Initial imports
-from hoki.age_utils import AgeWizard
+from hoki.age_utils import AgeWizard
import hoki
import pandas as pd
import matplotlib.pyplot as plt
@@ -469,6 +506,7 @@ Motivation
How it works¶
By matching the observed properties of your sources to the BPASS models you can find the most likely age of a location on the HRD/CMD.
+
If all your sources belong to the same region, you can then combine the PDFs of your individual sources to find the age PDF of the whole cluster.
All of this can be done step by step using the chain find_coordinates() -> calculate_pdfs -> multiply_pdfs(), or you could simply use AgeWizard() which handles this pipeline and facilitates loading in the models.
Note: In this tutorial we will focus on AgeWizard().
@@ -937,11 +975,11 @@ Most Likely Ages[8]:
-
-array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,
+
+
+array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,
6.9, 6.9, 6.9])
-
-
+
Different metallicities will give different results. In this example we summarise our results into a new DataFrame:
@@ -1215,10 +1253,10 @@ Plotting Aggregate Ages[17]:
-
-array([6.9])
-
-
+
+
+array([6.9])
+
Down below we create a quick function to plot our aggregate ages for z=0.006 and z=0.008.
As an example of the
@@ -1387,10 +1425,10 @@ Plotting Aggregate Ages[24]:
-
-<matplotlib.legend.Legend at 0x7f6b320e16d8>
-
-
+
+
+<matplotlib.legend.Legend at 0x7f6b320e16d8>
+
@@ -1419,8 +1457,9 @@ Probability given an age range[28]:
-
-name
+
+
+name
Star1 0.857630
Star2 0.932043
Star3 0.779729
@@ -1438,8 +1477,7 @@ Probability given an age range
@@ -1625,11 +1663,19 @@ Ages from Colour-Magnitude Diagram
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -1641,7 +1687,6 @@ Ages from Colour-Magnitude Diagram
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/AgeWizard.ipynb b/docs/AgeWizard.ipynb
new file mode 100644
index 00000000..ccbd06bd
--- /dev/null
+++ b/docs/AgeWizard.ipynb
@@ -0,0 +1,1479 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Age Wizard\n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
+ "\n",
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki.age_utils import AgeWizard\n",
+ "import hoki\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A systematic ageing method \n",
+ "\n",
+ "### Motivation\n",
+ "One of the classic methods for ageing clusters is to fit isochrones to observational data in Colour-Magnitude Diagrams (CMDs) or in Hertzsprung-Russel Diagrams (HRDs).\n",
+ "\n",
+ "There are a number of issues with this ubiquitous technique though:\n",
+ "\n",
+ "* Fitting is usually done by eye\n",
+ "* They only take into account single stars\n",
+ "* It cannot be used if your sample only contains a few sources\n",
+ "\n",
+ "**Our goal is to provide a method that answers all of the issues metioned above**\n",
+ "\n",
+ "### How it works\n",
+ "\n",
+ "By matching the observed properties of your sources to the BPASS models you can find the **most likely age** of a **location on the HRD/CMD**.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Drawing\"](\"age_method.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If all your sources belong to the same region, you can then combine the PDFs of your individual sources to find the age PDF of the whole cluster.\n",
+ "\n",
+ "All of this can be done step by step using the chain `find_coordinates()` -> `calculate_pdfs` -> `multiply_pdfs()`, or you could simply use `AgeWizard()` which handles this pipeline and facilitates loading in the models. \n",
+ "\n",
+ "Note: In this tutorial we will focus on `AgeWizard()`.\n",
+ "\n",
+ "\n",
+ "### Remember to exercise caution in your interpretation\n",
+ "\n",
+ "This is true of all methods but it is worth repeating. This technique helps you get to the answer, but it doesn't give you the answer. \n",
+ "\n",
+ "The **most likely age** of the **matching location** is not necessarily the age of your source - as we'll see below Helium Stars created through binary interactions and envelope stripping are found at similar locations as WR stars but at different ages.\n",
+ "\n",
+ "# Ages from Hertzsprung-Russell Diagram\n",
+ "\n",
+ "First, we need some observational data to compare to our models. \n",
+ "\n",
+ "`AgeWizard()` expects a `pandas.DataFrame`. If you want to use the HRD capabilities you will need to provide a `logT` and a `logL` column:\n",
+ "* logT: log10 of the stellar effective temperature\n",
+ "* logL: log10 of the stellar luminosity **in units of Solar Luminosities**\n",
+ "\n",
+ "Additionally, your DataFrame will ideally contain a `name` column with the name of the sources you're working on. If you don't provide one, `AgeWizard()` will make it's own sources names: source1, source2, etc...\n",
+ "\n",
+ "_Data source: McLeod et al. 2019, Stevance et al. in prep._"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stars = pd.DataFrame.from_dict({'name':np.array(['Star1','Star2','Star3','Star4', \n",
+ " 'Star5','Star6','Star7','Star8','Star9',\n",
+ " 'Star10','Star11','Star12','Star13','Star14', \n",
+ " 'WR1', 'WR2']),\n",
+ " 'logL':np.array([5.0, 5.1, 4.9, 5.9, 5.0, 5.4, 4.3, 5.7, 4.5, 4.5, \n",
+ " 4.9, 4.5, 4.3, 4.5, 5.3, 5.3]), \n",
+ " 'logT':np.array([4.48, 4.45, 4.46, 4.47, 4.48, 4.53, 4.52, 4.52, 4.52,\n",
+ " 4.56, 4.46, 4.52, 4.52, 4.52, 4.9, 4.65])})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialise our `AgeWizard()`. Each instance corresponds to one model and one model only: i.e. 1 IMF and 1 metalicity.\n",
+ "\n",
+ "If we want to have a look at 2 different metallicities for example, we can just instanciate `AgeWizard` twice:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agewiz006 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z006.dat')\n",
+ "agewiz008 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z008.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "During instanciation, the observations and models are matched and the age PDFs are calculated. They are summarised in a `pandas.DataFrame` that you can access as follows. "
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.pdfs.head(15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But those are just numbers... PDFs are meant to be plotted:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_all_pdfs(agewiz):\n",
+ " # creating the figure\n",
+ " f, ax = plt.subplots(4, 4, figsize=(15,15))\n",
+ " plt.subplots_adjust(hspace=0.4)\n",
+ " axes = ax.reshape(16)\n",
+ "\n",
+ " # We plot each star an individual axis \n",
+ " for source, axis in zip(agewiz.sources, axes):\n",
+ " \n",
+ " # Plotting one PDF\n",
+ " axis.step(hoki.BPASS_TIME_BINS, agewiz.pdfs[source],where='mid')\n",
+ " axis.fill_between(hoki.BPASS_TIME_BINS, agewiz.pdfs[source], step='mid', alpha=0.3)\n",
+ " \n",
+ " # Labels and limits\n",
+ " axis.set_title(source)\n",
+ " axis.set_ylabel('Probability (%)')\n",
+ " axis.set_xlabel('log(years)')\n",
+ " axis.set_ylim([0,0.6]) \n",
+ " axis.set_xlim([6,8.5]) \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Age PDFs at z=0.006"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_all_pdfs(agewiz006)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Age PDFs at z=0.008"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_all_pdfs(agewiz008)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Most Likely Ages\n",
+ "\n",
+ "The most likely age for **each source** is summarised by `AgeWizard.most_likely_ages`. This is returned as a numpy array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,\n",
+ " 6.9, 6.9, 6.9])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.most_likely_ages"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Different metallicities will give different results. In this example we summarise our results into a new DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "most_likely_ages = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
+ " 'z006': agewiz006.most_likely_ages, \n",
+ " 'z008': agewiz008.most_likely_ages}) \n",
+ "\n",
+ "# Note that the attribute AgeWizard.sources is the list of names you provided, or if you didn't the ones it created"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " name \n",
+ " z006 \n",
+ " z008 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Star1 \n",
+ " 6.9 \n",
+ " 6.8 \n",
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+ " Star2 \n",
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+ " \n",
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+ " Star10 \n",
+ " 7.3 \n",
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+ " 6.8 \n",
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+ " 12 \n",
+ " Star13 \n",
+ " 6.8 \n",
+ " 6.8 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Star14 \n",
+ " 6.9 \n",
+ " 6.9 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " WR1 \n",
+ " 6.9 \n",
+ " 6.8 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " WR2 \n",
+ " 6.9 \n",
+ " 6.9 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " name z006 z008\n",
+ "0 Star1 6.9 6.8\n",
+ "1 Star2 6.7 6.7\n",
+ "2 Star3 6.8 6.9\n",
+ "3 Star4 6.5 6.5\n",
+ "4 Star5 6.9 6.8\n",
+ "5 Star6 6.7 6.7\n",
+ "6 Star7 6.8 6.8\n",
+ "7 Star8 6.7 6.5\n",
+ "8 Star9 6.9 6.9\n",
+ "9 Star10 7.3 7.3\n",
+ "10 Star11 6.8 6.9\n",
+ "11 Star12 6.9 6.9\n",
+ "12 Star13 6.8 6.8\n",
+ "13 Star14 6.9 6.9\n",
+ "14 WR1 6.9 6.8\n",
+ "15 WR2 6.9 6.9"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "most_likely_ages"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IMPORTANT\n",
+ "The `most_likely_ages` (and `most_likley_age`, see below) tools **do not give you a direct answer** - they are a way to summarise the data and be used in your interpretation but **please do not use these as a black box**.\n",
+ "\n",
+ "Looking at the PDFs plotted above and the table of most likely ages, we can see that a few stars stand out:\n",
+ "\n",
+ "* Star 4 has a lower (most likely) age than the rest of the sample\n",
+ "* Star 10 has a higher (most likely) age than the rest of the sample\n",
+ "* The WR PDFs show peaks beyond log(age/years) - which souldn't be possible for WR stars.\n",
+ "\n",
+ "All of these are discussed an interpreted in details in Stevance et al. (in prep). Star 4 is an example of a star that is probably the result of a merger and rejuvination, Star 10 is more likely to be on the young tail of the distribution and not as old as the most likley age implies, and the WR stars share their HRD locations with helium stars which appear at later times, which populate the later peak. \n",
+ "\n",
+ "\n",
+ "# Plotting Aggregate Ages\n",
+ "\n",
+ "If you are looking for the age of a whole population then you can ask `AgeWizard` to combine the PDFs it calcualted on initialisation. This is done with the method `AgeWizard.calcualte_sample_pdfs()`. \n",
+ "\n",
+ "It can be called as such and it will multiply all the PDFs, or you can provide a list of columns to ignore in the paramter `not_you`. You can also ask it to return the resulting PDF by setting `return_df` to True. If you don't, it will store the result in `AgeWizard.sample_pdf`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Returns None\n",
+ "agewiz006.calculate_sample_pdf()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " pdf \n",
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+ " 0.018170 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.025543 \n",
+ " \n",
+ " \n",
+ " 2 \n",
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+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.042060 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.052826 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 0.057152 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0.085512 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 0.152308 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 0.196053 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 0.206209 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " pdf\n",
+ "0 0.018170\n",
+ "1 0.025543\n",
+ "2 0.030998\n",
+ "3 0.042060\n",
+ "4 0.052826\n",
+ "5 0.057152\n",
+ "6 0.085512\n",
+ "7 0.152308\n",
+ "8 0.196053\n",
+ "9 0.206209"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.sample_pdf.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also quickly retrieve the **most likely age of the whole sample** from the PDF you just calculated using `AgeWizard.most_likely_age`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.9])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.most_likely_age"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " pdf \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.024695 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.025288 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.019613 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.034380 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.058792 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 0.072591 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0.132265 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 0.174126 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 0.211425 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 0.190401 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " pdf\n",
+ "0 0.024695\n",
+ "1 0.025288\n",
+ "2 0.019613\n",
+ "3 0.034380\n",
+ "4 0.058792\n",
+ "5 0.072591\n",
+ "6 0.132265\n",
+ "7 0.174126\n",
+ "8 0.211425\n",
+ "9 0.190401"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Or return directly\n",
+ "agewiz008.calculate_sample_pdf(return_df=True).head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.8])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz008.most_likely_age"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Down below we create a quick function to plot our aggregate ages for z=0.006 and z=0.008. \n",
+ "\n",
+ "As an example of the "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster1_sources = ['Star1', 'Star2', 'Star3', 'Star4', 'WR1']\n",
+ "cluster2_sources = [item for item in agewiz006.sources if item not in cluster1_sources]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_aggregate_age(agewiz, ax):\n",
+ " \n",
+ " # Aggregate age PDF for all sources\n",
+ " cluster12 = agewiz.calculate_sample_pdf(return_df=True).pdf\n",
+ " \n",
+ " # Aggregate age PDF for cluster1\n",
+ " cluster1 = agewiz.calculate_sample_pdf(not_you=cluster2_sources, return_df=True).pdf\n",
+ " \n",
+ " # Aggregate age PDF for cluster2\n",
+ " cluster2 = agewiz.calculate_sample_pdf(not_you=cluster1_sources, return_df=True).pdf\n",
+ "\n",
+ "\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster1, where='mid', alpha=1, lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster1, step='mid', alpha=0.1, label='Cluster1')\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster2, where='mid', alpha=1, lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster2, step='mid', alpha=0.3, label='Cluster2')\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster12, where='mid',alpha=0.5, c='k', lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster12, step='mid', alpha=0.2, label='All', color='k')\n",
+ "\n",
+ "\n",
+ " ax.set_ylabel('Probability')\n",
+ " ax.set_xlabel('log(ages)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(ncols=2, nrows=1, figsize=(7,5))\n",
+ "\n",
+ "plot_aggregate_age(agewiz006, ax[0])\n",
+ "plot_aggregate_age(agewiz008, ax[1])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_ylim([0,0.4])\n",
+ " axis.set_xlim([6,8.5])\n",
+ " \n",
+ "# Cool legend\n",
+ "ax[0].legend(fontsize=14, loc='center right', bbox_to_anchor=(1.9, 1.1), ncol=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Probability given an age range\n",
+ "\n",
+ "The BPASS time bins are separated by 0.1 dex in log space (log(age/years) = 6.0, 6.1, 6.2, 6.3 etc..) and the PDFs for your individual sources will cover a range of BPASS time bins.\n",
+ "\n",
+ "Consequently, giving a single age may not make a lot of sense. Also the PDFs will rarely, if ever, resemble a Gaussian distribution: that means that you can't give an error bar in the \"classical\" sense. \n",
+ "\n",
+ "Another interesting measure is the probability that the age of your star falls into a chosen age range. \n",
+ "In this case our aggregate age is most likely to be between 6.7-6.9, so let's investigate how likely it is for each individual star to fall in that range. \n",
+ "\n",
+ "For that we use `AgeWizard.calculate_p_given_age_range()` which returns a Series containing the probabilities corresponding to each source."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "name\n",
+ "Star1 0.857630\n",
+ "Star2 0.932043\n",
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+ "dtype: float64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.calculate_p_given_age_range([6.7, 6.9])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can collate those in one single DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p_ages_679 = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
+ " 'z006': agewiz006.calculate_p_given_age_range([6.7,6.9]), \n",
+ " 'z008': agewiz008.calculate_p_given_age_range([6.7,6.9])}) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "Star3 Star3 0.779729 0.659319\n",
+ "Star4 Star4 0.127003 0.134967\n",
+ "Star5 Star5 0.857630 0.784335\n",
+ "Star6 Star6 0.948770 0.738479\n",
+ "Star7 Star7 0.500073 0.469008\n",
+ "Star8 Star8 0.666553 0.278005\n",
+ "Star9 Star9 0.446666 0.549145\n",
+ "Star10 Star10 0.023493 0.023109\n",
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+ "WR1 WR1 0.850846 0.721919\n",
+ "WR2 WR2 0.784204 0.789553"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p_ages_679"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To learn more about how we use and interpret this statistic, you can checkout Stevance et al. in prep an the associated Jupyter Notebook (link)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Ages from Colour-Magnitude Diagram \n",
+ "\n",
+ "[TO WRITE]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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index b3d215fe..2d2bba36 100644
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+++ b/docs/CMDs.html
@@ -1,8 +1,7 @@
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@@ -80,6 +82,7 @@
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+ - »
- Colour Magnitude Diagrams
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-div.nboutput,
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+div.nbinput.container div[class*=highlight],
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+div.nboutput.container,
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+div.nboutput.container div.output_area,
+div.nboutput.container div[class*=highlight],
+div.nboutput.container div[class*=highlight] pre {
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padding: 0 0;
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+div.nboutput.container div[class*=MathJax] {
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+div.nboutput.container div.output_area:not(.rendered_html) > img{
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+/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
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@@ -439,8 +476,8 @@ Initial Imports
import matplotlib.pyplot as plt
-from hoki.cmd import CMD
-from hoki.load import set_models_path, unpickle
+from hoki.cmd import CMD
+from hoki.load import set_models_path, unpickle
import pickle
import numpy as np
@@ -513,6 +550,7 @@ Making the CMDsCMD() instance is.
+
You can easily access the grid by simply calling the attribute CMD.grid
[4]:
@@ -527,8 +565,9 @@ Making the CMDs[4]:
-
-array([[[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
@@ -577,8 +616,7 @@ Making the CMDs
But CMD objects are also indexable!
@@ -612,16 +650,16 @@ Making the CMDs[6]:
-
-array([[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
-
-
+
This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use CMD.at_log_age():
@@ -905,11 +943,19 @@ Creating a publication-ready figure
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -921,7 +967,6 @@ Creating a publication-ready figure
jQuery(function () {
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diff --git a/docs/CMDs.ipynb b/docs/CMDs.ipynb
new file mode 100644
index 00000000..3be3f90a
--- /dev/null
+++ b/docs/CMDs.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Colour Magnitude Diagrams\n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from hoki.cmd import CMD\n",
+ "from hoki.load import set_models_path, unpickle\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Getting the Stellar models and input files\n",
+ "\n",
+ "Colour Magnitude Diagrams (CMDs) are created by reading in the [BPASS](https://bpass.auckland.ac.nz/9.html) stellar models listed in the `model_input` files that can be found in the BPASS output folders (e.g. *bpass_v2.2.1_imf135_300*). These stellar models are in a separate directory (because it is absolutely massive), so you will have to download it separately if you want to run the following cells or make your own CMDs.\n",
+ "\n",
+ "**NOTE: You will be able to run the cells in the section \"Loading a pickled CMD file\" even if you can't download the full set of stellar models **\n",
+ "\n",
+ "The stellar models and input files can be downloaded from [the google drive](https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl) (*bpass-v2.2-newmodels* for the models and e.g. *bpass_v2.2.1_imf135_300* to get the required `model_input` files).\n",
+ "Then you will have to change the path to the models in the `settings.yaml` file -- this can be done using the `set_models_path` function contained in the `hoki.load` module: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looks like everything went well! You can check the path was correctly updated by looking at this file:\n",
+ "/home/fste075/hoki/hoki/data/settings.yaml\n"
+ ]
+ }
+ ],
+ "source": [
+ "# The following path is valid on my machine - make sure you put the right ABSOLUTE path for your system\n",
+ "set_models_path(path='/home/fste075/BPASS_hoki_dev/bpass-v2.2-newmodels/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**NOTE: You'll probably have to reload hoki or restart the kernel at this point if you've jsut updated the yaml file :)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# CMD objects\n",
+ "\n",
+ "### Making the CMDs\n",
+ "\n",
+ "To create a synthetic CMD, `hoki` creates a grid in colour-magnitude space and then consults the entire set of stellar models to fill that grid. It is basically a histogram - the value of each cell/bin increases according to the proportion of the stellar population that falls into that bin. You could just plot this grid with a colour-map like an image, but we traditionally create contour plots for visualisation. \n",
+ "\n",
+ "In `hoki` you will be creating a `CMD()` object instanciated with a model of your choosing (a particular IMF and metallicity)- for this you need to provide the **location of a BPASS input file**.\n",
+ "\n",
+ "To know WHAT to do with this information, we also need to give the **two broad-band filters** we are interested in to make the plot filter2 Vs filter1-filter2 (e.g. V Vs B-V). This is given in the `CMD.make()` method, which actually creates and fills the CMD grids. \n",
+ "\n",
+ "**NOTE: This step is the most time consuming because there are thousands and thousands of models to look at. For that reason it also take much longer for the binary stellar models than the single star models to make a CMD. In the next section we will show you how to avoid having to go repeat his step in the future**\n",
+ "\n",
+ "The good news is that **once you have instanciated and 'made' the CMD object, plotting it is VERY fast** and you have a CMD for **each time bin**, which are also trivial and quick to access. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/fste075/hoki/hoki/cmd.py:191: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n",
+ "/home/fste075/hoki/hoki/cmd.py:207: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ONLY RUN IF YOU HAVE THE MODELS IN YOUR MACHINE\n",
+ "\n",
+ "# Update this path if you want to run this cell\n",
+ "input_file = '/home/fste075/BPASS_hoki_dev/bpass_v2.2.1_imf135_300/input_bpass_z020_bin_imf135_300'\n",
+ "mycmd = CMD(input_file)\n",
+ "\n",
+ "# actually makes and fills the grids - this is the time and memory consuming step\n",
+ "mycmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below is a summary illustration of what the synthetic CMDs are and what a `CMD()` instance is. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Drawing\"](\"CMDs.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can easily access the grid by simply calling the attribute `CMD.grid`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "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",
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+ "\n",
+ " [[0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
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+ "\n",
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+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " [0., 0., 0., ..., 0., 0., 0.],\n",
+ " ...,\n",
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+ " [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": [
+ "mycmd.grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(51, 240, 100)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.grid.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But `CMD` objects are also indexable! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(240, 100)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use `CMD.at_log_age()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.at_log_age(log_age=6.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Changing the resolution of the CMD grids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see we have 51 time bins, 240 magnitude intervals and 100 colour intervals. \n",
+ "\n",
+ "The number of time bins is fixed by BPASS but you can chose the size of your colour-magnitude grid and its resolution when you instanciate a `CMD` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "blurry_cmd = CMD(input_file, col_lim=[-3, 7], mag_lim=[-14, 10], res_el=0.75)\n",
+ "blurry_cmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the CMDs\n",
+ "\n",
+ "Like I said above, once the grid is made and filled, plotting is quick and straight forward. As in other `hoki` tools the plotting function returns the plot, which you can store in a variable to add your own labels and customize limits. \n",
+ "\n",
+ "Similarly to the `hoki.HRDiagrams` plots, the contours are on a log scale. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = mycmd.plot(log_age=6.8) # Here you can chose the time bin you want to plot.\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with the HRDiagrams, you can also tell the plotting function where your want it to plot your data!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "mycmd.plot(log_age=6.8, loc=ax[0]) # Here you can chose the time bin you want to plot.\n",
+ "ax[0].set_xlim([-3,3.0])\n",
+ "ax[0].set_ylim([2,-10])\n",
+ "ax[0].set_title('High resolution')\n",
+ "\n",
+ "blurry_cmd.plot(log_age=6.8, loc=ax[1]) # Here you can chose the time bin you want to plot.\n",
+ "ax[1].set_xlim([-3,3.0])\n",
+ "ax[1].set_ylim([2,-10])\n",
+ "ax[1].set_title('Low resolution')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pickle CMDs - don't make them twice!\n",
+ "\n",
+ "Because it is a little time consuming to create the synthetic CMDs, we actually recommend you [pickle](https://www.datacamp.com/community/tutorials/pickle-python-tutorial) your CMD objects. This will allow you to re-use them in the future and plot them virtually instantly, by-passing the `CMD.make()` step. \n",
+ "\n",
+ "### Loading a pickled CMD file\n",
+ "\n",
+ "We've provided a couple of pickled CMD files in the `./data/cmds/` directory for you to try even if you couldn't download the full sets of stellar models. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pickled_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Pickling a CMD file\n",
+ "\n",
+ "Now let's make your own pickle file! If you have the stellar models and made the CMD in the previous sections of this tutorial, you can now save your work!\n",
+ "\n",
+ "All you need is the following code (feel free to change the output file name)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First we open a file we can write into\n",
+ "outfile = open('./data/cmds/BV_CMD.pckl', 'wb')\n",
+ "# Then we call the 'dump' function from the pickle module to dump our cmd in our output file\n",
+ "pickle.dump(mycmd, outfile)\n",
+ "# And to avoid funny business we close our file. \n",
+ "outfile.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_pickled_cmd = unpickle(path='./data/cmds/BV_CMD.pckl')\n",
+ "\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = new_pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just like with the HRDiagrams, the tool provided by `hoki` will allow you to make publication ready figures in not time!\n",
+ "\n",
+ "Let's make a plot comparing the CMDs for Cygnus OB2 and Upper Sco in B-V and U-V plots.\n",
+ "\n",
+ "First we need to load our data (which is provided in the ./data/cmds/ repository) - we also need to make sure our observational data is in **absolute** magnitude, because that's what our synthetic CMDs provide. If **extinction** is important in your osbervational data, you also need to take that into account. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Cygnus OB2 data\n",
+ "Av, cyg_U, cyg_B, cyg_V = np.genfromtxt('./data/cmds/cygnusob.dat', unpack=True, usecols=(7, 8, 9,10), skip_header=54)\n",
+ "\n",
+ "# Assumes Milky Way extinction\n",
+ "Ab = (1.324*Av)\n",
+ "Au = (1.531*Av)\n",
+ "\n",
+ "# Distance to Cyg OB2 and distance modulus\n",
+ "d_cygob2 = 1750 #pc\n",
+ "mu_cygob2 = 5*np.log10(d_cygob2)-5\n",
+ "\n",
+ "# Taking away the extinction and turning our mags into absolute mags\n",
+ "# Note it was derived from single star models so extinction may be a tad off\n",
+ "# for your science, feel free to do a better job of it ;)\n",
+ "cyg_U, cyg_B, cyg_V = cyg_U-Au-mu_cygob2 , cyg_B-Ab-mu_cygob2 , cyg_V-Av-mu_cygob2 \n",
+ "\n",
+ "# Now calculating colours\n",
+ "cyg_UV = cyg_U-cyg_V\n",
+ "cyg_BV = cyg_B-cyg_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Upper Sco data\n",
+ "p, usco_U, usco_B, usco_V = np.genfromtxt('./data/cmds/usco.dat', unpack=True, usecols=(1,2,3,4), skip_header=1)\n",
+ "\n",
+ "# Distance modulus - this time based on parallax.\n",
+ "# (Note I inverted parallax to make this tutorial quick - don't @ me.)\n",
+ "# Extinction is not a problem for this data set\n",
+ "mu = 5*np.log10(1/p)-5\n",
+ "usco_U, usco_B, usco_V = usco_U+mu, usco_B+mu, usco_V+mu\n",
+ "\n",
+ "# Calculating colours\n",
+ "usco_UV = usco_U-usco_V\n",
+ "usco_BV = usco_B-usco_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unpickling our BV and UV cmds!\n",
+ "BV_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')\n",
+ "UV_cmd = unpickle(path='./data/cmds/cmd_uv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Now let's plot the data!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(2,2, figsize=(15,15))\n",
+ "\n",
+ "# This is a bit off on the colour axis which is probs just because of my conversion from Av to Ab and Au\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].scatter(cyg_UV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].scatter(cyg_BV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "\n",
+ "# this is not the same data as the paper\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[1,0])\n",
+ "ax[1,0].scatter(usco_UV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[1,1])\n",
+ "ax[1,1].scatter(usco_BV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "for axis in ax.reshape(4):\n",
+ " axis.set_ylabel('V', fontsize=14)\n",
+ " axis.set_ylim([2,-10])\n",
+ "\n",
+ "for i in [0,1]:\n",
+ " ax[i,0].set_xlim([-2,4])\n",
+ "\n",
+ " ax[i,1].set_xlim([-1,2])\n",
+ "\n",
+ " \n",
+ "ax[0,0].text(1,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[0,1].text(0.5,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,0].text(1,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,1].text(0.5,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/HR_diagrams.html b/docs/HR_diagrams.html
index 329f2671..d7fd06b8 100644
--- a/docs/HR_diagrams.html
+++ b/docs/HR_diagrams.html
@@ -1,8 +1,7 @@
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-
+
@@ -12,6 +11,10 @@
+
+
+
+
@@ -19,24 +22,23 @@
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+
-
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+
+
+
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@@ -80,6 +82,7 @@
+
+
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@@ -647,10 +684,10 @@ Multiple plots[6]:
-
-<matplotlib.legend.Legend at 0x7fc159d9beb8>
-
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+
+
+<matplotlib.legend.Legend at 0x7fc159d9beb8>
+
@@ -690,10 +727,10 @@ Multiple plots[7]:
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-
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+
+
+<matplotlib.legend.Legend at 0x7fc159ec4b38>
+
@@ -740,10 +777,10 @@ Customizing your plots with matplotlib key word arguments[8]:
-
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-
-
+
+
+<matplotlib.legend.Legend at 0x7fc15dd46d68>
+
@@ -959,10 +996,10 @@ Plotting HRDs of stacked ages[16]:
-
-Text(0.5, 1.0, 'Stacked Age: log(Age/yrs)= 6.0 - 7.0')
-
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+
+
+Text(0.5, 1.0, 'Stacked Age: log(Age/yrs)= 6.0 - 7.0')
+
@@ -987,11 +1024,19 @@ Plotting HRDs of stacked ages
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -1003,7 +1048,6 @@ Plotting HRDs of stacked ages
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/HR_diagrams.ipynb b/docs/HR_diagrams.ipynb
new file mode 100644
index 00000000..98676bd2
--- /dev/null
+++ b/docs/HR_diagrams.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HR diagrams\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
+ "\n",
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Learn about the HR-diagram data structure of BPASS\n",
+ "- Learn how to load them in using hoki\n",
+ "- Use the tools in `hoki` to explore your HR diagram data and make science plots\n",
+ "- Learn to customise the `hoki` plots\n",
+ "\n",
+ "\n",
+ "# Loading in the data\n",
+ "\n",
+ "### About the tools you're about to use. \n",
+ "HR diagram data can be loaded using the `hoki.load.population_output()` function. This function will automatically know that you are trying to load an HR diagram from the name of the text file.\n",
+ "\n",
+ "**Important**:\n",
+ "You should note that when loading an HR diagram you need to specify what type of HR diagram you want to load form the file. The HR diagram files are large and quite complex - see left hand side of the diagam below - so we'll be loading in one type of HR diagram at a time. \n",
+ "\n",
+ "You can choose from 3 options:\n",
+ "- `'TL'`: For a temperature/Luminosity HR diagram\n",
+ "- `'Tg'`: For a temperature/surface gravity HR diagram\n",
+ "- `'TTG'`: For a temperature/(temperature**4/surface gravity) HR diagram\n",
+ "\n",
+ "These options are there for 2 reasons: \n",
+ "* 1.They tell the object which segment of the text file to load in \n",
+ "* 2.They tell the plotting function what grid to create in order to plot the contours (also picks the right axis labels)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading the HR diagrams"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the HR diagrams for signle stars and binary star populations \n",
+ "sin_hr_diagram = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type = 'TL')\n",
+ "bin_hr_diagram = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type = 'TL')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading some observational data\n",
+ "\n",
+ "We're going to want to plot some observational data to compare to our models. A nice way to do that is to use `pandas`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the observational data of Upper Scorpio\n",
+ "usco = pd.read_csv('./data/USco.dat', sep='\\t', engine='python', names=['Temperature', 'Luminosity'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Temperature \n",
+ " Luminosity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 3.756 \n",
+ " 0.38 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 3.742 \n",
+ " 0.34 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3.742 \n",
+ " 0.36 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.750 \n",
+ " 0.08 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.728 \n",
+ " 0.27 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Temperature Luminosity\n",
+ "0 3.756 0.38\n",
+ "1 3.742 0.34\n",
+ "2 3.742 0.36\n",
+ "3 3.750 0.08\n",
+ "4 3.728 0.27"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usco.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting HR diagrams\n",
+ "\n",
+ "Now that we have loaded in our data, it's time to create some visualisation. The advantage of using the `HRDiagrams` object is that it comes with an **easy-to-use plotting method** that is highly **versatile** and designed to create **publication-ready figures**. \n",
+ "\n",
+ "Let's run through a couple of ways you can use this tool and make it your own. \n",
+ "\n",
+ "### Making a single plot\n",
+ "First we are going to make just one HR diagram plot. The plotting method returns the plote object, which **allows you to customize you plot**. \n",
+ "\n",
+ "For example you can **add observational data**, some **text**, a **title**, a **legend** and redefine the axis **limits** to create a more effective visualisation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Single stars')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Just maing sure teh figure size is sensible\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "# Plotting the hr_diagram (all hydrogen abundances added up) at the 10 million year time bin\n",
+ "my_plot = sin_hr_diagram.plot(log_age=7.0)\n",
+ "\n",
+ "# Addind the observational data of Upper Scorpio\n",
+ "my_plot.scatter(usco.Temperature, usco.Luminosity, label='Usco', s=50)\n",
+ "\n",
+ "# Customizing the visualisation\n",
+ "my_plot.set_xlim([5.4, 3.4])\n",
+ "my_plot.set_ylim([2.8, 6.2]) # better limits\n",
+ "my_plot.text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 14) # Informative text\n",
+ "my_plot.legend() # a legend\n",
+ "my_plot.set_title('Single stars') # A title"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multiple plots\n",
+ "\n",
+ "There might be times you want to put multiple plots on the same figure. The good news is the `HRDiagrams.plot()` allows you to do that intuitively! There are actually more than one way to do it, depending on how you like making your subplots in Python, either with `add_subplot()` or `subplots()`.\n",
+ "\n",
+ "Here is the same HR diagram as presented above, but with the Single star population on the left and the Binary star population on the right. \n",
+ "\n",
+ "**a) Using `subplots()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "sin_hr_diagram.plot(log_age=7.0, loc=ax[0])\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single stars')\n",
+ "ax[0].text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7.0, loc=ax[1])\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='pink', s=100, label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Binary stars')\n",
+ "ax[1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[1].legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**b) Using `add_subplot()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "sin_plot = sin_hr_diagram.plot(log_age=7, loc=121)\n",
+ "sin_plot .scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "sin_plot .set_xlim([5.4, 3.4])\n",
+ "sin_plot .set_ylim([2.8, 6.2])\n",
+ "sin_plot .set_title('Single stars')\n",
+ "sin_plot .text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "sin_plot .legend()\n",
+ "\n",
+ "\n",
+ "bin_plot = bin_hr_diagram.plot(log_age=7, loc=122)\n",
+ "bin_plot.scatter(usco.Temperature, usco.Luminosity, s=100, c='pink', label='Usco')\n",
+ "bin_plot.set_xlim([5.4, 3.4])\n",
+ "bin_plot.set_ylim([2.8, 6.2])\n",
+ "bin_plot.set_title('Binary stars')\n",
+ "bin_plot.text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "bin_plot.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Customizing your plots with matplotlib key word arguments\n",
+ "\n",
+ "Althought the default parameters for the HR diagram pltos were chosen to allow the creating of publication-ready plots with as few modifications as possible, the plotting function of `HRDiagrams` objects are designed to take any subplementary key word arguments and pass them on to `matplotlib.pyplot.contour`. \n",
+ "\n",
+ "This allows a much greater level of flexibility when making graphics. \n",
+ "For example if you wish to cahnge the colormap you can do the following. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0], cmap='jet_r') # levels adjusted for visualisation\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('A terrible colormap')\n",
+ "ax[0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[1], cmap='magma_r') # levels adjusted for visualisation\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('A less terrible colormap')\n",
+ "ax[1].text(5.25, 5.0, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='w', fontsize = 18)\n",
+ "ax[1].legend()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure \n",
+ "\n",
+ "In this last section, we will reproduce with `hoki` Figure 23 of Eldirdge, Stanway et al. (2018). This will show you how you can create a publication ready figure just using `hoki` and other ubiquitous python packages.\n",
+ "\n",
+ "*Note: This figure will look a little different frmo the published version as the latter was created in IDL*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# renaming our already laoded HR diagram just for readability \n",
+ "hr_usco_sin, hr_usco_bin = sin_hr_diagram, bin_hr_diagram \n",
+ "\n",
+ "hr_census_bin = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type='TL')\n",
+ "hr_census_sin = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type='TL')\n",
+ "\n",
+ "hr_6067_bin = load.model_output('./data/hrs-bin-imf135_300.z030.dat', hr_type='TTG')\n",
+ "hr_6067_sin = load.model_output('./data/hrs-sin-imf135_300.z030.dat', hr_type='TTG')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that it is not recommended to call a column 'T' for a pandas data frame. \n",
+ "# If you do, you won't be able to call it as mydataframe.T, because .T will default to \n",
+ "# returning the transpose. \n",
+ "\n",
+ "cyg_L, cyg_L_e, cyg_T, cyg_T_e = np.genfromtxt('./data/census.dat', unpack=True, usecols=(14,15,17,18))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ngc_T, ngc_T_e, ngc_logg, ngc_logg_e = np.loadtxt('./data/ngc6067hr.dat', unpack=True, usecols=(3,4,5,6), skiprows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the case of the NGC 6067 we want to make a spectroscopic HR diagram. That means that the y axis will be $\\log(T^4/g)$ -- this quatity is refered to as TG or log(TG). The trick is, we need to make sure we are using the right units when we do the calculations. \n",
+ "\n",
+ "We want everything to be with respect to solar units, so we'll have to divide by solar surface temperature (5778K) and surface gravity (274 m/s or a log g = 2.4377). We also nee to convert to SI from cgs, so there is a factor of $10^{-2}$ in the g, i.e a `-2` in the log g exponent, that needs to be taken into account. \n",
+ "\n",
+ "We also need to propagate errors. Since it's a mess of logs I'll just use bootstrapping for that. \n",
+ "\n",
+ "*Boostrapping: In this particular case I sample each of my values/data points according to a normal distribution where the mean is the value of that data point and the standard deviation is the error. I do this 1000 times, calculate the corresponding TG, and take the mean and standard deviation of these 1000 TG values.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TG = []\n",
+ "TG_e = []\n",
+ "for T_i, T_e_i, logg_i, logg_e_i in zip(ngc_T, ngc_T_e, ngc_logg, ngc_logg_e):\n",
+ " TG.append(np.mean(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))\n",
+ " TG_e.append(np.std(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also need to make sure we get the log(T) errors (and no, you can't just do `log(ngc_T_e)`). I'm using bootstrapping - because I can. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "T_e = []\n",
+ "for T_i, T_e_i in zip(ngc_T, ngc_T_e):\n",
+ " T_e.append(np.std(np.log(np.random.normal(T_i, T_e_i, 1000))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now it's time to plot all of this!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(3,2, figsize=(15,20))\n",
+ "\n",
+ "hr_census_sin.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,0].set_xlim([5.5, 3.4])\n",
+ "ax[0,0].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_census_bin.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,1].set_xlim([5.5, 3.4])\n",
+ "ax[0,1].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_usco_sin.plot(log_age=7, loc=ax[1,0]) # levels adjusted for visualisation\n",
+ "ax[1,0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,0].set_xlim([5.4, 3.4])\n",
+ "ax[1,0].set_ylim([2.8, 6.2])\n",
+ "ax[1,0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "\n",
+ "hr_usco_bin.plot(log_age=7, loc=ax[1,1]) # levels adjusted for visualisation\n",
+ "ax[1,1].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,1].set_xlim([5.4, 3.4])\n",
+ "ax[1,1].set_ylim([2.8, 6.2])\n",
+ "ax[1,1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "hr_6067_sin.plot(log_age=8, loc=ax[2,0])\n",
+ "ax[2,0].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,0].set_xlim([4.8, 3.4])\n",
+ "ax[2,0].set_ylim([1, 5.2])\n",
+ "ax[2,0].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "hr_6067_bin.plot(log_age=8, loc=ax[2,1])\n",
+ "ax[2,1].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,1].set_xlim([4.8, 3.4])\n",
+ "ax[2,1].set_ylim([1,5.2])\n",
+ "ax[2,1].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "for axis in ax.reshape(6):\n",
+ " axis.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stacking a range of Ages\n",
+ "\n",
+ "But what if you are interested in a stellar population that has a range of ages? \n",
+ "\n",
+ "Using the `HRDiagram` class you can stack HR Diagrams of a range of ages across all three surface abundance categories in a single line!\n",
+ "\n",
+ "Let's stack every age bin between 1 and 10 Myrs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ }
+ ],
+ "source": [
+ "bin_hr_diagram.stack(log_age_min=6.0, log_age_max=6.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This should return the following message: \n",
+ "```\n",
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n",
+ "```\n",
+ "\n",
+ "It indicates that the stacking was performed successfully, and four attributes of the `HRDiagram` class were update. \n",
+ "\n",
+ "`HRDiagram.all_stacked`, `HRDiagram.high_H_stacked`, `HRDiagram.medium_H_stacked`, `HRDiagram.low_H_stacked`, are the stacked equivalents to `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H`(see right panel of the Figure at the top of this tutorial).\n",
+ "\n",
+ "This means that the original `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H` attributes **remain untouched** when you run the `.stack()` method - only the \"stacked\" attributes will be updated.\n",
+ "\n",
+ "Note, before you run the `.stack()` method for the first time the \"stacked\" HRDs are just 100x100 numpy arrays filled with zeros. \n",
+ "\n",
+ "## Plotting HRDs of stacked ages\n",
+ "\n",
+ "Now, it's possible you don't actually need to do any opperations on the stacked HRDs, **you just want to plot** HRDs for a range of ages. \n",
+ "\n",
+ "For this you don't even need to use the `.stack()` method, **you can do it directly** in the `.plot()` method: you need to use the parameter `age_range` instead of '`log_age` in the plotting method. \n",
+ "\n",
+ "Here is an example of an HRD at 10 Myrs Vs an aggregate of HRDs from 1 to 10 Myrs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0]) #levels adjusted for visualisation\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single Age: log(Age/yrs)=7.0')\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(age_range=[6.0, 7.0], loc=ax[1]) # levels adjusted for visualisation\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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def extract_sdss_spectrum(dataloc):
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- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
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+ Built with Sphinx using a
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- © Copyright 2019, H. F. Stevance
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+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
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+ Built with Sphinx using a
+
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jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/Transient_rates.ipynb b/docs/Transient_rates.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Transient rates \n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki_tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Feel free to use your own style sheet\n",
+ "plt.style.use('tuto.mplstyle')\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Load BPASS transient rates from hoki\n",
+ "- Combine single and binary population transient rates\n",
+ "- Calculate total Core Collapse Supernovae rates\n",
+ "- Make sure the rates are in the right units\n",
+ "- Plot the rates of Type Ia SNe, CCSNe, LGRBs, and PISNe together. \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Loading in the data and initial set-up\n",
+ "\n",
+ "Transient rates in BPASS are one of the several types of stellar population outputs. We can simply use the `hoki.load.population_ouput()` function to laod the data. The function will automatically know what type of data it is loading from the file name and create an appropriate data frame to put it in. \n",
+ "\n",
+ "If you are not familiar with `pandas` and `pandas.DataFrame`, you should pick it up easily. If you want to look into them more check out this [Data Camp article](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python).\n",
+ "\n",
+ "In this tutorial we want to plot the rates corresponding to the stellar populations that include binary systems for a standard (Salpeter) IMF with slope -1.35 from 0.1 to 0.5 M_sun and a maximum mass of 300 solar masses, all of this at a tenth solar metalicity (so Z=0.002):\n",
+ "> `supernova-bin-imf135_300.z002.dat`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the binary and single star population transient rates.\n",
+ "# Note we chose this particular IMF and metallicity in order to reproduce the plot\n",
+ "# shown on the left hand sife of Figure 1 in Eldridge et al. 2018 \n",
+ "\n",
+ "bin_rates = load.model_output('./data/supernova-bin-imf135_300.z002.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at one of our data frames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " log_age \n",
+ " Ia \n",
+ " IIP \n",
+ " II \n",
+ " Ib \n",
+ " Ic \n",
+ " LGRB \n",
+ " PISNe \n",
+ " low_mass \n",
+ " e_Ia \n",
+ " e_IIP \n",
+ " e_II \n",
+ " e_Ib \n",
+ " e_Ic \n",
+ " e_LGRB \n",
+ " e_PISNe \n",
+ " e_low_mass \n",
+ " age_yrs \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 6.0 \n",
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+ " 0.0 \n",
+ " 0.0 \n",
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+ " 1122019.00 \n",
+ " \n",
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+ " 0.0 \n",
+ " 579664.00 \n",
+ " \n",
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\n",
+ ""
+ ],
+ "text/plain": [
+ " log_age Ia IIP II Ib Ic LGRB PISNe low_mass e_Ia \\\n",
+ "0 6.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "1 6.1 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "2 6.2 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "3 6.3 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "4 6.4 0.0 0.0 0.0 3.847896 0.0 0.0 5.109734 0.0 0.0 \n",
+ "\n",
+ " e_IIP e_II e_Ib e_Ic e_LGRB e_PISNe e_low_mass age_yrs \n",
+ "0 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 1122019.00 \n",
+ "1 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 290520.12 \n",
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+ "4 0.0 0.0 0.496761 0.0 0.0 0.80792 0.0 579664.00 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bin_rates.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Most column names are pretty self-explanatory, appart from `low_mass`, which just detones the rate of low-mass supernovae (< 2M$_{\\odot}$). The `age_yrs` bin is th size of each time bin in years; indeed since BPASS works in log(time) space, each time bin has a different width in years. \n",
+ "\n",
+ "If you need more detail on each of these rates, you should have a look at the [BPASS user manual](https://bpass.auckland.ac.nz/8/files/bpassv2_1_manual_accessible_version.pdf)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The last time bin in BPASS does some weird stuff so it's better to just ignore it. \n",
+ "bin_rates = bin_rates[:-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We are going to use the log_age and the size of the bin in years a lot, so I'm just renaming them for ease.\n",
+ "age = bin_rates.log_age.values\n",
+ "bin_size = bin_rates.age_yrs.values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Core Collapse Supernovae rates\n",
+ "\n",
+ "Core collapse supernovae comprise the type IIP, II, Ib and Ic. To get the total rate we need to sum these columns as well as put the single star and binary populations together.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne = ( bin_rates[['IIP', 'II', 'Ib', 'Ic']].sum(axis=1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting the Units right\n",
+ "\n",
+ "We want to plot our rates as **events/M$_{\\odot}$/year**, this means we need to normalise by the total mass and the number of years in each time bin. \n",
+ "\n",
+ "BPASS calulates stellar populations with 10$^6$ M$_{\\odot}$ and we've already put the bin size in years in a convenient variable called `bin_size`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne_norm = ccsne/bin_size/(10**6)\n",
+ "typeIa_norm = bin_rates.Ia.values /bin_size/(10**6)\n",
+ "lgrbs_norm = bin_rates.LGRB.values /bin_size/(10**6)\n",
+ "pisne_norm = bin_rates.PISNe.values/bin_size/(10**6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the transient rates\n",
+ "\n",
+ "Now we have everything we need to plot our transient rates! The only trick here is to remember to **log rate axis** to be able to see everything. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (10,7))\n",
+ "\n",
+ "plt.step(age, typeIa_norm, label='SN Ia')\n",
+ "plt.step(age, ccsne_norm, label='CCSNe')\n",
+ "plt.step(age, lgrbs_norm, label='LGRB')\n",
+ "plt.step(age, pisne_norm, label='PISNe')\n",
+ "\n",
+ "plt.yscale(\"log\")\n",
+ "\n",
+ "plt.text(9, 10**(-11), r\"Z=0.1Z$_{\\odot}$\", fontsize=18)\n",
+ "\n",
+ "plt.ylabel(r\"Event Rate (events/M$_{\\odot}$/year)\")\n",
+ "plt.xlabel(\"log(age/yrs)\")\n",
+ "plt.legend(fontsize=16)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/docs/_sources/cmd.rst.txt b/docs/_sources/cmd.rst.txt
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--- a/docs/_sources/cmd.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**cmd**
-################
-
-
-.. automodule:: hoki.cmd
- :members:
-
diff --git a/docs/_sources/constants.rst.txt b/docs/_sources/constants.rst.txt
deleted file mode 100644
index 5f9c5ff1..00000000
--- a/docs/_sources/constants.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-################
-**constants**
-################
-
-This module just contains BPASS constants and defines variables used in hoki.
-
-.. automodule:: hoki.constants
- :members:
-
-path_to_settings
- path to the `settings.yaml` file located in the package resources
-
-MODELS_PATH
- Path to the BPASS models as defined in the settings file. If you have not downloaded the models and updated this path with `load.set_models_path()`, it will not correspond to a valid path on your machine. This is not going to cause issues unless you're using functionalities that require the full set of BPASS models (like making CMDs)
-
-BPASS_TIME_BINS
- 1D array containing the 51 BPASS time bins (from log(age) = 6.0 to 11.0)
-
-BPASS_TIME_INTERVALS
- Time interval between BPASS time bins in years
-
-BPASS_TIME_WEIGHT_GRID
- Grid of weights to apply to HR Diagrams based on BPASS definitions
-
-dummy_dict
- Dictionary linking column number to column name for the large BPASS array traditionally called dummy. See the BPASS manual for a text version of the numner/name correspondance.
diff --git a/docs/_sources/hrdiagrams.rst.txt b/docs/_sources/hrdiagrams.rst.txt
deleted file mode 100644
index 58d1d07c..00000000
--- a/docs/_sources/hrdiagrams.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**hrdiagrams**
-################
-
-
-.. automodule:: hoki.hrdiagrams
- :members:
-
diff --git a/docs/_sources/load.rst.txt b/docs/_sources/load.rst.txt
deleted file mode 100644
index 69c38f09..00000000
--- a/docs/_sources/load.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-##########
-**load**
-##########
-
-
-.. automodule:: hoki.load
- :members:
-
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index ea6972d5..56f5efc6 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -316,22 +316,29 @@ img.align-default, .figure.align-default {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px 7px 0 7px;
+ padding: 7px;
background-color: #ffe;
width: 40%;
float: right;
+ clear: right;
+ overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
+div.admonition, div.topic, pre, div[class|="highlight"] {
+ clear: both;
+}
+
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px 7px 0 7px;
+ padding: 7px;
margin: 10px 0 10px 0;
+ overflow-x: auto;
}
p.topic-title {
@@ -346,16 +353,13 @@ div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
+ overflow-x: auto;
}
div.admonition dt {
font-weight: bold;
}
-div.admonition dl {
- margin-bottom: 0;
-}
-
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -366,9 +370,19 @@ div.body p.centered {
margin-top: 25px;
}
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
/* -- tables ---------------------------------------------------------------- */
table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -416,13 +430,13 @@ table.citation td {
border-bottom: none;
}
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
margin-top: 0px;
}
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
margin-bottom: 0px;
}
@@ -468,6 +482,10 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
+table.hlist {
+ margin: 1em 0;
+}
+
table.hlist td {
vertical-align: top;
}
@@ -495,17 +513,34 @@ ol.upperroman {
list-style: upper-roman;
}
-li > p:first-child {
+ol > li:first-child > :first-child,
+ul > li:first-child > :first-child {
margin-top: 0px;
}
-li > p:last-child {
+ol ol > li:first-child > :first-child,
+ol ul > li:first-child > :first-child,
+ul ol > li:first-child > :first-child,
+ul ul > li:first-child > :first-child {
+ margin-top: revert;
+}
+
+ol > li:last-child > :last-child,
+ul > li:last-child > :last-child {
margin-bottom: 0px;
}
+ol ol > li:last-child > :last-child,
+ol ul > li:last-child > :last-child,
+ul ol > li:last-child > :last-child,
+ul ul > li:last-child > :last-child {
+ margin-bottom: revert;
+}
+
dl.footnote > dt,
dl.citation > dt {
float: left;
+ margin-right: 0.5em;
}
dl.footnote > dd,
@@ -546,7 +581,7 @@ dl {
margin-bottom: 15px;
}
-dd > p:first-child {
+dd > :first-child {
margin-top: 0px;
}
@@ -560,6 +595,11 @@ dd {
margin-left: 30px;
}
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -644,22 +684,57 @@ span.pre {
hyphens: none;
}
+div[class^="highlight-"] {
+ margin: 1em 0;
+}
+
td.linenos pre {
- padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- margin-left: 0.5em;
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
}
table.highlighttable td {
- padding: 0 0.5em 0 0.5em;
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
}
div.code-block-caption {
+ margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -668,8 +743,9 @@ div.code-block-caption code {
background-color: transparent;
}
-div.code-block-caption + div > div.highlight > pre {
- margin-top: 0;
+table.highlighttable td.linenos,
+div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
}
div.code-block-caption span.caption-number {
@@ -681,11 +757,7 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
- margin: 0;
+ margin: 1em 0;
}
code.descname {
@@ -736,8 +808,7 @@ span.eqno {
}
span.eqno a.headerlink {
- position: relative;
- left: 0px;
+ position: absolute;
z-index: 1;
}
diff --git a/docs/_static/css/badge_only.css b/docs/_static/css/badge_only.css
index 3c33cef5..e380325b 100644
--- a/docs/_static/css/badge_only.css
+++ b/docs/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
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new file mode 100644
index 00000000..855c845e
--- /dev/null
+++ b/docs/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+
+
+
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diff --git a/docs/_static/css/fonts/lato-normal.woff b/docs/_static/css/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
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diff --git a/docs/_static/css/fonts/lato-normal.woff2 b/docs/_static/css/fonts/lato-normal.woff2
new file mode 100644
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diff --git a/docs/_static/css/theme.css b/docs/_static/css/theme.css
index aed8cef0..8cd4f101 100644
--- a/docs/_static/css/theme.css
+++ b/docs/_static/css/theme.css
@@ -1,6 +1,4 @@
-/* sphinx_rtd_theme version 0.4.3 | MIT license */
-/* Built 20190212 16:02 */
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tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) 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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index b33f87fc..daccd209 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -283,10 +283,11 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keyup(function(event) {
+ $(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+ && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index ec430720..ada26f7c 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,7 +3,9 @@ var DOCUMENTATION_OPTIONS = {
VERSION: 'beta',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
+ BUILDER: 'html',
FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false
diff --git a/docs/_static/fonts/FontAwesome.otf b/docs/_static/fonts/FontAwesome.otf
new file mode 100644
index 00000000..401ec0f3
Binary files /dev/null and b/docs/_static/fonts/FontAwesome.otf differ
diff --git a/docs/_static/fonts/Inconsolata-Bold.ttf b/docs/_static/fonts/Inconsolata-Bold.ttf
deleted file mode 100644
index 809c1f58..00000000
Binary files a/docs/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata-Regular.ttf b/docs/_static/fonts/Inconsolata-Regular.ttf
deleted file mode 100644
index fc981ce7..00000000
Binary files a/docs/_static/fonts/Inconsolata-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata.ttf b/docs/_static/fonts/Inconsolata.ttf
deleted file mode 100644
index 4b8a36d2..00000000
Binary files a/docs/_static/fonts/Inconsolata.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Bold.ttf b/docs/_static/fonts/Lato-Bold.ttf
deleted file mode 100644
index 1d23c706..00000000
Binary files a/docs/_static/fonts/Lato-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Regular.ttf b/docs/_static/fonts/Lato-Regular.ttf
deleted file mode 100644
index 0f3d0f83..00000000
Binary files a/docs/_static/fonts/Lato-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff b/docs/_static/fonts/Roboto-Slab-Bold.woff
new file mode 100644
index 00000000..6cb60000
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff2 b/docs/_static/fonts/Roboto-Slab-Bold.woff2
new file mode 100644
index 00000000..7059e231
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff b/docs/_static/fonts/Roboto-Slab-Light.woff
new file mode 100644
index 00000000..337d2871
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff2 b/docs/_static/fonts/Roboto-Slab-Light.woff2
new file mode 100644
index 00000000..20398aff
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff b/docs/_static/fonts/Roboto-Slab-Regular.woff
new file mode 100644
index 00000000..f815f63f
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff2 b/docs/_static/fonts/Roboto-Slab-Regular.woff2
new file mode 100644
index 00000000..f2c76e5b
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff b/docs/_static/fonts/Roboto-Slab-Thin.woff
new file mode 100644
index 00000000..6b30ea63
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff2 b/docs/_static/fonts/Roboto-Slab-Thin.woff2
new file mode 100644
index 00000000..328f5bb0
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff2 differ
diff --git a/docs/_static/fonts/RobotoSlab-Bold.ttf b/docs/_static/fonts/RobotoSlab-Bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/RobotoSlab-Regular.ttf b/docs/_static/fonts/RobotoSlab-Regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff b/docs/_static/fonts/lato-bold-italic.woff
new file mode 100644
index 00000000..88ad05b9
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff2 b/docs/_static/fonts/lato-bold-italic.woff2
new file mode 100644
index 00000000..c4e3d804
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-bold.woff b/docs/_static/fonts/lato-bold.woff
new file mode 100644
index 00000000..c6dff51f
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff differ
diff --git a/docs/_static/fonts/lato-bold.woff2 b/docs/_static/fonts/lato-bold.woff2
new file mode 100644
index 00000000..bb195043
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff2 differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff b/docs/_static/fonts/lato-normal-italic.woff
new file mode 100644
index 00000000..76114bc0
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff2 b/docs/_static/fonts/lato-normal-italic.woff2
new file mode 100644
index 00000000..3404f37e
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-normal.woff b/docs/_static/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff differ
diff --git a/docs/_static/fonts/lato-normal.woff2 b/docs/_static/fonts/lato-normal.woff2
new file mode 100644
index 00000000..3bf98433
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff2 differ
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.5.1.js
similarity index 91%
rename from docs/_static/jquery-3.4.1.js
rename to docs/_static/jquery-3.5.1.js
index 773ad95c..50937333 100644
--- a/docs/_static/jquery-3.4.1.js
+++ b/docs/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
/*!
- * jQuery JavaScript Library v3.4.1
+ * jQuery JavaScript Library v3.5.1
* https://jquery.com/
*
* Includes Sizzle.js
@@ -9,7 +9,7 @@
* Released under the MIT license
* https://jquery.org/license
*
- * Date: 2019-05-01T21:04Z
+ * Date: 2020-05-04T22:49Z
*/
( function( global, factory ) {
@@ -47,13 +47,16 @@
var arr = [];
-var document = window.document;
-
var getProto = Object.getPrototypeOf;
var slice = arr.slice;
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
var push = arr.push;
@@ -86,6 +89,8 @@ var isWindow = function isWindow( obj ) {
};
+var document = window.document;
+
var preservedScriptAttributes = {
@@ -142,7 +147,7 @@ function toType( obj ) {
var
- version = "3.4.1",
+ version = "3.5.1",
// Define a local copy of jQuery
jQuery = function( selector, context ) {
@@ -150,11 +155,7 @@ var
// 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 );
- },
-
- // Support: Android <=4.0 only
- // Make sure we trim BOM and NBSP
- rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+ };
jQuery.fn = jQuery.prototype = {
@@ -220,6 +221,18 @@ jQuery.fn = jQuery.prototype = {
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 );
@@ -353,9 +366,10 @@ jQuery.extend( {
return true;
},
- // Evaluates a script in a global context
- globalEval: function( code, options ) {
- DOMEval( code, { nonce: options && options.nonce } );
+ // 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 ) {
@@ -379,13 +393,6 @@ jQuery.extend( {
return obj;
},
- // Support: Android <=4.0 only
- trim: function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
- },
-
// results is for internal usage only
makeArray: function( arr, results ) {
var ret = results || [];
@@ -472,7 +479,7 @@ jQuery.extend( {
}
// Flatten any nested arrays
- return concat.apply( [], ret );
+ return flat( ret );
},
// A global GUID counter for objects
@@ -489,7 +496,7 @@ if ( typeof Symbol === "function" ) {
// Populate the class2type map
jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( i, name ) {
+function( _i, name ) {
class2type[ "[object " + name + "]" ] = name.toLowerCase();
} );
@@ -511,17 +518,16 @@ function isArrayLike( obj ) {
}
var Sizzle =
/*!
- * Sizzle CSS Selector Engine v2.3.4
+ * 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: 2019-04-08
+ * Date: 2020-03-14
*/
-(function( window ) {
-
+( function( window ) {
var i,
support,
Expr,
@@ -561,59 +567,70 @@ var i,
},
// Instance methods
- hasOwn = ({}).hasOwnProperty,
+ hasOwn = ( {} ).hasOwnProperty,
arr = [],
pop = arr.pop,
- push_native = arr.push,
+ 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 ) {
+ 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",
+ 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]",
- // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
- identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+ // 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)
"*([*^$|!~]?=)" + whitespace +
- // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
- "*\\]",
+
+ // "Attribute values must be CSS identifiers [capture 5]
+ // or strings [capture 3 or capture 4]"
+ "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+ whitespace + "*\\]",
pseudos = ":(" + identifier + ")(?:\\((" +
+
// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
// 1. quoted (capture 3; capture 4 or capture 5)
"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
// 2. simple (capture 6)
"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
// 3. anything else (capture 2)
".*" +
")\\)|)",
// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+ rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+ whitespace + "+$", "g" ),
rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+ rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+ "*" ),
rdescend = new RegExp( whitespace + "|>" ),
rpseudo = new RegExp( pseudos ),
@@ -625,14 +642,16 @@ var i,
"TAG": new RegExp( "^(" + identifier + "|[*])" ),
"ATTR": new RegExp( "^" + attributes ),
"PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
- "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
- "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+ "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+ whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+ whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
"bool": new RegExp( "^(?:" + booleans + ")$", "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 + "*\\)|)(?=[^-]|$)", "i" )
+ "needsContext": new RegExp( "^" + whitespace +
+ "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+ "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
},
rhtml = /HTML$/i,
@@ -648,18 +667,21 @@ var i,
// CSS escapes
// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
- funescape = function( _, escaped, escapedWhitespace ) {
- var high = "0x" + escaped - 0x10000;
- // NaN means non-codepoint
- // Support: Firefox<24
- // Workaround erroneous numeric interpretation of +"0x"
- return high !== high || escapedWhitespace ?
- escaped :
+ 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 ?
- // BMP codepoint
String.fromCharCode( high + 0x10000 ) :
- // Supplemental Plane codepoint (surrogate pair)
String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
},
@@ -675,7 +697,8 @@ var i,
}
// Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+ return ch.slice( 0, -1 ) + "\\" +
+ ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
}
// Other potentially-special ASCII characters get backslash-escaped
@@ -700,18 +723,20 @@ var i,
// Optimize for push.apply( _, NodeList )
try {
push.apply(
- (arr = slice.call( preferredDoc.childNodes )),
+ ( 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 ) {
- push_native.apply( target, slice.call(els) );
+ pushNative.apply( target, slice.call( els ) );
} :
// Support: IE<9
@@ -719,8 +744,9 @@ try {
function( target, els ) {
var j = target.length,
i = 0;
+
// Can't trust NodeList.length
- while ( (target[j++] = els[i++]) ) {}
+ while ( ( target[ j++ ] = els[ i++ ] ) ) {}
target.length = j - 1;
}
};
@@ -744,24 +770,21 @@ function Sizzle( selector, context, results, seed ) {
// Try to shortcut find operations (as opposed to filters) in HTML documents
if ( !seed ) {
-
- if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
- setDocument( context );
- }
+ 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 )) ) {
+ if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
// ID selector
- if ( (m = match[1]) ) {
+ if ( ( m = match[ 1 ] ) ) {
// Document context
if ( nodeType === 9 ) {
- if ( (elem = context.getElementById( m )) ) {
+ if ( ( elem = context.getElementById( m ) ) ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
@@ -780,7 +803,7 @@ function Sizzle( selector, context, results, seed ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
// getElementById can match elements by name instead of ID
- if ( newContext && (elem = newContext.getElementById( m )) &&
+ if ( newContext && ( elem = newContext.getElementById( m ) ) &&
contains( context, elem ) &&
elem.id === m ) {
@@ -790,12 +813,12 @@ function Sizzle( selector, context, results, seed ) {
}
// Type selector
- } else if ( match[2] ) {
+ } else if ( match[ 2 ] ) {
push.apply( results, context.getElementsByTagName( selector ) );
return results;
// Class selector
- } else if ( (m = match[3]) && support.getElementsByClassName &&
+ } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
context.getElementsByClassName ) {
push.apply( results, context.getElementsByClassName( m ) );
@@ -806,11 +829,11 @@ function Sizzle( selector, context, results, seed ) {
// Take advantage of querySelectorAll
if ( support.qsa &&
!nonnativeSelectorCache[ selector + " " ] &&
- (!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+ ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
// Support: IE 8 only
// Exclude object elements
- (nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+ ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
newSelector = selector;
newContext = context;
@@ -819,27 +842,36 @@ function Sizzle( selector, context, results, seed ) {
// 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 ) ) {
+ if ( nodeType === 1 &&
+ ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
- // 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) );
+ // 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 + " " + toSelector( groups[i] );
+ groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+ toSelector( groups[ i ] );
}
newSelector = groups.join( "," );
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
}
try {
@@ -872,12 +904,14 @@ 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[ key + " " ] = value );
}
return cache;
}
@@ -896,17 +930,19 @@ function markFunction( fn ) {
* @param {Function} fn Passed the created element and returns a boolean result
*/
function assert( fn ) {
- var el = document.createElement("fieldset");
+ var el = document.createElement( "fieldset" );
try {
return !!fn( el );
- } catch (e) {
+ } catch ( e ) {
return false;
} finally {
+
// Remove from its parent by default
if ( el.parentNode ) {
el.parentNode.removeChild( el );
}
+
// release memory in IE
el = null;
}
@@ -918,11 +954,11 @@ function assert( fn ) {
* @param {Function} handler The method that will be applied
*/
function addHandle( attrs, handler ) {
- var arr = attrs.split("|"),
+ var arr = attrs.split( "|" ),
i = arr.length;
while ( i-- ) {
- Expr.attrHandle[ arr[i] ] = handler;
+ Expr.attrHandle[ arr[ i ] ] = handler;
}
}
@@ -944,7 +980,7 @@ function siblingCheck( a, b ) {
// Check if b follows a
if ( cur ) {
- while ( (cur = cur.nextSibling) ) {
+ while ( ( cur = cur.nextSibling ) ) {
if ( cur === b ) {
return -1;
}
@@ -972,7 +1008,7 @@ function createInputPseudo( type ) {
function createButtonPseudo( type ) {
return function( elem ) {
var name = elem.nodeName.toLowerCase();
- return (name === "input" || name === "button") && elem.type === type;
+ return ( name === "input" || name === "button" ) && elem.type === type;
};
}
@@ -1015,7 +1051,7 @@ function createDisabledPseudo( disabled ) {
// Where there is no isDisabled, check manually
/* jshint -W018 */
elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
+ inDisabledFieldset( elem ) === disabled;
}
return elem.disabled === disabled;
@@ -1037,21 +1073,21 @@ function createDisabledPseudo( disabled ) {
* @param {Function} fn
*/
function createPositionalPseudo( fn ) {
- return markFunction(function( argument ) {
+ return markFunction( function( argument ) {
argument = +argument;
- return markFunction(function( seed, matches ) {
+ 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]);
+ if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+ seed[ j ] = !( matches[ j ] = seed[ j ] );
}
}
- });
- });
+ } );
+ } );
}
/**
@@ -1073,7 +1109,7 @@ support = Sizzle.support = {};
*/
isXML = Sizzle.isXML = function( elem ) {
var namespace = elem.namespaceURI,
- docElem = (elem.ownerDocument || elem).documentElement;
+ docElem = ( elem.ownerDocument || elem ).documentElement;
// Support: IE <=8
// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -1091,7 +1127,11 @@ setDocument = Sizzle.setDocument = function( node ) {
doc = node ? node.ownerDocument || node : preferredDoc;
// Return early if doc is invalid or already selected
- if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+ // 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;
}
@@ -1100,10 +1140,14 @@ setDocument = Sizzle.setDocument = function( node ) {
docElem = document.documentElement;
documentIsHTML = !isXML( document );
- // Support: IE 9-11, Edge
+ // Support: IE 9 - 11+, Edge 12 - 18+
// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- if ( preferredDoc !== document &&
- (subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+ // 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 ) {
@@ -1115,25 +1159,36 @@ setDocument = Sizzle.setDocument = function( node ) {
}
}
+ // 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 ) {
+ support.attributes = assert( function( el ) {
el.className = "i";
- return !el.getAttribute("className");
- });
+ 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.getElementsByTagName = assert( function( el ) {
+ el.appendChild( document.createComment( "" ) );
+ return !el.getElementsByTagName( "*" ).length;
+ } );
// Support: IE<9
support.getElementsByClassName = rnative.test( document.getElementsByClassName );
@@ -1142,38 +1197,38 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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 ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
- return elem.getAttribute("id") === attrId;
+ return elem.getAttribute( "id" ) === attrId;
};
};
- Expr.find["ID"] = function( id, context ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode("id");
+ 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 ) {
+ Expr.find[ "ID" ] = function( id, context ) {
if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
var node, i, elems,
elem = context.getElementById( id );
@@ -1181,7 +1236,7 @@ setDocument = Sizzle.setDocument = function( node ) {
if ( elem ) {
// Verify the id attribute
- node = elem.getAttributeNode("id");
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1189,8 +1244,8 @@ setDocument = Sizzle.setDocument = function( node ) {
// Fall back on getElementsByName
elems = context.getElementsByName( id );
i = 0;
- while ( (elem = elems[i++]) ) {
- node = elem.getAttributeNode("id");
+ while ( ( elem = elems[ i++ ] ) ) {
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1203,7 +1258,7 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Tag
- Expr.find["TAG"] = support.getElementsByTagName ?
+ Expr.find[ "TAG" ] = support.getElementsByTagName ?
function( tag, context ) {
if ( typeof context.getElementsByTagName !== "undefined" ) {
return context.getElementsByTagName( tag );
@@ -1218,12 +1273,13 @@ setDocument = Sizzle.setDocument = function( node ) {
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++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem.nodeType === 1 ) {
tmp.push( elem );
}
@@ -1235,7 +1291,7 @@ setDocument = Sizzle.setDocument = function( node ) {
};
// Class
- Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+ Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
return context.getElementsByClassName( className );
}
@@ -1256,10 +1312,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// See https://bugs.jquery.com/ticket/13378
rbuggyQSA = [];
- if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+ if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
// Build QSA regex
// Regex strategy adopted from Diego Perini
- assert(function( el ) {
+ 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,
@@ -1273,78 +1333,98 @@ setDocument = Sizzle.setDocument = function( node ) {
// Nothing should be selected when empty strings follow ^= or $= or *=
// The test attribute must be unknown in Opera but "safe" for WinRT
// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+ if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
}
// Support: IE8
// Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll("[selected]").length ) {
+ 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("~=");
+ 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");
+ 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(".#.+[+~]");
+ rbuggyQSA.push( ".#.+[+~]" );
}
- });
- assert(function( el ) {
+ // 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");
+ 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 ) {
+ 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 ) {
+ 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 ) {
+ 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(",.*:");
- });
+ el.querySelectorAll( "*,:x" );
+ rbuggyQSA.push( ",.*:" );
+ } );
}
- if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+ if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
docElem.webkitMatchesSelector ||
docElem.mozMatchesSelector ||
docElem.oMatchesSelector ||
- docElem.msMatchesSelector) )) ) {
+ docElem.msMatchesSelector ) ) ) ) {
+
+ assert( function( el ) {
- assert(function( el ) {
// Check to see if it's possible to do matchesSelector
// on a disconnected node (IE 9)
support.disconnectedMatch = matches.call( el, "*" );
@@ -1353,11 +1433,11 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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("|") );
+ rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+ rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
/* Contains
---------------------------------------------------------------------- */
@@ -1374,11 +1454,11 @@ setDocument = Sizzle.setDocument = function( node ) {
adown.contains ?
adown.contains( bup ) :
a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ));
+ ) );
} :
function( a, b ) {
if ( b ) {
- while ( (b = b.parentNode) ) {
+ while ( ( b = b.parentNode ) ) {
if ( b === a ) {
return true;
}
@@ -1407,7 +1487,11 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Calculate position if both inputs belong to the same document
- compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+ // 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
@@ -1415,13 +1499,24 @@ setDocument = Sizzle.setDocument = function( node ) {
// Disconnected nodes
if ( compare & 1 ||
- (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+ ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
// Choose the first element that is related to our preferred document
- if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+ // 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;
}
- if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+
+ // 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;
}
@@ -1434,6 +1529,7 @@ setDocument = Sizzle.setDocument = function( node ) {
return compare & 4 ? -1 : 1;
} :
function( a, b ) {
+
// Exit early if the nodes are identical
if ( a === b ) {
hasDuplicate = true;
@@ -1449,8 +1545,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// Parentless nodes are either documents or disconnected
if ( !aup || !bup ) {
- return a === document ? -1 :
- b === document ? 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 eqeqeq */
+ return a == document ? -1 :
+ b == document ? 1 :
+ /* eslint-enable eqeqeq */
aup ? -1 :
bup ? 1 :
sortInput ?
@@ -1464,26 +1566,32 @@ setDocument = Sizzle.setDocument = function( node ) {
// Otherwise we need full lists of their ancestors for comparison
cur = a;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
ap.unshift( cur );
}
cur = b;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
bp.unshift( cur );
}
// Walk down the tree looking for a discrepancy
- while ( ap[i] === bp[i] ) {
+ while ( ap[ i ] === bp[ i ] ) {
i++;
}
return i ?
+
// Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[i], bp[i] ) :
+ siblingCheck( ap[ i ], bp[ i ] ) :
// Otherwise nodes in our document sort first
- ap[i] === preferredDoc ? -1 :
- bp[i] === preferredDoc ? 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 eqeqeq */
+ ap[ i ] == preferredDoc ? -1 :
+ bp[ i ] == preferredDoc ? 1 :
+ /* eslint-enable eqeqeq */
0;
};
@@ -1495,10 +1603,7 @@ Sizzle.matches = function( expr, elements ) {
};
Sizzle.matchesSelector = function( elem, expr ) {
- // Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== document ) {
- setDocument( elem );
- }
+ setDocument( elem );
if ( support.matchesSelector && documentIsHTML &&
!nonnativeSelectorCache[ expr + " " ] &&
@@ -1510,12 +1615,13 @@ Sizzle.matchesSelector = function( 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 ) {
+
+ // 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) {
+ } catch ( e ) {
nonnativeSelectorCache( expr, true );
}
}
@@ -1524,20 +1630,31 @@ Sizzle.matchesSelector = function( elem, expr ) {
};
Sizzle.contains = function( context, elem ) {
+
// Set document vars if needed
- if ( ( context.ownerDocument || context ) !== 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 ( ( context.ownerDocument || context ) != document ) {
setDocument( context );
}
return contains( context, elem );
};
Sizzle.attr = function( elem, name ) {
+
// Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== 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 ( ( 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 ) :
@@ -1547,13 +1664,13 @@ Sizzle.attr = function( elem, name ) {
val :
support.attributes || !documentIsHTML ?
elem.getAttribute( name ) :
- (val = elem.getAttributeNode(name)) && val.specified ?
+ ( val = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
null;
};
Sizzle.escape = function( sel ) {
- return (sel + "").replace( rcssescape, fcssescape );
+ return ( sel + "" ).replace( rcssescape, fcssescape );
};
Sizzle.error = function( msg ) {
@@ -1576,7 +1693,7 @@ Sizzle.uniqueSort = function( results ) {
results.sort( sortOrder );
if ( hasDuplicate ) {
- while ( (elem = results[i++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem === results[ i ] ) {
j = duplicates.push( i );
}
@@ -1604,17 +1721,21 @@ getText = Sizzle.getText = function( elem ) {
nodeType = elem.nodeType;
if ( !nodeType ) {
+
// If no nodeType, this is expected to be an array
- while ( (node = elem[i++]) ) {
+ 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 );
@@ -1623,6 +1744,7 @@ getText = Sizzle.getText = function( elem ) {
} else if ( nodeType === 3 || nodeType === 4 ) {
return elem.nodeValue;
}
+
// Do not include comment or processing instruction nodes
return ret;
@@ -1650,19 +1772,21 @@ Expr = Sizzle.selectors = {
preFilter: {
"ATTR": function( match ) {
- match[1] = match[1].replace( runescape, funescape );
+ 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 );
+ match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+ match[ 5 ] || "" ).replace( runescape, funescape );
- if ( match[2] === "~=" ) {
- match[3] = " " + match[3] + " ";
+ 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)
@@ -1673,22 +1797,25 @@ Expr = Sizzle.selectors = {
7 sign of y-component
8 y of y-component
*/
- match[1] = match[1].toLowerCase();
+ match[ 1 ] = match[ 1 ].toLowerCase();
+
+ if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
- if ( match[1].slice( 0, 3 ) === "nth" ) {
// nth-* requires argument
- if ( !match[3] ) {
- Sizzle.error( match[0] );
+ 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" );
+ 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] );
+ // other types prohibit arguments
+ } else if ( match[ 3 ] ) {
+ Sizzle.error( match[ 0 ] );
}
return match;
@@ -1696,26 +1823,28 @@ Expr = Sizzle.selectors = {
"PSEUDO": function( match ) {
var excess,
- unquoted = !match[6] && match[2];
+ unquoted = !match[ 6 ] && match[ 2 ];
- if ( matchExpr["CHILD"].test( match[0] ) ) {
+ if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
return null;
}
// Accept quoted arguments as-is
- if ( match[3] ) {
- match[2] = match[4] || match[5] || "";
+ 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 )) &&
+ ( excess = tokenize( unquoted, true ) ) &&
+
// advance to the next closing parenthesis
- (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+ ( 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 );
+ 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)
@@ -1728,7 +1857,9 @@ Expr = Sizzle.selectors = {
"TAG": function( nodeNameSelector ) {
var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
return nodeNameSelector === "*" ?
- function() { return true; } :
+ function() {
+ return true;
+ } :
function( elem ) {
return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
};
@@ -1738,10 +1869,16 @@ Expr = Sizzle.selectors = {
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") || "" );
- });
+ ( 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 ) {
@@ -1757,6 +1894,8 @@ Expr = Sizzle.selectors = {
result += "";
+ /* eslint-disable max-len */
+
return operator === "=" ? result === check :
operator === "!=" ? result !== check :
operator === "^=" ? check && result.indexOf( check ) === 0 :
@@ -1765,10 +1904,12 @@ Expr = Sizzle.selectors = {
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 ) {
+ "CHILD": function( type, what, _argument, first, last ) {
var simple = type.slice( 0, 3 ) !== "nth",
forward = type.slice( -4 ) !== "last",
ofType = what === "of-type";
@@ -1780,7 +1921,7 @@ Expr = Sizzle.selectors = {
return !!elem.parentNode;
} :
- function( elem, context, xml ) {
+ function( elem, _context, xml ) {
var cache, uniqueCache, outerCache, node, nodeIndex, start,
dir = simple !== forward ? "nextSibling" : "previousSibling",
parent = elem.parentNode,
@@ -1794,7 +1935,7 @@ Expr = Sizzle.selectors = {
if ( simple ) {
while ( dir ) {
node = elem;
- while ( (node = node[ dir ]) ) {
+ while ( ( node = node[ dir ] ) ) {
if ( ofType ?
node.nodeName.toLowerCase() === name :
node.nodeType === 1 ) {
@@ -1802,6 +1943,7 @@ Expr = Sizzle.selectors = {
return false;
}
}
+
// Reverse direction for :only-* (if we haven't yet done so)
start = dir = type === "only" && !start && "nextSibling";
}
@@ -1817,22 +1959,22 @@ Expr = Sizzle.selectors = {
// ...in a gzip-friendly way
node = parent;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (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 ] ||
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
// Fallback to seeking `elem` from the start
- (diff = nodeIndex = 0) || start.pop()) ) {
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
// When found, cache indexes on `parent` and break
if ( node.nodeType === 1 && ++diff && node === elem ) {
@@ -1842,16 +1984,18 @@ Expr = Sizzle.selectors = {
}
} else {
+
// Use previously-cached element index if available
if ( useCache ) {
+
// ...in a gzip-friendly way
node = elem;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
cache = uniqueCache[ type ] || [];
nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
@@ -1861,9 +2005,10 @@ Expr = Sizzle.selectors = {
// 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()) ) {
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
if ( ( ofType ?
node.nodeName.toLowerCase() === name :
@@ -1872,12 +2017,13 @@ Expr = Sizzle.selectors = {
// Cache the index of each encountered element
if ( useCache ) {
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] ||
+ ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
uniqueCache[ type ] = [ dirruns, diff ];
}
@@ -1898,6 +2044,7 @@ Expr = Sizzle.selectors = {
},
"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
@@ -1917,15 +2064,15 @@ Expr = Sizzle.selectors = {
if ( fn.length > 1 ) {
args = [ pseudo, pseudo, "", argument ];
return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction(function( seed, matches ) {
+ 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] );
+ idx = indexOf( seed, matched[ i ] );
+ seed[ idx ] = !( matches[ idx ] = matched[ i ] );
}
- }) :
+ } ) :
function( elem ) {
return fn( elem, 0, args );
};
@@ -1936,8 +2083,10 @@ Expr = Sizzle.selectors = {
},
pseudos: {
+
// Potentially complex pseudos
- "not": markFunction(function( selector ) {
+ "not": markFunction( function( selector ) {
+
// Trim the selector passed to compile
// to avoid treating leading and trailing
// spaces as combinators
@@ -1946,39 +2095,40 @@ Expr = Sizzle.selectors = {
matcher = compile( selector.replace( rtrim, "$1" ) );
return matcher[ expando ] ?
- markFunction(function( seed, matches, context, xml ) {
+ 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);
+ if ( ( elem = unmatched[ i ] ) ) {
+ seed[ i ] = !( matches[ i ] = elem );
}
}
- }) :
- function( elem, context, xml ) {
- input[0] = elem;
+ } ) :
+ function( elem, _context, xml ) {
+ input[ 0 ] = elem;
matcher( input, null, xml, results );
+
// Don't keep the element (issue #299)
- input[0] = null;
+ input[ 0 ] = null;
return !results.pop();
};
- }),
+ } ),
- "has": markFunction(function( selector ) {
+ "has": markFunction( function( selector ) {
return function( elem ) {
return Sizzle( selector, elem ).length > 0;
};
- }),
+ } ),
- "contains": markFunction(function( text ) {
+ "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
@@ -1988,25 +2138,26 @@ Expr = Sizzle.selectors = {
// 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 || "") ) {
+ if ( !ridentifier.test( lang || "" ) ) {
Sizzle.error( "unsupported lang: " + lang );
}
lang = lang.replace( runescape, funescape ).toLowerCase();
return function( elem ) {
var elemLang;
do {
- if ( (elemLang = documentIsHTML ?
+ if ( ( elemLang = documentIsHTML ?
elem.lang :
- elem.getAttribute("xml:lang") || elem.getAttribute("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 );
+ } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
return false;
};
- }),
+ } ),
// Miscellaneous
"target": function( elem ) {
@@ -2019,7 +2170,9 @@ Expr = Sizzle.selectors = {
},
"focus": function( elem ) {
- return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+ return elem === document.activeElement &&
+ ( !document.hasFocus || document.hasFocus() ) &&
+ !!( elem.type || elem.href || ~elem.tabIndex );
},
// Boolean properties
@@ -2027,16 +2180,20 @@ Expr = Sizzle.selectors = {
"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);
+ 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;
}
@@ -2045,6 +2202,7 @@ Expr = Sizzle.selectors = {
// 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.)
@@ -2058,7 +2216,7 @@ Expr = Sizzle.selectors = {
},
"parent": function( elem ) {
- return !Expr.pseudos["empty"]( elem );
+ return !Expr.pseudos[ "empty" ]( elem );
},
// Element/input types
@@ -2082,39 +2240,40 @@ Expr = Sizzle.selectors = {
// Support: IE<8
// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+ ( ( attr = elem.getAttribute( "type" ) ) == null ||
+ attr.toLowerCase() === "text" );
},
// Position-in-collection
- "first": createPositionalPseudo(function() {
+ "first": createPositionalPseudo( function() {
return [ 0 ];
- }),
+ } ),
- "last": createPositionalPseudo(function( matchIndexes, length ) {
+ "last": createPositionalPseudo( function( _matchIndexes, length ) {
return [ length - 1 ];
- }),
+ } ),
- "eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
return [ argument < 0 ? argument + length : argument ];
- }),
+ } ),
- "even": createPositionalPseudo(function( matchIndexes, length ) {
+ "even": createPositionalPseudo( function( matchIndexes, length ) {
var i = 0;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "odd": createPositionalPseudo(function( matchIndexes, length ) {
+ "odd": createPositionalPseudo( function( matchIndexes, length ) {
var i = 1;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
var i = argument < 0 ?
argument + length :
argument > length ?
@@ -2124,19 +2283,19 @@ Expr = Sizzle.selectors = {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "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"];
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
// Add button/input type pseudos
for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
@@ -2167,37 +2326,39 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
while ( soFar ) {
// Comma and first run
- if ( !matched || (match = rcomma.exec( soFar )) ) {
+ if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
if ( match ) {
+
// Don't consume trailing commas as valid
- soFar = soFar.slice( match[0].length ) || soFar;
+ soFar = soFar.slice( match[ 0 ].length ) || soFar;
}
- groups.push( (tokens = []) );
+ groups.push( ( tokens = [] ) );
}
matched = false;
// Combinators
- if ( (match = rcombinators.exec( soFar )) ) {
+ if ( ( match = rcombinators.exec( soFar ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
+
// Cast descendant combinators to space
- type: match[0].replace( rtrim, " " )
- });
+ 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 ))) ) {
+ if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+ ( match = preFilters[ type ]( match ) ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
type: type,
matches: match
- });
+ } );
soFar = soFar.slice( matched.length );
}
}
@@ -2214,6 +2375,7 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
soFar.length :
soFar ?
Sizzle.error( selector ) :
+
// Cache the tokens
tokenCache( selector, groups ).slice( 0 );
};
@@ -2223,7 +2385,7 @@ function toSelector( tokens ) {
len = tokens.length,
selector = "";
for ( ; i < len; i++ ) {
- selector += tokens[i].value;
+ selector += tokens[ i ].value;
}
return selector;
}
@@ -2236,9 +2398,10 @@ function addCombinator( matcher, combinator, base ) {
doneName = done++;
return combinator.first ?
+
// Check against closest ancestor/preceding element
function( elem, context, xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
return matcher( elem, context, xml );
}
@@ -2253,7 +2416,7 @@ function addCombinator( matcher, combinator, base ) {
// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
if ( xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
if ( matcher( elem, context, xml ) ) {
return true;
@@ -2261,27 +2424,29 @@ function addCombinator( matcher, combinator, base ) {
}
}
} else {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || (elem[ expando ] = {});
+ outerCache = elem[ expando ] || ( elem[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+ uniqueCache = outerCache[ elem.uniqueID ] ||
+ ( outerCache[ elem.uniqueID ] = {} );
if ( skip && skip === elem.nodeName.toLowerCase() ) {
elem = elem[ dir ] || elem;
- } else if ( (oldCache = uniqueCache[ key ]) &&
+ } 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 ]);
+ 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 )) ) {
+ if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
return true;
}
}
@@ -2297,20 +2462,20 @@ function elementMatcher( matchers ) {
function( elem, context, xml ) {
var i = matchers.length;
while ( i-- ) {
- if ( !matchers[i]( elem, context, xml ) ) {
+ if ( !matchers[ i ]( elem, context, xml ) ) {
return false;
}
}
return true;
} :
- matchers[0];
+ matchers[ 0 ];
}
function multipleContexts( selector, contexts, results ) {
var i = 0,
len = contexts.length;
for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[i], results );
+ Sizzle( selector, contexts[ i ], results );
}
return results;
}
@@ -2323,7 +2488,7 @@ function condense( unmatched, map, filter, context, xml ) {
mapped = map != null;
for ( ; i < len; i++ ) {
- if ( (elem = unmatched[i]) ) {
+ if ( ( elem = unmatched[ i ] ) ) {
if ( !filter || filter( elem, context, xml ) ) {
newUnmatched.push( elem );
if ( mapped ) {
@@ -2343,14 +2508,18 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
if ( postFinder && !postFinder[ expando ] ) {
postFinder = setMatcher( postFinder, postSelector );
}
- return markFunction(function( seed, results, context, xml ) {
+ 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, [] ),
+ elems = seed || multipleContexts(
+ selector || "*",
+ context.nodeType ? [ context ] : context,
+ []
+ ),
// Prefilter to get matcher input, preserving a map for seed-results synchronization
matcherIn = preFilter && ( seed || !selector ) ?
@@ -2358,6 +2527,7 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
elems,
matcherOut = matcher ?
+
// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
@@ -2381,8 +2551,8 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
// 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 ( ( elem = temp[ i ] ) ) {
+ matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
}
}
}
@@ -2390,25 +2560,27 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
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]) ) {
+ if ( ( elem = matcherOut[ i ] ) ) {
+
// Restore matcherIn since elem is not yet a final match
- temp.push( (matcherIn[i] = elem) );
+ temp.push( ( matcherIn[ i ] = elem ) );
}
}
- postFinder( null, (matcherOut = []), temp, xml );
+ 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 ) {
+ if ( ( elem = matcherOut[ i ] ) &&
+ ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
- seed[temp] = !(results[temp] = elem);
+ seed[ temp ] = !( results[ temp ] = elem );
}
}
}
@@ -2426,14 +2598,14 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
push.apply( results, matcherOut );
}
}
- });
+ } );
}
function matcherFromTokens( tokens ) {
var checkContext, matcher, j,
len = tokens.length,
- leadingRelative = Expr.relative[ tokens[0].type ],
- implicitRelative = leadingRelative || Expr.relative[" "],
+ 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)
@@ -2445,38 +2617,43 @@ function matcherFromTokens( tokens ) {
}, implicitRelative, true ),
matchers = [ function( elem, context, xml ) {
var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- (checkContext = context).nodeType ?
+ ( 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) ];
+ if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+ matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
} else {
- matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+ 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 ] ) {
+ 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 === " " ? "*" : "" })
+
+ // 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 && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
j < len && toSelector( tokens )
);
}
@@ -2497,28 +2674,40 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
unmatched = seed && [],
setMatched = [],
contextBackup = outermostContext,
+
// We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+ 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),
+ dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
len = elems.length;
if ( outermost ) {
- outermostContext = context === document || context || 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++ ) {
+ for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
if ( byElement && elem ) {
j = 0;
- if ( !context && elem.ownerDocument !== 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 ( !context && elem.ownerDocument != document ) {
setDocument( elem );
xml = !documentIsHTML;
}
- while ( (matcher = elementMatchers[j++]) ) {
- if ( matcher( elem, context || document, xml) ) {
+ while ( ( matcher = elementMatchers[ j++ ] ) ) {
+ if ( matcher( elem, context || document, xml ) ) {
results.push( elem );
break;
}
@@ -2530,8 +2719,9 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// Track unmatched elements for set filters
if ( bySet ) {
+
// They will have gone through all possible matchers
- if ( (elem = !matcher && elem) ) {
+ if ( ( elem = !matcher && elem ) ) {
matchedCount--;
}
@@ -2555,16 +2745,17 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// numerically zero.
if ( bySet && i !== matchedCount ) {
j = 0;
- while ( (matcher = setMatchers[j++]) ) {
+ 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 );
+ if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+ setMatched[ i ] = pop.call( results );
}
}
}
@@ -2605,13 +2796,14 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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] );
+ cached = matcherFromTokens( match[ i ] );
if ( cached[ expando ] ) {
setMatchers.push( cached );
} else {
@@ -2620,7 +2812,10 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
}
// Cache the compiled function
- cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+ cached = compilerCache(
+ selector,
+ matcherFromGroupMatchers( elementMatchers, setMatchers )
+ );
// Save selector and tokenization
cached.selector = selector;
@@ -2640,7 +2835,7 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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) );
+ match = !seed && tokenize( ( selector = compiled.selector || selector ) );
results = results || [];
@@ -2649,11 +2844,12 @@ select = Sizzle.select = function( selector, context, results, seed ) {
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 ] ) {
+ 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];
+ context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+ .replace( runescape, funescape ), context ) || [] )[ 0 ];
if ( !context ) {
return results;
@@ -2666,20 +2862,22 @@ select = Sizzle.select = function( selector, context, results, seed ) {
}
// Fetch a seed set for right-to-left matching
- i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+ i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
while ( i-- ) {
- token = tokens[i];
+ token = tokens[ i ];
// Abort if we hit a combinator
- if ( Expr.relative[ (type = token.type) ] ) {
+ if ( Expr.relative[ ( type = token.type ) ] ) {
break;
}
- if ( (find = Expr.find[ type ]) ) {
+ 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 = 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 );
@@ -2710,7 +2908,7 @@ select = Sizzle.select = function( selector, context, results, seed ) {
// One-time assignments
// Sort stability
-support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
// Support: Chrome 14-35+
// Always assume duplicates if they aren't passed to the comparison function
@@ -2721,58 +2919,59 @@ 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 ) {
+support.sortDetached = assert( function( el ) {
+
// Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
-});
+ 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 ) {
+if ( !assert( function( el ) {
el.innerHTML = "";
- return el.firstChild.getAttribute("href") === "#" ;
-}) ) {
+ 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 ) {
+if ( !support.attributes || !assert( function( el ) {
el.innerHTML = "";
el.firstChild.setAttribute( "value", "" );
return el.firstChild.getAttribute( "value" ) === "";
-}) ) {
- addHandle( "value", function( elem, name, isXML ) {
+} ) ) {
+ 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;
-}) ) {
+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 = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
- null;
+ null;
}
- });
+ } );
}
return Sizzle;
-})( window );
+} )( window );
@@ -3141,7 +3340,7 @@ jQuery.each( {
parents: function( elem ) {
return dir( elem, "parentNode" );
},
- parentsUntil: function( elem, i, until ) {
+ parentsUntil: function( elem, _i, until ) {
return dir( elem, "parentNode", until );
},
next: function( elem ) {
@@ -3156,10 +3355,10 @@ jQuery.each( {
prevAll: function( elem ) {
return dir( elem, "previousSibling" );
},
- nextUntil: function( elem, i, until ) {
+ nextUntil: function( elem, _i, until ) {
return dir( elem, "nextSibling", until );
},
- prevUntil: function( elem, i, until ) {
+ prevUntil: function( elem, _i, until ) {
return dir( elem, "previousSibling", until );
},
siblings: function( elem ) {
@@ -3169,7 +3368,13 @@ jQuery.each( {
return siblings( elem.firstChild );
},
contents: function( elem ) {
- if ( typeof elem.contentDocument !== "undefined" ) {
+ if ( elem.contentDocument != null &&
+
+ // Support: IE 11+
+ //
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+
+
@@ -158,7 +162,7 @@
-from hoki.age_utils import AgeWizard
+from hoki.age_utils import AgeWizard
import hoki
import pandas as pd
import matplotlib.pyplot as plt
@@ -469,6 +506,7 @@ Motivation
How it works¶
By matching the observed properties of your sources to the BPASS models you can find the most likely age of a location on the HRD/CMD.
