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@@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a81c2794", - "metadata": {}, - "source": [ - "# The extra mile\n", - "\n", - "## Investigating NIfTI images with *NiBabel*\n", - "\n", - "[NiBabel](https://nipy.org/nibabel/) is a Python package for reading and writing neuroimaging data.\n", - "To learn more about how NiBabel handles NIfTIs, check out the [Working with NIfTI images](https://nipy.org/nibabel/nifti_images.html) page of the NiBabel documentation, from which this lesson is adapted from." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a1b7a08e", - "metadata": {}, - "outputs": [], - "source": [ - "import nibabel as nib" - ] - }, - { - "cell_type": "markdown", - "id": "7e077e7b", - "metadata": {}, - "source": [ - "First, use the `load()` function to create a NiBabel image object from a NIfTI file.\n", - "We'll load in an example dMRI file from the `data` folder." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dc7b8062", - "metadata": {}, - "outputs": [], - "source": [ - "dwi = \"../../data/sub-01_dwi.nii.gz\"\n", - "\n", - "dwi_img = nib.load(dwi)" - ] - }, - { - "cell_type": "markdown", - "id": "0183127c", - "metadata": {}, - "source": [ - "Loading in a NIfTI file with `NiBabel` gives us a special type of data object which encodes all the information in the file.\n", - "Each bit of information is called an **attribute** in Python's terminology.\n", - "To see all of these attributes, type `dwi_img.` followed by Tab.\n", - "There are three main attributes that we'll discuss today:\n", - "\n", - "### 1. [Header](https://nipy.org/nibabel/nibabel_images.html#the-image-header): contains metadata about the image, such as image dimensions, data type, etc." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8a09bb27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " object, endian='<'\n", - "sizeof_hdr : 348\n", - "data_type : b''\n", - "db_name : b''\n", - "extents : 0\n", - "session_error : 0\n", - "regular : b'r'\n", - "dim_info : 57\n", - "dim : [ 4 128 128 66 65 1 1 1]\n", - "intent_p1 : 0.0\n", - "intent_p2 : 0.0\n", - "intent_p3 : 0.0\n", - "intent_code : none\n", - "datatype : int16\n", - "bitpix : 16\n", - "slice_start : 0\n", - "pixdim : [-1. 2. 2. 2. 8.8 0. 0. 0. ]\n", - "vox_offset : 0.0\n", - "scl_slope : nan\n", - "scl_inter : nan\n", - "slice_end : 0\n", - "slice_code : unknown\n", - "xyzt_units : 10\n", - "cal_max : 0.0\n", - "cal_min : 0.0\n", - "slice_duration : 0.0\n", - "toffset : 0.0\n", - "glmax : 0\n", - "glmin : 0\n", - "descrip : b'TE=81;Time=122951.000'\n", - "aux_file : b''\n", - "qform_code : scanner\n", - "sform_code : scanner\n", - "quatern_b : 0.0\n", - "quatern_c : 1.0\n", - "quatern_d : 0.0\n", - "qoffset_x : 126.844\n", - "qoffset_y : -94.824005\n", - "qoffset_z : -26.2652\n", - "srow_x : [ -2. 0. -0. 126.844]\n", - "srow_y : [ -0. 2. -0. -94.824005]\n", - "srow_z : [ 0. 0. 2. -26.2652]\n", - "intent_name : b''\n", - "magic : b'n+1'\n" - ] - } - ], - "source": [ - "dwi_hdr = dwi_img.header\n", - "print(dwi_hdr)" - ] - }, - { - "cell_type": "markdown", - "id": "2579083a", - "metadata": {}, - "source": [ - "### 2. Data\n", - "\n", - "As you've seen above, the header contains useful information that gives us information about the properties (metadata) associated with the dMRI data we've loaded in.\n", - "Now we'll move in to loading the actual *image data itself*.\n", - "We can achieve this by using the `get_fdata()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "25e64938", - "metadata": { - "tags": [ - "output_scroll" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - 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" [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - "\n", - "\n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " ...,\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dwi_data = dwi_img.get_fdata()\n", - "dwi_data" - ] - }, - { - "cell_type": "markdown", - "id": "460b7b2f", - "metadata": {}, - "source": [ - "What type of data is this exactly? We can determine this by calling the `type()` function on `dwi_data`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4e7d2b57", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dwi_data)" - ] - }, - { - "cell_type": "markdown", - "id": "1cc74f1f", - "metadata": {}, - "source": [ - "The data is a multidimensional **array** representing the image data.\n", - "\n", - "How many dimensions are in the `dwi_data` array?" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "af8e1e48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dwi_data.ndim" - ] - }, - { - "cell_type": "markdown", - "id": "7337aec9", - "metadata": {}, - "source": [ - "As expected, the data contains 4 dimensions (*i, j, k* and gradient number).\n", - "\n", - "How big is each dimension?" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "77b9f659", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(128, 128, 66, 65)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dwi_data.shape" - ] - }, - { - "cell_type": "markdown", - "id": "56d0b719", - "metadata": {}, - "source": [ - "This tells us that the image is 128, 128, 66\n", - "\n", - "Lets plot the first 10 volumes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "21bff7f0", - "metadata": { - "tags": [ - "output_scroll" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.7.10/x64/lib/python3.7/site-packages/nilearn/datasets/__init__.py:90: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", - " \"Numpy arrays.\", FutureWarning)\n" - ] - }, - { - "data": { - "image/png": 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X60W5XIbdbpe2LMBqzYrH4/I7HLzhdDrlGnkvbrdbvFrW5nXrB6NqAkNm8usJSxDMSOgyCcFVOtogbzx1lZkePchCs4JxbbQe8vv4HWzZAyA8ACxtLCwsAACuXbuGRCKBBw8eSORRrVYxHA4lY8TvYiaG16PZ8AiCDAQCEt0z8tF9+8RcAJAWwemBMDN5McJnr6NU6uo0MEuT1ujPD4dDA1sxre/E61Cfp0s8brfb2AfTWSbdw8/MDPXJZrMZ6XLuOb0PflswNi/NIT09DYq1Np3e4/tsNhtarZZRswOsejVTvwTzNJtNqa8xJQPgGTSt3+/H1atXAVi8raFQyEgl//jHP8Z4PEY0GjWM9MnJCYLBIAKBgJFOIhsUnQqHw4FcLod6vW7UrFutFjqdjrxGNDvRkoBV12QNfrqnVRtK3htTVp935X6ewmene9P5fKf7pPUgFMDSKdb8NF6g3+8bLHOsHYdCIakVDwYDJBIJGYlK8Xq9aLfb8Pv9WFtbAwBkMhm89dZbCAaD+Nu//VsAVgq80Wig1WrJNbZaLQE+atAX0bt6EAeR5HRCx+Ox3B/1UYPKtD7O5MUJ15FEOgDEMdT2SZPVaOHaaRS4rj8DE8fS4/EYZcdphjsAwmCn69K06dPlSjrAvIdutyslFg3SJLjx827HLswh/YsAJjwMSe4ATCJceoo0AjoypeLV63WpIWoO4/F4LDU/fR3z8/NYXV0FYEXIrBEyUtCjJePxOABgbW0NJycnCAQCcLvdUn8uFAp48uQJotEoMpkMAAskVqlUBOAGWIjv5eVloS7ltRA8xtf0OMtpcIVGU9LATz9PRtKfd8X+TctZzEnUp+nWIw22IWf8dD2NQC9dzyZIUSOxibqmnng8Hni9XsTjcfm9UqkkHPFra2sCUIxEIrhx4wa+9rWv4c6dOwCsunIwGMT777+P9957DwBwenoqdUDqlM/nk+wNr7vVasHtdiMSiUiUzUhG1ypZu2TWh89vGkU8k9+cTNtP7TjqaLZerxsZSWCSoaSONZvNZwCQ4/FYkNz8bpKbUG+5D3w+n2TxdFsW20x1dsXn86HVahl6wi4VHV1Pt4SRwErjNfT9f57kwhzS0+hV/XcNRNARpe4J5vtpCLS3HwqF0Gw20W63jZ5or9f7TK9qKpXCzZs3DaQje0qZ9nM6nTKAY319HYDVluXz+YROlC1c+Xweu7u7uH79uuFIpNNpw4McjUZIJBLo9/vY3t4GYG0CskjpwQf0cOk8OBwOQfjq9gZuGt3ewE3yy8ZaziJtU/SzoKFib7z+N4fDYUSfDocDXq/XGA5fr9fFydIDNkgbynVh+jkWi0l0vLCwgBs3bsDj8aBUKgEA7t+/jw8//FCyLHzvm2++iUKhgF6vhz/+4z8GACSTScTjcWxtbeHf/tt/C8Ai3jg+PhZ6UWACJtKoXfLG+/1+yU6RdpcpT75ver/S0dQO9/RzncmvJ1wrOkfApKVPBzBMX09nHnXpBpj0RGvGMI7I9Xq9RkcLD1/NS8HvYuQOTDJ7BLkCltPX6/UQCATEIdSZUJ0WZ2SvS0vTXBi63fTzJDPg2ExmMpOZzGQmF1QuTCStRXt0wCStqMEEOr2jJ/YwRcyaLzCppQ0GA/Hs/X7/M0MBOp0OwuGwRMSAlboh/zbr1KzhjMdjmYLS7/eRSCRw+/ZtpFIp8SA3NjbwySefoFqtYmlpCQBwdHQkgxb428FgUABq9Hw3NzfR7XZRq9UkMmeqWtdn7HY72u023G633DP/XbeiMWojaE3X4mcpyM8WPUyAool09LPTPfbApK9dTykjyEq3zEWjUYRCISMS6HQ6uHLlClZWViRjc/XqVaRSKZycnEha+wtf+ALa7TY++OADJJNJ3Lx5E4CVOSGrHaOf+fl5YbVjNByNRlGv1+HxeAREmc/n4fF4jDZF8s+3220jq8ToSEc+06xjJAjq9/vP9M/Ooul/vDB61ZlGDfzie6jLOrpmupq4h2AwKHZvesSpXmeS5fj9fnS7XSnTMFtCrAxgRc0kTtFlQ2aLzmrVoo4xla7bxHge6Iwh8PnUpwt5SFM04ImAmum61zRVHWcr+3w+4/ClguiD2+l0IhqNyuEbDAaF+IEGzO12Y2FhQcZPAhZdJ9POpPXs9XqIRCJYW1tDMBg06PCSySRarZYYQP4mwWK87kwmg8uXL8uBTBT46emppDap7KPRSEhSbDYbKpWKYQB1H69mumJfIqd9UabT39NMQL/NMs2oxdRbt9uFy+UyQDhMuWmnkWUIXXejY6l7mhcWFnDr1i353f/5P/8n0uk0Xn31VTFitVoNjx49QqlUEkfytddeQyKRQDAYxCuvvGL02XMvUH/InlcqlbC3tye/7ff7UavV8MUvfhEAcHx8jL//+7+XQ5n33Ww2MRgMpCZN4QQuYFKT17VGzZI33Sv7eUxRvgg5i5zkLNIajZXgAciDkjax2WzKmnBULv+d39tqtWCz2YzDczQaCeGOJiVxOp3yPgBSe9bMe7TROtVOMO00OJFARzqMo9FIbD2F58PnDXNz7of0WYAx1ki08ZuO9qanPel2FHpYlUpFvpt1DX4uHo8jkUggHA5jcXERgFXvKxaLz9QQiWqksWq1WgiFQgiHw+K5sgbE6EYrz8bGBvb29uS1ZDIJn88nhCcAZLbv4uKi/HYmk8GdO3cwGo1w//59AMD//b//F/v7+4Zi88DQz8fj8cDn84lDQSHpBZ8RX+NaTJNufNYa/TYJHR7NskVhqwmFYEAaORondiLw+/r9Pnw+H65cuQIA+J3f+R288cYbuHXrFv7H//gfACxjeeXKFSQSCcFAnJycoFAoIJVKiXN5enqKTqeDmzdv4vLlyxJZ1Go17O3tIRAIyPWQvz6fzyORSACwatcffPAB6vU6fv/3fx8A5AA/PDwUvS8UCuLoUZfdbjf8fj/q9bowk7G+rsE/zCToNhrKzBn89UQ/R3LDa3AibYTevzzIqIv8nmkK0FKpZIBTdfuq3getVkvWWncxNBoN9Pt9g+HR4/EYzh0Dhk6nIwey1+tFo9EwInaPxyOtY5o9jYQ+ZyG+aQs/D3p17of0dGqbr+m+u7P4gfUItGAwaHiLfr8frVbLeG8wGDQOsq9//et4++23EQ6HJRom7N9utwtDU7VaRb/fN3i2e70e5ubm4PF4JEV0dHSEbrcrrGW83tPTU7TbbfR6PYlmwuGwKBkNZTQaRavVQqvVksOT7TJ2u13av95//308evQI8XhcrpspLJfLJR5ys9kUJKfm89bPbRpReVZEM21Qf1tkOkrR7XKa35qGD4AYDN0+QiS3RqYyst7Y2MA/+2f/DIAFQLTZbNja2pJ2qWg0inQ6jdPTU+M3h8Mh8vm8XF8oFEIwGEQkEoHdbhdDaLPZUK/XEYlEZK339/fh9/sRj8flkF9eXobNZkOxWDT045vf/Ca2traws7MDwDr0SbFLoA/1X/ODM2I7i52KkZSmEJ3JrycaLAuYg110mYEOGltQaQv5HeyZ14cvSzFcJ02TzLX3+/0IhULCQ6GBsMwS6XZSOrTUGZbtdD+13isatObz+WSQB1/j3tN93sxifZ7KdzPg2ExmMpOZzGQmF1TOJZI+K32qPWrWmnVdT3Ns8zUCJZxOp0SVgBVZ6HQOo/Jer4e3334bAPCNb3wDiUQCt27dkrrw6ekpisWiRM6A5X2yV5nXSO9Rt6wQfFOpVAwGs2aziW63a0Ts4XAY8XgcrVZL2MXm5uZQLBZRrVaNDECtVsP+/r6kwCORCILBoKQ/AStFFI1G4XQ6jbYsjoHjZzXBiRYStpxVy9GR4vSa/TYIMwzERQAw2q6mQVIE73EdWLfW/OtcB0bPAPDo0SOEQiEUCgU8fvwYAPDlL38ZvV4P5XLZmJbldDpRqVTk+xYWFhAMBrG/v49qtYrNzU0AQCwWQyqVgsfjEX05ODjA9evXsbKyIrpXr9exvLyMcrmMo6MjeY1sd4yistks7t27Z2Rs6vU66vW6EOzw+bTbbWlHAyatg9M91TPmu3+4THNDTA/WASYTpvREKYIcuTaAlYau1+uGftJ26JauZDIpWUU9mY+lNuIs+DssEfG3Sb6kr5VtsPpeyuUyUqkUAMgeajQaks2icFqW7pPmdzA1DpjjY19WOfd0t64xABOmI90HzOZ2XZcmuICMNVROGrXhcCgL5fP5sLKygoWFBXzlK18BYB2eH374IR48eCAkI91uF0+ePEGz2ZQ6NZGr2uD4fD4hACDI68tf/rLQck7XTvr9PjKZjKSJyuUyksmkARLrdDoyXYipdgI4SA4AAHfv3sX8/DzeeecdfPrppwAmNXvNQqXRyBrhyXSsJjng5/nvgKnUL7OC/7qi61ysx2lHkQcNe+2BCZhR9zqzq4D90oD1vFOpFDKZjJRQHj16hI2NDQAWAhuwKGV1HzJgGa5AIIC5uTn5jVarhXg8jocPH6LdbguRzqNHjzAajWR4BmDpPWlCqXvEcZTLZQFHjkYjZLNZBINBed+NGzfwySefCBGPfg6sBQIQR0VfN9G+usTCz/826tevK3zWuq5MkJXGnrCmq+d8R6NR2Gw2tNttef7ESwAwbBF77jVQi4evHpFqs9lk7C+F72G/MzAhgNJrzSl9LMsBlu4Ui0X4fD7B9xCg2+/35RqbzaaURfX16N/m83rZ5VwO6WlwEkkhgIkXp2up3W5XIPgabejz+YQ1jK8TANHtdsXbf/vtt/H2229jbm5O6nX1eh25XA7vv/8+vv71rwOAjOprNps4Pj4GYCk2SVA0cCwajSKXy8lvpFIpRCIRBAIBjMdj+Xw2m0W328Xp6akocqvVwtHREW7cuCFRNyOYxcVFo42i0+kglUrJZwuFAjweD/7kT/5E0JM/+tGPhL1Ke5N8ntO0gJpDmsJNSKWe3lC/TaLpLLnhdVTB5+z1euHz+WRt2M5CEhpgUp8FJpHB0tISwuEw2u226I/X68XBwQGuXbuGV155BYClj6OROVKy3W4jHo8jFAqJLlerVbjdbhQKBWxtbeFP//RPAVjG9t69ezg5ORFHjW1QZBkDrGxRIBDA0tKS/FahUEC/3zfITOx2O27evInd3V3DCeEBTL3VE7R0JEWkOLMBFA0CnckvFm3/dDuSZrYDYAQofB8PbA06BSZRtwb5MdtDe0AWMpIuAZZun5ycyPs0voZTAafbP5eWlgSbo++J9XBy0jMDxX+n/mhHWUf0+v8aFKsDkJdVzj2SpuhojxtZp27Y76tfY+9lp9Mxxvsx+nn99dcBAF/60pfkYNSUm6FQCOPxGE+fPgVgGatcLicHLGABetiapRGDRGtrYAWBOpwDzPcuLCzIZ/g7nU4HuVxO2qgYiUQiEYniu90unj59Kt4jYKUrO50Obt26hT/5kz8BAOzs7OD4+FgQ4oBl9NmWpdNBBHNoSkpmLzTw5PMGvvhVZbofmuxYGiULTBwefXiPRiMxTvwO/R4ygV2/fh2VSgVHR0cSPV++fBnvvfcestmsALpqtRoSiQQGg4F0KtD4MPoFLD1pNptIp9MIBAIyGpWGU7c5ud1uLC4uotfrySHf7XalxYX7g4BMYEKH6/F4EI1Gsb6+jnfeeQeA1U/NCIb6Uq1WEQwGjbRjs9kUUKY23pqVivr426h3v0imS4FM8WqnRg/UACbBiu5p1x0qunxC4Jh25qcHbFCPWW4DIOW+crmMer0ug12+/vWvIxKJIJvNSinx8ePH2NvbQz6flwi5XC5LtM7fZkDkdrtF59mu6Pf7RWf1mGIe3BpszGfD8p4uS71sMgOOzWQmM5nJTGZyQeXcI+lpNifWFBgNA5NaA9OJgJW2K5VKsNvtSCaT4ol1Oh00Gg0sLS3ha1/7mvzGu+++i0qlIqxfmUwG8XgcmUwG+/v7AIDd3V2pkXBwBklHdOvB6ekpbDYb5ubmhMmJqfJ8Po9kMilMZJT19XUBRLz//vvI5XIC2KJUq1Xcu3dPIu5YLIbhcIhKpSLe5+LiIg4ODvDo0SN5Fm+88Qb+8i//0kgjMoLWpC5MV02nvQnUY9oLMEkRfpvAYtNkJPTOyTvN19hm1+12JbLQXj3XhmnIcDiMN954AwCwurqKWCyG/f19AW8tLi4ikUggn88LuxjLQMlkUiJSllMAiI653W74fD7E43G43W4ZiToej5FKpeByuQQ41ul0JKPEjE06nUalUkG5XJasze3bt9Hr9XB8fCztXul0GvF4HMFgUH6jUChImUSnsImP0OBPDfLRfdZnTWGaybNCIBhBVLqvmf9NtzxpQB/tl06Vcz04VZD/Tj3T0XUsFsPGxoZkhNjmSnt0/fp1AFbm0ul0Ip1Oyz64c+cO9vb28JOf/ASHh4fynQSx6YlXxHloUh7yYVA/WQLVWCE+F80dzxIpM50voy0710NaH8463UEaRaZumYrhhCnASveRpq5Wq0kahJv9rbfewsrKCgBIXa5UKomxYl1Pp4Ptdjvcbjfm5+dFiXO5nKTbaZg8Hg9WV1fh9/vFKDebTdy/fx8ejwfr6+vGYce0EA/ajY0NVKtVmR9NIV2jHl9Zr9dRLpfFGWAfeDAYlE336quvIpfL4Z133jF6C3XfLjBh82EKSNcaaQDOQof+tojWR25y3Zupx0+yL3g0GhnGz2azIRaLiT7S2YzFYmJ0iMxeW1uTA5Cpbfa387Pj8RiZTEZSf/V6XfqsmUq8evUqRqMRgsEg5ubmjD0yHA4Ffc1r3NnZwdzcnBjlXq8nXAM0eJVKBaVSCaVSSX672WxK6lEbeV2/ByCTsjRwKBgMClZkmqqSB4/mRXhZU5PPQ3Q3zFmDSehY6n/vdDrw+/3weDzGSNtkMmmUSjj0ol6vG5gLpqBZjvP7/QiHw/j2t78tE9VarRZOTk7wox/9CNlsVmxUoVDA4uIiBoMBTk5OAFjBzptvvomNjQ38n//zfwAAW1tbKJVKBv6I99Dtdo0hLqxRa1Q6cUoaLEtnhffi9XoFfT7ds/+yyLkf0vTWCKBi25De/KRe1FzHuhasUY3j8Rhzc3O4c+eOGKZ2u41YLAaXyyWGbXNzE4FAAKurq+LteTweZLNZgxaU0YWOCu7evSuKzd9+5513UKvV8MYbb6DZbArCNpFISATBz6+srKDX6+HevXvyec6hpmEFLGUvl8totVpGy9W1a9eM+7tx4wYymQw+/PBDqTnFYjF0u12Dw5yo2+nWBRJP6PaI32ZmKM0gpsFT08h3EjFoDnS73W4cRHSMdAQMWGA/r9crjmQikRBEKz9Lqka22FFGoxGazaaAvPr9PlZWVqS9ic7g8fEx4vE4vF6vUcteXl6G1+uV+3j69CnW19eRyWSMcYWNRgOZTEZqjSQz0Y4la/YaZKSRxdQn7mmCerSDMF3T/m3QsX+ITGcfdOQMTFjfiHUBJpzxjUbDaMHiv+usR6PRkMAIsGyHz+fD5cuXRZcajQa+9rWvYWVlBQ8ePABgZTO9Xi+Ojo7EfgHAJ598glqthtPTU3E419bWpBvmD//wDwFMiHA6nY4xWctmsxlBiB6tqbN/7Lrgc9D3x/OEQYZunwReLh174Ye0NvQEOmgQBPvfOLYPgDB5sTcZMOelRqNRefhEzOpewLt37yIcDuPx48cGdSFTdJorezgcYn9/XxY7kUhIDyAVhHR0bK0CrIOz0Wjg2rVrePDggbFZyuUyfD6fpNorlQrcbjdu3rwp6c5qtQqHw4FarSaRS6FQQCAQkIMegERuh4eH8tuFQgHLy8v4xje+gb/5m7+RZ8bBIBTy75LPWX/nNL3ltDHmen1eROshjSDTbto714hRfeAQmKOfGw9nPnvAWv9wOIxQKCT96ul0Gna7HT/84Q8lO0OAYigUkuvJ5/Pw+XyCIud1dDod9Hq9Zwa+sM/+8uXLAKy9xIOSzhv7+On0AhYLWaPRwOnpqZRkqN/dblfum22E4/EYly5dAmAdstlsFsViUXSt0+mg0+kYI1trtZp0EDAlCZhgMX0IfZ507deVafY6m80maVu9N4fDoTh0LL14PB5x2qb7hzWwka2qfr9f1m88HuNLX/oSXn/9dbGX3//+9zEajfD06VMBJi4tLcHn8yEYDKJcLuP09BSApbf8M0s3nEFerVbltbfffhsffPABWq2WlByZ6SPQkK+RelQfuuFw2CiTkMZUtwNq/gedVXyZAo4XfkhPPxjWs2gwgEnagoZOD8jQHiA5XQOBgKQXvV4vFhYWMBwOJWr2eDxiuHQ7TL1el3nNgBU9RKNRMTCA5ZEdHBxgMBgI13axWMR4PDZ6E51OJ0qlEo6Pj7G4uIitrS0AlsIyimeU4nA4pGeUBp5TiPR0KtYc2fDP7zs6OjKQnPRCL126hEePHgGwOJc9Ho/Rw9hsNiVi1qQEmvxeP1t6qS+LMv+6Mn1Y6/ozMyg6RctOA04T4/Pl6w6HQ3qdr1+/LhEzv7NSqWBtbQ31eh0PHz4EYLXqaYMMTCa16Yg0l8tJGpnrx98qlUp49dVXjYg2n8+jXq+L0STil/8OWEQ64XAYXq9XopfHjx+j3+8jmUzKvTMd7/V6pf44Go3Q6XRQq9Xk+0gb2mg05NDgvHM6GNPG0uVyGXX836Yyy2cJs4zULx5K5N/XpZnRaCRrSOHzpjA9rDMrrVYLDocDzWbTmEP+xhtvoFarGVnKo6MjLC8vy2uffvop3G43XnvtNYOqNpPJ4MqVK0Z7YrFYxJMnT4ypfF/60pewvLyMH/zgB2KXHzx4ILaNukOHVAcdmmJW90mz/qwzgnS+SaYCTCLxl0Fm6O6ZzGQmM5nJTC6onGtNmgAFHaUwPaHTrUzvaC+RwAESu9M7+53f+R382Z/9GQaDgUSYjUYDbrcbq6urUtt9/PixeFusm/T7feTzedjtdkkllkoloaXTs6j9fj8cDoekg4hmfPLkCZaWlmTk36NHj6R2qfv+KpWKkbKan59Ht9tFIBCQ62bqh4h1wIpIHA4HSqUS7t27BwAykcvv90uEc3h4KKQbuj4KQDIPuj7FdC4jOa/XKyxo0zSuL1OqaFrOoqTV3jX1T1MksiRDj51rz5Qio08iSfv9Pr70pS8BAP7oj/5I9JOlDdZ219fXRQfZqZDL5SSCmJubQy6XQ6PRMCKiUChkzJ4ej8c4ODiAy+VCJBIxEOaRSAThcFh0irPMdf9sMBiU/zhMg1mXbrcrOuX1emWsJbkESqUSbt26hXA4jA8++ECukdShfLYsWX0Wipv8BtNr89ssjAxpJ7iunC7GZ8l68GAwMEaSsqxF+8bMn+aVACYT88gr8Qd/8AfIZrPY398XgpLLly8jmUwK2Qhgldk4yW1+fl4yl9euXcPy8jJOT0/lNXZEhMNhY9jL0tKSsOIBwB/+4R8im83io48+Mnq62devWfJCoZCRcWUJT8+d1gBaXdt+mTI158rdTUXSvKy6jsIHGgwGxajw4RYKBUFC64PkypUruHHjBn784x8LSUkqlRKqTT2xiNOyePhqdCkPRbvdjnQ6LbVAYMJX7HK5pFUrFothfn4e3/nOd/D//X//n6Sqk8mkpLJp2HgAhkIhUcRgMChIXoKJcrkcKpWKkbrvdDpIJpPw+/1GzSYYDMLtduP27dsAgI8++gjlctngz+WfSa/Hw0WPmOMz58QwnZ6afs9Zf7/oMn2t0+QlBD3p8Z1MN+uD2+fzCQqVuuf3+4WIgSMo2+02CoUCwuGwwQfP6UQkM1laWsLW1hay2awAaqrVqgEYAyx9DAaDSCQScugXCgV4vV4kk0mcnp4KViGVSuHw8BDD4RBf/vKXAQAffPABTk9PsbS0JPVnu90uOAndGkVHgEaVz+Dg4EDS5wSWraysiAH94Q9/KHuYtXTA0l0eJhqsybGG021Buob4MunYb0JIwKHbIjVFp953o9HIYHYDJvSx03qrqWsBa00TiQTS6bSUDN99910JUrj2Pp8PoVBIuk0AC1uxsLCARqOBBw8eGNie09NT9Pt90TGn04nT01M4HA7Rz3q9jmKxiGvXromDd/PmTayvr8PtdkvJkAe0zWYTZzMQCIh90mQ7DKg0y5omI9JdAy+L7ToX4Jj+M2sI08jZQCBgALUAa7H4GusR/DyVbn9/Hz/96U8RDAbloN3f34fL5RJmJsCq72QyGcMjpccZCATk8B2NRtje3jao7AgCqtfrBhjo/v372NzcxLVr1wyqSK/Xi2w2K5E0I6FQKCQG+eDgQA5l3gsj4V6vJ2CLra0tAazRg+Thm81mZZOGw2GUy2UDDMaDl0qrwXeAWadh7VpH4p/HtpjpNjPdhgJM0KYagEO+Y7K38cBh5MkMCGCtVy6Xwxe+8AXBNLB/1e/3i14NBgMsLS0JXgGwotR4PC7jBQEruh6PxxLFAJY+ZTIZDAYD/OAHP5Aonkh+IroBq6d+Z2fH2Es+nw+BQACNRkO4u1k7LhQKePLkCQCrK2FpaQkul0sirPn5eQEj3b17F4CFKievPJ1QcuDzgNaDN/hMphm09DpQXhbD+o8VjXbXjpM+jPWIRrZ60lby78ROAJOMotvtFtu4tLSEV199FT6fT3Sk2+0il8tJZwBgdcOUSiVcvXrVAKexwyUYDErGpVQqye9znfnnTCYjYElyOCwtLYkuLiwsSA2a+/LJkycSjOlMX6PRMIIIOiR6r3JPTtegNTj0ossLPaS5wXTKkRGznlfKNA8XhD18GrEdiUTQbDZlzqgGnTx9+hR37tzBa6+9BgD4u7/7O5ycnBi0eEzbae+q0WiIEeH3kVqx0+mIMuzt7eHu3btIJpNyCD558gTf/e53kU6n8cUvflG+88mTJ+LRcrM4HA7s7+8jFovJPXIecLlcxv379wFAaD7r9bo4F0wL6gjr6OhIWoW4Scmfq1OlNNps89EtbpofGpj0T+q5rmcZyM+DwdRpfHrhuiea+qqRygSxdLtdwwiORiN89atfFaIQArWY1eH7otEoarWaHMj9fh/pdFpQ44AVCZMil+tNlKyeykXg5d7enkFyY7fbMTc3J4QTgJlBotHi99dqNcnihEIhIdUhsNJut+ash0IhA8jIEgwPZjo1JMgBrD3MdKcGjulDWv9ZDy05a60+78IyYKfTMTJ93Iu6pY+AV7fbLVFuMpmUllVdVqNt43z6a9euIRaLwW63S8BRrVZltj0PWcCye3Qa+b5msyngSK5VLBZDPp83gqJEIoF2u43T01Ojj7/dbqPdbot+Z7NZ9Pt9JBIJ2UMff/yx6Byvh0M4dBbV4/HA6/UaU+M00lsPmqGuvQxO3ww4NpOZzGQmM5nJBZVzrUkDlkczPY3JbrdLHReAjKkkDSMwYcCJRqMySAKwIk2CGOgZXr9+HS6XC81mU6ZN9Xo97O/vG6mfSqUCr9eLZrMpHmswGITT6YTX65WaTS6XQ7/fx927d4Xo4ZNPPkG1WsWNGzdQrVYlpcMU8+LiokQfm5ubOD4+htvtlsiFKSSmqQFI3RKAtDf4/X40m010Oh1JT1UqFfES6TUeHBxITZrPhm0b9LCZVXC73c8QJrCnWpOevAxe5z9EGCXznji5SQOcGL0wpQdMdM/lciGfz0umYjgc4urVq/jjP/5j+Xyr1ZJsD6MKzR7HZ+tyuVCtVtHr9SRi7/f70pfMGdGAlQVyuVzC5sReZvavarY8lm00GQ5JS3jd2WwWo9EI9XpdZlk3Gg1Uq1Wjlk7d05kv6sji4qKk7nO5HObm5gwiHc1op/c/MRu6tk8OA6170ziI6dc+b8Jsjm4xAiClD9JiAhP7yMwOYGXSmJHQTHDNZtNIgR8cHKBYLOLWrVuSOWEKmjVjwNK5K1euIBAIiA1ldpLAU753eXlZ+uI10Yjf78enn36Kt99+G4BVFslkMgLsAiB77/DwUOzWm2++ia2tLdRqNbFv0WhU0vc8J1KpFHK5nPBsANbe8Hg86Ha7Bnsgn/HLUMJ7oYf0WT3SrI9q0ggurqZjJMpWI/6GwyGq1aoMqAesxYvH48jlcpLii0ajWFlZQT6fl4Xvdrs4OjrC4uKiKEggEJAanp4cs7+/bxz6kUgEm5ubKBaLUv/b29vDcDhEKBRCpVKRA51zf5vNptSkG42G1CNpkFmfj0ajWF1dBWAZ/VKphKdPn0palJvx8PBQ0kbxeFx+g0rM9JPD4ZD6IfmUOWmGTkO73RbQE3/H5/NJ3ZWG9iyFflkPbs01rUldiIjlcxgOh7LJaQBZHiD5jeZQ7vf72N3dFT3zeDxIJBIoFArGMPunT58KeBCATI2iM8n3sSTDA3VzcxOXLl0SwhDA4v0ul8uoVCr41re+ZfTzj0YjXLt2TQ7w9957T/YY9ZHUiU6nE5988gkAk5mJIMhcLiezq/UoTo7JpJA5z+fziXPJflWmtafBejqtytd02pt/n2bB+zyJpq6kc81xvPx3j8cjyHtNXet2u42penTYNfudx+NBOp3G/Py82Mbr169jdXXVeLbxeFwQ4DpVPh6PxXEDrJJIPp/HycmJdIIAkNT58fGx6HKlUkEmk8GDBw+EuKRcLgv9p17/fr8vfAAA8M1vfhOffPIJ/u7v/k4AtCxNcooXYGGPeEBrvI4OUvg7utx30eVcI2nWnjWROwBpd+L7GF2QUQaYzFBlYz+NRjabRaFQQCgUMuoPHPnHRTo6OkKz2RSucMA6mNiaxA3QbreFKYeHdDAYxKVLl9BsNiXC4fsLhQLq9bqAejhO7fj4GNvb2/J5Gh8eBmTQ8Xq9UgPc2dlBqVRCJpOR6378+LHUXGhI19bWjFnGlOlDyO12o1arSTuE3lShUEg4ofnMSIWp0b3TwsjoZVF4LcwsaK+bmATNJUyUMp8DIz8aAz1qMZfLYXt7G9/4xjcAWIYxHA5LFANY9bnT01Ps7OwIUItZk2kuedb9+BucGW2326WOF41Gsba2huPjYwGR8f44N5oHssZ0EN3v9/uRyWQMpjTugX6/Lw7n8fEx7HY7ms2mXHcikZCxqAQO3bx5E9///vexv79vzD9mVMc/A5MhDXx+/P802x2R3i+jnv1DZJpER0d/zEawhY72KBAIIJ/PIxgMylrpPcs1b7VaiEQiuHv3Lv78z/8cgOXIbW9vC5MZP7u9vY1+vy9rGovFJNCg3vh8Pty6dUsOakbYDocDy8vLmJubE4T24eEh5ubmDPrQlZUVcdA0Fa7f78fdu3flesLhMFZWVrC6uirg3na7jVQqhVarZQQR8Xhc+Or5POl8E2wMWPuS41kvejvWuaC7tdJN90kTOKb7khmtaI87Go2i3W6j2+3K4QpYIINXX33VmFBFcFq5XDZSk+vr6zLXmRIMBg2lcTqduHLlioEC7na70jPIVpRAICApKs64BiykI9NQNJ4ctjHdz8dNQC83Eokgk8lIuxlgzWr98MMPhW8XsAwqoz0+s3w+j8FggFgsZhg2XhvRu7yeVqslnjq/k5H4LzKML3Nkw4wN749RtAbLcc2Xl5cNhL+ecsbyRCAQQCwWw7179+Tzr7zyilB2Us+CwaA4B7qlkEAYOml+v18QrNRlu92a+qZZ9h49eoRAIIBvf/vb2NraEmP55MkTeDwe1Go1yQzF43ExYIy6er0ecrkc6vW6OG78DDNLACQro6+73W6j2WwiGAyKfrPlix0HvGeC7HSLm251416gg6hpID9PWZzPEpvNZmTC+DwYbPCwIUuY7ogh+900XTJLKIDlUL3xxht4/fXXRT8PDw/RbDYRCAREv+kQJZNJaRvd2dlBOp0WgCJg7Y1Op4OlpSUsLCzIdS4uLsJms8Hr9YreMTsZDofFuXA4HKhWq0aWkgNBTk9PDeZFTiLk9zmdTrTbbQNYF4vFxFHhtfA5Mhg7a475RS+fzIBjM5nJTGYyk5lcUDmXdLfu++MUK51qoUfJHj0CSfSQi/F4MjmLaQsA4nFxhi9gRSQEFDCaGY/HUnfVjf3sYaUXd3JygmazCZfLJb/B+h+jacCaJERvPxKJGNe2vb2NSCQiILHV1VXhntUEF6yf0Hu9efMmkskk8vm8zGC9du0abty4gc3NTUlhbm9v49KlS4hEIuIN83s1Gxu5zpki0i1h9XrdYORhVoAMXHwfnxPlonqfv0zIF617J4PBoBBu8PVwOIxOp/OMd85hBWxpA6ye4bW1NcFJAJZe8PlTV8iTvrKyIrVim80mJDO6HZGfYSRNIhQ9fjISiSCbzSKZTCIej0t2h+u5s7NjYB8AGDV28sXbbDbZM4yWNTNdJBKR9CmvK51Oy6hYRvapVAqrq6t488038YMf/AAABAfBdD4jJ97TNAMc8Sq/KBX5sureZ4nmbtec8Hr4EMly+v2+6Bhg2TgCRfne0WiEhYUFWb8bN27g7bffRq/XEx25cuUKisUistmsgAxpB1mmAayIO5fLwe/3G0A0zkDw+/0yQCgYDOLo6MgoETLyTSQSYrcikQjm5ubw5MkTyR4Ck3Q5S4Hs29YkOMQhORwOg3+CpRjaNpaqiOuYLqO8DLMJnush/Vl9tXqaEOujuoaiawjAZCCFrh8yfUJSBm1sj4+PpVYNWIvndDrx5ptvilHc3NyUkZRUTvbssREfgKTNtRJx+IROIxLdyjQ9N8bBwQFsNhu+8Y1vyAZsNBqS8uZ1e71eMdKawrHT6WBtbc0YzrC8vIxyuSxpzWAwKCAhHvButxuJRALVatVA2PIZaqAI02S6FsiyhGaDc7lcz6SIP2udL5rwfrQTQqpZXR9lD69GtJPog+8j25HT6US1WhWE/1e+8hX4fD5sb28L+UwymZSDjjpEljs9dIX90KypAdbBnM/n4Xa7pQbscrlkDjl1jyj/YrEIh8MhLGaj0UimU1HP0um0sEZp3AQPYOqtRv/z4I5Go+j3+zJxCbCMN8st1FvA2jfLy8vyHD799FMhNOl0OvIs2duqeQhY8tLOu17HlwGR+w8V6ibXvtvtGuBOABKktNtto9xCm6kJYQi2JU4BgPQdk4UOmIy0DIfDcugnk0kpD1K3Ll++LMNaGGzooS2c4gdYB+g06IyBk9/vl0P14OBAatf64L906RL6/b6UkbxeLwKBAJaXl6VG/ujRI9y/f9/QBw570Wh4l8uFer0uziV1bJr1kp+/iPLcI+mzeJ8prKFOo2l5MGgaPNLS0ag1Gg0Ui0V4PB4sLy8b7SjNZhPxeFwW5Pj4WA51Ip1XVlYEKMTWgWg0iqtXr2Jvb08UJBaLCXkIvbPxeGy0CADmGDjNG55MJvHmm2/C6/Ua49hYO+HGYJvXycmJHLThcBjJZFIQtMCkTej69etiuMm7HYlE5Df4HRrFDFi1Zq/XK/fM76TDo4FnvC7dGkN07svEBPVZNSdiH2iwCCzR7ET0zBcXF8W45PN5aZ8aDAZitG7duoXRaISDgwN8+OGHAIBvfetbCAaDot/ApK1LU9Jy5J+e/mOz2RCJRFAoFMSQnHVQhkIhrKys4KOPPsLx8TFeeeUVABDnlxEZYOmFzWYT3QGsdU0mk+j3+3KP8XgcrVYLa2trco2tVgudTgfdblf0bGlpCaFQCB6PR9DrnKb16NEjg0iH+1rv7XA4LPVCOuB0sG02mzGaVjvenzehY6gn5bXbbcNJbjQaQgYzGAzEdpD5zm63Gzie4XCIWCwmVMGA5eQHg0EBvB4eHgqnNwFhrVZLbC2n6mUyGaytreHDDz8UfYpEIgIG5CQ0wMooLSwswG63y+8QwX14eCg2uNvt4tNPP0UkEpEDMpFIwOVy4Xvf+57cy1tvvSX7ghPfAEuXs9ms6B0BiXrCH+vQrLNrDBD/XQOUuRYXSZ7bIa15dyk8MDTqmIxMfC+9PqL8AAhIQs/aZZ/qaDRCLpczHv6HH34Iv98vi8ff0sCqVCqFQqGASqUiKeJerycHFr+Pqbd4PC6GjTScuVxOmKRCoRAWFhYEqMMohKxkGnQUDodRqVQEFMTrY/TLz9ZqNZRKJWPO78bGBj788EM8ePBAopRMJoNcLmd4kNrx0SlOHhhsaeMzo1GcXjPdp8m+zbOU+CIf0gCMCFq/xtnjAJ7JGgCTjRsMBo1BBeQxBiBpPrJA3bp1S/qN3333XSSTSWNIij5caZSr1Sra7TbcbrdBfTgajZDP540MCdv6+NpPf/pTbG5u4qOPPkIkEsGrr74KwFqvYrGIk5MT2UvshKjVasI65XQ6kcvlEIvFxFjSmLdaLdHxarX6jKEkla3mIW82m/jggw/w3nvvSSmAkbbH44Hf75fDu1gsCm2jnn3O1K+e6/15RnezvMTnT/CizraQJ0L39QOQTCKzEsCkfe+NN96QQ/rk5ATZbBaZTEayI+12W2yFBp1WKhWsr69LJF0ul2WIC9eODhy7ZAgom5+fx+LiIjqdjthMglN1ic/n8wl4l9+5tbWFwWCA27dvG7ax0WhIuy5gZY/eeOMN/M3f/I0BqmR2Vmcez+Isb7Va8qx09vAiRtMz4NhMZjKTmcxkJhdUnlsk/VnRlv4/24ZYIwUmU376/b685vP5UKvVsLOzIx5gKpVCp9ORKVGMPpaWluD1evGzn/1M0i/JZBJzc3PIZDJSx6WXyik+fO309FSYdIAJCYNOOdtsNly5ckVqMYAVUWQyGUk9azavYrEIr9crfa1HR0eo1WoIBAJGtmAwGCAcDktrDBl2tra2JD09Ho8xPz+PnZ0dee3y5cviibPuVCqVsLu7axCcsBaoCRKAyRAETt0BIB7zNJmJ9uR/2XpfFDnr2nT7m+7l1S0aAGSQSqlUkjQdwS8kAKFesG3E5XLhK1/5ivwWU7vMErGEoPvSg8Gg6AKJQbLZrIB1GLWT0GdtbU2u9eTkRIA97XYb7777rnyeNXcCJr1er/TLU3dfeeUVFItFiar0/bPFCrD2Kznvec9HR0colUr4yle+IpHJ0dERvF4vYrGYpM+ZchwMBp9ZVtGgKc0fwOvRmaLPo2hAJmuomi+CrIehUMggveHgidFoJBk7jgnljAPA2r+Hh4d4+vSplETI2U6bA0zaL09PT3Ht2jUA1j7Y3d1FIBCQtSfItlwuo1AoCEhsaWlJdJa1c9bMQ6GQMb2r2Wyi1+sJLiifz6PRaCCVSokOct37/b609I3HY1y9ehUffPCBYD2SyaTcK/WGuBw+S/3vtGXTYLKLJs/1kNap0WlgBDBhgNHv46GtUz9M07IODVgHGFNnujbc6/UQiUQMJN+DBw8wHA5x+/ZtATsUCgUsLy/LDGnAOlBTqRQajYbxPsBKM2tjzDmqegiI3+9HMBhELpeT69Sf4wbqdDpYWVlBLpeToQQrKyuS6qRiE9Chn1kul0MymcSlS5fkfaxh5vN5YYziUBLWl4BJWodkKHpsJ2AZAR4aerylTou/jKluyvQENj4fTQHK++X9MMVPvQQgwwf6/T7m5ubEODWbTWSzWaOOb7dbM6KZEgQmKPJutytOJw/hVCol68HuB03v6XQ6UalUhMqTMhgMcP36ddhsNjl8I5GI4DY0aHE8HsPr9cpBmMvlUCgUsLS0JLrabDZl/CXT26SQ1EDNRCKBVquFzc1NSfv3+32srq5icXERP/7xjwFAQJV6KhZgpUZJ2KIHvpAohmvD3mk9Hepl0LlfRQhq1Cl/dhmw+wWAdBLoMY0AZK6yTuGOx2MZH0pbxrp3vV6XEgZxFZlMRuzIYDAQACNT2D6fT7AU/L7hcIg7d+5gfX0dPp9PcAp2u11IT7hWfr9fuiIePnwov0O6ZD3x6uTkBJVKRcoxpDidptYdDAbY2NgQ+91oNGSP0tZSh5vNprAEAhBQogaJsuR4ljN4njbuuR3S09y7AIwHwr8D5tQbGpBerycLwsjU5/MZNTyCK9xut0TDjABv3Lgh9JrValUGotM4LCwsYH9/X1oNgEl0rhek2+0iHo+jUqmIwh0cHGBhYUF4wwHIxJjl5WWDiCWfz+P27dsGW1o6ncbx8THK5TIuX74MALKZKpWKzCJOp9MIBAJClAJMSE/02LpKpYJQKIRoNCqR0Pvvvw+bzWbMjQ6HwwiHw7I2fJa1Wk1IJvgaD2RdB9J1M00ycVGN5S/ifXa73WfeH+9RA084VpLrz/nHXq/XaAEZDAYoFAoSNQIQdH2/3xfHrdVqyV7gepFpjmxS/G0CgjSQDbBAMzToBErSedAZAkZZPJBJEKJZ8cgh7/V6jbF/PDh5UOrpcJRms4lut4uf//znct2pVArdbheJREJq0pwcxmetecOJ/OW9+f1+AWKeBfa7qPr2q8hZOsnau84e8uDSU9iIG+l0OqjX6/IdfO6aHTASieC1115DMpmUSHM4HMLn88Hj8QgTmN1ux5UrV1Cv1+WwI5tXIpGQA6tUKhn4DMDCE+zv7wtIjetXLBYFzMYMTiaTQTQaxcnJyTMBAMlUAKsl7PT0FNFo1GC/4z3TltHxvHr1qhzI9+/flznofA6cic7sgG5H5fnE69EtqBdJnsshfVb6gH2pmgQemLCJUUjXxl5AwDIEfKBkNXK73dIyk0wmJVp8+vQp7Ha74WGxJ5Vc3wAkEgqHw1hbW5Prfvz4MVwulwAeisWijHukUdvY2EA6ncb+/r6Raul2uyiXy2i328KfzKEe4/FYNhNnFMdiMYNLNhgMisfI7+T4SRrZw8NDtFotzM3NSSqq2+0KeOeNN94AYG209957T7hxAct7LBaL0vvL6I1tGJVKRe5RR2nasEw7X9obvWjMPb8oLc/DR8+DbrfbckDzeevoTqdep9uGAAhNbSqVEqeT6FGdXu/1eojFYigWi9jZ2QFgRZQ2mw3lclkiUjJ02e120WU6Wna7XToQVlZWpMzicrlkDcnIx/54AAbtKIFspP9kmhGwHMRSqWSkMdkRwd5YwHJCfT4fbt++LXuTNKGNRkPSqMfHx5Jy5Ix1AMJDPR6PZS+Qk5nAPq5Dp9MxImnKy5LJAc7WSXJ0O51OYyTtNF0qAMnWcR0ASLuTz+cTu3Xp0iXMzc1hf39f1oqf0SU+2mOdJSN/RLFYNMaUcpiK7s8HLB0g6pz3c+3aNYPONhgMSibmzp07cj37+/vY39+XwOTatWtIp9PodrtGBpBAOs3kWKvVDJY2lpA0rXOtVhNHV7e0UZfo+PA507ad5Ryel1zsZPxMZjKTmcxkJr/F8huLpHUa+7N4dukRaWg960y67loul+FyucSDJ0sWUxeA5T0yXeL3+6V+8frrr+Pg4AD1et2IcldXVxGNRuW1nZ0dLC4uIhwOS3TNtKJOdzL64YAPwALqcPAAvUmSQezs7KDdbhtpY6ZzNBcvvTWdliEQjenTjY0N6T9kTZJe6MHBgURhi4uL6PV6qFQqEjH9wR/8Aebn53H//n3xZvf29mCz2cS7pOcbCAQkutfTtoBJBoSi0+B63V+GSEanSsm6Nh6PJaJkmoxtgYD1bFiD5fNi9ofRC9NtCwsLWFlZMXqvCZ4aDoeSXSkUCjg+Pka9XpdSy+XLlxEIBHBycmKk+R49evQMWQ/XQOMHeC/z8/PCTgdM+kSZTanVajLukNH54eEhksnkMzVRAnpYIjo5OZF+fj2BaXl5GSsrKxL17u3tCb89sz0nJyfY2dkRIB6FtUq2PwKQ0pJuwSJAT2dxLmpf668qmnmROsXnT3up66OBQADtdhu1Wg2NRkP0aTgcolwuG0xuJNHJ5XJi38bjsbB0aa7sQqEgYx8BC2dAO0HdZgqbugBYNq9er6PZbIr+AJZtikQiwoAHQNLUTqcTT548AWBlazhnQQ9xYVaQ98eJVw6HQ15j2e6TTz4Rgiqv1yvMfdRFDsAZDoeIRqMSdTNToUHHfEYXTZ9+Y4f0dN3vrLQoH5hmeGH6hA+KtVuN9mRdULPQMO0xHo+xvr6Ob37zmwAsEoZ3330X//W//lcxQn/6p38qzfVUMrKLraysyO+cnp7CZrMhn8+LUQuHw6jVajg9PZUFXlhYkB5tKjYP3qWlJUSjUTGU7XZbUjIkXAkGg0KwcuPGDXlu3KR8Pvv7++j3+yiVSnI9Pp8Ply5dwhtvvCG9uMViUQAhGi2+vLyMH/3oR3Lo8zDWhg+AAZ7SI+OmWY+4nnpGLQ/3aR24CDINFNN/54xoHgzABP2uR5WGw+FnSE80Mr/f7+PBgwcAgN3dXcRiMaytrUna0ev1Ip1O4+TkRJ7PxsYG4vE4SqWSlH5+/vOf4/XXX8fCwoIcYo8fPxaWJs3+xfIG16pYLCIYDAqBD9eL9LUaq6DrmLxGt9uNZDJp6EY4HMb29jZGo5H0PwcCASwtLSEYDIp+Ly0twePxoFQqiT6l02kxrBSyBvr9fqOPlZ0TzWZTHBE633rQCWuIepLRWet80XSQctYeoaPT6/Xg8/mMKWTj8VhmJvN9evgKyxGApcsE1lInvF6vjAul7QAg5RPqHfv3bTabAARpj3Vvv9/vRzqdFqwA74nftbW1JQ5+MBhEu91GoVAQHIbX6xXgGO1RuVwWFLumUa5Wq8YwJGACbuX1tFot5PN5FItFKdGUSiUpI+mOBM0aye8JBALy/bqT4SLyQDyXmvQ0lR9gUkry3zXEXisxQVisSej6Fo0N638AxGsDrCglGAziypUrgjZ8+vQp5ufnBSUOAK+++ipKpZJBUUjAAlnQACtKfeedd9BsNqUFh0Ytn89LHa5SqaDT6SCTyaDT6ciBHI1GBV3I2rfb7ZaJQ/QgnU6nAIxohNrtNhYWFrC4uCieKOuT5XJZDhjWmvx+v9Sa+Bs3b97E3//93wOY1P/8fr/hEbPVKpVKiUEgD/D0eEqy+kwbxs9S7osg9I51XZitLRoTMR6PpUatW0dOTk4QCoVEJ8gFzANHI6I5lY3GaTQaSVscOwU8Hg/m5+cN/uX19XVEo1Hj4I5EIuKcch1KpRIikQgSiYTocqlUQqPRQC6XwyuvvGKAMOfm5gz07NHRkTCk6bGIAAy8SCAQwMLCArLZrBg8jhrs9/sSiVUqFTx69AjBYFCiZlKokoQCmDjVNOrUlV6vJ7zxul5I51Dz6gMwuKP1+l50OesatfNLtkItfE583ev1wufzyfPRXPBkxuO6jMdjAWvx2fP/2pGrVqu4ceMGGo2GtKcGAgFEo1E0m00Jijjxj8hvfp/L5UIul0M2mxUdOz09lcOR9q1arYrO0tHgnAL+G+9xfn7eAEDyfnTgNh6PkUgk4Ha7RUd+9KMfYX9/32hbI7MfD2C+12aziX3Th7XT6ZSAS6/beerYczmkCSSafshut1tmpAKQNoter2ccGoy66Sm6XC5EIhHDM2e6MRKJIBaLCVqRc1VfeeUVmd88NzcnwB/+TrVaFe5lLsjCwoIYbRomGnKCGQBrHKbH40EkEsHHH38MYIKaZXqR15pIJOB0OlEoFMTw8zCNx+MSIcViMdjtdmMGbzAYRKlUksgdsFKJLpcLqVRKUj8bGxviRXNTlUoloWukYtIQ0HDyHqmc+lDWfLj6Nd2/ydcuKsL7LMY7vXkZremDm5zVfI5EvJMDGJhE3KPRSAwKAFy/fh3r6+syCAGAGCmtE0y1uVwuiXISiQSOj48F7AVMIuFwOGwgy202m5HxIAd5vV7H3NycMJHVajUkEgmMx2O5Dhq2eDwu7Xq9Xg9f+MIX0Gq1JP1ut9vFwLM3lU7kcDgUJ/TJkye4du0abt26Jb+RzWZllCxbDMfjMRYWFsR5oaEeDAYyJpUHDIe6uFyuZxjHut3uhWSF+scIUcWa8Yo83fowDofDODw8NIC3fC/tG9HUtVoNmUwGTqdTbB7Boix3AJMSj8PhEGey0WhI+5tmO8vlcmL3gMlM8rW1NaTTaSklDgYD7O3tYTAYCCvi4eEh7Ha7cDwAELrb1dVVWftgMCiRNvdGJpMRumTaHZfLhUuXLuH+/fviXDALabNNBsWUy2W551gsJjpKcBn3PGA5RdNtfhdBZsCxmcxkJjOZyUwuqDzXSFoX4XWtRU+80mQFAGQkJFtSAEhdRtcFUqmUtAiwfgJMxkRGIhG89dZbACyOWZLDM+LOZrNIJBKYn58XIANHUnq9XvHE9vb2ZNIRU0QEFjFdDlhe39raGgqFgtSX+Du1Wg2RSES8QI7MbDQaEkkXCgVhDuN3zs/PyxB0zf0dDAaxsrIiHufW1hbW1tae6V8mj61+3hzlqVsPvF6v0S8MTNJBGkzU7/elp5Xr9bIQS9BD5r1ogn2Nb9CvA5B2l2azKRFzKBRCp9PB5cuXcfnyZQGuBINB4fRmFLCxsYFSqYRsNis1v1AohFarhWQyKdkVpnZZegAsghNGVLplqdVqGZkltu0lEgmp0wHWSNRGo4FEIiH6Mx6PhWRC14Wr1apRu2TqnqxVgKWj4/EYsVhMIiS2f3U6Hck+DIdDAcuxBevq1avo9/v4i7/4C4P5KRQKSTslywF6kMv0VDaSmvC6p8sxL4swQ8c/A5NIEJjgP7R+ApOZCJodkNkNzQQHQBgZmQkhaMzv94suPnr0CM1mU0oggFVmazQaCAQCgj2gjSADIr8vHo8jHA5jfn5e1n8wGODKlStS3gMm9j2XyxkkLBycoif6cazlz3/+cwBWyZFkKswC6j56lgLZ8hUKhcSuer1edLtdxGIxtNttSdVzH3HyIJ+jzqwBuBD69Rs9pH8ZwttutyMQCBgsXcCEYUh/Tk+8YW+nRtj6/X7E43HcunXL6GElKYjb7ZZDulAoCBqaaTpOdRmNRqJcRAG6XC4xaoPBQBwCpmRu3bolBCqaUpIgOK/XK6T2sVgMKysrohCAZSjr9Tp2d3dlc/R6PTkE+P9er4eFhQWhggSslDzroz/5yU8AWEj1b3/728YaBINB7O3tIRwO44/+6I/kfQ8fPkSr1YLP55MDgkbd7/c/c3BzE/G72betCTOmD/iLINNAD6bqdd8zDaWme+33+/D7/QYTWKVSMYa1VCoV3L17F1/60pdwenoqfc2kYNTMTwSMEYMATJjJAJPcn+QlHMfHg1LXyBYWFuB2u1GpVGR/XL58Gfv7+2i320LcA0DISdjLDEx64kmAAlj6WCgUjJJTt9uVFDrLRo1GA8lk0phHrEfN8hldvnwZo9HImMFNnZmbm8PR0ZEc/CR2ITKX7yXAlM+MzqLmTziLNeqi6eFnCUfDUjjARTPVDQYD+Hw+sZOnp6diKzXxhsPhEBwED1/WeDUgLJPJCKCPdnA8HmN5edkIdOg46Q6b0WiEQCBgpORJEFUqlbC+vi62eX5+HvF4XGafA1adm8Nd6Awmk0npDaczsL29LdMKqbOkaV5cXJTPDodD5HI5BINBvP766wCsctXBwYGMcgUg/ed0WPh6s9kUXAkP4s8aMnTe8hs9pHlzuvbH/xMYpSMFXb/VBXouHA9Av9+PQqEAm20yuo7EG9FoFIFAQA7Ver3+DLtMPp/HeDzGK6+8Igq7t7eHnZ0dpNNp8fhOTk7ksNX1FYKtqDSAVfNNp9Ny0B0cHAgZBj03wJrWwsZ7DUZbXFw0+JpPT0/RarUkegasw9PtdiMcDgvXbjqdxtHREba2tvD+++8DsEBHdrsdpVJJvFQSUayvrxsAvU6ng8PDQ5k1C0BIVEajkVxjo9EQ9q1pNCfXk89Hr/u0LpyXnFWPZjsVMGGI03rCg7XT6UjtinVBTR5BEFqhUJCImJ9nyxVpFxmJdzodY3b48vIyDg4O5HkPh0OEw2FxKHnNu7u7WF1dlT2TzWbl3hilbm5uyjQv3aao0dt6YpLH40E4HJbuAOJEarWa7A+Px4NqtYrxeCyHL0GSPIABy/iShpcZhWKxiLm5OdTrdXGek8mkGOr//b//tzHPuFKpIJlMivHP5XIGCyF/ky1vOjt31jqft+79KqKvkTaGswyASWDSaDQEDEoGOb6HzjX3N58xANy8eRN2u12IbwDIoaSn7xGYqMmaBoOBRJcMIvL5PNrttuANAEhLEw9UOgapVEpq5/xtu92O5eVlQxdJiuPxePDmm28CsPTh3r17KJVKUjcn5oLT+wBrv4RCIdEpwOo0oFOsa9Kj0Qh+v99o6SP5CtsX+RwZmGjsynnr03NNd2ujzcNPbz7dgqXp8FwuF2q1mjwcjgbUIx+j0SgymYykxOgF8nDX85KbzSYWFxcxPz9vsOL0ej0UCgXxVJvNJjKZDIrFoqSSGeHcuHFDPttsNrGwsCDeGd/H8ZWdTkcMaDablbYRXqPb7cba2hru378vqfZ4PC484hrB2Ol0sLS0ZPSHj8djPHnyRF4LBoMyLEQf0uwdZzrI7XYjGo1ia2sLc3NzAhTJZrOSMmMaks+do964XkQ06wNrOoo5C91/3jIej430qU7587qJptUsZEz5sy8fsHSHACxmTgDLIJDbWrNvPX78GE6nU6JHRqJMUQMQ5476Alg9o5lMRhwK3sdoNEIwGBSHNRaLScQwPz8vkW8+n0ez2UQkEpHIl61V169fFwfR6XQik8nIIBtg0kVgs9mMA77T6RiMektLSwJ443U/fPgQBwcHSKVScmgAE0eE9wpAQD768+Qk0IcwI0tyj/O16XLLRdS9s0SDsgBrX2vUOrMGPp/PYIZjm5bf75f9y9G57AYArD2/vLxscG17PB45+HTpzePxYGlpSfb+p59+KuUyvtZutxGNRgWIyN+NRCLCcqcDqEQiAa/XK58/OjrC9vY20um03GelUkE8HhenDLAcwXw+j+FwKPfHSJhZLj4L2iNNixoOh1Eul43ecGYiqb8AjAwBf5t7lLS/+vMzxrGZzGQmM5nJTGbyjDy3mvRZni0Zg7QXDUDYYPi5TqcDv99vkP3Ti+JnAoEAvvWtb0nzOmsadrsdiURCAE4A8O677+Lu3btoNpviSb7++uvI5/NGGrNSqaDdbiMSicj7mCYpFosSHQWDQZm2xajX4/Hg8ePHaDabwh7F6/F6vQbI7MGDB5L+0Sl59gty0LpuOWOdmmmwGzduYHNzE4DV5hOPx2V6DJ8Zv4P3x0k5BF1oFiuS+/P5+v1+4bedXj9GmwCeKWHotT8v0RGWll6vJ9dNcIoek+pyuaS3U/f1RqNRiSoBa/3D4TBWV1dxcnKCR48eAZj0JbNVCZi0+ulUJssMusWILHupVEpqxW63G/fu3Xtm2hXBOrxGrsFoNMKTJ0+MSJM84kyZ9no9rKysYH9/X3S0Xq/j8PAQd+7ckRYe9usHAgFJg2azWYRCIaN+mc/nMRgMcPv2bclSbG9vSz89IzvqHqezaeISAt+YYiVo0e12G6A+lsL4zM5qlTlv3ftVhOAvHTnT5mnO+Gg0KqUVYEJG5HA4DIKa4+Nj+Hw+A1/D/ayxELr/njiKVColTHKabTCRSMjwE75GnAL3QTgcRjQaxc7ODqrVqjHhTPOEA5N+Zb/fL+UTptoBiC27c+cOLl26ZLA2ajAc15dtY+R+4O8Wi0WDEZF2zG63I5lMyjVyQJPOBPZ6PWFu0yDh89apX+uQ/qza4zQ6GJjUSzTdnf43ApN4uIRCIUFxahAZ62AaGcj0V61WkxoE6RJHo5GRct7a2jJo7RYWFnB6eiqbgddWKBSkHxCAkKCkUil5H9G6Gmm6vLyMRCKBRqNhjM9stVpSG+T/aXCWlpbkGr/73e9KippGulKpyKxWTstiaeDatWuSDnr48CGePn0qfeeUcrmM1dVVed4czpFKpcS4ch2YEtIp0OFwKP2qgKXEHo/H6HXXjsRFAu/oFJV2PDTjFg9zPUOb60rjwj5LPgvK97//ffT7faysrBikItlsVvRXXwt7rYFJDywnrAETIx2JRIxZ5CRw0MxPNKa6n56AoHg8LvfIGjcwqV/rVDlLR4uLi4IN0VSxwGR8J3/H6/VibW3NqBc3Gg2jbKTTjzwYut2uYCZ0OcHn82EwGBijM8lMqB1tHlaamOWi6t6vIrrPnXtKM4axrNDr9US/CPZaWVlBr9cTsqbBYIDl5WUsLCyIA1gqlbC2toZkMinPZDgcSg1Wd9X4/X7UajX5bDQalV5/6gOpWx89eiSlDk6nWlpaQqVSkdKdy+VCMBjE3NycjLr0eDy4fv06dnd3jYCDKXiuH0GSLHnyuh0Oh0G2xClu5XJZvi+fzwuAkfsFsGr1rO3ThrdaLXQ6HYP45Kzz6yLIr3VI/yobgR4KDytGYLr1g1B53Rqjo2i+7/r162i1Wtjd3RVgS7lcxr17955h1SGwZn5+XoxFJBLBzZs3sbGxIc4AJ7/kcjkxSLdv38bx8TH29/fl+xqNhqAVGWUUi0UkEgmD6pGgBo/HIyAeXid5u7mpVldXRen5+Vu3bgm9J+lMT09PEYvFZIIMYFJSssbZbrexvb2N27dvC4iJrQ4kJgAsZfX7/bh//z7GY5OzmpGZ3rx6mDwwqZtOA0LOqguep7GkQee96DWaBr4Nh0ODhchmsyGRSMj6kwaVTiEAmb5z7949DIdDyXzU63UUCgUDqzAYDDA3N4dUKmXU+3u9ntSjAchULRoOyvb2Nu7evSt/D4fDaDQaaDQaxgEPQGYA09GikatUKqIDOkKg00iCEA0Y4pzo/f19YwZvp9NBPB43oqHNzU1sbm7K+8j9HAqFhDJ1f38fb7/9NiKRCC5fvox33nlHnj8jS64JMx6aFpSv6yleZ+nZeeveWXJWp4Fm/eKa69YfHd1xTzNYaTabqNfrBjnON7/5TaysrOAv//IvAVgUtWz7Y8RNMhFyfwOT+eV7e3uyj1utFoLBIJLJpAHUos2ifs7NzaHdbmNrawvtdlucCZ/Ph0wmg0QiIbVh6jcjfr5GR4znRDabFWeQv8351KQRBaxDn6RTfC0QCGB5eRnZbFY+y64am81mzLcmxWqn05H9T3tH/BR/+7yJTZ5LuvusVDdZrXRKlf3PRBcDEBYmek98H+H8BISFQiGsrq4iHo9LnyYASS0Wi0VR4q9//evirdEwsgWG7QyAhfgul8uiJIA18s3r9UrvIDCJcJgK53XTwcjlcmIgV1dXMRgM0Ol0pLfUZrPh6dOnQgUJWBG72+3GycmJPJ9QKIS1tTVsbGwYBwQRh2zVsdvtAmQjwjYWi2F1dRXZbFaiGYI/vvjFL+JnP/uZOB1s/dBRGg2G7nVn/6o2JgRgaRak8zSSfHZ6Y9FIckgDMAEiaWAU0ch0bgCIg8V5yJThcCiHIg/kS5cuCU83DRZ7Uzl+EbCMcjKZxGAwEFBPMplEuVyGz+eTg/L+/fuIRCLY3d2Vg5dRMNuwKGQi8/l8xiHN6EN3EXg8HmSzWeHk5nXpNPbu7i6CwaCkXAHL4ex2uzg8PJQInW1i169fNyJoZltoAFdXV8V5Wl9flx5YHsQ2m032AtPhg8HA4NrXKGVeL/Xuoh3MWqavje1/mjuCWTlmBIPBoHQWMOPh8XjgdrvlsKKTFQqF0Gg0kM/njVJJOp2WkgUwAUYGAgHZB6enpzg8PMR4PMalS5cAWCCvTz/9FLdv35YyCWeLa1pWZj1PT0+NFrz19XU0Gg2DUtbpdOLk5ASvvvqq7LfT01PJPFLvCDiMxWKSmWEQx5Q5AKFFzmQy8hsEG2qObtp9r9eLbDYrv81nTtvMdeEz1wj085bn0oKl/64jLioiDza73S4eM43i/Py8pDK4AYn+83q9xoHBaIRoa37n7u4uIpGIKA3Tfnt7e7Lww+FQIktuAqYVG40GvvjFLwKwUoFE7HLBiLhMJpPyffV6XRCR2vt1uVyycTT5QCwWw2AwkBQT22JyuZwYJqIr8/m8vKbb1nh/nJPt8XhkLiufH0khuB5erxeXL19Gp9PB3/zN3wCwIp/pqV6kTGXth9fDZ0ynodVqSUpWR67nFdGc9Zv6Nf0s+B8NYzgclgk8PCiBCee5Ti+HQiFcunQJnU5HDiyv1yt1exrGeDwu9VYe3NVqVSISHdnfvXsXh4eH4mjxcNc0jkdHR3C5XFhYWDAiEr/fj3w+b0TopVJJ2hZZg2Q0EYlEZH+xh3xubk5+p16vyyQrGkZSo+qJcE+fPpUMjOZA5nPlc9A0oJVKRX47Ho9Lqp57iQdLu90+02mcHvhykQ/os2QwGEh9GTBLgDrFOx6PpZcYsNK5PHg5MQ+Y8PSHw2HR20AgAIfDYZAg0b7pKVFMEc/Pz4t9Wlpakk4V7g06SQxu+Lterxd37941nFVyVRQKBUnRe71epFIpPHr0yOC0oEOse7Tp2GphOYR29fj4GB999BHeeustKeW0223RGxKuxGIxdDod2cM6e9jr9STwoDDjqx3y846mZ+jumcxkJjOZyUwuqDyXPmmKnjpEz1s34zONrGuzzWZT6DnpQTJdQdJ4wPLi/vqv/xr9fh8bGxviQa6urqJer0sNBJhMDopEIpJSGwwGODw8lGZ3wAJMLCwswOPxSGRer9eRz+cFcEXZ3983mG34/cViURDdAGSkoNPplAjJ4/FgeXnZYHhqtVqIx+O4c+eOINXn5uawsbFheMScD60p9wjAGA6HBuEKBzTQS5+bm0Ov10M+n8ft27cl5UjaUjJRARPPmZkOPjMyHWnQzkWgzqN8FqiR0ZmuSTG1xYiEURlrr4CVIkyn0zJJB7B0jPSzNptN0o4sBZA2lL87HA6xu7trpM4CgQCuXbsmkSunWOnJT6wBhsNhY1SfJnAALN0hAIhjHwEIaIhRA18rFAoYjUZG6ajRaAgmAoCAEg8PDw12MfZ4c2/6fD4Z+8drjEQiKBQKEvFzLVqtlpQNuD8ajQZCoZDBTsXXNbhqPB5L7/BZoMWLLDrap00kGh+YgDRZZ9Xva7VaBuZD04VyDUKhEO7evWvQ9ZJUR1MKe71eKS1qOmNm+nSJJ51OI5PJyGdbrZbMrWeZ7N1330W/38dXvvIVGQcJWJlPr9eLSCQia1qtVlGv12XfABNCEeJ2+NtOpxP1el2eWSgUErutI9y1tTW4XC7JRpJQhd0PgMU2yXHBBLTxegi41FTBBCzqAUtaL89Dntshrenjpl/Xhy8fguaXJtewpnYjEQUP6bW1NWxtbSGfzwsZAmClbmkkaCCYplteXpbFo5F2u90GwQk5uOlUcEqPdiTGY4u/mIcjABmaTnSiTo1yeovm4O10OgY9InmXdftOKpUSnmddiyG6UtdXut0u/H6/AMwCgYCkxnmIMh1LEg1uFhrrZrMpICi3242dnZ1n5vdOc1sTyU6SFcp5pSDP+l1SSurauV5L3h/TXxpVrEclMq12+fJlOdg0I5TX68Xu7i6Gw6GBX/D5fAa3MQ3v8fGxACHJ7NZoNCQ1ff36dWSzWRSLRbluHrgaBENqTqfTiQcPHkitmdPXXC6XpB3j8bjcJ++bDuNwOBRdOD4+ljognwGn2FUqFdHRTCYDj8djOCCkk+R/wGQfZLNZXLp0Sa7nwYMHzxASkZp3GudALnrdHcIympaLlgKfBo3xgJ2mPdZIej0OVAPE2Faln+3i4iIqlYqBm2DQUiwW5c/pdFpKaBrQyzWnHrNVTgc60WgUfr8fa2trUs8Oh8OiD5zQBViBydraGhwOh9S5XS6XtFnp0kUgEDBArOysWVpaEhtTr9fRbDbh9XrlWoPBINLptDH/gROwNGAtFouh1WqJI0ldZqlS6wrXRXft0Fk6z0DkuR3Smi0MgPThDQYDY8zhcDhEIBCQDUkeYD3ur9/vywBzRrPz8/NIpVISifPAyeVyEgnRC7x3756AdVg34WbXxmE8HiMejyMej0vrQLvdxpUrV1Aul+UADIVCWFhYMBwJwKwtaaNK46JbsPib9AJtNpsMDOH7iD7k/FjAOsyTyaTMlQUmCFmHwyEbkvO23W63bGa/3y8zaTngg8+i1+shlUoZ9JPtdlu4noHJqEuNK2Adfrp94SIZSTqBGvRGY8aDB7CMYTgcNpiSyInc7/eFmjUWi0k7l+Zk51hHbUD1QULdJbub7mAga1ytVpO6H9c1EAjI9bIdMJPJCPVoMpnE3NwcyuUyNjY28NprrwEAvvOd76DdbgvWgc8inU5jPB4b3Mhf/vKX4Xa7xWm02WxSO+dak2dbI9WpEwT7AJbDWqlUcHx8LHuQWSyfzyetQHyd+qLBcTpan15His7yXFT5rL3Q6XQMkCP1ibaRIC9N18mZAqQQpdNIGs5MJiM12/39fezt7aFQKGB1dRUApOWULGO8juXlZeFpByAdL9r5aTQaqFarRufK0tKSrFWz2ZS10TVg7bxx4IsO0ph5pI1ZXFyEw+FAPB4XrAfBspqVLZPJSFsuMRjdbhflctmYTa6pmjUQkSBZnfUkOE4PH+IePU+b9tzT3bwxnSrlBuXA7mkvvNFooNlsGv14gJWupSHg0ACSRdAL7Ha7CAaD8Pl8slCLi4t48uQJ/uqv/koO2t/7vd+TKIa/Q5Qie0H5WqVSQbFYlCicqZd4PG4c+u12G5ubmwiHw2KQfT6fRKc6/V6r1UQxAUuZ2DbGw5MAG6at+HwSiQSWlpbE8DJ9qaMKcoGT0AKwNhVJAjQlIdcnHA6L0aNjoD1+jczXmQYAxvvOG909ncWhUaIOAhCACefmAhDOeM05zdJLv98XPWm326hUKojFYkin02JYadw0ctflcknkybRiMBgUZ5VOFR1Qu90u68qWPa/XK6+RSEe3yzFDUCgUkMlkZD/Rwbt8+bLo7unpqVAn0mCRG+BLX/qSTCMi+I+dCIDluPGApv6wf7zb7UrrIFvYCLADJns9n8/jZz/7mURdJL7Qs96ZqdKIbn2fFE3Go+UiOYiAeVATVKcHhehggRmzpaUlmTqnS4F0VBwOh3AnkPfg9PTU4NX2eDxYW1sTMBqdeE33SUeeBD0ABDkeCoUkciU/gyZlajQaKJfLgpDmWpGm1ufzye/U63VkMhnE43FxOnq9Ho6OjgzAG/WApRFeTyQSwXA4FAfW7XYjlUoZyPBSqYQrV64Y7Wl0Hrxer6DjgQk5i856aR4DfW7pNsHzkBlwbCYzmclMZjKTCyq/sUj6rHSApmdjaluDydgexEkq+rt0JMyo6MqVK5JGZH2jVCoZPaMk8Cd1HmD1r7rdbnz88cdGWiUYDOLw8NAYVOHz+bCzsyNeJdOhmhyl2WxKGxY9ezbRs8Xhu9/9LgCrR/vKlSvodrviLbKncW5uTu6bIxMbjYZkCwAYgxEAay4vU+18Pk6nE7VaDaVSSZ4DW7IIFONzZD+kbu0g4xNTTwCELlMPRGEEMD0shGv7y/ThRcg0WAyARC0aVEZiCEa+gHXN9XodCwsLkvpj1OJyuSTKyWQyqNVqePDggYz0BCBMZUdHR/L5fD6Pfr+P+fl5A0zmcrkQCoUEoMVnShpSwGpvqtVqcLvdEmV6PB6h8eReaLVaKJfLODo6wuLioujp8vIynjx5IlgLvpcDEVhqmZubg8fjMUogBHLp8gvHYOp2qVgsJnuOdWZyHUSjUWO4y7Vr11CtVpHP5w3gWDgcNkaG8jPEWQCQAQmaspEZk4sEXJyW6T1AG6iHvQCWTWHdFIDMFmdpBICUzcbjMSKRiGToDg8PhUGQ2Z7l5WV4PB64XC7JHqZSKfT7fWSzWXkfp5VVKhWpH8diMZRKJezs7Ag+gtTGBFbxNYK8Go2G6JjH48HBwYFkq4BJeTGVSgl4iwAztkkBECpijhsGIC2eq6urYssGgwEikYhQ4VKY0dQMlyzJDIdDSaGzHq0BeMweagCfvvbzasP6jR3SZzH/6BtjXyDrWsCkSZ08ysDk4elxeDy4Ll++bBz6ZNAhIpzfSWPKhQ+Hw5ibm8Pbb78ttWbAWuhkMmlM/snlctjY2DBGNhI8RtAalbJUKokROTk5wfe+9z2EQiEjLVytVoUVh/dNDlk9W7Ver+PJkycIBoPS40dkbjAYNFJM+/v72N3dNWop7XYbtVpNNgD7HlnjB6weW6Lhs9msHOisQ56cnIhBJk2oBsGxtqMPYPatTlM0nltqSAE9NBgRgJHuJgin2+0ajEOApS/UPSJvdd09Ho8LuMZmsxkzyolA1mleGjbNq85pa1xDHpKcHw5YeAb2xOqeWuI7NNag2+3iypUrWFpaEoAaSyPVatUYLel0OhGPx0WfNYJX09wyDa6pIXk487OxWAzlchm5XM4AILLTgYb25s2bwiGg683Em2jAJA3tYDAw9iYdZh5umgjloqW4p4VrynS15hUg5wMxIMBkDrnWOx66ZCzjmhITUKvVxI5ub28jEokgHA6LjTo+PkYoFEI8HjeYxFjq0nrQ6XRklCNgHaharwAI3uHq1avY29uT7hUSsQCQ+6nVagJiY8ARj8dFx+kwHhwcyDxx2vTxeCw8FHRqU6kUwuGw9PMDEM4LdkAAE058ngsayU2mM12mY01a6xNHhJ6XPJeatI5M+GcafL2heNBqhDXr1hp1S+Owv78vh0gikcD+/r54RgRM8FAdDAZCSZjL5YQyUxtkkj0QgEHgQbPZFIU9OjqSsWus7RItfvnyZVn0H/7wh4jH46hWq7hy5YoRQei6CQCJwvQMVg5s1yxU0WhU2NRIUhIIBHDv3j2pJQGWMxIIBOTA5zU6HA4kk0lZA7fbjcPDQ+zs7GBnZ8cAYbRaLSFF4PMlj7Rmc+P38DsZ/U0f3C9aNNvdWTNgadz5OvVR16lIMsExkvw+OiKso5LpbjAYYGFhQYxJpVKB1+vFK6+8Inp5dHQkLVgaZev1epHP50VPVlZWkE6nsbe3Z+AP4vG4UZP2er2w2+0GiJLGMxqNythIYEKxWyqV5L6DwSCq1arhYEYiEWG7osHiPHGHwyHXyGtiFwNgHfAEmHEfkfDk2rVr4igNBgN89NFH+OCDD7C1tSXGlmh6tgACkMOGtoBC54u/rW3FRZaz9oZ2Sug8MpMGTOqiwWBQnr/f7xdGRL/fL3pXKBQEkEcnndnIXq8ne9rpdCKdThutnwDEoWIAQ+S0x+MR3aJj2Ov1JILvdrs4Pj5Gs9lENBo1DsZ4PG7gVMgMdnp6Ko7pwsICXC4XPv74Y9FZ6iDJnQDIfWpaX2YxO52O3F+r1cLR0ZEA1wAIXWkikRAaaMCyo/w+/g5nQ4xGI3EuOAdCD8h50fr23OZJT4v2VnTvNFnEeGD4fD5B1+rpL2TY0ujHZDKJvb09rK6uigE8Pj6WFNH0oIpoNCpgsdPTUxSLRVksYMJrrKNtDgLX3l6v14PP5zP4uMfjMW7cuIFqtSqtOfwdl8slgzoAy2lIp9MGZ3kqlRLmKEa2TLUOBgNJWa2srMDpdEpbF2B5pAsLC1hfXzeyD7wuRnC9Xg/Xr1+X9Dhlf39fflcDPQDL8DNC4nppYJTX670Qk2LO+n0dvfDg1v2qRM/yfewh5SEITFLlOtvD3mgepnwvGd74XYClu5VKRXQAsIzOvXv3MBgMcOfOHbleu92OWCwma10oFASsxbUm6KxWq4nT1O12Be3aaDTkt1OplAB9ptvwnE6n6EWz2cTBwQEuXbokz6dSqUiUo1ueiMLmb9frdVy/fh0ul0uMLxkES6WSTAj74IMPkMlkcPPmTYxGIwGjUXc0gx6zIYFA4JmMBiNnYILQ14fgRR+0wba2ZrNp6CfvgY42KZOr1aoEJmx1YuaBhwszNbu7u7LvmYFZWFgw2k45f1lndQKBAEqlkqDA3W43jo6OjDnmpGGuVCqiS9StWq2GVqsleqtBgDy4Q6GQ/Bbvx2634/Hjxzg9PRVbz/vvdrtig7a3t7G2toaFhQX5bYLWdLmJYMVisWgwizWbTezt7cHv98uZwKhaz5cg14QG4BLYrLnWX3R5ZQYcm8lMZjKTmczkgspzbcHSXi8wmROrPWBOddHgiOlxiMPhEKlUSsaaAZNabyaTEW8JsDy3+fl5STcCVjrnk08+MQYnbG9vw2azSV8sf9vtdhtpFbaNaHKF+fl5hEIhPH78WOrHnEPqdrsNLvHRaISDgwOD25icvLpGwusPhUISce3v76PZbGJ+fl6ez9bWFgKBAMLhsNFPzQEdujafTCaNXkfOll5cXDTWJhqNyrXoepPT6RRwBZ+PHiPI56NxAFzX8wRaMCrRAxnYUqRBR5rHG4Dwdus5xayP1Wo1Wb+joyMMBgPRRWYaarWa1MAYdYdCIfmPHnuxWJTv4muMbNheR+n1eiiVSqKj7EsdjUbyG+vr6/D7/QJcpK4UCgVJG/L3tra24PP5jNYxu92OS5cuyXcAkwlFmiiIfa/UX8BK3XNKm554xCEzXINvfetb+MIXviB7cWdnR+7R5/MhHA7LOjDybzabsl7abmhyFd07y3XVa3oRZJrkJ5VKoVqtGhk8lvyYRet0OsJXrZ8/AWdOp9Oozfv9fszPz4t9Y+81Z6QDFojx0aNHaLfb8p0bGxuSqWHEbbfbcXJyItcAWJEtwalce7ZycjoZ9S6XyyGTycDr9YpOABC7xvXL5XI4OTmR58HXmEHhNQaDQSlzMLpuNptYWVkx3heLxeD1emXoEmDtq3g8Lv3QenRyo9FAq9WSTBjr8xyfymfL7zovgOJzPaSnDTVT27omfVYvJBv4+UBLpRLC4TBGo5H0cdpsNqGYYw8f30uKTW7efD4vaQ2msm02G+LxuPQiAhMUIevAvB6SPHBTFYtF/PznPzco5cjWtLKygkgkYgAn7HY73nnnHTHqdrtdyCv4WqVSgcPhwMrKimwqHg5UHADCpBaLxeS5uVwuPHnyBJFIROqCsVhMDgP+BsFORJJSeBBVq1WjHgpY6Xo9dYj1fn0AEnV7njXps0T3P+sZupTRaCS1ZWCSpuWUKcA6CMiARWMwHo+RTqeRy+WQzWaNEaZkaaJxo3M5HA4FYBYKhQQHwDo3hxQcHBxISo5o6oWFBSlPFAoFI/UGWF0PGjFNKZfLCIVCuHr1qjgSrAn6fD5Z12QyiWaziVQqJWt3enoqoEM6p4FAQPYacRNLS0totVrSrQBYRnV5eRlOp1OuyW63Zktvb28L/S4APHnyROgu+cw5Z16voXa0uA7dbleQt1r3Lor+nSU2mw25XM4YKUpgog4iSAerU6+aASyXy8nzymQyzwwpYdlGA+3IDNjr9aQ8yGls8XhcDs/t7W1UKhVj/ZgGZv82AKNct7a2Jp/PZrOo1WpGSYaMjTxwAat7gQxqGgVOXAnLH7TLeq46Udgej0eCFaLUqScApJOBYD3W2Mmw5/f75TkSpNdut+VeeJgT3Hce8lwPaWDSBqPH4GnOWhp+PeUpEAigVqtJdME6nOawrtVqomisdQDWYXdwcCBgAQByoHHkJWBFn9VqFVevXpUF3d3dFcXT4+FYx6FwSkulUhGvMpPJCOiBBCT8HdaPaezoqXFCEjChqdvZ2RFHgp6fBjJ0Oh2Uy2UkEgl5bWlpSdCQfO34+Fj4gXU7QiQSgc/nM2j3GBXqNjGbzYZyuSwtVsAEya1pMz+rBea8ELfTPMnABAWsN6TuJuA90yjpyWGMEHRGiEQL5XIZ2WzW4H5nOxuN2+bmJqLRqJBDAJM6fiKRkMNT14D172SzWfj9/memIzmdTtHvXC5n1G5pGK9du4ZarYanT5/KGnGG+8cffyy6SyeG2AQAQo6iWewILms2m5KpIpGQzhTF43EkEgns7e2JUaUDQzpargMNrSav4IF1FqMYSSm4htN4iIt6QGsGq+k553Q29L1Qj+nAABC2LmaE9Hhf1o9ZV3748CF2d3eRyWQkOucEPDoFgKU7g8EAa2trBuCX2TZd241GowaPfKFQkOAkmUzKmt64cUOQ27QTW1tbyGazWFtbM3i4CTxl+1e73cbh4SHC4bC8LxaLicPKcb+NRgM7OzuIxWKyDz799FMEg0G5dmDSNdHpdBCLxQymPAaIehoc9x9fYxD3uaQFpVCRqIiaw5rgHU1izvSF0+kU40CYvT7MfT6ftGQQnANYhkQrHGClRthSQAXj3N1sNivpxWAwCLvdGjCuB1UcHR0JXzIAQXu73W45zNnKY7PZDI+Wm2ptbU0MMqM13UaxtbWFVCqFXq8nRoigNrfbLaA1PpvV1VVRTvKXe71e8RQ1YpTPbGFhAXa7HeVyGcVi0aDnI4G+Hr7OwSBUbK6X7p0eDAbPeJnTY+ZetGgDB0yM4XRKHrD0k7ozHo9lKAR1tFgsCnKW9zgYDAQ4pgFT8XhcuJD53vfffx8PHjzAF77wBUn3xeNxHB8fw+Px4OrVqwAmiFgiYHk9nI2uM1L8HFG2HO3KKOCTTz4BYKUX5+fnjb7tubk5GbTCe6zVapJRoK4sLi6i0+lICQCY0J5mMhnR2w8//FC+h/f8ySefwOv1YnV1VfY1jft4PEatVpN75PN0u90SiZfLZTQaDWMQh571rctGF12mnVUOUGHEqIUBCmA5bdVq1QDpdTodeVZETwOToUW6vYnOEMs3gKX/LKdwuI7D4ZDsG8F83W4Xy8vL6PV64oxx9O3JyYnB7aBHYnJ/FQoFLC4uCrocsCJXMtDxuhkoXb9+XdjT9vb2JMVMW5TP58WxYFYwGo1KKyFlfX0dh4eHCAaD8hsEt4VCIYzHY9ExtgPr32F7nB4hS7uuOT9etMyAYzOZyUxmMpOZXFD5jUbSOnoFzPoQozLW9wAIUXyz2RTPnLUy3WMdiUSQy+VQr9fFEwKs6HMwGAhoCpjw2GazWfHC2dYVDAblfbp2oeuw7JGmd8WeWT3uL5/Po9frCVAMsCIFm80m96KHUiwtLeHx48cynYjgnnw+L57hrVu3cHR0ZAwhb7VaaDQaBnG+Bg3x+dTrdWERIiMPGdcSiYS8jyM7+To9X/Zrl8tl8cS5duPxWLz7ZrMJp9Np9H33+/1nRrmdd11wOnohU9r0xDP+m04xxmIxuN1uyXpwvKXmjH748CFu3bolveTM+JAoZDgcYnt7G4Clux6PB7VazeBq9vl8AmgBICWVXC5nYA2CwSBarZZEs+xv5iAC/q7f75cWHWaBUqmUEIOw9s3U9pUrV+R6gsGgYC7I3tRsNqWfm2u9tLQkPd96lOeDBw8Qi8XkOTIC1m2QXq9Xomfdm97tdsUO8B6Z+p6ONh0OB4LBoNFS+MvW/rzlrGshWEmvX6fTkWcBTNgYnU6nZNaY9iWYU4O66vU6CoUCVlZWAFgT1MbjMfL5vNhWRuUOh0PSxrlcDu12W54tYGVb2BamyXbG47FkMAErq1ev1xGLxZDJZARzMR6PUSwWjVT9wsICrl+/jkKhIHsLsLI9gUBAMoVMyevWSGYQ9KAhl8sFj8eDzc1N0dnBYIB0Oi3RNABhQmOpjs+X3PWhUMjIXNGmU7dImHJeIFjgN3xIT9dTALMmTXQwF9lms6HRaGA8Hkvxv1arSc1KG8put4vd3V289dZbAKwF4fSWUqlksEs5nU6jRkLiAF0X93q9WFhYEFYlXuvx8bGAqwALYe1wOJBKpSS9CECI5Zn64X2k02lsbGwI+06r1UIoFMJXv/pVvP/++wCsVJTD4cDq6ioeP34MwDKUpPskwlYfijz0Wbt0u92inE6nEysrK0Jryudos9lwfHwsKavT01MsLi4inU4bShcIBKQmrUFC1WrVSG2TpUuj3wms0qIZ1160TPdCAxMSFj2ekaBGXWohs5Ge3hMIBKTjQL8vm81KDZcgqM3NTUHo0gCvrq4iGo1ic3NT9Jl0rXa7XUg9QqGQoLN5/fl8Xg4uPQQgFoshFouJ88RZvcVi0RiUQAax+fl5eW1/f1/oYakDRL7qNB9JU9rttpRQPB4PQqGQwUI3NzcnXQGakGJlZQU+n094BJLJJJLJpPyWHi4DwBieQKpg3VHBddIjOrUx1et/kWS69MLr1XVh/p3kQcBkOhgRx/qzTGETIX90dCTlApbfSM0aj8dlf5bLZSEgoX56vV4UCgWjGyIQCAiCnPaSQMIrV67Ib/T7fUHma1IRdg9o550T3sg8Blj6RGeTwQUxFSzd8BkypU8dKRQKRsmNQtIVfcYQbKixJtwber/RvrXbbdlb59mlQnnuZCa8QR44ul5JQM50pNJut9FsNiVCoVfVbrflUOt0OlhZWRGjwe/tdDrIZDJwOBziQRLgoIkBgsGgRKNUkGaziVarZTTDM1IajUZirE5PT4Wq8+HDhwAsI3v9+nVpJdEsQbFYDDdv3pRo+ODgALlcDqenp8aYxFgshng8Locv61fA5KAhX67f75frDgQCKBQKxuQwYEKlSCXmiE2Xy4X79+8b60COaYI1mPHQkRRgGUZNPsOau5bzjmbOiqRJ7sDrJrWiBsYBkIlM9MQBPFPb93g8CAQCqNfrqFarYjiAyUhGHr7BYFBaaWjE+v0+qtUqKpWK0GvSQESjUYlI9vf3cfv2beFxBqyI5MqVK1hZWXlmkhEjDt57OBwWHAYdiX6/j5OTE1SrVcPRyufzcDgcgs9IpVJiyHSLyuHhoewTfX82mw03b94EAAFpFotFuT/A2oeRSATZbFbuMZlMChiORlnPu9agRXIw8/6mkd1ce77/IgrHvJIkCYBQ7Q6Hw2dAVfr+uM6kVuVh1+128dprr2FtbU30pFwuY29vT8hngEnLWiAQENu6sbGB7e1tHB4eytpfunQJ29vbcLvdBhWy1+vF4eGh2CJOV9ve3sb29rZcD9uv9vf3jZn3jUbDiM5puwlSBawgJJVKyQQ3/jYP2Ol2wFQqJWC5nZ0d2Gw2LC8vS3Ts8/lkb9GpACARM7nwARhOq+7k4Cz689Kp5w4c0yhg0knSuACQuaUaTOb3+1Gv10Vhs9ms9Bdq+k+73S79vfwdv98v4ChGDxyxVq/XDdAJlYUGh2k3AOKJAdahXKvVZHwlAV3RaNRIV5PWb29vT7xNto598MEH8ntzc3MySpKfJ/uVjoaJNhyNRvI+IoNjsZgol91ul1F1TGtyIzLND0CAasPh0NhA4XBYesHpXBA8pw9kct3q56RR75TzNpC6xY+iW3kACDpWgwkJnGLrCgBhMOr3+6I7BBhyVJ5Gz3NMo35vJBLBYDCQ7EqxWEQsFsPy8rLBluRyudDpdERv19bWxGmcHgl6enoqGSDd2rW4uCjOG1vEhsMhrl27BsAywNQbfifbGklLC0B6YpvNpuGckLVKAziZ+eKe4Xx3DogArEOD3PJsNwQmvPOaaY/jQ/k615IOlOZiP6vEdp5jBaflrOsYDAaS2QMgmbFKpSK2iG1yjUbDiHo5njebzUoWL5PJIBAIGLTJyWQS2WwWBwcHuH79OgBLn9jSpalBg8Gg9EsDkOzJeDyWg5v0t/p+ONMgEAhI+hmAOKjMxgCT2d/j8dgYS6mZJgFrWAjLfFzbUCiERCKBer0u+jQcDjE3N4fBYCDdMJVKBevr6wZrX7PZFIDxcDg0ODGoPzxn6LhqxDezRuepTzPg2ExmMpOZzGQmF1ReWCRNUo9er2eMWNSTlYAJ4xj774BJ7Yl9enwfgTea2UiPmaTXlE6npU2LKXC2Ajx8+FCinmazKQAFRketVgvz8/NGfyZBbBsbG+IVBoNBzM3N4cGDBwaLDYEZHo9HPDZGpEz9AZORf+VyWSJ2tkk9ffpUPnvr1i2JMJhV6HQ6wiDFLMXTp09RKBSEsIO/Qe/1+PhYnhkBGbpmq4ldeI96gpmu+553zWZaplOejLx0alvrHKNHEjrokZ31eh1+vx+FQkEiRXr65HDXpBSMSqmDHKyxtLQkemaz2QS0Qo+fkWa1WpWe0dXVVWxvbxtkNtQjDrbga2QHYyoUsKKfxcVFI9M0Ho+xsLCA7e1tyZrMz89jY2MD0WjUIAUiTzNf454ZDAZyjQBk6IIubeXzeWxubkrUvra2hqWlJYTDYTx69EieRSKREF3TBCaafUyvqcYQMA05LRdJH5mu1kx3BDJqxjsyuWnOaw7ToLBHHZjMDwCs8bWZTAanp6fyvAlOzGQyErETs6C5uxmZ22w2wa5UKhUp5egRkhroyvdtbGzg0qVLiEaj+Nu//Vv57UqlgmAwKLan1+uh1WrJQA4A0pJXLpelHEO+iCtXrsiziEajGA6H2NnZMdLVAIyomWXP4+PjZwaDsFWX68AeaabBgUm0T954AM9kD85DntshzZSTBkpw0IGm+uNcXx6K+qExlUqCiWw2K0pyenoqhyr7kwGr3kuqQvb9hUIhRCIR6csDLFCOz+dDp9OR1zhMgQcm39ftdnF4eCjpuEQigVu3bhlgonA4jMePH+NHP/qRQTlJWr5r165JCvzp06cC3OF0K53u4qZqNBrweDyIRCLyO/Pz83JwEMi2t7cngCEe3Ezb6j7AWq2Gubk5SUNyA6bTaYzHY2ET4zrQGdA932eRLlwk+ayaJJGduu7EMgL1TKfANAUl6/N8NvxzPB43Dv7NzU1JeX/66acArGd+48YNYZkCrH5OOqF0vpg6bDab8ry73S4CgQAymYykNpkC5zhB3lur1cJoNJIuA8DSn6WlJbjdbnz00UcALKMTDodl4AF/u1KpIBaLiSNKNK9GxO7v7yMUChm/TcxFvV6XNDuNfCKRkN7wZDIpI1Lv379v1Ll9Ph8uX74s5YButysoee4F2gibzSapyLN6jS+S6Hqyrq0Tsa/xKH6/X1i5AMtJCgQCSKVS4nRx3T0ej8GJsLq6KuvE500nfWVlxRggwfQt7S2nmhGTAFhryolXPACJsyAzIWDpUqVSgdvtxunpqdg3h8MhOAWNHyBeh79zfHyMYDCIK1euiC5evXpVBvlwbxB0bLPZ8OMf/xgAhFZV9/bPz88jn8/j4cOHBmEWB9eQa4Ova/pPPl/+Fh3yaUzOechzacHi4aBRv9xwRGoDE6PIzwCTiJsIQwDCpFUoFKTpndEtkeFUxHQ6DafTicePH8viOZ1OAT7wNaLM/X6/KBLH+Gmvfn19HblczqAPjUajWF5eNoaac570NMAgkUggkUigVCrJ8yAzlXZiyHPr9/sFtFYoFIRRh54d2zRIp8fv6/f7qNVqsqmWlpZgs9nw6aefCqCHtI5bW1toNpuSaWDLgs/nEw+ba6RpWxnhaPpJTRCi9eC8jCfbKKavgQerRgZ3u12Mx5PJQ2yTIhc6MPHE6UwC1rPp9XrY2Ngw+IQ3NjYQj8dRLBYNPcvn82g0GuLkMUPh8/nkNR5ysVjMAK2QHEdnM1gn5hp1Oh0UCgWEQiHZP7xnotAZ4QPWmqbTabnHk5MT+Hw+acUBLJ1yu93IZrOGjnq9XnS7XYP0pl6vC588AAG1TQMRd3Z28MknnxhTj6j3x8fHBniUz4fPguAfOg/8zvMGKf4iYX18uttBT70CrGfQbreNGi3R1azPAxNSJjIy0iZsbm7i1q1bUhsGIBPDotGo7GlmQXq9ngQ7+XweBwcHxmGUSqUwHA7hdrvlfez88Pv9ci+MiHnAMxpmC97p6alBj7y7uwuPxyOZQmZG/X6/ZE3D4TAcDgey2awRXJD+WeNrOHqSdfhGo4GDgwP0+33ZL5wNQfwP7VUikZBOAe1I0OnWvOp65OZ5yHOLpHnzOrJhC5ZOlTLNSuNAZWB/KQBh9mJPNAAhdt/b20OlUnlmJNra2tozc0hLpZJ852AwEJYcXmO1WhWyeR6U1WpVmG648NevX5eeQ/YlEh1ZLBaNDfT2229LlECgTzKZRDqdRqFQEO9zd3cX6XQaxWJRFIRozUajIRuoUCjIcA6d1mS0p1vR0uk0FhcX5XknEgncv38f77zzjgyEB6wDgrSrehA9SwQ6xf2rGMTzNppn/T75yXWKezweG21nuj+d7/N6vQKs03zAxWIRp6enCIfDgsb/4IMPcPPmTdy5c0dGUJZKJRwcHEgvKWCtIcdL0mOPRqOIRCLI5/OSdiT/dSqVEj1jK4w2OD6fTww8e6gBS8+Oj48xHA7lt5nN4kAY/nY0GkUymZRnUSwW4fF4sLGxIWBEpvl1BoAUuHqk6draGmKxmDEcIp/P4/Hjx8hms6LngJXmZ7aC90MQkl4HlsH473ztost0JM22UzpfAAzbR+fO6/VKuxKzKDz8eMDzOz/++GNcvXpVxlgCVjRMx51ONTtpmEHkb5OJkA4CgbwclclrSyQSGI/Hcj0EthWLRckWAta6lEolo83uk08+wcnJCTY2NmTdWF7SjHia+4FZFLZP0dYDk3Jcv98XO7+9vS0saLo3nDPVGdQB1sHP69djXHVJj+t23gyKM+DYTGYyk5nMZCYXVJ5bJD1dGyS0XXOgsqbkdDrF42Y6RfO+st62u7sr0eOrr76Kra0t6asjCIYc3Q6HQ7yzQqGA+fl5o3bNFCQAo07R6XTg8XiMqVORSETS5QDw+uuvY3t7G5988ol4vvF4HLdu3cLe3p7RYjY/P4+lpSU4HA6JUhj5s+UBsDw7Ri5k3zk6OhI+b00ywoEjevRhPp9HNBqVnkE++xs3bsj9dTodPHr0SLxMRvGLi4vSx8tnzj5wvZa6tWU6Wr1IaUdep74eRpCMXHWrDiM0RqSa15wRXywWEx0bj8fY2dnB3NwcUqmURK5utxubm5v42c9+JliDRCJhAF8AK9vjcrkMIA4HwmSzWflNMuzpdCAjIX4HAAHFsPee980pb5cvXzZGXfb7fZkUB1h6wAiJaXHqk+YgcDqdOD4+Nuqp+XxeeOK5n+r1OlZXVzE3NyeZL5/Ph52dHUmVky+a05b0gA69D3kvLDGwHgtc/Ej6s/AazLLRxnDKEksEwCRS1Knpk5MTw1bxvQcHBygWi6IDwGTwTbFYxI0bNwBMmLvY7w7A4I3g8y+VStLPTHuwurqKVCqFbDZrAPwGgwFSqZRwMgDWnrl06RIikYjREvr7v//7BoNaOByWLCXvpdFoCGsjM30k7+l2uwaQjcN++BpZ8/R4TGaYOFFQlxMqlYqAOAEIb0YgEBBd1oOR9Lq+SFv3XBjHCBKbRv4y58+bJkhH5/xJpN9sNsWIBINBAUCx5kpwAxF7XCiyNGllWFlZkalVrAGyVqPrytwQmlFpYWFBQFSvvfYaAAvw8OmnnxooSfYUHhwcoNvtCohmb29P0lQkGiFAjY32gFVLHw6H+PTTT+WgjUajMr2FqaS5uTl4vV5ks1lBhvPPdrtdNr7P55PRawSYeb1eHBwcyPt4jQcHB0Kgrw8xImlpbDwej9RrNNqW770oh/RnMZ7pEaIk19FEOna7XabtcOP2+32cnp7C7XbLWjEtmclkEI1GxWh94QtfwJ/+6Z8KOQR/h0AYXX8+ODiQvmZgMqo0k8kYZRqCAulQdTodwWjwoKvVashms4LmpSPLXlL2F/O1fr+P5eVlqQuzHKSBNT6fT+hM+Xx4cHDQBn87GAwa5QCSTmiug6WlJaHWJQiUsri4iEKhYOAuOD9ap4lHo5HQhQIwyjMXUT4LH8GBKLQ7xH3QWQEsveG0Pb4WCATke1qtlvH6/fv38bu/+7tGzfbSpUvY3d2V0ksikcD+/r5QNAOTNfV4PAJEI7iLpTbAciwbjYYAuPg+6pwmjyL72MHBgegtU+o7OztCj8zOAc034fP5UC6XUalURO8IWNOBCbE+m5ubYsfW19clCNS0v+Te0EEaGf/0XPbpoRr881nr+iLluUTS2nBTGNnw4QAQ2j8eJvrzoVBIFp3UlIPBQKa35HI5QTg7nU4xBpFIxKgjAtYm4OFFQ8DWL81iQ6IGKhYwoeH85//8n8tnCYAgmQVgeY+kDmXbDWBF/Jw6RY92PB7j5ORE0LjApB7i8/kMaj+C2EgqQEVzOBxi9KvVKtLptAA++H2MCnXbBAFQNptNPk+iGW1Ui8WiMDpxQ5M4RnN3X5SDeVqmr2s8HqPZbBozpgEY0QLbshYWFuSw4vfoeeJ0XqLRqEQXAGRNK5WKRL7kEyYmA4C027DdC7DWIJ1OI5lMyr45OTkRZ5cGVNduqTt8ze12y9QrAHj06BGWl5eNaKHb7SIcDiMSiQh7n9frlWiI699utwVso6d/ZTIZeDweg2t5eXkZkUhEuikASM383r17ACxHmRH7rVu3BG3Olkd+FzDpatAkEm63W+5bI6Uvupx1jQQxTmeqtM202WzCkMhgxeVyoVAoCHkM7cRoNML7778vMwaASQZnZWVFMkCrq6uCFaAtYwTMYIOfpV3mmpycnAgSnPqwtLSEbrcrQECS6lSrVTx58gT1el1s3urqKvb29uDxeERvB4OBXBulUCig3W4LQQvv+9NPP8X9+/fl8Eyn0zg4OMDJyYkEMMwG6PG57HwgqI3PjIRMjMiBCaEQR6Xyfefd0vfcDulpdLdOk04DJvQQe7J5dTodASgQlasRzVtbW7Jpk8mkKFk6nZYokgYHsDZ5JBIRo8hIqFgsGjOdGS1SEZeXl/GHf/iHgtoFrHmpbrcb1WrVgOozRVgoFPDqq68CsKjz3nvvPfz0pz812rXoSWuGJ1Kd0oDFYjH4/X74fD5JjQ+HQwHq3L59G4CVPajX60gmk4ZjE4vFZKg7ADx+/FhaOzSrTjqdRjweRy6XM4aqk/GMURi5nfVQDd2ydVFkOt1Nw6f1kY6OdhBZfqlWq7L+brdbMh509FqtlvQBA5BhGqFQCJ1OR2ZX871smeNhHgwGkU6njUwM+dzb7bYxG1rPkuZn6/U6Njc35ZmzlYQ9ozSs165dg81mw+PHjyU658g+MjEBk7SjRuoTZa7LU4y4Xn/9ddy6dQuAlVViPzWzAsxSkUGPr/GZ1+t12e/MmpE2EpiUvDTame2ANLrAxU13/yJqUv08NeWv1+uVllL+W7PZRLfbFeeHo0zpxOhBF+PxGAcHB4aTxcwMszqFQgFLS0uIRCJyaNVqNbRaLeRyObEHPIiDwaCsX6lUkt8lCLHX6+Hw8BDVahX5fF7sKHuNv/rVrxrsYq+88oqcDYB1KHIuO8si6XQa+/v7qFQqsjfK5TIODw+xvr4uUfjOzg5qtRreeOMNCTbeeecdafXiOaGHHelMIdeCTI/AZPwx2e4A065NO1MvykmcAcdmMpOZzGQmM7mg8twGbJzlZbCAz1QjPRLNasRe3NFoJOkTj8cjXhC9Ino6g8EAGxsbkrrb2dkRNh7d48emdnrwvV5PatJf/vKXAUBaHgKBgHhcN27cwNWrV/Hee++JpxmJRKRGyddYv7t58yb+7M/+DN///vcBAN/5zndkQMN7770HAPjWt76F1157Dfv7+3j06BEAK3W0srKCR48eGRmGYDCIO3fuSBTPlpxYLGaMzmTrDr1Kgu0ikQh++MMfAgD+6q/+ShiyqtWqeN1MMZE0Bpi0uQwGA4nCtBc8LRetJq2FNTZNSKLTXToi8/v9Uq8GrIiSmQrdEjgajfD06VNcu3bNIF1g9Ee98Pv9ku7WY/+KxaJkOwCrN9Xr9cpEIwDCw61LFiTvcbvdEsE3Gg0kk0lJ12mgVzqdRi6Xk7auer0uGRyOK+T3cQocnw9rz9SzlZUVySwwml1eXsaNGzeM9qFWqyUc5pqs5fDwUMYyarazfD5vpBXJsEaiDF4jy1Zn9axepMEav+wadPkOmLBxjUYjAxTHcheff7lclj2qwba6JEAdLZVKSKVSuH37tkSpjx8/xu7uLpaWlqR80u/3EQgEEIvFJHJl9i4YDMo+cLvdyOVyMpyF77t8+TKazaZgXQDLPnLMKb+z3+8jk8kgFArJbw8GA2G50yBetpSybENubq/XKyRBZHwEIAQnGkRG4QjUWq1m8ADY7XbZ50yrExCpyUw+q6z30gLHKBp0NG0wOdgAmKSrSIAOQBjJeEABVoqOoxf5GdazK5UK8vm81GyDwSD29vYMlB8n+VApgEkfYrVaNYg7iH7UjGEE6vAAPD09hc1mM/qhm80mjo+PZXIU0zyAZUDW1tZkUxLRmslkxLA1m030ej3U63W5706nI8QaBAkRmUmkJgAhPNEoyV6vh3g8jo8//hgffPCB3HO73Za509xU8XgcR0dH8Pv98nzYj0vHis+CoLdpEpOLYBx/kTB9qp1B3SMJTGb4Mq3K18LhMIbDoeGQDYdDbG5uotvtipN35coVqRvqoS3NZhP7+/tSW4xGozKYgwaLQ1kIcgEsPSHRiKaUpSHh9cTjcSGq2djYEODYvXv3sLy8jGQyKQQSjx49kvKSHjYSDAbh8XjkswTudLtduZeHDx9iPB7L3GrAYiFLJpN488035Rq3t7eFqII69qMf/QiHh4cCtKNzQsYxIsQBCHmMpgDlcATeP2COKLyo+vdZzisnUgHm6FI9c5xgMg2qJVkNa6/8PAGOmpDk5OQE29vbckhnMhm8//77+NnPfiakUGSQ8/v9op/9fl8YCVlyZOAVi8UEQd7v96V+vbS0JDZ4d3dXRkCyy4EgNE2qc3x8jHg8joWFBWGbe/jwIRYXF3Hz5k3Rh2w2K/aRz4LAsZ/+9KfyfUxxAxBdJNiQaWzqE88m/Z3j8dgAUU6v43k5gs+du1vP4GWEzJslMIToZwAy11i3y9hsNmGioYIQXJbP5/HgwQPj8F1aWsJ4PJZaDGur5XJZgAMk8yDiGbBQzuPxGMlkUhSEs3rdbrfUcQCI0eYCs2aWSqWwv78vGwOwjM76+rpED6VSSchPNKAnk8kgHo+LUVxaWsJrr72G0WhkeHvZbBabm5tiKBOJBNrtNsLhMK5evSrP8Z133sF/+k//SX43nU6LY6JbOJi9IAMXALmu6ewD3891vUi16F8kw+FQZkUDMCISbkgytzGqASzjQmIIDerp9Xro9/vY3t4W3Y3FYohGo6jX6xJBRKNROJ1OWRdgMury9PTUQOjGYjGD9pK1bLJyAROEutfrlelGi4uLsNmsUa6aD/zTTz8VLIZmfuLhqTMxNFA8PDmv2GazyWu9Xg+xWMzAKXzpS1/Cz3/+c/yH//Af8K1vfQuAxbxWq9WQz+flOfzd3/2d3H+pVBL6WtYMO52OrA0dCE7NAyCYFBpc/dpFFx2YaHS27qQgelrXmcnfzWfV6/UEqKidS+J4AoGAZL0CgQDu3r0rNgKw9vIrr7yCSqUiGZNwOIxYLIbV1VXR452dHZycnKDX64mDv7i4iFdeeQUAjAzceDzG8fGx8L8DVuvoycmJXD8AYXssFouypvF4HKlUygD+rqysoFAoGCMxqW8adX14eIiHDx8aZES0+xoRz8CJWRg+f7L4afwRO1ymsSsU3WlAeREH9nM7pD/r4tnnCUAeCNsrgAm7DDckYHl7HJ+mqRk1/Sg3+Pb2Nux2OxYWFuRwKZVKksLRUfz8/LzwGwOWp7m5uYl2uy0bgy1YmtXM5XJhd3cXwWDwGXBcrVbDycmJbJbhcIhCoWC0M6yvr+PKlSuo1Wri/aVSKVy+fBl+v1+GF1y7dg1+vx/Hx8fY398HAAG6HR8fC3qd6MpUKiVReCQSwU9+8hNhdONno9GoREv0nIFJOwufDznUQ6GQPNt+v29kGbS8aMX9h8r0NblcLoP6D5hkOJgaByCOC4FZAAy9rNfrAqThIa+fGaNwRgLAxMk5ODiQSOXatWsoFosGpWgoFEI6nUalUpEIpN1uo9PpyFADwDKKbDU5PT0VoJHf70cikTDmUdMQ61nBwGRuOo13JpNBqVRCuVyWDBKfBVtpAMtB/PM//3N8/PHH4pjS8B0fH8vc8kKhIA6L3u+kANZZjna7LY6IXrd2u204VS+DTJf+ph09AMbgBzr9NpvNsCHAJPp2uVwynAKY8FM3Gg3Z/7qcwD9Th1999VUDXR+JRGCz2WTt2brH8h9gpdpJNUoQazqdRjabRaPRQCaTMeYyswuF+4CDYXjwAtbeIOWyvu9EIoFer2ew/pH6VrPOEaDG84QseTp7sbKyIllP9nUDE2pWTbuqByvpFi7eB9/3ou3bDDg2k5nMZCYzmckFleea7p72OLTHB0wmE2kPh20nutWCBX9d+N/Y2ECpVJK0nI4Kg8GgUW/MZDI4Pj7GwcGBRA9MQdlsNmMa1MbGBvL5vETN6+vrAsDRpB6MZlnrY0qF0RA9ssXFRUmraFIRn8+HQCBg1KBY49b8tYFAAH6/X9KGjL7J6ARYUfhoNEIikZB02YcffohisWiQcPC36NEyMiRL1TRegK1sFJYNziKcv2jR81m1QM2exQk6nKwETABmrPHxM6FQyMhA8L3D4RAejwfvvvsuACvyJWe8Ti+zvU6zdPn9fszPzxstIeyRZuRDzEUmk5FoIZ/PYzgcolQqSemGQC5mi5gG39vbQ7fbxZMnT2StibFgDy5g6QP54RkNeTwe3L17V1oNeX/j8VjGagLARx99hEajYQBw7HY7wuEw7t+/L21qJA2q1WpYWlqS/cE97Pf7Dd54pjE1QZJmIHsZZJpj+6x/ByZc8QTH8d8YMWvehFKpJBPB9ICVeDwuOAvA0p2//Mu/RLvdxte+9jUAls7eu3cPh4eHYi/ZW+9yuSSS7vV6AnZk1DwajXB8fIz19XXJAOzs7CCfz2N+fh7z8/MGUVCtVsO1a9eEc77dbqPZbCKTyRjlReKF+IzK5bJkn7jf8vk8XC4XVlZWpIRZKpXQbDZht9vFFi0uLqJUKhnAL66BztACE04MnRUcjUbGEB69TudZVnmuh/R0CpRDN/RsW5I68EFzRJoeA1mtVmWWLWV3d1eQj/rQWF5elpobU3uRSATXrl3D4eGhMaKtUCjIVB/AUi6iUpmGPDo6Qi6Xw/Lyshg10ncmk0lRYk6w8nq9cDqd0hPL2gpTmPra19fXRbE10pH3yRpTtVqVzUfE9qVLlyT1QxCE7gN89OgRdnd3DTCF3++XwRK6VsU0YjgcFhQwmaW4BtPredHll/WoApPDRM8TJ8uVHnVHwCJTifV6XQ4QDWb88Y9/jIWFBYzHY1nDer0uDo/u1x6NRlhaWpL3kVSnVCoZALXV1VUsLy/La1evXkW328X+/r4YMaKA+b3U3ePjY6OWDlgHbbPZlBo4AEGKE2wIQA7mS5cuiVHlQfDw4UNJtV+5cgX1el2YAgFrD+7v7+Pjjz+W62Z5ijgUTbGqnztgTiPiOjK9yfLYZ63xRZJfdH2auIRENxqbwwEcGhPCKWTjsTXukiUwPkM9OZBr8YMf/EDS5nNzc9jf30epVJL93+/3kcvlEI1Gpebc6XQEmMrvcTqdyOfzqFarUv7w+/1466234HA40Gw25X7K5TJyuZwEZbwfv9+PnZ0dsY1zc3MSRFAXS6WS8ACwnk1bXq1WRedJ/wxM6FU57pXUnhSSDE2PSSY4mXZUl1M15uYsYq4XKS80kqbQUJJNaZqUwOPxCOIRmEDjvV6vvJdTcdi+QEDZ1atXZX60ZoJiixOj82g0KuhqKvv+/j4CgQBeeeUVAYnt7u5ib2/PQN1yZJzX65VDcG9vz9hUGnh28+ZN2Gw2QTCenp6KkaQR42bQB2ij0cAHH3wgTgtgIR2J5qUQEBGNRuU55PN5jEYjoz5KIhS3243xeCwHMu+FERGvhyhg/jaN6UU3jpSzNpY+fNkiouuEZ7HlkciBz1EfKP1+XzIajG4XFxfl4OfhpLNAgKX7i4uLRvS5u7uLQqEgvz03N4dgMIj3339f1uW1116TGplGynY6Hcna8DvJQU9MB2DpnsPhQCqVEt27d+8ems0mQqGQ7K96vY5yuYw7d+6I8SYbnnYabTYbNjY2xFHgc/irv/oriZSASXRMEN70WEq+B5g4MWyT4/0x8/Wy6N9Zctb1O51OBINBaRMCrKxKoVAwnivH63Y6HaOlj0hsttEB1loxE/eTn/wEgHWIra2tYWNjQ5wsu92Ozc1NuN1uyQpyOqDH4zEOtUQiYZDr0A5GIhHhp+fveDwew57Mzc3h6dOnODo6EtKbVCqFarVqzDWw2WxIp9M4PT01KGrz+TyOjo7EfrMdl6QvgOWU8llSSDjFWr7G3BBrMg3qOyty/txG0tMyzWAFQJRND5En0Eaj+wg444ZOJBLI5XJC48b+uXa7jbt37xoL1Ww2cXh4aMyO7vV6WFxcNIZhkN9Ve4tvvPGG0CBqYEQulxPeWcACaESjUZTLZVSrVVnUxcVF6aGmMWf6W6N719fXcf36ddTrdWxtbQGw2hE0mhWwvOlAIIDBYCDgtv39ffES6XCwhUbPtyZa1mazoVgsihNECkbNY800VK/Xk2fEfu+zZki/LKIRxBzEMr05p6Oc0WhkUK6Wy2Vj4+sRiw8ePMDKyopkWCqVChqNhjiZAAT1+uGHH4pOkCkvk8nIb5Pr2mazyVofHBxgOBzi8uXLYtADgQCi0aiUkqgvdMa0EXS5XIhGo2JggQljme5LXltbQy6Xk/nVgLVnVlZWJALiMwsEAuj1eoLgLhQKAmpkZE5Hk2MSNX1tt9s1RgISWKcNo86WTfN1X/SD+7OiMK13lUoFXq/XaNNkhlFzy7OHnP2/AGQ4SaVSMbI9LJ3wuc7Pz8Nms2FnZ0eyLZcuXcJgMMDTp09lZgBZv3Qmg4M0vF6vvO/JkydyCPKaAAhr3k9/+lODQnRvb0/sJGDZ0YWFBdTrdaGPXV1dxcLCgjHb/unTp8KkR71zOp1oNptwuVzSngpAnqF2NpmV0dzo4XBY9q9en7Pahs9bt17oIf3Mj///DwbN580evUgkYrQr0OMhgpRDEEhVR4Pzs5/9DKlUykCusrE+EonIIjUaDTGgdBqCwaC0xpAcxeVyIRKJoFwuS+SSzWZlcalwtVoNu7u7WF5eRiaTESUJhUIolUrIZrPiQTJi4UEIWCnCfr+PYrEoNSgadka8vMZ6vW5QV+7t7UnK6eOPPwYwaT0gzSkwQS8Sicnny/5p0kMCkANek2O8LC0vFL25uPk0Ycb05qMBYN8nYHnirLHx/RwAwbQsXw8EArh37x729vbwL/7FvwAwmdamnyO5iXXm4+joCHNzc8bhS67xGzduiEH+6U9/ilQqhVAoZOwF9rtGIhE5GJnVIUc9ANFN1uQA65Aol8uSNgUsvY/H40L9yGdBLm/u10QigXg8jh/96Ef467/+a7keRmGMusgdwJohHRaui9PpNND2dCj1NCJdf/ysdb6IMo3u1oczMHE6WMoCIHOgdfTHNjmi9vWkvlKphEgkIs87HA5L7znt29HRERKJBBqNhhCEsLwITJDlly9fxmAwkCiX3xeJRHBwcCA6SwwPyx0si/R6Pdy+fRs+n0+ymQcHB1hcXMTCwoKxzx49eoS7d+/id3/3dwFYOv/o0SM8fPhQ7O1oNBK9o44wQ6g7UljL1ihtni8sS/HZcz+Px2Mj89TpdD6zJ/q8yiwzdPdMZjKTmcxkJhdUnnskPe196GiGgxA06pZeOic7ATDSXHpqEBGzTI8DVupuc3MT165dM7wm9tnRa+IMXtbIgEkP6dWrVyVd95Of/ERm2+p6ZjweN4BVR0dHqNfruHz5MmKxmIAestksUqkUEomE0YOXy+Xg8XjEc2a9/L333jP6cOPxOBKJhDHkgLVPXQ4gUpheLlOIkUhEnk0wGDRQyroXsN/vC4qSz0fXavm+s3qkL6pMp6y1ML2qST2Y3h+Px8ZzZAqN+pROpyUiJOUsYEXSly5dwtbWFr73ve8BsModnABFXeEoVU6PAiCz0dknyuvv9Xqo1WqyVhsbG/D7/UZ9Lh6PCxp6cXFR7pWgnGw2K5kmplE15oODWPb29mR/7e3tYW5uDslkUiK2bDaLaDSKSCQiwLL5+Xncv38f3/nOd+T52O12hEIhIVjhb2iSGOqk3W6XwRGaAY3kE7QLL1Nv9LScFT2fFV1r4Bhn2BOVDEzKEoCFsyF2xeFwYGNjwyCeIUERgVh8X6lUgtfrldT0vXv34HA4sLi4iHfeeUc+u7S0ZEzkazQaqNVqaDabsjdogxqNhugNABmOsbKyIrozNzeHq1evGqOKS6WSkALxfU+fPsW7776Lra0to6eZnBS8P+5Lt9tt8Ff0+32USiV5H88bljr5/MnR0e/3jTLmdLqbfz/PkspzP6TPaoOh0CC63W4jjUWGren0V71eF2NDEEW32zUQepw5vb29LZR09Xodo9HIMBKsMRLwBVipO8L3Seu5trYmi8TDbX9/H/l8HpcvX5bUzdzcHK5fvy4Gm9+5vb2NeDyOSCRijESbm5tDIpEQ/uRkMik1ahrAYDCItbU1pFIpY+xmMBh8pu43HA6Ry+Uk5cR6FsEj/D6OY9QgPLZ0BINBoxWCjgkdBA2sml7Pi5ZyPGtT6Q3IVJcmXCCVoaYF1BgGPge/3y8HFds7AMuINptNzM/PSzvLf/yP/xFvvfUW1tbWpP7MKVFPnz41pqgRmMPfOTw8RK1WQzQaletZWFhALBbD0dGRvFar1TAajZBMJtHtdiU93ev1sLq6atw3mfN0mYf3kslkpA7YbDblz9wL5LRfX1+XtrP//t//O959912DKpT1Z7fbLcZyMBgIGQVnwfP5VqtVQf8CEwdTg/Uuet35F4m+7rPKRfoApy6ROESPUh0Oh9J1occ8hsNhpNNpAy+inT2W5Fqtljhz/E62gxK5DVjgwm9/+9tYW1uTtd/d3cXp6anQFAMQHbbb7VhfX5d6eKFQQLFYxGAwMNptq9UqqtWqHPzsCPhf/+t/yWu5XE7a7fQzq9VqCIfDcj08tHUJJJfLiePNvRGLxQT4qdtReSATiMdnNs0qxr1znrpnA/DCf11HaGzJ0Ow5g8FAkNIUjVwEJnUvgp10XZBDE/7JP/kn8hrn6BKAQ4rMR48eife6trYmCkKF9Xq9mJ+fh9PpxJMnTwAAH3/8MSqVCu7evSvfRyaewWAAm80mn3/w4AEWFhaMGrvdbpe5vESRBwIBGSlIRWG9JRKJSI39/v37cDgcuHPnjhjZfD6Px48f4/3335eaNLMTdGb4DCuVyjP12Xg8jtHIGgOqWXUYbU7T5L1MdWktGiSmoxMNjtEtPsBkuAsdR2BC6ah77QHr2bRaLczNzYmxZLZnbW1NdOVrX/sa5ufn8eTJEzGk2WwWrVYLGxsbYkAJ+qF+ApYTGo/HxRkFIHW+o6MjZDIZcd4ajQbefPNNVKtVGVbDjI6mP+T8369//etyMFcqFSSTSQNYU6lUpE+X/c8ff/yxHB58pmR9YmaC90Jgpb4n1k15+ACQNdAsTzSUL+tBreUXtTLy3/RoVD4DOtV8VjxwwuGw9JvzeZMfPRqNyrOu1+sSBfN9fr9fWjc1EI2zBYj4jsViaDQaBiiWo1ndbjfu3LkjOv+9730PuVwO7XZbnI6NjQ2srq4aA1uazaZ0jPAwX1xcRLfbFQeY95JOpw19IH2vBrFGo1FUq1Vj9PE0UxgdSbasaWAdsw9nrclvxSGtozDtObJ3ksLNSE8HmBzcmgqPBjISiUhfHwCJhNiHDABf/epX0el0EAgEBFTDdB9n4QJW6u7k5AT9fl+AQ06nUwyMbglj/7IG5EQiEWxvb2M8HguPLZVSz2ENBoOw2+3IZrNymPNaNZBjOBzi8ePHcLvdeP311wFYhzmBIiSt2Nrawqeffop33nlH0oipVOr/1961xMZ1Vv/fnffjjsePSWKnSU3dl1MCgZbySJRKKIIVKhFFNJUQXSAhNkggIQqLyhJCLEBlUSQWLEBQiRV0U6miCEhRSysVlZI2iWkhCQS/PeOZ8bzH9nz/xfx/Z869Gb+SNB4735Gu4ty573vu953H7/wOjDFCgA+0Q2RENGraPXLn1mo1T4kDr4Hvh5OV/73uloFTA0I4uWqjgwaJrmknyMpxHPEUdAlWX1+f3H+9Xhf+Xz14HDhwQCZJoD2YPPbYYzh69Ki8m6mpKaFtpTClQrAQ0J54l5aWcOjQIU/d/traGs6dO4dUKiXG29TUFD7zmc8gmUyK8dbX14eDBw9ienpawpsk+8lkMnjwwQflPCQZInBxamoK77zzjpSUAW197e/v95Sy0JvRyHBN4ctJnOv9JBLkTgCunaB2q/gjTt1C4P7t6cTwdxqRNDY1wr5arQr5CaXRaODw4cMC3orFYhgYGEAqlRLgGMu3iLwH2mFo1vrrSM/q6qqHT50kKiMjI0in0zJev/nmm8hms2i1Ol3e7r//funLzs5/pVIJzWbTw/FNEKH2XiORiHwLNDg4/mrnjffBqCsAQXSn02mJLAJt47xWq3kcP56/2/vYyTHOAsesWLFixYqVHpVbBhzze9EUskBpphxajBQStmv2JNbIsWZVW9y0tlh7V61W8clPftJD7VkoFDAyMoK7775bvFnmHXUYhRSjhUJBQpOu6+LOO+/E3Nyc1CUPDg4ikUhgdHQUc3NzEookaYXu9MJ+w+xdrO9R50hIPF8oFPDWW28BAB566CFMTU3hV7/6lXjmDK9rLzccDksoSwPFyIimn7nrulLmRiHRjM7FbpRP62XRURydc6I+anYhlmroe6a3R50YGBiQtqY8FuAl4+cxk8mkhJBJC5vL5fDqq69ibW1Nmqn09/djfn4eCwsLEu1h9Mh1XcnZpVIpHD16FM1mU0pUSCf7sY99DMVi0VMj+sorr+DUqVMS0svlchgaGvKU13zoQx/CHXfcgcuXL0vrP+rP1NSUHI+AINaYAp3uYvSyuG88Hkc+n/dEw3QKRQPCuE73MKfX7a+J3q2yETaHv+t19KD1c6VnzefFKIx+pnwv4XAYmUwGi4uLUrN/9epV1Go1TxmU67rSiUwTipASmOFh3cKRUQ62Nl1cXEQ6nZaIG3ulswEN0I7CMOqiQYx33HGH1H7z3OVyWXAfvBdSyvJbYr9rPSfodJZmStOlgPp+CDTb7F3ttNzSnPR6bDuarjGRSEieWTMRtVrtrkxUBMdpc9syPKkL6Qnm0X1CR0dHcfr0aZloa7UaDh065EFOLy0tIZvNejpMsSZ2eHjYU089NjaGfD4vkzSv9eDBgzKYAe0QOq9PAytYN8o8p+M4mJ2d9YR0iFTXzD3pdBqvvPIK/va3v3mUeHFxEbFYTM6RSCSkfpGKSFYjPk/NoEUF1+xkfD+7CdG9nuhQN4BrDBoAEuJj20DKysrKNRSVfIYcBIBOns9fg81nrfm8yUD20Y9+FABw8uRJRCIRzM7Oyr4Mx917772iT8FgEKOjo57rM8bg8uXLgtHQte6VSgVHjx6VwYmI1unpaVk3Pj6Oq1evYnl5WYwBdt7SpDfz8/PyffKeSYWrB28ajDo9kk6n5f+cSIDO909wKK+RaZmNDMTdLDrMutG9aSKjtbU1SVXpVrMkiGI1AABhCmSdsD5Xq9USA5910+RuB9oT2P79+1EsFmWdft+abY6G19jYmKR0iPjWjHHpdBrNZhP5fF7GN9bGa/pR5o0Z3gY6vBT6efC81GegbTRUq1UPGxv7w5PcRbO06XlH3w+rjnienZZbDhxbDzShJwei8DjJcoCMRCKexgcEnFQqFVEi5j10M2++TNd1PSUOx48fx/333y/ezMzMDLLZLGq1mky+ZIsiGxjPvW/fPg+9IYlMjhw5gnw+L3kgx3EwOjrqIRqgaOKJ9957TwZi5h+Hh4elgTqvu1Ao4A9/+AMuXbokx4nH4wKA0s+xWCwiHo8LYIk84hwY+RGQaEMT0+v8FxV5L3g1fq9XPzOgM+BpY9DfO1zrZjKZ9HzQnHx5HjaW0K0uCZ7SbGX33XcfDh8+7GlbuLCwAMdxcOzYMdF7lqwEg0FPP/FLly5henraEyEhf/Lg4KC817vuukvKcKj3s7OzOHfuHEZHR0VHyQ2ey+XkWZAwZXV11WNI8ti8RmJEaPRwXTgclooMjQ2g/vGZsqytG3HJXpLNKiOYE9aYh1QqhUQiIQAw7u84jofUiURJelINBAKCqOa3TEdB4zBmZmakj7qmsmUfer57XSanq0gqlYpcC6+dzVH0e+V4w97uQPt7Yf5bG6Ich7XjFgqFUCgUPOdgtFCzAMZiMfmGtUHN778bI1wvVa3ccsaxbg9FU4USYKJrkAny0hY40AnTaiBKqVRCLBYTRCAAKVnQIb5sNovnnnsOmUxGvJkjR44IYIwDD1HUgUDAQyEaDAaRTqfFeiQfcjAYhOu6ovR//etfsbKygo9//OMycR88eBCxWAz/+9//JBRND6a/v9/TiWhgYAAzMzMCtnj33Xdx+fJlD0jEdV0Z/HiNfC7GGLlGEvATDMUBlM+XSGYA8iHvBS9aS7ewo/ZkjDGeygJytutafuoDPUDN0kWGJ11vTiAUBx2WaDmOI+jZubk5GRx1Lf8jjzyCTCYjKRljDBqNhgcQ+Je//AX5fF46q3H95OQkXNdFPp+XOlZ6JQ888IBQ6b777rsCDmMIvVwuS7MCCgFhGsDJwTkajUr0ic9HA4JoGFarVTEU+fw1GJTr9voE7Zdu4W9dJwxA0mP0PDlGadpWXUFCR4UTdzKZlOoX6jy9ZV1jffjwYVSrVU8TC9Jrkp0Q6FSfEBnOcYvAXRoOvB8NEAba41Y6nZYIH6VQKCAcDss6Rm70+OQ4Dkql0jWd+1hWpTkpeJ262qDbc++FCbmbWOCYFStWrFix0qNyyz1pTR4BdKwxWjgE7mjOWoZNNCkDLSjWAdNrZo1dJBIRS4xEIrp+mZ1bFhcX8fvf/x5A27t+9NFHUalUpH553759WF5exurqqid/fODAAQwODor1SEYb1jnz3OzrXK1Wcc8998i1sw0hLUOSi0SjUQknkWFtcnJSSibIWT4yMiJc4PV6HW+++SZc15Wc9NramngyuoSKuSwN1tBMYv5yj91UYnU90g3I6A93s6sZnxFz1PQidO010zA69MuSJb4bkncEAgF5r9FoFP39/Z4+6isrK3jjjTeQTCaFxGFpaQn//e9/sbi4KGmMarUq7zKXy0kolPnLXC4nfX2Hh4eRy+Xw9ttvi6eWz+dRrVbx1ltveRqsMAzN7zWbzSKdTkuDGKCtU/TSeX/ValVaKlKSyaQAlHTvaZar6Trr20X8pajduMl1VIe5aHZW0xgJ4iD4/Wr8gw5Pcx9G+tjsZHZ2VtJt0WgUmUwGpVLJQ47Cdpr0jtnrYHFx0eMpc8zQvQkYldElTuTDyGazMi7zPOzhzmtdWFgQQBnvhaVV1FmOn7pRDHP1HOP0892o1KqXxrwdyUnrTkR+BCfD3X5ieT8KnOFd5rSolJwANd0j8yi1Ws2DlCQFKZUul8thfHwc4+PjngHVdV0cOnTI04PZdV1kMhkP+cPg4CAOHDiA+fl5aTbwwgsvSJiZjFMf+MAHcOLECXz2s5+V0BFpHjlgAu3Q5Pnz54W6D2iHw3Vokc9neXnZk1cql8sC1tBkBhoAxfVEhWoAB9C9NnWvTNobfaCcaLmOXccorOnls9RGJ0Eruk46k8kgn8+LTkWjUZRKJekIBXRobomsBTqhukwmI7qztraGcrl8zXvUzWqok9VqVQYuGojj4+OYmZnB3//+d09TG+7Pb4Ehxng8LpMw9Uv3dq9WqxLG1zX2bOPq72zFwZvHosEDdKg/94J+XY/4AUsbsebFYjHRYQ3Q0wY+iWL0pEjd0tgVkpkQiLq0tISDBw8K+I/Xw3p4pkY4JtNI1B2qSqUS+vv7RSdoILDREdDJM3OC1vet67GZu9b4GNaGA51Oa4VCQXgKNAeEP03K/TWBFtC7hDk7ChzjB9yt9EczYhGkpWkCNfkBuz0BEPaxvr4+efkERdE6BDrUnLVaTQYZTvZkrgHaL/CDH/wgRkdHcfToUQDtdmr0MDiRzc7O4sKFC0gmk8jlctJu8t///jeazSb2798vg10kEsH4+DgefvhhyUXPzc2h0Whgfn5ePjTS+Olm5fV6XTq46N6xzGHpTkv8cPyUqzRe/Axu2oCiRbvXpVsuEOg8GxqNLPcAIJ3XgA6FLYWlHTxurVZDf38/Wq2W6NTQ0JBEOTQFLCdpjcavVCoe1iR6TewKBHR6VtPA0pUOXMcBbWhoCLVaTbwyAFLOo3OGHNzJ1MfryefzHtpGDr5kVuMzpc7yGyXCnfl6PfhTd/2gvm7vZa+L3+DrhqGgTvLZEozFyY3bMYpD3WGuWJctcXudz2b/8bW1NYlQMvqhc+XsicDz8jxDQ0OC3NZkNiROYVSo1WpJ9zVGhYwxyOVyEr0C2mMeS2G1pNNpMUSB9mTNsj/t4HUTrZt6m150QnaUFlSzEHV7MBxgEokEwuGwhzJTW+C6SYbmZNXWJdv2MQxNhCpD6wDk91QqJYo4MDAglipBPo1GA+l02lPTPDMzg6tXrwoCXXs5NBp43wTlJBIJsfKMMRgeHvYoHcP8d955p4e7m2UwtIrJx0svhcLr03SN9Jb1RFIqla4p1/ArLqXXFPj9Fno3uj85Q5Ds9asHRg6OuhEAw798tul0GuFw2ONplstluK6LeDzuAYm1Wi3s379f9KRer6PRaHgMAw4spH3ltbIXOQddoP3dMDKgS2no1evKAg7CTP0A7QoBHfKkzulJQ5dPcTLWpTO6ioBsY37pJXTtrZSt3reeiKhbfgpflghqHgCOtbqVYyKR8ADMgHY9cbVaFS+VoNSDBw96xjE2U2HoGmgD2YaGhjxjGb3rZrMp41az2USz2cTIyIg4MKyxr1arnrGxr68P5XJZzhGNRuUeaAiwQkA7G916j2vZDfplgWNWrFixYsVKj8qOeNLXXITjbbJN60Z7KAzpUbR3qEMtuhCfVnxfX594yLp5hT+cG4lExAOnVcmwom50r8uUdN02+a9XVlbEIyGPLMOUAMQL8neJIdEKPa5yuSxWIa1KnXNiiGhpaQmRSAS1Ws3DBsRog87X12o1Dycv74dW6GbgnV4MB92obOS96N+0N810jCYuIdmEHxzD1pS6hI/vj/m52dlZJJNJ9PX1SdlSsViU8ji+65WVFQHG8F3t379f2J8ajYbHU2HKgzrLsLfuFMSSRcdx5HoIfhwcHJSUjNYjnXaid0zh+cgpDkDKthzH8QCY/K0BrbRF1/Cu973piCTD2jq6uLq6Kh420Blbuz1zXS7Fd0p95r4sceL4xXUE1XKMYi48k8mIXiwtLUmprC4tY7hcn8dPYMM6Z50SIWZC11jrcXI9ndoIi9Kr0hOTNMPeQCd5r5WTQB4/ubtO+OuuJ9yWABoicBnuATr9pKl4AIQkQoeNV1dXUalUZCCj1Go12Z/nIHqQqFigDfRaXl721IbW63UJB3HSTSaTmJ+f95DAM6Qdi8UkTJ9MJsXA0LSU0WjUA4zj5OxvYMLBUuf9WA/d7cMFel+Jb0S2GsrXRo0GHGo9ZVqEwEWgM3HrYzMFEY/HZWCkkRSPx2XfUqkkNdaceFknq5HArM3m5K3ZltgZiYYfa1p1uojfAgA5D/sH69QNr3FwcNBDm9toNAQnweuh4a1DrQzH6rymv07dSkf4/Py5U//EzWetKxCIztfvmWk/4iaADqBPdyHj+KcdJoagk8mkp/868RkEKAKdroPsRghAjEoNZGU6TlOfcjLWaPF0Oi1gSQrTl7qSolarrTtm8Rn50du7wenoiUl6MyEwwXVdT/cdDijacyHZA7ueABAO2uXlZU8xOzuoaN7vZDIpjDk8Hj1Mno8gIs1BrDmWY7GYfARkoGKeHIDk1rXCBgIBTE9PC9kA0FF2nX9kazWWYvD+OQjqSZofrabNI4iJ9HkAxOK20pbNJm79YWsiHVr6flpQRlG6URLq/uP0YmmQDQwMCIGF9obC4TAGBwflneXzeTSbTaRSKU9bS15HMBgU71yjeHke6pgmZiHiu1gsSilMIBCQlojaq6YhoideXqcGjhFBrpHpvT5A7qT4vb719HIjT1sbSgRvkdGL+1KHNaud/9wcJ/V75t/EAenoiKYV5nl0xEkfV//LY2ngMJ0zTWVMoKMe+7Ve+a99N+tbT07S65UeaMRfIBAQuL1+oUSZGmM8wCoAHsYZepqapo4eN71uoDMI6RAxwzS6KQAnXNLw0Rs2xkgNNSd+AoY08IzgCE01So9Mh+ZpcLAtGwCpC/dHGqjA2sOhV6dD2zpMZsUr3XSRRhcnQb4HeiN+L8fvCRHlrEPTej9tuNFj5cTKsLSfIhHo8N7rbwRov1/Ng8wSKBp/5XIZ2WwWw8PDonvs56v1nmkgfosU3f+ZQnCYrr/vxgPfDdHNZ74bB9T3U/wh8G5eo3+dNpSA9rvSpX/8W0f/WHrVzWPXVSW6rE6fm7qwHiCY+slIH710oBPp0f0K6vU6+vr6PAh0lu5upidb5UjvZbHAMStWrFixYqVHpWc9aeBaC1GHl/v6+iRXy4J6oG05MZyjQy7awwY6nYyq1aoHhEYwhA5jsy5Vh1ocx/HkSVKplJSc+IkmtPfOe0mlUqhUKh4CAX+zc1rLOnzEUint/YZCISG0180O6MXpECjTBRp4ZEPdXtlKnko3KaA3zNSG9jbYFlTnBnWrUP2uNfEE0KnBJrgGgJRJ6bwivY9arSZ5Q6BT/+w4jgAZS6WSgIp0FIjeE8GLhUJBQqCaJEez/wGQfKIxXtY6EqHoOtmtllrthjzhTorfkwY2DuPyXXGfVqslKYxisSh6yO2o2zptQ/Y6djwD2npPvdG6zHP60znUZZ0qYkRQg3xbrZZ0reL9rhfK3uqz2s3Sk5N0N/F/zESiOo7jqZ/WdXa6IwxDv5yw2PBAv8BYLOZhgNLnHRoakrwekdw0EHheoIMa18f1gzCIkCSgjOuo9Dw3Q9KsJQe8EzKFuRqd4yYYTCNx2SNW01pa2Vg2As8RVasnO9aia8Qpc4OaRIbvTIf0GK7WbVvZx5cTHFMsuv7dr8Pcn3lvfxtIhqepK6wi0KFyTZChAWrkFdB5eOq3ZoPS98m/b7emGe+3aN30T5IbVSpoo58MjZrchrX/muCGYEVdi80xjSkR3TrTj81g0wtNc0qHppvOan3itXcDg+32CXgrsmsmaYpWTMLvNeiG3bK0ZciJSpd60VLUZSJ86Zqggtanv3QgkUh4coYsYyJQh/tTqXXZCSdZjfqNxWKIRqMoFosejnB6NLxuUk76i/m7FenzvrUlrT2ebs/2dlD67chWEO7chpEebVQBnQHOP+B1Q9PrwVbrpQbRNJtNDAwMyHtknpjn1d3NOJnrSohWq+UpW+REq6MzPKfWKx5fr6MBrCNG/gHWyvsrOld8PYY39VYTj2gwFgAx9uhRczsalZrBS5ciEq/hOI7wd2tHi98F11E/9X2sZyDzN78Hr9fthQqVnp2ktxrKATo0iAA8E6n2cNjajeE3huR0aJsocs39yoFLK5zruhIm0tdLTmZuA7Qn1Uql4vGuOSjSIwIglqhuc0ivVwPUNB+vPp7+QIAOOKO/v1/WlctlAWtsVke4m5X6Zst2QrL0QPUgwvI7P9JZA2vIMUymJ6AdnWF6grpHfWAJGOANQTuO44mycACmjvt7OQOdumZdCklgm04REWnO6wW8DSAo1mPeGekG9Oomm33b/omU//qPT/5wUhdrQ5DGpE7xcHzze+LXc3/+KM1697UXxrGeBY5ZdKcVK1asWLnd5X2fpL/5zW/i0qVLKBaLmJ6exk9+8hNP+cbo6Cj+/Oc/o1KpYHJyEqdOnbrmGLTiulmGuqyI3gQXkjgYY1Cr1SQsrEE4DNMQAEHAC70P5oCZN1lbWxNvWV8XQ4sEmFUqFVQqFeRyOU8dczAYhOu6ArbR10rPvlarSVeqaDTqISPRYU1uR4+M3lU0GoXrugIcqtfr4l3p0Gk32SvG0cTEBJrNJkqlkix33XUXgDa+4NVXX0U2m0U+n8drr72G48ePr3usbs9kvZyfrj/lNiSeYZREe57UKYb9mBbRugF0eI5Z69psNrG8vCy1/9RfAnD08bkvt/OXr5CIQn8LxDdoHWdUifvo7lk85mZh7vW8u70iL774okfnGo0G3n77bfn9ypUrqFar8vtLL710087dTUe7LQDWHU/9x/Hvq8PR5NemTmgdoB7rULofl7AVj57LRjl3HR7vdq97Qcz7uYyNjZl0Om0AmIGBAfOnP/3JfOtb35LfX3vtNfPMM8+YWCxmvvCFL5h8Pm8ymcyWj+84jgFgYrGYicVixnVdEwqFjOu6JhKJmEgkYuLxuInFYiYQCJhwOGzC4bBnPdc5jmMikYhJJBImmUyaZDJpEomEGRgYMK7rynbxeNyEw2GTyWRkSSaTJpVKmQMHDhgAJhqNmmg0ahKJhIlGoyaZTMr1cF04HDaJRMIkEgkTDAblPrhdMBg0kUhEzhsOh+UeHMcxgUDABAIBEwqF5FnE43ETj8fN0NCQrL8dl4mJCfPcc891/S0ajZr77rtPdOfzn/+8yeVy8g62uziOI4v/t0AgIO/VdV3RT/4WDAZNMBg0rut6dIY6QT0IhUImFAqZcDgs+3AJBAJyfv5fH1v/ze2oQ/x7cHDQxONxOXcsFjOhUMgkk0nRKeofryMcDnt0bzvf6+2ynD171jz99NPy/ytXrphTp07t+HVRR7rp8U5cz0bnpn7r697Ja73l7wqbyJe+9CWPZViv13H27NnNdhO5fPmyp6F3q9WS3rb33nsvHnzwQUxMTKBer+P555/HO++8g8cee2zLx6e1RC+CiEGWR2mGI+YLWZ5Fcgauo6WovRl6KOztS8AZ0PZuisUiisWieG26zzPQ8aLIoxyPx7G2tiZlM37WMu3haBIT3gfLYrQVrMkk9HVvZC33utyo3m0kjUYD7733noAC19bWMDg4KB1/tiPdsBN6Hd9DvV5HuVyWTj4Ey/AdsaQq8P+c2tRjetMkqyFfMT1vRpL4N6M/7H5EVj3qmaYh1dEiP8sUdYmRGl1Gxu9Fdz7aqtfS697NzdS70dFRnDx5Er/+9a9v8lXefGG+11/adSvOC2BDz1eXX+nxvNd16WbKlmf0VCplLl68aL72ta+Zp556yuTz+XUXvd8TTzxhisWiMcaYhYUF8+EPf9gAMKdPnzYXL170bPvTn/7UPPvss1uyvNZbTw9EW4v0QOlRaG+GXoH2NvzH83vc3J/r+Hc0GjWhUEiOBUA8EXrIoVDIxGIxj3cdDAZNKpUSb4XXzf/7vaJ4PC6eF683EomIt8Zr8lvLu3G5Hr2bmJgwhULB5HI5c/78efP1r3/9muOeO3fONBoNY4wxP//5z6/7+vxWfTfPutvffo+A75jvlfoUCATkvWr91R4z9VGvp97RC9eeOHVO76vPzb/1fYZCoW15MLvd07ne8Y7L008/bc6ePetZd+XKFTM3N2cWFhbMSy+9JGNhLy7+96f1mr9t9o63s61d1l22/sJeeOEF87Of/ey6T3bPPfeY73//+xIS/vKXv2xef/11zzY/+MEPzC9/+cttK5BebyfpvTNJX6/eHTlyxIyMjJhAIGA+9alPmZmZGXPmzJlrtotGo+bMmTPmK1/5yg1do52kt/6N7oblZox3//rXv8yTTz7pWXf8+HETi8VMPB433/3ud83s7KykA3ttsZN0zyxb2/CHP/yhefnll284z/n444+b3/3udwZoe9IXLlzw/P7ss89uyZPebAmFQjIBciALBAKSa+ZEGw6HTSqVMqlUyiSTSRnEOKhxW06uoVDIJBIJA8CT/3McxyQSCRMIBEwikZBj6nwKB0V9TVTiQCAgAyfXcaCORCIyePI4zHMnk0lPvt1vmPSAgt3QcrP07qmnnjK//e1v1/394sWLN+zV+AekboOafi98V34doZ5xGz1ZchIGOrgH5pJpUHL/SCRiAMi/vK54PC56rXPZ/F4ikYjHkNjOfe+V5Ub17sSJE6ZUKplkMrnhdpOTk+Zzn/vcjt/vRu9V67TGQnTT6c10YyPnaq/p0M1atoTufvzxx/HEE0/gi1/8ouSgvve973lyN/5lPQmFQrj77rsBABcuXMDY2JinT++xY8dw4cKFrVzWhuLPmREVTZQrc4ErKyuoVqtSaM/CfC7MH+o8SLecIolNiHrUz0EzAmlGHX9uhdflr5XWaFqifVdXV+W6iRzXNd3A7iWUp9xMvdss1xYOhzE2NnZD17vdvKwfM0Ed0ShpzUAGdGpCg6oFZaPRkK5mGmlNBjSiwnWeuVKpeCoh+C3oigiefyv3s5fygzdD75588kk8//zznh7i3eRW54C3I36UNHXTTzZCrJH+v/8YXL+enuw1HbrZsuEs/pGPfMQsLCyYY8eOXZcV8NWvftXs27fPAO0Q5Pnz580zzzwjv7/++uvmxz/+sYlGo+b06dPbRnd3W7pZZLQC+f9gMCiha66LRqOCptZeBa1pHZqm58tFe7H6NyJltTdMq1Hvw9C39mjoYcdiMY8XzuvTIfC9huS+Ub179NFHTX9/vwFgHn74YTM1NSUh7U984hPmxIkTJhwOm1gsZr7zne+Y5eVlMzIy8r7cSzfvuts264UXux2LyGrtcTM6o7fn//Xv2nO3y83VO6CN5C8UCubTn/60Z/3hw4fN8ePHJY3x7W9/2ywsLJjBwcEdv+/rXTbyjPmvDXff8LLxBhMTE2ZlZcWUSiVZXnzxxS2f4Be/+IWZm5sz5XLZXLlyxfzoRz/yhO9GR0fN2bNnTbVaNf/85z9vWXkCJ0CdF+Q6oJNr1jlnLgxX6/V6sPRvyxyf/1iciB3HkTwVJw6GsAF4Jm6ea6+XIdyo3v3mN78x2WzWlEolMzk5ab7xjW/Ib4888oj5xz/+YZaXl00ulzMvv/yyOXny5C3Rue2+s422vxXvf6/q13rLjeodAHPmzBnzn//855r1DzzwgDl37pwpl8smm82aP/7xj+ahhx7a8Xu2etDbS8/Sgr7f4qjyJD+VXqvVknAzhaVQQJtGkdSemu+2Gyczezxr+kTuo0np2XidxwU63Yl0mRaJ8P3dsqz0vvjDgNvZZz3ylO0c63pkoxClldtHrB7snNy2k7QVK1asWLHS69Kz3N1WrFixYsXK7S52krZixYoVK1Z6VOwkbcWKFStWrPSo2EnaihUrVqxY6VGxk7QVK1asWLHSo2InaStWrFixYqVHxU7SVqxYsWLFSo+KnaStWLFixYqVHhU7SVuxYsWKFSs9KnaStmLFihUrVnpU7CRtxYoVK1as9KjYSdqKFStWrFjpUfk/Ae7D85dbMVUAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/extra/nifti_15_10.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "from nilearn import image\n", - "from nilearn.plotting import plot_epi\n", - "\n", - "selected_volumes = image.index_img(dwi, slice(0, 10))\n", - "\n", - "for img in image.iter_img(selected_volumes):\n", - " plot_epi(img, display_mode=\"z\", cut_coords=(30, 53, 75), cmap=\"gray\")" - ] - }, - { - "cell_type": "markdown", - "id": "b9687a37", - "metadata": {}, - "source": [ - "### 3. [Affine](https://nipy.org/nibabel/coordinate_systems.html): tells the position of the image array data in a reference space\n", - "\n", - "The final important piece of metadata associated with an image file is the **affine matrix**.\n", - "Below is the affine matrix for our data." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f6e0601c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ -2. , 0. , -0. , 126.84400177],\n", - " [ -0. , 2. , -0. , -94.82400513],\n", - " [ 0. , 0. , 2. , -26.26519966],\n", - " [ 0. , 0. , 0. , 1. ]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dwi_affine = dwi_img.affine\n", - "dwi_affine" - ] - }, - { - "cell_type": "markdown", - "id": "9cbde29f", - "metadata": {}, - "source": [ - "To explain this concept, recall that we referred to coordinates in our data as *voxel coordinates (i,j,k)* coordinates such that:\n", - "\n", - "* i is the first dimension of `dwi_data`\n", - "* j is the second dimension of `dwi_data`\n", - "* k is the third dimension of `dwi_data`\n", - "\n", - "Although this tells us how to access our data in terms of voxels in a 3D volume, it doesn't tell us much about the actual dimensions in our data (centimetres, right or left, up or down, back or front).\n", - "The affine matrix allows us to translate between *voxel coordinates* and *world space coordinates* in (left/right,bottom/top,back/front).\n", - "\n", - "An important thing to note is that in reality in which order you have:\n", - "\n", - "* left/right\n", - "* bottom/top\n", - "* back/front\n", - "\n", - "depends on how the affine matrix is constructed. For the data we're dealing with, it always refers to:\n", - "\n", - "* Right\n", - "* Anterior\n", - "* Superior\n", - "\n", - "Applying the affine matrix is done through using a *linear map* (matrix multiplication) on the voxel coordinates (defined in `dwi_data`)." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "md:myst", - "text_representation": { - "extension": ".md", - "format_name": "myst" - } - }, - "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": "3.7.10" - }, - "source_map": [ - 11, - 20, - 22, - 27, - 31, - 40, - 43, - 51, - 56, - 60, - 62, - 68, - 70, - 76, - 78, - 84, - 96, - 103, - 106 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sources/notebook/01-intro.ipynb b/_sources/notebook/01-intro.ipynb index 8b9c297..b9856e0 100644 --- a/_sources/notebook/01-intro.ipynb +++ b/_sources/notebook/01-intro.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "630e4332", "metadata": {}, "source": [ "# The problem of head-motion in dMRI" @@ -10,6 +11,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1ce9af2e", "metadata": { "tags": [ "hide-cell" @@ -26,6 +28,7 @@ }, { "cell_type": "markdown", + "id": "8fb8ee58", "metadata": {}, "source": [ "A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object.\n", @@ -38,6 +41,7 @@ { "cell_type": "code", "execution_count": null, + "id": "cb07fe37", "metadata": {}, "outputs": [], "source": [ @@ -46,6 +50,7 @@ }, { "cell_type": "markdown", + "id": "2076c0f1", "metadata": {}, "source": [ "## Dimensions of the head-motion problem\n", @@ -102,6 +107,8 @@ "- Memory fingerprint: DWIs can be large, and storing them in memory (and subsequent derivatives thereof) can be cumbersome, or even prohibitive.\n", "- Parallelism: simulation and registration are CPU-intensive processes - for the runtime to be in a manageable scale, we'll need to leverage parallelism.\n", "\n", + "![eddymotion](https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg)\n", + "\n", "**Sketch out an API (Application Programming Interface)**: Plan how the new software will expose the implementation downstream.\n", "Assuming our DWI data is encapsulated in an object (holding not just the data array, but also metadata such as the gradient table)\n", "pointed at by the variable `data`, and assuming we have a list of rigid-body transform matrices to initialize the algorithm (`mats`),\n", @@ -117,15 +124,35 @@ "corrected = estimator.predict(data)\n", "```" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c133e5f7-927d-468c-822a-eb4dfd2c6635", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/_sources/notebook/02-data.ipynb b/_sources/notebook/02-data.ipynb index 264253d..aac8189 100644 --- a/_sources/notebook/02-data.ipynb +++ b/_sources/notebook/02-data.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "f5123198", "metadata": {}, "source": [ "# Introduction to dMRI data" @@ -10,6 +11,7 @@ { "cell_type": "code", "execution_count": null, + "id": "89569811", "metadata": { "tags": [ "hide-cell" @@ -26,6 +28,7 @@ }, { "cell_type": "markdown", + "id": "74fe239e", "metadata": {}, "source": [ "Diffusion imaging probes the random, microscopic movement of water molecules by using MRI sequences that are sensitive to the geometry and environmental organization surrounding these protons.\n", @@ -37,6 +40,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a1098cf6", "metadata": {}, "outputs": [], "source": [ @@ -45,6 +49,7 @@ }, { "cell_type": "markdown", + "id": "62545c69", "metadata": {}, "source": [ "*Sourced from Dr. A. Rokem, DIPY Workshop 2021*\n", @@ -77,6 +82,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0be2bc8e", "metadata": {}, "outputs": [], "source": [ @@ -114,6 +120,7 @@ }, { "cell_type": "markdown", + "id": "5d0d7f99", "metadata": {}, "source": [ "This code implements several *attributes* as well as a *behavior* - the `__len__` *method*.\n", @@ -125,6 +132,7 @@ { "cell_type": "code", "execution_count": null, + "id": "875e9e63", "metadata": {}, "outputs": [], "source": [ @@ -140,6 +148,7 @@ }, { "cell_type": "markdown", + "id": "9cbdce98", "metadata": {}, "source": [ "The output of this `print()` statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.\n", @@ -156,11 +165,12 @@ { "cell_type": "code", "execution_count": null, + "id": "0760d003", "metadata": {}, "outputs": [], "source": [ "# import the class from the library\n", - "from eddymotion.dmri import DWI\n", + "from eddymotion.data.dmri import DWI\n", "\n", "# load the sample file\n", "dmri_dataset = DWI.from_filename(\"../../data/dwi.h5\")\n", @@ -169,6 +179,7 @@ }, { "cell_type": "markdown", + "id": "e400e285", "metadata": {}, "source": [ "In this case, the dataset is reporting to have 102 diffusion-weighted samples.\n", @@ -180,6 +191,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4eb3a412", "metadata": {}, "outputs": [], "source": [ @@ -188,6 +200,7 @@ }, { "cell_type": "markdown", + "id": "b3d42b3a", "metadata": {}, "source": [ "We'll go over some of the components of `dmri_dataset` through this lesson.\n", @@ -207,6 +220,7 @@ { "cell_type": "code", "execution_count": null, + "id": "11ae0396", "metadata": { "tags": [ "hide-cell" @@ -219,6 +233,7 @@ }, { "cell_type": "markdown", + "id": "6ba5b5fb", "metadata": {}, "source": [ "When calling `plot_mosaic()` without any arguments, the *b=0* reference is plotted.\n", @@ -237,6 +252,7 @@ { "cell_type": "code", "execution_count": null, + "id": "de6b4724", "metadata": { "tags": [ "hide-cell" @@ -248,6 +264,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1bde9b81", "metadata": { "tags": [ "hide-cell" @@ -258,6 +275,7 @@ }, { "cell_type": "markdown", + "id": "abe42cf8", "metadata": {}, "source": [ "Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal.\n", @@ -280,6 +298,7 @@ { "cell_type": "code", "execution_count": null, + "id": "501c5de7", "metadata": { "tags": [ "hide-cell" @@ -290,6 +309,7 @@ }, { "cell_type": "markdown", + "id": "7783f6f2", "metadata": {}, "source": [ "We get a $4\\times102$ -- three spatial coordinates ($b_x$, $b_y$, $b_z$) of the unit-norm \"*b-vector*\", plus the gradient sensitization magnitude (the \"*b-value*\"), with a total of 102 different orientations for the case at hand.\n", @@ -305,6 +325,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ca843407", "metadata": { "tags": [ "hide-cell" @@ -315,6 +336,7 @@ }, { "cell_type": "markdown", + "id": "92e63a6c", "metadata": {}, "source": [ "Later, we'll refer to this array as the gradient table.\n", @@ -337,12 +359,14 @@ { "cell_type": "code", "execution_count": null, + "id": "85677774", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", + "id": "a4a43cdc", "metadata": {}, "source": [ "We've projected all of the gradient directions onto the surface of a sphere, with each unique gradient strength colour-coded.\n", @@ -359,6 +383,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2023e87a", "metadata": {}, "outputs": [], "source": [ @@ -413,6 +438,7 @@ }, { "cell_type": "markdown", + "id": "99e429f3", "metadata": {}, "source": [ "This function is contained in the `DWI` class shown earlier and will allow us to easily partition the dataset as follows:" @@ -421,6 +447,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1d4df79a", "metadata": {}, "outputs": [], "source": [ @@ -432,6 +459,7 @@ }, { "cell_type": "markdown", + "id": "4b9a33f1", "metadata": {}, "source": [ "`data_train` is a tuple containing all diffusion-weighted volumes and the corresponding gradient table, excluding the left-out, which is stored in `data_test` (the 11th gradient indexed by `10`, in this example).\n", @@ -447,6 +475,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c3fe53a2", "metadata": { "tags": [ "hide-cell" @@ -457,6 +486,7 @@ }, { "cell_type": "markdown", + "id": "4b6a79e7", "metadata": {}, "source": [ "**Exercise**\n", @@ -469,6 +499,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e4e4a23d", "metadata": { "tags": [ "hide-cell" @@ -479,6 +510,7 @@ }, { "cell_type": "markdown", + "id": "a6937911", "metadata": {}, "source": [ "## Next steps: diffusion modeling\n", @@ -489,11 +521,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/_sources/tutorial/data.ipynb b/_sources/tutorial/data.ipynb deleted file mode 100644 index b118db5..0000000 --- a/_sources/tutorial/data.ipynb +++ /dev/null @@ -1,859 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9be5b928", - "metadata": {}, - "source": [ - "# Introduction to dMRI data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4d10654a", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from IPython.display import HTML" - ] - }, - { - "cell_type": "markdown", - "id": "392aebe8", - "metadata": {}, - "source": [ - "Diffusion imaging probes the random, microscopic movement of water molecules by using MRI sequences that are sensitive to the geometry and environmental organization surrounding these protons.