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When it is not found, a full rebuild will be done. -config: 4ead2cc44134efa04f34882b20b4f742 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_preview/77/README.html b/_preview/77/README.html deleted file mode 100644 index f4fe116..0000000 --- a/_preview/77/README.html +++ /dev/null @@ -1,587 +0,0 @@ - - - - - - - - CMIP6 Cookbook — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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CMIP6 logo

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CMIP6 Cookbook

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nightly-build -Binder -DOI

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This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.

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Motivation

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From the CMIP6 website:

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The simulation data produced by models under previous phases of CMIP have been used in thousands of research papers … and the multi-model results provide some perspective on errors and uncertainty in model simulations. This information has proved invaluable in preparing high profile reports assessing our understanding of climate and climate change (e.g., the IPCC Assessment Reports).

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With such a large amount of model output produced, moving the data around is inefficient. In this collection of notebooks, you will learn how to access cloud-optimized CMIP6 datasets, in addition to a few examples of using that data to analyze some aspects of climate change.

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Authors

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Ryan Abernathey, Henri Drake, Robert Ford, Max Grover

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Structure

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Foundations

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This section includes three variations of accessing CMIP6 data from cloud storage.

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Example workflows

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There are currently four examples of using this data to

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  • Estimate equilibrium climate sensitivity (ECS)

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  • Plot global mean surface temperature under two different Shared Socioeconomic Pathways

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  • Plot changes in precipitation intensity under the SSP585 scenario

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  • Calculate changes in ocean heat uptake after regridding with xESMF

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Running the Notebooks

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

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Running on Binder

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The simplest way to interact with a Jupyter Notebook is through -Binder, which enables the execution of a -Jupyter Book in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -Shift+Enter. Complete details on how to interact with -a live Jupyter notebook are described in Getting Started with -Jupyter.

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Running on Your Own Machine

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

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  1. Clone the https://github.com/ProjectPythia/cmip6-cookbook repository:

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  2. Move into the cmip6-cookbook directory

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  4. Move into the notebooks directory and start up Jupyterlab

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At this point, you can interact with the notebooks! Make sure to check out the “Getting Started with Jupyter” content from the Pythia Foundations material if you are new to Jupyter or need a refresher.

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How to Cite This Cookbook

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-[![nightly-build](https://github.com/ProjectPythia/cmip6-cookbook/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/cmip6-cookbook/actions/workflows/nightly-build.yaml) -[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/cmip6-cookbook.git/main) -[![DOI](https://zenodo.org/badge/507993770.svg)](https://zenodo.org/badge/latestdoi/507993770) - -This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools. - -## Motivation - -From the [CMIP6 website](https://esgf-node.llnl.gov/projects/cmip6/): - -> The simulation data produced by models under previous phases of CMIP have been used in thousands of research papers ... and the multi-model results provide some perspective on errors and uncertainty in model simulations. This information has proved invaluable in preparing high profile reports assessing our understanding of climate and climate change (e.g., the IPCC Assessment Reports). - -With such a large amount of model output produced, moving the data around is inefficient. In this collection of notebooks, you will learn how to access cloud-optimized CMIP6 datasets, in addition to a few examples of using that data to analyze some aspects of climate change. - -## Authors - -[Ryan Abernathey](https://github.com/rabernat), [Henri Drake](https://github.com/hdrake), [Robert Ford](https://github.com/r-ford), [Max Grover](https://github.com/mgrover1) - -### Contributors - - - - - -## Structure - -### Foundations - -This section includes three variations of accessing CMIP6 data from cloud storage. - -### Example workflows - -There are currently four examples of using this data to -- Estimate equilibrium climate sensitivity (ECS) -- Plot global mean surface temperature under two different [Shared Socioeconomic Pathways](https://unece.org/fileadmin/DAM/energy/se/pdfs/CSE/PATHWAYS/2019/ws_Consult_14_15.May.2019/supp_doc/SSP2_Overview.pdf) -- Plot changes in precipitation intensity under the SSP585 scenario -- Calculate changes in ocean heat uptake after regridding with xESMF - -## Running the Notebooks -You can either run the notebook using [Binder](https://binder.projectpythia.org) or on your local machine. - -### Running on Binder - -The simplest way to interact with a Jupyter Notebook is through -[Binder](https://binder.projectpythia.org), which enables the execution of a -[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with -a live Jupyter notebook are described in [Getting Started with -Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html). - -### Running on Your Own Machine -If you are interested in running this material locally on your computer, you will need to follow this workflow: - -1. Clone the `https://github.com/ProjectPythia/cmip6-cookbook` repository: - - ```bash - git clone https://github.com/ProjectPythia/cmip6-cookbook.git - ``` -1. Move into the `cmip6-cookbook` directory - ```bash - cd cmip6-cookbook - ``` -1. Create and activate your conda environment from the `environment.yml` file - ```bash - conda env create -f environment.yml - conda activate cmip6-cookbook-dev - ``` -1. Move into the `notebooks` directory and start up Jupyterlab - ```bash - cd notebooks/ - jupyter lab - ``` - -At this point, you can interact with the notebooks! Make sure to check out the ["Getting Started with Jupyter"](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html) content from the [Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) material if you are new to Jupyter or need a refresher. diff --git a/_preview/77/_sources/notebooks/example-workflows/ecs-cmip6.ipynb b/_preview/77/_sources/notebooks/example-workflows/ecs-cmip6.ipynb deleted file mode 100644 index 37e508d..0000000 --- a/_preview/77/_sources/notebooks/example-workflows/ecs-cmip6.ipynb +++ /dev/null @@ -1,4881 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estimating Equilibrium Climate Sensitivity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "Equilibrium Climate Sensitivity (ECS) is defined as change in global-mean near-surface air temperature (GMST) change due to an instantaneous doubling of CO$_2$ concentrations and once the coupled ocean-atmosphere-sea ice system has acheived a statistical equilibrium (i.e. at the top-of-atmosphere, incoming solar shortwave radiation is balanced by reflected solar shortwave and outgoing thermal longwave radiation).\n", - "\n", - "This notebook uses the “[Gregory method]( https://doi.org/10.1029/2003GL018747)” to approximate the ECS of CMIP6 models based on the first 150 years after an abrupt doubling of CO$_2$\n", - "concentrations. The Gregory method extrapolates the quasi-linear relationship between GMST and radiative imbalance at the top-of-atmosphere to estimate how much warming would occur if the system were in radiative balance at the top-of-atmosphere, which is by definition the equilibrium response. In particular, we extrapolate the linear relationship that occurs between 100 and 150 years after the abrupt quadrupling. \n", - "\n", - "Since the radiative forcing due to CO$_2$ is a logarithmic function of the CO$_2$ concentration, the GMST change from a first doubling is roughly the same as for a second doubling (to first order, we can assume feedbacks as constant), which means that the GMST change due to a quadrupling of CO$_2$ is roughly $\\Delta T_{4\\times\\mathrm{CO}_2}=2\\times\\mathrm{ECS}$. See also [Mauritsen et al. 2019](https://doi.org/10.1029/2018MS001400) for a detailed application of the Gregory method (with modifications) for the case of one specific CMIP6 model, the MPI-M Earth System Model.\n", - "\n", - "For another take on applying the Gregory method to estimate ECS, see [Angeline Pendergrass’ code](https://github.com/apendergrass/cmip6-ecs)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| Dask | Helpful | |\n", - "| Climate sensitivity | Helpful | |\n", - "\n", - "- **Time to learn**: 30 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:34.128622Z", - "iopub.status.busy": "2023-12-18T19:45:34.128419Z", - "iopub.status.idle": "2023-12-18T19:45:37.029636Z", - "shell.execute_reply": "2023-12-18T19:45:37.028889Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_98/1804016931.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", - " from tqdm.autonotebook import tqdm # Fancy progress bars for our loops!\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import sys\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import cartopy\n", - "import dask\n", - "from tqdm.autonotebook import tqdm # Fancy progress bars for our loops!\n", - "import intake\n", - "import fsspec\n", - "from dask_gateway import Gateway\n", - "from dask.distributed import Client\n", - "\n", - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = 12, 6" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Compute Cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we use a dask cluster to parallelize our analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:37.033644Z", - "iopub.status.busy": "2023-12-18T19:45:37.033396Z", - "iopub.status.idle": "2023-12-18T19:45:37.036667Z", - "shell.execute_reply": "2023-12-18T19:45:37.036096Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "platform = sys.platform\n", - "\n", - "if (platform == 'win32'):\n", - " import multiprocessing.popen_spawn_win32\n", - "else:\n", - " import multiprocessing.popen_spawn_posix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initiate the Dask client:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:37.038789Z", - "iopub.status.busy": "2023-12-18T19:45:37.038607Z", - "iopub.status.idle": "2023-12-18T19:45:38.056263Z", - "shell.execute_reply": "2023-12-18T19:45:38.055513Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "

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    " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client = Client()\n", - "client" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data catalogs\n", - "This notebook uses [`intake-esm`](https://intake-esm.readthedocs.io/en/latest/) to ingest and organize climate model output from the fresh-off-the-supercomputers Phase 6 of the Coupled Model Intercomparison Project (CMIP6).\n", - "\n", - "The file `https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv` in Google Cloud Storage contains thousands of lines of metadata, each describing an individual climate model experiment’s simulated data.\n", - "\n", - "For example, the first line in the `.csv` file contains the precipitation rate (`variable_id = 'pr'`), as a function of latitude, longitude, and time, in an individual climate model experiment with the BCC-ESM1 model (`source_id = 'BCC-ESM1'`) developed by the Beijing Climate Center (`institution_id = 'BCC'`). The model is forced by the forcing experiment SSP370 (`experiment_id = 'ssp370'`), which stands for the Shared Socio-Economic Pathway 3 that results in a change in radiative forcing of $\\Delta F=7.0$ W m$^{-2}$ from pre-industrial to 2100. This simulation was run as part of the `AerChemMIP` activity, which is a spin-off of the CMIP activity that focuses specifically on how aerosol chemistry affects climate." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:38.059748Z", - "iopub.status.busy": "2023-12-18T19:45:38.059048Z", - "iopub.status.idle": "2023-12-18T19:45:40.268192Z", - "shell.execute_reply": "2023-12-18T19:45:40.267506Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
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NaN 20170706 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The file `pangeo-cmip6.json` describes the structure of the CMIP6 metadata and is formatted so as to be read in by the `intake.open_esm_datastore` method, which categorizes all of the data pointers into a tiered collection. For example, this collection contains the simulated data from 28691 individual experiments, representing 48 different models from 23 different scientific institutions. There are 190 different climate variables (e.g. sea surface temperature, sea ice concentration, atmospheric winds, dissolved organic carbon in the ocean, etc.) available for 29 different forcing experiments." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Use Intake-ESM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Intake-ESM](https://intake-esm.readthedocs.io/en/stable/) is a new package designed to make working with these data archives a bit simpler." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:40.271613Z", - "iopub.status.busy": "2023-12-18T19:45:40.270931Z", - "iopub.status.idle": "2023-12-18T19:45:45.090106Z", - "shell.execute_reply": "2023-12-18T19:45:45.089077Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. 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    pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

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    unique
    activity_id18
    institution_id36
    source_id88
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    member_id657
    table_id37
    variable_id700
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    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n", - "col" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we show the various forcing experiments that climate modellers ran in these simulations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.093915Z", - "iopub.status.busy": "2023-12-18T19:45:45.093646Z", - "iopub.status.idle": "2023-12-18T19:45:45.117644Z", - "shell.execute_reply": "2023-12-18T19:45:45.117020Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['highresSST-present', 'piControl', 'control-1950', 'hist-1950',\n", - " 'historical', 'amip', 'abrupt-4xCO2', 'abrupt-2xCO2',\n", - " 'abrupt-0p5xCO2', '1pctCO2', 'ssp585', 'esm-piControl', 'esm-hist',\n", - " 'hist-piAer', 'histSST-1950HC', 'ssp245', 'hist-1950HC', 'histSST',\n", - " 'piClim-2xVOC', 'piClim-2xNOx', 'piClim-2xdust', 'piClim-2xss',\n", - " 'piClim-histall', 'hist-piNTCF', 'histSST-piNTCF',\n", - " 'aqua-control-lwoff', 'piClim-lu', 'histSST-piO3', 'piClim-CH4',\n", - " 'piClim-NTCF', 'piClim-NOx', 'piClim-O3', 'piClim-HC',\n", - " 'faf-heat-NA0pct', 'ssp370SST-lowCH4', 'piClim-VOC',\n", - " 'ssp370-lowNTCF', 'piClim-control', 'piClim-aer', 'hist-aer',\n", - " 'faf-heat', 'faf-heat-NA50pct', 'ssp370SST-lowNTCF',\n", - " 'ssp370SST-ssp126Lu', 'ssp370SST', 'ssp370pdSST', 'histSST-piAer',\n", - " 'piClim-ghg', 'piClim-anthro', 'faf-all', 'hist-nat', 'hist-GHG',\n", - " 'ssp119', 'piClim-histnat', 'piClim-4xCO2', 'ssp370',\n", - " 'piClim-histghg', 'highresSST-future', 'esm-ssp585-ssp126Lu',\n", - " 'ssp126-ssp370Lu', 'ssp370-ssp126Lu', 'land-noLu', 'histSST-piCH4',\n", - " 'ssp126', 'esm-pi-CO2pulse', 'amip-hist', 'piClim-histaer',\n", - " 'amip-4xCO2', 'faf-water', 'faf-passiveheat', '1pctCO2-rad',\n", - " 'faf-stress', '1pctCO2-bgc', 'aqua-control', 'amip-future4K',\n", - " 'amip-p4K', 'aqua-p4K', 'amip-lwoff', 'amip-m4K', 'aqua-4xCO2',\n", - " 'amip-p4K-lwoff', 'hist-noLu', '1pctCO2-cdr',\n", - " 'land-hist-altStartYear', 'land-hist', 'omip1', 'esm-pi-cdr-pulse',\n", - " 'esm-ssp585', 'abrupt-solp4p', 'piControl-spinup', 'hist-stratO3',\n", - " 'abrupt-solm4p', 'midHolocene', 'lig127k', 'aqua-p4K-lwoff',\n", - " 'esm-piControl-spinup', 'ssp245-GHG', 'ssp245-nat',\n", - " 'dcppC-amv-neg', 'dcppC-amv-ExTrop-neg', 'dcppC-atl-control',\n", - " 'dcppC-amv-pos', 'dcppC-ipv-NexTrop-neg', 'dcppC-ipv-NexTrop-pos',\n", - " 'dcppC-atl-pacemaker', 'dcppC-amv-ExTrop-pos',\n", - " 'dcppC-amv-Trop-neg', 'dcppC-pac-control', 'dcppC-ipv-pos',\n", - " 'dcppC-pac-pacemaker', 'dcppC-ipv-neg', 'dcppC-amv-Trop-pos',\n", - " 'piClim-BC', 'piClim-2xfire', 'piClim-SO2', 'piClim-OC',\n", - " 'piClim-N2O', 'piClim-2xDMS', 'ssp460', 'ssp434', 'ssp534-over',\n", - " 'deforest-globe', 'historical-cmip5', 'hist-bgc',\n", - " 'piControl-cmip5', 'rcp26-cmip5', 'rcp45-cmip5', 'rcp85-cmip5',\n", - " 'pdSST-piArcSIC', 'pdSST-piAntSIC', 'piSST-piSIC', 'piSST-pdSIC',\n", - " 'ssp245-stratO3', 'hist-sol', 'hist-CO2', 'hist-volc',\n", - " 'hist-totalO3', 'hist-nat-cmip5', 'hist-aer-cmip5',\n", - " 'hist-GHG-cmip5', 'pdSST-futAntSIC', 'futSST-pdSIC', 'pdSST-pdSIC',\n", - " 'ssp245-aer', 'pdSST-futArcSIC', 'dcppA-hindcast', 'dcppA-assim',\n", - " 'dcppC-hindcast-noPinatubo', 'dcppC-hindcast-noElChichon',\n", - " 'dcppC-hindcast-noAgung', 'hist-resIPO', 'ssp245-cov-modgreen',\n", - " 'ssp245-cov-fossil', 'ssp245-cov-strgreen', 'ssp245-covid', 'lgm',\n", - " 'ssp585-bgc', '1pctCO2to4x-withism', '1pctCO2-4xext', 'past1000',\n", - " 'pa-futArcSIC', 'pa-pdSIC', 'historical-ext', 'pdSST-futArcSICSIT',\n", - " 'pdSST-futOkhotskSIC', 'pdSST-futBKSeasSIC', 'pa-piArcSIC',\n", - " 'pa-piAntSIC', 'pa-futAntSIC', 'pdSST-pdSICSIT'], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['experiment_id'].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Loading Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Intake-ESM enables loading data directly into an `xarray.DataArray`, a metadata-aware extension of numpy arrays. Xarray objects leverage [Dask](https://www.dask.org/) to only read data into memory as needed for any specific operation (i.e. lazy evaluation). Think of Xarray Datasets as ways of conveniently organizing large arrays of floating point numbers (e.g. climate model data) on an n-dimensional discrete grid, with important metadata such as units, variable, names, etc.\n", - "\n", - "Note that data on the cloud are in [Zarr](https://zarr.readthedocs.io/en/stable/) format, an extension of the metadata-aware format [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) commonly used in the geosciences.\n", - "\n", - "Intake-ESM has rules for aggegating datasets; these rules are defined in the collection-specification file.\n", - "\n", - "Here, we choose the `piControl` experiment (in which CO$_2$ concentrations are held fixed at a pre-industrial level of ~300 ppm) and `abrupt-2xCO2` experiment (in which CO$_2$ concentrations are instantaneously doubled from a pre-industrial control state). Since the radiative forcing of CO$_2$ is roughly a logarithmic function of CO$_2$ concentrations, the ECS is roughly independent of the initial CO$_2$ concentration. \n", - "\n", - "
    \n", - "