+
If all your sources belong to the same region, you can then combine the PDFs of your individual sources to find the age PDF of the whole cluster.
All of this can be done step by step using the chain find_coordinates() -> calculate_pdfs -> multiply_pdfs(), or you could simply use AgeWizard() which handles this pipeline and facilitates loading in the models.
Note: In this tutorial we will focus on AgeWizard().
@@ -937,11 +975,11 @@ Most Likely Ages[8]:
-array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,
+
+
+array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,
6.9, 6.9, 6.9])
-
-
+Plotting Aggregate Ages[17]:
[17]:
-array([6.9])
-
+array([6.9])
+Down below we create a quick function to plot our aggregate ages for z=0.006 and z=0.008.
As an example of the
@@ -1387,10 +1425,10 @@Plotting Aggregate Ages[24]:
[24]:
-<matplotlib.legend.Legend at 0x7f6b320e16d8>
-
+<matplotlib.legend.Legend at 0x7f6b320e16d8>
+
@@ -1419,8 +1457,9 @@ Probability given an age range
-Probability given an age range[28]:
[28]:
-name
+
+
+name
Star1 0.857630
Star2 0.932043
Star3 0.779729
@@ -1438,8 +1477,7 @@ Probability given an age range
@@ -1625,11 +1663,19 @@ Ages from Colour-Magnitude Diagram
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -1641,7 +1687,6 @@ Ages from Colour-Magnitude Diagram
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/AgeWizard.ipynb b/docs/AgeWizard.ipynb
new file mode 100644
index 00000000..ccbd06bd
--- /dev/null
+++ b/docs/AgeWizard.ipynb
@@ -0,0 +1,1479 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Age Wizard\n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
+ "\n",
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki.age_utils import AgeWizard\n",
+ "import hoki\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A systematic ageing method \n",
+ "\n",
+ "### Motivation\n",
+ "One of the classic methods for ageing clusters is to fit isochrones to observational data in Colour-Magnitude Diagrams (CMDs) or in Hertzsprung-Russel Diagrams (HRDs).\n",
+ "\n",
+ "There are a number of issues with this ubiquitous technique though:\n",
+ "\n",
+ "* Fitting is usually done by eye\n",
+ "* They only take into account single stars\n",
+ "* It cannot be used if your sample only contains a few sources\n",
+ "\n",
+ "**Our goal is to provide a method that answers all of the issues metioned above**\n",
+ "\n",
+ "### How it works\n",
+ "\n",
+ "By matching the observed properties of your sources to the BPASS models you can find the **most likely age** of a **location on the HRD/CMD**.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If all your sources belong to the same region, you can then combine the PDFs of your individual sources to find the age PDF of the whole cluster.\n",
+ "\n",
+ "All of this can be done step by step using the chain `find_coordinates()` -> `calculate_pdfs` -> `multiply_pdfs()`, or you could simply use `AgeWizard()` which handles this pipeline and facilitates loading in the models. \n",
+ "\n",
+ "Note: In this tutorial we will focus on `AgeWizard()`.\n",
+ "\n",
+ "\n",
+ "### Remember to exercise caution in your interpretation\n",
+ "\n",
+ "This is true of all methods but it is worth repeating. This technique helps you get to the answer, but it doesn't give you the answer. \n",
+ "\n",
+ "The **most likely age** of the **matching location** is not necessarily the age of your source - as we'll see below Helium Stars created through binary interactions and envelope stripping are found at similar locations as WR stars but at different ages.\n",
+ "\n",
+ "# Ages from Hertzsprung-Russell Diagram\n",
+ "\n",
+ "First, we need some observational data to compare to our models. \n",
+ "\n",
+ "`AgeWizard()` expects a `pandas.DataFrame`. If you want to use the HRD capabilities you will need to provide a `logT` and a `logL` column:\n",
+ "* logT: log10 of the stellar effective temperature\n",
+ "* logL: log10 of the stellar luminosity **in units of Solar Luminosities**\n",
+ "\n",
+ "Additionally, your DataFrame will ideally contain a `name` column with the name of the sources you're working on. If you don't provide one, `AgeWizard()` will make it's own sources names: source1, source2, etc...\n",
+ "\n",
+ "_Data source: McLeod et al. 2019, Stevance et al. in prep._"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stars = pd.DataFrame.from_dict({'name':np.array(['Star1','Star2','Star3','Star4', \n",
+ " 'Star5','Star6','Star7','Star8','Star9',\n",
+ " 'Star10','Star11','Star12','Star13','Star14', \n",
+ " 'WR1', 'WR2']),\n",
+ " 'logL':np.array([5.0, 5.1, 4.9, 5.9, 5.0, 5.4, 4.3, 5.7, 4.5, 4.5, \n",
+ " 4.9, 4.5, 4.3, 4.5, 5.3, 5.3]), \n",
+ " 'logT':np.array([4.48, 4.45, 4.46, 4.47, 4.48, 4.53, 4.52, 4.52, 4.52,\n",
+ " 4.56, 4.46, 4.52, 4.52, 4.52, 4.9, 4.65])})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialise our `AgeWizard()`. Each instance corresponds to one model and one model only: i.e. 1 IMF and 1 metalicity.\n",
+ "\n",
+ "If we want to have a look at 2 different metallicities for example, we can just instanciate `AgeWizard` twice:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agewiz006 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z006.dat')\n",
+ "agewiz008 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z008.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "During instanciation, the observations and models are matched and the age PDFs are calculated. They are summarised in a `pandas.DataFrame` that you can access as follows. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
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+ "5 0.000000 0.000000 0.015330 7.629112e-01 0.000000 0.000000 \n",
+ "6 0.075321 0.011572 0.142812 1.100859e-01 0.075321 0.003387 \n",
+ "7 0.238335 0.430026 0.233166 8.879082e-02 0.238335 0.558455 \n",
+ "8 0.295138 0.348300 0.279178 2.892240e-02 0.295138 0.345761 \n",
+ "9 0.324156 0.153717 0.267386 9.289685e-03 0.324156 0.044554 \n",
+ "10 0.023544 0.025031 0.023663 0.000000e+00 0.023544 0.032551 \n",
+ "11 0.016194 0.017432 0.015374 0.000000e+00 0.016194 0.011823 \n",
+ "12 0.017234 0.011523 0.014033 0.000000e+00 0.017234 0.003118 \n",
+ "13 0.008077 0.001189 0.006150 0.000000e+00 0.008077 0.000000 \n",
+ "14 0.000061 0.000322 0.001203 0.000000e+00 0.000061 0.000134 \n",
+ "\n",
+ "name Star7 Star8 Star9 Star10 Star11 Star12 \\\n",
+ "0 0.025706 5.594794e-11 0.021803 0.000000e+00 0.000000 0.021803 \n",
+ "1 0.036928 0.000000e+00 0.030149 0.000000e+00 0.000000 0.030149 \n",
+ "2 0.046310 0.000000e+00 0.035243 0.000000e+00 0.000000 0.035243 \n",
+ "3 0.058174 0.000000e+00 0.052005 7.820472e-08 0.000000 0.052005 \n",
+ "4 0.081692 0.000000e+00 0.057408 1.012331e-04 0.000654 0.057408 \n",
+ "5 0.063921 4.216232e-03 0.074740 9.941176e-07 0.015330 0.074740 \n",
+ "6 0.078904 2.914504e-01 0.086366 1.476180e-04 0.142812 0.086366 \n",
+ "7 0.135883 4.335802e-01 0.093930 1.763084e-03 0.233166 0.093930 \n",
+ "8 0.187286 1.529185e-01 0.147844 4.403392e-03 0.279178 0.147844 \n",
+ "9 0.176904 8.005393e-02 0.204892 1.732683e-02 0.267386 0.204892 \n",
+ "10 0.072708 3.722405e-02 0.165809 1.310383e-02 0.023663 0.165809 \n",
+ "11 0.004711 5.566887e-04 0.006944 6.358691e-03 0.015374 0.006944 \n",
+ "12 0.003887 0.000000e+00 0.006177 6.590932e-02 0.014033 0.006177 \n",
+ "13 0.004152 0.000000e+00 0.006443 6.874719e-01 0.006150 0.006443 \n",
+ "14 0.007283 0.000000e+00 0.003896 4.532046e-02 0.001203 0.003896 \n",
+ "\n",
+ "name Star13 Star14 WR1 WR2 \n",
+ "0 0.025706 0.021803 0.000000e+00 0.107142 \n",
+ "1 0.036928 0.030149 0.000000e+00 0.004603 \n",
+ "2 0.046310 0.035243 0.000000e+00 0.006676 \n",
+ "3 0.058174 0.052005 0.000000e+00 0.012032 \n",
+ "4 0.081692 0.057408 0.000000e+00 0.003365 \n",
+ "5 0.063921 0.074740 4.525980e-08 0.009378 \n",
+ "6 0.078904 0.086366 0.000000e+00 0.000145 \n",
+ "7 0.135883 0.093930 2.155362e-14 0.015148 \n",
+ "8 0.187286 0.147844 3.207127e-01 0.023512 \n",
+ "9 0.176904 0.204892 5.301331e-01 0.745544 \n",
+ "10 0.072708 0.165809 2.254815e-02 0.060667 \n",
+ "11 0.004711 0.006944 9.453766e-03 0.002344 \n",
+ "12 0.003887 0.006177 3.787228e-06 0.001189 \n",
+ "13 0.004152 0.006443 6.385924e-05 0.000039 \n",
+ "14 0.007283 0.003896 1.007283e-03 0.000000 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.pdfs.head(15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But those are just numbers... PDFs are meant to be plotted:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_all_pdfs(agewiz):\n",
+ " # creating the figure\n",
+ " f, ax = plt.subplots(4, 4, figsize=(15,15))\n",
+ " plt.subplots_adjust(hspace=0.4)\n",
+ " axes = ax.reshape(16)\n",
+ "\n",
+ " # We plot each star an individual axis \n",
+ " for source, axis in zip(agewiz.sources, axes):\n",
+ " \n",
+ " # Plotting one PDF\n",
+ " axis.step(hoki.BPASS_TIME_BINS, agewiz.pdfs[source],where='mid')\n",
+ " axis.fill_between(hoki.BPASS_TIME_BINS, agewiz.pdfs[source], step='mid', alpha=0.3)\n",
+ " \n",
+ " # Labels and limits\n",
+ " axis.set_title(source)\n",
+ " axis.set_ylabel('Probability (%)')\n",
+ " axis.set_xlabel('log(years)')\n",
+ " axis.set_ylim([0,0.6]) \n",
+ " axis.set_xlim([6,8.5]) \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Age PDFs at z=0.006"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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W0tLSwsjICENDQwB0dHQwMzOz78aOjY2N1NTU0NPTw+TkJFVVVaxatYrh4WGGh4cpKiqio6ODqakpenp6AGhqaqK6upru7m727t3LihUraG5uZmhoiJGRkX2Pef755/ett7m5maqqKrq7u5mamqK6upqmpiYGBwcZHR2luLiYjo4OJiYm6O3tBZKfZFRWVtLV1cX09DQ1NTU0NjYyMDDA2NgYJSUlrFmzhvHxcfr6+gBoaWmhoqKCzs5OZmZmqK2tpaGhgf7+fnbu3ElpaSnt7e3s2rWL/v5+56/X0NAQu3fv9uL1mpycpLKyMkMscqKsKWveZm18fJy6ujpvXq8lZE05U868zVmM3tNim7OJiXFKEgmmEwn6JpLnhdWWlVJdWsrAnkkmp2eYmUxOV86UszByVpRIJOadGabUpzXDwFHW2ifSpu8ATrXW2hye43+BK6y135kzPZHDw5e1zZs3s3btWtfD8JqHNSpazIOUNbc87COveFqfBWdNOXPL0z7yioc1Us7SPH3e2UyN7qDtjI9mnN9z47WU1tRy2NXXFXhkf+JhD3nHwxrNm7Ocz+kzxhxijHmjMeZtxphXGWMW9JGNtXYE6AKOTnvOtSQ/qdmY49PMsMg/hJe7jg4d+h7Elxr19fWxceNGlLVo8qWPfOVLfWZz9pvf/AblLHp86SOf+VAj5SzafOgh30WpRlkP7zTGHAx8BDgJaOWFoZk0xvwKuAG41Vo7k8P6bgA+ZYy5j+QVmK4ENlhrt2RY93Ekg74ZqAIuAJqBDTmsR+aYnp6mpKTE9TC85rJGzz//PD/72c+4//77GRoaIrUH/j9Ss5W1CFHWsvMwZwAPo5xFinIWzFWNlLP4UM6CRalG8+7pM8Z8E/gdsBb4DPAyoBYoB1YBbwMeAL4IbDTGvCqH9V0B3Ak8CmwFSoBTUuv7gDFmZ9qyRwL3AmMkw/sa4M3W2u4F/H6SMntcsMzPVY1uuOEGzj33XPr6+jj11FO55ppruOWWW0BZiyRlLTvfcnbbbbeBchY5ylkwFzVSzuJFOQsWpRrNe06fMebLwJcz3fwyw7JvA6qstT8OeXw5cX1cdhR4eMyxd1zV6KabbuI973kPNTX7nZO+3+Eoypr/lLXsPMwZzMmacuY/5SyYixopZ7nTOX3x4GGN5j2Ued7DO621F+b67Nbany50RFJYBx10kOsheM9VjU477bScl1XW/KesZaecSRiUs2AuaqScxYtyFixKNVrwLRuMMY3Aq0nuXn/EWtsX+qgkdCtXrgxeaJnzrUbKWjT51ke+8a0+o6OjnHLKKW9HOYsU3/rIRz7VSDmLJp96yFdRqlHOV+8EMMa8E/hf4PPAJcBmY8wp+RiYhCtKxxy74lONHnroIVDWIsmnPvKRT/V56KGHOOuss0A5ixyf+shXvtRIOYsuX3rIZ1GqUdaNPmNMxZxJXwRea609xlr7CpJX9bwiX4OT8ExOTroegvdc1mjv3r0v+Pnmm28GZS2SlLXsfMvZlVdeiXIWPcpZMFc1Us7iQzkLFqUaBe3pe8IY88a0nxPAVNrPuVxqVzxQVVXlegjec1mjc889l40b/3TLoaKiIlDWIklZy863nM251LZyFhHKWTBXNVLO4kM5CxalGgWd03cycIMx5kngk8DngIeNMc8AZcBLgLPzO0QJw6pVq1wPwXsua/TJT36Sb3/723R0dHD66adz8sknc9lllylrEaSsZedbzi644AJ27dr1MMpZpChnwVzVSDmLD+UsWJRqlHVPn7X2cZIXkvg98BvgAODFwKXAxcCfWWu/n+9BytINDQ25HoL3XNbo0EMP5aqrrmLNmjWcf/75s4cLKGsRpKxl51vOrrvuOlDOIkc5C+aqRspZfChnwaJUo8Crd1prZ4CvGmNuA64F/gY421rble/BSXhGRkaor693PQyvua5RcXExJ554IuvWrZt9g/weylrkuO4j37muz9yc3XfffQC/U86ixXUfRYHLGiln8aCcBYtSjQKv3mmMebkx5j1AmbX2rcDNwC+MMecaY+a9AaD4JXWOmGThukZdXV38+te/Znp6mi984QugrEWS6z7ynev6zM3ZG97wBlDOIsd1H0WByxopZ/GgnAWLUo2Crt55PvAo8PfAg8aYv7XW3gy8CjgKeMgYc0T+hylL1dHR4XoI3nNZozvuuIPzzz+f2267jQsvvJANGzagrEWTspadbzlbv349KGeRo5wFc1Uj5Sw+lLNgUapR0J6+C4G3W2tfAxwNnA9grd1urf0Q8Bngh3kdoYRi7iWUZX8ua3Tbbbfxuc99jquuuoqvf/3r/OQnPwGUtShS1rJTziQMylkwVzVSzuJDOQsWpRoFbfQV8adL606nft7HWnsPyU9txHNbt251PQTvuaxRIpGguDgZx+LiYhKJxAvmK2tLM3Dn7Tx93tnzfg3ceXto61LWslPOJAzKWTBXNVLO4kM5CxalGgVdyOUrwE+NMb8jeSXBT89dwFo7kY+BiSwn7373u7n44os55JBD2Lp1K6eeeup+yyhri7f93g3s/uNTlK9u3W/eZG/yP+yDTjix0MOSAlPORPJPORPxU9aNPmvtVcaYDcBhwJPW2qcLMywJW1NTk+sheM9ljU488USOOuooenp6OPjgg2lra3M2lrgqX91K2xkf3W96z43XhroeZS075UzCoJwFc1Uj5Sw+lLNgUapRLrdseBJ4sgBjkTxasWKF6yF4z3WNDj74YA4++GCnY5Clc91HvnNdH+UsHlz3URS4rJFyFg/KWbAo1Wjec/qMMZ81xuT0mxhjXmuMOSG8YUnYuru7XQ/Be65q9MMf/pCJidyOdFHW/KesZaecSRiUs2AuaqScxYtyFixKNcq2p+9QoMsYcytwJ/Aba20vgDGmEngZ8DrgFKCR5E3bszLGlABXAB8CKoG7gbOstYMBj/sIyRvD/6O19tKg9cj+pqamXA/Be65q1NfXx+mnn866des45phjeNGLXrTvRp/KWvQoa9kpZ8pZGJSzYC5qpJzFi3IWLEo1mnejz1p7mjHmcOBjwL8CNcaYBLAXKCd5Jc/fAjcA37PW7slhfRcB7wReDWwHvgt8H3jrfA8wxnQAn0SHmC5JdXW16yF4z1WNzj33XLZs2cJ//ud/8vWvf53x8XEAZmZmxlHWIkdZy863nJWWlgLsQjmLFOUsmIsaKWfxopwFi1KNgi7k8iRwVurTkiOADuAAYBB4IuhTlgzOBC6x1m4GMMZcCGwyxnRYazvnecx3SN7T5SMLXJekidKJpq64rNHBBx/MOeecw0c+8hG2bNlCf38/l1122Wkoa5GjrGXnW84mJye56qqr3oJyFinKWTBXNVLO4kM5CxalGgVeyAXAWjsDPJH6WhRjTB2wBngs7XmfNcaMAkcC+wXXGHMWsMta+8PUhqcs0vbt22loaHA9DK/5UKPi4mLWrl3L2rVrsdb+v8U8h7Lmlg995DMf6pOeM4D/83/+zz0LfQ7lzC0f+sh3rmuknEWf6x6KgijVKKeNvpCsTP27Y870EaBm7sLGmDXAZ4HX5PLk55xzDgDl5eWsX7+eww8/HIDW1lbKysro7OwkkUhQV1dHfX09fX197N69m/Lyctra2hgbG2NgYACAtrY2SkpK6OxM/l9SX19PXV0dvb29jI+PU1FRQWtrK6OjowwOJj+wam9vp6ioiK6uLgAaGhqora1l27ZtTExMUFlZSUtLCyMjIwwNDQHQ0dHBzMzMvpNAGxsbqampoaenh8nJSaqqqli1ahXDw8MMDw9TVFRER0cHU1NT9PT0AMlPGKqrq+nu7mbv3r2sWLGC5uZmhoaGGBkZ2feYwcFBduxIlr65uZmqqiq6u7uZmpqiurqapqYmBgcHGR0dpbi4mI6ODiYmJujt7QVg9erVVFZW0tXVxfT0NDU1NTQ2NjIwMMDY2BglJSWsWbOG8fFx+vr6AGhpaaGiooLOzk5mZmaora2loaGB/v5+du7cSWlpKe3t7ezatYv+/n7nr9eOHTvYs2ePF6/X5OQklZWVubR+JsranNeORIJEIsHW8eQFBurKylhRWkL/xB4mp2eYmZwEUNYK8HqNj4/T0NDgzf+NS8iacqb3NG9zFqP3tNjmbGJinJJEgulEgr6J5FGutWWlVJeWMrBnMvXelJyunClnYeSsKJFI5JKLJUt9WjMMHGWtfSJt+g7gVGutnbP83cCt1trrUz//Argn08m4xpjEnIfLHFu2bNHlkwN4WKOixTxIWdvf0+edzdTojnnv01daU8thV18Xyro87COveFqfBWdNOXPL0z7yioc1Us7SZHtfgvDfmxbDwx7yjoc1mjdn896yIWzW2hGgCzh6dpoxZi3JT2o2ZnjIm4HLjDGDxphB4LXAPxhjflWI8cZNR0eH6yF4Ly41Utbciksf5Utc6qOcuRWXPsqnONRIOXMrDj2Ub1GqUSEP74Tk1Zo+ZYy5j+QVmK4ENlhrt2RYtn3Ozz8CfgV8Na8jjKk9e/Ys5XDBZSFmNVLWHIlZH4UuZvVRzhyJWR/lRYxqpJw5EqMeypso1SinjT5jzLuAO62100tc3xXAgcCjQAXwc5L3asEY8wHgemttNYC1tmfOGPYAo9ba55c4hmVp27Zt+06mlsx8qNFDDz3Eq171KkpKSpb6VMqaIz70kc98qI9yFn0+9JHvXNdIOYs+1z0UBVGqUa57+m4Gxowx3wO+Y619ZjErS200XpD6mjvv5tR65nvs+sWsUyRKvvrVr3LAAQdw3HHHcdttt71YWRMJX3rO3vzmN9Pa2rqo51HOROannIn4JdeNvlXAycBpwAXGmAdJ3gPl3621u/I1OAnPqlWrXA/Bez7U6Hvf+x73338/99xzD8AflLXo8aGPfOZDfdJzdscddzAzM/MAylmk+NBHvnNdI+Us+lz3UBREqUY5XcjFWjtmrb3eWvsakjdpfxi4HOg1xvyzMSanS+OKOwcccIDrIXjPhxpVVVVx/PHHc9VVV4GyFkk+9JHPfKhPes6++c1vgnIWOT70ke9c10g5iz7XPRQFUarRgq/eaa39PfB1kifWlgN/DfzKGPOwMeaIkMcnIZm9p4jMz7caKWvR5Fsf+ca3+qxZswaUs8jxrY985FONlLNo8qmHfBWlGuW80WeMKTPGvM8YcxfwHHAccDbQDHQAfwB+mJdRypJNTy/1Gjzx50uNpqameOCBB1DWosmXPvKVL/WZzdnnP/95UM4ix5c+8pkPNVLOos2HHvJdlGqU69U7vwWcBCSA7wPnW2ufSltk3BhzEbAt/CFKGFauXOl6CN7zoUbXX389999/P0VFRQBPoaxFjg995DMf6pOesze+8Y08/vjjRypn0eJDH/nOdY2Us+hz3UNREKUa5Xohl5cBHwNus9ZOzrPMIPDGUEYloTvooINcD8F7PtSou7ubs846i3Xr1lFWVnb+PIspax7zoY985kN95uSM008//akMiylnHvOhj3znukbKWfS57qEoiFKNct3ouxj4tbV2Kn2iMaYUONZae39q3i/DHqCEY3BwkMbGRtfD8JoPNXr/+9/PS1/60v3ua6SsRYcPfeQzH+qjnEWfD33kO9c1Us6iz3UPRUGUapTrOX33AfUZptem5onnRkdHXQ/Bez7U6LOf/SxjY2OZZilreTS+6RmePu/seb8G7rw95+fyoY985kN9lLPo86GPfOe6RspZ9LnuoSiIUo1y3dNXRPJ8vrkaAN1rJQLmftIm+/OhRolEYvZ8vrmUtTxZecRRjG18nKnRHRnnT/ZuBeCgE07M6fl86COf+VAf5Sz6fOgj37mukXIWfa57KAqiVKOsG33GGJv6NgH8wBizJ212CfDnwK/zNDYJUepyyZKFyxpdeumlABQVFfG1r32NsrIyHnnkkdn8KWt5VHvMOmqPWTfv/J4br13Q8ylr2fmWM2A2a8pZhChnwVzVSDmLD+UsWJRqFLSnb3vq3yJgGBhPmzcJPAD8cx7GJSGbmJiI1A0kXXBZo9mrPyUSCaqrqykvL4c/5U9ZixBlLTsPcwbJrClnEaKcBXNVI+UsPpSzYFGqUdaNPmvtaQDGmC3AVdZa7Y6PqN7eXtauXet6GF5zWaNzzwF0aLAAACAASURBVD0XgKamJk488UQqKys599xzT3MyGFkSZS0733KWmq6sRYxyFsxVjZSz+FDOgkWpRjmd02etvTjfAxEROOmkk1wPQST2lDOR/FPORPwy70afMWYj8AZr7bAx5kkyX8gFAGvtEfkYnMxv4M7b2X7vhqzLNLzpLfsuPrF69epCDCvSXNXo4x//OJdffjnV1dV8/OMf3ze9s7Nz49xllTX/KWvZ+ZYz2D9rypn/lLNgLmqknMWLchYsSjXKtqfvVmD2wi0/LsBYZAG237uB3X98ivLVrRnnz73i4OzhFTI/VzU69thj953ofuyxx+6b3tnZeauTAcmSKGvZ+ZYzUNaiSDkL5qJGylm8KGfBolSjeTf60g/p1OGdfipf3UrbGR/NOG/uFQc7Ozs5+OCDCzCq6HJVo/RDYOZ8r9xFkLKWnW85S/2srEWMchbMRY2Us3hRzoJFqUa53pxdIm5mZsb1ELynGkkY1EfZqT4SBvVRMNVIlko9FCxKNcp2Tl/W8/jS5XpctjGmBLgC+BBQCdwNnGWtHcyw7OuBbwAHk7yvy7PApdba23JZl7xQTU2N6yF4z1WN5p73MGsp5/Qpa+4oa9n5ljNY/LlGypk7ylkwFzVSzuJFOQsWpRplO6cvH+fxXQS8E3g1yfu1fBf4PvDWDMv+ETgR6Er9/HrgLmPMK621f8jD2GKtsbHR9RC856pGc897mLXE8x+UNUeUtex8yxksKWvKmSPKWTAXNVLO4kU5CxalGuV0Tl+IzgQusdZuBjDGXAhsMsZ0WGs756y/f/Z7Y0wxMEPycNQXAQruAvX399PU1OR6GF5zVaP5Lmu9xPMflDVHlLXsfMtZat5is6acOaKcBXNRI+UsXpSzYFGqUU736QuDMaYOWAM8NjvNWvusMWYUOBLonOdxI8AKkmO9n+Ru/f2cc845AJSXl7N+/XoOP/xwAFpbWykrK6Ozs5NEIkFdXR319fX09fWxe/duysvLaWtrY2xsjIGBAQDa2tooKSmhszM5pPr6eurq6ujt7WV8fJyKigpaW1sZHR1lcDB5dEF7eztFRUV0dSU/XGpoaKC2tpZt27YxMTFBZWUlLS0tjIyMMDQ0BEBHRwczMzN0d3cDyU8Lampq6OnpYXJykqqqKlatWsXw8DDDw8MUFRXR0dHB1NQUExPjzEzPsHtqmqrSEp6f2MNUIkFlcTENFeVMz8wwNTHOc889R0dHB8PDw+zcuROA5uZmqqqq6O7uZmpqiurqapqamhgcHGR0dJTi4mI6OjqYmJigt7cXSF6StrKykq6uLqanp6mpqaGxsZGBgQHGxsYoKSlhzZo1jI+P09fXB0BLSwsVFRV0dnYyMzNDbW0tDQ0N9Pf3s3PnTkpLS2lvb2fXrl309/c7f7127tyZqm34r1dPTw+QvFltdXU13d3d7N27lxUrVtDc3MzQ0BAjIyP7HjM5ObnoK0Ipa/u/diQSJBIJto5PAFBXVsaK0hL6J/awN5GgoriYxopyRvdOMTY1BcDqygqmEwkmp5NZ2rlzZ06vnbKW/fUaHx+nqakpb/83Fiprypnb1005Wx7vaXHO2cTEOCWJBNOJBH0TyYvl15aVUl1aysCeSSanZ5iZTE5XzpSzMHJWlEhkPm0v7Pv0GWPaSe5uX2utfS5teifwGWvtD7I8toLkbvyXAFdZa6fnzE9Ya4OGEDnZ7sU3vukZypqas169s7SmlsOuvg6Arq4u1qxZk7exxoGrGmW5T9+Tc5dV1hbn6fPOZmp0x7x5yWZuloIoa9n5ljPYP2vKmf+Us2AuaqSc5S7ofWmh7z35oJwF87BGRfPNKOR9+sZS/9bOmV4HjGZ7oLV2D3CHMeanwAhwfQjj8V62e/GVNTWz8oijcn6u9vb2MIcWS65qlIf79ClrDilr2fmWM1h01pQzh5SzYC5qpJzFi3IWLEo1Kth9+qy1I8aYLuBo4AkAY8xaoAbY7yqF8ygF/mypY4mSbPfiW4jdu3ezYsWKEEYUX65qFPZ9+pQ1t5S17HzLWernBWdNOXNLOQvmokbKWbwoZ8GiVKMFndNnjDkUeGnqxz9Ya59d4PpuAD5ljLmP5BWYrgQ2WGu3ZFjXe4BnSJ54WwqcChwHfGWB6xTg+eefZ+3ata6H4TWfatTb28tZZ531jtSPylqE+NRHPvKpPr29vfT09PDFL37xHShnkeJTH/nKlxopZ9HlSw/5LEo1ymmjzxjTAHwHMCSvhARQZIz5D+DD1trtOa7vCuBA4FGgAvg5cEpqHR8ArrfWVqeWXZ1afjUwSfIyvCdZa3+e47pEImd0dJRvfetbPPLIIwB3pCYrayIhSs9ZUVERJLOmnOVZtvPUARre9BYOOuHEAo5I8kk5E/FLrnv6biR5udvXAw+npr0a+Cfgn4F35/IkqZNoL0h9zZ13M3Bz2s/XANfkOD4J0NLS4noI3vOhRtdccw29vb1cfvnlXHTRRbOXYFLWIsSHPvKZD/VJz9lLXvISTjzxxEqUs7zLdp76ZO9WgJw3+nzoI9+5rpFyFn2ueygKolSjXDf63gK8yVr7YNq0/zbGnAXcE/6wJGzl5eWuh+A9H2r029/+lksvvZTDDjsMa+1UarKyFiE+9JHPfKhPes6A2awpZwUw33nqPTdeu7Dn8aCPfOe6RspZ9LnuoSiIUo1y3egbAHZlmL6b5PHV4rnOzk4OOeQQ18Pwmg81qq2tpaKiItMsZS0ifOgjn/lQH+XMT+ObnuHp887OOG/uoZ8+9JHvXNdIOYs+1z0UBVGqUXGOy10CXG2M2XdMRur7r6bmiefmux+j/IkPNfrrv/5rbrzxRrZv/9P7obIWLT70kc98qI9y5p+VRxxFWVMzU6M79vva/cen9jsX0Ic+8p3rGiln0ee6h6IgSjWad09fhhuyHwJsMcZsTf3cCkwATSTP+ROP1dXVuR6C91zVaO4NbPv7+znjjDOYnp7ekpqkrEWIspadbzlraGigv79/C8qZU7XHrKP2mHUZ52U69FM5C+aiRspZvChnwaJUo2yHd4ZxQ3bxRH19vesheM9VjebewHbWLbfcclOBhyIhUNay8y1noKxFkXIWzEWNlLN4Uc6CRalGOd2cXaLv+eefp7m52fUwvOaqRnNvYJs2XRmMIGUtO99ylpqnrEWMchbMRY2Us3hRzoJFqUa5ntMnEbdrV6br8Eg61UjCoD7KTvWRMKiPgqlGslTqoWBRqlGuN2cvBz4DnASsAcrS51trS8IfmoSprKwseKFlzoca7d27lx/96Efcf//9bNu2bQJlLXJ86COf+VCf9JwNDAywd+/e6fT5ypn/fOgj37mukXIWfa57KAqiVKNcb9nwReCvgcuBrwN/DxwMvB/4x7yMbJkYuPP2/a5KNmt80zOUNYWzy7i9vT2U54kzH2p0880388ADD/De976Xa6+9dgZlLXJ86COf+VCf9JzdeOONoJxFjg995DvXNVLOos91D0VBlGqU6+Gd7wPOttZeD0wDP7HWfgL4PPDmfA1uOdh+7wZ2//GpjJepLmtqZuURR4Wynp07d4byPHHmQ40eeOABPvKRj3D88ceDshZJPvSRz3yoT3rOSkpKQDmLHB/6yHeua6ScRZ/rHoqCKNUo1z19zcBTqe93ArPXJ70LuDLsQS035atbaTvjo3ldR39/P9XV1XldR9T5UKORkRHWrFkz+6OyFkE+9JHPfKhPes4qKysZHx9XzkKQ7cgVCPfoFR/6yHeua6ScRZ/rHoqCKNUo1z19XUBL6vtNwFtS368DxsMelMhyddBBB6XfyFZZE8mD9JytXr0alLNQZDtyJeyjV8R/ypmIX3Ld03c78CbgIeAbwC3GmL8leZPNr+RpbBKitrY210Pwng81es1rXsPGjRs57LDDQFmLJB/6yGc+1Cc9ZyeccAJPPfXUxcpZOApx5Ar40Ue+c10j5Sz6XPdQFESpRjlt9Flr/yHt+x8bY3qAY4FnrLX/ka/BSXhKS3Pdvl++fKjRBz/4wX3fK2vR5EMf+cyH+qTn7LWvfS3A61HOIsWHPvKd6xopZ9HnuoeiIEo1WtRIrbUPkdzrJxHR2dnJIYcc4noYXvOxRspa9PjYRz7xsT7KWfT42Ee+8a1Gyln0+NZDPopSjXLe6DPGHA2cB7wsNekPwNettb/Nx8AkXIlEwvUQvOdLjZ599lmstdx3332/SU1S1iLElz7ylS/1mc1Zd3c3mzZt+g3KWaT40kc+86FGylm0+dBDvotSjXK9OfsHgH8F/gv4aWrya4BHjDEfstb+IMfnKQGuAD4EVAJ3A2dZawczLPs24ALgCKAE+B/g09baX+WyLnmhAw880PUQvOdDjX7xi19w9dVXc/jhh4OyFkk+9JHPfKhPes5e+cpXsmnTpp+inEWKD33kO9c1Us6iz3UPRUGUapTrnr4vAf9orb0sfaIx5h+AS4GcggtcBLwTeDWwHfgu8H3grRmWPRD4FnAfyUvX/y3wM2PMS6213TmuT1Ki1JSu+FCjH/zgB5x88sm8733vA/jc7HRlLTp86COf+VCfOTnjAx/4wOdAOYsSH/rId65rpJxFn+seioIo1SjXjb6DgH/PMP1HwD8uYH1nApdYazcDGGMuBDYZYzqstZ3pC1prb57z2H8yxnweeBWg4C5QX18fq1atcj0Mr/lQox07dvC6170u0yxlLSJ86COf+VAf5Sz6fOgj37mukXIWfa57KAqiVKNcN/ruA9aTvG9YuvXAL3N5AmNMHbAGeGx2mrX2WWPMKHAk0DnfY1OPPxxoBJ7MNP+cc84BoLy8nPXr188eHkdraytlZWV0dnaSSCSoq6ujvr6evr4+du/eTXl5OW1tbYyNjTEwMAAkL79aUlJCZ2dySPX19dTV1dHb28v4+DgVFRW0trYyOjrK4GDy6IL29naKioro6uoCoKGhgdraWrZt28bExASVlZW0tLQwMjLC0NAQAB0dHSQSCSanZ9g6PkFdWRkrSkvon9jD3kSCiuJiGivKGd07xdjUFACrKyuYTiTo3zMJwIFlZVSVlvD8xB6mEgkqi4tpqChnemaGqYlxnnvuOTo6OtixYwe7d+8GoLm5maqqKrq7u5mamqK6upqmpiYGBwcZHR2luLiYjo4OJiYm6O3tTa539WoqKyvp6upienqampoaGhsbGRgYYGxsjJKSEtasWcP4+Dh9fX0AtLS0UFFRQWdnJzMzM9TW1tLQ0EB/fz87d+6ktLSU9vZ2du3aRX9/v/PXa/fu3YGv18zMDN3dyfeNxsZGampq6OnpYXJykqqqKlatWsXw8DDDw8MUFRXR0dHB1NQUPT09ADQ1NVFdXU13dzd79+5lxYoVNDc3MzQ0xMjICIceeihPPvkk9fX1VFZWprf4epQ1YHFZI5EgkUiwdXwCYEFZm5xOZmnnzp1ZX7vZ11tZy/56jY+Ps2rVKqdZO/TQQ/nFL37B+9//fiYnJ9Ozth7lLOPrlu+cZXtPA5ia2svmzZuVswi9pyln2V+3iYlxShIJphMJ+ib2AFBbVkp1aSkDeyaZnJ5hZjI5vdB/iyhn0clZ+us1J2f7KZrvBERjzLvTflwNfAG4lT9deek1wLuBL1hrr513DX96vnaSN3lfa619Lm16J/CZbMd2G2OagAeA26y1F2WYn7DWBg3BS0+fdzZToztCv69Rz43XUlpTy2FXX5f8uacnUvcSccFVjX7961/v+354eJhbbrmFY489lrvuuuu01GRlbYmWkrO5WQpcXlnLyrecveQlL+Eb3/jGaShnS1ao9zNQznLhokbKWe6C8rLQ9558UM6CeVijovlmZNvT9+MM085MfaX7FhAYXGAs9W/tnOl1wOh8DzLGtAA/J3ni7j/Mt5xk51lDeslVja688sr9pm3YsAGS5y2kU9YiQFnLzrecZciachYBylkwFzVSzuJFOQsWpRrNu9FnrS0Oc0XW2hFjTBdwNPAEgDFmLVADbMz0GGPMwcC9wO3W2gvCHM9yMzo6Sk1NjetheM1VjX7yk5/MN2tRGVTW3FLWsvMwZ7CIrClnbilnwVzUSDmLF+UsWJRqVOjbyN8AfMoYcx/JKzBdCWyw1m6Zu6Ax5jDgHuBfrLWfLegoY2hwcDAyTelKzGqkrDkSsz4KXczqo5w5ErM+yosY1Ug5cyRGPZQ3UarRQm7O/nbgUyRvzp4AngKutNb+NOsDX+gKkpfTfRSoILnr/ZTU838AuN5aW51a9lNAK3CeMea8tOc4K8PVmURi49FHH+XWW2/lqaeeGkRZE8mL2Zx1d3czNjY2gHImEjrlTMQf817IJZ0x5gySx17fTPKkWIDXAycBH7HWzj33qKBcn4y7FIU68X3v3r2UlZWFuo648aFGd999N9dddx1veMMbuPfee89ITVbWlqiQF3LxoY985kN90nP20pe+lGuuueYMlLMlK+SFXHzoI9+5rpFyll0ULuTiuoeiwMMaLepCLuk+BZxvrb0mbdp3jDGPkbxpptPgSrDi4lBP0YwlH2p066238uEPf5h3vOMdnHvuud9JTVbWIsSHPvKZD/VJzxnAX/7lX34H5SxSfOgj37mukXIWfa57KAqiVKNcR7oGuCvD9J8BHeENR/Jl9j4kMj8fajQwMMDRRx+daZayFhE+9JHPfKiPchZ9PvSR71zXSDmLPtc9FAVRqlGuG31dwJszTP9LAm6MKSK5O+igg3jiiScyzVLWREKinInkn3Im4pdcD++8CviWMeZoYPbOm68FTgU+no+BSbjq6+tdD8F7PtToxBNP5IYbbmDz5s3cfffdszdnV9YixIc+8pkP9UnP2WGHHcY3v/nN01DOIsWHPvKd6xopZ9HnuoeiIEo1ymmjz1p7vTGmH/gk8O7U5D8A77PWZr0pi/ihrq7O9RC850ONjj/+eGpra7njjjsg+WELKGuR4kMf+cyH+qTn7MEHH4Rk1pSzCPGhj3znukbKWfS57qEoiFKNAjf6jDGlJHfF32+tvT3/Q5J82LZtGy0tLa6H4TXXNZqenubxxx/n5S9/OevWrQNocDYYeYHxTc/w9HlnZ5zX8Ka3cNAJJ+772XUf+c51fTLkDJS1yHHdR1HgskbKWTwoZ8GiVKPAc/qstVPAbcDK/A9H8mViYsL1ELznukYlJSVcfvnljI+POx2HvNDKI46irKmZqdEd+33t/uNTbL93wwuWd91HvnNdH+UsHlz3URS4rJFyFg/KWbAo1SjXc/p+B7wI2JK/oUg+VVZWuh6C93yo0SGHHEJvby/Nzc2uhyIptceso/aYdRnn9dx47X7TfOgjn/lQH+Vs8QbuvH2/DzpmjW96hrKmwtTUhz7ynesaKWfR57qHoiBKNcp1o+8LwFeNMZ8HHgN2pc+01g6FPC4JWVR2PbvkQ41OOukkvvvd73LyySdz2WWXtaOsRY4PfeQzH+qTnrNDDz2U008//QVn4itn89t+7wZ2//Epyle37jevrKmZlUccVZBx+NBHvnNdI+Us+lz3UBREqUa5bvT9Z+rf24BE2vSi1M8lYQ5Kwrdjxw5qa2tdD8NrPtTokksuAeDyyy+HF+5ZV9Yiwoc+8pkP9UnPWVFREcBAapZyloPy1a20nfFRp2PwoY9857pGyln0ue6hKIhSjXLd6DuOF27sScRs3749Mk3pig81uvTSS2ffHPn0pz99nNPByKL40Ec+86E+6TkDZS2KfOgj37mukXIWfa57KAqiVKNcb9nwizyPQ0SAww8/fN/31tpfOhyKSGyl5wyUNZF8UM5E/JJ1o88YUwV8BXgXUAbcA3zCWjtYgLFJiNasWeN6CN5zWaM9e/Zw00038dBDDzE9Pc2RRx7J/fff36isRY+ylp1vOTvzzDOpqalxNiZZHOUsmKsaKWfxoZwFi1KNgm7ZcDHwIZLn9P0/4M3AP+V5TJIHiYSOzg3iskb/9m//xr333stf/MVf8PrXv54nnngClLVIUtay8y1n1167/xVYxX/KWTBXNVLO4kM5CxalGgVt9L0bON1ae6a19hPA24F3GWN08m3EdHd3ux6C91zW6MEHH+TjH/84H/vYxzjzzDP53Oc+B8paJClr2fmWs4cffhjlLHqUs2CuaqScxYdyFixKNQra6GsHfjX7g7X2EWAKiM71SUUiYHBwkJe//OX7fn7xi18MyppIqDLlrKSkBJQzkdAoZyJ+CrqQSwkwOWfaVA6Pyyj1Kc8VJA8ZrQTuBs7KdN6SMaYVuBZ4BbAGONVa+4PFrFegsbHR9RC857JGMzMzlJbuF6tFZU05c0tZy863nKX+GNV7WsQoZ8Fc1Ug5C8/4pmd4+ryzM85reNNbOOiEE/O6fuUsWJRqFBTAIuAHxpg9adMqgX82xuyenWCtNTmu7yLgncCrge3Ad4HvA2/NsOwMyWB/meT5hLIEOoE6mMsaJRIJvva1r1FWVpY+ebFZU84cUtay8y1nk5OToPe0yFHOgrmqkXIWjpVHHMXYxseZGt2x37zJ3q0Aed/oU86CRalGQRt938swbSmfmJwJXGKt3QxgjLkQ2GSM6bDWdqYvaK3tBb6dWm56CesUYOvWrbS2troehtdc1ui44zLevmixWVPOHFLWsvMtZ+vXr+fee+9d7EkZypojylkwVzVSzsJRe8w6ao9Zl3Fez42FuTCOchYsSjXKutFnrT0trBUZY+pI7mp/LO35nzXGjAJHAp3zPVaWbs+ePcELLXMua3Tuuedmmrbg/Cln7ilr2fmWs9R0ZS1ilLNgrmqknMWHchYsSjVa1PHVi7Qy9e/c/dQjwJL3jZ5zzjkAlJeXs379+n03BW1tbaWsrIzOzk4SiQR1dXXU19fT19fH7t27KS8vp62tjbGxMQYGBgBoa2ujpKSEzs7k/yX19fXU1dXR29vL+Pg4FRUVtLa2Mjo6yuBg8pDy9vZ2ioqK6OrqAqChoYHa2lq2bdvGxMQElZWVtLS0MDIywtDQEAAdHR0kEgkmp2fYOj5BXVkZK0pL6J/Yw95EgoriYhoryhndO8XY1BQAqysrmE4k6N+TPNXywLIyqkpLeH5iD1OJBJXFxTRUlDM9M8PUxDjPPfccHR0dlJaWsnnzZgCam5upqqqiu7ubqakpqquraWpqYnBwkNHRUYqLi+no6GBiYoLe3t7kelevprKykq6uLqanp6mpqaGxsZGBgQHGxsYoKSlhzZo1jI+P09fXB0BLSwsVFRV0dnYyMzNDbW0tDQ0N9Pf3s3PnTkpLS2lvb2fXrl309/c7f70OOOCAwNdrZmZm35WaGhsbqampoaenh8nJSaqqqli1ahXDw8MMDw9TVFRER0cHU1NT9PT0ANDU1ER1dTXd3d3s3buXFStW0NzczNDQECMjI/seMzk5SWVl5WKikNecQTSzRiJBIpFg6/gEQGhZA5ia2svmzZv3vXbKWvbXa/by1spadr7mbHJyD1PTMwzumaSxopyxvVOMpjKzqrKCRCLB86nMKGd6T1PO5n/dJibGKUkkmE4k6JtIbjjUlpVSXVrKwJ5JJmdm9mUmU84mJsbZvHlzXl835SxeOSsq1P0lUp/WDANHWWufSJu+g+SJtjbLY7cAn53vZFxjTCLLw7329HlnMzW6g7YzPhrq8/bceC2lNbUcdvV1oT6vFFTRQh+Qz5yllolk1pQzCeBV1nzOWb6ylI1yFhvKWZqlZEmZkCzmzVnQLRtCY60dAbqAo2enGWPWkvykZmOhxrFcjYyMuB6C9+JQI+XMvTj0UT7FpT7Kmltx6aN8ikONlLP5zV7ZM9PXwJ23h7KOOPRQvkWpRoU8vBPgBuBTxpj7SF6B6Upgg7V2S6aFjTGz+yiLgLLUz1PW2qlCDDZOhoaGqKurcz0Mr8WoRsqZQzHqo7yIWX2UNUdi1kd5EaMaKWdzFOrKnjHqobyJUo0KvdF3BXAg8ChQAfwcOAXAGPMB4HprbXXa8uNp33839XUx8IVCDFYkopQzkcJQ1kTyTzmbw4cre0r0FHSjz1o7DVyQ+po772bg5jnTFnz8t28G7ryd7fdumHf++KZnKGtqzvs4Ojo68r6OqItLjZZjznwSlz7KlzjVR1lzJ059lC9xqZFy5k5ceiifolSjQu/pW3a237uB3X98ivLVme/hUdbUzMojjsr7OKanpykpKcn7eqJMNZIwqI+yU30kDOqjYKqRLJV6KFiUaqSNvgIoX91a0CudZdLT08PatWudjsF3qpGEQX2UneojYVAfBVONZKnUQ8GiVKOCXb1TRERERERECk8bfcvEQQcd5HoI3lONJAzqo+xUHwmD+iiYaiRLpR4KFqUaaaNvmVi5cqXrIXhPNZIwqI+yU30kDOqjYKqRLJV6KFiUaqSNvmWip6fH9RC8pxpJGNRH2ak+Egb1UTDVSJZKPRQsSjXSRt8yMTk56XoI3lONJAzqo+xUHwmD+iiYaiRLpR4KFqUaaaNvmaiqqnI9BO+pRhIG9VF2qo+EQX0UTDWSpVIPBYtSjbTRt0ysWrXK9RC8pxpJGNRH2ak+Egb1UTDVSJZKPRQsSjXSRt8yMTQ05HoI3lONJAzqo+xUHwmD+iiYaiRLpR4KFqUaaaNvmRgZGXE9BO+pRhIG9VF2qo+EQX0UTDWSpVIPBYtSjUpdD0AKo6ioyPUQvKcaSRjUR9mpPhIG9VEw1Wj5Gt/0DE+fd3bGeQ1vegsHnXBiTs+jHgoWpRppo2+Z6OjocD0E76lGshj7v7kmeJrkm8BC3lyXC+VMwqA+CqYaLU8rjziKsY2PMzW6Y795k71bAXJ+X1IPBYtSjbTRF2Ppf4wmZmYoKv7T0bz6Y3R/e/fupaKiwvUwJEIyvbkmEgmKiooW/Oa6XChnshhzP1zRe1owZW15qj1mHbXHrMs4r+fGaxf0XOqhYFGqkTb6YmruH6OT0zOUlyTfICeee5adv/st2+/dsODnjfMb69atW1m7fpZkzgAAIABJREFUdq3rYUiEZHpz3To+QesBlfTceG1oh9jEiXImC5Xpw5X09zR9wJKZsiZLpR4KFqUaaaMvpub+MTr7hyjAjkcenHfXfzZ6YxXJXZiH2IgU0sCdt8/7oeD4pmcoa2ou6HiyfbgCBH7Aks1y/fBFRJafgm70GWNKgCuADwGVwN3AWdbawXmWPx74KrAWeBY431p7d2FGGy/15WX7vs+26z+bhR4WEDVNTU2uhxCa5Zg1X/5Qnc1a0CE2QX+kxvWPUeXM/5xtv3cDu//4FOWrW/ebV9bUzMojjnIwqhdKf0/L9gFLNnH/8CUuWYtrzlxZyHtPXHoon6JUo0Lv6bsIeCfwamA78F3g+8Bb5y5ojFkL3AacCfw78FfA7caYl1trtxRqwHHx4MYnOe6oVyz5eeL8h+oDDzzA8ccf73oYYVl2WfPlD9Vcshb0R2qc/xhVzqKRs/LVrbSd8VHXw5hXes6W8kFmnA/BjlHWYpuzQlvoe0+MeihvolSjQm/0nQlcYq3dDGCMuRDYZIzpsNZ2zln2g8Bj1tofpH6+2Rhzdmr6xQUbcUz8/PHfLXmjz9Ufqtn24EB4b8x33nlnZIKbg2WZNR/+UM0la0F/pMb5j1HlLPo580G+39NyOfd9MTkMej9b7PNmEqOsKWchyeW9J12MeihvolSjgm30GWPqgDXAY7PTrLXPGmNGgSOBucE9Mn3ZlN+mpu9nMcfyF4KL8x/yZal/qC7Wzt/9FoDKQw7db95SLkoz1/Fb/terPjrs6usW9ThlLfqW+seoz3zLGSwua3HOWZyylE2297Sgc98Xm8Ns72dLed5MfMuacvZCvuYs/e8433rIR77VKFvOCrmnb2Xq37n/g44ANfMsn2nZl2d68gs3b1vS4PKmuBoGd8EVX3E9Ev6mEGMoroawX4uVq5L/Du6af14Y6yxZEf7Yl8KYhLV2MXf9VNYcy2vWwux5F3zLGSw2a/HNmUdZyqYg72nzWWwOs72fLeV5M/Eta8rZC/mas/S/43zrIR/5VqMsOSvkRt9Y6t/aOdPrgNF5ls9p2UX+YSwSV8qaSP4pZyL5p5yJhKQ4eJFwWGtHgC7g6NlpqRNua4CNGR7yu/RlU45KTReReShrIvmnnInkn3ImEp5CX8jlBuBTxpj7SF6B6UpgwzxXVPpX4O+NMScBPwbeC7wS+JsCjVUkypQ1kfxTzkTyTzkTCUHB9vSlXAHcCTwKbAVKgFMAjDEfMMbsnF3QWvss8G7gsyR3y38WOFGX3BXJibImkn/KmUj+KWciIShKJBKux7Akxpj/C1wK/DkwAfy7tdbfmwsVkDHm90BH2qQSkjc2faW19rduRuUfY8wq4BvAcST3fj8O/J21VoeDpFHW5qesBVPOcqOczU85C6ac5UY5m59ylpsoZq3Qh3eGyhiznuTu+zNIfgpUBLzM5Zh8Yq19wdWqjDFfAt6l0O7nWpJX/HoxsIvkG8F/GGPWWGuj/alISJS17JS1nChnAZSz7JSznChnAZSz7JSznEUua5He6AMuB66z1v44bZqaMgNjTCnwYZI1kxd6EXCNtXYYwBjzHeDvgQZg0OXAPKKs5UhZm5dyFkw5y5FyNi/lLJhyliPlLKvIZS2yG33GmBXAMcB/G2N+S/Lmnf8DXGCt/Y3TwfnpXSQvY/yvrgfioa8Apxhjbid5ueczgQestV6GttCUtQVT1jJTzrJQzhZMOctMOctCOVsw5Wx+kctaoS/kEqYDSY7/JOBDQAtwN/BTY0ydw3H56izgh6nLH8sL/TfJY9b7gZ0kTwL/W6cj8ouytjDKWmbKWXbK2cIoZ5kpZ9kpZwujnM0vclmL8kbf7A07b7LWbrTWTpLc/VwGHOtuWP4xxhwKvAm4zvVYfGOMKQbuAZ4h+WlWFfAl4FfGmGaXY/OIspYjZS0z5SwnylmOlLPMlLOcKGc5Us7mF9WsRXajz1q7A9gCzD1ZMpFh2nJ3FvA7a+3DrgfioXrgEOBb1tpRa+2ktfZGktlY53ZoflDWFkRZy0w5C6CcLYhylplyFkA5WxDlbH6RzFpkz+lLuRY41xhzC8mt7fOBPcCvnY7KI8aYcpKHMPyj46F4yVo7aIx5BvioMeYikv3zNySvyLTR6eD8oqwFUNbmp5zlTDkLoJzNTznLmXIWQDnLLqpZi/pG31UkC/xfJO8h8jjw1tQnOZL0bpK1udn1QDz2LpIn5HaSPMRjE/BX1trNTkflF2UtmLKWnXIWTDkLppxlp5wFU86CKWfBIpe1yN+cXUREREREROYX2XP6REREREREJJg2+kRERERERGJMG30iIiIiIiIxpo0+ERERERGRGNNGn4iIiIiISIxpo09ERERERCTGon6fvmXDGPMvQKO19h0hP+9NwHPW2kvCfN6wGWMOB+4CXmyt3eV6PBJfypqyJvmnnClnkn/KmXKWTnv6lrFUGN4FXO16LEGstU8CDwHnux6LyEIpayL5p5yJ5J9yFl3a6FvePg7caq0ddTkIY0yxMaYkh0VvAj5ijNEeaokaZU0k/5QzkfxTziJq2RcgiowxFcCVwElALfAEcIG19oG0Zd4OfA3oAB4BrgVuAQ6x1m5JBeV9wAfTHvM54H3W2j+fs77/Bh6z1n4i9fNpwN8Da4Eu4J+Ab1hrZ1Lzzwc+BBwKjAA/S41vJDX/Q8A1qfV/GTgMeIUxpojkJ0evIvmBxLPAedba+1JDuRuoB9YD9yy2fiK5UtaUNfn/7N19fFxlnf//1+R2mqZJSNKkuR0oriJui0VFi3cVV0FXLkV/unK3iLAUqG5ZZAHvAZGbXdYbVBYQEb/Cuq5S8GJViiJaWUEQgbKLgFCamzYhSZM0aZs0mcz8/jiTOkxn5kySmbmu6+TzfDzyaHLmzMyVz3zemZ4551yn8CRnkjNReJIzyZns6XPTvwB/B3wcWAM8BdyrlGoBUEp1ApuAnwJHAdcn7pNsNV7o/5C07FbgCKXUMbMLlFKvAo4FvpP4+R+Aq4AvAK8GPgVcApyf9Dgx4ALgNcApwDHAN1KePwx8HlgPHAl0Af8B9CXWfy1wGTA5ewet9RTeH6m3+9RHiHyRrAlReJIzIQpPcrbIyZ4+xyillgLnAWdrrX+aWHYucBywAfhc4vZtWuvZY5ifVUq9Evhy0kNFgDheUADQWvcqpe7F+4PwSGLxx/E+qXky8fPngYu11j9O/PyiUuoavOB+M/E4ycd5b1dKXQz8RCl1xuwnOkAp8Amt9WNJv1sEuE5r/Uxi0fNpSrATODRbjYTIB8maZE0UnuRMciYKT3ImOQPZ6HPR4UA58D+zC7TWM0qph/A+9QBvl/ejKff7fcrPS4DppCDN+jbwPaXUPwFTwOnAlwCUUsuBDuAmpdS/J92nDAjN/qCUOg74NN6nObV4Ia0AVuAFDyCK98lLsq8AtyilzgDuxztm/JmUdSYSYxei0CRrkjVReJIzyZkoPMmZ5EwO7wyY+BzWHQIqlFJVKct/CuwDPgS8F6jD23UOf+mXc/F2oc9+/TXe7vjZT1x+CvwJ+DDwOrxPfMAL76z9WuuZ5CfWWl+G98fnbrzDArYqpT7Oy9UDg3P4PYUoBMmaEIUnOROi8CRni4Ts6XPPC3iforw58T2JE2vX8peAPQO8P+V+x6T8PPtJyZEkHZuttY4q77ouHwd2A5u01rsTt72klNoJHK61/n8Zxvd6vID+02wwlVI5Xx9Ga/1n4M/A9YlPhM7GO1581l/jHXMuRKFJ1iRrovAkZ5IzUXiSM8mZbPS5Rmu9N9HQ1yqlhoAXgX8CmvFmWQK4EbhQKXUd3i731+Cd9AqJT3S01oNKqT8Cb+HlJ+QC3IJ3gm0MeHfKbV8EvqGUGgV+hne4wNFAm9b6arzQlQAXKKU2AW/COzE3K6XUEuA64EfA9sTv8xaSDi1QSh0KtOHNxCREQUnWJGui8CRnkjNReJIzyRnI4Z2uugT4Id61R57Am03pBK11H4DWugtvF7sCnsQL9uWJ+04mPc7NwKmpD6613gb8Bm9K3V+n3HYL3ic5pyce+7fAOXh/QNBabwU24l0I82m8T1suyuF3mgEOAW4DngXuAh7i5RfUPBm4L/H7CVEMkjUhCk9yJkThSc4WuVA8PpdDeYWrlFIbgSuAOq11PLEsjLc7/3St9W9T1n8auENr/eWDHswA5V1f5s/AyVrr//FbXwhTJGtCFJ7kTIjCk5wFixzeGVBKqQ14szAN4u0m/zxw22xoAbTWk0qpv8c7wXX2fsuB/w9vatubijlmHxHgyxJaYRvJmhCFJzkTovAkZ8EmG33B9QrgM0AD0It3rPYVqStprbekLBrAm51pvdZ6qNCDzJXW+jngOdPjECINyZoQhSc5E6LwJGcBJod3CiGEEEIIIUSAyUQuQgghhBBCCBFgstEnhBBCCCGEEAEmG31CCCGEEEIIEWCy0SeEEEIIIYQQASYbfUIIIYQQQggRYLLRJ4QQQgghhBABJht9QgghhBBCCBFgstEnhBBCCCGEEAEmG31CCCGEEEIIEWBlpgcgCk8p9QHgQuAIYBkwADwO3Ki1vjexzjpgHXCF1jqW5+dfBzyQ5qbdWuu6fD6XEKaYzlnSON4LXAocDcSA54CLtda/KsTzCVFsprOmlPo18PYMN2/WWp+Qz+cTwgTTOUs8/puBLwKvBZYAfwa+qbW+Nd/PtRjInr6AU0r9I3AXXlDOAv4WuDJx83FJq67DC1Yhe+IfgbVJX39TwOcSomhsyZlSaj3wE+Ax4CTgw8CPgKpCPJ8QxWZJ1s7n5e9la/H+cwygC/B8QhSVDTlTSq0GfgmUA/8AfBB4FPiOUuq8fD/fYiB7+oLvIuBurfVZSct+BXxbKVXQjX6lVKXWen/Soj9prR8u5HMKYYjxnCmlDgW+Bvyz1vprSatsLuTzC1FkxrOmtX46zW3/AEwB/1nIMQhRJMZzBnwUKAVO1FrvSdz8i8TG4N8D/17IcQSRbPQFXz3Qn+6G2V3xSqnL8D6pAZhWSs3eHkrcfjlwIvAKvDe1rcBnkjfgkg7h/BDwHuADeJ/OyOGbYjGwIWcfxzuc88Z8/mJCWMaGrL2MUqoKb6/6PVrr4QX+fkLYwIacVQDTwETKEHYDhyzs11ucZKMv+B4BzlBKbQN+orV+Ls06twDteLvw3wLMpNzeBnwV6AWWAqcBW5RSr9NaP5Wy7jeAnwOnA+GU2+5QSjUCo3h7Hy7VWnfP+zcTwh425OwtwDPAR5VSnwciwHbgq1rrby3s1xPCGjZkLdVJeOc8fW/uv44QVrIhZ7cB5wHXK6W+DOzD+3DlnYn1xByF4vG46TGIAlJKvRL4MbAqsWgX8Avgu1rr+5LWuwzvE5tyrXU0y+OVAiHg/4B7tdYbE8vX4X1ac7fW+qSU+6wBTgV+A4wBa4DP4H2Cs0ZrPbDgX1QIgyzJ2TNAK7AfL18v4L1BngtcoLX++oJ/USEMsyFraR5jM95EE23ZnksIV9iSM6XUG/DOLWxLLJoGztNaf2chv99iJXv6Ak5r/Vxio+vNwLuBN+F9KvlRpdTntdZXZn0AQCn1N8BngdV4u/xnvZhm9bvSjOFxvBmfZv1GKbUF75OkfwQ+l+OvI4SVbMgZ3on0y4CPaa03JZb9KnGu36eVUtdrreVTPuE0S7KW/FiteJOSfV02+ERQ2JAzpdRfAXfibSiei3eY5/uBG5VSk1rrO+b2WwnZ6FsEtNYzwJbE1+yb1L3AF5VS39Jaj2S6r1LqaOBneIdjngX04e3Cv4X0h7r05TimPyqlngPeMIdfRQhrWZCzXcBf4X0am+w+4ASgBdg5h19JCCtZkLVkp+F94CKHdopAsSBnV+Ht2Xuf1no6sex+pVQD8HWl1A8KdemjoJJLNixCWuudeMErw/tPYjYfAqLAB7XWd2utf6+1/gOZT6Kd654E2fMgAslAzv7P5znkzVEEkuH3tDOAJ7XWT85lzEK4xkDOVuFlazpl+SNAA9CU8+AFIBt9gaeUaslw0xGJf2dnZ5q9tMKSlPWq8D6dORBIpdRxQOcCx/V64FV44RXCaZbkbPbwmONTlp8A9Gqt087EJoRLLMna7P1eDxyJ7OUTAWNJzvqB1yqlKlKWvxGYBGSm3DmSwzuD73+VUr/E283+IlADvBfv+Oj/Spo9c/a6Q59SSv0cmEl8KnMvcAFwm1Lqu8Argc8DO3IdgFLqjsRz/xFv5s41wKcTj3H9wn49IaxgPGeJ534AuCkxS+42vIlc3g2cuZBfTgiL2JC1WX+PtzdDzi0SQWNDzr4J/Ai4Ryl1A945fQo4GW9W6qmF/IKLkezpC77P4n0CcwXeuT0/BNYCl/LyKW//G7gBOB94CHgUQGu9GW+ylTcn1vk43hvd83MYw//iBfW7eMd3XwBsAt6otR6a5+8lhE2M5ywxScsH8C4OfXnicd4InKq1vm3ev5kQdjGeNQClVDnefz7vlRmoRQAZz5nW+sd4G5qVeIeV3ol3aYgNwD/P+zdbxOSSDUIIIYQQQggRYLKnTwghhBBCCCECrKjn9CUuzngN8DG8KVvvA9ZnOsRPKdUE/CvwPqAc7xyV9yZmEBJCZCBZE6LwJGdCFJ7kTIj8KPaevkvxLqz4RqA9sez76VZUSoWB+4EpvFke64BTgT2FH6YQzpOsCVF4kjMhCk9yJkQeFHv2znOAK7TW2wCUUhcDzyulIlrrrpR1z8AL6/lJ1+jwuw6VEMIjWROi8CRnQhSe5EyIPCjaRp9Sqg7v+hyPzS7TWr+glBoDjgJSg/sO4M94072eAAwCN2mtv5rmseMdHR0AVFRUsG7dOlatWgVAW1sb5eXldHV1EY/Hqauro76+nv7+fvbt20dFRQXt7e2Mj48zODgIQHt7O6WlpXR1eUOqr6+nrq6Ovr4+JiYmqKyspK2tjbGxMYaGvKMLOjo6CIVCdHd7s9g2NDRQW1vLzp07mZycJBwO09rayujoKMPD3qVFIpEIsViMnp4eABobG6mpqaG3t5epqSmqqqpYsWIFIyMjjIyMEAqFiEQiRKNRent7AWhqaqK6upqenh6mp6dZunQpzc3NDA8PMzo6euA+O3fuZGrKm922ubmZqqoqenp6iEajVFdX09TUxNDQEGNjY5SUlBCJRJicnKSvrw+AlpYWwuEw3d3dzMzMUFNTQ2NjI4ODg4yPj1NaWkpnZycTExP093uXb2ltbaWyspKuri5isRi1tbU0NDQwMDDAnj17KCsro6Ojg7179zIwMGD89dq/fz/RaNSK12tqaopwOBzKGqoMJGuSNZuzFgqFOOyww6x5veabNcmZ5MzmnAXlPU1yJjmTnOUvZ0WbvVMp1QF0Ayu11i8mLe8CPqu1vj1l/V8C78Sb3v/fgdV41/3YqLW+I2XduNa6wL+B27Zt28bKlStND8NqFtZovht9kjWDLOwjq1han/n8Z1RyZpClfWQVC2skOXOMhT1kHQtrlDFnxTy8czzxb23K8jpgLMP6O7TWX0/8/Ael1O14x3XLhVDnaPbTLJFZgGokWTMoQH1UEAGqj+TMoAD1UcEEpEaSM4MC0kMF5VKNijaRi9Z6FO/TmqNnlymlVgI1wNY0d3kCSLcbUi4sOA8lJXJ1Dj9BqZFkzayg9FGhBKU+kjOzgtJHhRSEGknOzApCDxWaSzUq9kQuNwOXKKUeAHYB1wKbtdbb06x7W2LdDcCNwF/jzcD0ieIMNVi6urps2/1snYDVSLJmSMD6KO8CVh/JmSEB66OCCFCNJGeGBKiHCsalGhV78/Qa4B7gUWAHUAqcBqCUOlUpdWBK3cSMTO8Fzsbbhf9j4DKt9Q+LPGYhXCRZE6LwJGdCFJ7kTIg8KNpELoUkJ+P6Gx0dpa6uzvQwrGZhjeY1kUshSdb8WdhHVrG0PlZlTXLmz9I+soqFNZKcOcbCHrKOhTXKmDN3DkQVC2JZQ1pJaiTyQfooO6mPyAfpI39SI7FQ0kP+XKqRbPQtEjt37jQ9BOtJjUQ+SB9lJ/UR+SB95E9qJBZKesifSzWSjb5FYnJy0vQQrCc1EvkgfZSd1Efkg/SRP6mRWCjpIX8u1ajYs3cKQ8LhsOkhWE9qFFyD99zFrvs3Z7y94Z3Hs/zEk/LyXNJH2Ul9RD5IH/mTGomFkh7y51KNZKNvkWhtbTU9BOtJjYJr1/2b2ffs01S0tB1021TfDoC8bfRJH2Un9RH5IH3kT2okFkp6yJ9LNZKNvkVi9+7d1NbWmh6G1aRGwVbR0kb72ecftLz3lhvy+jzSR9lJfUQ+SB/5kxqJhZIe8udSjeScvkVi165dpodgPamRyAfpo+ykPiIfpI/8SY3EQkkP+XOpRrLRJ4QQQgghhBABJht9i0RnZ6fpIVhPaiTyQfooO6mPyAfpI39SI7FQ0kP+XKqRbPQtEvF43PQQrCc1EvkgfZSd1Efkg/SRP6mRWCjpIX8u1Ug2+haJnp4e00OwntRI5IP0UXZSH5EP0kf+pEZioaSH/LlUI9noE0IIIYQQQogAk42+RaKxsdH0EKwnNRL5IH2UndRH5IP0kT+pkVgo6SF/LtVINvoWiZqaGtNDsJ7USOSD9FF2Uh+RD9JH/qRGYqGkh/y5VCPZ6FskduzYYXoI1pMaiXyQPspO6iPyQfrIn9RILJT0kD+XaiQbfYvE/v37TQ/BelIjkQ/SR9lJfUQ+SB/5kxqJhZIe8udSjcqK+WRKqVLgGuBjQBi4D1ivtR5Ks+464AFgb9LirVrrYws/0uBZsmSJ6SFYL0g1kqyZE6Q+KoQg1UdyZk6Q+qhQglIjyZk5QemhQnKpRkXd6AMuBd4PvBHYBdwKfB94T4b1Z7TW1UUaW6C1tLSYHoL1AlYjyZohAeujvAtYfSRnhgSsjwoiQDWSnBkSoB4qGJdqVOzDO88BrtVab9Na7wYuBk5QSkWKPI5FZ3R01PQQrBewGknWDAlYH+VdwOojOTMkYH1UEAGqkeTMkAD1UMG4VKOi7elTStUBncBjs8u01i8opcaAo4CuNHcrVUr1AOWJ+31Ga/1kMcYbNMPDw9TV1ZkehtWCUiPJmllB6aNCCUp9JGdmBaWPCikINZKcmRWEHio0l2pUzMM7lyX+3Z2yfBRIN9/pM8Brgf8DqoFLgF8ppVZprXemrrxhwwYAKioqWLduHatWrQKgra2N8vJyurq6iMfj1NXVUV9fT39/P/v27aOiooL29nbGx8cZHBwEoL29ndLSUrq6vL8l9fX11NXV0dfXx8TEBJWVlbS1tTE2NsbQkHdIeUdHB6FQiO7ubgAaGhqora1l586dTE5OEg6HaW1tZXR0lOHhYQAikQixWIyenh7Au9ZHTU0Nvb29TE1NUVVVxYoVKxgZGWFkZIRQKEQkEiEajdLb2wtAU1MT1dXV9PT0MD09zdKlS2lubmZ4eJjR0dED95menmbbtm0ANDc3U1VVRU9PD9FolOrqapqamhgaGmJsbIySkhIikQiTk5P09fUB3u7rcDhMd3c3MzMz1NTU0NjYyODgIOPj45SWltLZ2cnExAT9/f0AtLa2UllZSVdXF7FYjNraWhoaGhgYGGDPnj2UlZXR0dHB3r17GRgYMP56Ada8XlNTU4TD4TSxyIlkLeW1Ix4nHo+zY2ISgLrycpaWlTIwuZ+pmRixqSkAyVoRXq+JiQkgEFmTnMl7mrU5C9B7muRMciY5y1POQvF4POON+ZT4tGYEWKO1fiJp+W7gdK21zuEx/gxco7X+TsryeA53X9RmZmYoLS01PQyrWVij0HzuJFk72DMXnEt0bDftZ59/0G29t9xAWU0tR3ztxrw8l4V9ZBVL6zPnrEnOzLK0j6xiYY0kZ46xsIesY2GNMuYs53P6lFKHKaXeoZR6r1LqDUqpOX1ko7UeBbqBo5MecyXeJzVbc3yYGPP8j/BiNzMzY3oI1rOlRv39/WzduhXJmpts6SNb2VKf2Zz94Q9/QHLmHlv6yGY21Ehy5jYbesh2LtUo6+GdSqlDgfOAk4E2Xh6aKaXUb4GbgTu11rEcnu9m4BKl1AN4MzBdC2zWWm9P89zH4QV9G1AFXAQ0A5tzeB6Rore3l5UrV5oehtVM1uill17i5z//OVu2bGF4eJjEHvj/TtwsWXOIZC07C3MG8HskZ06RnPkzVSPJWXBIzvy5VKOMe/qUUtcDTwIrgc8CRwK1QAWwAngv8CDwJWCrUuoNOTzfNcA9wKPADqAUOC3xfKcqpfYkrXsUcD8wjhfeNwHv0lr3zOH3E8J6N998Mxs3bqS/v5/TTz+db37zm/zgBz8AyZoQeZMpZ5s2bQLJmRB5ITkTwl4Zz+lTSv0L8C/pLn6ZZt33AlVa6x/neXw5keOy/Y2Pj7Ns2TL/FRcxUzX67ne/y4c+9CFqag46J/2gw1Eka/NTzHP6JGvZWZgzSMma5Mx+kjN/JmokOQsWyZk/C2uU8VDmjId3aq0vzvXRtdY/m+uIRHFZ1pBWMlWjM888M+d1JWv2k6xlJzkT+SA582eiRpKzYJGc+XOpRnO+ZINSqhF4I97u9Ue01v15H5XIu97eXtrb200Pw2q21Uiy5ibb+sg2ttVnbGyM00477W+RnDnFtj6ykU01kpy5yaYespVLNcp59k4ApdT7gT8DXwSuALYppU4rxMBEfk0lrkMmMrOpRg8//DBI1pxkUx/ZyKb6PPzww6xfvx4kZ86xqY9sZUuNJGfusqWHbOZSjbJu9CmlKlMWfQl4s9b6GK31a/Fm9bymUIMT+VNVVWV6CNYzWaPp6emX/XzHHXeAZM1JkrXsbMvZtddei+TMPZIzf6ZqJDkLDsmZP5dq5Len7wml1DuSfo4D0aSfc5lqV1hgxYoVpodgPZM12rhxI1u3/uWSQ6FQCCRrTpKsZWdbzlIuqis5c4TkzJ+pGknOgkNy5s+lGvmd03cKcLNS6ingU8AXgN8rpZ7BRd+DAAAgAElEQVQDyoFXAecWdogiH4aHh6mvrzc9DKuZrNGnPvUpvvWtbxGJRDjrrLM45ZRTuOqqqyRrDpKsZWdbzi666CL27t37eyRnTpGc+TNVI8lZcEjO/LlUo6x7+rTWj+NNJPF/wB+AJcArgSuBy4G/0lp/v9CDFAs3OjpqegjWM1mjww8/nOuuu47Ozk4uvPDC2WPEJWsOkqxlZ1vObrzxRpCcOUdy5s9UjSRnwSE58+dSjXxn79Rax4B/U0ptAm4A/h44V2vdXejBifxJHC4osjBdo5KSEk466STWrl07+wb5PSRrzjHdR7YzXZ/UnD3wwAMAT0rO3GK6j1xgskaSs2CQnPlzqUa+s3cqpV6jlPoQUK61fg9wB/BrpdRGpZQ7v+kiF4lETA/BeqZr1N3dze9+9ztmZma47LLLQLLmJNN9ZDvT9UnN2dvf/naQnDnHdB+5wGSNJGfBIDnz51KN/GbvvBB4FPhn4CGl1D9ore8A3gCsAR5WSq0u/DDFQqXOpiUOZrJGd999NxdeeCGbNm3i4osvZvPmzUjW3CRZy862nK1btw4kZ86RnPkzVSPJWXBIzvy5VCO/wzsvBv5Wa/2AUioC3At8W2u9C/iYUupvgB8Cry7wOMUC7dixg5UrV5oehtVM1mjTpk184QtfYPXq1QwMDHDZZZdx/PHHI1lzj2QtO8mZyAfJmT9TNZKcFd7gPXex6/7NGW9veOfxLD/xpAU/j+TMn0s18ju8M8RfptadSfx8gNb6l3if2gghFiAej1NS4sWxpKSEeDz+stsla0IsnORMiMKTnBXervs3s+/Zp4mO7T7oa9+zT2fdIBSLl9+evn8FfqaUehJvJsHPpK6gtZ4sxMBEfjU1NZkegvVM1uiDH/wgl19+OYcddhg7duzg9NNPP2gdyZobJGvZSc5EPkjO/JmqkeSsOCpa2mg/+/yDlvfeckPenkNy5s+lGmXd6NNaX6eU2gwcATyltX6mOMMS+bZ06VLTQ7CeyRqddNJJrFmzht7eXg499FDa29uNjUUsjGQtO8mZyAfJmT9TNZKcBYfkzJ9LNcrlkg1PAU8VYSyigHp6eujs7DQ9DKuZrtGhhx7KoYceauz5RX6Y7iPbma6P5CwYTPeRC0zWSHIWDJIzfy7VKOM5fUqpzymlctp8VUq9WSl1Yv6GJfItGo2aHoL1TNXohz/8IZOTuR3pIlmzn2QtO8mZyAfJmT8TNZKcBYvkzJ9LNcq2p+9woFspdSdwD/AHrXUfgFIqDBwJvAU4DWjEu2h7VkqpUuAa4GNAGLgPWK+1HvK533l4F4b/vNb6Sr/nEQerrq42PQTrmapRf38/Z511FmvXruWYY47hFa94BfX19YBkzUWStewkZ5KzfJCc+TNRI8lZsEjO/LlUo4wbfVrrM5VSq4BPAP8PqFFKxYFpoAJvJs8/AjcD39Na78/h+S4F3g+8EdgF3Ap8H3hPpjskLhXxKeQQ0wVx6URTU0zVaOPGjWzfvp2f/vSnfPWrX2ViYgKAWCw2gWTNOZK17GzLWVlZGcBeJGdOkZz5M1EjyVmwSM78uVQjv4lcngLWJz4tWQ1EgCXAEPCE36csaZwDXKG13gaglLoYeF4pFdFad2W4z3eAzwLnzfG5RJJdu3bR0NBgehhWM1mjQw89lA0bNnDeeeexfft2BgYGuOqqq85EsuYcyVp2tuVsamqK66677ngkZ06RnPkzVSPJWXBIzvy5VCPfiVwAtNYx4InE17wopeqATuCxpMd9QSk1BhwFHBRcpdR6YK/W+oeJDU8xT7t373amKU2xoUYlJSWsXLmSlStXorX+z/k8hmTNLBv6yGY21Cc5ZwBve9vbfjnXx5CcmWVDH9nOdI0kZ+4z3UMucKlGOW305cmyxL+7U5aPAjWpKyulOoHPAW/K5cE3bNgAQEVFBevWrWPVqlUAtLW1UV5eTldXF/F4nLq6Ourr6+nv72ffvn1UVFTQ3t7O+Pg4g4ODALS3t1NaWkpXl/e3pL6+nrq6Ovr6+piYmKCyspK2tjbGxsYYGvI+sOro6CAUCtHd3Q1AQ0MDtbW17Ny5k8nJScLhMK2trYyOjjI8PAxAJBIhFovR09MDQGNjIzU1NfT29jI1NUVVVRUrVqxgZGSEkZERQqEQkUiEaDRKb28v4O1Wrq6upqenh+npaZYuXUpzczPDw8OMjo4euM/MzAzbtm0DoLm5maqqKnp6eohGo1RXV9PU1MTQ0BBjY2OUlJQQiUSYnJykr68PgJaWFsLhMN3d3czMzFBTU0NjYyODg4OMj49TWlpKZ2cnExMT9Pf3A9Da2kplZSVdXV3EYjFqa2tpaGhgYGCAPXv2UFZWRkdHB3v37mVgYMD461VSUmLN6zU1NUU4HM6l9dORrKW8dsTjxONxdkx4EwzUlZeztKyUgcn9TM3EiE1NAUjWivB67d/vHc0VgKxJzuQ9zdqcBeg9TXKW5nWLx+NMzcTYMTH5svez6Xic6ViMMuT9THJ2sFA8Hs8lFwuW+LRmBFijtX4iaflu4HSttU5Z/z7gTq31TYmffw38Mt3JuEqpeMrdRYp4PE4oFDI9DKtZWKN5DUaydrBnLjiX6NjujBeyLaup5Yiv3ZiX57Kwj6xiaX3mPCDJmVmW9pFVLKyR5CxPivWeZmEPWcfCGmUcTMZLNuSb1noU6AaOnl2mlFqJ90nN1jR3eRdwlVJqSCk1BLwZ+LRS6rfFGG/QzH66LjILSo0ka2YFpY8KJSj1kZyZFZQ+KqQg1EhyZlYQeqjQXKpRMQ/vBG+2pkuUUg/gzcB0LbBZa709zbodKT//CPgt8G8FHWFA7dy588Bx9SK9gNVIsmZIwPoo7wJWH8mZIQHro4IIUI0kZ4YEqIcKxqUa5bTRp5T6AHCP1npmgc93DXAI8ChQCfwC71otKKVOBW7SWlcDaK17U8awHxjTWr+0wDEIYa2HH36YN7zhDZSWli70oSRrQmQgOROi8CRnQtglp3P6lFJ7gXHge8B3tNbPFXpgc+HycdnFsm/fPqqqqkwPw2o21OjDH/4wS5Ys4bjjjmPTpk2vkqzlRzHP6bOhj2xmQ32Sc/aud72LtrY2q07IcDVnxWRDH9nOdI0kZ4VTrPc00z3kAgtrlDFnuR7euQI4BTgTuEgp9RDeNVD+S2u9d+HjE4W2ZMkS00Owng01+t73vseWLVv45S9/CfAnyZp7bOgjm9lQn+Sc3X333cRisQeRnDnFhj6ynekaSc7cZ7qHXOBSjXKayEVrPa61vklr/Sa8i7T/Hrga6FNKfVspldPUuMKc2ellRWY21KiqqooTTjiB6667DiRrTrKhj2xmQ32Sc3b99deD5Mw5NvSR7UzXSHLmPtM95AKXajTn2Tu11v8HfBXvxNoK4O+A3yqlfq+UWp3n8Yk8mZlZ6OmYwWdbjSRrbrKtj2xjW306OztBcuYc2/rIRjbVSHLmJpt6yFYu1SjnjT6lVLlS6iNKqXuBF4HjgHOBZiAC/An4YUFGKRZs2bJl/istcrbUKBqN8uCDDyJZc5MtfWQrW+ozm7MvfvGLIDlzji19ZDMbaiQ5c5sNPWQ7l2qU6+yd3wBOBuLA94ELtdZPJ60yoZS6FNiZ/yGKfFi+fLnpIVjPhhrddNNNbNmyZfZCn08jWXOODX1kMxvqk5yzd7zjHTz++ONHSc7cYkMf2c50jSRn7jPdQy5wqUa5TuRyJPAJYJPWeirDOkPAO/IyKpF3Q0NDNDY2mh6G1WyoUU9PD+vXr2ft2rWUl5dfmGE1yZrFbOgjm9lQn5SccdZZZz2dZjXJmcVs6CPbma6R5Mx9pnvIBS7VKNeNvsuB32mto8kLlVJlwLFa6y2J236T7wGK/BgbG3OmKU2xoUYf/ehHefWrX33QdY0ka+6woY9sZkN9JGfus6GPbGe6RpIz95nuIRe4VKNcN/oeAFqAgZTltYnbFnzlTVFYebg4auDZUKPPfe5z3HbbbdTV1aXeJFkroInnn+OZC87NeHvDO49n+Ykn5fRYNvSRzWyoj+TMfTb0ke1M10hy5j7TPeQCl2qU60ZfCO98vlQNgFxrxQGJmbNEFjbUKB6Pz57Pl0qyViDLVq9hfOvjRMd2p719qm8HQM4bfTb0kc1sqI/kzH029JHtTNdIcuY+0z3kApdqlHWjTymlE9/GgduVUvuTbi4F/hr4XYHGJvJocnLSqQtImmCyRldeeSUAoVCIr3zlK5SXl/PII4/M5k+yVkC1x6yl9pi1GW/vveWGOT2eZC0723IGzGZNcuYQyZk/UzWSnAWH5MyfSzXyu2TDrsRXCBhJ+nkX0AvcCJxWyAGK/Ojr6zM9BOuZrNGyZctYtmwZ8Xic6urq2SmAJWsOkqxlZ1vOkrImOXOI5MyfqRpJzoJDcubPpRpl3dOntT4TQCm1HbhOay2744UogI0bNwLQ1NTESSedRDgcZuPGjWcaHpYQgZIuZ4nlkjUh8kRyJoSdcjqnT2t9eaEHIgqrpaXF9BCsZ0ONTj75ZNNDEAtkQx/ZzIb6SM7cZ0Mf2c50jSRn7jPdQy5wqUYZN/qUUluBt2utR5RST5F+IhcAtNarCzE4kdngPXex6/7NWddJnnFw9pM2kZmpGn3yk5/k6quvprq6mk9+8pMHlnd1dW1NXVeyZj/JWna25QwOzprkzH6SM38maiQ5CxbJmT+XapRtT9+dwOzELT8uwljEHOy6fzP7nn2aipa2tLenzjjY1dXFoYceWqzhOclUjY499tgDJ7ofe+yxyeO5s+iDEQsmWcvOtpwlxiRZc4zkzJ+JGknOgkVy5s+lGmXc6Es+pFMO77RTRUsb7Wefn/a21BkHY7FYMYbkNFM1Sj4EJuV7yZ2DJGvZ2ZazxM+SNcdIzvyZqJHkLFgkZ/5cqlGu1+nLC6VUKXAN8DEgDNwHrNdaD6VZ963A14FD8ab4fQG4Umu9qVjjDZKamhrTQ7BekGokWTMnSH1UCEGqj+TMnCD1UaEEpUaSM3OC0kOF5FKNsp3Tl/U8vmRzOC77UuD9wBvxpu69Ffg+8J406z4LnAR0J35+K3CvUup1Wus/5fh8IqGxsdH0EKxnqkap5z3MWuA5fZI1QyRr2dmWM1jQuUaSM0MkZ/5M1EhyFiySM38u1Sjbnr5CnMd3DnCF1nobgFLqYuB5pVREa92VvKLWemD2e6VUCRDDu67gKwAJ7hwNDAzQ1NRkehhWM1Wj1PMeZi3w/AfJmiGStexsyxksKGuSM0MkZ/5M1EhyFiySM38u1Sinc/ryQSlVB3QCjyU9xwtKqTHgKKArw/1GgaWJsW7B260v5mjPnj3ONKUppmqUaVrr+Z7/IFkzS7KWnW05S9w256xJzsySnPkzUSPJWbBIzvy5VKNintO3LPHv7pTlo0DGA2K11nVKqUq83fivAqLp1tuwYQMAFRUVrFu3jlWrVgHQ1tZGeXk5XV1dxONx6urqqK+vp7+/n3379lFRUUF7ezvj4+MMDg4C0N7eTmlpKV1d3t+S+vp66urq6OvrY2JigsrKStra2hgbG2NoyDukvKOjg1AoRHe3d0RBQ0MDtbW17Ny5k8nJScLhMK2trYyOjjI8PAxAJBIhFovR09MDeLuIa2pq6O3tZWpqithDW4g++jDRaJRodBoIEQ5XEo/H2ffn5wgtb2ZfdIaqslJemtxPNB4nXFJCQ2UFM7EY0ckJXnzxRSKRCPF4nG3btgHQ3NxMVVUVPT09RKNRqquraWpqYmhoiLGxMUpKSohEIkxOTtLX1wd41yEJh8N0d3czMzNDTU0NjY2NDA4OMj4+TmlpKZ2dnUxMTNDf3w9Aa2srlZWVdHV1EYvFqK2tpaGhgYGBAfbs2UNZWRkdHR3s3buXgYEB469XWVnZgl6vqqoqVqxYwcjICCMjI4RCISKRCNFolN7eXsC7WG11dTU9PT1MT0+zdOlSmpubGR4eZnR09MB9pqamFjINsGQt5bUjHicej7NjYhKAuvJylpaVMjC5n+l4nMqSEhorKxibjjIe9X7tlnAlM/E4UzNelvbs2ZPTaydZy/56TU1NAQQha5Izg6+b5GzRvKdJztK8bvHEe9OOicmD3s+mYzHKQHImOTtIKB5Pf9pevq/Tl/i0ZgRYo7V+Imn5buB0rbXO4TF+BvxEa31TyvJ4Dnd3zjMXnJv1sgzLVq+h9pi1aW/rveUGympqOeJrNwIQj8cJhUIFG2sQmKpRluv0PZW6rmRtfp654FyiY7szznabTWqW/EjWsrMtZ3Bw1iRn9pOc+TNRI8lZ/mS7HvPE889R3tSc9j1tru9Z2UjO/FlYo4yDKdp1+rTWo0qpbuBo4AkApdRKvE9qDpqwIoMy4K8WOhaXZLssw1zs27ePpUuX5mFEwWWqRvm+Tp9kzSzJWna25QzmlzXJmVmSM38maiQ5y59s12Mub2pm2eo1BR+D5MyfSzUq9nX6bgYuUUo9gDcD07XAZq319tQVlVIfAp7DO/G2DDgdOA741zyNZVF56aWXWLlypelhWM1UjQp0nT7JmiGStexsy1ni5/lmTXJmiOTMn4kaSc7yK18f/M+X5MyfSzWa0zl9SqnDgVcnfvyT1vqFOT7fNcAhwKNAJfAL4LTEY58K3KS1rk6s25JYvwWYwpuG92St9S/m+JxCOKevr4/169e/L/GjZE2IAujr66O3t5cvfelL70NyJkRBSM6EsEPGc/qSKaUagO8ACm/6W/COGf1v4ONa610FG2EObD8ue77yeR7S7AmmIjMbajQ2NsY3vvENHnnkEeLx+ExisWRtgYp5Tp8NfWQzG+qTnLNQKEQsFptBcuYUG/rIdqZrJDlbmPm+b+XznD7TPeQCC2uU8Zy+khwf4Ba8a5y8FQgnvt4GHAZ8e6GjE4VXUVFhegjWs6FG3/zmN+nr6+Pqq68GyZqTbOgjm9lQn+Sc3XnnnSA5c44NfWQ70zWSnLnPdA+5wKUa5brRdzzwD1rr/9FaRxNf/wOsT9wmLDc7Ja3IzIYa/fGPf+QTn/gERx55JJI1N9nQRzazoT7JOSstLUVy5h4b+sh2pmskOXOf6R5ygUs1ynWjbxDYm2b5PryTaoXlcjmMd7GzoUa1tbVUVlamu0my5ggb+shmNtRHcuY+G/rIdqZrJDlzn+kecoFLNcp1o+8K4GtKqQPzxia+/7fEbcJydXV1podgPRtq9Hd/93fccsst7Nr1l/dDyZpbbOgjm9lQH8mZ+2zoI9uZrpHkzH2me8gFLtUo4+ydaS7IfhiwXSm1I/FzGzAJNOGd8ycsVl9fb3oI1jNVo9QL2A4MDHD22WczMzOzPbFIsuYQyVp2tuWsoaGBgYGB7UjOnCI582eiRpKzYJGc+XOpRtku2bDgC7ILe7z00ks0NzebHobVTNUo9QK2s37wgx98t8hDEXkgWcvOtpyBZM1FkjN/JmokOQsWyZk/l2qU08XZhfv27k13SqZIZqpGqRewTVouGXSQZC0723KWuE2y5hjJmT8TNZKcBYvkzJ9LNcr1nD7huPLyctNDsJ7USOSD9FF2Uh+RD9JH/qRGYqGkh/y5VKNsh3ceoJSqAD4LnAx0Ai/7DbXWpfkfmsinjo4O00Owng01mp6e5kc/+hFbtmxh586dk0jWnGNDH9nMhvok52xwcJDp6emZ5NslZ/azoY9sZ7pGkjP3me4hF7hUo1z39H0JOANvxqUY8M/At/Cm3D2/MENbHAbvuYtnLjg37dfE88/l7Xn27NmTt8cKKhtqdMcdd/CrX/2KD3zgAyBZc5INfWQzG+qTnLNQKASSM+fY0Ee2M10jyZn7TPeQC1yqUa4bfR8BztVa3wTMAD/RWv8j8EXgXYUa3GKw6/7N7Hv2aaJjuw/6Km9qZtnqNXl5noGBgbw8TpDZUKMHH3yQ8847jxNOOAEka06yoY9sZkN9knNWWloKkjPn2NBHtjNdI8mZ+0z3kAtcqlFOh3cCzcDTie/3ALMXpbgXuDbfg1psKlraaD9bPvQSMDo6Smdn5+yPkjUhCiA5Z+FwmImJCcmZEHkmORPCLrnu6esGWhPfPw8cn/h+LTCR70GJ/Gtvbzc9BOvZUKPly5cnX8hWsuYgG/rIZjbUJzlnLS0tIDlzjg19ZDvTNZKcuc90D7nApRrlutF3F/DOxPdfBy5XSr0I3IZcXNMJZWW57tRdvGyo0Zve9Ca2bt06+6NkzUE29JHNbKhPcs5OPPFEkJw5x4Y+sp3pGknO3Ge6h1zgUo1yGqnW+tNJ3/9YKdULHAs8p7X+70INTuRPV1cXhx12mOlhWM2GGp1xxhkHvpesucmGPrKZDfVJztmb3/xmgLciOXOKDX1kO9M1kpy5z3QPucClGs1r81Rr/TDwcJ7HIgooHo+bHoL1bKyRZM09NvaRTWysj+TMPTb2kW1sq5HkzD229ZCNXKpRzht9SqmjgQuAIxOL/gR8VWv9xzk8RilwDfAxIAzcB6zXWg+lWfe9wEXAaqAU+F/gM1rr3+b6fOIvDjnkENNDsJ4tNXrhhRfQWvPAAw/8IbFIsuYQW/rIVrbUZzZnPT09PP/8839AcuYUW/rIZjbUSHLmNht6yHYu1Sinc/qUUqcCjwItwM8SX83AI0qp0+bwfJcC7wfeCMye+fj9DOseAnwDeAWwHPgP4OdKKXeugmgRl5rSFBtq9Otf/5pPfepTDA8Pg2TNSTb0kc1sqE9yzl73uteB5Mw5NvSR7UzXSHLmPtM95AKXapTrnr4vA5/XWl+VvFAp9WngSuD2HB/nHOAKrfW2xP0vBp5XSkW01l3JK2qt70i5778rpb4IvAHoyfH5REJ/fz8rVqwwPQyr2VCj22+/nVNOOYWPfOQjAF+YXS5Zc4cNfWQzG+qTkjNOPfXUL4DkzCU29JHtTNdIcuY+0z3kApdqlOvsncuB/0qz/EdAUy4PoJSqAzqBx2aXaa1fAMaAo3K4/yqgEXgql+cTL7dv3z7TQ7CeDTXavXs3b3nLW9LdJFlzhA19ZDMb6iM5c58NfWQ70zWSnLnPdA+5wKUa5bqn7wFgHd51w5KtA36T42MsS/y7O2X5KFCT7Y5KqSbgTuA6rfWf062zYcMGACoqKli3bh2rVq0CoK2tjfLycrq6uojH49TV1VFfX09/fz/79u2joqKC9vZ2xsfHGRwcBLxrbpSWltLV5X2AVF9fT11dHX19fUxMTFBZWUlbWxtjY2MMDXmHlHd0dBAKheju7gagoaGB2tpadu7cyeTkJOFwmNbWVkZHR2cP3SMSiRCPx5maibFjYpK68nKWlpUyMLmf6XicypISGisrGJuOMh6NAtASrmQmHmdg/xQAh5SXU1VWykuT+4nG44RLSmiorGAmFiM6OcGLL75IJBIhFAqxbds2AJqbm6mqqqKnp4doNEp1dTVNTU0MDQ0xNjZGSUkJkUiEyclJ+vr6vOdtaSEcDtPd3c3MzAw1NTU0NjYyODjI+Pg4paWldHZ2MjExQX9/PwCtra1UVlbS1dVFLBajtraWhoYGBgYG2LNnD2VlZXR0dLB3714GBgaMv14VFRW+r1csFqOnx/uwsLGxkZqaGnp7e5mamqKqqooVK1YwMjLCyMgIoVCISCRCNBqlt7cXgKamJqqrq+np6WF6epqlS5fS3NzM8PAwo6OjHH744Tz11FPU19cTDoeTW3wdkjVgflkjHicej7NjYhJgTlmbmvGytGfPnqyv3ezrLVnL/npFE/U1mbXDDz+cX//613z0ox9lamoqOWvrkJwB839PK/TfSMmZO+9pkrOF5myCeCwGwPh0lLHE384V4Uri8TgvJf4fmPp+Nh2LUQaSs0WSs+TXKyVnBwllmnVGKfXBpB9bgMvwwjM789KbgA8Cl2mtb8j4DH95vDpgBFijtX4iaflu4HSttc5wv1bgF3gbnp/UWh80YKVUPMPdrffMBecSHdtN+9nn5/Vxe2+5gbKaWo742o15fVyRf7/73e8OfD8yMsIPfvADjj32WO69994zE4slawu0kJxJloIhU85e9apX8fWvf/1MJGdCLJjkLH/m+74l71mLXijTDdn29P04zbJzEl/JvgH4BldrPaqU6gaOBp4AUEqtxPukZmu6+yilDgXuB+7SWl/k9xwis7GxMWpqsn4otuiZqtG111570LLNmzcD3JqyWLLmAMladrblLE3WJGcFMnjPXey6f3PG2xveeTzLTzwpp8eSnPkzUSPJWbBIzvy5VKOMG31a61zP95uLm4FLlFIPALuAa4HNWuvtqSsqpY4AfgncprX+XAHGsqgMDQ0505SmmKrRT37yk0w3LSSDkjVDJGvZWZgzmH/WJGdzsOv+zex79mkqWtoOum2qbwdAzht9kjN/JmokOQsWyZk/l2o0r4uzL8A1eNPpPgpU4u16Pw0OXBbiJq11dWLdS4A24AKl1AVJj7E+zexMQoiXk6wJUXiSszmqaGlLe7ha7y2+O33E4iU5EyIPMp7Tl0op9bd4YToSiANPA9dqrX9WuOHlxvbjsrMp1jl909PTlJeX5/U5gsaWGj366KPceeedPP3007uQrOVFMc/ps6WPbGVLfWZz1tPTw/j4+BCSs4LLlsPeW25geuAllrzilWnvm3ropy19ZDMbaiQ5mz8bzumzoYdsZ2GNMp7Tl+vF2c8G7gJewNvwuxR4EbhLKfXxfIxQFFZJSSGO1g0WG2p03333cfXVV9PS0gKSNSfZ0Ec2s6E+yTk744wzQHJm3LLVayhvaiY6tvugr33PPn3QuYA29JHtTNdIcuY+0z3kApdqlOvhnZcAF2qtv5m07DtKqcfwQpw64YSwTFdXFytXrjQ9DKvZUKM777yTj3/847zvfe9j48aN30kslqw5xIY+spkN9UnOGcC73/3u7yA5M6r2mLXUHrM27W3pDv20oY9sZ7pGkjP3me4hF7hUo1w3TzuBe9Ms/zkQyd9whEeDHQ8AACAASURBVFjcBgcHOfroo9PdJFkTIk8kZ0IUnuRMCLvkutHXDbwrzfJ3A135G44olPr6etNDsJ4NNVq+fDlPPPFEupska46woY9sZkN9JGfus6GPbGe6RpIz95nuIRe4VKNcD++8DviGUupoYPbKm28GTgc+WYiBifyqq6szPQTr2VCjk046iZtvvplt27Zx3333zV6cXbLmEBv6yGY21Cc5Z0cccQTXX3/9mUjOnGJDH9nOdI0kZ+4z3UMucKlGOW30aa1vUkoNAJ8CPphY/CfgI1rrrBdlEXbYuXMnra2tpodhNRtqdMIJJ1BbW8vdd98N3octIFlzig19ZDMb6pOcs4ceegi8rEnOHGJDH9nOdI0kZ+ZMPP8cz1xwbtrbUmfCzcZ0D7nApRr5bvQppcrwdsVv0VrfVfghiUKYnJw0PQTrma7RzMwMjz/+OK95zWtYu3YtQIPRAYl5Md1HtjNdnzQ5A8mac0z3kQtM1khyZs6y1WsY3/o40bHdB9021bcDIOeNPsmZP5dq5HtOn9Y6CmwClhV+OKJQwuGw6SFYz3SNSktLufrqq5mYmDA6DrEwpvvIdqbrIzkLBtN95AKTNZKcmVN7zFrazz4/7VdFS9ucHkty5s+lGuU6kcuTwCsKORBRWK7sejbJhhoddthh9PX1mR6GWAAb+shmNtRHcuY+G/rIdqZrJDlzn+kecoFLNcp1IpfLgH9TSn0ReAzYm3yj1no4z+MSebZ7925qa2tND8NqNtTo5JNP5tZbb+WUU07hqquu6kCy5hwb+shmNtQnOWeHH344Z5111sumX5Oc2c+GPrKd6RpJztxnuodc4FKNct3o+2ni301APGl5KPFzaT4HJfJv165dzjSlKTbU6IorrgDg6quvBtiedJNkzRE29JHNbKhPcs5CoRDAYOImydkCDN5zF7vu35zx9onnn6O8qTkvz2VDH9nOdI0kZ+4z3UMucKlGuW70HcfLN/aEEAVw5ZVXzr458pnPfOY4w8MRIpCScwaStXzZdf9m9j37dMbzhsqbmlm2ek2RRyVMkZwJYZdcL9nw6wKPQxRYZ2en6SFYz4YarVq16sD3WuvfGByKmCcb+shmNtQnOWcgWcunipY22s8+v+DPY0Mf2c50jSRn7jPdQy5wqUZZN/qUUlXAvwIfAMqBXwL/qLUeKsLYRB7F47Kj1o/JGu3fv5/vfve7PPzww8zMzHDUUUexZcuWRsmaeyRr2dmWs3POOYeamhpjYxLzIznzZ6pGkrPgkJz5c6lGfrN3Xg58DO+cvv8E3gX8e4HHJAqgp6fH9BCsZ7JG//Ef/8H999/P61//et761rfyxBNPgGTNSZK17GzL2Q033GBsPGL+JGf+TNVIchYckjN/LtXI7/DODwJnaa3/E0ApdTvwP0qpUq31TMFHJ8Qi8dBDD/HJT36St73tbQCsW7eOiy666AOSNSHyJ13OLrnkEiRnQuSP5EwIO/lt9HUAv539QWv9iFIqCrQCc960VUqVAtfg7T0MA/cB69MdwqaUagNuAF4LdAKna61vn+tzCk9jY6PpIVjPZI2GhoZ4zWtec+DnV77ylQDzyprkzCzJWna25ay0tJSZmRl5T3OM5MyfqRpJzoJDcubPpRr5Hd5ZCkylLIuS+6yfqS4F3g+8EWhPLPt+hnVjeME+Beid5/OJBDmW3p/JGsViMcrKDorVfLMmOTNIspadbTkrLS0FeU9zjuTMn6kaSc6CQ3Lmz6Ua+QUwBNyulNqftCwMfFsptW92gdZa5fh85wBXaK23ASilLgaeV0pFtNZdyStqrfuAbyXWk8MBFmjHjh20taWfRlt4TNYoHo/zla98hfLy8uTF882a5MwgyVp2tuVsamoK5D3NOZIzf6ZqJDkLDsmZP5dq5Len73vATmBX0tfteLvnk5f5UkrV4e1qf2x2mdb6BWAMOGquAxdzs3//fv+VFjmTNTruuOOor69n2bJlB76YR9YkZ+ZJ1rKzLWfr1q0DeU9zjuTMn6kaSc6CQ3Lmz6UaZd3Tp7U+M4/PtSzx7+6U5aPAgveNbtiwAYCKigrWrVt34PowbW1tlJeX09XVRTwep66ujvr6evr7+9m3bx8VFRW0t7czPj7O4OAgAO3t7ZSWltLV5X2AVF9fT11dHX19fUxMTFBZWUlbWxtjY2MMDXmHlHd0dBAKheju7gagoaGB2tpadu7cyeTkJOFwmNbWVkZHRxkeHgYgEokQj8eZmomxY2KSuvJylpaVMjC5n+l4nMqSEhorKxibjjIejQLQEq5kJh5nYL931O0h5eVUlZXy0uR+ovE44ZISGiormInFiE5O8OKLLxKJRCgrK2Pbtm0ANDc3U1VVRU9PD9FolOrqapqamhgaGmJsbIySkhIikQiTk5P09fV5z9vSQjgcpru7m5mZGWpqamhsbGRwcJDx8XFKS0vp7OxkYmKC/v5+AFpbW6msrKSrq4tYLEZtbS0NDQ0MDAywZ88eysrK6OjoYO/evQwMDBh/vZYsWeL7esVisQMzNTU2NlJTU0Nvby9TU1NUVVWxYsUKRkZGGBkZIRQKEYlEiEaj9PZ6R5k0NTVRXV1NT08P09PTLF26lObmZk4//XRGR0cP3GdqaoqNGzfOJ38FzRm4mTXiceLxODsmJgHmlLWpGS9Le/bsSfvaDQ8Pv+y1k6xlf71mp7c2kbWNGzce9HrZmrXFlrNs72kA0eg027Ztk5w58J4mOctXziaIx2IAjE9HGUtkZkW4kng8zkuJzMj72eLMWbrXa2pqinA4nLHfQ8W6vkTi05oRYI3W+omk5bvxTrTVWe67HfhcppNxlVLxLHe32jMXnEt0bHfeL2bbe8sNlNXUcsTXbszr44qiCs31DoXMWWIdJ7O2kJxJlhYFq7K2GHOWjWQwMCRnc1CIPEmWFoWMOfM7vDNvtNajQDdw9OwypdRKvE9qthZrHIvV6Oio6SFYLwg1kpyZF4Q+KqSg1EeyZlZQ+qiQglAjyZlZQeihQnOpRvOdSWm+bgYuUUo9gHc897XAZq319nQrK6Vm91GGgPLEz1GtdbQYgw2S4eFh6urqTA/DagGqkeTMoAD1UUEErD6SNUMC1kcFEaAaSc4MCVAPFYxLNSr2Rt81wCHAo0Al8AvgNACl1KnATVrr6qT1J5K+vzXxdTlwWTEGK4SjJGdCFIezWRu85y523b854+0N7zye5SeeVMQRCZGRszmz0cTzz/HMBedmvF2yH1xF3ejTWs8AFyW+Um+7A7gjZdmcj/8W6UUiEdNDsF5QaiQ5MysofVQoQaqPy1nbdf9m9j37NBUtB081PtW3AyDtf/yybSxOPP8c5U3N+R1oBkHqo0IJSo1czpltlq1ew/jWx4mOpc6L40nNflB6qJBcqlGx9/QtOn6fphbrTXJmZmb24qgiA6mRyAfpo+ykPvaoaGlLO0lE7y03ZLxPto3F8qZmlq1ek9cxzkrdOxGPxwiF/jItgeydOJhkTaSqPWYttceszXh7avalh/y5VCPZ6CuwbG+QUNg3yWS9vb2sXLmy4M/jMqmRyAfpo+ykPu7LtLFYKOn2TkzNxKgo9Tb6su2ZXMwka2KhpIf8uVQj2egrgmK/QQohhBBBkW7vxI6JSdqWePN1ZNszKYQQwiMbfYvE8uXLTQ/BelIjkQ/SR9lJfdyQabKHYp63l80h5eWmh2A9yZpYKOkhfy7VSDb6Folly5aZHoL1pEYiH6SPspP6FMdCzifPNtlDsU5J8FNV5sY5NCZJ1sRCSQ/5c6lGstG3SPT29tLe3m56GFaTGol8kD7KTupTHAs5n9xvsgcbvDS5n+ZwpelhWE2yJhZKesifSzWSjb5FYmpqyvQQrCc1EvkgfZSd1Kd4gnw+eTQeNz0E60nWxEJJD/lzqUay0bdIVFVVmR6C9aRGIh+kj7KT+uSPLdfMMyFcUuK/0iInWRMLJT3kz6UayUbfIrFixQrTQ7Ce1EhkkmlSCzj4+mDSR9lJffLH1DXzbNBQWWF6CNaTrImFkh7y51KNZKNvkRgeHqa+vt70MKwmNRLpZJvUIt31waSPspP65FeQD+HMZmw6Sk25/BcmG8maWCjpIX8u1Uj+Yi4So6OjzjSlKVIjkU62SS3SXR9M+ig7qc/cLOZDOLMZj8pGnx/JmpiP5CNbJicnGAgvOXBb6pEtwq2cyV/MRSIUCpkegvWkRmI+Ug/9nJyc5Jmwd9FoeYM8mORsbhbzIZzZSBf5k6yJuUo9siU2EyOamKgk3ZEtwq2cyUbfIhGJREwPwXpSIzFX6Q79LIvHiU7tlzfIDCRnc7dYD+HMZoVcrsGXZE3MVeqRLbF4nJLERk26I1uEWzmTjb4AS94DEY/FCCXNdiZ7IA42PT1NZaX8R0LkLt2hn1OxGBUlJfIGmYHkLDezf7sX8yGc2UTjcSoc+oTdBMmaWCjJmT+XciZzHgfUstVrKG9qJjq2m+jYbiZ27z7w/b5nn854jshitmPHDtNDEAEwuN+da/aYIDnLzezf68V8CGc2kjN/kjWxUJIzfy7lTPb0BVTqHogdE5O0LfHOM+q95YasU9BnI3sIhRCi8ORwTiGEEPkkG32LRH1F+YHvs01Bn03Qz1FqamoyPQQRAMlZm8v1/RYLyZnIh+ScifQka2KhJGf+XMpZUTf6lFKlwDXAx4AwcB+wXms9lGH9E4B/A1YCLwAXaq3vK85og+WhrU9x3JrXAtmnoM8mlz2ELv9H9sEHH+SEE04wPYy8kKyZM5u1uV7fb7GQnEnO8iH5Pa0Qsl0qIxc2vBcGJWuSM3MKnbMgcClnxd7TdynwfuCNwC7gVuD7wHtSV1RKrQQ2AecA/wV8GLhLKfUarfX2Yg04KH7x+JMLDq7fHkLX/yN7zz33OBPcHCy6rNlyPbPZrPld3y/IH6BkIzlzO2e2SH1Pm+8pC5nsefKPAIQPO3zO95188QX2PPnHjH+PipXtAGUtkDnz+2DBhkmc8vF/x6BzKWfF3ug7B7hCa70NQCl1MfC8Uiqite5KWfcM4DGt9e2Jn+9QSp2bWH556gPn8499PtkQ2nzx20O4kHMFbXDC9j9bNfYjvnbjQu6+6LKW7T9ptk2G4fcBit9/Gl1mW85gQVkrWM5E7uZ7ykI24cMOZ9nqNfM6Kmb3Iw9lHE8xs21b1mzMmcn6+H2wYNv7FuT/w5UgcClnRdvoU0rVAZ3AY7PLtNYvKKXGgKOA1OAelbxuwh8Tyw9y8bad+RtsPpVUw9BeuOZfTY+Evy/GGEqqwdbXwk/pUrvGrlRcaz3nuZIXbdaWrfD+Hdqb/vZfPeh9FcGCszb7u9ha64WwLWcwr6wVOmdF+XvtuILXqBB/M4qZbduyZmHOjL6f+b1nQVHftzJ5Wc5c/j9eoTiUs2Lu6VuW+Df1o69RoCbD+unWfU3qivP5j7EQASZZE6LwJGdC/P/s3Xt8XHWd//FX7iENSUhC0uZaiqv1UhBUBNS14k/F24eLu7rcFAQpF7GICKwXVEQuioqKLLAorsq6rnLx46qAIFpRuamIioClNJc2IUmTNGmTNJlkfn+cSR3SZM6kmcn5fief5+PRR5szZ+Z8+8nnncyZc873ZJ/lzJgMWcz79A0n/q6csbwKGJpj/XTXNcb8g2XNmOyznBmTfZYzYzJk0Xb6VHUQaAcOnV6WuOC2Anh8lqf8KXndhEMSy40xc7CsGZN9ljNjss9yZkzmLPZELjcBF4vI/QQzMF0N3D3HjErfBj4qIicAPwT+BXgF8N5FGqsxPrOsGZN9ljNjss9yZkwGLPZO31XAfsAjQAnwc+BkABE5CbhRVcth94W6xxPca+WbwCbguJkhF5H/B1wOvAwYA/5XVc9ZlP+N40Tkr0Br0qICgnvcvEJV/xDNqNwjIsuBrwBHEWTij8CHVdXnTwYta4vIshbOcmY5WyjLWTjLmeVsoSxn6fExa3nxeDzqMew1EVkL3AmcAfwYyANeYk05OxH5HHCsqu5xQfNSJiK3E1z8/W5gJ8EvghOAFlX1NyAZZFmbH8vanixn4Sxn82M525PlLJzlbH4sZ7PzMWuLfaQv064EblDVHyYts9DOQkQKgfcT1Mw83wuA61R1AEBEvgF8FKgB+qIcmEMsa2myrM3JchbOcpYmy9mcLGfhLGdpspyl5F3WvN3pE5FlwGHAb0TkDwT3cfkLcKGqPhrp4Nx0LMGMVt+OeiAO+gJwsojcQTDz15nAA6rqZGgXm2Vt3ixrs7OcpWA5mzfL2ewsZylYzubNcjY377K2mLdsyLT9CMZ/AnAq0ADcA/w0cTNP83zrgO8nZsIyz/cbgnPWe4AdwPHAByIdkVssa/NjWZud5Sw1y9n8WM5mZzlLzXI2P5azuXmXNZ93+qbv3XKLqj6uquMEh5+LgCOjG5Z7RORA4I3ADVGPxTUikg/cCzxN8GlWGfA54NciUh/l2BxiWUuTZW12lrO0WM7SZDmbneUsLZazNFnO5uZr1rzd6VPV7cBmYObFkvFZli1164A/qepDUQ/EQdXAAcDXVHVIVcdV9WaCbBwR7dDcYFmbF8va7CxnISxn82I5m53lLITlbF4sZ3PzMmveXtOXcD2wXkS+R7C3fQGwC/htpKNyiIgUE5zC8MmIh+IkVe0TkaeBc0TkEoL+eS/BjEyz3fh1qbKshbCszc1yljbLWQjL2dwsZ2mznIWwnKXma9Z83+m7hqDAvyC4h8gfgbcmPskxgeMJanNr1ANx2LEEF+S2EZzisRH4V1XdFOmo3GJZC2dZS81yFs5yFs5ylprlLJzlLJzlLJx3WfP6Pn3GGGOMMcYYY1Lz9po+Y4wxxhhjjDHhbKfPGGOMMcYYY3KY7fQZY4wxxhhjTA6znT5jjDHGGGOMyWG202eMMcYYY4wxOcx2+owxxhhjjDEmh/l+n74lQ0S+BdSq6jsy/Lq3AM+q6mWZfN1ME5E1wF3AC1V1Z9TjMbnLsmZZM9lnObOcmeyznFnOktmRviUsEYZjgWujHksYVf0z8CBwQdRjMWa+LGvGZJ/lzJjss5z5y3b6lrbzgNtUdSjKQYhIvogUpLHqLcDZImJHqI1vLGvGZJ/lzJjss5x5askXwEciUgJcDZwAVAKPAReq6gNJ67wd+BLQCjwMXA98DzhAVTcngvJu4H1Jz7kUeLeqvmzG9n4D/F5VP5T4+jTgo8AqoB34D+ArqjqVePwC4FTgQGAQ+FlifIOJx08Frkts//PAauDlIpJH8MnRqwg+kHgGOF9V708M5R6gGlgL3Lu39TMmXZY1y5rJPsuZ5cxkn+XMcmZH+vz0eeA9wPuBQ4A/A3eJyAoAEWkBbgd+AhwMfDXxnGQHEYT+0aRl3wRWi8hh0wtE5EXAkcA3El9/ALgCuBR4MfAR4GLgnKTXmQLOB14KnAgcBnxtxvZLgU8C64CXAG3AfwNdifVfDnwaGJt+gqqOE/yQen1IfYzJFMuaMdlnOTMm+yxnS5wd6fOMiCwDzgbOUNWfJJadBRwFnAt8IvH4JlWdPof5KRF5IfC5pJdqBeIEQQFAVTtF5C6CHwgPJxa/n+CTmj8lvv4kcJGq/jDx9bMichVBcK9LvE7yed6bReQi4Eci8r7pT3SAAuCDqvr7pP9bK3CNqj6ZWLRxlhJsBVamqpExmWBZs6yZ7LOcWc5M9lnOLGdgO30+OhAoAn4zvUBVJ0XkdwSfekBwyPuRGc97aMbX+wATSUGa9p/Af4nIh4Fx4BTgswAisj/QDNwoIv+R9JxCIG/6CxE5Cvh3gk9zKglCWgwsJwgeQIzgk5dkXwJuFpH3AfcRnDP+5Ix1RhNjNybbLGuWNZN9ljPLmck+y5nlzE7vzDHxeazbBxSLSNmM5T8BRoB3AW8DqggOncM/+uUsgkPo039eRnA4fvoTl58AfwP+FXgFwSc+EIR32i5VnUzesKp+muCHz50EpwU8LiLv5/mqgd55/D+NyQbLmjHZZzkzJvssZ0uEHenzzzMEn6K8JvFvEhfWHsE/AvYkcMyM5x024+vpT0peQtK52aoak+C+Lu8HtgO3q+r2xGPPichW4EBV/fYc43slQUA/PB1MEUn7/jCq+nfg78BXE58InUFwvvi0lxGcc25MtlnWLGsm+yxnljOTfZYzy5nt9PlGVXcmGvpqEekDngU+DNQTzLIEcANwgYhcQ3DI/aUEF71C4hMdVe0VkT8Ar+X5F+QC3Exwge0U8OYZj30K+JqIDAI/JThd4FCgUVWvJAhdPnC+iNwOHE5wYW5KIrIPcA3wA2Bz4v/zWpJOLRCRlUAjwUxMxmSVZc2yZrLPcmY5M9lnObOcgZ3e6auLge8T3HvkMYLZlI5W1S4AVW0jOMQuwJ8Igv2ZxHPHkl7nJuCkmS+uqpuAXxFMqfvLGY/dTPBJzimJ1/41cCbBDxBU9XFgPcGNMJ8g+LTlwjT+T5PAfsC3gKeAO4Df8fwbap4A3JP4/xmzGCxrxmSf5cyY7LOcLXF58fh8TuU1vhKR9cBlQJWqxhPLSgkO55+iqr+esf4TwK2q+rk9XiwCEtxf5u/ACar6m7D1jYmKZc2Y7LOcGZN9lrPcYqd35igROZdgFqZegsPknwS+NR1aAFUdE5H3ElzgOv28/YF/IZja9sbFHHOIVuBzFlrjGsuaMdlnOTMm+yxnuc12+nLXC4CPATVAJ8G52pfNXElVN8xY1EMwO9M6Ve3L9iDTpapPA09HPQ5jZmFZMyb7LGfGZJ/lLIfZ6Z3GGGOMMcYYk8NsIhdjjDHGGGOMyWG202eMMcYYY4wxOcx2+owxxhhjjDEmh9lOnzHGGGOMMcbkMNvpM8YYY4wxxpgcZjt9xhhjjDHGGJPDbKfPGGOMMcYYY3KY7fQZY4wxxhhjTA6znT5jjDHGGGOMyWGFUQ/AZJ+IHAtcAKwG9gV6gD8CN6jqXYl11gJrgctUdSoLY3gD8FngFcAo8BPgQlV9LtPbMibbos6UiDQBFwOvBA4G9gEOUNXNs6xbSpC9k4Eq4DHgYlXdkMkxGZNpnuXsisR6rwCqgdNU9VuZHI8x2eJL1kTklcCZwD8DLUAf8GvgE6r6bCbHlIvsSF+OE5EPAXcAfwdOB94OXJ54+KikVdcCnyILPSEirwPuAQaBdwHrCQJ7n4iUZHp7xmSTC5kCXgC8Gxgg+IWXyjeADwCXAu8AuoC7ReTlWRiXMRnhYc7OI3ij+n9ZGIcxWeNZ1v4NeCnwVeCtwCXAocCjItKchXHlFDvSl/suBO5U1dOTlv0C+E8RyepOv4iUqOough8SbcCxqhpLPPY34BGCHzDXZ3McxmSYC5naoKr1iWVnAG+eY/2DgROB96vqLYllvwL+ClwGSDbHa8wCeJOzhEpVnRKRFwDvzeb4jMkwn7J2tar2zniN3wDP8o8PN80cbKcv91UD3bM9MH14XkQ+TbBjBjAhItOP5yUe/wzwToJPYsaBx4GPqeqD06+VOOx/P8GRvLcCxwJFBKeTHQ58Z3qHL/Haj4rINuA4bKfP+CXyTM3j1BoBJoDvJ40xJiL/A1yS9AvXGNf4lDPms64xjvEmazN3+BLL2kSkF2hM5zWWMju9M/c9DLxPRD4qIi+cY52bCU4BA3gtcETiz7RG4MvAMcCpBOd6bxCRNbO81teAPOCUxLoAkwQ/BGbaBbws3f+IMY5wIVPpeinwrKqOzFj+V6CY4Be0MS7yKWfG+MzrrInIi4E64G8Lfa1cZ0f6ct9ZwA+BzwOfTxxd+zlwi6reA6CqnSLSmVj/oeQjconHz5j+t4gUAHcRvGk8g+D6vGQPJ6+f8BTB0b7dRKQVWEFwFMIYn7iQqXRVE1wjMVN/0uPGuMinnBnjM2+zJiKFwA1AL//YKTVzsCN9OU5VnwYOAV4PfI5g5r7jCCZy+EQ6ryEi/09E7k/8IIgR7Ki9EHjRLKvfMcuyrwCHicjlIlInIquB7wBTiT/GeMORTBmT0yxnxiwOz7N2HXAkcLKqzvYBp0liR/qWAFWdBDYk/iAiDQSfwnxKRL6eKigicijwU+BugklXughO17wZKJ3lKV2zbP/WxI7ehcDHgTjBNUY/xU7vNB6KOlPzMAC0zrJ8+ghf/yyPGeMEj3JmjNd8zJqIXEVw+4b3TR+RNKnZkb4lSFW3EoSxEPinkNXfRfCpzfGqeqeqPqSqjwL7zbF+fI5tfhKoBQ4CVqjqCYltP7AX/wVjnBJFptL0V+AAESmbsfwlBNfZblzAaxuzqBzOmTE5xfWsicjHCe7r9yFV/c5CX2+psJ2+HCciK+Z4aHXi7+kZm6Zn8NtnxnplBJ/Y7A6piBxFcFPMeVHVnar6Z1V9TkSOTozhhvm+jjFRcilTafgxwexo/5q0rULgPcA9NnOncZVnOTPGW75lLXFfwcuBj6vqddnYRq6y0ztz319E5F6CQ+/PAhXA2wgu3P1fVW1PrPdE4u+PiMjPgMnEJzV3AecD3xKRWwjO0f4ksCXdAYjIIQTT8/4hsei1wEeBz6vqbxfynzMmApFnCkBE/iXxz1ck/n5rYtrqXlX9FYCq/lFEvg9cKyJFifGeDRwAnDTP/7cxi8mbnCXWez2wP7A8seiVIrIDQFV/OJ9tGrPIvMmaiPwbcG1im78QkeRJAodU9QnMnGynL/d9nCC8lwH1BJ/GPA1cQhCcaf9HcL+8cwhubpkH5Knq3YlPVS4gOIT/F4Ibz6Z1cW/CeGIMFwElBNPqnjV9s2hjPONCpgB+MOPr6ftd/gpYm7T8NIKL8y8nuG/mn4CjVfUPGOMu33L2GYKJMKadm/hDYkzGuMqnrB2d2O7RiT/JZmbSzJAXj9tp7MYYY4wxxhiTq+yaPmOMMcYYY4zJYYt6emfiho1XAacSTON6D7BOVfvmWL8O+ALwDoLJCDYBb0vMKmSMmYNlzZjss5wZk32WM2MyY7GP9F0CHAO8GmhKLJt1qlURxFDEPwAAIABJREFUKQXuI7ge7EUE16KcBOzI/jCN8Z5lzZjss5wZk32WM2MyYLEncjkTuExVNwGIyEXARhFpVdW2Geu+jyCs56jqRGLZXxdvqMZ4zbJmTPZZzozJPsuZMRmwaEf6RKSK4J4dv59epqrPAEPAwbM85Q3A3wmmgN0mIk+KyIcXZbDGeMyyZkz2Wc6MyT7LmTGZs5hH+vZN/L19xvJBgnuCzFRLEN7zCaYcPwi4S0R6VPXW5BVFJN7c3AxAcXExa9euZc2aNQA0NjZSVFREW1sb8Xicqqoqqqur6e7uZmRkhOLiYpqamhgeHqa3txeApqYmCgoKaGsLPkCqrq6mqqqKrq4uRkdHKSkpobGxkaGhIfr6glPKm5ubycvLo709uJ1JTU0NlZWVbN26lbGxMUpLS2loaGBwcJD+/n4AWltbmZqaoqOjI/gP19ZSUVFBZ2cn4+PjlJWVsXz5cgYGBhgYGCAvL4/W1lZisRidnZ0A1NXVUV5eTkdHBxMTEyxbtoz6+nr6+/sZHBzc/ZytW7cyPj4OQH19PWVlZXR0dBCLxSgvL6euro6+vj6GhobIz8+ntbWVsbExurq6AFixYgWlpaW0t7czOTlJRUUFtbW19Pb2Mjw8TEFBAS0tLYyOjtLdHdzHs6GhgZKSEtra2piamqKyspKamhp6enrYsWMHhYWFNDc3s3PnTnp6eiL/fu3atYtYLObE92t8fJzS0tK9nebbsuZ51tr+/cOMDAxQ8t4zWFZQQFVxEQPjE4xMTrLr2zdTtt9+tFzxJS+zlpeXxwEHHODM92sBWbOceZ4z+51mObOcWc6WUs4W7ZYNiU9rBoBDVPWxpOXbgVNUVWesfwfwKlVtSlp2LdCgqu+esW58xtPNDJs2bWLVqlVRD8NpDtZor3b6LGvRykQfPXn+WcSGttN0xjl7PNZ58/UUVlSy+tobFrSNqDiYM9iLrFnOouVoHznFwRpZzjzjYA85x8EazZmzRTu9U1UHgXbg0OllIrKK4JOax2d5ymPAbHukdmPBvTD9aZaZW67UyLIWrVzpo2zJlfpYzqKVK32UTblQI8tZtHKhh7LNpxot9kQuNwEXi8j9wDbgauBuVd08y7rfSqx7LnAD8DKCGZg+uDhDzS35+XZLxjA5ViPLWkRyrI8yLsfqYzmLSI71UVbkUI0sZxHJoR7KGp9qtNgjvQr4MfAIsAUoAE4GEJGTRGT3lLqJGZneBpxBcMHuD4FPq+r3F3nMOWH6nGUztxyrkWUtIjnWRxmXY/WxnEUkx/ooK3KoRpaziORQD2WNTzVatGv6ssnOyw7n4DnHznGwRns7kUvWWNbC2TV9qTmYM3Asa5azcI72kVMcrJHlzDMO9pBzHKxR9Nf0mWhVV1dHPQTnWY1MJlgfpWb1MZlgfRTOamQWynoonE81sp2+JaKqqirqITjPamQywfooNauPyQTro3BWI7NQ1kPhfKqR7fQtEVu3bo16CM6zGplMsD5KzepjMsH6KJzVyCyU9VA4n2pkO31LxNjYWNRDcJ7VyGSC9VFqVh+TCdZH4axGZqGsh8L5VCPb6VsiSktLox6C86xGJhOsj1Kz+phMsD4KZzUyC2U9FM6nGtlO3xLR0NAQ9RCcZzUymWB9lJrVx2SC9VE4q5FZKOuhcD7VyHb6lojt27dHPQTnWY1MJlgfpWb1MZlgfRTOamQWynoonE81sp2+JWLbtm1RD8F5ViOTCdZHqVl9TCZYH4WzGpmFsh4K51ONbKfPGGOMMcYYY3KY7fQtES0tLVEPwXlWI5MJ1kepWX1MJlgfhbMamYWyHgrnU41sp2+JiMfjUQ/BeVYjkwnWR6lZfUwmWB+FsxqZhbIeCudTjWynb4no6OiIegjOsxqZTLA+Ss3qYzLB+iic1cgslPVQOJ9qZDt9xhhjjDHGGJPDbKdviaitrY16CM6zGplMsD5KzepjMsH6KJzVyCyU9VA4n2pkO31LREVFRdRDcJ7VyGSC9VFqVh+TCdZH4axGZqGsh8L5VCPb6VsitmzZEvUQnGc1MplgfZSa1cdkgvVROKuRWSjroXA+1ahwMTcmIgXAVcCpQClwD7BOVftmWXctcD+wM2nx46p6ZPZHmnt27doV9RCcl0s1sqxFJ5f6KBtyqT6Ws+jkUh9lS67UyHIWnVzpoWzyqUaLutMHXAIcA7wa2AZ8E/gO8NY51p9U1fJFGltO22effaIegvNyrEaWtYjkWB9lXI7Vx3IWkRzro6zIoRpZziKSQz2UNT7VaLFP7zwTuFpVN6nqduAi4GgRaV3kcSw5K1asiHoIzsuxGlnWIpJjfZRxOVYfy1lEcqyPsiKHamQ5i0gO9VDW+FSjRTvSJyJVQAvw++llqvqMiAwBBwNtszytQEQ6gKLE8z6mqn9ajPHmmsHBQaqqqqIehtNypUaWtWjlSh9lS67Ux3IWrVzpo2zKhRpZzqKVCz2UbT7VaDFP79w38ff2GcsHgdmmvnkSeDnwV6AcuBj4hYisUdWtM1c+99xzASguLmbt2rWsWbMGgMbGRoqKimhrayMej1NVVUV1dTXd3d2MjIxQXFxMU1MTw8PD9Pb2AtDU1ERBQQFtbcHPkurqaqqqqujq6mJ0dJSSkhIaGxsZGhqiry84pby5uZm8vDza29sBqKmpobKykq1btzI2NkZpaSkNDQ0MDg7S398PQGtrK1NTU7tv7FhbW0tFRQWdnZ2Mj49TVlbG8uXLGRgYYGBggLy8PFpbW4nFYnR2dgJQV1dHeXk5HR0dTExMsGzZMurr6+nv72dwcHD3c5577rnd262vr6esrIyOjg5isRjl5eXU1dXR19fH0NAQ+fn5tLa2MjY2RldXFxB8klFaWkp7ezuTk5NUVFRQW1tLb28vw8PDFBQU0NLSwujoKN3d3QA0NDRQUlJCW1sbU1NTVFZWUlNTQ09PDzt27KCwsJDm5mZ27txJT09P5N+v/v5+RkZGnPh+jY+PU1paOkss0mJZ8zxrAOOTU2wZHWNZQQFVxUUMjE8wMjnJ+OQUhcDIyIiXWRsdHaWqqsqZ79cCsmY58zxn9jvNcmY5s5wtpZzlxePxOR/MpMSnNQPAIar6WNLy7cApqqppvMbfgatU9RszlsfTePqStmnTJlatWhX1MJzmYI3y9uZJlrVoZaKPnjz/LGJD22k645w9Huu8+XoKKypZfe0NC9pGVBzMGexF1ixn0XK0j5ziYI0sZ55xsIec42CN5sxZ2tf0icgBIvIGEXmbiLxKROb1kY2qDgLtwKFJr7mK4JOax9N8mSn28o3wUtfaaqe+h3GlRt3d3Tz++ONY1vzkSh+5ypX6TOfs0UcfxXLmH1f6yGUu1Mhy5jcXesh1PtUo5emdIrISOBs4AWjk+aEZF5FfAzcBt6nqVBrbuwm4WETuJ5iB6WrgblXdPMu2jyII+iagDLgQqAfuTmM7ZobJyUkKCgqiHobToqzRc889x89+9jM2bNhAf38/iSPw/5d42LLmEctaag7mDOAhLGdesZyFi6pGlrPcYTkL51ON5jzSJyJfBf4ErAI+DrwEqASKgeXA24AHgM8Cj4vIq9LY3lXAj4FHgC1AAXByYnsniciOpHUPBu4DhgnCezjwJlXtmMf/zyRMnxds5hZVjW666SbWr19Pd3c3p5xyCtdddx3f+973wLLmJctaaq7l7PbbbwfLmXcsZ+GiqJHlLLdYzsL5VKM5r+kTkc8Dn5/t5pezrPs2oExVf5jh8aXFzssO5+A5x86Jqka33HIL73rXu6io2OOa9D1OR7Gsuc+u6UvNwZzBjKxZztxnv9PCRVEjy1lusZyFc7BGc57KPOfpnap6Ubqvrqo/ne+IzOLaf//9ox6C86Kq0WmnnZb2upY191nWUrOcmUywnIWLokaWs9xiOQvnU43mfcsGEakFXk1weP1hVe3O+KhMxu27777hKy1xrtXIsuYn1/rINa7VZ2hoiJNPPvntWM684lofucilGlnO/ORSD7nKpxqlPXsngIgcA/wd+BRwGbBJRE7OxsBMZvl0znFUXKrRgw8+CJY1L7nURy5yqT4PPvgg69atA8uZd1zqI1e5UiPLmb9c6SGX+VSjlDt9IlIyY9Fngdeo6mGq+nKCWT2vytbgTOaMj49HPQTnRVmjiYmJ53196623gmXNS5a11FzL2dVXX43lzD+Ws3BR1chyljssZ+F8qlHYkb7HROQNSV/HgVjS1+lMtWscUFZWFvUQnBdljdavX8/jj//jlkN5eXlgWfOSZS0113I2Y6pty5knLGfhoqqR5Sx3WM7C+VSjsGv6TgRuEpE/Ax8BLgUeEpGngSLgRcBZ2R2iyYTly5dHPQTnRVmjj3zkI3z961+ntbWV008/nRNPPJErrrjCsuYhy1pqruXswgsvZOfOnQ9hOfOK5SxcVDWynOUOy1k4n2qU8kifqv6RYCKJvwKPAvsALwQuBz4D/JOqfifbgzQL19/fH/UQnBdljQ488ECuueYaWlpauOCCC6ZPF7CseciylpprObvhhhvAcuYdy1m4qGpkOcsdlrNwPtUodPZOVZ0CvigitwPXA+8FzlLV9mwPzmTO4OAg1dXVUQ/DaVHXKD8/n+OOO44jjjhi+hfkf2FZ807UfeS6qOszM2f3338/wJ8sZ36Juo98EGWNLGe5wXIWzqcahc7eKSIvFZF3AUWq+lbgVuCXIrJeROa8AaBxS+IaMZNC1DVqb2/nt7/9LZOTk3z6058Gy5qXou4j10Vdn5k5e/3rXw+WM+9E3Uc+iLJGlrPcYDkL51ONwmbvvAB4BPgo8DsR+YCq3gq8CjgEeFBEDsr+MM1Ctba2Rj0E50VZozvvvJMLLriA22+/nYsuuoi7774by5qfLGupuZaztWvXguXMO5azcFHVyHKWOyxn4XyqUdiRvouAt6vq4cChwAUAqrpNVU8FPg58P6sjNBkxcwpls6coa3T77bdz6aWXcs011/DlL3+ZH/3oR4BlzUeWtdQsZyYTLGfhoqqR5Sx3WM7C+VSjsJ2+PP4xte5k4uvdVPVegk9tjOO2bNkS9RCcF2WN4vE4+flBHPPz84nH48973LLmD8taapYzkwmWs3BR1chyljssZ+F8qlHYRC5fAH4qIn8imEnwYzNXUNWxbAzMmKXk+OOP5zOf+QwHHHAAW7Zs4ZRTTtljHcuaMQtjOTMm+yxnxrgp5U6fql4jIncDq4E/q+qTizMsk2l1dXVRD8F5UdbouOOO45BDDqGzs5OVK1fS1NQU2VjMwljWUrOcmUywnIWLqkaWs9xhOQvnU43SuWXDn4E/L8JYTBYtW7Ys6iE4L+oarVy5kpUrV0Y6BrNwUfeR66Kuj+UsN0TdRz6IskaWs9xgOQvnU43mvKZPRD4hImn9T0TkNSLyzswNy2RaR0dH1ENwXlQ1+v73v8/YWHpnuljW3GdZS81yZjLBchYuihpZznKL5SycTzVKdaTvQKBdRG4Dfgw8qqpdACJSCrwEeC1wMlBLcNN246hYLBb1EJwXVY26u7s5/fTTOeKIIzjssMN4wQtesPtGn5Y1/1jWUrOcmUywnIWLokaWs9xiOQvnU43m3OlT1dNEZA3wQeDbQIWIxIEJoJhgJs8/ADcB/6Wqu8I2JiIFwFXAqUApcA+wTlX7Qp53NnA98ElVvTyN/5eZoby8POohOC+qGq1fv57Nmzfzk5/8hC9/+cuMjo4CMDU1NYplzTuWtdRcy1lhYSHATixnXrGchYuiRpaz3GI5C+dTjcImcvkzsC4RnIOAVmAfoA94LCxws7gEOAZ4NbAN+CbwHeCtcz1BRFqBj2DXFS6ITxeaRiXKGq1cuZJzzz2Xs88+m82bN9PT08MVV1xxGpY171jWUnMtZ+Pj41xzzTVvwXLmFctZuKhqZDnLHZazcD7VKHQiFwBVnQIeS/xZiDOBy1R1E4CIXARsFJFWVW2b4znfILiR59kL3PaStm3bNmpqaqIehtNcqFF+fj6rVq1i1apVqOr/LOClLGsRcaGPXOZCfZJzBvDP//zP9+7lS1nOIuJCH7ku6hpZzvwXdQ/5wKcapbXTlwkiUgW0AL+fXqaqz4jIEHAwsEdwRWQdsFNVv5842jinc889F4Di4mLWrl3LmjVrAGhsbKSoqIi2tjbi8ThVVVVUV1fT3d3NyMgIxcXFNDU1MTw8TG9vLwBNTU0UFBTQ1hYMqbq6mqqqKrq6uhgdHaWkpITGxkaGhobo6ws+sGpubiYvL4/29nYAampqqKysZOvWrYyNjVFaWkpDQwODg4P09/cD0NraytTU1O6LQGtra6moqKCzs5Px8XHKyspYvnw5AwMDDAwMkJeXR2trK7FYjM7OTiD4hKG8vJyOjg4mJiZYtmwZ9fX19Pf3Mzg4uPs5fX19bN++HYD6+nrKysro6OggFotRXl5OXV0dfX19DA0NkZ+fT2trK2NjY3R1dQGwYsUKSktLaW9vZ3JykoqKCmpra+nt7WV4eJiCggJaWloYHR2lu7sbgIaGBkpKSmhra2NqaorKykpqamro6elhx44dFBYW0tzczM6dO+np6Yn8+7V9+3Z27drlxPdrfHyc0tLSVC0/J8ua/1kDGJ+cYsvoGMsKCqgqLmJgfIKRyUnGJ6coBEZGRrzM2ujoKDU1Nc58v/Y2a5Yz/3Nmv9MsZ5Yzy9lSyllePB5PlYeMEZFmoB1YparPJi1vAz6uqt+dsX4L8BvgcFXdIiK/BO6d7bxsEYmralbH77vNmzfb9MkhHKxR3t48ybIWrUz00ZPnn0VsaDtNZ5yzx2OdN19PYUUlq6+9YUHbiIqDOYO9yJrlLFqO9pFTHKyR5cwzDvaQcxys0Zw5m/OWDVkwnPi7csbyKmBolvVvBi5X1S1ZHdUS0draGvUQnJdDNbKsRSiH+igrcqg+lrMI5VAfZU2O1MhyFqEc6aGs8qlGi7bTp6qDBJ/WHDq9TERWARXA47M85U3AFSLSJyJ9wGuAfxeRXy/GeHPNrl2hE2QteblSI8tatHKlj7IlV+pjOYtWrvRRNuVCjSxn0cqFHso2n2qU1k6fiBybmDJ3oW4CLhaRA0SkArgauFtVN8+ybjPB+dovT/x5FPg68C8ZGMeSs3Xr1qiH4DwXavTggw8yOTmZiZeyrEXEhT5ymQv1sZz5z4U+cl3UNbKc+S/qHvKBTzVKdyKXW4FhEfkv4Buq+vRebu8qYD/gEaAE+DnBDToRkZOAG1W1HEBVO5OfKCK7gCFVfW4vt22M8774xS+yzz77cNRRR3H77be/0LJmTOYl5+xNb3oTjY2Ne/tSljNj5mA5M8YtaU3kIiL7AicCpwGvAn5HMB3u/6rqzqyOMA12MW64kZERysrKoh6G01yo0cjICBs2bODee+/l6aefnsKy5p1M9FEuT+TiWs42btzI1NTUb7CcecWFPnJd1DWynPkv6h7ygYM1WthELqo6rKo3qurhBDdpfwi4EugSkf8UkcMzM06TLfvss0/UQ3CeCzUqKyvj6KOP5pprrgHLmpdc6COXuVCf5Jx99atfBcuZd1zoI9dFXSPLmf+i7iEf+FSjeU/koqp/Bb5McI51MfAe4Nci8pCIHJTh8ZkMmb6niJmbazWyrPnJtT5yjWv1aWlpAcuZd1zrIxe5VCPLmZ9c6iFX+VSjtHf6RKRIRN4tIncBzwJHAWcB9UAr8Dfg+1kZpVmwDF1MndNcqVEsFuOBBx7AsuYnV/rIVa7UZzpnn/rUp8By5h1X+shlLtTIcuY3F3rIdT7VKK2JXETka8AJQBz4DnCBqj6RtMqoiFwC+DOFzRKz7777Rj0E57lQoxtvvJENGzaQl5cH8ASWNe+40Ecuc6E+yTl7wxvewB//+MeDLWd+caGPXBd1jSxn/ou6h3zgU43Snb3zJcAHgdtVdXyOdfqAN2RkVCbj9t9//6iH4DwXatTR0cG6des44ogjKCoqumCO1SxrDnOhj1zmQn1m5IzTTz/9iVlWs5w5zIU+cl3UNbKc+S/qHvKBTzVKd6fvM8BvVTWWvFBECoEjVXVD4rFfZXqAJjP6+vqora2NehhOc6FG//Zv/8aLX/xiCgqef1tMy5o/XOgjl7lQH8uZ/1zoI9dFXSPLmf+i7iEf+FSjdK/pux+onmV5ZeIx47ihoaGoh+A8F2r0iU98guHh4dkesqx5woU+cpkL9bGc+c+FPnJd1DWynPkv6h7ygU81SnenL4/ger6ZaoDI77Viws38pM3syYUaxePx6ev5ZrKsecKFPnKZC/WxnPnPhT5yXdQ1spz5L+oe8oFPNUp5eqeITN+1Mg58V0R2JT1cALwM+G2WxmYyKDFdskkhyhpdfvnlAOTl5fGlL32JoqIiHn744en8WdY8YllLzbWcAdNZs5x5xHIWLqoaWc5yh+UsnE81Crumb1vi7zxgABhNemwceAD4zyyMy2TY2NiYVzeQjEKUNZqe/Skej1NeXk5xcTH8I3+WNY9Y1lJzMGcQZM1y5hHLWbioamQ5yx2Ws3A+1SjlTp+qngYgIpuBa1TVDsd7qquri1WrVkU9DKdFWaP169cDUFdXx3HHHUdpaSnr168/LZLBmAWxrKXmWs4Syy1rnrGchYuqRpaz3GE5C+dTjdKavVNVP5PtgRhj4IQTToh6CMbkPMuZMdlnOTPGLXPu9InI48DrVXVARP7M7BO5AKCqB2VjcCZzVqxYEfUQnBdVjc477zyuvPJKysvLOe+883Yvb2tre3zmupY191nWUnMtZ7Bn1ixn7rOchYuiRpaz3GI5C+dTjVId6bsNmJ645YeLMBaTRdOnV5i5RVWjI488cveF7kceeeTu5W1tbbdFMiCzIJa11FzLGVjWfGQ5CxdFjSxnucVyFs6nGuXF43MewPOGiMRVNXzFJWzz5s2sXLky6mE4zcEazTrXdZQsa+Ey0UdPnn8WsaHtNJ1xzh6Pdd58PYUVlay+9oYFbSMqDuYMHMua5Syco33kFAdrZDnzjIM95BwHazRnztK9T5/x3NTUVNRDcJ7VyGSC9VFqVh+TCdZH4axGZqGsh8L5VKNU1/SlvI4vWbrnZYtIAXAVcCpQCtwDrFPVvlnWfR3wFWAlwX1dngEuV9Xb09mWeb6Kioqoh+C8qGo087qHaQu5ps+yFh3LWmqu5Qz2/lojy1l0LGfhoqiR5Sy3WM7C+VSjVNf0ZeM6vkuAY4BXE9yv5ZvAd4C3zrLuU8BxQHvi69cBd4nIK1T1b1kYW06rra2NegjOi6pGM697mLbA6x8saxGxrKXmWs5gQVmznEXEchYuihpZznKL5SycTzWac6cvS7dpOBO4TFU3AYjIRcBGEWlV1bYZ2++Z/reI5ANTBKejvgCw4M5TT08PdXV1UQ/DaVHVaK5prU844YSFZNCyFhHLWmqu5Szx2N5mzXIWEctZuChqZDnLLZazcD7VKK379GWCiFQBLcDvp5ep6jMiMgQcDLTN8bxBYBnBWDcQHNbfw7nnngtAcXExa9euZc2aNQA0NjZSVFREW1sb8Xicqqoqqqur6e7uZmRkhOLiYpqamhgeHqa3txeApqYmCgoKaGsLhlRdXU1VVRVdXV2Mjo5SUlJCY2MjQ0ND9PUFZxc0NzeTl5dHe3vw4VJNTQ2VlZVs3bqVsbExSktLaWhoYHBwkP7+fgBaW1uZmpqio6MDCD4tqKiooLOzk/HxccrKyli+fDkDAwMMDAyQl5dHa2srsViMzs5OILj5aXl5OR0dHUxMTLBs2TLq6+vp7+9ncHBw93MGBgbYsWMHAPX19ZSVldHR0UEsFqO8vJy6ujr6+voYGhoiPz+f1tZWxsbG6OrqAoIpaUtLS2lvb2dycpKKigpqa2vp7e1leHiYgoICWlpaGB0dpbu7G4CGhgZKSkpoa2tjamqKyspKampq6OnpYceOHRQWFtLc3MzOnTvp6emJ/Pu1Y8cOYrGYE9+v8fHxvZ4RyrLmf9YAxien2DI6xrKCAqqKixgYn2BkcpLxySkKgZGRES+zNjo6Sl1dnTPfr73NmuXM/5zZ7zTLmeXMcraUcjbn7J2Zvk+fiDQTHG5fparPJi1vAz6uqt9N8dwSgsP4LwKuUdXJGY/bDEwh2tvbaWlpiXoYTouqRinu0/fnmeta1tyXiT7K5dk7XcsZ7Jk1y5n77HdauChqZDnLLZazcA7WaM7ZOxfzPn3Dib8rZyyvAoZSPVFVdwF3ishPgUHgxgyMZ0lpbm6OegjOi6pGWbhPn2UtQpa11FzLGex11ixnEbKchYuiRpaz3GI5C+dTjdK6pi8T1/ep6qCItAOHAo8BiMgqoALYY5bCORQC/7TQsSxFIyMjLFu2LOphOC2qGiVfAzHj33uVO8tatCxrqbmWs8TX886a5SxalrNwUdTIcpZbLGfhfKrRvK7pE5EDgRcnvvybqj4zz+3dBFwsIvcTzMB0NXC3qm6eZVvvAp4muPC2EDgFOAr4wjy3aYDnnnuOVatWRT0Mp7lUo66uLtatW/eOxJeWNY+41Ecucqk+XV1ddHZ28tnPfvYdWM684lIfucqVGlnO/OVKD7nMpxqltdMnIjXANwAhmAkJIE9E/g94v6puS3N7VwH7AY8AJcDPgZMT2zgJuFFVyxPrrkisvwIYJ5iG9wRV/Xma2zLGO0NDQ3zta1/j4YcfBrgzsdiyZkwGJecsLy8PgqxZzrKs98d3sO2+u+d8vOaNb2H/dx63iCMy2WQ5M8Ytc07kkkxE7iA4NL4OeCix+NXAfwAbVfX4rI0wDXYxbrjpWYXM3Fyo0RVXXMHWrVs555xzuOSSS4oSiy1rHslEH+XyRC6u5exFL3oRxx13XBGWs6x78vyzGHnqCYpXNO7x2HjXFspe9JK0+9qFPnJd1DWynPkv6h7ygYM12quJXJK9BXijqv4uadlvRGQdcO9CRmYWR3FxcdRDcJ4LNfrDH/7A5ZciPMR7AAAgAElEQVRfzurVq1HVWGKxZc0jLvSRy1yoT3LOgOmsWc4WQfGKxjk/zJjX6zjQR66LukaWM/9F3UM+8KlG6e709QI7Z1k+QnB+tXFcW1sbBxxwQNTDcJoLNaqsrKSkpGS2hyxrnnChj1zmQn0sZ24a3fg0T55/1qyPzTz104U+cl3UNbKc+S/qHvKBTzXKT3O9y4BrRWT3ORmJf38x8ZhxXDqn8S51LtToPe95DzfffDPbtv3j96FlzS8u9JHLXKiP5cw9+x50CEV19cSGtu/xZ+SpJ/a4FtCFPnJd1DWynPkv6h7ygU81mvNI3yw3ZD8A2CwiWxJfNwJjQB1wc9ZGaDKiqqoq6iE4L6oazbyBbU9PD2eccQaTk5ObE4ssax6xrKXmWs5qamro6enZjOUsUpWHHUHlYUfM+thsp35azsJFUSPLWW6xnIXzqUapTu/MxA3ZjSOqq6ujHoLzoqrRzBvYTvve9753yyIPxWSAZS0113IGljUfWc7CRVEjy1lusZyF86lGad2c3fjvueeeo76+PuphOC2qGs28gW3ScsughyxrqbmWs8RjljXPWM7CRVEjy1lusZyF86lG6V7TZzy3c+ds8/CYZFYjkwnWR6lZfUwmWB+FsxqZhbIeCudTjdK9OXsx8HHgBKAFKEp+XFULMj80k0lFRUXhKy1xLtRoYmKCH/zgB2zYsIGtW7eOYVnzjgt95DIX6pOcs97eXiYmJiaTH7ecuc+FPnJd1DWynPkv6h7ygU81SvdI32eB9xHMuDQFfBT4OsGUu3vecMc4p7m5OeohOM+FGt1666384he/4NhjjwXLmpdc6COXuVCf5Jzl5eWB5cw7LvSR66KukeXMf1H3kA98qlG6O33vBs5S1RuBSeBHqvoh4FPAm7I1OJM5O3bsiHoIznOhRg888ABnn302Rx99NFjWvORCH7nMhfok56ygoAAsZ95xoY9cF3WNLGf+i7qHfOBTjdK9OXs98ETi3zuA6flJ7wKuzvSgTOb19PRQXl4e9TCc5kKNBgcHaWlpmf7SsuYhF/rIZS7UJzlnpaWljI6OWs4yoPfHd+xxP71koxufpqguMxMeuNBHrou6RpYz/0XdQz7wqUbpHulrBxoS/94IvCXx7yOA0UwPypilav/990++ka1lzZgsSM7ZihUrwHKWEdvuu5uRp56Y9QbrsaHtFNXVs+9Bh0Q9TLNILGfGuCXdI313AG8EHgS+AnxPRD5AcJPNL2RpbCaDmpqaoh6C81yo0eGHH87jjz/O6tWrwbLmJRf6yGUu1Cc5Z+985zt54oknPmM5y4ziFY00nZH9y7Vc6CPXRV0jy5n/ou4hH/hUo7R2+lT135P+/UMR6QSOBJ5W1f/L1uBM5hQWprt/v3S5UKP3ve99u/9tWfOTC33kMhfqk5yz17zmNQCvw3LmFRf6yHVR18hy5r+oe8gHPtVor0aqqg8SHPUznmhra+OAAw6IehhOc7FGljX/uNhHLnGxPpYz/7jYR65xrUaWM/+41kMu8qlGae/0icihwPnASxKL/gZ8WVX/kI2BmcyKx+NRD8F5rtTomWeeQVW5//77H00ssqx5xJU+cpUr9ZnOWUdHBxs3bnwUy5lXXOkjl7lQI8uZ31zoIdf5VKN0b85+EvBt4BfATxOLDwceFpFTVfW7ab5OAXAVcCpQCtwDrFPVvlnWfRtwIXAQUAD8BfiYqv46nW2Z59tvv/2iHoLzXKjRL3/5S6699lrWrFkDljUvudBHLnOhPsk5e8UrXsHGjRt/iuXMKy70keuirpHlzH9R95APfKpRukf6Pgd8UlWvSF4oIv8OXA6kFVzgEuAY4NUEN+f8JvAd4K2zrLsf8DXgfoKp6z8A/ExEXqyqHWluL2eFTY0NUPPGt7D/O48D/GrKqLhQo+9+97uceOKJvPvd7wa4dHq5Zc0fLvSRy1yoz4yccdJJJ10KljOfuNBHrou6RpYz99h7x8zzqUbp7vTtD/zvLMt/AHxyHts7E7hMVTcBiMhFwEYRaVXVtuQVVfXWGc/9DxH5FPAqYMkHd3pq7OIVjbM+Pt61BWB3cLu7u1m+fPmijc9HLtRo+/btvPa1r53tIcuaJ1zoI5e5UB/Lmf9c6CPXRV0jy5l77L1j5vlUo3R3+u4H1hLcNyzZWuBX6byAiFQBLcDvp5ep6jMiMgQcDLTN9dzE89cAtcCf0xxzzks1NXbnzdc/7+uRkZHFGJLXXKjRmjVr+Mtf/kJDQ8PMh9ZiWfOCC33kMhfqYznznwt95Lqoa2Q5c5O9d8wsn2o0506fiByf9OXPgCtF5JX8Y+alw4HjgU+nua19E39vn7F8EKhI9UQRqQNuA65R1b/Pts65554LQHFxMWvXrp2+JorGxkaKiopoa2sjHo9TVVVFdXU13d3djIyMUFxcTFNTE8PDw/T29gLBPTcKCgpoawt+llRXV1NVVUVXVxejo6OUlJTQ2NjI0NAQfX3BKeXNzc3k5eXR3t4OQE1NDZWVlWzdupWxsTFKS0tpaGhgcHCQ/v5+AFpbW5mamqKjI/jwqba2loqKCjo7OxkfH6esrIzly5czMDDAwMAAeXl5tLa2EovFGBsbZWpyipHYJGWFBTw3totYPE5pfj41JcVMTk0RGxvl2WefpbW1lby8PDZt2gRAfX09ZWVldHR0EIvFKC8vp66ujr6+PoaGhsjPz6e1tZWxsTG6urqA4MaqpaWltLe3Mzk5SUVFBbW1tfT29jI8PExBQQEtLS2Mjo7S3d0NQENDAyUlJbS1tTE1NUVlZSU1NTX09PSwY8cOCgsLaW5uZufOnfT09ET+/SouLs7a96uzsxOAuro6ysvL6ejoYGJigmXLlvHMM8+wY8cOdu7cycqVK/n2t7/Nk08+yb333ntqor0taxFmLdX3rr6+nv7+fgYHB3c/JxNZAxifnGLL6BjLCgqoKi5iYHyCkclJxienKCT4ReNj1mKxGMCif7+eeuopKisr2bFjB62trdxyyy38/e9/58ADD+T6668/FcvZgnNGPE48HmfL6BgAVUVFLCssoGdsFxPxOCX5+dSWFDM0EWM40QcrSkuYjMfp2TUOwH5FRXv8TgOIxSbYtGlTRnNmv9MsZz7mzN47Ws7mev8xPj5OaWnpnJnIm2vWGRGZmvNZzxdX1YKwlRKf1gwAh6jqY0nLtwOnqKrO8bwG4OcERxvPU9U9Biwi8TmenrOePP8sYkPbU35aU1hRyeprb1jkkZn5OuaYY2ZdHt8znJa1JSJVvi3be2eunMEeWbOc7aWw30t7y3reH5Yzt9l7xyUhb64H5jzSp6r5mRyBqg6KSDtwKPAYgIisIvik5vHZniMiK4H7gDtU9cJMjmepGRoaoqIi5YdiS15UNfrRj34010N7lUHLWrQsa6k5mDPYi6xZzqJlOQsXRY0sZ7nFchbOpxot9m3kbwIuFpH7CWZguhq4W1U3z1xRRFYD9wLfUtVPLOooc1BfX583TRmVHKuRZS0iOdZHGZdj9bGcRSTH+igrcqhGlrOI5FAPZY1PNZrPzdnfDlxMcHP2OPAEcLWq/jTlE5/vKoLpdB8BSggOvZ+ceP2TgBtVtTyx7sVAI3C+iJyf9BrrZpmdyZic8cgjj3DbbbfxxBNP9GFZMyYrpnPW0dHB8PBwL5YzYzLOcmaMO+a8pi+ZiJwBXA/cCjyQWPw64ATgbFX9ZtZGmAY7L3tPM8/LnpiYoKioaDGH6B0XanTPPfdwww038PrXv5777rvvjMRiy5pHMtFHuXxNn2s5e/GLX8x11113BpazBVvMa/pc6CPXRV0jy5l77L1j5jlYo/lf0zfDxcAFqnpd0rJviMjvCW6aGWlwTbj8/IxeopmTXKjRbbfdxvvf/37e8Y53sH79+m8kFlvWPOJCH7nMhfok5wzgzW9+8zewnHnFhT5yXdQ1spz5L+oe8oFPNUp3p68FuGuW5T8DrsnccEwmjW58mifPPwuAsbFRSkv32f1YzRvfsvvmmybQ1tbGqlWrIh1Db28vhx566GwPWdY84UIfucyF+ljO/OdCH7ku6hpZzvxk7x3nJ+qczUe6u6ftwJtmWf5mQm6MaaKx70GHUFRXT2xoO7Gh7UyNju3+98hTT7DtvrujHqKZxf77789jjz0220OWNWMyxHJmTPZZzvxj7x1zW7pH+q4BviYihwK/TSx7DXAKcF42BmYWpvKwI6g87IjdXw9PxNi3KPh2d958fVTDclp1dXXUQ+C4447jpptuYtOmTdxzzz2nJRZb1jziQh+5zIX6JOds9erVfPWrXz0Ny5lXXOgj10VdI8uZf+y94/xFnbP5SGunT1VvFJEe4CPA8YnFfwPeraopb8pi3DAdWjO3qqqqqIfA0UcfTWVlJXfeeSf84/QXy5pHXOgjl7lQn+Sc/e53v4Mga5Yzj7jQR66LukaWM//Ze8dwUedsPkK/myJSSHAofoOq3pH9IZls6Ns1Tm1JcdTDcNrWrVtpaGiIbPuTk5P88Y9/5KUvfSlHHHEEQE1kgzF7Leo+cl3U9ZklZ2BZ807UfeSDKGtkOcsN9t4xnE8/i0J3+lQ1JiK3A6sJboppFknvj++Y8/zp0Y1PU1RXn/Zr7ZqaytSwctbY2Fik2y8oKODKK6/k+uuv9+ZGn2ZPUfeR66Kuj+UsN0TdRz6IskaWs+jYe8fF5dPPonSP2/4JeAGwOXtDMTNtu+9uRp56guIVjXs8VlRXz74HHZL2axV7NKVsVEpLS6MeAgcccABdXV3U16f/Q9m4xYU+cpkL9bGc7b1MvqFcCBf6yHVR18hyFg1777i4os7ZfKS70/dp4Isi8ing98DO5AdVtT/D4zIJxSsaM3Kj2/3t8HwoFw7Pn3DCCXzzm9/kxBNP5IorrmjGsuYdF/rIZS7UJzlnBx54IKeffvrzrsS3nM0tk28oF8KFPnJd1DWynEXH3jsunqhzNh/p7vT9JPH37UA8aXle4uuCTA7KZN6OWIzyQrsgN5Xt27dTWVkZ6Rguu+wyAK688kp4/pF1y5onXOgjl7lQn+Sc5eXlAfQmHrKcpSFTbygXwoU+cl3UNbKc+c/eO4aLOmfzke538iiev7NnPLN9woIbZtu2bZEH9/LLL5/+5cjHPvaxoyIdjNkrLvSRy1yoT3LOwLLmIxf6yHVR18hy5j977xgu6pzNR7q3bPhllsdhjAHWrFmz+9+q+qsIh2JMzkrOGVjWjMkGy5kxbkm50yciZcAXgGOBIuBe4EOq2rcIYzMZtLy0JOohOK+lpSWybe/atYtbbrmFBx98kMnJSQ4++GA2bNhQa1nzT5R95APXcnbmmWfa7IIespyFi6pGlrPcYe8dw/n0syhsWp7PAKcSXNP3P8CbgP/I8phMFsTt5NxQ8QiL9N///d/cd999vPKVr+R1r3sdjz32GFjWvBRlH/nAtZxdf/31kY3H7D3LWbioamQ5yx0Ws3A+/SwK2+k7HjhdVc9U1Q8BbweOFRG7+NYzz+3aFfUQnNfR0RHZtn/3u99x3nnn8cEPfpAzzzyTSy+9FCxrXoqyj3zgWs4eeughLGf+sZyFi6pGlrPcYe8dw/n0syhsp68Z+PX0F6r6MBAD/Jmf1BgP9PX18dKXvnT31y984QvBsmZMRs2Ws4KCArCcGZMxljNj3BQ2kUsBMD5jWSyN580q8SnPVQSnjJYC9wDrZrtuSUQageuBlwMtwCmq+t292a6ByiKbfSlMbW1tZNuempqicM8ZsvYqa5azaEXZRz5wLWeJN6P2O80zlrNwUdXIcpY77L1jOJ9+FoV9N/OA74pI8vHdUuA/RWRkeoGqSprbuwQ4Bng1sA34JvAd4K2zrDtFEOzPE1xPaBbAptwNF+VF5vF4nC996UsUFRUlL97brFnOImSTFaTmWs7Gx8fBfqd5x3IWLqoaWc5yh713DOfTz6Kw7+Z/zbJsIZ+YnAlcpqqbAETkImCjiLSqalvyiqraBXw9sd7kArZpgJ6xXdTZLEwpbdmyhcbGxki2fdRRs96+aG+zZjmLUJR95APXcrZ27Vruu+++vb0ow7IWEctZuKhqZDnLHfbeMZxPP4tS7vSp6mmZ2pCIVBEcav990us/IyJDwMFA21zPNQs34dHsQlHZFeEFy+vXr59t2bzzZzmLXpR95APXcpZYblnzjOUsXFQ1spzlDnvvGM6nn0WLedx238Tf22csHwQWfGz03HPPBaC4uJi1a9fuviloY2MjRUVFtLW1EY/Hqaqqorq6mu7ubkZGRiguLqapqYnh4WF6e3sBaGpqoqCggLa24GdJdXU1VVVVdHV1MTo6SklJCY2NjQwNDdHXF5xS3tzcTF5eHu3t7QDU1NRQWVnJ1q1bGRsbo7S0lIaGBgYHB+nv7wegtbWV3h/fQfddPwagqKiIgoJCdu3aRTw+xWRHOyX1yxmaiDEciwGworSEyXicnl3BpZb7FRVRVljAc2O7iMXjlObnU1NSzPaJCXbEJslLPKcA2DI6BsBUIsTt7e3EYjHKy8upq6ujr6+PoaEh8vPzaW1tZWxsjK6urmC7K1ZQWlpKe3s7k5OTVFRUUFtbS29vL8PDwxQUFNDS0sLo6Cjd3d0ANDQ0UFJSQltbG1NTU1RWVlJTU0NPTw87duygsLCQ5uZmdu7cSU9PT+Tfr3322Sf0+zU1NbV7pqba2loqKiro7OxkfHycsrIyli9fzsDAAAMDA+Tl5dHa2kosFqOzsxOAuro6ysvL6ejoYGJigmXLllFfX09/fz+Dg4O7nzM+Pk5paeneRCGrOQN/s7ZY37vCwkI2bdoEQH19PWVlZXR0dMwrawDjk1NsGR1jWUEBVcVFDIxPMDI5yfjkFIXAyMiIl1mbnt7ale+Xq1lzNWfj47uITU7Rt2uc2pJihidiDCV+Py0vLSEej/Nc4vdTVVERywoL6BnbxUQ8Tkl+PrUlxfP+nQYQi02wadOmjObMfqdZzlzNmb13tJxlI2d5i3V/icSnNQPAIar6WNLy7QQX2mqK524GPjHXxbgiEk/xdKc9ef5ZjDz1BMUrZj80vO9Bh1B52BEZ3WbnzddTWFHJ6mtvyOjrmozLm+8TspmzxDreZs0nT55/FrGh7TSdcc4ej1l+s8KprLmcs1S9mS3W8znDcpYh9t7RpDBnzhbtSJ+qDopIO3Ao8BiAiKwi+KTm8cUax2Lr/fEdbLvv7jkfH934NMUrGrP+C3R4Isa+NgtTSoODg1RVVUU9jAVZqjlzSS70UTblSn0sa9H6/+zde3xcdZ3/8VeS5kKaJiEpSdu0DRRX8QIK/kTBW8Wfiq5+BF1dQRARFBAVFhFcr4jKRXG9oAgsiq6y/lzl4hcvFEEUURFEbisiQiFNaEqStmnSNmmaZH5/nEkd0mTOpJnJ+X5P3s/Ho49mzpyZ+eaTzzuZM+ec70lLH5VSGmo0n3OW7/2j3jv6I6SczfVP8krgXDO7jWgGpouBNc65J6Za2cwm9lGWAZXZ26POudG5GGwxbLx1Td5PYypbWll00MElH8fAqIIbZ9OmTcEEN8a8y5lPUtRHJZGy+ihrCUlZH5VEimo0L3OW7/2j3jv6I6SczfVP8iJgb+BuoBr4JXAcgJm9E7jCOVeXs/5Qztffzv77DHDeXAy2WObi0xiRHPMyZyIJUNZESm/e5kzvH6WY5nSjzzk3Bpyd/Tf5vmuAayYtm/Hx30mI2wVf2dI6xyPa3VJNuRurvb096SEURVpzFoq09FGppKk+ylpy0tRHpZKWGqU1Z4Wc/pP0+0e9d4wXUs60z7YIfNgFH2csk6G8LIjfg4kZGxujoqIi6WFI4NRH+ak+Ugzqo3iqkd98Of0nH713jBdSzrTRVyS+74Lv2TFC2157NF3yvNHV1cWqVauSHoYETn2Un+ojxaA+iqca+U/vHcMXUs600SciMsd8PKwnbkzNr34d+7zp6DkckYiIiBSLNvrmib0rK5Megvf22WefpIcgKVBIH5XqsJ64DTeYfuMt35hGup8EKMpGn3ImxaA+iqcayWzpvWO8kHKmjb4ChTBZSz61C8I43jhJixYtSnoIkgKF9lEpDuuJ25iM23ibbkxdV11WtDEqZ1IM6qN4qlHy9N4x/ULKmTb6cuQL59b7/wxAzX7773afDyfbxnlqeAetmoUpr66uLpYvX570MCRwSfdRvo3JYm687amk6yPpoD6KpxrNDb13nN9Cypk2+nLk+5S8Zr/9WXTQwTQcelgCI5u90Uwm6SF4b2RkJOkhSAqoj/JTfaQY1EfxVKO5ofeO81tIOdNG3yS+z6S0p2rKy5Megvdqa2uTHoKkwFz00dCjj/DwmadOudz7w4WUMykC9VE81Wju6L3j/BVSzrTRN080V1clPQTvLVmyJOkhSAqUuo8WHXQwgw/cy+jAlt3uC+FwIeVMikF9FE81ktnSe8d4IeVMG33zxMDOUeor9ePOZ9OmTTQ1NSU9DAlcqfuo4dDDgj1UCJQzKQ71UTzVSGZL7x3jhZSzefWT9PHaWHNlcFTBjdPf3x9McMVf6qP8VB8pBvVRPNWoOPTeUe8d8wkpZ6n7Se7pLEoQxqFRe6os6QEEoKxMVZLZUx/lp/pIMaiP4qlGhdN7x6mpg+KFlLPUbfSleRal2ViiKXdjtbe3Jz0ESQH1UX6qjxSD+iiealQ4vXecmt47xgspZ6nZ6JuYyW7o0UdSO4vSbIxmMlQF9GlEEnbu3El1tX7Byeyoj/JTfWRPTJ6xNjM+TlnOzILNr34d+7zp6CSG5i1lLZ7eO+an947xQspZajb6JmayS/Nu9tno3TFC2141SQ/Da08++SSrVq1KehjimbjzOSa/2VQf5af6yExNNWPtyNg4VRXRRt9I95MA2uibRFmLp/eO+em9Y7yQcpaajT59OiMipZDvsJ/hxx9j6/1/ftpG4fDwEA/X7AVo74OEKd8HHUlMWjHVjLVPDg3vejPaddVl0167Mo4yOr/pvaPMJ3O60WdmFcBFwLuBGuBm4BTnXN806x8JfAlYBTwGnOWcu3luRpsuTVWVSQ/Bey0tLUkPoWiUteKa7rCfLXf9Ybc9EOWZDKMjI6nb+7Cnb6rh6W+slTP/c5bvgw5f9ojk/k3Ld+3KfNKW0cnSkrW05iwEeu8YL6SczfWevo8CbwZeDGwEvg18D3j95BXNbBVwHfA+4H+AtwHXm9lznXNPzNWA0+IPDzzIEQe/YNft2byByyfkT03vuOMOjjzyyKSHUSzK2hyYag/Er+69jyMOfkHevQ+hTfG9p2+qYfc31spZGDnz/fym3L9pe3rtyrg9hCH/PYNUZS21OfOd3jvGCylnc73R9z7gfOfcWgAzOwd41MzanXMdk9Y9AbjHOff97O1rzOzU7PLPzNmIU+KX996/K7izeQOXz1SHuhXLXPxCuPHGG4MJbgGUtRxx5+XlM9MNtIms5ctZkntL9mRDdDYXhO+66rKn3c7N2UzPl/SQcpaQ3L9peypfRgv5e+Z7f6bob5pylhC9d4wXUs7mbKPPzBqBlcA9E8ucc4+Z2QDwfGBycJ+fu27Wn7PLZRZm8wYun6kOdSuGUv5CyHXkE38vySdYe+qAr1y+R48rddZ8qlGh4q6zlM+ebqCVKmezkdSGaO6GZm7O8v1c5ir3sGdZS3POQtsTvafyZTTu79lc9ueeSsPfNL139IfeO04tpJzN5Z6+Rdn/J/9U+4H6adafat3nTvXk77roi7Ma3HwQbI0WLYn+X7u+tK9TsbD0rzETZhnn3J7MlVzSrJ3jU40KNdFDfdv27PG/uiP6V6BgszbD77Ng5XX/yFZuzvL9XOYq97CnWUtvzsrrop+J532caM7msj/3VDr+pum9Y8KCrZHeO+5mLjf6BrP/N0xa3ggMTLN+Qevu4RtjkbRS1kRKTzkTKT3lTKRIyuNXKQ7nXD+wDjhkYln2hNt64IEpHnJ/7rpZB2eXi8g0lDWR0lPOREpPORMpnrmeyOVK4Fwzu41oBqaLgTXTzKj0X8BHzOwY4MfAvwAvBN41R2MVCZmyJlJ6yplI6SlnIkUwZ3v6si4CbgTuBp4EKoDjAMzsnWa2dWJF59xjwFuATxDtlv8EcLSm3BUpiLImUnrKmUjpKWciRVCWyWSSHsOsmNn/BT4HPA8YBv7HOefvxYXmkJn9BWjPWVRBdGHTFzrn/pzMqPxjZkuArwJHEO39vhf4N+ecDgfJoaxNT1mLp5wVRjmbnnIWTzkrjHI2PeWsMCFmba4P7ywqM1tNtPv+ZKJPgcqA5yQ5Jp845542W5WZfR44SqHdzWVEM349E9hG9Ifgp2a20jkX9qciRaKs5aesFUQ5i6Gc5aecFUQ5i6Gc5aecFSy4rAW90QdcCFzunPtxzjI15RTMbAHwHqKaydM9A/i6c24zgJl9C/gI0Az0JTkwjyhrBVLWpqWcxVPOCqScTUs5i6ecFUg5yyu4rAW70WdmC4FDgd+Z2Z+JLt75v8DZzrk/JTo4Px1FNI3xfyU9EA99ETjOzK4nmu75fcAdzjkvQzvXlLUZU9amppzloZzNmHI2NeUsD+VsxpSz6QWXtbmeyKWY9iYa/zHAu4FlwM3Az82sMcFx+eoU4IfZ6Y/l6X5HdMx6D7CV6CTw9yY6Ir8oazOjrE1NOctPOZsZ5Wxqyll+ytnMKGfTCy5rIW/0TVyw82rn3APOuRGi3c+VwOHJDcs/ZrY/8Grg8qTH4hszKwduAR4h+jSrFvg88Fsza01ybB5R1gqkrE1NOSuIclYg5WxqyllBlLMCKWfTCzVrwW70Oee2AE8Ak0+WzEyxbL47BbjfOffHpAfioSZgP+BS59yAc27EOXcVUTYOS3ZoflDWZkRZm5pyFkM5mxHlbGrKWQzlbEaUs+kFmbVgz+nLugw4w8x+QLS1fRawA/h9oqPyiJlVER3C8MmEh+Il51yfmT0CvN/MPkrUP34W5DsAACAASURBVO8impHpgUQH5xdlLYayNj3lrGDKWQzlbHrKWcGUsxjKWX6hZi30jb5LiAr8K6JriNwLvD77SY5E3kJUm2uSHojHjiI6IbeD6BCPR4G3OefWJjoqvyhr8ZS1/JSzeMpZPOUsP+UsnnIWTzmLF1zWgr84u4iIiIiIiEwv2HP6REREREREJJ42+kRERERERFJMG30iIiIiIiIppo0+ERERERGRFNNGn4iIiIiISIppo09ERERERCTFQr9O37xhZt8BFjvn3ljk570aeNw5d34xn7fYzOxA4Cbgmc65bUmPR9JLWVPWpPSUM+VMSk85U85yaU/fPJYNw1HAV5IeSxzn3IPAncBZSY9FZKaUNZHSU85ESk85C5c2+ua3DwLXOucGkhyEmZWbWUUBq14NnGZm2kMtoVHWREpPORMpPeUsUPO+ACEys2rgYuAYoAG4DzjbOXdHzjr/DPwH0A7cBVwG/ADYzzn3RDYobwdOyHnMp4C3O+eeN+n1fgfc45z7UPb2icBHgFXAOuCbwFedc+PZ+88C3g3sD/QDv8iOrz97/7uBr2df/wvAAcALzKyM6JOjFxF9IPEYcKZz7rbsUG4GmoDVwC17Wj+RQilrypqUnnKmnEnpKWfKmfb0hekLwL8C7wEOBh4EbjKzpQBmthK4DvgZ8Hzga9nH5DqIKPR/yln2beAAMzt0YoGZPQs4HPhW9vZ7gQuATwHPBj4MnAu8P+d5xoEzgecCxwKHApdOev0a4JPAKcBzgA7gv4Hu7PovAM4Dhice4JwbIfol9cqY+ogUi7ImUnrKmUjpKWfznPb0BcbMFgKnASc7536WXXYqcARwOvCJ7P1rnXMTxzD/zcyeCXw+56nagQxRUABwznWZ2U1EvxDuyi5+D9EnNfdnb38SOMc59+Ps7cfN7CKi4H49+zy5x3k/YWbnAD8xsxMmPtEBKoAPOOfuyfne2oFLnHMPZxc9OkUJ1gP75quRSDEoa8qalJ5yppxJ6Slnyhlooy9E+wOVwO8mFjjnxszsD0SfekC0y/vuSY/746TbewE7c4I04T+B75rZvwEjwPHAZwHMbB9gBXCFmX0z5zELgLKJG2Z2BPDvRJ/mNBCFtApYQhQ8gFGiT15y/QdwlZmdANxKdMz4w5PWGcqOXaTUlDVlTUpPOVPOpPSUM+VMh3emTGYG6/YBVWZWO2n5z4DtwFuBNwCNRLvO4R/9cirRLvSJf88j2h0/8YnLz4C/Am8DXkj0iQ9E4Z2wwzk3lvvCzrnziH753EB0WMADZvYenq4J6J3B9ylSCsqaSOkpZyKlp5zNE9rTF57HiD5FeWn2a7In1h7GPwL2MPDmSY87dNLtiU9KnkPOsdnOuVGLruvyHmALcJ1zbkv2vqfMbD2wv3Puv6YZ3/8hCui/TQTTzAq+Poxz7u/A34GvZT8ROpnoePEJzyM65lyk1JQ1ZU1KTzlTzqT0lDPlTBt9oXHObcs29MVm1gc8Dvwb0Eo0yxLA5cBZZnYJ0S735xKd9ArZT3Scc71m9mfgZTz9hFyAq4hOsB0HXjvpvk8Dl5pZP/BzosMFDgHanHMXEoWuHDjTzK4DXkJ0Ym5eZrYXcAnwI+CJ7PfzMnIOLTCzfYE2opmYREpKWVPWpPSUM+VMSk85U85Ah3eG6lzgh0TXHrmPaDalI51z3QDOuQ6iXewG3E8U7M9kHzuc8zxXAu+c/OTOubXAb4im1P31pPuuIvok5/jsc/8WeB/RLxCccw8AZxBdCPMhok9bzi7gexoD9ga+A/wNuB74A0+/oOYxwM3Z709kLihrIqWnnImUnnI2z5VlMjM5lFdCZWZnAOcDjc65THZZDdHu/OOdc7+dtP5DwDXOuc/v9mQJsOj6Mn8HjnHO/S5ufZGkKGsipaeciZSecpYuOrwzpczsdKJZmHqJdpN/EvjORGgBnHPDZvYuohNcJx63D/AvRFPbXjGXY47RDnxeoRXfKGsipaeciZSecpZu2uhLr2cAHwOagS6iY7XPn7ySc+72SYt6iGZnOsU511fqQRbKOfcI8EjS4xCZgrImUnrKmUjpKWcppsM7RUREREREUkwTuYiIiIiIiKSYNvpERERERERSTBt9IiIiIiIiKaaNPhERERERkRTTRp+IiIiIiEiKaaNPREREREQkxbTRJyIiIiIikmLa6BMREREREUkxbfSJiIiIiIik2IKkByDFZWZHAWcBBwCLgB7gXuBy59xN2XVWA6uB851z40V+/eXAucD/AZ4P7AXs55x7YtJ67cDXgBcALcA24C/Axc65nxdzTCLFFkrOpnjcR4ELgd85515WzDGJzCUzOwb4b+CVzrnbc5a3AhuAHudc66THnA58HTgQ+Bfg0zl3jwCPAVcDX8rN7J7mTSR0c5yzfwGOIcpZC7AOuA64wDk3WPzvbv7Rnr4UMbMPAdcDfwdOAv4Z+Fz27iNyVl1NFMJS/PyfAbwd2Az8Ns96dUAf8AngDUTjHQR+ZmZvKcG4RIoisJztYmariPLWU4LxiMy1iTegr5i0/BXAdqDFzA6Y4r6NRB8wTngZcBhwNPC/wBeAf5v0uBnnTSQl5jJnZwNjwMeAI4FvAqcBvzQzba8Ugfb0pcvZwA3OuZNylv0K+M9SB8bMqp1zO4DbJz71MbOTgddOtb5z7i9Eb5hzn+NnwOPAiUSf7oj4KJicTfJN4BrgWeh3vwTOOfekmT3G1G9GfwU8O/v1wzn3vRy4wzmXMbOJZX90zo0CmNlNwEHAe4Ev5TxuT/ImErw5ztmbnHO9Obd/Y2abgO8SfYj6q6J8U/OY/vCnSxPR7vbdTOxCN7Pz+Meu9p0TgXTOlWXv/wzwJqJPNkeAB4CPOefunHiu7GFrtwFvBV4PHAVUAo2zOYzNOTdqZluA0T19DpE5EFzOzOxY4BCiQ2f0gYqkxe3A28xswcQbSqI3oNcQ7Wl4BXAlgJn9E7AU+M10T+acGzez+4my+bTlJRi7SCjmKme9U6x+d/b/tll9BwLo8M60uQs4wcw+YmbPnGadq4BvZb+e2N1+WM79bcCXgTcD7yY6FOx2Mztwiue6FCgDjs+uO2NmVm5mC8xsiZl9Cngm0bHgIr4KKmdmtnf2tc5xzm2a6eNFPHY70akChwCYWSPwPKJDMH9LtMdhwityHpPPvkTnHIlIJMmcvTL7/18LHKvkoT196XIq8GOiY6W/YGYbgV8CVzvnbgZwznWZWVd2/T/mfGpD9v6TJ742swrgJqLjsk8Gzpj0enflrr+HvgB8OPv1VuAdzrlbZ/mcIqUUWs6+CDwCfGcWzyHio4m9Ca8g+jDm5cAO4B6iPRArzWzf7IQrrwAGgPsmPUdFdk/83kT5eyHR5BMiEkkkZ2bWBpwP3OKc+1NRvpN5Tnv6UsQ59whwMNEnI58nCt3RwBoz+0Qhz2Fm/9fMbsu+kR0FdhLtfXvWFKtfX4RhfwV4EdFu/l8A/21mbyzC84qUREg5M7OXA+8CTnPOZfb0eUR85Jx7HOjiH3sXXkH0IctINqc9k+77nXNubNLTDBPlrwe4APh359wNJR+8SCCSyJmZ1QE/Ifr7eGKxvpf5Tnv6UiYbtNuz/zCzZUR7ET5tZt9wzm2e7rFmdgjwc2AN0SQr3UQzKV0F1EzxkO4ijLeL6JcJwE/N7NfAJcBPZ/vcIqUSUM6uIDrMtCt7SA5Ev/crsreHshPDiITqduD1ZlZG9IZzTc59dwCvMLNfER1OdsUUj38JME50yPUngYvM7G7n3K9LOWiRwMxZzsxsL+BGYBXRpSK6Jq8je0Z7+lLOObee6M3kAuCfYlZ/K9GnKm9xzt3gnPtjdpf63tOsX4o9B38imtxCJBge5+zZRIejbs7591KiP8CbiabDFgnZb4iy8xKic45yL6nwW6I3qBPnBU11ntE9zrm7s3sdXkeUi0s1RbzI08xJzsyskuj0if8DvME592BRv4t5Tr/UUsTMlk5z18Q1VCZmHJz4ZH+vSevVEu1x2PUm08yOAFYWa4z5ZMP/MnQSvXgssJy9aop/9xNdJ+lVRH9cRUI28Qbzo0QTHv0h5747iD6EeTvRNcXuJg/nXB/ROUTPI/pwRkQiJc9Z9j3gNUTXuz0qdzZrKQ4d3pku/2tmtxAdOvY4UE904fNTgf9xzq3LrvdQ9v8Pm9kvgLHsnoabgDOB75jZ1UTnGH0SeHImgzCziZNzX5j9//Vm1gv0Oud+k13nPKKp739H9CZ5CdGhbocCx87k9UTmWDA5m+bQmX5ggQ5fkzRwzj1sZj1E54Xf45zbmnP3vUQThL0JuM05t7OAp7wC+AjwCTP78cS5sIXkTSSt5ihn3wDeRnSu/DYze0nO+l06zHP2tKcvXT5OtFfhfOBm4IdE08R/lGi69wk/BS4D3k/0ac3dAM65NcCHiA7/+inwHqJJIB6d4Th+lP13avb2Zdnbn8lZ589En/Jcmh3rF4hO9H25c+7/zfD1ROZSSDkTmQ9uJ9r7kHvI2cS5t3/I3hc3hfzEY3YAnyW6ePRROXcpbzLflTpnr8/+//Hs8+X+m+1M8QKUZTKa0E1ERERERCSttKdPREREREQkxeb0nL7sRYgvAt5NNDX5zcAp2ZM6p1q/hejCwm8EKoG1RLP5rJ+TAYsESlkTKT3lTKT0lDOR4pjrPX0fBd4MvBhYnl32valWNLMa4FZghOiCxY3AO4lOFhWR/JQ1kdJTzkRKTzkTKYK5nr3zfcD5zrm1AGZ2DvCombU75zomrXsCUVjfnzMT0F/mbqgiQVPWREpPORMpPeVMpAjmbKPPzBqJrkN1z8Qy59xjZjYAPB+YHNxXAX8nmtb8SKAXuMI59+UpnjuzYsUKAKqqqli9ejUHHnggAG1tbVRWVtLR0UEmk6GxsZGmpiY2bNjA9u3bqaqqYvny5QwODtLb2wvA8uXLqaiooKMjGlJTUxONjY10d3czNDREdXU1bW1tDAwM0NcXHV2wYsUKysrKWLcumq29ubmZhoYG1q9fz/DwMDU1NSxbtoz+/n42bdoEQHt7O+Pj43R2dgKwePFi6uvr6erqYmRkhNraWpYsWcLmzZvZvHkzZWVltLe3Mzo6SldXNHNtS0sLdXV1dHZ2snPnThYuXEhrayubNm2iv79/12PWr1/PyMgIAK2trdTW1tLZ2cno6Ch1dXW0tLTQ19fHwMAA5eXltLe3Mzw8THd3NwBLly6lpqaGdevWMTY2Rn19PYsXL6a3t5fBwUEqKipYuXIlQ0NDbNgQXaZs2bJlVFdX09HRwfj4OA0NDTQ3N9PT08PWrVtZsGABK1asYNu2bfT09CT+89qxYwejo6Ne/LxGRkaoqakpyxuqaShryprPWSsrK2O//fbz5ue1p1lTzpQzn3OWlr9pyllxf24Pfei9jA8Ns+Sk06hdUMFTwzsYzWSoKS+nubqKjiu/TqamhoZzz1POUpizOZu908xWAOuAVc65x3OWdwAfd859f9L6twCvJrqe1TeJpnW9CTjDOXfNpHUzzrkSfwdhW7t2LatWrUp6GF7zsEZ7utGnrCXIwz7yiqf12ZM3o8pZgjztI694WCPlLGEPn3kqowNbWH7y+6e8v+uqy1hQ38ABX7kc8LKHvONhjabN2Vwe3jmY/b9h0vJGYGCa9Z90zn01e/tPZvZ9ouO6r5lifclj4tMsmV6KaqSsJShFfVQSKaqPcpagFPVRyaSkRspZglLSQyUVUo3mbCIX51w/0ac1h0wsM7NVQD3wwBQPuQ+YajekLiy4B8rLdXWOOGmpkbKWrLT0UamkpT7KWbLS0kellIYaKWfJSkMPlVpINZrriVyuBM41s9uAjcDFwBrn3BNTrPud7LqnA5cDzyOagekDczPUdOno6PBt97N3UlYjZS0hKeujoktZfZSzhKSsj0oiRTVSzhKSoh4qmZBqNNebpxcBNwJ3A08CFcBxAGb2TjPbNaVudkamNwAnE+3C/zFwnnPuh3M8ZpEQKWsipaeciZSeciZSBHM2kUspzceTcWeqv7+fxsbGpIfhNQ9rtEcTuZSSshbPwz7yiqf18Sprylk8T/vIKx7WSDlL2EwncvGwh7zjYY2mzVk4B6LKrHjWkF5SjaQY1Ef5qT5SDOqjeKqRzJZ6KF5INdJG3zyxfv36pIfgPdVIikF9lJ/qI8WgPoqnGslsqYfihVQjbfTNE8PDw0kPwXuqkRSD+ig/1UeKQX0UTzWS2VIPxQupRtromydqamqSHoL3VCMpBvVRfqqPFIP6KJ5qJLOlHooXUo200TdPLFu2LOkheE81kmJQH+Wn+kgxqI/iqUYyW+qheCHVSBt988SWLVuSHoL3VCMpBvVRfqqPFIP6KJ5qJLOlHooXUo200TdPbNy4MekheE81kmJQH+Wn+kgxqI/iqUYyW+qheCHVSBt9IiIiIiIiKaaNvnli5cqVSQ/Be6qRFIP6KD/VR4pBfRRPNZLZUg/FC6lG2uibJzKZTNJD8J5qJMWgPspP9ZFiUB/FU41kttRD8UKqkTb65onOzs6kh+A91UiKQX2Un+ojxaA+iqcayWyph+KFVKMFSQ9ARERE5rfeG69n461rpr2/+dWvY583HT2HIxIRSRdt9M0TixcvTnoI3lONpBjUR/mpPjKVjbeuYfvfHqJqadtu9410PwnwtI0+9VE81UhmSz0UL6QaaaNvnqivr096CN5TjaQY1Ef5qT4ynaqlbSw/+f27Le+66rLdlqmP4qlGMlvqoXgh1Ujn9M0TTz75ZNJD8J5qJMWgPspP9ZFiUB/FU41kttRD8UKqkTb65okdO3YkPQTvqUZSDOqj/FQfKQb1UTzVSGZLPRQvpBrN6eGdZlYBXAS8G6gBbgZOcc71TbHuauA2YFvO4gecc4eXfqTps9deeyU9BO+lqUbKWnLS1EelkKb6KGfJSVMflUpaaqScJSctPVRKIdVors/p+yjwZuDFwEbg28D3gNdPs/6Yc65ujsaWakuXLk16CN5LWY2UtYSkrI+KLmX1Uc4SkrI+KokU1Ug5S0iKeqhkQqrRXB/e+T7gYufcWufcFuAc4Egza5/jccw7/f39SQ/BeymrkbKWkJT1UdGlrD7KWUJS1kclkaIaKWcJSVEPlUxINZqzPX1m1gisBO6ZWOace8zMBoDnAx1TPKzCzDqByuzjPuacu38uxps2mzZtorGxMelheC0tNVLWkpWWPiqVtNRHOUtWWvqolNJQI+UsWWnooVILqUZzeXjnouz/WyYt7wemmu/0YeAFwF+AOuBc4FdmdqBzbv3klU8//XQAqqqqWL16NQceeCAAbW1tVFZW0tHRQSaTobGxkaamJjZs2MD27dupqqpi+fLlDA4O0tvbC8Dy5cupqKigoyP6XdLU1ERjYyPd3d0MDQ1RXV1NW1sbAwMD9PVFh5SvWLGCsrIy1q1bB0BzczMNDQ2sX7+e4eFhampqWLZsGf39/WzatAmA9vZ2xsfH6ezsBKJrfdTX19PV1cXIyAi1tbUsWbKEzZs3s3nzZsrKymhvb2d0dJSuri4AWlpaqKuro7Ozk507d7Jw4UJaW1vZtGkT/f39ux6zc+dO1q5dC0Brayu1tbV0dnYyOjpKXV0dLS0t9PX1MTAwQHl5Oe3t7QwPD9Pd3Q1Eu69rampYt24dY2Nj1NfXs3jxYnp7exkcHKSiooKVK1cyNDTEhg0bAFi2bBnV1dV0dHQwPj5OQ0MDzc3N9PT0sHXrVhYsWMCKFSvYtm0bPT09if+8AG9+XiMjI9TU1EwRi4Ioa8qat1kbGhoCUpE15ayIP7fh4SHGx8bZPjpG7YIKnhrewWgmQ015dEDS6GiUK+Vs3v1NU87mKGfN1VWMjY8zOjzE448/rpylMGdlmUxm2juLKftpzWbgYOfcfTnLtwDHO+dcAc/xd+Ai59y3Ji3PFPDweW1sbIyKioqkh+E1D2tUticPUtaS5WEfecXT+sw4a8pZcT185qmMDmyZ9jp9C+obOOArl+9a5mkfecXDGilnCcuXM9g9ax72kHc8rNG0OSv4nD4z28/MXmVmbzCzF5nZjD6ycc71A+uAQ3KecxXRJzUPFPg04+zhG+H5bmxsLOkheM+XGm3YsIEHHngAZS1MvvSRr3ypz0TO/vSnP6GchceXPvKZDzVSzsLmQw/5LqQa5T2808z2BU4DjgHaeHpoRszst8CVwLXOufECXu9K4Fwzu41oBqaLgTXOuSemeO0jiIK+FqgFzgZagTUFvI5M0tXVxapVq5IehteSrNFTTz3FL37xC26//XY2bdpEdg/8T7N3K2sBUdby8zBnAH9EOQuKchYvqRopZ+mhnMULqUbT7ukzs68B9wOrgI8DzwEagCpgCfAG4A7gs8ADZvaiAl7vIuBG4G7gSaACOC77eu80s6056z4fuBUYJArvS4DXOOc6Z/D9iXjvyiuv5IwzzmDDhg0cf/zxfP3rX+cHP/gBKGsiRTNdzq677jpQzkSKQjkT8de05/SZ2ReAL0x18csp1n0DUOuc+3GRx1eQ+Xhc9kwNDg6yaNGi+BXnsaRqdPXVV/PWt76V+vrdzknf7XAUZc1/ylp+HuYMJmVNOZt7Mz2nTzmLl0SNlDO/zfScPuUsnoc1mvZQ5mkP73TOnVPoszvnfj7TEcnc8qwhvZRUjU488cSC11XW/Kes5aecSTEoZ/GSqJFyli7KWbyQajTjSzaY2WLgxUS71+9yzm0o+qik6Lq6uli+fHnSw/CabzVS1sLkWx/5xrf6DAwMcNxxx/0zyllQfOsjH/lUI+UsTD71kK9CqlHBs3cCmNmbgb8DnwbOB9aa2XGlGJgU18jISNJD8J5PNbrzzjtBWQuST33kI5/qc+edd3LKKaeAchYcn/rIV77USDkLly895LOQapR3o8/Mqict+izwUufcoc65FxDN6nlRqQYnxVNbW5v0ELyXZI127tz5tNvXXHMNKGtBUtby8y1nF198McpZeJSzeEnVSDlLD+UsXkg1itvTd5+ZvSrndgYYzbldyFS74oElS5YkPQTvJVmjM844gwce+Mclh8rKykBZC5Kylp9vOZt0UV3lLBDKWbykaqScpYdyFi+kGsWd03cscKWZPQh8GPgU8EczewSoBJ4FnFraIUoxbNq0iaampqSH4bUka/ThD3+Yb3zjG7S3t3PSSSdx7LHHcsEFFyhrAVLW8vMtZ2effTbbtm37I8pZUJSzeEnVSDlLD+UsXkg1yrunzzl3L9FEEn8B/gTsBTwT+BzwGeCfnHPfK/UgZfb6+/uTHoL3kqzR/vvvzyWXXMLKlSs566yzJo4RV9YCpKzl51vOLr/8clDOgqOcxUuqRspZeihn8UKqUezsnc65ceBLZnYdcBnwLuBU59y6Ug9Oiid7uKDkkXSNysvLOfrooznssMMm/kB+F2UtOEn3ke+Srs/knN12220A9ytnYUm6j0KQZI2Us3RQzuKFVKPY2TvN7Llm9lag0jn3euAa4NdmdoaZhfOdznPt7e1JD8F7Sddo3bp1/P73v2dsbIzzzjsPlLUgJd1Hvku6PpNz9spXvhKUs+Ak3UchSLJGylk6KGfxQqpR3OydZwF3Ax8B/mBm73XOXQO8CDgYuNPMDir9MGW2Js+mJbtLskY33HADZ511Ftdddx3nnHMOa9asQVkLk7KWn285W716NShnwVHO4iVVI+UsPZSzeCHVKG5P3znAPzvnXgIcApwF4Jzb6Jx7N/Bx4IclHaEUxZNPPpn0ELyXZI2uu+46PvWpT3HJJZfw5S9/mZ/85CeAshYiZS0/5UyKQTmLl1SNlLP0UM7ihVSjuI2+Mv4xte5Y9vYuzrlbiD61EZFZyGQylJdHcSwvLyeTyTztfmVNZPaUM5HSU85E/BQ3kcsXgZ+b2f1EMwl+bPIKzrnhUgxMiqulpSXpIXgvyRq95S1v4TOf+Qz77bcfTz75JMcff/xu6yhrYVDW8lPOpBiUs3hJ1Ug5Sw/lLF5INcq70eecu8TM1gAHAA865x6em2FJsS1cuDDpIXgvyRodffTRHHzwwXR1dbHvvvuyfPnyxMYis6Os5aecSTEoZ/GSqpFylh7KWbyQalTIJRseBB6cg7FICXV2drJy5cqkh+G1pGu07777su+++yb2+lIcSfeR75Kuj3KWDkn3UQiSrJFylg7KWbyQajTtOX1m9gkzK2jz1cxeamZvKt6wpNhGR0eTHoL3kqrRD3/4Q4aHCzvSRVnzn7KWn3ImxaCcxUuiRspZuihn8UKqUb49ffsD68zsWuBG4E/OuW4AM6sBngO8DDgOWEx00fa8zKwCuAh4N1AD3Ayc4pzri3ncaUQXhv+kc+5zca8ju6urq0t6CN5LqkYbNmzgpJNO4rDDDuPQQw/lGc94Bk1NTYCyFiJlLT/lTDkrBuUsXhI1Us7SRTmLF1KNpt3oc86daGYHAh8A/guoN7MMsBOoIprJ88/AlcB3nXM7Cni9jwJvBl4MbAS+DXwPeP10DzCzduDD6BDTWQnpRNOkJFWjM844gyeeeIKf/exnfPnLX2ZoaAiA8fHxIZS14Chr+fmWswULFgBsQzkLinIWL4kaKWfpopzFC6lGcRO5PAickv205CCgHdgL6APui/uUZQrvA853zq0FMLNzgEfNrN051zHNY75FdE2X02b4WpJj48aNNDc3Jz0MryVZo3333ZfTTz+d0047jSeeeIKenh4uuOCCE1HWgqOs5edbzkZGRrjkkkteh3IWFOUsXlI1Us7SQzmLF1KNYidyAXDOjQP3Zf/tETNrBFYC9+Q872NmNgA8H9gtuGZ2CrDNOffD7Ian7KEtW7YE05RJ8aFG5eXlrFq1ilWrVuGc+3978hzKWrJ86COf+VCf3JwBvOIVr7hlps+hnCXLhz7yXdI1Us7Cl3QPhSCkGhW00Vcki7L/b5m0vB+on7yyma0EPgG8pJAnP/300wGoLEQLDQAAIABJREFUqqpi9erVHHjggQC0tbVRWVlJR0cHmUyGxsZGmpqa2LBhA9u3b6eqqorly5czODhIb28vAMuXL6eiooKOjuh3SVNTE42NjXR3dzM0NER1dTVtbW0MDAzQ1xd9YLVixQrKyspYt24dAM3NzTQ0NLB+/XqGh4epqalh2bJl9Pf3s2nTJgDa29sZHx+ns7MTgMWLF1NfX09XVxcjIyPU1tayZMkSNm/ezObNmykrK6O9vZ3R0VG6urqAaLdyXV0dnZ2d7Ny5k4ULF9La2sqmTZvo7+/f9ZixsTHWrl0LQGtrK7W1tXR2djI6OkpdXR0tLS309fUxMDBAeXk57e3tDA8P093dDcDSpUupqalh3bp1jI2NUV9fz+LFi+nt7WVwcJCKigpWrlzJ0NAQGzZsAGDZsmVUV1fT0dHB+Pg4DQ0NNDc309PTw9atW1mwYAErVqxg27Zt9PT0JP7zKi8v9+bnNTIyQk1NTSGtPxVlTVnzNms7dkRHc/ny85pF1pSzIv7choeHGB8bZ/voGLULKnhqeAejmQw12Yt8j47uZO3atcrZ/PubppzNUc6aq6sYGx9ndHiIxx9/XDlLYc7KMplMIbmYteynNZuBg51z9+Us3wIc75xzk9a/GbjWOXdF9vavgVumOhnXzDKTHi6TZDIZysrKkh6G1zys0R4NRllLlod95BVP6zPjASlnxfXwmacyOrCF5Se/f7f7uq66jAX1DRzwlct3LfO0j7ziYY2Us4TlyxnsnjUPe8g7HtZo2sFMe8mGYnPO9QPrgEMmlpnZKqJPah6Y4iGvAS4wsz4z6wNeCvy7mf12LsabNhOfrsv00lIjZS1ZaemjUklLfZSzZKWlj0opDTVSzpKVhh4qtZBqNJeHd0I0W9O5ZnYb0QxMFwNrnHNPTLHuikm3fwT8FvhSSUeYUuvXr991XL1MLWU1UtYSkrI+KrqU1Uc5S0jK+qgkUlQj5SwhKeqhkgmpRgVt9JnZUcCNzrmxWb7eRcDewN1ANfBLomu1YGbvBK5wztUBOOe6Jo1hBzDgnHtqlmMQ8dadd97Ji170IioqKmb7VMqayDSUM5HSU85E/FLQOX1mtg0YBL4LfMs590ipBzYT8/G47Jnavn07tbW1SQ/Daz7U6G1vext77bUXRxxxBNddd92zlLXw+NBHPvOhPrk5e81rXkNbW5tXJ2TMx5zN9Jw+H/rId0nXSDnzz0zP6Uu6h0LgYY2mzVmhh3cuAY4FTgTONrM/EF0D5X+cc9tmPz4ptb322ivpIXjPhxp997vf5fbbb+eWW24B+KuyFh4f+shnPtQnN2c33HAD4+Pjd6CcBcWHPvJd0jVSzsKXdA+FIKQaFTSRi3Nu0Dl3hXPuJUQXaf8jcCHQbWb/aWYFTY0ryZmYXlam50ONamtrOfLII7nkkktAWQuSD33kMx/qk5uzr33ta6CcBceHPvJd0jVSzsKXdA+FIKQazXj2TufcX4AvE51YWwX8K/BbM/ujmR1U5PFJkYyNzfZ0zPTzrUbKWph86yPf+FaflStXgnIWHN/6yEc+1Ug5C5NPPeSrkGpU8EafmVWa2dvN7CbgceAI4FSgFWgH/gr8sCSjlFlbtGhR/ErznC81Gh0d5Y477kBZC5MvfeQrX+ozkbNPf/rToJwFx5c+8pkPNVLOwuZDD/kupBoVOnvnpcAxQAb4HnCWc+6hnFWGzOyjwPriD1GKYZ999kl6CN7zoUZXXHEFt99++8SFPh9CWQuOD33kMx/qk5uzV73qVdx7773PV87C4kMf+S7pGiln4Uu6h0IQUo0KncjlOcAHgOuccyPTrNMHvKooo5Ki6+vrY/HixUkPw2s+1Kizs5NTTjmFww47jMrKyrOmWU1Z85gPfeQzH+ozKWecdNJJD02xmnLmMR/6yHdJ10g5C1/SPRSCkGpU6EbfZ4DfO+dGcxea2QLgcOfc7dn7flPsAUpxDAwMBNOUSfGhRu94xzt49rOfvdt1jZS1cPjQRz7zoT7KWfh86CPfJV0j5Sx8SfdQCEKqUaHn9N0GNE2xvCF7n3iuCBdHTT0favSJT3yCwcHBqe5S1gLhQx/5zIf6KGfh86GPfJd0jZSz8CXdQyEIqUaFbvSVEZ3PN1kzoGutBCA7c5bk4UONMpnMxPl8kylrgfChj3zmQ32Us/D50Ee+S7pGyln4ku6hEIRUo7yHd5qZy36ZAb5vZjty7q4Angf8vkRjkyIaHh4O6gKSSUiyRp/73OcAKCsr4z/+4z+orKzkrrvumsifshYQZS0/33IGTGRNOQuIchYvqRopZ+mhnMULqUZxe/o2Zv+VAZtzbm8EuoDLgeNKOUApju7u7qSH4L0ka7Ro0SIWLVpEJpOhrq5uYgpgZS1Aylp+vuUsJ2vKWUCUs3hJ1Ug5Sw/lLF5INcq7p885dyKAmT0BXOKc0+54kRI444wzAGhpaeHoo4+mpqaGM84448SEhyWSKlPlLLtcWRMpEuVMxE8Fzd7pnPtMqQcipbV06dKkh+A9H2p0zDHHJD0EmSUf+shnPtRHOQufD33ku6RrpJyFL+keCkFINZp2o8/MHgBe6ZzbbGYPMvVELgA45w4qxeCkeCY+aZPpJVWjD37wg1x44YXU1dXxwQ9+cNfyjo6OByavq6z5T1nLz7ecwe5ZU85Ko/fG69l465op7xt69BEqW1oLfi7lLF4SNVLO0kU5ixdSjfLt6bsWmJi45cdzMBYpoY6ODvbdd9+kh+G1pGp0+OGH7zrR/fDDD88dz7VzPhiZNWUtP99ylh2TsjYHNt66hu1/e4iqpW273VfZ0sqigw4u+LmUs3hJ1Eg5SxflLF5INZp2oy/3kE4d3hm+8fHxpIfgvaRqlHsIzKSvlbsAKWv5+Zaz7G1lbY5ULW1j+cnvn/XzKGfxkqiRcpYuylm8kGpU0Dl9xWJmFcBFwLuBGuBm4BTnXN8U674c+CqwL9EUv48Bn3POXTdX402T+vr6pIfgvTTVSFlLTpr6qBTSVB/lLDlp6qNSSUuNlLPkpKWHSimkGuU7py/veXy5ZnBc9keBNwMvJpq699vA94DXT7Hu34CjgXXZ2y8HbjKzFzrn/lrg60nW4sWLkx6C95Kq0eTzHibM8pw+ZS0hylp+vuUMZnWukXKWEOUsXhI1Us7SRTmLF1KN8u3pK8V5fO8DznfOrQUws3OAR82s3TnXkbuic65n4mszKwfGia4r+AxAwZ2hnp4eWlpakh6G15Kq0eTzHibM8vwHZS0hylp+vuUMZpU15Swhylm8JGqknKWLchYvpBoVdE5fMZhZI7ASuCfnNR4zswHg+UDHNI/rBxZmx3o70W59maGtW7cG05RJSapG001rvafnPyhryVLW8vMtZ9n7Zpw15SxZylm8JGqknKWLchYvpBrN5Tl9i7L/b5m0vB+Y9oBY51yjmVUT7cZ/FjA61Xqnn346AFVVVaxevZoDDzwQgLa2NiorK+no6CCTydDY2EhTUxMbNmxg+/btVFVVsXz5cgYHB+nt7QVg+fLlVFRU0NER/S5pamqisbGR7u5uhoaGqK6upq2tjYGBAfr6okPKV6xYQVlZGevWRUcUNDc309DQwPr16xkeHqampoZly5bR39/Ppk2bAGhvb2d8fJzOzk4g2kVcX19PV1cXIyMj1NbWsmTJEjZv3szmzZspKyujvb2d0dFRurq6gOjip3V1dXR2drJz504WLlxIa2srmzZtor+/f9djMpkMa9euBaC1tZXa2lo6OzsZHR2lrq6OlpYW+vr6GBgYoLy8nPb2doaHh+nu7gai65DU1NSwbt06xsbGqK+vZ/HixfT29jI4OEhFRQUrV65kaGiIDRs2ALBs2TKqq6vp6OhgfHychoYGmpub6enpYevWrSxYsIAVK1awbds2enp6Ev95LViwwJuf18jIyGymAVbWlDVvszYyMgLgzc9rFllTzmb4cxsZGaEcGNg5yuBo9G0vralmLJOhZ0fUF3tXVlK7oIKnhncwmslQU14OwOjoTtauXauczb+/acpZEX9uw8NDjI+Ns310bLecNVdXMTY+zujwEI8//rhylsKclWUyU5+2V+zr9GU/rdkMHOycuy9n+RbgeOecK+A5fg78xDl3xaTlmQIePq9lMhnKysqSHobXkqpRnuv0PTh5XWXNf8pafr7lDHbPmnJWGg+feSqjA1tmPHtn11WXsaC+gQO+cvmuZcpZvCRqpJz5LS6Dk7OmnMXzsEbTDmbOrtPnnOs3s3XAIcB9AGa2iuiTmt0mrJjGAuCfZjuW+Wj79u0sXLgw6WF4LakaFfs6fcpaspS1/HzLGexZ1pSzZCln8ZKokXKWLspZvJBqNNfX6bsSONfMbiOageliYI1z7onJK5rZW4FHiE68XQAcDxwBfLFIY5lXnnrqKVatWpX0MLyWVI1KdJ0+ZS0hylp+vuUse3tPs6acJUQ5i5dEjZSzdFHO4oVUoxmd02dm+wPPzt78q3PusRm+3kXA3sDdQDXwS+C47HO/E7jCOVeXXXdpdv2lwAjRNLzHOOd+OcPXFAlOd3c3p5xyyhuzN5U1kRLo7u6mq6uLz372s29EORMpCeVMxA/TntOXy8yagW8BRjT9LUTHjP4UeI9zbmPJRliA+Xhc9kxNnGAq0/OhRgMDA1x66aXcddddZDKZsexiZS0gPvSRz3yoT27OysrKGB8fH0M5K7lintPnQx/5LukaKWf+mek5fUn3UAg8rNG05/SVF/gEVxFd4+TlQE323yuA/YD/nO3opPSqqqqSHoL3fKjR17/+dbq7u7nwwgtBWQuSD33kMx/qk5uza6+9FpSz4PjQR75LukbKWfiS7qEQhFSjQjf6Xge81zn3O+fcaPbf74BTsveJ5yampJXp+VCjP//5z3zgAx/gOc95DspamHzoI5/5UJ/cnFVUVKCchceHPvJd0jVSzsKXdA+FIKQaFbrR1wtsm2L5dqKTasVzhRzGO9/5UKOGhgaqq6unuktZC4QPfeQzH+qjnIXPhz7yXdI1Us7Cl3QPhSCkGhW60Xc+8BUza5tYkP36S9n7xHONjY1JD8F7PtToX//1X7nqqqvYuPEffw+VtbD40Ec+86E+yln4fOgj3yVdI+UsfEn3UAhCqtG0s3dOcUH2/YAnzOzJ7O02YBhoITrnTzzW1NSU9BC8l1SNJl/Atqenh5NPPpmxsbEnsouUtYAoa/n5lrPm5mZ6enqeQDkLinIWL4kaKWfpopzFC6lG+S7ZMOsLsos/nnrqKVpbW5MehteSqtHkC9hO+MEPfnD1HA9FikBZy8+3nIGyFiLlLF4SNVLO0kU5ixdSjQq6OLuEb9u2qU7JlFxJ1WjyBWxzliuDAVLW8vMtZ9n7lLXAKGfxkqiRcpYuylm8kGpU6Dl9ErjKysqkh+A91UiKQX2Un+ojxaA+iqcayWyph+KFVKN8h3fuYmZVwMeBY4CVwNO+Q+dcRfGHJsW0YsWKpIfgPR9qtHPnTn70ox9x++23s379+mGUteD40Ec+86E+uTnr7e1l586dY7n3K2f+86GPfJd0jZSz8CXdQyEIqUaF7un7LHAC0YxL48BHgG8QTbn7/tIMTYpp69atSQ/Bez7U6JprruFXv/oVRx11FChrQfKhj3zmQ31yc1ZWVgbKWXB86CPfJV0j5Sx8SfdQCEKqUaEbfW8HTnXOXQGMAT9xzn0I+DTwmlINToqnp6cn6SF4z4ca3XHHHZx22mkceeSRoKwFyYc+8pkP9cnNWUVFBShnwfGhj3yXdI2Us/Al3UMhCKlGhW70tQIPZb/eCkxclOIm4LXFHpTIfNXf38/KlSsnbiprIiWQm7OamhpQzkSKTjkT8UuhG33rgGXZrx8FXpf9+jBgqNiDkuJbvnx50kPwng812meffXIvZKusBciHPvKZD/XJzdnSpUtBOQuOD33ku6RrpJyFL+keCkFINSp0o+964NXZr78KfMbMHge+gy6uGYQFCwqas2de86FGL3nJS3jggQcmbiprAfKhj3zmQ31yc/amN70JlLPg+NBHvku6RspZ+JLuoRCEVKOCRuqc+/ecr39sZl3A4cAjzrmflmpwUjwdHR3st99+SQ/Daz7U6IQTTtj1tbIWJh/6yGc+1Cc3Zy996UsBXo5yFhQf+sh3SddIOQtf0j0UgpBqtEebp865O4E7izwWKaFMJpP0ELznY42UtfD42Ec+8bE+yll4fOwj3/hWI+UsPL71kI9CqlHBG31mdghwJvCc7KK/Al92zv15Bs9RAVwEvBuoAW4GTnHO9U2x7huAs4GDgArgf4GPOed+W+jryT/svffeSQ/Be77U6LHHHsM5x2233fan7CJlLSC+9JGvfKnPRM46Ozt59NFH/4RyFhRf+shnPtRIOQubDz3ku5BqVNA5fWb2TuBuYCnw8+y/VuAuMztuBq/3UeDNwIuBiTMfvzfNunsDlwLPAPYB/hv4hZmFcxVEj4TUlEnxoUa//vWv+fCHP8ymTZtAWQuSD33kMx/qk5uzF77whaCcBceHPvJd0jVSzsKXdA+FIKQaFbqn7/PAJ51zF+QuNLN/Bz4HfL/A53kfcL5zbm328ecAj5pZu3OuI3dF59w1kx77TTP7NPAioLPA15OsDRs2sGTJkqSH4TUfavT973+fY489lre//e0An5pYrqwlp/fG69l465pp729+9evY501H77rtQx/5zIf6TMoZ73znOz8FyllIfOgj3yVdI+UsfEn3UAhCqlGhs3fuA/zPFMt/BLQU8gRm1gisBO6ZWOacewwYAJ5fwOMPBBYDDxbyevJ027dvT3oI3vOhRlu2bOFlL3vZVHcpawnZeOsatv/tIUYHtuz2b/vfHtptg9CHPvKZD/VRzsLnQx/5LukaKWfhS7qHQhBSjQrd03cbsJroumG5VgO/KfA5FmX/3zJpeT9Qn++BZtYCXAtc4pz7+1TrnH766QBUVVWxevVqDjzwQADa2tqorKyko6ODTCZDY2MjTU1NbNiwge3bt1NVVcXy5csZHBykt7cXiK65UVFRQUdH9AFSU1MTjY2NdHd3MzQ0RHV1NW1tbQwMDNDXFx1SvmLFCsrKyli3bh0Azc3NNDQ0sH79eoaHh6mpqWHZsmX09/dPHLpHe3s74+PjdHZGHz4tXryY+vp6urq6GBkZoba2liVLlrB582Y2b95MWVkZ7e3tjI6O0tXVBUBLSwt1dXV0dnayc+dOFi5cSGtrK5s2baK/v3/XY8rKyli7di0Ara2t1NbW0tnZyejoKHV1dbS0tNDX18fAwADl5eW0t7czPDxMd3c3EF1jp6amhnXr1jE2NkZ9fT2LFy+mt7eXwcFBKioqWLlyJUNDQ2zYsAGAZcuWUV1dTUdHB+Pj4zQ0NNDc3ExPTw9bt25lwYIFrFixgm3bttHT05P4z6uqqirxn9f+++/Pgw8+SFNT08TFbCesRlkD5j5rw8ND0LKUpnefQu2CCp4a3sFoJkNNeTl87ypGR3eydu1aZa3An9fo6ChAolnbf//9+fWvf8073vEORkZGcrO2GuUMKE3ORkZGKAcGdo4ymO2DpTXVjGUy9OwYAWDvysrdcwbKWYB/05QzP/+ejY+Ns310bLecNVdXMTY+zujwEI8//rhyFkjOct/rT8rZbsqmm3XGzN6Sc3MpcB5ReCZmXnoJ8BbgPOfcZdO+wj+erxHYDBzsnLsvZ/kW4HjnnJvmccuAXxJteH7QObfbgM0sM83DRbz3+9//ftfXmzdv5gc/+AGHH344N91004nZxcpagh4+81RGB7aw/OT373Zf11WXsaC+gQO+cnkCI5OZmC5nz3rWs/jqV796IspZyeXLUj7KWTiUM7/FZVBZS4Wy6e7It6fvx1Mse1/2X65LgdjgOuf6zWwdcAhwH4CZrSL6pOaBqR5jZvsCtwLXO+fOjnuNtJnpuUT5DAwMUF+f90OxeS+pGl188cW7LVuzZg3AtyctVtYCoKzl51vOpsiachYA5SxeEjVSztJFOYsXUo2m3ehzzhV6vt9MXAmca2a3ARuBi4E1zrknJq9oZgcAtwDfcc59ogRj8d7EuURVS9t2u2+k+0mAgjf6+vr6gmnKpCRVo5/85CfT3TWbDCprCVHW8vMwZ7DnWVPOEqKcxUuiRspZuihn8UKq0R5dnH0WLiKaTvduoJpo1/txsOuyEFc45+qy654LtAFnmtmZOc9xyhSzM6VW1dK2aQ8rE8lDWRMpPeVMpPSUM5EimPacvsnM7J+JwvQcIAM8BFzsnPt56YZXmLQelx13LtHOnqfY6xnPnPbxuYd/7ty5k8rKypKNNQ18qdHdd9/Ntddey0MPPbQRZS1xMz2nz5c+8pUv9ZnIWWdnJ4ODg30oZyVXzHP6fOkjn/lQI+XMLzM9p8+HHvKdhzWa9py+Qi/OfjJwPfAY0YbfR4HHgevN7D3FGKHMzKKDDqaypXXKaeSnmkq+vLwUR+umiw81uvnmm7nwwgtZunQpKGtB8qGPfOZDfXJzdsIJJ4ByFhwf+sh3SddIOQtf0j0UgpBqVOjhnecCZznnvp6z7Ftmdg9RiCdPOCEl1nDoYTQceti0908+/LOjo4NVq1aVelhB86FG1157Le95z3t44xvfyBlnnPGt7GJlLSA+9JHPfKhPbs4AXvva134L5SwoPvSR75KukXIWvqR7KAQh1ajQjb6VwE1TLP8FcEnxhiMyv/X29nLIIYdMdZey5qmhRx/h4TNP3XV7eHiIh2v2AmY2w67MHeVMpPSUMxG/FLpPch3wmimWvxboKN5wpFSampqSHoL3fKjRPvvsw3333TfVXcqah6Y6zLpseMeUh1hLRDmTYvChj3yXdI2Us/Al3UMhCKlGhe7puwS41MwOASauvPlS4Hjgg6UYmBRXY2Nj0kPwng81Ovroo7nyyitZu3YtN99888TF2ZU1T+U7zFoz7E7Nt5wdcMABfO1rXzsR5SwoPvSR75KukXIWvqR7KAQh1aigjT7n3BVm1gN8GHhLdvFfgbc75/JelEX8sH79epYtW5b0MLzmQ42OPPJIGhoauOGGG+Afh78oawHp2zHC4uqqpIfhLd9y9oc//AGirClnAfGhj3yXdI2Us/Al3UMhCKlGsRt9ZraAaFf87c6560s/JCmF4eHhpIfgvaRrNDY2xr333stzn/tcDjvsMIDmRAcke2TH+HjSQ/CahzkDZS04SfdRCJKskXKWDspZvJBqFLvR55wbNbPrgAOAjaUfkpRCTU1N0kPwXtI1qqio4MILL+Syyy6jvr4+0bHMJ703Xp/33LuhRx+hsqW14OerCmj65iQoZ1IMSfdRCJKskXKWDspZvJBqVOi7k/uBZ5RyIFJaoex6TpIPNdpvv/3o7u5OehjzysZb17D9bw9Ne83LypZWFh10cMHPt48O7cxLOZNi8KGPfJd0jZSz8CXdQyEIqUaFTuRyHvAlM/s0cA+wLfdO59ymIo9LimzLli00NDQkPQyv+VCjY445hm9/+9sce+yxXHDBBStQ1uZE1dI2lp/8/qI819bRUeoWFPqrdf7xLWf7778/J5100tOmX1PO/OdDH/ku6RopZ+FLuodCEFKNCn1n8rPs/9cBmZzlZdnbFcUc1HyS79CymR5Wls/GjRuDacqk+FCj888/H4ALL7wQ4Imcu5S1QGzZqY2+fHzLWVlZGUBv9i7lLBA+9JHvkq6Rcha+pHsoBCHVqNB3Jkfw9I09KZKJQ8uqlrbtdt9MDyuT8H3uc5+b+OPIxz72sSMSHo5IKuXmDJQ1kVJQzkT8UuglG35d4nHMa8U8tGw6K1euLOnzp4EPNTrwwAN3fe2c+02CQ5E9tKSmOukheM23nIGyFiIf+sh3SddIOQtf0j0UgpBqlHejz8xqgS8CRwGVwC3Ah5xzfXMwNimiTEY7auMkWaMdO3Zw9dVXc+eddzI2Nvb/27v3MLnq+o7j793NXhKyybIJSciFFfChVCgQpVis2jzYVqztT6RqH1QuUgwRShMoN/uIFVoqKOXx1ggaURHwUgv2y4MVFG25lIpyiy0NIYRcNiQkS7K7IdnN7OxO/zgzyWQye87O7Jw5l/m8nmefZM6eM/Pb35zPOed3zu/8DieffDKPPPLIbGUteXI5vM5LUlbccrZ06VKNLphA2qcFi6qOlLP0UM6CJamOgkbvvB64AO+evu8BfwR8NeQySQg2b94cdRFiL8o6uueee3j44Yc59dRTecc73sGzzz4LyloivbpvX9RFiLW45WzlypWRlUeqp31asKjqSDlLD+UsWJLqKKh759nAX5rZ9wCcc3cBjzvnWsxsNPTSiTSIJ554gssuu4x3vvOdACxZsoQrr7zyLGVNpHbK5eyaa65BOROpHeVMJJ6CGn2LgEcLL8zsSedcFpgPVNy0dc61ADfhXT3sAB4CLi7Xhc05twBYCZwCHAWca2Z3VfqZ4pk9e3bURYi9KOuor6+PE044Yf/r4447DqCqrCln0ZrZqpE7/cQtZy0tLYyOjmqfljDapwWLqo6Us/RQzoIlqY6Cune2AJmSaVkmPupnqWuB9wFvBRbmp31nnHnH8IL9YaC3ys+TPPWlDxZlHY2NjTHl0GH+q82achYhPa7BX9xy1tLSAtqnJY72acGiqqMk5WzNimX7f3bcf1+VxUsv5SxYkuooKIBNwF3OueKbVDqArzvn9hYmmJmb4OctBW4ws/UAzrmrgXXOuR4z21g8o5ltBf45P5+6A0zSli1bWLDg0MdCyAFR1lEul+PWW2+ltbW1eHK1WVPOIrR9eB9zNILnuOKWs0wmA9qnJY72acGiqqMk5Sw7OOCVb+sWAI74s/dPsEiNQTkLlqQ6CrrS923gFeC1op+78C7PF08L5JzrwrvU/lRhmpm9BAwCJ1dacKnMPg0uESjKOjrjjDPo7u6ms7Nz/w9VZE05i95IgkbyikJ1VvfSAAAUXklEQVTccrZkyRLQPi1xtE8LFlUdJSlnCy+6hIUXXVL2WcminE1EkurI90qfmX2shp/Vmf93oGR6PzDpa6OXXnopAG1tbSxZsmT/82EWLFhAa2srGzduJJfL0dXVRXd3N9u2bWPv3r20tbWxcOFCdu/ezY4dOwBYuHAhLS0tbNzonUDq7u6mq6uLrVu3MjQ0RHt7OwsWLGBwcJC+Pq9L+aJFi2hqamLTpk0AzJo1i5kzZ/LKK68wPDxMR0cH8+fPp7+/n507dwLQ09NDLpcjMzrGlqFhulpbOWxKC9uH9zGSy9He3Mzs9jYGR7LszmYBOLKjndFcju37vF63h7e2Mm1KC68O7yOby9HR3Mys9jZGx8bIDg/x8ssv09PTw5QpU1i/fj0Ac+fOZdq0aWzevJlsNsv06dOZM2cOfX19DA4O0tzcTE9PD8PDw2zdutX73COPpKOjg02bNjE6OsqMGTOYPXs2O3bsYPfu3bS0tHDUUUcxNDTEtm3bAJg/fz7t7e1s3LiRsbExZs6cyaxZs9i+fTuvv/46U6ZMYdGiRezZs4ft27dH/n1NnTo18PsaGxvbP1LT7NmzmTFjBr29vWQyGaZNm8a8efPYtWsXu3btoqmpiZ6eHrLZLL29Xi+TOXPmMH36dDZv3szIyAiHHXYYc+fO5dxzz6W/v3//MplMhuXLl1eTv1BzBsnN2njf3fDwELmxMYCqsjYwMsLr2VGa8su0AFuGhsmMjtE8Okoul1PWir6vwvDWUWRt+fLl7Ny5MxFZS1vOMpkMmUyGZirPGUA2O8L69ev3f2/ap8V3n5aknH3yjm8D8N5t2zg8M7J/nUpyzvy+t+HhIcZGx9ibHdWxY8JzNnfu3LI56+joGHd9b6rX8yXyZ2t2AYvN7Nmi6QN4N9qaz7IbgE+NdzOucy7ns3isrVmxjOzgQM0fzt67aiVTZszk+C/cVtP3lbqq+GlvYeYsP08is7bj/vt47eEHy/5uaN1aWufMVQYbW6yyltScBal2f6cspUbscnbntVcBjbOOBWWwUeoh5cbNWVD3zpoxs35gE/DmwjTn3DF4Z2pW16scjaq/vz/qIsReGupIOSvvtYcfZO8Lz5MdHDjkp3XOXDpPWlyzz9o9kq3Ze6VRGnIGylrU0rIehSkNdaScRSsN61DYklRH9R5m7mvANc65X+D1574ZeNDMNpSb2TlXuEbZBLTmX2fNTEdVFdq5cyddXV1RFyPWUlRHDZmzoKt5bUcuqPnVvHIGs1k69diGcaUoZ9CgWYuDlK1HoUhRHSlnEUnROhSaJNVRvY9MbgIOB34FtAM/BT4K4Jz7CHC7mU0vmn+o6P935H+uBz5Tj8IW8zugBJj1rndr1CeJi8TmbDIKV/PK3ZBf66t5InkNmTWJn3LHKMPDQ6zpmAok/hhFOROpgbo2+sxsFLgy/1P6u7uBu0umVdz/Oyx+B5R+Q/0GNRYL9xOFraenJ/TPSLq01FGSczZZ9bqa5+dIPa7BV1pyBo2dtailaT2qhXLHKFNyObKZTOIfR6CcRUc5C5akOlIfpAqMd0DZu2rluMv4NRYh3CsQQ+vWsmbFMgByuTGamg7cwpnws36hGB0dLTxAVmJqIgOyRG00l6O5Sccc41HOpBa0Hh2q9BhlZGyM1uZm32MUET/KWbAk1ZEafXUQxdWHzpMWs3v1MwcePDo6RluL1+hL+lm/sPT29nLMMcdEXQzxkYQunNv3ZVgwdfwhkxudcia1oPUomLZFMlnKWbAk1ZEafSk187TTmXna6ftfbxka3r/x11k/SbI4dOEUERERSRI1+mqkuCtl6fQ4dDk7vLU16iLE3hFHHBF1ESQFlDV/ypnUgtajYNoWSTWKj2dHR0dZU9R1UbcGHSpJ2yI1+opUe79QaVfKYnHpcjZtSjL6G0eps7Mz6iI0vLgMfDQZypo/5UxqQetRMG2LpFLljmcLz7nQrUHlJWlbpEZfkWrvFyrtShlHrw7vY65GFfTV29vLwoULoy5GQ4ty4KNaUdb8KWdSC1qPgmlbJJUqPZ4tXod0a1B5SdoWqdFXIq33C2VzuaiLEHuZTCbqIjSEuDxEPSzKmj/lTGpB61EwbYtksrQOBUvStqihGn1p6DpWrY7m5uCZGty0adOiLkJDSMIInJOhrPlTzqQWtB4F07ZIJkvrULAkbYsaqtGXhq5j1ZrV3hZ1EWJv3rx5URehYST9ap4fZc2fcia1oPUomLZFMllah4IlaVvUUI0+SPfBpp/BkSwzWhvu667Izp076e7ujroYknDKmj/lTGpB61EwbYtksrQOBUvStih132S1I3Cm3e6sghukv78/McGNu0bOobLmTzmTWtB6FEzbIpksrUPBkrQtSt03mfb7harVFHUBEqCpSbU0UUH3x77+3NMAdBx97CG/S3sOtRb5U86kFrQeBVMNyWRpHQqWpG1R6hp90LhdOP3M07DNgXp6eqIuQmIE3R/bcfSxdJ60OPaPMgmDsuZPOZNa0HoUTNsimSytQ8GStC1KTaNvzYplQPq7jlUrm8vRlqCzEVEYGRmhvV0buInSyZXylDV/yplUY2jd2v37eYDc2BhNRSMLznrXu/XQ6BLaFjWmWt5eoXUoWJL2aakZizU7OEB2cCD1XceqtWNfcp4jEpUtW7ZEXYREWLNiGUPr1kZdjNhS1vwpZ1KpzpMW0zpn7v79fHZwgKGBA//f+8Lzvt3NG5W2RY2p0BOnOC/VHiNrHQqWpH1aaq706YqDSH3o5IqI1NPM004/pKv4lqFhFkztAKB31cooihWKoPuldUVTIPhqnnriSDmpafSJv+621qiLEHtz5syJugiJoB2JP2XNn3ImtZDWnPndL53Z6l1RmGijL611FDdRNNTrNWih1qFgSdqn1bXR55xrAW4CLgA6gIeAi82sb5z5zwT+CTgGeAm4wsweqk9p0+WJ1b/hjMWnhPoZQRs+P3E4e/nYY49x5plnRlqGWlHWolOctdL7kIrFYZ2PgnKmnNVC6T4tTVkb7ypNpVc067Hfr4e456zahvpEjpn81t16XM1LyzoUpiTt0+p9pe9a4H3AW4HXgDuA7wDvKZ3ROXcMcC+wFPgB8EHgPufcCWa2oV4FToufPvPchHeQ1fIbpt9PpWcvw3L//fcnJrgToKxFpJC1zpMWs3v1M2QHBw6ZZ/jll3j9uacjuQ8p6gNg5Uw5q4XifZpf1sLav1R7dSdouaCBNsbbd5dbrnS/n2B1yVm1x0V+3Sl7V60c932DjpnicGwU9To0mYsJUJ/9XZL2afVu9C0FbjCz9QDOuauBdc65HjPbWDLv+cBTZnZX/vXdzrll+enX163EKeS3g5yMaofp99so1tOZG16MvAzFjv/CbZNZXFmLWLn7kAoGnnwilAwGibKxWRC3nMGkshZazuJWR7UQ1ujaflkLa//id8Dul7OgA32/rnl+++6gLn1x2MfGMWcFkzkuqvY7Czpm8lt3oxqpvt7rUbUXE6B++7u47dP8cla3Rp9zrgs4CniqMM3MXnLODQInA6XBPbl43ryn89MPcd5Nn69dYVOqLnX088e8n0o1T4f1r9S+PJVoOSz6MhRzLmdmFY+VrKxFL7Z11DnP+zfK9TxuOYOqshZ2zq6OWx3VQvN06NsDNcrHhHMWxv6lkKW+PeP/rtxn+i1XUO1+tMxy++soDvvYGOasZtvqGn5nBxnve6txlvwcVEf1Xo8mkpegZcMub9z2aT45q+eVvs78v6WnO/qBGePMX27eE0pnrObAWCTFlDWR8ClnIuFTzkRqpJ7P6dud/3dmyfQuYHCc+Sc6r4gcoKyJhE85EwmfciZSI3Vr9JlZP7AJeHNhWv6G2xnA6jKLPFc8b97i/HQRGYeyJhI+5UwkfMqZSO3UeyCXrwHXOOd+gTcC083Ag+OMqHQncJVz7hzgh8AHgLcA59WprCJJpqyJhE85EwmfciZSA/Vu9N0EHA78CmgHfgp8FMA59xHgdjObDvtv1D0b71krdwDrgfeXhtw594fAPwAnAsPAD8xMT48GnHP/C/QUTWrBe8bNW8zs6WhKFT/OuXnAF4Ez8DLxDHC5mSX5zKCyVkfKWjDlTDmbLOUsmHKmnE2WcjYxScxaUy6Xi7oMVXPOLQF+BFwE3A80AW/SSlmec+5G4CwzO+SG5kbmnLsX7+bvDwF78HYE5wBHmVlyA1JDylpllLVDKWfBlLPKKGeHUs6CKWeVUc7KS2LW6n2lr9Y+C9xmZj8smqbQluGcmwJciFdncrA3Al8xs10AzrlvAFcBs4C+KAsWI8raBClr41LOgilnE6ScjUs5C6acTZBy5itxWUtso885dxhwGvC4c+5pvOe4/A9wpZn9OtLCxdNZeCNa3Rl1QWLo88BHnXP34Y38tRR4zMxiGdp6U9YqpqyVp5z5UM4qppyVp5z5UM4qppyNL3FZq+cjG2rtcLzynwNcAMwHHgJ+nH+YpxzsYuD7+ZGw5GCP4/VZ3w68DpwNfDzSEsWLslYZZa085cyfclYZ5aw85cyfclYZ5Wx8ictakht9hWe3fNPMVptZBu/ycyvwtuiKFT/OuWOBdwG3RV2WuHHONQM/A9binc2aBtwIPOqcmxtl2WJEWZsgZa085WxClLMJUs7KU84mRDmbIOVsfEnNWmIbfWY2AGwASm+WzJWZ1uguBp4zs19GXZAY6gaOBr5sZoNmljGzVXjZOD3aosWDslYRZa085SyAclYR5aw85SyAclYR5Wx8icxaYu/py1sJLHfOfRevtX0FsA/4r0hLFSPOuTa8LgzXRVyUWDKzPufcWuAS59y1eOvPeXgjMpV78GujUtYCKGvjU84mTDkLoJyNTzmbMOUsgHLmL6lZS3qj7xa8Cv453jNEngHekz+TI56z8erm7qgLEmNn4d2QuxGvi8c64INmtj7SUsWLshZMWfOnnAVTzoIpZ/6Us2DKWTDlLFjispbo5/SJiIiIiIiIv8Te0yciIiIiIiLB1OgTERERERFJMTX6REREREREUkyNPhERERERkRRTo09ERERERCTF1OgTERERERFJsaQ/p69hOOe+Bcw2sz+t8ft+E3jZzG6o5fvWmnPud4CfAMeZ2Z6oyyPppawpaxI+5Uw5k/ApZ8pZMV3pa2D5MJwFfCHqsgQxs98A/w1cEXVZRCqlrImETzkTCZ9yllxq9DW2y4B/NbPBKAvhnGt2zrVMYNZvAp9wzukKtSSNsiYSPuVMJHzKWUI1fAUkkXOuHbgZOAeYCTwLXGlmjxXN817gVqAHeBJYCXwXONrMNuSD8iHg/KJlPg18yMxOLPm8x4GnzOyv868/BlwFHANsAr4KfNHMxvK/vwK4ADgW6Af+PV++/vzvLwC+kv/8zwHHA6c455rwzhz9Lt4JiZeAFWb2i3xRHgK6gSXAz6qtP5GJUtaUNQmfcqacSfiUM+VMV/qS6XPAXwAXAouB3wA/cc4dCeCcOwq4F3gAOBn4Un6ZYifhhf7XRdPuAI53zp1WmOCc+y3gbcA38q8/Dvwj8Gngt4G/Aa4BLil6nzFgBXAC8GHgNODLJZ/fAVwHXAy8CdgI3ANszc9/CvAZYLiwgJll8DZSfxBQPyK1oqyJhE85EwmfctbgdKUvYZxzhwGfAC4yswfy05YBZwCXAp/K/369mRX6ML/gnDsOuLHorXqAHF5QADCzXufcT/A2CE/mJ1+Id6bmufzr64CrzeyH+dcvO+duwgvuV/LvU9zPe4Nz7mrg35xz5xfO6AAtwF+Z2VNFf1sPcIuZrclPWlemCl4B3uBXRyK1oKwpaxI+5Uw5k/ApZ8oZqNGXRMcCrcDjhQlmNuqcewLvrAd4l7x/VbLcL0teTwVGioJU8HXg2865y4EMcC7w9wDOuSOARcDtzrmvFi0zBWgqvHDOnQF8Eu9szky8kLYB8/CCB5DFO/NS7FZglXPufOBhvD7ja0rmGcqXXSRsypqyJuFTzpQzCZ9yppype2fK5CqYtw9oc85NK5n+ALAX+HPgT4AuvEvncGB9WYZ3Cb3wcyLe5fjCGZcHgP8DPgi8Be+MD3jhLdhnZqPFH2xmn8Hb+PwIr1vAaufchRysG9hRwd8pEgZlTSR8yplI+JSzBqErfcnzEt5ZlN/P/5/8jbWncyBga4D3lSx3WsnrwpmSN1HUN9vMss57rsuFwABwr5kN5H/3qnPuFeBYM7tznPKdihfQywvBdM5N+PkwZvYi8CLwpfwZoYvw+osXnIjX51wkbMqasibhU86UMwmfcqacqdGXNGa2J79C3+yc6wNeBi4H5uKNsgRwG3CFc+4WvEvuJ+Dd9Ar5MzpmtsM59zTwdg6+IRdgFd4NtmPAH5f87u+ALzvn+oEf43UXeDOwwMw+ixe6ZmCFc+5e4Pfwbsz15ZybCtwC/AuwIf/3vJ2irgXOuTcAC/BGYhIJlbKmrEn4lDPlTMKnnClnoO6dSXUN8H28Z488izea0plmthXAzDbiXWJ3wHN4wb4+v+xw0ft8DfhI6Zub2XrgP/GG1P2Pkt+twjuTc27+vR8FluJtQDCz1cByvAdhPo93tuXKCfxNo8DhwLeAF4D7gCc4+IGa5wAP5f8+kXpQ1kTCp5yJhE85a3BNuVwlXXklqZxzy4EbgC4zy+WndeBdzj/XzB4tmf954G4zu/GQN4uA854v8yJwjpk9HjS/SFSUNZHwKWci4VPO0kXdO1PKOXcp3ihMO/Auk18HfKsQWgAzG3bOnYd3g2thuSOAD+ANbXt7PcscoAe4UaGVuFHWRMKnnImETzlLNzX60uuNwN8Cs4BevL7aN5TOZGaPlEzajjc608Vm1hd2ISfKzNYCa6Muh0gZyppI+JQzkfApZymm7p0iIiIiIiIppoFcREREREREUkyNPhERERERkRRTo09ERERERCTF1OgTERERERFJMTX6REREREREUkyNPhERERERkRT7f/dRoTkbGXZKAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_all_pdfs(agewiz006)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Age PDFs at z=0.008"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_all_pdfs(agewiz008)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Most Likely Ages\n",
+ "\n",
+ "The most likely age for **each source** is summarised by `AgeWizard.most_likely_ages`. This is returned as a numpy array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,\n",
+ " 6.9, 6.9, 6.9])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.most_likely_ages"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Different metallicities will give different results. In this example we summarise our results into a new DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "most_likely_ages = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
+ " 'z006': agewiz006.most_likely_ages, \n",
+ " 'z008': agewiz008.most_likely_ages}) \n",
+ "\n",
+ "# Note that the attribute AgeWizard.sources is the list of names you provided, or if you didn't the ones it created"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " name \n",
+ " z006 \n",
+ " z008 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Star1 \n",
+ " 6.9 \n",
+ " 6.8 \n",
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+ " Star2 \n",
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+ " \n",
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+ " Star10 \n",
+ " 7.3 \n",
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+ " 6.8 \n",
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+ " 12 \n",
+ " Star13 \n",
+ " 6.8 \n",
+ " 6.8 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Star14 \n",
+ " 6.9 \n",
+ " 6.9 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " WR1 \n",
+ " 6.9 \n",
+ " 6.8 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " WR2 \n",
+ " 6.9 \n",
+ " 6.9 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " name z006 z008\n",
+ "0 Star1 6.9 6.8\n",
+ "1 Star2 6.7 6.7\n",
+ "2 Star3 6.8 6.9\n",
+ "3 Star4 6.5 6.5\n",
+ "4 Star5 6.9 6.8\n",
+ "5 Star6 6.7 6.7\n",
+ "6 Star7 6.8 6.8\n",
+ "7 Star8 6.7 6.5\n",
+ "8 Star9 6.9 6.9\n",
+ "9 Star10 7.3 7.3\n",
+ "10 Star11 6.8 6.9\n",
+ "11 Star12 6.9 6.9\n",
+ "12 Star13 6.8 6.8\n",
+ "13 Star14 6.9 6.9\n",
+ "14 WR1 6.9 6.8\n",
+ "15 WR2 6.9 6.9"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "most_likely_ages"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IMPORTANT\n",
+ "The `most_likely_ages` (and `most_likley_age`, see below) tools **do not give you a direct answer** - they are a way to summarise the data and be used in your interpretation but **please do not use these as a black box**.\n",
+ "\n",
+ "Looking at the PDFs plotted above and the table of most likely ages, we can see that a few stars stand out:\n",
+ "\n",
+ "* Star 4 has a lower (most likely) age than the rest of the sample\n",
+ "* Star 10 has a higher (most likely) age than the rest of the sample\n",
+ "* The WR PDFs show peaks beyond log(age/years) - which souldn't be possible for WR stars.\n",
+ "\n",
+ "All of these are discussed an interpreted in details in Stevance et al. (in prep). Star 4 is an example of a star that is probably the result of a merger and rejuvination, Star 10 is more likely to be on the young tail of the distribution and not as old as the most likley age implies, and the WR stars share their HRD locations with helium stars which appear at later times, which populate the later peak. \n",
+ "\n",
+ "\n",
+ "# Plotting Aggregate Ages\n",
+ "\n",
+ "If you are looking for the age of a whole population then you can ask `AgeWizard` to combine the PDFs it calcualted on initialisation. This is done with the method `AgeWizard.calcualte_sample_pdfs()`. \n",
+ "\n",
+ "It can be called as such and it will multiply all the PDFs, or you can provide a list of columns to ignore in the paramter `not_you`. You can also ask it to return the resulting PDF by setting `return_df` to True. If you don't, it will store the result in `AgeWizard.sample_pdf`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Returns None\n",
+ "agewiz006.calculate_sample_pdf()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " pdf \n",
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+ " 0.018170 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.025543 \n",
+ " \n",
+ " \n",
+ " 2 \n",
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+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.042060 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.052826 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 0.057152 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0.085512 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 0.152308 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 0.196053 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 0.206209 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " pdf\n",
+ "0 0.018170\n",
+ "1 0.025543\n",
+ "2 0.030998\n",
+ "3 0.042060\n",
+ "4 0.052826\n",
+ "5 0.057152\n",
+ "6 0.085512\n",
+ "7 0.152308\n",
+ "8 0.196053\n",
+ "9 0.206209"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.sample_pdf.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also quickly retrieve the **most likely age of the whole sample** from the PDF you just calculated using `AgeWizard.most_likely_age`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.9])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.most_likely_age"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " pdf \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.024695 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.025288 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.019613 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.034380 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.058792 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 0.072591 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0.132265 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 0.174126 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 0.211425 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 0.190401 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " pdf\n",
+ "0 0.024695\n",
+ "1 0.025288\n",
+ "2 0.019613\n",
+ "3 0.034380\n",
+ "4 0.058792\n",
+ "5 0.072591\n",
+ "6 0.132265\n",
+ "7 0.174126\n",
+ "8 0.211425\n",
+ "9 0.190401"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Or return directly\n",
+ "agewiz008.calculate_sample_pdf(return_df=True).head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([6.8])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz008.most_likely_age"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Down below we create a quick function to plot our aggregate ages for z=0.006 and z=0.008. \n",
+ "\n",
+ "As an example of the "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster1_sources = ['Star1', 'Star2', 'Star3', 'Star4', 'WR1']\n",
+ "cluster2_sources = [item for item in agewiz006.sources if item not in cluster1_sources]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_aggregate_age(agewiz, ax):\n",
+ " \n",
+ " # Aggregate age PDF for all sources\n",
+ " cluster12 = agewiz.calculate_sample_pdf(return_df=True).pdf\n",
+ " \n",
+ " # Aggregate age PDF for cluster1\n",
+ " cluster1 = agewiz.calculate_sample_pdf(not_you=cluster2_sources, return_df=True).pdf\n",
+ " \n",
+ " # Aggregate age PDF for cluster2\n",
+ " cluster2 = agewiz.calculate_sample_pdf(not_you=cluster1_sources, return_df=True).pdf\n",
+ "\n",
+ "\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster1, where='mid', alpha=1, lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster1, step='mid', alpha=0.1, label='Cluster1')\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster2, where='mid', alpha=1, lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster2, step='mid', alpha=0.3, label='Cluster2')\n",
+ " \n",
+ " ax.step(hoki.BPASS_TIME_BINS, cluster12, where='mid',alpha=0.5, c='k', lw=3)\n",
+ " ax.fill_between(hoki.BPASS_TIME_BINS, cluster12, step='mid', alpha=0.2, label='All', color='k')\n",
+ "\n",
+ "\n",
+ " ax.set_ylabel('Probability')\n",
+ " ax.set_xlabel('log(ages)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(ncols=2, nrows=1, figsize=(7,5))\n",
+ "\n",
+ "plot_aggregate_age(agewiz006, ax[0])\n",
+ "plot_aggregate_age(agewiz008, ax[1])\n",
+ "\n",
+ "for axis in ax:\n",
+ " axis.set_ylim([0,0.4])\n",
+ " axis.set_xlim([6,8.5])\n",
+ " \n",
+ "# Cool legend\n",
+ "ax[0].legend(fontsize=14, loc='center right', bbox_to_anchor=(1.9, 1.1), ncol=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Probability given an age range\n",
+ "\n",
+ "The BPASS time bins are separated by 0.1 dex in log space (log(age/years) = 6.0, 6.1, 6.2, 6.3 etc..) and the PDFs for your individual sources will cover a range of BPASS time bins.\n",
+ "\n",
+ "Consequently, giving a single age may not make a lot of sense. Also the PDFs will rarely, if ever, resemble a Gaussian distribution: that means that you can't give an error bar in the \"classical\" sense. \n",
+ "\n",
+ "Another interesting measure is the probability that the age of your star falls into a chosen age range. \n",
+ "In this case our aggregate age is most likely to be between 6.7-6.9, so let's investigate how likely it is for each individual star to fall in that range. \n",
+ "\n",
+ "For that we use `AgeWizard.calculate_p_given_age_range()` which returns a Series containing the probabilities corresponding to each source."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "name\n",
+ "Star1 0.857630\n",
+ "Star2 0.932043\n",
+ "Star3 0.779729\n",
+ "Star4 0.127003\n",
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+ "Star14 0.446666\n",
+ "WR1 0.850846\n",
+ "WR2 0.784204\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agewiz006.calculate_p_given_age_range([6.7, 6.9])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can collate those in one single DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p_ages_679 = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
+ " 'z006': agewiz006.calculate_p_given_age_range([6.7,6.9]), \n",
+ " 'z008': agewiz008.calculate_p_given_age_range([6.7,6.9])}) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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\n",
+ ""
+ ],
+ "text/plain": [
+ " name z006 z008\n",
+ "name \n",
+ "Star1 Star1 0.857630 0.784335\n",
+ "Star2 Star2 0.932043 0.737951\n",
+ "Star3 Star3 0.779729 0.659319\n",
+ "Star4 Star4 0.127003 0.134967\n",
+ "Star5 Star5 0.857630 0.784335\n",
+ "Star6 Star6 0.948770 0.738479\n",
+ "Star7 Star7 0.500073 0.469008\n",
+ "Star8 Star8 0.666553 0.278005\n",
+ "Star9 Star9 0.446666 0.549145\n",
+ "Star10 Star10 0.023493 0.023109\n",
+ "Star11 Star11 0.779729 0.659319\n",
+ "Star12 Star12 0.446666 0.549145\n",
+ "Star13 Star13 0.500073 0.469008\n",
+ "Star14 Star14 0.446666 0.549145\n",
+ "WR1 WR1 0.850846 0.721919\n",
+ "WR2 WR2 0.784204 0.789553"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p_ages_679"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To learn more about how we use and interpret this statistic, you can checkout Stevance et al. in prep an the associated Jupyter Notebook (link)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Ages from Colour-Magnitude Diagram \n",
+ "\n",
+ "[TO WRITE]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/CMDs.html b/docs/CMDs.html
index b3d215fe..2d2bba36 100644
--- a/docs/CMDs.html
+++ b/docs/CMDs.html
@@ -1,8 +1,7 @@
-
-
+
@@ -12,6 +11,10 @@
+
+
+
+
@@ -19,24 +22,23 @@
-
+
-
-
-
-
-
+
+
+
+
+
+
-
-
-
-
@@ -80,6 +82,7 @@
+
+
-
-
- Docs » +
- »
- Colour Magnitude Diagrams @@ -211,16 +215,16 @@ /* CSS for nbsphinx extension */ /* remove conflicting styling from Sphinx themes */ -div.nbinput, -div.nbinput div.prompt, -div.nbinput div.input_area, -div.nbinput div[class*=highlight], -div.nbinput div[class*=highlight] pre, -div.nboutput, -div.nbinput div.prompt, -div.nbinput div.output_area, -div.nboutput div[class*=highlight], -div.nboutput div[class*=highlight] pre { +div.nbinput.container, +div.nbinput.container div.prompt, +div.nbinput.container div.input_area, +div.nbinput.container div[class*=highlight], +div.nbinput.container div[class*=highlight] pre, +div.nboutput.container, +div.nboutput.container div.prompt, +div.nboutput.container div.output_area, +div.nboutput.container div[class*=highlight], +div.nboutput.container div[class*=highlight] pre { background: none; border: none; padding: 0 0; @@ -229,13 +233,13 @@ } /* avoid gaps between output lines */ -div.nboutput div[class*=highlight] pre { +div.nboutput.container div[class*=highlight] pre { line-height: normal; } /* input/output containers */ -div.nbinput, -div.nboutput { +div.nbinput.container, +div.nboutput.container { display: -webkit-flex; display: flex; align-items: flex-start; @@ -243,92 +247,104 @@ width: 100%; } @media (max-width: 540px) { - div.nbinput, - div.nboutput { + div.nbinput.container, + div.nboutput.container { flex-direction: column; } } /* input container */ -div.nbinput { +div.nbinput.container { padding-top: 5px; } /* last container */ -div.nblast { +div.nblast.container { padding-bottom: 5px; } /* input prompt */ -div.nbinput div.prompt pre { +div.nbinput.container div.prompt pre { color: #307FC1; } /* output prompt */ -div.nboutput div.prompt pre { +div.nboutput.container div.prompt pre { color: #BF5B3D; } /* all prompts */ -div.nbinput div.prompt, -div.nboutput div.prompt { - min-width: 5ex; - padding-top: 0.4em; - padding-right: 0.4em; - text-align: right; - flex: 0; +div.nbinput.container div.prompt, +div.nboutput.container div.prompt { + width: 4.5ex; + padding-top: 5px; + position: relative; + user-select: none; } + +div.nbinput.container div.prompt > div, +div.nboutput.container div.prompt > div { + position: absolute; + right: 0; + margin-right: 0.3ex; +} + @media (max-width: 540px) { - div.nbinput div.prompt, - div.nboutput div.prompt { + div.nbinput.container div.prompt, + div.nboutput.container div.prompt { + width: unset; text-align: left; padding: 0.4em; } - div.nboutput div.prompt.empty { + div.nboutput.container div.prompt.empty { padding: 0; } + + div.nbinput.container div.prompt > div, + div.nboutput.container div.prompt > div { + position: unset; + } } /* disable scrollbars on prompts */ -div.nbinput div.prompt pre, -div.nboutput div.prompt pre { +div.nbinput.container div.prompt pre, +div.nboutput.container div.prompt pre { overflow: hidden; } /* input/output area */ -div.nbinput div.input_area, -div.nboutput div.output_area { - padding: 0.4em; +div.nbinput.container div.input_area, +div.nboutput.container div.output_area { -webkit-flex: 1; flex: 1; overflow: auto; } @media (max-width: 540px) { - div.nbinput div.input_area, - div.nboutput div.output_area { + div.nbinput.container div.input_area, + div.nboutput.container div.output_area { width: 100%; } } /* input area */ -div.nbinput div.input_area { +div.nbinput.container div.input_area { border: 1px solid #e0e0e0; border-radius: 2px; background: #f5f5f5; } /* override MathJax center alignment in output cells */ -div.nboutput div[class*=MathJax] { +div.nboutput.container div[class*=MathJax] { text-align: left !important; } /* override sphinx.ext.imgmath center alignment in output cells */ -div.nboutput div.math p { +div.nboutput.container div.math p { text-align: left; } /* standard error */ -div.nboutput div.output_area.stderr { +div.nboutput.container div.output_area.stderr { background: #fdd; } @@ -372,6 +388,27 @@ .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } + +div.nbinput.container div.input_area div[class*=highlight] > pre, +div.nboutput.container div.output_area div[class*=highlight] > pre, +div.nboutput.container div.output_area div[class*=highlight].math, +div.nboutput.container div.output_area.rendered_html, +div.nboutput.container div.output_area > div.output_javascript, +div.nboutput.container div.output_area:not(.rendered_html) > img{ + padding: 5px; +} + +/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */ +div.nbinput.container div.input_area > div[class^='highlight'], +div.nboutput.container div.output_area > div[class^='highlight']{ + overflow-y: hidden; +} + +/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */ +.prompt a.copybtn { + display: none; +} + /* Some additional styling taken form the Jupyter notebook CSS */ div.rendered_html table { border: none; @@ -409,13 +446,13 @@ /* CSS overrides for sphinx_rtd_theme */ /* 24px margin */ -.nbinput.nblast, -.nboutput.nblast { +.nbinput.nblast.container, +.nboutput.nblast.container { margin-bottom: 19px; /* padding has already 5px */ } /* ... except between code cells! */ -.nblast + .nbinput { +.nblast.container + .nbinput.container { margin-top: -19px; } @@ -439,8 +476,8 @@
Initial Imports
import matplotlib.pyplot as plt
-from hoki.cmd import CMD
-from hoki.load import set_models_path, unpickle
+from hoki.cmd import CMD
+from hoki.load import set_models_path, unpickle
import pickle
import numpy as np
@@ -513,6 +550,7 @@ Making the CMDsCMD() instance is.
+
You can easily access the grid by simply calling the attribute CMD.grid
[4]:
@@ -527,8 +565,9 @@ Making the CMDs[4]:
-
-array([[[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
@@ -577,8 +616,7 @@ Making the CMDs
But CMD objects are also indexable!
@@ -612,16 +650,16 @@ Making the CMDs[6]:
-
-array([[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
-
-
+
This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use CMD.at_log_age():
@@ -905,11 +943,19 @@ Creating a publication-ready figure
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -921,7 +967,6 @@ Creating a publication-ready figure
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/CMDs.ipynb b/docs/CMDs.ipynb
new file mode 100644
index 00000000..3be3f90a
--- /dev/null
+++ b/docs/CMDs.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Colour Magnitude Diagrams\n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from hoki.cmd import CMD\n",
+ "from hoki.load import set_models_path, unpickle\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Getting the Stellar models and input files\n",
+ "\n",
+ "Colour Magnitude Diagrams (CMDs) are created by reading in the [BPASS](https://bpass.auckland.ac.nz/9.html) stellar models listed in the `model_input` files that can be found in the BPASS output folders (e.g. *bpass_v2.2.1_imf135_300*). These stellar models are in a separate directory (because it is absolutely massive), so you will have to download it separately if you want to run the following cells or make your own CMDs.\n",
+ "\n",
+ "**NOTE: You will be able to run the cells in the section \"Loading a pickled CMD file\" even if you can't download the full set of stellar models **\n",
+ "\n",
+ "The stellar models and input files can be downloaded from [the google drive](https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl) (*bpass-v2.2-newmodels* for the models and e.g. *bpass_v2.2.1_imf135_300* to get the required `model_input` files).\n",
+ "Then you will have to change the path to the models in the `settings.yaml` file -- this can be done using the `set_models_path` function contained in the `hoki.load` module: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looks like everything went well! You can check the path was correctly updated by looking at this file:\n",
+ "/home/fste075/hoki/hoki/data/settings.yaml\n"
+ ]
+ }
+ ],
+ "source": [
+ "# The following path is valid on my machine - make sure you put the right ABSOLUTE path for your system\n",
+ "set_models_path(path='/home/fste075/BPASS_hoki_dev/bpass-v2.2-newmodels/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**NOTE: You'll probably have to reload hoki or restart the kernel at this point if you've jsut updated the yaml file :)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# CMD objects\n",
+ "\n",
+ "### Making the CMDs\n",
+ "\n",
+ "To create a synthetic CMD, `hoki` creates a grid in colour-magnitude space and then consults the entire set of stellar models to fill that grid. It is basically a histogram - the value of each cell/bin increases according to the proportion of the stellar population that falls into that bin. You could just plot this grid with a colour-map like an image, but we traditionally create contour plots for visualisation. \n",
+ "\n",
+ "In `hoki` you will be creating a `CMD()` object instanciated with a model of your choosing (a particular IMF and metallicity)- for this you need to provide the **location of a BPASS input file**.\n",
+ "\n",
+ "To know WHAT to do with this information, we also need to give the **two broad-band filters** we are interested in to make the plot filter2 Vs filter1-filter2 (e.g. V Vs B-V). This is given in the `CMD.make()` method, which actually creates and fills the CMD grids. \n",
+ "\n",
+ "**NOTE: This step is the most time consuming because there are thousands and thousands of models to look at. For that reason it also take much longer for the binary stellar models than the single star models to make a CMD. In the next section we will show you how to avoid having to go repeat his step in the future**\n",
+ "\n",
+ "The good news is that **once you have instanciated and 'made' the CMD object, plotting it is VERY fast** and you have a CMD for **each time bin**, which are also trivial and quick to access. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/fste075/hoki/hoki/cmd.py:191: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n",
+ "/home/fste075/hoki/hoki/cmd.py:207: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ONLY RUN IF YOU HAVE THE MODELS IN YOUR MACHINE\n",
+ "\n",
+ "# Update this path if you want to run this cell\n",
+ "input_file = '/home/fste075/BPASS_hoki_dev/bpass_v2.2.1_imf135_300/input_bpass_z020_bin_imf135_300'\n",
+ "mycmd = CMD(input_file)\n",
+ "\n",
+ "# actually makes and fills the grids - this is the time and memory consuming step\n",
+ "mycmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below is a summary illustration of what the synthetic CMDs are and what a `CMD()` instance is. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can easily access the grid by simply calling the attribute `CMD.grid`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "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",
+ "\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": [
+ "mycmd.grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(51, 240, 100)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.grid.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But `CMD` objects are also indexable! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(240, 100)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use `CMD.at_log_age()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.at_log_age(log_age=6.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Changing the resolution of the CMD grids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see we have 51 time bins, 240 magnitude intervals and 100 colour intervals. \n",
+ "\n",
+ "The number of time bins is fixed by BPASS but you can chose the size of your colour-magnitude grid and its resolution when you instanciate a `CMD` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "blurry_cmd = CMD(input_file, col_lim=[-3, 7], mag_lim=[-14, 10], res_el=0.75)\n",
+ "blurry_cmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the CMDs\n",
+ "\n",
+ "Like I said above, once the grid is made and filled, plotting is quick and straight forward. As in other `hoki` tools the plotting function returns the plot, which you can store in a variable to add your own labels and customize limits. \n",
+ "\n",
+ "Similarly to the `hoki.HRDiagrams` plots, the contours are on a log scale. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = mycmd.plot(log_age=6.8) # Here you can chose the time bin you want to plot.\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with the HRDiagrams, you can also tell the plotting function where your want it to plot your data!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "mycmd.plot(log_age=6.8, loc=ax[0]) # Here you can chose the time bin you want to plot.\n",
+ "ax[0].set_xlim([-3,3.0])\n",
+ "ax[0].set_ylim([2,-10])\n",
+ "ax[0].set_title('High resolution')\n",
+ "\n",
+ "blurry_cmd.plot(log_age=6.8, loc=ax[1]) # Here you can chose the time bin you want to plot.\n",
+ "ax[1].set_xlim([-3,3.0])\n",
+ "ax[1].set_ylim([2,-10])\n",
+ "ax[1].set_title('Low resolution')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pickle CMDs - don't make them twice!\n",
+ "\n",
+ "Because it is a little time consuming to create the synthetic CMDs, we actually recommend you [pickle](https://www.datacamp.com/community/tutorials/pickle-python-tutorial) your CMD objects. This will allow you to re-use them in the future and plot them virtually instantly, by-passing the `CMD.make()` step. \n",
+ "\n",
+ "### Loading a pickled CMD file\n",
+ "\n",
+ "We've provided a couple of pickled CMD files in the `./data/cmds/` directory for you to try even if you couldn't download the full sets of stellar models. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pickled_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Pickling a CMD file\n",
+ "\n",
+ "Now let's make your own pickle file! If you have the stellar models and made the CMD in the previous sections of this tutorial, you can now save your work!\n",
+ "\n",
+ "All you need is the following code (feel free to change the output file name)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First we open a file we can write into\n",
+ "outfile = open('./data/cmds/BV_CMD.pckl', 'wb')\n",
+ "# Then we call the 'dump' function from the pickle module to dump our cmd in our output file\n",
+ "pickle.dump(mycmd, outfile)\n",
+ "# And to avoid funny business we close our file. \n",
+ "outfile.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_pickled_cmd = unpickle(path='./data/cmds/BV_CMD.pckl')\n",
+ "\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = new_pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just like with the HRDiagrams, the tool provided by `hoki` will allow you to make publication ready figures in not time!\n",
+ "\n",
+ "Let's make a plot comparing the CMDs for Cygnus OB2 and Upper Sco in B-V and U-V plots.\n",
+ "\n",
+ "First we need to load our data (which is provided in the ./data/cmds/ repository) - we also need to make sure our observational data is in **absolute** magnitude, because that's what our synthetic CMDs provide. If **extinction** is important in your osbervational data, you also need to take that into account. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Cygnus OB2 data\n",
+ "Av, cyg_U, cyg_B, cyg_V = np.genfromtxt('./data/cmds/cygnusob.dat', unpack=True, usecols=(7, 8, 9,10), skip_header=54)\n",
+ "\n",
+ "# Assumes Milky Way extinction\n",
+ "Ab = (1.324*Av)\n",
+ "Au = (1.531*Av)\n",
+ "\n",
+ "# Distance to Cyg OB2 and distance modulus\n",
+ "d_cygob2 = 1750 #pc\n",
+ "mu_cygob2 = 5*np.log10(d_cygob2)-5\n",
+ "\n",
+ "# Taking away the extinction and turning our mags into absolute mags\n",
+ "# Note it was derived from single star models so extinction may be a tad off\n",
+ "# for your science, feel free to do a better job of it ;)\n",
+ "cyg_U, cyg_B, cyg_V = cyg_U-Au-mu_cygob2 , cyg_B-Ab-mu_cygob2 , cyg_V-Av-mu_cygob2 \n",
+ "\n",
+ "# Now calculating colours\n",
+ "cyg_UV = cyg_U-cyg_V\n",
+ "cyg_BV = cyg_B-cyg_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Upper Sco data\n",
+ "p, usco_U, usco_B, usco_V = np.genfromtxt('./data/cmds/usco.dat', unpack=True, usecols=(1,2,3,4), skip_header=1)\n",
+ "\n",
+ "# Distance modulus - this time based on parallax.\n",
+ "# (Note I inverted parallax to make this tutorial quick - don't @ me.)\n",
+ "# Extinction is not a problem for this data set\n",
+ "mu = 5*np.log10(1/p)-5\n",
+ "usco_U, usco_B, usco_V = usco_U+mu, usco_B+mu, usco_V+mu\n",
+ "\n",
+ "# Calculating colours\n",
+ "usco_UV = usco_U-usco_V\n",
+ "usco_BV = usco_B-usco_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unpickling our BV and UV cmds!\n",
+ "BV_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')\n",
+ "UV_cmd = unpickle(path='./data/cmds/cmd_uv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Now let's plot the data!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(2,2, figsize=(15,15))\n",
+ "\n",
+ "# This is a bit off on the colour axis which is probs just because of my conversion from Av to Ab and Au\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].scatter(cyg_UV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].scatter(cyg_BV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "\n",
+ "# this is not the same data as the paper\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[1,0])\n",
+ "ax[1,0].scatter(usco_UV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[1,1])\n",
+ "ax[1,1].scatter(usco_BV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "for axis in ax.reshape(4):\n",
+ " axis.set_ylabel('V', fontsize=14)\n",
+ " axis.set_ylim([2,-10])\n",
+ "\n",
+ "for i in [0,1]:\n",
+ " ax[i,0].set_xlim([-2,4])\n",
+ "\n",
+ " ax[i,1].set_xlim([-1,2])\n",
+ "\n",
+ " \n",
+ "ax[0,0].text(1,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[0,1].text(0.5,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,0].text(1,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,1].text(0.5,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/HR_diagrams.html b/docs/HR_diagrams.html
index 329f2671..d7fd06b8 100644
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-
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+
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+
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+
@@ -987,11 +1024,19 @@ Plotting HRDs of stacked ages
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -1003,7 +1048,6 @@ Plotting HRDs of stacked ages
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/HR_diagrams.ipynb b/docs/HR_diagrams.ipynb
new file mode 100644
index 00000000..98676bd2
--- /dev/null
+++ b/docs/HR_diagrams.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HR diagrams\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
+ "\n",
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Learn about the HR-diagram data structure of BPASS\n",
+ "- Learn how to load them in using hoki\n",
+ "- Use the tools in `hoki` to explore your HR diagram data and make science plots\n",
+ "- Learn to customise the `hoki` plots\n",
+ "\n",
+ "\n",
+ "# Loading in the data\n",
+ "\n",
+ "### About the tools you're about to use. \n",
+ "HR diagram data can be loaded using the `hoki.load.population_output()` function. This function will automatically know that you are trying to load an HR diagram from the name of the text file.\n",
+ "\n",
+ "**Important**:\n",
+ "You should note that when loading an HR diagram you need to specify what type of HR diagram you want to load form the file. The HR diagram files are large and quite complex - see left hand side of the diagam below - so we'll be loading in one type of HR diagram at a time. \n",
+ "\n",
+ "You can choose from 3 options:\n",
+ "- `'TL'`: For a temperature/Luminosity HR diagram\n",
+ "- `'Tg'`: For a temperature/surface gravity HR diagram\n",
+ "- `'TTG'`: For a temperature/(temperature**4/surface gravity) HR diagram\n",
+ "\n",
+ "These options are there for 2 reasons: \n",
+ "* 1.They tell the object which segment of the text file to load in \n",
+ "* 2.They tell the plotting function what grid to create in order to plot the contours (also picks the right axis labels)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading the HR diagrams"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the HR diagrams for signle stars and binary star populations \n",
+ "sin_hr_diagram = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type = 'TL')\n",
+ "bin_hr_diagram = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type = 'TL')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading some observational data\n",
+ "\n",
+ "We're going to want to plot some observational data to compare to our models. A nice way to do that is to use `pandas`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the observational data of Upper Scorpio\n",
+ "usco = pd.read_csv('./data/USco.dat', sep='\\t', engine='python', names=['Temperature', 'Luminosity'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Temperature \n",
+ " Luminosity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 3.756 \n",
+ " 0.38 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 3.742 \n",
+ " 0.34 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3.742 \n",
+ " 0.36 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.750 \n",
+ " 0.08 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.728 \n",
+ " 0.27 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Temperature Luminosity\n",
+ "0 3.756 0.38\n",
+ "1 3.742 0.34\n",
+ "2 3.742 0.36\n",
+ "3 3.750 0.08\n",
+ "4 3.728 0.27"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usco.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting HR diagrams\n",
+ "\n",
+ "Now that we have loaded in our data, it's time to create some visualisation. The advantage of using the `HRDiagrams` object is that it comes with an **easy-to-use plotting method** that is highly **versatile** and designed to create **publication-ready figures**. \n",
+ "\n",
+ "Let's run through a couple of ways you can use this tool and make it your own. \n",
+ "\n",
+ "### Making a single plot\n",
+ "First we are going to make just one HR diagram plot. The plotting method returns the plote object, which **allows you to customize you plot**. \n",
+ "\n",