\n", - "This is a popular technique for studying the white matter of the brain.\n", - "The diffusion within biological structures, such as the brain, are often restricted due to barriers (e.g., cell membranes), resulting in a preferred direction of diffusion (anisotropy).\n", - "A typical dMRI scan will acquire multiple volumes (or ***angular samples***), each sensitive to a particular ***diffusion direction***." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6f6eb389", - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(\"\"\"\"\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "8df7db38", - "metadata": {}, - "source": [ - "*Sourced from Dr. A. Rokem, DIPY Workshop 2021*\n", - "\n", - "These *diffusion directions* (or ***orientations***) are a fundamental piece of metadata to interpret dMRI data, as models need to know the exact orientation of each angular sample.\n", - "\n", - "```{admonition} Main elements of a dMRI dataset\n", - "- A 4D data array, where the last dimension encodes the reconstructed **diffusion direction *maps***.\n", - "- Tabular data or a 2D array, listing the **diffusion directions** (`.bvec`) and the encoding **gradient strength** (`.bval`).\n", - "```\n", - "\n", - "In summary, dMRI involves ***complex data types*** that, as programmers, we want to access, query and manipulate with ease.\n", - "\n", - "## Python and object oriented programming\n", - "\n", - "Python is an [object oriented programming](https://en.wikipedia.org/wiki/Object-oriented_programming) language.\n", - "It allows us to represent and encapsulate data types and corresponding behaviors into programming structures called *objects*.\n", - "\n", - "```{admonition} Data structures\n", - "How you feed in data into your algorithm will impose constraints that might completely hinder the implementation of nonfunctional requirements down the line.\n", - "Therefore, a careful plan must also be thought out for the data structures we are going to handle.\n", - "```\n", - "\n", - "Therefore, let's leverage Python to create *objects* that contain dMRI data.\n", - "In Python, *objects* can be specified by defining a class.\n", - "In the example code below, we've created a class with the name `DWI`.\n", - "To simplify class creation, we've also used the magic of a Python library called [`attrs`](https://www.attrs.org/en/stable/)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7f687d6b", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"Representing data in hard-disk and memory.\"\"\"\n", - "import attr\n", - "\n", - "def _data_repr(value):\n", - " if value is None:\n", - " return \"None\"\n", - " return f\"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>\"\n", - "\n", - "\n", - "@attr.s(slots=True)\n", - "class DWI:\n", - " \"\"\"Data representation structure for dMRI data.\"\"\"\n", - "\n", - " dataobj = attr.ib(default=None, repr=_data_repr)\n", - " \"\"\"A numpy ndarray object for the data array, without *b=0* volumes.\"\"\"\n", - " brainmask = attr.ib(default=None, repr=_data_repr)\n", - " \"\"\"A boolean ndarray object containing a corresponding brainmask.\"\"\"\n", - " bzero = attr.ib(default=None, repr=_data_repr)\n", - " \"\"\"A *b=0* reference map, preferably obtained by some smart averaging.\"\"\"\n", - " gradients = attr.ib(default=None, repr=_data_repr)\n", - " \"\"\"A 2D numpy array of the gradient table in RAS+B format.\"\"\"\n", - " em_affines = attr.ib(default=None)\n", - " \"\"\"\n", - " List of :obj:`nitransforms.linear.Affine` objects that bring\n", - " DWIs (i.e., no b=0) into alignment.\n", - " \"\"\"\n", - "\n", - " def __len__(self):\n", - " \"\"\"Obtain the number of high-*b* orientations.\"\"\"\n", - " return self.gradients.shape[-1]" - ] - }, - { - "cell_type": "markdown", - "id": "7d68d740", - "metadata": {}, - "source": [ - "This code implements several *attributes* as well as a *behavior* - the `__len__` *method*.\n", - "The `__len__` method is special in Python, as it will be executed when we call the built-in function `len()` on our object.\n", - "\n", - "Let's test out the `DWI` data structure with some *simulated* data:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "573bc14a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "64\n" - ] - } - ], - "source": [ - "# NumPy is a fundamental Python library for working with arrays\n", - "import numpy as np\n", - "\n", - "# create a new DWI object, with only gradient information that is random\n", - "dmri_dataset = DWI(gradients=np.random.normal(size=(4, 64)))\n", - "\n", - "# call Python's built-in len() function\n", - "print(len(dmri_dataset))" - ] - }, - { - "cell_type": "markdown", - "id": "847da9c6", - "metadata": {}, - "source": [ - "The output of this `print()` statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.\n", - "\n", - "## Using the new data representation object\n", - "\n", - "The code shown above was just a snippet of the `DWI` class. For simplicity, we will be using the full implementation of this class from our [`eddymotion` package](https://github.com/nipreps/EddyMotionCorrection/blob/main/eddymotion/dmri.py)\n", - "Under the `data/` folder of this book's distribution, we have stored a sample DWI dataset with filename `dwi.h5`.\n", - "Please note that the file has been minimized by zeroing all but two diffusion-weighted orientation maps.\n", - "\n", - "Let's get some insights from it:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7187b4a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "102\n" - ] - } - ], - "source": [ - "# import the class from the library\n", - "from eddymotion.dmri import DWI\n", - "\n", - "# load the sample file\n", - "dmri_dataset = DWI.from_filename(\"../../data/dwi.h5\")\n", - "print(len(dmri_dataset))" - ] - }, - { - "cell_type": "markdown", - "id": "431cd2f4", - "metadata": {}, - "source": [ - "In this case, the dataset is reporting to have 102 diffusion-weighted samples.\n", - "\n", - "Python will automatically generate a summary of this object if we just type the name of our new object.\n", - "This pretty-printing of the object informs us about the data and metadata that, together, compose this particular DWI dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2ede3760", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DWI(dataobj=<118x118x78x102 (int16)>, affine=<4x4 (float64)>, brainmask=None, bzero=<118x118x78 (int16)>, gradients=<4x102 (float32)>, em_affines=None, fieldmap=None)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dmri_dataset" - ] - }, - { - "cell_type": "markdown", - "id": "1ce1a334", - "metadata": {}, - "source": [ - "We'll go over some of the components of `dmri_dataset` through this lesson.\n", - "\n", - "## Visualizing the data\n", - "\n", - "````{admonition} Exercise\n", - "Let's start out by seeing what the data looks like.\n", - "The fully-fledged `DWI` object has a convenience function to plot the dataset.\n", - "\n", - "```{hint}\n", - "To see all of the instances and behaviors available to an object, try typing the object name, followed by `.` and Tab\n", - "```\n", - "\n", - "````\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f58daf80", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/data_13_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dmri_dataset.plot_mosaic();" - ] - }, - { - "cell_type": "markdown", - "id": "48f2d3d1", - "metadata": {}, - "source": [ - "When calling `plot_mosaic()` without any arguments, the *b=0* reference is plotted.\n", - "This *b=0* reference is a map of the signal measured ***without gradient sensitization***, or in other words, when we are not measuring diffusion in any direction.\n", - "The *b=0* map can be used by diffusion modeling as the reference to quantify the signal drop at every voxel and given a particular orientation gradient.\n", - "\n", - "We can also get some insight into how a particular diffusion-weighted orientation looks like by selecting them with the argument `index`.\n", - "\n", - "```{admonition} Exercise\n", - "Try calling `plot_mosaic` with an index of 10 or 100.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9c798c6d", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/data_16_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dmri_dataset.plot_mosaic(index=100, vmax=5000);" - ] - }, - { - "cell_type": "markdown", - "id": "df40c12a", - "metadata": {}, - "source": [ - "Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal.\n", - "As we can see, ***diffusion-weighted*** images consistently drop almost all signal in voxels filled with cerebrospinal fluid because there, water diffusion is free (isotropic) regardless of the direction that is being measured.\n", - "\n", - "We can also see that the images at `index=10` and `index=100` have different gradient strength (\"*b-value*\").\n", - "The higher the magnitude of the gradient, the more diffusion that is allowed to occur, indicated by the overall decrease in signal intensity.\n", - "Stronger gradients yield diffusion maps with substantially lower SNR (signal-to-noise ratio), as well as larger distortions derived from the so-called \"*Eddy-currents*\".\n", - "\n", - "## Visualizing the gradient information\n", - "\n", - "Our `DWI` object stores the gradient information in the `gradients` attribute.\n", - "\n", - "```{admonition} Exercise\n", - "Let's see the shape of the gradient information.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7a05d344", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 102)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dmri_dataset.gradients.shape" - ] - }, - { - "cell_type": "markdown", - "id": "ab5e7311", - "metadata": {}, - "source": [ - "We get a $4\\times102$ -- three spatial coordinates ($b_x$, $b_y$, $b_z$) of the unit-norm \"*b-vector*\", plus the gradient sensitization magnitude (the \"*b-value*\"), with a total of 102 different orientations for the case at hand.\n", - "\n", - "```{admonition} Exercise\n", - "Try printing the gradient information to see what it contains.\n", - "Remember to transpose (`.T`) the array.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "92ecfe0d", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.04914188e-01 -6.79875135e-01 -4.14546251e-01 3.00000000e+02]\n", - " [ 7.51762651e-03 9.17056799e-01 -3.98685753e-01 3.00000000e+02]\n", - " [-8.86579394e-01 3.21218759e-01 -3.32859576e-01 3.00000000e+02]\n", - " [ 8.16364467e-01 2.68300444e-01 -5.11433184e-01 3.00000000e+02]\n", - " [ 4.34362024e-01 -7.33946085e-01 -5.22161603e-01 3.00000000e+02]\n", - " [ 9.60559174e-02 -7.31587410e-02 9.92683768e-01 3.05000000e+02]\n", - " [ 5.85605621e-01 7.62755051e-02 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2.00500000e+03]\n", - " [ 3.74817163e-01 -9.27097678e-01 -1.41696935e-03 2.00000000e+03]\n", - " [ 6.10155761e-01 -5.36033213e-01 5.83419502e-01 2.00500000e+03]\n", - " [ 9.50321257e-01 -1.07752405e-01 2.92025506e-01 2.00000000e+03]\n", - " [-8.09516788e-01 2.45182857e-01 -5.33449113e-01 1.99500000e+03]\n", - " [ 9.56617057e-01 -2.84275055e-01 6.38084337e-02 2.00000000e+03]\n", - " [ 4.44206297e-01 6.86384022e-01 5.75810552e-01 2.00500000e+03]\n", - " [ 9.54805911e-01 1.77383393e-01 2.38497064e-01 2.00000000e+03]\n", - " [ 6.78448856e-01 4.65783864e-01 -5.68113148e-01 1.99500000e+03]\n", - " [-1.22661926e-01 -5.87784886e-01 7.99664319e-01 2.00500000e+03]\n", - " [ 4.10763621e-01 -1.32623598e-01 -9.02044475e-01 1.99000000e+03]\n", - " [-9.28761899e-01 -1.47203013e-01 3.40195030e-01 2.00500000e+03]\n", - " [-4.32663143e-01 4.41911817e-01 -7.85822213e-01 1.99500000e+03]\n", - " [-1.73938900e-01 -7.22244203e-01 -6.69409096e-01 1.99500000e+03]\n", - " [ 3.93927962e-01 4.76803184e-01 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3.80590975e-01 -6.98121727e-01 6.06445849e-01 2.00500000e+03]\n", - " [ 3.18718821e-01 -6.50580406e-01 -6.89320982e-01 1.99500000e+03]\n", - " [ 4.12478864e-01 2.02624902e-01 8.88146579e-01 2.00500000e+03]\n", - " [ 1.32435739e-01 9.71343756e-01 -1.97362855e-01 1.99500000e+03]\n", - " [-9.72415447e-01 1.24751076e-01 1.97092354e-01 2.00000000e+03]\n", - " [-2.45995522e-01 -8.56899619e-01 4.53000307e-01 2.00500000e+03]\n", - " [ 1.21794799e-02 7.88615406e-01 -6.14766181e-01 1.99500000e+03]\n", - " [-8.40300739e-01 -2.33427882e-01 -4.89291459e-01 1.99500000e+03]\n", - " [ 4.91548866e-01 1.66130796e-01 -8.54856849e-01 1.99000000e+03]\n", - " [ 6.91248178e-01 -6.99574709e-01 1.81028187e-01 2.00000000e+03]\n", - " [-5.76671958e-01 -8.02036345e-01 1.55522212e-01 2.00000000e+03]\n", - " [-7.08378106e-02 2.88473666e-01 -9.54863846e-01 1.99000000e+03]\n", - " [-1.85595229e-01 3.32799673e-01 9.24553275e-01 2.01000000e+03]]\n" - ] - } - ], - "source": [ - "print(dmri_dataset.gradients.T)" - ] - }, - { - "cell_type": "markdown", - "id": "94c2a888", - "metadata": {}, - "source": [ - "Later, we'll refer to this array as the gradient table.\n", - "\n", - "It consists of one row per diffusion-weighted image, with each row consisting of 4 values corresponding to [ R A S+ b ].\n", - "\n", - "[ R A S+ ] are the components of the **gradient direction**.\n", - "Note that the directions have been re-oriented with respect to *world space coordinates*.\n", - "For more information on this, refer to {doc}`the Affine section in The extra mile <../extra/nifti>`.\n", - "\n", - "The last column, b, reflects the **timing and strength of the gradients** in units of s/mm².\n", - "\n", - "To get a better sense of which gradient directions were sampled, let's plot them!" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7ed6ef85", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/data_22_0.png" - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dmri_dataset.plot_gradients();\n" - ] - }, - { - "cell_type": "markdown", - "id": "a4a64c71", - "metadata": {}, - "source": [ - "We've projected all of the gradient directions onto the surface of a sphere, with each unique gradient strength colour-coded.\n", - "Darkest hues correspond to the lowest *b*-values and brighter to the highest.\n", - "\n", - "## The *LOGO* (leave-one-gradient-out) splitter\n", - "\n", - "One final behavior that will make our endeavor easier in the long run is a convenience method for data splitting.\n", - "In particular, we are implementing some sort of cross-validation scheme where we will iterate over different data splits.\n", - "In this case, the splitting strategy is a simple leave-one-out.\n", - "Because one \"*datapoint*\" in our DWI dataset corresponds to one gradient, we will refer to this partitioning of the dataset as *leave-one-gradient-out (LOGO)*:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7b4cd7fb", - "metadata": {}, - "outputs": [], - "source": [ - "def logo_split(self, index, with_b0=False):\n", - " \"\"\"\n", - " Produce one fold of LOGO (leave-one-gradient-out).\n", - "\n", - " Parameters\n", - " ----------\n", - " index : :obj:`int`\n", - " Index of the DWI orientation to be left out in this fold.\n", - " with_b0 : :obj:`bool`\n", - " Insert the *b=0* reference at the beginning of the training dataset.\n", - "\n", - " Return\n", - " ------\n", - " (train_data, train_gradients) : :obj:`tuple`\n", - " Training DWI and corresponding gradients.\n", - " Training data/gradients come **from the updated dataset**.\n", - " (test_data, test_gradients) :obj:`tuple`\n", - " Test 3D map (one DWI orientation) and corresponding b-vector/value.\n", - " The test data/gradient come **from the original dataset**.\n", - "\n", - " \"\"\"\n", - " dwframe = self.dataobj[..., index]\n", - " bframe = self.gradients[..., index]\n", - "\n", - " # if the size of the mask does not match data, cache is stale\n", - " mask = np.zeros(len(self), dtype=bool)\n", - " mask[index] = True\n", - "\n", - " train_data = self.dataobj[..., ~mask]\n", - " train_gradients = self.gradients[..., ~mask]\n", - "\n", - " if with_b0:\n", - " train_data = np.concatenate(\n", - " (np.asanyarray(self.bzero)[..., np.newaxis], train_data),\n", - " axis=-1,\n", - " )\n", - " b0vec = np.zeros((4, 1))\n", - " b0vec[0, 0] = 1\n", - " train_gradients = np.concatenate(\n", - " (b0vec, train_gradients),\n", - " axis=-1,\n", - " )\n", - "\n", - " return (\n", - " (train_data, train_gradients),\n", - " (dwframe, bframe),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "6608b576", - "metadata": {}, - "source": [ - "This function is contained in the `DWI` class shown earlier and will allow us to easily partition the dataset as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "840558c8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/data_26_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from eddymotion.viz import plot_dwi\n", - "\n", - "data_train, data_test = dmri_dataset.logo_split(10)\n", - "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "5e08df08", - "metadata": {}, - "source": [ - "`data_train` is a tuple containing all diffusion-weighted volumes and the corresponding gradient table, excluding the left-out, which is stored in `data_test` (the 11th gradient indexed by `10`, in this example).\n", - "`data_test[0]` contains the held-out diffusion-weighted volume and `data_test[1]`, the corresponding gradient table.\n", - "\n", - "```{admonition} Exercise\n", - "Try printing the shapes of elements in the `data_train` tuple.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8b50d36d", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data_train[0] is the DW maps dataset and has (118, 118, 78, 101) dimensions\n", - "data_train[1] is a gradient table and has (4, 101) dimensions\n" - ] - } - ], - "source": [ - "print(f\"data_train[0] is the DW maps dataset and has {data_train[0].shape} dimensions\")\n", - "print(f\"data_train[1] is a gradient table and has {data_train[1].shape} dimensions\")" - ] - }, - { - "cell_type": "markdown", - "id": "05e2e95e", - "metadata": {}, - "source": [ - "```{admonition} Exercise\n", - "Likewise for the left-out gradient, try printing the shapes of elements in the `data_test` tuple.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f9a53aad", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data_test[0] is left-out DW map and has (118, 118, 78) dimensions\n", - "data_test[1] is the corresponding DW gradient and has (4,) dimensions\n" - ] - } - ], - "source": [ - "print(f\"data_test[0] is left-out DW map and has {data_test[0].shape} dimensions\")\n", - "print(f\"data_test[1] is the corresponding DW gradient and has {data_test[1].shape} dimensions\")" - ] - }, - { - "cell_type": "markdown", - "id": "a3a5670a", - "metadata": {}, - "source": [ - "## Next steps: diffusion modeling\n", - "\n", - "By modeling the diffusion signal, the acquired images can provide measurements which are related to the microscopic changes and estimate white matter trajectories." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "md:myst", - "text_representation": { - "extension": ".md", - "format_name": "myst" - } - }, - "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": "3.7.10" - }, - "source_map": [ - 11, - 15, - 23, - 30, - 34, - 62, - 93, - 100, - 109, - 121, - 128, - 135, - 137, - 155, - 159, - 173, - 179, - 183, - 202, - 206, - 217, - 221, - 235, - 239, - 251, - 299, - 303, - 308, - 319, - 324, - 332, - 337 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sources/tutorial/intro.ipynb b/_sources/tutorial/intro.ipynb deleted file mode 100644 index 9cb8cf5..0000000 --- a/_sources/tutorial/intro.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f3d4a56e", - "metadata": {}, - "source": [ - "# The problem of head-motion in dMRI" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5af3d1ae", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from IPython.display import HTML" - ] - }, - { - "cell_type": "markdown", - "id": "70ed0890", - "metadata": {}, - "source": [ - "A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object.\n", - "Rigid-body, bulk-motion of the head will degrade every image, even if the experimenters closely followed all the standard operation procedures and carefully prepared the experiment (e.g., setting correctly the head paddings), and even if the participant was experienced with the MR settings and strictly followed indications to avoid any movement outside time windows allocated for rest.\n", - "This effect is exacerbated by the length of the acquisition (longer acquisitions will have more motion), and is not limited to humans.\n", - "For instance, although rats are typically acquired with head fixations and under sedation, their breathing (especially when assisted) generally causes motion.\n", - "Even the vibration of the scanner itself can introduce motion!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6b3bec7e", - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(\"\"\"\"\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "9c52b7e6", - "metadata": {}, - "source": [ - "## Dimensions of the head-motion problem\n", - "\n", - "These sudden and unavoidable motion of the head (for instance, when the participant swallowed) result in two degrading consequences that confuse the diffusion model through which we will attempt to understand the data:\n", - "\n", - "- **Misalignment** between the different angular samplings (between-volume), which means that the same *(i, j, k)* voxel in one orientation will not contain a diffusion measurement of exactly the same anatomical location of the rest of the orientations (see [these slides by Dr. A. Yendiki in 2013](http://ftp.nmr.mgh.harvard.edu/pub/docs/TraculaNov2013/tracula.workshop.iv.pdf)).\n", - "- **Attenuation** in the recorded intensity of a particular orientation (within-volume), especially present when the sudden motion occurred during the diffusion-encoding gradient pulse.\n", - "\n", - "While we can address the between-volume motion using head motion correction, the within-volume motion is often identified as a signal outlier and omitted from further analysis.\n", - "\n", - "## Objective: Implement a head-motion estimation code\n", - "\n", - "This tutorial focuses on the misalignment problem.\n", - "We will build from existing software (DIPY for diffusion modeling and ANTs for image registration), as well as commonplace Python libraries (NumPy), a software framework for head-motion estimation in diffusion MRI data.\n", - "\n", - "The algorithmic and theoretical foundations of the method are based on an idea first proposed by [Ben-Amitay et al.](https://pubmed.ncbi.nlm.nih.gov/22183784/) and later implemented in *QSIPREP* (see this [OHBM 2019 poster](https://github.com/mattcieslak/ohbm_shoreline/blob/master/cieslakOHBM2019.pdf)).\n", - "The idea works as follows:\n", - "\n", - "```{admonition} Algorithm\n", - " 1. Leave one DWI (diffusion weighted image) orientation out.\n", - " 2. Using the rest of the dataset, impute the excluded orientation using a diffusion model.\n", - " Because it was generated based on the remainder of the data, the simulated volume will be\n", - " free of head-motion and eddy-current spatial distortions.\n", - " 3. Run a volumetric registration algorithm between the imputed volume and the original volume.\n", - " 4. Iterate over the whole dataset until convergence.\n", - "```\n", - "\n", - "### Step 1: Identify an I/O (inputs/outputs) specification\n", - "Briefly anticipate what are the inputs to your new algorithm and the expected outcomes.\n", - "\n", - "```{admonition} Inputs\n", - "- A *b=0* reference - this is a 3D file resulting from a varyingly sophisticated average across the *b=0* volumes in the dataset.\n", - "- Orientation matrix in \"RAS+b\" format. This means that b-vectors are given in \"scanner\" coordinates (as opposed to \"voxel\" coordinates) and must have unit-norm. An additional column provides the sensitization intensity value (*b* value) in *s/mm2*.\n", - "- *high-b* DWI data (4D file) - in other words, the original DWI dataset after extracting the *b=0* volumes out.\n", - "- DWI prediction model specification (model name + parameters)\n", - "- Image registration framework specification (including parameters)\n", - "```\n", - "\n", - "```{admonition} Outputs\n", - "- List of affine matrices estimated by algorithm, which collapse the distortion from both sources.\n", - "- List of rigid-body transformation matrices decomposed from the latter, representing the estimated head-motion parameters.\n", - "- List of the residuals of the previous decomposition, representing the affine distortions attributed to eddy-currents.\n", - "- A new DWI file (4D) resampling the data via the estimated affine matrices.\n", - "- New orientation matrix in \"RAS+b\" format, after rotation by the rigid-body motions estimated.\n", - "```\n", - "\n", - "```{warning}\n", - "**What this idea doesn't cover:**\n", - "\n", - "- Conversion into RAS+b format of the gradient matrix.\n", - "- Calculation of Framewise-Displacement or any other data quality estimation.\n", - "- Outlier removal or correcting intensity dropout\n", - "```\n", - "\n", - "```{admonition} Nonfunctional requirements\n", - "Briefly anticipate further requirements that are important, but do not alter the goal of the project.\n", - "\n", - "- Memory fingerprint: DWIs can be large, and storing them in memory (and subsequent derivatives thereof) can be cumbersome, or even prohibitive.\n", - "- Parallelism: simulation and registration are CPU-intensive processes - for the runtime to be in a manageable scale, we'll need to leverage parallelism.\n", - "```\n", - "\n", - "### Step 2: Sketch out an API (Application Programming Interface)\n", - "Plan how the new software will expose the implementation downstream.\n", - "Assuming our DWI data is encapsulated in an object (holding not just the data array, but also metadata such as the gradient table)\n", - "pointed at by the variable `data`, and assuming we have a list of rigid-body transform matrices to initialize the algorithm (`mats`),\n", - "a potential API would have a `.fit()` and `.predict()` members which run the algorithm (the former) and generate an EM-corrected\n", - "DWI (the latter):\n", - "\n", - "```python\n", - "from eddymotion import EddyMotionEstimator\n", - "\n", - "estimator = EddyMotionEstimator()\n", - "estimator.fit(data, model=\"DTI\")\n", - "\n", - "corrected = estimator.predict(data)\n", - "```\n", - "\n", - "### Step 3: Software architecture\n", - "\n", - "Once the problem is well defined, we will leverage object orientation programming to implement the solution.\n", - "In our case, we have four major components that will allow us to meet the requirements above:\n", - "\n", - "* A ***powerful but resource-lightweight DWI data representation***.\n", - "* A uniform interface to interchangeable models of the left out gradient.\n", - "* A reliable registration framework to estimate the misalignment.\n", - "* The integration of the three elements above in an iterable loop." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "md:myst", - "text_representation": { - "extension": ".md", - "format_name": "myst" - } - }, - "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": "3.7.10" - }, - "source_map": [ - 11, - 15, - 23, - 31, - 35 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sources/tutorial/models.ipynb b/_sources/tutorial/models.ipynb deleted file mode 100644 index 4686c6f..0000000 --- a/_sources/tutorial/models.ipynb +++ /dev/null @@ -1,667 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "35cf9785", - "metadata": {}, - "source": [ - "# Diffusion modeling" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b86e77e3", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "b09fa0d1", - "metadata": {}, - "source": [ - "The proposed method requires inferring a motion-less, reference DW map for a given diffusion orientation for which we want to estimate the misalignment.\n", - "Inference of the reference map is achieved by first fitting some diffusion model (which we will draw from [DIPY](https://dipy.org)) using all data, except the particular DW map that is to be aligned.\n", - "This data splitting scheme was introduced in {doc}`the LOGO splitter section in Introduction to dMRI data `.\n", - "\n", - "All models are required to offer the same API (application programmer interface):\n", - "\n", - "1. The initialization takes a DIPY `GradientTable` as the first argument, and then arbitrary parameters as keyword arguments.\n", - "2. A `fit(data)` method, which only requires a positional argument `data`, a 4D array with DWI data.\n", - "3. A `predict(gradient_table)` method, which only requires a `GradientTable` as input.\n", - " This method produces a prediction of the signal for every voxel in every direction represented in the input `gradient_table`.\n", - "\n", - "```{attention}\n", - "By default, the code running in each Jupyter notebook is its own process.\n", - "We must reload the dataset again to use it in this notebook.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "04a03a0c", - "metadata": {}, - "outputs": [], - "source": [ - "from eddymotion.dmri import DWI\n", - "from eddymotion.viz import plot_dwi\n", - "dmri_dataset = DWI.from_filename(\"../../data/dwi.h5\")" - ] - }, - { - "cell_type": "markdown", - "id": "90449cf4", - "metadata": {}, - "source": [ - "## Implementing a trivial model\n", - "\n", - "We will first start implementing a *trivial* model.\n", - "This model will always return the reference *b=0* map, regardless of the particular diffusion orientation model.\n", - "In other words, it is just a ***constant*** model.\n", - "\n", - "Its simplicity does not diminish its great usefulness.\n", - "First, when coding it is very important to build up iteratively in complexity.\n", - "This model will allow to easily test the overall integration of the different components of our head-motion estimation algorithm.\n", - "Also, this model will allow a very straightforward implementation of registration to the *b=0* reference, which is commonly used to initialize the head-motion estimation parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d8dbea71", - "metadata": {}, - "outputs": [], - "source": [ - "class TrivialB0Model:\n", - " \"\"\"\n", - " A trivial model that returns a *b=0* map always.\n", - "\n", - " Implements the interface of :obj:`dipy.reconst.base.ReconstModel`.\n", - " Instead of inheriting from the abstract base, this implementation\n", - " follows type adaptation principles, as it is easier to maintain\n", - " and to read (see https://www.youtube.com/watch?v=3MNVP9-hglc).\n", - "\n", - " \"\"\"\n", - "\n", - " __slots__ = (\"_S0\",)\n", - "\n", - " def __init__(self, gtab, S0=None, **kwargs):\n", - " \"\"\"Implement object initialization.\"\"\"\n", - " if S0 is None:\n", - " raise ValueError(\"S0 must be provided\")\n", - "\n", - " self._S0 = S0\n", - "\n", - " def fit(self, *args, **kwargs):\n", - " \"\"\"Do nothing.\"\"\"\n", - "\n", - " def predict(self, gradient, **kwargs):\n", - " \"\"\"Return the *b=0* map.\"\"\"\n", - " return self._S0" - ] - }, - { - "cell_type": "markdown", - "id": "6229504c", - "metadata": {}, - "source": [ - "The model can easily be initialized as follows (assuming we still have our dataset loaded):" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ccea757a", - "metadata": {}, - "outputs": [], - "source": [ - "model = TrivialB0Model(\n", - " dmri_dataset.gradients,\n", - " S0=dmri_dataset.bzero,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "132ac076", - "metadata": {}, - "source": [ - "Then, at each iteration of our estimation strategy, we will fit this model to the data, after holding one particular direction (`data_test`) out, using the `logo_split` method of the dataset. In every iteration, this finds the b=0 volumes in the data and averages their values in every voxel:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "edcf4a3c", - "metadata": {}, - "outputs": [], - "source": [ - "data_train, data_test = dmri_dataset.logo_split(10)\n", - "model.fit(data_train[0])" - ] - }, - { - "cell_type": "markdown", - "id": "384108ac", - "metadata": {}, - "source": [ - "Finally, we can generate our registration reference with the `predict()` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "67dcc872", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_11_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "predicted = model.predict(data_test[1])\n", - "plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "d2fbf1e2", - "metadata": {}, - "source": [ - "As expected, the *b=0* doesn't look very much like the particular left-out direction, but it is a start!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5cb43020", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_13_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "eb7b2cfc", - "metadata": {}, - "source": [ - "## Implementing a *regression to the mean* model\n", - "\n", - "```{admonition} Exercise\n", - "Extend the `TrivialB0Model` to produce an average of *all other* diffusion directions, instead of the *b=0*.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "60f6a019", - "metadata": {}, - "outputs": [], - "source": [ - "class AverageDWModel:\n", - " \"\"\"A trivial model that returns an average map.\"\"\"\n", - "\n", - " __slots__ = (\"_data\",)\n", - "\n", - " def __init__(self, gtab, **kwargs):\n", - " \"\"\"Implement object initialization.\"\"\"\n", - " return # do nothing at initialization time\n", - "\n", - " def fit(self, data, **kwargs):\n", - " \"\"\"Calculate the average.\"\"\"\n", - " # self._data = # Use numpy to calculate the average.\n", - "\n", - " def predict(self, gradient, **kwargs):\n", - " \"\"\"Return the average map.\"\"\"\n", - " return self._data" - ] - }, - { - "cell_type": "markdown", - "id": "753d898b", - "metadata": {}, - "source": [ - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1fa5a7ca", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "class AverageDWModel:\n", - " \"\"\"A trivial model that returns an average map.\"\"\"\n", - "\n", - " __slots__ = (\"_data\",)\n", - "\n", - " def __init__(self, gtab, **kwargs):\n", - " \"\"\"Implement object initialization.\"\"\"\n", - " return # do nothing at initialization time\n", - "\n", - " def fit(self, data, **kwargs):\n", - " \"\"\"Calculate the average.\"\"\"\n", - " self._data = data.mean(-1)\n", - "\n", - " def predict(self, gradient, **kwargs):\n", - " \"\"\"Return the average map.\"\"\"\n", - " return self._data" - ] - }, - { - "cell_type": "markdown", - "id": "7df36167", - "metadata": {}, - "source": [ - "```{admonition} Exercise\n", - " Use the new `AverageDWModel` you just created.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1cc65d0e", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_19_1.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "model = AverageDWModel(\n", - " dmri_dataset.gradients,\n", - ")\n", - "model.fit(data_train[0])\n", - "predicted = model.predict(data_test[1])\n", - "plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]);\n", - "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "3cfbf2de", - "metadata": {}, - "source": [ - "## Investigating the tensor model\n", - "\n", - "Now, we are ready to use the diffusion tensor model.\n", - "We will use the wrap around DIPY's implementation that we distribute with `eddymotion`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a5b7de73", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from tempfile import mkstemp\n", - "from pathlib import Path\n", - "import requests\n", - "\n", - "if dmri_dataset._filepath.exists():\n", - " dmri_dataset._filepath.unlink()\n", - "url = \"https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2\"\n", - "datapath = Path(mkstemp(suffix=\".h5\")[1])\n", - "if datapath.stat().st_size == 0:\n", - " datapath.write_bytes(\n", - " requests.get(url, allow_redirects=True).content\n", - " )\n", - "\n", - "dmri_dataset = DWI.from_filename(datapath)\n", - "datapath.unlink()\n", - "data_train, data_test = dmri_dataset.logo_split(88, with_b0=True)" - ] - }, - { - "cell_type": "markdown", - "id": "aa65ad09", - "metadata": {}, - "source": [ - "### The model factory\n", - "\n", - "To permit flexibility in selecting models, the `eddymotion` package offers a `ModelFactory` that implements the *facade design pattern*.\n", - "This means that `ModelFactory` makes it easier for the user to switch between models:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "747f8380", - "metadata": {}, - "outputs": [], - "source": [ - "from eddymotion.model import ModelFactory\n", - "\n", - "# We are using now a full dataset, we need to split the data again\n", - "\n", - "model = ModelFactory.init(\n", - " gtab=data_train[1],\n", - " model=\"DTI\",\n", - " S0=dmri_dataset.bzero,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "47f957d4", - "metadata": {}, - "source": [ - "### Leveraging the `fit()` / `predict()` API\n", - "\n", - "The `ModelFactory` returns a model object that is compliant with the interface sketched above:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b6cf7b5a", - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(data_train[0])\n", - "predicted = model.predict(data_test[1])" - ] - }, - { - "cell_type": "markdown", - "id": "8ca5e38a", - "metadata": {}, - "source": [ - "Now, the predicted map for the particular ***b*** gradient looks much closer to the original:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c4df1b23", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_27_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);" - ] - }, - { - "cell_type": "markdown", - "id": "1ed76b0f", - "metadata": {}, - "source": [ - "Here's the original DW map, for reference:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "01d049e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_29_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "93a0c59d", - "metadata": {}, - "source": [ - "```{admonition} Exercise\n", - "Use the `ModelFactory` to initialize a `\"DKI\"` (diffusion Kurtosis imaging) model.\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "3f9dd5c7", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "model = ModelFactory.init(\n", - " gtab=data_train[1],\n", - " model=\"DKI\",\n", - " S0=dmri_dataset.bzero,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "62273023", - "metadata": {}, - "source": [ - "Once the model has been initialized, we can easily generate a new prediction." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "04abea25", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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hhPAt4avqoIMFUnDt8WgIAS4EkFqtZnZ2QkIChoaG+HtUVBTCwsLQ2dkJQBoyotVq+blKpZLVFwOHW8PBgtfv98u6iol/F58FdzjLzc1Fb2+vbKCQUqlEamoqACnW6/P5aP36/X44HA5ZxrnoOibWPlqQBwIBJowNDg4iPj6e3w0EAidNSAcPZQGkgTZvvfXWN7rm8SAUkw4hhBBCCCGEr0BwTDo7OxslJSW44oorTvl9Q5Z0CCGEEMK3hK+KkQZ/fiQrWliMwTXPgGRxipi0y+WCy+VCQUEBACl+2traymuImHKwZXwkiN7cXq8Xfr+ftcmjx/GKtYjOY4FAAOnp6QCA2tpatg0V11SpVGhvbwcguemHh4f5udlshkaj4WhYo9HILHPx/WBXvYDo6a1Wq/lciYmJ6Ojo4J59057iwWhsbMT69esxderUU25Nh4R0CCGEEMIZBo1Gc1jsNxgqlQpqtZrfCQsLYyx2eHgYarUaNTU1AKQY8pgxY5go5nQ6D0sEG+1CFg1LACnOOzIycpiCcKSWpkJIDwwMAABMJhPMZjOF7uLFi2E0GtHd3Q0AqKmpQVdXFz93u92IiIhAWFgYALA3t3Cfizhz8DAPAIxJazQaKgAqlQrR0dGw2+3H3OuvC5vN9q20dA65u0MIIYQQTjJG19R+G27REL5d7N69G/PmzTvl9wlZ0iGEEEIIJxEnI7P7aAMqhAUsLFbh7g4EAnSPq9Vq+P1+WaJUXV0drU2dTieznHU6ncxd7vP54PF4eG1hoQe3AfX7/fw8ePqUUqlEWFgY0tLSeO/U1FROWzOZTNi3bx+/P3PmTPT09GD//v0AgObmZqjValrtOp2OzUwAySugUqlkXga1Ws01irWJPVGpVDIPQXAL0RNBdHT0Yf/21ltvfSuJYyEhHUIIIYRwhuFIox79fj/dwEIIC2Hl9/sp2EYLaBETFkLa6/XC5XKxB4bb7YbJZKIgam1tlXXsioyMhN1uZ6xXZEwHr0XMkxZTqpYsWQIAOPvss3HgwAF25ioqKpIJabVajaKiImZ/2+12DA4O8tkTEhLQ0dHBe5lMJvT09MieZXTNtlAm/H4/BgcHZfXho+difxcQEtIhhBBCCGcYhNAVwko0IAmuJ/b5fBTMIyMjMqFqs9lkMWSDwcAyJoVCgZGREbS0tAD40roUwk4IdBFvVSgUMJlMyMrKAgCkp6ejpqYGCQkJ/Fyr1VJ4/+1vf8OCBQsAAO+88w4GBwd5723btrFMC5DiunFxcZg6dSoAIDk5GX6/H/39/bJrixh3IBDA5MmTUVtby7UHC2kxG1tAzLsWeyri7N8lhGLSIYQQQgghhHCGImRJhxBCCCGcZoi4a3DjDWEBi9+DLevh4WFkZmaiubkZgGRxCpfw0NAQY7mAlKk9btw4Ngzp6OhAIBBAcnIyAGD//v0YP348rcz+/n44nU5cdtllACRr9JZbbqFLuq2tDTNnzsTs2bMBAG+88QaGh4eh0+mgVquxYMEC9Pb2cl1Go5EZ1/39/YiOjmZL1JiYGDidTt57ypQpyM7OZuOVN998E1OmTKGXYGRkBPX19Yx5HzhwgNa22Bfx3eTkZFitVsaoR2fMBzddOZNxzHnSIYQQQgghfDsQLTWBL7PDRRxY9KsWn/v9foSHh1O4BbtxIyMj4fF4KJSvu+46AF+OfRQjMBsbGwFIwuqzzz5jG+j58+ejsrIS//73vwEA+/btw4QJE4667r6+Prz99tuYPXs2J2F9/PHHfI7NmzfTTd/T04NJkyZR4LvdbqSkpKCtrQ2A1KEsPj4eUVFRAIA5c+bg0UcfxcGDB3m9qqoqllXZbDbYbLbDSrQASQFwOBz8Xa1Wsy5b7GlISIcQQgghhPCVOFKDENG4A5AEjMlkgsViASA1/RgeHpbNT3Y6nQAki1Gv1+Pee+8FIAlRrVaLSZMmAZCszfDwcCZzqdVqqNVqzJgx45hrFA1DhMW/du1aAEBaWhrq6uowbdo0pKeno7y8HLt37wYgKQSxsbE4cOAAAEngh4eHy5qVZGZm8ve4uDgkJSVh4sSJACSh/s477zAmbbPZkJiYiL179/L3kZERfh6cQKdWqxEbG8t9sdvtbMzyXUIoJh1CCCGEEEIIZyhCMekQQgghhNMMEUcVVp5SqZTFpPV6vaw0SafTyX4PrpkeGhpCUVERY859fX3Q6/Xs8qVUKmGz2RATEwMAmDx5Mv/2SLDZbNDr9XSfA0BnZyeKiooAAHl5eQgEArDb7SgrK0NZWRmqq6sBAOPHj4fL5UJ4eDgAqYTK7XYzpuxwONDf3w+TycS1p6Sk4JNPPgHwZdxYlIsZjUZotVqcd955AICXX34ZWq2WZVfDw8P0Pvj9fvT19THOr1AoYDAYDhsmcqYj5O4OIYQQQjjNCG7DKaBQKCgYXS4X63yBL13boxuSiP//61//ShdwfX09APkUqLS0NFx++eUAwDiy+Ly9vR1Go5FxYY/Hg+rqagreyMhIBAIBuqhFjfXAwAA+//xz+Hw+llAlJSXB6/UyhqxSqXDo0CEqECaTCSMjIyzJCg8PZ8kXAFRXV6Oqqgrjxo3jPnV3dyMuLg4A2AhFxLSDy7tEzDm4TjoiIoLrDh7nKXAmJpP9Rwjp45m7KhIwzGYzzGYzCgsLAUgdbiwWC2bOnAkA2L59O3p7e6lt6XQ62bV1Op1s1FpTUxM8Hg+mTZvGv1cqldQcIyIiSFC1tbVISEhAQ0MDAKnZvM/n42Ho6+tj/1vxt+Hh4bzX0NAQVCoVD2N9fT2Gh4epBXs8nqOOtgvh60OtVrNmdHBwEImJidzn/Px87N+/n8zU6XTKGKvX62XWrd1uRyAQID319/ezOxTwpRUlGPHod6lSqfgZICXdKBQKWkRWq/Ww5CIBkZQkslvFDN/gGtLgRhCBQAAmk4kJPjqdDmFhYaQ9k8kEj8fD74eFhcHtdpPRd3d3h2jxOBA8knH08AeTycT3LYZmBAtllUrFxhwpKSkcoqHRaNDb24tnn30WgJTlfODAAWZYt7a2Ijk5GXl5eQCAsWPH4qqrrsLmzZt5r+HhYeTn5wOQBGVLSwvftdfrhdvtJt8aGhrC2LFjYTabMTAwgDfffJP88/LLL8fIyAhj0g6HA319fRTC4nqCxxUWFqK/v59CvqGhQdZoxWq1oru7m4qFWq3Gnj17sGnTJgASHQbXf8fFxZEm7XY7hoeHZXH/70J8OhSTDiGEEEIIIYQzFN8JS/qrLGWlUolAICCzIIKnpKSlpfHvJ0yYAKvVSleQUqmEw+Gg5dvV1YWhoSFZHCM6OpqanUajgd/vp/tGaLbCWlar1bRoACAnJ4daZCAQQHFxMd0tIjNRWBxarRYej0dmWSmVSsZjIiIikJeXR6+AsNrFs7hcLlkd4FdN0gnhyAj2Zuh0OhQXF/Mzm83GGk9A2vORkRFazxEREcyCFf8uvq/VammJBkPcS6VSydxzgqZFaYzdbkdYWBi/b7Va4XQ6Sat+vx8ajQYpKSkApHpYEdMcHByEwWCg5aVQKHg/4MuRgeLa4nrBrSmVSiW7TLlcLqjVarpdlUol9Ho9/16UEAGSx0CcsxCOD8JjJzKwhbtb8Aq9Xs96aECyUMW7fvHFF3HxxRfTcq6urkZdXR3q6uoASO9DuJWBL/ttC3d7YWEhkpOT8dprrwEAJk2ahKioKPT09AD4ssRL0NmuXbuwcOFCBAIBbNy4ERaLhdZrSUkJYmNjsXXrVgCSx8BsNrOkqqioSOZ2TktLQ1paGr0COp0Obreb3c6ERyAzM5NrKSkpwY9+9CMAkierrKwMAFhXLvbIaDTCYrFwnwTtCpysMZYnG9+JxLGjCejRo9OCN1kwn8jISBgMBpYudHZ2wuPxkIDNZjM6OjpkCRgFBQWMcQwMDJA5AhKzGxkZobvG7XZjZGSEQnzRokX49NNPSfBnnXUWqqqquK6KigquW8w7FbNXOzs7YTKZyMg1Gg0GBwfZGKCrqwsNDQ0k0MzMTAwMDPD7arWaCgAAmYAePYouhKNj9F61tbVR+BiNRjidTtKcVquFTqejYO7v75e5q4PdmAqFAkqlku/f4/HIFNBgpin+W1BQQCXP6/XC6XSylKa9vR0pKSlMCBL9k0UjiGClQNTVitCIQqFAb28v7+3z+RAZGUk693g8cDgcFOo6nQ4+n4/Xy8zMRFVVFd21oq+0YHyCIYprq1QqMke32y1TEP6bEUwrwXTn8XhkAsZqtSIQCMjGRCqVSr4fv9/P/XznnXcQFRXF2ubW1lZERUUhPj4egCTwVSoVQxnh4eEMmQBAVVUVNm/ezDiw0+mEw+GQ1ToHx9Db2toYemlvb0dvby+nQ+3cuRMZGRkMF6lUKjQ1NWFwcJDXDhbKoq+3oDOv14uGhgaGCHNycpCcnEwjKSwsDPv378eVV14JAPjf//1f0mQgEIDX6+WejYyMyBSdyMhIDAwMcM/PVKPmO2FJnyjUajUJMjExEcPDw3zpw8PDsFgssibraWlpFG4jIyPweDxkyhkZGWhvbycDTU1NRXp6OmMkHo8HBQUFeOSRRwBI2Yyib+2f//xnJCQkUMgGAgESM/ClkiEstaGhIdTU1PDfTSaTzKoTTE8gIyMDubm5/PekpCQcOHCAzPDQoUP8WyEsQoL62NBoNDAajWR4CQkJyMvLI300Nzejr6+Pe2wymTA4OEiGFTyY4EjKpdlsJoMKTmgBpOYLgUAAGRkZACSGJeYGA9IkHpfLxb9fvnw5du3ahdjYWADAli1bcNddd8Hn8+HJJ59EQkICxo4dCwBoaWlBc3Mz1x0TE4PExERm2a5evRo6nY6xwebmZuZMiGdRKpV8Tq1WC6/XSyGi1WphNpvJbMX5AyQhEB8fz/3o6urC4OAgFdsQTUoIFtIxMTHw+Xx81zqdTiZE1Gq1bE5ycnIyre6UlBRMmDABF1xwAQDgvffew+DgIPmMVquFw+EgX2ppaYHJZOLnBw4ckHloVCoV3G63jP9kZ2ejr68PgGQNn3feeejp6cFrr70Gr9eL3NxcANL5qKqq4t/q9XqYzWbWQdtsNgwPD5MW3G43xo8fj/HjxwOQxkHGxMSQJ7rdbmRmZqK8vBwAEBsbi+HhYSqqhw4dopU+MDCAwcFB2TQvj8dDb5C495lOf6c8Jn2mb8CpQG5uLon0PxFCqw/+WblyJZlECKcPubm5TPgJIYQQvvv4Tri7jxfBWr2wGAYGBuB2u/lZZ2cnfD4ftSuR0i+67VRWVsJqtfL7hw4dkt1j7NixiIyMZKbjoUOHEBUVRYvl4MGD0Ov1iImJwZ133omqqipOeLniiitw0003MWYirLU9e/YAkKy4c889l5mKYsSccIc7nU6Eh4dTa2xqakJ4eDgtlOrqang8Hl43KSkJHR0dAA7P7v2mKCkpYdefyMhIlJaW4v7778d99913Uq5/upCYmIju7m66IcPDw7F//37ZvFoxpxb4cmyfcLGJzFeBiIgIWiRarRZ6vZ60FRkZyfAJIL3/9vZ2WkgKhQJpaWl0Iff09MDj8VDx/ec//4nzzjuP1zv33HOh1+uhUqkwadIkHDx4EFu2bAEguZ+DY8gejwfNzc3YtWsXP582bRrn+mo0GiQlJdEyHu2aFq5D8e9+vx+tra30Egn3v/huT08PsrOzAUjWT09Pz3+lAj8aYr9E6U9wfkJUVBTp0Gq1QqPRcM8cDgerPwCJrwn39uDgoCzUMTAwAIfDwWv39PTAarUy10WEI6ZMmQJACmV0d3ejqakJgNQ1rKmpie9arVYjLCyMa3c6nfB6vYiIiMCUKVNQU1ND3hATEyPL9E9JSUFCQgJzOMaMGYNDhw6R/7788stQKpUs91IqlSgrK+P5SkhIQEtLC/ntpk2bYDAYuE8qlQqTJ08GAJSVlaG/v5/nyWAwyHqWq9Vqhj0FgudPH0/m9/FUFn1TfGeFdHBMRKlUylL6c3Nz6Y4eGBhAY2Mjkx4UCgU0Gg3dfF1dXfD7/fjiiy8ASIw0NTWVzEngmmuuASARXX5+Pu99zjnnYGBggIL0scceQ3h4OAwGA3JycugWBKSXvmDBArpbNm7cCLfbLRtovmXLFrz99tsAgHfffRdvvvkmCd5gMGBoaIilD3a7HZ2dnVRIrFYrDAYDiTA5OZkCwmq1Qq1WM8ns6ww+PxoGBgawevVqMuEzHUca/C6UroGBAVx66aV0z5WVlclisR6PB3q9nkzBaDSiv79fFvsNLmsyGAx8B4KhCiVrcHBQFnYR8WqRJFNbW4vOzk5axuL9Cqaj1+vh8XhI+xqNhglB+fn5iI+Pp0tTKIJ33303AODpp59GbGwsP9fpdNi0aRPPRUpKCjo7O3H99dcDAF5//XWkp6eTeQq3vlD6RJKOeFaTyUR3qFar5TMBkssyOTmZCoPNZjssiee/BcE1vWq1mmdZCG2h7Ph8PqSnp7PmGZDegaBDkVwISMmxfr8fn332GQApbqtUKvluIiIiEB0dLSsFNRqNNA58Ph+qqqq4FpvNBo1Gw3vl5+ejo6ND1txEjMZsbm6GTqcjjfb19cHtdvM5xYhNQTdjx45FY2Mjlcn8/Hzk5eXxd7VajYKCAp4T4f4XZVbjxo2D3+/n2gGQv8bFxckUR0BSqoU7XCjXgper1eoTNmK+jRKu76yQdrvddK/GxcXRegWk+KvIJtTr9ejv76cmJog1OHswmNENDg4eJqCjo6PJYDUaDYaGhij0HQ4HmpqamBxmMBjgdDphMBjQ2tqK3t5ervPNN99EUlISHnvsMQASgS1btgxvvfUWAKnzz7x58/Dqq68CkKy6m2++GRs2bAAgEWZRURGtHfGs4iCLbjti/cEKwOTJk7F//35qiSezaD8rKwvz5s3Do48+elKud6oRLKCDm0AAkpW3b98+xqADgYDMCgGkgymEm8jQF4wgmJmNjIwgNjaW70AwFmFVhIeHQ6fT8T1ERETImv6PGTMGarWa187KysLAwADXHxkZia6uLjIWn8/HpLbt27fLaraFsvjEE0/wOYQQBaSEt0AgwKlKPT09KCkpwSuvvAJAiqWPjIzQWtPpdOjq6qLSJ/4r1ipqwIEvcytElrvT6aQiKfYhuKfyf0JC2fFaWKOVHBHLHxwcRGxsLBW+zs5ONDc3U6Do9XpMnDiRDUlmzZpFL1ZHRwdpC5DeRUxMDOPANTU18Pl8si5fxcXFzL7v6urCuHHjUFNTA0Ciq7POOotWp9/vR2dnJ5Nr8/Pz4fV64fV60dLSgrS0NJnVHdxfPD4+Hhs3bmRM2WAwoLi4mEbQ9u3bmdAGSGeisbGRybJOp5N9wgEpzjxp0iTeY+vWreT1u3btkgleMSkseJJYcF+BM5XuQnXSIXwtlJWVMR4tsi+FshFCCCGEEMLJwXfSkhYdv0RMxWq14uabb6aLZOPGjYyv7NixA5GRkbSM9Hq9rDbZbDazD664dnB96DnnnIP09HRaqwaDgdYTADQ2NqK3t5fZiX19fSxXcTqdmDBhAi3p1atXo6enh+7O6OhorFq1ii5Fg8GA+Ph4WnZKpRIGgwGPP/44AOCXv/wlKioqaA0Ld6fQmEU8Rfze29vL+u3W1lZcdNFFWLNmDb/r9XplbtwTQXBMOisrC+vWrcMVV1zxnRDUwSMBjUYj9Ho9QwgKhQLd3d10v4ra42BrOzguLPZb7HlSUhLfb2JiIhQKBS0OtVoNh8Mhc4drtVqGZiIiItDT00NLPCwsjO50QKpCSE9Pp2fF5XLhjjvuoOXR2toKnU6HQCCAvLw87Nu3j9na48ePh9/vp7u6rq4OKpUK//u//wtA8iBUVFTQIv7oo49k/QZ6e3v5XALx8fG0vtLS0tDb20tLLzo6mt6GwcFBdHd3s7wrPDycZTxiDwsLC1k61t/f/1/TrSx4frTBYOC79Pl8OHToEOkqPDycHQcByXu4du1aljY99NBDOPvsswFIYZK2tjZ6K8aOHQu3201vYHt7OzweDz0yOp0OPT095JGiREtY2k1NTfjggw8Yduno6IDf76enYN++fZg3bx68Xi/6+vqQkpJCj0BLSws8Hg/OP/98AFLVQEREBHOCNmzYgPb2dpSWlgKQ8ip0Oh3P4759+2S/R0REyPI8IiMjMTIyQlqaO3cuVq5cCUA6y5GRkaRDn8+HQCBAfqxUKjE0NESaDQQCR2zPerrxnRLSIm6o0+mQmJhIhjI8PIzXX3+dzNBisTAhZnSDCJfLBYPBwBc1ODiIyZMn46qrrgIguS/ff/99zJo1C4DEzIITz3JycuDz+eiuSU9PR0xMDD799FOuU7SCLCwsxPvvv8/2e16vFw6Hg4zSbDbjgQceIIGpVCp0dXWRgP1+P1paWlBZWQkAmDZtGnp7e0l0Q0NDyMrKoosyJiYGLpeLrvhgV09UVBTWrVsnO/Stra3HjE0fr8uusbER69evx9SpU894IS2aeAjhMzIygrS0NO5Td3c31Go1mYD4d7FvIqQghHxaWhomTpyIdevWAZDyAAQd1tfXy/oQA9I7LikpASDlFPT19ZE2BfMSjDoxMZGlUuL7SUlJFOI+nw9lZWU8F52dnfB6vXC5XNi7dy/UajVdlqJphGDqSUlJyMzM5LpNJhNSU1PZLnfv3r3YuXOnLLkr2DWo1+vR09PDfZo8eTKcTifDTD09PdzjgYEB+P1+PodIGguOx7vdbrrmo6KicOjQoe9Ey8aj4XjXLr4nmiQJAZGZmYnGxkbSHSDtf3BC4yeffCIbHymUudjYWJnyNzg4iNtuuw3bt28HIPEwh8PB2GxOTg6am5vJlxQKBYxGI8NmWq0WkZGR5Cv9/f1wu91UALu6umRxZ6vVyradGo0G/f39jH9bLBbo9Xq++7Fjx6KyspJCfebMmZg7d64sMbOtrY1hlrVr18rOjNlslgnW7du3M1HX5XKhoqKCNC8aRYkzLXIqhPLocDhkJbJnSmLjd0ZIazQaMqPc3FyMjIzw95GREVRUVHBz4+PjqYkLBiFqT4uLi+F0Okn8t956K+Lj41FbWwsAmDdvHtxuN5nPnDlzZJmQLpcLmZmZJOCuri5s2bLlMGHndDpRXV2NkpISJmCcc845aGtrI5HU1tYiLi6OBNrc3Cwb1m42m5Gdnc0M83HjxuHdd9+ldSym4IjmFhUVFUhLS6OQMBgMvJbb7ZZNyhEHWTw3cDhRngiTtNlsPAxnGoKFMiAJN8HsUlJSYDQaZUJaNKgBJLoLVlbUajUSEhJIa2azGV988QU/Ly8vZ2wrPDwcDoeDVsD06dOxdu1a1q8+//zzMJlMzMAfHh7G1KlTyWSEtSmE/NatW1FWVsbr6/V6WCwWzJ07FwD4rkUYore3lwwoPz9flotxySWXoK6uju88KioKqampzLKdOnUqbr75Zu7D6H4DwoMj9u2dd96ByWTC9OnTAUiVBuK5RLxVJOOp1Wro9XpabsLaEcpqUlKS7NpnaqzwRHGkhMVguhT5LICk4MXExPC82mw2eDweXHvttQCk5Kjt27dTUIpKAEASkrGxsfR8zJw5kwILkHiByWQiXTY3N8NsNpOfms1meL1ezJkzB4B0Xtra2sjHcnNzUV5eLjtD0dHRMBgMuOGGG7Bp0yZa3S6XC52dnVxLZGQk4uLi+LdutxuxsbG08p955hlcdtllmD17NvfkrrvuIk9LSkqSNWxyOByora2lUJ8yZQp52iWXXMJGK8CX1TEiqS07Oxsul4u5Inq9Xjaj+0zx5nwrMenRNbVXXHHFt3HbEL5F7N69m12GQgghhBBCODk45ZZ0cPr7N0HwpCm73U7tT/weXIZls9mosWZnZ2P69OnUQHt6elBeXs5aura2NmzYsIGa3MjICBYvXoyXXnoJgBRjzsnJ4ZSsd999F5999hnT/LOzs3HddddR83vkkUeY3W2xWODxeLjO6OhodHV18ffw8HA0NTVxrcnJyRgeHmacz263y0pVHA4HOjo6cPvttwOQOkxNnjwZH330EQCwxCq477jQGkdGRhAWFkZrpaenB/n5+QwZdHd3H1HTPxKEGy0Yb7311hnj6h6dsR3cZtBoNCIsLEyWfarX62XlQh6PhxaMw+GQxbDT09MxMDBAC7W1tRUul4uWdHJysqxLVHJyMjuCxcbGwmAwsIxGeDLEO5kxYwbq6uqwe/duAGAcUXh9RB95Uep20UUXYd26dbQy1q1bh5kzZ8Lr9cJiseD666+nNTpjxgz09vbSle71epGQkMBri25kf/rTnwBIrSGD3atOpxPFxcW0vhobG2EwGPDAAw8AAB544AFUVFSw7AcALTvRNzy457nf72fIKTk5GR6Ph3ReXV19xsUFjxfCGlUoFIdZYqPPlkKhIB2KumhBO0lJSUhOTiZdDgwMQK1W00UtrFpR6eFwONhLOyoqCn6/n6ELrVaLd999l/trMBjQ3d3N92MymbB582Zes6WlBampqaT5qKgoREZGYtu2bQAkWnjqqafw0EMPAZA8QLGxsbDb7fB6vbLSP4fDgcHBQT67KCMVIUCfz4fU1FTuwwUXXIDLLruM+QkdHR1oaWlhieukSZNgt9tJl0NDQ9i2bRvpZcyYMbKpcIWFhZzuJfKExDuqr6+Xzapua2uTtQ09Xn54qnFGu7uDBXxGRgbjL+3t7cjNzUVjYyMAyc3ndDrpBgmuCUxISEBKSgoZY09PD1QqFT7//HMAEiM1GAxkZhaLBS+88AJdyoB0QD7++GOuaWBggMyqo6MDFosF//73vwFILm3RVjQqKgoGg4HulAMHDqCqqor3MhgMslGFn3/+uazNp9FohN/vp4DZvXs3GhsbceGFFwKQ3EyNjY34+c9/DkA6PCtWrJC5kkT95MjICIaGhmQxrOrqaj5HcB/n7zqGh4dlfaIBeZ93g8HAz3t6etDY2HjYwJRgF6vP56NSZrFYZGVWFotF1gChubmZtJObm4u2tjY2r9m7d6+MecbGxuKDDz6gEG9ra0NHRwebSIyMjCAnJ4eCND4+Hvn5+Ywtbtq0CWq1Gjt27AAAzJ8/n3FjnU6HsrIyulMrKysxfvx47kdFRQUMBgMFfEJCArZu3cq1T5o0CfX19aRVv9+P/fv3y1rU5ubmsqZ/woQJaGxspNInnkfs6dDQEMtoOjo6YLfbuRbRk1ycWVFSKc5/cAzyTEdw7+0TgcidCW5hWV5eTjo1GAyIjo7GDTfcAAD45JNP4Ha7GcYbP3486SYuLg4Oh4P7PzQ0BK/XSwWgoaEBRqNR5tbt6OhgK86SkhJotVr87W9/AwDcf//9sFgsVBjUajWeeuop9pQ3mUxob29HWloapk+fjrq6OtKoy+WC2+2mUiv4nXCl79+/H9HR0bImOwB4HltbW9HY2EgXdlFREdra2vh5ZGQkDh06RLoNHj+p1+uRlJREHlhfX4+IiAhZXbTP52OsXafTsUmPuNaZgO9E726j0Yi4uDgyTjHvVGy2yPYLnsgjrO5Zs2ZBq9WSSFQqFXp6evi3Go2GfXIBYPbs2UhMTGQimMhWFLG6999/H3a7XZZRe9FFFzHW19zcjLvvvhvt7e1Yt24dRkZGSCRiwIGwKEQ3MpFUMTAwIOsUlJiYyP6ygDSdJniYR3p6OhoaGsjMLr30Urz//vv48MMPAUhEKhQEj8eD8ePHU/M2mUzQ6XRkCqL/dHCP8+DZx98lBPdAVqlUiI+Pp1AVXdeCe20LK0V8HjxzNrhbGCB5EXp6emSxfp/Ph4KCAgAScxHvU1QJiDBAd3c3UlJS+Lc7duxAVlYW44oxMTHYtWsXhbrH48H06dOplJWVlaGvr4+09sYbb2Djxo38/dJLL0V8fDwaGxvxxhtvICYmRtYIJz4+nszsnXfeQXJyMr0iLS0tuPjii9nUx2azwWg0sv5fdFYT5yQnJwdKpZLXKykpwXPPPXeYB0P8N1jpMZvNnHEMSGdQrVbTEhSMMrhfs9VqlTWM+U9AcK6DGG4SPBEvOjqaSaFGoxFz5sxhf+2amhqeaUBSkkTil91uR0VFBZXB4uJidHR0kGfV1tYe1skuJSVFViWSnZ1Na3jfvn2YNGmSLGE1eKJZSUkJ5s+fj8rKSrz44oucqgVIBlV/fz/f7QMPPID9+/fTEJk+fTo++ugjrtXlcnEKFwD85S9/QVpaGp8zMTEReXl5sgTFpqYmDgPJyMggDTc0NMDv92Pnzp3cQ5vNRmVDp9PBYrHw/LW3tzMDHDhzhHSoTjqEEEIIIYQQzlCc0e5uoeWPHz8e3d3d7Hjj9Xo5dQeQYibNzc0yd5mIS7jdbuTk5FALr66uhl6vp5souDQEAF555RVMnTqV8bKKigocOnSI7hbRX1loiiKOLDTD7Oxszm09dOgQMjMzWW4wbtw4REZG0ioTLlTRMjEQCKCvr49WncfjgcFgkM2+HhoaksWtBgcHGa9paWlBdHQ0y8cqKytl8RmbzcbYe19fH2JjY+n+NplMiIyMpOUt2vd9FxE8W1zMOBZuQ7vdDpvNRkva5/NBp9PRanM6nSxBASSrwul00sKxWCwIBAK0QsaMGYPq6mqW5I2GyWRiOaBotSloKSoqCn/605/4Dp599lkUFRUx7hgZGQm3202XckZGhqw2+cUXX0RTUxN/37dvH3784x/D5/Ohq6sL3d3dpD2Px4PW1lZaU0NDQ+xjL54zOjqaLtJgDw4gj58CUkw6OTmZ4ZJXXnlF5oEIBAL0GOj1eowfP57noqmpCXa7nbWzGzduxMjICD0QxcXFaGtrY6zearXKatP/UxDcRjUsLEw25aqvr09W/yu6JorzaTAYMHv2bPKd4C6I8fHxmD9/Pm677TYAEl3V1NSwLMrhcKCyspKx2QkTJsDn8/HaOp0OsbGx5L8PPfQQvF4v1/rcc8+hpqaG789ut8tmHERERPA8BAIBLFq0CIsWLQIArF+/HsuWLWNZ6XPPPYerrrqKZYlPP/00Dh48SB4JSN5JsQ+zZs1CdHQ0PVcqlQo5OTnM47BarbTCq6urkZ2dTbqqrKxEeHg4n1vMqRZnWavVwm63n7Q8qpOFM1ZIG41GuoldLhf6+/uZGNbX1weTycTkAtFmUAjxqKgo5OTkAADOP/98tLe3U/AVFBRg5syZeP311wF82exfCMJx48ahvr6eU6wuvvhiVFdX88WKRibCTTVt2jQ0NDTImjqIJI45c+agp6eHyoTD4cDQ0BDdLd3d3RgaGuJz5eTkoKurS9ZMvrm5GUVFRQCkGIpWq5XFR8eMGUNmODQ0BKPRSNeSy+XCu+++C0A6eE1NTWR0Wq0Wvb29shpCERsETl7C3+mAQqGgUE5KSoLT6aQgdLlcMuXDaDRieHiYQlihUCA5OZl7arfbkZ6eTsUoJycHgUCA7raKigoolUrGlQ0GA/cuNjYWnZ2dpL309HR4vV6+74KCAmzevJnxu8bGRsTGxuK8884DIClv6enpdNc5nU6Ul5eT+RoMBrjdbrpA6+vr4XA4EB8fj2nTpmHhwoV48803AUjvt66ujszt/fffR3h4OAXx4sWLkZ6eTiEuYqRi7ffddx/a29vx3HPPAZDOQWdnJ12gGRkZ7AMg8NOf/hSARGuLFy+mEPjJT36CQ4cOyXpFp6amUsgcPHiQyhEghYlcLpdsNOOZOvv3eDF6XK1InBO/q1QqhIeHc3+VSiW2bNmCc845B4BE1xaLhaEzkXsjvpuUlIS//OUvAED6FmVQwp0sYthbtmzBpEmTGDYRSWPi3k8++SQSExNx7rnnApAMkdLSUgri2tpa5OfnQ6fTISUlBWazmX+bn5+P2bNnY+3atXzuN998k8aC2+1GUVERhbYYJCLc27m5uRgaGuLaDx48CJfLRf6cmJiIqKgo8u++vj7SsFarhdPp5Lr37dsHt9vN72q1WnR3d9MgE82FgsezngkIubtDCCGEEEII4QzFGWlJC3ej0GhE96Fg95vFYqHL2W63ywYTnHXWWdTkduzYgaGhISa49Pb2oqKigtZMc3MzFAoFLSWv14vY2Fi6b7Zt24aYmBjeu62tDVdffTUtr3Xr1iEpKYnJBzqdDm63G16vF/39/YiMjKTG2djYCLPZTCu/oaEBQ0ND1PxGRkZkyVvNzc3IysrivUQmonhOMQJOlFIITVxY2tdeey01ztdeew19fX0yC9lgMPBewkoXFmRwctV3BcIdJ1p9AhJt+P1+7llwkxJAet8+n49/C0hJdMKKO/vss7Fnzx5ZeKOmpoadvF599VX85S9/YVmUUqmk9ZmdnY0lS5bIEgM9Hg+TsS666CJ4PB4OvXjsscfw+eef8x10dnZi69atdI/39/cjPT2dFqSwmoWl3dbWhpKSErS2tuKJJ57Axx9/zHUpFAr09fWxoYVKpYLL5eLQBXE/0RgnKioKy5Yt44CNAwcOIBAIMJQSExODuro6Jn/V1tbKLA+DwcAExqioKHz66aekcxHyCc6yDQQCPKMdHR1Qq9VsiFFXVyejTbvdDo/HQ1pWKBRnjNVzvAgEAjK6E4mxwSWT6enppFen04mwsDBamGI0pKhoKSsro1fszjvvxO7du0nzH3/8Mfx+P2666SYAX+7/+vXrAUj7O3bsWHoysrOzsW/fPtJZQkICDhw4QMv7+uuvx8jICCtm2traEBYWBpvNhtraWgwPD/PeWVlZ+OCDD9im2Wq1YteuXSxpTUxMlHVCFJ4tcWbCwsIQGRlJD01PTw+io6NJx729vUhOTibddnR04JNPPgEgWeFdXV14/vnnAUihp6GhIXoT4+LiZF5Wk8mErq6uM86LeEYK6YiICERGRtKdLQhXuBQ1Gg26urrISGNjY9lvFgAWLlzI2rjs7GxZ7XF2djY6Ojr4e15eHhITExm7E+MfRUxkzJgx6Ovro0tR1JH+9re/BSCVXA0PD/N+IyMjGDNmDFwuF+rr6/G9732PLe2cTiciIyPp2klJScE111zDbESlUgm73c4OZFqtFl1dXSQijUaD4uJiZnf/+Mc/xqeffsrv19TUwGKxMMa9YMEClr10d3fj1VdflXXKUqvVdCN5PB4UFxfzoFdWVjLD+0j4NuaoHi8MBgNiY2PpxgoLC+PanE4nYmNjSSt2u11WJy3aBIrvBwsDQGozGJx1u2bNGiQlJbFrmMvlgsViIW0WFxdzT0VZoHBJNjY2Ij8/n7Qk6pmFWzIsLIzVCIAkhMvKymSzmYuLiym0o6OjodfreT5Gt5C0WCxUCGfNmoUbbriBysm2bdvw6quvsr7f7XbD7XZj/vz5ACSF8K233mK+Q1ZWFnQ6HYXKoUOHUFxczBBTZWUlmpqaqLwYjUbSocPhwA033MDn+uSTT1BXV0cF0ev1oq2tjW7HpKQk9Pb2MlvZZDLJWvOKqVrB8XaVSsV3+F2JXev1eu5nT08PcnNzKXR1Oh2VOUDaQ61Wy3hrd3c38vPzcckllwCQJj6JktTo6Gicd955pMn+/n6kpqayG9wnn3yCBQsWUBCKsMddd90FQHJ/b9y4kffeu3cvsrKy8P3vfx8A8NJLL+Hyyy8nbWzduhXt7e1ITEzElClTUFdXRyPI5XIhLy+PAr26uhpKpZK9HZxOJ/x+v4y3W61WWfe8/Px85jMMDAzIZkSHhYWhtLSUSvTAwAAzxWNiYrBt2zaGvvR6vaztssPhgNFo5DvQaDQoLCyk4hlcJXI6cUYJaZHAlZ6ejp6eHm4uIG2+OJQWiwVer5dE9eqrr8JqteLll18GIDHa4L7eNpuNlq7dbofL5WKCTFNTEzIzM5m4ICwlETcWhfmit/cTTzwBh8NBjfbTTz+FXq/n2qOjoxmTWbVqFaqqqiikY2NjkZmZybKKoaEhWbMTh8OBgYEBDjTv6+uD0+lkbH7//v2IioqioN25cycsFgtruMPCwhAREUELxuv1ch+mTp0Kq9XK7/p8PlgsFhlj279/Py6++GIAYJlacLJQMMGeKQIaAN+nEECjD153dzfXK0ZDCgER3IcbkJSs4eFhWa9uq9XKg20ymZCQkCDLQYiKimKC3owZM+iVKS8vR1dXl6x+/4svvqB129PTg+XLl5NBbdiwAc3NzaTzmTNnYtOmTbJ3EBMTg4suuggA8OGHH6KlpYX0sWvXLpjNZmRkZGDDhg3o6Oigotvb24v333+fXhbRdnL16tUAJGW0sbGRa42Li0NFRQW+973vAZCUlcrKSj7b8PAwbrnlFtKyWq1GeHg4mbPdbqfltnbtWuzYsYOMd2BgAHfddRct59LSUnz44Yf0fmk0GlpmgGR9BTekEQNPRPLn0NCQzNPmcrnOCOZ6JATT1cjIiGy8rkhkAiRFZceOHaQzj8eDK6+8kjReUlKCtrY20nVwnsxf/vIXOJ1O8qi8vDzU1NSwQZNOp8PatWu5vwUFBUhISKDnsq+vDxMnTqTQj4mJwdDQEP7whz8AkHjcli1bGFeeOXMmYmJiWM566NAhLF26FADw2WefoaysjM89e/Zs1NTUUGmOioqCUqlkaejEiRPhcDj4nE6nE729vaRx0eo2uJlLQUEBv9/b28t7+Xw+XHnllRy8VFNTg8TERNKwzWaTNSrKzMyUjQEWeRmnG6GYdAghhBBCCCGcoTijmpkIzVo0Phfusb1790KlUlFbcjgcyMzMZOby9u3bmfEKAH/84x/pEg4PD8cVV1zBaT/CbSm0qdjYWFxzzTXUzDo7OxEeHk6tVLjaxLAAm82G/fv302LIzc1lW1JAyvadMGEC1Go1enp6cNlll/Fer732GicAAZL2PGXKFMbqRMmAiIcnJSWhv79f1ogjKiqKlpjwBgQ3r7darbSWxNoBKcvSZrPh97//PQDJ+mhpaZFNmwlup5mTk4OqqqrvTBmWGEEKSO9clAaJshXxWSAQkE0HKyoqQm1tLTV10VL28ssvByC9k5aWFmrnBQUFiImJIX3V1dUhLCyM+5yRkYE33ngDAHDvvfdieHiYrr64uDgsXryYmeFutxsej4fZ3a2trbjllltYgtXR0cGhKwJmsxn33XcfAOn97t27l2tNT09Hb28v1Go1W4AGT1nSaDTcF4fDgc7OTnpdIiIiMDg4SDru7u6WDRuYPHkyUlNTOeq0vr4eZ511Fr1ZKSkp2LRpE/75z38CkGhRuOXj4+PR3t5OyystLQ1z587FNddcAwB46qmnsHnzZtK9iLcK75bIAhdn1OVyQaPRMO9gZGQE4eHhXPvIyIjMAj1TEVwaODw8jLy8PMZ9RUhGWHJ2ux1Tpkxhd0HR6Eic9eAOYxaLBUNDQ/jlL38JQNrf6Ohonu0vvvgCSqWSoRCr1Yrx48czdyYuLg7V1dX0bIgOZmeddRYAKUtao9HQOzkyMoILLriAQ3vWr1/PLnjt7e3Q6XRs6DNr1iz88pe/ZJx48+bN+OKLL9gmVEzUErQSGRkJo9HIe23duhVGo5H74vV6sXz5ctJOQkICB9S8+OKLuO2223DjjTcCkPKT+vv7ZSW3LpeLZ1+M8AzumqfRaE77oI0zQkiLcX4ifipG7gn3mEKhQFdXFw/clVdeiejoaNnnIyMjdBM3NTVxY00mE5xOJ3t1C/eziAs7HA6Eh4dTcBYWFspibYKJis9nzJiBzz//nC86LCwMAwMDdN80NTXhqquugtvtxtq1azFjxgweHr1ej6amJjL0s846C1arle5Nl8sli6loNBpUV1dTIdDr9airq+M++Hw+mEwmWVvJ5uZmxqizs7MZu0lISGBMHAAefPBBAGDtZFNTk6w8SCTAibUGx0XPNIh4pBCkwUlIgMQMxTsQc3CFwufz+WA0Ghkn9ng8aGxspHCKj49HcXExFaOqqioMDAyQvvR6PTQaDekjOztbloDT2trK5CtAmtLz/vvvAwB+85vfoKGhgfOhfT4foqKiGBdetWoV/H4/XaAtLS1Qq9UyppGRkcF61iVLlmD69OkYHh7Ghx9+iN27d9N9J2pyRWjl2muvRWNjI5lhV1cXent7GY/LysrCj370I9KJSCwU7tmysjLo9XrGORMTE5GRkUElYMaMGXjttdcASLT0z3/+k2dErVajsLCQaxHxRNGqNzhBFJDcq/39/XRL+v1+Cmixb8Hv32w2M1wl6qvPtPGDgmbEeRPhAqFwi3GOIszi8/mQlZWFRx55BICk0O3atYudtmw2G/lAX18foqKimDfx0UcfobOzk/vj9/vR3d1NQTdmzBh2OxTXjouLI480m82yEq8DBw7AaDSStuLj47F8+XLExMSgr68Pt99+O2uoDx06hEcffZSKa1ZWFhobG8k/DQaDrMZalJaJM1RSUoLe3l52kNyyZQvGjRvHXIp///vfmD9/Pte+adMmzkj/+OOPMXbsWLY33bp1K+rq6mh4mEwmmRHi9/sRHx/P9ZwpZX5nRExao9EgMjKS1qoYciAEZVxcHOLi4iiUY2Nj8dRTT7HW2eFwoKGhgQKns7OTROL3+9HW1kbB1dnZifLycmpLVqsVl1xyCZMgEhIS0NjYSCZusVhQWFjIGsKGhgZ4PB4mXSQlJSEnJ4eMs6CggAk2Y8aMgVqtphYpErEuvfRSAF82jBCxoaGhIVgsFjJdo9EIrVbLa7e1tSE2NpYH22KxIC4ujoll0dHRHCko7ieeW8S8xLUuvPBCfPzxx0z0Ef2phWY/NDTE3raAdJiC5/+ebgTHkYOzuoEvB40AkrUaFRXF5wgEAjKhGh4ejrCwMLbijI2NhdFoJBOZNWsW2tvbmcSj1Woxbtw4Ht64uDhkZ2fTKhkcHOSebtq0CQcOHMDNN9/Mdd1xxx1ktEuWLMHzzz9PYdXb2wuFQsGh9WPGjMGCBQvwxz/+EYAktCsrK/nsVVVV2L9/P+uqZ82axfpip9OJjIwMWhVerxfd3d2k29bWVkyePJl0bTab4XA4+P377rsPHo+HQuDss8/Gvn37WKerVqsxdepUegVmzJiB2267jR6r7du3M48jMzMTK1eupIL485//HJ2dnfQgzZkzB6tXrz5MOAu4XC5ZLFE0lwnuxx78Tt1ut2yWstPplLWJPRO8Qz6fD3q9nufT6/VCpVJR0AKQDZLo6+tDdXU1vYeTJk1CX18fvS5RUVHYt28fAOmst7S0sOpjYGAARqORiovFYoFaraaQ7uzsREREBOmwv78fgUCA70un00GhUFCBU6vVGB4eplH1/PPP44MPPsDg4CCraYRA3717N9atW8cz+t5772HGjBl890ajEXa7nXynq6sLLS0trErR6XRIS0ujLLDZbGhra6P3acGCBWhrayPvt1qtVIInTpwIl8uFv//97wAkOgz2Lg4PD8sG4Ozbtw/t7e0yBf5MSB4LxaRDCCGEEEII4QzFGeHu1ul0MJlMstiAGK0IfBmDEnOo//jHP8JoNLJ06dNPP5V12hocHJTVyVosFlqrQvsT8bLZs2fz7wDJPbZt2za6P8W4SLG2PXv2YGBggG7Czs5OuFwuWvnR0dHIzMyk5tnU1ESNVqPRwGw20+1ns9lwzjnn0BIuLy+HxWKhK+fTTz/F1KlTac2IMgvhCnK73bKJTGeffTbef/99WdN9oRXu2LGDmjQgZbH/7Gc/k7XE02g01Co7OzthNptp3Qgr5nRrlQLB5UBiPJ5wtYoWqICUmWq1WmWdsNRqNa0Gl8tFVy4guaN37drFus7+/n4UFRUxftrd3Y3NmzfTkm5qaoLRaOT1cnNz2Qa0vb0deXl5fL8LFizAypUrGV9bvHgx1qxZw/GOLS0tUCqVjDHffffdeOSRR0hr2dnZKC4upiWQlJRE9zMglYede+658Hq92LZtGwYGBngOmpqa0N/fjwULFgCQsv3LyspoyXV3d+Oaa66hp6Wurg47duygxVNXV4ehoSHSw1VXXYW1a9fSpapSqRAREcHwyqpVq0j3iYmJmDt3Lr0TH330EXp7e+lxqKqqYhY6AIwdOxYLFy6kB+LnP/85enp6GLIQrXMF7YrhH8GlioLuRXmWeP9nghUNfOkJEtaq8AyIHAJRciboSrjBn376aQDSfqtUKpZK5eXlyfJm6uvr6RXJzc1FIBCgtTo4OIimpiaeEfG34vOoqCjU1tYyTvzuu+9yOBAgeQurqqpYIrdq1SqWYHV2duKqq67CqlWrAEhtP2fOnEkarqqqwvTp03nvxsZG2bjIlpYWtLW1sUuY6IwmyhjnzJkDl8slK/FyOp20pG02G70oarUajzzyCN3yWq1W1lZZqVTCYDBQzojeGyK0JyoKTjfPO61CWhBqWFiYbKbo0NAQnE6nbHxgZmYmY3UGgwFJSUlsrLB3717s2bOHzQ9yc3OZ2FNbWyuLXxuNRuzbt49C97zzzoPNZqN71Gg0MtEFkJJ3fD4fhZdoRxrMYJYsWcLPDx48iOLiYsTFxcFqtbIxg1hLW1sbXYZpaWno7u5mDETUFIqDKmq/RanJ1q1bkZ2dTQY0ODgIrVZL4dvb2wutVsuYdmNjI0tksrKykJ+fT+WksLAQDz30EAlaMEFxUNVqNcLCwnhQKyoqyLBPF4QgFdO5BP1otVqYTCYKpKGhIVlTCJPJRNeoKOcRf6tWq6FUKsksPR4Pzj33XL7/CRMmID8/n3FjETcVQuWvf/0rXcGApDwI5dFut0OtVvPeYha0aI8ZGxsLlUrFmPWYMWMwefJk3HnnnQCk2F9LSwsZVHFxMVQqFdfe2NiI3bt3U1A2NTUhNzcXCoUC7733nqxBS3NzM2w2G8NA6enpGB4epmIzffp0lJaWcvpbREQE4uPjea/169cjPz+fwkKr1aKxsVHWbjU/P5+hHI/HQ1f4LbfcgpUrV1JAejweWCwWviOfz4fKykq+3yVLlqCoqIi1sRkZGdiyZQtLgkSehmC2AoKWRd6BQHDYRjS3Od2MF5CUCcGXRkZG6AIHJFesUqmkcnHOOedgy5YtHIkrXMIiXBEZGYk9e/YAkEJ2NpsNv/vd7wB8WTMvBLrNZsOuXbsonJqbm1FQUMCkNZVKhcmTJ9MYEDxM7Fl9fT30ej0F7w9/+ENotVp4vV5UVVXhxz/+Md/Fww8/LEtSe+2115CVlcXpbCUlJThw4AB52qJFi7Bq1Srydr/fj02bNvFec+bMkc2ENplMjMEDkBlkgUAAOTk5VLgjIyOxcOFCxtqjoqKYCyDuNTw8zHyWiooKGZ3o9frT0uDpjIhJR0ZGIisri/GVgYEB5OTkUKAIohFISUlBV1cXsxHLy8tlXWqC6w1vvPFGfPbZZxRWFosFOp2OL+LgwYNYvHgxmWxtbS3r5wDJkvJ4POzMFAgE0N3dzYQZjUYDi8XCbMYrrrgCY8eOxaFDh7B27VoUFRWRYYgYiGBeDz/8MNra2ijEx44dC7fbzWt/+umnUCgU7M7zxRdfQKPR8GAqFAp0d3eTCIuLi7Fq1SpZ/C3Y2qyqquKhHzduHJRKJQ/21KlT0dLSws8LCgpgtVq5p6I7UvD1vk2IEZOA9A6ioqLoAdHr9bIuciqVigw/LS0NPT09VNpEb15hFQwPD8NsNssGamzatIn0duDAAezZs4cdjZ555hm8/vrrWLx4MQCJ6b/zzjuMGx84cIAZt2lpadi8eTNpbd68eWhubma8TaPRYOXKlUxKMxqNGBwcpEJQU1ODWbNm8bl1Oh18Ph8effRRAMBNN92EJUuWyLrGjR07FmVlZaioqIDRaMS1117LPTvvvPNkzTPS0tKonP7gBz9AdnY2hYbZbMbg4CCVtIsvvpi11YAUO0xOTibdazQaKBQKZneXlJSw5/ymTZvQ0dFBK72yshKZmZmMcapUKiQlJdGaqqysRGVlJRWnsrIydHR0sD48EAjAZrNRSAtFIrhroLBA/X4/wsPDybhFJr6g39FjNE8lgvNLxMxvQbNWqxWRkZEyAZOcnEyFbufOnfD7/ey0JaoMBF0Gx+g1Gg1mzpzJ39977z34/X4qPTfccAM6Ojr43Pn5+XC5XNw/vV4Pk8lEoa7VapGamkp+29bWhh/84AdU6ASv9vv96OzshN/vZ/7K66+/jvj4eNKZy+XC22+/jeuvvx6A5B2MjIzk96urq2npA5Iim5ycTB65b98+TJkyhTlEZWVlqKurI+2YzWaeN5fLhZ6eHmaSX3nllbIBGiK3QfBnpVKJ3Nxcyh2z2YyBgQGZd+N0IBSTDiGEEEIIIYQzFKfN3R1s5YkMWRFz6ujogNFopPZVVFSEWbNmscQqJycHmzZtontboVCgs7OTsb5gTVlooiKe/eGHH2J4eJiWU1JSEjOZAeDcc8/FmjVr6NYzGAwYP368rL1mV1cXXdRutxtjx47lWgCpJKGvrw9vvvkmbrrpJsZL/vCHPyAiIoL1i8888wz6+/tpSWVmZqKqqoqxpezsbOh0OvaiPeuss2i5AJKrvre3l/tWWFgIj8dDV3xycjJd1KLsSMSr33nnHej1esbe//znP0OtVsvclwaDQZYh2tPTc1ozvAW9qFQqpKSkMJ46ODiI7u5uutiamprothf9zIVVMTIycpi7OyYmhhZNa2srTCYTaW3+/PmIiIhgf22TyYSpU6fSCgwPD5e5wZKTk3mv5uZmdHZ2kpYsFgumTJnC9/3DH/4Q7777Lt2KIkwjSmVmzZqFxsZGrvWDDz5AV1cXrSe9Xg+9Xk9X31VXXcXe7jU1NXj99ddxyy23AJBcd2lpaYzPZWVlobm5mVZCWFiYzLITVrugLavVCqfTiTlz5gCQ6lsDgQC9XyMjI0hMTMQPfvADAFJrXpGl/vnnnyMqKoox5Pr6euzevZsuaKVSiRUrVjCUUFtbC4fDQXdsR0eHrOeySqWC0WiU9RsIjjUHW8cqlUrWfjMmJgY2m03WUvRUdc8LziRXKBSymltRdirenc1mo0cRkLwJFouFdCzi7qI15+DgIMaMGUOvjMPhkIW2SkpK6BWpqanBgQMHeO1Fixbh+uuvp2s9PT0dHR0d3JPh4WGkp6fTqt+5cyfbwgJSxnZOTg7r/59//nlotVp6gd544w3SeGRkJC688ELS+IEDB3DgwAHea9myZUhPT6d30OVykRaBLzPLhRcgISEBF1xwgaxk9qWXXmL4KT8/nxZ/Tk4O1Go1wySpqal48MEHZfkKGo1G1iJYq9XSCxcREYG2trav9LQEVxmcCpw2d7eobQQkRmm321k87/P5EB4eTsF53nnnYdq0abKY6AUXXED3jMvlwsyZM0mwL7zwAolk6tSpMBgMJNDU1FRYrVYmJthsNlx88cV0Me7fvx/FxcWyecSJiYl80RaLBZdccgmF/65du7Br1y4ygR/84AeoqamB0WhEYWEh9u7dy/jLwYMHERsbK3PL+Xw+xms8Hg+mT58uG5gRGRmJ5cuX87mtVisJNiwsDFlZWWwy4Ha7UVVVRcFrNBrZ1nHTpk248cYb2RZ07ty5dK8DknuyoaGB8U9RmiBcrWazmQwbkJjMqRxsMLpUJjgxSKFQIDw8nAdfHOjgsX5iXUajEW63mwdPzOgWbn3RL138LnpOCwVx3bp1sFgsDHeIxjbinYnYuBDEUVFRPNT79u1DVFQU3bY+nw+zZs3CT37yEwAS3Tc1NdE9NzAwAJ1Ox/e5Z88ejBkzhuUmU6ZMwYIFC3D22WcDkGLSTzzxBB5//HEAEm1ed911CAQCmDBhAhYuXEjXe19fHxISElgOqFKpkJycTKFdXl4ua/TQ2tqKu+66i8xTCGQxzGPhwoUYGRnBkiVLAEgx+tbWVsak//SnP5FezjrrLKhUKr6viy66CHa7HT/84Q/5ft1uN0MIlZWV+OCDD5iYNBoiCUrEEk0mE8rLy0kfPp9PxjiHh4cp8K1WK/R6/WGu8lOB0WcjuOZWrVYjLi6O589kMsFut1PpSU1NhcPhIC+oq6vDRx99xHiq3+/HoUOHSLfJycmkm7Vr16KxsRH3338/AIkm3333Xc6erqqqQnx8PIW4iEcLhcDtdmPv3r283hVXXMFwICDFxz/88EMaOtXV1cjIyIDP58PAwABaWlpYqzw0NISmpib2HC8rK+MMaEBSHoXiBEiu+srKSn4+duxYBAIBhl3CwsIwODhIQRsTE4Pvf//7bAktwnaApMiEh4dTblitVvzsZz+j8igarQjeEBcXh87OTipOLS0tsnGtRxPWpzoZ8bQmjgnBlpaWxi5JgETMwY0XbrrpJlm/VoVCAbvdToszKioKY8eO5YGrrq6WxekyMjJ4KA0GA4aGhlj3+uCDDzLeA0gvLiMjg1ppUVER2tvbea/m5ma0trayNvXTTz9FWFgYLZC8vDwsWLAADocD69atw5IlSziQvL+/H4WFhYy3eL1eJCUl0QLcuHEjxo0bR4WiqakJw8PD/H5JSQn0ej0zJxUKBbKysiggHA4HlEoliS4sLIwWYVtbG7RaLT0A48ePR3h4OGNc06ZNw9tvv83uThqNBpmZmSTw2NhYNDY2yrKkTzWCGajoFAaAgkfQz+DgIFQqFROR/H4/mbQ4WEJQDg4Owmw2k2nExcXBZrNR0J999tmorKykVyY+Ph56vZ60WFVVhVtvvRW//vWvAQAvv/wyampqSE9dXV30ZCQmJmLjxo20+IWiIzppXX755fB4PLQg33vvPbzzzjvMWeju7obFYqHFMmXKFHg8Htx+++18/l//+tey5jZXXXWVbDqc+P/c3Fw0NTVx73bv3o3y8nLGeadNmwaXy8UY9ueff44lS5Yw7p+amsr8BODLudni+hEREaioqOAZXrJkCd555x0AkvLR2trKPTebzZgwYQKWLVsGQIp3X3nllWSus2bNQlRUFPdl9+7dqKurY7et1tZWaDQa1mSr1Wo4HA6ZwBPnVaFQIDIykrQUEREBv9/Pz7/tbO/gGtzCwkKe1fr6eiZrApJXbevWrXjqqacASIJ35syZFKyiO5bgQxaLhWd1/PjxiI6OpmDbs2cP/vrXv1JB6+npQUtLC/d/+/btsl7r7e3t5DmA1Ftd1E4DUox67NixjAunpqZCp9NhYGAAH3zwAc4//3zua29vLxITE/HBBx8AAC677DL8/e9/57UKCgrg8/mY/Go0GtHQ0EBl0e12Y+7cuTS6EhMT8f7779PIys3NxY4dO3g+dTodeVwgEIDL5aJR4/V6UV1dzXcgcpmEVS9kgMjxqaqqwsjIiKxb4elAKCYdQgghhBBCCGcoTqslLayd3NxcThsCJEt6eHiYpUSzZs3CxIkTGdMSvVyFFQhIGpSwfoKnrJjNZlnrzJaWFigUClrlN998MyoqKqjFC4tTaP2iVZ8oyenv74dKpWJWdHZ2Nn784x9TU7z77rtRVVWFpqYmfPLJJ7Jyg+HhYVqo4rkuuugivPnmmwC+9Ai89dZbACStUaFQMEt2/PjxWLNmDV3zYma10Jj1ej0iIiIYa4qIiKDbtqGhAdOmTeN3P/vsM7qnAGns5cKFC9l5S1gcwvKKjY1FaWkprZVvs3OTVqtFfHw8rXhR1hQ8ZxsAPw92S2k0GkRHR9Pqdrvd8Pv9/N3v92P27NmkpYsuuggLFy6kZRwWFoYDBw7IOkMFz97dsGEDli5dSnddbW0tY8YVFRWw2+3MA+jv78dvfvMbltHExMSgt7eXtJWeng6z2YwLL7wQgFQfPHnyZL6D0tJS9PX10UvU3d2NrKwsel4OHTqEG2+8Ea2trdi4cSPGjBlDS3nVqlVobW1l+V9LSwsyMzMZv7vqqqvQ39+P0tJSAJKl/Ic//IH78stf/hI5OTl45plnAADf//730dHRQe/F+eefj5iYGLZv3LNnDy2Smpoa3HrrrbRe4+PjMWXKFFnG9YYNG2jpaTQa2Gw20rFKpcIXX3yB6667DoBEu9HR0XzfcXFx+Oc//0mrNLgEy2AwwOv1klZEFz8Br9f7rba8FZay2+3GWWedxUxig8EAj8dDulIoFBwXCkihjdbWVoYXKisrMXXqVHougvsnTJo0CXq9nnTldDqhUCgYTvj000+xbds2enQyMjIQHx9PHlhdXS2b7Ld79260trbyfYlxvGL/586dy9LRXbt24X//938ZO09JScGECRP4PiorK7Fjxw6uZe3atQC+9I6dc845ssx9p9OJ5ORkrrWurg4Wi4Wey5GRESQnJ/M8T5kyhSEcp9OJ2tpaWTvUnTt3kv8G95kHJLrs7OyUdWkMrkoQ+LZbzJ5WIR085zMlJYVxX7/fj8jISDKjCy+8kO0aAYm5BccxamtrYbfbKaQzMjJIFGLWrziYoj1i8Mi36Oho2Ygyh8NBgv35z3+OvXv3MjFhxowZePTRR6lQiGYZgqCvuOIKCuMtW7Zg//79PDzjxo3DgQMHmDj25z//GSkpKRwPGRERAa1WS3dpR0cHlEoliWbSpEn4/PPP6ZJOTExkbBGQGG1iYiL+7//+j9cLblYSERFBJuHz+dDZ2UmC++Mf/4jHH38cv/rVrwBI7jaLxSIbVFFdXU3XqkKhgNfrPaUEK64tkgzFYRvdBjQsLAx2u51u/+BezWq1WtbaT6fToaSkhHFe4bYN7sU9a9Ysulbb29tRUVHB+KfFYsHkyZM5mGL27NlYtGgR7rnnHgCSqzW4h3hVVRXH9k2ZMgUrV65kzoBWq4XdbqeSIZJWBO3Nnz8f7e3tfN/p6en48MMP6YqMiIhAZmYmn/s3v/kNbDYbae/gwYN0E8bGxrLtLCApDMH9zSdNmoTy8nIyv/r6ejgcDirK0dHR2L59O8/c1VdfjXfeeYfhlAsvvBBVVVU8V1OmTOGZueyyywCA7VBfeeUVlJSUcJ9cLhccDgfj2e3t7Th48CD3obm5GdOmTcP69esBSGc4EAhQYbzsssvw7rvvkh6ClTDRElS8f1H6JNzGNpsNgUBA1jb2VEGpVHJ/IiIikJ+fzxwQp9OJnp4eum1tNhvy8/NJO7/97W9x3333sTRQ0E1wIqfgE6K8TdD0yMiIbCDGzp07YTKZ+P2uri5ERkZS4Is8CJGP0NXVheuuu4489P3330dmZib5ltVqRVxcHAwGAwYGBvDuu+9i4cKFAEB6FUJ5YGAAFRUVvPY111yDPXv2UFG6/PLLsWHDBtKOzWbDddddJ+s1kZWVRT5WX18v6x0RFhbGXIb29nZcfPHFDItkZGRwnjUghao8Hg/ffUREBDQajcwYEKWbX/VegVM3vveMqJM2GAyyzky9vb1QKpV8cQ0NDTj77LPJKEXPW2EFhIeHIzs7mzGSgYEBKgCi+YgQ+CIjVlgrDQ0NqKmpYS1ya2srdu/eTWvo4MGD2L9/Pz/Pz8/Hb3/7W8bbWlpaYDabmVjx+eef46KLLoLD4YDH40FWVhaJ22w2Y/PmzbIEqI8//pgCQySJBM/NveOOO2SxQKVSyQ5ksbGxSE1NZeF/ZWUlysvL2bAiOPGmoKAAzc3N1NyLi4sxYcIEKhv19fUyC1RokEKjLSkpQWZmpiy+A5w6pmYwGLgPoq+xoAcxpD64Zjs4KUdk9AISg9Lr9Yz7A9LhFbXovb29MBgMVGbCw8OxYcMGJj0tXrwYBw8elHUxWr16NQ9kR0cHfvazn/EwZ2dnkyFVV1fjuuuuYw11d3c3hoaGZIML1Go1E3TefvttWK1WWqObNm1CQkIChVF3dzfWrl3LDmX//Oc/kZ+fzySbF154Af/zP/+DoaEh2Gw2xMTEUEHMz89HWlqabJ/Ky8spKHt6ehAXFycbyDJmzBgqgG1tbUhOTuYZ27x5s6xZyrx58zA0NETB0NraSktFCOkHHniA/508eTLvlZiYiNtvv53Xi46OhslkwowZMwBI3q4dO3Zw7YKxCn6wZs0a2Zx2hULBMxcVFQWPx0PaERPQhHBLTk6moARObf1/IBDg/iiVSlgsFj6T0+lEfn4+hVdcXBx0Oh1+/OMf8++NRiOtY5PJBKvVyvfR0NBAxaOqqgrt7e3MJxgZGUFubi4VLDFFULzb8vJyXH755TzrP/zhD6HT6aigp6amQqlUkk/FxsZi7969VBjCw8PR0dGBqVOnYsaMGdi+fTv5b0FBASZPniyjm507d/JsDwwMoLKykt6g8vJyJCcnU8kWCbjivZSWlqK3t5eK7sSJE6HVajkru6SkhNfOzMyE2+3mvZOSkqDT6fDcc88B+HLuuPDoCjoRzy2GJwkcTQgLHhgZGSn7/slCKCYdQgghhBBCCGcoTqslLbQlk8nEEYCApEXW19dTG5swYQKioqJYI6xSqWSuooSEBGi1WrpAROcbQNKUFy1aRE2ssbER27dv5+i01NRUjIyMMJMUkDRNobEqlUqkpKTQKqisrMT3v/99/v0XX3yBxx9/nG1GOzo60NvbC41Gg+7ubqhUKtx9990AgJkzZ+Kxxx7jfebPn48nnniCLsmGhgbWhANSCU9tbS3dnYFAAFu2bGFMRcx8FpNx9Ho9wsLC6KI0m830APj9flRXV1MDXb9+PcLDw2khNjQ04NFHH6XrZ2BgAElJSbI6aXFNQLL2T2Usb3h4mJaSmN4l6GV4eFhWgiPid8GlEsJi8fv98Hq9tIyjo6Ph9XppNSQlJUGpVNL1GwgEMG7cOO5TTk4OHnroIeYgVFZWynoNG41GXHHFFZgwYQIAaXTen/70JwCS+zF4bGogEMAdd9zBfXvrrbfgdDrZY7m3txcxMTF8vxdeeCEef/xxuhWffvppTJo0iTkKHo8H+/fvp9cgMzMTvb29UKlU6O/vx3nnnUdrqr29He3t7bQgExMTodfraWmLjHdxrc8//xyNjY3MNB8aGkJWVhbv7XK5YLFYeAarq6tRVVWFt99+m2sRuPjii2GxWGiJ3HTTTYx9A5I79ZxzzuH7LCgokLlj3333XY6UBSS3ZUVFhazSoLm5mRZTV1cXXd/CvS8gXOXCirfb7dDpdIxLjv7+yURwhYIYYyq8JGKsrKBxi8WCs88+W9ab3e/302J8+OGHYbPZ6MEzGo0sQW1vb4fZbCY/nD17NkpLS2lhRkdH47PPPqM3aWBgAGvXruUZt9vtSExMlPXCF30VAOCSSy5BSUkJr19WVoY5c+ZgZGQEHR0dsFqttC7POeccvP/++wxldnR0oLCwkHQ4Z84cbN++nfxTtEcVz1JXV4cPP/yQtBAXF4ecnBze2+l0oqamhnkfWVlZPNtGo5Fz04EvK31Ez4OLL75YVmOfnJwsG9+anp4uy3I/2uhK8azBVvTJzNk5rUJaEIFOp0NPTw834KKLLsLrr79Od8qdd96JlJQUxh1aW1uh1+txySWXAJDKpJqbm+n+1uv1dBk6HA588cUXFNJhYWFIS0tjrWlfXx9qamrobikqKkJ5eTkJcs+ePVi6dClfvN/vx49//GMK8YKCAtTW1uLhhx8GICVZxMXFwWKxwOVyIT4+nrXJDz74IP72t78xUWzfvn2orq4mgW3btg02m43lCFVVVYiMjJT17xXJYYDUGCA3N5duwvDwcM67BiQiFUlm+fn5iIuLY8yxpKQEKSkpdNPqdDrExMTw9+LiYtTU1PBgX3nllfjXv/4lq1UPrkc+2VAoFNxzMbxBvBOr1Yrc3FwyOL/fD7fbLYtVi89EGYb4TMSnxRCTvXv3YsKECdyX6OhouFwu0mZ/fz8GBwcpnNavX4+kpCS6rF0uF9auXUv36tixY3HrrbcCkATH8uXLKeBfeOEF9PT0ULA9/vjj2Llzp2xgxr59++gef+KJJ7BmzRr84x//ACAx8rFjx1IApaSkYOLEiWQq8+bNw6ZNm+D1enHWWWdh7969DG8MDAygo6NDVoJVVFSEG2+8EQDw0ksvYePGjVQogS+VIUAS4sHlLWIfxb4+/fTT8Pv9TBa79NJLqVRnZWVhz549VPCE4iiQmJiI2tpa1vTr9XqMGzeOCsb3vvc9HDx4kMqqqCEW56isrAwmk4kxVb/fT4Y5mrEeKTwzPDx81DGZJxuCD1VXV8v6NwBf9osApPO3evVq2d/++te/ppAPni8ASGcxOGS3detWhkE++eQTDA0NkX8aDAakpqaSFjQaDfLy8vgu33rrLSQkJJCnrVq1CmVlZTQmAoEApk2bhhdeeAGAxFtSUlLg9/sRHR2Nq666ivy1trYWcXFxVJLj4+ORkpJC5d/r9SI3N5dr+fjjjxETE0PjobOzE2PGjOG7tdlsslyKgYEB2awGpVJJpXjChAk4ePAgZYGYxS7OelZWlqyUt6GhAWFhYaTTmJgYpKWlMYwCfDmXHTi8fC849+VkJtWeViEt4oCRkZEwGAzs4lRZWYn09HROE3rvvfdgs9n4opKTk9l0H5CSE6KioshQWlpa+NL27t2Lvr4+Wjp33nknfvrTn/K78+bNQ09Pj+yABwIBCsri4mJs2LCBSS0ibic6K2VnZ2PTpk1kXvv375e9qOBOWNdddx2zvwX+7//+j8/p8Xjg9XoZO3rggQfwwQcfkKjmzJmDsLAw9sx1OBzssQ1Ih7OpqYnJPHV1dWyOMHfuXPT09FCrnDBhAvbs2UPBp1QqERYWxrWLGbNCUxeJOt9W7+7giUAxMTGyOnq/3y/TcIEvY3TA4QfEYDCQuQ0ODsLv91OAiMk9Ys8tFguWLFnC53a73airq2OWs9vthtvtpsUzMDCArKwsMob9+/fTmszIyMB9993HrGSfz4eNGzfyff70pz9FUlISaTEuLg7PPfcctm3bxrUUFhZS4IjhGoJpxMbGoq+vDw8++CAAKfnr5ptvhkKhgFqtliW89fX1YfHixTxjSUlJMBqN/P2vf/0rfvnLX9K6Sk9Px7x582Qds+bNm0flZdq0adi1axdj2jU1NfD5fBQ669evp5It5r4/+eSTAKS45MqVK3HllVfK3pPIege+zAIGJKGcmppK5eb111+HSqXiPhYWFsJut3MQzZ49e2TDO4IZq1Asvu3e8wJiv0Q+QnB/BpE/AQC33XYbADBBMTU1FRMmTKDFeejQIURFRdEiFRnygJT4FR0dzTMiOjKKe3s8HrS1tZH/ZmRkIDU1lXOXZ8+ejYkTJ+LZZ58FICm9wQNNent7sWHDBq6lra0NDocDfX19WLlyJa666ip+99///je6u7spKC0WC1JTU2lZ/+pXv8J5551HRfa2227Du+++Szrs7+9HW1sbPVexsbFobm4mbQwNDWHBggX0Rj3zzDPktxs2bEBCQgJ598svv4zNmzfz2pdddhnWrFkj89iFh4eTdhobG5Gamirzuozubhc8l/tU5eeEYtIhhBBCCCGEcIbitJRgCbeA0MrT0tLQ0dHBEoGuri74/X5qR7fffjt6enroomxqakJ3d7es9nXfvn10G0dFRVFrtFgsMJvNdBlfddVVqKyspNZTXl6OsLAwZlWazWYolUpqqDNnzoRCoWBGNQCsXLlSZv1mZmbSHZqdnY2enh7Gxevr61lKEhkZyVmsgOQmam9v59pWrFiBoaEhuoby8vKwfv167ovoniPc3Q6HA3Fxcaw1POecc1BTU0PXkcPhkGU6VlRUcM8vvvhi9l8GpNKyxx9/HO+//z7vFVzmlpubC4vFQg+DcCGfKnc3AFkZVXDmpNfrhcFgoEar0+lk7srgaUNiVq6wpMW8WdHmMz8/HxMmTGB/dK/XS7cgIFkoY8aMYWwwKSkJNpuNveDb29txyy23MB5qNBqpqcfFxWFkZITzodva2rBmzRq+g/b2dtx7770cVanVanH11VfjtddeAyCNJhX9vwHJ4jGbzZg7dy4AyU179913k1ajo6OxZs0amEwmuiuFB+nGG2/E2WefzT7yWVlZiIiIoLWVnp6O3t5eWm4NDQ1QqVSsgti0aROWLl1KF+r777+P6dOn06pPTExET08Pfw8LC2NuRXNzs2yUqE6nQ3h4OGdbb9q0CQqFgnvc3d2N2tpazpMWniFxpo+EPXv2sMZ/w4YNLEtraWlBa2sr6SEiIuJbrYseDZFJnJGRAY1GQxoWuQHCKnvjjTeQnJxMT4fD4UBRURGfY9euXdBqtfQeaDQa0nRDQwN27txJGu3q6sKMGTPw17/+FYAUJlm/fj3Pbl9fH84//3yuraamBkuWLOHI1UAggLlz59JbqNPp2PoYkHKCCgoKaEkvWrSIfEKU7d1xxx0ApPDg9u3bue7HH38c7e3tPHMZGRlISEggDxscHERbWxtp3u12Y2BggLxg5syZmD17NmXBiy++SP6ZkZGBcePG0WpvbW1FR0cHs9rFmRBrFdPxxNp8Ph88Hg8rWmJjY0lXAsGW9LHe9zdxf58WIa1SqRAIBBj8nz59OrZv307hM3bsWJSWlrKuMj8/H/v27WNyR11dHQYGBujSjoyMhFqtpotFo9EwrpCRkYG+vj4yyqamJvT19ZFZbdu2DcXFxWy9KF7EokWLAEhDDdRqNd3bmzZtwpNPPkmhnJqaitjYWLrqpk+fjilTpqCrqwu///3vcdFFF3EowSeffILa2loSQVRUFMrLy9nizuv1IjMzk8qIyWTCypUrGTOZM2cOqqurufa+vj7Z2DeNRoPzzz8fv//97wFIh0e4BOPj4xEbG4uPPvoIgNQUf+nSpSToX/7yl5g5cyaZRVJSEhISEhjT1Ov1iImJIZGeilKDo0GlUiE2NpZCe2RkBNHR0dyXtLQ0WZ20z+fjoRetK8Vh0el0iI6OpgAR7jChnGi1Wnz44YdsnlBbW4tbbrmFNbqZmZnwer2Mnw4MDJCmAEnpEwwmMjIStbW1HC355ptvYvfu3YxfG41GqFQqCtLOzk44nU7mVnR1dSEQCMjmSet0OoYwysvLMWfOHCaWRUZGIi0tDS6XCx9++CEGBwf53djYWMTHxzP+fcUVV+DQoUNUwgTE5xs2bEBubi6V0bFjx0Kj0TCRLC4uDmFhYXRDTp8+HS0tLSwXVKvVpD2v14uEhAQqo42NjZgxYwbdiIODg7I2wNdeey10Oh3rW99++20kJyfTfR4bG4tDhw5R6F922WXYunUrXn31VV5PuHI9Hg+TkQAprBNcF/1tQyRAiTCLUB6tVivbugJSHXpXVxdpRTyPUDDa29sxefJkKqc9PT1UYu6++2488cQTFKI2mw33338/eVRZWRl+8IMfMMExKSkJw8PDbLr0y1/+Ep988gnDRxMmTEBzczP3zOfzYdKkSeQl8fHx5CUrV65EQkICDQuTySRrm6xSqWTlts888ww2bNjAJktGoxG7d+8mTzz33HOxefNmhlG6u7uRl5dH/qNUKjFt2jQOOPrXv/7F+eqHDh1Cc3MzldikpCR4vV7mNixfvhyrVq3ic3i9XjidTp6B4IY5wJehMgFRvnWqaemUxqSPVuQ9Ok40NDQEv99PJms2m5GTk4O//OUvAIC//OUvcDgcPJRnn302Zs+eTa1STKUKrmUV1khGRgZuuukm9rsGJGtXXCsrK0s2gH78+PGw2WwcJCAagoisypSUFFx44YWYP38+r3XLLbfwGePj4xETE4OoqCj861//wpNPPskEkKKiIuTl5ZGR/eY3v8HKlSsZE6mtrYXT6WRSxYsvvgiPx8MEGjFz+7333gMgDR6ZN28eNcWEhAQ88sgjVF4KCwspdIVWLRjf008/DY/Hw8OUlZWFBx98EHfddRcAibkdOnSIB1+j0aCoqIhrFVZLMMSkHwCybOyvE6sRzBSQ6Ke3t1eWvW2328nUBSMW70A0WgEkwTVz5kxaAe3t7Zg7dy5rcMUQA8Ekdu/ezfsAEoMLthocDgeampoYr5s6dSr8fj+Z79KlS9l9buzYsYiOjqYC8K9//QszZ87kZKq2tjacddZZtDI2bdqEiIgIMgm9Xo+xY8dSEE6bNg3d3d20vP1+P2bNmsV39n//938oLi6GwWDA3/72N7zwwgvck7vuugudnZ30urz33nuw2+30EGVnZzOjG5AUgkWLFlFo1NXV4dFHH6XQ8Pl8GBwcJPPcv3+/bKJQcCc1t9sNh8PBwSJlZWW4/fbbZZav6HoFSOd/8uTJVJxLSkowNDREpaytrQ15eXl8JxdccAHS09P5LO+++y5pbzTvCW5scjoQTMORkZE0LCIjI5Gdnc3ch+XLl6Orq0tWt56cnMw9AaR9FQInJyeHyl5bWxuam5tpDOj1erz66qvci5SUFMTFxXGPYmNjodfrOWNAqVQiJyeHRtP3vvc9rF27lt4mv9+Pf/3rXxROaWlpnMt+3nnnYcOGDfRM5ubmQq/X03t4//33o6WlhXT0+9//HhUVFfjd734HQOpLUVBQQANu69atsNls5Lf79+/H/PnzyccaGhrw1FNPsfY5OE6/YcMGWRJoZmYmpk6dSoXa4XDge9/7Hulw7969UKlUsn4couEQINH8aO+hkGUCp4K2QjHpEEIIIYQQQjhDcdragiqVStbzZmZm4rPPPqMWkpiYKOtJLTQ8gZdffpmtIAEpNjd37lxqV7W1tezUY7Va4ff7GSvLyMiAy+Vil6dXXnkFer2ecQcxGk1YXomJibj66qvpVhoZGcH69esZTwsLC8O6deto7b7xxhtYsmQJPB4Py3uEKyYrKwuPPPIIY9SzZ89GeHg4XestLS1ob2+nlWe327F27Vq6LFtbWzFv3jxeT8xwFeUO69atw+uvv07LymAw0IV14MABGAwGZhKPHz8eSqWSVndvby9+8Ytf0DppaWmRjaocGRlBWloav+9wOOiCOxXQaDT0KAitXKxFqVQiIiKC4Q9AogFBD8Hd6rRarSxj0+fzYfz48SgoKAAgWcJqtZqZsePGjcP+/fsZotiyZQt27dpFi/HBBx/E22+/TfqIj4/HyMgI13L11VfjvvvuAyDN7U1NTWVJXElJCTZu3Mj3bbfboVar6TES1qOwUMxmMxQKBd3nqamp+MMf/kArvrS0FH//+9/pNXK73cjLy8Pg4CDWrl0ri72KCUoivvvFF1+gsbGRPe1FH3ixx/Pnz4dGo+E17r77bhQXF+MPf/gD1/rWW29h48aNAL6s8RbtdRMTE9neVvQoF9nAH3/8Mf7xj38wi/3yyy/HJZdcQjpvbm7GhRdeSKuyoqICZWVl7Afg9XplvRAMBgO0Wi3XKiwe4NS1ajweHMkVKs5qQkICduzYwfMkrFERVgsEAhgYGKA13N/fjyuuuIK05PP5MH36dFrWdXV1rO1fsGABHn30UeYbLF++HOnp6axd/8UvfoHe3l5eOyUlBTU1NQyr3HnnnWhra+O1NRoNDAYDe6tv2rQJVVVVPFN5eXmYMWMGRkZG0NLSgq6uLtJFcnIyHnzwQa5lZGQETqeTORx+v5+9xQHJsl62bBnj4TExMYiPj6f3yWazyapWUlNTmb0NSDzi+eefByB51FpaWmRVIvPmzWPehdlsRmFhIUseg+uxgS/nvws67u/vl5VZfVs4rb27hZCOiopCd3c3YyAqlQputxs33HADAKn1W1NTE+OKo5vmT5kyBdHR0XwZH3/8MVsnxsXF4ZJLLqGbKS4ujoMPAOnAx8fHc+PFoG9BwJ2dnWhsbORhMxqNuOOOO8goW1tbccUVVzCh5oMPPsC5556L8PBw1NTUoKamhuvOyclBeXk53fJDQ0NoaGgg47ztttvwr3/9i4X8tbW1UKvVFLSFhYUYP348FQa3240PP/yQI+fEKDbR7/ftt9+m+9Hn86G1tZWx2C1btiAsLIxupfj4eBgMBrZuFO8luI9tamoqY0Hd3d0yYhWNGsQ7+KbM0Wg0cu2xsbHo6upiiEKUggmmY7fbodVq6Q4F5P29J02ahM2bNwOQ3n9eXh5rSq+66ip0dXVRaD///PMICwtjXOvOO++E3+9n7bPT6URhYSEZmGi/KFxqS5culbm/Pv/8cyb/qNVqTJkyhcKmp6cHaWlpvNb+/ftZVgVILtDExES6Me+55x7Y7XbGhRcsWIAHHniAymxiYiJnlK9evRo5OTn8rK2tDXfffTfDEU8//TQOHDhAZnnBBRfgpZdeoqvd6/XCYrHQZf3II4/g3nvvZb5EZGQktm3bxutv3bpV9s4CgQAbg6jValgsFrpPExMTodVqOXO4tbVVlkdw6NAhjBkzRuaufeyxx8iYxUxo4dYUA3OC6+q/qsRKvCPhoj/VwlzcR+xBcnIyhoaG+AxTp05FdXU1ez/Exsaira2N+ScZGRkwGAykMyHsf/Ob3wCArBd6TU0Nvv/97zMGPTQ0hKSkJBoqNTU1SE1NJR8ZGhqCw+GgkJ89ezYGBwepBFVXV6O2tpbGQWlpKe644w6GBDs7O7F48WI4HA689NJLiIuLo1KrUCiwYMEC0vSYMWOQnZ1NRVb0hxc5Pjt27EB1dTVzJWw2G+68807miLS1taGhoYHnOzc3F+Hh4Qx1vPXWW7JESlGmC0gKuNFoJN+aMGEC1q5dyxLB0tJSmfEXGxuLrKwsGlV+vx99fX0y2jrVybPAVwhpIYhO2s2CYpTBMWmRnRvcC9jr9fJFC20reCpOsHUkhkeIzfN6vbQIe3p6kJSURIITtaZCaLtcLll3pP7+fjidThbMd3Z2Qq/XU0iLrkDBWXtut5uHZ2hoCImJifD7/bBarTCbzTLhMjw8zN9jY2NhtVrJGIEvp+SIew0MDMh6ywrGDUiJYwqFgr8DEgMTzM5ut3NP3G43AoEAmYTb7YbBYOAe5uTkoL29nbXIdrtdJoTFnGrxDsU0qVOFYzUNEOsR61Or1fD5fLIhCsF1scGdntRqNfR6PWPxImlJ3EP0/RYWZV1dHZRKJfdYTNUSzy6G0Iv3JhrzABKTGBoaYtKM2+1GWFgYhZdCoZBli0ZERMDhcFD5VKlUGB4epgXa09MDu91OBUMId/FO4uPj4fF4aIWZzWZZBrzb7ZYNJQmek56cnAy/309GPzQ0JGukI7qTiX1UqVTQ6XRkiBaLBSkpKfy+RqMhU6+qqkJKSgoZtRCywdn7om4YkOhanBFASj7y+Xx8XrE/Yq0i/+FY9Cjo4eDBg3jyySdlA3dEbwQgxPOAEM8T+DZ5nqiLPxJCMekQQgghhBBCOENxUt3dwfWpXwWlUsk4Xk5ODpqbmxkPEB1xHnroIQBSLPezzz6jS1mr1SI3N5daz/bt2xEdHc1s371799L98fzzz+Ouu+5ivKW7u1tmmRUXF2PPnj10d8bGxqKhoYHxLb1ejzlz5rAUye/3w2w20/U2duxYNDQ00AWp0+lw3XXXQaVS4dNPP8WUKVOwfPlyAFJbv9zcXFoUMTEx+N73voef/vSnAKTaZY/HQwvh7bffRl9fH0tPJk6ciK1bt9KiyM/Ph1KppAtSWBuihCcuLo5upKGhIfT399N9mZOTg+7ubsaKiouL8fDDD1PDFSMeg+dHJyQkUJPs7OyE1+s9ZbG/4BrS4Kzt4LUJ7TkhIQHDw8O0fq1WK2lrcHAQgUCAtDFlyhTY7XbGeUdGRpCens6pZy0tLbL50qI7nbj2xRdfjLS0NIYoDhw4gEsvvZT3e+mll/j/DocD+fn5PBNerxcLFy6kG9LhcOAvf/kLLeXExERER0fLpqTFxsZyJvQdd9wBtVrNbPDbbrsNDQ0NzNnYt28fLrvsMqSnpyMsLAxlZWVci6h3FpavyAoWuRWA1BFMZKbn5+dDpVJxfvHatWuxb98+hjtefPFF3HjjjcyMXb16NfLy8lh54HK5uC6TyYSIiAjmjCxduhQ9PT2kHbvdjvT0dMa7Kyoq8MILL3AfhoeHceONN+IXv/gFAClUk5GRwXDZxo0bZSMbgWNn2ZrNZln8Wuz1V/3dN4GgZ2GtqlQq2Gw2hqa2bdsGlUpFd3dSUhJaW1vJ86ZMmYKWlhbynRtuuAH9/f0yD49w286YMQNTpkxhRcvixYvxpz/9idZsYWEhdu3aRY9MVVUVLrvsMllOz5gxY5hn8/nnnyM7O5u8YMmSJfjHP/7BM+NyuZCfn4/e3l5s2bIFO3bsoDs+NjaW+QqAlH+watUq8uPCwkKcc845/Ly+vh6PPfYYw3JXXXUV7rvvPv7e2dmJ7OxslkACUqWC6JGxatUqmYcl+DleeOEFxMTEkIZ/+MMf4oc//CFpQMSvhWUdExMDpVJJ13twa+hvE6ctJq1SqZgYZDQaER0dzc3o7e1FVFQU3WXLly+Hz+fj6MX4+HhZy8qUlBTZaDWfz0dBeMMNN8Dj8ZBJr1mzRubu3Lx5M0wmE2NzO3fuRG1tLeuqzz33XLS1tdGVdOjQIWi1WiYWBQIBbN26lf25ExMTMW3aNKjVami1Wvztb3+j+3PlypVwOp1M8Rdzb8XBKy0tRU5ODj+vr6/Hli1bSKCXXXYZHnvsMSa9ZWdnY/PmzWz6otfrsW3bNjKgBQsWsH3e5MmTsXPnTrqWFi1aJHuuffv2Yc+ePUyicDqdGBwcZFxweHgYMTExPKjDw8OwWq2ycZKjEVxucqIQtfRin0b3YFYqlXQjJycnY2RkhAff4/HQxeXxeGC32ynQzWYzYmJiyODHjx8Pu93O78fGxiI9PZ2lSkqlEq2trWQCGzdulLWgLCgowOzZsykEf/aznzEZS5QFiWEs7e3tyMvL4yjR6667Dk1NTYw5WywWDA4OkmEtWLAAs2fPZp11TU0NnnnmGcbX8/LycO+993JPamtr0d/fD5VKBZVKhZqaGgrKmTNnIikpiQk5999/v2y/P/74Y1x44YWyXI1Vq1YxL0Sr1eLZZ58lgxPjXwUzfvnll3H55ZdTWb3ooot4BoeGhrBs2TK2SxXjNQXtiRwMAbfbjba2NiYMnnfeeQDA2H5JSQnmz58vS1ILTi4Nds0KN7NgrB6PR6YAis+EABst7E8WFAoFIiIiyIdMJhNaW1tpHHi9XpSXl3OOeWJiIqxWK2khLi4OTqeTSvZZZ52FgoIC7tHw8DDL56KiojBu3Dgq4FOnTkV3dzeTuQQvFe8yISEBP/3pT1kqajAYUFhYiJdffhmApERdfvnlTBRtb2/H5ZdfzsS/hoYGTJs2DVqtFuXl5bjzzjupIFitVvT19fFMuFwu7Ny5kzwhNzcX48eP53nq6+vDunXryMvPP/987Nixg4ZKR0cHbr31VoaJ7rzzTixbtozhi56eHio2g4ODqK2tJa9vb2/HmDFjZO1m33vvPe7TwMAAbDYbm53YbDaYTCbShN1uh8vlOqaQ/iY872gIubtDCCGEEEII4QzFaRuwIZIPAEn7OHDgAN0J559/PhobG/F///d/AIB33nkHIyMj1DrdbjeGhoZoadlsNiQmJlLjiYqKopb36KOP4k9/+hNdQQaDAf39/SzMVygUsnGCBw8eRHx8PL7//e8DAD766CMolUo2gJ86dSqio6PZ5WnKlClYuHAhrToxkUmMqjz33HPZrerWW29FSkoKXWu//vWvce+99zJ7UJQ4CTdUeXk5jEYjSwZKS0uxb98+av0DAwNIS0ujpVZQUIDMzEy6oR588EHZ4AfhigeAf/7zn6itrWVJViAQgF6vZ4bnp59+CrVazbWJJCFhkcbFxaGjo4PZvcLLEYxvqk0Gu7n9fr+syYhKpaJVYrPZ4PP56Prr6OggLbhcLqSnp8syNhcuXMiyjZycHGzcuJFuSDF9S0yDevLJJ1FYWMgJQBEREbJxoKtXr4bBYKD7OD09nZq7GLAi9mbq1Km4//776Spvbm5Gbm6ubCpPY2Mjradzzz0XTz/9NMMXZ511Fm644QbSnvAkXXPNNQAk6+zaa69FQkICxowZg+LiYjYj+fvf/44JEyYcZkEL/O53v8OWLVtYiqbRaFBYWEj3dVdXF/Ly8njmmpqaYDAY+I4uueQS3HbbbbRKnnrqKbraMzMz8ZOf/ITPVVFRISvvE5n7wnoqLCzEtm3bSOcajQZnn302PU4RERHsJAdIJZp+v1/WwESsS6vVwmw28/17PB6oVCreW6FQyCzvU4VAIACv10s6C04QBKTzOXnyZJ6n1NRU9Pf3M5GstLRU1qGssrISfX199LqVl5czXJidnQ2n00nPw5w5c1BVVcV7V1ZWIj4+nt0GAcmlLdaTm5sLnU7HASjCwhaT3zQaDVwuF/e7tLQUU6dOhcFgwJgxY1BdXc2GTdHR0YiPjydvb2hogFqtZojO4XDg0KFD9Aikp6dj8eLF9MK0trbSMwRIVQXNzc1c08SJE7F//36GjMLCwvhc/f39sjG1FosFGo2GtNDc3IydO3fy94iICPh8PnoHBQ8X+xY8gvNoOBWDW06LkNbpdBgZGSGzioiIQHl5OV96eXk5Oz0BwKxZs2AymeieaWlpoUACpAMQPCUr2B0dExOD3//+96wlnTt3LlpaWljulZeXh88//5yM7uWXX8bSpUvpDmtqakJubi7dMSKeJlyUt99+O1544QW65pVKJQwGA2w2G8rLy5GdnY0f/ehHXLder+e1nn76aeTl5cn6QO/fv58uraKiIqxcuZKuVp/PB7PZjDVr1gCQ4sjDw8P42c9+BkByBzY0NNDtu3DhQroUJ0+ejE8//ZTdrwoKClBUVMR+vx999BH8fj9d6b29vfD7/TzoIgNaXK+/vx9KpZLx8a/qYXuiCM4bEG0cgzskiRZ/gOSG8vv9zJoOBAIsc/P5fLL3JXIVxHOuXr0an332GWPUAwMDuPLKK1leMnXqVOj1ej7nhAkT0NLSQqYjwh2CSURGRtI15/P5cOedd3KdTz31FLq7uyl8fD4fent7+T57e3vR1dVFwbplyxa6tgEpJt3S0sLQjHCbCwX0448/ptI1Gj/4wQ/Q0tLCPI9f/epX2LVrF/fsqaeewvPPP88eyb29vRgzZgz+9re/8d4Oh4P3zMvLw+7du6kM3XnnnTCbzVTirrvuOj6XuIcIwwwODmLSpEnsIvXRRx/BaDTSda7VapGXl0cXaXC3NwC4+eab8corr5B2m5qaZLMA+vr6KMycTid6enrIXEV5p4BwfQtGfaqg0+lgMplIw3a7HUajkUpwREQEoqOjSUc2m4119MCX8w5GZ+eLZ4mPj2eMf3BwEPX19ezyJVp1CtrIyclBW1sblUOz2SzroaBSqTA4OEihn5qaCq/Xyzrp9evX44svvqDgFP9eX1+Phx9+GFu3bmUexapVq2C323km+vv7MW7cOCoXycnJsha/+/fvx+zZs6nYVlRUIDU1lftisVhgtVqZK/HYY48hPz+fJVovvvgiz3hGRgaam5t5/kpKShAVFcU9DwQCHJMq9kX0kwAkGt+2bRv3WFSQiJh3sDAHIAvHnUyctpi02WymJWQ2m1FdXU3rpKenBwMDA9y86upq7Ny5k5a01WqFyWRiXEKtViM+Pp4xk7a2NhJBSUkJenp6GGdwu92ora1lYhAgzWEWBFlfX4+oqCgy9YyMDNTV1bEW+c0338Tw8LAslrRz506uXdRsivGI69evp1CeM2cOzGYzYz1tbW3IzMzkYSosLIROpyNTa2lpwSWXXEIrTZQ4COVlypQpskYa+fn5KC0txdVXXw1AijWJWHlfXx9MJhMJS6vVcuwmII05VCgUvPeKFSuwYcMGEuD48eNlow/FTOVvqweyTqfjYRP9gMWzREZGyuaRh4eHy8aFDgwM0LpdtGgRDAYDE+pcLhfef/99KlnZ2dnw+XxUdKZPnw6n08l6S6FcCsUpNTUVDz74IA/um2++yWSsnJwcmEwmKjrbt2+H1+ulsFEoFIiPj2dMdvfu3XC73bI9FQl7gNTaNSkpidbVddddJ1OMrr/+etx66620FkQ8D5Bi6f39/ZwfPXbsWOzatYtxShELFdi4cSP27t3LJj8//OEPccstt9AqWbZsGdasWYNrr70WgFSvW1RURMGq0WioVEdHR8NqtXLWdFFRET755BO+r+7ubhQVFTEnRSTPiQYWsbGxSEtLI2PevHmzLInRZDLh4MGDsriyEG5+vx9RUVFUyoXFLaxwMW9c0MupbFQRFRXFPBy324329nYKykAggGXLluGyyy4DICVDTZ48mcrg2LFjYbFYyBOdTqesDOnAgQPkcRdeeCH27dtHz4PD4cCCBQuwcOFCAFK7zE8//VR27Z6eHno68vPzYTAYqMi+9tpriIiIoFFUX18Pk8nE30WuxbZt23DvvfciJyeH97rkkkvQ2tpK5eTQoUPYv38/1zo4OIjFixfT8hbWvPCEajQaxMTEcC3Nzc2YMmUKlVe3241p06ZRyL/99ts8m5mZmejt7eUZKS4uls2AEPMfhNx47bXXoFarua/Dw8Pw+/08ry6X64g1+Ke6cU4oJh1CCCGEEEIIZyhOW0za5XLR4qurq8PkyZMZh7BYLJzCBEjlIllZWdTcFi1ahPLycsYxvF4v3G43vy/GSwKSqyclJYUZ1gaDAZs3b2ac4Qc/+AH+8Y9/cCj5pZdeyuxyQHIhitZ9gORmMhgMtMTF5CHhnhkeHsbEiRMRFxeHMWPGQKfTMQtVWBmiJCAsLAwxMTHU3ESGqggDFBUVYfv27bRmcnJy8JOf/IT3qq2tRVtbGy23ffv2YdasWfQKNDQ0ML69Z88eTJkyhS6qDz74AMXFxdRAs7KyUFdXx+kzTU1NqK2tpeVcXV2N6Oholox8/vnnx2y/eLLb5wWPohwcHIRer2dGtl6vh06nY4anVqslLel0Otno0aGhIcybN4/uuL/+9a8wm820YLKzs5GamkovjXg3IoZWXV2N1NRUelJqamqQnJzMDOyf/OQnbMv6+OOPo7u7m6Mr77//fmRnZ9Ny/uKLL/DAAw/Qte52uxkjBb4sUxQZ083NzaisrKTX5sknn0RlZSWtioULF8LpdCImJgZms5kjIAHg3nvvxccff8x2pk8++SR6e3vZsernP/85Wltb6UGYOXMmtm7dyn0vKCjAfffdh3/9618AJC/OOeecwzaVF1xwAXw+Hy3z2267jZZxWVkZcnJyuM6PP/4YOp2O1mxvby+uvfZatnMUA0RGQ7jelyxZgv/5n/+RTb0KbsE5enSpaIAhaEOv19NiExUWpyKWOBpqtVoWGw/u0KbRaGTZ2wqFAjU1NcyFGBgYQGNjI12xwl0ueMrQ0BCfqa2tDdnZ2bQY9+/fj56eHk4027NnD5YsWcJs+3Xr1uH8888nTSsUCuzcuZNnKCoqCmFhYaSdGTNmICkpiVb9qlWrMGfOHPj9frhcLixevJgu5W3btsHj8ZA/LFu2DNXV1aSTc845B0qlklb9okWL8Mknn3BM6c6dO2G1Wsnbe3p6sGXLFoZKKioq8Pbbb/Pzs846i3siPFiCl2s0GmRlZdFyFuE7kYfz6quv8ruAxFfS0tJ4Jurq6mR0ImgumPedCtf3aXN363Q6xr8OHjyIQCBAAtbpdPD5fBTi6enpdFsCkhDLzMwkE/j444+xYMEC/l5QUED3VnV1NaxWKxl6fX09zGYzx2B+9NFHmDVrFh588EEAUrlHcFvQgYEBxMXFkYhaWlrwxRdf4KabbgIgzdVtbGzkvadMmYKlS5fC7XbjrbfeYvce4Mv5piK+8vDDD2Pu3Lls/ffOO+8gNjaWgrGtrQ0xMTF0pVZVVUGhULDkSjyTYE733HMP3nvvPcZQPB4P4/QOhwNhYWEsLSstLYVOp2PZRnR0NLxeL4XS+vXr8e9//1vWySc6Oppr3bNnz2GdgU4lRFcxQGIiaWlpdGmKPuKCMScmJnIPxo4dix07dlAxevnll6HT6UhLotRL0CIgCVoh8A0Gg6xExGq1oquri/vm8/mg1WrZRtTn8+HPf/4zgC/nGotr5+bmIiUlBb/61a9kzybeZ2lpKQKBgEzxSU1NZResDRs24Kc//SlpccyYMaioqMCbb74JQDonWVlZGB4expo1a3DZZZfhnXfeASAlZ6WkpMi6O8XExJCh7dy5ExqNhs9VXl5O5QeQlJTgWdnLly/HTTfdxKRGoVQL5XXq1KlU+H73u99Br9eTVgYGBlBUVEQXZ1ZWFnw+H7//8ssvw+/3U0BNnjwZM2bMYCnWF198gbfeegulpaV8DqfTybaXwJcuyIiICPT39/M5PB4P9Ho9z2R/f/8prXsNjilnZGRw/zs6OhAREcF72+12TJw4kXXSfX19SE5OpsJts9lkpaTV1dWIi4uTJVeKUFVYWBhuuOEGJgGqVCrExMSQv6ampmLr1q0UpEqlEm63WzYqtL+/n0LdarUiOTkZ55xzDu8lwkOApFy+//77UCgUeOmll7B27Vq2fFWr1Vi+fDkVsKamJvz85z/nuy8tLcW4cePIy+Lj4/HZZ58xLGA2m/HJJ5+Q7qZOnYp///vfuPPOO3m9xsZGCmSFQkF3t8irESVZe/fuxeDgIPmvXq9HIBBgTkdFRQUSExN5vtxuNwoKCmRlh8GdDb8tnBZLWqlUQqlUMpOxtbUVLpdLlu346KOPMsP69ddfR2RkJAVvWlqaLNMuIyMD9fX1fNEdHR3UIpuamhAeHk5NKz8/H4sWLWKm4iuvvIJXX32VVrlOp8O5557LJJfrrrsOzzzzDGOaYWFhKCgo4PVVKhU2bNjAZJ/W1lY2b7jyyitlDV7mzJmDNWvWcH7pnXfeiY0bN1KzW758OSwWCxlIUVERfvvb31IhmDNnDs477zxahfX19RgeHmZS04oVKzBhwgTGDYuKiugh8Hg8aGpqYmw1Li6ODAuQBirodDompmRnZ2Pbtm08yJMnT0ZpaSm9AiqVilbIqUJwNrfX66UVIVomindQUFCA9vZ2arxer5fKRVNTEzweD/ds3759qK2tpbeiv78f4eHhFAi9vb1YsmQJaenzzz9HT08PLbHo6GgUFhbSyhg3bhzKy8s5m/fdd98lE5g2bRqefvppCumFCxfKhoIICKZy4MAB2bCBmJgY0iUgNVLZsmWLbE5wcnIyFc6nnnoKJpMJXq8XCxYswNatW/l+TSYTzGYzrZ+0tDQ8+OCDtOI3b96MCy64gMx1/PjxeOqpp+gVOHToEFQqFZPBbrnlFjz99NP0MGRnZ+ODDz4gPXg8HlrZ6enpqK+v53OJNpXi/IpRsSKT3Ol0YmRkhHQ8ffp0/M///A/3IThmCEjejKysLO5LVFQULZ6BgQHodDpZm1G1Ws2/PdWNKcQ6FAoFM8sByaIPPutijUIon3POOYz9AlJ8tbS0lAJErVYjPT2dCZLr1q1jwtSaNWvQ2dlJwRYdHQ2LxcKY86ZNm5Cenk6P3Pjx47Fw4UImlT733HNISUmh5QxI+S3i976+PiQlJTHf5Te/+Q2efvppJCYmYu3atXjooYdo6fb39+MXv/gFaTw5ORmpqan8XeTjCAXC7XbLWppu3boVhYWF9D6dddZZOHjwIP7+978DkOgsKyuLFQ+7du2SVaT09/eT5ru6uhAdHc130NraCqVSyedSqVRwOBxU6Ds7O9HZ2UkFz2AwyPIXvq18nFBMOoQQQgghhBDOUJwWd7fRaIRWq2UG9c6dO6FQKGQ1hOXl5XR5rFy5EtOmTWMXKK/Xi6GhIbpYU1JScPPNNzNeZrVaaYVddNFFWL9+PTXamJgY2Gw2ak/Nzc0ICwuj5ZyXlweNRsN6z3//+9/YsmUL48Ljxo3DlClTqOX7fD689957dFm++eabWLhwIXw+H7Zs2QKXy0Ut0Ww2Y3BwkC7owcFBPPTQQ7LxaMKiE3jxxRfZenHu3LmccgRImp/VaqWrR6VSIS4uji41hULBdZ577rlIS0ujC2zLli1Yvnw5XVwFBQWwWCy0RkQpkdDcY2JiYLFYqLnb7XZ0d3fTEvk2XUCirlWsde7cudi0aZNsPGXwFDObzUYrbtKkSbj00kuZXZqfn4/x48fTzfurX/0K11xzDRYvXgxAirf19fXRYnS73VCpVPSONDQ04L777mMGqFarpdu2tbUVJSUldPV9Fd58801cffXVtEbvvfdedHR00LW4cOFClh0JiDgtIHkBZs2ahYSEBMTExGDWrFnMxXA4HPjDH/5Aj8LmzZvR2dmJ22+/HYBEm/n5+fS8DA0N4R//+Ae9XVqtFlu3bmUJFyCVZQWPi21vb6f7vKKigrTV09ODMWPGMA4pJg0JK1HUsgtaEjQn9nT16tW4+OKL2U519erVrOMXa506dSrrydVqNWlDDH8QrmCn0ykbqPFtQXRYFM8oBk8Ii14MtBAegx/96EdYv3697DwCkGUaZ2dn0xJtamqSlWp2dHTIrPCioiJalH19feju7qbFeddddyE7OxsvvfQS/z4lJYWWcnh4OEpLS2X1+fX19aTLnp4eXHnllQgPD8eHH36I4uJiWsKJiYnYsWMH6SQ6OhpTp05ltrbL5UJzczP5cUlJCQKBAP75z38CkPhKRkYG85EmTpwIi8VCV3xnZycuueQS8ufe3l7uaUREBGJjY5nlHhsbC4fDQcv4oosuwieffIJ//OMfACSrX6/X05J2Op3QarXk1x6P51uZmDYap8XdLeKHwe5Ls9nMh09KSkJTUxOZbn19vSzmlJCQAJfLRYK12+146623KORHRkZkjQ6mTJnCcY9ffPHFESfdiGYl1157LR588EG6Q1evXo0LL7yQZU/btm1DXV0dCaGjowPZ2dl0f6alpSEmJgbNzc2or6/H1KlT6YbasmUL+vr6mJwwZswYWK1WutKbmppQXl5Ogt20aRPGjRvH2LzL5cLBgwe5T5mZmfB6vTycl1xyCf74xz+SYBMSEsjIysrKUF5ezj3My8tDRUUF3f7Z2dmYOHEim3ysW7cOLpeL1961axfUajWFk2g/OXpc5egawpOJ4P7Kfr+fgjYsLEym6NjtdipZBQUF6OnpIZOuq6vDli1b8Pvf/x7Al6PuRPKIEFoieem1117DE088QYGzZ88eWK1WvtPGxkZER0dzbRs3buRM4LvuugvXX3+97BnefvttKjqill/gqquuwtVXX00XaHR0NC666CIK5t7eXrz33ntknuPHj0dsbCyV3eTkZNTX16O+vh6PPvooent72Tf+b3/7G1555RW+b/Fswo24b98+xMfHkxFXVVXhmmuu4fvduXMnenp62Ca0tbUVd911F92OYlqXaLASPF1IqVRi06ZNVD7EdCLhdhT1woKuhUIt3LWPP/64rC43MzMTJpNJVvvc0dFBWg8LC+N5VCqV6OrqktVGnw6IZxM8KyoqCuHh4VSiTSYTRkZGOCq2q6uLIRtA4ml9fX0Mw4geBeKZDQYDaX7ZsmWyOcnR0dFobW2lMh8ZGQm/30+hvW3bNqSnpzNB9tChQ/jggw941gsLC3HzzTczv6GjowM6nY5rGRkZgU6ng9/vx8jICHbt2sVyO5vNhlmzZrGkymQywWq1UnEVyW/i8+HhYVx88cVUSoqKimCxWHhWuru70dbWRjotKyvD4OCgrJGNWJfNZpMp6OK/QlENBAJs3CIQzEvKysoQHx9Po0jsx6nu8z4apy1xTK/XU2sXcTEhZN1uN3bv3k3BuGvXLvj9fhJRbm4u9u3bx2zEyy+/HFVVVTyYeXl5TCr53ve+hxtvvJFM9tZbb8XZZ5+Nhx9+GIDExG02GzVWvV6PgYEBCvni4mJUVFRQa7Tb7di3bx+t2+zsbBiNRjZDsdlsqK+vh16vR21tLc477zweFp/Ph6GhISYyCI1NCOG33noL8fHxXKtI1BCZw5s3b8b555/PjmKim5I4PDabDYsWLWIiUWtrK70RdrsdIyMjsnrxvr4+Mun09HQ8+eSTfE7R7EAI/Li4ODgcDmrISqWSSpTAqRyIHjzmT6VSwWAwyEZZBs+ydjqdsiSzqKgomfIwefJkZo9+9tlnGDNmDAXj2WefjcTERHpltm3bhokTJ7KBSCAQQHx8PGnzySefRGtrK/d11qxZVAKFJRMMl8vFPY2Pj4der5fFqRUKBe+1Y8cOJCUlsbuS0WhETEwMY96Dg4P48MMPme29b98+lJSUoLOzE/fffz/sdjsWLVrEawXXljc0NCAQCDABZ3BwEC+99BKz2hMSErB27Vpap7t27UJ4eDjzQq6//nqsWLGCQmfu3LlISkrCY489BkBKThKWmhg1KZIrR9PIaLq59dZbERYWxne2Y8cOdHV1yWKHwR3CREc6EasWGdLiXj6fTzZm8XRA0KGgCZ/Ph/j4eJ6h3t5eDA4O0oJcvnw53njjDXomDh48iObmZvKG/Px81NXVkWcmJiZyD8XsaGF9iuRV8e5qa2uRk5PDszxhwgRo/197Xx4fZ1mufc0+k5lMtpkszdKkadOdpvtOsQXKpoAVUMQjVWvBTw4fohTUTwT0AKKCcBQ4RZQK59CWAupBoBst3dekS5a22fdlkkwyM0lm//6Y7776vm1ZZGs533v9fvw0TTJ5l+d57u26r9tsphEvKCjA+vXr6djk5+fj4MGDNF5CNJNMZnFxMRYsWACj0Yi1a9eipKSE152WlobMzEzWw0+cOEExGyCRqVIqOqampmLlypUka/X29qK+vp6ZypMnT+IrX/kKncHy8nLY7XYSWpubm7km29vbUVJSwp9NTk5GSUkJdSfGjh2L5cuX46GHHgKQIMtK5gk43fEiZ2hDQwPPGuDTJ8sKtJq0Bg0aNGjQcIHivKS7bTYbQqEQ21yMRiMHvwOJGsfvfvc7FUs5GAzSUzx69CgH2wMJec2ioiJGFM8++yyVkHbv3o3LL7+ckc7EiRPx3//93/QydTodzGYzvf7CwkJ0dXWxRWDhwoX4yle+QlnRkpISjB49mjXLP/7xj9i6dSu9teeeew7V1dXo6urCv/3bv+Hqq69m60hHRwcqKioYrYicntx3KBRCTk4O2d9vvvkmZs6cyfTM7bffjr/85S9M/bz00kvIyspi+rSnpwd/+MMfGFFaLBbs2bMHwOl2Han3dXd3w263M4p77bXXEIlEGK0MDg7iyJEjKiW2rKwsRv91dXV8j/JOBwcH+Y4+rkyo1WqlpyptD8roWDl0Pj09nW1YwOlyCJCIpAoKCvh+RR9barlf//rX0dDQwOiro6MDDoeDzykrKwvHjh1j6rGgoADNzc3sMRVJSlEBGzt2LD/L6XRiw4YNlETU6XSMaIEE81jZz9rV1YUNGzYw4rn44otVE9mOHz/O1LZA0u5AYmqWpMKlNUyi8OLiYjLzAXBCljynlpaWsyLaa665hi1B//7v/47vf//7XLNHjx7F5Zdfjrlz5wJIpPF/9atfsd/87bff5vuSDJekISWqkoxSdnY2urq6uJYqKipw6tQp/l5JSQl6e3uxYcMGAIkWuba2NmZSsrKy0NfXp2rBEkhq8nxF0IJ4PA6j0cjsQGtrq6p/Xzol5B4vueQSOBwOVbQmLHkgcWYUFRWppi5JJ0BbWxuCwSCzfePGjUNHRwefV2pqKo4cOUIdfyCREZTztLW1FYsWLWJUefz4ccqWAokMntPp5DlTXl6OxYsXQ6fTce9IJA2cloEGEueK1+tlpvPWW29FIBAgs/zw4cOYMmWKqitB+uDlGfT09DBaTk5Ohtvt5nNxu9382WAwiFAoxP3X1dWFmpoaZjNaWlrQ2NjI8ayxWAxms5nndVlZGWw2m2rS35nT+D4LnNc+aSH31NfXIycnRzUGUTlA44YbbsCiRYtY48rJycHu3bv5+zIcQhZCcnIyX5T0d4oUZzQaxeTJk3lQXnPNNfjd737HGvTll1+ONWvWMP1tNpthNBo5mKCqqgoHDx5k/Uca3pWEnsOHD6O6uhq//e1vcfDgQaawli9fjrVr1zLVI6llESN5+OGH8dZbb7FNatmyZTh16hQ3R01NDa6//noa8csvvxz//u//TrLZvHnzsHHjRqb9BgYGmJaVepF8LxAIIBAI0NAdOXJEJSoQCAQosACAi1WuubOzk+P2ADDdLIdGKBT6WAQLpQyokigHnK5JywE8ZswYmM1mrh9lDVIcC5EB3bdvH5qbm3kIGI1GTJgwgaIvOTk56OzsVM2XLikpYU1bp9Ph5ZdfZrtQfn4+Wlpa+BzvvfdeSsgWFhbC7/ez7x1IEBXlYBbJUGkdMxqNLH0AiXXkcDiYnrv66quZ6gYSM57nzZtHx2rkyJFob2/H4cOHceedd8JkMnFQyKJFi0hoAxKtYiKzeS5UVVVh/PjxuOeeewAkDn6Hw8G1eMstt6gGcNx9990Ih8MUxxE5VSBRnlq2bBk2btwIINFW09LSQofvRz/6EZYuXUri0oYNG1BRUcE91dTUhJ6eHnJYsrKyEI/HeVB7PB6kpKQwnW4wGGgMBwcHzylWIt8XEtlnIWaSkpLCNQsk0sbioA8MDCAcDtPpWr16tUooY+PGjTCZTDwTw+EwgxzgdJkOSJSySkpK+NmdnZ3Izc1lufDYsWMIBoN49NFHAST2y89+9jMGJrt27UJGRgZWrlwJIMHLycrK4pn4la98hf3LQOL9/Ou//iuSk5MRDAaxf/9+OvcTJ05EdXU1r8XtduPvf/87tbY3btyIhoYGagdIuUicPznfJOh64403MHXqVJLBGhoa4Ha7uZZisRiDIJvNhq6uLt53MBiE0+nkugyHwygpKeEZ+fvf/x5Wq5XvyOl0wmazsSyg1Ov/LPGZGmllXSg7O5uLLCkpCbFYjNGO2+3G3XffzT7L9PR01WCC7u5uOBwOHrw1NTUwm800QDU1NSTsmEwmHDx4kItky5YtuOWWW2jY9uzZg7S0ND74vr4+GI1GRk533HEH7rrrLtZMduzYgcHBQTz//PMAwChKPMP6+nrceuutsFqt8Pl8qKur43UCifqaeHKhUAjf+973SMbJyclBbm4ua/RZWVno6emh53bRRRdhx44dzAJ0dXWpxE/MZjNee+01Pue5c+fS4BsMBpXRTUpKUhmMOXPmoKamhsZI/l2eqQwtUG58vV7/qbG79Xr9e+op6/V6Rs9Agn1aUlLC+l59fT3ZpCkpKbDZbIzerFYrWltbeYjcd9992Lt3L2vUHR0dWLRoEf9mTU0NcnJy6G3/5S9/QWpqKmuBNpsNK1asUIk7KOH1ehkZd3Z2UmwESGQnlCIqZxrOBx54AMXFxTywcnJysGnTJq7Fhx56CMePH8fvf/97AAnewbe//W1EIhEcOnQIhYWFzADt2bOHkakSEoFIVCuYPXs2LrroIh7EkUgEFouFUb/JZEJeXh7fQWpqKg4cOMAIavLkyfzb4vgIQVKn08Hr9XKdRiIRlWEV4SKZo15RUYFoNMo1KQxd2ReRSATJycl8Zy6Xi9mMaDSKaDSq4iTE43Hu78/KQMt9K2vILS0t3I9GoxFDQ0PcbzKPWd5PKBRCZmYm19LAwABisRgdl8bGRp5pwWAQZrOZjmNbW5sq85STk4OBgQGSr06ePIl58+ZxT7S2tuK73/0ug560tDRUVlbSQZs3bx5uu+02iva8/vrruPvuuzkoKRAI0KgODQ2psocdHR0YP348n3lOTg5uvPFGZovWrFmDZ599lu9n//79sNvtPEM7Ozsxf/58nlOxWAy7du3iWli8eDGdjezsbBw7dozrpr+/H5mZmVyHQIL9L+u0pqYGRUVFDDxkBoNkFIxGI9/XZwmtJq1BgwYNGjRcoPjUatLKtI5Op1P1LgaDQTgcDnpXvb290Ol09J5yc3PxzDPPsDYg7RlSc0pPT8f48ePZI+hwODBr1izS+CdOnEhW6rXXXovRo0fTi5SISmp3s2bNgsfjoacWi8Xg9/uZ7gQSaWeJdrds2YIlS5Ywohw1ahQOHz7MqGDx4sWwWq1ISkpCXl4ewuEwPbMTJ04gPz+f3vGkSZPwy1/+khrEQEKFTHpTy8vL0dTURLW0V199FXq9nukbm82G3t5e/vwVV1yBhoYG1neUUYLL5UJdXR295ZaWFuTk5DCbsXPnTphMJn5tsVig0+kYKcl7lAhVGLXvF0F/nOkwynS2KNRJNCSzbJVpqTFjxjDaVWqK9/f3qxTIpGVNarsdHR3wer1syZoyZQpOnTpFb7ygoAAWi4UtIwcPHsTrr7+Oxx57DADwyCOPnBVFSy0wEokgEomwFlxQUIAjR45wLUqJRbIwZ6af77//fu4RILEerr32WqbHf/rTn8JqtTIiSktLg9FopMRjOBzmWmhra0NRURHXuU6nw549e7i2otEo3n77bUYdTzzxBL70pS8xhTp9+nQ0NTWxnzw5ORl+v58pQGW7EJDIIEj3RUNDA8aOHcuIpLi4mOldINH2ouSYXH/99Th16hTT3eXl5SgpKWEGwW63Y9asWcy0JSUlYWBggO/c7/eratHCYVDis64rAqfLNMDpdh6JTgcGBpCcnMxywVNPPYWnnnqKvJwxY8Zg/Pjx5EIIT0OyMhkZGTwXTCYT8vPzGTEODAygq6sLV1xxBYAET0cY3kBinTqdTv6tBx98EHq9nu16RqMR6enpzADOnz8fRqOR63jr1q1444034PF48Prrr+OJJ57AU089BSDR0tjX18czMD09HXv27GFGISkpCT//+c+Z8XM4HKirq2M6XM5SyRI0Nzdj06ZNzNjk5+djxIgRVE+TbCyQyER6PB6234p8rGQfbDYbDhw4wD2m1+tVfdTJycmqdRIKhT6wg0WyvZ9ky9+nZqTNZjNriVarlUMwgMQNKOt47e3tsNvtKknBSCTCVhWpqYjBGRoawvbt20mN7+vrw+uvv64SBhCSyqFDhxCNRjmkYNSoUSgsLKSxisfj2LlzJ4UWJkyYoNKJvvjii/H666/j17/+NYBEGuqVV15hHfnFF1/EuHHjuAgDgQA6OzthMpnwpz/9CXPmzOGLln5CkXE8fvw4Bz0ACfnTSZMmcSMGg0HMmjWL4wZPnDiB8ePHcyPPmzcPNpuNad7Vq1er6swiGwokDkqj0cj6SmpqKvx+v6o2GwwG+Y6kfiaOVSQSQVJSEtNtSUlJKgN+LnycmrRyM8TjcVUNUq/Xo7e3l+vF4/Fg06ZNdMBk7jaQMMJ2u101k7ipqYl8hl//+te46667aJS3bt0Kg8HAVGt+fj7+9V//lX2fa9euxWWXXUatbmVdUCAHzOHDh3H06FGSFh0OB2cmA4l1vHv3bpYrROJVCJDd3d3Q6/Vcu9dddx02bNjAWuHx48dhsVjY7/r3v/8d8+bNQzwex/bt2/Gf//mfNPwPP/wwCgsLaRwikQiqq6tV88R/8pOfUHu7q6sLdruddd62tjY88MADFDPxer3YvHkz1++NN94Ik8lEo/PUU0+RQ2K1WlFQUMBnXldXh9TUVH4/Foth5MiRPDz37t0Lj8fDgTPSUiTrwe/3Y/Pmzaqv5b2fC/F4XBU0fNY1ReD0ehZDJzLHcu1Go1F1jbt378ZLL71EQ5uRkYFNmzbxTCwpKUF5eTnXjl6vV+kvAKedxba2NrhcLp4TkUgE3/72t2nURdNaHFEh7Umg0tbWhuuuu45tUEVFRXjhhRdw4403Akg4BVlZWZxBHovF2EYoI3Jl/y1ZsgTr1q3jZ0WjUfzgBz/Atm3bACRmJ7zwwgsMPDweD2677TbycAoKCnD8+HGuy7y8PJhMJrbzmkwmFUk0Ly+PZLx58+ahsbGR66SyspK91EDCgRgYGKDTLQNPZP+ei5h4Jj6N0smnVpM+M/I5E7m5uVw0MrdTvHH5efn9UCiEtLQ0euY1NTWssQKJ2quwBoHEISB61z//+c/xve99j3Xe5cuXo7e3lwe+yWRCW1sb63WhUAj33nsvGdQPPfQQh28I9u3bR6EFgZAPjEYj+vr60NPTg127dmHixIncNBMmTDjrUH/66acpfjFz5kx8+ctf5kEYCoVQUlLCCKS/vx8XXXQRZwKLIISQKF566SXY7XaSKE6dOqWKfJWHk0TB8oytVivcbjcPAdE7l78diUSo1gYk3q/f76chVtanPwlILzRw+hBWQq/Xq6Ili8XCe3G73aqpVz6fjwdYUlISTCYTN9PEiRNRVVXFQ6O7uxvRaJQOYGFhIUaPHo2f/OQnABJrdcmSJVi1ahWA08MxlBBlvOPHj6O0tJQRTG1tLdra2nhgFRcXq/rczWYzli5dquoBbmpqYiQwb9487NmzR9WfarPZuL6ARKSfnJzM3m9xutxuN5YtW8Yas8fjwTe+8Q0efkVFRdi+fTuNiKhhybXIc5I1NH/+fNWs3u7ubkyePJnvqrW1lXXDL3/5ywgGg3SqRMdA9mBdXR1cLhcjmv7+ftVaSktLQ1JSEr8v1yWfdy4VKGVPfSwWOy/KeOeCvHvh5IjTHAqFmNUAEp0a3/rWt+gcdnV1wWaz0WhHIhGkpaVxvzY2NrLDIDU1FcPDwzxfBwcH4Xa7Wfd9+OGH8dBDD+Hll18GkMjoXHXVVXQu9Xo9LrvsMq7tOXPmoLe3lwRFUQsTdHR0oK6uDkNDQygvL4fJZKLz19/fj6SkJDoTkjGRNT1//ny8++67+OlPf8qvd+3aRW2ITZs24eWXX8Zf//pX/u2ysjJGzm63G3l5eTwLhoeHydlZsGAB1qxZw+zPFVdcgddff50ZrH379uGdd95R8XT6+vpoxE0mE7xer0qY6bNWGwO0mrQGDRo0aNBwweK8tGBJb7J4tbFYDFlZWfRgAoEAZ+sCp5mP4ml2dnbCaDTS6x8zZgyqq6sZYZjNZkaTGRkZZFoDCdZpeno6UzvZ2dmYOHEiPaRXX30V1dXV+I//+A8ACVbs4sWLGdXde++96O/vP+c0I4FE1WPHjkVnZyfTTkuXLlXVev7whz/gscceoyTlpZdeCrfbzTTUf/3Xf8Hr9TIrsHbtWowcOZK1+rfffhtGo5Gp99TUVDz++OO8tmg0qpoMpazjmkwmmEwmRj4SmUitJxQKwWg0sjUpNTUV+/fvJ2t2YGAAJpOJ6VOj0fip1/kkC6HT6VTercFggNFoZHuS1+tV9R5L+xmQyLqYzWYqrc2aNQv79u0jRwFIeNzyHP7yl79gyZIl/OyFCxeyd/jDoLe3lylgACxVAIlSzMiRI1nO8Pv9+MEPfsCJWr/85S/R3d3N9TNt2jQ0NDQwJW2z2Zh2F0j/88MPP4yKigqmu7ds2aIqhSxZsgTl5eVsJRRtaVl7hYWF6Ozs5PVmZWXB6XQymk1KSkJSUpJqGtg111zDlqyuri5G8fPnz4fb7Wbm6/Dhw8jPz+f+j0QiOHjwIPfBiRMnEA6HWaNevnw5duzYwbUMJKJDafcLBoOqKXqy1uW+4vE41+b5iISUUHYFDA8PqzJT6enpzFw5HA488sgjXMe9vb0YHBxke5+UDoSZ3N7ezhJfOBzG5MmTWZrauXMnotEoI+C33noL9913H2v8DQ0N2LFjBzsNBgcHMX/+fNXa+tvf/sZIurW1FT/4wQ+YbXr33XeRlZWF5uZmlJeXY8SIEYza//u//xsLFixgOVCn06G+vp5lko0bN+LYsWM8yxsaGvDAAw9wnV199dV45JFHVLMV0tPT2a7n9XpRVFTE9HhtbS0Z8tKRIBmC3t5e1fl63333wWKxUAc8IyMDoVCIWZihoSH09vayJHimwuJnhfMiZmI0GhGPx1XjAmfNmsW0RGtrKywWCxfw4OAgHA4H02MjR45EKBRiTUsIOkoBCyFBtLW1obe3l/XKf/mXf8HKlSt5OG3cuBF9fX1coMuWLcNtt92GzZs3A0ikG5977jnce++9vP6UlBQScCoqKrBp0yaOZsvMzORweyBheGVeqdyH6GE//fTT+OIXv8iD8zvf+Q6++c1vMiWWlJSEnp4eEjxuueUWDA0Nqfpue3p62K7R2dmpIuQpN6bBYMCOHTtofHp7ezkyUJ6ZyP3JM87Ly+NGFllOWbAyek/SnZ+0gZaZ4gKz2awaTqBM34twh7xzl8vFZ+rxeDBy5EimBY8fP47CwkKSubZt24apU6fy0PjTn/6EYDDI9pJLL70UZWVlHHxwLqlPpTDPmVAaaCDxnOT95Obmwul04rvf/S6/X1tbS86C3+/Hiy++qBIsKSwsZDpPKU5yLuh0Oh5YycnJCIVCXLc7d+5EIBDgMwyFQojH43TKQqEQotEoli1bBiAxmOSFF14gUUwkTZUjXA8cOMDUe1VVFddpaWkpIpEIje62bds4ShM4PXBHSlKjRo3CvHnzWAZ4/vnnkZGRQW2DxsZGVQrbZrMhPT2dRlqv19OJMxgM8Hg8vE+73Y6hoSE6DEK0+6zS4VICGDFihGpegRBrle1BNTU1JDiGw2GMGzeO5YdXXnkFRUVF3I/Z2dl83q2trSgtLWV/vcvlgslkInfB6XRi9+7dfNe1tbUIhUI0ZkNDQ5g9ezZ/PhgMktsBJMosv/jFLyh/m5ycjEAgAKfTiZkzZ6p0CPR6PbKzsyn2JMM+lK19DQ0NqhnPmzdvZukqJSVFNfgnPz8fjY2NPM8LCgpUwYiSaClfC99k7ty5eOaZZ7g2WlpaMHbsWK7LjIwMldypaGRIOvyTLul9WJwXI20wGBCNRslyNhgMOHTokMqrzM/PV9WyZfoQkKiBKBXIysrKVMxJs9msOkzkoAWAH//4x3jttddUNceWlhYa0rS0NAwPD9MrdblcqK+vJ9FMPN2f//znABJCDML0BRJEs3379sHhcOC+++5DS0sLF+S3vvUtHDhwgLX1lJQUvPbaaxSwEPKERNpf/vKXMXXqVNaGtmzZgoaGBpIkNmzYgC9+8Ys0BOnp6WhtbaW3rtPpuHiHhoZUgg9ikOUZ6nQ6WK1WepESVYkn7/F4VEL0ZrMZ/f39POw+aUWnM5njys0hwipKB0PpMAQCAd6HaIwrh5gMDw/zuciADfGu09PTUVlZqVJTUjJfpQtByCROpxN1dXWqSE0pbNPY2MgsTGZmJiwWi4pzoTTi48ePR3V1NQ+ko0ePIhAIvGcPttJ4Awkm7bx58xAMBjn/We5LmKliGIHEvhOnIxAIoL6+nusjOzsb4XCY++TgwYMYM2YMo46srCw8/vjjFCgJBALo6Ojgc1Be86FDh7B//346DAsXLkQkElGRfSoqKnh47tu3D/v27aNRiEQiaGpqUmWBgsEgI2m9Xo+Wlhb+fDweZ4bIbrerHLqhoSEYDAb+rU9jEMz7QelopqSk0CkWRT35OiMjA5MmTSLR0OFwwO12q4YDifIXkOAEiJMyZswYVSZj4sSJqK+v59cifCJrY3h4GC6Xi2t86tSp2LZtm0qtMCkpiUa9srISl112Gb8/d+5cVFZWore3F11dXdDr9VwXFRUVyM3N5RQ5i8WC//N//g+JZZmZmbDb7Txvpd9dAjJxUoS0tWnTJsRiMV6rDAZROrbirPz4xz9WzY8eP348MjIy8OCDDwJIcCFE7RIAo33lOQd8+LPt0zLiWk1agwYNGjRouEBx3rS7pYcNALWzxVNOTU1FdXU1vV+n0wmfz0ePxul0wmq10mOSyFw8osbGRnpDgUAAhw4dojdls9lQVlbGyHr8+PGw2Wz8+dLSUmzdupV6r8eOHcNjjz1Gre65c+di6dKljKTb2tpwxRVXsC+7q6sL1157LQYGBrBv3z4YDAZ6eb/97W8xd+5ctlhlZGTgjjvu4HU/8sgjsNvtZA77fD5s3bqV16rX6zF//ny24Nxzzz2IRCJM1TqdTnq3QCLjIGmnGTNm4MUXX6Tn3d7ezrIDkIgQh4aGmErdtWuXqmfQ4XCgra3trNGUn1YdWnR0gdNsXklh9/T08J3LdShVpXw+HzMpKSkpqtm6ZrMZnZ2dZOf39PTg1KlTzG7YbDbMnj2bbRzisUvGQCnbCSSecWFhIdfunj17WO6YOHEia4eCvr4+RtazZs3Cs88+y6hPol+JeO666y5cddVVZ0XMgurqaowbN051jRI1RiIR9pgCieggEAiotArC4TDrbKK5LOnY3t5eJCcn42c/+xmfm0Q/QCIVe/XVVzM6njlzJkwmE/kYJ0+eZBampaUFU6dOZYanoqICo0aN4tSl/Px8lJSUMFqSNkNJM8pMYSXa2tpY7vJ6vbDb7VzryrUwPDzMNKb8rDLa+bga8/8slJmnQCCgkvAVeVMgcY5UV1dTn8Hn8+Gvf/0r07lerxfDw8PcI1VVVcz2hcNh9PT08J4lFS37pba2Funp6SyjuVwu2O12lsaSkpIQjUbZmzxnzhzU1tayZGS327F7926eiXL90oLn8/mYkbn55ptx9OhR7rfXXnsNZrOZZ5ak1mVSYGdnJ8LhMMuHR44cQWtrK9sSI5EIfD4f+UkDAwPk5ACJ6Fj28j333KPqfhkeHkZbWxt1CeLxOE6dOsV3YrPZVO16fr9ftTZE+vi9ouVPKxV+XohjVqtV1QYDJA4NeVg5OTkwGAysG8diMZKDgEQ9R0QqAOCqq67Cli1buODb2tr4EquqqmA2m9lucN111yEpKYnEoZycHLS0tLBukZ+fj5tuuomtKSJ1J4dEWVkZysvLuaiSkpJw8OBBvngATEnddttt0Ov1vK/h4WE6KEDipefl5fFQj0ajrN8BiYM1IyOD5J3u7m50dnZyY95xxx3Yvn07hTh++tOfssYDJA5eOchE31aMT1tbG4UegNO1ODEgQhyT+rm8D0n3C/HlU1uYihSl9JDK1yIJKeshHo/zeuXaxaBPmzYNJpMJW7ZsAZA4wHQ6HdOxqamp0Ov1PFRkLrI4RlIHlHrph8G6desAJAhog4ODXJdnYs+ePZg7dy6NkzhIgnPVR9966y22dAlEDOf222/Hb3/7W+h0Ovyv//W/MHbsWJXs5KlTp+is5Ofnc28AiWeWlJSkamtTppTHjRuHuro6rgePx4P6+nqmI61WK/x+P1OGubm5LAkcPXoU6enpqlKLy+UisUlahqSe3dPTg5MnT9IhstvtKsJcR0cHyaTyt5VrWTlDODk5GWlpaUztijN65ozgzwImk0m1ZuXagESrUiAQYPpd+CSSmg2Hw6ipqeHzlYEXSslK4ZsYjUY0NTWRiNfX1weLxUJnsLa2Fj6fj7PjW1paMGXKFK7xL3/5y6oadHd3N2pqanjtfX190Ol0rCNffvnlaGpqwtDQEOLxOFJTU9mGeNddd6Grq4sOxEMPPaTSkf/1r3+Nf/mXf+EZVlBQgP/9v/83ib1Go1E1PMflcqnOneTkZDQ3NzNYGB4eJlnS6XTi4MGDXFeFhYV49NFHuT/NZrOKGDY8PKzi5ZhMJhVv43zhM42klZ6u0ngFAgHY7XbWIfr6+lQvQnL9SmEOm83GRbNu3TqyI4GEtyWflZubi7Fjx5IFecMNN+DAgQPcnP39/WhtbcVtt90GAFi1ahUuvvhiepGHDh1CJBKhtyZkDTlgjEajiqxwpmiCaAfLfQhjEEh4gZ2dnaw5h0IhlJeX8zllZGSgtraWWYC0tDT09vZyY//5z3+GwWDApk2bACQ8v7q6Ol6D1WrlAuvo6EBTUxNrtUajEcXFxWRrS6+6cqhFPB7ntfb29qoOmU/TQANQ6SvLNYm4Qn19PWw2GzfmwMAALBYLN5dycIHH44Hf71fNgNXpdORD6PV6TJ48mVFCNBrF/PnzGeVdd9117OF8L/j9fpJ0rrrqKoo8AGqiWX9/PxwOBx0Al8uFSCSiGhRTWFhIJ6usrEylfAeAB6cSot0t92+xWOiQyd+XGemSYaisrFQNpYnFYgiHw/x8k8mElJQUvoMvfOELCIfDqsElXq+XNesnn3wS119/Pa/X4/EwSpcZ1xLh1NfXw+FwcJ3bbDbVWiotLcXcuXNZP29ublZpdRuNRthsNu7xWCwGl8vFLMGMGTN4X83NzfD5fKx59vX1Qa/Xc0/JVCNZYx93TcvzOleGKRaLqRwLpRKaEGWVw2T27t1LkaUbb7wRR44c4fspKSlRGd5QKMR7slqtsFqtdEwyMzO5Z+Szx48fTwe+tLQURUVF/Kw//vGPuOuuu5iprKurw6233so6s5zHsofKysoofGQymTBmzBhGvm+++Says7PpoM2YMQNVVVV83tnZ2fjrX//KtSJ8AhGyefDBB7Fu3Tr+rZSUFJjNZjrZXV1dCAQCvLdp06bxPo4cOaLSdmhubkZ7ezv5KKmpqfD5fOwvr6ysRE5ODt9dIBCA2WzmWjpfxlqrSWvQoEGDBg0XKD7TSFqpZxqJRFTazhJNA2D/rXjakgoXL9NisVBjW35+eHiY0VBKSgqVfILBIJYtW0avcdu2bVi8eDGVfJ566imMHj2a6ZTDhw/j1ltvpS7wpEmTEIvF8MwzzwBIaDsHg0F+ntTwJFKz2WywWq0wGo0oLS1FdXU1az+SOhEv8uKLL8bPf/5zqus4nU488sgjVDiLx+Po7OykhyxpOmkZKC0txR133MEsQDQahdPpZApMr9erIgjljGa73Y7W1lZGcdFoFCdOnOAznzZtGiorK/nMY7EYBgcHP5DpqJxv+3GQn5/PyKioqAhNTU2MbmVtiGcrI/yUU9aUo0QzMzPZsqHX61Wzi6PRKI4cOUJPPCkpCbt372YE+vbbb+OWW245KxUtKeYvfOELOH78OPWzleju7kZvby+jgltuuQU1NTXsix4eHsbll1/ODFBeXh6vGwAef/xxfO1rX+O+SElJUamLCWRm9A033ECVsYaGBhgMBqYkZ86cicrKSkYkI0aMQHt7u6oFaHh4mLXcSy+9FIODg1xrr732Gi666CK88847ABKs27y8PHIgFi5ciFgsRjbyhAkT8I9//ANAYt1nZmaS5xEIBJhFAsAUuqTe58+fj0cffZS1RL1ej5SUFFVfdVpaGjktwWAQpaWlvJaenh6mU0ePHo05c+YwUnvggQcwZ84cfv+FF15gq9EngffjaCg15TMyMtDf3886fXd3N/Lz8xnlBQIBFZ9leHgYBQUFbJMKBoOqcZ7t7e2qqWIzZ87kGWU2m3HttddyHdrtdkyePFk1O0Gpw56Xlwev18tI+tJLL8XJkydZs66ursaePXv4+Ur9ioaGBng8HmYMSktLVcx7GWeqVM0bGBhgzfrSSy+Fw+Hgu83OzsbAwAB/v62tDSaTidfi8XhUcsbbt29nlD1q1CgcPXqU2Z7f/OY3qKioYKaxv78fBQUFfA4mkwl2u533BZzusz+fOG/zpPV6PV/u0NAQhoeHVaPSbDYbDaccJLIIpUYt/x6LxZCSksL0VzQapbEBoKpBL1y4EMeOHVMZ15tvvpmN+2PHjsUrr7xC4zVlyhS88847NE6jR4/GsWPHSKgJBAJwu91M5cns4lAohHvuuQd+v181AzYej7N2NDg4iHA4zN+dOHEiTpw4wfr29ddfj+rqai54GXZ+9dVXAwAOHDiAwcFBLuBQKMTaIgDVOEBpK1OKxwthD0hsZGU9RgRnzhyooSRrnQtn1rg/KkaOHMm/PW7cOBQUFPDrd999FyaTiQdeUlISh7DINcj3nE6nqsdWJGWVIxYvuugiEp4KCwvR0dHBtVhcXMxSiRJSBkhPT0d9fT3TnMo+VyBxqEjatri4GNOmTWMbk7Qgyfu12Wyora2lU/fjH/8YBQUFdMIEytaZ9vZ2HtwvvPACCXX3338/IpEInY+6ujqkpaXROQmHwyqRH3F0xZkZHh5GUVERDVg0GsWMGTNw8803A0iMEBw1ahRr/ULoEXJZR0cHr3NoaAh5eXksGbW0tKClpYW6CAMDA7jssstUEqPKtWg2m9HY2MivJ0yYAJ/Px3c2ZcoUDA0NsXSjJPxccsklGBgYIFFpy5Yt6Ojo4PdHjBgBv9/P96l0kj5N2O12EpWAxH6MRCIs6UmtVM7AL33pS1i2bBkdncLCQgwODtLhq66upqGrqKiA0WhU9SqbTCaOPN2xYwfy8/NJdrVYLHA6nUz7+v1+LF68mH34+/bt49ATIFGmSU5OZh/7ZZddRrJYU1MTxowZw3cj7bDKoUHd3d2qYKClpYXX5vP5cODAAa7LSy65BDU1NXwvQlCT81xaKuW5eDwelSCM8gx86623cPLkSVXJwWg08kz0+/1wOp10EM638I3gvLC7gcQDkGhX6kLycOPxOGw2Gx+mLDiJ0oT8IQdxWloa2traaCDk+4KkpCQawuHhYcyZMwfPPfccgMShunXrVrz22msAEgPNX3nlFR6MM2fORDAYpLjJf/3XfyE1NZWG8LLLLkNycjIXsNfrxTe+8Q0MDw9j27Zt+Nvf/sYF9fzzz+Ott96iQ9Db24trr72Ww9dra2uRl5fHQ7qjo0NlWFesWIEnn3ySurVerxdWq5UesM1mQygUogFSquvIpCBZsH6/HyNHjuTmaWlp4Sxt4PTmkmcqvckfNkJWsq+V///DQqfTsfY+NDSEXbt20UPOycnB4OCgqr7udDpVU5DEUx8aGoLFYiF5q6GhAT6fj1Gbz+dDdXU1WbW1tbXweDy873PVgAG1SIkcbgIhgolXLipRcm2S3fD7/di5cycdpRkzZtDgAsC//du/obq6mu8wLS0Nd955J4VWDh48yIwQAHzzm99ktLx06VKsWbOGRlp0tcUoDA4Ooru7W0XGi0aj/FuxWAzHjx9X1TmrqqrYeTBnzhzs37+fB5nVasX8+fMZrXm9XnZAbNu2DW+//Tb+8z//E0Biz5jNZq6l4eFhvPPOOzyoU1JScPToUfblKqe+yd8aHBxUORSBQEA1bGLhwoUAEgbG4/FQtU2cUSXJUEk006DhQsN5M9IaNGjQ8D8NH3Y8q0gfS1lFpgKK4xKPx+F2u9leV15ejgkTJjDiz8zMhNvtVk0aFGJXbm4uotEoHcyRI0eqnL9FixZh7dq1qixLMBikUyz3oGx5dTgcLJXIhDQpAfX09CAcDsPpdGL27NloaGhgRqm/vx/JycmUMs7Pz8eRI0d4nx0dHbjmmmv42WvXrkVPTw/+8Ic/AEgERWazmRkGEQNSZr7sdjuzbC6Xi9ff0dGBrKwsfnZNTY3KGVOqJ8p9i3ohkMg2fdA43s8C5y3dfSaUutEDAwNnzeNUprclQlT2QiojNYfDoapXx2Ix1n6cTifa29v5gqZOnYoTJ07g+9//PoBEW8yqVavI0NXr9ZgxYwaj2+eeew7hcJje/bmg1O4+FySdMnr0aASDQd6HaA5LFkDa1CSaCQaDKvlJiaKFySzsUdl8ypGNMoNZIO01ckh4PB6kpqayJmYwGDhOTxCLxT70IaRkRX4UzJo1i61noiAm0XE0GkUsFlOxg0OhENfM7Nmz2U4yceJENDc3M3Jyu93w+Xy8r/7+flWtcMKECbj//vs5acxsNmP+/PnkDQhrVXD8+HHk5uYym9HQ0EB1pYGBgbNkRP1+Pw/ePXv2YPny5SqlNI/Hw7X21FNP4fbbb+ezbmxsRDgcZvbDZDJh8+bNTDk//PDD1L3OyclRac7v3bsXixYtYquh2WxGa2sr14Skv+XnHQ4HZWyBxB7r6elhxsFkMqGzs5M93NLOJbVeGScKnJb5lT2an5+Puro6rrWMjAzk5+ezft7e3q6ahGU2m+FwOJgCFW6HXGtKSgpmz57NkYRK1rTD4WC9HEiUSoLBIMsZsVhM1VMsNfiPin+m3GOz2XgPOp1ONf1M2pzkul0uF7761a/y+Q8PD8NutzObINk14PTEsksvvRRAYmZAb2+vSj2wq6sLu3fvBpDIqhQWFpIFnZ+fjwULFtAw7ty5E7m5uUy9NzU1wWKxMCM0b948tLS0wGg0YuzYsdi7dy/XhXKkJAD84x//QG1tLe644w4ACSZ5T08PM11bt27F7NmzmVUZHh7GwYMHVVnV7OxsliuBhMOiTFFLRtblciE9PR0PPfQQALDzRc7bkSNHor29XTVKdmBggG1u50ur+0xcMJG08uEqU6xAYsGnpKSwwC9tNMragrKeo+xtk5+VxSJSlrIhjh49Cp1Oxz7qqqoqHDx4kGSDffv2oa2tTdXrqjTQO3bsYGrtw0LS20BiISlTzN3d3TwYnU4namtreXj19/erUrUGgwGFhYUkOhiNRlWbDXDaGxYPUb7OzMzkQQ2AAvnKkoFSetXtdrP2CyRqaMFgUKWXnJOTw2ejFAX4KJ7o4OAgf19az5Q1Z6vVSgPo9XoRCoV4CFVUVDDlX1FRAYPBoGq5EVlZuW6lt9zW1obbb7+d309OTkZtbS0PNDHScigEAgGYTCZyGHw+Hz/rySefxFVXXUWHITk5GYWFhVxry5cvx/bt22nUU1JSVINbBgcH8fzzz7MdcNasWfjd737Hlr1x48Zh+/btqh59uceMjAz86Ec/YlkGAO68807s3LkTQIKQ8+CDDzJ6EkKOcp8A4NqS0aDiGHk8Hg6xARLveMSIEUwrf+c736GmvMPhwJVXXkn95kAggJ/97GckZ06fPh3RaJRtbMeOHaMWgnw/EolQd7y2thZNTU2s6ff09GDNmjUsUblcLtZLm5ub8eqrr9JJD4fDSE1Nxde+9jUACaOQlJREA/RxjbQyCv0goRSlJKnNZlNxaSQoUXJKGhsbeR8LFy5Ea2srn/fIkSO59ySlL73K0WgU1157Lffu7t274XK5eKbpdDo0NDTQcRk/fjxOnjxJzlBpaSkOHDjA59/c3EziGwC88cYbGD16NGKxGDZu3Ai/389hHAUFBZg7dy77nquqqrBgwQL+rtynci3cfvvtJGYKf0Ceg91ux/Tp0xkdS4uaOCvz58/H008/DQD4xje+gZ6eHp6HFosFwWCQgYvf78fkyZN5Vng8HtTW1qoEns53FA1oLVgaNGjQoEHDBYvzku4+MxUqka14S2azGV6vl14pkPBQxauRIRjiPTU1NXGKE5Dw6iVCkM9R1h3i8Ti9Rr/fj9zcXFL6x44di7/+9a+q1qQ333yTnx0Oh5GUlMT0WF5eHioqKlT3p0x3v/POO6zHCETW8+jRo5g+fTreeOMNftbx48d5nw6HA11dXYzq/X4/9Ho9n50QvSQ6FrF4+X3luE9hvysnBw0NDbHEsHTpUqxfv16lxKQUFAmHwzCbzcxuyLhO8ULlPSoFRQQfZfhGZmamKhU4depU/q2TJ0/C4XCwRScjI4Myl8DZwzl0Oh3rc263m0xkuc7i4mK+Q4vFQvUsuY7k5GSyaN8LknIeO3YsPysrKwuFhYX09uvq6pCXl0ci2aOPPgq3283WwSuvvBILFy5k2laEa+T5FRcXw+12qwYpAFANOlCuveXLl+NPf/oTr/Hhhx8mYXLp0qXo6OhgBJOfn4/6+nqV2p3f71dlL5QlJ5vNhqKiIq7N4uJinDx5khGpx+Mhqa28vFwl65udnY14PM7Uu16vR1FRETNG3d3d6OrqYrSVk5OD+vp6VapX2jABULBCIqbU1FRGpP39/Rg7dqwqom1ubuZ6CAQCKC4uZhZPmTb+KFC2AX4QRAAEADNgst+ARKQteyAQCGDixIn44he/CCCxtsaMGcP9FovF+DwHBwcxNDSEJUuWAEiUD66++mpVGa21tZWR/KFDh2A2m7m/7rzzTlRWVpL9PXnyZBw+fJgSpc8//zx8Ph/321e/+lV0dHRgaGgILS0tKCoqwvPPPw8g0RY4cuRI/i232w2Px8ORppMnT1YNfRHlMdkD+/fvR2Fhoao+Pn78eN7L9u3bEY/HmY1cvnw5x1jm5uaiq6uLmcvs7GycPHmSGZju7m74fD5mLmXal9yXkl1/PnFe0t1yyEjaVupLskiGhoZgtVpVWs1KQ9vX1weDwcCalt1uP2sOqLJtS1LeQKLloqOjg3/L7/cjKyuLacD9+/ejuLiYh3JhYSEWLVrE+ahPPPEEjEYjWaxAwoApN5cSZxpoINGvByTScuXl5dyIZrMZo0aNYi1PCBpygAwPD6sUcUwmE50N4HT/qbInUflMlDUuMUKyyf/+97+fNRVJJgbJO0pLS1P1ECoPcen/lucci8U+FmM2MzOT5Y1IJKLqZRUJUEm3dnV1wWq18lozMzP5zMThUx7wo0eP5n2Xl5dj//79ZGv39vYiOzuba7O7uxuDg4P44x//CAD49re/fc7rlTrXmjVrSKiZNm0aenp6KFN45MgRVFRU8P2tWrUKdXV1LJeI4yDjIZ944gnU1dXx66ysLDz99NNkkz/55JPIycnh+5Y6nkBpoIHE/FyR1jxw4AB6e3tVh7zNZmPtsKamRsXAtlqtGBoaUim3VVRU0CFJSUlBOBxmn7Tb7aYDMG/ePOzbtw+XXHIJgIRzceDAAV5vRkYG3nnnHfbBx+NxLFq0iE5YQ0MDiouLWat/5513EI/H+cwHBwfhcrlUPd9i4EV+Uz67r68P6enp3HNAgrT1SbXbKPfL+32mpFLlHBIVLeFhpKamoqWlhWvCYrGgpaWFDt3YsWOxdOlSvr9jx46R65CcnIyuri6WstxuNzo7O+kEbdu2DSaTiS1x4XAY27Zt47Xs27cPubm5PF9vuukmFBUV0Wh3dHTg61//OssseXl5qKysxNDQEP7xj3/gm9/8JvfmzJkzcejQIabO29raUFlZSaO9cOFCbN68mYZUOiuUbYn19fU8R/v7+xEMBrnm58yZgx07drCDZmBggKp3Y8aMwf3330/SnGhMSKmqq6sLkUhEpVSpVMJUdgidT3wqkfTUqVOxfv16pKenY926dao+T4fDwfqyeJHK0YLAaW1medEyfEO8KZPJBLPZrJrzeWbtRw4T6e2VTRkMBmmogdPDPpR13dGjR/MwmzRpErxeLxdwKBTC/PnzKQAvhDMlPog4JkSGDRs2oKKigjWScePGYceOHcwQBAIB5OTkcFGFw2GVxrnIjiq999zcXJIqIpGIqg1K6ayI9rZ8bTabVYIgwWBQ1ejvdDrR3d1N46XX62no5Z04nU7+m7KnWv72PwMR5fikIOvBYDAgKSmJG7empga9vb0qwf6+vj7e5/Tp07Fjxw4SySoqKvDss89SF7mxsRGdnZ00SAMDA/TMv/rVr+KZZ57hOl+1ahVaWloYAZ8JGbP3gx/8AECCcHX33XfT829pacHatWu5Xs7lMHzQ2hPxi87OTng8HkbC/f39GBoaUnEMjEYj+57r6+tRUFDA9jLhP5x5/fJvfr+fTnZHRwfq6+t5X4ODg1i8eDFr82fKvLpcLlV/q8fjUY22PHHiBHw+H+bPnw8g4cR7PB5mhRYuXKhydJXzhkOhELKyslSkoK6uLr5vMZIfBcpMg9FofN81f2a90+12q7JkwrmQPVRXV0cCHZCIEn/xi1+QHHbixAnuZeHUyN6VudXyPsxmM06dOoVbbrkFQCLb+OKLL/KcMRqN+PrXv05Hp729HXq9nmfgiRMnYDAYaMQyMzO5TjZt2qTqyQ4EAujv76fz1tTUpCKjNjU1YdSoUVyHkUhEJXEKJMh+X/nKVwAkHJCWlhZ+xqJFi7Bz504K4lRUVPC5Hjt2DL/61a+4Js1mMzweDwOblpYWWK1Wfl9agmW/XghRNPAp1aS3bNmClStXIj09HaNGjcKKFSs+jT+j4X8wPqqBnjp1Kg2vqMRp0KBBw+cV7xtJP/PMMzh06BCZeVIvuPfee9/zA5csWYJHH32U9agzvwYSXqROp2NqNT09HR0dHfQE5d+VXr1SRUj65pSpVokEgYRnJx5xSUkJWltb6fUp5UjlWpxOp0owwufzUeYxFoupWgAkwhDv68SJE3jwwQfxi1/8AkBiAtasWbOQlJSEsWPH4qGHHsKaNWsAgClbSdvJ0Ar521/4whdw8OBB3r/UfSSt19fXh1gsxr8dDAbhdrsZFYoymrAwpf9RnpEyfS3pTbkvmUSkFHmQ1ia5byXbW9irEi0YDAZG5/Kczwd6e3txww03YMuWLdi4cSPWr1+P1atXs11ERmDKMzaZTEyhAeAwDomsxo0bh/b2dv5+dXU1nnzySZV06yuvvEIm7ciRIxkxfv/730dPTw/TjFOmTMHkyZMZhYu8qaz7VatW4ZFHHsH9998PIJHenjhxIp95WVkZLBYLI56+vj7ce++9zAIsXrwYv/nNb2AwGJCeno6tW7dySlVpaSk6OzvxwAMPAEjUFZXlEJkiJbXc9PR06HQ6tqNYLBZVGSEpKQnJycnMQEktX8naFbnSwcFB/OlPf2Lk/PLLL+PFF19kpJKdnQ29Xk+mMpCQdJTIxul0qhTlnE6nqpYZCARwySWX8PtKERYgEa3JMxKxH+XkI+Vz+DijV/V6PT9Hp9O972cp9x6QiF7dbjf3eigUQnZ2NhnZkuUQrkxXVxdcLhd++ctfAkiUDCTLkZaWhuLiYj7PtrY2LFy4kDV7l8uFhoYGjBs3DkDi+W/fvl3F6bjyyisZcfb19eHll19mZC6SvLLXx40bh8HBQcyaNQsulwt/+9vfyL6eMGECuy+AhIJYUlISFcT0ej0aGhooVCQts5Kqv+iii9Dd3c11lZ+fD5/Px593uVzYtWuXSjJYMom7d+/Gnj17mI3Iy8tDNBrltUh5T5TvpGQqmUol+/584n2N9LJly7By5UoqZNXU1OCGG27gFJ9zYcWKFbjsssvYZ5ySkoL6+nrm/ZUEMPlfGUOpTP8UFBSwxpKeng6Xy8XD7OjRo5ydC5wmOSnTvnLIikSkGA2bzUb5NyBxKGdnZzN9YrPZ4PP5WCecPn06fvOb37DO0dvby95aAHjppZeQlJSEt99+G0CCnLNmzRrMnj2bKUeZuzp27Fjk5+dzY8ViMWRmZvKgGzNmjKrH8OjRo+zTBRIL+OTJkzy8pEVDOR3IZDLxAFJO3ZF6ovJ7RqNRVTIQ/WaBso1KerblvmVqmbJvW6fTcQMo698fBStWrMANN9ygWnsrV64kQetceD8HUUkkTEpKUjk68XicNS2z2axqacrNzeXoP3lOSpLj0qVLsWrVKrZ9JCcns1xRWloKu93O52CxWJCTk8NnJCp4yqllkyZNwp133gkgcfCGw2EeaE6nE1OmTGFqffny5bDZbExxl5aWYs2aNRg1ahRaW1tx00038aBet24dnn76ada3//znP3MEKACuBTEyYnCUkrPiXMtzMhgMPNBEAUw5k1ju8w9/+APvFUgYkSVLlrBFS8QyxMgMDQ2pRrbW19djwoQJbGWTOdliNDIzM2G1Wvmcxo0bR0lLp9MJt9vNdOqRI0cwPDzM2nt9fb1qnX5cIy34sGtfjHIsFoPD4aAz4ff7kZqaynsUB1tSznl5eWhvb2c73q9+9Suu+4aGBpXG/9atW5GcnEwDn5KSgt27d/OcycjIwMSJEylX29jYiLa2NhXBqqWlhc/s5MmTcLlcDDquu+461NXV4eKLL8bYsWNx8OBBEsOKi4tRW1tLTsjs2bMxPDzM8sOCBQtw4sQJGtbS0lLU1dXxPD5+/DiWLVvGr2U0pZRCFy5ciH379tGZ3LJlC7kMooindLhbWlp4JqalpaGzs5PGWFp5P8vxpR8G70sc27BhA71hIau8n4EGEhGZsuYAgAckcJolqmRjy2xYefA9PT1ISkpik7nX60VVVRUNq7AixTNUkpUESnalUqxicHAQeXl5KoJGV1cXF2x9fT30ej2JYTabDePHj+dhN2nSJDidTpIoMjMzMWPGDPa6AqCHKvjRj34EILGglIMaQqEQampqeDD09/fDZrNxkel0OhiNRpUohHJAh5BT5N7y8vJQW1urioZlQRoMBuh0OhplkTYVxygYDCI5OZnfF6lWiSADgcBZYijA6Yg5PT0dAwMDZzlgHxWrV6/GypUrsWzZMowaNQqHDx9+XwMNJKIvYbgCidqc9BjLgSUMdXlGDocDSUlJJOTI2EvZ2F6vV1VfF210ue/m5ma88sordCb+/Oc/U99aiH/Hjh0DkCC5GI3GswRR5Fm99dZbePnll8mHuPLKK+FwOLB48WIAicO3ubmZ7/vXv/41/vGPf9ChfOqppzBu3DikpKSwBij160WLFuHZZ58lmcxkMiE1NZUOopAflSzakpISOqe7d+9WCeXodDrk5eWxvv7GG2+gra2NTp7oaQMJoqREPkAi4vd6vawhC0FN1ldJSQlOnjzJ+nlaWhqqq6t5HnR0dMDn89GgjR8/HpWVlazXnjx5kkYhGo2isbGRDn9GRgYMBgOjJxnOouxS+Kj4KE6p7COdTge/30/nz2azob29nedFMBiEwWBg/3FfXx8ikQj797/73e/SufvOd76DcDhMZ1Ov16Ovr48OVV9fH+x2Ox2Xqqoq7Nixgxmem266CX/729/4TAKBAKZPn85zLRAIwOl08kxcsmSJykERUR4g8S7D4TDXTW9vL6ZNm0aH7NixY/D5fFwLGzZsQFFREb8uKSlBTk4O68RZWVno7e3l75eUlGDOnDkc/BIKhdiPH41Gcemll/IM27VrF3w+Hz9LNBLkvV0oRLEz8YHs7k2bNmHJkiWYNm0aDfayZctUghyClStXwuv1qnSNAbyvOpcGDe+FFStWMNoSAf33wwc5iBo0aNDwecOHMtI33HADZsyYQQLYhg0b2Md2Lkj0AiSGBiijG9FeVUZaoVAIJpOJXn0oFMKxY8dUilXJyck8cKPRKIaHh5m6VdZFgcRhLcpJdrsdQ0NDzATISEzllKwbb7yRtTYZJCH9khs3boTL5SJ7sa2tDbm5ufTaf/WrX2HdunVMr02ePBk5OTl46aWXsHbtWpSVlTHSWbVqFYeBKK9bnBq/349AIECP1efzIRAIsN4i/aTKCV5KuUyppypbCJQ9gDk5OTRioVAIAwMDzF60tLTA6/XyGQvjXhlZeb1eKh5JZKocaqHsT/8kHLOysjJ4vV4cPHiQf++jOojKWrpEEkCCVSt1SeA0q1yeW2pqqqodxe1248CBA0xT1tfXo7KykmvV5/NxHaalpWHKlCmsxZaXl7N2rbw+4XzceOONOHHiBN/f7t27UVpaymipvb1dpbx1+eWXc5QhkEhLSv0bAK655hpGU8LhUNag+/v7+bXb7VYNYElPT0dfXx+HYkyZMgW1tbVM5VssFnR0dDBilfS3RFBtbW0cVTlhwgT2XQOJNKTZbOY+8Pv9GDVqFNOrd999N+68805qUVutVuTk5DC6am5uRmZmJqPQBQsWqCIoZb1USkqyjoeHhznhS56/sq3wfCElJUXV6y2Sn8oIX/l+LBYLLBYLr72pqYm8mFdffRU333wzW97Gjh2LoaEhtlzV1dVh9+7d/Oy2tjakpqbyjDt69Ch6e3u5xkU5TbJNkg1VquOZTCY0NjZi/fr1mDt3LtPwJpMJLpeL7zYUCql60WOxGEKhEM+4WbNmUWFQvg4Gg2Rv+/1+eL1etv7pdDocPHgQb731FoDEupZnWFJSwolgQOI8TEpKYlng1KlTqpHIn2Q3ySeJD2zBKioq4kaXA/D9DsotW7agt7cXS5YsQVlZGdatW4cDBw7gscceA5BIBZ88eVKVGpKXrhQjiUQifHhSj1HWb5SpWTEOytGGUj8ZGBhAeXk5yVqRSAQDAwN8kT09Pdi/fz/T1SNGjEBzczNTH1lZWfB6vUxvfu1rX8Pu3buZbmlubkZWVhbrxo8++igWLFjA1qyioiJulltvvRUzZ85kTbGxsREWi0WVnk5OTubmycjIgMfj4QHjdrvhcrnoEFitVkSjURI8ZD6wbORx48bxPnp7e1WtDXq9Hm63m5s8EAggEonwWoBEilQMhEwdk1SR1PbFmMnkqQ8aZfnPQGrKo0aNwvTp05mi/KCfP1dNWoyoxWJBamqq6vqMRiPXg9FohNfrZZpY+AmSgh4/fjyqq6tV86dDoRAPFaVgzEUXXYTRo0czTXvPPfcgLy8PP/zhDwGc7peXlGZ9fT28Xi979K+44gqUlZXRMdq/fz8efvhhfPWrXwWQ6HV++eWX+U62bt2KOXPm8PPffvttEniEFCN7SkQcRBpy5cqVMJlM/KyysjL09vbSeS0rK0MwGKSR7u7uxve+9z06GOI0K8mdkmZ0OByqUoscjOJAzZs3D++++y5T60ajEZdffjnLBF6vFyUlJVi3bh2ARA20vr6epTe32w2z2UwHpaioCNu2beP77e7u5gEcCASQmpqqSm2eqf1/PiCpVymzSFAi5YTm5mYMDg7SQU9JSeGoWwAqAZ65c+fi1KlTdLDmzZuHN998k+8uPT0dVVVVfN7d3d2q2eJA4pmLY5qenq46b2XEqVxrbm4uS2979+7FxRdfzPO3sbEReXl5fN7vvPMOpkyZwnXX2NiIUCjEdymT0yRrK7VvCRA7Ozvx6quvqsbWKseeyrOR6z5x4gTLRYcPH0ZycjLb76S1T35ehK8+qZLdJ4UP1SddU1ODzZs3nzXX9r0wdepUrF69GqmpqWf9nkS2wGltbhHaUBK/hD0MJBZBJBLhYWW1WnH8+HFGbcK+VdashSSxZMkSPPPMM4zuDxw4gGg0yhcwceJE1NbWcpO6XC60t7ez3uXz+TBixAgazvb2ds5fBRJGPS0tjVrA99xzD4aHh1mj3L9/PwlkBoMB7777LqM4n8+HxYsX0xDKQSfPIRQKwWKxMBJ/++23VSz2cDiMSCRCg9Lb2wu73c7nFo1G+YwmTZqEI0eO0BHq6+uDw+FgXaqyshKhUEg1AUbZgy01bSFViHavUudW1NgAqHqoPyqEqDhjxgySyD6qgygOntlsRlZWlqr/2+v1MiqLRqNITk7mfUYiEVgsFlWfvVIMw2KxkFQGqHkAJpMJ2dnZNBDTpk3Dk08+yXqxXIOstS996UsoLi5mFmj8+PEYHBxkxNnS0oIpU6aw9ijXJ+97wYIFKCsrY9RSVVVFQlxFRQW8Xi8Pz+7ubpSWlnLtSKZCard2ux3Nzc2qucvK3uKBgQFMnjyZ3Ay9Xg+n06niNAiz/Pjx4wiFQnQmhWshDN4zh78IYVH2SV5eHhoaGngtTqdTxSwfGhqC2Wzm2lVOPhJCqlI3QcRw5DpDodB5N9JnanWbzWakp6czus3Ly8P+/fu5TjMyMlibBxLrThzmSCSCeDzO3/3JT36CefPm8XlKRk0M/P79++F2u7ln9+7di6ysLK7jwsJC1RQsj8eDBQsWqEhrs2fPhsFgQHl5OfLy8lQDjAYGBujktrS04Oabb+a1HTlyBH//+9/58z/84Q9RVVVFXo7BYMCxY8dY7qqqqoJOp+OeECdVonzl+SndJnLGGY1GGAwGfrasBWV2SZylCwkfSnHM6/WyNvhhUFZWpmq50qDhn8UjjzyCzZs3o6ysDGVlZVi5ciWWLFnygaWWJUuWqBxEyeBo0KBBw+cRHxhJT506FVu2bDmr1vdRkZubC4PBoOpnC4fDZHwDCa9cajRAIn2hlKDMy8vDRRddRK9d2mp+//vfA0iwYKUftKamBoWFhfTapWYoNY7KykosW7aMXuE999yjml8qI/skJdLR0YF4PM6IUdqSpFfZaDSiqqqKkVgoFKIHW1JSghMnTtBDTU5OhtfrZerHbDYjLS2NrSQ9PT0oKSlhaj0zMxMul4tRT319PZxOJ59LcnIyrFYr22C+/vWv4/HHHweQqDPl5eWpphr5fD6mwGpra1WqTzIWU6ITiQYlAlEyzIFERGKxWFT3fb6jEyUkC+PxeGCxWJhZ0ev16Onp4XMYHh5GfX09o75Jkyahq6uL7yQSiWDGjBlMYe/cuROxWIyevTJFJv3KknXJyMjAyJEjVQ6v8pnde++9OHDgAB588EEA4MhJWbstLS3Izs6mylRycjKMRqOqx99gMDAistls+M53vgMgsYfWrl3LtKDVasX111/PNGNFRQUsFgsjs1gsRuU/+VoJadGS6C0ej2NwcJD7IhqN8swoLi7GiBEj2CpWWVmp6qM3Go0oKSnhZ5WXl8Nut7NufPz4ccyePZtRe1pamiriaWlpwfDwMN9xNBrlM+/s7GTGCTit9KWc9nYhRE5S91Vq5VutVpbpZMKcfL+jo0OljNfX18c17PP5mIUDEuvO7XbznLnkkkvwwx/+UCUvvH79en7Wtddei9raWrZkpaeno7S0lNLJzc3N+MY3vsGIdNu2bZgzZw6sViva29uxf/9+Pt+CggLk5+erxgz7fD5ceeWVqvuX9/PCCy8gLS2Na0euQYK+7du3Y//+/Tz7jx49qmodLCwsZNYzLS0NHo+He31gYAD19fU8y3Q6HSKRyAXx/t8PH9gnvXr1aqxYseJ9oxcNGjRo0PDJQWYViOG0Wq3IzMxkaSQlJQUHDhxQzT8QZy81NRWRSIR8gng8jtTUVDpBtbW1+OMf/0g9bJPJhLKyMtWoSmVvc319PTweDx306upq+P1+zJs3D8DpEmZaWhrC4TA2b95MZ9DpdMLlcjEwycnJwbXXXksOSEtLC6644gqW/F5++WVceumlLNkcPnwYAwMDvJfNmzfj1KlTrJeLIysp7WPHjqnESux2O41yamoqPB4PDbyM473QcV6mYGnQoEGDhrNx5lAaId/F43FkZGTQSBcWFmLPnj0qgqt00YiKomQT6urqVJyf5ORkJCUlMeN20003Yfbs2cx0iF6DUpTnueeeI7fG6/VScxtIdBHs3bsXsVgM3d3dKCoq4mS/wcFB5OTkkH9hNBqRlpZGI221WnHdddcxo9Pc3Ayz2UwuhcPhwO7du6mRsGfPHpXYyNixY9HQ0EDmf0dHh4p/otPpmGkKhUKqwT8Oh0PFmL+QMn9KaPOkNWjQoEGDhgsU52VUpQYNGjRoOBtnqidKOlaiQIkSDx06hIkTJ5JDkJKSwjbA4eFh9Pb2qmaDm81m1XjaMWPGkD+xd+9e/g5wtgqbXq/H1KlT2RL37W9/G3v37lV1NbS0tMBiseDNN9/Et771LVx99dUAEnVg5QStzMxMVFZWMk1vsViQnp7OdPWoUaNw5MgRyop2dHSgsbGRf9tsNiMcDjN139fXh8LCQvbn2+12VYdKMBhUzS9QtpgODAyoWrIuVGhGWoMGDRouABgMhrNSrmJgDAYDBgcHmdJOTU3Fzp07mZIeGhpSDeax2Ww0XA6HQzWqMhaLIT8/n3oLFRUVJK3KZ5lMJhpWEVkRxyESicDj8ZB8++KLLwJIpIsrKiqwfv16yixPmzYNo0ePZsviyZMn4Xa7cdNNNwFItGoqJUiBRJuVzEJob28nIRA4LYV88cUXA0hI70pbFpAgOsszPH78OMnHwGmimLKkcKEbaECrSWvQoEHD5wKiVQCc7iUXtrfD4aA2w6lTpzgICEhExoODgzRIEydOhNls5qCecDiMcePGscNlyZIlSE5OZmQdjUZVk8IKCwtx9OhRisvs27cPjz/+OPR6PR588EHodDoKiEyePBnXXHMNr6WiogIGg4HdEdFoFJWVleye6O7uxrvvvsuvHQ4HAoEABX8GBgZUUwhF6VCyBOnp6axZixa6cujPhdZ18mGg1aQ1aNCgQYOGCxRaJK1BgwYNFwCkV/q9MGLECKZvbTYbBgYG+PNGo5F1WtFRF3W5uro6lQyq3W6H0+lkOjwYDGLkyJGs16akpODiiy/G3LlzASQ05IPBIGVE169fj/r6ekav3d3deOKJJxCLxXD77bcjKSmJ7Gy32w2j0cge7dTUVJw6dYr34XK54PF4+PXw8DCCwSDZ4EAiPS/3IhO9lJoaeXl51JIIBAIqrQKHw8EMgmQhPm+RtGakNWjQoOECwPtJUup0OqSnp9OYSbpbSTSTum8sFkNqairFSmSOshjxcDiMxsZGGvjc3Fy0tbWphq1YLBb2E8sca2kHGzFiBE6ePMmUc39/P377298CSAxHkbG3QKK+bbfbVRr/Q0NDFCtxOBzo7OxUyQgHg0Fei8vlgsvl4vcNBgMKCgrYVmU2m9HZ2UlhLL1ez75oZYpensuFosf9z0AjjmnQoEHDBYAP0o2WaYHA6SFEYmil7gycJpkJY7q2thbFxcUYP348gITqXkZGBqeGyYAR+dv9/f2q2eFAInIXB6GnpwfhcJhfi7Mg0wmlZxlIkM6GhoZYk5YZ9qKiJ4xrqRvLnPMnnngCAPDSSy+hsbFRxco+evQoI/NQKIS+vj4a/dbWVpUKotFoVD2Xz1sUDWg1aQ0aNGjQoOGChRZJa9CgQcMFgEgk8p7qVzI3Xv5dJl0pf1e+jkQi8Pl8nAyVl5eH+vp6TqKyWq1YvHixqo47atQo6vrH43E4nU62fwWDQVWrksViOSvil1nlZrOZvdFAYl5Bf38/o9loNKqqj0ejUZhMJv58VlYW0tPT8eyzzwI4PStAIvGsrCzEYjHVaMqcnBxO6FJmI0KhkEqdLBqNUrv98wTNSGvQoEHDeYakYt8rHavX6xEKhd5z1vGZX8diMdZmGxoaYDAYaAgDgQB27drF1HlBQQFaW1tVo4BlJC8A5Ofno62tjT8fCARgNptZkw6Hw0xbO51O6mwDiXYwo9HI1Png4CDbqoDE7O+mpiZqdQ8ODqoGGs2aNQsbNmzg/Q0NDSESiahGx9bX19OYf9DAlM+bgQa0dLcGDRo0aNBwwUKLpDVo0KDhAsC5UrHCkg4GgzCZTGRs6/V6pm8BNSlKPkPZcqRU2jKZTPB4PIycZQCHpKEDgQBMJhPVzBoaGpCWlsbvp6enw+fzqT5Pp9PBaDQiJSUFwWCQhDa73a5KdxcXF8Pn83E4R3t7OxwOB8deyuhdScU3NjbC4XCQpNbZ2YlQKKRSKIvH4yoW9/tB+Zw+L0QyzUhr0KBBw3mGGN0zIcZN0t1ijM6sryrbiyS1LWnfaDSKlJQUVV04FApxBnpycjLi8Thr0iaTCUlJSRwHOXLkSAwMDFA2tLm5GU6nE1arFcDpvuxwOIyuri6YzWZeSygUQkZGBmvtLpcL3d3d1P0eMWIEQqEQp2J5vV6OlJRr6e/vV7WeKT9fHAVl7fn9oHzGnwcDDWhGWoMGDRrOO84lYpKWlkbClnxfqTsdj8dp/JQjGGOxmCrKBk4Tr5QQI+Xz+aDX6/k3IpEIhoeH+fs+nw8mk4m9zi6XCz6fT9WPHIlEYLfbUVBQoCKOhUIheL1eFBcXA0gM4ujv76fBt9lsjJqBRDtYR0cHr03ESZQZA6VB/jzWmP9ZaDVpDRo0aPiYmDp1KmpqatDb24tnnnnmfF+Ohv9B0Iy0Bg0aNHxMbNmyBStXrkR6ejpGjRqFFStW/FO/f66IUKJZ+U+i5nNB+T0Z1RiPx8/5uTqd7qwarlLBTH5PxFKEKe71euH1etHR0YHBwUEMDQ1haGiIUXwgEMDx48fh8/lgtVphtVqRmpoKt9uNlpYWtLS0IBAIwGAw8HdlEpeMlDQYDAgGg2S6SwRvMplgMpk+NynqTxKakdagQYOG/4dly5ahpqaGUXE8HseSJUve93eWLFmCuro6bNmyBQDw6KOPYuXKlR/7WmKxGMLhMMLhMGvO8p+SFKYkjMXjcQSDQVWqG1AbcRn3KBBiWSwW498RwytKYkajkX9Lfl7+i0ajqKqqwokTJxAOh+HxeNDc3Izm5mb09vaiqakJfr8ffr8fPT09CIVCcDqdcDqdvD8x6kJOk3sJhULw+/1n9Tzr9XqVo2Gz2VQKaf+ToGl3a9CgQcM5sGzZMtx3332YMWPG+/7cihUrcNlll+HGG28EkBhQUV9fT6nKjwKDwaAig+l0OlgsFgqMiKH+NGuyyrGYANgnLYZb+XPKmroy6heRE3kWkUgEqampHIMpfc//P0bIHxYacUyDBg0azkBRURFWr16N6dOnf+DPKo2OQIhRGjR8XGhGWoMGDRrOwPr167Fq1SrKTy5btgyPPvroWT+3cuVKeL3es6JmmdL0UXFmWltSv2emsT8NiHqX/O/w8DAsFgsj6WAwCKPRyJ5tg8HwntdlNpsRDAbJBI/FYvD7/fxaonKJvCWt/n4jOz9JfB5kQjUjrUGDBg0K/OhHP0Jvby9Wr17Nf9uwYQM2bNjwnr8j4hwAMGPGDNTV1X2sa5CasdKAKNusPq5hUdZ0z2zvikajsNlsZ02TEiMdiURYQwYSvdzKdjCj0YicnBwACbES4HRNfGhoCDqdjvXj/v5+VY+4xWJBJBKhLOiZrWM6nQ4mk+lD90V/EC50Aw1oRlqDBg0aiKlTp+K+++5DUVGR6t/fL5LesmULRo0ahalTp6KsrAwrV67E2rVrP9Z1vNfYyk/KqJxrtrKy/n1mL7KwrwEwihYjr9fr4XA4aGhjsRg6OzsBnO7Fls+T6FvuTchiErVLVK4UcVGy1EXU5b2clc+Litg/A81Ia9CgQcP/w3333Ye0tDQcOnSI/7Zq1aoPjKSXLFmC1atXIzU1FZs3b8Zjjz32WVyuhv8PoLG7NWjQoOECw/mMCKUnWaLfM9XLsrKyEAgEmO4eHh5WjZ+Mx+OckDU0NMToGThb/lSn08HpdFKLW6fTIT8/n1+Lspkyko5Go0hKSuLPK3W8/ydCM9IaNGjQoEHDBQpNzESDBg0aNGi4QKEZaQ0aNGjQoOEChWakNWjQoEGDhgsUmpHWoEGDBg0aLlBoRlqDBg0aNGi4QKEZaQ0aNGjQoOEChWakNWjQoEGDhgsUmpHWoEGDBg0aLlBoRlqDBg0aNGi4QKEZaQ0aNGjQoOEChWakNWjQoEGDhgsUmpHWoEGDBg0aLlBoRlqDBg0aNGi4QKEZaQ0aNGjQoOEChWakNWjQoEGDhgsUmpHWoEGDBg0aLlBoRlqDBg0aNGi4QKEZaQ0aNGjQoOEChWakNWjQoEGDhgsUmpHWoEGDBg0aLlBoRlqDBg0aNGi4QPF/AY3yX+0G0x8xAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/models_33_1.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "model.fit(data_train[0])\n", - "predicted = model.predict(data_test[1])\n", - "plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);\n", - "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "a00ed146", - "metadata": {}, - "source": [ - "## Next steps: image registration\n", - "\n", - "Once we have our model factory readily available, it will be easy to generate predictions that we can use for reference in image registration." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "md:myst", - "text_representation": { - "extension": ".md", - "format_name": "myst" - } - }, - "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": "3.7.10" - }, - "source_map": [ - 11, - 15, - 21, - 39, - 43, - 56, - 83, - 87, - 92, - 96, - 99, - 103, - 106, - 110, - 112, - 120, - 137, - 141, - 160, - 168, - 178, - 185, - 204, - 211, - 221, - 227, - 230, - 234, - 236, - 240, - 242, - 250, - 258, - 262, - 267 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sources/tutorial/registration.ipynb b/_sources/tutorial/registration.ipynb deleted file mode 100644 index f389ea1..0000000 --- a/_sources/tutorial/registration.ipynb +++ /dev/null @@ -1,7852 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "825417bd", - "metadata": {}, - "source": [ - "# Image registration (spatial alignment)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af12e340", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "from IPython.display import HTML\n", - "import requests\n", - "from tempfile import mkstemp\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import nibabel as nb\n", - "\n", - "from eddymotion.dmri import DWI\n", - "from eddymotion.viz import plot_dwi\n", - "from eddymotion.estimator import _advanced_clip\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "url = \"https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2\"\n", - "datapath = Path(mkstemp(suffix=\".h5\")[1])\n", - "if datapath.stat().st_size == 0:\n", - " datapath.write_bytes(\n", - " requests.get(url, allow_redirects=True).content\n", - " )\n", - "\n", - "dmri_dataset = DWI.from_filename(datapath)\n", - "datapath.unlink()\n", - "\n", - "\n", - "def _to_nifti(\n", - " data, affine, filename, clip=True\n", - "):\n", - " data = np.squeeze(data)\n", - " if clip:\n", - " data = _advanced_clip(data)\n", - " nb.Nifti1Image(\n", - " data,\n", - " affine,\n", - " None,\n", - " ).to_filename(filename)" - ] - }, - { - "cell_type": "markdown", - "id": "18dd4f85", - "metadata": {}, - "source": [ - "At this point of the tutorial we have covered two of the three initial requirements:\n", - "\n", - "* we have a powerful data structure to access our dMRI dataset with agility, and\n", - "* we have a reliable (thanks to DIPY!) model factory to generate *motion-less* references.\n", - "\n", - "Therefore, we are only one step away from our goal - aligning any given DW map with the *motion-less* reference.\n", - "The estimation of the ***spatial transform*** that brings two maps into alignment is called ***image registration***.\n", - "\n", - "**Image registration** is therefore the process through which we bring the structural features of two images into alignment.\n", - "This means that, brain sulci and gyri, the ventricles, subcortical structures, etc. are located exactly at the same place in the two images.\n", - "That allows, for instance, for **image fusion**, and hence screening both images together (for example, applying some transparency to the one on top) should not give us the perception that they *are not aligned*.