    Warning

    \n", - "The version of this notebook in the \n", - "Pangeo Gallery\n", - "uses the abrupt-4xCO2 forcing experiment, but fewer abrupt-2xCO2 datasets are currently avaiable in Google Cloud Storage, which significantly reduces run time. If you want to run this notebook on your own computer with the abrupt-4xCO2 experiment instead, change co2_option in the cell below. You will also need to take half of ecs, as described in the Overview. \n", - "
    " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.120114Z", - "iopub.status.busy": "2023-12-18T19:45:45.119916Z", - "iopub.status.idle": "2023-12-18T19:45:45.123396Z", - "shell.execute_reply": "2023-12-18T19:45:45.122812Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "co2_option = 'abrupt-2xCO2'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Prepare Data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.125561Z", - "iopub.status.busy": "2023-12-18T19:45:45.125372Z", - "iopub.status.idle": "2023-12-18T19:45:45.463297Z", - "shell.execute_reply": "2023-12-18T19:45:45.462582Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    experiment_idvariable_idtable_id
    source_id
    CESM2241
    GISS-E2-1-G241
    GISS-E2-1-H241
    GISS-E2-2-G241
    IPSL-CM6A-LR241
    MIROC6241
    MRI-ESM2-0241
    \n", - "
    " - ], - "text/plain": [ - " experiment_id variable_id table_id\n", - "source_id \n", - "CESM2 2 4 1\n", - "GISS-E2-1-G 2 4 1\n", - "GISS-E2-1-H 2 4 1\n", - "GISS-E2-2-G 2 4 1\n", - "IPSL-CM6A-LR 2 4 1\n", - "MIROC6 2 4 1\n", - "MRI-ESM2-0 2 4 1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query = dict(\n", - " experiment_id=[co2_option,'piControl'], # pick the `abrupt-2xCO2` and `piControl` forcing experiments\n", - " table_id='Amon', # choose to look at atmospheric variables (A) saved at monthly resolution (mon)\n", - " variable_id=['tas', 'rsut','rsdt','rlut'], # choose to look at near-surface air temperature (tas) as our variable\n", - " member_id = 'r1i1p1f1', # arbitrarily pick one realization for each model (i.e. just one set of initial conditions)\n", - ")\n", - "\n", - "col_subset = col.search(require_all_on=[\"source_id\"], **query)\n", - "col_subset.df.groupby(\"source_id\")[\n", - " [\"experiment_id\", \"variable_id\", \"table_id\"]\n", - "].nunique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following functions help us load and homogenize the data. We use some [`dask.delayed`](https://docs.dask.org/en/latest/delayed.html) programming to open the datasets in parallel." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.466304Z", - "iopub.status.busy": "2023-12-18T19:45:45.465744Z", - "iopub.status.idle": "2023-12-18T19:45:45.471979Z", - "shell.execute_reply": "2023-12-18T19:45:45.470966Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def drop_all_bounds(ds):\n", - " \"\"\"Drop coordinates like 'time_bounds' from datasets,\n", - " which can lead to issues when merging.\"\"\"\n", - " drop_vars = [vname for vname in ds.coords\n", - " if (('_bounds') in vname ) or ('_bnds') in vname]\n", - " return ds.drop(drop_vars)\n", - "\n", - "def open_dsets(df):\n", - " \"\"\"Open datasets from cloud storage and return xarray dataset.\"\"\"\n", - " dsets = [xr.open_zarr(fsspec.get_mapper(ds_url), consolidated=True)\n", - " .pipe(drop_all_bounds)\n", - " for ds_url in df.zstore]\n", - " try:\n", - " ds = xr.merge(dsets, join='exact')\n", - " return ds\n", - " except ValueError:\n", - " return None\n", - "\n", - "def open_delayed(df):\n", - " \"\"\"A dask.delayed wrapper around `open_dsets`.\n", - " Allows us to open many datasets in parallel.\"\"\"\n", - " return dask.delayed(open_dsets)(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a nested dictionary of models and experiments. It will be structured like this:\n", - "```\n", - "{'CESM2':\n", - " {\n", - " 'piControl': ,\n", - " 'abrupt-2xCO2': \n", - " },\n", - " ...\n", - "}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.474907Z", - "iopub.status.busy": "2023-12-18T19:45:45.474491Z", - "iopub.status.idle": "2023-12-18T19:45:45.483603Z", - "shell.execute_reply": "2023-12-18T19:45:45.482019Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "dsets = defaultdict(dict)\n", - "for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):\n", - " dsets[group[0]][group[1]] = open_delayed(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Open one of the datasets directly, just to show what it looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:45.486203Z", - "iopub.status.busy": "2023-12-18T19:45:45.485731Z", - "iopub.status.idle": "2023-12-18T19:45:59.315323Z", - "shell.execute_reply": "2023-12-18T19:45:59.314579Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2.72 s, sys: 94.1 ms, total: 2.82 s\n", - "Wall time: 13.7 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "Dimensions:  (lat: 160, lon: 320, time: 8412)\n",
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-       "  * time     (time) object 1850-01-16 00:00:00 ... 2550-12-16 00:00:00\n",
-       "Data variables:\n",
-       "    tas      (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>\n",
-       "    rsut     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>\n",
-       "    rsdt     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>\n",
-       "    rlut     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>\n",
-       "Attributes: (12/47)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
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-       "    ...                     ...\n",
-       "    tracking_id:            hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6\n",
-       "    variable_id:            tas\n",
-       "    variant_label:          r1i1p1f1\n",
-       "    status:                 2019-10-25;created;by nhn2@columbia.edu\n",
-       "    netcdf_tracking_ids:    hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6\n",
-       "    version_id:             v20190222
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 160, lon: 320, time: 8412)\n", - "Coordinates:\n", - " height float64 ...\n", - " * lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14\n", - " * lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 355.5 356.6 357.8 358.9\n", - " * time (time) object 1850-01-16 00:00:00 ... 2550-12-16 00:00:00\n", - "Data variables:\n", - " tas (time, lat, lon) float32 dask.array\n", - " rsut (time, lat, lon) float32 dask.array\n", - " rsdt (time, lat, lon) float32 dask.array\n", - " rlut (time, lat, lon) float32 dask.array\n", - "Attributes: (12/47)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 365243.0\n", - " cmor_version: 3.4.0\n", - " ... ...\n", - " tracking_id: hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6\n", - " variable_id: tas\n", - " variant_label: r1i1p1f1\n", - " status: 2019-10-25;created;by nhn2@columbia.edu\n", - " netcdf_tracking_ids: hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6\n", - " version_id: v20190222" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time open_dsets(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now use dask to do this in parallel on all of the datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:45:59.318565Z", - "iopub.status.busy": "2023-12-18T19:45:59.317789Z", - "iopub.status.idle": "2023-12-18T19:46:24.556662Z", - "shell.execute_reply": "2023-12-18T19:46:24.555556Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - } - ], - "source": [ - "dsets_ = dask.compute(dict(dsets))[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Reduce Data via Global Mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We don’t want to load all of the raw model data into memory right away. Instead, we want to reduce the data by taking the global mean. We need to remember to weight this global mean by a factor proportional to `cos(lat)`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:24.561018Z", - "iopub.status.busy": "2023-12-18T19:46:24.560705Z", - "iopub.status.idle": "2023-12-18T19:46:24.566082Z", - "shell.execute_reply": "2023-12-18T19:46:24.565259Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def get_lat_name(ds):\n", - " \"\"\"Figure out what is the latitude coordinate for each dataset.\"\"\"\n", - " for lat_name in ['lat', 'latitude']:\n", - " if lat_name in ds.coords:\n", - " return lat_name\n", - " raise RuntimeError(\"Couldn't find a latitude coordinate\")\n", - "\n", - "def global_mean(ds):\n", - " \"\"\"Return global mean of a whole dataset.\"\"\"\n", - " lat = ds[get_lat_name(ds)]\n", - " weight = np.cos(np.deg2rad(lat))\n", - " weight /= weight.mean()\n", - " other_dims = set(ds.dims) - {'time'}\n", - " return (ds * weight).mean(other_dims)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now apply this function, plus resampling to annual mean data, to all of the datasets. We also concatenate the experiments together into a single Dataset for each model. This is the most complex cell in the notebook. A lot is happening here." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:24.569527Z", - "iopub.status.busy": "2023-12-18T19:46:24.568578Z", - "iopub.status.idle": "2023-12-18T19:46:26.800675Z", - "shell.execute_reply": "2023-12-18T19:46:26.799970Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ac233d8e3e3e4589b899f5c08a0adcbe", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/7 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:        (year: 250, experiment_id: 2, source_id: 7)\n",
-       "Coordinates:\n",
-       "  * year           (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0\n",
-       "  * experiment_id  (experiment_id) <U12 'piControl' 'abrupt-2xCO2'\n",
-       "  * source_id      (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MRI-ESM2-0'\n",
-       "Data variables:\n",
-       "    rsut           (source_id, experiment_id, year) float64 99.36 99.8 ... nan\n",
-       "    tas            (source_id, experiment_id, year) float64 287.0 287.1 ... nan\n",
-       "    rsdt           (source_id, experiment_id, year) float64 340.3 340.3 ... nan\n",
-       "    rlut           (source_id, experiment_id, year) float64 240.2 240.5 ... nan
    " - ], - "text/plain": [ - "\n", - "Dimensions: (year: 250, experiment_id: 2, source_id: 7)\n", - "Coordinates:\n", - " * year (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0\n", - " * experiment_id (experiment_id) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:        (year: 250, experiment_id: 2, source_id: 7)\n",
-       "Coordinates:\n",
-       "  * year           (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0\n",
-       "  * experiment_id  (experiment_id) <U12 'piControl' 'abrupt-2xCO2'\n",
-       "  * source_id      (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MRI-ESM2-0'\n",
-       "Data variables:\n",
-       "    tas            (source_id, experiment_id, year) float64 0.06312 ... nan\n",
-       "    imbalance      (source_id, experiment_id, year) float64 -0.04953 ... nan
    " - ], - "text/plain": [ - "\n", - "Dimensions: (year: 250, experiment_id: 2, source_id: 7)\n", - "Coordinates:\n", - " * year (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0\n", - " * experiment_id (experiment_id) " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds_anom.tas.plot.line(col='source_id', x='year', col_wrap=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the models cover different time intervals. Let’s limit the rest of our analysis to the first 150 years." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:49.261522Z", - "iopub.status.busy": "2023-12-18T19:46:49.261297Z", - "iopub.status.idle": "2023-12-18T19:46:50.826284Z", - "shell.execute_reply": "2023-12-18T19:46:50.825237Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "first_150_years = slice(0, 149)\n", - "ds_anom.tas.sel(year=first_150_years).plot.line(col='source_id', x='year', col_wrap=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Same thing for radiative imbalance:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:50.830923Z", - "iopub.status.busy": "2023-12-18T19:46:50.830610Z", - "iopub.status.idle": "2023-12-18T19:46:53.002912Z", - "shell.execute_reply": "2023-12-18T19:46:53.001909Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds_anom.imbalance.sel(year=first_150_years).plot.line(col='source_id', x='year', col_wrap=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate ECS" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:53.008648Z", - "iopub.status.busy": "2023-12-18T19:46:53.008418Z", - "iopub.status.idle": "2023-12-18T19:46:53.043444Z", - "shell.execute_reply": "2023-12-18T19:46:53.042460Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.Dataset>\n",
-       "Dimensions:    (year: 150, source_id: 7)\n",
-       "Coordinates:\n",
-       "  * year       (year) float64 0.0 1.0 2.0 3.0 4.0 ... 146.0 147.0 148.0 149.0\n",
-       "  * source_id  (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MIROC6' 'MRI-ESM2-0'\n",
-       "Data variables:\n",
-       "    tas        (source_id, year) float64 0.5582 0.8175 1.095 ... 1.646 1.616\n",
-       "    imbalance  (source_id, year) float64 4.026 3.355 3.579 ... 1.022 0.7031\n",
-       "    ecs        (source_id) float64 3.526 2.659 2.932 2.501 3.827 2.235 2.552
    " - ], - "text/plain": [ - "\n", - "Dimensions: (year: 150, source_id: 7)\n", - "Coordinates:\n", - " * year (year) float64 0.0 1.0 2.0 3.0 4.0 ... 146.0 147.0 148.0 149.0\n", - " * source_id (source_id) " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds_abrupt = ds_abrupt.set_coords(['tas', 'imbalance'])\n", - "\n", - "fg = ds_abrupt.plot.scatter(x='tas', y='imbalance', col='source_id', col_wrap=4, add_colorbar=False)\n", - "\n", - "def calc_and_plot_ecs(x, y, **kwargs):\n", - " x = x[~np.isnan(x)]\n", - " y = y[~np.isnan(y)]\n", - " a, b = np.polyfit(x, y, 1)\n", - " ecs = -1.0 * b/a\n", - " plt.autoscale(False)\n", - " plt.plot([0, 10], np.polyval([a, b], [0, 10]), 'k')\n", - " plt.text(2, 3, f'ECS = {ecs:3.2f}', fontdict={'weight': 'bold', 'size': 12})\n", - " plt.grid()\n", - "\n", - "fg.map(calc_and_plot_ecs, 'tas', 'imbalance')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:54.287339Z", - "iopub.status.busy": "2023-12-18T19:46:54.286991Z", - "iopub.status.idle": "2023-12-18T19:46:54.459164Z", - "shell.execute_reply": "2023-12-18T19:46:54.458401Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds_abrupt.ecs.plot.hist();" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:54.461811Z", - "iopub.status.busy": "2023-12-18T19:46:54.461598Z", - "iopub.status.idle": "2023-12-18T19:46:54.665870Z", - "shell.execute_reply": "2023-12-18T19:46:54.665093Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds_abrupt.ecs.to_dataframe().sort_values('ecs').plot(kind='bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're at the end of the notebook, so let's shutdown our Dask cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:54.669330Z", - "iopub.status.busy": "2023-12-18T19:46:54.669095Z", - "iopub.status.idle": "2023-12-18T19:46:55.428004Z", - "shell.execute_reply": "2023-12-18T19:46:55.427151Z" - } - }, - "outputs": [], - "source": [ - "client.shutdown()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we estimated ECS for a subset of CMIP6 models using the Gregory method.\n", - "\n", - "### What's next?\n", - "We will plot global average surface air temperature for a historical run and two branching emissions scenarios." - 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"cells": [ - { - "cell_type": "markdown", - "id": "7d1ea66b-fa0c-4fa4-9b65-3d7b07716e62", - "metadata": {}, - "source": [ - "# Calculating ENSO Using Intake-ESGF" - ] - }, - { - "cell_type": "markdown", - "id": "4a415308-0e9a-470c-bb68-da75b349c006", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "In this workflow, we combine topics covered in previous Pythia Foundations and CMIP6 Cookbook content to apply the [Niño 3.4 Index](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) to a broader range of datasets. As a refresher of what the ENSO 3.4 index is, please see the following text, which is also included in the [ENSO Xarray](https://foundations.projectpythia.org/core/xarray/enso-xarray.html) content in the Pythia Foundations content.\n", - "\n", - "> Niño 3.4 (5N-5S, 170W-120W): The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more.\n", - "\n", - "> Niño X Index computation: a) Compute area averaged total SST from Niño X region; b) Compute monthly climatology (e.g., 1950-1979) for area averaged total SST from Niño X region, and subtract climatology from area averaged total SST time series to obtain anomalies; c) Smooth the anomalies with a 5-month running mean; d) Normalize the smoothed values by its standard deviation over the climatological period.\n", - "\n", - "![](https://www.ncdc.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)\n", - "\n", - "The previous example in the Pythia Foundations content detailed a single simulation. In this example, we aim to apply this computation more generically across a variety of datasets.\n", - "\n", - "The overall goal of this tutorial is to produce a plot of ENSO data using Xarray and intake-ESGF. The plots will resemble the Oceanic Niño Index plot shown below.\n", - "\n", - "![ONI index plot from NCAR Climate Data Guide](https://climatedataguide.ucar.edu/sites/default/files/styles/extra_large/public/2022-03/indices_oni_2_2_lg.png)" - ] - }, - { - "cell_type": "markdown", - "id": "2d4c6aed-a9c5-4d29-bfa3-c8e8be230567", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [hvPlot Basics](https://hvplot.holoviz.org/getting_started/hvplot.html) | Necessary | Interactive Visualization with hvPlot |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| [Calculating ENSO with Xarray](https://foundations.projectpythia.org/core/xarray/enso-xarray.html) | Neccessary | Understanding of Masking and Xarray Functions |\n", - "| Dask | Helpful | |\n", - "\n", - "- **Time to learn**: 30 minutes" - ] - }, - { - "cell_type": "markdown", - "id": "7ff38f37-8f14-443f-b0c7-188baf75d1be", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "52bcfa1a-3907-446d-b384-29e97b5c8cb9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:20.384471Z", - "iopub.status.busy": "2023-12-18T19:52:20.384252Z", - "iopub.status.idle": "2023-12-18T19:52:23.263119Z", - "shell.execute_reply": "2023-12-18T19:52:23.262298Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - " var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", - " var reloading = 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[\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n", - " var js_modules = [];\n", - " var js_exports = {};\n", - " var css_urls = [];\n", - " var inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - "\tvar NewBokeh = root.Bokeh;\n", - "\tif (Bokeh.versions === undefined) {\n", - "\t Bokeh.versions = new Map();\n", - "\t}\n", - "\tif (NewBokeh.version !== Bokeh.version) {\n", - "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - "\t}\n", - "\troot.Bokeh = Bokeh;\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " }\n", - " root._bokeh_is_initializing = false\n", - " }\n", - "\n", - " function load_or_wait() {\n", - " // Implement a backoff loop that tries to ensure we do not load multiple\n", - " // versions of Bokeh and its dependencies at the same time.\n", - " // In recent versions we use the root._bokeh_is_initializing flag\n", - " // to determine whether there is an ongoing attempt to initialize\n", - " // bokeh, however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", - " setTimeout(load_or_wait, 100);\n", - " } else {\n", - " Bokeh = root.Bokeh;\n", - " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", - " root._bokeh_is_initializing = true\n", - " root._bokeh_onload_callbacks = []\n", - " if (!reloading && (!bokeh_loaded || is_dev)) {\n", - "\troot.Bokeh = undefined;\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - "\tconsole.debug(\"Bokeh: BokehJS 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root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 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element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no 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however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", - " setTimeout(load_or_wait, 100);\n", - " } else {\n", - " Bokeh = root.Bokeh;\n", - " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", - " root._bokeh_is_initializing = true\n", - " root._bokeh_onload_callbacks = []\n", - " if (!reloading && (!bokeh_loaded 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scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 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require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof 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(!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = 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metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import hvplot.xarray\n", - "import holoviews as hv\n", - "import numpy as np\n", - "import hvplot.xarray\n", - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs\n", - "from intake_esgf import ESGFCatalog\n", - "import xarray as xr\n", - "import cf_xarray\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "hv.extension(\"bokeh\")" - ] - }, - { - "cell_type": "markdown", - "id": "7aa56cff-6a80-47fc-b99e-fd9fa960032c", - "metadata": {}, - "source": [ - "## Access ESGF-hosted CMIP6 Data\n", - "We will use the Climate Model Intercomparison Project version 6 (CMIP6) dataset, which is available from the Earth System Grid Federation (ESGF) data servers.\n", - "\n", - "There is a toolkit, `intake-esgf`, we can use to interface with the data servers, making it easier to search for our datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b82ae2da-1928-4f6b-b17b-551b82465845", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:23.267166Z", - "iopub.status.busy": "2023-12-18T19:52:23.265840Z", - "iopub.status.idle": "2023-12-18T19:52:24.155028Z", - "shell.execute_reply": "2023-12-18T19:52:24.154297Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 0%| |0/1 [ ?index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 100%|##########|1/1 [ 1.17index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 100%|##########|1/1 [ 1.17index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "Summary information for 2 results:\n", - "mip_era [CMIP6]\n", - "activity_id [CMIP]\n", - "institution_id [NCAR]\n", - "source_id [CESM2]\n", - "experiment_id [historical]\n", - "member_id [r11i1p1f1]\n", - "table_id [Omon]\n", - "variable_id [tos]\n", - "grid_label [gn, gr]\n", - "dtype: object" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat = ESGFCatalog()\n", - "cat.search(\n", - " activity_id='CMIP',\n", - " experiment_id=[\"historical\",\"ssp585\"],\n", - " source_id=\"CESM2\",\n", - " variable_id=[\"tos\"],\n", - " member_id='r11i1p1f1',\n", - " table_id=\"Omon\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "29bc9830-67b0-40ed-aae3-ad5b759878d5", - "metadata": {}, - "source": [ - "### Load into a DataTree\n", - "Once we subset for our data, we can load the data into a datatree, which is a nested structure of `xarray.Dataset`s, which include the climate grid cell statistics as well!" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2b8d3bc8-f568-44f7-bee2-833cf532d823", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:24.157678Z", - "iopub.status.busy": "2023-12-18T19:52:24.157468Z", - "iopub.status.idle": "2023-12-18T19:52:55.884044Z", - "shell.execute_reply": "2023-12-18T19:52:55.883118Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Obtaining file info: 0%| |0/2 [ ?dataset/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Obtaining file info: 50%|##### |1/2 [ 1.11dataset/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Obtaining file info: 100%|##########|2/2 [ 1.33dataset/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Obtaining file info: 100%|##########|2/2 [ 1.29dataset/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "tos_Omon_CESM2_historical_r11i1p1f1_gn_190001-194912.nc: 0%| |0.00/150M [?B/s]" - ] - }, - { - "name": "stderr", - 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-       "    lon        (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8\n",
-       "  * nlat       (nlat) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384\n",
-       "  * nlon       (nlon) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320\n",
-       "  * time       (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: d2, vertices\n",
-       "Data variables:\n",
-       "    tos        (time, nlat, nlon) float32 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
-       "    time_bnds  (time, d2) object dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (time, nlat, nlon, vertices) float32 dask.array<chunksize=(600, 384, 320, 4), meta=np.ndarray>\n",
-       "    lon_bnds   (time, nlat, nlon, vertices) float32 dask.array<chunksize=(600, 384, 320, 4), meta=np.ndarray>\n",
-       "    areacello  (nlat, nlon) float32 ...\n",
-       "    sftof      (nlat, nlon) float32 ...\n",
-       "Attributes: (12/45)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   674885.0\n",
-       "    branch_time_in_parent:  219000.0\n",
-       "    case_id:                972\n",
-       "    ...                     ...\n",
-       "    sub_experiment_id:      none\n",
-       "    table_id:               Omon\n",
-       "    tracking_id:            hdl:21.14100/b0ffb89d-095d-4533-a159-a2e1241ff138\n",
-       "    variable_id:            tos\n",
-       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
-       "    variant_label:          r11i1p1f1
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1980, nlat: 384, nlon: 320, d2: 2, vertices: 4)\n", - "Coordinates:\n", - " lat (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n", - " lon (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8\n", - " * nlat (nlat) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384\n", - " * nlon (nlon) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320\n", - " * time (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00\n", - "Dimensions without coordinates: d2, vertices\n", - "Data variables:\n", - " tos (time, nlat, nlon) float32 dask.array\n", - " time_bnds (time, d2) object dask.array\n", - " lat_bnds (time, nlat, nlon, vertices) float32 dask.array\n", - " lon_bnds (time, nlat, nlon, vertices) float32 dask.array\n", - " areacello (nlat, nlon) float32 ...\n", - " sftof (nlat, nlon) float32 ...\n", - "Attributes: (12/45)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 674885.0\n", - " branch_time_in_parent: 219000.0\n", - " case_id: 972\n", - " ... ...\n", - " sub_experiment_id: none\n", - " table_id: Omon\n", - " tracking_id: hdl:21.14100/b0ffb89d-095d-4533-a159-a2e1241ff138\n", - " variable_id: tos\n", - " variant_info: CMIP6 20th century experiments (1850-2014) with C...\n", - " variant_label: r11i1p1f1" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = tos_tree[\"gn\"].to_dataset()\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "f3ac774f-7a6f-425b-82c9-c5b3099eb203", - "metadata": {}, - "source": [ - "## Calculate ENSO\n", - "The calculation is covered in more detail in the Pythia Foundations book, here, we apply the calculation to our datasets!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c01c56eb-71b9-4b4b-92c3-31ff3714b55b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:55.926555Z", - "iopub.status.busy": "2023-12-18T19:52:55.926361Z", - "iopub.status.idle": "2023-12-18T19:52:55.931482Z", - "shell.execute_reply": "2023-12-18T19:52:55.930822Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def calculate_enso(ds):\n", - " \n", - " # Subset the El Nino 3.4 index region\n", - " dso = ds.where(\n", - " (ds.cf[\"latitude\"] < 5) & (ds.cf[\"latitude\"] > -5) & (ds.cf[\"longitude\"] > 190) & (ds.cf[\"longitude\"] < 240), drop=True\n", - " )\n", - " \n", - " # Calculate the monthly means\n", - " gb = dso.tos.groupby('time.month')\n", - " \n", - " # Subtract the monthly averages, returning the anomalies\n", - " tos_nino34_anom = gb - gb.mean(dim='time')\n", - " \n", - " # Determine the non-time dimensions and average using these\n", - " non_time_dims = set(tos_nino34_anom.dims)\n", - " non_time_dims.remove(ds.tos.cf[\"T\"].name)\n", - " weighted_average = tos_nino34_anom.weighted(ds[\"areacello\"]).mean(dim=list(non_time_dims))\n", - " \n", - " # Calculate the rolling average\n", - " rolling_average = weighted_average.rolling(time=5, center=True).mean()\n", - " std_dev = weighted_average.std()\n", - " return rolling_average / std_dev" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "193b0055-35a4-4f48-8d00-b24059f00160", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:55.933687Z", - "iopub.status.busy": "2023-12-18T19:52:55.933493Z", - "iopub.status.idle": "2023-12-18T19:53:08.374799Z", - "shell.execute_reply": "2023-12-18T19:53:08.373810Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.DataArray 'tos' (time: 1980)>\n",
-       "array([       nan,        nan, 0.06341499, ..., 0.79205155,        nan,\n",
-       "              nan], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00\n",
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    " - ], - "text/plain": [ - "\n", - "array([ nan, nan, 0.06341499, ..., 0.79205155, nan,\n", - " nan], dtype=float32)\n", - "Coordinates:\n", - " * time (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00\n", - " month (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enso_index = calculate_enso(ds).compute()\n", - "enso_index" - ] - }, - { - "cell_type": "markdown", - "id": "0a6dba6d-f305-4909-bbde-63366c5cb2b5", - "metadata": {}, - "source": [ - "## Visualize ENSO\n", - "\n", - "### Basic Visualization\n", - "We can create a basic visualization of the dataset using hvplot!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "67cac155-bb58-4c80-a0e4-1c3fd8de8eb4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.378704Z", - "iopub.status.busy": "2023-12-18T19:53:08.378159Z", - "iopub.status.idle": "2023-12-18T19:53:08.515073Z", - "shell.execute_reply": "2023-12-18T19:53:08.514290Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:param.CurvePlot00970: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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    \n", - "" - ], - "text/plain": [ - ":Curve [time] (tos)" - ] - }, - "execution_count": 7, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1004" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "enso_index.hvplot(x='time')" - ] - }, - { - "cell_type": "markdown", - "id": "629d1d24-accb-426d-858c-6422d568d97d", - "metadata": {}, - "source": [ - "### Identify El Niño and La Niña\n", - "Including the indices as we showed above is not always the most helpful. We need to add additional context to help the reader understand when we reach El Niño and La Niña, which are helpful thresholds for the wider community to use.\n", - "\n", - "A typical threshold to use is 0.4, which means El Niño occurs when the ENSO 3.4 index is equal to or greater than 0.4, and La Niña occurs when the ENSO 3.4 index is equal to or less than 0.4.\n", - "\n", - "We apply this using the following function." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0bf6b460-f64a-47d2-b819-53abdc0b03f6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.517811Z", - "iopub.status.busy": "2023-12-18T19:53:08.517593Z", - "iopub.status.idle": "2023-12-18T19:53:08.522843Z", - "shell.execute_reply": "2023-12-18T19:53:08.522108Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def add_enso_thresholds(da, threshold=0.4):\n", - " \n", - " # Conver the xr.DataArray into an xr.Dataset\n", - " ds = da.to_dataset()\n", - " \n", - " # Cleanup the time and use the thresholds\n", - " try:\n", - " ds[\"time\"]= ds.indexes[\"time\"].to_datetimeindex()\n", - " except:\n", - " pass\n", - " ds[\"tos_gt_04\"] = (\"time\", ds.tos.where(ds.tos >= threshold, threshold).data)\n", - " ds[\"tos_lt_04\"] = (\"time\", ds.tos.where(ds.tos <= -threshold, -threshold).data)\n", - " \n", - " # Add fields for the thresholds\n", - " ds[\"el_nino_threshold\"] = (\"time\", np.zeros_like(ds.tos) + threshold)\n", - " ds[\"la_nina_threshold\"] = (\"time\", np.zeros_like(ds.tos) - threshold)\n", - " \n", - " return ds" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "13680161-2f71-40f0-9c56-df8cac9621a5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.525219Z", - "iopub.status.busy": "2023-12-18T19:53:08.525012Z", - "iopub.status.idle": "2023-12-18T19:53:08.576100Z", - "shell.execute_reply": "2023-12-18T19:53:08.575371Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.Dataset>\n",
-       "Dimensions:            (time: 1980)\n",
-       "Coordinates:\n",
-       "  * time               (time) datetime64[ns] 1850-01-15T13:00:00.000007 ... 2...\n",
-       "    month              (time) int64 1 2 3 4 5 6 7 8 9 ... 4 5 6 7 8 9 10 11 12\n",
-       "Data variables:\n",
-       "    tos                (time) float32 nan nan 0.06341 ... 0.7921 nan nan\n",
-       "    tos_gt_04          (time) float32 0.4 0.4 0.4 0.4 ... 0.6829 0.7921 0.4 0.4\n",
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    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1980)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1850-01-15T13:00:00.000007 ... 2...\n", - " month (time) int64 1 2 3 4 5 6 7 8 9 ... 4 5 6 7 8 9 10 11 12\n", - "Data variables:\n", - " tos (time) float32 nan nan 0.06341 ... 0.7921 nan nan\n", - " tos_gt_04 (time) float32 0.4 0.4 0.4 0.4 ... 0.6829 0.7921 0.4 0.4\n", - " tos_lt_04 (time) float32 -0.4 -0.4 -0.4 -0.4 ... -0.4 -0.4 -0.4\n", - " el_nino_threshold (time) float32 0.4 0.4 0.4 0.4 0.4 ... 0.4 0.4 0.4 0.4\n", - " la_nina_threshold (time) float32 -0.4 -0.4 -0.4 -0.4 ... -0.4 -0.4 -0.4" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enso_ds = add_enso_thresholds(enso_index)\n", - "enso_ds" - ] - }, - { - "cell_type": "markdown", - "id": "b502d1d8-e4d7-497d-84fa-c478ec7b4e91", - "metadata": {}, - "source": [ - "### Configure a Function to Plot the Data\n", - "We will use the `hvplot.area` functionality here, which enables us to shade the area between values. We use the newly added variables in our dataset to help here." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d2e8e487-5158-4825-a7aa-9dfe582192c4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.578476Z", - "iopub.status.busy": "2023-12-18T19:53:08.578269Z", - "iopub.status.idle": "2023-12-18T19:53:08.583500Z", - "shell.execute_reply": "2023-12-18T19:53:08.582642Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_enso(ds):\n", - " el_nino = ds.hvplot.area(x=\"time\", y2='tos_gt_04', y='el_nino_threshold', color='red', hover=False)\n", - " el_nino_label = hv.Text(ds.isel(time=40).time.values, 2, 'El Niño').opts(text_color='red',)\n", - "\n", - " # Create the La Niña area graphs\n", - " la_nina = ds.hvplot.area(x=\"time\", y2='tos_lt_04', y='la_nina_threshold', color='blue', hover=False)\n", - " la_nina_label = hv.Text(ds.isel(time=-40).time.values, -2, 'La Niña').opts(text_color='blue')\n", - "\n", - " # Plot a timeseries of the ENSO 3.4 index\n", - " enso = ds.tos.hvplot(x='time', line_width=0.5, color='k', xlabel='Year', ylabel='ENSO 3.4 Index')\n", - "\n", - " # Combine all the plots into a single plot\n", - " return (el_nino_label * la_nina_label * el_nino * la_nina * enso)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ed848ecc-1731-46a3-a898-a4cfcaa55d71", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.585868Z", - "iopub.status.busy": "2023-12-18T19:53:08.585666Z", - "iopub.status.idle": "2023-12-18T19:53:08.851680Z", - "shell.execute_reply": "2023-12-18T19:53:08.850937Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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    \n", - "" - ], - "text/plain": [ - ":Overlay\n", - " .Text.I :Text [x,y]\n", - " .Text.II :Text [x,y]\n", - " .Area.I :Area [time] (el_nino_threshold,tos_gt_04)\n", - " .Area.II :Area [time] (la_nina_threshold,tos_lt_04)\n", - " .Curve.I :Curve [time] (tos)" - ] - }, - "execution_count": 11, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1075" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "plot_enso(enso_ds)" - ] - }, - { - "cell_type": "markdown", - "id": "c3bf5d22-57fa-4fad-b194-064a2aae342b", - "metadata": {}, - "source": [ - "## Apply to Multiple Datasets\n", - "Now that we have the workflow, let's apply this to multiple datasets. We focus here on two different instiutions:\n", - "- The National Center for Atmospheric Research (NCAR)\n", - "- Model for Interdisciplinary Research on Climate (MIROC)\n", - "\n", - "Both of these modeling centers produced output for CMIP6.\n" - ] - }, - { - "cell_type": "markdown", - "id": "0624f670-f805-4813-8853-d7f94f2c2a86", - "metadata": {}, - "source": [ - "### Setup a Function for Searching and Combining Datasets\n", - "We can use the query mentioned previously to configure our search. Here, we parameterize based on the institution id (ex. NCAR, MIROC)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "256207a2-0275-4897-b380-334de13e9ce1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.855635Z", - "iopub.status.busy": "2023-12-18T19:53:08.854955Z", - "iopub.status.idle": "2023-12-18T19:53:08.859354Z", - "shell.execute_reply": "2023-12-18T19:53:08.858651Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def search_esgf(institution_id, grid='gn'):\n", - " \n", - " # Search and load the ocean surface temperature (tos)\n", - " cat = ESGFCatalog()\n", - " cat.search(\n", - " activity_id=\"CMIP\",\n", - " experiment_id=\"historical\",\n", - " institution_id=institution_id,\n", - " variable_id=[\"tos\"],\n", - " member_id='r11i1p1f1',\n", - " table_id=\"Omon\",\n", - " )\n", - " try:\n", - " tos_ds = cat.to_datatree()[grid].to_dataset()\n", - " except KeyError:\n", - " tos_ds = cat.to_dataset_dict()[\"tos\"]\n", - "\n", - " return tos_ds" - ] - }, - { - "cell_type": "markdown", - "id": "28fd9293-4b6e-4881-af47-8f95ece00c62", - "metadata": {}, - "source": [ - "### Apply the Search and Computations" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3a173bf5-c152-4e63-a0c3-c1055ea1439b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:08.861769Z", - "iopub.status.busy": "2023-12-18T19:53:08.861571Z", - "iopub.status.idle": "2023-12-18T19:53:31.775821Z", - "shell.execute_reply": "2023-12-18T19:53:31.774831Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 0%| |0/1 [ ?index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 100%|##########|1/1 [ 1.15index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " Searching indices: 100%|##########|1/1 [ 1.15index/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - 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"text": [ - "\r", - "Adding cell measures: 100%|##########|1/1 [ 3.38s/dataset]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Adding cell measures: 100%|##########|1/1 [ 3.38s/dataset]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "miroc_ds = search_esgf(\"MIROC\")\n", - "enso_index_miroc = add_enso_thresholds(calculate_enso(miroc_ds).compute())" - ] - }, - { - "cell_type": "markdown", - "id": "d2f0a6b0-07d7-4c95-9598-0d746cfcaf72", - "metadata": {}, - "source": [ - "### Visualize our ENSO Comparison\n", - "Now that we have our data, we can plot the comparison, stacking the two together using hvPlot." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "54594fe4-136b-4150-bb1f-25f70df82364", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:53:59.131853Z", - "iopub.status.busy": "2023-12-18T19:53:59.131617Z", - "iopub.status.idle": "2023-12-18T19:53:59.700629Z", - "shell.execute_reply": "2023-12-18T19:53:59.699243Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
    \n", - "
    \n", - "
    \n", - "" - ], - "text/plain": [ - ":Layout\n", - " .Overlay.I :Overlay\n", - " .Text.I :Text [x,y]\n", - " .Text.II :Text [x,y]\n", - " .Area.I :Area [time] (el_nino_threshold,tos_gt_04)\n", - " .Area.II :Area [time] (la_nina_threshold,tos_lt_04)\n", - " .Curve.I :Curve [time] (tos)\n", - " .Overlay.II :Overlay\n", - " .Text.I :Text [x,y]\n", - " .Text.II :Text [x,y]\n", - " .Area.I :Area [time] (el_nino_threshold,tos_gt_04)\n", - " .Area.II :Area [time] (la_nina_threshold,tos_lt_04)\n", - " .Curve.I :Curve [time] (tos)" - ] - }, - "execution_count": 15, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1210" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "ncar_enso_plot = plot_enso(enso_index_ncar).opts(title=f'NCAR {ncar_ds.attrs[\"source_id\"]} \\n Ensemble Member: {ncar_ds.attrs[\"variant_label\"]}')\n", - "miroc_enso_plot = plot_enso(enso_index_miroc).opts(title=f'MIROC {miroc_ds.attrs[\"source_id\"]} \\n Ensemble Member: {miroc_ds.attrs[\"variant_label\"]}')\n", - "\n", - "(ncar_enso_plot + miroc_enso_plot).cols(1)" - ] - }, - { - "cell_type": "markdown", - "id": "b3bfceb9-124a-4d72-9d49-3ca255965e29", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we searched for and accessed two different CMIP6 datasets hosted through ESGF, calculated the ENSO 3.4 indices for the datasets, and created interactive plots comparing where we see El Niño and La Niña.\n", - "\n", - "### What's next?\n", - "We will see some more advanced examples of using the CMIP6 and other data access methods as well as computations\n", - "\n", - "## Resources and references\n", - "- [Intake-ESGF Documentation](https://github.com/nocollier/intake-esgf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5b2864f-6661-4aa4-8d65-8dc10c961b36", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/77/_sources/notebooks/example-workflows/esgf2-arm-comparison.ipynb b/_preview/77/_sources/notebooks/example-workflows/esgf2-arm-comparison.ipynb deleted file mode 100644 index e4d192e..0000000 --- a/_preview/77/_sources/notebooks/example-workflows/esgf2-arm-comparison.ipynb +++ /dev/null @@ -1,4292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "26550474-a788-4c90-a6fc-fb520acc8d41", - "metadata": {}, - "source": [ - "\"ESGF\n", - "\"ARM" - ] - }, - { - "cell_type": "markdown", - "id": "b26398c8-01f4-40e7-bf6b-db9e4c466eac", - "metadata": {}, - "source": [ - "# Compare Data from ESGF and ARM" - ] - }, - { - "cell_type": "markdown", - "id": "dd0adfff-43d4-4874-9d6d-511693ce8613", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This notebook details how to compare CMIP6 data hosted through the Earth System Grid Federation (ESGF) to observations collected and hosted through the Department of Energy's Atmospheric Radiation Measurement (ARM) user facility.\n", - "\n", - "The measurement of focus is 2 meter air temperature, collected at the Southern Great Plains (SGP) site in Northern Oklahoma. This climate observatory has collected state-of-the-art observations since 1993." - ] - }, - { - "cell_type": "markdown", - "id": "5ffd1461-464c-4e04-ad8f-e2aa1a710d59", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Search and Load CMIP6 Data via ESGF/OPeNDAP](https://projectpythia.org/cmip6-cookbook/notebooks/foundations/esgf-opendap.html) | Necessary | Familiarity with data access patterns |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| [Dask Arrays with Xarray](https://foundations.projectpythia.org/core/xarray/dask-arrays-xarray.html) | Helpful | Familiarity with lazy-loading |\n", - "\n", - "- **Time to learn**: 25 minutes" - ] - }, - { - "cell_type": "markdown", - "id": "a40b8cb6-ba5a-470e-90ef-3e21e0a0dc97", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ace2396c-6182-4488-8f4c-d4993fffbe00", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:46:59.390881Z", - "iopub.status.busy": "2023-12-18T19:46:59.390384Z", - "iopub.status.idle": "2023-12-18T19:47:03.706867Z", - "shell.execute_reply": "2023-12-18T19:47:03.706188Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - " var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", - " var reloading = false;\n", - " var Bokeh = root.Bokeh;\n", - " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && 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['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\n", - " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\n", - " } var existing_scripts = []\n", - " var scripts = document.getElementsByTagName('script')\n", - " for (var i = 0; i < scripts.length; i++) {\n", - " var script = scripts[i]\n", - " if (script.src != null) {\n", - "\texisting_scripts.push(script.src)\n", - " }\n", - " }\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " for (var i = 0; i < js_modules.length; i++) {\n", - " var url = js_modules[i];\n", - " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " element.type = \"module\";\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " for (const name in js_exports) {\n", - " var url = js_exports[name];\n", - " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.type = \"module\";\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " element.textContent = `\n", - " import ${name} from \"${url}\"\n", - " window.${name} = ${name}\n", - " window._bokeh_on_load()\n", - " `\n", - " document.head.appendChild(element);\n", - " }\n", - " if (!js_urls.length && !js_modules.length) {\n", - " on_load()\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n", - " var js_modules = [];\n", - " var js_exports = {};\n", - " var css_urls = [];\n", - " var inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - "\tvar NewBokeh = root.Bokeh;\n", - "\tif (Bokeh.versions === undefined) {\n", - "\t Bokeh.versions = new Map();\n", - "\t}\n", - "\tif (NewBokeh.version !== Bokeh.version) {\n", - "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - "\t}\n", - "\troot.Bokeh = Bokeh;\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " }\n", - " root._bokeh_is_initializing = false\n", - " }\n", - "\n", - " function load_or_wait() {\n", - " // Implement a backoff loop that tries to ensure we do not load multiple\n", - " // versions of Bokeh and its dependencies at the same time.\n", - " // In recent versions we use the root._bokeh_is_initializing flag\n", - " // to determine whether there is an ongoing attempt to initialize\n", - " // bokeh, however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", - " setTimeout(load_or_wait, 100);\n", - " } else {\n", - " Bokeh = root.Bokeh;\n", - " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", - " root._bokeh_is_initializing = true\n", - " root._bokeh_onload_callbacks = []\n", - " if (!reloading && (!bokeh_loaded || is_dev)) {\n", - "\troot.Bokeh = undefined;\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - "\trun_inline_js();\n", - " });\n", - " }\n", - " }\n", - " // Give older versions of the autoload script a head-start to ensure\n", - " // they initialize before we start loading newer version.\n", - " setTimeout(load_or_wait, 100)\n", - "}(window));" - ], - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n Bokeh = root.Bokeh;\n bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", - " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", - "}\n", - "\n", - "\n", - " function JupyterCommManager() {\n", - " }\n", - "\n", - " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", - " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) 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element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\n", - " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\n", - " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", - " var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", - " for (var i = 0; i < urls.length; i++) {\n", - " skip.push(urls[i])\n", - " }\n", - " } var existing_scripts = []\n", - " var scripts = document.getElementsByTagName('script')\n", - " for (var i = 0; i < scripts.length; i++) {\n", - " var script = scripts[i]\n", - " if (script.src != null) {\n", - "\texisting_scripts.push(script.src)\n", - " }\n", - " }\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " for (var i = 0; i < js_modules.length; i++) {\n", - " var url = js_modules[i];\n", - " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " element.type = \"module\";\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " for (const name in js_exports) {\n", - " var url = js_exports[name];\n", - " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", - "\tif (!window.requirejs) {\n", - "\t on_load();\n", - "\t}\n", - "\tcontinue;\n", - " }\n", - " var element = document.createElement('script');\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.type = \"module\";\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " element.textContent = `\n", - " import ${name} from \"${url}\"\n", - " window.${name} = ${name}\n", - " window._bokeh_on_load()\n", - " `\n", - " document.head.appendChild(element);\n", - " }\n", - " if (!js_urls.length && !js_modules.length) {\n", - " on_load()\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [];\n", - " var js_modules = [];\n", - " var js_exports = {};\n", - " var css_urls = [];\n", - " var inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - "\tvar NewBokeh = root.Bokeh;\n", - "\tif (Bokeh.versions === undefined) {\n", - "\t Bokeh.versions = new Map();\n", - "\t}\n", - "\tif (NewBokeh.version !== Bokeh.version) {\n", - "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - "\t}\n", - "\troot.Bokeh = Bokeh;\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " }\n", - " root._bokeh_is_initializing = false\n", - " }\n", - "\n", - " function load_or_wait() {\n", - " // Implement a backoff loop that tries to ensure we do not load multiple\n", - " // versions of Bokeh and its dependencies at the same time.\n", - " // In recent versions we use the root._bokeh_is_initializing flag\n", - " // to determine whether there is an ongoing attempt to initialize\n", - " // bokeh, however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", - " setTimeout(load_or_wait, 100);\n", - " } else {\n", - " Bokeh = root.Bokeh;\n", - " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", - " root._bokeh_is_initializing = true\n", - " root._bokeh_onload_callbacks = []\n", - " if (!reloading && (!bokeh_loaded || is_dev)) {\n", - "\troot.Bokeh = undefined;\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - "\trun_inline_js();\n", - " });\n", - " }\n", - " }\n", - " // Give older versions of the autoload script a head-start to ensure\n", - " // they initialize before we start loading newer version.\n", - " setTimeout(load_or_wait, 100)\n", - "}(window));" - ], - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = true;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n Bokeh = root.Bokeh;\n bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run 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    Worker: 7