+ "For example you can **add observational data**, some **text**, a **title**, a **legend** and redefine the axis **limits** to create a more effective visualisation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Single stars')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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QW1RURGtra9L7Z3S4m11kSxv79PS0cXMex2Kyn8luIH5uR/zci81uzraxvxpIparEjZjsZ7IbiJ/bET/34oSbBHYLiQwHMRWT/Ux2A/FzO+LnXpxwk8BuIU5MRJBJTPYz2Q3Ez+2In3txwk0Cu4VUVFQ4nQRbMdnPZDcQP7cjfu7FCTfpPCcIgiAI7kQ6z9lNZNpGUzHZz2Q3ED+3I37uxQk3CewWMjEx4XQSbMVkP5PdQPzcjvi5FyfcJLBbSLqTEGQ7JvuZ7Abi53bEz7044SZt7BYiCzW4F5PdQPzcjvi5F5vdpI3dbiJLY5qKyX4mu4H4uR3xcy9OuElgt5C9e/c6nQRbMdnPZDcQP7cjfu7FCTcJ7IIgCIJgEBLYLaS5udnpJNiKyX4mu4H4uR3xcy9OuElgt5CCggKnk2ArJvuZ7Abi53bEz7044SaB3UJMXsgAzPYz2Q3Ez+2In3uRRWBcjglDB5fCZD+T3UD83I74uRcn3MydFcAB+vr62L59e8LXN27cyNe//nXbrr97926uvfZadu7cSVlZGSeddBLve9/7lhxDOTExwY9+9CP+9re/AbB582bOO+88ysrKAHj22We5/fbb2blzJ5OTkzQ1NaGU4q1vfattHk5QVVXldBJsRfzcjfi5FyfcJLBbyObNm7npppv22/7444/zwx/+kJNPPtm2a09NTXHJJZewYcMGLr/8cnp6erjqqqsoLCzk1FNPTXjcd77zHQYHB7n00ksB+N73vsd3v/td/ud//geAF154gQMOOICtW7dSU1PDk08+yQ9+8AMKCgo47rjjbPPJNDU1NU4nwVbEz92In3txwk2q4i1keHiY6urqBT8TExP85Cc/4bTTTuONb3yjbde+//77mZ2dZfv27bS3t/OGN7yBrVu3cvvttyesCuru7ubJJ59k27ZtHHrooRx66KFs27aNJ554gp6eHgBOP/10PvjBD7J+/Xo8Hg+nnHIKxxxzDI888ohtLk5g8jhaED+3I37uxQk3KbFbyOTk5IL/JyYm+PrXv87GjRv5wAc+sOSx999/P1dfffWS+3z84x9ny5YtcV978cUX2bBhA4WFhdFtRx11FD/72c/Yu3cvjY2N+x2zY8cOiouLOeyww6LbDjvsMIqKitixYwetra1x/aampqirq1syrW5jcd6Zhvi5G/FzL064SWC3kPz8/OjfwWCQyy+/nNzcXD75yU8uO1fw5s2bOfjgg5fcZ6m2mpGREWpra+PuPzo6Gjewj4yMUFFRsSBtHo+HyspKRkZG9ts/Pz+fJ554gmeeeYZvfetbS6bVbcTmnYmIn7sRP/fihJsEdgtpa2uL/n3zzTezY8cOLr/8ckpKSpY9tqSkJKn9nGR8fJzvfOc7nHvuucs+hLiN2LwzEfFzN+LnXpxwk8BuIRMTE5SVlfHggw9y2223cckllyQ961C6VfHV1dWMjY0t2DY6OgokLulXV1fj9XoXrD4UCoUYGxujurp6wb7PP/88l112Ge9///s55ZRTklFyFZG8MxXxczfi516ccJPAbiEDAwMMDAzwve99jw9/+MMcddRRSR+bblX8IYccwk033YTP54vOdPTPf/6TmpoaGhoa4h5z6KGHMj09zY4dO6Lt7Dt27GBmZoZDDz00ut9zzz3HV7/6VU488UTe9a53Je3kJgYGBoz9YgHxczvi516ccJPAbiETExNcddVVbNy4kS1btsRtp15cEo6QblX8cccdxy9/+UuuvPJKTj/9dPbs2cNvf/tbzjjjjGhp/NFHH+WnP/0pX/va16itraWtrY2jjjqKq6++mm3btgFw9dVX87rXvS7ace7ZZ5/lK1/5CqeccgpHHnlk1CknJ4fKysoVp1cQBEGwB48JM/4opUJaa6eTwV133cX3v//9JfexM527d+/mmmuuWTBBTWxgv+eee7jyyiu57rrroqX4iYkJrr322gUT1PzXf/1X9Anziiuu4N57793vWvX19Vx//fW2uWSa2JoOExE/dyN+7sVmt7i9siWwW0gwGCQnx9ypAUz2M9kNxM/tiJ97sdktbmA38510CJMXMgCz/Ux2A/FzO+LnXmQRGJdjQu3HUpjsZ7IbiJ/bET/34oSbBHYLSdQxzhRM9jPZDcTP7Yife3HCTQK7hZh8c4LZfia7gfi5HfFzLxLYXU5/fz9XXHEFX/nKVyw/95VXXskvf/lLy8+bCv39/bae/yc/+QnXXnutrddIhN1uTiN+7kb83IsTbjKO3UKmpqZsOe/u3bt57LHH+PGPf7zfa4888gjf/va3edOb3sSnPvUpW64f4cYbb6S2tpZzzz3XlvNv3bqV8847j3e9611x57ZPhkTD8woLC/nNb36T8Div18vtt9/Ogw8+iM/nY9OmTXzsYx8zZrEbu+7NbEH83I3Jfk64SYndQuwaq/iHP/yBY489Nu4ENnfffTennnoqjz32GBMTE7ZcH8IdQDo6Ojj66KNXdPzc3Nyy+1RWVnLkkUfypz/9aUXXADj33HO56aabFvw0NjYuu2Tu7bffzqOPPsqnP/1pvvnNbzI1NcVXv/pVAoHAitOSTZg6RjiC+Lkbk/2ccJMSu4UsXuZ0bm6OG2+8kQcffJCpqSnWrFnDf/7nf7J+/froPk888QQ33HADAwMDrFu3jlNOOYXvfOc70UlkAoEAf/3rX7nwwgv3u97Q0BDPPvssF110ETt37uT+++/nHe94x4J9ljs/wAsvvMBPf/rT6MQ2mzdv5uyzz17wILFz506CwSDr16/nyiuvZGxsjEsuuST6ejAY5JxzzkEpxbvf/W4uvvhiWltbKSoq4t5776W+vp7//d//5c477+R3v/sdg4ODFBUVcdBBB3HJJZeQm5sLhCfIufnmm/nIRz6yojwoLS2ltLQ0+v/zzz9Pf38/F110UcJjJicnefTRR/nEJz7BkUceCcBFF13EOeecw9NPP53S1MDZyuJ70zTEz92Y7OeEmwR2C/F6vQv+v/HGG3n44Yf5xCc+QWNjI7fffjuXXnop11xzDTU1NQwODvKNb3yDt7/97bztbW+js7Nzv+r23bt3Mzk5ybp16/a73l/+8heOOOIIKioqOP7449FaLwjsyZ7/y1/+MmeeeSb//d//zfj4ONdffz1XXXUVn//856P7PfbYY2zatInc3Fze9ra38fnPf57h4WFqamoAeOqppxgdHeX444+PHvPAAw9w4okn8s1vfpNQKMTOnTu55ppr2L59O+vXr2dycpJnnnlmQXoOPvhg9u3bR19fH01NTQCcfvrpS77v69ev59JLL4372l133cXq1asXrDm/mJdffhm/3x8N6gCrVq2itbWVHTt2GBHYvV4vFRUVTifDNsTP3Zjs54SbBHYLGRoaiv49MzPDn/70Jy644AJe97rXAXD++efzzDPPcMcdd/DBD36QO+64g8bGRj760Y8C4Se7PXv2cMstt0TPMzg4iMfj2a9nZSgU4p577uHss88G4Nhjj41OJxt5CEjm/LfddhtvfOMbOfXUU6Pbzj//fLZv387o6Gh04ZnHH3+ct7zlLUB48ZjW1lbuvfde3vve9wLhh4zNmzcvmD++vr4+em0I9wcoKipi8+bN0dqANWvWLPCKPCgMDAxEA/sVV1yx5PteWFgYd/vk5CQPP/wwZ5111pLHj46OkpOTs9+Hr6qqKu58/25kaGjI2C9OED+3Y7KfE24S2G2ir68Pv9+/oKSYm5vLIYccQldXFwB79uzZryR+yCGHLPh/dnaW3Nzc/aYkfPrpp5mYmGDz5s0AFBcXc/TRR3P33XdHz5nM+V9++WX6+vp4+OGHo9siEyr09/dTVVVFb28ve/fuXbDi24knnsif/vQn3vve9zI+Ps7jjz/OxRdfvODcBx100IL/jzjiCFatWsW5557LUUcdxRFHHMExxxyzoMo/0h7l8/mi25Jd+nYx999/P6FQaEEtgiAIgulkLLArpW4EPgDMxmz+rNY67iLkSqktwH3AZMzmZ7TWx9qVxnRpa2tLar/IoizJUFFRgd/vZ3Z2dkHJ9O6772ZycpLTTjttwf7FxcV89KMfTViKXUwoFOLEE09EKbXfa7W1tUC4tH744YcvCNTHH388N910E88//zy7du2KdnyLZXEaSkpKuOKKK3juued46qmnuPXWW7n55pu5/PLLo9caHx+PekdYaVX8XXfdxbHHHkt5efmSx1dVVREMBvF6vQtqHEZHR9mwYcOSx7qFZO9NtyJ+7sZkPyfcMl1iv0lrfU4K+we01q5ZpDe2VN3U1EReXh4vvPBCtEo5EAjw4osv8uY3vxmAlpaW6KpqEV566aUF/69duxaArq6uaOl7fHycxx57jO3bt3PggQcu2P9LX/oSf/3rXznhhBOSOv+BBx5IV1fXkqXixx9/nBNOOGGBX3l5Occccwx33303u3bt2u/1ROTm5rJp0yY2bdrE+9//fs466yyeeOIJTjrppKhnXl4eBxxwQPSYlVTFv/TSS/zrX//inHOWv90OOugg8vLyeOqppzjuuOOAcPVZT0/PgloKN2PqAhsRxM/dmOznhJu576YDxE72X1RUxMknn8xNN93E3//+d7q7u/nhD3/I6Ogop5xyCgAnn3wyfX193HDDDfT09PDII4/w5z//Gfh3qb6yspIDDzyQ559/Pnru++67j5KSEo477jja29sX/BxzzDHcddddSZ9/69atvPTSS1x99dW88sor9Pb28sQTT/CDH/wAgLGxMV588UVe97rX7beYwYknnsgDDzzA7t27o+3vS/HEE0+gteaVV15hYGCABx98kOnp6QVPtB0dHaxfv35BsG5ubl7yJ1Laj+XPf/4zzc3NbNy4cb/XXnrpJc4///zoQ05paSmbN2/mxhtv5KmnnuKVV17hu9/9LgcccACbNm1a1ssNmLzIBoif2zHZzwm3TJfY36OU2goMAbcDl2mtlxp8nauU6gbygX8AF2utn85AOi0h0rHtyiuvZHJykrVr13LppZdGO4jV19fzhS98gR//+Mf88Y9/ZN26dZxxxhlcddVV5OfnR8/ztre9jbvuuot3vetdQLga/vWvf310iFgsb3jDG7jzzjvZs2cPLS0ty55/zZo1fOMb3+CWW27h4osvJhgM0tjYGB2v/re//Y1169ZRXV29X0eyjRs3UldXx6pVq5KaUKa0tJTHH3+cX/3qV8zOztLY2MgFF1ywoLr7oYce4swzz0zhXd6fqakpHnroId73vvfFfX12dpY9e/YwO/vvVqGtW7dy33338f/+3/9jdnaWTZs2sX379rjvsSAIQjaTsfXYlVKvAXqAQeAw4CfAK1rruN/iSqlGoAHoAMqAzwHnARu11r2L9g1FSn0FBQVs2bIlWlJraWkhPz+fzs5OQqEQVVVV1NTU0N/fz9TUFAUFBbS2tjI+Ps7g4CAQ7j2em5sbfdKqqamhqqqKvr4+pqenKSwspKWlBa/XG+0J39bWhtfrZWxsDAi3T1dWVtLb28vMzAxFRUU0NzczOjrK8PAwAO3t7QSDQbq7uwGoq6vj/vvv55ZbbuEb3/gGpaWlNDY2snfvXj772c/yoQ99iBNOOAG/309PTw8QfjgoKyuju7ububk5SktLaWhoYHh4mNHRUTweD+3t7fh8Pnp7e3nggQe48847+cUvfkFPTw9+v5+ysjLq6+sZGhrC6/WSk5NDe3s7MzMzfPWrX2XNmjV88IMfZGZmBq/XSyAQoKKigvLycj784Q+zdetWXv/617N69Wqmp6ejUyg2NzdTWFhIZ2cnwWCQyspKamtrGRgYYGJigry8PNra2picnGRgYICOjg7uuOMOrrrqKnp6emzPL4/HE+3IGDlvqvlVUVFBT08PPp+PkpISGhsbGRkZYWRkJPrep5NfAA0NDZSUlNDd3b1sfvX19QHhpqCioiK6uroIBALk5ORwwAEHMDg4yPj4OLm5uWnnV6Y/X7H5tfjzFQwGOeigg4zJr4qKCurq6qL5NTs7y6GHHmpMfi3+fE1PT3PooYcak1+Rz9fY2BhdXV0UFxfT1tZmeX6tXbs2boetjAX2xSil3gDcD5RprWeX2T1yzE7gm1rrHy/aHtJaW5/IDBApSVdUVPDiiy/yo5mfb9kAACAASURBVB/9iC1btuw3betzzz3HxMREyjO/JXv+RPz2t7/lzW9+M6tWrYpui3Q0+/3vf88999zD9ddfT15e+pU/Dz/8MKtWrdqv574gCIKbiB3VsxiLZ6KLG9idHO4WnP+dfBfx8DGp7J9Rent7Ux6a1dfXx29+8xvGx8epra3l5JNPjluF/B//8R8rSlOy50/Ee97znujfEb/BwUHOPfdc6urquPDCCy0J6sCy077ayUryzk2In7sRv+wnUTDv7+9f0FQZu59d081mcrjbGcCdWutRpdQ64HJAa61nEux/AtAF7AJKgE8Trpr/c4aSnDIzM3FVluScc85Jquf2SrHy/BG/hoYG3FpDkoiV5J2bED93I37Zy1Klc2BBX55Ex1od4DPZK/5jwC6l1CRwF/AYEJ0QXCn1AaVUbEe6TcA9wDjh4H408FatdXfmkpwaRUVFTifBVkz2M9kNxM/tiF/24fP5lg3qIyMjzM3NLTuDZTLnSgXH2titxM1t7IIgCII7SDb4LhXIF08PHo8USvBxm6ZlHLuFRHrEm4rJfia7gfi5HfFzlmRL1JEe+7FMTk4uu0+i6620FC+B3UL27dvndBJsxWQ/k91A/NyO+DlDstXtSwXryDTZqR6XShoWI4vACIIgCMI8VlS3p0rkXEtV06fS0U7a2C3E7/dbNvQrGzHZz2Q3ED+3I372Y1dADwQCKc9gmUw7PEBBQYG0sduNCQ9JS2Gyn8luIH5uR/yyAytL6XYigd1CIlMhmorJfia7gfi5HfFzLy+++GLKx6T7ACGBXRAEQRBsIDK/fqZXeJPAbiF1dXVOJ8FWTPYz2Q3Ez+2In/OkWoqOBPNIZ7dUg3s6pXYJ7BZSUVHhdBJsxWQ/k91A/NyO+LmL2CAeuwR3pkruEtgtZM+ePU4nwVZM9jPZDcTP7Yifs6RSel4cvKenp5d83arrxiKB3UKWmuzfBEz2M9kNxM/tiJ87iBe0g8FgUvtZiQR2CykuLnY6CbZisp/JbiB+bkf8nCPZUnOiYJ1oDLudwV0Cu4U0NTU5nQRbMdnPZDcQP7cjftnNUkF6qZXrkgnuK6mOl8BuIaOjo04nwVZM9jPZDcTP7Yife5mbm8v4NSWwW8jw8LDTSbAVk/1MdgPxczvi5wzJlJaXK3UvN1WtHVXyEtgFQRAEwSb6+voyfk0J7BbS3t7udBJsxWQ/k91A/NyO+GUny5W2Ozo6mJ2dpaOjI63rpNrOLoHdQgKBgNNJsBWT/Ux2A/FzO+LnXjyeuAuwLcDq6ngJ7BbS09PjdBJsxWQ/k91A/NyO+LmPSCk9MqVsuqX2VJDALgiCIAgpYnUp28rzSWC3kFWrVjmdBFsx2c9kNxA/tyN+7iK2dB473C1TpXYJ7BZSXl7udBJsxWQ/k91A/NyO+GWepTqsLVW6Xhy8400pazcS2C3ExHaiWEz2M9kNxM/tiJ97ibSxR1iq1L7UA0MqPePzkt5TWJblJiJwOyb7mewG4ud2xC+z17aqtA7J9Yq3GgnsFlJSUuJ0EmzFZD+T3UD83I74WU+ih4mVBvVEOFEVL4HdQhobG51Ogq2Y7GeyG4if2xE/a1iuZiCdoJ6oil3minc52TrfsVWY7GeyG4if2xG/9PD5fI4EdYi/bKvdveMlsFuIySsUgdl+JruB+Lkd8VsZywX0kZGR6E88Ojs70x5fnpeX+YpxqYq3ECc6SWQSk/1MdgPxczvilzzJdMSzYtW2CJmcUS5ZJLBbiFsXMkgWk/1MdgPxczvitzxWBXSwdha42dlZy86VLFIVbyFOdJLIJCb7mewG4ud2xC8x6Va3LyaVoJ5Mad2J2hYJ7BayZ88ep5NgKyb7mewG4ud2xG8hkWCeTEBPBauDOuw/QY2VaUiEVMULgrBirJ5YJNUvQeHVRzq92xORajDNxnb1WCSwW0h9fb3TSbAVk/1MdgNr/eycJWyl566urjZ6dja3+K30wczJz58dpfRYnGhGkcBuIaWlpU4nwVZM9jPZDazxSzT1ZnV1ddrnTheZmS07iHePJBPsrfr82VX1nk4JPdWZ56zoSCht7BbS3d3tdBJsxWQ/k90gPb94bZmx7ZexnZMW/2SK3t7ejF3LCdzsl0x7uBOfv2SCekdHR9rV7k40L0mJ3UL8fr/TSbAVk/1MdoPU/VJdGCMRSx1jZUlf8s8dxN5XsQHPCr9U7s90ZpJLlXi94jds2GDZ+eMhgd1CysrKnE6CrZjsZ7IbJO9nVUBPBiuDvluqqleKiX6x91qmPn+ZDOgRjF4ERil1I/ABIHa0/me11lcvccxJwOXAWuAV4JNa67vsTGc6SAcs92KyGyzvZ9XSlVZNpJLqw0ReXl5Gq/6TwcoaiVWrVll2rmykqqoKn8+3bLX1SlZki5DqcqtW8WroPHeT1vqcZHZUSq0F/g84D/g1cBpwm1Jqg9Z6t31JXDn79u2jtrbW6WTYhsl+JrtBfL+VlM6XK/Esfj1TM6Z5vV4qKioycq1ksfJBw+v1Gj373PDwMDU1NQmr6tPFqaAO4YdOK5oaUnlQzOaq+A8D/9Ba3zL//8+UUh+b336Zc8lKzNjYmNHBwWQ/k91goZ8dAT3Z4+wKTlNTU1kX2K1kamoqYR5lw6iEdBkfH6empmbBtmSDfDr3bibGo+fm5i4I7Eu1r1v1+ch0YH+PUmorMATcDlymtZ5IsO8m4B+Ltj05vz0ryckxe5CByX4mu0HYz6rq9gixX4rJdgayK9C/mhdJyVQHRTtZLv9WOoY/0T2cbED3BAI0/PNxSvv3MNnYwt4jX08ozjKs2UYmA/v3gM8Bg8BhwE+A64AzE+xfDowt2jYKxP0G2bZtGxB+stuyZQsbN24EoKWlhfz8fDo7OwmFQlRVVVFTU0N/fz9TU1MUFBTQ2trK+Pg4g4ODALS2tpKbmxu9KWpqaqiqqqKvr4/p6WkKCwtpaWnB6/UyNDQEQFtbGy0tLezatQuA2tpaKisr6e3tZWZmhqKiIpqbmxkdHY2uPdze3k4wGIwO9airq6OiooKenh58Ph8lJSU0NjZGhw55PB7a29vx+/309PQA4bbTsrIyuru7mZubo7S0lIaGBoaHhxkdHY0e4/P5okNmGhoaKCkpobu7G7/fT1lZGfX19QwNDeH1esnJyaG9vZ2ZmRn6+voAaGpqYvXq1XR2dhIIBKioqKCuro7BwUHGx8fJzc1l9erVTE9P09/fD0BzczOFhYV0dnYSDAaprKyktraWgYEBJiYmyMvLo62tjcnJSQYGBjKeXx6Ph66urugxgFH5VVRURFdXF4FAgKKiIgCGhoaYnJwkJyeH0tJSZmdno0tm1tTUkJ+fz0B/H5NPPE7guacprKxk5oST8IdCeDweiouLCQQC7N69m8LCQnw+H6FQiJdffhmA1atXU1BQwMzMDIFAgJycHIqLi/H7/dHFMIqLi/F4PExNTfH888+zevVqysrKGB4exufzkZ+fT21tLVNTU3i9XuDfbcyRPC8vL6e0tJR9+/YxNzdHfn4+AJOTk4yPj0ff62AwGM3ziooKSkpKGBoawu/3U1hYSHV1NRMTE0xMTESPCQQC7Nu3D4DKykqKi4sZHBwkEAhEjxkfH2dychKPx0N9fT1zc3PR+6SqqorCwkIGBwcJBoMUFRVRVVWF1+tlamoq7jHV1dUUFBREjykuLqayspKxsTGmp6fxeDyEQqH4+TUwQCgUoqSkhIqKCkZHR5mZmSE3Nzd6TCQwNjY2kp+fH72HKysrqaqqYmBggOnpafLz82lubmZiYiL6HjQ3N5OTkxO9h6uqqqisrGTv3r3MzMxQUFBAU1MT4+PjUZ+Wlhbg31PFVldXU1FRQV9fHz6fj8LCQhobGxkbG2N0dJRQKEQgECAYDEbv+5qaGsrLy+nt7WVubo6ioiIaGhoYHR1lbGws+jmem5uLm1+Dg4Pk5uZSVFSEz+eLtnWXlJTg8XiitQBzc3MEg0EKCgrweDwEAgH8fj95eXlU7+2lOC+P4uEh8v1+Am0H4F29JnpMYWEhwIJjIu+7z+eLFhhiPytTU1OEQiHy8/MpKChgdnYWv98fN49ra2vJzc1lYGAAr9e7X36tW7eOeHhCoVDcF+xGKfUG4H6gTGu93/I3SqnfAbu11ttjtl0JtGmtty7aN6S1tjnFyxMJCKZisp/JbhBuo13sl6ikN3LzDYzf9Qc8c3OE8vPh6DcReseCj9ySJZ5Uh/JYtbKXydPRWuWXrSX4dD5/K611SqbUfuAffkPJ8FD0/6maOl55x2kppS8SsNOpgl+cbzH3QtyqDifb2CNjABLVwTwNHL9o25HAPbalKE16e3tZu3at08mwDZP9THYD2Lt3b9IBdPb5Z/DMl248c3OEdu20M2l0dnamHdyHh4dpbGy0KEXZh1V+kSCYbQE+lfszFSLnjBfgI4F2qQA/2dhC0dgoOQE/wdw8JhtblrxevOA9OTmZcGa9pZzj5VGyD3eZHO52BnCn1npUKbWO8DA2rbWeSXDIT4HPKKXOBG4F3gu8BjgrIwkWhFcphesPx9fT9e8S+9qF1X3LlXQ6Ojpsn4BDSI9smQrYCqqrq5ftQLdcgF98T0fv30MOwXOnJrRrJ56166g9SVGbl37YTCWgr6SmJpMl9o8BVyulCoEB4Dbg0siLSqkPANdqrcsAtNavzHe0uxy4AdgFnJqtQ90Ao0sMYLafyW6Q2jjoqjM/xLh3LFxSX7uO0EnKxpRZQ1VVldNJsBU7/LKp9J6pcfrt7e1Llt73Iy9vv2aoVIm0w6dR3Z4yjrWxW0m2tLGH5jsYmYrJfia7AczOzi7ws7NdMkIqpfZ0q2FNz79M+DkZ4NP1y8RSrakSuaeXckunun2euCc2e4xPhon0sDYVk/1MdgOiPZqzlXS/ZCO95U0lE35OztyX7v25kocSK9r029vbE/5EiJd31dXVcUvoVnUAzeYJalxHIBBwOgm2YrKfyW7gzHzVmWxrd8Ivk2TKz6nqeafyb6m299jX0yHWzcrq9qWQwG4h5eXlTifBVkz2M9kNrFnveiWzdGUquBcXF9t+DSfJtF+mA7xV67GvFLtmRKyuriYQCGQsoEeQwG4hpi/UYLKfyW4Qnkwn20ln2FtlZaXFqckunPLLVO95K+7PZHrH28VS71GsW6bmWpA2dguJzHBlKib7mewGRGfmWg47OhR1dHQkXdpf6fUjM5GZilN+mSqxJ3t/ZgOR9vHYn6Xwer2Wtp8ng5TYLcTr9bqiZLRSTPYz2Q1gYmLC8UVuIsF9uar5SHBPpfQ+PT1tVKl9cbDwer1JBdl0SqxO9oq36v60u9S+kvfIie8WCewWkuuCxQHSwWQ/k90guxa5SbbdPZWq+WzyW4wVATNZv2wYk74SrMw/J6vk4+HEd4uMY7cQGUvrXkx2g+THsae7GlaqJBPgkwnuVuZfNgZH0+9Pq/3sCOwrvS/y8/PtzDsZx243MzOJZsc1A5P9THaD7PVL5oGhs7Nz2bb3VJb1jNdGmmx7qVNka/5ZhdV+2ZSPTuSdBHYLiSyZaSom+5nsBkSXxc1Gku1ct1RwT7aElk1f+KmQzflnBXb4WZnX6ZzLie8WCeyCIGQF6Qb35XBrUBdWjhV57sb7RgK7hTQ1NTmdBFsx2c9kN4CGhgank5AUyZTe4wX3mpqaJY9x45dzLG7Jv5Vip5/Tee/Ed4sEdgspKipyOgm2YrKfyW7w7xWmrMauTnXJBPfYAJ+fn59wX6e/2K3ArvzLFuz2W+k9YMW948R3iwR2C7F7tSCnMdnPZDewdxEYp4J7LHa1QUcmFnH6p6+vz5bzZgvZuEiRVQ+ETny3yDh2C5GFKNyLyW7gTr/lhsLFDoOLN2zXgiUxswa78m/x+5HK6AIrycT96dT4dic+exLYLaSiosLpJNiKyX4mu4G7FrlZKqAnGtNeUlIS/dukgB4hU/dn7PuUySCfqfsz2eBuZfONE98tEtgtxOQpScFsP5PdYPnOZSvBjir4REF9uUlq4r3u9mAeixP3Z+T9y0SAt+P+TESmS+5O5J20sVuIjDV1Lya7QXqL3NjVhh7Lhg0b4gb19vb2JYN6ZFKZWL9saz+2Aifvz0y8n5lehCmTHSqdyDsJ7BYyMTHhdBJsxWQ/k90AJicnnU5CXNIN6BEmJyeNDOgRsuH+tLPTnRP3Z6LgbnXQdyLvpCreQvLyzH47TfYz2Q2yc5GbRAE9EUu1nZs+HCzb7k+rq+mduj/trpYvKChwJO+y625xOW1tbU4nwVZM9jPZDaClpWXZfTI1LCfVdvRkOsOZnn/Z6mdVgE/m/rSL2OBuRxW9E3kngd1CpqamKC0tdToZtmGyn8luEF6vPLbnuBPYEdAjmJ5/2e6Xbm/6bLg/7cKJvJM2dgvZu3ev00mwFZP9THYDGBwcdPT6qbSjL24/T6Zt1/T8c5PfSibKGR4ednRCHTtX9nMi76TELgiCbaRSSl/8xWpqRzhh5WRyCJ6bkcBuIc3NzU4nwVZM9jPZDaCxsdHpJNiK6fknfgvJ9gAf+1DqRN5JYLcQ00sYJvuZ7AZLL5JiAqbnn/gtfVy2BPh4Hk7knbSxW4jpC4mY7GeyG2TnIhtWYnr+id/SOLmwzXLXlkVgXE68hShMwmQ/k91A/NyO+CVPpkrxyT5EOJF3EtgtpKqqyukk2IrJfia7gfmL3Jief+KXOnYF+FRrBZzIOwnsFpLJhQycwGQ/k90gs3NjO4Hp+Sd+K8eKFevSqeJ3Iu8sa2NXSpk5u0AKuGms6Uow2c9kN3B+HLvdmJ5/4mcNqbbDW9Fu70TeWRLYlVKbgHErzuVmsnWhDasw2c9kNwjPfmUypuef+FnLcgHbyo54TuSdVMVbiOlDikz2M9kNsm8REasxPf/Ezx4y0YveCTezP+0ZJlsXarAKk/1MdoMVLLLh9+O5U8OunTRW1rD3yNcTysIV4iKYnn/i516ccJNx7BaSDWsm24nJfia7QerVgZ47NTz2EJ493dS+2EHDPx+3KWXWYHr+iZ97ydr12JVSy3XrM3ssRpIMDAxQVlbmdDJsw2Q/k90AhoaGUlthatdOPHNzAOQE/JT277EpZdZgev6Jn3txwi3ZqvghYKlR9p5lXhcEwU2sXUdooB/P3BzB3DwmG51bL1sQhNRINrAfb2sqDKG1tdXpJNiKyX4muwE0NTWltH/oJBV+Wt+1k33zbezZjOn5J37uxQm3pAK71voBKy+qlMoBHgaOAdq01vtNZK2U2gLcB8Q2Dj6jtT7WyrRYiek9j032M9kNVuCXl0foHVsB6O/osCFF1iL5525M9nPCLaUrKqVWJ3gpBMxorZOdBeMiIJmBtQGttWsaXjo7O1mzZo3TybANk/1MdgPo7u6Ouwa6KZief+LnXpxwS/VRYjdLtKUrpbzAT4DPaq39CfY5GPg48B7gnyleP6uRhRrci8lurwZMzz/xcy9uWATmTODbwDVAZPzL64HzgEsJ947/EuFZ6L68+OD5KvgbgE8Do0lcL1cp1Q3kA/8ALtZaP51imjOG6fNxm+xnshtAZWWl00mwFdPzT/zcixNuqQb284GLtNb/F7PtXqXUi8CFWuvjlFIDwGXECezAhUC/1vo2pdQBy1xrB3AE0AGUAZ+bv9ZGrXXv4p23bdsGhGcS2rJlCxs3bgTCE3Pk5+fT2dlJKBSiqqqKmpoa+vv7mZqaoqCggNbWVsbHx6Pzabe2tpKbmxtdR7empoaqqir6+vqYnp6msLCQlpYWvF4vQ0NDQHgSgvLycnbt2gVAbW0tlZWV9Pb2MjMzQ1FREc3NzYyOjjI8PAxAe3s7wWCQ7u5uAOrq6qioqKCnpwefz0dJSQmNjY2MjIwwMjKCx+Ohvb0dv98fXV+7vr6esrIyuru7mZubo7S0lIaGBoaHhxkdHY0e4/P56O0Nv20NDQ2UlJTQ3d2N3++nrKyM+vp6hoaG8Hq95OTk0N7ezszMDH19fUC481VVVRWdnZ0EAgEqKiqoq6tjcHCQ8fFxcnNzWb16NdPT0/T39wPQ3NxMYWEhnZ2dBINBKisrqa2tZWBggImJCfLy8mhra2NycpKBgYGM55fH46GrqyuaX4BR+VVUVERXVxeBQIDi4mIgPOxtcnKSmZkZVq1axezsLKOj4WfsQCBATk4O09PThEIh8vPzKSgoID8/n5ycHEKhED6fj5ycHPLz82lqamJoaAi/309hYSEAfr+fQCAQPWZ6epri4mL8fj+zs7MAFBcXEwwGo3leVlZGWVkZe/fuZWZmhoKCApqampbNr8Wfr+rqamPyK97nq6qqytWfr+W+DysqKozKr8j3YSS9duTX2rVriYcnlWoCpdQ0cLjWeuei7QcDT2mtS+YD9vNa65JF+xwE3A+8VmvdP7/fv0jQeS7B9XcC39Ra/3jR9pDWOmkPu+jv76exsdHpZNiGyX4mu0G4jb2hoSH6/8jIyH77RL64F9ORoPNc7PYNGzbE3SfR9njt/YtLNqlM92l6/omfe7HZzRNvY6ozz3USrnZfzLlA1/zfq4DhOPu8cf6155RSQ8CT89ufUUp9PMnrB0kgkg2YvtCGyX4muwHMzMw4nQRbMT3/xM+9OOGWalX8p4DfKqVOAZ6Y3/Za4EDCneEAXgf8Os6xvwb+EvN/K/AocCLhavcFKKVOIPywsAsoIdwu3wD8OcU0Z4xMLCjgJCb7mewG5i8iYnr+iZ97ccItpcCutf6jUmod4V7th0Q2A9dorbvm97k6wbFTxAxxU0pFrt2vtZ5QSn0AuDZmeNsmwj3s6wiPZX8SeKvWujuVNGcSkydZALP9THaDcHusyZief+LnXpxwS6mNPVvJljZ2r9dLRUWF08mwDZP9THYD2LdvH+Xl5dH/TWtjNz3/xM+92OwWt2k65SlxlFINwDZgPeEx7R3AD7XWe9NKngEMDQ0Ze3OC2X4muwEMDw8vCOzxaG9vTxjcsx3T80/83IsTbil1nlNKvQF4GXg/MA3MAB8EdiqljrE+eYIgmIbJM+AJQjaQaon9O8AvgI9prYMQnXTmGuByIGvncc8EbW1tTifBVkz2M9kNzG9jNz3/xM+9OOGW6nC3I4DLI0EdYP7v/wWOtDJhbiQnJ9W3012Y7GeyG4if2xE/9+KEW6pXHAPizWa/huSmiDUat7ZPJovJfia7AdGZubIBO95r0/NP/NyLE26pVsX/EvixUuqzwCPz294AfItwFb0gCC7F5C9XQXg1kWpg/yzh7vU3zB/rAXzAD4HPW5s091FTU+N0EmzFZD+T3QCqqqqcToKtmJ5/4udenHBLdYIaH3ChUuoLhGebA3hlfvKZVz2mf3ma7GeyG5i/upvp+Sd+7sUJt2UDu1Jq2ZlflFIAaK2VBWlyLb29vUb3PjbZz2Q3sH4hCk8gwKaeV2gYH2NveSXPBoMcNr+iohOYnn/i516ccEumxL7P9lQYgukLbZjsZ7IbEF0y1Spy//JH1g30kRcKUjEdrrDzpxnYR0ZGVrx2ten5J37uxQm3ZQO71vojmUiICRQVFTmdBFsx2c9kN9h/etaQf47RX9zM7PPPULj+cKrO/FBK52sYHyMvFB71mhcK0jA+xh7LUps6puef+LkXJ9xSnlJWSIypVUkRTPYz2Q2gqakp+vfIyAijv7iZybv/SMjnw79nfl2lN/9/+x2XaJ74veWVVExPkRcK4vfksLc8fht+R0dHwvnircT0/BM/9+KEm7mzAjjA2NiY00mwFZP9THaD8EIUscw+/wwhnw+AkM/H7PPPLHm8JxCg8e+PcOAffkPj3x+ho7GdnfVNDJeUsbO+iWeb401vkTlMzz/xcy9OuElgt5B9+8zujmCyn8lusP9qboXrD8czXz3vKSigcP3hSx7f8M/HqX2xg5LhIWpf7GBDfydPtx7IXYcdxdOtBxJ0eOYw0/NP/NyLE25SFS8Ir0IibeqxbezePb0J9y/t30NOwA9ATsBPw7i5JSxBcDsS2C1k9erVTifBVkz2M9kNoKWlZcH/nrx8qj/0n0kfP9nYQtHYKDkBP8HcvIRt6k5hev6Jn3txwk0Cu4WEQiGnk2ArJvuZ7GYFe498PRAuuU82tvBs4f5ru2eqo1w8TM8/8XMvTrhJG7uFdHd3O50EWzHZz2Q3gD170huMFsrNpf+1x/LKO07jnuJKx9vUF2N6/omfe3HCLbs+nYIgCIIgpIUEdgupq6tzOgm2YrKfyW5g9iIbYH7+iZ97ccJNAruFVFRUOJ0EWzHZz2Q3gPLy/dvETcL0/BM/9+KEmwR2C0m3HTPbMdnPZDeAvr4+p5NgK6bnn/i5FyfcJLBbiNULbWQbJvuZ7Abgm59lzlRMzz/xcy9OuElgt5Di4mKnk2ArJvuZ7AZmL7IB5uef+LkXJ9wksFtI7EIbJmKyn8luAA0NDZadK9FYdafGsIP5+Sd+7sUJNwnsFjI6Oup0EmzFZD+T3cDsRTbA/PwTP/fihJsEdgsZHh52Ogm2YrKfyW6w8Muluro66ePSLYVnqhRvev6Jn3txwk0CuyAIKbM4YFsRwFN54BAEITES2C2kvb3d6STYisl+JrsBtLa2LruPm98DN6c9GcTPvTjhJoHdQgKBgNNJsBWT/Ux2AwgGgys+dqWl8UTH2fFFZ3r+iZ97ccJNAruF9PT0OJ0EWzHZz2Q3gN7exGutr5RI4HayN3wE0/NP/NyLE24S2AVBWDHZENQFQViIBHYLWbVqldNJsBWT/Ux2A6itrU3r+GwP4Kbnn/i5FyfcJLBbiOkLbZjsZ7IbQFlZWVrHd3R0WJQSezA9/8TPvTjhJoHdQkxuJwKz/Ux2A3va2CF7Ar7p+Sd+7kXa2F2O6QttmOxnshvA3NycbefOhuBuev6Jn3txwk0Cu4WUlJQ4nQRbMdnPZDdIbyGKRIF7uYCe6PXOzs4VpyURpuef+LkXJ9wksFtIenix5gAAIABJREFUY2Oj00mwFZP9THYDqK+vX3YfOwJupjA9/8TPvTjhlpfxKwJKqRzgYeAYoE1rHbcRQil1EnA5sBZ4Bfik1vqujCU0RYaHh6mpqXE6GbZhsp/JbhCeK76qqsqy8y0ujXd0dDjac970/BM/9+KEm1Ml9ouAqaV2UEqtBf4P+AZQOf/7NqXUAbanboWYvEIRmO1nshssXN1tZGQkY9dNpf09nXSZnn/i516ccMt4iV0pdTDwceA9wD+X2PXDwD+01rfM//8zpdTH5rdfZm8qV4bH43E6CbZisp/JbumQDR3jksH0/BM/9+KEW0ZL7PNV8DcAnwaWe4zZBPxj0bYn57dnJSYvZABm+5nsBtDW1mbZuVbamc5OTM8/8XMvTrhlusR+IdCvtU6mSr0cGFu0bRSI25C3bds2AAoKCtiyZQsbN24EoKWlhfz8fDo7OwmFQlRVVVFTU0N/fz9TU1MUFBTQ2trK+Pg4g4ODQHglrNzc3GhnopqaGqqqqujr62N6eprCwkJaWlrwer0MDQ0B4S9On8/H3r17gfBMX5WVlfT29jIzM0NRURHNzc2Mjo5G1+dtb28nGAzS3d0NQF1dHRUVFfT09ODz+SgpKaGxsZGRkRFGRkbweDy0t7fj9/ujYyPr6+spKyuju7ububk5SktLaWhoYHh4mNHR0egxPp8vOpa5oaGBkpISuru78fv9lJWVUV9fz9DQEF6vl5ycHNrb25mZmaGvrw+ApqYmPB4Pe/fuJRAIUFFRQV1dHYODg4yPj5Obm8vq1auZnp6mv78fgObmZgoLC+ns7CQYDFJZWUltbS0DAwNMTEyQl5dHW1sbk5OTDAwMZDy/PB4PXV1d4ZutvJxVq1YZlV9FRUV0dXURCATIy8ujsbGRoaEhBgcHycnJYdWqVczOzkarCgOBADk5OUxPTxMKhcjPzwcgPz+fnJwcQqEQPp+PoqKi6BKrQ0ND+P3+aAeh3NxcAoHAgmMA/H4/s7OzQLiHfjAYjOZ5WVkZZWVlDA8P4/V6KSgooKmpacn8Wvz5ysvLY/Xq1cbk1+LPVygUYu3ata79fC33fej3+znwwAONya/Y78Oenh7y8vJsya+1a9cSD0/kg2c3SqmDgPuB12qt++cD+79I0HlOKfU7YLfWenvMtivn99+6aN+Q1trO5CfFrl27Er7RJmCyn8luADt37oyWHBK1ZcfrFR+vFL5UyTxeB7p42xKVYmLXZC8oKEh4ncWYnn/i515sdotbz5/Jqvg3AquA55RSQ4Sr1QGeUUp9PM7+TwNHLdp25Px2QRAyhCcQoPHvj3DgH35D498fISeNJWAFQbCfTFbF/xr4S8z/rcCjwInAjjj7/xT4jFLqTOBW4L3Aa4CzbE7niklmrLCbMdnPZDcIV2uulIZ/Pk7tix3kBPwUjY2ysa6Bp1sPtDB16WN6/omfe3HCLWOBXWs9RcwQN6VU5Nr9WusJpdQHgGu11mXz+7+ilNpKeBz7DcAu4FSt9e5MpTlVSktLnU6CrZjsZ7IbpDf7VWn/HnICfgByAn4axhd3fXEe0/NP/NyLE26OTFADMB+gPTH//wz42aJ97gTuzGzKVk53dzerV692Ohm2YbKfyW4QXgSmpaVlRcdONrZQNDZKTsBPMDePveWVFqcufUzPP/FzL064ORbYTcTv9zudBFsx2c9kN0jPb++RrwfCJffJxhaeLcy+JTYl/9yNyX5OuElgt5B017zOdkz2M9kN0quKD+Xm0v/aY6P/B7Nw0hrT80/83IsTbrIIjIWY3AEEzPYz2Q1g1apVTifBVkzPP/FzL064SWC3kH379jmdBFsx2c9kNyA6aYupmJ5/4udenHCTwG4hsQttmIjJfia7AYyPjzudBFsxPf/Ez7044SaB3UJycsx+O032M9kNzF5kA8zPP/FzL064mftuOoDJCxmA2X4mu4G1i8BkI6bnn/i5FyfcJLBbSGSRC1Mx2c9kNxA/tyN+7sUJNwnsFhJZLchUTPYz2Q2IrjpoKqbnn/i5FyfcJLALgiAIgkFIYLeQyJrUpmKyn8luYP44dtPzT/zcixNuEtgtpLi42Okk2IrJfia7gfi5HfFzL064SWC3kK6uLqeTYCsm+5nsBtDT0+N0EmzF9PwTP/fihJsEdgsJBAJOJ8FWTPYz2Q0gGAxm5DodceaRj7ets7OTzs7O/baPjIxE//b5fPh8vqSua3r+iZ97ccJNAruFlJdn36pXVmKyn8lukNk1oTs6OvYL5vG2QfwAPzIyknKANz3/xM+9OOEmgd1CTO+gZLKfyW4AdXV1Gb9motK7HQHe9PwTP/fihJsEdgsZGhpyOgm2YrKfyW7g3CIbiQL5SgJ8JMhHAnxskDc9/8TPvTjhJoHdQrxer9NJsBWT/Ux2A5iYmHD0+vGCeGR7qm3w8Urxpuef+LkXJ9wksFtIbm6u00mwFZP9THaD7FhkI1EQX+q1ZAN8KBRKuqOdGzH9/jTZzwk3TygUyvhFrUYpFdJaO50MQqGQ0atomexnshuE56uO+MUGxFjiBdBke7mvhA0bNqT8WqIFNUKhEDU1NdH/CwoK0ktclmH6/Wmyn81ucU/s/GO8QczMzDidBFsx2c9kN8hOv6UeEJYrwS9+CPH5fMu2w7uZbMw/KzHZzwk3CewW0tfX53QSbMVkP5PdAAYGBiw711Il7VRZqno+9vXlquljayFWMlwu2zH9/jTZzwk3CeyCIDjOcgF+qX06OzsZHh7eb7uJAV4QkkECu4U0NTU5nQRbMdnPZDeAhoYGp5OwLOnUBBQVFVmYkuzD9PvTZD8n3KTznIWY3AEEzPYz2Q3+7be4xBqvI128TnSQuCOdlVXzi6+V6NyLt0f8EnWuq66ujv7txo51r5b700Sk85zLSfSFaAom+5nsBv/2KygoiP5AOODFBj0I9zyP/MSyYcOG/QKqHUF9JUxPTzudBFt5tdyfJuKEmwR2C8nUQhtOYbKfyW4Q32+5AA8kHeCtZCXD6UyoeVyKV+P9aQpOuOVl/IoGU1FR4XQSbMVkP5PdYGm/SHD3+XwLgntsNX0kuMeWPmKDu1Vj2xeTbFV/Xp7ZX2Wv5vvT7TjhJm3sgiDsR2xbfLoT2qRCvOOTbWeHxBPYuL2NXRASIG3sdmPlWOFsxGQ/k90gdb9sqqZP5mFhdnYWMLetVu5P9+KEmwR2C3F6oQ27MdnPZDdYuV+8AJ+os10sdrfDL8bv92fsWk4g96d7ccJNAruFmN7OZ7KfyW6Qvl8qveljSSXAp1qNH7u/qUOlIsj96V6ccJM2dgsxeSwmmO1nshvY47dcO3wq4+GX2h4h3gNCZFusX7x2dre3scv96V5kHLvLmZqacjoJtmKyn8luYI9fbIBMtgQP9ox9DwQClp8zm5D707044SaB3UL27t3rdBJsxWQ/k93AfL9I5zlTMT3/TPZzwk0CuyAIgiAYhAR2C2lubnY6CbZisp/JbmC+n+mLwJiefyb7OeEmgd1C3NgpJxVM9jPZDcz3y8kx+6vM9Pwz2c8Jt4z2w1dKfR14P1ALzAAPAp/UWnfF2XcLcB8wGbP5Ga31sRlI6oro7OxkzZo1TifDNkz2M9kNzPebmpqitLTU6WTYhun5Z7KfE26ZHmB3M/BtrfWYUqoE+BrwSyBRsA5orcsylro0MWHo4FKY7GeyG5jvZzqm55/Jfk64ZTSwa613xPzrAYLAIZlMg51UVVU5nQRbMdnPZDcw3y8/P9/pJNiK6flnsp8TbhmfEkcp9X7gh0AF4Ac+ucTuuUqpbiAf+Adwsdb6aftTuTJqamqcToKtmOxnshu432+5se8mt9GC+/NvOUz2c8It44Fda/1z4OdKqUbgo8CzCXbdARwBdABlwOeAe5VSG7XWvYt33rZtGxD+gG/ZsoWNGzcC0NLSQn5+Pp2dnYRCIaqqqqipqaG/v5+pqSkKCgpobW1lfHycwcFBAFpbW8nNzY3OnFVTU0NVVRV9fX1MT09TWFhIS0sLXq+XoaEhANra2hgcHGRmZgaA2tpaKiv///bOPTiuqzzgP1mSpehpy7JkyZIVXBpc7DiQ0A7QVyYTmDRMvxkenULSAkMpSZNOSdJQSoY+QgstlMCUMASGoaSAU0hp0n4pQ4DSUkiHQieGgF3cmrjWI7KRZUnW+7Fa9Y+7u2zWK1sr37tn9+P7zWQSnXuv8/387dlP595zz2lndHSUxcVFGhsb6e3tZWpqiomJCSBawCOdTjM8PAxAZ2cnbW1tjIyMsLy8TFNTE7t27WJycpLJyUlqamoYGBgglUoxMjICQFdXFy0tLQwPD7OyskJzczPd3d1MTEwwNTWVu2Z5eZnR0eivrbu7m6amJoaHh0mlUrS0tNDV1cX4+DjT09Ns2bKFgYEBFhcXOXXqFAA9PT2cO3eOpaUlVldXaWtro7OzkzNnzjAzM0NtbS179uxhYWGB06dPA9Fs0IaGBgYHB0mn07S3t7Njxw7GxsaYnZ2lrq6O/v5+5ubmchsllDNfNTU1DA39eHrH3r17TeWrsbGRoaEhVldXSaVSXHHFFbHna+fOndTX1+f+TmpqamhtbWVycpKlpaXccpqpVCr3rvlll10GQENDQ+7Y6uoqHR0dNDQ0sLKywvj4OE1NTbS3t0dfBseOsW/fvvOuWVhYIJ1O5z6TKysrOZ+uri7S6XTu76mjo4PW1taqyFdh/5qbm+PAgQNV278u9n04MzPDgQMHqrZ/Xej78Pjx47S2tiaSr71791KMoEvKikgXcALYo6oTGzj/OPCXqvqJgvaKWFL2xIkT6/5FW8Cyn2U3SM4vf1lZ2PjSssWWj72UJWXn5uZyk+csLinrn8/qJWG3ilxStg5oBjb6ol+adUQqAevP+Sz7WXYD+35W1xnPYj1/lv1CuJXtVryIbAFuAx5W1TER6QPuB04S3XYvPP86YIhoRN8E3A10A18qV8yl0t/fHzqERLHsZ9kN7Ps1NTWFDiFRrOfPsl8It3KP2G8EjojIHPAtYB64XlVTInKziORvXHsV8FVghqi4vxh4maoOlznmDWN5T2Gw7WfZDez7+X7s1Y1lvxBuZRuxq2qaqLCvd/wQcCjv5w8CHyxDaLExNjZGS0vVvHZfMpb9LLuBfb/8iXoWsZ4/y34h3EI/Y3ccx3EcJ0a8sMdIX19f6BASxbKfZTew75d9hc4q1vNn2S+Emxf2GLF8KxBs+1l2A/t+1mfFW8+fZb8Qbl7YY6TY+7qWsOxn2Q3K41fsHfZyMT8/H+z/XQ7881m9hHDzwh4jljcyANt+lt3Avp91rOfPsl8INy/sMZK/upVFLPtZdgP7fpYXOAH7+bPsF8LNC3uMWP5wgm0/y25g368al4ktBev5s+znhb3KyW7OYBXLfpbdwL5fdvMlq1jPn2W/EG5e2GPE+gQey36W3cC+3+rqaugQEsV6/iz7hXDzwh4j1m8HWvaz7Ab2/bZssf1VZj1/lv1CuNnuDWXG8iILYNvPshvY9/MFaqoby36+QE2VMz09HTqERLHsZ9kN7PutrKyEDiFRrOfPsl8INy/sMTI+Ph46hESx7GfZDez7LS8vhw4hUaznz7JfCDcv7I7jOI5jCC/sMdLf3x86hESx7GfZDez7WX/Gbj1/lv1CuHlhjxHrM3Mt+1l2A/t+1jeBsZ4/y34h3Oz+bQbA8kYGYNvPshvY97P8HjTYz59lP98ExnEcx3GcS8ILe4x0dHSEDiFRLPtZdgP7fpYXOAH7+bPsF8LNC3uMbNu2LXQIiWLZz7Ib2Pezvrub9fxZ9gvh5oU9RkZHR0OHkCiW/Sy7gX0/65vAWM+fZb8Qbl7YY8T6l4tlP8tuYN/P+iYw1vNn2S+Emxf2GGlsbAwdQqJY9rPsBvb9LL8uBfbzZ9kvhJvt3lBment7Q4eQKJb9LLuBfT/rC9RYz59lvxBuXthj5Ny5c6FDSBTLfpbdwL6f9U1grOfPsl8INy/sMXL27NnQISSKZT/LbmDfz/omMNbzZ9kvhJsXdsdxEsPyimKOU6l4YY+RPXv2hA4hUSz7WXaDyvI7evRo7H+m9WfslZS/JLDsF8LNC3uMrK2thQ4hUSz7WXaDyvdLothbotLzd6lY9gvh5oU9RoaHh0OHkCiW/Sy7gX2/hYWF0CEkivX8WfYL4eaF3XEcx3EM4YU9Rjo7O0OHkCiW/Sy7gX0/65vAWM+fZb8Qbl7YY6StrS10CIli2c+yG9jwK/YcPtuWvwlMsZn4k5OTuf9eXl6uutfjLOTvQlj2C+HmhT1GnnnmmdAhJIplP8tuYMfv6NGj5xX4o0ePcvz48We1DQ4OnlfgJycnq7bAW8nfelj2C+FWV/b/o2GWlpZCh5Aolv0su4E9v2xx379/PwA1NTXntcGPR+8DAwO5tvzivn379lxxr+Tb+dbyV4hlvxBuXthjxPq7tJb9LLuBXb9sMe/o6KCnp+dZbRcr8PDjIl/pBd5q/rJY9gvhVmPh/UERWVPV0GE4zk8c+bey80fCWYo9777Qs/I4yC/oF2orLPIQFfh8KrHIO04eNcUa/Rl7jExNTYUOIVEs+1l2A/t+LS0tuf9e7zl8YduFnsNnf0mplOfw1vNn2S+EW1lvxYvIu4GbgB3AIvB14C5VHVrn/BuA+4C9wNOZc79cpnBLZmJigm3btoUOIzEs+1l2g8ry279//3lFtlhbKbS2tjI7O/ustmK35Kv1Nn0l5S8JLPuFcCv3iP3TwAtUtQ24HBgCPlvsRBHZCzwC/AXQnvn3oyJyeVkidRzHBKWO4C80mz47gq+EUbzjrEdZR+yqeizvxxogDTxvndPfADypqp/J/HxIRG7NtN+bXJSbp9gzO0tY9rPsBuH8BgYGyrLD2+nTpy96zkZH8FB5s+n981m9hHAr+6x4EbkJeABoA1LAXeucehXwZEHb4Ux7RbK6ukptbW3oMBLDsp9lN7DvV1tbSyqV2tC5Fyrwhe2Vcpveev4s+4VwK3thV9WHgIdEZBfwW8D31zm1FThX0DYFnD+9Fbj99tuBqHNde+21XHnllQDs3r2b+vp6BgcHWVtbY9u2bXR0dHD69Gnm5+fZunUrfX19zMzMcObMGQD6+vqora3NdeqOjg62bdvGqVOnWFhYoKGhgd27dzM9Pc34+DgA/f39DA0NsWVL9HRjx44dtLe3Mzo6yuLiIo2NjfT29jI1NcXExAQQfVmk0+ncJgGdnZ20tbUxMjLC8vIyTU1N7Nq1K3crsKamhoGBAVKpFCMjIwB0dXXR0tLC8PAwKysrNDc3093dzcTEBFNTU7lrlpeXGR0dBaC7u5umpiaGh4dJpVK0tLTQ1dXF+Pg409PTbNmyhYGBARYXFzl16hQAPT09jI6OUldXx+rqKm1tbXR2dnLmzBlmZmaora1lz549LCws5EZPvb29NDQ0MDg4SDqdpr29nR07djA2Nsbs7Cx1dXX09/czNzfH2NhY2fNVU1PD0FA0vWNhYYH9+/ebyldjYyNDQ0Osrq4yOzvLwYMHY8/Xzp07qa+vZ3h4mPn5eZqbm2ltbWVycpKlpSXq6qKvmFQqlXufN/v6T0NDQ+7Y6uoqHR0dNDQ0sLKywvj4OE1NTbS3twMwNjbG2toa3d3dAExPTzM3N0dnZyf19fU0Nzfzwx/+kObm5txKXz/60Y+oqamhq6sLgHPnzjE/P8/OnTupq6vj1KlT9PT0UFtbm4tzaWmJdDqd21SmoaGBuro6fvCDH7B9+3YaGhrYvn07MzMzzM3NMTMzQ19fHysrKwwODlJbWxtLvgr718TEBNdcc03V9q+LfR+ePXuWq6++umr714W+D48cOUJnZ2ci+dq7dy/FCPq6m4h0ASeAPao6UXDsH4GTqnpHXttfA/2q+qqCcyvidbcTJ06s+xdtAct+lt0geb/CZ87lfvWtp6cn96VbCht9NQ7Cvh7nn8/qJWG3inzdrQ5oBnqLHHsKuLqg7YWZ9opk586doUNIFMt+lt0geb+tW7fm/oGo4BUWvYGBgfOK4/79+88rpMXaLobl16XAP5/VTAi3st2KF5EtwG3Aw6o6JiJ9wP3ASeBYkUs+BbxNRF4HfB54DXAN8PryRFw6ra2toUNIFMt+lt2gvH7Z4r68vJwr7vkj+Gxxzx/BZwt5sWfdGxnBx7kf+9GjR0v+xSJp/PNZvYRwK/eI/UbgiIjMAd8C5oHrVTUlIjeLSO5FVFV9GngV8E5gOvPvV6rqyTLHvGGyz3msYtnPshuE8Ss2gs8fxWdH8Pmj+OxoPb+wFmsrZLOjojhXvEsS/3xWLyHcyjZiV9U0UWFf7/gh4FBB2+PA4wmHFhvW32217GfZDcL65T93TmoUn538ZhX/fFYvIdxCP2M3RVNTU+gQEsWyn2U3qBy/pJ7DLy4uJhRxZVAp+UsKy34h3HwTGMdxgrGZ2fRxbyJzKZvGwLNnxvumMU6ZqchZ8abIvu9sFct+lt2gcv02M5u+2DP3gwcPbmo2PWz8F4VyrKC3HpWav7iw7BfCzQt7jFh/5cayn2U3qA6/S7lNn/+MfbMFvpKphvxdCpb9Qrh5YY+Rmpqid0XMYNnPshtUl99mCnxPT08s78NXKtWUv81g2S+Emxf2GLG8kQHY9rPsBvb9LE++Avv5s+wXws0Le4ysrKyEDiFRLPtZdgP7ful0OnQIiWI9f5b9Qrh5YY+RZ555JnQIiWLZz7Ib2Pez/rqb9fxZ9gvh5oXdcRyzVMvKco4TJ17YYyS7PaRVLPtZdgP7ftktYK1iPX+W/UK4eWGPkSeeeCJ0CIli2c+yG9j3e+qpit30MRas58+yXwg3L+wx8thjj4UOIVEs+1l2A/t+hw8fDh1ColjPn2W/EG5e2B3HqXosLI3tOHFhZq340DE4juM4TrlR1fNWwDFR2B3HcRzHifBb8Y7jOI5jCC/sjuM4jmMIL+yO4ziOY4i6i5/yk4eIPAjcDCzlNf+Bqn5knfNvBO4GDgK1wBHgHlX9Rt45a8ACkL+o9W5VPRdv9BenVL/MNTcA9wF7gaeBu1T1y3nHnwt8FHgJMAl8UFXviz/60hCRLcATRHH1q+pIkXPuAe4paG4G7lfV38uccxLYBaTyznmJqn4/ibg3wgbdrgX+DZjLa/6eqr4075xqzl1V9b18NuKXOa+q+p6IvBu4CdgBLAJfJ4p5qMi5Vdf3SvS7lgD9zwv7+vytqr55g+duB+4nSuAs8NvAF0XkZ1R1OO+8l6tqpazEsGE/EdkLPAK8BXgY+DXgURHZr6onRaQWeAz4F0CAfcDjIjKiqp9LJvwNcycwf6ETVPU9wHuyP4vIFcAx4DMFp75ZVQvbQnJRtwyrqtpS7EC1547q7HtZLupXpX3v08D7VPWciDQBfw58Fnhp4YlV2vc27Jeh7P3PC3sMqOqhgqYHRORPgJ8FhotcUm28AXgyr2MdEpFbM+33Ar8EDADvUNV54LCIfAy4FQhWHDJfErcBrwa+U8KlbwG+o6rfTiSwGLgEt0KqOnfV2vdKyF/V9T1VPZb3Yw3RnZLnbfDyiu97l+hXSCL588K+Pq8WkVcB48A/Afeq6uxGLhSRK4FOoPBW0d+LSD3R7bT3quojcQZcIqX4XQU8WdB2ONOePf6/BdcfBm6PMd6SyNzm/Bui27RTJVzXALyR828PAnxARD4EDAEPqOrHYgi1ZDbhVisiw0A9UR7vUdXsGqxmcpe5tuL7Xol+Vdf3AETkJuABoI3oFvpdG7im4vtelhL9yt7/fPJcce4nuiXSCbwS+GXg4xu5UES6gH8A3q+qx/MOXQ88B+gDPkD0m/cNcQZdAqX6tQKFzyOniD7UGzkegrcCp1X10RKvew2wFXiooP0NRM84u4G3Ae8RkVsuOcrNUYrbMeAFRJ+9fcD3gH8Vkd7McTO5q5K+B6X5VWPfQ1UfUtV2oAf4U87/RasY1dD3gJL8gvQ/H7EXQVXzf0M+KiJ3Al8TkTeq6tJ612WS9RXgy8A7Cv7Mr+b9+DkRuZ5oAtvj8UW+MTbhNwO0F7RtA6Y3eLysZCaj/D7wok1cfgtwqPDuhar+e96PXxGRDwC/AZR15FCqm6qeBk5nfpwC3iEirwF+BfgERnJXLX1vE35V1fcKUdXTIvJx4ISI7FHViQucXtF9rxgX8wvV/3zEvjGys2nPW7ovi4hcDnwD+KKq/q6qXmxJv/SF/rwyczG/p4CrC9pemGnPHr9CRJrXOV5ufgHYCRwRkXGiW1sA3xOR29a7SESeD/wi0QzVixEqf5tyKyA/9qrPXZX1vVL9qq3vFaOOaKZ773onVEnfW4+L+hWQeP/zEXsRROS1wOOqOiUiP030qomq6uI65+8jmtX4oKq+s8jxA0AT8F1gDXgF8JvAaxNSuCCl+gGfAt4mIq8DPk90y+wa4PWZ418HBolukf0h0USSW4huOYbgYaJ8ZOkDvgm8nOjW2HrcAvxn3vMvAERkgOhW4DeBFaIv5zuBP4sx5o1SkpuIXEf0XPIE0WfwbqJbml/KnFLVuau2vkfpn82q6nuZ+QO3AQ+r6piI9BE9+jtJ9fe9kv1C9T8v7MW5FfhIZjLHGPAo0XMUAETkZuBjea8wvB3YDdwhInfk/Tm3ZGbt7gQ+DFwOLBNN4HmTqmrCHutRkp+qPp2ZaHcf0aSfE8ArVfVk5viqiPwq0a2xs0S3nP5KVT9bNqM8MrNLc68RiUj2c35aVWeL5A8RuYzoy/IOzqeZ6Nnsc4mKwxDwLlX9cEIK67IJt6uATxLNp5gjGiG+LPsqmIHcVVXfK9Wv2vpehhuBP86MQqeArwHXq2qqmvteHqX4Bel/vgmM4ziO4xjCn7E7juM4jiG8sDuO4ziOIbywO47jOI4hvLA7juM4jiG8sDuO4ziOIbywO47jOI4hvLA7jgOAiDwoIv8cOg7HcS4NX6DGcZzYEZEHiTbvWBdVraRlQR1t9lE6AAAB1UlEQVTHDD5idxwnCd5KtPNV9p95opXF8tscx0kAH7E7jnMemeWG3wu8jmj3qe8Cd6vqE3nnvIJouc8B4NvAR4C/A56TWfL0XN65a8C5zG5XjuMkiI/YHccpxvuAXwfeRLTb1PeBx0WkB0BE9gCPAF8gWg/7Q5lrHMcJjI/YHcd5FpnNLX4HeLOqfiHTditwHXA78M7M8ROqelfmsv8RkSuAdwcI2XGcPHzE7jhOIT8F1AP/kW1Q1VWirTOfn2naB/xXwXXfKkt0juNcEC/sjuOUgm8H6TgVjhd2x3EKeZpo7/KfzzaISC3wEuC/M03HgBcVXPdzZYnOcZwL4s/YHcd5Fqo6JyIPAO8VkXHg/4A7gW6ime8AHwXuEpH3Ax8H9gO3ZI75qN5xAuIjdsdxivF24HPAJ4ledTsI3KCqpwBUdRB4NSDAU0SF/97MtYtlj9ZxnBw1a2v+y7XjOJeOiLwVeBewTVX9i8VxAuG34h3H2RQicjvRzPgzwIuBPwIe9KLuOGHxwu44zmZ5LnAPsAMYIXru/q6gETmO47fiHcdxHMcSPnnOcRzHcQzhhd1xHMdxDOGF3XEcx3EM4YXdcRzHcQzhhd1xHMdxDOGF3XEcx3EM8f8HtmJMQnuJqAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Just maing sure teh figure size is sensible\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "# Plotting the hr_diagram (all hydrogen abundances added up) at the 10 million year time bin\n",
+ "my_plot = sin_hr_diagram.plot(log_age=7.0)\n",
+ "\n",
+ "# Addind the observational data of Upper Scorpio\n",
+ "my_plot.scatter(usco.Temperature, usco.Luminosity, label='Usco', s=50)\n",
+ "\n",
+ "# Customizing the visualisation\n",
+ "my_plot.set_xlim([5.4, 3.4])\n",
+ "my_plot.set_ylim([2.8, 6.2]) # better limits\n",
+ "my_plot.text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 14) # Informative text\n",
+ "my_plot.legend() # a legend\n",
+ "my_plot.set_title('Single stars') # A title"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multiple plots\n",
+ "\n",
+ "There might be times you want to put multiple plots on the same figure. The good news is the `HRDiagrams.plot()` allows you to do that intuitively! There are actually more than one way to do it, depending on how you like making your subplots in Python, either with `add_subplot()` or `subplots()`.\n",
+ "\n",
+ "Here is the same HR diagram as presented above, but with the Single star population on the left and the Binary star population on the right. \n",
+ "\n",
+ "**a) Using `subplots()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "sin_hr_diagram.plot(log_age=7.0, loc=ax[0])\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single stars')\n",
+ "ax[0].text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7.0, loc=ax[1])\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='pink', s=100, label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Binary stars')\n",
+ "ax[1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[1].legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**b) Using `add_subplot()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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4z3/+w/nz5wGYMWMGQ4YMYceOHfz444/o9XquvfZa7r33Xs+KFwgEAsGfB70e8ougtBzCQqB1S3Dhqpunx0e3G32SJN0M/B/QB6gEVsiy/JiVtuOBd4GuwFngaVmW/+curbZo3bq1pyU4jJq1g9DvSdSsHdStvznatRXlVJz7neCuV+EbHOJEVQ356aef+Pzzz/H396e0tBSA5cuXExoayr///W8ASktLycvL49tvv+X9998nLCyMl19+maSkJG644QaX6vN2roQxUs3fM7iy9RcUFDj9fK1atXLaufz9/c2Os9a3t6LmZ0fN2kHRf+nSJfsP0OvhWAoUl4FOBz4+EB4K/eJdZvh5enx0q9EnSVIC8CPwIPAzSgHBq6207QqsAh4CVgDTgdWSJPWWZfmCO/TaIiIiwtMSHEbN2kHo9yRq1g7q1u+odm1FOcfv+Sva0mJ8w8Lp8+3yZht+tuIPYmNjeffddxk8eLBxgPrll1+YP3++sU1YWBjHjx+nb9++tGzZEoARI0Zw4sSJP7XRd6WMkWr+nsGVqd8Vxp4r+g4LC3NZ3+7CE5qdYRxfCc99k4y+/KI/DD5Q/i4uU7a3cfxaePP46O6Vvn8An8iy/KPJtsNW2t4DJMuyvKTu/99LkvRI3fZXXajRLjIzM2nfvr2nZTiEmrWD0O9J1Kwd1K3fUe0V535HW1qMrqLC+P+w3v2apaVFixbGWUoDpaWlREVF8fLLL3PixAkOHjzIypUrjbOXNTU1zTrnn4QrYoxU8/cMriz9lgyQlJQUm8fHx8e7RJc95ObmEhkZ6bHzNxdP6Tfc5+YYf1fCc98kSsv/MPgM6HTK9mYYfd48PrrN6JMkKRS4HtgjSdJhoDNwHHhWluVDFg7pDyTX23a4bnsDHn/8cQACAgJISEigb9++AHTo0AF/f38uXryIXq8nIiKC1q1bk5WVRXl5OQEBAXTs2JGSkhJycnIA6NixI76+vsag0NatWxtnECoqKggMDKSqqori4mJyc3MBJVWsRqMhNTUVgDZt2tCyZUsyMzOprKwkKCiI9u3bU1hYSH5+PqBY/DqdjrS0NAAiIyONxZerq6sJCQkhOjqagoICCgoK0Gg0xMbGUltbS3p6OgDt2rUjLCyMtLQ0ampqCA0NJSoqivz8fAoLC43HVFdXG78Q1dXVxMTEkJaWRm1tLWFhYbRr147c3FyKi4vx8fEhNjaWyspK46xJTEwMQUFBpKamotVqCQ8PJzIykpycHEpKSvD19aVz585UVFSQlZUFQPv27QkMDOTixYvodDpatmxJmzZtyM7OprS0FD8/Pzp16kRZWRnZ2dl236/q6uom368OHTp4zf2qrKxs0v2KiooiJCTEK+5XTU0NpaWlTbpfjny/XHW/KisrXf79ctX9qq2tpby8nNraWqqqqvD390ej0RgDwH19fY33SFc3kAUEBBAQ2wXfsBagB9+wcIK6dKOqqsriMRqNhoCAALRaLbW1tQAWzxMcHExERASHDh2iX79+VFVVkZyczLhx48jMzKRPnz706tWLnTt3UlRURL9+/di4cSP3338/er2esrIy4uPj+eyzz8jJySE0NJSdO3cyYcIEozY/Pz/j5z537pzZ/VKby5c9XEljZGFhIe3bt/ea39w/2xhZWFiIXq83GgLt2rXj3LlzlJeXU1JSYnwmIiIi0Gq1+Pj4EBwcTE1NDdXV1Rw5coTg4GAAKuomiwICAvD396eyshKtVouvry9BQUHGYwBCQkLQ6/XGYwIDA/Hz86OiogKdTtfgGI1GQ3BwsNkxOp2O/Px84zF+fn4EBgYSFRVFaWkpGo2GmJgYampqjM9j69atCQoK4vLly2i1WkJCQmjVqhWFhYWUlZXh4+NDdHQ01dXVxuexTZs2BAYGkpWVhU6nIzQ0lIiICAoKCigvL8fX15eoqCiqqqrIy8szPn8BAQHGY8LCwmjZsqVRr+G3tKKiwvgMt23bFj8/P7KystDr9bRo0YLw8HDy8vKorKzE39+fdu3aUV5ebna/fH19jceEh4fTokULcnNzqaqqIiAggLZt21JWVkZhYSGgjDsajYbjx48TEhLi0PersrKS9u3bq3qM1Ol0xjGksTHSJ9AfPx8fNCaGn97HB0KDqW7GGOnv70/r1q1JTk6mT58+lJaWcvjwYW699VYyMzPp0aMHPXv2NI6Pffr0YcOGDTz66KPG99suXbrw+eefk5+fT1BQEDt27GDSpEnodDqjgejnp5hw9e+XLTR6vd5mA2chSVJHIA3IBG4BTgPPAnOBeFmWC+u13wLslmV5ocm2V4GhsizfXK+tXpZlF38Cc9Q8I6Jm7SD0exI1awd16zdoP3XqFL169WrSsa6I6UtNTeXTTz81zmhOnTqVYcOG8cILL1BeXo5erychIYFp06ZRUVHBxx9/zPnz5/Hx8eGOO+6wO1Ddyue94uo/XEljpJq/Z6Be/QajIScnh7Zt2wJ/rOoZDM36NPaSaA/OXBk01a5GPK3/z77SV1RUZP/46MKYPm8dH93p3mmYXvpaluVjAJIk/QOYDwwBNlho37Letgig2JUi7UXNXww1aweh35OoWTuoW39ztPsGhzTbpbM+nTt35o033miw/e23326wLTg4mGeeeabB9hEjRjBixAin6lIxV8wYqebvGahLvyX3TcPKjDVDz5tRs8EHntPvDO8HNT33lmjfvj1FRUX2H6DRKAaeC7J3euv46DajT5blIkmSLgD1lxb1FrYB/AKMrLdtILDF+eqaTlFRkTHAUm2oWTsI/Z5EzdpB3frVrB2gtrbW6I4iaMiVNEaq/VlVg35bsXpNMfaysrKatdrn7Pi/0tJSwsLCVOvCrYZnxxpq1g40zeAzoNEo8XvNiOFzBu4aH909An8MPCVJ0jIgBXgaqAL2Wmi7GJgvSdIMlGxm04BrgLvdpNUmeXl5qv1yqFk7CP2eRM3aQd361awdQKvVCqOvca6IMVLtz6q36neWoVcfRw0/Zxp8BiNP7TG73vrs2IOatQPG2Es14q7x0cflZzDnn8BXwFYgFyVu4Za6Gc6ZkiQZ093IsnwW+AvwIoq7yovAVE+nohYIBAKBwEWIMVLQAIO7pikpKSns3LmTrKwsVbpxGmjVqpWqjTyBQE24LZGLK/FEIhc1uyqpWTsI/Z5EzdpB3foN2k+dOkXPnj1t1gLyRvR6fZM16/V6Tp8+/adI5OJK3D1Gqvl7Bt6h31WrerZoympfc1b5bBl53nDtm4Oa9atZOyj6z5w5I8ZHL0nkckWh1+uRJMmutm+88YYxPbY7KSsrY8mSJezbt4+SkhKio6OZMGECN998c+MHN9LHLbfcYvaAZmRksH37do4cOUJWVhY1NTVER0czdOhQJEkiKCjIaZ9L7RMVatavZu2gbv0G7UFBQeTl5dGmTRtVDWxNHdT0ej15eXlO/e0QuAc1f8/As/qtGXvuWM2z183TEYPP3tU88ex4DjVrB0W/GB9tI4w+B0lLS2PevHlW91++fJmlS5cSHh5Ohw4d3KhMoaamhpdffplz584xceJEOnbsSHJyMp988gnnz5831mxytI/CwkLuvPNOY9vExEQ2bNjA9ddfT0JCAr6+vvz6668sWbKE3bt3s2jRIgIDA53y2dLS0ujatatT+vIEatavZu2gbv0G7R07diQ9Pd1Yo0otODKLHBQURMeOHV2kSOAq1Pw9A/fr98SqnrtoquumeHY8h5q1g6K/U6dOYny0gTD6msHIkfUTpylUVVUxf/58fHx8mD9/Pq1bt3azMti8eTNnzpzhoYceYuLEiQCMGzeOf/zjHyQmJjJ9+nTatWvncB8//vgjN998s7GPIUOGMG3aNEJDQ43H33LLLbRv354VK1awefNmYx8CgcBx/P396dKli6dlNJlz586p+oVCIHA2njT2GlvRc5X7pkDgSsT4aBth9DlIZGSk1X0ffvghFy5c4L777qN///5uVPUHO3bsIDAwkLFjx5ptlySJffv2sWvXLm677Tan9dG9e3eLfQwbNowVK1aQmprajE9jjq1rrwbUrF/N2kHd+tWsHdSvX2A/ar/XrtbvSRdOg7Hn7FIL4BxjTzw7nkPN2kHd+t2lXRh9DhIeHm5x+6pVq9i1axc33XQTU6dOtasvnU5HaWlp4w3rCAsLw8fHeuJVnU7H2bNn6datGwEBAWb74uPj0Wg0nDlzplFNze0D/kihGxHhvBoo1q69WlCzfjVrB3XrV7N2UL9+gf2o/V67Qr+nXTijo6Obbei5YwVPPDueQ83aQd363aVdGH0OkpGR0SBW7+jRoyxevJjY2FjmzJljd185OTnMnj3b7vaff/45UVFRVveXlpZSXV1NmzZtGuzz9/cnNDSU/Px8m+dorI/w8PBG+9BqtSxfvhxfX1+GDx9us21TsHTt1YSa9atZO6hbv5q1g/r1C+xH7ffamfq9wYXTkrHnrS6Y4tnxHGrWDurW7y7twuhzkKqqKrP/X758mUWLFhEcHMzzzz/fpIw6rVq14rXXXmtSe3u0+fv7W9zv5+fXQH9T+/D392+0jy+++ILTp09z1113OTUZQ2Pn9XbUrF/N2kHd+tWsHdSvX2A/ar/XztDvSRfOFi1aMHDgwAbbvdXQM0U8O9ax9EzVpzn3uDnaXa3NHkz126PHmygoKCAkJMTl10gYfQ4SHBxs/HdVVRVvvvkmZWVlvPTSS8TExDSpr4CAAAYMGOA0bYYsmTU1NRb3a7XaRjNpNtZHTU2NzT6WLFnC+vXrGTduHNOnT7dHtt2YXns1omb9atYO6tavZu2gfv0C+1H7vXZUv7es6uXm5prtU4OxZ7h2Wq1WdS/spnhaf3PO7Wrtrr4unr72zcHwPm2q3xXfW2H0OYipYffRRx9x/vx5Zs6cyTXXXNPkvrRaLcXFxXa3Dw8Px9fX1+r+sLAwAgICjPF0ptTU1FBaWtpoRtHG+iguLqZ3794Wj126dCkrVqxg9OjRPPbYY418mqbTVKPa21CzfjVrB3XrV7N2UL9+gf3ExMTYXXNq27ZtJCQkuFaQBYqKinjxxRdZtWoVeXl5dOvWjSeeeIJHHnnE7mfV0MdPP/1Efn4+cXFxzJ49m/vuu88Y856VlUVGRgY7duzg6NGjZGVlUV1dbaxjO2nSpGbVorTkwmlICqEmY8+AmpNxgLr1q1k7OE+/YaLG3eTn55t9jw3fDWd+j4XR5yCFhYVERESwZs0aduzYweDBg7n99tsd6is3N9epMX0+Pj5069aNc+fOUVNTY+aimZKSgl6vt5pts7l9LF26lB9++IFRo0YxZ84clxTHNFx7taJm/WrWDurWr2btoH79AvspLCzku+++s7r/3LlzLFy4kMjISHr06OFGZQrV1dWMGTOGI0eOMGfOHHr16sXGjRt57LHHuHz5MnPnzm30WTXtY/bs2cTHx7NlyxaeffZZTp06xezZs42re1u2bDHWsR0+fDh+fn78+uuvfP/99+zZs4e33367yXVsrSVmadWqldd/12ytxpSUlNCiRQs3qnHuS3799yUDlu6VO40Le5L42HPtXaG5uQmGDJqsXfvm4u5amfWvR0FBgdMMP2H0OUh+fj6pqal88803dOjQgXnz5jls4Dg7pg9g+PDhnDp1iv/+979m9fFkWcbHx4dhw4YZt9XW1pKVlUVgYCBt27a1qw9fX1+zPgB++OEHfvjhB0aOHMmTTz5pM8Noc8jPz/fqAa0x1KxfzdpB3frVrB3Ur19gP/n5+cyaNcvivvLycm688UZ8fX1Zvny5R1aAv/jiCw4ePMiHH35oTLo2e/ZsbrvtNt58801Gjx7NTTfdZFcfb731Fg899BAA99xzD3fffTfffvstEydONL4TDBkyhNtuu82sju348eNp3749K1euJDExkQkTJtil3bS+nuFlt/7qQEZGhtd91+x1uysuLm6W0eepVRoD1dXVFg0PT+tq7Pzx8fEUFxdz6dIlNyn6A2ddG2vXvjm42+BzNcLoc5CioiLeffdddDodQ4YMYf/+/VbbxsXF2SwW6eyYPoCxY8eyZcsWvvzySy5fvkynTp04dOgQSUlJjB071mylMC8vj8cee4w+ffrw5ptv2tXH7bffbkl72GsAACAASURBVNbH+vXrWbp0KW3btqV///7s2LHDTE9ERITFwHKBQCAQ/Hm4//77OXbsGP/85z8ZNWqURzQsXbqUkJCQBh42c+fOZdWqVaxbt65Ro8/Qx9133222/dFHH2XdunUkJiYyZswYAK666iqLfQwdOpSVK1c2u46t6UuzoaSSq2OD7KWpMVb2TJ572oC6EklJSaGsrMxsYuLPjCeNvZSUFJet9gmjz0F8fHyMcXgrV6602faOO+6wafS5An9/f1577TWWLFnCrl27KC4uJiYmhoceeojx48c3u4/6s5KG+IWcnBw++OCDBn316dPHaUZfbGysU/rxFGrWr2btoG79atYO6tcvsB9r93rRokUsX76cv/71rzzzzDN29aXT6RotD2RK69atG61je/jwYQYNGtQglu76669Ho9Fw9uzZRjVZ62PQoEFoNBpOnTplNPqs4Yo6tobwC1NcERtki+Yk0zCsZHqbYZeVlWW2ymqNkJAQN6hxDWrWDs7Rf6Wt7NVHGH0O0qtXL2RZ9rQMm4SFhfHII4/wyCOPmG2vrq42+39UVJTVz2Ktj/rMnTuXuXPnNk+wnWi1WpuJbLwdNetXs3ZQt341awf16xfYj6V7nZiYyN///nf69u3Ll19+aXdfqampTZo0PX/+PHFxcVb3FxQUUFFRYbEmVmBgIJGRkWRkZNg8R2N9REREkJOTY7MPrVbLihUrnF7HFkCv11t1/QTXGH+OGnr1jTudTuey0JDmvtDbOt5gEOr1epfkMnAHatYOzdd/pRt8IIw+h0lPT6dr166eluEQatYOQr8nUbN2ULd+NWsH9esX2E/9e33hwgXuuOMOWrRowerVq5vkQhYdHc3mzZub1N4W5eXlAFYTpwQFBVFUVNSsPgICAqisrLTZx5dffslvv/3GrFmzGi3KbM8KkykVFRXGa2zL+HM39qzemWpvKp58abfHIPR2mnPtvQFH9Xva2EtPTyc0NNQtK/HC6BMIBAKBQOASysvLmTp1KgUFBaxbt45u3bo16figoCBuvvlmp+kxuIBZK0RdWVnZaOr3xvqorq62WYbh+++/Z8OGDYwdO5Zp06ZZbedMY8GS8ecq3OGa6ekX9aZgTatajMErFXc/Q+np6Y22sdeN2FGE0ecgplku1YaatYPQ70nUrB3UrV/N2kH9+gX2Y3qvZ8+ezdGjR3n99de55ZZbmtyXVqtt1FWy/rltuRG3atWK4OBgiy6cVVVV5ObmMnToUJvnaKyPwsJCqzHsy5YtY+XKlYwePZpHH320wX5nvPDZKv/gbbFy9bGm3V0v6Pa8mHfs2NEp5/K21cGmlg3xNpqi31XPkz3PjyWsTSA5G2H0OYi768g4EzVrB6Hfk6hZO6hbv5q1g/r1C+zHcK/fe+89li5dyuTJk3nhhRcc6istLc2pMX0+Pj4MGjSII0eOUFVVZfaieODAAfR6PTfccIPNc9jqY+3atej1eouGwbJly1i+fDkjR47k8ccfN4s/cuZLvp+fel/t/Pz8vO6F3FY/zjIA62PpGrjaEFTzcwP263fm8+WsZ6q2ttYp/TSGuu+wB0lPT3fZl93VqFk7CP2eRM3aQd361awd1K9fYD/p6emcOXOGBQsW0KNHDxYvXuxwggVnx/QBzJgxgz179vDZZ58Z6/QBfPDBB/j5+ZklVqmpqeHs2bOEhITQuXNnYzzc5MmT2bNnD99++y0JCQmA8jL5zTff4Ovr22C1cPny5SxfvpyEhATmzJnTIFmJ4UXUGS/2FRUVBAcHN7uf5uJpF0xnvZA3dg53/a6ZXk9XGIDe8tw4ij36vdHgA9x23YXR5yD1M2CqCTVrB6Hfk6hZO6hbv5q1g/r1C+wnPT2d22+/Ha1Wy2233WYz03W/fv3o16+f1f3OjukDxeX066+/5umnn+bChQv06tWLDRs2sHr1al588UWzGrQZGRn06tWLoUOH8vPPPxu333333Xz11Ve88MILTJgwgU6dOpGcnExSUhLTp08362PDhg0sW7bMWMd2586dZnoiIiKMtXqdYfzpdDqHj/W0oeYo7jDwbJ3b3RNa9e+TM4zA5jw33oCpflc/x85+3upPAlmq1ecMhNHnIIZA7l9//ZUXXniBp556itGjR7vl3O+88w65ubm88847Dh3/Z6jFkpSUxDvvvMNHH31E+/bt3aDKftR8/dWsHdStX83aQf36BfaTmZlJbm4uAG+++abNtgsXLrRp9LmCgIAAEhMTefHFF1m2bBl5eXl069aNf//73zz++ONcvnwZULJc1s/kaYiJy8rK4sUXX2Tp0qXs2rWLkpISoqOjmT17NrfeeqvZMaZ1bP/1r3810NO7d2+j0WegOcZf/ZhGtRpytvCkkeeNOCM+0NMldZzxnJaUlDhBiW1c8exptVq3TB5o6hfxVCOSJOk9VTPP3UbfyZMnee655/i///s/qwPlN998w6pVq4iJieHTTz91uSZrnDlzhmeeeYZXXnmFQYMGuf388+bNo23btjz//PNO63Pp0qX88MMPVvf7+vqyevVqu/o6dOgQK1as4Pz58/j7+9OvXz/uvfdekdFLIGgc9RaT8gCeHCPViKWSBqbGniewNS5ciUadKWox8NTovu7o+8aV/szZwpXPY8eOHY33pP5KXxNKOlgdH8VKn4Pk5+fTunVrt593+fLldOnSxarBp9Vq2bZtG9HR0Vy6dInjx4/Tp08fszbu0p6UlERoaKjTZ3Ht1T9p0iQ++OADUlNT6dy5s1POfeONNxITE9Ng+4ULF1i9ejXXXXddo33k5+dz+vRp3n77beLi4rjvvvsoKytDlmWee+453n33Xdq0aeMUvc7GU8+9s1CzfjVrB/XrF9iPGu+1qaFXXFxMeHi4xw09U7xBg7tQi5FXHzXGLf+Znitn4KpnMyAgwC0hED6NNxFYorCw0O3nzMzM5OjRo4waNcpqm4MHD1JQUMDjjz9Oy5YtLQbAu0t7UlIS11xzjVMyQun1eioqKgD79d94440EBgaycePGZp/fQJcuXRg5cmSDP4ZivWPGjGm0j7y8PD777DMiIyN56623uPXWW5k+fTqvvvoqhYWFLFu2zGl6nY0nnntnomb9atYO6tcvsB813euCgoIGK3tnz54lJSWFrKws8VLsBtLT041/PFU4XiBoDFdORvj7+7usb1PESp+D2MpEVllZyfLly9mzZw+5ubmEhYUxcOBAZs6cSbt27czaFhcX8/XXX3PgwAFqamro3r07999/P19++SXZ2dl88cUXxrZ79+5Fr9dzzTXXWD13YmIi0dHR9OvXj4SEBDZt2sTDDz9sFk9j0H7+/Hm+/vprTp06RUBAANdeey0PPPAAs2bNYtSoUcydO9es7127drFu3TouXLiAVqslLi6OqVOnWqxplJ6eTlpaGjNmzADgySefpLS0lC+++KJBwOru3bt55513mDt3LqNGjTJzma2srGTDhg1cunSJadOmceedd5KVlcWKFSs4ffo0xcXFhIWF0alTJ6ZMmWK20hYcHMzVV1/N3r17efjhh61es+ZSWVnJrl27iIyMtMuN9ezZs+Tn53PnnXeaZWzq2rUrffr0Yffu3TzyyCNemT7Z0Qx83oKa9atZO6hfv8B+1HCvrblwGow8d8QG/RlR6yqevRQUFDTFDU8gsBtnPFve91apEmJjYy1ur62tZeHChZw6dYohQ4YwZcoUMjMz2bhxI0eOHOG9994jMjISUFJBv/TSS5w/f57Ro0fTvXt3Lly4wMsvv0xYWFiDvo8fP05oaCgdOnSweO6CggKSk5O5/fbb0Wg0jBo1irVr17Jz507Gjx9vpj0zM5O///3v6PV6Jk6cSJs2bUhOTuaVV16x2PeSJUtYsWIFgwYN4s4778THx4ekpCTefvttHn74YSZMmGDWPikpCX9/f6OBOnbsWD777DOOHj3awDDavHkzoaGhDYxHWZYpKSlh7NixRERE0LZtW4qLi/nPf/4DwC233GLc9vvvv5OSktLAvbJnz54cOXLEzO1Cp9NRWlpq8XNaIiwsrIGhasqePXsoLy9n4sSJdgVCFxcXG7XVp0ePHhw7dozMzEynuaQ6E2vPvVpQs341awf16xfYj7fea2+M1fuzYK+xV1ZW5mIlrqWsrIyysjLVuXkKbOPqyQrT5z4rK8tluR2E0ecgNTU1ZgVZDWzZsoVTp04xdepU7rvvPuP2/v378/rrr7N48WKefvppQDF2zp8/z6xZs7j99tuNbePi4vjkk08arAqmpaURFRVldRZ169at6HQ6Ro4cCSiuiF26dCExMdHM6KupqeG7776jvLyct956i6uvvhqAiRMn8s477/D777+b9Xv27FlWrFjBtGnTuPvuu43bJ02axBtvvMHixYsZOXKk2WpiUlIS/fv3N65kJSQk8M0337B582Yzoy8nJ4dffvmFcePGNbieOTk5fPzxx0RERBi37d+/n6KiIhYsWMCwYcMsXgdTDF+c1NRU449wTk4Os2fPbvRYA59//rlZ+u36bN68GY1GY3dacUNWO0txe4ZteXl5Xmn0WXvu1YKa9atZO6hfv8B+vO1eN7aqZ8DwYufj44NOpxMv7h7AcO3Vitr1CzyDu54bYfQ5SEZGBl27dm2wPSkpCR8fH6ZPn262/brrrqNLly7s378fnU6Hj48PBw4cwMfHh0mTJpm1HTNmDN99912DvouKimyWH0hMTOTqq682myEYPXo0X3zxhVkyk7S0NJKTk4mPjzcafAamTJnC7t27zbZt377duHJoWKUycP3117N//35+++03Bg4cCCgGy5kzZ3j88ceN7cLCwhg2bBg7d+40BsmDYiTrdDqLsXAjR440M/gAQkNDAUhOTmbQoEGNpoE3nMc0xqRVq1a89tprNo8zxdZyenp6OidPnqR///52z8wYXkAs+XAbtlVVVdmtz51Ye+7Vgpr1q1k7qF+/wH684V43ZVWv/ix+cHAwZWVlZtuFAeg4TVklMVx7tWL67IhnRmAv7nruhdHnZC5fvkzr1q0tumd27tyZ8+fPU1xcTEREhLGtaVwXKC/+7dq1a/AAaDQarJXYOHHiBBkZGYwYMYLMzEzj9vj4eHx8fNi8eTMPPPAAAKWlpVRWVlp0E7W0LT09Hb1ez2OPPWb1c5saVfv370ej0XD99debtRk3bhxbt25l27ZtTJ48Gb1eT2JiIl26dOGqq66yS0ufPn247rrr2LJlCzt27OCqq65iwIABDBs2zOLKmOF6ma6OBgQENKiJ5CiGRDn2JHAxPT8os+H1MWzzpllygUAgcITt27czcuRIvv76a+699163nHPq1KlkZmayadMm4zZLxl5T3bXqt1fzC/3+/ftZtGgRH374odfVsb2SEIafwNsQRp+D1He9dActW7a0GlxuMD6WLl3K0qVLG+zfvn0799xzD35+fsaYQnvR6/VoNBoWLlxoNbbN1ODat28fPXv2bLBK16tXL2JjY0lMTGTy5Mn88ssvZGdnW02yYs3wmTdvHnl5eSQnJ3Py5EnWrFnDihUrePDBB5k4caJZW8P1atmypXGbVqttsGJpi/DwcIuxeobyGC1atODGG2+0uz/DIJuXl0enTp3M9uXl5QGWXT+9AU88985EzfrVrB3Ur19gP5641wUFBSQlJbFmzRrWrl0LNHThTE9PZ82aNSQmJtK2bVsWLlxosa/KyspGz9dcI/D333/n2WefZeHChUYvGXcxePBgOnfuzOLFi3nuueec1u+yZctYvny51f0+Pj58+OGHNvswXPsTJ06wadMmMjIy8PPzo0ePHkyePLnJ7y/uxp5nRyAw5dKlSwQGBrqlzI3bjD5Jkr4BZgKmfmsLZFn+2Er7BGAbYLrcdUyW5SGu0tgUDG6G9YmOjubw4cOUlpY2WO1LS0sjJCTE6HLYrl07fvnlFyoqKsxW+2pra8nOzm5wjs6dO3PixAmje6iB8vJy9u7dy4ABAxg3blwDTRcuXGD58uUcOHCAIUOGEB0dTVBQEBkZGQ3aWtrWvn17Dh8+TNu2bRsYKfUpLS3l+PHj3HPPPRb3jx07ls8//5yUlBQ2b95MQEAAI0aMsNlnfUJDQwkLCyM2Npa//OUvlJaWMn/+fBYvXsyECRPMVvUuXboEmBulubm5TonpO3DgAIWFhUyaNKlJ6XYNLrWnT59usOL422+/ERIS4rWzr9aee7WgZv1q1g7q1+9qvH2MbEoqfb1eT0FBgXHSrayszC2p+BctWkR8fDxRUVHs3LkTMDfOtFotBw4cIDIykpycHM6cOUP37t0b9KPVapt87qa6giYlJRESEkLfvn2bfC5nMGnSJP71r3+5vI5tfn4+mZmZJCYm2vVZtVotR48e5csvv6RDhw5MmTKFiooKtm/fznvvvceCBQsaTChbwjD2W6qr60rqPztitU9gC8Nz6q6QHnev9H0ry/KDTWivlWW5oZ+kF5CWlmbxh3Lw4MEcOnSIn376yczwSU5O5ty5cyQkJBgNtuuvv54jR47w888/myVy+d///kdZWVmDl6S+ffty6NAh0tLSzLKj7dq1i8rKSsaPH8+QIQ3H+2uvvdY4uzlkyBAyMzMZNGgQe/fu5eTJk2ZxfWvWrGlw/MiRI1m3bh3fffcdf/vb3xqsepmmkT148CBarZYbbrjB4nUbOXIk3377LatWreLgwYMMGTLEoiusNUpKSsjLyyMuLs64LSwsjHbt2pGZmUl1dbXZCuFvv/1GRESE2Y+us2L67HHtzM/Pp7y8nLZt2xp1RURE0Lp1azZv3szkyZONBv/58+c5fvw4o0eP9spyDWD9uVcLatavZu2gfv1uwqvGSEcNtezsbJvJr1zBli1b2L59O/fddx+HDh2y2ObEiRMUFxczZ84cvvnmG/bt22fR6AsJCTHWXnUEa66jpuOQs+vYVlZWNggVscUNN9zAJ598wqZNm3jooYearQGUJHSmY7PhOhhqz9rjERMQEMDKlSuJiIhg3rx5xnGzd+/evP3222zYsIE777zT2N7w0myN+vtdbQQ299kR/HkwfTbd5d3lnW+WKqC2ttbi9tGjR7N161Z++uknsrOz6d27N5cuXWLDhg1ERERw1113GduOHTuWTZs2sWTJEi5dumQs2bBnzx5iYmIazBjdeOONfPvttxw6dMjM6Nu8eTOBgYFW6/cZ9iUlJZGXl0dtbS2zZs3iyJEjvPrqq9x6661ERkZy6NAhioqKAPMYuO7duzNjxgyWLVvG3LlzGTp0KK1btyY/P5+zZ8+SnJzMqlWrAMW1My4uzmpSk7CwMIYMGcL27duN16ApbN26lVWrVjFs2DBiYmLw8/Pj+PHjHDlyhGHDhpkZfBUVFZw8ebJBVk1nxPTl5eVx+PBh4uPjzQa5+ixevJitW7fyxhtvGGc59Xo9Dz74IIsWLeK5555j3LhxlJeXs3btWsLDw80GNG/D2nOvFtSsX83aQf36/0zYSoJiD2VlZRQVFZGWlgYosXSG4ysqKli7di1r1qwhMzOTsLAwBg8ezEMPPdTghbyoqIh///vf7Ny5k5qaGq6++mqefPJJ3nnnHbKzs/n888+NbWVZRq/XWy1pBEqt28jISOLj47n22mvZs2cP06dPb2AsaTQaMjIyWL16NefOncPPz48+ffrwl7/8heeee47BgwebjeWgTOzu2LGDjIwMdDod7du35+abbzZz3TQYQZcvXyY9PZ2xY8eSnp7OP/7xD8rLy3n11VcbhFAcPnyYr776irvuuovBgweTkpLChx9+yKxZs6iqqmLfvn1kZWVx2223MWPGDFJTU/nhhx/M6th27NiRKVOmcO211xr7NdSx3bdvn9OMPktUVVWRnJxMREREg8RxlkhJSaGoqIgJEyaYjee+vr7ExsaSnJzM8OHD7SqPZAnTF21XGICWsquL1T5BfepPRjj6PDcVdxt9t0mS9BcgF1gLvCrLsq2Cab6SJKUB/kAy8Lwsy79YamjIFBkQEEBCQoLxBbtDhw74+/tz8eJF9Hq9cZUlKyuL8vJyAgIC6NixIyUlJeTk5ADKbJyvry8XL14EoHXr1kRERHDp0iUqKioIDAwkLCyM4uJiY9KU2tpaamtrSU1N5d5772XXrl0cPHiQvXv3EhwczKBBg5g9ezb+/v6cO3cOUGoZLVy4kP/85z/s27eP3bt3Ex8fzxNPPMGSJUuMn62goICCggI0Gg0DBgxg27ZtxoGkurqalJQUBgwYQEZGBqGhoURFRZGfn09hYSEajYbY2FiuvfZa9u7dy48//sgtt9xChw4deOqpp1i1ahU///wzAQEB9O3blxkzZvD6668TEBBARUWF8cGcMmUKV111FStXrmTNmjVUV1cTERFBdHQ0U6dO5eLFi0RFRXHkyBESEhI4d+4c7du3JzAwkIsXL6LT6WjZsiVt2rTh+uuvZ/v27bRt25bevXtTWlpKdna28X4Z0tZmZ2eTn59vdr/atWtHz549OXDggPGaREdHc++999KnTx/OnTtnvF8bN26kqqrKeK2Ki4uN5RI6deqERqMhNTUVUGZZWrZsSWZmJpWVlQQFBdG+fXsKCwvJz8833i+dTkdaWhqbN282Zh1NT0+nurqakJAQoqOjze6XIZFMZmYmoaGhtGvXzuia+uCDD5KYmMhXX32Fn58f3bt3R5IkWrVqRWVlpfHZioqKIiQkhLS0NGpra40rm7m5uRQXF+Pj40NsbCyVlZVmLi1BQUGkpqai1WoJDw83ujSVlJTg6+tL586dqaioMMa8WLtf2dnZlJaW4ufnR2hoaIP75YrvV4cOHZx6vwAiIyMJCwuzeb9iY2Opra01vpwZ7ldaWho1NTVWv1/V1dUuv1+hoaGUl5c36X516tSJsrIyj9+v8PBwAgMDm3y/wsPDLd6vK7j4scfHSF9fX8LDw8nLy6OyshJ/f38KCwupra013l97CA0NNU5cVlVVodPpqK2tZc6cORw/fpyEhASmTZvGhQsXWLt2LUlJSSxatMisju2CBQs4f/48o0aNonv37ly8eJEnnnjCzDskPT0df39/jh49SkhICO3atcPHx8doyFVVVVFbW0tNTQ2nTp0yhgDcdNNNbNu2jePHj3PdddeZHXPp0iXef/999Ho9N998My1btuSXX34x1og1rM4Zjvnpp59Yt24dV199NVOnTsXHx4fk5GS+/PJL7rzzTsaMGYNer6e8vBxfX19OnTplNCRBKWn0/fffc/bsWbp3705gYCB+fn7o9Xr27dtHcHAww4YNIyAgwGgU7tixg9LSUm666SZCQkKIioqiuLiYl156CYDx48c3qGNravSBUhfWVXVsCwoK8PPzIykpicrKSsaMGUNISIjxmTIkNCsrKzO79idPngSU3xCtVktVVRVFRUWEhYUZJ8bz8/OJiYkxxkAVFRVRVVVFmzZt8PX1pbKy0mjwhoSEoNPpyM3Nxd/f3/jbUVhYyKVLl4iMjCQiIoLa2lqqqqrMrr3hfgUFBQHKhIVOpzN6YdXU1Bi9iwzH1NbWWjzmCv/duuIJCgrC19cXnU5HRUUFfn5+xkmJ8vJy9Hq98bmorq6mpqbG5jGG74zh987wrpiXl0ebNm2MsaGlpaUUFRWh0WiIioqipqam0THSFhpr2SCdjSRJ1wDpQA7QC/gaOCvL8gwr7aOBKOAEEAb8DXgI6CvLcma9tnpZll2o3r1otVpmzZpFfHw8r776qtm+06dPs2DBAl577TWnZaA05ffff+fpp5/m7rvvZtq0aU06NikpiTfffJMPPvjAZrrulJQUnn32We66664GpS2cydy5c2nXrh3PP/+8y84hEAgsU11dbXWf4YWvGVguVqpivGWMdGYB819//ZWXXnqJOXPmMHPmTNasWcNbb73FzJkzmTNnjrHdnj17eOaZZxgxYgTz5s0DYMOGDXz22WfMnDnTbJwwbK+fiOWll14iNDTUamKSzZs3I8syCxcuNL5ovfXWW/j6+jJ//nyztl9++SVHjhxh3rx5dOvWzbj9q6++4vDhw2YrfWlpabz99tuMHTsWSZLM+vnss884c+YMX331ldGoSU9PZ9GiRYSFhfHoo48CykvjCy+8QJ8+fYxZtkG5Fy+//DLDhg3jr3/9K4BxpS8sLIyPPvrILL7twIEDvPnmmzz77LN21bHdvn07H3zwAQsWLDCGhly+fNlqcjVLfPrpp2auvPVdW99//33OnTtndt0tYZgA++9//8uhQ4d4+OGHG7Q/dOgQ//3vf5kxY4ZTS4K4K+5PrPapG2cVaLfmkmx4Djt27Eh0dDTx8fEN2tg5cWB1fHTbSp8sy8km/z0hSdI8YLskSffKstwgglGW5SzAMMIUAn+XJGkacAvwpcsFN4LBGm8uhpklUzZt2kRZWZlFo65nz57cdNNNLF261GGjz6C9/rn1er3RTdORvgMDA7nrrrsa/TFev349fn5+dhczr4891z4pKYnU1FQWLFjg0DlcibOeHU+gZu2gbv1q0W7N2DOs2tdv4wQD8IrgShsj65OVlcX//vc/fHx8zOLds7Ky6NatG126dOHAgQPGRGWHDh3Cx8enQUZmQx3b+uEPpaWlNrOG7tu3j27dupkZEoMHD+ann37i0qVLxhcunU7HyZMniY2NNTP4AEaNGsXhw4fNth08eBCNRsPgwYPNVsjat2/P8OHDOXbsGKdPnzZ6nISEhJCamspjjz1mZgQMGzaMXbt2ER4ebkz2tmfPHvR6PZMnTza2NRjlCQkJDRKaGOrWHj582K46ti1atAAwhnWA8lJZf7LZFoaXUEsvxJcvX+bs2bP06NHDLoPPFEvuboYVVkvljryFgIAAq7+BV7Kbp711ipuKYaLJnv6rq6sbHU+aOnFlSseOHZ1m+NWnKbktmoMnY/oMpeebMmOra2J7l1FUVOSUF7CPPvqImpoaevbsib+/P6dP8Sh6qAAAIABJREFUn2bnzp3ExMRYzMQJNJiVbCoG7XPnzqVv377ExcVRWVnJgQMHOHnyJDfddJPFunmNMXDgQKuppw39p6amsn37dsaNG+ewq4M91/6GG24wGrDehrOeHU+gZu2gbv3erN3Wqp6BkpISiympDccK468Bqh4jLZGdnU2rVq0oLy9vkOzCnjq2hheuNm3aNDjeVh3b33//nezsbK677jqj2zIoiUc0Gg179+7ltttuAxTjsaqqymIiGkvbsrKy0Ov1vP7661Y/t2kd26SkJDQaDdddd51Zm3HjxrFt2za2b9+OJEno9Xq2bNlitY6tpQzPffr0YeTIkWzdupWdO3dy1VVX0b9/f4YNG2Yx87a1Orb9+/e3+lksYe1FeN++fYDtBC71DT7D/baUQdUQF9yUbNnuxt/f367fQ2+kRYsWXpdluSnGZE1NTaPjiKG/5hh/rsDSBE1KSorF1b7m4M6SDXcAm2RZLpQkqTvwLiDLsmyxqIkkSaOAVOAcEAI8i+LK8l83SbaJtXp1TWXgwIFs2LCB5cuXU1lZSUREBGPGjGHmzJmNztI5imn20IMHD7J9+3a0Wi1RUVHMnDnTOPg5k6KiIv75z38SHBzMkCFDmlWo11nX3lOoWb+atYO69Xuj9sZebkxdBSsqKmzGtfzZjb8rbYx0JvbMroeFhVnNmmgwPtavX8/69esb7D948CBTpkxxOJmCRqPh5ZdftvodNTW49u/fT48ePRqs0vXs2ZPOnTuTmJiIJEkcO3aM7Oxsq0lWrNWxfeqpp5gyZQqHDx/m5MmTrF27lpUrV/LAAw8wYcIEs7aGlUnDyiI0rY7tpUuXrMb0abVa9u/fT2hoqFUj0tIKn0FLSUlJg9VBQwkQwwqloPmYGlVqzzpqKYmONaKjox0y/Fy12ueuUDt3rvQ9AnwsSVIgkA2sBl4x7JQkaSbwqUn66f4oMQ2RKHWIDgNjZFlOc6Nmq5hmz2wOo0aNYtSoUU7py14M2u+77z7uu+8+t5wzKioKZ8VdOuvaewo161ezdlC3fm/S3hRjz4DB9c50nyUD8E/s+nlFjZGWMCT7akod27Nnz5oZOFqtlry8vAYZN2NiYjh79myDOraVlZUcOXKEnj17MnTo0AaaMjIy2LRpE8eOHWPgwIGEhYUREBDA5cuXG7S1tK1Lly6cPHmSyMhIu+vY3n333Rb3jx07li+++IKUlBQSExMJCAhg+PDhNvu0RGxsLLGxsUydOpXS0lIWLFjAd999x6233mqxjq3pb0tubm6TYvpeffVVix4Ix48fp6SkhISEhAYrc7bKLLRs2RJQDP0uXbqY7cvMzHRbEWtHKSsrs7nfW1w8La2gNaXkhzfSVP3eZPjl5OS4JbbUnTF9CY3s/x743uT/7wPvu1iWw1RVVRmzM6kNNWsHod+TqFk7qFu/p7U7YuiZYsn1xnCMWP278sZISwwePNhY4sfU8DHUsR0xYoTRYLvqqqs4cuQI27ZtY/z48ca2e/bsoaKiosELXvfu3Tlx4gRZWVlmro/JyclUV1czbNgwi7HqvXv3ZuvWrSQlJTFw4EB8fHzo3bs3R44c4ezZs2ZxfVu3bjX+2/DinpCQwPr161myZAkLFiwwWy2Mjo42i8XduHGjzTq2CQkJLF68mDVr1jhcxzY0NNTM6A0LCyMqKopLly41qGObkpJCRESEWZmLxmL6TN1jwXyV0JS9e/cCDV07TQ2+kpISqqqqaNmypdEw7NatG2FhYRw9epTBgwcbv/uXL1/m4sWL9OvXz23p7R3Bx8fHmIHc22jMVVKn03n1tW0MR/Q76u7pqOFnbcLDXS7Lok6fg2RmZjo1e5Q7UbN2EPo9iZq1g7r1e0p7c409A/n5+VZfOuw1/uDPYQB6G/Hx8U2q02eNUaNGsW3bNlatWkV2djZXX301WVlZbNy4kYiICGbNmmV8kRoyZAh79uxh3bp15OTkEBcXR0ZGBocPH6Zt27YNXqwHDBjA2rVrOXHihJnRt2/fPgICAqzWiDPsO3bsGIWFhURERDB9+nROnTrFxx9/zPDhw4mIiODEiRNGd0jTuKfu3btzxx138MMPPzBv3jyGDh1KXFwceXl5nD59mr1797J7925AyZZpiLOrT1ZWFmFhYdx4443s2LEDoMnJzrZt28bPP//M4MGDG9SxHTp0qMU6tqNHj25wPay5Y6anp9sVV1xYWMipU6eIjY01Myjrv/Bu376dY8eOMWvWLONqY2RkJGPHjmXVqlUsXryYAQMGUF1dzYEDBwgJCXFo5dOdBAcHe91qn71xcZWVlV4X09cUmqPf0VU/Z+Guch7C6BMIBAKBRZxl8NlLY8afQZMw/NSJn58fCxcuZOXKlezevZukpCRCQ0MZMmQIM2fOpKrqjySl/v7+zJkzhzVr1vDrr79y5MgR4uLiePLJJ/n+++/NMjgajIlevXpx4MABxowZY9x+4cIF+vfvb/OZGTBgAEePHmX//v2MGzeOmJgY5s6dy+rVq9m+fTv+/v5cf/31PPXUUzz88MMN+rrjjju46qqr2Lx5M+vXr6eiooJWrVrRtWtXnn76aUB5Id2/fz8zZ860qMHwYj5jxgx27NhBx44dufnmm9FoNHa/jPbp04fz589z6NAhCgoK8PHxISoqinvvvbdBPN++ffuoqqqymjCuPk1Z1di/fz86nc5YBgJsu3TWp1evXtx+++3s3r2bLVu24OfnR1xcHKNGjbK6sigwx1XZNK9kmrrq52w3T9Mswq5CGH0OouYvlJq1g9DvSdSsHdSt39u0N9Xgq5+4QuCdGAzu+ve3fhY5e16M+vbty5o1a8y2BQUFcddddxnr3NXH9CWqRYsWDdoZCm3HxcUZtxlelMaPH897773H6dOn6dmzJzExMXz00UeN6rz22mvNCpdXVlbSsWNH5syZY7Yi8/vvvwPQtm1bq31Y+54eOHCAyspKRowYYVOLwc1r0qRJxvg70z6jo6ONRm19oqOjGTJkCLW1tcbyBtZYt24dN9xwg0tihceNG2eXMTlp0iQmTZpkts2Q6bR79+50797d6drq4+yXbENRbWu4Y4XP0bHCWnIgteDt+m1NfJhm+HUlwuhzEDUHvKpZOwj9nkTN2kHd+tWq/eLFi4CSncz0Rd0WtrJ8CtyDJePPYPilpKRYfLF0hnuU6ey5pVXdXbt2UVFRQc+ePRsc27VrVwYNGsT69est7rcXrVZLdXW1mTu1Xq9n9erVAE0uaQDKC+mjjz7aaAr2H3/8ET8/vwb1CZtCY3FNO3bsIDU1lWeffdbhc7gKtZY7MFC/1IS7k7Y0Z3JQzfF8oG797nruhdHnIKmpqV6VTa8pqFk7CP2eRM3aQd36vUm7vat8BoMPlBiiixcves1nENiHLeMPMIv1M7xwlpeXExIS4rARaDD8li1bRk1NDV26dMHPz48LFy5w6NAh2rZtazETJ8D999/v0DlNCQkJ4c0336Rfv3507tyZqqoqDh48yMmTJxk2bJjNOrZZWVkWX7z/P3tvHiNNet/3ffru6Tl7pue+3n2pHZJ+8Yo6NmQoOdJah2MdKcukgoRUHNFIIhhcAQZFRY4ECo7gBEGCMASSgLaVWLYUE6IERwYLIkzLJKyDVmRlSYq7esPlS+3LnfOd++iZnu7pM3/MW82enqruquq6fvM+H2Cw+/b08ZnnebqqvvVc73nPe3jPe95j+ppyucwXv/hFnjx5wuc+9zl+4id+YqA9Ocvlcs8tn77/+7+/Pc8wrKFsVhQKBQ4PD33/HPC+lw+CD3mdDDoapF+7iTqS/YNq9yr0ucRs41ApSHYH5R8mkt1Btr80987AB9/ah8hu8FO9fdGisy76BUCjrgfpDVxaWuId73gHf/iHf8jXv/51rq6uGBsb473vfS8//uM/7ttKtsZF+7vf/W5effXVG/vYfvCDH+R973tf3/ewCn5WnJyc8Mu//Mvkcjl+4Ad+gJ/92Z917Q/O9vwKewGLboLaj9SrwBeF7RfAm+H/Qe0V5xeS/bvbvdNjiF1U6HOJ5M1BJbuD8g8Tye4g21+Se3fgA27MMVI9frLpN+/v9ddft3xt54VMv7Dx/ve/n/e///2B9DB1X7x/6EMf4kMf+pDr93Ny0bawsMCf/MmfWP7eakio1Yqq/ebzdWM3+Hm9TL0Z5XLZ8fsHTVSCHng719tpu4kaUfbv9x0Iqt1Ht4QijtlEbilIdgflHyaS3UG2f1Tc3a7Y2T3JXgU/+Vj1/j18+LD9/722evA7bNjBzwv4Qe7W95v7Z/Yco6zdLGgRlR6/8/Nz3z/DTS9fGEEv6MW7or4QSj8k+wfR7gGC6Ue/gwQ15twPJLuD8g8Tye4g21+Ku1kvH3BjOf5+zzXweksIhX/k8/l2COxciW5tba1ngJmbm7N1cev1RffS0lIgF/JOg1S/8rLz2pmZGVfvEYUVgv0e0eA08PnZToy2b/UTNGbHaElI9g9qJI8KfS4pFothK7hGsjso/zCR7A6y/aPg3i+E9Qpx9Xrd8WsU8sjn87RarVvzMe2Ev354cfEdVNjrxE7wGyTsdWNsDm68p5P3Djv4+blKsZvA5yVhh7p+WB2jpeCFv5ObNHZHH9gZ3hzU6txqeKdLJC8NK9kdlH+YSHYH2f6S3YH2nmOKu83JyQmVSuXWDYJewzydMOhQT+O1YQS/Xhf6Rvl4Efw6F4VwWu5eD/F0Mp8Prvdh9Asnm1/70T78WpzDK6Qfowf19zrwOWn7naMjtra2fDs+qdDnkpWVlbAVXCPZHZR/mEh2B9n+UXfv1WP36NEjAB48eBCUjiJgOkNe54Wt3dAR9HyyMMKf8Tf6Gf4eP35Mq9VyNUeoXx04DdtOAx/IGcbuls4yjloAlLoXrIFb/7B697o/P4j2oEKfSyqVitgviGR3UP5hItkdZPtLdTcCX6+l2Hst6qK2bog2ZkN+33jjDUc9004Dn5cLu3S+T1AB0E6Pj5Pw1x2sm81m6CMD3AQ+gHQ6LX6DdrtYtfuwwmAU2s0gOPV3etzxM/DZ3YJm0POhCn0uefr0Kffv3w9bwxWS3UH5h4lkd5DtH2V3O/PyUqkUjx49Ur19dwCruZ1G+KhUKgwPD9t6ryisGGkQZO+f3bv7ZuGvX++pk/Lv9rHCSch2G/gAJiYm2N/fd/36u0CvuvAzELppN1HCr+OOn2HPYG5ujkqlYvv1blGhT6FQKBS+YPTy2UFt4RB9+oW9IPFzG4eTk5P2Yih+B0C787z8LOMoBW+/cTKvL4qY1VXUholGGT/CHgwW+Lrfp7N9Pn782LMFnkCFPtdIPmhIdgflHyaS3UG2f9jug26fYAzZUr19snAT9OwOVYpq2OjcKDmI4Z9ez+mxW/52CaqXD9Q2LW7wap6g1+0maMz83R5jgg58u7u7TExM2H4ft6jQ5xLJXw7J7qD8w0SyO8j2j6q71dDO7l6+Vqt143cq+EWbQXr1es3fhOiGPQOrFST9DoBere7Yr/y7P9MrBg18ALVazQOT55dBAqCTdhNFDP9B2rSfNzh6eQUxtBPUPn2ukby3lGR3UP5hItkdZPsH6V6tVm0tpmA38AFkMhnX76cIlkGHcXb2lHUT9cAH2JoXtLW11f7xkt3d3faPW3qVf/dn9cOvIbRWFAqFQD/vLtPZluy0K7vtJmoYf9fe3l5kA18/VlZWAjk2qp4+l/i5l4zfSHYH5R8mkt1Btn8Q7mZBzyoAqIB2d8nn86b1vra2Ziv4dfbq+kHQQaQffi0AY7bYgx3slH8UAx8E09tkZ16fn3ulhU2vui8Wi+3/j/JcQQk3jzrp5xtUL6sKfS4ZGxsLW8E1kt1B+YeJZHeQ7e+nu1WvnpvAZ7V4S6PRcC6mCI3OZcE724GxqECv8JdKpXxxCiqEuB1i6HdQcHKh62afvk7CCtaXl5ehfK7iNl4Hq0FDpJ9BL8xePrgZtv1EhT6XSB6CINkdlH+YSHYH2f5eu/cbvul1D1+9Xnf1OkX4GAHQbvhLp9Oefn7QAWSQfeLC2PTda9yW9/z8/MAXxBcXFwO93kvucm9fGEjrnfMKO3/38fFxACZqTp9rJO8jI9kdlH+YSHYH2f5eufear3dyctL+MaNf4Ou1RUMyqe4xSiefz9/aGHhtbe3WkuJXV1eW7+Hkbr8fc+bsYGf+aT/Cch+UsJ2jNhoj7PJQ+E/YvXwQ3A1pFfpcEqW7UU6R7A7KP0wku4Ns/0Hd7YQ9K9bX1wcKfACJRKK/pAVqGfdo0S/8DdqrG3Zg8vIGReeCL34t/hIlBt1aJqhVip1cvN/l+lJEg5GRkVuP+dEzqm69ukTyXWvJ7qD8w0SyO8j2d+PudghnJ14t2NK9uITatkE+VsM+d3d3Xc1RicrFtd8L0UDvvzWsIYVRKH+Jc387yy2Xyw08L1ENKQ0Ov3v57Aa3oKY/yL0CCpnl5eWwFVwj2R2Uf5hIdgfZ/k7ce4U9J71mdgNfv16+fk4K2XSHv9nZ2fYwTjurfUYhbHQS9mIiYQRCL+tgkLl9R0dHnnl4id3y8aLt2PksFQzvFkEdA1Xoc8nl5aWtvXyiiGR3UP4Q3gX05eUluVwulM/2Ajf+Xi9K4Ra77SaKgQ+ul6SWvGWGoj9G+CuVSu122DnfzywARi3wwfVQ5Kj2ONkpr25/q4AQxbLPZDI954R6iZ2tG5wSVNtxWncqJN4mKr184E0PsR1U6HPJ3t4e9+/fD1vDFZLd4fn2D7u35ODggNXV1VAdBsGNf1Bl3i9cDtruvQx8doNeJ6lUKrCLOUW47O3t3Zrz100UA4dBNpulVCqFreGabv+gy3qQxS7Gx8cDXXDLcPUq/EW17dhpA8PDw213FRK/hdP27GYu3szMDG+99Zbj1zlFhT6FIuJ0h45+F+/9LrYU0aSznsPqXfQj7FnhZD6fatMKhQz8Wt3Qb/zo9ZPMXR9iavdGiJP2HLV9Dc1Qoc8lCwsLYSu4RrI7PD/+TsOenecNevHsx0EoSKT4mwXAQdr9oAu2eBH2wu6lVgTHwsIC5XI5bA3XSHaH8Py9CHxe7VdmXIA7OeZ70esnue04db/rwdBue/Yi7O3s7Az8HnZQoc8lUZnn4wbJ7nC3/c0ujL1crn7QQJhKpWz7RLF3JpVKha3gGCdhyW2wsgp8XvbsBbEioiIapNNp0Re/0ueehuHvVQ+f21UMrS68d3d3Hd/sG6TXT3Lb8cPdLBiGHQTthFU77dnLnr1arRbITenAQp+maf8U+Cmgc1LHL+i6/sker/lrwMeB+8CbwM/puv57fnraZX19nRdeeCFsDVdIdoe76e+kV69Xj8wg8+3shLm9vT1mZ2c9ez+vsBswt7a2WFlZ8dnGHzrdndz46FcPZu3Jy7BnYGeBBsnzRQflLp0j19fXmZiYMP2dH3tPeU3n3CaJBO3v5ZDO6elpDg4O+j7PSTsKstdPctsJyt0qdIUdBp3g9XFsZWUlkDnvQff0/bqu6/+lnSdqmnYf+B3gZ4DfBv5j4F9omvZA1/W3/FO0h+S71pLd4W75exX2rJ7j9UV0VMvebsAslUo3nhvF3kgrrNqN257vIHr3rFD781lyJ86RUT1OKLzBz3l7sVjM9HEvLrKD7vVTOCOIXkEvevms2mIsFuPhwjIrkwU2jg95fWfT9rHQqt17TZSHd/408CVd1//Zs39/StO0v/3s8V8JT+saq7uYEpDsDvL9h4eHbYe9fvOs+l08ex0CJW+VAbf9rco9imFwbGzM9HE3WzQMEvjitRrZ0yMqE1M0HQyXjeoS+IKJ7Dky6GN090XaoBfptVptoNeHjV3/KC660rlsvR8X126DH9hrV5LbThTdnaw8m06nTc+HToLjIIHvIz/4Y9wvzJBOJqnW6zw53OcTX/isrbZ5dnZGJpOx7emWoEPf+zVNex9wCHwG+BVd1y8snvsu4Etdj3352eO3eOWVV4DrSn/55Zd5+PAhAIuLi6RSKdbX12m1WkxMTDA5Ocnu7i6Xl5ek02mWlpY4Pz9vDylYWloikUi0L4wmJyeZmJjg6dOnlMtlMpkMi4uLFItFDg8PgeuNk2OxGBsbGwBMTU0xPj7Ozs4OlUqFbDbLwsICp6en7YnKq6urNJtNNjc3ASgUCoyNjbG1tUW1WiWXyzE3N8fJyQknJyfEYjFWV1ep1+vtL8LMzAwjIyNsbm5Sq9UYHh5mdnaW4+NjTk9P26+pVqvtiaKzs7O0Wi02Nzep1+uMjIwwMzPD4eEhxWKReDzO6uoqlUrlxsEum82ysbFBo9FgbGyMQqHAwcEB5+fnJBIJVlZWKJfL7S/FwsICmUyG9fV1ms0m4+PjTE1Nsb+/z8XFBclkkuXlZUqlUnuJ5rteX/l8nnw+z/b2NvV6nVwuRzKZ5Pz8nFKpRCwWY2ZmhurFOW9+9c9gbILM8DCJRIJyuUyr1SKZTPIXf/EX7f8CvO1tb6PZbFKpVIDrZaPj8fiN12QyGR4/fky9XqdQKDA9Pc3V1RWnp6ftckulUuzv79NqtcjlcoyNjXF6ekqlUiGRSFAoFKhUKu3XTE1NkUgk2vU3PDzM6OgoJycnXF1dkUwmKRQKlMtlzs7O2uUWj8fbrxkZGWFkZITj42Oq1SqpVIqpqSkuLy8pFovA9ZAfoF3no6OjDA8Pc3R0RK1WI51OMzk5SalU4vz8vP3daDab7TofGxtjdHSUw8ND6vU6mUyGfD7PxcUFFxcX7dc0Gg2+9rWvAdfLhw8NDXF5edmur+npaU5OTigWi8RiMZaWlqjVau12Pz09zdDQENvb2zQaDYaHhykUChwfH3N+fk48HmdpaYmrqyv29vban5vNZtna2qLZbDIyMsLU1BSHh4eUSqX2ay4vL9tlMDs7SyaTYXPz+oJndHSUyclJDg4OuLy8JJlMMjQ0dKOOjfoyhvCkUinS6TSVSoWtra32STMej7fnP1arVVqtFplMhlijwcLvf47szhaXL76D3fe+TDMep1qtkkgkSCavTynGMBXjJFav16nX66RSKeLxePs7VqvV2ifpXC5HvV6/UV+5XK59XMpms8zOznJ6esrMzAx3lDtzjszlcpRKpXbbm52dvTFnKJVKUavVGBoaIh6P02g0qFQq7TYJtI+Jxt6aV1dX1Ov1G6/55je/yfDwMMPDw7RaLQ4PD9nf32dqagq4vpi6urpqt/2rqyvOzs4YGRkhl8vRarU4ODggmUxy7949gPZxNJfLEYvFqNfrXF1dkU6n298L43s5NDTUfk2j0bj1mkwmQzKZpNVqcXl5SSKRIJvNAtcLZzSbzfbNKOP74OY12WyWRCJBs9mkXC6TTCbb38OjoyOazWb7OHpxccHl5SUTExOk02lqtRonJycMDQ0xOjoK0P4eFgoFAM7PzymXy+TzeVKpFNVqldPTU3K5HCMjI8D18Tkej7fLvlgsUqlUmJycbPucnZ3dqq/O15ydnbG7u8vi4iL37t3j8vKS/f198vk84+PjtFot3vfiQ5amChw2a7yz2eT7i+f875//LIuLiySTSS4uLjg8PGRycpKxsTGazSYbGxtks9l24Gs2m1Sr1fb5qFwuc35+zujoKENDQ+1zRzqdbt/EODw8pFarMT09TSwW4/LykouLC8bGxshmszQaDZ4+fcrS0pKr+jKOl5eXl7RarfZrqtUqtVqt72uMNtv5GqffL6DvazrbvfGdNNq98Ro33xW/vl/GzU8j1Fq9plgskkwmaTQaFAoFYrEYpVKJUqnE+Pg4V1dXLC4usr29zcjISPu7sb29zTtnF0iOj7LTrDLZgIlUmpGJcb7nXd/Bk71dnj59yujoaLudG9eRy8vLwLcWMMrn8zduTtdqNba3t4nFYu1r9ydPnrS/m2bXoL2IBTUMQ9O07wa2gAPgncA/Ad7Udf0DFs//AvBFXdf/XsdjvwJ8r67rP9T13Jau6765m+FkblPUkOwOsv2r1SoHBwftk69ZT0yzUmb3ox+mfn4OQ0O0PvrL0HEHyKo3xsmQObc9ficnJ5HsBbPLIP5h/92d7cYuTocL9+vpyx3scu9f/y6Jeo1GMslbP/wfcTlt7655KpW6sVF3N1Ztsrvcn118BDMWJkDu0jlyb2/PdNjx48eP2zdHvNo7zo/eqvHx8fZNKjOiONyvsxz6+UeZZrPZc5++b19c4Wf+gx8im/pW+6rUqvzqH32e17Y3HH2WHwtnjI+Pt0OUNLLZbPuGh0S88Hfb0/fjD78L7V0vEY/F2481W00+89VX+ezrX+77uTMzM8Tj8faxZWlpibm5OdNzpo1rEcvzY2A9fbqud96RfKRp2keA39c07UO6rpvNXjwHxrsemwCKfjk6QepEXZDtDvL9O4evmFHbWKdZuiBWvaIFsLsNq/035bYz3HNQpG+uPYi/EaDCCn/92o1TVldXbwU/o/1Yhb/KxBSNdBpo0UhnqExMmb7eDLPvrcOgd6e5S+fIUqkUSJ35NTyx3zCrfp/rZyi08zcHMUzML/oFppXJAunkzUvXdDLJ8mTBcehzM9SzH5lMRuw8wEQiEbbCQITpv3F8SLVev3Ezolqvs3l8aOv1c3Nz7ZsdvYajDnr9EeacPmOch1Ui/SrwV7oe+07gC74ZOUDi0u8Gkt1Bvn8y2ftrl1pZpZnJQrMFQ0Mwt9j+nVeLbKyvr7vq7VMnhfDCX792Y0Y+n+/Z22e0AavwZ8o73wm72yTnFnmng4vLzonqKuzZQuw5stcxem5uLvIreA46/zTsuXJS588awzp7MejFtdlnehn8pJY9yF+AKQh/q+PX6zubPDncvzWn7/WdTdP36Ka73fi1fUOQWzb8p8DndF0/1TTtRa6XmdZ1Xbfqi/0N4L/WNO0DwD8HfhL4buA/D0S4D8Y4XIlIdgf5/v1OavHssyGdu9vXgc/BhbXfvX1OhxdGDS/9gw5//drNIJj1+lmSydjqee4Q/AMUAAAgAElEQVTmne98p+XvOsvweQ16d+kcuby87PuWLX4Gq6OjI9/eOwgk+29vb/f8vZOL6zCQXPZejyYJGi/85+fnXR1bWq0Wn/jCZ3m4sMzyZIHNjgWG7AS4o6OjQHqH4/2f4hl/G3iiaVoJ+D3gT4C/ZfxS07Sf0jStPWFd1/U3gfcBH+N6uMrHgL8R9lLUBsbCDxKR7A7y/W0NTzUurF0M0/FzyX3JGy6DP/5B7Ufodliz3VC6uro68OquxnuY/XSXvbGokeGXTqef28D3jDtzjrRzjI7ynlySh0eCbP9+K0QbF9e/+kef5zNffZVf/aPP214h0Qove56Nsg+7t9cNbkaTRIl+/l4dc6xCXKvV4rXtDb50sMN+o8rs7KztHjuj3XQ69poD75Yg5/S93Of3nwI+1fXY54DP+ajlmv39/faKVdKQ7A7y/Q8PD11tfRDE/mn9ODs7a6+mJRG//IPo9XPbbpzSr9fPbTA0yl4N4TTnLp0j9/f3uX///q0bImtrazx+/Lj976WlJVcLuvh9QT0+Pt5zMZGoI9G/c/Xjfje4jItrp3P4+n2+F0PqJJa9QSaToV6vh63hmiD9jWGeXg3DNFay9xvZsV6huIPYHmbXA7t7+Hm9efvzTtiLvXiFl+3CKItisXijV0+hgG/d2fZqNU+Fwi1+LOyiiAZOevnsDvEctK1YDee0el8vritU6HNJlIem9EOyO8j3D2pVLz+Cn7HHjFSC8vdja4tB2k2/BV28oN/fOz8/r8Lec0K/Y7TZYgh2e/386uXr9DH2/zSQFgQkzyvrN6fP6fs4nQs9aPDrLHtpq3je1ekbUbpmtGoP5XI5EE8V+lwieeyzZHdQ/k4whoT2Cn9Ogp9avdMav3v3otju7f7N6XQ6kv4KfzDq2uxmgzHE023wc7vQQi+6PbqHiHX+XkIAbDab/Z8UUbwenre9vR1o8Osse0mBD2S3G7jpbxWg7N5cMurOi2ONnXawsLDQbnMu9+azhToLu2R9fZ0XXnghbA1XSHYH+f6bm5uOh88NOp+vX6+fMaS0n9f+/n6kLnqcHgglD2l102468bq3z03ZS/7eKuzTWded7cRof8ZFjREAO0NV0MM9zRbxWFlZsRxmLyEAFgoFDg4OwtZwhHEToFfZ28WL3kKjnp3WscSyNxgeHha3D7Kb3jEnc4nd3mSyG/iNTdgvLy9vhT0/biSr0OcSyfuZSHYH+f5h4XWv3yBIn/MmlSCGeVqhvrfPD1Z1bXzvO9vg2tqaZfizujDzo7evk849JXsR1QBo198pvS5kvaoPP9zd9PYZOO3186vsn3e8HvroNPiBvTZuJ+wZf8vc3Fw76HXeqPDz+kiFPpdIvmiV7A7y/cfHx0P9fDu9flbBz86qqVGun7DLfhC8cvci+Lmp4yi3C4W39Ktru+HP714/q6X63Xw/ohQAB+2tcTMs0YvhcHNzc6HdlOqFk+BnlL20oZ0A1WrV8/eM0ny6bpyuHmzVxp326sHtIZxjY2OBnCNjd+Huq6ZpLV3Xw9ZQKPrSfVA1O8GZDW3xa7uGfgu9uOn1Uxf3MnB7ceWmfn1YwEXdTndA1M+RZm2xu+fP7OJskIDh5d5s/Qg7BFrhVzAJs156De1029tn4KQeoxT6ohy8ooCfQ8nNevU68el6yfL8qHr6XCJ5aV/J7iDff29vj9nZ2bA1gP5DPrt7/fqtShn1wBelsneKZHeQ/71V2MdpXdvp+TPr9XM7zLNfsJidnWVvb8/x+/b7vKDa//j4OGdnZ5a/9zuQDNLr53XZe4mddj0+Pk4ul3P0viqUhYsfIwqchr2gzo9x3z/hjnJ5eRm2gmsku4N8/0ql4sn7eNn71+u9Onser66uLJ8X9cAH3pV9GHjtHvQQTenfW4V93NZ1Pp+/1cbW1tb4vu/7vvYF0aAXyHZ6koaGhgb6jF6fHUQPYyaTMX18fn4+0B4oN5/l540tLxd4scKq7DtZWlq68aOIBl7UxdLSEi+99BJzc3N83/d9343AZxzfzM6jQZ0fVU+fSyTvNyXZHeT7p1Ipz97Lzl58XryX0eNntex+0IHPbRsYGhrypP34MfehH162G4MgF3aR/r1V2GfQuu7X82ewtbXly6Iufn+//b6rb7btQVjDDZ32+g2yZYNXe/wNwsjISM/97lTIizZO5/kZr4HBhnAGdX5Uoc8lkr+4kt1Bvv/CwgLgfk6VH/QKjp3DOwuFwo3fdR/Mon5h71XbMfs7/b5QNNqN19gNfoMGe+nfW4V9vKprq33+gPaQT6cXaGb7A3azs7PjTDRiHB8ft//f77DnZN8zO8Gv010iKvDJx8lxxas5e0G1DRX6XFIsFhkbGwtbwxWS3UG+//n5OaOjo2FrtLEKfGaLuFxeXpLL5cSFPQM/245RBn6FPz/bTRA9ftK/twr7SK/rkZERLi4uwtZwTTab9WX0RRAXptls1vfPGIRePbTz8/Mkk0nPN5hXBI+bG0qDENQxU4U+lxweHoo9qUl2B/n+x8fHlhfvYa/c2Wu1znw+T7FYvHExISXsGQTRdvwKf73ajRf0Cn5eXEBK/94q7BP1uu7X21coFMSGvvn5eU822Q6rV8ptuwliaGe/wAfXc/rMQp/q5ZOH31vGdBLUMVOFPoVCIIMEQTdhrxNpQS8sOsspjDmAbghz83aFwgyrNmlnmObzxCDDOFUg6Y+dwKe4mwTd6+cnKvS5ZHl5OWwF10h2B/n+fs3NsoPdwGfWs5NOp7l3754fWoERVtvxovcvqHbTfZHt1TAx6d9bhX2Crms3WwT0Co2SLvDMAoedlQCjGvSOjo7CVriFk0V3zMo+qmWtsI/fwS+oY6YKfS6Jx+XudiHZHZS/G9yGve5ePVX2gzFI+AvbfVCk+yvsE1ZdO13J0yr4NZtNL7V8oVfvUqvVMn3cr/Dh5QWx1yuxDkq/wNddD1Zlr5CPVTv3YiXeoI6Z6izsErO5V1KQ7A7y/YO8i/zgwYNbgW91dfVW4OveOyadTpsO45Re9lHxN8rXyVDZINtNr/2E3BKVslf4T5h17cVQu5WVFQ9M/MHOXnvDw8M3/i1pPzg3Ze/HfL65uTnHgQ/My15xd/CrPoM6ZqqePoXijmKnd0/N1wsfu2WeSCRsPVfK/EGFwi2d+/WZ3X130uMXxtxAtz0DTgOtChzusFM/ah7f80uQC7x4jQp9LpmcnAxbwTWS3UG+/8TEhO+f4XSxFrAXPqSXvWR/u+5+bx3hFMNHctkrnBGFuh5k0/Yo7hVnN2QYF6R+bNkQBFEseydE5bir8B8vb6oEdcxUwztdEsSFu19Idgf5/uPj42EruEZ62Uv2d+rudPiol5gNX5Vc9gpnBFXXbobf2XmfYrHo2skP+v0dxvDNsHr2vOzxyOVynr1XGNRqtbAVFAIJ6pipQp9LdnZ2wlZwjWR3kO8veZlx6WUv2d+tu5v5g27p9TmSy17hjKDrulfYsTMHDm4GPy8WZrDzOf3o5+4m6Blz1fz8GwdB+s2hqG8ur4gmQR0zVehzSaVSCVvBNZLdQb7/1dVV2AqukV72kv29cPcj/NkNlZLLXuEMr+vabKji2trajX/3Cz9Ogl8ULtytfJ326nWGvO6gF8XgJ31eeSKRCFtBEQLdxyNwNsQ6qPOjZ6FP0zTZffIOicJJwS2S3UG+v+STmvSyl+zvpbsX4c/pe0gu+0FR50f/6AwuXgW/oaGhgb0GwczTSdCbmJiw3ZsXteAnfXikhO0+FNEjqGOmJwu5aJr2LuDLwHNziyPMDbYHRbI7yPeXvOqX9LKX7O+He2dos7sAgduwKLnsB0GdH4Ol38p6dhZ4OTk5aYchL4fju52DaCfsDRLeBvlbvV7B8OTkxNP3C5pyuXzj31tbW2oV1TuMVzdNgjpmqtU7XXJ2diZ2QQ7J7iDfv1gsMjY2FraGK6SXvWR/v917rfrpRe+05LJXOCOouu7cuqGbXhuG9wt+Q0ND7Yv3zos6P+djuwl8VhectVqNVCrl2CGM7Su6sdvLGovFeLiwzMpkga9kh3n1yTdoRmBj9FQqJb63Mgq4CVN2233YbdyMoI6ZKvS55OjoSOwFjGR3kO9/cnJiL/RdXcHuNvFajaaLE7gfSC97yf5Bufs1/Fhy2SucEXRdW4WVXr1+vYLf6OjorR4b43PA+4tGq+GcZti5GK5Wq65Cn/H+YV4Uj46O9nWIxWJ85Ad/jPuFGdLJJP/hX3oXb+xs8bHf+vXQg186nQ4s9PVqC1EMNv0YtNfMbrv363s8CEEdM1XoUyiiyNUVsY//fSiXeTGZ5Bt//QORCX4KhUIRNPl8vufQPzdhxe1efm4uGq0uaL0OfF4QdvDrx8OFZe4XZsimrm9QDaUzvGNhiZfuv8ifvmne8xsmfgzx7NcWzH4ftToNez6phDLyGluhT9O0frsGyl5j1wUrKythK7hGsjvI919cXOz/pN1tKJeJVa9INBtkT4+4nA5/wr30spfsL9kd5Ptboc6Ptwmrrnv1+DkZ6nl4eGj78wzcXCz6FfjCXohmEOyU/cpkgXTy5uVrJpni/ux86KHv8vLS989wG5a6Xxd0wPE75HnR7sMKgkEdM+329B0CvfrMY31+f+doRWDsuFsku4N8f1vMLcLQEC2gkUxSmZgK2wiQX/aS/SW7g3z/HqjzYxdB1nWveX2d9Ap+XtCr92/Qi92ge0TsXuT6WZ692Dg+pFqvt3v6AK7qNZ7sOe+17cZOWbtZjM2r3j4v20L3ezWbTeJxtZNbN/3K3Gy7BqcEdcy0G/r+iq8WAtnc3OT+/ftha7hCsjvI99/e3mZ1dbX3kzIZWh/9Zdjd5hsnxcgM7ZRe9pL9JbuDfP8eqPNjF2HWtRdDEwuFAvv7+64/36CXh91ePjcX+eVymeHhYceviwJ2yv71nU2eHO635/Rd1Wq8sbPFq0++EZClNblcjlKpZPq7QYOf3+FfcrsB2f5BHTNthT5d1//Ayw/VNC0OfBF4L7Cs6/qt20Wapr0M/Bug89vzmq7r3+Oli0IRWTIZWL1P8+JR2CYKhcICdX6MHk6Hebqd22fHwwwnwzoVt2m1WnziC5/l4cIyy5MF/uwbXw9s9c6wtlwKe/6b4m7gaCEXTdOsBp22gIqu6wc23+ojgJ2Bzw1d10dsvmegFAqFsBVcI9kd5PtPTvabAhRdpJe9ZH/J7iDfvx/q/Pgt/Krrfou5dDJIj9/5+bmr13mN2wt9v1bgDQKj7PvVX6vV4rXtDV7b3mB7ezsovb5cXV31/L2b3r6gAp/kdgOy/YM6PzpdvfMtesxN0DStCPwT4Bd0Xa9bPGcN+DDwfuArDj8/MkjdZw1ku4N8/9HR0bAVXCO97CX7S3YH+f42eAt1fgSCr2u78/rA3tw+s+0avGKQYZ3dc4es/ma32zVEATdlv7i4aCv49VtErV+4stPLV6+bfrVv4CT4BdnDJ7ndQLT88/m8o+cHdcx0Gvo+APxPwD8E/t2zx94D/Azw33K9StnHgHPg73W/+NmwlV8Dfh44tfF5CU3TNoEU8CXgl3Rd/6rZE1955RXgOum//PLLPHz4ELj+kqdSKdbX12m1WkxMTDA5Ocnu7i6Xl5ek02mWlpY4Pz/n4OD6RuzS0hKJRIL19XXgumdmYmKCp0+fUi6XyWQywPXFu7HS1PLyMrFYjI2NDQCmpqYYHx9nZ2eHSqVCNptlYWGB09NTjo+PAVhdXaXZbLK5uQlcJ/2xsTG2traoVqvkcjnm5uY4OTnh5OSEWCzG6uoq9Xq9fdKamZlhZGSEzc1NarUaw8PDzM7Ocnx8zOnpafs11WqVnZ2ddnm98MILbG5uUq/XGRkZYWZmhsPDQ4rFIvF4nNXVVSqVSnvIy/z8PNlslo2NDRqNBmNjYxQKBQ4ODjg/PyeRSLCyskK5XG7fnVtYWCCTybC+vk6z2WR8fJypqSn29/e5uLggmUyyvLxMqVRqj+G3U1+xWIyxsTFH9bW4uEixWAy9vvL5PMVikWazSbFYJJPJkM/nOT8/p1QqUSqVyOVyNJtNKpUKAPF4nGaz2f7bG40GcH2QGB4eJpPJcHV1RSwWa9/pajQaxONxyuUyrVaL5LOVzs7OziiXy8Tjcaanp7m8vGyX4/Lyct/6isfjTExMOKovN98vv+rr/PycVqvl6/drdnaWXC7n+fcrkUi0y9bP75df9XVxcUEsFvPk++X0hBoQkT0/QrDnyFKpxMOHD335DgPs7+9Tq9XIZrNMTU1xfn7O8PAwBwcHDA0N0Wq1KJfLjI6OWvbapdPp9kViqVRicXGxvb9aq9Xi4OCAqakpEokElUqFYrHIyMhI+/h8eHhIKpVqt8XT01Oq1SqFQqF97D0/P2d0dJShoaH2axKJBNlsFrgOOAsLC7fc8vk8pVKJeDxONpul0WgwPj7O9vY209PTJJNJdnd3GR4eZnR0lLGxMY6OjqhUKu3yTqVS7V6noaEhYrFYe2VJ42+vVCrtc8XQ0BC1Wo1qtWrVhExJpVLt806pVCIWi5HL5YDrXq96vc7Q0BDxeJzx8XHOzs4YHh5meHiYVqvF4eEh8XicqalvLVa2v7/P1NQU2WyWy8tL9vf3yefzjI+P02q12NjYIJVKtcvOOL69/e1vJ5lMcnZ2xtbWFvPz80xOTtJsNvna175GNpu9seBOpVJhZWWFeDzebifd9ZVOp5mYuF6A1zgXG/PGjPLKZDIkk0larVb7/NJZx2avscPo6CilUslWfRkrVxqhufs1RrvrfE0ul2t/V+C63Y+MjLSdu18Ti8VufL+A9t9uvCaZTJLJZKhWq9RqtfZrOq9pMpkMiUTixvVJv9dks9lb1zTGtU+9XicWi7X/bqPdd7/GaKvGazq/X93fle7XdJd9vV5vv6ZWq5FIJNjd3aXVajE2NkY+n3d0jmw0Grz44oueXIP2IuZkxRhN034f+F91Xf+drsffB/wdXde/X9O0DwC/ouv6reVsNE37CPC9uq7/pKZp94BvYj1nYQ6YBR4BI8Df5frk+VDX9Z2u57Z0Xbf9d3jBkydPxC5KINkdZPtXq1XW19dZXV01HaZkXER18ujR7Tl9nY89ePDg1u/NHjNbPKbz4tnO0AjJZQ+y/SW7g+f+Ma/eyCuien589vxAz5F+t9VeQzzNer/Mhgma9fYZN2FmZmZcL+TSCy97+Xqxvb1tuZiIHfxavdPOvMnOsncyPDcqPX3Dw8N9y95OL5+dHj4vVo3sZHt7296WUhElKv5ubkoGdX502tP3HuB1k8f/HPj3nv3//wPcatGapn0b8FHgJTsfpOv6LmB840+BX9Q07SeBHwH+sTNt75G8D45kd5Dvb9wBlIj0spfsL9kd5PvbQJ0fn+F3XXdeVHUHQONCuDP8mW2nYDbM01jQxWlvV9QwehbsDnmNEtLL3hiJY0ZUw56BMYpNKmH7DzICJajzo9MNOda5vpvYzX8FbDz7/2ng2OQ5f/nZ7/5c07RD4MvPHn9N07QP2/z8JhG5wxvWCk5eINkd5PvPzs6GreAa6WUv2V+yO8j3t4E6Pz4jyLrO5/OmF1tra2u3Lo67L6jNLsLn5+cZGhry/G/wc4uGboxhsH6FAz85PbUzsjm6GMMRu+kV+Obm5to/VhjtubtOjfZv9uMU6YttheE/SHl3EtQx02lP30eB/1vTtB8F/t9nj70EvI3riedwfUfzt01e+9vA5zv+vcT1Xc+/CrzR/WRN036A6xPlEyDH9TyHWeBfOXT2hdPT0/Y4b2lIdgf5/mdnZ4yPj4et4QrpZS/ZX7I7yPe3gTo/PiOMujYuusx6/rp7/fr1+KVSKWq12o0LMT+2dLCL0/BmzCWUSC6Xa8899GLPRa+we1FutB2DfmGvH1Z1bydkOA0i0o/Rkv2DcncU+nRd/6ymaS9yvbrY242HgX+o6/rGs+d80uK1l3QsQ61pmvHZu7quX2ia9lPAP+pYgvpdXK90VuB6L6IvAz+s6/qmE2e/OD4+Ftu4JLuDfP/T01OxoU962Uv2l+wO8v37oc6P3yLMujYLf91DPvuFiXQ6fePCHYhMALRDsVhshz4nK5sahBm2RkZG2qFPIkbbCTPsuUX6MVqyf1DuTnv6eHZS+cVBP1jX9bfoGIqi6/qngE91/PsTwCcG/RyFQqFQKIJAnR+jQ7/w1znPz842Dp109/r0C4FBD202VjI0cBP8/MCYM+kHdrdtsMLLrRHcBr4wgp7i+cJx6NM0bRZ4BfhLXO9J9Aj4B7qu73nsFmnMVkKUgmR3kO/vdGPWKCG97CX7S3YH+f52UOfHa6JU13bCX3fwc7rypZteQD/PA0Hu7eY1xrYgXhPEqo7Sw16UvrdukOwflLujhVw0Tfte4C+ADwJloAL8Z8A3NE17r/d60aXXCk1RR7I7yPdvNpthK7hGetlL9pfsDvL9+6HOj98iinVtttiCcbFtXJAbF+3dPWVOmJ+fb/8Y/w4as/KXsqhLPO50fcFoYBX4ei3QYrYwC1gvTuQ3UfzeOkGyf1DuTr9d/zPwm8Carut/U9f1vwmsAZ8GPu61XJRxuj9NlJDsDvL9jU28JSK97CX7S3YH+f42UOfHZ0iqa7PgZ2wuPihOA59XPXRWewxarQAZJTo3afeSfkM/+81h7NWD2yvwmdFvFc6wkPS9NUOyf1DuTkPfdwAf13W93VXx7P//F+A7vRRTKBQKhUIQ6vwoAKvtHbqf4+fwSydbNfgV0Pq9r7Qhop2hLh6L8e63rfGB73mZd79tjfgAPbf9cBL4eoU9hSIInM7pOwNeAL7e9fgLXG8Q+9wwPT0dtoJrJLuDfH8v7mQmGw3GyyXOhoY9MLKP9LKX7C/ZHeT720CdH58hta7NtnOA8HoQ3AY+ySGiWCwO9Pp4LMZ/95/8NO9YWCKTTHFVr/HGzhYf+61f98iwP92Bz2oIZ9SQ+r01kOwflLvT0Pdp4B9rmvYLwB8/e+x7gf+R62Etzw1S98AB2e4g339kZKT/k3oQr9X4kUevkm7UqCZSvPXOd9JMpTyy6430spfsL9kd5PvbQJ0fnyGxrnutcOll+LPbyzdID5/d4an9VvUMY+sGq83N7fLS/Rd5x8ISQ+kMAEPpDO9YWOKl+y+yXXG2QE8/uutSatgzkPi97USyf1DuTod3/gLwz4Ff43rC+pvA/8n1xrL/jbdq0UaNHQ4P6f6DzunLnh6RbtRINZukGzV2vvyqR2b9kV72kv0lu4N8fxuo8+Mzol7XvS68ew1rXFpa8nzYp9eBD6zn9JkRtfl9k5OT7f+PAd++uMKPP/wuvn1xxdYCO2+bXSCTvHkTNJNMcX92fuB5fZ1YBb5yuSx2GGfUv7f9kOwflLvTzdmrwN/RNO0Xgbc9e/jNZxvLPldUq9WwFVwj2R3k+3du+tuslKltrJNaWSWeHbL1+q883WcukQKue/rOhoYJavaF9LKX7C/ZHeT790OdH7+F9LoeHR1leHjYMgS47fmzExi9CGHdG8vb+UyrHr+ge/uSyevL0hjwoe/6HhbHJkgnk1TrdZ4c7vOJL3yWVqt14zVPd3Z499vWeNvsAol4jKt6rd3TB3BVr/Fkz5/9AeFmcC8UCjd+F/Wg14n0761k/6Dc+4Y+TdN0G88BQNd1zQMnEXi1ulcYSHYH+f5DQ9fhrlkps/vRD9MsXRAfHmHu45/s+bp4rUb29AiAf/ngpfacvnoiceu5jx494sGDB567Sy97yf6S3UG+vxnq/GiO1Lo2wk/i2TG1cwN3MwYd9mlnOKAbstms49cEtYF7vw3ar66urn0KsyyNT5J5FgKzqTT3CzM8XFjmte2N9vNjJnP4ao0GVK9uzOl79ck3PPsbjHrvrD+j7o6Ors/RksKegdTvrYFk/6Dc7fT0HfluIRBpK1t1Itkd5PvPzMwAUNtYp1m6oFWp0Hz2bzI3e/sePXoEXAe+Fz/zmySqVeZicf7lg5c4GhkLWl182Uv2l+wO8v0tUOdHE6TXdXdoshv+wDoAupn/5Ra3i4VZBb8ge/vOzs4AmB+dINV1QzOdTLI8WbgR+h4uLN+aw0f1it/5039Lvdnkyd5TXn3yDZpdvYODYtRfd71927d9m6efEyTSv7eS/YNy7xv6dF3/W0GISOP4+PjG2HNJSHYH+f6np6dMTExcD+kcHqEJxIdHSK2swp75XIzs6RGJapVEvUY6Hme8XAol9Ekve8n+kt1Bvr8Z6vxojoS6zufznJycmP6uWq2STqdvPe5X+PF6Xl2xWGRszN35wUmP39LSkqtezl69fcPDw5RKJZ6en1JrNNo9fQDVep3N48Mbz1+ZLJjO4as3m3z6j//g1vtvb2+zuLjo2NmgM7yb1ZuEtm+FZHeQ7R+Uu9OFXBTPOD2VuwK3ZHeQ72/cyYxnh5j7+CeZ/qW/z9zHP9lzTl9lYopGOk0jmWzP4wsD6WUv2V+yO8j3V9hHel07nRPXiZ15e27u6jvZvPv8/Nzx+/fD656I+fl5083rh4evz22PD/fYOjvmql6n2WpSqVV5crjP6zubN56/cXzIVf1mffk9h68Xktu+ZHeQ7R+Uu9MtGxTPsLOKVFSR7A7y/TuJZ4fIrL2j7/OaqRTf+OsfIHt6xFee7pvO4wsC6WUv2V+yO8j3V9hHcl2vra3xZ3/2Z5a/97q3z04vX3fQM/5t1VM5aPk77e0D9/MajeBn9PwZi7S0gH/65T9mrTDL/Og4/9/GW7y+s3lrEZfXdzZ5Y2fr1r58Xs7hc4Lkti/ZHWT7B+WuQp9LVldXw1ZwjWR3kO+/vLzs6nXNVIrL6Tnq++FNI5Je9pL9JbuDfH+FfaTXtbHYlld09v6ZLf5hRb9evc7fdwZAL3rlzIJfr8DrdqingRH+Ot+/BXz9cI+vH+5Zfm6r1eKTX/w8C5kc92fnfZvDZxfJbV+yO8j2D8pdDe90ySDDP8JGshA4YY8AACAASURBVDso/zCR7A6y/SW7g3x/hX2k1/ULL7zg+2cMGvjMnm/81Ov1QdTamDn6vY9hwmIUS6/PbbVa/Ombj/n0H/8Bf/rm49ACH8hu+5LdQbZ/UO4q9Lmk3yafUUayO8j3D3LPI6+RXvaS/SW7g3x/hX2k1/XBwUHPUObnSntebOJdKpU82zLAzSIzg4S/e/fuuXpdVJDc9iW7g2z/oNxV6FMoFAqFQqHwGatl/g283tvNyeIvTrAbet2GP7MFXpx8rkKhMEeFPpcYe61JRLI7yPcvFAphK7hGetlL9pfsDvL9FfaRXtfG0uleb6Vg9Z5eBzOz8h8kAA5aDk7CX6VSAayDX5TJ5/Oi275kd5DtH5S7Cn0uMZYVlohkd5Dvn8vlwlZwjfSyl+wv2R3k+yvsI72ujc3Ze61g2W9um5Pne02/8vci+Ln5e+wEv0aj0fP3fpZjr/fuDKG9nie57Ut2B9n+Qbmr0OeSzc3N/k+KKJLdQb7/zs6O7ec+ePDA1We4fV0/pJe9ZH/J7iDfX2Ef6XW9v79v63lOg19Q2Cl/P4Z92qFfr5/fN0WtNma3E/j6bcwOstu+ZHeQ7R+Uuwp9LvFqdawwkOwOyr870PkV8Mx43ss+TCS7g3x/hX2k17VX/naGNfoRvJz4O/l8L4e7WpVL535lToZ4WoW5Qel26NfTKLntS3YH2f5BuavQ55KRkZGwFVwj2R3k+0se3im97CX7S3YH+f4K+0iv66GhIdubkzsZbujHHEEznJZ/mL1+3fh58eukl29+fv5G4LPbcyu57Ut2B9n+Qbmr0OcSNWE0PKT7T09Ph63gGullL9lfsjvI91fYR3pdGwu52CVqq0q6LX87wa8zuHrxd3eHqaurq76v8bK8rQJfJ92OVovxgOy2L9kdZPurhVwiztHRUdgKrpHsDvL9j4+PAfMT7Orq6q3HzIZvGo/1+l2/93WD9LKX7C/ZHeT7K+wjva7Pzs4cv6ZXEAk6FA5S/mH0+nWGqnQ6feN3fq7i6TTw2alHyW1fsjvI9g/KXYU+l7g5KUQFye4g3//8/NyT9xl0Lp+bE7v0spfsL9kd5Psr7CO9rp88eRK2wkB4Uf69zg9+DFM1wlUqlXL9Hr3m9dmZ89evhw/6/+2S275kd5DtH5S7Cn0uicflFp1kd5Dv3zlRXRrSy16yv2R3kO+vsI/0unZ7jI7KME+vyj/oXj8nK54OWtbdr+8X+Hp9XmcZSW77kt1Btn9Q7nJLKGS8Gi4XBpLdQb7/8vJy2AoAnJycOH6N9LKX7C/ZHeT7K+wjva7f9a53ha0wEJLLv1Qqha0wEJLLXrI7yPYPyl2FPpfYmWwcVSS7g/I3ePTokSfv4wRV9uEh2R3k+yvsI72uq9Vq2AoDIbn8JffWgOyyl+wOsv2Dcpf97QoRJxtsRw3J7iDff29vDzDvaVtfX7/1mFm4Mx7r9bt+7+sG6WUv2V+yO8j3V9hHel0fHh4Gtr2CH0gu/6GhobAVBkJy2Ut2B9n+Qbmr0KdQKBQKhUKhUCgUd5hkGB+qaVoc+CLwXmBZ1/Uti+f9NeDjwH3gTeDndF3/vcBEexCVCdtukOwO8v293qfv0aNHA6/kaRfpZS/ZX7I7yPcPCnV+DJ+pqSngeqVGu5u0RwnJ5V+pVG49Nj8/z9OnT0OwscfJyUl7MRfJZS/ZHWT7B+UeVk/fR4DLXk/QNO0+8DvA/wCMP/vvv9A07Z7vdjaQPARBsjsofztz+fya7/e8l32YSHYH+f4Bos6PIZPJZNr/L3GYp+Ty93NfviCQXPaS3UG2f1DugYc+TdPWgA8DP9/nqT8NfEnX9X+m63pV1/VPAV9+9njobGxshK3gGsnuIN9/a8v0xr0pYSzW0gvpZS/ZX7I7yPcPAnV+jAbGvGupSC9/t2xvb4etILrsJbuDbP+g3AMd3vls2MqvcX1CO+3z9HcBX+p67MvPHr/FK6+8AkA6nebll1/m4cOHwPWGnKlUivX1dVqtFhMTE0xOTrK7u8vl5SXpdJqlpSXOz885ODgArvdnSSQS7cUvJicnmZiY4OnTp5TLZTKZDI1Gg2KxyOHhIXC9DH8sFmtX3NTUFOPj4+zs7FCpVMhmsywsLHB6esrx8TFwvURrs9lkc3MTgEKhwNjYGFtbW1SrVXK5HHNzc5ycnHByckIsFmN1dZV6vd4ODjMzM4yMjLC5uUmtVmN4eJjZ2VmOj485PT1tv6ZarbYnilarVVqtFpubm9TrdUZGRpiZmeHw8JBisUg8Hmd1dZVKpdIeUjE/P082m2VjY4NGo8HY2BiFQoGDgwPOz89JJBKsrKxQLpfZ3d0FYGFhgUwmw/r6Os1mk/Hxcaamptjf3+fi4oJkMsny8jKlUon9/X3b9dVoNBzX1+LiYiTqK5/P02w22d7eplgskslkyOfznJ+fUyqVKJVK5HI5ms0mlUqFTCZDrVaj2WySTqeJxWJMTExwenrK2NgYw8PDtFot4HpvqXQ6DUCtVqPValEul2m1WiST11/1s7MzyuUy8Xic6elpLi8v2+W4vLzct77q9ToXFxeO6svN98uv+mo0Gr5/v2ZnZ8nlcp5/v+r1OpeXl75/v/yqr0aj4dn3K8i9w4LCz/MjBHuOPD09ZXV1NRLH3F7f4Z2dHWKxGPPz89RqtfbfZxw/9/b2aDQaTE9Pc3BwQLVapVarEYvFGBoaah+nAbLZrOXKk6VSiVgsRqvV4urqiqOjo/bf02q1In+OvLy8ZHx8nJGRkXY5GHXmF7lcjlgsRqPRoFKpMDIyQi6Xo9VqcXBwwPHxMffu3QNgf3+fy8tL3v72t5NMJjk7O2Nra4v5+XkmJydpNpsUi0Wy2Wx7GN3u7i4TExMUCgXi8TgXFxeMjIyQyWRIJpMcHx8zOTnZ9tnd3WV6eppWq8Xu7i7NZpORkRHGx8c5Pj7m5ORE/DnSaMvqHBl8fZXLZQBPjoe9iBkXjEGgadpHgO/Vdf0nnw1D+SYWcxY0TfsC8EVd1/9ex2O/8uz1P9T13Jau6/7Kd3FwcOD53KygkOwOsv2r1SqHh4cUCgVbq3faXZ3TbE5f92Nm+8B0XjwbgbEXksseZPtLdgfP/d3tnh1h/Do/PvtdoOdIaW21+1hsdWPBzvw+44KzE+NCzGyoqB83MPwof7Pz1ePHj03/3kExGw3TPaev83NjsRgPF5aZSGZ4c2+HV598g6bJte3i4uKNf7vdoL1XPUpr+51IdgfZ/kGdHwPr6dM07duAjwIv2XzJOddzFTqZAIpeerlFasMC2e4g379QKDh+TbxWI3t6RGViygcj+0gve8n+kt1Bvr+fqPNjuBgX7EawsQpiUhZ2CbL8O3vO/KLXIi6xWIyP/OCPcb8wQzqR5Kpe442dLT72W79uGvz6fU5n8Nva2roV/Pohre13ItkdZPsH5R7knL6/DEwDf65p2iHXQ1EAXtM07cMmz/8q8F1dj33ns8dDx+iilYhkd5DvbwztsUu8VuPFz/wm9/717/LiZ36TZKPhk1l/pJe9ZH/J7iDf32fU+TEC5PN58vk8p6fWo2vdLOziZyAyI6jy7yyLubm5UFZPfLiwzP3CDNlUmng8zlA6wzsWlnjp/ot9X2unXpzMwQe5bR9ku4Ns/6Dcgwx9vw28DfiOZz8/+uzxvwr8hsnzfwN4SdO0D2ialtI07QPAdwO/HoRsP4rFSNxQdYVkd5Dvf3Fx4ej52dMjEtUqiXqNRPWK8XLJJ7P+SC97yf6S3UG+v8+o82OEaLVaPYddrq2tWYa/KCwb70f52x2GGnT4W5kskE7eHLSWSaa4P+tuFdBevYp2QqLkti/ZHWT7B+Ue2PBOXdcv6ViGWtM047N3dV2/0DTtp4B/pOv6yLPnv6lp2vu43ofo14AnwN/Qdf2toJx7kUgkwlZwjWR3kO9vNdnfisrEFI10GmjRSGc4Gxr2R8wG0stesr9kd5Dv7yfq/BgtDP/uYZ/dRHW4Z5Dlb4Tf7nJwO+zTWLTCLhvHh1TrdbKpb81Jv6rXeLJ3O7xtb2/fmtdnh+5hno8fP74V+o15oJLbvmR3kO0flHugC7n4RRgLubRaLWIxmWsJSHYH2f7GyqmxWMzRQi6dc/peN7nICGohF8llD7L9JbuD5/5yCyIEgj5H3tW2ahX+ugOP1WIuQS3k4mf5W5WBgVUIthv+7Czi0vl+Tuf0mYU+s55Js/0Cl5aW+i7mIrntS3YH2f5BnR8D3bLhLlGpVMRuBCnZHZ5P/2YqxeV0+MOGnseyjwqS3UG+v8I+0uvayt+q56+7t2tubi7weXydhFn+g/b8JRIJGg7mrbdaLT7xhc/ycGGZ8WSaJ3tPLVfvdEL3oi52kdz2JbuDbP+g3APfnP2u0Gvcd9SR7A7y/Y39YCQivewl+0t2B/n+CvtIr+t+/saCL930W+QlqKGgUSh/q3mP/eb8udn7r9Vq8dr2Bp/+4z/gT9983DPwmW3gvru7axpGe/UwWhGFsneLZHeQ7R+Uuwp9CoVCoVAoFA7xY2hm1LEKvFZEccGb7e1ty/DXzdOnT9sX5J1DT6M4l1Oh6IcKfS5x0+0fFSS7g3z/2dnZsBVcI73sJftLdgf5/gr7SK9rv/zdbPfghiDK3wh/dgKgk787yCDtttfPqrfv5OREdNuX7A6y/YNyV6HPJW6GIEQFye4g3z+TyYSt4BrpZS/ZX7I7yPdX2Ed6XSt/Z7gNflHY3qJXr59ZuOu3b5/ktiPZHWT7B+WuQp9LuldZlIRkd5Dv73Sz1yghvewl+0t2B/n+CvtIr2vl7xw7vX5uezqD6AWxE/66512ZDfGU3HYku4Ns/6DcVehzSbPZDFvBNZLdQfmHiWR3kO0v2R3k+yvsI72uB/EPaghnL8Is/37hr7t8otDb14lZ8IPbQzqthnhKbvuS3UG2f1DuKvS5ZGxsLGwF10h2B/n+o6OjYSu4RnrZS/aX7A7y/RX2kV7Xyn9weoW/KATjXlj1+sF1b1+v0TpRKHu3SHYH2f5BuavQ55JCoRC2gmsku4N8/8nJybAVXCO97CX7S3YH+f4K+0iva+XvHb2CnxH+3PT2BdFD2B3+zHr3uod4JhIJ3738Ikrtxg2S/YNyV6HPJZL3WpPsDvL9Dw8PPX/PR48e8ejRo1uPdbK+vn5r3HjnJsPVarXv50gve8n+kt1Bvr/CPtLrWvl7i+ReP3C2xUPnOVUaUWs3TpHsH5S7Cn0uubi4CFvBNZLdQb5/qVTy7b3Ngl+/8HdyctI+UVWr1faPGdLLXrK/ZHeQ76+wj/S6Vv7+0GuuX2fP3dLSUhA6A2E1xPPy8jJgE++Iaruxi2T/oNxV6HNJMpkMW8E1kt1Bvr/fwz+sev3shr/uANiJ9LKX7C/ZHeT7K+wjva6Vv0wWFxfDVhA9vFN6u5HsH5S7Cn0uWV5eDlvBNZLdQb5/UCcmt+EPzHv/QH7ZS/aX7A7y/RX2kV7XXvsHvUKl9PKXzOzsbNgKrpHebiT7B+WuQp9LJHfhS3YH+f7lcjnQz+sOecZjbsLf2dmZrbl/UUVy25HsDvL9FfaRXtde+Ie5FYH08jcjiL367NK9X18nlUolQBNvkd5uJPsH5a5Cn0v29vbCVnCNZHeQ739wcBD4Z5qFPKvHjfBnNvTTeKzXvL8oI7ntSHYH+f4K+0iva+WvcMvx8XHYCq6R3m4k+wflLncArEKhcIwR8B48eGD6ePfvjJC3urrafszo+cvn8zeCXzqd9l5YoVAoFAoT5ubmLDdJ94vd3d3IbSivUNhF9fS5ZGFhIWwF10h2B/n+UThhmPX6df7Oqvevc49BOwu/RA3JbUeyO8j3V9hHel0rf4UZVhu2d+7VNz09HZSO50hvN5L9g3JXoc8lkns1JLuDfP9UKhW2AtA7+Fn9fmdnx/S5Tvf7CwvJbUeyO8j3V9hHel0rf7mEvYJnVM7vbpDebiT7B+Wuhne6ZH19nRdeeCFsDVdIdgfZ/ul0mm9+85ssLi629yzqDEyrq6s35tIZQy3Nhl+ahbLuYZt2efToka3XSp4oDbLbjmR3kO+vsI/0unbib3Yc795w3GwD8l571g2K9PKH6736uvfCm5+fv7WIShhDPHvx9OnTGyNiJCG93Uj2D8pdhT6XtFqtsBVcI9kd7oa/cVenWq3eumjonD9nBMBe4W9QjPc0C35mj62vr99wlITktiPZHeT7K+wjva7d+NsJf53P8xPp5W/gNvgZvX1WwzH9RHLZS3YH2f5BuavhnS6ZmJgIW8E1kt3hbvmn0+n2Tz6fv3VBsLq6eiNgPXjwoP0TBpKHroDstiPZHeT7K+wjva4H8Tc7jvd63A+klb9ZOO6F2fYNZnPlwxjqOTIyEvhneoW0dtONZP+g3FXoc4nU7nuQ7Q531787/HVeIHSHP8CX8Ndvnp/kMfMgu+1Idgf5/gr7SK9rL/w7j+NBhT0D6eXfydLSkunjToJfkOFvfHw8sM/yGuntRrJ/UO4q9LlE7QcSHnfd3wh/cPvusBH+zHr/3NAv5HU/R/LGsyC77Uh2B/n+CvtIr2vlLwO7wQ+CC39qn77wkOwflLsKfS4plUphK7hGsjs8P/5mQz979f4FMfSz0Wj49t5BILntSHYH+f4K+0iva+XvH716Pa0Cm1VvHzgLfuB/+CuXy769t99Eud3YQbJ/UO4q9LlE8twmye7wfPrb6f3rxE74s+rl69X7F4vFAG6sMCoJyW1HsjvI91fYR3pdK//o0Sv4mTE3NxdK+Esm5a6PKL3dSPYPyl1u6wyZ5eXlsBVcI9kdnm9/L1b9HIRcLufJ+4SF5LYj2R3k+yvsI72ulb8szFb0NDCCn9W2Dl6v9Dk7O+vJ+4SB9HYj2T8od9XT55KLi4uwFVwj2R2UP9xcUMXOIgFeDfms1+uevE9YSG47kt1Bvr/CPtLrWvmHR6FQsPyd02GeTvCq10/yXraS2w3I9g/KXYU+l+zv74et4BrJ7qD8w+Tq6ipshYGQXPaS3UG+v8I+0uta+YeH9HNM5z6N0pDcbkC2f1DuKvQpFAqFQqFQKBQKxR1GhT6XOJ1UHCUku4PyD5OhoaGwFQZCctlLdgf5/gr7SK9r5R8e0s8xMzMzYSu4RnK7Adn+QbkHupCLpmn/PfBBYAqoAH8I/Jyu6xsmz30Z+DdA5zqmr+m6/j0BqPZF8gpNkt1B+YeJsXqnVCSXvWR3kO8fBHflHCm9rqPobwwbtDOHO4r+dpF+jkkkEmEruEZyuwHZ/kG5B93T938B36Hr+hhwD9gAPt3j+Q1d10c6fkI/mRlIXbIeZLuD8rdD9xYOXiF5kjrIbjuS3UG+f0DciXOk9LqOiv/JyUn7p9dj3UTF3w1+7XPXa/sGL7FaJVQCktsNyPYPyj3QWKzr+hsd/4wBTeDtQTp4RavVClvBNZLdQfkr3CO57CW7g3z/ILgr50jpdR22v93FQKx6/8L2HwTJ7iDbX7I7yPYPyj3wvlBN0z4I/ANgDKgDP9fj6QlN0zaBFPAl4Jd0Xf+q2RNfeeUV4Hop+5dffpmHDx8C18vwplIp1tfXabVaTExMMDk5ye7uLpeXl6TTaZaWljg/P+fg4AC4HlubSCTayXtycpKJiQmePn1KuVwmk8mQz+cpFoscHh4C13tsxGIxNjauR+FMTU0xPj7Ozs4OlUqFbDbLwsICp6enHB8fA9e9Mc1mk83NTeB6qeKxsTG2traoVqvkcjnm5ubad/VisRirq6vU63W2traA6/HjIyMjbG5uUqvVGB4eZnZ2luPjY05PT9uvqVar7OzsAJDNZmm1WmxublKv1xkZGWFmZobDw0OKxSLxeJzV1VUqlUp775v5+Xmy2SwbGxs0Gg3GxsYoFAocHBxwfn5OIpFgZWWFcrncvtO1sLBAJpNhfX2dZrPJ+Pg4U1NT7O/vc3FxQTKZZHl5mVKp1F65yE595fN5x/W1uLgYmfrK5/OO6mt2dpZcLnervo6Pjzk/PycejzM8PEytVmu75vN5Wq0W5XKZVqvVHjqQTCZJJBLtA0wmk2FychKAw8NDarUac3NzZDIZGo0G9XqdVCpFqVQiFouRTCap1+tcXV2xu7vL1NQUiUSC/f19isUi4+PjTExMDPz98qu+8vm8798vq/oa9Ps1MTHB5eWl798vv+orn8979v2yM8RNKnfhHGls7RKVY66kc2Q8Huf4+JhyuUwymWRmZoZKpcIbb1zfD3jw4AHJZJLd3V1arRajo6PA9aqXUs6R5+fnFItFYrEYc3NzNBoNSqXrUcr9hkcODw9TKpWIx+Pt+X+VSoVGo9E+H1UqFYrFIiMjI+RyOZrNJoeHh2Sz2Rv79lUqFVZWVojH45yfn7O9vc3i4iITExPU63W+/vWvMzo6ysrKCgDf/OY3AZienqbRaLSPq/l8nq985SssLCwwOjraLiO/vl9+1ZexFZQ6RwZfX8bm7F58v3oRCysZa5o2B/wXwL/Vdf33LX4/CzwCRoC/C/wM8FDX9Z2u57Z0XffdWaGIEtVqtf3/3XeGzYYKdG/Q3mvD9u59/cz2+eseQtp5Id65j6BC4QOyJ/7YQJ0jn0+sevkeP34MwNramunvJd0IMfsbjb/PoNcwSSNwmGG1SXu/9wR7G7Qb+/nNzc219wY0FuGYm5u7VT+S6kVxZ7A8P4a2eqeu67vA/wH8rqZpk2a/13X9q7qu13VdP9V1/ReBY+BHgnY1Q/K4bcnuoPzDxLh7KRXJZS/ZHeT7B43kc6T0ulb+4WHnHLO0tGS52uH8/LzlRu1ez+szAqYRQnd3d3nttdduBNh+8y+jhOR2A7L9g3IPe8uGJDAMLNh8fpOI3OGVvKCFZHdQ/mHSaDTCVhgIyWUv2R3k+4eEyHOk9LpW/uFhnGPsBLR+4c+MXu9r9OL1wugNNC7SO4Pf1tYWp6en7O7u3uq5lBD8JLcbkO0flHtgc/o0TYsDHwZ+W9f1fU3TloD/DXgLeMPk+T/A9cplT4Ac8PNcD2X5V0E590Ly8DXJ7qD8wyQeD/s+0WBILnvJ7iDf32/u0jlSel0r//Bwc45ZWloyHfI5Pz9vOtxzbm7Ok56V3d1d5ubmePr0aTtkHh0dMTU1deP9jSGfTrbdCAPJ7QZk+wflHvQV3I8Cf65pWgn4d8Al8EO6rtc1TfspTdMuOp77LuALwDnXJ7V/H/hhXdc3A3Y2RW0CGR7KPzykb5wruewlu4N8/4C4E+dI6XWt/MOj8xzjZDimVa+fVY+f1xjhslwu3xjuafT6SRjyKbndgGz/oNxDW8jFS8KYpF4sFhkbGwv0M71Csjsof4MwFnKp1WrtVaYkLuQiue1IdgfP/UMfwiiJoM+Rqq26x4uFXKJe/r0WcqnX67c2qnbTK9fd82fW42f1vk4WdIGb4XR5ebm9em3nhbzxHLP6i0rPX9TbTT8k+wd1fpQ9VitEjGVXJSLZHZR/mHQGTYlILnvJ7iDfX2Ef6XWt/MPj6urKk/fp7jkx6/HzY8P2Tv/O4GnV6wff6vkLu/dPcrsB2f5BuavQp1AoFAqFQqGIJG7DWVDBz8n2EsZzu4OfQhEEKvS5ZHl5OWwF10h2B+UfJtLn9Ekue8nuIN9fYR/pdR2mfz6fNx3ut7a2Zjk0sPv5US//Xn+f1TnGz+DnJUdHRz33CowyUW83/ZDsH5R7YKt33jUkr2Io2R2Uf5jEYrKnUkkue8nuIN9fYR/pdR0FfyMYWQ356zUPLAr+/ej07/wbX3zxRRKJhGlPmBH8orwfW7PZHOj1Jycnoc3xk9BueiHZPyh3uSUUMmYLZUhBsjso/zCRvA8OyC57ye4g319hH+l1HSV/oyev+6cXUfK3Q+fftLe3B1j3bII/c/G8Ynp6+tZjZttJRBFp7aYbyf5BuavQp1AoFAqFQqEIlXw+f2t4pxH+ugPg3NxcoOHvrbfe8uy9otxTqbjbqNDnksnJybAVXCPZHZR/mER1Kwa7SC57ye4g319hH+l1rfzDY3Jy0rJH0yr8RYmLi4v+T4ooktsNyPYPyl2FPpdMTEyEreAaye6g/MPE2KNPKpLLXrI7yPdX2Ed6XSv/8Oh2txP+gu71A+u9/JxMgYjaCp6S2w3I9g/KXYU+l+zs7ISt4BrJ7qD8w6RSqYStMBCSy16yO8j3V9hHel0r//Cwcrcb/sLCGLIpOXhIbjcg2z8od7V6p0skX/xKdgflHyaNRiNshYGQXPaS3UG+v8I+0uta+YdHP3ezVU27h3x6PWfOyXw+YwrE06dPfd8ewmsktxuQ7R+Uu+rpc0k2mw1bwTWS3UH5h4nkJZFBdtlLdgf5/gr7SK9r5R8edt17rWAaZo9frVYL7bMHRXK7Adn+QbnLvoILkYWFhbAVXCPZHZR/mEjfnF1y2Ut2B/n+CvtIr2vlHx6S3cF6X0UJSC97yf5BuavQ55Kzs7OwFVwj2R2Ufxg8evQIkH0XE2SWvYFkd5Dvr7CP9LpW/uEh2R1k3xiVXvaS/YNyV6HPJUdHR2EruEayOyj/MKlWq2ErDITkspfsDvL9FfaRXtfKPzwkuwOMjo4CiJvPB/LLXrJ/UO4q9CkUCoVCoVAo7iRLS0s3/i0xkCkUXqBCn0tWVlbCVnCNZHdQ/mEieegKyC57ye4g319hH+l1rfzDY1D37pU87eDlwi+Hh4eevVfQSG43INs/KHcV+lzSarXCVnCNZHdQ/gr3SC57ye4g319hH+l1rfzDQ7K7dKSXvWT/oNxV6HPJ5uZm2AqukewOyj9MyuVy2AoDIbnsJbuDfH+FfaTXtfIPD8nuAIVCAbjep6+Tra2tMHQcIb3sJfsH5a5Cn0KhUCgUCoVC4YDt7e0b/97d3aVYLLb//fTp0xvhrzP4eb2BvEJhBxX6XGLczZGIZHdQ/mGSTqfDVhgIyWUv2R3k+yvsI72ulX94SHaH61UYuwNdd/DrDH+Ps4aGhwAAFodJREFUHz++9R5h7fUnvewl+wflrkKfS8bGxsJWcI1kd1D+YZJKpcJWGAjJZS/ZHeT7K+wjva6Vf3hIc+/u7Ts/Pwdu9+R1D/c0e07YSCv7biT7B+WuQp9Lur/okpDsDso/TKTP6ZNc9pLdQb6/wj7S61r5h4dE907nzu0gzIKfEf769faFgcSy70Syf1DuKvS55OrqKmwF10h2B+UfJs1mM2yFgZBc9pLdQb6/wj7S61r5h4dkd4BMJnPj32a9eWa9flFAetlL9g/KXYU+l0jer0yyOyh/Nzx69MiT90kkEp68T1hIbjuS3UG+v8I+0uta+YeHVHejp8ZsNMzu7q5p+Nva2rIc4hnGvD6pZW8g2T8odxX6XNLZhS8Nye6g/MMkm82GrTAQkstesjvI91fYR3pdK//w8Mrdyw3X7bK9vc3e3p7l7zsDXndvXxSGeEpuNyDbPyh3Ffpccnp6GraCayS7g/IPgkePHt3qHXz06BG1Wq397/X19Ru/Pzk5ad+drFar/ku6QELZWyHZHeT7K+wjva6Vf3g4cc/n8z6auKPfMD2znr2oLOgiud2AbP+g3FXoc8nx8XHYCq6R7A7KP0i6w9/6+vqtfxs/Bkb4q1ar7Z+oIKnsu5HsDvL9FfaRXtfKPzycupsFv7W1NeBmb9/S0tKN5/TqWVlcXHTk0Mns7GzfRTmiEvK6kdxuQLZ/UO4q9CkUir50hz+znkCr8AdELvwpFAqF4m6Qz+fbPwZra2usra3dCn6d4a8z+PUaDnrv3j3u3bvnyMnuaoydq3gqFH4Ta7VaYTsMjKZpLV3XA/3MRqMhdlELye6g/A06Q1T3pO/uoZdwezEXN4u7xGIxWq0WDx48uPF4978BVldXbz3WeVIOY6N3yW1Hsjt47h/z6o2eB4I+R6q2Gi6S/b1y7z4nPn78+EYPW2fY6pxf1/kcN8voJxIJGo1G+99WvYZzc3Pt0Lm0tMTc3Fy7h9Ig6OGrktsNyPYP6vyoevpc0vmlloZkd1D+YWIclKx6/syGfnby/7d3/7F1nfUdx9+2kziOk9jOD9dOnDikacnoSimwacDWVahDXdG+UoFpUDaKGKNdO422gzGqbqxsMMFoJ1FEQdVGB5RBx1rtWxAdjI1BEYOpgQCtukxpncRprhLHdeP4B/4R749z7+X2+F773uvrc85z83lJUeXzw/fTc557v37uc85z0h79C/nYh5wdws8v1Qv9XCt/ehqVvdzI3xVXXFEc0as04rdS8T/cl+o4LvfohqRn8Ay53UDY+ZPKviaRV8kzsw8B1wFbgWng28Bt7n60wvZXA3cBe4HD+W2/nlDcJQ0PD7N37960Y9Ql5Oyg/Gnavn37CwpVoZNXOtIXX1ba8SuM/hWKWU9PT7Hjl8TIX8jHPuTsEH7+JDRLjQz9XCt/ehqdvaen5wWdp8JoWi6XK3b8hoeH6e/v58SJE/T19a3onruLLrpo0VU0hY7fcvcKHjp0aNFoX5JCbjcQdv6ksic90vc54GXuvhnYAxwFvlhuQzPbCzwE/A3Qlf/vw2a2J5GkIlK1ciN9tdz3l9WJX0QSphop0mDVjvoVRvwKy1cyoUs5paN+WZ3MRZpboiN97v5UyY8twDngxRU2vx543N0/n//5ATO7Mb/8ztVLWZ3t27enHaFuIWcH5U9TNdMKx0f6Sjt+8dG/0vv+khj9C/nYh5wdws+fhGapkaGfa+VPz2pmL3T8CrWmMNlL4Rl5jRjxW+4+wOPHj5ftTOZyuVSeLVgq5HYDYedPKnuinT4AM7sOuBfYDMwBt1XY9DLg8diyA/nli9x8881A9EfilVdeyaWXXgpE39SsXbuWI0eOsLCwQHd3N1u2bCGXyzE5Ocm6desYGBhgfHycU6dOAdG3Pm1tbcU/TLds2UJ3dzcnTpxgamqK9vZ2du7cyZkzZxgZGQFg165dtLS0cPRodBXO1q1b6erq4tlnn2V6epr169ezY8cOxsbGilOzDg4Ocu7cOY4dOwbAtm3b2Lx5M8PDw8zMzLBhwwb6+vqKIyEtLS0MDg4yNzdXvAm5t7eXjRs3cuzYMWZnZ+ns7OSCCy5gdHSUsbGx4j4zMzM8++yzQDSl8MLCAseOHWNubo6NGzfS29vLyMgIZ86cobW1lcHBQaanp4uX8vX397N+/XqOHj3K/Pw8mzdvZtu2bZw6dYrx8XHa2trYvXs3U1NTxQ/KHTt20N7ezpEjRzh37hxdXV1s3bqVkydPcvbsWdasWcOuXbuYmJjg5MmT58352rRpU83na8OGDYvO1+joKOPj47S2ttLZ2cns7Gwx6/z8PK2trUxNTbGwsMCaNdFbfc2aNbS1tVGYwKm9vZ0tW7YAMDIywuzsLH19fbS0tDAxMcGZM2fo7u6mo6OD+fl5Tp48yfr164vFc2RkhLm5uWKxOnv2LOPj4/T09DA6OkpfXx8zMzO0traydu1aJiYm6OjooKWlhcnJSZ588kl2797Nxo0bGR0dZWZmhtOnT7Nv3z7Gx8cZHR2lra0tuPdXufPViPfX5OSk3l/PPZfJ53M1SrPUyE2bNmXmM1c1Mnvv4aXOF7Dqn7kdHR20t7eTy+Xo7Oykp6en2NHr7OxkbGyMXC7Hvn37aG9vB+CZZ55hamqK/fv309rayunTp4uXiXZ1dTE7O8uhQ4fYvHkzu3btAuDw4cPMzMywf/9+WlpaOHXqFMePH+fyyy+nt7eXjo4OhoeHGRgYYGJiguPHj9Pb20tbWxu5XI7nnnsu0fMFqEam9P4CGvL+Wkpqs3eaWR/w+8B33f1bZdZ/E3jM3T9QsuxO4DXuflVs28Rn7yy8SUMUcnZQ/rhaZvEsN2NnLbN4bt++vfhBV41ys3rGlyU5y2fIbSfk7NDw/E0/e2fINVJtNV0h51/t7OVm9SxV6DAMDw9z4sQJcrlcVbN4Fkbvdu7cueT2pff3ZW0Gz5DbDYSdP6n6mNrsne6eA+4DvmJmW8psMk50n0KpbuDMamerRsj3HYWcHZQ/bt26dcWOUfzehcHBwRd0qi655JLiv/iyahRGDEMVctsJOTuEnz9pIdfI0M+18qdnNbNX2+Er6O/vp6+vj507dy77r2Dt2rUVX79cZ3C5GTyTFHK7gbDzJ5U97Uc2rAE6gR1l1h0EXh5bdnl+eeo2bNiQdoS6hZwdlL+SQudv3bp1FTt/5TqAlX4uZ3p6uuG5yz1TcLWE3HZCzg7h509JkDUy9HOt/OlZjeyljwqCqLMX7/DF1TvqMjk5WXZ5uQ5f1iZzCbndQNj5k8qe2Nf2ZtYK3AQ86O4nzWwAuAcYAp4qs8tngfea2VuALwNvAl4BvC2ZxEtL+4bblQg5Oyh/NUpH/goKRa/Q8St0tuITrpR7/EL8d1TriSeeqHoUMQkht52Qs0P4+VdbM9XI0M+18qen0dnjnb1axDt+1XTSCveGhSjkdgNh508qe9IjfdcAPzWzCeD7wCRwlbvPmdlbzexsYUN3Pwy8AbiD6HKVO4Br3X0o4cxlFW6sDFHI2UH5a9HoSz83bdqUQOrVE3LbCTk7hJ8/IU1RI0M/18qfnkZlLze6t5RqOnR9fX3L/nHe3d29aFm5Ub5K9/3V2jFtpJDbDYSdP6nsiY30ufs5ooJWaf0DwAOxZY8Cj65ytLqMjY0VZz0MTcjZQfnrUToZSnz0r7Tjt9zoX2HG0JVIc/Qv5LYTcnYIP/9qa6YaGfq5Vv70rCR7uStRVtqJKh3tK9x/V+j4lesodnd3L/too6GhIfbs2bNoedqPbQi53UDY+ZPKHvasDClqaQl38riQs4Pyr1ShAzgzM7PouUXLXfrZ399f/GCqZdbPrEj72K9EyNkh/PxSvdDPtfKnp57sq9HZKzUwMFB8hh8s3fmLz4gfH9EbGhoq+xpZmHky5HYDYedPKnvaE7kEq9w086EIOTsof6NUM/FLQeEyzwsvvHDRsuVkqXOYlWNfj5CzQ/j5pXqhn2vlT0+t2Wu9z7xepR2y/v7+YgcQXnjZZ+G5a42S1P8fhN1uIOz8SWVXp69Os7OzaUeoW8jZQflXU/yZQPEPonPnzi3aJ0sTtSwny8d+OSFnh/DzS/VCP9fKn55as8e/sCyIP++uEeIjcaUdP4g6f0s9siHrQm43EHb+pLKr01enah7WmVUhZwflT9NqPLIhSSEf+5CzQ/j5pXqhn2vlT0+92ct1/i6++OLiv+VUM0lLNXbsKPd0lTCE3G4g7PxJZVenT0RERESCV+j8VeoALme5jl/a992JrIQ6fXXq7e1NO0LdQs4Oyp+m9vb2tCOsSMjHPuTsEH5+qV7o51r509PI7PV2/lYy4hfyc/pCbjcQdv6ksqvTV6fHHnss7Qh1Czk7KH+aDh48mHaEFQn52IecHcLPL9UL/Vwrf3pWI3s9nb9qO37x+/pOnz5de8CMCLndQNj5k8quTl+dHnnkkbQj1C3k7KD8aTpw4MCiZVmanXM5IR/7kLND+PmleqGfa+VPz2pmr/W+v3pG/Obn5+vOl7aQ2w2EnT+p7HpOn4hULf4MIhERkZCUdvxKH4lQ6PiVPuNvqQexi4SmpRn+iDOz8P8nRESkau4e7pN4E6YaKSJy/qhUH5ui0yciIiIiIiLl6Z4+ERERERGRJqZOn4iIiIiISBNTp09ERERERKSJnfezd5rZ/cBbgZ+VLP5Td/9khe2vAd4DvBRoA34K3O7u3ynZZgGYAs6V7LrT3Z9vbPra8+f3uRq4C9gLHAZuc/evl6zfB3wKeBXwHPB37n5Xo7PHMrUCj+Vfc5e7D5fZ5nbg9tjiTuAed//j/DZDQB8wV7LNq9z9J6uRuyRbNfmvBP4TmChZ/GN3f3XJNlk99plq97Fsy+bPb5epdm9mHwKuA7YC08C385mOltk2U22/xuxXksF2L9VRjUz/syL/msHWyJDrY/51g62Rqo/ptPus1sjzvtOX94/u/s4qt+0B7iE6QWeBPwC+Zma/4O7HSrZ7nbsn9aTIqvOb2V7gIeBdwIPAbwMPm9kl7j5kZm3AI8C/AwbsBx41s2F3/9LqxAfgVmByqQ3c/cPAhws/m9nFwFPA52ObvtPd48tW27L58+bdfWO5FVk+9mSz3Rcsmz+j7f5zwEfd/Xkz2wD8NfBF4NXxDTPY9qvOnpfFdi/VU41UjVyJkOsjhF0jVR9/Lsl2n8kaqU5fjdz9gdiie83sA8AvAcfK7JI11wOPlzT8B8zsxvzyO4ErgEHg/e4+CRwws08DNwKr8ubOv0FvAt4I/LCGXd8F/NDdf7Aauaq1gvxxmT32WW33NRz7zLV7d3+q5McWom9/X1zl7qm2/RVmj0v82MvqyepnRQ0y91kRco0MuT5C2DVS9TG9dp/VGqlOX+SNZvYGYAT4V+BOdz9bzY5mdimwDYgPEf+zma0lGib/iLs/1MjAMbXkvwx4PLbsQH55Yf2h2P4HgJsbmLcof+nBPxBdFjFWw37twNtZPKQPcLeZfRw4Ctzr7p9uQNRKOWrN32Zmx4C1ROfhdnc/mF8XxLHP75t6u68xf6bafYGZXQfcC2wmuuzktir2yUrbryV7Ztq91EU1UjWyZiHXRwi7Rqo+plsf81kyVyM1kUs0HL+f6M15LfDrwH3V7GhmvcC/AB9z9/8rWXUV8CJgALib6FuTqxsZukSt+TcB8evHx4gaZTXrG+3dQM7dH65xvzcB64AvxJZfT3RN+gXAe4EPm9kNK05ZWS35nwJeRtQ29gM/Bv7DzHbk1wdx7DPS7qG2/Flr9wC4+xfcvQvoB/6SxX8glJOJtl9D9qy1e6mNaqRqZL1Cro8Qdo1UfXyhpP82zGSNPO9H+ty99NuNJ8zsVuBbZvZ2d/9Zpf3yJ+MbwNeB98d+5zdLfvySmV1FdCP5o41LXnytWvOPA12xZd3AmSrXN4xFN6b+CfDKOna/AXgg/m2tu/9XyY/fMLO7gd8FGv6NTq353T0H5PI/jgHvN7M3Ab8J/D0BHPustPs68mem3Zfj7jkzuw942sx2u/voEpun3vZjr7tk9iy1e6mdaiSgGlmzkOsjhF0jVR+zUx/zr52ZGqmRvsUKsyq1VNrAzPYA3wG+5u5/5O4LVfzOir+vwZbLfxB4eWzZ5fnlhfUXm1lnhfWN9KvAduCnZjZCNFwN8GMzu6nSTmb2EuDXiGYyWs5qHvu68seU5sv0sc9Yu681f5bafSVriGYc21Fpgwy1/bhls8ek1e5l5VQjM/w5DZn5nAi5PkLYNVL1cXlJfuZARmrkeT/SZ2ZvBh519zEzu4hoylp39+kK2+8nmkHnfne/o8z6XwQ2AD8CFoDXA78HvDkL+YHPAu81s7cAXyYaCn8F8Lb8+m8DR4iGvv+M6MbTG4guFWi0B4mOZcEA8D3gdUTD3ZXcAPx3yfXOAJjZINHw/feAWaIPvluBv2pg5lI15Tez1xJdS/40URt5D9GlBv+W3ySzxz5r7Z7a206W2n3hfoubgAfd/aSZDRBdhjZUIX9B6m2/1uwZa/dSI9VI1cg6hVwf68mfpXav+vjC35fo34ZZrpHnfaePaPabT1p08+dJ4GGia28BMLO3Ap/2n0+l+j5gJ3CLmd1S8ntuyM/etB34BLAHmCG6Wfcd7u5ZyO/uhy26of0uopt8nwaudfeh/Pp5M/stoiHv00RDzX/r7l9sdPD8LETFqYTNrNAec+5+tsyxx8w6iD6IbmGxTqLr5PcRfageBT7o7p9odPY6818GfIbo3pIJom/ffsPz0zln/Nhnqt3Xmj9L7b7ENcBf5L+9GwO+BVzl7nNZb/s1Zs9Mu5e6qEaqRiaRPVOfEyHXSNXHRZKuj7XmT6zttywsLDf6LCIiIiIiIqHSPX0iIiIiIiJNTJ0+ERERERGRJqZOn4iIiIiISBNTp09ERERERKSJqdMnIiIiIiLSxNTpExERERERaWLq9IlkgJndb2ZfSTuHiIhIlqg+ijSGHs4u0mTM7H7g+qW2cfeWZNKIiIhkg+qjnM800ifSfN4N9Jf8mwRuiS0TERE536g+ynlLI30iGWNm7cBHgLcAXcCPgPe4+2Ml27weuBsYBH4AfBL4J+BF7j4EPF+y7QLwvLvnkvp/EBERaTTVR5H6aaRPJHs+CvwO8A7gcuAnwKNm1g9gZruBh4CvApcBH8/vIyIi0sxUH0XqpJE+kQwxs07gD4F3uvtX88tuBF4L3AzckV//tLvflt/tf83sYuBDKUQWERFZdaqPIiujkT6RbLkQWAt8t7DA3eeB7wEvyS/aD/xPbL/vJ5JOREQkHaqPIiugTp9IOBbSDiAiIpJBqo8iy1CnTyRbDgMzwGsKC8ysDXgV8GR+0VPAK2P7/XIi6URERNKh+iiyArqnTyRD3H3CzO4FPmJmI8AzwK3ABUQzkAF8CrjNzD4G3AdcAtyQX6dvO0VEpOmoPoqsjEb6RLLnfcCXgM8QTUf9UuBqdz8B4O5HgDcCBhwkKnp35vedTjytiIhIMlQfRerUsrCgLz5EQmdm7wY+CHS7u97UIiIiqD6KFOjyTpEAmdnNRDOUnQJ+Bfhz4H4VNBEROZ+pPoqUp06fSJj2AbcDW4FhovsYPphqIhERkfSpPoqUocs7RUREREREmpgmchEREREREWli6vSJiIiIiIg0MXX6REREREREmpg6fSIiIiIiIk1MnT4REREREZEmpk6fiIiIiIhIE/t/MpCAcBvl8b8AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "sin_plot = sin_hr_diagram.plot(log_age=7, loc=121)\n",
+ "sin_plot .scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "sin_plot .set_xlim([5.4, 3.4])\n",
+ "sin_plot .set_ylim([2.8, 6.2])\n",
+ "sin_plot .set_title('Single stars')\n",
+ "sin_plot .text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "sin_plot .legend()\n",
+ "\n",
+ "\n",
+ "bin_plot = bin_hr_diagram.plot(log_age=7, loc=122)\n",
+ "bin_plot.scatter(usco.Temperature, usco.Luminosity, s=100, c='pink', label='Usco')\n",
+ "bin_plot.set_xlim([5.4, 3.4])\n",
+ "bin_plot.set_ylim([2.8, 6.2])\n",
+ "bin_plot.set_title('Binary stars')\n",
+ "bin_plot.text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "bin_plot.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Customizing your plots with matplotlib key word arguments\n",
+ "\n",
+ "Althought the default parameters for the HR diagram pltos were chosen to allow the creating of publication-ready plots with as few modifications as possible, the plotting function of `HRDiagrams` objects are designed to take any subplementary key word arguments and pass them on to `matplotlib.pyplot.contour`. \n",
+ "\n",
+ "This allows a much greater level of flexibility when making graphics. \n",
+ "For example if you wish to cahnge the colormap you can do the following. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0], cmap='jet_r') # levels adjusted for visualisation\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('A terrible colormap')\n",
+ "ax[0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[1], cmap='magma_r') # levels adjusted for visualisation\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('A less terrible colormap')\n",
+ "ax[1].text(5.25, 5.0, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='w', fontsize = 18)\n",
+ "ax[1].legend()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure \n",
+ "\n",
+ "In this last section, we will reproduce with `hoki` Figure 23 of Eldirdge, Stanway et al. (2018). This will show you how you can create a publication ready figure just using `hoki` and other ubiquitous python packages.\n",
+ "\n",
+ "*Note: This figure will look a little different frmo the published version as the latter was created in IDL*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# renaming our already laoded HR diagram just for readability \n",
+ "hr_usco_sin, hr_usco_bin = sin_hr_diagram, bin_hr_diagram \n",
+ "\n",
+ "hr_census_bin = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type='TL')\n",
+ "hr_census_sin = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type='TL')\n",
+ "\n",
+ "hr_6067_bin = load.model_output('./data/hrs-bin-imf135_300.z030.dat', hr_type='TTG')\n",
+ "hr_6067_sin = load.model_output('./data/hrs-sin-imf135_300.z030.dat', hr_type='TTG')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that it is not recommended to call a column 'T' for a pandas data frame. \n",
+ "# If you do, you won't be able to call it as mydataframe.T, because .T will default to \n",
+ "# returning the transpose. \n",
+ "\n",
+ "cyg_L, cyg_L_e, cyg_T, cyg_T_e = np.genfromtxt('./data/census.dat', unpack=True, usecols=(14,15,17,18))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ngc_T, ngc_T_e, ngc_logg, ngc_logg_e = np.loadtxt('./data/ngc6067hr.dat', unpack=True, usecols=(3,4,5,6), skiprows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the case of the NGC 6067 we want to make a spectroscopic HR diagram. That means that the y axis will be $\\log(T^4/g)$ -- this quatity is refered to as TG or log(TG). The trick is, we need to make sure we are using the right units when we do the calculations. \n",
+ "\n",
+ "We want everything to be with respect to solar units, so we'll have to divide by solar surface temperature (5778K) and surface gravity (274 m/s or a log g = 2.4377). We also nee to convert to SI from cgs, so there is a factor of $10^{-2}$ in the g, i.e a `-2` in the log g exponent, that needs to be taken into account. \n",
+ "\n",
+ "We also need to propagate errors. Since it's a mess of logs I'll just use bootstrapping for that. \n",
+ "\n",
+ "*Boostrapping: In this particular case I sample each of my values/data points according to a normal distribution where the mean is the value of that data point and the standard deviation is the error. I do this 1000 times, calculate the corresponding TG, and take the mean and standard deviation of these 1000 TG values.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TG = []\n",
+ "TG_e = []\n",
+ "for T_i, T_e_i, logg_i, logg_e_i in zip(ngc_T, ngc_T_e, ngc_logg, ngc_logg_e):\n",
+ " TG.append(np.mean(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))\n",
+ " TG_e.append(np.std(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also need to make sure we get the log(T) errors (and no, you can't just do `log(ngc_T_e)`). I'm using bootstrapping - because I can. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "T_e = []\n",
+ "for T_i, T_e_i in zip(ngc_T, ngc_T_e):\n",
+ " T_e.append(np.std(np.log(np.random.normal(T_i, T_e_i, 1000))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now it's time to plot all of this!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(3,2, figsize=(15,20))\n",
+ "\n",
+ "hr_census_sin.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,0].set_xlim([5.5, 3.4])\n",
+ "ax[0,0].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_census_bin.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,1].set_xlim([5.5, 3.4])\n",
+ "ax[0,1].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_usco_sin.plot(log_age=7, loc=ax[1,0]) # levels adjusted for visualisation\n",
+ "ax[1,0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,0].set_xlim([5.4, 3.4])\n",
+ "ax[1,0].set_ylim([2.8, 6.2])\n",
+ "ax[1,0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "\n",
+ "hr_usco_bin.plot(log_age=7, loc=ax[1,1]) # levels adjusted for visualisation\n",
+ "ax[1,1].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,1].set_xlim([5.4, 3.4])\n",
+ "ax[1,1].set_ylim([2.8, 6.2])\n",
+ "ax[1,1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "hr_6067_sin.plot(log_age=8, loc=ax[2,0])\n",
+ "ax[2,0].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,0].set_xlim([4.8, 3.4])\n",
+ "ax[2,0].set_ylim([1, 5.2])\n",
+ "ax[2,0].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "hr_6067_bin.plot(log_age=8, loc=ax[2,1])\n",
+ "ax[2,1].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,1].set_xlim([4.8, 3.4])\n",
+ "ax[2,1].set_ylim([1,5.2])\n",
+ "ax[2,1].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "for axis in ax.reshape(6):\n",
+ " axis.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stacking a range of Ages\n",
+ "\n",
+ "But what if you are interested in a stellar population that has a range of ages? \n",
+ "\n",
+ "Using the `HRDiagram` class you can stack HR Diagrams of a range of ages across all three surface abundance categories in a single line!\n",
+ "\n",
+ "Let's stack every age bin between 1 and 10 Myrs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ }
+ ],
+ "source": [
+ "bin_hr_diagram.stack(log_age_min=6.0, log_age_max=6.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This should return the following message: \n",
+ "```\n",
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n",
+ "```\n",
+ "\n",
+ "It indicates that the stacking was performed successfully, and four attributes of the `HRDiagram` class were update. \n",
+ "\n",
+ "`HRDiagram.all_stacked`, `HRDiagram.high_H_stacked`, `HRDiagram.medium_H_stacked`, `HRDiagram.low_H_stacked`, are the stacked equivalents to `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H`(see right panel of the Figure at the top of this tutorial).\n",
+ "\n",
+ "This means that the original `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H` attributes **remain untouched** when you run the `.stack()` method - only the \"stacked\" attributes will be updated.\n",
+ "\n",
+ "Note, before you run the `.stack()` method for the first time the \"stacked\" HRDs are just 100x100 numpy arrays filled with zeros. \n",
+ "\n",
+ "## Plotting HRDs of stacked ages\n",
+ "\n",
+ "Now, it's possible you don't actually need to do any opperations on the stacked HRDs, **you just want to plot** HRDs for a range of ages. \n",
+ "\n",
+ "For this you don't even need to use the `.stack()` method, **you can do it directly** in the `.plot()` method: you need to use the parameter `age_range` instead of '`log_age` in the plotting method. \n",
+ "\n",
+ "Here is an example of an HRD at 10 Myrs Vs an aggregate of HRDs from 1 to 10 Myrs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0]) #levels adjusted for visualisation\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single Age: log(Age/yrs)=7.0')\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(age_range=[6.0, 7.0], loc=ax[1]) # levels adjusted for visualisation\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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def extract_sdss_spectrum(dataloc):
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- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
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+ Built with Sphinx using a
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- © Copyright 2019, H. F. Stevance
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+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
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+ Built with Sphinx using a
+
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jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/Transient_rates.ipynb b/docs/Transient_rates.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Transient rates \n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki_tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Feel free to use your own style sheet\n",
+ "plt.style.use('tuto.mplstyle')\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Load BPASS transient rates from hoki\n",
+ "- Combine single and binary population transient rates\n",
+ "- Calculate total Core Collapse Supernovae rates\n",
+ "- Make sure the rates are in the right units\n",
+ "- Plot the rates of Type Ia SNe, CCSNe, LGRBs, and PISNe together. \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Loading in the data and initial set-up\n",
+ "\n",
+ "Transient rates in BPASS are one of the several types of stellar population outputs. We can simply use the `hoki.load.population_ouput()` function to laod the data. The function will automatically know what type of data it is loading from the file name and create an appropriate data frame to put it in. \n",
+ "\n",
+ "If you are not familiar with `pandas` and `pandas.DataFrame`, you should pick it up easily. If you want to look into them more check out this [Data Camp article](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python).\n",
+ "\n",
+ "In this tutorial we want to plot the rates corresponding to the stellar populations that include binary systems for a standard (Salpeter) IMF with slope -1.35 from 0.1 to 0.5 M_sun and a maximum mass of 300 solar masses, all of this at a tenth solar metalicity (so Z=0.002):\n",
+ "> `supernova-bin-imf135_300.z002.dat`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the binary and single star population transient rates.\n",
+ "# Note we chose this particular IMF and metallicity in order to reproduce the plot\n",
+ "# shown on the left hand sife of Figure 1 in Eldridge et al. 2018 \n",
+ "\n",
+ "bin_rates = load.model_output('./data/supernova-bin-imf135_300.z002.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at one of our data frames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " log_age \n",
+ " Ia \n",
+ " IIP \n",
+ " II \n",
+ " Ib \n",
+ " Ic \n",
+ " LGRB \n",
+ " PISNe \n",
+ " low_mass \n",
+ " e_Ia \n",
+ " e_IIP \n",
+ " e_II \n",
+ " e_Ib \n",
+ " e_Ic \n",
+ " e_LGRB \n",
+ " e_PISNe \n",
+ " e_low_mass \n",
+ " age_yrs \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 6.0 \n",
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+ " 0.0 \n",
+ " 0.0 \n",
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+ " 1122019.00 \n",
+ " \n",
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+ " 0.0 \n",
+ " 579664.00 \n",
+ " \n",
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\n",
+ ""
+ ],
+ "text/plain": [
+ " log_age Ia IIP II Ib Ic LGRB PISNe low_mass e_Ia \\\n",
+ "0 6.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "1 6.1 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "2 6.2 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "3 6.3 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "4 6.4 0.0 0.0 0.0 3.847896 0.0 0.0 5.109734 0.0 0.0 \n",
+ "\n",
+ " e_IIP e_II e_Ib e_Ic e_LGRB e_PISNe e_low_mass age_yrs \n",
+ "0 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 1122019.00 \n",
+ "1 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 290520.12 \n",
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+ "4 0.0 0.0 0.496761 0.0 0.0 0.80792 0.0 579664.00 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bin_rates.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Most column names are pretty self-explanatory, appart from `low_mass`, which just detones the rate of low-mass supernovae (< 2M$_{\\odot}$). The `age_yrs` bin is th size of each time bin in years; indeed since BPASS works in log(time) space, each time bin has a different width in years. \n",
+ "\n",
+ "If you need more detail on each of these rates, you should have a look at the [BPASS user manual](https://bpass.auckland.ac.nz/8/files/bpassv2_1_manual_accessible_version.pdf)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The last time bin in BPASS does some weird stuff so it's better to just ignore it. \n",
+ "bin_rates = bin_rates[:-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We are going to use the log_age and the size of the bin in years a lot, so I'm just renaming them for ease.\n",
+ "age = bin_rates.log_age.values\n",
+ "bin_size = bin_rates.age_yrs.values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Core Collapse Supernovae rates\n",
+ "\n",
+ "Core collapse supernovae comprise the type IIP, II, Ib and Ic. To get the total rate we need to sum these columns as well as put the single star and binary populations together.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne = ( bin_rates[['IIP', 'II', 'Ib', 'Ic']].sum(axis=1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting the Units right\n",
+ "\n",
+ "We want to plot our rates as **events/M$_{\\odot}$/year**, this means we need to normalise by the total mass and the number of years in each time bin. \n",
+ "\n",
+ "BPASS calulates stellar populations with 10$^6$ M$_{\\odot}$ and we've already put the bin size in years in a convenient variable called `bin_size`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne_norm = ccsne/bin_size/(10**6)\n",
+ "typeIa_norm = bin_rates.Ia.values /bin_size/(10**6)\n",
+ "lgrbs_norm = bin_rates.LGRB.values /bin_size/(10**6)\n",
+ "pisne_norm = bin_rates.PISNe.values/bin_size/(10**6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the transient rates\n",
+ "\n",
+ "Now we have everything we need to plot our transient rates! The only trick here is to remember to **log rate axis** to be able to see everything. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (10,7))\n",
+ "\n",
+ "plt.step(age, typeIa_norm, label='SN Ia')\n",
+ "plt.step(age, ccsne_norm, label='CCSNe')\n",
+ "plt.step(age, lgrbs_norm, label='LGRB')\n",
+ "plt.step(age, pisne_norm, label='PISNe')\n",
+ "\n",
+ "plt.yscale(\"log\")\n",
+ "\n",
+ "plt.text(9, 10**(-11), r\"Z=0.1Z$_{\\odot}$\", fontsize=18)\n",
+ "\n",
+ "plt.ylabel(r\"Event Rate (events/M$_{\\odot}$/year)\")\n",
+ "plt.xlabel(\"log(age/yrs)\")\n",
+ "plt.legend(fontsize=16)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/docs/_sources/cmd.rst.txt b/docs/_sources/cmd.rst.txt
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--- a/docs/_sources/cmd.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**cmd**
-################
-
-
-.. automodule:: hoki.cmd
- :members:
-
diff --git a/docs/_sources/constants.rst.txt b/docs/_sources/constants.rst.txt
deleted file mode 100644
index 5f9c5ff1..00000000
--- a/docs/_sources/constants.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-################
-**constants**
-################
-
-This module just contains BPASS constants and defines variables used in hoki.