\n", - "\n", - "## ANTs - Advanced Normalization ToolS\n", - "\n", - "The [ANTs toolbox](http://stnava.github.io/ANTs/) is widely recognized as a powerful image registration (and *normalization*, which is registration to some standard space) framework.\n", - "\n", - "The output of an image registration process is the *estimated transform* that brings the information in the two images into alignment.\n", - "In our case, the head-motion is a rigid-body displacement of the head.\n", - "Therefore, a very simple (*linear*) model --an affine $4\\times 4$ matrix-- can be used to formalize the *estimated transforms*.\n", - "\n", - "Only very recently, [ANTs offers a Python interface](https://doi.org/10.1101/2020.10.19.20215392) to run their tools.\n", - "For this reason, we will use the very much consolidated [*Nipype* wrapping of the ANTs' command-line interface](https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.ants.html#registration).\n", - "The code is *almost* as simple as follows:\n", - "\n", - "```python\n", - "from nipype.interfaces.ants import Registration\n", - "\n", - "registration_framework = Registration(\n", - " fixed_image=\"reference.nii.gz\",\n", - " moving_image=\"left-out-gradient.nii.gz\",\n", - " from_file=\"settings-file.json\"\n", - ")\n", - "```\n", - "\n", - "At the minimum, we need to establish our registration framework using the *fixed* (our synthetic, motion-less reference) and the *moving* (the left-out gradient) images.\n", - "We can *easily* configure registration by creating a `settings-file.json` that may look like the following:\n", - "\n", - "```JSON\n", - "{\n", - " \"collapse_output_transforms\": true,\n", - " \"convergence_threshold\": [ 1E-5, 1E-6 ],\n", - " \"convergence_window_size\": [ 5, 2 ],\n", - " \"dimension\": 3,\n", - " \"initialize_transforms_per_stage\": false,\n", - " \"interpolation\": \"BSpline\",\n", - " \"metric\": [ \"Mattes\", \"Mattes\" ],\n", - " \"metric_weight\": [ 1.0, 1.0 ],\n", - " \"number_of_iterations\": [\n", - " [ 100, 50, 0 ],\n", - " [ 10 ]\n", - " ],\n", - " \"radius_or_number_of_bins\": [ 32, 32 ],\n", - " \"sampling_percentage\": [ 0.05, 0.1 ],\n", - " \"sampling_strategy\": [ \"Regular\", \"Random\" ],\n", - " \"shrink_factors\": [\n", - " [ 2, 2, 1 ],\n", - " [ 1 ]\n", - " ],\n", - " \"sigma_units\": [ \"vox\", \"vox\" ],\n", - " \"smoothing_sigmas\": [\n", - " [ 4.0, 2.0, 0.0 ],\n", - " [ 0.0 ]\n", - " ],\n", - " \"transform_parameters\": [\n", - " [ 0.01 ],\n", - " [ 0.01 ]\n", - " ],\n", - " \"transforms\": [ \"Rigid\", \"Rigid\" ],\n", - " \"use_estimate_learning_rate_once\": [ false, true ],\n", - " \"use_histogram_matching\": [ true, true ],\n", - " \"verbose\": true,\n", - " \"winsorize_lower_quantile\": 0.0001,\n", - " \"winsorize_upper_quantile\": 0.9998,\n", - " \"write_composite_transform\": false\n", - "}\n", - "```\n", - "\n", - "Yes, configuring image registration is definitely not *straightforward*.\n", - "The most relevant piece of settings to highlight is the `\"transforms\"` key, where we can observe we will be using a `\"Rigid\"` transform model.\n", - "\n", - "## Example registration\n", - "\n", - "It is beyond the scope of this tutorial to understand ANTs and/or image registration altogether, but let's have a look at how registration is integrated.\n", - "First, we'll need to generate one target gradient prediction following all the steps learned previously.\n", - "For this example, we have selected the 8th DW map (`index=7`) because it contains a sudden motion spike, resembling a nodding movement." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ac920c9f", - "metadata": {}, - "outputs": [], - "source": [ - "from eddymotion.model import ModelFactory\n", - "\n", - "data_train, data_test = dmri_dataset.logo_split(7, with_b0=True)\n", - "\n", - "model = ModelFactory.init(\n", - " gtab=data_train[1],\n", - " model=\"DTI\",\n", - " S0=dmri_dataset.bzero,\n", - ")\n", - "model.fit(data_train[0])\n", - "predicted = model.predict(data_test[1])" - ] - }, - { - "cell_type": "markdown", - "id": "2569eb23", - "metadata": {}, - "source": [ - "Since we are using the command-line interface of ANTs, the software must be installed in the computer and the input data is provided via files in the filesystem.\n", - "Let's write out two NIfTI files in a temporary folder:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "35cc633e", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from tempfile import mkdtemp\n", - "\n", - "tempdir = Path(mkdtemp())\n", - "\n", - "# The fixed image is our prediction\n", - "fixed_path = tempdir / \"fixed.nii.gz\"\n", - "_to_nifti(predicted, dmri_dataset.affine, fixed_path)\n", - "\n", - "# The moving image is the left-out DW map\n", - "moving_path = tempdir / \"moving.nii.gz\"\n", - "_to_nifti(data_test[0], dmri_dataset.affine, moving_path)" - ] - }, - { - "cell_type": "markdown", - "id": "c8ea3083", - "metadata": {}, - "source": [ - "We can now visualize our reference (the prediction) and the actual DW map.\n", - "Please notice the subtle *nodding* of the head, perhaps more apparent when looking at the corpus callosum in the sagittal views:" - 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "filenames": { - "image/svg+xml": "/home/runner/work/nipreps-book/nipreps-book/docs/_build/jupyter_execute/tutorial/registration_7_0.svg" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from niworkflows.viz.notebook import display\n", - "\n", - "display(\n", - " fixed_path,\n", - " moving_path,\n", - " fixed_label=\"Predicted\",\n", - " moving_label=\"Left-out gradient\",\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c8095cab", - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(\"\"\"\"\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "caa87916", - "metadata": {}, - "source": [ - "Let's configure ANTs via NiPype:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "57302190", - "metadata": {}, - "outputs": [], - "source": [ - "from os import cpu_count\n", - "from pkg_resources import resource_filename as pkg_fn\n", - "from nipype.interfaces.ants.registration import Registration\n", - "\n", - "registration = Registration(\n", - " terminal_output=\"file\",\n", - " from_file=pkg_fn(\n", - " \"eddymotion\",\n", - " f\"config/dwi-to-dwi_level1.json\",\n", - " ),\n", - " fixed_image=str(fixed_path.absolute()),\n", - " moving_image=str(moving_path.absolute()),\n", - ")\n", - "registration.inputs.output_warped_image = True\n", - "registration.inputs.num_threads = cpu_count()" - ] - }, - { - "cell_type": "markdown", - "id": "de998c20", - "metadata": {}, - "source": [ - "which will run the following command-line:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7cb7827b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initialize-transforms-per-stage 0 --interpolation BSpline --output [ transform, transform_Warped.nii.gz ] --transform Rigid[ 0.01 ] --metric GC[ /tmp/tmpoqu912td/fixed.nii.gz, /tmp/tmpoqu912td/moving.nii.gz, 1, 32, Regular, 0.05 ] --convergence [ 100x50x10, 1e-06, 5 ] --smoothing-sigmas 4.0x2.0x2.0vox --shrink-factors 4x2x1 --use-estimate-learning-rate-once 0 --use-histogram-matching 1 --transform Affine[ 0.001 ] --metric GC[ /tmp/tmpoqu912td/fixed.nii.gz, /tmp/tmpoqu912td/moving.nii.gz, 1, 32, Random, 0.1 ] --convergence [ 50, 1e-07, 2 ] --smoothing-sigmas 0.0vox --shrink-factors 1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 -v --winsorize-image-intensities [ 0.0001, 0.9998 ] --write-composite-transform 0'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "registration.cmdline" - ] - }, - { - "cell_type": "markdown", - "id": "077b5fcd", - "metadata": {}, - "source": [ - "Nipype interfaces can be submitted for execution with the `run()` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9bb6ce2c", - "metadata": {}, - "outputs": [], - "source": [ - "result = registration.run(cwd=str(tempdir.absolute()))" - ] - }, - { - "cell_type": "markdown", - "id": "cf151722", - "metadata": {}, - "source": [ - "If everything worked out, we can now retrieve the aligned file with the output `result.outputs.warped_image`.\n", - "We can now visualize how close (or far) the two images are:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d9eefbf", - "metadata": { - "tags": [ - "remove-output" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - 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"id": "cd35b58a", - "metadata": {}, - "source": [ - "## Resampling an image\n", - "\n", - "Once we have estimated what is the *transform* that brings two images into alignment, we can *bring* the data in the *moving* image and *move this image* into the *reference*'s grid through *resampling*.\n", - "\n", - "The process works as follows:\n", - "\n", - "![nitransforms](https://raw.githubusercontent.com/poldracklab/nitransforms/master/docs/_static/figure1-joss.png)\n", - "\n", - "[*NiTransforms*](https://doi.org/10.1109/ISBI45749.2020.9098466) ([open-access pre-print here](https://doi.org/10.31219/osf.io/8aq7b)) is an ongoing project to bring a compatibility layer into *NiBabel* between the many transform file formats generated by neuroimaging packages.\n", - "We will be using *NiTransforms* to *apply* these transforms we estimate with ANTs -- effectively *resampling* moving images into their reference's grid.\n", - "\n", - "To read a transform produced by ANTs with *NiTransforms*, we use the following piece of code:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "80cb9be9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.99606636, -0.00534955, 0.00189532, 0.14150404],\n", - " [ 0.00441187, 0.9964563 , -0.01533533, -0.2574495 ],\n", - " [-0.00354967, 0.02485858, 0.99336894, -0.91179065],\n", - " [ 0. , 0. , 0. , 1. ]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import nitransforms as nt\n", - "\n", - "itk_xform = nt.io.itk.ITKLinearTransform.from_filename(result.outputs.forward_transforms[0])\n", - "matrix = itk_xform.to_ras(reference=fixed_path, moving=moving_path)\n", - "matrix" - ] - }, - { - "cell_type": "markdown", - "id": "44b8c7e6", - "metadata": {}, - "source": [ - "Resampling an image requires two pieces of information: the *reference* image (which provides the new grid where we want to have the data) and the *moving* image which contains the actual data we are interested in:" - 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}, - "source_map": [ - 11, - 15, - 55, - 143, - 155, - 160, - 173, - 178, - 191, - 195, - 199, - 215, - 219, - 221, - 225, - 227, - 232, - 243, - 247, - 262, - 268, - 272, - 288, - 292, - 301, - 312 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sources/tutorial/solution.ipynb b/_sources/tutorial/solution.ipynb deleted file mode 100644 index 17f3551..0000000 --- a/_sources/tutorial/solution.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6f77191d", - "metadata": {}, - "source": [ - "# Putting everything together" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4f2fc29c", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from tempfile import mkstemp\n", - "from pathlib import Path\n", - "import requests\n", - "\n", - "from eddymotion.dmri import DWI\n", - "\n", - "url = \"https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2\"\n", - "datapath = Path(mkstemp(suffix=\".h5\")[1])\n", - "if datapath.stat().st_size == 0:\n", - " datapath.write_bytes(\n", - " requests.get(url, allow_redirects=True).content\n", - " )\n", - "\n", - "dmri_dataset = DWI.from_filename(datapath)\n", - "dmri_dataset.dataobj = dmri_dataset.dataobj[..., :32]\n", - "dmri_dataset.gradients = dmri_dataset.gradients[..., :32]\n", - "datapath.unlink()" - ] - }, - { - "cell_type": "markdown", - "id": "e34e0eae", - "metadata": {}, - "source": [ - "Once we have finalized the main components of the solution, it is time for integration.\n", - "We now want to iterate over all the *LOGO* partitions of the dataset, generate a synthetic reference through the model of choice, and finally estimate the misalignment between the left-out gradient and the synthetic reference.\n", - "This solution, must also abide by the API we have envisioned.\n", - "\n", - "```{admonition} Exercise\n", - "Complete the code snipet below to integrate the different components into the final solution to the dMRI head-motion problem.\n", - "```\n", - "\n", - "```python\n", - "class EddyMotionEstimator:\n", - " \"\"\"Estimates rigid-body head-motion and distortions derived from eddy-currents.\"\"\"\n", - "\n", - " @staticmethod\n", - " def fit(\n", - " dwdata,\n", - " *,\n", - " n_iter=1,\n", - " align_kwargs=None,\n", - " model=\"b0\",\n", - " **kwargs,\n", - " ):\n", - " r\"\"\"\n", - " Estimate head-motion and Eddy currents.\n", - "\n", - " \n", - "\n", - " \"\"\"\n", - " align_kwargs = align_kwargs or {}\n", - "\n", - " if dwdata.brainmask is not None:\n", - " kwargs[\"mask\"] = dwdata.brainmask\n", - "\n", - " kwargs[\"S0\"] = dwdata.bzero\n", - "\n", - " for i_iter in range(1, n_iter + 1):\n", - " for i in np.arange(len(dwdata)):\n", - " # run a original-to-synthetic affine registration\n", - " with TemporaryDirectory() as tmpdir:\n", - " # Invoke `dwdata.logo_split()` on an index.\n", - " data_train, data_test = ...\n", - "\n", - " # Factory creates the appropriate model and pipes arguments\n", - " dwmodel = ...\n", - "\n", - " # fit the model\n", - "\n", - "\n", - " # generate a synthetic dw volume for the test gradient\n", - " predicted = ...\n", - "\n", - " # Write arrays in memory to harddisk as NIfTI files\n", - " tmpdir = Path(tmpdir)\n", - " moving = tmpdir / \"moving.nii.gz\"\n", - " fixed = tmpdir / \"fixed.nii.gz\"\n", - " _to_nifti(data_test[0], moving)\n", - " _to_nifti(predicted, fixed)\n", - "\n", - " # Prepare ANTs' antsRegistration via NiPype\n", - " registration = Registration(\n", - " fixed_image=str(fixed.absolute()),\n", - " moving_image=str(moving.absolute()),\n", - " **align_kwargs,\n", - " )\n", - "\n", - " # execute ants command line\n", - " result = registration.run(cwd=str(tmpdir)).outputs\n", - "\n", - " # read output transform\n", - " xform = nt.io.itk.ITKLinearTransform.from_filename(\n", - " result.forward_transforms[0]\n", - " )\n", - "\n", - " # update\n", - " dwdata.set_transform(i, xform)\n", - "\n", - " return dwdata.em_affines\n", - "```\n", - "\n", - "**Solution**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a56b3be1", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "class EddyMotionEstimator:\n", - " \"\"\"Estimates rigid-body head-motion and distortions derived from eddy-currents.\"\"\"\n", - "\n", - " @staticmethod\n", - " def fit(\n", - " dwdata,\n", - " *,\n", - " n_iter=1,\n", - " align_kwargs=None,\n", - " model=\"b0\",\n", - " **kwargs,\n", - " ):\n", - " r\"\"\"\n", - " Estimate head-motion and Eddy currents.\n", - "\n", - " Parameters\n", - " ----------\n", - " dwdata : :obj:`~eddymotion.dmri.DWI`\n", - " The target DWI dataset, represented by this tool's internal\n", - " type. The object is used in-place, and will contain the estimated\n", - " parameters in its ``em_affines`` property, as well as the rotated\n", - " *b*-vectors within its ``gradients`` property.\n", - " n_iter : :obj:`int`\n", - " Number of iterations this particular model is going to be repeated.\n", - " align_kwargs : :obj:`dict`\n", - " Parameters to configure the image registration process.\n", - " model : :obj:`str`\n", - " Selects the diffusion model that will generate the registration target\n", - " corresponding to each gradient map.\n", - " See :obj:`~eddymotion.model.ModelFactory` for allowed models (and corresponding\n", - " keywords).\n", - "\n", - " Return\n", - " ------\n", - " affines : :obj:`list` of :obj:`numpy.ndarray`\n", - " A list of :math:`4 \\times 4` affine matrices encoding the estimated\n", - " parameters of the deformations caused by head-motion and eddy-currents.\n", - "\n", - " \"\"\"\n", - " align_kwargs = align_kwargs or {}\n", - "\n", - " if dwdata.brainmask is not None:\n", - " kwargs[\"mask\"] = dwdata.brainmask\n", - "\n", - " kwargs[\"S0\"] = dwdata.bzero\n", - "\n", - " for i_iter in range(1, n_iter + 1):\n", - " for i in np.arange(len(dwdata)):\n", - " # run a original-to-synthetic affine registration\n", - " with TemporaryDirectory() as tmpdir:\n", - " # Invoke `dwdata.logo_split()` on an index.\n", - " data_train, data_test = dwdata.logo_split(i, with_b0=True)\n", - "\n", - " # Factory creates the appropriate model and pipes arguments\n", - " dwmodel = ModelFactory.init(\n", - " gtab=data_train[1], model=model, **kwargs\n", - " )\n", - "\n", - " # fit the model\n", - " dwmodel.fit(data_train[0])\n", - "\n", - " # generate a synthetic dw volume for the test gradient\n", - " predicted = dwmodel.predict(data_test[1])\n", - "\n", - " # Write arrays in memory to harddisk as NIfTI files\n", - " tmpdir = Path(tmpdir)\n", - " moving = tmpdir / \"moving.nii.gz\"\n", - " fixed = tmpdir / \"fixed.nii.gz\"\n", - " _to_nifti(data_test[0], moving)\n", - " _to_nifti(predicted, fixed)\n", - "\n", - " # Prepare ANTs' antsRegistration via NiPype\n", - " registration = Registration(\n", - " fixed_image=str(fixed.absolute()),\n", - " moving_image=str(moving.absolute()),\n", - " **align_kwargs,\n", - " )\n", - "\n", - " # execute ants command line\n", - " result = registration.run(cwd=str(tmpdir)).outputs\n", - "\n", - " # read output transform\n", - " xform = nt.io.itk.ITKLinearTransform.from_filename(\n", - " result.forward_transforms[0]\n", - " )\n", - "\n", - " # update\n", - " dwdata.set_transform(i, xform)\n", - "\n", - " return dwdata.em_affines" - ] - }, - { - "cell_type": "markdown", - "id": "74332836", - "metadata": {}, - "source": [ - "The above code allows us to use our estimator as follows:\n", - "\n", - "```python\n", - "from eddymotion.estimator import EddyMotionEstimator\n", - "\n", - "estimated_affines = EddyMotionEstimator.fit(dmri_dataset, model=\"b0\")\n", - "```\n", - "\n", - "## What's next? - Testing!\n", - "\n", - "Once we have our first implementation functional, we should think of some unit-tests for our code.\n", - "\n", - "```{admonition} Exercise\n", - "Write a unit test for the `TrivialB0Model`.\n", - "This test would just make sure that, regardless of the particular partition of the input dataset, a *b=0* map is always returned.\n", - "```\n", - "\n", - "**Solution**: in this solution, we are using `pytest` to integrate the test into a higher-level test suite." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2abb297e", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pytest\n", - "\n", - "@pytest.mark.parametrize(\"split_index\", list(range(30)))\n", - "def test_TrivialB0Model(split_index, dmri_dataset):\n", - " model = TrivialB0Model(\n", - " dmri_dataset.gradients,\n", - " S0=dmri_dataset.bzero,\n", - " )\n", - " data_train, data_test = dmri_dataset.logo_split(split_index)\n", - " model.fit(data_train[0])\n", - " predicted = model.predict(data_test[1])\n", - "\n", - " assert np.all(dmri_dataset.bzero == predicted)" - ] - }, - { - "cell_type": "markdown", - "id": "51f1cd05", - "metadata": {}, - "source": [ - "## And after testing? - Validation!\n", - "\n", - "Once we have a sufficient portion of our code *covered* by unit-tests, then we would add some *integration* tests that give us confidence that our bullet-proof individual components also work together.\n", - "Only after we have both steps secure, we can run benchmarks and evaluations from which we learn whether our solution works, and characterize its limitations.\n", - "\n", - "The main strategy to validate this software would entail finding/acquiring a special dataset where motion is not present or extremely low, in which we *introduce* a known head-motion pattern with which we are going to challenge our estimator.\n", - "Some ideas to achieve this are:\n", - "\n", - "- a dataset acquired with special sequences that can do prospective motion correction, or\n", - "- a dataset that has been acquired under very controlled settings, with an extremely collaborative participant who was also wearing a personalized mold, or\n", - "- a fully synthetic dataset such as the Fiber Box, or\n", - "- a fully synthetic dataset containing a repeated *b=0* image (this evaluation would be limited to work with the `TrivialB0Model`, for instance).\n", - "\n", - "***Please head to [the GitHub repository](https://github.com/nipreps/EddyMotionCorrection) and share your ideas with us! We are welcoming new contributors!***" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "md:myst", - "text_representation": { - "extension": ".md", - "format_name": "myst" - } - }, - "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": "3.7.10" - }, - "source_map": [ - 11, - 15, - 39, - 121, - 214, - 235, - 252 - ] - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css new file mode 100644 index 0000000..eb19f69 --- /dev/null +++ b/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css @@ -0,0 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label.previousElementSibling.checked = true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/__init__.py b/_static/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/_static/__pycache__/__init__.cpython-37.pyc b/_static/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index 8c837fc..0000000 Binary files a/_static/__pycache__/__init__.cpython-37.pyc and /dev/null differ diff --git a/_static/basic.css b/_static/basic.css index 5d8ae08..2af6139 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -130,7 +130,7 @@ ul.search li a { font-weight: bold; } -ul.search li div.context { +ul.search li p.context { color: #888; margin: 2px 0 0 30px; text-align: left; @@ -222,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 450px; + min-width: 360px; max-width: 800px; } @@ -237,14 +237,8 @@ a.headerlink { visibility: hidden; } -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; +a:visited { + color: #551A8B; } h1:hover > a.headerlink, @@ -335,12 +329,16 @@ 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; @@ -379,6 +377,8 @@ div.body p.centered { 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; @@ -386,6 +386,8 @@ div.admonition > :last-child { div.sidebar::after, aside.sidebar::after, +nav.contents::after, +aside.topic::after, div.topic::after, div.admonition::after, blockquote::after { @@ -428,10 +430,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -508,6 +506,63 @@ table.hlist td { vertical-align: top; } +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + /* -- other body styles ----------------------------------------------------- */ @@ -558,19 +613,26 @@ ul.simple p { margin-bottom: 0; } -dl.footnote > dt, -dl.citation > dt { +aside.footnote > span, +div.citation > span { float: left; - margin-right: 0.5em; } - -dl.footnote > dd, -dl.citation > dd { +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { margin-bottom: 0em; } - -dl.footnote > dd:after, -dl.citation > dd:after { +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { content: ""; clear: both; } @@ -587,10 +649,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -616,6 +674,16 @@ 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; @@ -634,14 +702,6 @@ dl.glossary dt { font-size: 1.1em; } -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - .versionmodified { font-style: italic; } @@ -682,8 +742,9 @@ dl.glossary dt { .classifier:before { font-style: normal; - margin: 0.5em; + margin: 0 0.5em; content: ":"; + display: inline-block; } abbr, acronym { @@ -691,6 +752,14 @@ 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 { @@ -707,6 +776,7 @@ span.pre { -ms-hyphens: none; -webkit-hyphens: none; hyphens: none; + white-space: nowrap; } div[class*="highlight-"] { @@ -770,8 +840,12 @@ div.code-block-caption code { table.highlighttable td.linenos, span.linenos, -div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ } div.code-block-caption span.caption-number { @@ -786,16 +860,6 @@ div.literal-block-wrapper { margin: 1em 0; } -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; -} - code.xref, a code { background-color: transparent; font-weight: bold; diff --git a/_static/check-solid.svg b/_static/check-solid.svg new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js index 02c549e..54b3c46 100644 --- a/_static/clipboard.min.js +++ b/_static/clipboard.min.js @@ -1,7 +1,7 @@ /*! - * clipboard.js v2.0.4 - * https://zenorocha.github.io/clipboard.js - * + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * * Licensed MIT © Zeno Rocha */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return 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t},i=function(){function o(t,e){for(var n=0;n + diff --git a/_static/copybutton.css b/_static/copybutton.css index 75b17a8..f1916ec 100644 --- a/_static/copybutton.css +++ b/_static/copybutton.css @@ -1,30 +1,51 @@ /* Copy buttons */ -a.copybtn { +button.copybtn { position: absolute; - top: .2em; - right: .2em; - width: 1em; - height: 1em; - opacity: .3; - transition: opacity 0.5s; - border: none; + display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; } div.highlight { position: relative; } -a.copybtn > img { - vertical-align: top; - margin: 0; - top: 0; - left: 0; - position: absolute; +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; } -.highlight:hover .copybtn { - opacity: 1; +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); } /** @@ -44,11 +65,10 @@ a.copybtn > img { visibility: hidden; position: absolute; content: attr(data-tooltip); - padding: 2px; - top: 0; + padding: .2em; + font-size: .8em; left: -.2em; background: grey; - font-size: 1rem; color: white; white-space: nowrap; z-index: 2; @@ -65,3 +85,10 @@ a.copybtn > img { transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); transition-delay: .5s; } + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js index 65a5916..2ea7ff3 100644 --- a/_static/copybutton.js +++ b/_static/copybutton.js @@ -17,6 +17,30 @@ const messages = { 'copy_to_clipboard': 'In die Zwischenablage kopieren', 'copy_success': 'Kopiert!', 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', } } @@ -26,6 +50,31 @@ if( document.documentElement.lang !== undefined locale = document.documentElement.lang } +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + /** * Set up copy/paste for code blocks */ @@ -53,11 +102,25 @@ const clearSelection = () => { } } -// Changes tooltip text for two seconds, then changes it back -const temporarilyChangeTooltip = (el, newText) => { - const oldText = el.getAttribute('data-tooltip') +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) } const addCopyButtonToCodeCells = () => { @@ -69,16 +132,16 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) - const pre_bg = getComputedStyle(codeCell).backgroundColor; const clipboardButton = id => - ` - ${messages[locale]['copy_to_clipboard']} - ` + `` codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) }) @@ -86,13 +149,32 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) { - +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { var regexp; var match; + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + // create regexp to capture prompt and remaining line if (isRegexp) { regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') @@ -102,24 +184,31 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl const outputLines = []; var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; for (const line of textContent.split('\n')) { match = line.match(regexp) - if (match) { - promptFound = true - if (removePrompts) { + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { outputLines.push(match[2]) } else { outputLines.push(line) } - } else { - if (!onlyCopyPromptLines) { - outputLines.push(line) - } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) } } // If no lines with the prompt were found then just use original lines - if (promptFound) { + if (lineGotPrompt.some(v => v === true)) { textContent = outputLines.join('\n'); } @@ -133,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '', false, true, true) + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy @@ -142,11 +236,12 @@ var copyTargetText = (trigger) => { // Update UI with error/success messages clipboard.on('success', event => { clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy_success']) + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) }) clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy_failure']) + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) }) } diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js index 57caa55..dbe1aaa 100644 --- a/_static/copybutton_funcs.js +++ b/_static/copybutton_funcs.js @@ -2,13 +2,32 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) { - +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { var regexp; var match; + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + // create regexp to capture prompt and remaining line if (isRegexp) { regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') @@ -18,24 +37,31 @@ export function formatCopyText(textContent, copybuttonPromptText, isRegexp = fal const outputLines = []; var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; for (const line of textContent.split('\n')) { match = line.match(regexp) - if (match) { - promptFound = true - if (removePrompts) { + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { outputLines.push(match[2]) } else { outputLines.push(line) } - } else { - if (!onlyCopyPromptLines) { - outputLines.push(line) - } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) } } // If no lines with the prompt were found then just use original lines - if (promptFound) { + if (lineGotPrompt.some(v => v === true)) { textContent = outputLines.join('\n'); } diff --git a/_static/css/index.c5995385ac14fb8791e8eb36b4908be2.css 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solid;border-left-color:rgba(var(--pst-color-admonition-default),1);border-bottom-color:rgba(var(--pst-color-admonition-default),1);border-right-color:rgba(var(--pst-color-admonition-default),1);border-top-color:rgba(var(--pst-color-admonition-default),1);border-radius:.1rem;box-shadow:0 .2rem .5rem rgba(0,0,0,.05),0 0 .05rem rgba(0,0,0,.1);transition:color .25s,background-color .25s,border-color .25s}.admonition :last-child{margin-bottom:0}.admonition p.admonition-title~*{padding:0 1.4rem}.admonition>ol,.admonition>ul{margin-left:1em}.admonition .admonition-title{position:relative;margin:0 -.6rem!important;padding:.4rem .6rem .4rem 2rem;font-weight:700;background-color:rgba(var(--pst-color-admonition-default),.1)}.admonition .admonition-title:before{position:absolute;left:.6rem;width:1rem;height:1rem;color:rgba(var(--pst-color-admonition-default),1);font-family:Font Awesome\ 5 Free;font-weight:900;content:var(--pst-icon-admonition-default)}.admonition .admonition-title+*{margin-top:.4em}.admonition.attention{border-color:rgba(var(--pst-color-admonition-attention),1)}.admonition.attention .admonition-title{background-color:rgba(var(--pst-color-admonition-attention),.1)}.admonition.attention .admonition-title:before{color:rgba(var(--pst-color-admonition-attention),1);content:var(--pst-icon-admonition-attention)}.admonition.caution{border-color:rgba(var(--pst-color-admonition-caution),1)}.admonition.caution .admonition-title{background-color:rgba(var(--pst-color-admonition-caution),.1)}.admonition.caution .admonition-title:before{color:rgba(var(--pst-color-admonition-caution),1);content:var(--pst-icon-admonition-caution)}.admonition.warning{border-color:rgba(var(--pst-color-admonition-warning),1)}.admonition.warning .admonition-title{background-color:rgba(var(--pst-color-admonition-warning),.1)}.admonition.warning .admonition-title:before{color:rgba(var(--pst-color-admonition-warning),1);content:var(--pst-icon-admonition-warning)}.admonition.danger{border-color:rgba(var(--pst-color-admonition-danger),1)}.admonition.danger .admonition-title{background-color:rgba(var(--pst-color-admonition-danger),.1)}.admonition.danger .admonition-title:before{color:rgba(var(--pst-color-admonition-danger),1);content:var(--pst-icon-admonition-danger)}.admonition.error{border-color:rgba(var(--pst-color-admonition-error),1)}.admonition.error .admonition-title{background-color:rgba(var(--pst-color-admonition-error),.1)}.admonition.error .admonition-title:before{color:rgba(var(--pst-color-admonition-error),1);content:var(--pst-icon-admonition-error)}.admonition.hint{border-color:rgba(var(--pst-color-admonition-hint),1)}.admonition.hint .admonition-title{background-color:rgba(var(--pst-color-admonition-hint),.1)}.admonition.hint .admonition-title:before{color:rgba(var(--pst-color-admonition-hint),1);content:var(--pst-icon-admonition-hint)}.admonition.tip{border-color:rgba(var(--pst-color-admonition-tip),1)}.admonition.tip .admonition-title{background-color:rgba(var(--pst-color-admonition-tip),.1)}.admonition.tip .admonition-title:before{color:rgba(var(--pst-color-admonition-tip),1);content:var(--pst-icon-admonition-tip)}.admonition.important{border-color:rgba(var(--pst-color-admonition-important),1)}.admonition.important .admonition-title{background-color:rgba(var(--pst-color-admonition-important),.1)}.admonition.important .admonition-title:before{color:rgba(var(--pst-color-admonition-important),1);content:var(--pst-icon-admonition-important)}.admonition.note{border-color:rgba(var(--pst-color-admonition-note),1)}.admonition.note .admonition-title{background-color:rgba(var(--pst-color-admonition-note),.1)}.admonition.note .admonition-title:before{color:rgba(var(--pst-color-admonition-note),1);content:var(--pst-icon-admonition-note)}div.deprecated{margin-bottom:10px;margin-top:10px;padding:7px;background-color:#f3e5e5;border:1px solid #eed3d7;border-radius:.5rem}div.deprecated p{color:#b94a48;display:inline}.topic{background-color:#eee}.seealso dd{margin-top:0;margin-bottom:0}.viewcode-back{font-family:var(--pst-font-family-base)}.viewcode-block:target{background-color:#f4debf;border-top:1px solid #ac9;border-bottom:1px solid #ac9}span.guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}table.field-list{border-collapse:separate;border-spacing:10px;margin-left:1px}table.field-list th.field-name{padding:1px 8px 1px 5px;white-space:nowrap;background-color:#eee}table.field-list td.field-body p{font-style:italic}table.field-list td.field-body p>strong{font-style:normal}table.field-list td.field-body blockquote{border-left:none;margin:0 0 .3em;padding-left:30px}.table.autosummary td:first-child{white-space:nowrap}footer{width:100%;border-top:1px solid #ccc;padding:10px}footer .footer-item p{margin-bottom:0}.bd-search{position:relative;padding:1rem 15px;margin-right:-15px;margin-left:-15px}.bd-search .icon{position:absolute;color:#a4a6a7;left:25px;top:25px}.bd-search input{border-radius:0;border:0;border-bottom:1px solid #e5e5e5;padding-left:35px}.bd-toc{-ms-flex-order:2;order:2;height:calc(100vh - 2rem);overflow-y:auto}@supports (position:-webkit-sticky) or (position:sticky){.bd-toc{position:-webkit-sticky;position:sticky;top:calc(var(--pst-header-height) + 20px);height:calc(100vh - 5rem);overflow-y:auto}}.bd-toc .onthispage{color:#a4a6a7}.section-nav{padding-left:0;border-left:1px solid #eee;border-bottom:none}.section-nav ul{padding-left:1rem}.toc-entry,.toc-entry a{display:block}.toc-entry a{padding:.125rem 1.5rem;color:rgba(var(--pst-color-toc-link),1)}@media (min-width:1200px){.toc-entry a{padding-right:0}}.toc-entry a:hover{color:rgba(var(--pst-color-toc-link-hover),1);text-decoration:none}.bd-sidebar{padding-top:1em}@media (min-width:720px){.bd-sidebar{border-right:1px solid rgba(0,0,0,.1)}@supports (position:-webkit-sticky) or (position:sticky){.bd-sidebar{position:-webkit-sticky;position:sticky;top:calc(var(--pst-header-height) + 20px);z-index:1000;height:calc(100vh - var(--pst-header-height) - 20px)}}}.bd-sidebar.no-sidebar{border-right:0}.bd-links{padding-top:1rem;padding-bottom:1rem;margin-right:-15px;margin-left:-15px}@media (min-width:720px){.bd-links{display:block!important}@supports (position:-webkit-sticky) or (position:sticky){.bd-links{max-height:calc(100vh - 11rem);overflow-y:auto}}}.bd-sidenav{display:none}.bd-content{padding-top:20px}.bd-content .section{max-width:100%}.bd-content .section table{display:block;overflow:auto}.bd-toc-link{display:block;padding:.25rem 1.5rem;font-weight:600;color:rgba(0,0,0,.65)}.bd-toc-link:hover{color:rgba(0,0,0,.85);text-decoration:none}.bd-toc-item.active{margin-bottom:1rem}.bd-toc-item.active:not(:first-child){margin-top:1rem}.bd-toc-item.active>.bd-toc-link{color:rgba(0,0,0,.85)}.bd-toc-item.active>.bd-toc-link:hover{background-color:transparent}.bd-toc-item.active>.bd-sidenav{display:block}nav.bd-links p.caption{font-size:var(--pst-sidebar-caption-font-size);text-transform:uppercase;font-weight:700;position:relative;margin-top:1.25em;margin-bottom:.5em;padding:0 1.5rem;color:rgba(var(--pst-color-sidebar-caption),1)}nav.bd-links p.caption:first-child{margin-top:0}.bd-sidebar .nav{font-size:var(--pst-sidebar-font-size)}.bd-sidebar .nav ul{list-style:none;padding:0 0 0 1.5rem}.bd-sidebar .nav li>a{display:block;padding:.25rem 1.5rem;color:rgba(var(--pst-color-sidebar-link),1)}.bd-sidebar .nav li>a:hover{color:rgba(var(--pst-color-sidebar-link-hover),1);text-decoration:none;background-color:transparent}.bd-sidebar .nav li>a.reference.external:after{font-family:Font Awesome\ 5 Free;font-weight:900;content:"\f35d";font-size:.75em;margin-left:.3em}.bd-sidebar .nav .active:hover>a,.bd-sidebar .nav .active>a{font-weight:600;color:rgba(var(--pst-color-sidebar-link-active),1)}.toc-h2{font-size:.85rem}.toc-h3{font-size:.75rem}.toc-h4{font-size:.65rem}.toc-entry>.nav-link.active{font-weight:600;color:#130654;color:rgba(var(--pst-color-toc-link-active),1);background-color:transparent;border-left:2px solid rgba(var(--pst-color-toc-link-active),1)}.nav-link:hover{border-style:none}#navbar-main-elements li.nav-item i{font-size:.7rem;padding-left:2px;vertical-align:middle}.bd-toc .nav .nav{display:none}.bd-toc .nav .nav.visible,.bd-toc .nav>.active>ul{display:block}.prev-next-bottom{margin:20px 0}.prev-next-bottom a.left-prev,.prev-next-bottom a.right-next{padding:10px;border:1px solid rgba(0,0,0,.2);max-width:45%;overflow-x:hidden;color:rgba(0,0,0,.65)}.prev-next-bottom a.left-prev{float:left}.prev-next-bottom a.left-prev:before{content:"<< "}.prev-next-bottom a.right-next{float:right}.prev-next-bottom a.right-next:after{content:" >>"}.alert{padding-bottom:0}.alert-info a{color:#e83e8c}#navbar-icon-links i.fa,#navbar-icon-links i.fab,#navbar-icon-links i.far,#navbar-icon-links i.fas{vertical-align:middle;font-style:normal;font-size:1.5rem;line-height:1.25}#navbar-icon-links i.fa-github-square:before{color:#333}#navbar-icon-links i.fa-twitter-square:before{color:#55acee}#navbar-icon-links i.fa-gitlab:before{color:#548}#navbar-icon-links i.fa-bitbucket:before{color:#0052cc}.tocsection{border-left:1px solid #eee;padding:.3rem 1.5rem}.tocsection i{padding-right:.5rem}.editthispage{padding-top:2rem}.editthispage a{color:#130754}.xr-wrap[hidden]{display:block!important}.toctree-checkbox{position:absolute;display:none}.toctree-checkbox~ul{display:none}.toctree-checkbox~label i{transform:rotate(0deg)}.toctree-checkbox:checked~ul{display:block}.toctree-checkbox:checked~label i{transform:rotate(180deg)}.bd-sidebar li{position:relative}.bd-sidebar label{position:absolute;top:0;right:0;height:30px;width:30px;cursor:pointer;display:flex;justify-content:center;align-items:center}.bd-sidebar label:hover{background:rgba(var(--pst-color-sidebar-expander-background-hover),1)}.bd-sidebar label i{display:inline-block;font-size:.75rem;text-align:center}.bd-sidebar label i:hover{color:rgba(var(--pst-color-sidebar-link-hover),1)}.bd-sidebar li.has-children>.reference{padding-right:30px}div.doctest>div.highlight span.gp,span.linenos,table.highlighttable td.linenos{user-select:none!important;-webkit-user-select:text!important;-webkit-user-select:none!important;-moz-user-select:none!important;-ms-user-select:none!important} \ No newline at end of file diff --git a/_static/css/theme.css b/_static/css/theme.css deleted file mode 100644 index 3f6e79d..0000000 --- a/_static/css/theme.css +++ /dev/null @@ -1,117 +0,0 @@ -:root { - /***************************************************************************** - * Theme config - **/ - --pst-header-height: 60px; - - /***************************************************************************** - * Font size - **/ - --pst-font-size-base: 15px; /* base font size - applied at body / html level */ - - /* heading font sizes */ - --pst-font-size-h1: 36px; - --pst-font-size-h2: 32px; - --pst-font-size-h3: 26px; - --pst-font-size-h4: 21px; - --pst-font-size-h5: 18px; - --pst-font-size-h6: 16px; - - /* smaller then heading font sizes*/ - --pst-font-size-milli: 12px; - - --pst-sidebar-font-size: .9em; - --pst-sidebar-caption-font-size: .9em; - - /***************************************************************************** - * Font family - **/ - /* These are adapted from https://systemfontstack.com/ */ - --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", - Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; - --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, - Liberation Mono, Lucida Console, monospace; - - --pst-font-family-base: var(--pst-font-family-base-system); - --pst-font-family-heading: var(--pst-font-family-base); - --pst-font-family-monospace: var(--pst-font-family-monospace-system); - - /***************************************************************************** - * Color - * - * Colors are defined in rgb string way, "red, green, blue" - **/ - --pst-color-primary: 19, 6, 84; - --pst-color-success: 40, 167, 69; - --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ - --pst-color-warning: 255, 193, 7; - --pst-color-danger: 220, 53, 69; - --pst-color-text-base: 51, 51, 51; - - --pst-color-h1: var(--pst-color-primary); - --pst-color-h2: var(--pst-color-primary); - --pst-color-h3: var(--pst-color-text-base); - --pst-color-h4: var(--pst-color-text-base); - --pst-color-h5: var(--pst-color-text-base); - --pst-color-h6: var(--pst-color-text-base); - --pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: '\f058'; - --pst-icon-info-circle: '\f05a'; - --pst-icon-exclamation-triangle: '\f071'; - --pst-icon-exclamation-circle: '\f06a'; - --pst-icon-times-circle: '\f057'; - --pst-icon-lightbulb: '\f0eb'; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - -} diff --git a/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css new file mode 100644 index 0000000..eb19f69 --- /dev/null +++ b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) 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window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/doctools.js b/_static/doctools.js index 61ac9d2..4d67807 100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -2,320 +2,155 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return result; }; -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } }, - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, /** - * add context elements like header anchor links + * helper function to focus on search bar */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + * Initialise the domain index toggle buttons */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, + }; - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey - && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + 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 (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); } + break; } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js index 93b7c24..dab586c 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,12 +1,13 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), +const DOCUMENTATION_OPTIONS = { VERSION: '', - LANGUAGE: 'None', + LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, }; \ No newline at end of file diff --git a/_static/images/logo_deepnote.svg b/_static/images/logo_deepnote.svg new file mode 100644 index 0000000..fa77ebf --- /dev/null +++ b/_static/images/logo_deepnote.svg @@ -0,0 +1 @@ + diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.5.1.js deleted file mode 100644 index 5093733..0000000 --- a/_static/jquery-3.5.1.