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    " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client = Client()\n", - "client" - ] - }, - { - "cell_type": "markdown", - "id": "1bdcd8de-b9b3-4b65-924d-3292fa4d1e63", - "metadata": {}, - "source": [ - "## Access Data\n", - "Our first step is to access data from the ESGF data servers, and the Atmospheric Radiation Measurement (ARM) user facility, which has a long term site in Northern Oklahoma." - ] - }, - { - "cell_type": "markdown", - "id": "cf527623-1d78-4f62-8d52-427e4ecbd67d", - "metadata": {}, - "source": [ - "### Access ESGF Data\n", - "A tutorial on how to access ESGF-hosted CMIP6 data is included in the Foundations section of this cookbook:\n", - "- [ESGF OpenDAP Tutorial](https://projectpythia.org/cmip6-cookbook/notebooks/foundations/esgf-opendap.html)\n", - "\n", - "We use the following block of code to search for a single earth system model simulation, the Energe Exascale Earth System Model (E3SM), which is the Department of Energy's flagship coupled Earth System Model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a1721519-5b6f-465b-9089-bf9a8f3ce13c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:04.616504Z", - "iopub.status.busy": "2023-12-18T19:47:04.616049Z", - "iopub.status.idle": "2023-12-18T19:47:05.861921Z", - "shell.execute_reply": "2023-12-18T19:47:05.861130Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "conn = SearchConnection('https://esgf-node.llnl.gov/esg-search',\n", - " distrib=False)\n", - "ctx = conn.new_context(\n", - " facets='project,experiment_id',\n", - " project='CMIP6',\n", - " table_id='Amon',\n", - " institution_id = 'E3SM-Project',\n", - " experiment_id='historical',\n", - " source_id='E3SM-1-0',\n", - " variable='tas',\n", - " variant_label='r1i1p1f1',\n", - ")\n", - "result = ctx.search()[1]\n", - "files = result.file_context().search()\n", - "opendap_urls = [file.opendap_url for file in files]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f60b409e-cbea-43ac-907f-e31f03cfab95", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:05.865705Z", - "iopub.status.busy": "2023-12-18T19:47:05.865496Z", - "iopub.status.idle": "2023-12-18T19:47:11.806256Z", - "shell.execute_reply": "2023-12-18T19:47:11.805575Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.Dataset>\n",
-       "Dimensions:    (time: 1980, bnds: 2, lat: 180, lon: 360)\n",
-       "Coordinates:\n",
-       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
-       "  * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
-       "    height     float64 2.0\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    time_bnds  (time, bnds) object dask.array<chunksize=(300, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(300, 180, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(300, 360, 2), meta=np.ndarray>\n",
-       "    tas        (time, lat, lon) float32 dask.array<chunksize=(300, 180, 360), meta=np.ndarray>\n",
-       "Attributes: (12/54)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           36500.0\n",
-       "    contact:                         Dave Bader (bader2@llnl.gov)\n",
-       "    ...                              ...\n",
-       "    e3sm_source_code_reference:      https://github.com/E3SM-Project/E3SM/rel...\n",
-       "    doe_acknowledgement:             This research was supported as part of t...\n",
-       "    computational_acknowledgement:   The data were produced using resources o...\n",
-       "    ncclimo_generation_command:      ncclimo --var=${var} -7 --dfl_lvl=1 --no...\n",
-       "    ncclimo_version:                 4.8.1-alpha04\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1980, bnds: 2, lat: 180, lon: 360)\n", - "Coordinates:\n", - " * time (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n", - " * lat (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", - " * lon (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n", - " height float64 2.0\n", - "Dimensions without coordinates: bnds\n", - "Data variables:\n", - " time_bnds (time, bnds) object dask.array\n", - " lat_bnds (time, lat, bnds) float64 dask.array\n", - " lon_bnds (time, lon, bnds) float64 dask.array\n", - " tas (time, lat, lon) float32 dask.array\n", - "Attributes: (12/54)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 36500.0\n", - " contact: Dave Bader (bader2@llnl.gov)\n", - " ... ...\n", - " e3sm_source_code_reference: https://github.com/E3SM-Project/E3SM/rel...\n", - " doe_acknowledgement: This research was supported as part of t...\n", - " computational_acknowledgement: The data were produced using resources o...\n", - " ncclimo_generation_command: ncclimo --var=${var} -7 --dfl_lvl=1 --no...\n", - " ncclimo_version: 4.8.1-alpha04\n", - " DODS_EXTRA.Unlimited_Dimension: time" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "esgf_ds = xr.open_mfdataset(opendap_urls,\n", - " combine='by_coords',\n", - " chunks={'time':480})\n", - "esgf_ds" - ] - }, - { - "cell_type": "markdown", - "id": "f5a83d3a-938d-44d8-9350-5a9cdf218c99", - "metadata": {}, - "source": [ - "### Clean up the dataset\n", - "We need to adjust the 0 to 360 degree longitude to be -180 to 180 - we can do this generically using the climate forecast (CF) conventions." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "92ccf9da-5f3c-40e4-875c-49c662b4a8e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:11.811327Z", - "iopub.status.busy": "2023-12-18T19:47:11.810642Z", - "iopub.status.idle": "2023-12-18T19:47:11.832690Z", - "shell.execute_reply": "2023-12-18T19:47:11.832073Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "lon_coord = esgf_ds.cf['X'].name\n", - "esgf_ds[lon_coord] = (esgf_ds[lon_coord] + 180) % 360 - 180\n", - "esgf_ds = esgf_ds.sortby(lon_coord)" - ] - }, - { - "cell_type": "markdown", - "id": "44bfced7-1886-4fd5-bb2f-1810709c95e5", - "metadata": {}, - "source": [ - "## Access ARM Data\n", - "We use the ARM data API, which is included in the Atmospheric Data Community Toolkit (ACT) to access the data.\n", - "\n", - "### Setup the Search\n", - "\n", - "Before downloading our data, we need to make sure we have an ARM Data Account, and ARM Live token. Both of these can be found using this link:\n", - "- [ARM Live Signup](https://adc.arm.gov/armlive/livedata/home)\n", - "\n", - "Once you sign up, you will see your token. Copy and replace that where we have `arm_username` and `arm_password` below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e3d24fdf-517f-4f32-966b-e9e8f9e52174", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:11.835146Z", - "iopub.status.busy": "2023-12-18T19:47:11.834949Z", - "iopub.status.idle": "2023-12-18T19:47:37.217134Z", - "shell.execute_reply": "2023-12-18T19:47:37.215906Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DOWNLOADING] sgpmetE13.b1.20130101.000000.cdf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DOWNLOADING] sgpmetE13.b1.20130102.000000.cdf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DOWNLOADING] sgpmetE13.b1.20130103.000000.cdf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DOWNLOADING] sgpmetE13.b1.20130104.000000.cdf\n" - ] - }, - { - "name": "stdout", - 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"output_type": "stream", - "text": [ - "\n", - "If you use these data to prepare a publication, please cite:\n", - "\n", - "Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation\n", - "(MET). Atmospheric Radiation Measurement (ARM) User Facility.\n", - "https://doi.org/10.5439/1786358\n", - "\n" - ] - } - ], - "source": [ - "arm_username = os.getenv(\"ARM_USERNAME\")\n", - "arm_password = os.getenv(\"ARM_PASSWORD\")\n", - "\n", - "# Meteorological observations at the Southern Great Plains site\n", - "datastream = \"sgpmetE13.b1\"\n", - "\n", - "start_date = \"2013-01-01\"\n", - "end_date = \"2013-02-28\"\n", - "files = act.discovery.download_data(arm_username,\n", - " arm_password,\n", - " datastream,\n", - " start_date,\n", - " end_date\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "7d66ed98-e96d-44bb-a558-4a9bf46f7db5", - "metadata": {}, - "source": [ - "### Load the Data Using Xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "69ab2faa-3890-4e6d-b0fc-3f0486a6c79e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:37.220570Z", - "iopub.status.busy": "2023-12-18T19:47:37.220319Z", - "iopub.status.idle": "2023-12-18T19:47:39.405675Z", - "shell.execute_reply": "2023-12-18T19:47:39.404752Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "arm_ds = xr.open_mfdataset(files,\n", - " combine='nested',\n", - " concat_dim='time',\n", - " chunks={'time':86400})" - ] - }, - { - "cell_type": "markdown", - "id": "60dc491d-ff51-4dc0-bfd4-695b4884722d", - "metadata": {}, - "source": [ - "## Subset and Prepare Data to be Compared\n", - "We need to subset the climate model output for the nearest grid point, over the SGP site." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "079b0699-89cc-4bdb-a7a1-5f96859c13ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:39.412000Z", - "iopub.status.busy": "2023-12-18T19:47:39.411784Z", - "iopub.status.idle": "2023-12-18T19:47:39.416940Z", - "shell.execute_reply": "2023-12-18T19:47:39.416318Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(36.605, -97.485)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat = arm_ds.lat.values[0]\n", - "lon = arm_ds.lon.values[0]\n", - "lat, lon" - ] - }, - { - "cell_type": "markdown", - "id": "a6346398-7dbe-4507-8e18-5c703960ecb7", - "metadata": {}, - "source": [ - "Xarray offers this subsetting functionality, and we specify we want the **nearest** gird point to the site." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7c54eb44-29e5-4f33-ab01-b438bf0c77a9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:39.419622Z", - "iopub.status.busy": "2023-12-18T19:47:39.419424Z", - "iopub.status.idle": "2023-12-18T19:47:39.456549Z", - "shell.execute_reply": "2023-12-18T19:47:39.455980Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:    (time: 1980, bnds: 2)\n",
-       "Coordinates:\n",
-       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
-       "    lat        float64 36.5\n",
-       "    lon        float64 -97.5\n",
-       "    height     float64 2.0\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    time_bnds  (time, bnds) object dask.array<chunksize=(300, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (time, bnds) float64 dask.array<chunksize=(300, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (time, bnds) float64 dask.array<chunksize=(300, 2), meta=np.ndarray>\n",
-       "    tas        (time) float32 dask.array<chunksize=(300,), meta=np.ndarray>\n",
-       "Attributes: (12/54)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           36500.0\n",
-       "    contact:                         Dave Bader (bader2@llnl.gov)\n",
-       "    ...                              ...\n",
-       "    e3sm_source_code_reference:      https://github.com/E3SM-Project/E3SM/rel...\n",
-       "    doe_acknowledgement:             This research was supported as part of t...\n",
-       "    computational_acknowledgement:   The data were produced using resources o...\n",
-       "    ncclimo_generation_command:      ncclimo --var=${var} -7 --dfl_lvl=1 --no...\n",
-       "    ncclimo_version:                 4.8.1-alpha04\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1980, bnds: 2)\n", - "Coordinates:\n", - " * time (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n", - " lat float64 36.5\n", - " lon float64 -97.5\n", - " height float64 2.0\n", - "Dimensions without coordinates: bnds\n", - "Data variables:\n", - " time_bnds (time, bnds) object dask.array\n", - " lat_bnds (time, bnds) float64 dask.array\n", - " lon_bnds (time, bnds) float64 dask.array\n", - " tas (time) float32 dask.array\n", - "Attributes: (12/54)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 36500.0\n", - " contact: Dave Bader (bader2@llnl.gov)\n", - " ... ...\n", - " e3sm_source_code_reference: https://github.com/E3SM-Project/E3SM/rel...\n", - " doe_acknowledgement: This research was supported as part of t...\n", - " computational_acknowledgement: The data were produced using resources o...\n", - " ncclimo_generation_command: ncclimo --var=${var} -7 --dfl_lvl=1 --no...\n", - " ncclimo_version: 4.8.1-alpha04\n", - " DODS_EXTRA.Unlimited_Dimension: time" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cmip6_nearest = esgf_ds.cf.sel(lat=lat,\n", - " lon=lon,\n", - " method='nearest')\n", - "cmip6_nearest" - ] - }, - { - "cell_type": "markdown", - "id": "8d1ca239-a5cb-40e8-8704-9024f8600774", - "metadata": {}, - "source": [ - "We need to convert our time to datetime to make it easier to compare." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fb0e3c86-7c39-4248-8c8e-43b7b50e9e00", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:39.459232Z", - "iopub.status.busy": "2023-12-18T19:47:39.458669Z", - "iopub.status.idle": "2023-12-18T19:47:39.499047Z", - "shell.execute_reply": "2023-12-18T19:47:39.498425Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cmip6_nearest['time'] = cmip6_nearest.indexes['time'].to_datetimeindex()" - ] - }, - { - "cell_type": "markdown", - "id": "1581930c-df52-4954-b52b-c01da13cf72b", - "metadata": {}, - "source": [ - "Next, we select the times we have data from the SGP site, specified earlier in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b48285d8-7e65-4a4e-9df5-e0cdc1d4d4c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:39.502092Z", - "iopub.status.busy": "2023-12-18T19:47:39.501389Z", - "iopub.status.idle": "2023-12-18T19:47:39.529674Z", - "shell.execute_reply": "2023-12-18T19:47:39.528739Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cmip6_nearest = cmip6_nearest.sel(time=slice(start_date,\n", - " end_date)).resample(time='1M').mean()" - ] - }, - { - "cell_type": "markdown", - "id": "0eae53d6-ef15-4d5f-adda-ec3eb5012328", - "metadata": {}, - "source": [ - "### Calculate Monthly Mean Temperature at SGP\n", - "We can calculate the monthly average temperature at the SGP site using the `resample` method in `Xarray`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d16d08c4-e317-4590-a119-9f7171fd8793", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:39.533432Z", - "iopub.status.busy": "2023-12-18T19:47:39.532614Z", - "iopub.status.idle": "2023-12-18T19:47:40.050532Z", - "shell.execute_reply": "2023-12-18T19:47:40.049360Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "arm_ds = arm_ds.sortby('time')\n", - "sgp_monthly_mean_temperature = arm_ds.temp_mean.resample(time='1M').mean().compute().rename('tas (ARM)')" - ] - }, - { - "cell_type": "markdown", - "id": "716b8556-46be-4f32-a440-9e38f78bdea5", - "metadata": {}, - "source": [ - "We need to apply some data cleaning here too - converting our units of temperature to degrees Celsius for the CMIP6 data." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2d89cc9a-3160-4549-8221-e3f4e7f49dfe", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:40.055177Z", - "iopub.status.busy": "2023-12-18T19:47:40.054516Z", - "iopub.status.idle": "2023-12-18T19:47:44.073999Z", - "shell.execute_reply": "2023-12-18T19:47:44.073082Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cmip6_monthly_mean_temperature = cmip6_nearest.tas.compute().metpy.quantify()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e6e66da0-4d4c-4dcc-85ab-55d9036f9fa5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:44.078836Z", - "iopub.status.busy": "2023-12-18T19:47:44.078622Z", - "iopub.status.idle": "2023-12-18T19:47:44.083923Z", - "shell.execute_reply": "2023-12-18T19:47:44.083123Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cmip6_monthly_mean_temperature = cmip6_monthly_mean_temperature.metpy.convert_units('degC').rename(\"tas (CMIP6)\")" - ] - }, - { - "cell_type": "markdown", - "id": "7dca2cfb-c76c-4899-9e4d-f67515299f3e", - "metadata": {}, - "source": [ - "## Visaulize the Output\n", - "Once we have our comparisons ready, we can visualize using `hvPlot`, which produces an interactive visualization!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a4f46b86-e7ed-441f-9411-e7112702ed12", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:44.087875Z", - "iopub.status.busy": "2023-12-18T19:47:44.086952Z", - "iopub.status.idle": "2023-12-18T19:47:44.303269Z", - "shell.execute_reply": "2023-12-18T19:47:44.302534Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
    \n", - "
    \n", - "
    \n", - "" - ], - "text/plain": [ - ":Overlay\n", - " .Bars.I :Bars [time,Variable] (value)\n", - " .Bars.II :Bars [time,Variable] (value)" - ] - }, - "execution_count": 15, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1004" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "esgf_plot = cmip6_monthly_mean_temperature.hvplot.bar(title='Average Surface Temperature \\n near the Southern Great Plains Field Site',\n", - " xlabel='Time')\n", - "arm_plot = sgp_monthly_mean_temperature.hvplot.bar(ylabel='Average Temperature (degC)',\n", - " xlabel='Time')\n", - "\n", - "esgf_plot * arm_plot" - ] - }, - { - "cell_type": "markdown", - "id": "d4b169dd-67b5-45c0-9768-ea50c663f927", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we searched for and opened a CMIP6 E3SM dataset using the ESGF API and OPeNDAP, and compared to an ARM dataset collected at the Southern Great Plains climate observatory.\n", - "\n", - "### What's next?\n", - "We will see some more advanced examples of using the CMIP6 and obsverational data." - ] - }, - { - "cell_type": "markdown", - "id": "71dbbf10-c12b-459f-ac02-fc9015905ff9", - "metadata": {}, - "source": [ - "## Resources and references\n", - "- [ARM Surface Meteorological Handbook](https://www.arm.gov/publications/tech_reports/handbooks/met_handbook.pdf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27b4be06-a088-452b-8633-febce92610c6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/77/_sources/notebooks/example-workflows/gmst.ipynb b/_preview/77/_sources/notebooks/example-workflows/gmst.ipynb deleted file mode 100644 index 5cc0db1..0000000 --- a/_preview/77/_sources/notebooks/example-workflows/gmst.ipynb +++ /dev/null @@ -1,1896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Global Mean Surface Temperature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This notebook uses similar techniques to the [ECS notebook](ecs-cmip6.ipynb). Please refer to that notebook for details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| Seaborn | Helpful | |\n", - "\n", - "- **Time to learn**: 10 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:48.142848Z", - "iopub.status.busy": "2023-12-18T19:47:48.142611Z", - "iopub.status.idle": "2023-12-18T19:47:50.783305Z", - "shell.execute_reply": "2023-12-18T19:47:50.782520Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_526/1335193511.py:6: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import xarray as xr\n", - "import numpy as np\n", - "import dask\n", - "from dask.diagnostics import progress\n", - "from tqdm.autonotebook import tqdm\n", - "import intake\n", - "import fsspec\n", - "import seaborn as sns\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:50.787399Z", - "iopub.status.busy": "2023-12-18T19:47:50.787049Z", - "iopub.status.idle": "2023-12-18T19:47:54.816449Z", - "shell.execute_reply": "2023-12-18T19:47:54.815663Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "data": { - "text/html": [ - "

    pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    unique
    activity_id18
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    source_id88
    experiment_id170
    member_id657
    table_id37
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    grid_label10
    zstore514818
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    derived_variable_id0
    \n", - "
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n", - "col" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:54.819717Z", - "iopub.status.busy": "2023-12-18T19:47:54.819347Z", - "iopub.status.idle": "2023-12-18T19:47:54.846475Z", - "shell.execute_reply": "2023-12-18T19:47:54.845720Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['ssp585',\n", - " 'ssp245',\n", - " 'ssp370SST-lowCH4',\n", - " 'ssp370-lowNTCF',\n", - " 'ssp370SST-lowNTCF',\n", - " 'ssp370SST-ssp126Lu',\n", - " 'ssp370SST',\n", - " 'ssp370pdSST',\n", - " 'ssp119',\n", - " 'ssp370',\n", - " 'esm-ssp585-ssp126Lu',\n", - " 'ssp126-ssp370Lu',\n", - " 'ssp370-ssp126Lu',\n", - " 'ssp126',\n", - " 'esm-ssp585',\n", - " 'ssp245-GHG',\n", - " 'ssp245-nat',\n", - " 'ssp460',\n", - " 'ssp434',\n", - " 'ssp534-over',\n", - " 'ssp245-stratO3',\n", - " 'ssp245-aer',\n", - " 'ssp245-cov-modgreen',\n", - " 'ssp245-cov-fossil',\n", - " 'ssp245-cov-strgreen',\n", - " 'ssp245-covid',\n", - " 'ssp585-bgc']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[eid for eid in col.df['experiment_id'].unique() if 'ssp' in eid]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is currently a significant amount of data for these runs:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:54.849059Z", - "iopub.status.busy": "2023-12-18T19:47:54.848844Z", - "iopub.status.idle": "2023-12-18T19:47:55.111969Z", - "shell.execute_reply": "2023-12-18T19:47:55.111313Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    experiment_idvariable_idtable_id
    source_id
    ACCESS-CM2311
    AWI-CM-1-1-MR311
    BCC-CSM2-MR311
    CAMS-CSM1-0311
    CAS-ESM2-0311
    CESM2-WACCM311
    CIESM311
    CMCC-CM2-SR5311
    CMCC-ESM2311
    CanESM5311
    E3SM-1-1311
    EC-Earth3311
    EC-Earth3-CC311
    EC-Earth3-Veg311
    EC-Earth3-Veg-LR311
    FGOALS-f3-L311
    FGOALS-g3311
    FIO-ESM-2-0311
    GFDL-CM4311
    GFDL-ESM4311
    IITM-ESM311
    INM-CM4-8311
    INM-CM5-0311
    IPSL-CM6A-LR311
    KACE-1-0-G311
    KIOST-ESM311
    MIROC6311
    MPI-ESM1-2-HR311
    MPI-ESM1-2-LR311
    MRI-ESM2-0311
    NESM3311
    NorESM2-LM311
    NorESM2-MM311
    TaiESM1311
    \n", - "
    " - ], - "text/plain": [ - " experiment_id variable_id table_id\n", - "source_id \n", - "ACCESS-CM2 3 1 1\n", - "AWI-CM-1-1-MR 3 1 1\n", - "BCC-CSM2-MR 3 1 1\n", - "CAMS-CSM1-0 3 1 1\n", - "CAS-ESM2-0 3 1 1\n", - "CESM2-WACCM 3 1 1\n", - "CIESM 3 1 1\n", - "CMCC-CM2-SR5 3 1 1\n", - "CMCC-ESM2 3 1 1\n", - "CanESM5 3 1 1\n", - "E3SM-1-1 3 1 1\n", - "EC-Earth3 3 1 1\n", - "EC-Earth3-CC 3 1 1\n", - "EC-Earth3-Veg 3 1 1\n", - "EC-Earth3-Veg-LR 3 1 1\n", - "FGOALS-f3-L 3 1 1\n", - "FGOALS-g3 3 1 1\n", - "FIO-ESM-2-0 3 1 1\n", - "GFDL-CM4 3 1 1\n", - "GFDL-ESM4 3 1 1\n", - "IITM-ESM 3 1 1\n", - "INM-CM4-8 3 1 1\n", - "INM-CM5-0 3 1 1\n", - "IPSL-CM6A-LR 3 1 1\n", - "KACE-1-0-G 3 1 1\n", - "KIOST-ESM 3 1 1\n", - "MIROC6 3 1 1\n", - "MPI-ESM1-2-HR 3 1 1\n", - "MPI-ESM1-2-LR 3 1 1\n", - "MRI-ESM2-0 3 1 1\n", - "NESM3 3 1 1\n", - "NorESM2-LM 3 1 1\n", - "NorESM2-MM 3 1 1\n", - "TaiESM1 3 1 1" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expts = ['historical', 'ssp245', 'ssp585']\n", - "\n", - "query = dict(\n", - " experiment_id=expts,\n", - " table_id='Amon',\n", - " variable_id=['tas'],\n", - " member_id = 'r1i1p1f1',\n", - ")\n", - "\n", - "col_subset = col.search(require_all_on=[\"source_id\"], **query)\n", - "col_subset.df.groupby(\"source_id\")[\n", - " [\"experiment_id\", \"variable_id\", \"table_id\"]\n", - "].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:55.114484Z", - "iopub.status.busy": "2023-12-18T19:47:55.114268Z", - "iopub.status.idle": "2023-12-18T19:47:55.130718Z", - "shell.execute_reply": "2023-12-18T19:47:55.130119Z" - } - }, - "outputs": [], - "source": [ - "def drop_all_bounds(ds):\n", - " drop_vars = [vname for vname in ds.coords\n", - " if (('_bounds') in vname ) or ('_bnds') in vname]\n", - " return ds.drop(drop_vars)\n", - "\n", - "def open_dset(df):\n", - " assert len(df) == 1\n", - " ds = xr.open_zarr(fsspec.get_mapper(df.zstore.values[0]), consolidated=True)\n", - " return drop_all_bounds(ds)\n", - "\n", - "def open_delayed(df):\n", - " return dask.delayed(open_dset)(df)\n", - "\n", - "from collections import defaultdict\n", - "dsets = defaultdict(dict)\n", - "\n", - "for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):\n", - " dsets[group[0]][group[1]] = open_delayed(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:47:55.133041Z", - "iopub.status.busy": "2023-12-18T19:47:55.132844Z", - "iopub.status.idle": "2023-12-18T19:48:12.827361Z", - "shell.execute_reply": "2023-12-18T19:48:12.826483Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", - " array = array.get_duck_array()\n" - ] - } - ], - "source": [ - "dsets_ = dask.compute(dict(dsets))[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate global means:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:48:12.831159Z", - "iopub.status.busy": "2023-12-18T19:48:12.830151Z", - "iopub.status.idle": "2023-12-18T19:48:12.836208Z", - "shell.execute_reply": "2023-12-18T19:48:12.835610Z" - } - }, - "outputs": [], - "source": [ - "def get_lat_name(ds):\n", - " for lat_name in ['lat', 'latitude']:\n", - " if lat_name in ds.coords:\n", - " return lat_name\n", - " raise RuntimeError(\"Couldn't find a latitude coordinate\")\n", - "\n", - "def global_mean(ds):\n", - " lat = ds[get_lat_name(ds)]\n", - " weight = np.cos(np.deg2rad(lat))\n", - " weight /= weight.mean()\n", - " other_dims = set(ds.dims) - {'time'}\n", - " return (ds * weight).mean(other_dims)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:48:12.839059Z", - "iopub.status.busy": "2023-12-18T19:48:12.838861Z", - "iopub.status.idle": "2023-12-18T19:48:30.189837Z", - "shell.execute_reply": "2023-12-18T19:48:30.188887Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3b0cba825c8a4f58b141713dee7021d6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/34 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:        (year: 451, experiment_id: 3, source_id: 34)\n",
-       "Coordinates:\n",
-       "  * year           (year) float64 1.85e+03 1.851e+03 ... 2.299e+03 2.3e+03\n",
-       "  * experiment_id  (experiment_id) <U10 'historical' 'ssp245' 'ssp585'\n",
-       "  * source_id      (source_id) <U16 'ACCESS-CM2' 'AWI-CM-1-1-MR' ... 'TaiESM1'\n",
-       "Data variables:\n",
-       "    tas            (source_id, experiment_id, year) float64 287.0 287.0 ... nan
    " - ], - "text/plain": [ - "\n", - "Dimensions: (year: 451, experiment_id: 3, source_id: 34)\n", - "Coordinates:\n", - " * year (year) float64 1.85e+03 1.851e+03 ... 2.299e+03 2.3e+03\n", - " * experiment_id (experiment_id) \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    yearexperiment_idsource_idtas
    01900.0historicalACCESS-CM2287.019917
    11900.0historicalAWI-CM-1-1-MR286.958154
    21900.0historicalBCC-CSM2-MR287.996260
    31900.0historicalCAMS-CSM1-0287.084974
    41900.0historicalCAS-ESM2-0287.263682
    \n", - "" - ], - "text/plain": [ - " year experiment_id source_id tas\n", - "0 1900.0 historical ACCESS-CM2 287.019917\n", - "1 1900.0 historical AWI-CM-1-1-MR 286.958154\n", - "2 1900.0 historical BCC-CSM2-MR 287.996260\n", - "3 1900.0 historical CAMS-CSM1-0 287.084974\n", - "4 1900.0 historical CAS-ESM2-0 287.263682" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_all = big_ds.sel(year=slice(1900, 2100)).to_dataframe().reset_index()\n", - "df_all.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:08.976900Z", - "iopub.status.busy": "2023-12-18T19:49:08.976329Z", - "iopub.status.idle": "2023-12-18T19:49:09.427269Z", - "shell.execute_reply": "2023-12-18T19:49:09.426478Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/seaborn/axisgrid.py:854: FutureWarning: \n", - "\n", - "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n", - "\n", - " func(*plot_args, **plot_kwargs)\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.relplot(data=df_all,\n", - " x=\"year\", y=\"tas\", hue='experiment_id',\n", - " kind=\"line\", ci=\"sd\", aspect=2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we accessed data for historical, SSP245, and SSP585 runs from a collection of CMIP6 models and plotted the multimodel-mean global average surface air temperature for each run.\n", - "\n", - "### What's next?\n", - "We will use CMIP6 data to analyze precipitation intensity under a warming climate." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Resources and references\n", - "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/global_mean_surface_temp.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)" - 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"source": [ - "# Precipitation Frequency Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This notebook shows an advanced analysis case. The calculation was inspired by Angie Pendergrass’s work on precipitation statistics, as described in the following websites / papers:\n", - "\n", - "- [https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-16-0684.1](https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-16-0684.1)\n", - "- [https://climatedataguide.ucar.edu/climate-data/gpcp-daily-global-precipitation-climatology-project](https://climatedataguide.ucar.edu/climate-data/gpcp-daily-global-precipitation-climatology-project)\n", - "\n", - "We use [`xhistogram`](https://xhistogram.readthedocs.io/en/latest/) to calculate the distribution of precipitation intensity and its changes in a warming climate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| Dask | Helpful | |\n", - "\n", - "- **Time to learn**: 5 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:52.535983Z", - "iopub.status.busy": "2023-12-18T19:50:52.535773Z", - "iopub.status.idle": "2023-12-18T19:50:53.799207Z", - "shell.execute_reply": "2023-12-18T19:50:53.798468Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_803/106887996.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import fsspec\n", - "from tqdm.autonotebook import tqdm\n", - "from xhistogram.xarray import histogram\n", - "from dask_gateway import Gateway\n", - "from dask.distributed import Client\n", - "\n", - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = 12, 6" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Compute Cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we use a dask cluster to parallelize our analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:53.802587Z", - "iopub.status.busy": "2023-12-18T19:50:53.802389Z", - "iopub.status.idle": "2023-12-18T19:50:53.806002Z", - "shell.execute_reply": "2023-12-18T19:50:53.805436Z" - } - }, - "outputs": [], - "source": [ - "platform = sys.platform\n", - "\n", - "if (platform == 'win32'):\n", - " import multiprocessing.popen_spawn_win32\n", - "else:\n", - " import multiprocessing.popen_spawn_posix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initiate the Dask client:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:53.808385Z", - "iopub.status.busy": "2023-12-18T19:50:53.808079Z", - "iopub.status.idle": "2023-12-18T19:50:55.198646Z", - "shell.execute_reply": "2023-12-18T19:50:55.197929Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    4HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpslgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
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    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id member_id \\\n", - "0 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "1 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "2 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "3 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "4 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "\n", - " table_id variable_id grid_label \\\n", - "0 Amon ps gn \n", - "1 Amon rsds gn \n", - "2 Amon rlus gn \n", - "3 Amon rlds gn \n", - "4 Amon psl gn \n", - "\n", - " zstore dcpp_init_year version \n", - "0 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "1 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "2 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "3 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "4 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.786091Z", - "iopub.status.busy": "2023-12-18T19:50:56.785545Z", - "iopub.status.idle": "2023-12-18T19:50:56.844046Z", - "shell.execute_reply": "2023-12-18T19:50:56.843291Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "78" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_3hr_pr = df[(df.table_id == '3hr') & (df.variable_id == 'pr')]\n", - "len(df_3hr_pr)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.846623Z", - "iopub.status.busy": "2023-12-18T19:50:56.846426Z", - "iopub.status.idle": "2023-12-18T19:50:56.853736Z", - "shell.execute_reply": "2023-12-18T19:50:56.853005Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "source_id experiment_id \n", - "BCC-CSM2-MR historical 1\n", - " ssp126 1\n", - " ssp245 1\n", - " ssp370 1\n", - " ssp585 1\n", - "CNRM-CM6-1 highresSST-present 1\n", - " historical 3\n", - " ssp126 1\n", - " ssp245 1\n", - " ssp370 1\n", - " ssp585 1\n", - "CNRM-CM6-1-HR highresSST-present 1\n", - "CNRM-ESM2-1 historical 1\n", - " ssp126 1\n", - " ssp245 1\n", - " ssp370 1\n", - " ssp585 1\n", - "GFDL-CM4 1pctCO2 2\n", - " abrupt-4xCO2 2\n", - " amip 2\n", - " historical 2\n", - " piControl 2\n", - "GFDL-CM4C192 highresSST-future 1\n", - " highresSST-present 1\n", - "GFDL-ESM4 1pctCO2 1\n", - " abrupt-4xCO2 1\n", - " esm-hist 1\n", - " historical 1\n", - " ssp119 1\n", - " ssp126 1\n", - " ssp370 1\n", - "GISS-E2-1-G historical 2\n", - "HadGEM3-GC31-HM highresSST-present 1\n", - "HadGEM3-GC31-LM highresSST-present 1\n", - "HadGEM3-GC31-MM highresSST-present 1\n", - "IPSL-CM6A-ATM-HR highresSST-present 1\n", - "IPSL-CM6A-LR highresSST-present 1\n", - " historical 15\n", - " piControl 1\n", - " ssp126 3\n", - " ssp245 2\n", - " ssp370 10\n", - " ssp585 1\n", - "MRI-ESM2-0 historical 1\n", - "Name: zstore, dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_counts = df_3hr_pr.groupby(['source_id', 'experiment_id'])['zstore'].count()\n", - "run_counts" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.855873Z", - "iopub.status.busy": "2023-12-18T19:50:56.855682Z", - "iopub.status.idle": "2023-12-18T19:50:56.861776Z", - "shell.execute_reply": "2023-12-18T19:50:56.861207Z" - } - }, - "outputs": [], - "source": [ - "source_ids = []\n", - "experiment_ids = ['historical', 'ssp585']\n", - "for name, group in df_3hr_pr.groupby('source_id'):\n", - " if all([expt in group.experiment_id.values\n", - " for expt in experiment_ids]):\n", - " source_ids.append(name)\n", - "source_ids\n", - "\n", - "# Use only one model. Otherwise it takes too long to run on GitHub.\n", - "source_ids = ['BCC-CSM2-MR']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.863863Z", - "iopub.status.busy": "2023-12-18T19:50:56.863673Z", - "iopub.status.idle": "2023-12-18T19:50:56.868283Z", - "shell.execute_reply": "2023-12-18T19:50:56.867697Z" - } - }, - "outputs": [], - "source": [ - "def load_pr_data(source_id, expt_id):\n", - " \"\"\"\n", - " Load 3hr precip data for given source and expt ids\n", - " \"\"\"\n", - " uri = df_3hr_pr[(df_3hr_pr.source_id == source_id) &\n", - " (df_3hr_pr.experiment_id == expt_id)].zstore.values[0]\n", - "\n", - " ds = xr.open_zarr(fsspec.get_mapper(uri), consolidated=True)\n", - " return ds" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.870999Z", - "iopub.status.busy": "2023-12-18T19:50:56.870424Z", - "iopub.status.idle": "2023-12-18T19:50:56.875965Z", - "shell.execute_reply": "2023-12-18T19:50:56.875392Z" - } - }, - "outputs": [], - "source": [ - "def precip_hist(ds, nbins=100, pr_log_min=-3, pr_log_max=2):\n", - " \"\"\"\n", - " Calculate precipitation histogram for a single model.\n", - " Lazy.\n", - " \"\"\"\n", - " assert ds.pr.units == 'kg m-2 s-1'\n", - "\n", - " # mm/day\n", - " bins_mm_day = np.hstack([[0], np.logspace(pr_log_min, pr_log_max, nbins)])\n", - " bins_kg_m2s = bins_mm_day / (24*60*60)\n", - "\n", - " pr_hist = histogram(ds.pr, bins=[bins_kg_m2s], dim=['lon']).mean(dim='time')\n", - "\n", - " log_bin_spacing = np.diff(np.log(bins_kg_m2s[1:3])).item()\n", - " pr_hist_norm = 100 * pr_hist / ds.dims['lon'] / log_bin_spacing\n", - " pr_hist_norm.attrs.update({'long_name': 'zonal mean rain frequency',\n", - " 'units': '%/Δln(r)'})\n", - " return pr_hist_norm\n", - "\n", - "def precip_hist_for_expts(dsets, experiment_ids):\n", - " \"\"\"\n", - " Calculate histogram for a suite of experiments.\n", - " Eager.\n", - " \"\"\"\n", - " # actual data loading and computations happen in this next line\n", - " pr_hists = [precip_hist(ds).load()\n", - " for ds in [ds_hist, ds_ssp]]\n", - " pr_hist = xr.concat(pr_hists, dim=xr.Variable('experiment_id', experiment_ids))\n", - " return pr_hist" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:56.878454Z", - "iopub.status.busy": "2023-12-18T19:50:56.877899Z", - "iopub.status.idle": "2023-12-18T19:52:15.284088Z", - "shell.execute_reply": "2023-12-18T19:52:15.275386Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "68f379e1011e46a790e035f76e3466f8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "title = 'Change in Zonal Mean Rain Frequency'\n", - "for source_id, pr_hist in results.items():\n", - " plt.figure()\n", - " plot_precip_changes(pr_hist)\n", - " plt.title(f'{title}: {source_id}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're at the end of the notebook, so let's shutdown our Dask cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:52:16.131398Z", - "iopub.status.busy": "2023-12-18T19:52:16.129695Z", - "iopub.status.idle": "2023-12-18T19:52:16.644463Z", - "shell.execute_reply": "2023-12-18T19:52:16.643763Z" - } - }, - "outputs": [], - "source": [ - "client.shutdown()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we used CMIP6 data to compare precipitation intensity in the SSP585 scenario to historical runs.\n", - "\n", - "### What's next?\n", - "More examples of using CMIP6 data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Resources and references\n", - "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/precip_frequency_change.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Regridding with xESMF and calculating a multi-model mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "The main goal of this workflow is to calculate the mean change in ocean heat uptake (OHU) associated with the transient climate response (TCR) for CMIP6. TCR is defined as the change in global mean surface temperature at the time of CO$_2$ doubling in a climate model run with a 1% increase in CO$_2$ per year. The amount and pattern of heat uptake into the oceans are important in determining the strength of radiative feedbacks and thus climate sensitivity. See [Xie (2020)](https://doi.org/10.1029/2019AV000130) for an overview.\n", - "\n", - "In order to use as many models as possible, we will need to load the model output in its native grid, then regrid to a common grid (here 1°x1° lat-lon) using [xESMF](https://xesmf.readthedocs.io/en/latest/). From there, we can take the average across models and either plot the result or save it as a netCDF file for later use." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Computations and Masks with Xarray](https://foundations.projectpythia.org/core/xarray/computation-masking.html) | Necessary | |\n", - "| [Load CMIP6 Data with Intake-ESM](https://projectpythia.org/cmip6-cookbook/notebooks/foundations/intake-esm.html) | Necessary | |\n", - "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | |\n", - "| Familiarity with CMIP6 | Helpful | |\n", - "\n", - "- **Time to learn**: 30 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:12.276841Z", - "iopub.status.busy": "2023-12-18T19:49:12.276598Z", - "iopub.status.idle": "2023-12-18T19:49:14.931028Z", - "shell.execute_reply": "2023-12-18T19:49:14.930249Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as colors\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import intake\n", - "import xesmf as xe\n", - "from cartopy import crs as ccrs\n", - "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Access the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will open and search the Pangeo CMIP6 catalog for monthly `hfds` (downward heat flux at the sea surface) for the control (`piControl`) and 1%/year CO$_2$ (`1pctCO2`) runs for all available models on their native grids. The argument `require_all_on='source_id'` ensures that each model used has both experiments required for this analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:14.936186Z", - "iopub.status.busy": "2023-12-18T19:49:14.934874Z", - "iopub.status.idle": "2023-12-18T19:49:16.972166Z", - "shell.execute_reply": "2023-12-18T19:49:16.971316Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - } - ], - "source": [ - "cat_url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n", - "col = intake.open_esm_datastore(cat_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:16.974753Z", - "iopub.status.busy": "2023-12-18T19:49:16.974515Z", - "iopub.status.idle": "2023-12-18T19:49:17.245916Z", - "shell.execute_reply": "2023-12-18T19:49:17.245271Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "data": { - "text/html": [ - "
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    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0CMIPCSIRO-ARCCSSACCESS-CM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...NaN20191109
    1CMIPCSIRO-ARCCSSACCESS-CM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...NaN20191112
    2CMIPCSIROACCESS-ESM1-51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/1pct...NaN20191115
    3CMIPCSIROACCESS-ESM1-5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/piCo...NaN20191214
    4CMIPAWIAWI-CM-1-1-MR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/1pctCO...NaN20181218
    5CMIPAWIAWI-CM-1-1-MRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/piCont...NaN20181218
    6CMIPCAMSCAMS-CSM1-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/1pctCO2...NaN20190708
    7CMIPCAMSCAMS-CSM1-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/piContr...NaN20190729
    8CMIPNCARCESM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...NaN20190320
    9CMIPNCARCESM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/1pctCO2/r1i1p...NaN20190425
    10CMIPNCARCESM2-FV2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/piControl...NaN20191120
    11CMIPNCARCESM2-FV21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/1pctCO2/r...NaN20200310