-
-.. automodule:: hoki.constants
- :members:
-
-path_to_settings
- path to the `settings.yaml` file located in the package resources
-
-MODELS_PATH
- Path to the BPASS models as defined in the settings file. If you have not downloaded the models and updated this path with `load.set_models_path()`, it will not correspond to a valid path on your machine. This is not going to cause issues unless you're using functionalities that require the full set of BPASS models (like making CMDs)
-
-BPASS_TIME_BINS
- 1D array containing the 51 BPASS time bins (from log(age) = 6.0 to 11.0)
-
-BPASS_TIME_INTERVALS
- Time interval between BPASS time bins in years
-
-BPASS_TIME_WEIGHT_GRID
- Grid of weights to apply to HR Diagrams based on BPASS definitions
-
-dummy_dict
- Dictionary linking column number to column name for the large BPASS array traditionally called dummy. See the BPASS manual for a text version of the numner/name correspondance.
diff --git a/docs/_sources/hrdiagrams.rst.txt b/docs/_sources/hrdiagrams.rst.txt
deleted file mode 100644
index 58d1d07c..00000000
--- a/docs/_sources/hrdiagrams.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**hrdiagrams**
-################
-
-
-.. automodule:: hoki.hrdiagrams
- :members:
-
diff --git a/docs/_sources/load.rst.txt b/docs/_sources/load.rst.txt
deleted file mode 100644
index 69c38f09..00000000
--- a/docs/_sources/load.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-##########
-**load**
-##########
-
-
-.. automodule:: hoki.load
- :members:
-
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index ea6972d5..56f5efc6 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -316,22 +316,29 @@ img.align-default, .figure.align-default {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px 7px 0 7px;
+ padding: 7px;
background-color: #ffe;
width: 40%;
float: right;
+ clear: right;
+ overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
+div.admonition, div.topic, pre, div[class|="highlight"] {
+ clear: both;
+}
+
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px 7px 0 7px;
+ padding: 7px;
margin: 10px 0 10px 0;
+ overflow-x: auto;
}
p.topic-title {
@@ -346,16 +353,13 @@ div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
+ overflow-x: auto;
}
div.admonition dt {
font-weight: bold;
}
-div.admonition dl {
- margin-bottom: 0;
-}
-
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -366,9 +370,19 @@ div.body p.centered {
margin-top: 25px;
}
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
/* -- tables ---------------------------------------------------------------- */
table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -416,13 +430,13 @@ table.citation td {
border-bottom: none;
}
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
margin-top: 0px;
}
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
margin-bottom: 0px;
}
@@ -468,6 +482,10 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
+table.hlist {
+ margin: 1em 0;
+}
+
table.hlist td {
vertical-align: top;
}
@@ -495,17 +513,34 @@ ol.upperroman {
list-style: upper-roman;
}
-li > p:first-child {
+ol > li:first-child > :first-child,
+ul > li:first-child > :first-child {
margin-top: 0px;
}
-li > p:last-child {
+ol ol > li:first-child > :first-child,
+ol ul > li:first-child > :first-child,
+ul ol > li:first-child > :first-child,
+ul ul > li:first-child > :first-child {
+ margin-top: revert;
+}
+
+ol > li:last-child > :last-child,
+ul > li:last-child > :last-child {
margin-bottom: 0px;
}
+ol ol > li:last-child > :last-child,
+ol ul > li:last-child > :last-child,
+ul ol > li:last-child > :last-child,
+ul ul > li:last-child > :last-child {
+ margin-bottom: revert;
+}
+
dl.footnote > dt,
dl.citation > dt {
float: left;
+ margin-right: 0.5em;
}
dl.footnote > dd,
@@ -546,7 +581,7 @@ dl {
margin-bottom: 15px;
}
-dd > p:first-child {
+dd > :first-child {
margin-top: 0px;
}
@@ -560,6 +595,11 @@ dd {
margin-left: 30px;
}
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -644,22 +684,57 @@ span.pre {
hyphens: none;
}
+div[class^="highlight-"] {
+ margin: 1em 0;
+}
+
td.linenos pre {
- padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- margin-left: 0.5em;
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
}
table.highlighttable td {
- padding: 0 0.5em 0 0.5em;
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
}
div.code-block-caption {
+ margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -668,8 +743,9 @@ div.code-block-caption code {
background-color: transparent;
}
-div.code-block-caption + div > div.highlight > pre {
- margin-top: 0;
+table.highlighttable td.linenos,
+div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
}
div.code-block-caption span.caption-number {
@@ -681,11 +757,7 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
- margin: 0;
+ margin: 1em 0;
}
code.descname {
@@ -736,8 +808,7 @@ span.eqno {
}
span.eqno a.headerlink {
- position: relative;
- left: 0px;
+ position: absolute;
z-index: 1;
}
diff --git a/docs/_static/css/badge_only.css b/docs/_static/css/badge_only.css
index 3c33cef5..e380325b 100644
--- a/docs/_static/css/badge_only.css
+++ b/docs/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
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new file mode 100644
index 00000000..855c845e
--- /dev/null
+++ b/docs/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+
+
+
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diff --git a/docs/_static/css/fonts/lato-normal.woff b/docs/_static/css/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
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diff --git a/docs/_static/css/fonts/lato-normal.woff2 b/docs/_static/css/fonts/lato-normal.woff2
new file mode 100644
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diff --git a/docs/_static/css/theme.css b/docs/_static/css/theme.css
index aed8cef0..8cd4f101 100644
--- a/docs/_static/css/theme.css
+++ b/docs/_static/css/theme.css
@@ -1,6 +1,4 @@
-/* sphinx_rtd_theme version 0.4.3 | MIT license */
-/* Built 20190212 16:02 */
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tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) 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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index b33f87fc..daccd209 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -283,10 +283,11 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keyup(function(event) {
+ $(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+ && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index ec430720..ada26f7c 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,7 +3,9 @@ var DOCUMENTATION_OPTIONS = {
VERSION: 'beta',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
+ BUILDER: 'html',
FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false
diff --git a/docs/_static/fonts/FontAwesome.otf b/docs/_static/fonts/FontAwesome.otf
new file mode 100644
index 00000000..401ec0f3
Binary files /dev/null and b/docs/_static/fonts/FontAwesome.otf differ
diff --git a/docs/_static/fonts/Inconsolata-Bold.ttf b/docs/_static/fonts/Inconsolata-Bold.ttf
deleted file mode 100644
index 809c1f58..00000000
Binary files a/docs/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata-Regular.ttf b/docs/_static/fonts/Inconsolata-Regular.ttf
deleted file mode 100644
index fc981ce7..00000000
Binary files a/docs/_static/fonts/Inconsolata-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata.ttf b/docs/_static/fonts/Inconsolata.ttf
deleted file mode 100644
index 4b8a36d2..00000000
Binary files a/docs/_static/fonts/Inconsolata.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Bold.ttf b/docs/_static/fonts/Lato-Bold.ttf
deleted file mode 100644
index 1d23c706..00000000
Binary files a/docs/_static/fonts/Lato-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Regular.ttf b/docs/_static/fonts/Lato-Regular.ttf
deleted file mode 100644
index 0f3d0f83..00000000
Binary files a/docs/_static/fonts/Lato-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff b/docs/_static/fonts/Roboto-Slab-Bold.woff
new file mode 100644
index 00000000..6cb60000
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff2 b/docs/_static/fonts/Roboto-Slab-Bold.woff2
new file mode 100644
index 00000000..7059e231
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff b/docs/_static/fonts/Roboto-Slab-Light.woff
new file mode 100644
index 00000000..337d2871
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff2 b/docs/_static/fonts/Roboto-Slab-Light.woff2
new file mode 100644
index 00000000..20398aff
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff b/docs/_static/fonts/Roboto-Slab-Regular.woff
new file mode 100644
index 00000000..f815f63f
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff2 b/docs/_static/fonts/Roboto-Slab-Regular.woff2
new file mode 100644
index 00000000..f2c76e5b
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff b/docs/_static/fonts/Roboto-Slab-Thin.woff
new file mode 100644
index 00000000..6b30ea63
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff2 b/docs/_static/fonts/Roboto-Slab-Thin.woff2
new file mode 100644
index 00000000..328f5bb0
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff2 differ
diff --git a/docs/_static/fonts/RobotoSlab-Bold.ttf b/docs/_static/fonts/RobotoSlab-Bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/RobotoSlab-Regular.ttf b/docs/_static/fonts/RobotoSlab-Regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff b/docs/_static/fonts/lato-bold-italic.woff
new file mode 100644
index 00000000..88ad05b9
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff2 b/docs/_static/fonts/lato-bold-italic.woff2
new file mode 100644
index 00000000..c4e3d804
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-bold.woff b/docs/_static/fonts/lato-bold.woff
new file mode 100644
index 00000000..c6dff51f
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff differ
diff --git a/docs/_static/fonts/lato-bold.woff2 b/docs/_static/fonts/lato-bold.woff2
new file mode 100644
index 00000000..bb195043
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff2 differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff b/docs/_static/fonts/lato-normal-italic.woff
new file mode 100644
index 00000000..76114bc0
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff2 b/docs/_static/fonts/lato-normal-italic.woff2
new file mode 100644
index 00000000..3404f37e
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-normal.woff b/docs/_static/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff differ
diff --git a/docs/_static/fonts/lato-normal.woff2 b/docs/_static/fonts/lato-normal.woff2
new file mode 100644
index 00000000..3bf98433
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff2 differ
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.5.1.js
similarity index 91%
rename from docs/_static/jquery-3.4.1.js
rename to docs/_static/jquery-3.5.1.js
index 773ad95c..50937333 100644
--- a/docs/_static/jquery-3.4.1.js
+++ b/docs/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
/*!
- * jQuery JavaScript Library v3.4.1
+ * jQuery JavaScript Library v3.5.1
* https://jquery.com/
*
* Includes Sizzle.js
@@ -9,7 +9,7 @@
* Released under the MIT license
* https://jquery.org/license
*
- * Date: 2019-05-01T21:04Z
+ * Date: 2020-05-04T22:49Z
*/
( function( global, factory ) {
@@ -47,13 +47,16 @@
var arr = [];
-var document = window.document;
-
var getProto = Object.getPrototypeOf;
var slice = arr.slice;
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
var push = arr.push;
@@ -86,6 +89,8 @@ var isWindow = function isWindow( obj ) {
};
+var document = window.document;
+
var preservedScriptAttributes = {
@@ -142,7 +147,7 @@ function toType( obj ) {
var
- version = "3.4.1",
+ version = "3.5.1",
// Define a local copy of jQuery
jQuery = function( selector, context ) {
@@ -150,11 +155,7 @@ var
// 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 );
- },
-
- // Support: Android <=4.0 only
- // Make sure we trim BOM and NBSP
- rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+ };
jQuery.fn = jQuery.prototype = {
@@ -220,6 +221,18 @@ jQuery.fn = jQuery.prototype = {
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 );
@@ -353,9 +366,10 @@ jQuery.extend( {
return true;
},
- // Evaluates a script in a global context
- globalEval: function( code, options ) {
- DOMEval( code, { nonce: options && options.nonce } );
+ // 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 ) {
@@ -379,13 +393,6 @@ jQuery.extend( {
return obj;
},
- // Support: Android <=4.0 only
- trim: function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
- },
-
// results is for internal usage only
makeArray: function( arr, results ) {
var ret = results || [];
@@ -472,7 +479,7 @@ jQuery.extend( {
}
// Flatten any nested arrays
- return concat.apply( [], ret );
+ return flat( ret );
},
// A global GUID counter for objects
@@ -489,7 +496,7 @@ if ( typeof Symbol === "function" ) {
// Populate the class2type map
jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( i, name ) {
+function( _i, name ) {
class2type[ "[object " + name + "]" ] = name.toLowerCase();
} );
@@ -511,17 +518,16 @@ function isArrayLike( obj ) {
}
var Sizzle =
/*!
- * Sizzle CSS Selector Engine v2.3.4
+ * 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: 2019-04-08
+ * Date: 2020-03-14
*/
-(function( window ) {
-
+( function( window ) {
var i,
support,
Expr,
@@ -561,59 +567,70 @@ var i,
},
// Instance methods
- hasOwn = ({}).hasOwnProperty,
+ hasOwn = ( {} ).hasOwnProperty,
arr = [],
pop = arr.pop,
- push_native = arr.push,
+ 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 ) {
+ 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",
+ 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]",
- // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
- identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+ // 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)
"*([*^$|!~]?=)" + whitespace +
- // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
- "*\\]",
+
+ // "Attribute values must be CSS identifiers [capture 5]
+ // or strings [capture 3 or capture 4]"
+ "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+ whitespace + "*\\]",
pseudos = ":(" + identifier + ")(?:\\((" +
+
// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
// 1. quoted (capture 3; capture 4 or capture 5)
"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
// 2. simple (capture 6)
"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
// 3. anything else (capture 2)
".*" +
")\\)|)",
// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+ rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+ whitespace + "+$", "g" ),
rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+ rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+ "*" ),
rdescend = new RegExp( whitespace + "|>" ),
rpseudo = new RegExp( pseudos ),
@@ -625,14 +642,16 @@ var i,
"TAG": new RegExp( "^(" + identifier + "|[*])" ),
"ATTR": new RegExp( "^" + attributes ),
"PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
- "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
- "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+ "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+ whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+ whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
"bool": new RegExp( "^(?:" + booleans + ")$", "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 + "*\\)|)(?=[^-]|$)", "i" )
+ "needsContext": new RegExp( "^" + whitespace +
+ "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+ "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
},
rhtml = /HTML$/i,
@@ -648,18 +667,21 @@ var i,
// CSS escapes
// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
- funescape = function( _, escaped, escapedWhitespace ) {
- var high = "0x" + escaped - 0x10000;
- // NaN means non-codepoint
- // Support: Firefox<24
- // Workaround erroneous numeric interpretation of +"0x"
- return high !== high || escapedWhitespace ?
- escaped :
+ 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 ?
- // BMP codepoint
String.fromCharCode( high + 0x10000 ) :
- // Supplemental Plane codepoint (surrogate pair)
String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
},
@@ -675,7 +697,8 @@ var i,
}
// Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+ return ch.slice( 0, -1 ) + "\\" +
+ ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
}
// Other potentially-special ASCII characters get backslash-escaped
@@ -700,18 +723,20 @@ var i,
// Optimize for push.apply( _, NodeList )
try {
push.apply(
- (arr = slice.call( preferredDoc.childNodes )),
+ ( 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 ) {
- push_native.apply( target, slice.call(els) );
+ pushNative.apply( target, slice.call( els ) );
} :
// Support: IE<9
@@ -719,8 +744,9 @@ try {
function( target, els ) {
var j = target.length,
i = 0;
+
// Can't trust NodeList.length
- while ( (target[j++] = els[i++]) ) {}
+ while ( ( target[ j++ ] = els[ i++ ] ) ) {}
target.length = j - 1;
}
};
@@ -744,24 +770,21 @@ function Sizzle( selector, context, results, seed ) {
// Try to shortcut find operations (as opposed to filters) in HTML documents
if ( !seed ) {
-
- if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
- setDocument( context );
- }
+ 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 )) ) {
+ if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
// ID selector
- if ( (m = match[1]) ) {
+ if ( ( m = match[ 1 ] ) ) {
// Document context
if ( nodeType === 9 ) {
- if ( (elem = context.getElementById( m )) ) {
+ if ( ( elem = context.getElementById( m ) ) ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
@@ -780,7 +803,7 @@ function Sizzle( selector, context, results, seed ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
// getElementById can match elements by name instead of ID
- if ( newContext && (elem = newContext.getElementById( m )) &&
+ if ( newContext && ( elem = newContext.getElementById( m ) ) &&
contains( context, elem ) &&
elem.id === m ) {
@@ -790,12 +813,12 @@ function Sizzle( selector, context, results, seed ) {
}
// Type selector
- } else if ( match[2] ) {
+ } else if ( match[ 2 ] ) {
push.apply( results, context.getElementsByTagName( selector ) );
return results;
// Class selector
- } else if ( (m = match[3]) && support.getElementsByClassName &&
+ } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
context.getElementsByClassName ) {
push.apply( results, context.getElementsByClassName( m ) );
@@ -806,11 +829,11 @@ function Sizzle( selector, context, results, seed ) {
// Take advantage of querySelectorAll
if ( support.qsa &&
!nonnativeSelectorCache[ selector + " " ] &&
- (!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+ ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
// Support: IE 8 only
// Exclude object elements
- (nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+ ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
newSelector = selector;
newContext = context;
@@ -819,27 +842,36 @@ function Sizzle( selector, context, results, seed ) {
// 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 ) ) {
+ if ( nodeType === 1 &&
+ ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
- // 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) );
+ // 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 + " " + toSelector( groups[i] );
+ groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+ toSelector( groups[ i ] );
}
newSelector = groups.join( "," );
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
}
try {
@@ -872,12 +904,14 @@ 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[ key + " " ] = value );
}
return cache;
}
@@ -896,17 +930,19 @@ function markFunction( fn ) {
* @param {Function} fn Passed the created element and returns a boolean result
*/
function assert( fn ) {
- var el = document.createElement("fieldset");
+ var el = document.createElement( "fieldset" );
try {
return !!fn( el );
- } catch (e) {
+ } catch ( e ) {
return false;
} finally {
+
// Remove from its parent by default
if ( el.parentNode ) {
el.parentNode.removeChild( el );
}
+
// release memory in IE
el = null;
}
@@ -918,11 +954,11 @@ function assert( fn ) {
* @param {Function} handler The method that will be applied
*/
function addHandle( attrs, handler ) {
- var arr = attrs.split("|"),
+ var arr = attrs.split( "|" ),
i = arr.length;
while ( i-- ) {
- Expr.attrHandle[ arr[i] ] = handler;
+ Expr.attrHandle[ arr[ i ] ] = handler;
}
}
@@ -944,7 +980,7 @@ function siblingCheck( a, b ) {
// Check if b follows a
if ( cur ) {
- while ( (cur = cur.nextSibling) ) {
+ while ( ( cur = cur.nextSibling ) ) {
if ( cur === b ) {
return -1;
}
@@ -972,7 +1008,7 @@ function createInputPseudo( type ) {
function createButtonPseudo( type ) {
return function( elem ) {
var name = elem.nodeName.toLowerCase();
- return (name === "input" || name === "button") && elem.type === type;
+ return ( name === "input" || name === "button" ) && elem.type === type;
};
}
@@ -1015,7 +1051,7 @@ function createDisabledPseudo( disabled ) {
// Where there is no isDisabled, check manually
/* jshint -W018 */
elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
+ inDisabledFieldset( elem ) === disabled;
}
return elem.disabled === disabled;
@@ -1037,21 +1073,21 @@ function createDisabledPseudo( disabled ) {
* @param {Function} fn
*/
function createPositionalPseudo( fn ) {
- return markFunction(function( argument ) {
+ return markFunction( function( argument ) {
argument = +argument;
- return markFunction(function( seed, matches ) {
+ 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]);
+ if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+ seed[ j ] = !( matches[ j ] = seed[ j ] );
}
}
- });
- });
+ } );
+ } );
}
/**
@@ -1073,7 +1109,7 @@ support = Sizzle.support = {};
*/
isXML = Sizzle.isXML = function( elem ) {
var namespace = elem.namespaceURI,
- docElem = (elem.ownerDocument || elem).documentElement;
+ docElem = ( elem.ownerDocument || elem ).documentElement;
// Support: IE <=8
// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -1091,7 +1127,11 @@ setDocument = Sizzle.setDocument = function( node ) {
doc = node ? node.ownerDocument || node : preferredDoc;
// Return early if doc is invalid or already selected
- if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+ // 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;
}
@@ -1100,10 +1140,14 @@ setDocument = Sizzle.setDocument = function( node ) {
docElem = document.documentElement;
documentIsHTML = !isXML( document );
- // Support: IE 9-11, Edge
+ // Support: IE 9 - 11+, Edge 12 - 18+
// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- if ( preferredDoc !== document &&
- (subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+ // 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 ) {
@@ -1115,25 +1159,36 @@ setDocument = Sizzle.setDocument = function( node ) {
}
}
+ // 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 ) {
+ support.attributes = assert( function( el ) {
el.className = "i";
- return !el.getAttribute("className");
- });
+ 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.getElementsByTagName = assert( function( el ) {
+ el.appendChild( document.createComment( "" ) );
+ return !el.getElementsByTagName( "*" ).length;
+ } );
// Support: IE<9
support.getElementsByClassName = rnative.test( document.getElementsByClassName );
@@ -1142,38 +1197,38 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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 ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
- return elem.getAttribute("id") === attrId;
+ return elem.getAttribute( "id" ) === attrId;
};
};
- Expr.find["ID"] = function( id, context ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode("id");
+ 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 ) {
+ Expr.find[ "ID" ] = function( id, context ) {
if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
var node, i, elems,
elem = context.getElementById( id );
@@ -1181,7 +1236,7 @@ setDocument = Sizzle.setDocument = function( node ) {
if ( elem ) {
// Verify the id attribute
- node = elem.getAttributeNode("id");
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1189,8 +1244,8 @@ setDocument = Sizzle.setDocument = function( node ) {
// Fall back on getElementsByName
elems = context.getElementsByName( id );
i = 0;
- while ( (elem = elems[i++]) ) {
- node = elem.getAttributeNode("id");
+ while ( ( elem = elems[ i++ ] ) ) {
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1203,7 +1258,7 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Tag
- Expr.find["TAG"] = support.getElementsByTagName ?
+ Expr.find[ "TAG" ] = support.getElementsByTagName ?
function( tag, context ) {
if ( typeof context.getElementsByTagName !== "undefined" ) {
return context.getElementsByTagName( tag );
@@ -1218,12 +1273,13 @@ setDocument = Sizzle.setDocument = function( node ) {
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++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem.nodeType === 1 ) {
tmp.push( elem );
}
@@ -1235,7 +1291,7 @@ setDocument = Sizzle.setDocument = function( node ) {
};
// Class
- Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+ Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
return context.getElementsByClassName( className );
}
@@ -1256,10 +1312,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// See https://bugs.jquery.com/ticket/13378
rbuggyQSA = [];
- if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+ if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
// Build QSA regex
// Regex strategy adopted from Diego Perini
- assert(function( el ) {
+ 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,
@@ -1273,78 +1333,98 @@ setDocument = Sizzle.setDocument = function( node ) {
// Nothing should be selected when empty strings follow ^= or $= or *=
// The test attribute must be unknown in Opera but "safe" for WinRT
// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+ if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
}
// Support: IE8
// Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll("[selected]").length ) {
+ 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("~=");
+ 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");
+ 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(".#.+[+~]");
+ rbuggyQSA.push( ".#.+[+~]" );
}
- });
- assert(function( el ) {
+ // 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");
+ 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 ) {
+ 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 ) {
+ 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 ) {
+ 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(",.*:");
- });
+ el.querySelectorAll( "*,:x" );
+ rbuggyQSA.push( ",.*:" );
+ } );
}
- if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+ if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
docElem.webkitMatchesSelector ||
docElem.mozMatchesSelector ||
docElem.oMatchesSelector ||
- docElem.msMatchesSelector) )) ) {
+ docElem.msMatchesSelector ) ) ) ) {
+
+ assert( function( el ) {
- assert(function( el ) {
// Check to see if it's possible to do matchesSelector
// on a disconnected node (IE 9)
support.disconnectedMatch = matches.call( el, "*" );
@@ -1353,11 +1433,11 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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("|") );
+ rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+ rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
/* Contains
---------------------------------------------------------------------- */
@@ -1374,11 +1454,11 @@ setDocument = Sizzle.setDocument = function( node ) {
adown.contains ?
adown.contains( bup ) :
a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ));
+ ) );
} :
function( a, b ) {
if ( b ) {
- while ( (b = b.parentNode) ) {
+ while ( ( b = b.parentNode ) ) {
if ( b === a ) {
return true;
}
@@ -1407,7 +1487,11 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Calculate position if both inputs belong to the same document
- compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+ // 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
@@ -1415,13 +1499,24 @@ setDocument = Sizzle.setDocument = function( node ) {
// Disconnected nodes
if ( compare & 1 ||
- (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+ ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
// Choose the first element that is related to our preferred document
- if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+ // 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;
}
- if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+
+ // 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;
}
@@ -1434,6 +1529,7 @@ setDocument = Sizzle.setDocument = function( node ) {
return compare & 4 ? -1 : 1;
} :
function( a, b ) {
+
// Exit early if the nodes are identical
if ( a === b ) {
hasDuplicate = true;
@@ -1449,8 +1545,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// Parentless nodes are either documents or disconnected
if ( !aup || !bup ) {
- return a === document ? -1 :
- b === document ? 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 eqeqeq */
+ return a == document ? -1 :
+ b == document ? 1 :
+ /* eslint-enable eqeqeq */
aup ? -1 :
bup ? 1 :
sortInput ?
@@ -1464,26 +1566,32 @@ setDocument = Sizzle.setDocument = function( node ) {
// Otherwise we need full lists of their ancestors for comparison
cur = a;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
ap.unshift( cur );
}
cur = b;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
bp.unshift( cur );
}
// Walk down the tree looking for a discrepancy
- while ( ap[i] === bp[i] ) {
+ while ( ap[ i ] === bp[ i ] ) {
i++;
}
return i ?
+
// Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[i], bp[i] ) :
+ siblingCheck( ap[ i ], bp[ i ] ) :
// Otherwise nodes in our document sort first
- ap[i] === preferredDoc ? -1 :
- bp[i] === preferredDoc ? 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 eqeqeq */
+ ap[ i ] == preferredDoc ? -1 :
+ bp[ i ] == preferredDoc ? 1 :
+ /* eslint-enable eqeqeq */
0;
};
@@ -1495,10 +1603,7 @@ Sizzle.matches = function( expr, elements ) {
};
Sizzle.matchesSelector = function( elem, expr ) {
- // Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== document ) {
- setDocument( elem );
- }
+ setDocument( elem );
if ( support.matchesSelector && documentIsHTML &&
!nonnativeSelectorCache[ expr + " " ] &&
@@ -1510,12 +1615,13 @@ Sizzle.matchesSelector = function( 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 ) {
+
+ // 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) {
+ } catch ( e ) {
nonnativeSelectorCache( expr, true );
}
}
@@ -1524,20 +1630,31 @@ Sizzle.matchesSelector = function( elem, expr ) {
};
Sizzle.contains = function( context, elem ) {
+
// Set document vars if needed
- if ( ( context.ownerDocument || context ) !== 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 ( ( context.ownerDocument || context ) != document ) {
setDocument( context );
}
return contains( context, elem );
};
Sizzle.attr = function( elem, name ) {
+
// Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== 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 ( ( 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 ) :
@@ -1547,13 +1664,13 @@ Sizzle.attr = function( elem, name ) {
val :
support.attributes || !documentIsHTML ?
elem.getAttribute( name ) :
- (val = elem.getAttributeNode(name)) && val.specified ?
+ ( val = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
null;
};
Sizzle.escape = function( sel ) {
- return (sel + "").replace( rcssescape, fcssescape );
+ return ( sel + "" ).replace( rcssescape, fcssescape );
};
Sizzle.error = function( msg ) {
@@ -1576,7 +1693,7 @@ Sizzle.uniqueSort = function( results ) {
results.sort( sortOrder );
if ( hasDuplicate ) {
- while ( (elem = results[i++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem === results[ i ] ) {
j = duplicates.push( i );
}
@@ -1604,17 +1721,21 @@ getText = Sizzle.getText = function( elem ) {
nodeType = elem.nodeType;
if ( !nodeType ) {
+
// If no nodeType, this is expected to be an array
- while ( (node = elem[i++]) ) {
+ 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 );
@@ -1623,6 +1744,7 @@ getText = Sizzle.getText = function( elem ) {
} else if ( nodeType === 3 || nodeType === 4 ) {
return elem.nodeValue;
}
+
// Do not include comment or processing instruction nodes
return ret;
@@ -1650,19 +1772,21 @@ Expr = Sizzle.selectors = {
preFilter: {
"ATTR": function( match ) {
- match[1] = match[1].replace( runescape, funescape );
+ 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 );
+ match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+ match[ 5 ] || "" ).replace( runescape, funescape );
- if ( match[2] === "~=" ) {
- match[3] = " " + match[3] + " ";
+ 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)
@@ -1673,22 +1797,25 @@ Expr = Sizzle.selectors = {
7 sign of y-component
8 y of y-component
*/
- match[1] = match[1].toLowerCase();
+ match[ 1 ] = match[ 1 ].toLowerCase();
+
+ if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
- if ( match[1].slice( 0, 3 ) === "nth" ) {
// nth-* requires argument
- if ( !match[3] ) {
- Sizzle.error( match[0] );
+ 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" );
+ 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] );
+ // other types prohibit arguments
+ } else if ( match[ 3 ] ) {
+ Sizzle.error( match[ 0 ] );
}
return match;
@@ -1696,26 +1823,28 @@ Expr = Sizzle.selectors = {
"PSEUDO": function( match ) {
var excess,
- unquoted = !match[6] && match[2];
+ unquoted = !match[ 6 ] && match[ 2 ];
- if ( matchExpr["CHILD"].test( match[0] ) ) {
+ if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
return null;
}
// Accept quoted arguments as-is
- if ( match[3] ) {
- match[2] = match[4] || match[5] || "";
+ 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 )) &&
+ ( excess = tokenize( unquoted, true ) ) &&
+
// advance to the next closing parenthesis
- (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+ ( 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 );
+ 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)
@@ -1728,7 +1857,9 @@ Expr = Sizzle.selectors = {
"TAG": function( nodeNameSelector ) {
var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
return nodeNameSelector === "*" ?
- function() { return true; } :
+ function() {
+ return true;
+ } :
function( elem ) {
return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
};
@@ -1738,10 +1869,16 @@ Expr = Sizzle.selectors = {
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") || "" );
- });
+ ( 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 ) {
@@ -1757,6 +1894,8 @@ Expr = Sizzle.selectors = {
result += "";
+ /* eslint-disable max-len */
+
return operator === "=" ? result === check :
operator === "!=" ? result !== check :
operator === "^=" ? check && result.indexOf( check ) === 0 :
@@ -1765,10 +1904,12 @@ Expr = Sizzle.selectors = {
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 ) {
+ "CHILD": function( type, what, _argument, first, last ) {
var simple = type.slice( 0, 3 ) !== "nth",
forward = type.slice( -4 ) !== "last",
ofType = what === "of-type";
@@ -1780,7 +1921,7 @@ Expr = Sizzle.selectors = {
return !!elem.parentNode;
} :
- function( elem, context, xml ) {
+ function( elem, _context, xml ) {
var cache, uniqueCache, outerCache, node, nodeIndex, start,
dir = simple !== forward ? "nextSibling" : "previousSibling",
parent = elem.parentNode,
@@ -1794,7 +1935,7 @@ Expr = Sizzle.selectors = {
if ( simple ) {
while ( dir ) {
node = elem;
- while ( (node = node[ dir ]) ) {
+ while ( ( node = node[ dir ] ) ) {
if ( ofType ?
node.nodeName.toLowerCase() === name :
node.nodeType === 1 ) {
@@ -1802,6 +1943,7 @@ Expr = Sizzle.selectors = {
return false;
}
}
+
// Reverse direction for :only-* (if we haven't yet done so)
start = dir = type === "only" && !start && "nextSibling";
}
@@ -1817,22 +1959,22 @@ Expr = Sizzle.selectors = {
// ...in a gzip-friendly way
node = parent;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (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 ] ||
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
// Fallback to seeking `elem` from the start
- (diff = nodeIndex = 0) || start.pop()) ) {
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
// When found, cache indexes on `parent` and break
if ( node.nodeType === 1 && ++diff && node === elem ) {
@@ -1842,16 +1984,18 @@ Expr = Sizzle.selectors = {
}
} else {
+
// Use previously-cached element index if available
if ( useCache ) {
+
// ...in a gzip-friendly way
node = elem;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
cache = uniqueCache[ type ] || [];
nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
@@ -1861,9 +2005,10 @@ Expr = Sizzle.selectors = {
// 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()) ) {
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
if ( ( ofType ?
node.nodeName.toLowerCase() === name :
@@ -1872,12 +2017,13 @@ Expr = Sizzle.selectors = {
// Cache the index of each encountered element
if ( useCache ) {
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] ||
+ ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
uniqueCache[ type ] = [ dirruns, diff ];
}
@@ -1898,6 +2044,7 @@ Expr = Sizzle.selectors = {
},
"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
@@ -1917,15 +2064,15 @@ Expr = Sizzle.selectors = {
if ( fn.length > 1 ) {
args = [ pseudo, pseudo, "", argument ];
return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction(function( seed, matches ) {
+ 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] );
+ idx = indexOf( seed, matched[ i ] );
+ seed[ idx ] = !( matches[ idx ] = matched[ i ] );
}
- }) :
+ } ) :
function( elem ) {
return fn( elem, 0, args );
};
@@ -1936,8 +2083,10 @@ Expr = Sizzle.selectors = {
},
pseudos: {
+
// Potentially complex pseudos
- "not": markFunction(function( selector ) {
+ "not": markFunction( function( selector ) {
+
// Trim the selector passed to compile
// to avoid treating leading and trailing
// spaces as combinators
@@ -1946,39 +2095,40 @@ Expr = Sizzle.selectors = {
matcher = compile( selector.replace( rtrim, "$1" ) );
return matcher[ expando ] ?
- markFunction(function( seed, matches, context, xml ) {
+ 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);
+ if ( ( elem = unmatched[ i ] ) ) {
+ seed[ i ] = !( matches[ i ] = elem );
}
}
- }) :
- function( elem, context, xml ) {
- input[0] = elem;
+ } ) :
+ function( elem, _context, xml ) {
+ input[ 0 ] = elem;
matcher( input, null, xml, results );
+
// Don't keep the element (issue #299)
- input[0] = null;
+ input[ 0 ] = null;
return !results.pop();
};
- }),
+ } ),
- "has": markFunction(function( selector ) {
+ "has": markFunction( function( selector ) {
return function( elem ) {
return Sizzle( selector, elem ).length > 0;
};
- }),
+ } ),
- "contains": markFunction(function( text ) {
+ "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
@@ -1988,25 +2138,26 @@ Expr = Sizzle.selectors = {
// 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 || "") ) {
+ if ( !ridentifier.test( lang || "" ) ) {
Sizzle.error( "unsupported lang: " + lang );
}
lang = lang.replace( runescape, funescape ).toLowerCase();
return function( elem ) {
var elemLang;
do {
- if ( (elemLang = documentIsHTML ?
+ if ( ( elemLang = documentIsHTML ?
elem.lang :
- elem.getAttribute("xml:lang") || elem.getAttribute("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 );
+ } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
return false;
};
- }),
+ } ),
// Miscellaneous
"target": function( elem ) {
@@ -2019,7 +2170,9 @@ Expr = Sizzle.selectors = {
},
"focus": function( elem ) {
- return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+ return elem === document.activeElement &&
+ ( !document.hasFocus || document.hasFocus() ) &&
+ !!( elem.type || elem.href || ~elem.tabIndex );
},
// Boolean properties
@@ -2027,16 +2180,20 @@ Expr = Sizzle.selectors = {
"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);
+ 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;
}
@@ -2045,6 +2202,7 @@ Expr = Sizzle.selectors = {
// 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.)
@@ -2058,7 +2216,7 @@ Expr = Sizzle.selectors = {
},
"parent": function( elem ) {
- return !Expr.pseudos["empty"]( elem );
+ return !Expr.pseudos[ "empty" ]( elem );
},
// Element/input types
@@ -2082,39 +2240,40 @@ Expr = Sizzle.selectors = {
// Support: IE<8
// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+ ( ( attr = elem.getAttribute( "type" ) ) == null ||
+ attr.toLowerCase() === "text" );
},
// Position-in-collection
- "first": createPositionalPseudo(function() {
+ "first": createPositionalPseudo( function() {
return [ 0 ];
- }),
+ } ),
- "last": createPositionalPseudo(function( matchIndexes, length ) {
+ "last": createPositionalPseudo( function( _matchIndexes, length ) {
return [ length - 1 ];
- }),
+ } ),
- "eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
return [ argument < 0 ? argument + length : argument ];
- }),
+ } ),
- "even": createPositionalPseudo(function( matchIndexes, length ) {
+ "even": createPositionalPseudo( function( matchIndexes, length ) {
var i = 0;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "odd": createPositionalPseudo(function( matchIndexes, length ) {
+ "odd": createPositionalPseudo( function( matchIndexes, length ) {
var i = 1;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
var i = argument < 0 ?
argument + length :
argument > length ?
@@ -2124,19 +2283,19 @@ Expr = Sizzle.selectors = {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "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"];
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
// Add button/input type pseudos
for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
@@ -2167,37 +2326,39 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
while ( soFar ) {
// Comma and first run
- if ( !matched || (match = rcomma.exec( soFar )) ) {
+ if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
if ( match ) {
+
// Don't consume trailing commas as valid
- soFar = soFar.slice( match[0].length ) || soFar;
+ soFar = soFar.slice( match[ 0 ].length ) || soFar;
}
- groups.push( (tokens = []) );
+ groups.push( ( tokens = [] ) );
}
matched = false;
// Combinators
- if ( (match = rcombinators.exec( soFar )) ) {
+ if ( ( match = rcombinators.exec( soFar ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
+
// Cast descendant combinators to space
- type: match[0].replace( rtrim, " " )
- });
+ 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 ))) ) {
+ if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+ ( match = preFilters[ type ]( match ) ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
type: type,
matches: match
- });
+ } );
soFar = soFar.slice( matched.length );
}
}
@@ -2214,6 +2375,7 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
soFar.length :
soFar ?
Sizzle.error( selector ) :
+
// Cache the tokens
tokenCache( selector, groups ).slice( 0 );
};
@@ -2223,7 +2385,7 @@ function toSelector( tokens ) {
len = tokens.length,
selector = "";
for ( ; i < len; i++ ) {
- selector += tokens[i].value;
+ selector += tokens[ i ].value;
}
return selector;
}
@@ -2236,9 +2398,10 @@ function addCombinator( matcher, combinator, base ) {
doneName = done++;
return combinator.first ?
+
// Check against closest ancestor/preceding element
function( elem, context, xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
return matcher( elem, context, xml );
}
@@ -2253,7 +2416,7 @@ function addCombinator( matcher, combinator, base ) {
// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
if ( xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
if ( matcher( elem, context, xml ) ) {
return true;
@@ -2261,27 +2424,29 @@ function addCombinator( matcher, combinator, base ) {
}
}
} else {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || (elem[ expando ] = {});
+ outerCache = elem[ expando ] || ( elem[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+ uniqueCache = outerCache[ elem.uniqueID ] ||
+ ( outerCache[ elem.uniqueID ] = {} );
if ( skip && skip === elem.nodeName.toLowerCase() ) {
elem = elem[ dir ] || elem;
- } else if ( (oldCache = uniqueCache[ key ]) &&
+ } 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 ]);
+ 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 )) ) {
+ if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
return true;
}
}
@@ -2297,20 +2462,20 @@ function elementMatcher( matchers ) {
function( elem, context, xml ) {
var i = matchers.length;
while ( i-- ) {
- if ( !matchers[i]( elem, context, xml ) ) {
+ if ( !matchers[ i ]( elem, context, xml ) ) {
return false;
}
}
return true;
} :
- matchers[0];
+ matchers[ 0 ];
}
function multipleContexts( selector, contexts, results ) {
var i = 0,
len = contexts.length;
for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[i], results );
+ Sizzle( selector, contexts[ i ], results );
}
return results;
}
@@ -2323,7 +2488,7 @@ function condense( unmatched, map, filter, context, xml ) {
mapped = map != null;
for ( ; i < len; i++ ) {
- if ( (elem = unmatched[i]) ) {
+ if ( ( elem = unmatched[ i ] ) ) {
if ( !filter || filter( elem, context, xml ) ) {
newUnmatched.push( elem );
if ( mapped ) {
@@ -2343,14 +2508,18 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
if ( postFinder && !postFinder[ expando ] ) {
postFinder = setMatcher( postFinder, postSelector );
}
- return markFunction(function( seed, results, context, xml ) {
+ 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, [] ),
+ elems = seed || multipleContexts(
+ selector || "*",
+ context.nodeType ? [ context ] : context,
+ []
+ ),
// Prefilter to get matcher input, preserving a map for seed-results synchronization
matcherIn = preFilter && ( seed || !selector ) ?
@@ -2358,6 +2527,7 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
elems,
matcherOut = matcher ?
+
// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
@@ -2381,8 +2551,8 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
// 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 ( ( elem = temp[ i ] ) ) {
+ matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
}
}
}
@@ -2390,25 +2560,27 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
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]) ) {
+ if ( ( elem = matcherOut[ i ] ) ) {
+
// Restore matcherIn since elem is not yet a final match
- temp.push( (matcherIn[i] = elem) );
+ temp.push( ( matcherIn[ i ] = elem ) );
}
}
- postFinder( null, (matcherOut = []), temp, xml );
+ 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 ) {
+ if ( ( elem = matcherOut[ i ] ) &&
+ ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
- seed[temp] = !(results[temp] = elem);
+ seed[ temp ] = !( results[ temp ] = elem );
}
}
}
@@ -2426,14 +2598,14 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
push.apply( results, matcherOut );
}
}
- });
+ } );
}
function matcherFromTokens( tokens ) {
var checkContext, matcher, j,
len = tokens.length,
- leadingRelative = Expr.relative[ tokens[0].type ],
- implicitRelative = leadingRelative || Expr.relative[" "],
+ 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)
@@ -2445,38 +2617,43 @@ function matcherFromTokens( tokens ) {
}, implicitRelative, true ),
matchers = [ function( elem, context, xml ) {
var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- (checkContext = context).nodeType ?
+ ( 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) ];
+ if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+ matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
} else {
- matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+ 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 ] ) {
+ 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 === " " ? "*" : "" })
+
+ // 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 && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
j < len && toSelector( tokens )
);
}
@@ -2497,28 +2674,40 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
unmatched = seed && [],
setMatched = [],
contextBackup = outermostContext,
+
// We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+ 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),
+ dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
len = elems.length;
if ( outermost ) {
- outermostContext = context === document || context || 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++ ) {
+ for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
if ( byElement && elem ) {
j = 0;
- if ( !context && elem.ownerDocument !== 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 ( !context && elem.ownerDocument != document ) {
setDocument( elem );
xml = !documentIsHTML;
}
- while ( (matcher = elementMatchers[j++]) ) {
- if ( matcher( elem, context || document, xml) ) {
+ while ( ( matcher = elementMatchers[ j++ ] ) ) {
+ if ( matcher( elem, context || document, xml ) ) {
results.push( elem );
break;
}
@@ -2530,8 +2719,9 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// Track unmatched elements for set filters
if ( bySet ) {
+
// They will have gone through all possible matchers
- if ( (elem = !matcher && elem) ) {
+ if ( ( elem = !matcher && elem ) ) {
matchedCount--;
}
@@ -2555,16 +2745,17 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// numerically zero.
if ( bySet && i !== matchedCount ) {
j = 0;
- while ( (matcher = setMatchers[j++]) ) {
+ 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 );
+ if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+ setMatched[ i ] = pop.call( results );
}
}
}
@@ -2605,13 +2796,14 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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] );
+ cached = matcherFromTokens( match[ i ] );
if ( cached[ expando ] ) {
setMatchers.push( cached );
} else {
@@ -2620,7 +2812,10 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
}
// Cache the compiled function
- cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+ cached = compilerCache(
+ selector,
+ matcherFromGroupMatchers( elementMatchers, setMatchers )
+ );
// Save selector and tokenization
cached.selector = selector;
@@ -2640,7 +2835,7 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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) );
+ match = !seed && tokenize( ( selector = compiled.selector || selector ) );
results = results || [];
@@ -2649,11 +2844,12 @@ select = Sizzle.select = function( selector, context, results, seed ) {
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 ] ) {
+ 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];
+ context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+ .replace( runescape, funescape ), context ) || [] )[ 0 ];
if ( !context ) {
return results;
@@ -2666,20 +2862,22 @@ select = Sizzle.select = function( selector, context, results, seed ) {
}
// Fetch a seed set for right-to-left matching
- i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+ i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
while ( i-- ) {
- token = tokens[i];
+ token = tokens[ i ];
// Abort if we hit a combinator
- if ( Expr.relative[ (type = token.type) ] ) {
+ if ( Expr.relative[ ( type = token.type ) ] ) {
break;
}
- if ( (find = Expr.find[ type ]) ) {
+ 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 = 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 );
@@ -2710,7 +2908,7 @@ select = Sizzle.select = function( selector, context, results, seed ) {
// One-time assignments
// Sort stability
-support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
// Support: Chrome 14-35+
// Always assume duplicates if they aren't passed to the comparison function
@@ -2721,58 +2919,59 @@ 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 ) {
+support.sortDetached = assert( function( el ) {
+
// Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
-});
+ 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 ) {
+if ( !assert( function( el ) {
el.innerHTML = "";
- return el.firstChild.getAttribute("href") === "#" ;
-}) ) {
+ 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 ) {
+if ( !support.attributes || !assert( function( el ) {
el.innerHTML = "";
el.firstChild.setAttribute( "value", "" );
return el.firstChild.getAttribute( "value" ) === "";
-}) ) {
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+ addHandle( "value", function( elem, _name, isXML ) {
if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
return elem.defaultValue;
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- });
+ } );
}
// Support: IE<9
// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert(function( el ) {
- return el.getAttribute("disabled") == null;
-}) ) {
+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 = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
- null;
+ null;
}
- });
+ } );
}
return Sizzle;
-})( window );
+} )( window );
@@ -3141,7 +3340,7 @@ jQuery.each( {
parents: function( elem ) {
return dir( elem, "parentNode" );
},
- parentsUntil: function( elem, i, until ) {
+ parentsUntil: function( elem, _i, until ) {
return dir( elem, "parentNode", until );
},
next: function( elem ) {
@@ -3156,10 +3355,10 @@ jQuery.each( {
prevAll: function( elem ) {
return dir( elem, "previousSibling" );
},
- nextUntil: function( elem, i, until ) {
+ nextUntil: function( elem, _i, until ) {
return dir( elem, "nextSibling", until );
},
- prevUntil: function( elem, i, until ) {
+ prevUntil: function( elem, _i, until ) {
return dir( elem, "previousSibling", until );
},
siblings: function( elem ) {
@@ -3169,7 +3368,13 @@ jQuery.each( {
return siblings( elem.firstChild );
},
contents: function( elem ) {
- if ( typeof elem.contentDocument !== "undefined" ) {
+ if ( elem.contentDocument != null &&
+
+ // Support: IE 11+
+ //
"),i("table.docutils.footnote").wrap(""),i("table.docutils.citation").wrap(""),i(".wy-menu-vertical ul").not(".simple").siblings("a").each(function(){var e=i(this);expand=i(''),expand.on("click",function(n){return t.toggleCurrent(e),n.stopPropagation(),!1}),e.prepend(expand)})},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),i=e.find('[href="'+n+'"]');if(0===i.length){var t=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(i=e.find('[href="#'+t.attr("id")+'"]')).length&&(i=e.find('[href="#"]'))}0this.docHeight||(this.navBar.scrollTop(i),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",function(){this.linkScroll=!1})},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current"),e.siblings().find("li.current").removeClass("current"),e.find("> ul li.current").removeClass("current"),e.toggleClass("current")}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:e.exports.ThemeNav,StickyNav:e.exports.ThemeNav}),function(){for(var r=0,n=["ms","moz","webkit","o"],e=0;e
import matplotlib.pyplot as plt
-from hoki.cmd import CMD
-from hoki.load import set_models_path, unpickle
+from hoki.cmd import CMD
+from hoki.load import set_models_path, unpickle
import pickle
import numpy as np
@@ -513,6 +550,7 @@ Making the CMDsCMD() instance is.
+
You can easily access the grid by simply calling the attribute CMD.grid
[4]:
@@ -527,8 +565,9 @@ Making the CMDs[4]:
-
-array([[[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
@@ -577,8 +616,7 @@ Making the CMDs
But CMD objects are also indexable!
@@ -612,16 +650,16 @@ Making the CMDs[6]:
-
-array([[0., 0., 0., ..., 0., 0., 0.],
+
+
+array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
-
-
+
This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use CMD.at_log_age():
@@ -905,11 +943,19 @@ Creating a publication-ready figure
- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
+ theme
+
+ provided by Read the Docs.
@@ -921,7 +967,6 @@ Creating a publication-ready figure
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/CMDs.ipynb b/docs/CMDs.ipynb
new file mode 100644
index 00000000..3be3f90a
--- /dev/null
+++ b/docs/CMDs.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Colour Magnitude Diagrams\n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from hoki.cmd import CMD\n",
+ "from hoki.load import set_models_path, unpickle\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Getting the Stellar models and input files\n",
+ "\n",
+ "Colour Magnitude Diagrams (CMDs) are created by reading in the [BPASS](https://bpass.auckland.ac.nz/9.html) stellar models listed in the `model_input` files that can be found in the BPASS output folders (e.g. *bpass_v2.2.1_imf135_300*). These stellar models are in a separate directory (because it is absolutely massive), so you will have to download it separately if you want to run the following cells or make your own CMDs.\n",
+ "\n",
+ "**NOTE: You will be able to run the cells in the section \"Loading a pickled CMD file\" even if you can't download the full set of stellar models **\n",
+ "\n",
+ "The stellar models and input files can be downloaded from [the google drive](https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl) (*bpass-v2.2-newmodels* for the models and e.g. *bpass_v2.2.1_imf135_300* to get the required `model_input` files).\n",
+ "Then you will have to change the path to the models in the `settings.yaml` file -- this can be done using the `set_models_path` function contained in the `hoki.load` module: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looks like everything went well! You can check the path was correctly updated by looking at this file:\n",
+ "/home/fste075/hoki/hoki/data/settings.yaml\n"
+ ]
+ }
+ ],
+ "source": [
+ "# The following path is valid on my machine - make sure you put the right ABSOLUTE path for your system\n",
+ "set_models_path(path='/home/fste075/BPASS_hoki_dev/bpass-v2.2-newmodels/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**NOTE: You'll probably have to reload hoki or restart the kernel at this point if you've jsut updated the yaml file :)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# CMD objects\n",
+ "\n",
+ "### Making the CMDs\n",
+ "\n",
+ "To create a synthetic CMD, `hoki` creates a grid in colour-magnitude space and then consults the entire set of stellar models to fill that grid. It is basically a histogram - the value of each cell/bin increases according to the proportion of the stellar population that falls into that bin. You could just plot this grid with a colour-map like an image, but we traditionally create contour plots for visualisation. \n",
+ "\n",
+ "In `hoki` you will be creating a `CMD()` object instanciated with a model of your choosing (a particular IMF and metallicity)- for this you need to provide the **location of a BPASS input file**.\n",
+ "\n",
+ "To know WHAT to do with this information, we also need to give the **two broad-band filters** we are interested in to make the plot filter2 Vs filter1-filter2 (e.g. V Vs B-V). This is given in the `CMD.make()` method, which actually creates and fills the CMD grids. \n",
+ "\n",
+ "**NOTE: This step is the most time consuming because there are thousands and thousands of models to look at. For that reason it also take much longer for the binary stellar models than the single star models to make a CMD. In the next section we will show you how to avoid having to go repeat his step in the future**\n",
+ "\n",
+ "The good news is that **once you have instanciated and 'made' the CMD object, plotting it is VERY fast** and you have a CMD for **each time bin**, which are also trivial and quick to access. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/fste075/hoki/hoki/cmd.py:191: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n",
+ "/home/fste075/hoki/hoki/cmd.py:207: RuntimeWarning: divide by zero encountered in log10\n",
+ " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ONLY RUN IF YOU HAVE THE MODELS IN YOUR MACHINE\n",
+ "\n",
+ "# Update this path if you want to run this cell\n",
+ "input_file = '/home/fste075/BPASS_hoki_dev/bpass_v2.2.1_imf135_300/input_bpass_z020_bin_imf135_300'\n",
+ "mycmd = CMD(input_file)\n",
+ "\n",
+ "# actually makes and fills the grids - this is the time and memory consuming step\n",
+ "mycmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below is a summary illustration of what the synthetic CMDs are and what a `CMD()` instance is. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can easily access the grid by simply calling the attribute `CMD.grid`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "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",
+ "\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": [
+ "mycmd.grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(51, 240, 100)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.grid.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But `CMD` objects are also indexable! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(240, 100)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd[0].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This would give you the grid for log(age/years)=6.0, but it can get tricky to find the right age CMD grid just based on indices, so for that purpose you can use `CMD.at_log_age()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "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.]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mycmd.at_log_age(log_age=6.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Changing the resolution of the CMD grids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see we have 51 time bins, 240 magnitude intervals and 100 colour intervals. \n",
+ "\n",
+ "The number of time bins is fixed by BPASS but you can chose the size of your colour-magnitude grid and its resolution when you instanciate a `CMD` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "blurry_cmd = CMD(input_file, col_lim=[-3, 7], mag_lim=[-14, 10], res_el=0.75)\n",
+ "blurry_cmd.make(mag_filter='V', col_filters=['B', 'V'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the CMDs\n",
+ "\n",
+ "Like I said above, once the grid is made and filled, plotting is quick and straight forward. As in other `hoki` tools the plotting function returns the plot, which you can store in a variable to add your own labels and customize limits. \n",
+ "\n",
+ "Similarly to the `hoki.HRDiagrams` plots, the contours are on a log scale. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = mycmd.plot(log_age=6.8) # Here you can chose the time bin you want to plot.\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with the HRDiagrams, you can also tell the plotting function where your want it to plot your data!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "mycmd.plot(log_age=6.8, loc=ax[0]) # Here you can chose the time bin you want to plot.\n",
+ "ax[0].set_xlim([-3,3.0])\n",
+ "ax[0].set_ylim([2,-10])\n",
+ "ax[0].set_title('High resolution')\n",
+ "\n",
+ "blurry_cmd.plot(log_age=6.8, loc=ax[1]) # Here you can chose the time bin you want to plot.\n",
+ "ax[1].set_xlim([-3,3.0])\n",
+ "ax[1].set_ylim([2,-10])\n",
+ "ax[1].set_title('Low resolution')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pickle CMDs - don't make them twice!\n",
+ "\n",
+ "Because it is a little time consuming to create the synthetic CMDs, we actually recommend you [pickle](https://www.datacamp.com/community/tutorials/pickle-python-tutorial) your CMD objects. This will allow you to re-use them in the future and plot them virtually instantly, by-passing the `CMD.make()` step. \n",
+ "\n",
+ "### Loading a pickled CMD file\n",
+ "\n",
+ "We've provided a couple of pickled CMD files in the `./data/cmds/` directory for you to try even if you couldn't download the full sets of stellar models. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pickled_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Pickling a CMD file\n",
+ "\n",
+ "Now let's make your own pickle file! If you have the stellar models and made the CMD in the previous sections of this tutorial, you can now save your work!\n",
+ "\n",
+ "All you need is the following code (feel free to change the output file name)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First we open a file we can write into\n",
+ "outfile = open('./data/cmds/BV_CMD.pckl', 'wb')\n",
+ "# Then we call the 'dump' function from the pickle module to dump our cmd in our output file\n",
+ "pickle.dump(mycmd, outfile)\n",
+ "# And to avoid funny business we close our file. \n",
+ "outfile.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_pickled_cmd = unpickle(path='./data/cmds/BV_CMD.pckl')\n",
+ "\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "myplot = new_pickled_cmd.plot(log_age=6.8)\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "myplot.set_ylim([2,-10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just like with the HRDiagrams, the tool provided by `hoki` will allow you to make publication ready figures in not time!\n",
+ "\n",
+ "Let's make a plot comparing the CMDs for Cygnus OB2 and Upper Sco in B-V and U-V plots.\n",
+ "\n",
+ "First we need to load our data (which is provided in the ./data/cmds/ repository) - we also need to make sure our observational data is in **absolute** magnitude, because that's what our synthetic CMDs provide. If **extinction** is important in your osbervational data, you also need to take that into account. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Cygnus OB2 data\n",
+ "Av, cyg_U, cyg_B, cyg_V = np.genfromtxt('./data/cmds/cygnusob.dat', unpack=True, usecols=(7, 8, 9,10), skip_header=54)\n",
+ "\n",
+ "# Assumes Milky Way extinction\n",
+ "Ab = (1.324*Av)\n",
+ "Au = (1.531*Av)\n",
+ "\n",
+ "# Distance to Cyg OB2 and distance modulus\n",
+ "d_cygob2 = 1750 #pc\n",
+ "mu_cygob2 = 5*np.log10(d_cygob2)-5\n",
+ "\n",
+ "# Taking away the extinction and turning our mags into absolute mags\n",
+ "# Note it was derived from single star models so extinction may be a tad off\n",
+ "# for your science, feel free to do a better job of it ;)\n",
+ "cyg_U, cyg_B, cyg_V = cyg_U-Au-mu_cygob2 , cyg_B-Ab-mu_cygob2 , cyg_V-Av-mu_cygob2 \n",
+ "\n",
+ "# Now calculating colours\n",
+ "cyg_UV = cyg_U-cyg_V\n",
+ "cyg_BV = cyg_B-cyg_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Upper Sco data\n",
+ "p, usco_U, usco_B, usco_V = np.genfromtxt('./data/cmds/usco.dat', unpack=True, usecols=(1,2,3,4), skip_header=1)\n",
+ "\n",
+ "# Distance modulus - this time based on parallax.\n",
+ "# (Note I inverted parallax to make this tutorial quick - don't @ me.)\n",
+ "# Extinction is not a problem for this data set\n",
+ "mu = 5*np.log10(1/p)-5\n",
+ "usco_U, usco_B, usco_V = usco_U+mu, usco_B+mu, usco_V+mu\n",
+ "\n",
+ "# Calculating colours\n",
+ "usco_UV = usco_U-usco_V\n",
+ "usco_BV = usco_B-usco_V"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unpickling our BV and UV cmds!\n",
+ "BV_cmd = unpickle(path='./data/cmds/cmd_bv_z020_bin_imf135_300')\n",
+ "UV_cmd = unpickle(path='./data/cmds/cmd_uv_z020_bin_imf135_300')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Now let's plot the data!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(2,2, figsize=(15,15))\n",
+ "\n",
+ "# This is a bit off on the colour axis which is probs just because of my conversion from Av to Ab and Au\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].scatter(cyg_UV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].scatter(cyg_BV, cyg_V, s=70, marker='x')\n",
+ "\n",
+ "myplot.set_xlim([-1,2.0])\n",
+ "\n",
+ "# this is not the same data as the paper\n",
+ "UV_cmd.plot(log_age=6.8, loc=ax[1,0])\n",
+ "ax[1,0].scatter(usco_UV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "BV_cmd.plot(log_age=6.8, loc=ax[1,1])\n",
+ "ax[1,1].scatter(usco_BV, usco_V, s=100, marker='x')\n",
+ "\n",
+ "for axis in ax.reshape(4):\n",
+ " axis.set_ylabel('V', fontsize=14)\n",
+ " axis.set_ylim([2,-10])\n",
+ "\n",
+ "for i in [0,1]:\n",
+ " ax[i,0].set_xlim([-2,4])\n",
+ "\n",
+ " ax[i,1].set_xlim([-1,2])\n",
+ "\n",
+ " \n",
+ "ax[0,0].text(1,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[0,1].text(0.5,0, 'Cygnus OB2\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,0].text(1,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)\n",
+ "ax[1,1].text(0.5,0, 'USco\\nZ=0.020\\nlog(age)=6.8 yrs', fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
+
+
+ Built with Sphinx using a
+
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@@ -1003,7 +1048,6 @@ Plotting HRDs of stacked ages
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/HR_diagrams.ipynb b/docs/HR_diagrams.ipynb
new file mode 100644
index 00000000..98676bd2
--- /dev/null
+++ b/docs/HR_diagrams.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HR diagrams\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
+ "\n",
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "plt.style.use('tuto.mplstyle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Learn about the HR-diagram data structure of BPASS\n",
+ "- Learn how to load them in using hoki\n",
+ "- Use the tools in `hoki` to explore your HR diagram data and make science plots\n",
+ "- Learn to customise the `hoki` plots\n",
+ "\n",
+ "\n",
+ "# Loading in the data\n",
+ "\n",
+ "### About the tools you're about to use. \n",
+ "HR diagram data can be loaded using the `hoki.load.population_output()` function. This function will automatically know that you are trying to load an HR diagram from the name of the text file.\n",
+ "\n",
+ "**Important**:\n",
+ "You should note that when loading an HR diagram you need to specify what type of HR diagram you want to load form the file. The HR diagram files are large and quite complex - see left hand side of the diagam below - so we'll be loading in one type of HR diagram at a time. \n",
+ "\n",
+ "You can choose from 3 options:\n",
+ "- `'TL'`: For a temperature/Luminosity HR diagram\n",
+ "- `'Tg'`: For a temperature/surface gravity HR diagram\n",
+ "- `'TTG'`: For a temperature/(temperature**4/surface gravity) HR diagram\n",
+ "\n",
+ "These options are there for 2 reasons: \n",
+ "* 1.They tell the object which segment of the text file to load in \n",
+ "* 2.They tell the plotting function what grid to create in order to plot the contours (also picks the right axis labels)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading the HR diagrams"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the HR diagrams for signle stars and binary star populations \n",
+ "sin_hr_diagram = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type = 'TL')\n",
+ "bin_hr_diagram = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type = 'TL')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading some observational data\n",
+ "\n",
+ "We're going to want to plot some observational data to compare to our models. A nice way to do that is to use `pandas`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the observational data of Upper Scorpio\n",
+ "usco = pd.read_csv('./data/USco.dat', sep='\\t', engine='python', names=['Temperature', 'Luminosity'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Temperature \n",
+ " Luminosity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 3.756 \n",
+ " 0.38 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 3.742 \n",
+ " 0.34 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3.742 \n",
+ " 0.36 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.750 \n",
+ " 0.08 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.728 \n",
+ " 0.27 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Temperature Luminosity\n",
+ "0 3.756 0.38\n",
+ "1 3.742 0.34\n",
+ "2 3.742 0.36\n",
+ "3 3.750 0.08\n",
+ "4 3.728 0.27"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usco.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting HR diagrams\n",
+ "\n",
+ "Now that we have loaded in our data, it's time to create some visualisation. The advantage of using the `HRDiagrams` object is that it comes with an **easy-to-use plotting method** that is highly **versatile** and designed to create **publication-ready figures**. \n",
+ "\n",
+ "Let's run through a couple of ways you can use this tool and make it your own. \n",
+ "\n",
+ "### Making a single plot\n",
+ "First we are going to make just one HR diagram plot. The plotting method returns the plote object, which **allows you to customize you plot**. \n",
+ "\n",
+ "For example you can **add observational data**, some **text**, a **title**, a **legend** and redefine the axis **limits** to create a more effective visualisation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Single stars')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Just maing sure teh figure size is sensible\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "# Plotting the hr_diagram (all hydrogen abundances added up) at the 10 million year time bin\n",
+ "my_plot = sin_hr_diagram.plot(log_age=7.0)\n",
+ "\n",
+ "# Addind the observational data of Upper Scorpio\n",
+ "my_plot.scatter(usco.Temperature, usco.Luminosity, label='Usco', s=50)\n",
+ "\n",
+ "# Customizing the visualisation\n",
+ "my_plot.set_xlim([5.4, 3.4])\n",
+ "my_plot.set_ylim([2.8, 6.2]) # better limits\n",
+ "my_plot.text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 14) # Informative text\n",
+ "my_plot.legend() # a legend\n",
+ "my_plot.set_title('Single stars') # A title"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multiple plots\n",
+ "\n",
+ "There might be times you want to put multiple plots on the same figure. The good news is the `HRDiagrams.plot()` allows you to do that intuitively! There are actually more than one way to do it, depending on how you like making your subplots in Python, either with `add_subplot()` or `subplots()`.\n",
+ "\n",
+ "Here is the same HR diagram as presented above, but with the Single star population on the left and the Binary star population on the right. \n",
+ "\n",
+ "**a) Using `subplots()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "sin_hr_diagram.plot(log_age=7.0, loc=ax[0])\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single stars')\n",
+ "ax[0].text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7.0, loc=ax[1])\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='pink', s=100, label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Binary stars')\n",
+ "ax[1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[1].legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**b) Using `add_subplot()` syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "sin_plot = sin_hr_diagram.plot(log_age=7, loc=121)\n",
+ "sin_plot .scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "sin_plot .set_xlim([5.4, 3.4])\n",
+ "sin_plot .set_ylim([2.8, 6.2])\n",
+ "sin_plot .set_title('Single stars')\n",
+ "sin_plot .text(5.25, 5.5, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", fontsize = 18)\n",
+ "sin_plot .legend()\n",
+ "\n",
+ "\n",
+ "bin_plot = bin_hr_diagram.plot(log_age=7, loc=122)\n",
+ "bin_plot.scatter(usco.Temperature, usco.Luminosity, s=100, c='pink', label='Usco')\n",
+ "bin_plot.set_xlim([5.4, 3.4])\n",
+ "bin_plot.set_ylim([2.8, 6.2])\n",
+ "bin_plot.set_title('Binary stars')\n",
+ "bin_plot.text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "bin_plot.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Customizing your plots with matplotlib key word arguments\n",
+ "\n",
+ "Althought the default parameters for the HR diagram pltos were chosen to allow the creating of publication-ready plots with as few modifications as possible, the plotting function of `HRDiagrams` objects are designed to take any subplementary key word arguments and pass them on to `matplotlib.pyplot.contour`. \n",
+ "\n",
+ "This allows a much greater level of flexibility when making graphics. \n",
+ "For example if you wish to cahnge the colormap you can do the following. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0], cmap='jet_r') # levels adjusted for visualisation\n",
+ "ax[0].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('A terrible colormap')\n",
+ "ax[0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "ax[0].legend()\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[1], cmap='magma_r') # levels adjusted for visualisation\n",
+ "ax[1].scatter(usco.Temperature, usco.Luminosity, c='w', label='Usco')\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('A less terrible colormap')\n",
+ "ax[1].text(5.25, 5.0, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='w', fontsize = 18)\n",
+ "ax[1].legend()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating a publication-ready figure \n",
+ "\n",
+ "In this last section, we will reproduce with `hoki` Figure 23 of Eldirdge, Stanway et al. (2018). This will show you how you can create a publication ready figure just using `hoki` and other ubiquitous python packages.\n",
+ "\n",
+ "*Note: This figure will look a little different frmo the published version as the latter was created in IDL*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# renaming our already laoded HR diagram just for readability \n",
+ "hr_usco_sin, hr_usco_bin = sin_hr_diagram, bin_hr_diagram \n",
+ "\n",
+ "hr_census_bin = load.model_output('./data/hrs-bin-imf135_300.z020.dat', hr_type='TL')\n",
+ "hr_census_sin = load.model_output('./data/hrs-sin-imf135_300.z020.dat', hr_type='TL')\n",
+ "\n",
+ "hr_6067_bin = load.model_output('./data/hrs-bin-imf135_300.z030.dat', hr_type='TTG')\n",
+ "hr_6067_sin = load.model_output('./data/hrs-sin-imf135_300.z030.dat', hr_type='TTG')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that it is not recommended to call a column 'T' for a pandas data frame. \n",
+ "# If you do, you won't be able to call it as mydataframe.T, because .T will default to \n",
+ "# returning the transpose. \n",
+ "\n",
+ "cyg_L, cyg_L_e, cyg_T, cyg_T_e = np.genfromtxt('./data/census.dat', unpack=True, usecols=(14,15,17,18))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ngc_T, ngc_T_e, ngc_logg, ngc_logg_e = np.loadtxt('./data/ngc6067hr.dat', unpack=True, usecols=(3,4,5,6), skiprows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the case of the NGC 6067 we want to make a spectroscopic HR diagram. That means that the y axis will be $\\log(T^4/g)$ -- this quatity is refered to as TG or log(TG). The trick is, we need to make sure we are using the right units when we do the calculations. \n",
+ "\n",
+ "We want everything to be with respect to solar units, so we'll have to divide by solar surface temperature (5778K) and surface gravity (274 m/s or a log g = 2.4377). We also nee to convert to SI from cgs, so there is a factor of $10^{-2}$ in the g, i.e a `-2` in the log g exponent, that needs to be taken into account. \n",
+ "\n",
+ "We also need to propagate errors. Since it's a mess of logs I'll just use bootstrapping for that. \n",
+ "\n",
+ "*Boostrapping: In this particular case I sample each of my values/data points according to a normal distribution where the mean is the value of that data point and the standard deviation is the error. I do this 1000 times, calculate the corresponding TG, and take the mean and standard deviation of these 1000 TG values.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TG = []\n",
+ "TG_e = []\n",
+ "for T_i, T_e_i, logg_i, logg_e_i in zip(ngc_T, ngc_T_e, ngc_logg, ngc_logg_e):\n",
+ " TG.append(np.mean(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))\n",
+ " TG_e.append(np.std(np.log10((np.random.normal(T_i, T_e_i, 1000)/5778)**4/\n",
+ " ((10**(np.random.normal(logg_i-2.4377-2, np.abs(logg_e_i), 1000) ))))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also need to make sure we get the log(T) errors (and no, you can't just do `log(ngc_T_e)`). I'm using bootstrapping - because I can. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "T_e = []\n",
+ "for T_i, T_e_i in zip(ngc_T, ngc_T_e):\n",
+ " T_e.append(np.std(np.log(np.random.normal(T_i, T_e_i, 1000))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now it's time to plot all of this!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(3,2, figsize=(15,20))\n",
+ "\n",
+ "hr_census_sin.plot(log_age=6.8, loc=ax[0,0])\n",
+ "ax[0,0].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,0].set_xlim([5.5, 3.4])\n",
+ "ax[0,0].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_census_bin.plot(log_age=6.8, loc=ax[0,1])\n",
+ "ax[0,1].errorbar(cyg_T, cyg_L, xerr = cyg_T_e, yerr = cyg_L_e,\n",
+ " ls =' ', marker = 'o', alpha=0.5, label = \"Cygnus OB2\")\n",
+ "ax[0,1].set_xlim([5.5, 3.4])\n",
+ "ax[0,1].set_ylim([2,6.4])\n",
+ "\n",
+ "\n",
+ "hr_usco_sin.plot(log_age=7, loc=ax[1,0]) # levels adjusted for visualisation\n",
+ "ax[1,0].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,0].set_xlim([5.4, 3.4])\n",
+ "ax[1,0].set_ylim([2.8, 6.2])\n",
+ "ax[1,0].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "\n",
+ "hr_usco_bin.plot(log_age=7, loc=ax[1,1]) # levels adjusted for visualisation\n",
+ "ax[1,1].scatter(usco.Temperature, usco.Luminosity, label='Usco')\n",
+ "ax[1,1].set_xlim([5.4, 3.4])\n",
+ "ax[1,1].set_ylim([2.8, 6.2])\n",
+ "ax[1,1].text(5.25, 5.4, \"Z = 0.02 \\nlog(Age/yrs)=7.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "\n",
+ "hr_6067_sin.plot(log_age=8, loc=ax[2,0])\n",
+ "ax[2,0].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,0].set_xlim([4.8, 3.4])\n",
+ "ax[2,0].set_ylim([1, 5.2])\n",
+ "ax[2,0].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "hr_6067_bin.plot(log_age=8, loc=ax[2,1])\n",
+ "ax[2,1].errorbar(np.log10(ngc_T), TG, xerr=T_e, yerr=TG_e, label='NGC 6067', ls=' ', \n",
+ " marker='o')\n",
+ "ax[2,1].set_xlim([4.8, 3.4])\n",
+ "ax[2,1].set_ylim([1,5.2])\n",
+ "ax[2,1].text(4.6, 4.4, \"Z = 0.02 \\nlog(Age/yrs)=8.0\", color='k', fontsize = 18)\n",
+ "\n",
+ "for axis in ax.reshape(6):\n",
+ " axis.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stacking a range of Ages\n",
+ "\n",
+ "But what if you are interested in a stellar population that has a range of ages? \n",
+ "\n",
+ "Using the `HRDiagram` class you can stack HR Diagrams of a range of ages across all three surface abundance categories in a single line!\n",
+ "\n",
+ "Let's stack every age bin between 1 and 10 Myrs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ }
+ ],
+ "source": [
+ "bin_hr_diagram.stack(log_age_min=6.0, log_age_max=6.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This should return the following message: \n",
+ "```\n",
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n",
+ "```\n",
+ "\n",
+ "It indicates that the stacking was performed successfully, and four attributes of the `HRDiagram` class were update. \n",
+ "\n",
+ "`HRDiagram.all_stacked`, `HRDiagram.high_H_stacked`, `HRDiagram.medium_H_stacked`, `HRDiagram.low_H_stacked`, are the stacked equivalents to `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H`(see right panel of the Figure at the top of this tutorial).\n",
+ "\n",
+ "This means that the original `HRDiagram.all`, `HRDiagram.high_H`, `HRDiagram.medium_H`, `HRDiagram.low_H` attributes **remain untouched** when you run the `.stack()` method - only the \"stacked\" attributes will be updated.\n",
+ "\n",
+ "Note, before you run the `.stack()` method for the first time the \"stacked\" HRDs are just 100x100 numpy arrays filled with zeros. \n",
+ "\n",
+ "## Plotting HRDs of stacked ages\n",
+ "\n",
+ "Now, it's possible you don't actually need to do any opperations on the stacked HRDs, **you just want to plot** HRDs for a range of ages. \n",
+ "\n",
+ "For this you don't even need to use the `.stack()` method, **you can do it directly** in the `.plot()` method: you need to use the parameter `age_range` instead of '`log_age` in the plotting method. \n",
+ "\n",
+ "Here is an example of an HRD at 10 Myrs Vs an aggregate of HRDs from 1 to 10 Myrs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following attributes were updated: .all_stacked, .high_H_stacked, .medium_H_stacked, .low_H_stacked.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+ "\n",
+ "bin_hr_diagram.plot(log_age=7, loc=ax[0]) #levels adjusted for visualisation\n",
+ "ax[0].set_xlim([5.4, 3.4])\n",
+ "ax[0].set_ylim([2.8, 6.2])\n",
+ "ax[0].set_title('Single Age: log(Age/yrs)=7.0')\n",
+ "\n",
+ "\n",
+ "bin_hr_diagram.plot(age_range=[6.0, 7.0], loc=ax[1]) # levels adjusted for visualisation\n",
+ "ax[1].set_xlim([5.4, 3.4])\n",
+ "ax[1].set_ylim([2.8, 6.2])\n",
+ "ax[1].set_title('Stacked Age: log(Age/yrs)= 6.0 - 7.0')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "**YOU'RE ALL SET!**\n",
+ "\n",
+ "I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on `hoki` GitHub page [here](https://github.com/HeloiseS/hoki) or on the `hoki_tutorials` GitHub [there](https://github.com/HeloiseS/hoki_tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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def extract_sdss_spectrum(dataloc):
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- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
- Built with Sphinx using a theme provided by Read the Docs.
+
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+ Built with Sphinx using a
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+ provided by Read the Docs.
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- Built with Sphinx using a theme provided by Read the Docs.
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+ Built with Sphinx using a
+
+ theme
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+ provided by Read the Docs.
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- © Copyright 2019, H. F. Stevance
+
+ © Copyright 2020, H. F. Stevance
Plotting the transient rates
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
diff --git a/docs/Transient_rates.ipynb b/docs/Transient_rates.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Transient rates \n",
+ "---\n",
+ "\n",
+ "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki_tutorials"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initial imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hoki import load\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Feel free to use your own style sheet\n",
+ "plt.style.use('tuto.mplstyle')\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### In this tutorial you will:\n",
+ "\n",
+ "- Load BPASS transient rates from hoki\n",
+ "- Combine single and binary population transient rates\n",
+ "- Calculate total Core Collapse Supernovae rates\n",
+ "- Make sure the rates are in the right units\n",
+ "- Plot the rates of Type Ia SNe, CCSNe, LGRBs, and PISNe together. \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Loading in the data and initial set-up\n",
+ "\n",
+ "Transient rates in BPASS are one of the several types of stellar population outputs. We can simply use the `hoki.load.population_ouput()` function to laod the data. The function will automatically know what type of data it is loading from the file name and create an appropriate data frame to put it in. \n",
+ "\n",
+ "If you are not familiar with `pandas` and `pandas.DataFrame`, you should pick it up easily. If you want to look into them more check out this [Data Camp article](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python).\n",
+ "\n",
+ "In this tutorial we want to plot the rates corresponding to the stellar populations that include binary systems for a standard (Salpeter) IMF with slope -1.35 from 0.1 to 0.5 M_sun and a maximum mass of 300 solar masses, all of this at a tenth solar metalicity (so Z=0.002):\n",
+ "> `supernova-bin-imf135_300.z002.dat`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Loading the binary and single star population transient rates.\n",
+ "# Note we chose this particular IMF and metallicity in order to reproduce the plot\n",
+ "# shown on the left hand sife of Figure 1 in Eldridge et al. 2018 \n",
+ "\n",
+ "bin_rates = load.model_output('./data/supernova-bin-imf135_300.z002.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at one of our data frames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " log_age \n",
+ " Ia \n",
+ " IIP \n",
+ " II \n",
+ " Ib \n",
+ " Ic \n",
+ " LGRB \n",
+ " PISNe \n",
+ " low_mass \n",
+ " e_Ia \n",
+ " e_IIP \n",
+ " e_II \n",
+ " e_Ib \n",
+ " e_Ic \n",
+ " e_LGRB \n",
+ " e_PISNe \n",
+ " e_low_mass \n",
+ " age_yrs \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 6.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
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+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.00000 \n",
+ " 0.0 \n",
+ " 1122019.00 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 6.1 \n",
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+ " 365743.12 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 6.3 \n",
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+ " 460443.62 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 6.4 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 3.847896 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 5.109734 \n",
+ " 0.0 \n",
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+ " 0.0 \n",
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+ " 0.496761 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.80792 \n",
+ " 0.0 \n",
+ " 579664.00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " log_age Ia IIP II Ib Ic LGRB PISNe low_mass e_Ia \\\n",
+ "0 6.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "1 6.1 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "2 6.2 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "3 6.3 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n",
+ "4 6.4 0.0 0.0 0.0 3.847896 0.0 0.0 5.109734 0.0 0.0 \n",
+ "\n",
+ " e_IIP e_II e_Ib e_Ic e_LGRB e_PISNe e_low_mass age_yrs \n",
+ "0 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 1122019.00 \n",
+ "1 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 290520.12 \n",
+ "2 0.0 0.0 0.000000 0.0 0.0 0.00000 0.0 365743.12 \n",
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+ "4 0.0 0.0 0.496761 0.0 0.0 0.80792 0.0 579664.00 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bin_rates.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Most column names are pretty self-explanatory, appart from `low_mass`, which just detones the rate of low-mass supernovae (< 2M$_{\\odot}$). The `age_yrs` bin is th size of each time bin in years; indeed since BPASS works in log(time) space, each time bin has a different width in years. \n",
+ "\n",
+ "If you need more detail on each of these rates, you should have a look at the [BPASS user manual](https://bpass.auckland.ac.nz/8/files/bpassv2_1_manual_accessible_version.pdf)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The last time bin in BPASS does some weird stuff so it's better to just ignore it. \n",
+ "bin_rates = bin_rates[:-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We are going to use the log_age and the size of the bin in years a lot, so I'm just renaming them for ease.\n",
+ "age = bin_rates.log_age.values\n",
+ "bin_size = bin_rates.age_yrs.values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Core Collapse Supernovae rates\n",
+ "\n",
+ "Core collapse supernovae comprise the type IIP, II, Ib and Ic. To get the total rate we need to sum these columns as well as put the single star and binary populations together.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne = ( bin_rates[['IIP', 'II', 'Ib', 'Ic']].sum(axis=1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting the Units right\n",
+ "\n",
+ "We want to plot our rates as **events/M$_{\\odot}$/year**, this means we need to normalise by the total mass and the number of years in each time bin. \n",
+ "\n",
+ "BPASS calulates stellar populations with 10$^6$ M$_{\\odot}$ and we've already put the bin size in years in a convenient variable called `bin_size`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccsne_norm = ccsne/bin_size/(10**6)\n",
+ "typeIa_norm = bin_rates.Ia.values /bin_size/(10**6)\n",
+ "lgrbs_norm = bin_rates.LGRB.values /bin_size/(10**6)\n",
+ "pisne_norm = bin_rates.PISNe.values/bin_size/(10**6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting the transient rates\n",
+ "\n",
+ "Now we have everything we need to plot our transient rates! The only trick here is to remember to **log rate axis** to be able to see everything. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (10,7))\n",
+ "\n",
+ "plt.step(age, typeIa_norm, label='SN Ia')\n",
+ "plt.step(age, ccsne_norm, label='CCSNe')\n",
+ "plt.step(age, lgrbs_norm, label='LGRB')\n",
+ "plt.step(age, pisne_norm, label='PISNe')\n",
+ "\n",
+ "plt.yscale(\"log\")\n",
+ "\n",
+ "plt.text(9, 10**(-11), r\"Z=0.1Z$_{\\odot}$\", fontsize=18)\n",
+ "\n",
+ "plt.ylabel(r\"Event Rate (events/M$_{\\odot}$/year)\")\n",
+ "plt.xlabel(\"log(age/yrs)\")\n",
+ "plt.legend(fontsize=16)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/docs/_sources/cmd.rst.txt b/docs/_sources/cmd.rst.txt
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--- a/docs/_sources/cmd.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**cmd**
-################
-
-
-.. automodule:: hoki.cmd
- :members:
-
diff --git a/docs/_sources/constants.rst.txt b/docs/_sources/constants.rst.txt
deleted file mode 100644
index 5f9c5ff1..00000000
--- a/docs/_sources/constants.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-################
-**constants**
-################
-
-This module just contains BPASS constants and defines variables used in hoki.
-
-.. automodule:: hoki.constants
- :members:
-
-path_to_settings
- path to the `settings.yaml` file located in the package resources
-
-MODELS_PATH
- Path to the BPASS models as defined in the settings file. If you have not downloaded the models and updated this path with `load.set_models_path()`, it will not correspond to a valid path on your machine. This is not going to cause issues unless you're using functionalities that require the full set of BPASS models (like making CMDs)
-
-BPASS_TIME_BINS
- 1D array containing the 51 BPASS time bins (from log(age) = 6.0 to 11.0)
-
-BPASS_TIME_INTERVALS
- Time interval between BPASS time bins in years
-
-BPASS_TIME_WEIGHT_GRID
- Grid of weights to apply to HR Diagrams based on BPASS definitions
-
-dummy_dict
- Dictionary linking column number to column name for the large BPASS array traditionally called dummy. See the BPASS manual for a text version of the numner/name correspondance.
diff --git a/docs/_sources/hrdiagrams.rst.txt b/docs/_sources/hrdiagrams.rst.txt
deleted file mode 100644
index 58d1d07c..00000000
--- a/docs/_sources/hrdiagrams.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**hrdiagrams**
-################
-
-
-.. automodule:: hoki.hrdiagrams
- :members:
-
diff --git a/docs/_sources/load.rst.txt b/docs/_sources/load.rst.txt
deleted file mode 100644
index 69c38f09..00000000
--- a/docs/_sources/load.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-##########
-**load**
-##########
-
-
-.. automodule:: hoki.load
- :members:
-
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index ea6972d5..56f5efc6 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -316,22 +316,29 @@ img.align-default, .figure.align-default {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px 7px 0 7px;
+ padding: 7px;
background-color: #ffe;
width: 40%;
float: right;
+ clear: right;
+ overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
+div.admonition, div.topic, pre, div[class|="highlight"] {
+ clear: both;
+}
+
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px 7px 0 7px;
+ padding: 7px;
margin: 10px 0 10px 0;
+ overflow-x: auto;
}
p.topic-title {
@@ -346,16 +353,13 @@ div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
+ overflow-x: auto;
}
div.admonition dt {
font-weight: bold;
}
-div.admonition dl {
- margin-bottom: 0;
-}
-
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -366,9 +370,19 @@ div.body p.centered {
margin-top: 25px;
}
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
/* -- tables ---------------------------------------------------------------- */
table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -416,13 +430,13 @@ table.citation td {
border-bottom: none;
}
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
margin-top: 0px;
}
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
margin-bottom: 0px;
}
@@ -468,6 +482,10 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
+table.hlist {
+ margin: 1em 0;
+}
+
table.hlist td {
vertical-align: top;
}
@@ -495,17 +513,34 @@ ol.upperroman {
list-style: upper-roman;
}
-li > p:first-child {
+ol > li:first-child > :first-child,
+ul > li:first-child > :first-child {
margin-top: 0px;
}
-li > p:last-child {
+ol ol > li:first-child > :first-child,
+ol ul > li:first-child > :first-child,
+ul ol > li:first-child > :first-child,
+ul ul > li:first-child > :first-child {
+ margin-top: revert;
+}
+
+ol > li:last-child > :last-child,
+ul > li:last-child > :last-child {
margin-bottom: 0px;
}
+ol ol > li:last-child > :last-child,
+ol ul > li:last-child > :last-child,
+ul ol > li:last-child > :last-child,
+ul ul > li:last-child > :last-child {
+ margin-bottom: revert;
+}
+
dl.footnote > dt,
dl.citation > dt {
float: left;
+ margin-right: 0.5em;
}
dl.footnote > dd,
@@ -546,7 +581,7 @@ dl {
margin-bottom: 15px;
}
-dd > p:first-child {
+dd > :first-child {
margin-top: 0px;
}
@@ -560,6 +595,11 @@ dd {
margin-left: 30px;
}
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -644,22 +684,57 @@ span.pre {
hyphens: none;
}
+div[class^="highlight-"] {
+ margin: 1em 0;
+}
+
td.linenos pre {
- padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- margin-left: 0.5em;
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
}
table.highlighttable td {
- padding: 0 0.5em 0 0.5em;
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
}
div.code-block-caption {
+ margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -668,8 +743,9 @@ div.code-block-caption code {
background-color: transparent;
}
-div.code-block-caption + div > div.highlight > pre {
- margin-top: 0;
+table.highlighttable td.linenos,
+div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
}
div.code-block-caption span.caption-number {
@@ -681,11 +757,7 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
- margin: 0;
+ margin: 1em 0;
}
code.descname {
@@ -736,8 +808,7 @@ span.eqno {
}
span.eqno a.headerlink {
- position: relative;
- left: 0px;
+ position: absolute;
z-index: 1;
}
diff --git a/docs/_static/css/badge_only.css b/docs/_static/css/badge_only.css
index 3c33cef5..e380325b 100644
--- a/docs/_static/css/badge_only.css
+++ b/docs/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
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new file mode 100644
index 00000000..855c845e
--- /dev/null
+++ b/docs/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+
+
+
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diff --git a/docs/_static/css/fonts/lato-normal.woff b/docs/_static/css/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
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diff --git a/docs/_static/css/fonts/lato-normal.woff2 b/docs/_static/css/fonts/lato-normal.woff2
new file mode 100644
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diff --git a/docs/_static/css/theme.css b/docs/_static/css/theme.css
index aed8cef0..8cd4f101 100644
--- a/docs/_static/css/theme.css
+++ b/docs/_static/css/theme.css
@@ -1,6 +1,4 @@
-/* sphinx_rtd_theme version 0.4.3 | MIT license */
-/* Built 20190212 16:02 */
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tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) 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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index b33f87fc..daccd209 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -283,10 +283,11 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keyup(function(event) {
+ $(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+ && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index ec430720..ada26f7c 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,7 +3,9 @@ var DOCUMENTATION_OPTIONS = {
VERSION: 'beta',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
+ BUILDER: 'html',
FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false
diff --git a/docs/_static/fonts/FontAwesome.otf b/docs/_static/fonts/FontAwesome.otf
new file mode 100644
index 00000000..401ec0f3
Binary files /dev/null and b/docs/_static/fonts/FontAwesome.otf differ
diff --git a/docs/_static/fonts/Inconsolata-Bold.ttf b/docs/_static/fonts/Inconsolata-Bold.ttf
deleted file mode 100644
index 809c1f58..00000000
Binary files a/docs/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata-Regular.ttf b/docs/_static/fonts/Inconsolata-Regular.ttf
deleted file mode 100644
index fc981ce7..00000000
Binary files a/docs/_static/fonts/Inconsolata-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata.ttf b/docs/_static/fonts/Inconsolata.ttf
deleted file mode 100644
index 4b8a36d2..00000000
Binary files a/docs/_static/fonts/Inconsolata.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Bold.ttf b/docs/_static/fonts/Lato-Bold.ttf
deleted file mode 100644
index 1d23c706..00000000
Binary files a/docs/_static/fonts/Lato-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Regular.ttf b/docs/_static/fonts/Lato-Regular.ttf
deleted file mode 100644
index 0f3d0f83..00000000
Binary files a/docs/_static/fonts/Lato-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff b/docs/_static/fonts/Roboto-Slab-Bold.woff
new file mode 100644
index 00000000..6cb60000
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff2 b/docs/_static/fonts/Roboto-Slab-Bold.woff2
new file mode 100644
index 00000000..7059e231
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff b/docs/_static/fonts/Roboto-Slab-Light.woff
new file mode 100644
index 00000000..337d2871
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff2 b/docs/_static/fonts/Roboto-Slab-Light.woff2
new file mode 100644
index 00000000..20398aff
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff b/docs/_static/fonts/Roboto-Slab-Regular.woff
new file mode 100644
index 00000000..f815f63f
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff2 b/docs/_static/fonts/Roboto-Slab-Regular.woff2
new file mode 100644
index 00000000..f2c76e5b
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff b/docs/_static/fonts/Roboto-Slab-Thin.woff
new file mode 100644
index 00000000..6b30ea63
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff2 b/docs/_static/fonts/Roboto-Slab-Thin.woff2
new file mode 100644
index 00000000..328f5bb0
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff2 differ
diff --git a/docs/_static/fonts/RobotoSlab-Bold.ttf b/docs/_static/fonts/RobotoSlab-Bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/RobotoSlab-Regular.ttf b/docs/_static/fonts/RobotoSlab-Regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff b/docs/_static/fonts/lato-bold-italic.woff
new file mode 100644
index 00000000..88ad05b9
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff2 b/docs/_static/fonts/lato-bold-italic.woff2
new file mode 100644
index 00000000..c4e3d804
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-bold.woff b/docs/_static/fonts/lato-bold.woff
new file mode 100644
index 00000000..c6dff51f
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff differ
diff --git a/docs/_static/fonts/lato-bold.woff2 b/docs/_static/fonts/lato-bold.woff2
new file mode 100644
index 00000000..bb195043
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff2 differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff b/docs/_static/fonts/lato-normal-italic.woff
new file mode 100644
index 00000000..76114bc0
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff2 b/docs/_static/fonts/lato-normal-italic.woff2
new file mode 100644
index 00000000..3404f37e
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-normal.woff b/docs/_static/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff differ
diff --git a/docs/_static/fonts/lato-normal.woff2 b/docs/_static/fonts/lato-normal.woff2
new file mode 100644
index 00000000..3bf98433
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff2 differ
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.5.1.js
similarity index 91%
rename from docs/_static/jquery-3.4.1.js
rename to docs/_static/jquery-3.5.1.js
index 773ad95c..50937333 100644
--- a/docs/_static/jquery-3.4.1.js
+++ b/docs/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
/*!