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? 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t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Ft(e)?_e.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Ft(e)?_e.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const[n,s]of Object.entries(e)){const e=t[n],o=Ft(e)?"element":null==(i=e)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(o))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${o}" but expected type "${s}".`)}var i}}class ve extends be{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),Nt.set(this._element,this.constructor.DATA_KEY,this))}dispose(){Nt.remove(this._element,this.constructor.DATA_KEY),fe.off(this._element,this.constructor.EVENT_KEY);for(const t of 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e?we.find(e):[]}},Ee=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;fe.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Wt(this))return;const s=we.getElementFromSelector(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Te=`close${Ae}`,Ce=`closed${Ae}`;class Oe extends ve{static get NAME(){return"alert"}close(){if(fe.trigger(this._element,Te).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),fe.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Oe.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}Ee(Oe,"close"),Qt(Oe);const xe='[data-bs-toggle="button"]';class ke extends ve{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=ke.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}fe.on(document,"click.bs.button.data-api",xe,(t=>{t.preventDefault();const e=t.target.closest(xe);ke.getOrCreateInstance(e).toggle()})),Qt(ke);const Le=".bs.swipe",Se=`touchstart${Le}`,De=`touchmove${Le}`,$e=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},Me={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class je extends be{constructor(t,e){super(),this._element=t,t&&je.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return Me}static get NAME(){return"swipe"}dispose(){fe.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(fe.on(this._element,Ie,(t=>this._start(t))),fe.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(fe.on(this._element,Se,(t=>this._start(t))),fe.on(this._element,De,(t=>this._move(t))),fe.on(this._element,$e,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const Fe=".bs.carousel",He=".data-api",Be="next",We="prev",ze="left",Re="right",qe=`slide${Fe}`,Ve=`slid${Fe}`,Ye=`keydown${Fe}`,Ke=`mouseenter${Fe}`,Qe=`mouseleave${Fe}`,Xe=`dragstart${Fe}`,Ue=`load${Fe}${He}`,Ge=`click${Fe}${He}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:Re,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ve{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=we.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Ye,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Ke,(()=>this.pause())),fe.on(this._element,Qe,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Xe,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(Re)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return 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l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return we.findOne(ii,this._element)}_getItems(){return we.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?We:Be:t===ze?Be:We}_orderToDirection(t){return Kt()?t===We?ze:Re:t===We?Re:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,Ge,"[data-bs-slide], 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e=we.getSelectorFromElement(t),i=we.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(mi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi,fi),this._element.style[e]="",fe.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(mi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi),fe.trigger(this._element,di)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(gi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,ui,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ei=".bs.dropdown",Ai=".data-api",Ti="ArrowUp",Ci="ArrowDown",Oi=`hide${Ei}`,xi=`hidden${Ei}`,ki=`show${Ei}`,Li=`shown${Ei}`,Si=`click${Ei}${Ai}`,Di=`keydown${Ei}${Ai}`,$i=`keyup${Ei}${Ai}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,Mi=".dropdown-menu",ji=Kt()?"top-end":"top-start",Fi=Kt()?"top-start":"top-end",Hi=Kt()?"bottom-end":"bottom-start",Bi=Kt()?"bottom-start":"bottom-end",Wi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",Ri={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},qi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Mi)[0]||we.prev(this._element,Mi)[0]||we.findOne(Mi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Ri}static get DefaultType(){return qi}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),fe.trigger(this._element,Li,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===Ci,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ti,Ci].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:we.prev(this,Ni)[0]||we.next(this,Ni)[0]||we.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Di,Ni,Vi.dataApiKeydownHandler),fe.on(document,Di,Mi,Vi.dataApiKeydownHandler),fe.on(document,Si,Vi.clearMenus),fe.on(document,$i,Vi.clearMenus),fe.on(document,Si,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Yi="backdrop",Ki="show",Qi=`mousedown.bs.${Yi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ui={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Gi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Ui}static get NAME(){return Yi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ki),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Qi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Ji=".bs.focustrap",Zi=`focusin${Ji}`,tn=`keydown.tab${Ji}`,en="backward",nn={autofocus:!0,trapElement:null},sn={autofocus:"boolean",trapElement:"element"};class on extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return nn}static get DefaultType(){return sn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,Ji),fe.on(document,Zi,(t=>this._handleFocusin(t))),fe.on(document,tn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,Ji))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===en?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?en:"forward")}}const rn=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",an=".sticky-top",ln="padding-right",cn="margin-right";class hn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,ln,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e+t)),this._setElementAttributes(an,cn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,ln),this._resetElementAttributes(rn,ln),this._resetElementAttributes(an,cn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const dn=".bs.modal",un=`hide${dn}`,fn=`hidePrevented${dn}`,pn=`hidden${dn}`,mn=`show${dn}`,gn=`shown${dn}`,_n=`resize${dn}`,bn=`click.dismiss${dn}`,vn=`mousedown.dismiss${dn}`,yn=`keydown.dismiss${dn}`,wn=`click${dn}.data-api`,En="modal-open",An="show",Tn="modal-static",Cn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new hn,this._addEventListeners()}static get Default(){return Cn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,mn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(En),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,un).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,dn),fe.off(this._dialog,dn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Gi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new on({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,gn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,yn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,vn,(t=>{fe.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(En),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Tn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Tn),this._queueCallback((()=>{this._element.classList.remove(Tn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,mn,(t=>{t.defaultPrevented||fe.one(e,pn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),Ee(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Sn=`load${kn}${Ln}`,Dn="show",$n="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,Mn=`shown${kn}`,jn=`hide${kn}`,Fn=`hidePrevented${kn}`,Hn=`hidden${kn}`,Bn=`resize${kn}`,Wn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,Rn={backdrop:!0,keyboard:!0,scroll:!1},qn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Rn}static get DefaultType(){return qn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new hn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($n),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Dn),this._element.classList.remove($n),fe.trigger(this._element,Mn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,jn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Dn,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new hn).reset(),fe.trigger(this._element,Hn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Gi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Fn)}:null})}_initializeFocusTrap(){return new on({trapElement:this._element})}_addEventListeners(){fe.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Fn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Hn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),fe.on(window,Sn,(()=>{for(const t of we.find(Nn))Vn.getOrCreateInstance(t).show()})),fe.on(window,Bn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),Ee(Vn),Qt(Vn);const Yn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={allowList:Yn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Gn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Jn={entry:"(string|element|function|null)",selector:"(string|element)"};class Zn extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Un}static get DefaultType(){return Gn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Jn)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const ts=new Set(["sanitize","allowList","sanitizeFn"]),es="fade",is="show",ns=".modal",ss="hide.bs.modal",os="hover",rs="focus",as={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ls={allowList:Yn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},cs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class hs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ls}static get DefaultType(){return cs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(ns),ss,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[rs]=!1,this._activeTrigger[os]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(es,is),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(es),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Zn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(es)}_isShown(){return this.tip&&this.tip.classList.contains(is)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=as[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===os?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===os?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?rs:os]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?rs:os]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(ns),ss,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))ts.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=hs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(hs);const ds={...hs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},us={...hs.DefaultType,content:"(null|string|element|function)"};class fs extends hs{static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".bs.scrollspy",ms=`activate${ps}`,gs=`click${ps}`,_s=`load${ps}.data-api`,bs="active",vs="[href]",ys=".nav-link",ws=`${ys}, .nav-item > ${ys}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,gs),fe.on(this._config.target,gs,vs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(vs,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(bs),this._activateParents(t),fe.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(bs);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,ws))t.classList.add(bs)}_clearActiveClass(t){t.classList.remove(bs);const e=we.find(`${vs}.${bs}`,t);for(const t of e)t.classList.remove(bs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,_s,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Cs=".bs.tab",Os=`hide${Cs}`,xs=`hidden${Cs}`,ks=`show${Cs}`,Ls=`shown${Cs}`,Ss=`click${Cs}`,Ds=`keydown${Cs}`,$s=`load${Cs}`,Is="ArrowLeft",Ns="ArrowRight",Ps="ArrowUp",Ms="ArrowDown",js="Home",Fs="End",Hs="active",Bs="fade",Ws="show",zs=".dropdown-toggle",Rs=`:not(${zs})`,qs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Vs=`.nav-link${Rs}, .list-group-item${Rs}, [role="tab"]${Rs}, ${qs}`,Ys=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Ks extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ds,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Os,{relatedTarget:t}):null;fe.trigger(t,ks,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ls,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Bs)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,xs,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Bs)))}_keydown(t){if(![Is,Ns,Ps,Ms,js,Fs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([js,Fs].includes(t.key))i=e[t.key===js?0:e.length-1];else{const n=[Ns,Ms].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ks.getOrCreateInstance(i).show())}_getChildren(){return we.find(Vs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(zs,Hs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(Vs)?t:we.findOne(Vs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ks.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ss,qs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Ks.getOrCreateInstance(this).show()})),fe.on(window,$s,(()=>{for(const t of we.find(Ys))Ks.getOrCreateInstance(t)})),Qt(Ks);const Qs=".bs.toast",Xs=`mouseover${Qs}`,Us=`mouseout${Qs}`,Gs=`focusin${Qs}`,Js=`focusout${Qs}`,Zs=`hide${Qs}`,to=`hidden${Qs}`,eo=`show${Qs}`,io=`shown${Qs}`,no="hide",so="show",oo="showing",ro={animation:"boolean",autohide:"boolean",delay:"number"},ao={animation:!0,autohide:!0,delay:5e3};class lo extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ao}static get DefaultType(){return ro}static get NAME(){return"toast"}show(){fe.trigger(this._element,eo).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(no),qt(this._element),this._element.classList.add(so,oo),this._queueCallback((()=>{this._element.classList.remove(oo),fe.trigger(this._element,io),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,Zs).defaultPrevented||(this._element.classList.add(oo),this._queueCallback((()=>{this._element.classList.add(no),this._element.classList.remove(oo,so),fe.trigger(this._element,to)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(so),super.dispose()}isShown(){return this._element.classList.contains(so)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,Xs,(t=>this._onInteraction(t,!0))),fe.on(this._element,Us,(t=>this._onInteraction(t,!1))),fe.on(this._element,Gs,(t=>this._onInteraction(t,!0))),fe.on(this._element,Js,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=lo.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named 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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.3.2 (https://getbootstrap.com/)\n * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n const instanceMap = elementMap.get(element);\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n instanceMap.set(key, instance);\n },\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n return null;\n },\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key);\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n }\n return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n }\n\n // Get transition-duration of the element\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object));\n }\n return null;\n};\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])');\n if (!closedDetails) {\n return elementIsVisible;\n }\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n if (summary === null) {\n return false;\n }\n }\n return elementIsVisible;\n};\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n if (element.classList.contains('disabled')) {\n return true;\n }\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n if (element instanceof ShadowRoot) {\n return element;\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null;\n }\n return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement);\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n index += shouldGetNext ? 1 : -1;\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n return fn.apply(element, [event]);\n };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n hydrateObj(event, {\n delegateTarget: target\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n return fn.apply(target, [event]);\n }\n }\n };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string';\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n callable = wrapFunction(callable);\n }\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n if (!fn) {\n return;\n }\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n const evt = hydrateObj(new Event(event, {\n bubbles,\n cancelable: true\n }), args);\n if (defaultPrevented) {\n evt.preventDefault();\n }\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n return evt;\n }\n};\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value;\n }\n });\n }\n }\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n if (value === 'false') {\n return false;\n }\n if (value === Number(value).toString()) {\n return Number(value);\n }\n if (value === '' || value === 'null') {\n return null;\n }\n if (typeof value !== 'string') {\n return value;\n }\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n return attributes;\n },\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n static get DefaultType() {\n return {};\n }\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n return config;\n }\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n if (!element) {\n return;\n }\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n static get VERSION() {\n return VERSION;\n }\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href');\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n }\n return selector;\n};\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n return parents;\n },\n prev(element, selector) {\n let previous = element.previousElementSibling;\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n previous = previous.previousElementSibling;\n }\n return [];\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n next = next.nextElementSibling;\n }\n return [];\n },\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n },\n getSelectorFromElement(element) {\n const selector = getSelector(element);\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null;\n }\n return null;\n },\n getElementFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.findOne(selector) : null;\n },\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.find(selector) : [];\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target);\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n if (closeEvent.defaultPrevented) {\n return;\n }\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n }\n\n // Private\n _destroyElement() {\n this._element.remove();\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n if (!element || !Swipe.isSupported()) {\n return;\n }\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n this._initEvents();\n }\n\n // Getters\n static get Default() {\n return Default$c;\n }\n static get DefaultType() {\n return DefaultType$c;\n }\n static get NAME() {\n return NAME$d;\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n this._handleSwipe();\n execute(this._config.endCallback);\n }\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n if (!direction) {\n return;\n }\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n this._addEventListeners();\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$b;\n }\n static get DefaultType() {\n return DefaultType$b;\n }\n static get NAME() {\n return NAME$c;\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT);\n }\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n prev() {\n this._slide(ORDER_PREV);\n }\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n this._clearInterval();\n }\n cycle() {\n this._clearInterval();\n this._updateInterval();\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n this.cycle();\n }\n to(index) {\n const items = this._getItems();\n if (index > items.length - 1 || index < 0) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n const activeIndex = this._getItemIndex(this._getActive());\n if (activeIndex === index) {\n return;\n }\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n this._slide(order, items[index]);\n }\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause();\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n const direction = KEY_TO_DIRECTION[event.key];\n if (direction) {\n event.preventDefault();\n this._slide(this._directionToOrder(direction));\n }\n }\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n if (!element) {\n return;\n }\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n const activeElement = this._getActive();\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n if (nextElement === activeElement) {\n return;\n }\n const nextElementIndex = this._getItemIndex(nextElement);\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n const slideEvent = triggerEvent(EVENT_SLIDE);\n if (slideEvent.defaultPrevented) {\n return;\n }\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return;\n }\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n this._setActiveIndicatorElement(nextElementIndex);\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n if (isCycling) {\n this.cycle();\n }\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n if (slideIndex) {\n carousel.to(slideIndex);\n carousel._maybeEnableCycle();\n return;\n }\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n carousel._maybeEnableCycle();\n return;\n }\n carousel.prev();\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n this._initializeChildren();\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n if (this._config.toggle) {\n this.toggle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$a;\n }\n static get DefaultType() {\n return DefaultType$a;\n }\n static get NAME() {\n return NAME$b;\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n let activeChildren = [];\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n const dimension = this._getDimension();\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.style[dimension] = 0;\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n this._queueCallback(complete, this._element, true);\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n const dimension = this._getDimension();\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger);\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n this._element.style[dimension] = '';\n this._queueCallback(complete, this._element, true);\n }\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n config.parent = getElement(config.parent);\n return config;\n }\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element);\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {};\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n }\n\n // Getters\n static get Default() {\n return Default$9;\n }\n static get DefaultType() {\n return DefaultType$9;\n }\n static get NAME() {\n return NAME$a;\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n if (showEvent.defaultPrevented) {\n return;\n }\n this._createPopper();\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n this._element.focus();\n this._element.setAttribute('aria-expanded', true);\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n this._element.classList.add(CLASS_NAME_SHOW$6);\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n this._completeHide(relatedTarget);\n }\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n super.dispose();\n }\n update() {\n this._inNavbar = this._detectNavbar();\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n if (this._popper) {\n this._popper.destroy();\n }\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n this._element.setAttribute('aria-expanded', 'false');\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n _getConfig(config) {\n config = super._getConfig(config);\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n return config;\n }\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n let referenceElement = this._element;\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n const popperConfig = this._getPopperConfig();\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n _getPlacement() {\n const parentDropdown = this._parent;\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n };\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n if (!items.length) {\n return;\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n if (!context || context._config.autoClose === false) {\n continue;\n }\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n const relatedTarget = {\n relatedTarget: context._element\n };\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n context._completeHide(relatedTarget);\n }\n }\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n if (isInput && !isEscapeEvent) {\n return;\n }\n event.preventDefault();\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n instance._selectMenuItem(event);\n return;\n }\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n }\n\n // Getters\n static get Default() {\n return Default$8;\n }\n static get DefaultType() {\n return DefaultType$8;\n }\n static get NAME() {\n return NAME$9;\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._append();\n const element = this._getElement();\n if (this._config.isAnimated) {\n reflow(element);\n }\n element.classList.add(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n dispose() {\n if (!this._isAppended) {\n return;\n }\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n this._element.remove();\n this._isAppended = false;\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n this._element = backdrop;\n }\n return this._element;\n }\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n _append() {\n if (this._isAppended) {\n return;\n }\n const element = this._getElement();\n this._config.rootElement.append(element);\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n }\n\n // Getters\n static get Default() {\n return Default$7;\n }\n static get DefaultType() {\n return DefaultType$7;\n }\n static get NAME() {\n return NAME$8;\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return;\n }\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n deactivate() {\n if (!this._isActive) {\n return;\n }\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n }\n\n // Private\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n const elements = SelectorEngine.focusableChildren(trapElement);\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n hide() {\n const width = this.getWidth();\n this._disableOverFlow();\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n isOverflowing() {\n return this.getWidth() > 0;\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n this._element.style.overflow = 'hidden';\n }\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n this._saveInitialAttribute(element, styleProperty);\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty);\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$6;\n }\n static get DefaultType() {\n return DefaultType$6;\n }\n static get NAME() {\n return NAME$7;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._isTransitioning = true;\n this._scrollBar.hide();\n document.body.classList.add(CLASS_NAME_OPEN);\n this._adjustDialog();\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._isShown = false;\n this._isTransitioning = true;\n this._focustrap.deactivate();\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n dispose() {\n EventHandler.off(window, EVENT_KEY$4);\n EventHandler.off(this._dialog, EVENT_KEY$4);\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n handleUpdate() {\n this._adjustDialog();\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n this._element.style.display = 'block';\n this._element.removeAttribute('aria-hidden');\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_SHOW$4);\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n return;\n }\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n _hideModal() {\n this._element.style.display = 'none';\n this._element.setAttribute('aria-hidden', true);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n this._isTransitioning = false;\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n this._resetAdjustments();\n this._scrollBar.reset();\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n if (hideEvent.defaultPrevented) {\n return;\n }\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY;\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n this._element.classList.add(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n this._element.focus();\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const scrollbarWidth = this._scrollBar.getWidth();\n const isBodyOverflowing = scrollbarWidth > 0;\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](relatedTarget);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n });\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$5;\n }\n static get DefaultType() {\n return DefaultType$5;\n }\n static get NAME() {\n return NAME$6;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._backdrop.show();\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n this._element.classList.add(CLASS_NAME_SHOW$3);\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n this._queueCallback(completeCallBack, this._element, true);\n }\n hide() {\n if (!this._isShown) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._focustrap.deactivate();\n this._element.blur();\n this._isShown = false;\n this._element.classList.add(CLASS_NAME_HIDING);\n this._backdrop.hide();\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n this._queueCallback(completeCallback, this._element, true);\n }\n dispose() {\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n this.hide();\n };\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n });\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n });\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n }\n return true;\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n }\n\n // Getters\n static get Default() {\n return Default$4;\n }\n static get DefaultType() {\n return DefaultType$4;\n }\n static get NAME() {\n return NAME$5;\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n hasContent() {\n return this.getContent().length > 0;\n }\n changeContent(content) {\n this._checkContent(content);\n this._config.content = {\n ...this._config.content,\n ...content\n };\n return this;\n }\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n const template = templateWrapper.children[0];\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n return template;\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n this._checkContent(config.content);\n }\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n if (!templateElement) {\n return;\n }\n content = this._resolvePossibleFunction(content);\n if (!content) {\n templateElement.remove();\n return;\n }\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n return;\n }\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n templateElement.textContent = content;\n }\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this]);\n }\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n templateElement.textContent = element.textContent;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n super(element, config);\n\n // Private\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null;\n\n // Protected\n this.tip = null;\n this._setListeners();\n if (!this._config.selector) {\n this._fixTitle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$3;\n }\n static get DefaultType() {\n return DefaultType$3;\n }\n static get NAME() {\n return NAME$4;\n }\n\n // Public\n enable() {\n this._isEnabled = true;\n }\n disable() {\n this._isEnabled = false;\n }\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n this._activeTrigger.click = !this._activeTrigger.click;\n if (this._isShown()) {\n this._leave();\n return;\n }\n this._enter();\n }\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n this._disposePopper();\n super.dispose();\n }\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper();\n const tip = this._getTipElement();\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n const {\n container\n } = this._config;\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n if (this._isHovered === false) {\n this._leave();\n }\n this._isHovered = false;\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n hide() {\n if (!this._isShown()) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n if (hideEvent.defaultPrevented) {\n return;\n }\n const tip = this._getTipElement();\n tip.classList.remove(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n if (!this._isHovered) {\n this._disposePopper();\n }\n this._element.removeAttribute('aria-describedby');\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n update() {\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n return this.tip;\n }\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml();\n\n // TODO: remove this check in v6\n if (!tip) {\n return null;\n }\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n return tip;\n }\n setContent(content) {\n this._newContent = content;\n if (this._isShown()) {\n this._disposePopper();\n this.show();\n }\n }\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n return this._templateFactory;\n }\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element]);\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element]);\n }\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n context._leave();\n });\n }\n }\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n _fixTitle() {\n const title = this._element.getAttribute('title');\n if (!title) {\n return;\n }\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title');\n }\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n this._isHovered = true;\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n this._isHovered = false;\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n return config;\n }\n _getDelegateConfig() {\n const config = {};\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value;\n }\n }\n config.selector = false;\n config.trigger = 'manual';\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config;\n }\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n this._popper = null;\n }\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n static get DefaultType() {\n return DefaultType$2;\n }\n static get NAME() {\n return NAME$3;\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent();\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n }\n\n // Getters\n static get Default() {\n return Default$1;\n }\n static get DefaultType() {\n return DefaultType$1;\n }\n static get NAME() {\n return NAME$2;\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables();\n this._maybeEnableSmoothScroll();\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n dispose() {\n this._observer.disconnect();\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body;\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n return config;\n }\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height;\n }\n });\n }\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n this._process(targetElement(entry));\n };\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n this._clearActiveClass(targetElement(entry));\n continue;\n }\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry);\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return;\n }\n continue;\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor);\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n this._clearActiveClass(this._config.target);\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n this._activateParents(target);\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both