    12CMIPNCARCESM2-WACCMpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/piContr...NaN20190320
    13CMIPNCARCESM2-WACCM1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/1pctCO2...NaN20190425
    14CMIPNCARCESM2-WACCM-FV2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/piC...NaN20191120
    15CMIPNCARCESM2-WACCM-FV21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/1pc...NaN20200226
    16CMIPTHUCIESM1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/THU/CIESM/1pctCO2/r1i1p1...NaN20200220
    17CMIPTHUCIESMpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/THU/CIESM/piControl/r1i1...NaN20200220
    18CMIPCMCCCMCC-CM2-SR51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/1pctCO...NaN20200616
    19CMIPCMCCCMCC-CM2-SR5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/piCont...NaN20200616
    20CMIPCMCCCMCC-ESM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/1pctCO2/r...NaN20210127
    21CMIPCMCCCMCC-ESM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/piControl...NaN20210304
    22CMIPCCCmaCanESM51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CCCma/CanESM5/1pctCO2/r1...NaN20190429
    23CMIPCCCmaCanESM5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CCCma/CanESM5/piControl/...NaN20190429
    24CMIPEC-Earth-ConsortiumEC-Earth3-VegpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20200919
    25CMIPEC-Earth-ConsortiumEC-Earth3-Veg1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20200919
    26CMIPCASFGOALS-g3piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/piControl/...NaN20191126
    27CMIPCASFGOALS-g31pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/1pctCO2/r1...NaN20191126
    28CMIPFIO-QLNMFIO-ESM-2-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/piC...NaN20200921
    29CMIPFIO-QLNMFIO-ESM-2-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/1pc...NaN20200927
    30CMIPNOAA-GFDLGFDL-CM41pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/1pctC...NaN20180701
    31CMIPNOAA-GFDLGFDL-CM4piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/piCon...NaN20180701
    32CMIPNOAA-GFDLGFDL-ESM41pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/1pct...NaN20180701
    33CMIPNOAA-GFDLGFDL-ESM4piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/piCo...NaN20180701
    34CMIPNASA-GISSGISS-E2-1-GpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/pi...NaN20180824
    35CMIPNASA-GISSGISS-E2-1-G1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/1p...NaN20180905
    36CMIPNASA-GISSGISS-E2-1-H1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/1p...NaN20190403
    37CMIPNASA-GISSGISS-E2-1-HpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/pi...NaN20190410
    38CMIPNASA-GISSGISS-E2-2-G1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/1p...NaN20191120
    39CMIPNASA-GISSGISS-E2-2-GpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/pi...NaN20191120
    40CMIPIPSLIPSL-CM6A-LR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/1pctCO...NaN20180727
    41CMIPIPSLIPSL-CM6A-LRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piCont...NaN20200326
    42CMIPUAMCM-UA-1-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/1pctCO2/r1...NaN20190731
    43CMIPUAMCM-UA-1-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/piControl/...NaN20190731
    44CMIPMPI-MMPI-ESM1-2-HR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/1pct...NaN20190710
    45CMIPMPI-MMPI-ESM1-2-HRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/piCo...NaN20190710
    46CMIPMPI-MMPI-ESM1-2-LRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/piCo...NaN20190710
    47CMIPMPI-MMPI-ESM1-2-LR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/1pct...NaN20190710
    48CMIPNUISTNESM31pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NUIST/NESM3/1pctCO2/r1i1...NaN20190703
    49CMIPNUISTNESM3piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NUIST/NESM3/piControl/r1...NaN20190704
    50CMIPNCCNorCPM1piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCC/NorCPM1/piControl/r1...NaN20190914
    51CMIPNCCNorCPM11pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCC/NorCPM1/1pctCO2/r1i1...NaN20190914
    52CMIPSNUSAM0-UNICON1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/...NaN20190323
    53CMIPSNUSAM0-UNICONpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/piContro...NaN20190910
    54CMIPAS-RCECTaiESM11pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/1pctCO2/...NaN20201130
    55CMIPAS-RCECTaiESM1piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/piContro...NaN20210213
    \n", - "
    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id member_id \\\n", - "0 CMIP CSIRO-ARCCSS ACCESS-CM2 1pctCO2 r1i1p1f1 \n", - "1 CMIP CSIRO-ARCCSS ACCESS-CM2 piControl r1i1p1f1 \n", - "2 CMIP CSIRO ACCESS-ESM1-5 1pctCO2 r1i1p1f1 \n", - "3 CMIP CSIRO ACCESS-ESM1-5 piControl r1i1p1f1 \n", - "4 CMIP AWI AWI-CM-1-1-MR 1pctCO2 r1i1p1f1 \n", - "5 CMIP AWI AWI-CM-1-1-MR piControl r1i1p1f1 \n", - "6 CMIP CAMS CAMS-CSM1-0 1pctCO2 r1i1p1f1 \n", - "7 CMIP CAMS CAMS-CSM1-0 piControl r1i1p1f1 \n", - "8 CMIP NCAR CESM2 piControl r1i1p1f1 \n", - "9 CMIP NCAR CESM2 1pctCO2 r1i1p1f1 \n", - "10 CMIP NCAR CESM2-FV2 piControl r1i1p1f1 \n", - "11 CMIP NCAR CESM2-FV2 1pctCO2 r1i1p1f1 \n", - "12 CMIP NCAR CESM2-WACCM piControl r1i1p1f1 \n", - "13 CMIP NCAR CESM2-WACCM 1pctCO2 r1i1p1f1 \n", - "14 CMIP NCAR CESM2-WACCM-FV2 piControl r1i1p1f1 \n", - "15 CMIP NCAR CESM2-WACCM-FV2 1pctCO2 r1i1p1f1 \n", - "16 CMIP THU CIESM 1pctCO2 r1i1p1f1 \n", - "17 CMIP THU CIESM piControl r1i1p1f1 \n", - "18 CMIP CMCC CMCC-CM2-SR5 1pctCO2 r1i1p1f1 \n", - "19 CMIP CMCC CMCC-CM2-SR5 piControl r1i1p1f1 \n", - "20 CMIP CMCC CMCC-ESM2 1pctCO2 r1i1p1f1 \n", - "21 CMIP CMCC CMCC-ESM2 piControl r1i1p1f1 \n", - "22 CMIP CCCma CanESM5 1pctCO2 r1i1p1f1 \n", - "23 CMIP CCCma CanESM5 piControl r1i1p1f1 \n", - "24 CMIP EC-Earth-Consortium EC-Earth3-Veg piControl r1i1p1f1 \n", - "25 CMIP EC-Earth-Consortium EC-Earth3-Veg 1pctCO2 r1i1p1f1 \n", - "26 CMIP CAS FGOALS-g3 piControl r1i1p1f1 \n", - "27 CMIP CAS FGOALS-g3 1pctCO2 r1i1p1f1 \n", - "28 CMIP FIO-QLNM FIO-ESM-2-0 piControl r1i1p1f1 \n", - "29 CMIP FIO-QLNM FIO-ESM-2-0 1pctCO2 r1i1p1f1 \n", - "30 CMIP NOAA-GFDL GFDL-CM4 1pctCO2 r1i1p1f1 \n", - "31 CMIP NOAA-GFDL GFDL-CM4 piControl r1i1p1f1 \n", - "32 CMIP NOAA-GFDL GFDL-ESM4 1pctCO2 r1i1p1f1 \n", - "33 CMIP NOAA-GFDL GFDL-ESM4 piControl r1i1p1f1 \n", - "34 CMIP NASA-GISS GISS-E2-1-G piControl r1i1p1f1 \n", - "35 CMIP NASA-GISS GISS-E2-1-G 1pctCO2 r1i1p1f1 \n", - "36 CMIP NASA-GISS GISS-E2-1-H 1pctCO2 r1i1p1f1 \n", - "37 CMIP NASA-GISS GISS-E2-1-H piControl r1i1p1f1 \n", - "38 CMIP NASA-GISS GISS-E2-2-G 1pctCO2 r1i1p1f1 \n", - "39 CMIP NASA-GISS GISS-E2-2-G piControl r1i1p1f1 \n", - "40 CMIP IPSL IPSL-CM6A-LR 1pctCO2 r1i1p1f1 \n", - "41 CMIP IPSL IPSL-CM6A-LR piControl r1i1p1f1 \n", - "42 CMIP UA MCM-UA-1-0 1pctCO2 r1i1p1f1 \n", - "43 CMIP UA MCM-UA-1-0 piControl r1i1p1f1 \n", - "44 CMIP MPI-M MPI-ESM1-2-HR 1pctCO2 r1i1p1f1 \n", - "45 CMIP MPI-M MPI-ESM1-2-HR piControl r1i1p1f1 \n", - "46 CMIP MPI-M MPI-ESM1-2-LR piControl r1i1p1f1 \n", - "47 CMIP MPI-M MPI-ESM1-2-LR 1pctCO2 r1i1p1f1 \n", - "48 CMIP NUIST NESM3 1pctCO2 r1i1p1f1 \n", - "49 CMIP NUIST NESM3 piControl r1i1p1f1 \n", - "50 CMIP NCC NorCPM1 piControl r1i1p1f1 \n", - "51 CMIP NCC NorCPM1 1pctCO2 r1i1p1f1 \n", - "52 CMIP SNU SAM0-UNICON 1pctCO2 r1i1p1f1 \n", - "53 CMIP SNU SAM0-UNICON piControl r1i1p1f1 \n", - "54 CMIP AS-RCEC TaiESM1 1pctCO2 r1i1p1f1 \n", - "55 CMIP AS-RCEC TaiESM1 piControl r1i1p1f1 \n", - "\n", - " table_id variable_id grid_label \\\n", - "0 Omon hfds gn \n", - "1 Omon hfds gn \n", - "2 Omon hfds gn \n", - "3 Omon hfds gn \n", - "4 Omon hfds gn \n", - "5 Omon hfds gn \n", - "6 Omon hfds gn \n", - "7 Omon hfds gn \n", - "8 Omon hfds gn \n", - "9 Omon hfds gn \n", - "10 Omon hfds gn \n", - "11 Omon hfds gn \n", - "12 Omon hfds gn \n", - "13 Omon hfds gn \n", - "14 Omon hfds gn \n", - "15 Omon hfds gn \n", - "16 Omon hfds gn \n", - "17 Omon hfds gn \n", - "18 Omon hfds gn \n", - "19 Omon hfds gn \n", - "20 Omon hfds gn \n", - "21 Omon hfds gn \n", - "22 Omon hfds gn \n", - "23 Omon hfds gn \n", - "24 Omon hfds gn \n", - "25 Omon hfds gn \n", - "26 Omon hfds gn \n", - "27 Omon hfds gn \n", - "28 Omon hfds gn \n", - "29 Omon hfds gn \n", - "30 Omon hfds gn \n", - "31 Omon hfds gn \n", - "32 Omon hfds gn \n", - "33 Omon hfds gn \n", - "34 Omon hfds gn \n", - "35 Omon hfds gn \n", - "36 Omon hfds gn \n", - "37 Omon hfds gn \n", - "38 Omon hfds gn \n", - "39 Omon hfds gn \n", - "40 Omon hfds gn \n", - "41 Omon hfds gn \n", - "42 Omon hfds gn \n", - "43 Omon hfds gn \n", - "44 Omon hfds gn \n", - "45 Omon hfds gn \n", - "46 Omon hfds gn \n", - "47 Omon hfds gn \n", - "48 Omon hfds gn \n", - "49 Omon hfds gn \n", - "50 Omon hfds gn \n", - "51 Omon hfds gn \n", - "52 Omon hfds gn \n", - "53 Omon hfds gn \n", - "54 Omon hfds gn \n", - "55 Omon hfds gn \n", - "\n", - " zstore dcpp_init_year \\\n", - "0 gs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/... NaN \n", - "1 gs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/... NaN \n", - "2 gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/1pct... NaN \n", - "3 gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/piCo... NaN \n", - "4 gs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/1pctCO... NaN \n", - "5 gs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/piCont... NaN \n", - "6 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/1pctCO2... NaN \n", - "7 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/piContr... NaN \n", - "8 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i... NaN \n", - "9 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/1pctCO2/r1i1p... NaN \n", - "10 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/piControl... NaN \n", - "11 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/1pctCO2/r... NaN \n", - "12 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/piContr... NaN \n", - "13 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/1pctCO2... NaN \n", - "14 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/piC... NaN \n", - "15 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/1pc... NaN \n", - "16 gs://cmip6/CMIP6/CMIP/THU/CIESM/1pctCO2/r1i1p1... NaN \n", - "17 gs://cmip6/CMIP6/CMIP/THU/CIESM/piControl/r1i1... NaN \n", - "18 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/1pctCO... NaN \n", - "19 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/piCont... NaN \n", - "20 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/1pctCO2/r... NaN \n", - "21 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/piControl... NaN \n", - "22 gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/1pctCO2/r1... NaN \n", - "23 gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/piControl/... NaN \n", - "24 gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E... NaN \n", - "25 gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E... NaN \n", - "26 gs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/piControl/... NaN \n", - "27 gs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/1pctCO2/r1... NaN \n", - "28 gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/piC... NaN \n", - "29 gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/1pc... NaN \n", - "30 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/1pctC... NaN \n", - "31 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/piCon... NaN \n", - "32 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/1pct... NaN \n", - "33 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/piCo... NaN \n", - "34 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/pi... NaN \n", - "35 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/1p... NaN \n", - "36 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/1p... NaN \n", - "37 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/pi... NaN \n", - "38 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/1p... NaN \n", - "39 gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/pi... NaN \n", - "40 gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/1pctCO... NaN \n", - "41 gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piCont... NaN \n", - "42 gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/1pctCO2/r1... NaN \n", - "43 gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/piControl/... NaN \n", - "44 gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/1pct... NaN \n", - "45 gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/piCo... NaN \n", - "46 gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/piCo... NaN \n", - "47 gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/1pct... NaN \n", - "48 gs://cmip6/CMIP6/CMIP/NUIST/NESM3/1pctCO2/r1i1... NaN \n", - "49 gs://cmip6/CMIP6/CMIP/NUIST/NESM3/piControl/r1... NaN \n", - "50 gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/piControl/r1... NaN \n", - "51 gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/1pctCO2/r1i1... NaN \n", - "52 gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/... NaN \n", - "53 gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/piContro... NaN \n", - "54 gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/1pctCO2/... NaN \n", - "55 gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/piContro... NaN \n", - "\n", - " version \n", - "0 20191109 \n", - "1 20191112 \n", - "2 20191115 \n", - "3 20191214 \n", - "4 20181218 \n", - "5 20181218 \n", - "6 20190708 \n", - "7 20190729 \n", - "8 20190320 \n", - "9 20190425 \n", - "10 20191120 \n", - "11 20200310 \n", - "12 20190320 \n", - "13 20190425 \n", - "14 20191120 \n", - "15 20200226 \n", - "16 20200220 \n", - "17 20200220 \n", - "18 20200616 \n", - "19 20200616 \n", - "20 20210127 \n", - "21 20210304 \n", - "22 20190429 \n", - "23 20190429 \n", - "24 20200919 \n", - "25 20200919 \n", - "26 20191126 \n", - "27 20191126 \n", - "28 20200921 \n", - "29 20200927 \n", - "30 20180701 \n", - "31 20180701 \n", - "32 20180701 \n", - "33 20180701 \n", - "34 20180824 \n", - "35 20180905 \n", - "36 20190403 \n", - "37 20190410 \n", - "38 20191120 \n", - "39 20191120 \n", - "40 20180727 \n", - "41 20200326 \n", - "42 20190731 \n", - "43 20190731 \n", - "44 20190710 \n", - "45 20190710 \n", - "46 20190710 \n", - "47 20190710 \n", - "48 20190703 \n", - "49 20190704 \n", - "50 20190914 \n", - "51 20190914 \n", - "52 20190323 \n", - "53 20190910 \n", - "54 20201130 \n", - "55 20210213 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query = dict(experiment_id=['1pctCO2', 'piControl'], table_id='Omon', variable_id='hfds', \n", - " grid_label='gn', member_id='r1i1p1f1', require_all_on='source_id')\n", - "\n", - "cat = col.search(**query)\n", - "cat.df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conveniently, NCAR contributed some data to CMIP6 that has already been regridded to a 1x1 lat-lon grid, which is the resolution I am interested in for the ensemble mean. We will use the coordinates from this Dataset when we create the xESMF regridder." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:17.248304Z", - "iopub.status.busy": "2023-12-18T19:49:17.248108Z", - "iopub.status.idle": "2023-12-18T19:49:17.427630Z", - "shell.execute_reply": "2023-12-18T19:49:17.427030Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "data": { - "text/html": [ - "
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    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0CMIPNCARCESM2piControlr1i1p1f1Omonhfdsgrgs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...NaN20190320
    \n", - "
    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id member_id table_id \\\n", - "0 CMIP NCAR CESM2 piControl r1i1p1f1 Omon \n", - "\n", - " variable_id grid_label zstore \\\n", - "0 hfds gr gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i... \n", - "\n", - " dcpp_init_year version \n", - "0 NaN 20190320 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rg_query = dict(source_id='CESM2', experiment_id='piControl', table_id='Omon', variable_id='hfds', \n", - " grid_label='gr', member_id='r1i1p1f1', require_all_on=['source_id'])\n", - "\n", - "rg_cat = col.search(**rg_query)\n", - "rg_cat.df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, make the dictionaries with the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:17.429953Z", - "iopub.status.busy": "2023-12-18T19:49:17.429758Z", - "iopub.status.idle": "2023-12-18T19:49:33.702005Z", - "shell.execute_reply": "2023-12-18T19:49:33.701250Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_631/4245233592.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.\n", - " dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--> The keys in the returned dictionary of datasets are constructed as follows:\n", - "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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To remove this message, remove the ambiguity by padding your reference date strings with zeros.\n", - " warnings.warn(warning_msg, SerializationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "['CMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn',\n", - " 'CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn',\n", - " 'CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn',\n", - " 'CMIP.NCAR.CESM2.1pctCO2.Omon.gn',\n", - " 'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn',\n", - " 'CMIP.NCC.NorCPM1.piControl.Omon.gn',\n", - " 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn',\n", - " 'CMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn',\n", - " 'CMIP.THU.CIESM.piControl.Omon.gn',\n", - " 'CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn',\n", - " 'CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn',\n", - " 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn',\n", - " 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn',\n", - " 'CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn',\n", - " 'CMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn',\n", - " 'CMIP.UA.MCM-UA-1-0.piControl.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn',\n", - " 'CMIP.SNU.SAM0-UNICON.piControl.Omon.gn',\n", - " 'CMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn',\n", - " 'CMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn',\n", - " 'CMIP.CCCma.CanESM5.piControl.Omon.gn',\n", - " 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn',\n", - " 'CMIP.NCAR.CESM2.piControl.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn',\n", - " 'CMIP.CCCma.CanESM5.1pctCO2.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-FV2.piControl.Omon.gn',\n", - " 'CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn',\n", - " 'CMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn',\n", - " 'CMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn',\n", - " 'CMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn',\n", - " 'CMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn',\n", - " 'CMIP.CAS.FGOALS-g3.piControl.Omon.gn',\n", - " 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn',\n", - " 'CMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn',\n", - " 'CMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn',\n", - " 'CMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn',\n", - " 'CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn',\n", - " 'CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn',\n", - " 'CMIP.NUIST.NESM3.1pctCO2.Omon.gn',\n", - " 'CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn',\n", - " 'CMIP.THU.CIESM.1pctCO2.Omon.gn',\n", - " 'CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn',\n", - " 'CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn',\n", - " 'CMIP.NCC.NorCPM1.1pctCO2.Omon.gn',\n", - " 'CMIP.NUIST.NESM3.piControl.Omon.gn',\n", - " 'CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn',\n", - " 'CMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn',\n", - " 'CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn',\n", - " 'CMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})\n", - "list(dset_dict.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:33.705436Z", - "iopub.status.busy": "2023-12-18T19:49:33.704472Z", - "iopub.status.idle": "2023-12-18T19:49:43.501005Z", - "shell.execute_reply": "2023-12-18T19:49:43.500219Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--> The keys in the returned dictionary of datasets are constructed as follows:\n", - "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_631/2341675588.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.\n", - " rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "['CMIP.NCAR.CESM2.piControl.Omon.gr']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})\n", - "list(rg_dset_dict.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define some functions and organize" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's make a function to get the diagnostic of interest: the change in ocean heat uptake at the time of transient CO$_2$ doubling compared to the pre-industrial control:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.504474Z", - "iopub.status.busy": "2023-12-18T19:49:43.504265Z", - "iopub.status.idle": "2023-12-18T19:49:43.508247Z", - "shell.execute_reply": "2023-12-18T19:49:43.507684Z" - } - }, - "outputs": [], - "source": [ - "def get_tcr(ctrl_key, expr_key):\n", - " ds_1pct = dset_dict[expr_key].squeeze()\n", - " ds_piCl = dset_dict[ctrl_key].squeeze()\n", - " ds_tcr = ds_1pct.isel(time=slice(12*59, 12*80)).mean(dim='time') - ds_piCl.isel(time=slice(12*59, 12*80)).mean(dim='time')\n", - " return ds_tcr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the time slice is 20 years centered around year 70, which is when CO$_2$ doubles in a 1pctCO2 experiment ($1.01^{70}\\approx 2$). Just for convenience, we will also define a function that creates the xESMF regridder and performs the regridding. The regridder is specific to the input (`ds_in`) and output (`regrid_to`) grids, so it must be redefined for each model. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.510425Z", - "iopub.status.busy": "2023-12-18T19:49:43.510237Z", - "iopub.status.idle": "2023-12-18T19:49:43.513326Z", - "shell.execute_reply": "2023-12-18T19:49:43.512775Z" - } - }, - "outputs": [], - "source": [ - "def regrid(ds_in, regrid_to, method='bilinear'):\n", - " regridder = xe.Regridder(ds_in, regrid_to, method=method, periodic=True, ignore_degenerate=True)\n", - " ds_out = regridder(ds_in)\n", - " return ds_out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the following function takes the list of keys generated by Intake-ESM and splits them into two sorted lists of keys: one for the piControl experiment and another for 1pctCO2. This will work nicely with the `get_tcr()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.515350Z", - "iopub.status.busy": "2023-12-18T19:49:43.515162Z", - "iopub.status.idle": "2023-12-18T19:49:43.518643Z", - "shell.execute_reply": "2023-12-18T19:49:43.518084Z" - } - }, - "outputs": [], - "source": [ - "def sorted_split_list(a_list):\n", - " c_list = []\n", - " e_list = []\n", - " for item in a_list:\n", - " if 'piControl' in item:\n", - " c_list.append(item)\n", - " elif '1pctCO2' in item:\n", - " e_list.append(item)\n", - " else: print('Could not find experiment name in key:'+item)\n", - " return sorted(c_list), sorted(e_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's make the lists and look at them to make sure they are properly sorted:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.520759Z", - "iopub.status.busy": "2023-12-18T19:49:43.520537Z", - "iopub.status.idle": "2023-12-18T19:49:43.523449Z", - "shell.execute_reply": "2023-12-18T19:49:43.522906Z" - } - }, - "outputs": [], - "source": [ - "ctrl_keys, expr_keys = sorted_split_list(list(dset_dict.keys()))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.525474Z", - "iopub.status.busy": "2023-12-18T19:49:43.525284Z", - "iopub.status.idle": "2023-12-18T19:49:43.528636Z", - "shell.execute_reply": "2023-12-18T19:49:43.528060Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn\t\tCMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn\n", - "CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn\t\tCMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn\n", - "CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn\t\tCMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn\n", - "CMIP.CAS.FGOALS-g3.piControl.Omon.gn\t\tCMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn\n", - "CMIP.CCCma.CanESM5.piControl.Omon.gn\t\tCMIP.CCCma.CanESM5.1pctCO2.Omon.gn\n", - "CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn\t\tCMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn\n", - "CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn\t\tCMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn\n", - "CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn\t\tCMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn\n", - "CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn\t\tCMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn\n", - "CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn\t\tCMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn\n", - "CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn\t\tCMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn\n", - "CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn\t\tCMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn\n", - "CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn\t\tCMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn\n", - "CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn\t\tCMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn\n", - "CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn\t\tCMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn\n", - "CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn\t\tCMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn\n", - "CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn\t\tCMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn\n", - "CMIP.NCAR.CESM2-FV2.piControl.Omon.gn\t\tCMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn\n", - "CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn\t\tCMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn\n", - "CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn\t\tCMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn\n", - "CMIP.NCAR.CESM2.piControl.Omon.gn\t\tCMIP.NCAR.CESM2.1pctCO2.Omon.gn\n", - "CMIP.NCC.NorCPM1.piControl.Omon.gn\t\tCMIP.NCC.NorCPM1.1pctCO2.Omon.gn\n", - "CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn\t\tCMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn\n", - "CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn\t\tCMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn\n", - "CMIP.NUIST.NESM3.piControl.Omon.gn\t\tCMIP.NUIST.NESM3.1pctCO2.Omon.gn\n", - "CMIP.SNU.SAM0-UNICON.piControl.Omon.gn\t\tCMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn\n", - "CMIP.THU.CIESM.piControl.Omon.gn\t\tCMIP.THU.CIESM.1pctCO2.Omon.gn\n", - "CMIP.UA.MCM-UA-1-0.piControl.Omon.gn\t\tCMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn\n" - ] - } - ], - "source": [ - "for i in range(len(ctrl_keys)):\n", - " print(ctrl_keys[i]+'\\t\\t'+expr_keys[i])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Note\n", - "If you look at the `hfds` anomaly for SAM0-UNICON, you will see negative values around the North Atlantic, especially in the Labrador Sea and Denmark Strait. These are areas of deep water formation and ocean heat uptake. By the CMIP convention, as described in the `hfds` attributes, a negative `hfds` indicates an upward heat flux from the ocean to the atmosphere, so by physical reasoning, this data should have the opposite sign. We could do this manually, but for simplicity, let's just remove the model from our analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.530736Z", - "iopub.status.busy": "2023-12-18T19:49:43.530545Z", - "iopub.status.idle": "2023-12-18T19:49:43.571741Z", - "shell.execute_reply": "2023-12-18T19:49:43.571185Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "Dimensions:             (member_id: 1, dcpp_init_year: 1, time: 1800, j: 384,\n",
-       "                         i: 320, bnds: 2, vertices: 4)\n",
-       "Coordinates:\n",
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-       "    latitude            (j, i) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
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-       "  * time                (time) object 1850-01-17 00:29:59.999998 ... 1999-12-...\n",
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-       "  * member_id           (member_id) object 'r1i1p1f1'\n",
-       "  * dcpp_init_year      (dcpp_init_year) float64 nan\n",
-       "Dimensions without coordinates: bnds, vertices\n",
-       "Data variables:\n",
-       "    hfds                (member_id, dcpp_init_year, time, j, i) float32 dask.array<chunksize=(1, 1, 148, 384, 320), meta=np.ndarray>\n",
-       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
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-       "Attributes: (12/63)\n",
-       "    Conventions:                      CF-1.7 CMIP-6.2\n",
-       "    activity_id:                      CMIP\n",
-       "    branch_method:                    standard\n",
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-       "    cmor_version:                     3.4.0\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     hfds\n",
-       "    intake_esm_attrs:grid_label:      gn\n",
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-       "    intake_esm_attrs:version:         20190323\n",
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    " - ], - "text/plain": [ - "\n", - "Dimensions: (member_id: 1, dcpp_init_year: 1, time: 1800, j: 384,\n", - " i: 320, bnds: 2, vertices: 4)\n", - "Coordinates:\n", - " * i (i) int32 0 1 2 3 4 5 6 ... 313 314 315 316 317 318 319\n", - " * j (j) int32 0 1 2 3 4 5 6 ... 377 378 379 380 381 382 383\n", - " latitude (j, i) float64 dask.array\n", - " longitude (j, i) float64 dask.array\n", - " * time (time) object 1850-01-17 00:29:59.999998 ... 1999-12-...\n", - " time_bnds (time, bnds) object dask.array\n", - " * member_id (member_id) object 'r1i1p1f1'\n", - " * dcpp_init_year (dcpp_init_year) float64 nan\n", - "Dimensions without coordinates: bnds, vertices\n", - "Data variables:\n", - " hfds (member_id, dcpp_init_year, time, j, i) float32 dask.array\n", - " vertices_latitude (j, i, vertices) float64 dask.array\n", - " vertices_longitude (j, i, vertices) float64 dask.array\n", - "Attributes: (12/63)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 99645.0\n", - " cmor_version: 3.4.0\n", - " ... ...\n", - " intake_esm_attrs:variable_id: hfds\n", - " intake_esm_attrs:grid_label: gn\n", - " intake_esm_attrs:zstore: gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1...\n", - " intake_esm_attrs:version: 20190323\n", - " intake_esm_attrs:_data_format_: zarr\n", - " intake_esm_dataset_key: CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dset_dict['CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn']" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:43.573911Z", - "iopub.status.busy": "2023-12-18T19:49:43.573714Z", - "iopub.status.idle": "2023-12-18T19:49:47.560549Z", - "shell.execute_reply": "2023-12-18T19:49:47.559813Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "get_tcr('CMIP.SNU.SAM0-UNICON.piControl.Omon.gn', 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn').hfds.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:47.564084Z", - "iopub.status.busy": "2023-12-18T19:49:47.563876Z", - "iopub.status.idle": "2023-12-18T19:49:47.568166Z", - "shell.execute_reply": "2023-12-18T19:49:47.567591Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ctrl_keys.pop(-3)\n", - "expr_keys.pop(-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also remove AWI-CM because it raises a `MemoryError` that causes this notebook to fail to [execute via binderbot](https://github.com/ProjectPythia/cookbook-actions/blob/main/.github/workflows/build-book.yaml). Feel free to add it back if this notebook is being run locally." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:47.570321Z", - "iopub.status.busy": "2023-12-18T19:49:47.570133Z", - "iopub.status.idle": "2023-12-18T19:49:47.573746Z", - "shell.execute_reply": "2023-12-18T19:49:47.573176Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ctrl_keys.pop(1)\n", - "expr_keys.pop(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regrid the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will define the output grid. It does not matter what the data actually is, since we just want the structure of the Dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:47.575887Z", - "iopub.status.busy": "2023-12-18T19:49:47.575700Z", - "iopub.status.idle": "2023-12-18T19:49:47.599452Z", - "shell.execute_reply": "2023-12-18T19:49:47.598901Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.Dataset>\n",
-       "Dimensions:         (lat: 180, lon: 360, d2: 2)\n",
-       "Coordinates:\n",
-       "  * lat             (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
-       "    lat_bnds        (lat, d2) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
-       "  * lon             (lon) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5\n",
-       "    lon_bnds        (lon, d2) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>\n",
-       "    time            object 0001-01-15 13:00:00.999998\n",
-       "    time_bnds       (d2) object dask.array<chunksize=(2,), meta=np.ndarray>\n",
-       "    member_id       <U8 'r1i1p1f1'\n",
-       "    dcpp_init_year  float64 nan\n",
-       "Dimensions without coordinates: d2\n",
-       "Data variables:\n",
-       "    hfds            (lat, lon) float32 dask.array<chunksize=(180, 360), meta=np.ndarray>\n",
-       "Attributes: (12/61)\n",
-       "    Conventions:                      CF-1.7 CMIP-6.2\n",
-       "    activity_id:                      CMIP\n",
-       "    branch_method:                    standard\n",
-       "    branch_time_in_child:             0.0\n",
-       "    branch_time_in_parent:            48545.0\n",
-       "    case_id:                          3\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:variable_id:     hfds\n",
-       "    intake_esm_attrs:grid_label:      gr\n",
-       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piCont...\n",
-       "    intake_esm_attrs:version:         20190320\n",
-       "    intake_esm_attrs:_data_format_:   zarr\n",
-       "    intake_esm_dataset_key:           CMIP.NCAR.CESM2.piControl.Omon.gr
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 180, lon: 360, d2: 2)\n", - "Coordinates:\n", - " * lat (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", - " lat_bnds (lat, d2) float64 dask.array\n", - " * lon (lon) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5\n", - " lon_bnds (lon, d2) float64 dask.array\n", - " time object 0001-01-15 13:00:00.999998\n", - " time_bnds (d2) object dask.array\n", - " member_id \n", - "Attributes: (12/61)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 0.0\n", - " branch_time_in_parent: 48545.0\n", - " case_id: 3\n", - " ... ...\n", - " intake_esm_attrs:variable_id: hfds\n", - " intake_esm_attrs:grid_label: gr\n", - " intake_esm_attrs:zstore: gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piCont...\n", - " intake_esm_attrs:version: 20190320\n", - " intake_esm_attrs:_data_format_: zarr\n", - " intake_esm_dataset_key: CMIP.NCAR.CESM2.piControl.Omon.gr" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rg_ds = rg_dset_dict['CMIP.NCAR.CESM2.piControl.Omon.gr'].isel(time=0).squeeze()\n", - "rg_ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we create a new dictionary to store our regridded data. The for-loop goes through the two sorted lists of keys and tries to regrid each model. This allows us to avoid removing a model and rerunning the code every time there is an error. \n", - "\n", - "To summarize,\n", - "- Get the diagnostic of interest and try to regrid to a 1x1 lat-lon grid\n", - " - If that fails for any reason, print the error\n", - " - If the regridding is successful, add it to the new dictionary\n", - "- Repeat for all models" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:49:47.601865Z", - "iopub.status.busy": "2023-12-18T19:49:47.601365Z", - "iopub.status.idle": "2023-12-18T19:50:30.460671Z", - "shell.execute_reply": "2023-12-18T19:50:30.459830Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TaiESM1 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CAMS-CSM1-0 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FGOALS-g3 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CanESM5 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMCC-CM2-SR5 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMCC-ESM2 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ACCESS-CM2 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ACCESS-ESM1-5 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EC-Earth3-Veg regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIO-ESM-2-0 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IPSL-CM6A-LR regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPI-ESM1-2-HR regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPI-ESM1-2-LR regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GISS-E2-1-G regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GISS-E2-1-H regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GISS-E2-2-G regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Failed to regrid CESM2-FV2: lon and lat should be both 1D or 2D\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CESM2-WACCM-FV2 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CESM2-WACCM regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CESM2 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NorCPM1 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GFDL-CM4 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GFDL-ESM4 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NESM3 regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CIESM regridded and added to dictionary\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MCM-UA-1-0 regridded and added to dictionary\n", - "----------------------------------------\n", - "| 25/26 models successfully regridded! |\n", - "----------------------------------------\n" - ] - } - ], - "source": [ - "ds_regrid_dict = dict()\n", - "success_count = 0\n", - "model_count = 0\n", - "\n", - "for ctrl_key, expr_key in zip(ctrl_keys, expr_keys):\n", - " model = ctrl_key.split('.')[2]\n", - " try:\n", - " ds_tcr = get_tcr(ctrl_key=ctrl_key, expr_key=expr_key)\n", - " ds_tcr_hfds_regridded = regrid(ds_tcr, rg_ds, method='nearest_s2d').hfds\n", - " except Exception as e:\n", - " print('Failed to regrid '+model+': '+str(e))\n", - " else: \n", - " ds_regrid_dict[model] = ds_tcr_hfds_regridded\n", - " print(model+' regridded and added to dictionary')\n", - " success_count += 1\n", - " finally:\n", - " model_count += 1\n", - " \n", - "print('-'*40+'\\n| '+str(success_count)+'/'+str(model_count)+' models successfully regridded! |\\n'+'-'*40)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CESM2-FV2 fails because of some issue with the dimensions of the coordinates. If we remove `ignore_degenerate=True` from the regridder defined in `regrid()`, there may be a few more failures because of a degenerate element: a cell that has corners close enough that the cell collapses to a line or point." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we concat the results into a single DataArray:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:30.464135Z", - "iopub.status.busy": "2023-12-18T19:50:30.463453Z", - "iopub.status.idle": "2023-12-18T19:50:30.517123Z", - "shell.execute_reply": "2023-12-18T19:50:30.516517Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.DataArray 'hfds' (lat: 180, lon: 361)>\n",
-       "dask.array<concatenate, shape=(180, 361), dtype=float32, chunksize=(80, 144), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
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    " - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * lat (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n", - " * lon (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 360.5" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cmip6em_ohutcr = add_cyclic_point(ds_out_regrid.mean(dim='model'), 'lon', period=360)\n", - "# cmip6em_ohutcr.to_netcdf('cmip6_ohutcr.nc') # remove add_cyclic_point() and uncomment to save\n", - "cmip6em_ohutcr" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:50:30.546679Z", - "iopub.status.busy": "2023-12-18T19:50:30.546361Z", - "iopub.status.idle": "2023-12-18T19:50:49.499995Z", - "shell.execute_reply": "2023-12-18T19:50:49.499268Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip\n", - " warnings.warn(f'Downloading: {url}', DownloadWarning)\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(1, figsize=(12, 5), dpi=130)\n", - "ax_mean = plt.subplot(projection=ccrs.PlateCarree(central_longitude=-150))\n", - "mean_plot = ax_mean.contourf(cmip6em_ohutcr.lon, cmip6em_ohutcr.lat, cmip6em_ohutcr, transform=ccrs.PlateCarree(), \n", - " cmap='RdBu_r', levels=np.linspace(-35, 35, 15), extend='both')\n", - "ax_mean.set_title('CMIP6 ensemble-mean $\\Delta\\mathrm{OHUTCR}$')\n", - "ax_mean.coastlines()\n", - "ax_mean.set_xticks([-120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())\n", - "ax_mean.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n", - "ax_mean.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=True))\n", - "ax_mean.yaxis.set_major_formatter(LatitudeFormatter())\n", - "plt.colorbar(mean_plot, orientation='vertical', label='W m$^{-2}$')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the heat uptake is highest in the subpolar oceans, especially the North Atlantic. From this multi-model ensemble mean, we can see that this is a robust feature of climate models (and likely the climate system itself) in response to a CO$_2$ forcing. For more background and motivation, see [Hu et al. (2020)](https://journals.ametsoc.org/view/journals/clim/33/17/jcliD190642.xml)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "This notebook demonstrates the use of xESMF to regrid the CMIP6 data hosted in Pangeo's Google cloud storage. The regridded data allows us to use Xarray to take a multi-model mean, in this case, of changes in ocean heat uptake associated with each model's transient climate response.\n", - "\n", - "### What's next?\n", - "Other example workflows using this CMIP6 cloud data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "Hu, S., Xie, S.-P., & Liu, W. (2020). Global Pattern Formation of Net Ocean Surface Heat Flux Response to Greenhouse Warming. Journal of Climate, 33(17), 7503–7522. [https://doi.org/10.1175/JCLI-D-19-0642.1](https://doi.org/10.1175/JCLI-D-19-0642.1)\n", - "\n", - "Xie, S.-P. (2020). Ocean warming pattern effect on global and regional climate change. AGU Advances, 1, e2019AV000130. [https://doi.org/10.1029/2019AV000130](https://doi.org/10.1029/2019AV000130) \n", - "\n", - "\n", - "\n", - "Parts of this workflow were taken from a similar workflow in [this notebook by NordicESMhub](https://nordicesmhub.github.io/forces-2021/learning/example-notebooks/xesmf_regridding.html)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ] - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/77/_sources/notebooks/foundations/esgf-opendap.ipynb b/_preview/77/_sources/notebooks/foundations/esgf-opendap.ipynb deleted file mode 100644 index de6b027..0000000 --- a/_preview/77/_sources/notebooks/foundations/esgf-opendap.ipynb +++ /dev/null @@ -1,4629 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CMIP6\n", - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Search and Load CMIP6 Data via ESGF/OPeNDAP" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This notebook shows how to search and load data via [Earth System Grid Federation](https://esgf.llnl.gov) infrastructure. This infrastructure works great and is the foundation of the CMIP6 distribution system.\n", - "\n", - "The main technologies used here are the [ESGF search API](https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API), used to figure out what data we want, and [OPeNDAP](https://www.opendap.org/), a remote data access protocol over HTTP." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "\n", - "- **Time to learn**: 10 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" 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scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof 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    " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client = Client()\n", - "client" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Search using ESGF API\n", - "\n", - "Fortunately, there is an ESGF API implemented in Python - `pyesgf`, which requires three major steps:\n", - "- Establish a search connection\n", - "- Query your data\n", - "- Extract the urls to your data\n", - "\n", - "Once you have this information, you can load the data into an `xarray.Dataset`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the connection to a data server\n", - "First, we configure our connection to some server, using the distributed option (`distrib=False`). In this case, we are searching from the Lawerence Livermore National Lab (LLNL) data node." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:27.700934Z", - "iopub.status.busy": "2023-12-18T19:54:27.700461Z", - "iopub.status.idle": "2023-12-18T19:54:27.705797Z", - "shell.execute_reply": "2023-12-18T19:54:27.705033Z" - } - }, - "outputs": [], - "source": [ - "conn = SearchConnection('https://esgf-node.llnl.gov/esg-search',\n", - " distrib=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Query our dataset\n", - "We are interested in a single experiment from CMIP6 - one of the Community Earth System Model version 2 (CESM2) runs, specifically the historical part of the simulation.\n", - "\n", - "We are also interested in a single variable - temperature at the surface (**tas**), with a single ensemble member (`r10i1p1f1`)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:27.708542Z", - "iopub.status.busy": "2023-12-18T19:54:27.708065Z", - "iopub.status.idle": "2023-12-18T19:54:27.711491Z", - "shell.execute_reply": "2023-12-18T19:54:27.710925Z" - } - }, - "outputs": [], - "source": [ - "ctx = conn.new_context(\n", - " facets='project,experiment_id',\n", - " project='CMIP6',\n", - " table_id='Amon',\n", - " institution_id=\"NCAR\",\n", - " experiment_id='historical',\n", - " source_id='CESM2',\n", - " variable='tas',\n", - " variant_label='r10i1p1f1',\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract the OpenDAP urls\n", - "In order to access the datasets, we need the urls to the data. Once we have these, we can read the data remotely!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:27.713993Z", - "iopub.status.busy": "2023-12-18T19:54:27.713797Z", - "iopub.status.idle": "2023-12-18T19:54:28.861011Z", - "shell.execute_reply": "2023-12-18T19:54:28.860362Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = ctx.search()[0]\n", - "files = result.file_context().search()\n", - "files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The files object is not immediately helpful - we need to extract the `opendap_url` method from this." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:28.863373Z", - "iopub.status.busy": "2023-12-18T19:54:28.863182Z", - "iopub.status.idle": "2023-12-18T19:54:28.867382Z", - "shell.execute_reply": "2023-12-18T19:54:28.866823Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files[0].opendap_url" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use this for the whole list using list comprehension, as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:28.869657Z", - "iopub.status.busy": "2023-12-18T19:54:28.869470Z", - "iopub.status.idle": "2023-12-18T19:54:28.873694Z", - "shell.execute_reply": "2023-12-18T19:54:28.873131Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n", - " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n", - " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n", - " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opendap_urls = [file.opendap_url for file in files]\n", - "opendap_urls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read the data into an `xarray.Dataset`\n", - "Now that we have our urls to the data, we can use open multifile dataset (`open_mfdataset`) to read the data, combining the coordinates and chunking by time.\n", - "\n", - "Xarray, together with the netCDF4 Python library, allow lazy loading." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:28.875927Z", - "iopub.status.busy": "2023-12-18T19:54:28.875737Z", - "iopub.status.idle": "2023-12-18T19:54:34.364539Z", - "shell.execute_reply": "2023-12-18T19:54:34.363426Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:    (lat: 192, lon: 288, time: 1980, nbnd: 2)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