- * jQuery JavaScript Library v3.4.1
+ * jQuery JavaScript Library v3.5.1
* https://jquery.com/
*
* Includes Sizzle.js
@@ -9,7 +9,7 @@
* Released under the MIT license
* https://jquery.org/license
*
- * Date: 2019-05-01T21:04Z
+ * Date: 2020-05-04T22:49Z
*/
( function( global, factory ) {
@@ -47,13 +47,16 @@
var arr = [];
-var document = window.document;
-
var getProto = Object.getPrototypeOf;
var slice = arr.slice;
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
var push = arr.push;
@@ -86,6 +89,8 @@ var isWindow = function isWindow( obj ) {
};
+var document = window.document;
+
var preservedScriptAttributes = {
@@ -142,7 +147,7 @@ function toType( obj ) {
var
- version = "3.4.1",
+ version = "3.5.1",
// Define a local copy of jQuery
jQuery = function( selector, context ) {
@@ -150,11 +155,7 @@ var
// 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 );
- },
-
- // Support: Android <=4.0 only
- // Make sure we trim BOM and NBSP
- rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+ };
jQuery.fn = jQuery.prototype = {
@@ -220,6 +221,18 @@ jQuery.fn = jQuery.prototype = {
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 );
@@ -353,9 +366,10 @@ jQuery.extend( {
return true;
},
- // Evaluates a script in a global context
- globalEval: function( code, options ) {
- DOMEval( code, { nonce: options && options.nonce } );
+ // 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 ) {
@@ -379,13 +393,6 @@ jQuery.extend( {
return obj;
},
- // Support: Android <=4.0 only
- trim: function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
- },
-
// results is for internal usage only
makeArray: function( arr, results ) {
var ret = results || [];
@@ -472,7 +479,7 @@ jQuery.extend( {
}
// Flatten any nested arrays
- return concat.apply( [], ret );
+ return flat( ret );
},
// A global GUID counter for objects
@@ -489,7 +496,7 @@ if ( typeof Symbol === "function" ) {
// Populate the class2type map
jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( i, name ) {
+function( _i, name ) {
class2type[ "[object " + name + "]" ] = name.toLowerCase();
} );
@@ -511,17 +518,16 @@ function isArrayLike( obj ) {
}
var Sizzle =
/*!
- * Sizzle CSS Selector Engine v2.3.4
+ * 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: 2019-04-08
+ * Date: 2020-03-14
*/
-(function( window ) {
-
+( function( window ) {
var i,
support,
Expr,
@@ -561,59 +567,70 @@ var i,
},
// Instance methods
- hasOwn = ({}).hasOwnProperty,
+ hasOwn = ( {} ).hasOwnProperty,
arr = [],
pop = arr.pop,
- push_native = arr.push,
+ 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 ) {
+ 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",
+ 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]",
- // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
- identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+ // 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)
"*([*^$|!~]?=)" + whitespace +
- // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
- "*\\]",
+
+ // "Attribute values must be CSS identifiers [capture 5]
+ // or strings [capture 3 or capture 4]"
+ "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+ whitespace + "*\\]",
pseudos = ":(" + identifier + ")(?:\\((" +
+
// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
// 1. quoted (capture 3; capture 4 or capture 5)
"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
// 2. simple (capture 6)
"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
// 3. anything else (capture 2)
".*" +
")\\)|)",
// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+ rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+ whitespace + "+$", "g" ),
rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+ rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+ "*" ),
rdescend = new RegExp( whitespace + "|>" ),
rpseudo = new RegExp( pseudos ),
@@ -625,14 +642,16 @@ var i,
"TAG": new RegExp( "^(" + identifier + "|[*])" ),
"ATTR": new RegExp( "^" + attributes ),
"PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
- "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
- "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+ "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+ whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+ whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
"bool": new RegExp( "^(?:" + booleans + ")$", "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 + "*\\)|)(?=[^-]|$)", "i" )
+ "needsContext": new RegExp( "^" + whitespace +
+ "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+ "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
},
rhtml = /HTML$/i,
@@ -648,18 +667,21 @@ var i,
// CSS escapes
// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
- funescape = function( _, escaped, escapedWhitespace ) {
- var high = "0x" + escaped - 0x10000;
- // NaN means non-codepoint
- // Support: Firefox<24
- // Workaround erroneous numeric interpretation of +"0x"
- return high !== high || escapedWhitespace ?
- escaped :
+ 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 ?
- // BMP codepoint
String.fromCharCode( high + 0x10000 ) :
- // Supplemental Plane codepoint (surrogate pair)
String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
},
@@ -675,7 +697,8 @@ var i,
}
// Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+ return ch.slice( 0, -1 ) + "\\" +
+ ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
}
// Other potentially-special ASCII characters get backslash-escaped
@@ -700,18 +723,20 @@ var i,
// Optimize for push.apply( _, NodeList )
try {
push.apply(
- (arr = slice.call( preferredDoc.childNodes )),
+ ( 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 ) {
- push_native.apply( target, slice.call(els) );
+ pushNative.apply( target, slice.call( els ) );
} :
// Support: IE<9
@@ -719,8 +744,9 @@ try {
function( target, els ) {
var j = target.length,
i = 0;
+
// Can't trust NodeList.length
- while ( (target[j++] = els[i++]) ) {}
+ while ( ( target[ j++ ] = els[ i++ ] ) ) {}
target.length = j - 1;
}
};
@@ -744,24 +770,21 @@ function Sizzle( selector, context, results, seed ) {
// Try to shortcut find operations (as opposed to filters) in HTML documents
if ( !seed ) {
-
- if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
- setDocument( context );
- }
+ 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 )) ) {
+ if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
// ID selector
- if ( (m = match[1]) ) {
+ if ( ( m = match[ 1 ] ) ) {
// Document context
if ( nodeType === 9 ) {
- if ( (elem = context.getElementById( m )) ) {
+ if ( ( elem = context.getElementById( m ) ) ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
@@ -780,7 +803,7 @@ function Sizzle( selector, context, results, seed ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
// getElementById can match elements by name instead of ID
- if ( newContext && (elem = newContext.getElementById( m )) &&
+ if ( newContext && ( elem = newContext.getElementById( m ) ) &&
contains( context, elem ) &&
elem.id === m ) {
@@ -790,12 +813,12 @@ function Sizzle( selector, context, results, seed ) {
}
// Type selector
- } else if ( match[2] ) {
+ } else if ( match[ 2 ] ) {
push.apply( results, context.getElementsByTagName( selector ) );
return results;
// Class selector
- } else if ( (m = match[3]) && support.getElementsByClassName &&
+ } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
context.getElementsByClassName ) {
push.apply( results, context.getElementsByClassName( m ) );
@@ -806,11 +829,11 @@ function Sizzle( selector, context, results, seed ) {
// Take advantage of querySelectorAll
if ( support.qsa &&
!nonnativeSelectorCache[ selector + " " ] &&
- (!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+ ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
// Support: IE 8 only
// Exclude object elements
- (nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+ ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
newSelector = selector;
newContext = context;
@@ -819,27 +842,36 @@ function Sizzle( selector, context, results, seed ) {
// 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 ) ) {
+ if ( nodeType === 1 &&
+ ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
- // 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) );
+ // 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 + " " + toSelector( groups[i] );
+ groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+ toSelector( groups[ i ] );
}
newSelector = groups.join( "," );
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
}
try {
@@ -872,12 +904,14 @@ 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[ key + " " ] = value );
}
return cache;
}
@@ -896,17 +930,19 @@ function markFunction( fn ) {
* @param {Function} fn Passed the created element and returns a boolean result
*/
function assert( fn ) {
- var el = document.createElement("fieldset");
+ var el = document.createElement( "fieldset" );
try {
return !!fn( el );
- } catch (e) {
+ } catch ( e ) {
return false;
} finally {
+
// Remove from its parent by default
if ( el.parentNode ) {
el.parentNode.removeChild( el );
}
+
// release memory in IE
el = null;
}
@@ -918,11 +954,11 @@ function assert( fn ) {
* @param {Function} handler The method that will be applied
*/
function addHandle( attrs, handler ) {
- var arr = attrs.split("|"),
+ var arr = attrs.split( "|" ),
i = arr.length;
while ( i-- ) {
- Expr.attrHandle[ arr[i] ] = handler;
+ Expr.attrHandle[ arr[ i ] ] = handler;
}
}
@@ -944,7 +980,7 @@ function siblingCheck( a, b ) {
// Check if b follows a
if ( cur ) {
- while ( (cur = cur.nextSibling) ) {
+ while ( ( cur = cur.nextSibling ) ) {
if ( cur === b ) {
return -1;
}
@@ -972,7 +1008,7 @@ function createInputPseudo( type ) {
function createButtonPseudo( type ) {
return function( elem ) {
var name = elem.nodeName.toLowerCase();
- return (name === "input" || name === "button") && elem.type === type;
+ return ( name === "input" || name === "button" ) && elem.type === type;
};
}
@@ -1015,7 +1051,7 @@ function createDisabledPseudo( disabled ) {
// Where there is no isDisabled, check manually
/* jshint -W018 */
elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
+ inDisabledFieldset( elem ) === disabled;
}
return elem.disabled === disabled;
@@ -1037,21 +1073,21 @@ function createDisabledPseudo( disabled ) {
* @param {Function} fn
*/
function createPositionalPseudo( fn ) {
- return markFunction(function( argument ) {
+ return markFunction( function( argument ) {
argument = +argument;
- return markFunction(function( seed, matches ) {
+ 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]);
+ if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+ seed[ j ] = !( matches[ j ] = seed[ j ] );
}
}
- });
- });
+ } );
+ } );
}
/**
@@ -1073,7 +1109,7 @@ support = Sizzle.support = {};
*/
isXML = Sizzle.isXML = function( elem ) {
var namespace = elem.namespaceURI,
- docElem = (elem.ownerDocument || elem).documentElement;
+ docElem = ( elem.ownerDocument || elem ).documentElement;
// Support: IE <=8
// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -1091,7 +1127,11 @@ setDocument = Sizzle.setDocument = function( node ) {
doc = node ? node.ownerDocument || node : preferredDoc;
// Return early if doc is invalid or already selected
- if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+ // 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;
}
@@ -1100,10 +1140,14 @@ setDocument = Sizzle.setDocument = function( node ) {
docElem = document.documentElement;
documentIsHTML = !isXML( document );
- // Support: IE 9-11, Edge
+ // Support: IE 9 - 11+, Edge 12 - 18+
// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- if ( preferredDoc !== document &&
- (subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+ // 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 ) {
@@ -1115,25 +1159,36 @@ setDocument = Sizzle.setDocument = function( node ) {
}
}
+ // 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 ) {
+ support.attributes = assert( function( el ) {
el.className = "i";
- return !el.getAttribute("className");
- });
+ 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.getElementsByTagName = assert( function( el ) {
+ el.appendChild( document.createComment( "" ) );
+ return !el.getElementsByTagName( "*" ).length;
+ } );
// Support: IE<9
support.getElementsByClassName = rnative.test( document.getElementsByClassName );
@@ -1142,38 +1197,38 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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 ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
- return elem.getAttribute("id") === attrId;
+ return elem.getAttribute( "id" ) === attrId;
};
};
- Expr.find["ID"] = function( id, context ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode("id");
+ 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 ) {
+ Expr.find[ "ID" ] = function( id, context ) {
if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
var node, i, elems,
elem = context.getElementById( id );
@@ -1181,7 +1236,7 @@ setDocument = Sizzle.setDocument = function( node ) {
if ( elem ) {
// Verify the id attribute
- node = elem.getAttributeNode("id");
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1189,8 +1244,8 @@ setDocument = Sizzle.setDocument = function( node ) {
// Fall back on getElementsByName
elems = context.getElementsByName( id );
i = 0;
- while ( (elem = elems[i++]) ) {
- node = elem.getAttributeNode("id");
+ while ( ( elem = elems[ i++ ] ) ) {
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1203,7 +1258,7 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Tag
- Expr.find["TAG"] = support.getElementsByTagName ?
+ Expr.find[ "TAG" ] = support.getElementsByTagName ?
function( tag, context ) {
if ( typeof context.getElementsByTagName !== "undefined" ) {
return context.getElementsByTagName( tag );
@@ -1218,12 +1273,13 @@ setDocument = Sizzle.setDocument = function( node ) {
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++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem.nodeType === 1 ) {
tmp.push( elem );
}
@@ -1235,7 +1291,7 @@ setDocument = Sizzle.setDocument = function( node ) {
};
// Class
- Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+ Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
return context.getElementsByClassName( className );
}
@@ -1256,10 +1312,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// See https://bugs.jquery.com/ticket/13378
rbuggyQSA = [];
- if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+ if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
// Build QSA regex
// Regex strategy adopted from Diego Perini
- assert(function( el ) {
+ 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,
@@ -1273,78 +1333,98 @@ setDocument = Sizzle.setDocument = function( node ) {
// Nothing should be selected when empty strings follow ^= or $= or *=
// The test attribute must be unknown in Opera but "safe" for WinRT
// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+ if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
}
// Support: IE8
// Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll("[selected]").length ) {
+ 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("~=");
+ 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");
+ 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(".#.+[+~]");
+ rbuggyQSA.push( ".#.+[+~]" );
}
- });
- assert(function( el ) {
+ // 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");
+ 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 ) {
+ 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 ) {
+ 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 ) {
+ 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(",.*:");
- });
+ el.querySelectorAll( "*,:x" );
+ rbuggyQSA.push( ",.*:" );
+ } );
}
- if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+ if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
docElem.webkitMatchesSelector ||
docElem.mozMatchesSelector ||
docElem.oMatchesSelector ||
- docElem.msMatchesSelector) )) ) {
+ docElem.msMatchesSelector ) ) ) ) {
+
+ assert( function( el ) {
- assert(function( el ) {
// Check to see if it's possible to do matchesSelector
// on a disconnected node (IE 9)
support.disconnectedMatch = matches.call( el, "*" );
@@ -1353,11 +1433,11 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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("|") );
+ rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+ rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
/* Contains
---------------------------------------------------------------------- */
@@ -1374,11 +1454,11 @@ setDocument = Sizzle.setDocument = function( node ) {
adown.contains ?
adown.contains( bup ) :
a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ));
+ ) );
} :
function( a, b ) {
if ( b ) {
- while ( (b = b.parentNode) ) {
+ while ( ( b = b.parentNode ) ) {
if ( b === a ) {
return true;
}
@@ -1407,7 +1487,11 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Calculate position if both inputs belong to the same document
- compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+ // 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
@@ -1415,13 +1499,24 @@ setDocument = Sizzle.setDocument = function( node ) {
// Disconnected nodes
if ( compare & 1 ||
- (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+ ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
// Choose the first element that is related to our preferred document
- if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+ // 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;
}
- if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+
+ // 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;
}
@@ -1434,6 +1529,7 @@ setDocument = Sizzle.setDocument = function( node ) {
return compare & 4 ? -1 : 1;
} :
function( a, b ) {
+
// Exit early if the nodes are identical
if ( a === b ) {
hasDuplicate = true;
@@ -1449,8 +1545,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// Parentless nodes are either documents or disconnected
if ( !aup || !bup ) {
- return a === document ? -1 :
- b === document ? 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 eqeqeq */
+ return a == document ? -1 :
+ b == document ? 1 :
+ /* eslint-enable eqeqeq */
aup ? -1 :
bup ? 1 :
sortInput ?
@@ -1464,26 +1566,32 @@ setDocument = Sizzle.setDocument = function( node ) {
// Otherwise we need full lists of their ancestors for comparison
cur = a;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
ap.unshift( cur );
}
cur = b;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
bp.unshift( cur );
}
// Walk down the tree looking for a discrepancy
- while ( ap[i] === bp[i] ) {
+ while ( ap[ i ] === bp[ i ] ) {
i++;
}
return i ?
+
// Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[i], bp[i] ) :
+ siblingCheck( ap[ i ], bp[ i ] ) :
// Otherwise nodes in our document sort first
- ap[i] === preferredDoc ? -1 :
- bp[i] === preferredDoc ? 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 eqeqeq */
+ ap[ i ] == preferredDoc ? -1 :
+ bp[ i ] == preferredDoc ? 1 :
+ /* eslint-enable eqeqeq */
0;
};
@@ -1495,10 +1603,7 @@ Sizzle.matches = function( expr, elements ) {
};
Sizzle.matchesSelector = function( elem, expr ) {
- // Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== document ) {
- setDocument( elem );
- }
+ setDocument( elem );
if ( support.matchesSelector && documentIsHTML &&
!nonnativeSelectorCache[ expr + " " ] &&
@@ -1510,12 +1615,13 @@ Sizzle.matchesSelector = function( 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 ) {
+
+ // 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) {
+ } catch ( e ) {
nonnativeSelectorCache( expr, true );
}
}
@@ -1524,20 +1630,31 @@ Sizzle.matchesSelector = function( elem, expr ) {
};
Sizzle.contains = function( context, elem ) {
+
// Set document vars if needed
- if ( ( context.ownerDocument || context ) !== 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 ( ( context.ownerDocument || context ) != document ) {
setDocument( context );
}
return contains( context, elem );
};
Sizzle.attr = function( elem, name ) {
+
// Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== 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 ( ( 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 ) :
@@ -1547,13 +1664,13 @@ Sizzle.attr = function( elem, name ) {
val :
support.attributes || !documentIsHTML ?
elem.getAttribute( name ) :
- (val = elem.getAttributeNode(name)) && val.specified ?
+ ( val = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
null;
};
Sizzle.escape = function( sel ) {
- return (sel + "").replace( rcssescape, fcssescape );
+ return ( sel + "" ).replace( rcssescape, fcssescape );
};
Sizzle.error = function( msg ) {
@@ -1576,7 +1693,7 @@ Sizzle.uniqueSort = function( results ) {
results.sort( sortOrder );
if ( hasDuplicate ) {
- while ( (elem = results[i++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem === results[ i ] ) {
j = duplicates.push( i );
}
@@ -1604,17 +1721,21 @@ getText = Sizzle.getText = function( elem ) {
nodeType = elem.nodeType;
if ( !nodeType ) {
+
// If no nodeType, this is expected to be an array
- while ( (node = elem[i++]) ) {
+ 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 );
@@ -1623,6 +1744,7 @@ getText = Sizzle.getText = function( elem ) {
} else if ( nodeType === 3 || nodeType === 4 ) {
return elem.nodeValue;
}
+
// Do not include comment or processing instruction nodes
return ret;
@@ -1650,19 +1772,21 @@ Expr = Sizzle.selectors = {
preFilter: {
"ATTR": function( match ) {
- match[1] = match[1].replace( runescape, funescape );
+ 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 );
+ match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+ match[ 5 ] || "" ).replace( runescape, funescape );
- if ( match[2] === "~=" ) {
- match[3] = " " + match[3] + " ";
+ 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)
@@ -1673,22 +1797,25 @@ Expr = Sizzle.selectors = {
7 sign of y-component
8 y of y-component
*/
- match[1] = match[1].toLowerCase();
+ match[ 1 ] = match[ 1 ].toLowerCase();
+
+ if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
- if ( match[1].slice( 0, 3 ) === "nth" ) {
// nth-* requires argument
- if ( !match[3] ) {
- Sizzle.error( match[0] );
+ 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" );
+ 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] );
+ // other types prohibit arguments
+ } else if ( match[ 3 ] ) {
+ Sizzle.error( match[ 0 ] );
}
return match;
@@ -1696,26 +1823,28 @@ Expr = Sizzle.selectors = {
"PSEUDO": function( match ) {
var excess,
- unquoted = !match[6] && match[2];
+ unquoted = !match[ 6 ] && match[ 2 ];
- if ( matchExpr["CHILD"].test( match[0] ) ) {
+ if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
return null;
}
// Accept quoted arguments as-is
- if ( match[3] ) {
- match[2] = match[4] || match[5] || "";
+ 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 )) &&
+ ( excess = tokenize( unquoted, true ) ) &&
+
// advance to the next closing parenthesis
- (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+ ( 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 );
+ 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)
@@ -1728,7 +1857,9 @@ Expr = Sizzle.selectors = {
"TAG": function( nodeNameSelector ) {
var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
return nodeNameSelector === "*" ?
- function() { return true; } :
+ function() {
+ return true;
+ } :
function( elem ) {
return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
};
@@ -1738,10 +1869,16 @@ Expr = Sizzle.selectors = {
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") || "" );
- });
+ ( 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 ) {
@@ -1757,6 +1894,8 @@ Expr = Sizzle.selectors = {
result += "";
+ /* eslint-disable max-len */
+
return operator === "=" ? result === check :
operator === "!=" ? result !== check :
operator === "^=" ? check && result.indexOf( check ) === 0 :
@@ -1765,10 +1904,12 @@ Expr = Sizzle.selectors = {
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 ) {
+ "CHILD": function( type, what, _argument, first, last ) {
var simple = type.slice( 0, 3 ) !== "nth",
forward = type.slice( -4 ) !== "last",
ofType = what === "of-type";
@@ -1780,7 +1921,7 @@ Expr = Sizzle.selectors = {
return !!elem.parentNode;
} :
- function( elem, context, xml ) {
+ function( elem, _context, xml ) {
var cache, uniqueCache, outerCache, node, nodeIndex, start,
dir = simple !== forward ? "nextSibling" : "previousSibling",
parent = elem.parentNode,
@@ -1794,7 +1935,7 @@ Expr = Sizzle.selectors = {
if ( simple ) {
while ( dir ) {
node = elem;
- while ( (node = node[ dir ]) ) {
+ while ( ( node = node[ dir ] ) ) {
if ( ofType ?
node.nodeName.toLowerCase() === name :
node.nodeType === 1 ) {
@@ -1802,6 +1943,7 @@ Expr = Sizzle.selectors = {
return false;
}
}
+
// Reverse direction for :only-* (if we haven't yet done so)
start = dir = type === "only" && !start && "nextSibling";
}
@@ -1817,22 +1959,22 @@ Expr = Sizzle.selectors = {
// ...in a gzip-friendly way
node = parent;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (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 ] ||
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
// Fallback to seeking `elem` from the start
- (diff = nodeIndex = 0) || start.pop()) ) {
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
// When found, cache indexes on `parent` and break
if ( node.nodeType === 1 && ++diff && node === elem ) {
@@ -1842,16 +1984,18 @@ Expr = Sizzle.selectors = {
}
} else {
+
// Use previously-cached element index if available
if ( useCache ) {
+
// ...in a gzip-friendly way
node = elem;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
cache = uniqueCache[ type ] || [];
nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
@@ -1861,9 +2005,10 @@ Expr = Sizzle.selectors = {
// 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()) ) {
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
if ( ( ofType ?
node.nodeName.toLowerCase() === name :
@@ -1872,12 +2017,13 @@ Expr = Sizzle.selectors = {
// Cache the index of each encountered element
if ( useCache ) {
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] ||
+ ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
uniqueCache[ type ] = [ dirruns, diff ];
}
@@ -1898,6 +2044,7 @@ Expr = Sizzle.selectors = {
},
"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
@@ -1917,15 +2064,15 @@ Expr = Sizzle.selectors = {
if ( fn.length > 1 ) {
args = [ pseudo, pseudo, "", argument ];
return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction(function( seed, matches ) {
+ 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] );
+ idx = indexOf( seed, matched[ i ] );
+ seed[ idx ] = !( matches[ idx ] = matched[ i ] );
}
- }) :
+ } ) :
function( elem ) {
return fn( elem, 0, args );
};
@@ -1936,8 +2083,10 @@ Expr = Sizzle.selectors = {
},
pseudos: {
+
// Potentially complex pseudos
- "not": markFunction(function( selector ) {
+ "not": markFunction( function( selector ) {
+
// Trim the selector passed to compile
// to avoid treating leading and trailing
// spaces as combinators
@@ -1946,39 +2095,40 @@ Expr = Sizzle.selectors = {
matcher = compile( selector.replace( rtrim, "$1" ) );
return matcher[ expando ] ?
- markFunction(function( seed, matches, context, xml ) {
+ 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);
+ if ( ( elem = unmatched[ i ] ) ) {
+ seed[ i ] = !( matches[ i ] = elem );
}
}
- }) :
- function( elem, context, xml ) {
- input[0] = elem;
+ } ) :
+ function( elem, _context, xml ) {
+ input[ 0 ] = elem;
matcher( input, null, xml, results );
+
// Don't keep the element (issue #299)
- input[0] = null;
+ input[ 0 ] = null;
return !results.pop();
};
- }),
+ } ),
- "has": markFunction(function( selector ) {
+ "has": markFunction( function( selector ) {
return function( elem ) {
return Sizzle( selector, elem ).length > 0;
};
- }),
+ } ),
- "contains": markFunction(function( text ) {
+ "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
@@ -1988,25 +2138,26 @@ Expr = Sizzle.selectors = {
// 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 || "") ) {
+ if ( !ridentifier.test( lang || "" ) ) {
Sizzle.error( "unsupported lang: " + lang );
}
lang = lang.replace( runescape, funescape ).toLowerCase();
return function( elem ) {
var elemLang;
do {
- if ( (elemLang = documentIsHTML ?
+ if ( ( elemLang = documentIsHTML ?
elem.lang :
- elem.getAttribute("xml:lang") || elem.getAttribute("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 );
+ } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
return false;
};
- }),
+ } ),
// Miscellaneous
"target": function( elem ) {
@@ -2019,7 +2170,9 @@ Expr = Sizzle.selectors = {
},
"focus": function( elem ) {
- return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+ return elem === document.activeElement &&
+ ( !document.hasFocus || document.hasFocus() ) &&
+ !!( elem.type || elem.href || ~elem.tabIndex );
},
// Boolean properties
@@ -2027,16 +2180,20 @@ Expr = Sizzle.selectors = {
"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);
+ 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;
}
@@ -2045,6 +2202,7 @@ Expr = Sizzle.selectors = {
// 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.)
@@ -2058,7 +2216,7 @@ Expr = Sizzle.selectors = {
},
"parent": function( elem ) {
- return !Expr.pseudos["empty"]( elem );
+ return !Expr.pseudos[ "empty" ]( elem );
},
// Element/input types
@@ -2082,39 +2240,40 @@ Expr = Sizzle.selectors = {
// Support: IE<8
// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+ ( ( attr = elem.getAttribute( "type" ) ) == null ||
+ attr.toLowerCase() === "text" );
},
// Position-in-collection
- "first": createPositionalPseudo(function() {
+ "first": createPositionalPseudo( function() {
return [ 0 ];
- }),
+ } ),
- "last": createPositionalPseudo(function( matchIndexes, length ) {
+ "last": createPositionalPseudo( function( _matchIndexes, length ) {
return [ length - 1 ];
- }),
+ } ),
- "eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
return [ argument < 0 ? argument + length : argument ];
- }),
+ } ),
- "even": createPositionalPseudo(function( matchIndexes, length ) {
+ "even": createPositionalPseudo( function( matchIndexes, length ) {
var i = 0;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "odd": createPositionalPseudo(function( matchIndexes, length ) {
+ "odd": createPositionalPseudo( function( matchIndexes, length ) {
var i = 1;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
var i = argument < 0 ?
argument + length :
argument > length ?
@@ -2124,19 +2283,19 @@ Expr = Sizzle.selectors = {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "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"];
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
// Add button/input type pseudos
for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
@@ -2167,37 +2326,39 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
while ( soFar ) {
// Comma and first run
- if ( !matched || (match = rcomma.exec( soFar )) ) {
+ if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
if ( match ) {
+
// Don't consume trailing commas as valid
- soFar = soFar.slice( match[0].length ) || soFar;
+ soFar = soFar.slice( match[ 0 ].length ) || soFar;
}
- groups.push( (tokens = []) );
+ groups.push( ( tokens = [] ) );
}
matched = false;
// Combinators
- if ( (match = rcombinators.exec( soFar )) ) {
+ if ( ( match = rcombinators.exec( soFar ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
+
// Cast descendant combinators to space
- type: match[0].replace( rtrim, " " )
- });
+ 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 ))) ) {
+ if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+ ( match = preFilters[ type ]( match ) ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
type: type,
matches: match
- });
+ } );
soFar = soFar.slice( matched.length );
}
}
@@ -2214,6 +2375,7 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
soFar.length :
soFar ?
Sizzle.error( selector ) :
+
// Cache the tokens
tokenCache( selector, groups ).slice( 0 );
};
@@ -2223,7 +2385,7 @@ function toSelector( tokens ) {
len = tokens.length,
selector = "";
for ( ; i < len; i++ ) {
- selector += tokens[i].value;
+ selector += tokens[ i ].value;
}
return selector;
}
@@ -2236,9 +2398,10 @@ function addCombinator( matcher, combinator, base ) {
doneName = done++;
return combinator.first ?
+
// Check against closest ancestor/preceding element
function( elem, context, xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
return matcher( elem, context, xml );
}
@@ -2253,7 +2416,7 @@ function addCombinator( matcher, combinator, base ) {
// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
if ( xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
if ( matcher( elem, context, xml ) ) {
return true;
@@ -2261,27 +2424,29 @@ function addCombinator( matcher, combinator, base ) {
}
}
} else {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || (elem[ expando ] = {});
+ outerCache = elem[ expando ] || ( elem[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+ uniqueCache = outerCache[ elem.uniqueID ] ||
+ ( outerCache[ elem.uniqueID ] = {} );
if ( skip && skip === elem.nodeName.toLowerCase() ) {
elem = elem[ dir ] || elem;
- } else if ( (oldCache = uniqueCache[ key ]) &&
+ } 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 ]);
+ 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 )) ) {
+ if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
return true;
}
}
@@ -2297,20 +2462,20 @@ function elementMatcher( matchers ) {
function( elem, context, xml ) {
var i = matchers.length;
while ( i-- ) {
- if ( !matchers[i]( elem, context, xml ) ) {
+ if ( !matchers[ i ]( elem, context, xml ) ) {
return false;
}
}
return true;
} :
- matchers[0];
+ matchers[ 0 ];
}
function multipleContexts( selector, contexts, results ) {
var i = 0,
len = contexts.length;
for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[i], results );
+ Sizzle( selector, contexts[ i ], results );
}
return results;
}
@@ -2323,7 +2488,7 @@ function condense( unmatched, map, filter, context, xml ) {
mapped = map != null;
for ( ; i < len; i++ ) {
- if ( (elem = unmatched[i]) ) {
+ if ( ( elem = unmatched[ i ] ) ) {
if ( !filter || filter( elem, context, xml ) ) {
newUnmatched.push( elem );
if ( mapped ) {
@@ -2343,14 +2508,18 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
if ( postFinder && !postFinder[ expando ] ) {
postFinder = setMatcher( postFinder, postSelector );
}
- return markFunction(function( seed, results, context, xml ) {
+ 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, [] ),
+ elems = seed || multipleContexts(
+ selector || "*",
+ context.nodeType ? [ context ] : context,
+ []
+ ),
// Prefilter to get matcher input, preserving a map for seed-results synchronization
matcherIn = preFilter && ( seed || !selector ) ?
@@ -2358,6 +2527,7 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
elems,
matcherOut = matcher ?
+
// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
@@ -2381,8 +2551,8 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
// 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 ( ( elem = temp[ i ] ) ) {
+ matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
}
}
}
@@ -2390,25 +2560,27 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
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]) ) {
+ if ( ( elem = matcherOut[ i ] ) ) {
+
// Restore matcherIn since elem is not yet a final match
- temp.push( (matcherIn[i] = elem) );
+ temp.push( ( matcherIn[ i ] = elem ) );
}
}
- postFinder( null, (matcherOut = []), temp, xml );
+ 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 ) {
+ if ( ( elem = matcherOut[ i ] ) &&
+ ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
- seed[temp] = !(results[temp] = elem);
+ seed[ temp ] = !( results[ temp ] = elem );
}
}
}
@@ -2426,14 +2598,14 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
push.apply( results, matcherOut );
}
}
- });
+ } );
}
function matcherFromTokens( tokens ) {
var checkContext, matcher, j,
len = tokens.length,
- leadingRelative = Expr.relative[ tokens[0].type ],
- implicitRelative = leadingRelative || Expr.relative[" "],
+ 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)
@@ -2445,38 +2617,43 @@ function matcherFromTokens( tokens ) {
}, implicitRelative, true ),
matchers = [ function( elem, context, xml ) {
var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- (checkContext = context).nodeType ?
+ ( 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) ];
+ if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+ matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
} else {
- matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+ 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 ] ) {
+ 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 === " " ? "*" : "" })
+
+ // 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 && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
j < len && toSelector( tokens )
);
}
@@ -2497,28 +2674,40 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
unmatched = seed && [],
setMatched = [],
contextBackup = outermostContext,
+
// We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+ 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),
+ dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
len = elems.length;
if ( outermost ) {
- outermostContext = context === document || context || 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++ ) {
+ for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
if ( byElement && elem ) {
j = 0;
- if ( !context && elem.ownerDocument !== 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 ( !context && elem.ownerDocument != document ) {
setDocument( elem );
xml = !documentIsHTML;
}
- while ( (matcher = elementMatchers[j++]) ) {
- if ( matcher( elem, context || document, xml) ) {
+ while ( ( matcher = elementMatchers[ j++ ] ) ) {
+ if ( matcher( elem, context || document, xml ) ) {
results.push( elem );
break;
}
@@ -2530,8 +2719,9 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// Track unmatched elements for set filters
if ( bySet ) {
+
// They will have gone through all possible matchers
- if ( (elem = !matcher && elem) ) {
+ if ( ( elem = !matcher && elem ) ) {
matchedCount--;
}
@@ -2555,16 +2745,17 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// numerically zero.
if ( bySet && i !== matchedCount ) {
j = 0;
- while ( (matcher = setMatchers[j++]) ) {
+ 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 );
+ if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+ setMatched[ i ] = pop.call( results );
}
}
}
@@ -2605,13 +2796,14 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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] );
+ cached = matcherFromTokens( match[ i ] );
if ( cached[ expando ] ) {
setMatchers.push( cached );
} else {
@@ -2620,7 +2812,10 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
}
// Cache the compiled function
- cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+ cached = compilerCache(
+ selector,
+ matcherFromGroupMatchers( elementMatchers, setMatchers )
+ );
// Save selector and tokenization
cached.selector = selector;
@@ -2640,7 +2835,7 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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) );
+ match = !seed && tokenize( ( selector = compiled.selector || selector ) );
results = results || [];
@@ -2649,11 +2844,12 @@ select = Sizzle.select = function( selector, context, results, seed ) {
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 ] ) {
+ 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];
+ context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+ .replace( runescape, funescape ), context ) || [] )[ 0 ];
if ( !context ) {
return results;
@@ -2666,20 +2862,22 @@ select = Sizzle.select = function( selector, context, results, seed ) {
}
// Fetch a seed set for right-to-left matching
- i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+ i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
while ( i-- ) {
- token = tokens[i];
+ token = tokens[ i ];
// Abort if we hit a combinator
- if ( Expr.relative[ (type = token.type) ] ) {
+ if ( Expr.relative[ ( type = token.type ) ] ) {
break;
}
- if ( (find = Expr.find[ type ]) ) {
+ 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 = 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 );
@@ -2710,7 +2908,7 @@ select = Sizzle.select = function( selector, context, results, seed ) {
// One-time assignments
// Sort stability
-support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
// Support: Chrome 14-35+
// Always assume duplicates if they aren't passed to the comparison function
@@ -2721,58 +2919,59 @@ 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 ) {
+support.sortDetached = assert( function( el ) {
+
// Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
-});
+ 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 ) {
+if ( !assert( function( el ) {
el.innerHTML = "";
- return el.firstChild.getAttribute("href") === "#" ;
-}) ) {
+ 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 ) {
+if ( !support.attributes || !assert( function( el ) {
el.innerHTML = "";
el.firstChild.setAttribute( "value", "" );
return el.firstChild.getAttribute( "value" ) === "";
-}) ) {
- addHandle( "value", function( elem, name, isXML ) {
+} ) ) {
+ 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;
-}) ) {
+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 = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
- null;
+ null;
}
- });
+ } );
}
return Sizzle;
-})( window );
+} )( window );
@@ -3141,7 +3340,7 @@ jQuery.each( {
parents: function( elem ) {
return dir( elem, "parentNode" );
},
- parentsUntil: function( elem, i, until ) {
+ parentsUntil: function( elem, _i, until ) {
return dir( elem, "parentNode", until );
},
next: function( elem ) {
@@ -3156,10 +3355,10 @@ jQuery.each( {
prevAll: function( elem ) {
return dir( elem, "previousSibling" );
},
- nextUntil: function( elem, i, until ) {
+ nextUntil: function( elem, _i, until ) {
return dir( elem, "nextSibling", until );
},
- prevUntil: function( elem, i, until ) {
+ prevUntil: function( elem, _i, until ) {
return dir( elem, "previousSibling", until );
},
siblings: function( elem ) {
@@ -3169,7 +3368,13 @@ jQuery.each( {
return siblings( elem.firstChild );
},
contents: function( elem ) {
- if ( typeof elem.contentDocument !== "undefined" ) {
+ if ( elem.contentDocument != null &&
+
+ // Support: IE 11+
+ //
| \n", + " | log_age | \n", + "Ia | \n", + "IIP | \n", + "II | \n", + "Ib | \n", + "Ic | \n", + "LGRB | \n", + "PISNe | \n", + "low_mass | \n", + "e_Ia | \n", + "e_IIP | \n", + "e_II | \n", + "e_Ib | \n", + "e_Ic | \n", + "e_LGRB | \n", + "e_PISNe | \n", + "e_low_mass | \n", + "age_yrs | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "6.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.00000 | \n", + "0.0 | \n", + "1122019.00 | \n", + "
| 1 | \n", + "6.1 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.00000 | \n", + "0.0 | \n", + "290520.12 | \n", + "
| 2 | \n", + "6.2 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.00000 | \n", + "0.0 | \n", + "365743.12 | \n", + "
| 3 | \n", + "6.3 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.000000 | \n", + "0.0 | \n", + "0.0 | \n", + "0.00000 | \n", + "0.0 | \n", + "460443.62 | \n", + "
| 4 | \n", + "6.4 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "3.847896 | \n", + "0.0 | \n", + "0.0 | \n", + "5.109734 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.496761 | \n", + "0.0 | \n", + "0.0 | \n", + "0.80792 | \n", + "0.0 | \n", + "579664.00 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (10,7))\n",
+ "\n",
+ "plt.step(age, typeIa_norm, label='SN Ia')\n",
+ "plt.step(age, ccsne_norm, label='CCSNe')\n",
+ "plt.step(age, lgrbs_norm, label='LGRB')\n",
+ "plt.step(age, pisne_norm, label='PISNe')\n",
+ "\n",
+ "plt.yscale(\"log\")\n",
+ "\n",
+ "plt.text(9, 10**(-11), r\"Z=0.1Z$_{\\odot}$\", fontsize=18)\n",
+ "\n",
+ "plt.ylabel(r\"Event Rate (events/M$_{\\odot}$/year)\")\n",
+ "plt.xlabel(\"log(age/yrs)\")\n",
+ "plt.legend(fontsize=16)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_images/CMDs_17_1.png b/docs/_images/CMDs_17_1.png
deleted file mode 100644
index 150298ca..00000000
Binary files a/docs/_images/CMDs_17_1.png and /dev/null differ
diff --git a/docs/_images/CMDs_19_1.png b/docs/_images/CMDs_19_1.png
deleted file mode 100644
index 46f90218..00000000
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diff --git a/docs/_images/CMDs_22_1.png b/docs/_images/CMDs_22_1.png
deleted file mode 100644
index 7c35a1b8..00000000
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diff --git a/docs/_images/CMDs_25_1.png b/docs/_images/CMDs_25_1.png
deleted file mode 100644
index 150298ca..00000000
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diff --git a/docs/_images/CMDs_32_1.png b/docs/_images/CMDs_32_1.png
deleted file mode 100644
index b5302724..00000000
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diff --git a/docs/_images/Spectra_30_1.png b/docs/_images/Spectra_30_1.png
deleted file mode 100644
index 1e78a3f9..00000000
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diff --git a/docs/_images/Spectra_35_1.png b/docs/_images/Spectra_35_1.png
deleted file mode 100644
index b7e3c6e8..00000000
Binary files a/docs/_images/Spectra_35_1.png and /dev/null differ
diff --git a/docs/_images/Spectra_37_0.png b/docs/_images/Spectra_37_0.png
deleted file mode 100644
index 5f3949d5..00000000
Binary files a/docs/_images/Spectra_37_0.png and /dev/null differ
diff --git a/docs/_images/Spectra_41_0.png b/docs/_images/Spectra_41_0.png
deleted file mode 100644
index a75a241e..00000000
Binary files a/docs/_images/Spectra_41_0.png and /dev/null differ
diff --git a/docs/_images/Spectra_43_1.png b/docs/_images/Spectra_43_1.png
deleted file mode 100644
index 4c9f54b6..00000000
Binary files a/docs/_images/Spectra_43_1.png and /dev/null differ
diff --git a/docs/_images/cmds.png b/docs/_images/cmds.png
deleted file mode 100644
index 0bdb83d0..00000000
Binary files a/docs/_images/cmds.png and /dev/null differ
diff --git a/docs/_sources/cmd.rst.txt b/docs/_sources/cmd.rst.txt
deleted file mode 100644
index 640f1db7..00000000
--- a/docs/_sources/cmd.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**cmd**
-################
-
-
-.. automodule:: hoki.cmd
- :members:
-
diff --git a/docs/_sources/constants.rst.txt b/docs/_sources/constants.rst.txt
deleted file mode 100644
index 5f9c5ff1..00000000
--- a/docs/_sources/constants.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-################
-**constants**
-################
-
-This module just contains BPASS constants and defines variables used in hoki.
-
-.. automodule:: hoki.constants
- :members:
-
-path_to_settings
- path to the `settings.yaml` file located in the package resources
-
-MODELS_PATH
- Path to the BPASS models as defined in the settings file. If you have not downloaded the models and updated this path with `load.set_models_path()`, it will not correspond to a valid path on your machine. This is not going to cause issues unless you're using functionalities that require the full set of BPASS models (like making CMDs)
-
-BPASS_TIME_BINS
- 1D array containing the 51 BPASS time bins (from log(age) = 6.0 to 11.0)
-
-BPASS_TIME_INTERVALS
- Time interval between BPASS time bins in years
-
-BPASS_TIME_WEIGHT_GRID
- Grid of weights to apply to HR Diagrams based on BPASS definitions
-
-dummy_dict
- Dictionary linking column number to column name for the large BPASS array traditionally called dummy. See the BPASS manual for a text version of the numner/name correspondance.
diff --git a/docs/_sources/hrdiagrams.rst.txt b/docs/_sources/hrdiagrams.rst.txt
deleted file mode 100644
index 58d1d07c..00000000
--- a/docs/_sources/hrdiagrams.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-################
-**hrdiagrams**
-################
-
-
-.. automodule:: hoki.hrdiagrams
- :members:
-
diff --git a/docs/_sources/load.rst.txt b/docs/_sources/load.rst.txt
deleted file mode 100644
index 69c38f09..00000000
--- a/docs/_sources/load.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-##########
-**load**
-##########
-
-
-.. automodule:: hoki.load
- :members:
-
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index ea6972d5..56f5efc6 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -316,22 +316,29 @@ img.align-default, .figure.align-default {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px 7px 0 7px;
+ padding: 7px;
background-color: #ffe;
width: 40%;
float: right;
+ clear: right;
+ overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
+div.admonition, div.topic, pre, div[class|="highlight"] {
+ clear: both;
+}
+
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px 7px 0 7px;
+ padding: 7px;
margin: 10px 0 10px 0;
+ overflow-x: auto;
}
p.topic-title {
@@ -346,16 +353,13 @@ div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
+ overflow-x: auto;
}
div.admonition dt {
font-weight: bold;
}
-div.admonition dl {
- margin-bottom: 0;
-}
-
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -366,9 +370,19 @@ div.body p.centered {
margin-top: 25px;
}
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
/* -- tables ---------------------------------------------------------------- */
table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -416,13 +430,13 @@ table.citation td {
border-bottom: none;
}
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
margin-top: 0px;
}
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
margin-bottom: 0px;
}
@@ -468,6 +482,10 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
+table.hlist {
+ margin: 1em 0;
+}
+
table.hlist td {
vertical-align: top;
}
@@ -495,17 +513,34 @@ ol.upperroman {
list-style: upper-roman;
}
-li > p:first-child {
+ol > li:first-child > :first-child,
+ul > li:first-child > :first-child {
margin-top: 0px;
}
-li > p:last-child {
+ol ol > li:first-child > :first-child,
+ol ul > li:first-child > :first-child,
+ul ol > li:first-child > :first-child,
+ul ul > li:first-child > :first-child {
+ margin-top: revert;
+}
+
+ol > li:last-child > :last-child,
+ul > li:last-child > :last-child {
margin-bottom: 0px;
}
+ol ol > li:last-child > :last-child,
+ol ul > li:last-child > :last-child,
+ul ol > li:last-child > :last-child,
+ul ul > li:last-child > :last-child {
+ margin-bottom: revert;
+}
+
dl.footnote > dt,
dl.citation > dt {
float: left;
+ margin-right: 0.5em;
}
dl.footnote > dd,
@@ -546,7 +581,7 @@ dl {
margin-bottom: 15px;
}
-dd > p:first-child {
+dd > :first-child {
margin-top: 0px;
}
@@ -560,6 +595,11 @@ dd {
margin-left: 30px;
}
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -644,22 +684,57 @@ span.pre {
hyphens: none;
}
+div[class^="highlight-"] {
+ margin: 1em 0;
+}
+
td.linenos pre {
- padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- margin-left: 0.5em;
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
}
table.highlighttable td {
- padding: 0 0.5em 0 0.5em;
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
}
div.code-block-caption {
+ margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -668,8 +743,9 @@ div.code-block-caption code {
background-color: transparent;
}
-div.code-block-caption + div > div.highlight > pre {
- margin-top: 0;
+table.highlighttable td.linenos,
+div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
}
div.code-block-caption span.caption-number {
@@ -681,11 +757,7 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
- margin: 0;
+ margin: 1em 0;
}
code.descname {
@@ -736,8 +808,7 @@ span.eqno {
}
span.eqno a.headerlink {
- position: relative;
- left: 0px;
+ position: absolute;
z-index: 1;
}
diff --git a/docs/_static/css/badge_only.css b/docs/_static/css/badge_only.css
index 3c33cef5..e380325b 100644
--- a/docs/_static/css/badge_only.css
+++ b/docs/_static/css/badge_only.css
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tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) 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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index b33f87fc..daccd209 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -283,10 +283,11 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keyup(function(event) {
+ $(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+ && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index ec430720..ada26f7c 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,7 +3,9 @@ var DOCUMENTATION_OPTIONS = {
VERSION: 'beta',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
+ BUILDER: 'html',
FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false
diff --git a/docs/_static/fonts/FontAwesome.otf b/docs/_static/fonts/FontAwesome.otf
new file mode 100644
index 00000000..401ec0f3
Binary files /dev/null and b/docs/_static/fonts/FontAwesome.otf differ
diff --git a/docs/_static/fonts/Inconsolata-Bold.ttf b/docs/_static/fonts/Inconsolata-Bold.ttf
deleted file mode 100644
index 809c1f58..00000000
Binary files a/docs/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata-Regular.ttf b/docs/_static/fonts/Inconsolata-Regular.ttf
deleted file mode 100644
index fc981ce7..00000000
Binary files a/docs/_static/fonts/Inconsolata-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Inconsolata.ttf b/docs/_static/fonts/Inconsolata.ttf
deleted file mode 100644
index 4b8a36d2..00000000
Binary files a/docs/_static/fonts/Inconsolata.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Bold.ttf b/docs/_static/fonts/Lato-Bold.ttf
deleted file mode 100644
index 1d23c706..00000000
Binary files a/docs/_static/fonts/Lato-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Lato-Regular.ttf b/docs/_static/fonts/Lato-Regular.ttf
deleted file mode 100644
index 0f3d0f83..00000000
Binary files a/docs/_static/fonts/Lato-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff b/docs/_static/fonts/Roboto-Slab-Bold.woff
new file mode 100644
index 00000000..6cb60000
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Bold.woff2 b/docs/_static/fonts/Roboto-Slab-Bold.woff2
new file mode 100644
index 00000000..7059e231
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Bold.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff b/docs/_static/fonts/Roboto-Slab-Light.woff
new file mode 100644
index 00000000..337d2871
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Light.woff2 b/docs/_static/fonts/Roboto-Slab-Light.woff2
new file mode 100644
index 00000000..20398aff
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Light.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff b/docs/_static/fonts/Roboto-Slab-Regular.woff
new file mode 100644
index 00000000..f815f63f
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Regular.woff2 b/docs/_static/fonts/Roboto-Slab-Regular.woff2
new file mode 100644
index 00000000..f2c76e5b
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Regular.woff2 differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff b/docs/_static/fonts/Roboto-Slab-Thin.woff
new file mode 100644
index 00000000..6b30ea63
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff differ
diff --git a/docs/_static/fonts/Roboto-Slab-Thin.woff2 b/docs/_static/fonts/Roboto-Slab-Thin.woff2
new file mode 100644
index 00000000..328f5bb0
Binary files /dev/null and b/docs/_static/fonts/Roboto-Slab-Thin.woff2 differ
diff --git a/docs/_static/fonts/RobotoSlab-Bold.ttf b/docs/_static/fonts/RobotoSlab-Bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Bold.ttf and /dev/null differ
diff --git a/docs/_static/fonts/RobotoSlab-Regular.ttf b/docs/_static/fonts/RobotoSlab-Regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs/_static/fonts/RobotoSlab-Regular.ttf and /dev/null differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff b/docs/_static/fonts/lato-bold-italic.woff
new file mode 100644
index 00000000..88ad05b9
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff differ
diff --git a/docs/_static/fonts/lato-bold-italic.woff2 b/docs/_static/fonts/lato-bold-italic.woff2
new file mode 100644
index 00000000..c4e3d804
Binary files /dev/null and b/docs/_static/fonts/lato-bold-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-bold.woff b/docs/_static/fonts/lato-bold.woff
new file mode 100644
index 00000000..c6dff51f
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff differ
diff --git a/docs/_static/fonts/lato-bold.woff2 b/docs/_static/fonts/lato-bold.woff2
new file mode 100644
index 00000000..bb195043
Binary files /dev/null and b/docs/_static/fonts/lato-bold.woff2 differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff b/docs/_static/fonts/lato-normal-italic.woff
new file mode 100644
index 00000000..76114bc0
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff differ
diff --git a/docs/_static/fonts/lato-normal-italic.woff2 b/docs/_static/fonts/lato-normal-italic.woff2
new file mode 100644
index 00000000..3404f37e
Binary files /dev/null and b/docs/_static/fonts/lato-normal-italic.woff2 differ
diff --git a/docs/_static/fonts/lato-normal.woff b/docs/_static/fonts/lato-normal.woff
new file mode 100644
index 00000000..ae1307ff
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff differ
diff --git a/docs/_static/fonts/lato-normal.woff2 b/docs/_static/fonts/lato-normal.woff2
new file mode 100644
index 00000000..3bf98433
Binary files /dev/null and b/docs/_static/fonts/lato-normal.woff2 differ
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.5.1.js
similarity index 91%
rename from docs/_static/jquery-3.4.1.js
rename to docs/_static/jquery-3.5.1.js
index 773ad95c..50937333 100644
--- a/docs/_static/jquery-3.4.1.js
+++ b/docs/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
/*!
- * jQuery JavaScript Library v3.4.1
+ * jQuery JavaScript Library v3.5.1
* https://jquery.com/
*
* Includes Sizzle.js
@@ -9,7 +9,7 @@
* Released under the MIT license
* https://jquery.org/license
*
- * Date: 2019-05-01T21:04Z
+ * Date: 2020-05-04T22:49Z
*/
( function( global, factory ) {
@@ -47,13 +47,16 @@
var arr = [];
-var document = window.document;
-
var getProto = Object.getPrototypeOf;
var slice = arr.slice;
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
var push = arr.push;
@@ -86,6 +89,8 @@ var isWindow = function isWindow( obj ) {
};
+var document = window.document;
+
var preservedScriptAttributes = {
@@ -142,7 +147,7 @@ function toType( obj ) {
var
- version = "3.4.1",
+ version = "3.5.1",
// Define a local copy of jQuery
jQuery = function( selector, context ) {
@@ -150,11 +155,7 @@ var
// 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 );
- },
-
- // Support: Android <=4.0 only
- // Make sure we trim BOM and NBSP
- rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+ };
jQuery.fn = jQuery.prototype = {
@@ -220,6 +221,18 @@ jQuery.fn = jQuery.prototype = {
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 );
@@ -353,9 +366,10 @@ jQuery.extend( {
return true;
},
- // Evaluates a script in a global context
- globalEval: function( code, options ) {
- DOMEval( code, { nonce: options && options.nonce } );
+ // 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 ) {
@@ -379,13 +393,6 @@ jQuery.extend( {
return obj;
},
- // Support: Android <=4.0 only
- trim: function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
- },
-
// results is for internal usage only
makeArray: function( arr, results ) {
var ret = results || [];
@@ -472,7 +479,7 @@ jQuery.extend( {
}
// Flatten any nested arrays
- return concat.apply( [], ret );
+ return flat( ret );
},
// A global GUID counter for objects
@@ -489,7 +496,7 @@ if ( typeof Symbol === "function" ) {
// Populate the class2type map
jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( i, name ) {
+function( _i, name ) {
class2type[ "[object " + name + "]" ] = name.toLowerCase();
} );
@@ -511,17 +518,16 @@ function isArrayLike( obj ) {
}
var Sizzle =
/*!
- * Sizzle CSS Selector Engine v2.3.4
+ * 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: 2019-04-08
+ * Date: 2020-03-14
*/
-(function( window ) {
-
+( function( window ) {
var i,
support,
Expr,
@@ -561,59 +567,70 @@ var i,
},
// Instance methods
- hasOwn = ({}).hasOwnProperty,
+ hasOwn = ( {} ).hasOwnProperty,
arr = [],
pop = arr.pop,
- push_native = arr.push,
+ 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 ) {
+ 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",
+ 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]",
- // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
- identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+ // 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)
"*([*^$|!~]?=)" + whitespace +
- // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
- "*\\]",
+
+ // "Attribute values must be CSS identifiers [capture 5]
+ // or strings [capture 3 or capture 4]"
+ "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+ whitespace + "*\\]",
pseudos = ":(" + identifier + ")(?:\\((" +
+
// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
// 1. quoted (capture 3; capture 4 or capture 5)
"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
// 2. simple (capture 6)
"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
// 3. anything else (capture 2)
".*" +
")\\)|)",
// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+ rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+ whitespace + "+$", "g" ),
rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+ rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+ "*" ),
rdescend = new RegExp( whitespace + "|>" ),
rpseudo = new RegExp( pseudos ),
@@ -625,14 +642,16 @@ var i,
"TAG": new RegExp( "^(" + identifier + "|[*])" ),
"ATTR": new RegExp( "^" + attributes ),
"PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
- "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
- "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+ "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+ whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+ whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
"bool": new RegExp( "^(?:" + booleans + ")$", "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 + "*\\)|)(?=[^-]|$)", "i" )
+ "needsContext": new RegExp( "^" + whitespace +
+ "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+ "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
},
rhtml = /HTML$/i,
@@ -648,18 +667,21 @@ var i,
// CSS escapes
// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
- funescape = function( _, escaped, escapedWhitespace ) {
- var high = "0x" + escaped - 0x10000;
- // NaN means non-codepoint
- // Support: Firefox<24
- // Workaround erroneous numeric interpretation of +"0x"
- return high !== high || escapedWhitespace ?
- escaped :
+ 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 ?
- // BMP codepoint
String.fromCharCode( high + 0x10000 ) :
- // Supplemental Plane codepoint (surrogate pair)
String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
},
@@ -675,7 +697,8 @@ var i,
}
// Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+ return ch.slice( 0, -1 ) + "\\" +
+ ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
}
// Other potentially-special ASCII characters get backslash-escaped
@@ -700,18 +723,20 @@ var i,
// Optimize for push.apply( _, NodeList )
try {
push.apply(
- (arr = slice.call( preferredDoc.childNodes )),
+ ( 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 ) {
- push_native.apply( target, slice.call(els) );
+ pushNative.apply( target, slice.call( els ) );
} :
// Support: IE<9
@@ -719,8 +744,9 @@ try {
function( target, els ) {
var j = target.length,
i = 0;
+
// Can't trust NodeList.length
- while ( (target[j++] = els[i++]) ) {}
+ while ( ( target[ j++ ] = els[ i++ ] ) ) {}
target.length = j - 1;
}
};
@@ -744,24 +770,21 @@ function Sizzle( selector, context, results, seed ) {
// Try to shortcut find operations (as opposed to filters) in HTML documents
if ( !seed ) {
-
- if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
- setDocument( context );
- }
+ 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 )) ) {
+ if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
// ID selector
- if ( (m = match[1]) ) {
+ if ( ( m = match[ 1 ] ) ) {
// Document context
if ( nodeType === 9 ) {
- if ( (elem = context.getElementById( m )) ) {
+ if ( ( elem = context.getElementById( m ) ) ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
@@ -780,7 +803,7 @@ function Sizzle( selector, context, results, seed ) {
// Support: IE, Opera, Webkit
// TODO: identify versions
// getElementById can match elements by name instead of ID
- if ( newContext && (elem = newContext.getElementById( m )) &&
+ if ( newContext && ( elem = newContext.getElementById( m ) ) &&
contains( context, elem ) &&
elem.id === m ) {
@@ -790,12 +813,12 @@ function Sizzle( selector, context, results, seed ) {
}
// Type selector
- } else if ( match[2] ) {
+ } else if ( match[ 2 ] ) {
push.apply( results, context.getElementsByTagName( selector ) );
return results;
// Class selector
- } else if ( (m = match[3]) && support.getElementsByClassName &&
+ } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
context.getElementsByClassName ) {
push.apply( results, context.getElementsByClassName( m ) );
@@ -806,11 +829,11 @@ function Sizzle( selector, context, results, seed ) {
// Take advantage of querySelectorAll
if ( support.qsa &&
!nonnativeSelectorCache[ selector + " " ] &&
- (!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+ ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
// Support: IE 8 only
// Exclude object elements
- (nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+ ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
newSelector = selector;
newContext = context;
@@ -819,27 +842,36 @@ function Sizzle( selector, context, results, seed ) {
// 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 ) ) {
+ if ( nodeType === 1 &&
+ ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
- // 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) );
+ // 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 + " " + toSelector( groups[i] );
+ groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+ toSelector( groups[ i ] );
}
newSelector = groups.join( "," );
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
}
try {
@@ -872,12 +904,14 @@ 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[ key + " " ] = value );
}
return cache;
}
@@ -896,17 +930,19 @@ function markFunction( fn ) {
* @param {Function} fn Passed the created element and returns a boolean result
*/
function assert( fn ) {
- var el = document.createElement("fieldset");
+ var el = document.createElement( "fieldset" );
try {
return !!fn( el );
- } catch (e) {
+ } catch ( e ) {
return false;
} finally {
+
// Remove from its parent by default
if ( el.parentNode ) {
el.parentNode.removeChild( el );
}
+
// release memory in IE
el = null;
}
@@ -918,11 +954,11 @@ function assert( fn ) {
* @param {Function} handler The method that will be applied
*/
function addHandle( attrs, handler ) {
- var arr = attrs.split("|"),
+ var arr = attrs.split( "|" ),
i = arr.length;
while ( i-- ) {
- Expr.attrHandle[ arr[i] ] = handler;
+ Expr.attrHandle[ arr[ i ] ] = handler;
}
}
@@ -944,7 +980,7 @@ function siblingCheck( a, b ) {
// Check if b follows a
if ( cur ) {
- while ( (cur = cur.nextSibling) ) {
+ while ( ( cur = cur.nextSibling ) ) {
if ( cur === b ) {
return -1;
}
@@ -972,7 +1008,7 @@ function createInputPseudo( type ) {
function createButtonPseudo( type ) {
return function( elem ) {
var name = elem.nodeName.toLowerCase();
- return (name === "input" || name === "button") && elem.type === type;
+ return ( name === "input" || name === "button" ) && elem.type === type;
};
}
@@ -1015,7 +1051,7 @@ function createDisabledPseudo( disabled ) {
// Where there is no isDisabled, check manually
/* jshint -W018 */
elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
+ inDisabledFieldset( elem ) === disabled;
}
return elem.disabled === disabled;
@@ -1037,21 +1073,21 @@ function createDisabledPseudo( disabled ) {
* @param {Function} fn
*/
function createPositionalPseudo( fn ) {
- return markFunction(function( argument ) {
+ return markFunction( function( argument ) {
argument = +argument;
- return markFunction(function( seed, matches ) {
+ 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]);
+ if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+ seed[ j ] = !( matches[ j ] = seed[ j ] );
}
}
- });
- });
+ } );
+ } );
}
/**
@@ -1073,7 +1109,7 @@ support = Sizzle.support = {};
*/
isXML = Sizzle.isXML = function( elem ) {
var namespace = elem.namespaceURI,
- docElem = (elem.ownerDocument || elem).documentElement;
+ docElem = ( elem.ownerDocument || elem ).documentElement;
// Support: IE <=8
// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -1091,7 +1127,11 @@ setDocument = Sizzle.setDocument = function( node ) {
doc = node ? node.ownerDocument || node : preferredDoc;
// Return early if doc is invalid or already selected
- if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+ // 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;
}
@@ -1100,10 +1140,14 @@ setDocument = Sizzle.setDocument = function( node ) {
docElem = document.documentElement;
documentIsHTML = !isXML( document );
- // Support: IE 9-11, Edge
+ // Support: IE 9 - 11+, Edge 12 - 18+
// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- if ( preferredDoc !== document &&
- (subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+ // 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 ) {
@@ -1115,25 +1159,36 @@ setDocument = Sizzle.setDocument = function( node ) {
}
}
+ // 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 ) {
+ support.attributes = assert( function( el ) {
el.className = "i";
- return !el.getAttribute("className");
- });
+ 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.getElementsByTagName = assert( function( el ) {
+ el.appendChild( document.createComment( "" ) );
+ return !el.getElementsByTagName( "*" ).length;
+ } );
// Support: IE<9
support.getElementsByClassName = rnative.test( document.getElementsByClassName );
@@ -1142,38 +1197,38 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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 ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
- return elem.getAttribute("id") === attrId;
+ return elem.getAttribute( "id" ) === attrId;
};
};
- Expr.find["ID"] = function( id, context ) {
+ 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 ) {
+ Expr.filter[ "ID" ] = function( id ) {
var attrId = id.replace( runescape, funescape );
return function( elem ) {
var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode("id");
+ 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 ) {
+ Expr.find[ "ID" ] = function( id, context ) {
if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
var node, i, elems,
elem = context.getElementById( id );
@@ -1181,7 +1236,7 @@ setDocument = Sizzle.setDocument = function( node ) {
if ( elem ) {
// Verify the id attribute
- node = elem.getAttributeNode("id");
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1189,8 +1244,8 @@ setDocument = Sizzle.setDocument = function( node ) {
// Fall back on getElementsByName
elems = context.getElementsByName( id );
i = 0;
- while ( (elem = elems[i++]) ) {
- node = elem.getAttributeNode("id");
+ while ( ( elem = elems[ i++ ] ) ) {
+ node = elem.getAttributeNode( "id" );
if ( node && node.value === id ) {
return [ elem ];
}
@@ -1203,7 +1258,7 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Tag
- Expr.find["TAG"] = support.getElementsByTagName ?
+ Expr.find[ "TAG" ] = support.getElementsByTagName ?
function( tag, context ) {
if ( typeof context.getElementsByTagName !== "undefined" ) {
return context.getElementsByTagName( tag );
@@ -1218,12 +1273,13 @@ setDocument = Sizzle.setDocument = function( node ) {
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++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem.nodeType === 1 ) {
tmp.push( elem );
}
@@ -1235,7 +1291,7 @@ setDocument = Sizzle.setDocument = function( node ) {
};
// Class
- Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+ Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
return context.getElementsByClassName( className );
}
@@ -1256,10 +1312,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// See https://bugs.jquery.com/ticket/13378
rbuggyQSA = [];
- if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+ if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
// Build QSA regex
// Regex strategy adopted from Diego Perini
- assert(function( el ) {
+ 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,
@@ -1273,78 +1333,98 @@ setDocument = Sizzle.setDocument = function( node ) {
// Nothing should be selected when empty strings follow ^= or $= or *=
// The test attribute must be unknown in Opera but "safe" for WinRT
// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+ if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
}
// Support: IE8
// Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll("[selected]").length ) {
+ 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("~=");
+ 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");
+ 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(".#.+[+~]");
+ rbuggyQSA.push( ".#.+[+~]" );
}
- });
- assert(function( el ) {
+ // 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");
+ 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 ) {
+ 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 ) {
+ 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 ) {
+ 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(",.*:");
- });
+ el.querySelectorAll( "*,:x" );
+ rbuggyQSA.push( ",.*:" );
+ } );
}
- if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+ if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
docElem.webkitMatchesSelector ||
docElem.mozMatchesSelector ||
docElem.oMatchesSelector ||
- docElem.msMatchesSelector) )) ) {
+ docElem.msMatchesSelector ) ) ) ) {
+
+ assert( function( el ) {
- assert(function( el ) {
// Check to see if it's possible to do matchesSelector
// on a disconnected node (IE 9)
support.disconnectedMatch = matches.call( el, "*" );
@@ -1353,11 +1433,11 @@ setDocument = Sizzle.setDocument = function( node ) {
// 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("|") );
+ rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+ rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
/* Contains
---------------------------------------------------------------------- */
@@ -1374,11 +1454,11 @@ setDocument = Sizzle.setDocument = function( node ) {
adown.contains ?
adown.contains( bup ) :
a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ));
+ ) );
} :
function( a, b ) {
if ( b ) {
- while ( (b = b.parentNode) ) {
+ while ( ( b = b.parentNode ) ) {
if ( b === a ) {
return true;
}
@@ -1407,7 +1487,11 @@ setDocument = Sizzle.setDocument = function( node ) {
}
// Calculate position if both inputs belong to the same document
- compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+ // 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
@@ -1415,13 +1499,24 @@ setDocument = Sizzle.setDocument = function( node ) {
// Disconnected nodes
if ( compare & 1 ||
- (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+ ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
// Choose the first element that is related to our preferred document
- if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+ // 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;
}
- if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+
+ // 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;
}
@@ -1434,6 +1529,7 @@ setDocument = Sizzle.setDocument = function( node ) {
return compare & 4 ? -1 : 1;
} :
function( a, b ) {
+
// Exit early if the nodes are identical
if ( a === b ) {
hasDuplicate = true;
@@ -1449,8 +1545,14 @@ setDocument = Sizzle.setDocument = function( node ) {
// Parentless nodes are either documents or disconnected
if ( !aup || !bup ) {
- return a === document ? -1 :
- b === document ? 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 eqeqeq */
+ return a == document ? -1 :
+ b == document ? 1 :
+ /* eslint-enable eqeqeq */
aup ? -1 :
bup ? 1 :
sortInput ?
@@ -1464,26 +1566,32 @@ setDocument = Sizzle.setDocument = function( node ) {
// Otherwise we need full lists of their ancestors for comparison
cur = a;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
ap.unshift( cur );
}
cur = b;
- while ( (cur = cur.parentNode) ) {
+ while ( ( cur = cur.parentNode ) ) {
bp.unshift( cur );
}
// Walk down the tree looking for a discrepancy
- while ( ap[i] === bp[i] ) {
+ while ( ap[ i ] === bp[ i ] ) {
i++;
}
return i ?
+
// Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[i], bp[i] ) :
+ siblingCheck( ap[ i ], bp[ i ] ) :
// Otherwise nodes in our document sort first
- ap[i] === preferredDoc ? -1 :
- bp[i] === preferredDoc ? 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 eqeqeq */
+ ap[ i ] == preferredDoc ? -1 :
+ bp[ i ] == preferredDoc ? 1 :
+ /* eslint-enable eqeqeq */
0;
};
@@ -1495,10 +1603,7 @@ Sizzle.matches = function( expr, elements ) {
};
Sizzle.matchesSelector = function( elem, expr ) {
- // Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== document ) {
- setDocument( elem );
- }
+ setDocument( elem );
if ( support.matchesSelector && documentIsHTML &&
!nonnativeSelectorCache[ expr + " " ] &&
@@ -1510,12 +1615,13 @@ Sizzle.matchesSelector = function( 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 ) {
+
+ // 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) {
+ } catch ( e ) {
nonnativeSelectorCache( expr, true );
}
}
@@ -1524,20 +1630,31 @@ Sizzle.matchesSelector = function( elem, expr ) {
};
Sizzle.contains = function( context, elem ) {
+
// Set document vars if needed
- if ( ( context.ownerDocument || context ) !== 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 ( ( context.ownerDocument || context ) != document ) {
setDocument( context );
}
return contains( context, elem );
};
Sizzle.attr = function( elem, name ) {
+
// Set document vars if needed
- if ( ( elem.ownerDocument || elem ) !== 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 ( ( 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 ) :
@@ -1547,13 +1664,13 @@ Sizzle.attr = function( elem, name ) {
val :
support.attributes || !documentIsHTML ?
elem.getAttribute( name ) :
- (val = elem.getAttributeNode(name)) && val.specified ?
+ ( val = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
null;
};
Sizzle.escape = function( sel ) {
- return (sel + "").replace( rcssescape, fcssescape );
+ return ( sel + "" ).replace( rcssescape, fcssescape );
};
Sizzle.error = function( msg ) {
@@ -1576,7 +1693,7 @@ Sizzle.uniqueSort = function( results ) {
results.sort( sortOrder );
if ( hasDuplicate ) {
- while ( (elem = results[i++]) ) {
+ while ( ( elem = results[ i++ ] ) ) {
if ( elem === results[ i ] ) {
j = duplicates.push( i );
}
@@ -1604,17 +1721,21 @@ getText = Sizzle.getText = function( elem ) {
nodeType = elem.nodeType;
if ( !nodeType ) {
+
// If no nodeType, this is expected to be an array
- while ( (node = elem[i++]) ) {
+ 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 );
@@ -1623,6 +1744,7 @@ getText = Sizzle.getText = function( elem ) {
} else if ( nodeType === 3 || nodeType === 4 ) {
return elem.nodeValue;
}
+
// Do not include comment or processing instruction nodes
return ret;
@@ -1650,19 +1772,21 @@ Expr = Sizzle.selectors = {
preFilter: {
"ATTR": function( match ) {
- match[1] = match[1].replace( runescape, funescape );
+ 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 );
+ match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+ match[ 5 ] || "" ).replace( runescape, funescape );
- if ( match[2] === "~=" ) {
- match[3] = " " + match[3] + " ";
+ 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)
@@ -1673,22 +1797,25 @@ Expr = Sizzle.selectors = {
7 sign of y-component
8 y of y-component
*/
- match[1] = match[1].toLowerCase();
+ match[ 1 ] = match[ 1 ].toLowerCase();
+
+ if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
- if ( match[1].slice( 0, 3 ) === "nth" ) {
// nth-* requires argument
- if ( !match[3] ) {
- Sizzle.error( match[0] );
+ 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" );
+ 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] );
+ // other types prohibit arguments
+ } else if ( match[ 3 ] ) {
+ Sizzle.error( match[ 0 ] );
}
return match;
@@ -1696,26 +1823,28 @@ Expr = Sizzle.selectors = {
"PSEUDO": function( match ) {
var excess,
- unquoted = !match[6] && match[2];
+ unquoted = !match[ 6 ] && match[ 2 ];
- if ( matchExpr["CHILD"].test( match[0] ) ) {
+ if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
return null;
}
// Accept quoted arguments as-is
- if ( match[3] ) {
- match[2] = match[4] || match[5] || "";
+ 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 )) &&
+ ( excess = tokenize( unquoted, true ) ) &&
+
// advance to the next closing parenthesis
- (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+ ( 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 );
+ 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)
@@ -1728,7 +1857,9 @@ Expr = Sizzle.selectors = {
"TAG": function( nodeNameSelector ) {
var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
return nodeNameSelector === "*" ?
- function() { return true; } :
+ function() {
+ return true;
+ } :
function( elem ) {
return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
};
@@ -1738,10 +1869,16 @@ Expr = Sizzle.selectors = {
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") || "" );
- });
+ ( 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 ) {
@@ -1757,6 +1894,8 @@ Expr = Sizzle.selectors = {
result += "";
+ /* eslint-disable max-len */
+
return operator === "=" ? result === check :
operator === "!=" ? result !== check :
operator === "^=" ? check && result.indexOf( check ) === 0 :
@@ -1765,10 +1904,12 @@ Expr = Sizzle.selectors = {
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 ) {
+ "CHILD": function( type, what, _argument, first, last ) {
var simple = type.slice( 0, 3 ) !== "nth",
forward = type.slice( -4 ) !== "last",
ofType = what === "of-type";
@@ -1780,7 +1921,7 @@ Expr = Sizzle.selectors = {
return !!elem.parentNode;
} :
- function( elem, context, xml ) {
+ function( elem, _context, xml ) {
var cache, uniqueCache, outerCache, node, nodeIndex, start,
dir = simple !== forward ? "nextSibling" : "previousSibling",
parent = elem.parentNode,
@@ -1794,7 +1935,7 @@ Expr = Sizzle.selectors = {
if ( simple ) {
while ( dir ) {
node = elem;
- while ( (node = node[ dir ]) ) {
+ while ( ( node = node[ dir ] ) ) {
if ( ofType ?
node.nodeName.toLowerCase() === name :
node.nodeType === 1 ) {
@@ -1802,6 +1943,7 @@ Expr = Sizzle.selectors = {
return false;
}
}
+
// Reverse direction for :only-* (if we haven't yet done so)
start = dir = type === "only" && !start && "nextSibling";
}
@@ -1817,22 +1959,22 @@ Expr = Sizzle.selectors = {
// ...in a gzip-friendly way
node = parent;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (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 ] ||
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
// Fallback to seeking `elem` from the start
- (diff = nodeIndex = 0) || start.pop()) ) {
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
// When found, cache indexes on `parent` and break
if ( node.nodeType === 1 && ++diff && node === elem ) {
@@ -1842,16 +1984,18 @@ Expr = Sizzle.selectors = {
}
} else {
+
// Use previously-cached element index if available
if ( useCache ) {
+
// ...in a gzip-friendly way
node = elem;
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] || ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
cache = uniqueCache[ type ] || [];
nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
@@ -1861,9 +2005,10 @@ Expr = Sizzle.selectors = {
// 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()) ) {
+ while ( ( node = ++nodeIndex && node && node[ dir ] ||
+ ( diff = nodeIndex = 0 ) || start.pop() ) ) {
if ( ( ofType ?
node.nodeName.toLowerCase() === name :
@@ -1872,12 +2017,13 @@ Expr = Sizzle.selectors = {
// Cache the index of each encountered element
if ( useCache ) {
- outerCache = node[ expando ] || (node[ expando ] = {});
+ outerCache = node[ expando ] ||
+ ( node[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
uniqueCache = outerCache[ node.uniqueID ] ||
- (outerCache[ node.uniqueID ] = {});
+ ( outerCache[ node.uniqueID ] = {} );
uniqueCache[ type ] = [ dirruns, diff ];
}
@@ -1898,6 +2044,7 @@ Expr = Sizzle.selectors = {
},
"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
@@ -1917,15 +2064,15 @@ Expr = Sizzle.selectors = {
if ( fn.length > 1 ) {
args = [ pseudo, pseudo, "", argument ];
return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction(function( seed, matches ) {
+ 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] );
+ idx = indexOf( seed, matched[ i ] );
+ seed[ idx ] = !( matches[ idx ] = matched[ i ] );
}
- }) :
+ } ) :
function( elem ) {
return fn( elem, 0, args );
};
@@ -1936,8 +2083,10 @@ Expr = Sizzle.selectors = {
},
pseudos: {
+
// Potentially complex pseudos
- "not": markFunction(function( selector ) {
+ "not": markFunction( function( selector ) {
+
// Trim the selector passed to compile
// to avoid treating leading and trailing
// spaces as combinators
@@ -1946,39 +2095,40 @@ Expr = Sizzle.selectors = {
matcher = compile( selector.replace( rtrim, "$1" ) );
return matcher[ expando ] ?
- markFunction(function( seed, matches, context, xml ) {
+ 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);
+ if ( ( elem = unmatched[ i ] ) ) {
+ seed[ i ] = !( matches[ i ] = elem );
}
}
- }) :
- function( elem, context, xml ) {
- input[0] = elem;
+ } ) :
+ function( elem, _context, xml ) {
+ input[ 0 ] = elem;
matcher( input, null, xml, results );
+
// Don't keep the element (issue #299)
- input[0] = null;
+ input[ 0 ] = null;
return !results.pop();
};
- }),
+ } ),
- "has": markFunction(function( selector ) {
+ "has": markFunction( function( selector ) {
return function( elem ) {
return Sizzle( selector, elem ).length > 0;
};
- }),
+ } ),
- "contains": markFunction(function( text ) {
+ "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
@@ -1988,25 +2138,26 @@ Expr = Sizzle.selectors = {
// 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 || "") ) {
+ if ( !ridentifier.test( lang || "" ) ) {
Sizzle.error( "unsupported lang: " + lang );
}
lang = lang.replace( runescape, funescape ).toLowerCase();
return function( elem ) {
var elemLang;
do {
- if ( (elemLang = documentIsHTML ?
+ if ( ( elemLang = documentIsHTML ?
elem.lang :
- elem.getAttribute("xml:lang") || elem.getAttribute("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 );
+ } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
return false;
};
- }),
+ } ),
// Miscellaneous
"target": function( elem ) {
@@ -2019,7 +2170,9 @@ Expr = Sizzle.selectors = {
},
"focus": function( elem ) {
- return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+ return elem === document.activeElement &&
+ ( !document.hasFocus || document.hasFocus() ) &&
+ !!( elem.type || elem.href || ~elem.tabIndex );
},
// Boolean properties
@@ -2027,16 +2180,20 @@ Expr = Sizzle.selectors = {
"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);
+ 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;
}
@@ -2045,6 +2202,7 @@ Expr = Sizzle.selectors = {
// 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.)
@@ -2058,7 +2216,7 @@ Expr = Sizzle.selectors = {
},
"parent": function( elem ) {
- return !Expr.pseudos["empty"]( elem );
+ return !Expr.pseudos[ "empty" ]( elem );
},
// Element/input types
@@ -2082,39 +2240,40 @@ Expr = Sizzle.selectors = {
// Support: IE<8
// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+ ( ( attr = elem.getAttribute( "type" ) ) == null ||
+ attr.toLowerCase() === "text" );
},
// Position-in-collection
- "first": createPositionalPseudo(function() {
+ "first": createPositionalPseudo( function() {
return [ 0 ];
- }),
+ } ),
- "last": createPositionalPseudo(function( matchIndexes, length ) {
+ "last": createPositionalPseudo( function( _matchIndexes, length ) {
return [ length - 1 ];
- }),
+ } ),
- "eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
return [ argument < 0 ? argument + length : argument ];
- }),
+ } ),
- "even": createPositionalPseudo(function( matchIndexes, length ) {
+ "even": createPositionalPseudo( function( matchIndexes, length ) {
var i = 0;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "odd": createPositionalPseudo(function( matchIndexes, length ) {
+ "odd": createPositionalPseudo( function( matchIndexes, length ) {
var i = 1;
for ( ; i < length; i += 2 ) {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
var i = argument < 0 ?
argument + length :
argument > length ?
@@ -2124,19 +2283,19 @@ Expr = Sizzle.selectors = {
matchIndexes.push( i );
}
return matchIndexes;
- }),
+ } ),
- "gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+ "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"];
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
// Add button/input type pseudos
for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
@@ -2167,37 +2326,39 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
while ( soFar ) {
// Comma and first run
- if ( !matched || (match = rcomma.exec( soFar )) ) {
+ if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
if ( match ) {
+
// Don't consume trailing commas as valid
- soFar = soFar.slice( match[0].length ) || soFar;
+ soFar = soFar.slice( match[ 0 ].length ) || soFar;
}
- groups.push( (tokens = []) );
+ groups.push( ( tokens = [] ) );
}
matched = false;
// Combinators
- if ( (match = rcombinators.exec( soFar )) ) {
+ if ( ( match = rcombinators.exec( soFar ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
+
// Cast descendant combinators to space
- type: match[0].replace( rtrim, " " )
- });
+ 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 ))) ) {
+ if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+ ( match = preFilters[ type ]( match ) ) ) ) {
matched = match.shift();
- tokens.push({
+ tokens.push( {
value: matched,
type: type,
matches: match
- });
+ } );
soFar = soFar.slice( matched.length );
}
}
@@ -2214,6 +2375,7 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
soFar.length :
soFar ?
Sizzle.error( selector ) :
+
// Cache the tokens
tokenCache( selector, groups ).slice( 0 );
};
@@ -2223,7 +2385,7 @@ function toSelector( tokens ) {
len = tokens.length,
selector = "";
for ( ; i < len; i++ ) {
- selector += tokens[i].value;
+ selector += tokens[ i ].value;
}
return selector;
}
@@ -2236,9 +2398,10 @@ function addCombinator( matcher, combinator, base ) {
doneName = done++;
return combinator.first ?
+
// Check against closest ancestor/preceding element
function( elem, context, xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
return matcher( elem, context, xml );
}
@@ -2253,7 +2416,7 @@ function addCombinator( matcher, combinator, base ) {
// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
if ( xml ) {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
if ( matcher( elem, context, xml ) ) {
return true;
@@ -2261,27 +2424,29 @@ function addCombinator( matcher, combinator, base ) {
}
}
} else {
- while ( (elem = elem[ dir ]) ) {
+ while ( ( elem = elem[ dir ] ) ) {
if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || (elem[ expando ] = {});
+ outerCache = elem[ expando ] || ( elem[ expando ] = {} );
// Support: IE <9 only
// Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+ uniqueCache = outerCache[ elem.uniqueID ] ||
+ ( outerCache[ elem.uniqueID ] = {} );
if ( skip && skip === elem.nodeName.toLowerCase() ) {
elem = elem[ dir ] || elem;
- } else if ( (oldCache = uniqueCache[ key ]) &&
+ } 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 ]);
+ 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 )) ) {
+ if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
return true;
}
}
@@ -2297,20 +2462,20 @@ function elementMatcher( matchers ) {
function( elem, context, xml ) {
var i = matchers.length;
while ( i-- ) {
- if ( !matchers[i]( elem, context, xml ) ) {
+ if ( !matchers[ i ]( elem, context, xml ) ) {
return false;
}
}
return true;
} :
- matchers[0];
+ matchers[ 0 ];
}
function multipleContexts( selector, contexts, results ) {
var i = 0,
len = contexts.length;
for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[i], results );
+ Sizzle( selector, contexts[ i ], results );
}
return results;
}
@@ -2323,7 +2488,7 @@ function condense( unmatched, map, filter, context, xml ) {
mapped = map != null;
for ( ; i < len; i++ ) {
- if ( (elem = unmatched[i]) ) {
+ if ( ( elem = unmatched[ i ] ) ) {
if ( !filter || filter( elem, context, xml ) ) {
newUnmatched.push( elem );
if ( mapped ) {
@@ -2343,14 +2508,18 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
if ( postFinder && !postFinder[ expando ] ) {
postFinder = setMatcher( postFinder, postSelector );
}
- return markFunction(function( seed, results, context, xml ) {
+ 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, [] ),
+ elems = seed || multipleContexts(
+ selector || "*",
+ context.nodeType ? [ context ] : context,
+ []
+ ),
// Prefilter to get matcher input, preserving a map for seed-results synchronization
matcherIn = preFilter && ( seed || !selector ) ?
@@ -2358,6 +2527,7 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
elems,
matcherOut = matcher ?
+
// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
@@ -2381,8 +2551,8 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
// 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 ( ( elem = temp[ i ] ) ) {
+ matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
}
}
}
@@ -2390,25 +2560,27 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
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]) ) {
+ if ( ( elem = matcherOut[ i ] ) ) {
+
// Restore matcherIn since elem is not yet a final match
- temp.push( (matcherIn[i] = elem) );
+ temp.push( ( matcherIn[ i ] = elem ) );
}
}
- postFinder( null, (matcherOut = []), temp, xml );
+ 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 ) {
+ if ( ( elem = matcherOut[ i ] ) &&
+ ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
- seed[temp] = !(results[temp] = elem);
+ seed[ temp ] = !( results[ temp ] = elem );
}
}
}
@@ -2426,14 +2598,14 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
push.apply( results, matcherOut );
}
}
- });
+ } );
}
function matcherFromTokens( tokens ) {
var checkContext, matcher, j,
len = tokens.length,
- leadingRelative = Expr.relative[ tokens[0].type ],
- implicitRelative = leadingRelative || Expr.relative[" "],
+ 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)
@@ -2445,38 +2617,43 @@ function matcherFromTokens( tokens ) {
}, implicitRelative, true ),
matchers = [ function( elem, context, xml ) {
var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- (checkContext = context).nodeType ?
+ ( 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) ];
+ if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+ matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
} else {
- matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+ 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 ] ) {
+ 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 === " " ? "*" : "" })
+
+ // 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 && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
j < len && toSelector( tokens )
);
}
@@ -2497,28 +2674,40 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
unmatched = seed && [],
setMatched = [],
contextBackup = outermostContext,
+
// We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+ 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),
+ dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
len = elems.length;
if ( outermost ) {
- outermostContext = context === document || context || 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++ ) {
+ for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
if ( byElement && elem ) {
j = 0;
- if ( !context && elem.ownerDocument !== 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 ( !context && elem.ownerDocument != document ) {
setDocument( elem );
xml = !documentIsHTML;
}
- while ( (matcher = elementMatchers[j++]) ) {
- if ( matcher( elem, context || document, xml) ) {
+ while ( ( matcher = elementMatchers[ j++ ] ) ) {
+ if ( matcher( elem, context || document, xml ) ) {
results.push( elem );
break;
}
@@ -2530,8 +2719,9 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// Track unmatched elements for set filters
if ( bySet ) {
+
// They will have gone through all possible matchers
- if ( (elem = !matcher && elem) ) {
+ if ( ( elem = !matcher && elem ) ) {
matchedCount--;
}
@@ -2555,16 +2745,17 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
// numerically zero.
if ( bySet && i !== matchedCount ) {
j = 0;
- while ( (matcher = setMatchers[j++]) ) {
+ 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 );
+ if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+ setMatched[ i ] = pop.call( results );
}
}
}
@@ -2605,13 +2796,14 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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] );
+ cached = matcherFromTokens( match[ i ] );
if ( cached[ expando ] ) {
setMatchers.push( cached );
} else {
@@ -2620,7 +2812,10 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
}
// Cache the compiled function
- cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+ cached = compilerCache(
+ selector,
+ matcherFromGroupMatchers( elementMatchers, setMatchers )
+ );
// Save selector and tokenization
cached.selector = selector;
@@ -2640,7 +2835,7 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
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) );
+ match = !seed && tokenize( ( selector = compiled.selector || selector ) );
results = results || [];
@@ -2649,11 +2844,12 @@ select = Sizzle.select = function( selector, context, results, seed ) {
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 ] ) {
+ 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];
+ context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+ .replace( runescape, funescape ), context ) || [] )[ 0 ];
if ( !context ) {
return results;
@@ -2666,20 +2862,22 @@ select = Sizzle.select = function( selector, context, results, seed ) {
}
// Fetch a seed set for right-to-left matching
- i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+ i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
while ( i-- ) {
- token = tokens[i];
+ token = tokens[ i ];
// Abort if we hit a combinator
- if ( Expr.relative[ (type = token.type) ] ) {
+ if ( Expr.relative[ ( type = token.type ) ] ) {
break;
}
- if ( (find = Expr.find[ type ]) ) {
+ 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 = 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 );
@@ -2710,7 +2908,7 @@ select = Sizzle.select = function( selector, context, results, seed ) {
// One-time assignments
// Sort stability
-support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
// Support: Chrome 14-35+
// Always assume duplicates if they aren't passed to the comparison function
@@ -2721,58 +2919,59 @@ 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 ) {
+support.sortDetached = assert( function( el ) {
+
// Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
-});
+ 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 ) {
+if ( !assert( function( el ) {
el.innerHTML = "";
- return el.firstChild.getAttribute("href") === "#" ;
-}) ) {
+ 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 ) {
+if ( !support.attributes || !assert( function( el ) {
el.innerHTML = "";
el.firstChild.setAttribute( "value", "" );
return el.firstChild.getAttribute( "value" ) === "";
-}) ) {
- addHandle( "value", function( elem, name, isXML ) {
+} ) ) {
+ 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;
-}) ) {
+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 = elem.getAttributeNode( name ) ) && val.specified ?
val.value :
- null;
+ null;
}
- });
+ } );
}
return Sizzle;
-})( window );
+} )( window );
@@ -3141,7 +3340,7 @@ jQuery.each( {
parents: function( elem ) {
return dir( elem, "parentNode" );
},
- parentsUntil: function( elem, i, until ) {
+ parentsUntil: function( elem, _i, until ) {
return dir( elem, "parentNode", until );
},
next: function( elem ) {
@@ -3156,10 +3355,10 @@ jQuery.each( {
prevAll: function( elem ) {
return dir( elem, "previousSibling" );
},
- nextUntil: function( elem, i, until ) {
+ nextUntil: function( elem, _i, until ) {
return dir( elem, "nextSibling", until );
},
- prevUntil: function( elem, i, until ) {
+ prevUntil: function( elem, _i, until ) {
return dir( elem, "previousSibling", until );
},
siblings: function( elem ) {
@@ -3169,7 +3368,13 @@ jQuery.each( {
return siblings( elem.firstChild );
},
contents: function( elem ) {
- if ( typeof elem.contentDocument !== "undefined" ) {
+ if ( elem.contentDocument != null &&
+
+ // Support: IE 11+
+ //