In summary, dMRI involves complex data types that, as programmers, we want to access, query and manipulate with ease.

-
-

Python and object oriented programming

+
+

Python and object oriented programming#

Python is an object oriented programming language. It allows us to represent and encapsulate data types and corresponding behaviors into programming structures called objects.

Data structures

@@ -358,35 +456,35 @@

Python and object oriented programmingattrs.

-
"""Representing data in hard-disk and memory."""
+
"""Representing data in hard-disk and memory."""
 import attr
 
 def _data_repr(value):
     if value is None:
         return "None"
-    return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>"
+    return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>"
 
 
 @attr.s(slots=True)
 class DWI:
-    """Data representation structure for dMRI data."""
+    """Data representation structure for dMRI data."""
 
     dataobj = attr.ib(default=None, repr=_data_repr)
-    """A numpy ndarray object for the data array, without *b=0* volumes."""
+    """A numpy ndarray object for the data array, without *b=0* volumes."""
     brainmask = attr.ib(default=None, repr=_data_repr)
-    """A boolean ndarray object containing a corresponding brainmask."""
+    """A boolean ndarray object containing a corresponding brainmask."""
     bzero = attr.ib(default=None, repr=_data_repr)
-    """A *b=0* reference map, preferably obtained by some smart averaging."""
+    """A *b=0* reference map, preferably obtained by some smart averaging."""
     gradients = attr.ib(default=None, repr=_data_repr)
-    """A 2D numpy array of the gradient table in RAS+B format."""
+    """A 2D numpy array of the gradient table in RAS+B format."""
     em_affines = attr.ib(default=None)
-    """
+    """
     List of :obj:`nitransforms.linear.Affine` objects that bring
     DWIs (i.e., no b=0) into alignment.
     """
 
     def __len__(self):
-        """Obtain the number of high-*b* orientations."""
+        """Obtain the number of high-*b* orientations."""
         return self.gradients.shape[-1]
 

 

@@ -397,7 +495,7 @@ 
Python and object oriented programmingDWI data structure with some simulated data:

 

 

-
# NumPy is a fundamental Python library for working with arrays
+
# NumPy is a fundamental Python library for working with arrays
 import numpy as np
 
 # create a new DWI object, with only gradient information that is random
@@ -410,17 +508,17 @@ 
Python and object oriented programmingprint() statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.

-

-

+
+

Using the new data representation object#

The code shown above was just a snippet of the DWI class. For simplicity, we will be using the full implementation of this class from our eddymotion package Under the data/ folder of this book’s distribution, we have stored a sample DWI dataset with filename dwi.h5. Please note that the file has been minimized by zeroing all but two diffusion-weighted orientation maps.

Let’s get some insights from it:

-
# import the class from the library
-from eddymotion.dmri import DWI
+
# import the class from the library
+from eddymotion.data.dmri import DWI
 
 # load the sample file
 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
@@ -434,26 +532,32 @@ 
Using the new data representation object
 

-
dmri_dataset
+
dmri_dataset
 

 

 

 

 
We’ll go over some of the components of dmri_dataset through this lesson.

-

-
+
+

Visualizing the data#

Exercise

Let’s start out by seeing what the data looks like. The fully-fledged DWI object has a convenience function to plot the dataset.

Hint: To see all of the instances and behaviors available to an object, try typing the object name, followed by . and Tab

Solution

+
+ + +Hide code cell content +
-
dmri_dataset.
+
dmri_dataset.
+

When calling plot_mosaic() without any arguments, the b=0 reference is plotted. This b=0 reference is a map of the signal measured without gradient sensitization, or in other words, when we are not measuring diffusion in any direction. @@ -464,12 +568,12 @@

Visualizing the dataSolution

Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal. As we can see, diffusion-weighted images consistently drop almost all signal in voxels filled with cerebrospinal fluid because there, water diffusion is free (isotropic) regardless of the direction that is being measured.

-

We can also see that the images at index=10 and index=100 have different gradient strength (“b-value”). +

We can also see that the images at index=10 and index=100 have different gradient strength (”b-value”). The higher the magnitude of the gradient, the more diffusion that is allowed to occur, indicated by the overall decrease in signal intensity. Stronger gradients yield diffusion maps with substantially lower SNR (signal-to-noise ratio), as well as larger distortions derived from the so-called “Eddy-currents”.

-

-
-

Visualizing the gradient information

+ +
+

Visualizing the gradient information#

Our DWI object stores the gradient information in the gradients attribute.

Exercise Let’s see the shape of the gradient information.

@@ -490,17 +594,17 @@

Visualizing the gradient information -

The LOGO (leave-one-gradient-out) splitter

+

+
+

The LOGO (leave-one-gradient-out) splitter#

One final behavior that will make our endeavor easier in the long run is a convenience method for data splitting. In particular, we are implementing some sort of cross-validation scheme where we will iterate over different data splits. In this case, the splitting strategy is a simple leave-one-out. Because one “datapoint” in our DWI dataset corresponds to one gradient, we will refer to this partitioning of the dataset as leave-one-gradient-out (LOGO):

-
def logo_split(self, index, with_b0=False):
-    """
+
def logo_split(self, index, with_b0=False):
+    """
     Produce one fold of LOGO (leave-one-gradient-out).
 
     Parameters
@@ -553,7 +657,7 @@ 
The LOGO (leave-one-gradient-out) splitterThis function is contained in the DWI class shown earlier and will allow us to easily partition the dataset as follows:

 

 

-
from eddymotion.viz import plot_dwi
+
from eddymotion.viz import plot_dwi
 
 data_train, data_test = dmri_dataset.logo_split(10)
 plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
@@ -569,12 +673,12 @@ 
The LOGO (leave-one-gradient-out) splitterExercise

 
Likewise for the left-out gradient, try printing the shapes of elements in the data_test tuple.

 
Solution

-

-
+
+

Next steps: diffusion modeling#

By modeling the diffusion signal, the acquired images can provide measurements which are related to the microscopic changes and estimate white matter trajectories.

-
- + + - + - -
- -
- - - -
-
-

- - By The NiPreps developers
- - © Copyright 2021.
-

-
-
- + + + + +
+ +
+
+
+ + + + + +
+ + +
+
+ Contents +
+
+
+ + + +
+ +
+ +
+ +

+By The NiPreps developers +

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+ + +
- +

+ + © Copyright 2021. +
+ +

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+ + + + + + + + + + +
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\ No newline at end of file diff --git a/notebook/03-models.html b/notebook/03-models.html index 890f44d..529c1d3 100644 --- a/notebook/03-models.html +++ b/notebook/03-models.html @@ -1,346 +1,432 @@ - - - - - Diffusion modeling — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - + Diffusion modeling — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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Diffusion modeling

+ +
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+ + -
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Diffusion modeling

+ +
+ +
+

Diffusion modeling#

+
+ + +Hide code cell content +
-
import warnings
+
import warnings
 
 warnings.filterwarnings("ignore")
+

The proposed method requires inferring a motion-less, reference DW map for a given diffusion orientation for which we want to estimate the misalignment. Inference of the reference map is achieved by first fitting some diffusion model (which we will draw from DIPY) using all data, except the particular DW map that is to be aligned. This data splitting scheme was introduced in the LOGO splitter section in Introduction to dMRI data.

All models are required to offer the same API (application programmer interface):

-
    +

    1. The initialization takes a DIPY GradientTable as the first argument, and then arbitrary parameters as keyword arguments.

    2. A fit(data) method, which only requires a positional argument data, a 4D array with DWI data.

    3. A predict(gradient_table) method, which only requires a GradientTable as input. @@ -351,15 +437,15 @@

      Diffusion modeling
      -
      from eddymotion.dmri import DWI
+
      from eddymotion.dmri import DWI
 from eddymotion.viz import plot_dwi
 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
      -
      -

      Implementing a trivial model

      +
      +

      Implementing a trivial model#

      We will first start implementing a trivial model. This model will always return the reference b=0 map, regardless of the particular diffusion orientation model. In other words, it is just a constant model.

      @@ -369,8 +455,8 @@

      Implementing a trivial model
      -
      class TrivialB0Model:
-    """
+
      class TrivialB0Model:
+    """
     A trivial model that returns a *b=0* map always.
 
     Implements the interface of :obj:`dipy.reconst.base.ReconstModel`.
@@ -383,17 +469,17 @@ 
      Implementing a trivial model__slots__ = ("_S0",)
 
     def __init__(self, gtab, S0=None, **kwargs):
-        """Implement object initialization."""
+        """Implement object initialization."""
         if S0 is None:
             raise ValueError("S0 must be provided")
 
         self._S0 = S0
 
     def fit(self, *args, **kwargs):
-        """Do nothing."""
+        """Do nothing."""
 
     def predict(self, gradient, **kwargs):
-        """Return the *b=0* map."""
+        """Return the *b=0* map."""
         return self._S0
 
      
 
      
@@ -402,7 +488,7 @@ 
      Implementing a trivial model
 
      
-
      model = TrivialB0Model(
+
      model = TrivialB0Model(
     dmri_dataset.gradients,
     S0=dmri_dataset.bzero,
 )
@@ -413,7 +499,7 @@ 
      Implementing a trivial modeldata_test) out, using the logo_split method of the dataset. In every iteration, this finds the b=0 volumes in the data and averages their values in every voxel:
      
 
      
 
      
-
      data_train, data_test = dmri_dataset.logo_split(10)
+
      data_train, data_test = dmri_dataset.logo_split(10)
 model.fit(data_train[0])
 
      
 
      
@@ -422,7 +508,7 @@ 
      Implementing a trivial modelpredict() method:
      
 
      
 
      
-
      predicted = model.predict(data_test[1])
+
      predicted = model.predict(data_test[1])
 plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]);
 
      
 
      
@@ -431,34 +517,34 @@ 
      Implementing a trivial model
 
      
-
      plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
+
      plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
 
      
 
      
 
      
 
      
-
      
-

      +
      +

      Implementing a regression to the mean model#

      Exercise

      Extend the TrivialB0Model to produce an average of all other diffusion directions, instead of the b=0.

      Solution

      -
      class AverageDWModel:
-    """A trivial model that returns an average map."""
+
      class AverageDWModel:
+    """A trivial model that returns an average map."""
 
     __slots__ = ("_data",)
 
     def __init__(self, gtab, **kwargs):
-        """Implement object initialization."""
+        """Implement object initialization."""
         return  # do nothing at initialization time
 
     def fit(self, data, **kwargs):
-        """Calculate the average."""
+        """Calculate the average."""
         # self._data =  # Use numpy to calculate the average.
 
     def predict(self, gradient, **kwargs):
-        """Return the average map."""
+        """Return the average map."""
         return self._data
 
      
 
      
@@ -467,18 +553,18 @@ 
      Implementing a regression to the mean modelExercise
      
 
      Use the new AverageDWModel you just created.
      
 
      Solution
      
-
      -
      +
      +

      Investigating the tensor model#

      Now, we are ready to use the diffusion tensor model. We will use the wrap around DIPY’s implementation that we distribute with eddymotion.

      -
      -

      The model factory

      +
      +

      The model factory#

      To permit flexibility in selecting models, the eddymotion package offers a ModelFactory that implements the facade design pattern. This means that ModelFactory makes it easier for the user to switch between models:

      -
      from eddymotion.model import ModelFactory
+
      from eddymotion.model import ModelFactory
 
 # We are using now a full dataset, we need to split the data again
 
@@ -491,13 +577,13 @@ 
      The model factory
-
      Leveraging the fit() / predict() API
      
+
      +
      +

      Leveraging the fit() / predict() API#

      The ModelFactory returns a model object that is compliant with the interface sketched above:

      -
      model.fit(data_train[0])
+
      model.fit(data_train[0])
 predicted = model.predict(data_test[1])
 
      
 
      
@@ -506,7 +592,7 @@ 
      Leveraging the 
 
      Now, the predicted map for the particular b gradient looks much closer to the original:
      
 
      
 
      
-
      plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);
+
      plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);
 
      
 
      
 
      
@@ -514,7 +600,7 @@ 
      Leveraging the 
 
      Here’s the original DW map, for reference:
      
 
      
 
      
-
      plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
+
      plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
 
      
 
      
 
      
@@ -525,13 +611,13 @@ 
      Leveraging the 
 
      Exercise
      
 
      Once the model has been initialized, we can easily generate a new prediction.
      
 
      Solution
      
-
      
-
      
-
      
-
      Next steps: image registration
      
+
      +
      +
      +

      Next steps: image registration#

      Once we have our model factory readily available, it will be easy to generate predictions that we can use for reference in image registration.

      -
      -
      +

+ -
+ - -
- -
- -
-
-
-
-

- - By The NiPreps developers
- - © Copyright 2021.
-

-
-
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+ + + + +
+ +
+
+
+ +
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+ + +
+
+ Contents +
+
+
+ + +
+
+ +
+ +
+ +

+By The NiPreps developers +

-
-
+
+ +
- +

+ + © Copyright 2021. +
+ +

+
+ +
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+
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\ No newline at end of file diff --git a/notebook/04-registration.html b/notebook/04-registration.html index d0192d3..0fe126c 100644 --- a/notebook/04-registration.html +++ b/notebook/04-registration.html @@ -1,301 +1,409 @@ - - - - - Image registration (spatial alignment) — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + Image registration (spatial alignment) — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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Image registration (spatial alignment)

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Image registration (spatial alignment)

+ +
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Image registration (spatial alignment)#

At this point of the tutorial we have covered two of the three initial requirements:

-
-

Example registration

+ +
+

Example registration#

It is beyond the scope of this tutorial to understand ANTs and/or image registration altogether, but let’s have a look at how registration is integrated. First, we’ll need to generate one target gradient prediction following all the steps learned previously. For this example, we have selected the 8th DW map (index=7) because it contains a sudden motion spike, resembling a nodding movement.

-
from eddymotion.model import ModelFactory
+
from eddymotion.model import ModelFactory
 
 data_train, data_test = dmri_dataset.logo_split(7, with_b0=True)
 
@@ -394,7 +502,7 @@ 
Example registration
 

 

-
from pathlib import Path
+
from pathlib import Path
 from tempfile import mkdtemp
 
 tempdir = Path(mkdtemp())
@@ -414,7 +522,7 @@ 
Example registrationnodding of the head, perhaps more apparent when looking at the corpus callosum in the sagittal views:

 

 

-
from niworkflows.viz.notebook import display
+
from niworkflows.viz.notebook import display
 
 display(
     fixed_path,
@@ -429,7 +537,7 @@ 
Example registrationLet’s configure ANTs via NiPype:

 

 

-
# ensure that NiPype is able to find and execute our ANTs command
+
# ensure that NiPype is able to find and execute our ANTs command
 
 import os
 
@@ -440,7 +548,7 @@ 
Example registration
 

 

-
from os import cpu_count
+
from os import cpu_count
 from pkg_resources import resource_filename as pkg_fn
 from nipype.interfaces.ants.registration import Registration
 
@@ -462,7 +570,7 @@ 
Example registrationwhich will run the following command-line:

 

 

-
registration.cmdline
+
registration.cmdline
 

 

 

@@ -470,7 +578,7 @@ 
Example registrationNipype interfaces can be submitted for execution with the run() method:

 

 

-
result = registration.run(cwd=str(tempdir.absolute()))
+
result = registration.run(cwd=str(tempdir.absolute()))
 

 

 

@@ -479,7 +587,7 @@ 
Example registration
 

 

-
display(
+
display(
     fixed_path,
     result.outputs.warped_image,
     fixed_label="Predicted",
@@ -489,9 +597,9 @@ 
Example registration
 

 

-

-
+
+

Resampling an image#

Once we have estimated what is the transform that brings two images into alignment, we can bring the data in the moving image and move this image into the reference’s grid through resampling.

The process works as follows:

nitransforms

@@ -500,7 +608,7 @@

Resampling an image
-
import nitransforms as nt
+
import nitransforms as nt
 
 itk_xform = nt.io.itk.ITKLinearTransform.from_filename(result.outputs.forward_transforms[0])
 matrix = itk_xform.to_ras(reference=fixed_path, moving=moving_path)
@@ -512,7 +620,7 @@ 
Resampling an image
 

-
xfm = nt.linear.Affine(matrix)
+
xfm = nt.linear.Affine(matrix)
 xfm.reference = fixed_path
 resampled = xfm.apply(moving_path)
 resampled.to_filename(tempdir / "resampled-nitransforms.nii.gz")
@@ -529,8 +637,8 @@ 
Resampling an imagedisplay() function to visualize the image aligned as generated by ANTs vs. that generated by NiTransforms – they should be aligned!.

 
Solution

-

-

+

+ -
+ - -
- -
- -
-
-
-
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- - By The NiPreps developers
- - © Copyright 2021.
-

-
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+ + + + +
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+
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+ + +
+
+ Contents +
+
+
+ + +
+
+ +
+ +
+ +

+By The NiPreps developers +

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-
+
+ +
- +

+ + © Copyright 2021. +
+ +

+
+ +
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+
\ No newline at end of file diff --git a/notebook/05-solution.html b/notebook/05-solution.html index 931e0ac..38108bd 100644 --- a/notebook/05-solution.html +++ b/notebook/05-solution.html @@ -1,297 +1,414 @@ - - - - - Putting everything together — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - + Putting everything together — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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Putting everything together

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Putting everything together

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Putting everything together#

+
+ + +Hide code cell content +
-
import warnings
+
import warnings
 
 warnings.filterwarnings("ignore")
 
@@ -315,6 +432,7 @@ 
Putting everything together
+

+
+
+Hide code cell content
+
 

-
class EddyMotionEstimator:
-    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
+
class EddyMotionEstimator:
+    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
 
     @staticmethod
     def fit(
@@ -336,7 +459,7 @@ 
Putting everything togethermodel="b0",
         **kwargs,
     ):
-        r"""
+        r"""
         Estimate head-motion and Eddy currents.
 
         <please write a descriptive documentation of the function here>
@@ -394,6 +517,7 @@ 
Putting everything together
from eddymotion.estimator import EddyMotionEstimator
@@ -401,16 +525,16 @@ 
Putting everything togetherestimated_affines = EddyMotionEstimator.fit(dmri_dataset, model="b0")
 

 

-

-
What’s next? - Testing!

+

+
What’s next? - Testing!#

 
Once we have our first implementation functional, we should think of some unit-tests for our code.

 
Exercise

 
Write a unit test for the TrivialB0Model.
 This test would just make sure that, regardless of the particular partition of the input dataset, a b=0 map is always returned.

 
Solution

-

-

-
And after testing? - Validation!

+
+
+

And after testing? - Validation!#

Once we have a sufficient portion of our code covered by unit-tests, then we would add some integration tests that give us confidence that our bullet-proof individual components also work together. Only after we have both steps secure, we can run benchmarks and evaluations from which we learn whether our solution works, and characterize its limitations.

The main strategy to validate this software would entail finding/acquiring a special dataset where motion is not present or extremely low, in which we introduce a known head-motion pattern with which we are going to challenge our estimator. @@ -422,8 +546,8 @@

And after testing? - Validation!TrivialB0Model, for instance).

Please head to the GitHub repository and share your ideas with us! We are welcoming new contributors!

-

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- - © Copyright 2021.
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+By The NiPreps developers +

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+ + © Copyright 2021. +
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\ No newline at end of file diff --git a/preparation/step0.html b/preparation/step0.html index 40dd311..a8cf093 100644 --- a/preparation/step0.html +++ b/preparation/step0.html @@ -1,290 +1,407 @@ - - - - - Before we start: How to follow this tutorial — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - + Before we start: How to follow this tutorial — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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Before we start: How to follow this tutorial

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Before we start: How to follow this tutorial

+ +
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Before we start: How to follow this tutorial#

This tutorial contains a mix of lecture-based and interactive components. The interactive components can be executed in your personal computer locally or using the Binder service. You are welcome to follow along however you like, whether you just want to listen or code along with us.

@@ -292,8 +409,8 @@

Before we start: How to follow this tutorialAttention

Regardless of which setup method you choose, all of the Jupyter notebooks can be found in the docs/notebook folder.

-
-

Using Binder

+
+

Using Binder#

Clicking the Binder button below will launch an interactive computing environment with all of the necessary software packages pre-installed.

Binder

This is the easiest and quickest way to get started.

@@ -303,27 +420,27 @@

Using Binder

+

+
+

Local installation (“bare-metal”)#

If you would like to follow along using your own setup and you have a functional Python environment, you can run the following commands in your terminal:

# 1. clone this repository
-git clone https://github.com/nipreps/nipreps-book
+git clone https://github.com/nipreps/nipreps-book
 
 # 2. install the necessary python packages in your Python environment
-cd nipreps-book && pip install -r requirements.txt
+cd nipreps-book && pip install -r requirements.txt
 
 # 3. launch a Jupyter lab instance
-jupyter lab
+jupyter lab

The image registration lesson requires an installation of ANTs. Separate instructions can be found for Linux/MacOS users and Windows users.

-
-
-

Local installation (“docker containers”)

+ +
+

Local installation (“docker containers”)#

If you have a working Docker installation and would like to use the workshop’s Docker image, you can run the following command in your terminal:

-
docker run --rm -p 9999:8888 -e JUPYTER_ENABLE_LAB=yes nipreps/nipreps-book:latest
+
docker run --rm -p 9999:8888 -e JUPYTER_ENABLE_LAB=yes nipreps/nipreps-book:latest
 

 

 
This pulls the latest release of the nipreps/nipreps-book image from Docker Hub.
@@ -336,8 +453,8 @@ 
 Local installation (“docker containers”)jupyter_port

Docker destroys the container after notebook server exit.

-
-
+ + -
+ - -
- - Welcome! - NeuroImaging PREProcessing toolS (NiPreps) -
- -
-
-
-
-

- - By The NiPreps developers
- - © Copyright 2021.
-

-
-
-
+ + + + + + +
+ + + +
+ + +
+
+ Contents +
+
+
+ + +
+
+ +
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+By The NiPreps developers +

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-
+
+ +
- +

+ + © Copyright 2021. +
+ +

+
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\ No newline at end of file diff --git a/reports/tutorial/data.err.log b/reports/tutorial/data.err.log new file mode 100644 index 0000000..8e372e8 --- /dev/null +++ b/reports/tutorial/data.err.log @@ -0,0 +1,39 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +# import the class from the library +from eddymotion.dmri import DWI + +# load the sample file +dmri_dataset = DWI.from_filename("../../data/dwi.h5") +print(len(dmri_dataset)) +------------------ + + +--------------------------------------------------------------------------- +ModuleNotFoundError Traceback (most recent call last) +Cell In[5], line 2 + 1 # import the class from the library +----> 2 from eddymotion.dmri import DWI + 4 # load the sample file + 5 dmri_dataset = DWI.from_filename("../../data/dwi.h5") + +ModuleNotFoundError: No module named 'eddymotion.dmri' + diff --git a/reports/tutorial/models.err.log b/reports/tutorial/models.err.log new file mode 100644 index 0000000..ddea3b7 --- /dev/null +++ b/reports/tutorial/models.err.log @@ -0,0 +1,35 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +from eddymotion.dmri import DWI +from eddymotion.viz import plot_dwi +dmri_dataset = DWI.from_filename("../../data/dwi.h5") +------------------ + + +--------------------------------------------------------------------------- +ModuleNotFoundError Traceback (most recent call last) +Cell In[2], line 1 +----> 1 from eddymotion.dmri import DWI + 2 from eddymotion.viz import plot_dwi + 3 dmri_dataset = DWI.from_filename("../../data/dwi.h5") + +ModuleNotFoundError: No module named 'eddymotion.dmri' + diff --git a/reports/tutorial/registration.err.log b/reports/tutorial/registration.err.log new file mode 100644 index 0000000..468dda4 --- /dev/null +++ b/reports/tutorial/registration.err.log @@ -0,0 +1,70 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +import warnings +from IPython.display import HTML +import requests +from tempfile import mkstemp +from pathlib import Path +import numpy as np +import nibabel as nb + +from eddymotion.dmri import DWI +from eddymotion.viz import plot_dwi +from eddymotion.estimator import _advanced_clip + +warnings.filterwarnings("ignore") + +url = "https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2" +datapath = Path(mkstemp(suffix=".h5")[1]) +if datapath.stat().st_size == 0: + datapath.write_bytes( + requests.get(url, allow_redirects=True).content + ) + +dmri_dataset = DWI.from_filename(datapath) +datapath.unlink() + + +def _to_nifti( + data, affine, filename, clip=True +): + data = np.squeeze(data) + if clip: + data = _advanced_clip(data) + nb.Nifti1Image( + data, + affine, + None, + ).to_filename(filename) +------------------ + + +--------------------------------------------------------------------------- +ModuleNotFoundError Traceback (most recent call last) +Cell In[1], line 9 + 6 import numpy as np + 7 import nibabel as nb +----> 9 from eddymotion.dmri import DWI + 10 from eddymotion.viz import plot_dwi + 11 from eddymotion.estimator import _advanced_clip + +ModuleNotFoundError: No module named 'eddymotion.dmri' + diff --git a/reports/tutorial/solution.err.log b/reports/tutorial/solution.err.log new file mode 100644 index 0000000..064c902 --- /dev/null +++ b/reports/tutorial/solution.err.log @@ -0,0 +1,55 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +import warnings + +warnings.filterwarnings("ignore") + +from tempfile import mkstemp +from pathlib import Path +import requests + +from eddymotion.dmri import DWI + +url = "https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2" +datapath = Path(mkstemp(suffix=".h5")[1]) +if datapath.stat().st_size == 0: + datapath.write_bytes( + requests.get(url, allow_redirects=True).content + ) + +dmri_dataset = DWI.from_filename(datapath) +dmri_dataset.dataobj = dmri_dataset.dataobj[..., :32] +dmri_dataset.gradients = dmri_dataset.gradients[..., :32] +datapath.unlink() +------------------ + + +--------------------------------------------------------------------------- +ModuleNotFoundError Traceback (most recent call last) +Cell In[1], line 9 + 6 from pathlib import Path + 7 import requests +----> 9 from eddymotion.dmri import DWI + 11 url = "https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2" + 12 datapath = Path(mkstemp(suffix=".h5")[1]) + +ModuleNotFoundError: No module named 'eddymotion.dmri' + diff --git a/search.html b/search.html index a45c0bc..c8eeb48 100644 --- a/search.html +++ b/search.html @@ -1,297 +1,430 @@ - + + + - - Search — NiPreps - - - - - - - - + Search - NiPreps + + + + + + + + + - - + + + + + - - - - - - - - - - - + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - + + + - - - + + - - - - + + + + +
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10, 15], "valid": [8, 14], "variabl": 3, "veri": 3, "visual": [5, 10], "we": 9, "welcom": 15, "what": [8, 14]}}) \ No newline at end of file diff --git a/tutorial/data.html b/tutorial/data.html index 737f5ea..e6aba13 100644 --- a/tutorial/data.html +++ b/tutorial/data.html @@ -1,325 +1,412 @@ - - - - - Introduction to dMRI data — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + Introduction to dMRI data — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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    Introduction to dMRI data

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    Introduction to dMRI data

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    Introduction to dMRI data#

    Diffusion imaging probes the random, microscopic movement of water molecules by using MRI sequences that are sensitive to the geometry and environmental organization surrounding these protons. This is a popular technique for studying the white matter of the brain. The diffusion within biological structures, such as the brain, are often restricted due to barriers (e.g., cell membranes), resulting in a preferred direction of diffusion (anisotropy). @@ -338,8 +425,8 @@

    Introduction to dMRI data -

    Python and object oriented programming

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    Python and object oriented programming#

    Python is an object oriented programming language. It allows us to represent and encapsulate data types and corresponding behaviors into programming structures called objects.

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    Python and object oriented programmingdef _data_repr(value): if value is None: return "None" - return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>" + return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>" @attr.s(slots=True) class DWI: - """Data representation structure for dMRI data.""" + """Data representation structure for dMRI data.""" dataobj = attr.ib(default=None, repr=_data_repr) - """A numpy ndarray object for the data array, without *b=0* volumes.""" + """A numpy ndarray object for the data array, without *b=0* volumes.""" brainmask = attr.ib(default=None, repr=_data_repr) - """A boolean ndarray object containing a corresponding brainmask.""" + """A boolean ndarray object containing a corresponding brainmask.""" bzero = attr.ib(default=None, repr=_data_repr) - """A *b=0* reference map, preferably obtained by some smart averaging.""" + """A *b=0* reference map, preferably obtained by some smart averaging.""" gradients = attr.ib(default=None, repr=_data_repr) - """A 2D numpy array of the gradient table in RAS+B format.""" + """A 2D numpy array of the gradient table in RAS+B format.""" em_affines = attr.ib(default=None) - """ + """ List of :obj:`nitransforms.linear.Affine` objects that bring DWIs (i.e., no b=0) into alignment. """ def __len__(self): - """Obtain the number of high-*b* orientations.""" + """Obtain the number of high-*b* orientations.""" return self.gradients.shape[-1]

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    Python and object oriented programmingprint() statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.

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    Using the new data representation object

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    Using the new data representation object#

    The code shown above was just a snippet of the DWI class. For simplicity, we will be using the full implementation of this class from our eddymotion package Under the data/ folder of this book’s distribution, we have stored a sample DWI dataset with filename dwi.h5. Please note that the file has been minimized by zeroing all but two diffusion-weighted orientation maps.

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    Using the new data representation object -
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    ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[5], line 2
+      1 # import the class from the library
+----> 2 from eddymotion.dmri import DWI
+      4 # load the sample file
+      5 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
+
+ModuleNotFoundError: No module named 'eddymotion.dmri'
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    Using the new data representation object -
    DWI(dataobj=<118x118x78x102 (int16)>, affine=<4x4 (float64)>, brainmask=None, bzero=<118x118x78 (int16)>, gradients=<4x102 (float32)>, em_affines=None, fieldmap=None)
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    We’ll go over some of the components of dmri_dataset through this lesson.

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    Visualizing the data

    + +
    +

    Visualizing the data#

    Exercise

    Let’s start out by seeing what the data looks like. @@ -464,14 +554,17 @@

    Visualizing the data

    Solution

    +
    + + +Hide code cell content + 
    dmri_dataset.plot_mosaic();
    -
    -../_images/data_13_0.png -
    +

    When calling plot_mosaic() without any arguments, the b=0 reference is plotted. This b=0 reference is a map of the signal measured without gradient sensitization, or in other words, when we are not measuring diffusion in any direction. @@ -483,33 +576,39 @@

    Visualizing the data

    Solution

    +
    + + +Hide code cell content + 
    dmri_dataset.plot_mosaic(index=10, vmax=5000);
    -
    -../_images/data_15_0.png -
    +
    +
    + + +Hide code cell content + 
    dmri_dataset.plot_mosaic(index=100, vmax=5000);
    -
    -../_images/data_16_0.png -
    +

    Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal. As we can see, diffusion-weighted images consistently drop almost all signal in voxels filled with cerebrospinal fluid because there, water diffusion is free (isotropic) regardless of the direction that is being measured.

    -

    We can also see that the images at index=10 and index=100 have different gradient strength (“b-value”). +

    We can also see that the images at index=10 and index=100 have different gradient strength (”b-value”). The higher the magnitude of the gradient, the more diffusion that is allowed to occur, indicated by the overall decrease in signal intensity. Stronger gradients yield diffusion maps with substantially lower SNR (signal-to-noise ratio), as well as larger distortions derived from the so-called “Eddy-currents”.

    -

    -
    +
    +

    Visualizing the gradient information#

    Our DWI object stores the gradient information in the gradients attribute.

    Exercise

    @@ -517,16 +616,17 @@

    Visualizing the gradient information +
    + + +Hide code cell content + 
    dmri_dataset.gradients.shape
    -
    -
    (4, 102)
-
    -
    -
    +

    We get a \(4\times102\) – three spatial coordinates (\(b_x\), \(b_y\), \(b_z\)) of the unit-norm “b-vector”, plus the gradient sensitization magnitude (the “b-value”), with a total of 102 different orientations for the case at hand.

    @@ -536,117 +636,17 @@

    Visualizing the gradient information +
    + + +Hide code cell content + 
    print(dmri_dataset.gradients.T)
    -
    -
    [[-6.04914188e-01 -6.79875135e-01 -4.14546251e-01  3.00000000e+02]
- [ 7.51762651e-03  9.17056799e-01 -3.98685753e-01  3.00000000e+02]
- [-8.86579394e-01  3.21218759e-01 -3.32859576e-01  3.00000000e+02]
- [ 8.16364467e-01  2.68300444e-01 -5.11433184e-01  3.00000000e+02]
- [ 4.34362024e-01 -7.33946085e-01 -5.22161603e-01  3.00000000e+02]
- [ 9.60559174e-02 -7.31587410e-02  9.92683768e-01  3.05000000e+02]
- [ 5.85605621e-01  7.62755051e-02  8.06999445e-01  1.00500000e+03]
- [ 1.32749856e-01  3.81094962e-01 -9.14955795e-01  9.95000000e+02]
- [-9.10080016e-01  3.49084914e-01  2.23369867e-01  1.00000000e+03]
- [-1.42672375e-01 -9.87846315e-01 -6.16788231e-02  1.00000000e+03]
- [-3.14908028e-01  4.70339447e-01  8.24386895e-01  1.00500000e+03]
- [ 9.26234543e-01  3.46899480e-01 -1.47479758e-01  1.00000000e+03]
- [ 6.95218086e-01  6.17206156e-01 -3.68413389e-01  9.95000000e+02]
- [ 1.79834515e-01 -9.48279917e-01  2.61581242e-01  1.00000000e+03]
- [-5.72267652e-01  8.19448650e-01 -3.18384580e-02  1.00000000e+03]
- [ 6.62053525e-01 -3.44300926e-01  6.65689111e-01  1.00500000e+03]
- [-1.81866080e-01  7.04759300e-01 -6.85739756e-01  9.95000000e+02]
- [-6.36256397e-01 -7.67920136e-01 -7.40036741e-02  1.00000000e+03]
- [-2.50486493e-01 -7.25317061e-01  6.41226709e-01  1.00500000e+03]
- [-1.81372240e-01 -5.50868332e-01 -8.14646065e-01  9.95000000e+02]
- [ 2.51025885e-01 -9.41051543e-01 -2.26733208e-01  1.00000000e+03]
- [ 8.59194577e-01  4.16565776e-01  2.97081828e-01  1.00000000e+03]
- [-5.87923348e-01 -4.98144925e-01 -6.37336493e-01  9.95000000e+02]
- [-2.08524555e-01  6.58749491e-02  9.75796103e-01  1.00500000e+03]
- [-8.81649256e-01 -4.38893167e-03 -4.71884906e-01  9.95000000e+02]
- [-6.17551386e-01  6.92788363e-01  3.72390330e-01  1.00000000e+03]
- [-2.11339086e-01 -1.57031611e-01 -9.64715958e-01  9.95000000e+02]
- [ 5.63584268e-01  3.84537101e-01 -7.31097817e-01  9.95000000e+02]
- [-1.04902856e-01  7.80609608e-01  6.16152585e-01  1.00500000e+03]
- [-3.27980161e-01 -8.34259689e-01 -4.43215251e-01  1.00000000e+03]
- [ 9.97081280e-01 -3.60786468e-02  6.72850981e-02  1.00000000e+03]
- [-3.64686340e-01 -8.92125309e-01  2.66676456e-01  1.00000000e+03]
- [ 2.97879040e-01 -3.06824297e-01  9.03950751e-01  1.00500000e+03]
- [ 7.01884270e-01 -3.75511825e-01 -6.05267942e-01  9.95000000e+02]
- [ 5.85624397e-01 -2.57630497e-02 -8.10173035e-01  9.95000000e+02]
- [ 8.74492407e-01  2.43720673e-02 -4.84426469e-01  9.95000000e+02]
- [ 5.51209927e-01 -7.06570625e-01  4.43762958e-01  1.00500000e+03]
- [ 8.55460048e-01 -4.86520320e-01  1.77443221e-01  1.00000000e+03]
- [-6.43364310e-01 -2.89421439e-01 -7.08743691e-01  1.99500000e+03]
- [ 1.21839322e-01  3.67268413e-01  9.22100365e-01  2.01000000e+03]
- [ 8.67053568e-01 -2.20949143e-01 -4.46541786e-01  1.99500000e+03]
- [-7.46472776e-01  8.38321110e-04 -6.65415406e-01  1.99500000e+03]
- [ 3.49373311e-01  4.06477451e-01 -8.44224155e-01  1.99000000e+03]
- [-5.60097814e-01  1.76819220e-01 -8.09336364e-01  1.99500000e+03]
- [-4.30994742e-02  9.74978864e-01  2.18079522e-01  2.00000000e+03]
- [-2.40879446e-01  8.55759144e-01  4.57879245e-01  2.00500000e+03]
- [-7.79660225e-01 -6.15284383e-01  1.16426118e-01  2.00000000e+03]
- [ 4.48004901e-01 -8.49978268e-01  2.77179599e-01  2.00500000e+03]
- [ 3.74817163e-01 -9.27097678e-01 -1.41696935e-03  2.00000000e+03]
- [ 6.10155761e-01 -5.36033213e-01  5.83419502e-01  2.00500000e+03]
- [ 9.50321257e-01 -1.07752405e-01  2.92025506e-01  2.00000000e+03]
- [-8.09516788e-01  2.45182857e-01 -5.33449113e-01  1.99500000e+03]
- [ 9.56617057e-01 -2.84275055e-01  6.38084337e-02  2.00000000e+03]
- [ 4.44206297e-01  6.86384022e-01  5.75810552e-01  2.00500000e+03]
- [ 9.54805911e-01  1.77383393e-01  2.38497064e-01  2.00000000e+03]
- [ 6.78448856e-01  4.65783864e-01 -5.68113148e-01  1.99500000e+03]
- [-1.22661926e-01 -5.87784886e-01  7.99664319e-01  2.00500000e+03]
- [ 4.10763621e-01 -1.32623598e-01 -9.02044475e-01  1.99000000e+03]
- [-9.28761899e-01 -1.47203013e-01  3.40195030e-01  2.00500000e+03]
- [-4.32663143e-01  4.41911817e-01 -7.85822213e-01  1.99500000e+03]
- [-1.73938900e-01 -7.22244203e-01 -6.69409096e-01  1.99500000e+03]
- [ 3.93927962e-01  4.76803184e-01  7.85798609e-01  2.00500000e+03]
- [ 2.16246262e-01 -8.66339266e-01  4.50215280e-01  2.00500000e+03]
- [ 5.89937627e-01 -5.72140634e-01 -5.69761992e-01  1.99500000e+03]
- [ 3.72014821e-01  9.28178847e-01 -9.43302456e-03  2.00000000e+03]
- [ 8.53485584e-01 -5.17996848e-01 -5.69352657e-02  2.00000000e+03]
- [ 2.51451343e-01  9.32365298e-01  2.59744376e-01  2.00000000e+03]
- [ 6.35405779e-02 -5.95680475e-01 -8.00704300e-01  1.99500000e+03]
- [-7.51151621e-01 -6.28546119e-01 -2.01744899e-01  2.00000000e+03]
- [-1.09398387e-01  9.92227495e-01 -5.93008772e-02  2.00000000e+03]
- [-5.18000782e-01  3.63550693e-01  7.74277806e-01  2.00500000e+03]
- [-8.37094843e-01 -4.31998879e-01  3.35632533e-01  2.00500000e+03]
- [ 7.27129698e-01  1.49743423e-01 -6.69969618e-01  1.99500000e+03]
- [-5.17419696e-01 -7.40754366e-01  4.28438783e-01  2.00500000e+03]
- [ 7.27875590e-01  4.97373462e-01  4.72034663e-01  2.00500000e+03]
- [-3.26542199e-01  7.36291781e-02 -9.42310452e-01  1.99000000e+03]
- [-6.14776909e-02 -8.78977180e-01 -4.72884387e-01  1.99500000e+03]
- [-7.73990631e-01  5.15239596e-01 -3.68057936e-01  1.99500000e+03]
- [-3.58383171e-03  1.07271522e-01  9.94223297e-01  2.01000000e+03]
- [-1.60011083e-01  5.34122169e-01 -8.30126464e-01  1.99000000e+03]
- [ 9.88182724e-01  1.43299058e-01 -5.44080175e-02  2.00000000e+03]
- [-9.05943990e-01 -4.18793827e-01 -6.22674413e-02  2.00000000e+03]
- [ 2.03286484e-01  1.53597519e-01 -9.66996610e-01  1.99000000e+03]
- [ 4.36532289e-01  6.08863235e-01 -6.62363291e-01  1.99500000e+03]
- [ 6.82894647e-01 -7.17134774e-01 -1.39185578e-01  2.00000000e+03]
- [ 7.86479354e-01 -4.98139858e-01 -3.65112156e-01  1.99500000e+03]
- [-4.65987802e-01  8.39519799e-01  2.79395491e-01  2.00000000e+03]
- [ 7.21606970e-01 -1.46813065e-01 -6.76556945e-01  1.99500000e+03]
- [ 5.12317955e-01  8.17944586e-01  2.61719227e-01  2.00000000e+03]
- [ 3.80590975e-01 -6.98121727e-01  6.06445849e-01  2.00500000e+03]
- [ 3.18718821e-01 -6.50580406e-01 -6.89320982e-01  1.99500000e+03]
- [ 4.12478864e-01  2.02624902e-01  8.88146579e-01  2.00500000e+03]
- [ 1.32435739e-01  9.71343756e-01 -1.97362855e-01  1.99500000e+03]
- [-9.72415447e-01  1.24751076e-01  1.97092354e-01  2.00000000e+03]
- [-2.45995522e-01 -8.56899619e-01  4.53000307e-01  2.00500000e+03]
- [ 1.21794799e-02  7.88615406e-01 -6.14766181e-01  1.99500000e+03]
- [-8.40300739e-01 -2.33427882e-01 -4.89291459e-01  1.99500000e+03]
- [ 4.91548866e-01  1.66130796e-01 -8.54856849e-01  1.99000000e+03]
- [ 6.91248178e-01 -6.99574709e-01  1.81028187e-01  2.00000000e+03]
- [-5.76671958e-01 -8.02036345e-01  1.55522212e-01  2.00000000e+03]
- [-7.08378106e-02  2.88473666e-01 -9.54863846e-01  1.99000000e+03]
- [-1.85595229e-01  3.32799673e-01  9.24553275e-01  2.01000000e+03]]
-
    -
    -
    +

    Later, we’ll refer to this array as the gradient table.

    It consists of one row per diffusion-weighted image, with each row consisting of 4 values corresponding to [ R A S+ b ].

    @@ -661,15 +661,12 @@

    Visualizing the gradient information -../_images/data_22_0.png -

    We’ve projected all of the gradient directions onto the surface of a sphere, with each unique gradient strength colour-coded. Darkest hues correspond to the lowest b-values and brighter to the highest.

    -
    -
    -

    The LOGO (leave-one-gradient-out) splitter

    + +
    +

    The LOGO (leave-one-gradient-out) splitter#

    One final behavior that will make our endeavor easier in the long run is a convenience method for data splitting. In particular, we are implementing some sort of cross-validation scheme where we will iterate over different data splits. In this case, the splitting strategy is a simple leave-one-out. @@ -677,7 +674,7 @@

    The LOGO (leave-one-gradient-out) splitter 
    def logo_split(self, index, with_b0=False):
-    """
+    """
     Produce one fold of LOGO (leave-one-gradient-out).
 
     Parameters
@@ -737,9 +734,6 @@ 
    The LOGO (leave-one-gradient-out) splitter
    -
    -../_images/data_26_0.png -

    data_train is a tuple containing all diffusion-weighted volumes and the corresponding gradient table, excluding the left-out, which is stored in data_test (the 11th gradient indexed by 10, in this example). data_test[0] contains the held-out diffusion-weighted volume and data_test[1], the corresponding gradient table.

    @@ -749,18 +743,18 @@

    The LOGO (leave-one-gradient-out) splitter

    Solution

    +
    + + +Hide code cell content + 
    print(f"data_train[0] is the DW maps dataset and has {data_train[0].shape} dimensions")
 print(f"data_train[1] is a gradient table and has {data_train[1].shape} dimensions")
    -
    -
    data_train[0] is the DW maps dataset and has (118, 118, 78, 101) dimensions
-data_train[1] is a gradient table and has (4, 101) dimensions
-
    -
    -
    +

    Exercise

    @@ -768,25 +762,25 @@

    The LOGO (leave-one-gradient-out) splitter

    Solution

    +
    + + +Hide code cell content + 
    print(f"data_test[0] is left-out DW map and has {data_test[0].shape} dimensions")
 print(f"data_test[1] is the corresponding DW gradient and has {data_test[1].shape} dimensions")
    -
    -
    data_test[0] is left-out DW map and has (118, 118, 78) dimensions
-data_test[1] is the corresponding DW gradient and has (4,) dimensions
-
    -
    -
    +
    -

    -
    -

    Next steps: diffusion modeling

    + +
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    Next steps: diffusion modeling#

    By modeling the diffusion signal, the acquired images can provide measurements which are related to the microscopic changes and estimate white matter trajectories.

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    - - By The NiPreps developers
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    The problem of head-motion in dMRI

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    The problem of head-motion in dMRI#

    A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object. Rigid-body, bulk-motion of the head will degrade every image, even if the experimenters closely followed all the standard operation procedures and carefully prepared the experiment (e.g., setting correctly the head paddings), and even if the participant was experienced with the MR settings and strictly followed indications to avoid any movement outside time windows allocated for rest. This effect is exacerbated by the length of the acquisition (longer acquisitions will have more motion), and is not limited to humans. @@ -320,24 +415,24 @@

    The problem of head-motion in dMRI

    -
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    Dimensions of the head-motion problem

    +
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    Dimensions of the head-motion problem#

    These sudden and unavoidable motion of the head (for instance, when the participant swallowed) result in two degrading consequences that confuse the diffusion model through which we will attempt to understand the data:

    • Misalignment between the different angular samplings (between-volume), which means that the same (i, j, k) voxel in one orientation will not contain a diffusion measurement of exactly the same anatomical location of the rest of the orientations (see these slides by Dr. A. Yendiki in 2013).

    • Attenuation in the recorded intensity of a particular orientation (within-volume), especially present when the sudden motion occurred during the diffusion-encoding gradient pulse.

    While we can address the between-volume motion using head motion correction, the within-volume motion is often identified as a signal outlier and omitted from further analysis.

    -
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    Objective: Implement a head-motion estimation code

    + +
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    Objective: Implement a head-motion estimation code#

    This tutorial focuses on the misalignment problem. We will build from existing software (DIPY for diffusion modeling and ANTs for image registration), as well as commonplace Python libraries (NumPy), a software framework for head-motion estimation in diffusion MRI data.

    The algorithmic and theoretical foundations of the method are based on an idea first proposed by Ben-Amitay et al. and later implemented in QSIPREP (see this OHBM 2019 poster). The idea works as follows:

    Algorithm

    -
      +

      1. Leave one DWI (diffusion weighted image) orientation out.

      2. Using the rest of the dataset, impute the excluded orientation using a diffusion model. Because it was generated based on the remainder of the data, the simulated volume will be @@ -346,8 +441,8 @@

        Objective: Implement a head-motion estimation code

        Iterate over the whole dataset until convergence.

    -
    -

    Step 1: Identify an I/O (inputs/outputs) specification

    +
    +

    Step 1: Identify an I/O (inputs/outputs) specification#

    Briefly anticipate what are the inputs to your new algorithm and the expected outcomes.

    Inputs

    @@ -386,9 +481,9 @@

    Step 1: Identify an I/O (inputs/outputs) specification

    Parallelism: simulation and registration are CPU-intensive processes - for the runtime to be in a manageable scale, we’ll need to leverage parallelism.

    -
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    +
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    Step 2: Sketch out an API (Application Programming Interface)#

    Plan how the new software will expose the implementation downstream. Assuming our DWI data is encapsulated in an object (holding not just the data array, but also metadata such as the gradient table) pointed at by the variable data, and assuming we have a list of rigid-body transform matrices to initialize the algorithm (mats), @@ -402,9 +497,9 @@

    Step 2: Sketch out an API (Application Programming Interface)corrected = estimator.predict(data)

    -
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    Step 3: Software architecture

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    Step 3: Software architecture#

    Once the problem is well defined, we will leverage object orientation programming to implement the solution. In our case, we have four major components that will allow us to meet the requirements above:

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    + + -
    -

    Diffusion modeling

    + +
    + +
    +

    Diffusion modeling#

    The proposed method requires inferring a motion-less, reference DW map for a given diffusion orientation for which we want to estimate the misalignment. Inference of the reference map is achieved by first fitting some diffusion model (which we will draw from DIPY) using all data, except the particular DW map that is to be aligned. This data splitting scheme was introduced in the LOGO splitter section in Introduction to dMRI data.

    All models are required to offer the same API (application programmer interface):

    -
      +

      1. The initialization takes a DIPY GradientTable as the first argument, and then arbitrary parameters as keyword arguments.

      2. A fit(data) method, which only requires a positional argument data, a 4D array with DWI data.

      3. A predict(gradient_table) method, which only requires a GradientTable as input. @@ -355,9 +430,21 @@

        Diffusion modeling +
        ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[2], line 1
+----> 1 from eddymotion.dmri import DWI
+      2 from eddymotion.viz import plot_dwi
+      3 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
+
+ModuleNotFoundError: No module named 'eddymotion.dmri'
+
        +
        +

    -

    As expected, the b=0 doesn’t look very much like the particular left-out direction, but it is a start!

    @@ -436,13 +520,10 @@

    Implementing a trivial model -../_images/models_13_0.png -

    -
    -
    -

    Implementing a regression to the mean model

    + +
    +

    Implementing a regression to the mean model#

    Exercise

    Extend the TrivialB0Model to produce an average of all other diffusion directions, instead of the b=0.

    @@ -450,20 +531,20 @@

    Implementing a regression to the mean model 
    class AverageDWModel:
-    """A trivial model that returns an average map."""
+    """A trivial model that returns an average map."""
 
     __slots__ = ("_data",)
 
     def __init__(self, gtab, **kwargs):
-        """Implement object initialization."""
+        """Implement object initialization."""
         return  # do nothing at initialization time
 
     def fit(self, data, **kwargs):
-        """Calculate the average."""
+        """Calculate the average."""
         # self._data =  # Use numpy to calculate the average.
 
     def predict(self, gradient, **kwargs):
-        """Return the average map."""
+        """Return the average map."""
         return self._data
    @@ -471,26 +552,32 @@

    Implementing a regression to the mean model

    Solution

    +
    + + +Hide code cell content + 
    class AverageDWModel:
-    """A trivial model that returns an average map."""
+    """A trivial model that returns an average map."""
 
     __slots__ = ("_data",)
 
     def __init__(self, gtab, **kwargs):
-        """Implement object initialization."""
+        """Implement object initialization."""
         return  # do nothing at initialization time
 
     def fit(self, data, **kwargs):
-        """Calculate the average."""
+        """Calculate the average."""
         self._data =  data.mean(-1)
 
     def predict(self, gradient, **kwargs):
-        """Return the average map."""
+        """Return the average map."""
         return self._data
    +

    Exercise

    @@ -498,6 +585,11 @@

    Implementing a regression to the mean model

    Solution

    +
    + + +Hide code cell content + 
    model = AverageDWModel(
     dmri_dataset.gradients,
@@ -509,18 +601,15 @@ 
    Implementing a regression to the mean model
    -
    -../_images/models_19_0.png -../_images/models_19_1.png -
    +
    -

    -

    +
    +

    Investigating the tensor model#

    Now, we are ready to use the diffusion tensor model. We will use the wrap around DIPY’s implementation that we distribute with eddymotion.

    -
    -

    The model factory

    +
    +

    The model factory#

    To permit flexibility in selecting models, the eddymotion package offers a ModelFactory that implements the facade design pattern. This means that ModelFactory makes it easier for the user to switch between models:

    @@ -538,9 +627,9 @@

    The model factory -

    Leveraging the fit() / predict() API

    +

    +
    +

    Leveraging the fit() / predict() API#

    The ModelFactory returns a model object that is compliant with the interface sketched above:

    @@ -557,9 +646,6 @@

    Leveraging the

    -
    -../_images/models_27_0.png -

    Here’s the original DW map, for reference:

    @@ -568,9 +654,6 @@

    Leveraging the

    -
    -../_images/models_29_0.png -

    Exercise

    @@ -578,6 +661,11 @@

    Leveraging the

    Solution

    +
    + + +Hide code cell content + 
    model = ModelFactory.init(
     gtab=data_train[1],
@@ -587,6 +675,7 @@ 
    Leveraging the
    +

    Once the model has been initialized, we can easily generate a new prediction.

    @@ -598,18 +687,14 @@

    Leveraging the

    -
    -../_images/models_33_0.png -../_images/models_33_1.png -
    -
    -
    -
    -

    Next steps: image registration

    + + +
    +

    Next steps: image registration#

    Once we have our model factory readily available, it will be easy to generate predictions that we can use for reference in image registration.

    -
    -
    + + -
    + - -
    - - Introduction to dMRI data - Image registration (spatial alignment) -
    - -
    - -
    -
    -

    - - By The NiPreps developers
    - - © Copyright 2021.
    -

    -
    -
    - + + + + + + + + + + +
    + + +
    +
    + Contents +
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    +
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    +By The NiPreps developers +

    -
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    + + © Copyright 2021. +
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    +
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    \ No newline at end of file diff --git a/tutorial/registration.html b/tutorial/registration.html index 2ff0801..93c75ee 100644 --- a/tutorial/registration.html +++ b/tutorial/registration.html @@ -1,306 +1,409 @@ - - - - - Image registration (spatial alignment) — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + Image registration (spatial alignment) — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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    Image registration (spatial alignment)

    + +
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    Image registration (spatial alignment)

    + +
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    Image registration (spatial alignment)#

    At this point of the tutorial we have covered two of the three initial requirements:

    -
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    Example registration

    + +
    +

    Example registration#

    It is beyond the scope of this tutorial to understand ANTs and/or image registration altogether, but let’s have a look at how registration is integrated. First, we’ll need to generate one target gradient prediction following all the steps learned previously. For this example, we have selected the 8th DW map (index=7) because it contains a sudden motion spike, resembling a nodding movement.

    @@ -432,8 +535,6 @@

    Example registration

    -
    -

    Let’s configure ANTs via NiPype:

    @@ -464,11 +565,6 @@

    Example registration

    -
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    'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initialize-transforms-per-stage 0 --interpolation BSpline --output [ transform, transform_Warped.nii.gz ] --transform Rigid[ 0.01 ] --metric GC[ /tmp/tmpoqu912td/fixed.nii.gz, /tmp/tmpoqu912td/moving.nii.gz, 1, 32, Regular, 0.05 ] --convergence [ 100x50x10, 1e-06, 5 ] --smoothing-sigmas 4.0x2.0x2.0vox --shrink-factors 4x2x1 --use-estimate-learning-rate-once 0 --use-histogram-matching 1 --transform Affine[ 0.001 ] --metric GC[ /tmp/tmpoqu912td/fixed.nii.gz, /tmp/tmpoqu912td/moving.nii.gz, 1, 32, Random, 0.1 ] --convergence [ 50, 1e-07, 2 ] --smoothing-sigmas 0.0vox --shrink-factors 1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 -v --winsorize-image-intensities [ 0.0001, 0.9998 ]  --write-composite-transform 0'
-
    -
    -

    Nipype interfaces can be submitted for execution with the run() method:

    @@ -493,12 +589,10 @@

    Example registration

    -
    -
    -
    -
    -

    Resampling an image

    + +
    +

    Resampling an image#

    Once we have estimated what is the transform that brings two images into alignment, we can bring the data in the moving image and move this image into the reference’s grid through resampling.

    The process works as follows:

    nitransforms

    @@ -515,14 +609,6 @@

    Resampling an image -
    array([[ 0.99606636, -0.00534955,  0.00189532,  0.14150404],
-       [ 0.00441187,  0.9964563 , -0.01533533, -0.2574495 ],
-       [-0.00354967,  0.02485858,  0.99336894, -0.91179065],
-       [ 0.        ,  0.        ,  0.        ,  1.        ]])
-
    -
    -

    Resampling an image requires two pieces of information: the reference image (which provides the new grid where we want to have the data) and the moving image which contains the actual data we are interested in:

    @@ -543,13 +629,16 @@

    Resampling an image -
    -

    Exercise

    Use the display() function to visualize the image aligned as generated by ANTs vs. that generated by NiTransforms – they should be aligned!.

    Solution

    +
    + + +Hide code cell content + 
    display(
     result.outputs.warped_image,
@@ -560,13 +649,12 @@ 
    Resampling an image
-
    
-
    
-
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    + + -
    + - -
    - - Diffusion modeling - Putting everything together -
    - -
    -
    -
    -
    -

    - - By The NiPreps developers
    - - © Copyright 2021.
    -

    -
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    + Contents +
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    +By The NiPreps developers +

    -
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    + + © Copyright 2021. +
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    +
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    \ No newline at end of file diff --git a/tutorial/solution.html b/tutorial/solution.html index 61022e0..1fcb505 100644 --- a/tutorial/solution.html +++ b/tutorial/solution.html @@ -1,299 +1,406 @@ - - - - - Putting everything together — NiPreps - - - - - - - + - - + + + - - - - - - - - - - - - - - - - - - - - - - - - + Putting everything together — NiPreps + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - -
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    Putting everything together

    + +
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    Putting everything together#

    Once we have finalized the main components of the solution, it is time for integration. We now want to iterate over all the LOGO partitions of the dataset, generate a synthetic reference through the model of choice, and finally estimate the misalignment between the left-out gradient and the synthetic reference. This solution, must also abide by the API we have envisioned.

    @@ -302,7 +409,7 @@

    Putting everything together
    class EddyMotionEstimator:
-    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
+    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
 
     @staticmethod
     def fit(
@@ -313,7 +420,7 @@ 
    Putting everything togethermodel="b0",
         **kwargs,
     ):
-        r"""
+        r"""
         Estimate head-motion and Eddy currents.
 
         <please write a descriptive documentation of the function here>
@@ -372,9 +479,14 @@ 
    Putting everything together
+
    
+
+
+Hide code cell content
+
 
    
 
    class EddyMotionEstimator:
-    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
+    """Estimates rigid-body head-motion and distortions derived from eddy-currents."""
 
     @staticmethod
     def fit(
@@ -385,7 +497,7 @@ 
    Putting everything togethermodel="b0",
         **kwargs,
     ):
-        r"""
+        r"""
         Estimate head-motion and Eddy currents.
 
         Parameters
@@ -466,6 +578,7 @@ 
    Putting everything together
    from eddymotion.estimator import EddyMotionEstimator
@@ -473,8 +586,8 @@ 
    Putting everything togetherestimated_affines = EddyMotionEstimator.fit(dmri_dataset, model="b0")
 
    
 
    
-
    
-
    What’s next? - Testing!
    
+
    
+
    What’s next? - Testing!#
    
 
    Once we have our first implementation functional, we should think of some unit-tests for our code.
    
 
    
 
    Exercise
    
@@ -483,6 +596,11 @@ 
    What’s next? - Testing!
 
    Solution: in this solution, we are using pytest to integrate the test into a higher-level test suite.
    
 
    
+
    
+
+
+Hide code cell content
+
 
    
 
    import numpy as np
 import pytest
@@ -501,10 +619,11 @@ 
    What’s next? - Testing!
    
 
    
 
    
+
    
 
    
-
    
-
    
+
    
+
    And after testing? - Validation!#
    
 
    Once we have a sufficient portion of our code covered by unit-tests, then we would add some integration tests that give us confidence that our bullet-proof individual components also work together.
 Only after we have both steps secure, we can run benchmarks and evaluations from which we learn whether our solution works, and characterize its limitations.
    
 
    The main strategy to validate this software would entail finding/acquiring a special dataset where motion is not present or extremely low, in which we introduce a known head-motion pattern with which we are going to challenge our estimator.
@@ -516,8 +635,8 @@ 
    And after testing? - Validation!TrivialB0Model, for instance).
    
 
 
    Please head to the GitHub repository and share your ideas with us! We are welcoming new contributors!
    
-
    
-
    
+

    + -
    + - -
    + + + + + + + +
    - Image registration (spatial alignment) - The extra mile + + +
    -
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    - - By The NiPreps developers
    - - © Copyright 2021.
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    +By The NiPreps developers +

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    Welcome!

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    Welcome!

    -
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    Implementing a head-motion correction algorithm for diffusion MRI in Python, using DIPY and NiTransforms

    + +
    + +
    +

    Welcome!#

    +
    +

    Implementing a head-motion correction algorithm for diffusion MRI in Python, using DIPY and NiTransforms#

    Summary. This tutorial walks attendees through the development of one fundamental step in the pre-processing of diffusion MRI data using a community-driven approach and relying on existing tools. The tutorial justifies the NiPreps approach to pre-processing, describing how the framework attempts to enhance or extend the MRI scanner to produce “analysis-grade” data. This is important because data produced by the scanner is typically not digestible for statistical analyses directly.

    Researchers resort to either:

    -
      +

      1. modifying their experimental design so that it matches the requirements of large-scale studies that have made all of their software tools publicly available

      2. creating custom pre-processing pipelines tailored to each particular study

      @@ -293,6 +424,7 @@

      Implementing a head-motion correction algorithm for diffusion MRI in Pyt

    1. Discover how to integrate some of the tools in the NiPreps framework

    +
    @@ -301,8 +433,7 @@

    Implementing a head-motion correction algorithm for diffusion MRI in Pyt

    -
    -
    + -
    + - -
    + + + + + + + +
    - Before we start: How to follow this tutorial + + +
    -
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    - - By The NiPreps developers
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