-       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    time_bnds  (time, nbnd) object dask.array<chunksize=(480, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (time, lat, nbnd) float64 dask.array<chunksize=(600, 192, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (time, lon, nbnd) float64 dask.array<chunksize=(600, 288, 2), meta=np.ndarray>\n",
-       "    tas        (time, lat, lon) float32 dask.array<chunksize=(480, 192, 288), meta=np.ndarray>\n",
-       "Attributes: (12/46)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            674885.0\n",
-       "    branch_time_in_parent:           306600.0\n",
-       "    case_id:                         24\n",
-       "    ...                              ...\n",
-       "    table_id:                        Amon\n",
-       "    tracking_id:                     hdl:21.14100/e47b79db-3925-45a7-9c0a-679...\n",
-       "    variable_id:                     tas\n",
-       "    variant_info:                    CMIP6 20th century experiments (1850-201...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 192, lon: 288, time: 1980, nbnd: 2)\n", - "Coordinates:\n", - " * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", - " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n", - "Dimensions without coordinates: nbnd\n", - "Data variables:\n", - " time_bnds (time, nbnd) object dask.array\n", - " lat_bnds (time, lat, nbnd) float64 dask.array\n", - " lon_bnds (time, lon, nbnd) float64 dask.array\n", - " tas (time, lat, lon) float32 dask.array\n", - "Attributes: (12/46)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 674885.0\n", - " branch_time_in_parent: 306600.0\n", - " case_id: 24\n", - " ... ...\n", - " table_id: Amon\n", - " tracking_id: hdl:21.14100/e47b79db-3925-45a7-9c0a-679...\n", - " variable_id: tas\n", - " variant_info: CMIP6 20th century experiments (1850-201...\n", - " variant_label: r10i1p1f1\n", - " DODS_EXTRA.Unlimited_Dimension: time" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = xr.open_mfdataset(opendap_urls,\n", - " combine='by_coords',\n", - " chunks={'time':480})\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot a quick look of the data\n", - "Now that we have the dataset, let's plot a few quick looks of the data." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:34.368654Z", - "iopub.status.busy": "2023-12-18T19:54:34.367453Z", - "iopub.status.idle": "2023-12-18T19:54:36.103523Z", - "shell.execute_reply": "2023-12-18T19:54:36.102802Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds.tas.sel(time='1950-01').squeeze().plot(cmap='Spectral_r');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These are OPeNDAP endpoints. Xarray, together with the netCDF4 Python library, allow lazy loading." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute an area-weighted global average\n", - "Let's apply some computation to this dataset. We would like to calculate the global average temperature. This requires weighting each of the grid cells properly, using the area.\n", - "\n", - "### Find the area of the cells\n", - "\n", - "We can query the dataserver again, this time extracting the area of the cell (`areacella`)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:36.106518Z", - "iopub.status.busy": "2023-12-18T19:54:36.105989Z", - "iopub.status.idle": "2023-12-18T19:54:36.109515Z", - "shell.execute_reply": "2023-12-18T19:54:36.108926Z" - } - }, - "outputs": [], - "source": [ - "ctx = conn.new_context(\n", - " facets='project,experiment_id',\n", - " project='CMIP6',\n", - " institution_id=\"NCAR\",\n", - " experiment_id='historical',\n", - " source_id='CESM2',\n", - " variable='areacella',\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As before, we extract the opendap urls." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:36.111810Z", - "iopub.status.busy": "2023-12-18T19:54:36.111462Z", - "iopub.status.idle": "2023-12-18T19:54:37.262872Z", - "shell.execute_reply": "2023-12-18T19:54:37.262044Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r11i1p1f1/fx/areacella/gn/v20190514/areacella_fx_CESM2_historical_r11i1p1f1_gn.nc']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = ctx.search()[0]\n", - "files = result.file_context().search()\n", - "opendap_urls = [file.opendap_url for file in files]\n", - "opendap_urls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And finally, we load our cell area file into an `xarray.Dataset`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:37.265432Z", - "iopub.status.busy": "2023-12-18T19:54:37.265068Z", - "iopub.status.idle": "2023-12-18T19:54:37.888698Z", - "shell.execute_reply": "2023-12-18T19:54:37.888085Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    Conventions:            CF-1.7 CMIP-6.2\n",
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-       "    case_id:                972\n",
-       "    ...                     ...\n",
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-       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
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    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 192, lon: 288, nbnd: 2)\n", - "Coordinates:\n", - " * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", - " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", - "Dimensions without coordinates: nbnd\n", - "Data variables:\n", - " lat_bnds (lat, nbnd) float64 ...\n", - " lon_bnds (lon, nbnd) float64 ...\n", - " areacella (lat, lon) float32 ...\n", - "Attributes: (12/44)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 674885.0\n", - " branch_time_in_parent: 219000.0\n", - " case_id: 972\n", - " ... ...\n", - " sub_experiment_id: none\n", - " table_id: fx\n", - " tracking_id: hdl:21.14100/96455df2-979e-4cd4-8521-ddf307c6bc4a\n", - " variable_id: areacella\n", - " variant_info: CMIP6 20th century experiments (1850-2014) with C...\n", - " variant_label: r11i1p1f1" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds_area = xr.open_dataset(opendap_urls[0])\n", - "ds_area" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the global average\n", - "Now that we have the area of each cell, and the temperature at each point, we can compute the global average temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:37.891711Z", - "iopub.status.busy": "2023-12-18T19:54:37.891383Z", - "iopub.status.idle": "2023-12-18T19:54:38.244897Z", - "shell.execute_reply": "2023-12-18T19:54:38.244266Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.DataArray (time: 1980)>\n",
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-       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    " - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_area = ds_area.areacella.sum(dim=['lon', 'lat'])\n", - "ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area\n", - "ta_timeseries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:38.247659Z", - "iopub.status.busy": "2023-12-18T19:54:38.247136Z", - "iopub.status.idle": "2023-12-18T19:54:58.934628Z", - "shell.execute_reply": "2023-12-18T19:54:58.933787Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.21 s, sys: 269 ms, total: 1.48 s\n", - "Wall time: 20.7 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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    <xarray.DataArray (time: 1980)>\n",
-       "array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
-       "       287.06284], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    " - ], - "text/plain": [ - "\n", - "array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n", - " 287.06284], dtype=float32)\n", - "Coordinates:\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time ta_timeseries.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize our results\n", - "Now that we have our results, we can visualize using static and dynamic plots. Let's start with static plots using `matplotlib`, then dynamic plots using `hvPlot`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:58.938234Z", - "iopub.status.busy": "2023-12-18T19:54:58.937699Z", - "iopub.status.idle": "2023-12-18T19:54:58.979863Z", - "shell.execute_reply": "2023-12-18T19:54:58.979012Z" - } - }, - "outputs": [], - "source": [ - "ta_timeseries['time'] = ta_timeseries.indexes['time'].to_datetimeindex()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:58.983842Z", - "iopub.status.busy": "2023-12-18T19:54:58.983400Z", - "iopub.status.idle": "2023-12-18T19:54:59.385703Z", - "shell.execute_reply": "2023-12-18T19:54:59.384857Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Global Mean Surface Air Temperature')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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EEAR0bADAVlttlUjLtGnTMGrUKPzkJz9BEAShyfTRRx+NoUOH4u6778aVV16ZWqemXqEAEJq577HHHvT3eDv06NED3bp1s8K6dOmCfv36OWm7dOmC2tpaJ9zMn3iYmU+GpqOPPtpL99KlS9GzZ8/wb9YWv/jFLzBs2DBccMEF2GOPPdC7d+9QUNDUOelDnI7G1EWhUCi+LFBGW6FQKNoAqqursf/+++OVV17B/PnzrQOt8ayc5R1cwzDOnz/fedJo3rx52GCDDawwdt92wYIFVl6PPPIIOnfujOeff95iQp555pn0imXEySefjL///e/I5XIYMmQIjbP++uujuroaJ5xwAs4880wax3j+fuCBB7Dlllvi0UcftZj2pjjUqgTHHHMMrrzySnz44YdhWLdu3bBixQonbvzevEEl76M/8MAD2G+//XDLLbdY4atWrbL+3nDDDfHmm2+iWCx6me0NNtgAuVwOb7zxBrp27er8zsIk7r77bgRBgCeeeAJPPPGE8/u9996Lyy+/HNXV1Yn5NPZ9eAkz3p944glsscUWTc4vC8z8iYdts802Fk033HCD1+N6XMgRb4sVK1bg+eefx0UXXYS//vWvYbjxTZAV3bp1o3Piiy++cNYKRkdj6qJQKBRfFiijrVAoFG0E5513Hl588UX8+te/brTzp/333x9AifGSWrzx48dj8uTJ+Nvf/mbFX7VqFZ577jlLU/7QQw+hqqoqNN82T/hIxmjt2rW4//77K6bPhyFDhuDtt9/G4MGDsemmm9I4PXr0wPe+9z1MmjQJX/va17ye2Q3NXbp0sRiDBQsWUK/jTUFcKGKwevVqzJkzx/IQP2jQIDz++OOoq6sLmdUlS5ZgzJgx1PFbJcjlcg4D/P777+Ott96y3vE+7LDD8PDDD+Oee+7xmo//4Ac/wFVXXYXPP/8cxxxzTEV0FAoF3Hvvvdh6661x5513Or8///zzuO666/Diiy/iBz/4QUV5NwaHHHIIOnXqhOnTpzeLKXoWPPjgg1ZZY8aMwWeffYZf/vKXAIC99toLffv2xccff4yzzjqrUWXkcjkEQeD0+Z133um8GGDiMC33oEGD8P7771thn376KaZMmUIZ7Tiaoy4KhULRUaGMtkKhULQR7LXXXrjppptw9tlnY7fddsPpp5+OHXfcEVVVVZg/fz6efPJJAEhkyrbbbjucfvrpuOGGG1BVVYXDDjsMs2bNwgUXXIDNN98cf/jDH6z4/fv3x29+8xvMnj0bX/3qV/HCCy/gjjvuwG9+8xt85StfAVDyXPyvf/0Lv/jFL3D66adjyZIluPbaa1M1m5Vgk002yaQh/7//+z/svffe+O53v4vf/OY3GDRoEFatWoVp06bhv//9L4YPHw6gxCw+9dRTOOOMM3D00Udjzpw5uOyyyzBw4MDMd4Sz4B//+AdGjx6Nn/3sZ+FTWDNnzsSNN96IJUuW4JprrgnjnnDCCbjttttw/PHH47TTTsOSJUtw9dVXN5nJBkr1veyyy3DRRRdh3333xZQpU3DppZdiyy23RD6fD+P9/Oc/x9ChQ/HrX/8aU6ZMwfe+9z0Ui8VQyHHsscdir732wumnn46TTz4ZEyZMwD777IOePXti/vz5ePPNN7HzzjtbZvISL774IubNm4d//vOf9CmznXbaCTfeeCPuuuuuFmG0Bw0ahEsvvRR/+9vfMGPGDBx66KFYf/31sXDhQowbNw49e/a0vNU3ByZMmIBf/vKX+OlPf4o5c+bgb3/7GzbddFOcccYZAEr31W+44QYMGTIES5cuxdFHH40BAwZg8eLFeO+997B48WLHMiGO3r17Y5999sE111yDDTbYAIMGDcLrr7+Ou+66C3379rXi7rTTTgCA22+/Hb169UK3bt2w5ZZbon///jjhhBNw/PHH44wzzsBPfvITfPbZZ7j66qux4YYbZqprc9RFoVAoOixa1xebQqFQKOJ49913g5NPPjnYcsstg65duwbdunULttlmm+DEE08Mhg0bZsWNex0PgpLH33/+85/BV7/61aBz587BBhtsEBx//PHBnDlzrHjG8/fIkSOD3XffPejatWswcODA4Pzzzw8aGhqsuHfffXew3XbbBV27dg222mqr4MorrwzuuuuuAIDlXbsxXsd9YF7Hg6DklfuUU04JNt1006Bz587BhhtuGOy5556hV2eDq666Khg0aFDQtWvXYPDgwcEdd9xB2wseL94+j8wSY8eODc4888xgl112Cfr16xdUV1cHG264YXDooYcGL7zwghP/3nvvDQYPHhx069Yt2GGHHYJHH33U63X8mmuuoWUyeuvq6oI//elPwaabbhp069Yt2G233YJnnnnGyTsIgmDt2rXBhRdeGGy77bZBly5dgv79+wf7779/MGbMGCve3XffHXzrW98KevbsGXTv3j3YeuutgxNPPDGYMGGCtz2OOOKIoEuXLsGiRYu8cY499tigU6dOwYIFC8L6MK/jxnN4JfClfeaZZ4Lvfe97Qe/evYOuXbsGW2yxRXD00UcHr732WhhnyJAhQc+ePZ08fWN1iy22CA4//HCn7FdeeSU44YQTgr59+wbdu3cPvv/97wdTp0510r/++uvB4YcfHvTr1y/o3LlzsOmmmwaHH3548Pjjj4dxzHhdvHixk37u3LnBT37yk2D99dcPevXqFRx66KHBhx9+SMftf/7zn2DLLbcMqqurrTlVLBaDq6++Othqq62Cbt26BbvvvnswfPhwr9dxSVuldVEoFIovG3JBEAStweArFAqFQqFQdBTcc889OPnkkzF+/HjsvvvurU2OQqFQKFoZ+ryXQqFQKBQKhUKhUCgUzQhltBUKhUKhUCgUCoVCoWhGqOm4QqFQKBQKhUKhUCgUzQjVaCsUCoVCoVAoFAqFQtGMUEZboVAoFAqFQqFQKBSKZoQy2gqFQqFQKBQKhUKhUDQjOrU2AY1BsVjEvHnz0KtXL+RyudYmR6FQKBQKhUKhUCgUHRxBEGDVqlXYZJNNUFWVrLNul4z2vHnzsPnmm7c2GQqFQqFQKBQKhUKh+JJhzpw52GyzzRLjtEtGu1evXgBKFezdu3crU6NQKBQKhUKhUCgUio6OlStXYvPNNw/50SS0S0bbmIv37t1bGW2FQqFQKBQKhUKhULQYslxfVmdoCoVCoVAoFAqFQqFQNCOU0VYoFAqFQqFQKBQKhaIZoYy2QqFQKBQKhUKhUCgUzYh2eUc7KwqFAhoaGlqbDIWi3aBz586orq5ubTIUCoVCoVAoFIp2jQ7JaAdBgAULFmD58uWtTYpC0e7Qt29fbLzxxvpGvUKhUCgUCoVC0Uh0SEbbMNkDBgxAjx49lGFQKDIgCALU1NRg0aJFAICBAwe2MkUKhUKhUCgUCkX7RIdjtAuFQshk9+/fv7XJUSjaFbp37w4AWLRoEQYMGKBm5AqFQqFQKBQKRSPQ4ZyhmTvZPXr0aGVKFIr2CTN31L+BQqFQKBQKhULROHQ4RttAzcUVisZB545CoVAoFAqFQtE0dFhGW6FQKBQKhUKhUCgUitaAMtoKiosvvhhf//rXE+Pst99++P3vf98i9CgUCoVCoVAoFApFe4Ey2grkcjk888wzrU2GQqFQKBQKhUKhUHQIKKOtUCgUCoVCoVAoFApFM+JLwWgHQYCa+nyr/AuCIDOd++23H84++2z8/ve/x/rrr4+NNtoIt99+O9asWYOTTz4ZvXr1wtZbb40XX3wxTPP666/jm9/8Jrp27YqBAwfir3/9K/L5vJXnb3/7W5x77rno168fNt54Y1x88cXh74MGDQIAHHnkkcjlcuHfBvfffz8GDRqEPn364Nhjj8WqVaso7Zdeeil23nlnJ/wb3/gGLrzwwsxtoFAoFAqFQqFQKBTtHR3uHW2GtQ0F7HDhy61S9seXHoIeXbI387333otzzz0X48aNw6OPPorf/OY3eOaZZ3DkkUfi/PPPx7///W+ccMIJmD17NpYtW4bvf//7OOmkk3Dffffhk08+wWmnnYZu3bpZzPS9996Lc845B2+//TbeeustnHTSSdhrr71w0EEHYfz48RgwYACGDh2KQw891Ho3efr06XjmmWfw/PPPY9myZTjmmGNw1VVX4R//+IdD9ymnnIJLLrkE48ePxx577AEAeP/99zFp0iQ8/vjjjW9AhUKhUCgUCoVCoWhn+FJotNsTdtllF/z973/Htttui/POOw/du3fHBhtsgNNOOw3bbrstLrzwQixZsgTvv/8+br75Zmy++ea48cYbsf322+OII47AJZdcguuuuw7FYjHM82tf+xouuugibLvttjjxxBOx++67Y9iwYQCADTfcEADQt29fbLzxxuHfAFAsFnHPPfdgp512wne/+12ccMIJYbo4NttsMxxyyCEYOnRoGDZ06FDsu+++2GqrrdZFUykUCoVCoVAoFApFm8SXQqPdvXM1Pr70kFYruxJ87WtfC79XV1ejf//+lkn2RhttBABYtGgRJk+ejO985zvWu8d77bUXVq9ejblz5+IrX/mKkycADBw4EIsWLUqlZdCgQejVq1fmdKeddhpOOeUU/Otf/0J1dTUefPBBXHfddanlKBQKhUKhUCgUCkVHwpeC0c7lchWZb7cmOnfubP2dy+WsMMNUF4tFBEFgMdkAwjvhMpzlKTXeldCSlO6HP/whunbtiqeffhpdu3ZFXV0dfvKTn6SWo1AoFAqFQqFQKBQdCe2D+1RQ7LDDDnjyyScthnvMmDHo1asXNt1008z5dO7cGYVCocn0dOrUCUOGDMHQoUPRtWtXHHvssejRo0eT81UoFAqFQqFQKBSK9gS9o92OccYZZ2DOnDk4++yz8cknn+DZZ5/FRRddhHPOOQdVVdm7dtCgQRg2bBgWLFiAZcuWNYmmX/7ylxg+fDhefPFFnHLKKU3KS6FQKBQKhUKhUCjaI5TRbsfYdNNN8cILL2DcuHHYZZdd8Otf/xqnnnoq/v73v1eUz3XXXYdXX30Vm2++OXbdddcm0bTttttizz33xHbbbYdvfetbTcpLoVAoFAqFQqFQKNojckElDz23EaxcuRJ9+vTBihUr0Lt3b+u32tpazJw5E1tuuSW6devWShR+eREEAbbffnv86le/wjnnnNPa5CgaAZ1DCoVCoVAoFAqFiyQ+NA69o61oNixatAj3338/Pv/8c5x88smtTY5CoVAoFAqFQqFQtAqU0VY0GzbaaCNssMEGuP3227H++uu3NjkKhUKhUCgUCoViHWPRylps2Kur8xrSlx3KaCuaDe3wFoJCoVAoFAqFQqFoJB4Y+xn+/syHOHv/bfDHg7drbXLaFNQZmkKhUCgUCoVCoVAoKsYFz34IALhh+LRGpV9Z24DahqY/M9wWoYy2QqFQKBQKhUKhUCgqRqeqxpuL19Tn8bWLX8G3rxzWjBS1HSijrVAoFAqFQqFQKBSKilHdBEZ72qLVAIDlNQ0oFDveFdSKGO0rr7wSe+yxB3r16oUBAwbgiCOOwJQpU6w4CxcuxEknnYRNNtkEPXr0wKGHHoqpU6dacerq6nD22Wdjgw02QM+ePfGjH/0Ic+fObXptFAqFQqFQKBQKhULRIuhU1Xi9bY8u1eH31XX55iCnTaGilnn99ddx5plnYuzYsXj11VeRz+dx8MEHY82aNQBKzrCOOOIIzJgxA88++ywmTZqELbbYAgceeGAYBwB+//vf4+mnn8YjjzyCN998E6tXr8YPfvADFAod0z5foVAoFAqFQqFQKDoamqLRlkx6R2S0K/I6/tJLL1l/Dx06FAMGDMDEiROxzz77YOrUqRg7diw+/PBD7LjjjgCAm2++GQMGDMDDDz+MX/7yl1ixYgXuuusu3H///TjwwAMBAA888AA233xzvPbaazjkkEOaqWoKhUKhUCgUCoVCoVhXaMod7YJ4sWh1bcdjtJt0R3vFihUAgH79+gEomYQDQLdu3cI41dXV6NKlC958800AwMSJE9HQ0ICDDz44jLPJJptgp512wpgxY2g5dXV1WLlypfVP0X5xzz33oG/fvi1S1siRI5HL5bB8+XJa9sUXX4yvf/3rLUKLQqFQKBQKhULRkdAUjbZ8GnhVbUNzkNOm0GhGOwgCnHPOOdh7772x0047AQC23357bLHFFjjvvPOwbNky1NfX46qrrsKCBQswf/58AMCCBQvQpUsXrL/++lZ+G220ERYsWEDLuvLKK9GnT5/w3+abb95Ysts0Ro0ahR/+8IfYZJNNkMvl8Mwzz1i/NzQ04C9/+Qt23nln9OzZE5tssglOPPFEzJs3r3UIzoBBgwbhP//5T2uT4cWf/vQnDBvWMT0dKhQKhUKhUCgU6xJN0mgXo++rOqDpeKMZ7bPOOgvvv/8+Hn744TCsc+fOePLJJ/Hpp5+iX79+6NGjB0aOHInDDjsM1dXVCbmVGPdcjnfUeeedhxUrVoT/5syZ01iy2zTWrFmDXXbZBTfeeCP9vaamBu+88w4uuOACvPPOO3jqqafw6aef4kc/+lELU9r6CIIA+XzTJ+R6662H/v37NwNFCoVCoVAoFArFlwvV1Y1ntItqOu7i7LPPxnPPPYcRI0Zgs802s377xje+gXfffRfLly/H/Pnz8dJLL2HJkiXYcsstAQAbb7wx6uvrsWzZMivdokWLsNFGG9Hyunbtit69e1v/KkIQAPVrWudfkN1V/WGHHYbLL78cRx11FP29T58+ePXVV3HMMcdgu+22w7e//W3ccMMNmDhxImbPnu3N96STTsIRRxyBK664AhtttBH69u2LSy65BPl8Hn/+85/Rr18/bLbZZrj77rutdB988AH2339/dO/eHf3798fpp5+O1atXO/lee+21GDhwIPr3748zzzwTDQ0l04/99tsPn332Gf7whz8gl8s5gpSXX34ZgwcPxnrrrYdDDz00tHpgMCbgL7/8MnbffXd07doVb7zxBurq6vDb3/4WAwYMQLdu3bD33ntj/PjxqW1tEDcdT6sTAMyfPx+HH344unfvji233BIPPfRQqua+sX3w+eef42c/+xnWX3999O/fHz/+8Y8xa9as8Pfx48fjoIMOwgYbbIA+ffpg3333xTvvvGPlkcvlcOedd+LII49Ejx49sO222+K5557L3EYKhUKhUCgUCgVDU7yOyye9vvTO0IIgwNlnn42nn34aI0eODJlnhj59+gAApk6digkTJuCyyy4DUGLEO3fuHDKMQIlx+fDDD3H11Vc3th7JaKgBrthk3eSdhvPnAV16rrPsV6xYgVwul3rnefjw4dhss80watQojB49Gqeeeireeust7LPPPnj77bfx6KOP4te//jUOOuggbL755qipqcGhhx6Kb3/72xg/fjwWLVqEX/7ylzjrrLNwzz33hPmOGDECAwcOxIgRIzBt2jT87Gc/w9e//nWcdtppeOqpp7DLLrvg9NNPx2mnnWbRU1NTg2uvvRb3338/qqqqcPzxx+NPf/oTHnzwwcR6nHvuubj22mux1VZboW/fvjj33HPx5JNP4t5778UWW2yBq6++GocccgimTZsW+g6oFEl1AoATTzwRX3zxBUaOHInOnTvjnHPOwaJFi1LzbUwffO9738N3v/tdjBo1Cp06dcLll1+OQw89FO+//z66dOmCVatWYciQIbj++usBANdddx2+//3vY+rUqejVq1dY9iWXXIKrr74a11xzDW644QYcd9xx+OyzzxrdRgqFQqFQKBQKRdPuaEffv/TvaJ955pl44IEH8NBDD6FXr15YsGABFixYgLVr14ZxHn/8cYwcOTJ84uuggw7CEUccETo/69OnD0499VT88Y9/xLBhwzBp0iQcf/zx2HnnnUMv5IpsqK2txV//+lf84he/SNXy9+vXD9dffz222247nHLKKdhuu+1QU1OD888/H9tuuy3OO+88dOnSBaNHjwYAPPjgg1i7di3uu+8+7LTTTth///1x44034v7778fChQvDfNdff33ceOON2H777fGDH/wAhx9+eHjnuV+/fqiurkavXr2w8cYbY+ONNw7TNTQ04NZbb8Xuu++O3XbbDWeddVamu9KXXnopDjroIGy99dbo1q0bbrnlFlxzzTU47LDDsMMOO+COO+5A9+7dcddddzWmSVPr9Mknn+C1117DHXfcgW9961vYbbfdcOedd1pzwIdK++CRRx5BVVUV7rzzTuy8884YPHgwhg4ditmzZ2PkyJEAgP333x/HH388Bg8ejMGDB+O2225DTU0NXn/9davsk046CT//+c+xzTbb4IorrsCaNWswbty4RreRQqFQKBQKhULRXF7HgwqsgNsLKtJo33LLLQBKJsESQ4cOxUknnQSgpJ0+55xzsHDhQgwcOBAnnngiLrjgAiv+v//9b3Tq1AnHHHMM1q5diwMOOAD33HNP6j3uRqNzj5JmuTXQucc6ybahoQHHHnssisUibr755tT4O+64I6qEacdGG20UOrEDSt7h+/fvH2pmJ0+ejF122QU9e0ba+L322gvFYhFTpkwJzfx33HFHq98GDhyIDz74IJWeHj16YOutt7bSZdEK77777uH36dOno6GhAXvttVcY1rlzZ3zzm9/E5MmTU/PyIalOU6ZMQadOnbDbbruFv2+zzTaOcz9fvpX0wcSJEzFt2jRLMw2UBCzTp08HULpyceGFF2L48OFYuHAhCoUCampqnKsEX/va18LvPXv2RK9evTK1t0KhUCgUCoVCAQDPvvs5Lv/fZNx2wjew21dKZ9+maLTlHe2Ox2Y3wnQ8Db/97W/x29/+NjFOt27dcMMNN+CGG26opPjGI5dbp+bbLY2GhgYcc8wxmDlzJoYPH57pznrnzp2tv3O5HA0rFkvu/5Kc08nwpDwqpSfL+JKMv4kfpzOJ9ixIaxeGLLRX2gfFYhHf+MY3qDn9hhtuCKCkqV68eDH+85//YIsttkDXrl3xne98B/X19ZnrpFAoFAqFQqFoXRSLAeYuW4uv9F83SrrmwO8eeRcAcNq9EzDxgoMANE2jXSxKjXaTSGuTaNI72oqWh2Gyp06ditdee22deczeYYcd8O6772LNmjVh2OjRo1FVVYWvfvWrmfPp0qULCoXCuiAR22yzjfVGO1BqnwkTJmDw4MHrpMztt98e+XwekyZNCsOmTZsWvtPdnNhtt90wdepUDBgwANtss431z/hAeOONN/Db3/4W3//+97Hjjjuia9eu+OKLL5qdFoVCoVAoFArFusNfnnwf+1wzAg+97XdwXAkaCkX86MY38ftHJqVHrhArxZvXTdFoy3vZxQ7IaSuj3YawevVqvPvuu3j33XcBADNnzsS7774bmgHn83kcffTRmDBhAh588EEUCoXwnnxcg9lUHHfccejWrRuGDBmCDz/8ECNGjMDZZ5+NE044wesdnmHQoEEYNWoUPv/882ZnAHv27Inf/OY3+POf/4yXXnoJH3/8MU477TTU1NTg1FNPbdayDLbffnsceOCBOP300zFu3DhMmjQJp59+Orp3794kLTrDcccdhw022AA//vGP8cYbb2DmzJl4/fXX8bvf/Q5z584FUBI23H///Zg8eTLefvttHHfccejevXuz0qFQKBQKhUKhWLd4fGLpbPfv1z5tlvwmzV6O9+euwDPvzrM0x82BhkKUn/Q6Xuk9a0lWB+SzldFuS5gwYQJ23XVX7LrrrgCAc845B7vuuisuvPBCAMDcuXPx3HPPYe7cufj617+OgQMHhv/GjBnTrLT06NEDL7/8MpYuXYo99tgDRx99NA444ADvG98+XHrppZg1axa23nrr0Ny5OXHVVVfhJz/5CU444QTstttumDZtGl5++eVMd6Ybi/vuuw8bbbQR9tlnHxx55JE47bTT0KtXL3Tr1q1Zy+nRowdGjRqFr3zlKzjqqKMwePBgnHLKKVi7dm14XeDuu+/GsmXLsOuuu+KEE04InzpTKBQKhUKhULQ/NJdTMJnP8rWRBro+X0Rd3rY2LRQDjJn2BdY04okt+bpXpZ7DpRa7I2q0c0E7dPG2cuVK9OnTBytWrHDuJ9fW1mLmzJnYcsstm53xUSgY5s6di8033xyvvfYaDjjggNYmp8nQOaRQKBQKhULRshj01/8BAPr37BLef24KRkxZhJOHjgcAvPqHfbDtRr0QBAH2/ucIrFjbgIkXHIiunUrOf28aMQ3XvDwFe27dHw+d9u1UGgFg1lWHAwCOvf0tjJ2xFADw6eWHoUun7HrcN6Yuxgl3lV7B+fvhg/HL725VWSVbAUl8aBwVOUNTKBSl97BXr16NnXfeGfPnz8e5556LQYMGYZ999mlt0hQKhUKhUCgU7RjNpdldKbTYS9bUY1uUNM6fLy89SfvZkhp8daPSyzb3jJkFABgzfUnF5Uhygwp9h3f0O9rKaCsUFaKhoQHnn38+ZsyYgV69emHPPffEgw8+6Hj2VigUCoVCoVAoKkFzsZuS0V66puTLSb5bXZ+PXp+prW+842JJb6W8ctDB72gro61QVIhDDjkEhxxySGuToVAoFAqFQqHoYGgux2Ura6P71obplhrkvPhem8/GaHfpVGUx6IB9F7xSZtnWaFeWtj1AnaEpFAqFQqFQKBQKRRtAc2l2pUbbMLGSsW0oFMX3bIV2I/evm2I6Ls3FK03bHtBhGe126ONNoWgT0LmjUCgUCoVC0TporrvK9YKRNkxsUSijG2Ka6cZCUlupVrrYBG14e0CHY7TNPdmamppWpkShaJ8wc0fvnCsUCoVCoVC0LJrLhJrdf84LTlsy4o3J08BmlpvyjnbH47Q73B3t6upq9O3bF4sWLQJQeos4l8u1MlUKRdtHEASoqanBokWL0LdvX1RXV7c2SQqFQqFQKBRfKjSXCXVAGGDpDK2uERptpm23TccrgzRl74B8dsdjtAFg4403BoCQ2VYoFNnRt2/fcA4pFAqFQqFQKFoOzabRJt8lY1vbULmncUabVU6FvLtk3DuiM7QOyWjncjkMHDgQAwYMQENDQ3oChUIBoGQurppshUKhUCgUilZCMzGcFhNb5mIlo13X4HLF1VXJVsBco+06NFuyug5/feoDHLvH5jhg8EaZ8uuIztA6JKNtUF1drUyDQqFQKBQKhUKhaBeQzOecpTW4eeR0nLr3lthmwHoV5cNMugspT3oxr+K+PGk55e//fOkTvPrxQrz68ULMuupwb37SOVtH1Gh3OGdoCoVCoVAoFAqFQtEeIRntPz/xHh4eNxtH3DS6EflE302WaabjXVIYbarRRuD8vmhVXSYaC4xL70BQRluhUCgUCoVCoVAoCJbX1OOA60bi/16b2iLlSQZ5+uI1AIDVdfnkNMUAd4yagYmfLROhLgMsGeXaZjIdl87Lza9VGR1RBx38jrYy2gqFQqFQKBQKhUJBcOcbMzF98Rr8+7VPW7zsTfp2zxTvfx/Mxz9emIyf3DImDCsSx2R5wc3WE6/jaS81UWdo5C3srIy2zaR3PE5bGW2FQqFQKBQKhUKhIGhgHGsLYZM+3cLvSe9MzyhrviUk48pMxxljm6LQprCf6Cp9r87IYXZ0r+PKaCsUCoVCoVAoFApFG4O8M53EiHaqdjlkGd8wtJIpZvlJTfSIKYvw+0cmYWVt8gtOBctzeAlpJuhxuoAOeUVbGW2FQqFQKBQKhUKhaGuwHZqV/li2ph7nPPouxkz/IvyNmWqneR1n961lPicPHY9n3p2Hi5/7KJFG5s8szQQ9pCFFw97eoYy2QqFQKBQKhUKhULQxMNPqq178BE9N+hy/uOPt8Ddmqm07GnOdoTENchXJ59WPFybSyJj36qx3tDu203FltBUKhUKhUCgUCoWircFyNFbW+H621L2PXU04ZMm3mmzygrMtEttxphlfVZvs8dy+923oqdzreNId9PYKZbQVCoVCoVAoFAqFoo1B+mFL8ujdSTC2+bIrb/oUV4rzsazewiUYs5w1m7Q74+0dymgrFAqFQqFQKBQKRRsDM/Vm2uIqEVZfZrQln22018zMW2q2G8Fn287QDI1Z72ir6bhCoVAoFAqFQqFQKFoSFiMKoy12mVjJ2NY1uBpt5gzNaJ8lo9wYjXYho9adoUjukXckKKOtUCgUCoVCoVAoFG0MAdUWk3jiRnao0Ra/M2dohueWzHcl72gb2tg98qqsz3t1RHtxAWW0FQqFQqFQKBQKhaKNgWl8mbZYMstGo82YdMsZGnlbuxKNdsioE+Zd8tmFBGa6oBpthUKhUCgUCoVCoVA0B9bWFzB14arUeLbpeAnMdFwy0HX5Qik+Scs02vmMjHZc+xwQRt2EyXvkDdK2PJ6n3tFWKBQKhUKhUCgUivaJhkIRnyxY2WaekDr61jE46N+jMGLKosR43BmaGy8v3JPX5ckd7fL3PLujLRntBM6wEGs7k8xyukbuaEtG+705y7F4VV0U33oarG30TXNCGW2FQqFQKBQKhULRYfHbhyfh0P+8gQfGftbapAAAPpq3EgDwxMS5ifEsrTTRFjMG2jDaAdEWM6/jkknPwa/RjpuAG8bYDnfN2422/d05y/Hjm0bj21cOc2goffcW3W6hjLZCoVAoFAqFQqHosHjxwwUAgNvfmNHKlNhIcwbGNNrSdNww2AVmOk7yYYytfKs76Z60w2iTO9osudFoj5n+hZNPWtr2DmW0FQqFQqFQKBQKRYdHW2Pm8pUw2uVPqS2uL2uvZT6GkQ1IWssZWtHVaCe1T5wJN38GjHkXYQ3lwE7EEznT2HckKKOtUCgUCoVCoVAoOjwaw8slmVM3FUkeuQHbnDryOh6FGTNx2yGZ/Sm/My/mzJxcwpQXJ5WlZ+bkDWUaq8kFcEZ3R4Iy2gqFQqFQKBQKhaLDo61pTVM12oQRtZ7yKpuJNwitNDMTZ3e52TvaDEaDHm+7oBzGPIdLk3CjMWcabcb4dyQoo61QKBQKhUKhUCg6PNoaK1co+p++AmJ3mMvUW095ld/Mlne0Q422yCd83otor9M12rnyb3Z4MQgcLXTI5BMBgXTili/f27a9jnc8KKOtUCgUCoVCoVAoOjyaqjRtbo24ZJoZmGm1fC6rQDTVhiG3zM6LLlNt8sunmG+bK+GORrvoPvnFtO7mm9Ro14ZPkAkaVaOtUCgUCoVCoVAoFO0PTX2rOc3Murnzo4x2ylvYRknOnKEVCGPLNNoyrU+jHSDwMsfMm7h04ra2vuDE64gqbWW0FQqFQqFQKBQKRYdHU/nk5n7rOe2ONnM0lpca7aLJR3gON5/k7nRA7kQz7bMMq/JotIuB/TSYzJOZqMu61jYUnDw7IJ+tjLZCoVAoFAqFQqHo+GiqdXJjzJtvGDYVx9/5dui4TKISjbb5Kk3HQya24DK2UnsfaaohwkqfzHRcapqrqjwa7SDwm44TJl8KA0xbpN0Pb+9QRluhUCgUCoVCoVB8CdA0Zq4xzOB1r36KN6d9gWcnzXN+M0yu7+63bYJd+t4guNgCvXtttMpufswTOUsrw3Ikbelv8ra2+Y1o4iXdtQ3uHe0OyGcro61QKBQKhUKhUCg6Ppqu0W582uVr650w43Xcp9lOc4aW5NCMarRF3ux5L/NN5mc02nEKAwQWQy3LoU7XBN3GdJyZmHckKKOtUCgUCoVCoVAoOjyayso1xRma0eJKGIbWly1jWJmnbmZizrTF7N1qZr4tGeBq4wwtRmQQuO3BTM+ZMGCtYbT1jrZCoVAoFAqFQqFQtG809XmupqQ3WlyJYsho83yZCbakoRCakxedeJJzjdKKvMndacYUx3+Tf7vRXBoj0/GoHJO/fZe747HanVqbAIVCoVAoFAqFQqFY12iq1/CmaLTXEkY7n8Jop71xHd7HJgy0rb2249tpiTM0Yk7u3tEOkIvRxMzRmRM3EBo7IJ+tGm2FQqFQKBQKhUKhYLDvOjc+H2Y67nujOiqPmHVLjTZ7M5vcx46e93Lztvhf8hQXy8/87TcdJ3kSz+h6R1uhUCgUCoVCoVAo2jkaY57cFGZQlldHHICZN6pdhpU4FQt/E7QlaKVtWokztKJbdqgNp3e+YxrtYuB6HacMdOmzoZCcZwfks5XRVigUCoVCoVAoFB0fjWHmmPOxrGAOwKhHb/J0Vjxu5E3cpadAaLQY8qJLfxTPvU+dJ8IFJqSIPyHGTMcR5invgrva9Kaa9bdF6B1thUKhUCgUCoVC0eHRGF7ONtWukNEWHDC7jx29UW2nKzGiOdsZGtFeF4npOL2PTZyhMQ/hEaPM7lPbNBaDwKUbhvF3BQRMox0QrXtHgmq0FQqFQqFQKBQKRYdHU03HK00utbidytprS6NdvqMdZ+DpE13OF5/pODETJ4xtlFaUETLabhnc67jDaZfSENPxfIFotNV0XKFQKBQKhUKhUCjaH+wnshqRXiRK02g/PWkuHh43O/xbarSNmXih4DLarum4/+61jGkYVUsBnXD/mZmdp2rNfXe0g8B5WzvUiBPGP0/6oSlm+e0BajquUCgUCoVCoVAoOiQkg1edyyXE5CgQLTAArKxtQNdOVejaqbpUTqGIPzz6HgBgh4G9scvmfdEgOGBTstRyG3IKHiYzzes41QwzM3H4Gei0p7xYWhM3TncYl+Qpn/eiTD46HlSjrVAoFAqFQqFQKDokJCNptMqVgJlbr6hpwNcufgXfu2Zk+Jtk6F+bvNAp23xjTDW7/xwPp090JdzRTkvLtdcuk14kaU36uDO0gJTNBARJb313JCijrVAoFAqFQqFQKDokpFa5uhGMNnv+asJnSwEA81bUit+ieHX5Upl560FpN7+QMfXe0SaaaqH7LRCGnHoTp/FIGYTZZQy5Sc/MyeM0Rve23TwLxGy9I0EZbYVCoVAoFAqFQtEhYd+Jrjw9u6OdJ+pXyTRGZuIuo5y3vG/7mNgAQRBQL+FpWmmq+Q7vaLOyK8/P/O28/23ydp2Wp+bZAflsZbQVCoVCoVAoFApFx4Q01c414o42M61mmljLjLpcjPS0bX63NeQus1vKk4fJz1JeJm+XHslUB+Fvsl4uPZxJ9wsDfF7Hg1g8WZ4MY3fLOxKU0VYoFAqFQqFQKBQdEk3VmlpPVZUZW6mpNoyq/T52idOWb0cnvTHtaIYJExs5OUtm1Pkdbb85uf3etp2vLz9Du9d0nJqjJ9c7ft+7I0AZbYVCoVAoFAqFQtEhwZjLSsA0sVJTbZhu5uSMaYuZgzT3eS//29pcW5zCQBNmmWnnozvWNoKyKbtNT2C9t22Xw5h3t2ybyVeNtkKhUCgUCoVCoVA0GbUNBfz58ffw0ocL1lkZged7VkgG2nyXGm3zbBVzXCYdsZkk7I52nEkvxu5nA8lewplG29Lkkye/snonN3+ztmNPfrnlMGFA6ZNp9zsSlNFWKBQKhUKhUCgULY773pqFxyfOxa8fmLjOymBMX2XpXW1xwWK0y4yk1O4SptoEUkaZaIbdN6qj38L05HmvIPYp46W9y800zSbc9YzOw+J5hve2CeNveyJHh4My2gqFQqFQKBQKhaLFsWhl3TovI+35qtT0ghk0ptL1eWE6Xg4sEIY1LxJTR2OEAS7FIXe0iVl3gTD+1HM40UcnmXk7puPgd7QdYUASU03vfavpuEKhUCgUCoVCoVA0K6oa895WhWB3misBY6BrGwphWINxhkbuYzMzccZQsjeqA8/9Z/4+NtNKu2m5B3UZz62LiUu13E5VXGFApHW307r0xPNq/1BGW6FQKBQKhUKhULQ4GvHaViryhSIWrawN/w5s9Woi6vIFPDxuNuYuq6HpDQO6VjDaeXJHO7yPLTXaYX4Q8dw71ibcvbfN0rthnKlOKtsVEDgabXJHOwBjyN2yIy05uaNNNPEdCcpoKxQKhUKhUCgUihZH9TrgtH//6Lv45hXDMPGzZQAqu6N9y8jpOO+pD3DIv0eJNNHv5mttQ8RAh3e0STlSo52sVXbvY2cyHSdPizGmmt4PJ29w+97MDgJOj/ssmVs2u4ceeUaX8dDhoIy2QqFQKBQKhUKhaHFUrQNG+/n35wMArh82FUD8vnAyxkxbAgBYUx9prNkzWdJ03Git40wn4HnSivzumGUX2Tvadj4yHTNvDxjzLfMj2vTA+RLlmeUdbXYfO0nDbnkdR8eDMtoKhUKhUCgUCoWixcGuaOcLRQy5exz++dInTcrbmH9Lk+U0jXanapcgxizLfBry/nvSjH20zbw9Gu3A9UTO7l6HGu1Y3HjJSRpk9sRWnOoicc5WJIx2kjadM/lqOq5QKBQKhUKhUCgUzYoc0WiPmroYr3+6GLeMnG6Fj5yyCCM+WZQ57znL1gLgb0z70KnaZY3ofWORj3krm951pmbrTNtrlxnAZWyjZ7KYmbg0CSdMLEg8ovk2fzNtOotXcBy2JWjTmddxfd5LoVAoFAqFQqFQKJoX0nTcMGJr6lyz7dqGAk4aOh4n3zMeq2obMuVtnuCqxBlaF6LRZhpoyYiae9gFetfZzYeak2fQFjMdOS2H0J3kSI1p0x0ev+iGMRrjecsy0ywD9HkvhUKhUCgUCoVCoWgGSNNx5tirpnxXuk44H6sR96ezgDF4PnSqclkj5i1b5mK8jjN+nqZNMd82dGYxHQ+dqTFNtaxDgqk283jumoSTO9pE686eMDPfmWm4JZwg5u/tHcpoKxQKhUKhUCgUihaHfEc7X2a6jCYaAFbX5QHwe8RpiJTlrgm1D/SOtjRvhsvsmne02VNVjClm5tuuozFivp3VdJyEMQGB18M43HYKWDymdafaaxbm18R3JCijrVAoFApFC6OmPo9f3jsej46f3dqkKBQKRZNQU5/H+FlLnTeV46htKOCRcbMxb/naMEyajteXNcMra/NhmGG0GwryOa1sqk+TN78nzdE55Y62YbolE9tQFgykORVjpt8hY0rMt+NabpY+sgJwM7U17C5TXfSUXQwC6nXcvaNNTMwpk88EDKIsuGk6CpTRVigUCoWihfHQ27Px2uRF+MuTH7Q2KQqFQtEknHT3ePz01rdw9+iZifFuGD4Vf33qA/z4ptFhmPSFZhjWlWujO9hryoy21HJnZ7RLn+y+sA+d0+5okzDzvFeqo7EE5pJ573bvbSeZf6cxsbEKIGKwHYaeaKpLjDYyhVnlyXxjZXhp7EBQRluhUAAAXv90MX59/0QsXlXX2qQ0C+Yuq0mVrisUrYVK7xgqFApFW8W4WUsBAA+Piyx05i1fiytfnGxpr1/7uOQxXJ4zpLl1Q9mp2Erh7IxrtLPt7TmUNdqIM5L+9NLruCnTvmftMrYNoTM0WYb9aaVJMas2cZ0jDM0T5bIJkx/LL043ExqYv50mCjiNHhLpU2csjNHdkaCMtkKhAAAMuXscXvpoAa54YXJrk9JkPD1pLvb+5wic++T7rU2KQkHRvXN1a5OgUCjaAd6esQRnPfQOFq2qbW1SAACzvliDZyZ9nirIPuPBd3Db6zNw4t3jwrA88XZlM9ql36X22nggz5N4qSgrp33mzQydxZ3x2oZS2ey+scwiT573iuIRxlakZQynCXe8gZP3qE0cpnWXfURN2T1lcydnnrvcTuOycgw9btmM7o6ETq1NgEKhaFtYuLJtbOZNwX9emwoAeGLiXFz7011amRqFwkW3LhGjnS8U6dutCkVbxIqaBqypz2OTvt1bm5QvBX52+1gAJabvziF7tDI1wH7XjgRQYrqO2m0z6zf5Jva7c5YDAKYtWh2GMd5cMnn1oQY5AjMdr8tXajruMrFVcE3EAds529qGAnp16xxL7zKIRqNtMbYkHmUuA/s3GV6MsZ7sfjg3HXcZ8kjTLPIz2nBmtg7EwphzNr/peDqTXy5bNdoKheLLhOoqvvm0J0jnKgpFW0S3TtH2a0wjFYr2gF0ufQV7XjUcS1Z3jGtG7QUffr6ytUmw8PaMpYm/dyHCw6wabclvmd+byxkawLW6BpJBNE+KUY02UcVSZ2jUTJyYUGdxNBbLW6YrEno4k8+EAYyBZtrrGD0BMcsnNHJv6/ZvVuIOBGW0FQqFhY7AaCufrWjrkOeJVbXKaCsqR2t76J08f1Wrlv9lwypxb3lFTQMeHT/busvc0sgT9bTcetfr5hrNFsjdaplPvsCYwRLkvexKGe04DHM3YsoifP3SV/HyRwsoPQHhGql2NvTo7ZbBtMppmmaT3jHppnevy59FGUbaMYHxd83WXaa6WAwcpprF4+1j02XT6MbrSFBGW6FQWKjuAFxq+6+Boj1g+CcL8ZNbxmDG4tXpkWPIi0Njax6WFW0LQRBkcuJ45xszsMc/XsP0Roy95oLrBql9wtzDbetYIxwonvHQRPzlyQ9w7uOt54ekQLTTEj27un4o4lrbeD71RKNtGLK8YK7rM5qO5zym4+bPk4eOx4q1DfjV/RMFPS4zyN9/JtpZwqTbGlum2eVaZWaqzd/MTqCHMv4uZ8ucrjlBgc3MGzri8ZI9o4OEuXXpSFBGW6FQWOgIGm01HVe0BE65ZwImfrYMf3jsvYrTShPK1arRVpQxZOh4HPiv1y3m74UP5uNvT39gMRqX/28yvlhdj4uf+6g1yOww+NcrU7D9BS9h4mfJZtBtDaOnLQEAvCQ0sS0NptGWWK9rZycsrjkthUXfzfNejBmsb4TpuDkJVOIMLU8ZbVfDzu5ZU6aRaJAtbW9QYjpdh2TsTjth/IuMsTWxXboth2QmvwzO0HwO0rKYt7N76FS73/H4bGW0FQqFDWW0FYrKsGxNfcVppBkkO3wqvpwY9elizPhiDYZNXhSGnfHgO3jw7dl48p25TvyWPpi2trl6c+P64dMAAJc93z5f22jNrY6tW5Ke7p1dFoMz2hHnx8yoDXMn18z6hOe9JNNonLMxZ2g+2Bptlx5mvm0YVpk2iP0mv1PHZ44wgDkfc+lN0l6z98MlPfSeNDym4ySslBuXYqQJJ/i97Y61vgDKaCsUihg6AqOtfLairUNqJzve0ULRGMgD58wvXJPwL1a7Ap2WXuvYwb0joL3uGV07td4xnt/RjhqS8cIsDdMgs3u7ljO0BNNxmZ85zhDLaAA+h22uyTO768xNtVlaUW4AJ8yki2uVi4GraS4GLpNeKLppg4irdsKY0CDeV4zJL72ZnY0hL+Vhp5WfVjyrXHQ4KKOtUHRw3D5qOs5/+oPMmohOHYLRbv91UHRsyENjR5TiKyqH1Nh9vnxtK1LiB9PYdQS0Vyuorp3ce9AthTRLHOZrgKVJM3mONNruXe40unxex02enavdfpcadsY0hrQRDpEJorjHcrvMAO4+EATEEzmCbJrvsLw0rbIbL06zpMctm93lNhp/1q9wwwjj35GgjLZC0cFxxQuf4KG3Z+Od2cvCsMcmzMFvHphIncBUdQBG21eFJavr1Ez3S4D6fBEXP/cRRkxZlB65lSCZqg54tlA0Amn3TtsCM9hRhULt1QloN2KeHce62vfS7mgzzTDVaIu1kGq0y9+zeh2XzGkuZLRdJhYAOhOLAElP5E1c0Fi0fyv9Xi6baefhtkO8FYpBQN6ydt+oNve5rTC4DDDzeO5zulaqk8vQu2VzLXc8YsTkszC3LVjajgRltBWKLwlWCodL5z7xPl78cAHuf+szJ17H0Gi7YR/MXYFvXP4aTho6ruUJUrQo7h/7Ge4ZMwsnDx3fIuU15oyeJ/cSFesW0xevxoefr2htMryQzAO7wtMWeMECYZ46AtpC2zYG3Tona7Q/mlfa94bc3fz7HvM6LttRMm9m3LDzBbsTzZ/3yuZ1XD4hlvOZjoca7eR75KaKTDNsORVj95I92ut4PBOHMcvcfDteF79XdW4tEE/P74IzLbej0Sb50T407RN7gox5Wu9oUEZboehA+GjeCkxdyN82ZWZcS2tKd/7kYtcad7RHTFmEB8a6TH9jwTQ/Jv83pn7RbOUo2ibmLqtp0fIaM2OYFkfRfLh91HQ8PSlyHhYEAQ647nX84IY3sbQRzutaAlJj16nKPZ6xpVlek3l/7nL86v4JmPnFmnVCH2BrCzvSqG0L1gKNQbcU0/GH3p4NAHhzWvPve/kEh2QAv48rGW2m5aZvT1doOi6FmGlex9PuaCfRk+ZUjN1BZhp7E8607mmm9oaOLPekI8bfZaDjzUkU1aX6OjRyzbekIU5HYhkdaVEpw31NXqFQtEusrG3A4de/CQCYfsX3UV2VsxZUZrLFvHm2xoHDaB53/Upf7LhJnybnx+5ok3OrQuCTBStx3Suf4pyDvorBA3u3NjlNQlM36+U19Vhe04BBG/RsHoIIrINiBzxctCamLVqFK174BABw5K6bAQBqxBvEX6yuQ7+eXVqFtiRIRoJpi3MpIp2f3DIGDYUAUxetxvA/7tfc5AGwtYUdCe2Uz0bXFNPxdbmfp5mjFyym09Ug54sBOlfnbOFNApMmtdhJTH4W5tT8zZzJ2VYb9qf8zkyjbYbVTRx9Zcyuy2hz03E7rBiQu9zMPJy0rYmb6Q3voueONjMB99ATF0T4tOEdCXr0VCiagIfeno1/vfppa5MBAFgiPNKaDUku3HTzKbqS4tb0Or5oVV2z5JOm+VG4+NltY/HqxwvxizvGtjYpLYovVtdh0cpaK2zXy17FfteOxGdL1p1mkN0DVDQPltc0OGHLaqL1kZmLtgXYDvLc39OWMCMwnbG4ZTTaHWnYthWN9t1vzsSPbxqN5TXZrC7Srno193aeJryXYAxrJ+F8zIx3KbwJ7z8TJj1P8qPlMq2yo5116THIF11HlbbpuKuV9jGS8lOWy+8/22E+0/E4fKbjvqsd8dAAzOkav0eexZSd3S3nGn+Xwe9Qi0oZbXO3USjaCc5/+gNcP2wqJs9f2dqkWBuuYbTlxsQ12nZ8oHUZ7cY4pJm7rAZXvDAZ84SXXnZoaq/ObloKK9aWmJNlhEnpqCgUA+x++Wv45hXDLMeAZu9/e+bSdVa2dZhL9oGlaAKMMFEy3/kUp2OthQYifJEHUSYsbOlVjd277QhoK9vDpc9/jPfmLMe/Mwrw03qguQXMacJ7X1zDYMkrEeycwk217d/i3+OgztUcDXLgrQOjmzHQksqQxpSnwXxaZf6UV0C13G7dAwSxJY0zuy49Jtyhp8iYYJ9G2w3zeUH3CTzg+b0jQBlthaIZsEo4GmstyP20rlBiGuxN0T1cmt+T7juta8jFvDFahRPvHofbR83AqfdOEPm4+XcAH2+KjMjqpGl1XTRvW/rers1UKZoTchlpKK97ktFuzfUuCbbpeOlTruFtYQmTmq80jWZ7QmsKmBmmLXbfUWdgvlckmluAwN6otsuLAouEYZXtbOZhgTiGZMwp0yAzUM/fhBmMx2V3xhmDyE3Hy4w706ZbBdvxZdlZHKQFpC4lk+50jTQzgzd/c/Nv++8CYb6pczZw83TEwpOEAR0JymgrFI2E3ESaczNrrCdXyUeHpuPyUETuNJlFTWq00zbu5ob15mUjViRjJimtCuRdRsPQqOn4lwdZR7Ac99QEcx1OhTx5R7tYDPDr+yfiulemrLuCv2Qw6540HU9z4tRaYKbj+ZR9pqWXNcbEtFfIOdhWTMcN5i2v9f4mzwhxk984mlqvaYtW4Rd3jMXYGUsA2GOUPQUqQzjTGYWZvdm+j80ZY/lbKR8/LA15MYXRJjQy5lumZ5phZiZOBQSx8mV6l6nO6HUc/GmwLG9rG1qylZONSS+ldcsofQbJ8dDxoIy2QtFIWG81NlOeE2YtxdcvfRVPTJybHjkGaYpalyd3n6gZUFkKKzXfLXx4siXkzdOSlkarfDBoawepJavrcOGzH7bp54baK7IOYWkuzsb9urw7zd7RHjtzCV76aAFuGD5tnZXbHBjxySK8O2d5a5ORgGiuG6Z6Za0wHW+jtvoNlgAgKIe5HpR9YI6dmhuy6dq7RrveYrRbkRCCJAsbi5lLGcrMuqsSDLl7PMZMX4Jjby/57pBnhbTrWKkMa9Ew2oQhT9EgJwl52N1w311ndkayTc/d9EHs0yqHzAkZwkzjTTh3PuZw39Q7Ob+j7ZYhaY2y5O91u3ky5t331rdbRpn8ZBrb+ZrCoIy2QtFIrIv7ab9+YCJWrG3Anx5/r+K0cgFkd5/qiRbHnDMCEtZSkDQ2l/meZKojRrtZsm42/P2ZD3HfW5/hBze82dqkdDhkZZDXCkabaTmznksbYy3RYE20UkF1DW2TAZSYvaQGJ98zHkfcNLq1SUmA0JqVORF5gKvPt83DHDMdl+OSaRAlenZd9w/JSIEUu47UnmDPt6htv1hdh7vfnJnZIdm6QJKTM99d5RVrG3D1S5/gU/HEp1ybGiMY+Vz4PgFsYVCahQV/Hzv6nTHB/E400yr7aaam47E45m92P9wqm2nEPWbQfrpdxp8yy7FKFQL37nRJy22HFRljS7TFIO1tyib8fCate8lzePreyfvaSaYabYVCESHNpK8xaArvbjHVxOt4nWAqDKizjhaWKObXgVZBLvxGa5F2SG1pTFnA3ztXNB1ZGWT55NO61s7FD1bsENoeMKeF3yhPQxAEuH3UdIwTjuskc5on5qltV6PtXidIG5dyVcsqqAyCAB/NW4Ga+sp9i0jmujFC2YZCEe/OWd4mHKnV5d32BoBT752AS5//GH958v3WIAtA8n7lY7T//eqnuHnkdBz871FhmMxF1rexYNriLHGTLOrkdGT3moNYfF9+vFxDa1xbHDhxk5hBrnVnygvGVMu6EMYdRlscD2MO0uBwo9zEPEGjDTecOYtjHsEzac6LnB7zW5SWa8M7GpTRVigaibb2nqg8XNaFGu1oB2MOgMymINfy1jQdb6571JZ2MjB5N0vWzYa25nzHoI2StU6wVjDahoHIeg+wEkxZsAp7/OM13DtmVhhmmSK2raUkMxrrT6I58dKHC3DFC5/gmNveCsPkYdcwr7K9G9qRM7S8xdi67e1zPpWE1yYvwuHXv4mf3vpWeuQYZNM1RqN9wTMf4oibRuNfr7a+HwLZtrLt3ytfi3j144UtTVIIo9FOc1Ilu1xqskNIJ6lE2J4VG6xXenfeGqMpaVK1xcZ8O7CZr9InnHhWeQmFp2nDJQpkHba17u4ZKbsZvKFbKjLghJn8mTdxytgSTbWraSbjJka/DKfexGPp2fLid4YWL8PtQ1aXtrCnNDeU0VYoGol1oRVpCo8jD2Fco+0yEtEmIzeCFma0rbuqzVM28xoq75O1BW1KW2W02ypdlSBr78o72nmi4WiMs6cgCDBj8WprHv39mQ/wxep6XPTcRyJvSa9bTto8LBQDrKpt3afYGtqAsHEmeeu8wXptwT0UtwW6GZgnerk+pj6nlHG8PjZhDgDgo3mVP0spaWiMFcgj40tl3zRiesVpmxv2mHD38x5dbFP8ltwbzfvO1LyWMHgAsMF6XZ24UiHQFG/7/XuW8uYevaWwPEqTJ34omJCAeRiXqzgzt05am+Uvqc970afA3LyY4zOZZxD7zaab0EgYY4dhJQw0Nx13tc8BKcNHT1CEs2n6tM3xOeArh9UlXja3cnCC2j2U0VYoYlhdl8eB/3odV7wwOTGeLa1d11SlQx6y6snzXkbLnbYRtLRGm3na9WH64tW4ZeT0VJNHdhhg97ZbE+Yg1dbQ1pzGNQZZhzC7o20JaUQ+ny5chfvemkXfYJYtdvPI6dj/Onv94G/YJx/c0ubhkTePxs4Xv4JFq/zeidc12sQ8IoIhqWk1AlHZ3i3tdbymPo8f3zQ69W1kZjouw1IZ7Yz1aso74rId24LAsimwrRzcunTvUh1+nzR7GXa55BU8MPazFqHNCIaZQN9nQr1hr4jRrsuX1jY5fpJ8QARBgBPvHoeTho6jAu/1unVy6DHRfOMgTbtLn3wqwgnjWm5vVThT7GE6s2rdOfMNJx41HYcbj2qgqfl3ehhndrnHcvkZpefevzN5Ig88ZudFN168bHq3vAPe0q6I0b7yyiuxxx57oFevXhgwYACOOOIITJlim/+sXr0aZ511FjbbbDN0794dgwcPxi233GLFmT59Oo488khsuOGG6N27N4455hgsXNh6JjoKhcRj4+dg2qLVuH3UjMR4coFuroNbU3gcpr22Ge3SxstMES3T8RY+PFlMcUrZB1z3Ov750ie49uXsB1azkMvzeFvwllvdmLfMWgAdQaOdVae9ltzR9o3/g/89Chc++xEeHjc7Mc9rXi7tiXe+OTMxnm067paZNg/fn1vyVj988qLEeM0NOTraBqPtziPJNDEGqqXpfvKdz/HenOX4v2FTE+NZdBFGgK1bvueUkstp/PpnvVDRBtbRpiBNo91TMNp/ePRdrKrL4+/PfLjO6LE8epfXYesd9XJn24xb9H094QzPvBtfT66UMSxeVYdRny7GyCmLsWJtKa1skx7ltrAFka5wUoIJZRiznNWBWNY72pkZW8QYY5IBo5ExjUzrbpjGtHgm3GFiGdvp0XxXpFXOkJ69ce0tx2GWmUl4lG8Uj9elo6Gik97rr7+OM888E2PHjsWrr76KfD6Pgw8+GGvWRKZbf/jDH/DSSy/hgQcewOTJk/GHP/wBZ599Np599lkAwJo1a3DwwQcjl8th+PDhGD16NOrr6/HDH/4QxTbqoETROnh7xpJWuSOV9RBWqMCkryWQJyZi7D1J2/GIkdZm28DWBSTjn7UZx89amvg72zzlXcaGZnAK01S0UYV2h2C0faaVcUiNttGCsmsHEpMa8awVm1Jp211WYVCn6pYV2NiWM60/j5hlCHeGJpnvZLrfnrEEf3v6A6yuq9xZGENWDXKa6Xi0hrvMF5B9zDRF0GB7HW/9fa8SfDRvBR4Y+xm9T1tP9gRpOt4SNZX9YtZh61WOnDEn533ALLksjXbef0dbXrcw43CNGP/dO5cZ7ZSnr3JC9EOt50SZkTM0woiKeAFIWEKHxJn0wGNaXfoUcckrLIk0WvUzjLtMS+KRu8oRnSzMpZu9j53FmRmzIDBxs7yt7dNex8Ecu9G2YPVrX0tKJlT0FsRLL71k/T106FAMGDAAEydOxD777AMAeOuttzBkyBDst99+AIDTTz8dt912GyZMmIAf//jHGD16NGbNmoVJkyahd+/eYT79+vXD8OHDceCBBzZDtRStgfp8EV2a8R3Pn5XfbRz91/2xad/uzZZvc8HSDLeB1SFNox0e3CzGtrzoC/Jb+vDUQO5IpSEtHtOSyxQNbUCoxzRxbQFJz8q0F1jjOQhQ5fF+UEtMx4vsdOjJO0QjmkyuGXQeinmxqrYBxSLQp0dnJ5+W7i95cF/Xd50XrKjFRr27JjpJZFcdLCdXoel49Hsa3Wbv6VSVwyU/3qkSkimy9hFjlNg+49tvst4hbopFjyyjLVgGVYLDry89pdirWyf8+OubplpySdPxSmbZghW16N29k3PHOw32U5el/cF63q081gMiLAfsfc1kZc9X/74nrXvMuigFTYzxN0wXGweuybO7xjFtc3KYZHb9Y48xrK6zLzd9UjlpzLL5zhy72WXEEoiyObMci1cMeFrC2DrMfESYEx6P63OmRp2mZWHS4bYjd6TWvtaULGjSSW/FipLpWr9+/cKwvffeG8899xw+//xzBEGAESNG4NNPP8UhhxwCAKirq0Mul0PXrtFdkm7duqGqqgpvvsnfk62rq8PKlSutf4q2hWGTF+Krf38x1ayyMViwomXvIGad5rYkufUZN7nA1xGNtvmZScBtCfm6pNJFY5xPNcYpkDwgJh20gyDAbx+ehD88+m4mWhqLtnpHuyNotCWSxgrTDOXJ/PClaQr426rR74XwgBdg54tfwS6XvhIegKWGtKXHkWWWLbSA//faVFwsnL01FQ++/Rm+feUwXPXiJ4nxJBNr2oVptGXbZnVk2RhnYQxZr4kwE9MGYjnlG9PZTccbv8inzY844ofwrs0ojG8sPvx8RfmbK4j2OdzK+iLGvOVr8e0rh+HbVwyrmC45nzqFjK3oq5xLl8/3gNnvsvpAkX5PzDqzpi5ivpmptSFNCgVzIY12/szxGTMnTorHTLoZONMYZ2LddFQYQLXcfhr5/fDkOpu/2fRlzLITjTHVARc4SLrS4rrMuxtITczhf7YrvsYxAUFHQ6NXvCAIcM4552DvvffGTjtFEt/rr78eO+ywAzbbbDN06dIFhx56KG6++WbsvffeAIBvf/vb6NmzJ/7yl7+gpqYGa9aswZ///GcUi0XMnz+flnXllVeiT58+4b/NN9+8sWQr1hF+/cBEAMB5T33QLPkVG8F8tTTsA0crEhLSIJ7yIl7HIw2JSzeTMrcUKnGGZpBGYtoTIkmm40vW1OO59+bh6UmfY9ma+mwENQJtlaHtEM7QxLEjSesmx1EDYWK4E7NmIDCWDzuEmAO2ZLTmLlsLwL5r2dKWEUxDFgQB/v3ap7hnzCxMW7S6Wcq55L8fAwBuS/GVIeeRMWW3+pA+75WtE5vLdFxa9ydp4qwxUf6UQhXOaEf1Z1nPW74Wv39kEt6fuzwMa4olQrECRvvON2Zg18texZQF0bNTbYHRNk2apkm17kdnzPut6UsAACtrKx87UiNtluECGRSM6QPssWLGQn3eZb4ZaiyNdikfOf4p02jKJQJ9nyOugISlCdu5I61s67qh3cf4xeOVaHTryO9Zy7RuXbipttsOJk5jGeASYxvE4rnct88De8A0ywEzHeeKKFYXn9m60/9O/drmeb8paPSKd9ZZZ+H999/Hww8/bIVff/31GDt2LJ577jlMnDgR1113Hc444wy89tprAIANN9wQjz/+OP773/9ivfXWQ58+fbBixQrstttuqK6uZkXhvPPOw4oVK8J/c+bMaSzZigTcP/YznPnQO42Sdje3FKqt3ANLmvT2syvNxWk3nslh0mxmOm63Z3nx8xwuWgLM62ca0jQ3sjuYJj9Jo8W0is2BuHfotmqi3VYFAJUg63iWv0R3tKXHav+hryJ6SBgVBhEmhq3H0uS9NU3HDcMvGbe0FwGyImu1Ogsu1ggYGyzTcbf1s+5xzcdoRzSmWdMYcMGoG5aG3z/yLp55dx5+dONoQUPz3NGWdNTU5/FZ7Km1y/83GctrGvD3ZyIBvDTHbi1wE2E4YVZfkfFYLAZ4eNxsS7jUlPWTXaNKM6H2mfKzO9pJw0aajq8NNdrR+Gfa0Mis2qWB3e+Nx+X3dknfgMQTeTcUilgpnjpkZuvsXrKbZ7lsYprP1mtGIxOW2Qy5GxaV7tLNwhwhRpFr8dldbhM/Hu7eBXeZb26i7nGaFksbiPh2vHj90OFQ2QWSMs4++2w899xzGDVqFDbbbLMwfO3atTj//PPx9NNP4/DDDwcAfO1rX8O7776La6+9Nrx/ffDBB2P69On44osv0KlTJ/Tt2xcbb7wxttxyS1pe165dLVNzxbrBBWVvmt/bbgCO/sZmKbFtNLcWtBJP1M0NWZX6QhFdO/GDge9AvmhVLT5ftha7fmX9dUYjA9sI7Kc4/If5VnWGRgQEaUiLxw6s9jvjScyXODQ00x3Um0dOw9UvTcHfDx+MX353KwBt1+t4x9BoR5D9/tx787BJn27YfVDpuhMzu2TaUCvvZpoelnOd8mdD0aXHZopKYbVCo93iz/GJudNQcIUTzXVvuzHjkFnyGAFKMaVfGdY0gtEOggBTFq7CoP490a3sQEoKQ+ryBa8vE6sriYCQabTTmmnqolVOWFOe9/LtzYf93xv4bEkNnj97b+y0aR+7PBGvLfimMOOVMXiWlYtoJ9bMD42bHXohn3VV6cxb1QRGm2mkmXWfbfnC9yvODPrnZg25o20z2kkMZ5RP6BPFYcgMDTKM5WnKY/GiMLnHH3fn2xg3cynGnX8ABvTuxhnELF6wSfP4HIhlq4t7vgpIWJg/Y1hTyjV50Se/4vFIHxg6GbOcxekaY5ZLb33z9o4LNnxtnvWqRntARSteEAQ466yz8NRTT2H48OEOY9zQ0ICGhgZUxRbS6upq6lF8gw02QN++fTF8+HAsWrQIP/rRjxpRBUVzwzztUAma+6zn20haAnLRSDo0+rx97nnlcBx58xh83Ij7fU1ZW5i3ZPamo22mZf8W/70l0Biv46kabWsxL6ex7mj7D5qW9/Zm8k5+9UulJ58u/1/0trI8fLclc6nWuDteKAZY2oxm+raWp9SHH89bid8+PAlH3/pW+Bsb9/li8vxvLkEUMSyxLGMMPXIMGtpqLW/pzUPPsjX1OOfRdzFm+heJ8eoLLlPdYN2Jbp45U51xMbSfMHQ17Oa7bKaswgB5RzUrXvl4IQ79zxs45rZonFVZjLa/fRhTxPZCJkCtBE0Rzvj25s+W1AAA/veBewWwNa6C3TJyOs557F0qlDVBjMHzefRmB/83pi52wuS4rXRuMu0z05pKvsba18hemsZIGkhLFKPRbiD9ZgsnDDmizQjD6U8fWGkqKUeyFONmll4hef79+Z6yGeNvGF5GozsuQOhOE4xx5jKWryjbZaA5s0yfBnPyY/Hc+oXpmTbdYb49Ttcokx6P5woDmCbepO9IqIjRPvPMM/HAAw/goYceQq9evbBgwQIsWLAAa9eW7o317t0b++67L/785z9j5MiRmDlzJu655x7cd999OPLII8N8hg4dirFjx2L69Ol44IEH8NOf/hR/+MMfsN122zVv7RSNQls49Fvmci1Mj6XRTjgUMZO++nwxDP9w3gqabl0h7T62qRczL2tNwQaTwqchLR47XGR1umY/k1b5QTsrpIlhW/Leu67Z7Pp8EStqbGHeyfeMx26XvYqPmmnOWAfE8hSevXSNJ7adJu2KQfMx2mS+Ftx5KBlbsx5Z3tJTxs6iVbV4ZNxsyzR0xdoG3PfWLCxeVReGXfXiJ3hq0uf4xR1vJ+bH7mhL5rq5xnJWoaNlqRLe0Rb0GGdolgA1+v3Btz/Dbx6YSJ8+aszzZY+NL11tM++cAzajmbSn2JpBlOmWvwfWb4C/vdmdSAPJDKZZB01btArXvPxJOGdlk7ArU8z/RWOuBzUV/3zpEzz1zucYO3OJ8xvTujIGL02jvXKta/Eg7+NXKqi1nncjlliGySp6aMyT9KyODPKpQ7O+pDn2SrrX7jBkRTuNnSecsDTTcVaXL1bX2QlEXF/dedluOWkO2xLjSVo8tuOFImFECQfNzbcZQ84Zd1J02XSchTGGPB6P3e/mGvYszLeks6OgIkb7lltuwYoVK7Dffvth4MCB4b9HH300jPPII49gjz32wHHHHYcddtgBV111Ff7xj3/g17/+dRhnypQpOOKIIzB48GBceuml+Nvf/oZrr722+WqlaBLagvMxSwLeioxfkuaTabRnfBHd1dpwvcqvOzSFybE32TJdhIktWNqQ8qfIp8Xb2zpIZGS0U84vzMSsQDZuBqkVME5h1gUsJ05t4F1vg3Xd+/tfNxK7XPqKxeSN+rSkGXro7eZ5tcBitMLv7uyS4y28o02YXTuNW15j5m2a/4TojqW7HslxmaZBPubWt/DXpz7AFS9E1hR/ffJ9XPjsRxhy97gwbO7ymkx0yzWxPjQdj2hMequ3EvhMcOcsrbEdhIkOMc8aWhp28ryXpPdvT3+IFz9cgMcmzG0WupmAwG6fJP8Qcky4i3NkdeFaPlTyHq18ez3tqcMD/zUKN42Yjqte+sQqr/Tdjc/2TGZF1VKoIVYJjBk039l1EoD366o61/pPXnlImwsTP1uKvf85HK9+vBBAXKPtMrGGTqrlBrcOY4wfAzMdTzMTjzS2yQyn9bdkYotuXK/TLMTnhwvDaDuMHxmnjMljZt1JlgEydTh+yHmG0e0yp2TOUsaWzW3PPWnC2JZj2+FF9w67T3tNTcIzaNjj4zasS7wqaBvKvuZERXe0s1R+4403xtChQxPjXHXVVbjqqqsqKVrRgmgbHrSTD7vrElKLWalGe9HKiHlojDakKTVl7zcy5lKSxaT4LW1BkCfmcmlIFwaRehNpP6cn+k324efL12Lp6nrsvFmfbESmoFPMnLRnG3FD0ZzdP+uLNSgGAbbacL0wzHjOHj3tCxyx66ZW/Oa6H86eRLLGRFC6Aybryu9ou43BtBnSrDSXy9aGTDvD7lsy52N1FWi0Z5VNel/5eAEuO2Kn8vfSwf7j+dH1lqw+A9jzXnKdbC7hFBsLI6YswslDx2Pfr26Ie0/5JgCfRlsKJ8IOC8OYMHHxynX3jGTe6kM/88UP8+5YZgKZuJlyOOrJ8JBrT0MhQNcMJ8GpC1c55TCNdj2ZM5ZGu8Wvgrlg5s2MwZOMa46I01aleBZPE6Aef+c4rG0o4LT7JmDWVYenmo4b+hjdQEwjDndvT7bkiso21i/yrMAEEWyMRU+G2vkz5rRAxi5jyBGWnVyXJavrHXpMXM74eWgMZJhbH3ZuYtpwlh9YmP2T+Dtw5ktJq5welnwvnZSdRXtNAhmTzs3Js5mi++hrz2h9rxQKC29NX4Lj7hyLGYub54mUxqAtaLTTDrtpGDtjCY659S18sqDye9Jyc0zSPhSIMzTbY2nLSiyY1oDdF7Kl3mbDaL2DUIN1cMtWdlo8tinagogEeoipLgDsddVw/PDGNx3Pus2BrO/6NgYLV9bijamLs1sLZIjH8vrw8xWYv2Jt+Hd9voj9rh2J/a973TJ1NmB92Fz+T3z+Ewzy5DDItIXcGRo/SBqkVYF61SXzld3RDk3H8027o83uP2f1Xi7pMQd7yUhJE/WmgJFzz+hZAIDXP43uxsp2NLRZpuzh815RPkyYuJaM0cbBJdzSaJcFEZ8sWIljbnsL//fa1PA3enC3TLVdxiRP+qCU3l3bDaQfhqSnDiX6r9elVHaKVpExl9aTYG3gfME0qYyxlW0qp4xpU8ZoZ7VeANwxl2aybP62GUFRtmTUi+7vSXuA/Mk4W+Qa7eSwiLlMH4+csbXjW2nhpmVjK5On7sBjqg1XU83MoOM0mjILJIw5m2WaYVdA4M6xUl3ssACulV8QwEnsu6PN9nymvaYm6iCMP3OGRsop5eeWneS0rz1CGe02hp/fMRajpy3BWQ9NajUa5CSsyxcwvRWYfrlYNeae7LG3j8W4WUtx6j0TKk5bZx0k/Rsle4pDriONMQdukuk4ZaplmHtIY3eIWvogVCCMv4HvYNDUO9pJqW3v5G4fftQIJ3e0HOt00SxZUux51XCccNc4DP9kkTdOJaZaC1fW4jtXDse/Xv00DJv1xRr84IY38Z0rh4dh0rmOfHrFgPVhc2m00xht06/WvV0mLGOm4wn5ZoGZk4xhsTTa5XnB7mg3pJi3p4Epr7My2lIQYdZmSxvWTAwrcz7Fnk5izgvtdvQzAxLNR7cbxhy2PTJuDsbNXIp/v/apRwvoHvqjVyIi+NbCpDVSamezCoT7l69CFck+I8Hya2mNts3MueWFRg4yjWlvef/dI+RvINYm0W/cemHZmnrHN0UcrG0ZA2ULZPhawJjTJHkuEzoyk3DGFDN6XC2nf+ym3Wtmmtjwao2olFnDKMNKmFOXiTXl2fV2hlBCWxTZ+Yps9S6NXAPN5hhjlLlwgZKdycyca5u5cILlx85yrAx29mkDsrhmhTLabRQL1qEZWxrk5DzxrnE44LrXMWKK/5C+LmCZ/iU8xZSGeULDlhVMg8TAtAqtqtEm97us+9jlT8Z8y4Wtpa8OMPM0oOSc6TtXDo8cnAikndWYl9usT8ZZjATp/+by8pyV8W+uckZ96nrIDcsnBwYfbhk5HQtW1uL6YZEm7r25y73lAtzssjGb6araBjw2YU7qgVVmzRhRMzetcR869hJMeoLXcR8jkyYsYGUHZL6G72gTDbL1bm3GCSv7gGq0Pd7m35uzHHe9OTOcM7bpuBvGrBcaA0YjY7RtwYhrOh7EPuO/G6ytX3cLnxyDZk2RdVlVFkSlOVwKNXYyLGSK7DLDQzWhx7ICSBg/Uli2QZnRTnMq2RbuaFvmu+x3ykia+HwPz5G712w8MqFKbUMBu172Kna59JXE/cNmLvk6E9fOWmsd8dOSZm4d5ePuj3ZaNw+2FkZCI5duSVdanmmO2MxXufaY/khrM5OXT7Afp5FpYuPlcEulcn5WGXDCzN+uMMDjaMwJ8zDPjqY5oWyHUWcMva+cdBqZ9pppyE2eHQnKaLdRtOYLcnKheLv8bMKDYz9rWRrEXl2XcpCsbSiE98fiaMyErc+o0WZ3tOXGFGfSVtY2pErzmXlaVkiewHxlXkiZxNV352tdoFAMqMOQOB23vl5i6O58YybNIwmMcczsDE20GTP9a66rFWkatuZGUhHsgOeNyzREpD8Knj4OfydhaUzq357+EOc+8T7OfOidMOzThauw55XD8Mi4yJEaY0TkVDbMhXVIJWa5zFEUu5YhQasgmcE8O5CVfyOa6gbLZ0DBip9ERxJdzNGY7472j28ajcue/xgvfrigRJcURBitErnf2VQwBTvTulv1J32T9P6vRHMJCBgKxApAMmnLy4IjpollY5T1v+8+JoP8Lemps9XCNLp7+U3wtHWUa7T9VkvrAvbzZ+7v3Bmau4f7YPYFtl7lyf4xf0WkOEkaZ2mm7IbmOONmaE+7FpZUtTSmmrUPYxp9dCcx1bawnZSTcN9cCiKNkz+XycvGnDJtOjfVdlvShKTda/dd6WB7ZGZm10lZZtKd+rlrofmb1ZG2mRNGNNVg9eNWDmx+toXrq80JZbTbKFrzrXa20aTdNWpuyA0j7Q7ZsbePxUH/HoWXygfBpoKZajJY2ieygEmnMJ8sWImvXfwKzn3y/cx0VKo5tb2Js8Nl6ZOal5ENbF2gUAxw0L9fx+E3vEkPkqzO/P5QMo3sd+aBnSHtea+0flm0shZPvTM31eNsY541W1fwaUgYGJNGzbNTnnzipuPJZT/33jwAwJvToree//b0B5i3ohZ/feoDmneoGSbvP4PEyxPGTYI5GpRg2ntLAEccdrFrHYzuyOQ9QmNMx5kmrnNK489bXrIOYodGuV7Xpoz7JyfOxU9vHWN5nWfIajrO3uC1mAZyemaH3WYzHSdhtuk0nLJXrDWMdpTGfGX+M6w1k6zhpTj+cZHV8oreN00RyjJnaD4t+LoS6tryMUJPAtPo1xa7TCwbjw3EIiqr8J7tw66TO6aJNXknz4XE9k6bR6x9GDPvGY9s7LL0yabjgRNGn2BzGDoe5swZcm4qaWfjjKlNg0xDBRt0XjN6HIIQR0Died+3JmlZtjxPvqZQpjqDyTuzKjB5umEdC8poKwDAq2E0kIz2Cx/Mx7evGIaJny1dd/RYDGsyo/3unOUAgMcnzGmWsmX1k7TplmaHmKLJDeCy5z8GADwxMfkJGXlIr/QAze5os4MbvUMEN2xdYP6KtZixeA0mz18ZPSdCDjtpSLMMYOZyWe9Es4NSkTBAPhxx02ic89h7uGn4tBQa+YFuXSHjGSuV6WcmvdyENLnNTJvmLfPMxKLRi7hHTnuCK3yPmlwJKZJ4zOyYXUWo5K6p9dZt0TUdZ0IwQ0cdoVumbcxVBqYZTjOD7dezS7lsd62w+zq57D8+/h7Gz1qGa1+eEoatrstj5JRFVjsxBTulseDSk/Y8j5nizKRbojECb3pHO8Wfx/K1xnQcIh6ceMwsN1zrY2WGeZHhIfNMtNpiT0Z6rvqE+aVct2F1bG6kabS5MzQ3DJ52MlHYWshMx6UQh42zrp2qHFoj5jJGO2OKyp/seS8ZM4npZvJFtu6x+S/TMqWDFUf2fxFO3PB7CvNu4tWTfuHPV8XpycpAc8Y0Tnc4P0hdrOQeGnmIh9mNxwsYs8vesuZ94zNHZ31IrwQ4Y9TVkJs84/Ho/FxHArjWgjLabRYtq9Jmi4OE3BzOePAdLFhZi1/eW7mjscz0iL0oq1fUtKm5ZHUdrnn5E8z6IvIaHQQBJs1eFt6RK+UjD2T+XJn2yWc6/t6cFWnkO6j0jjdlBsjGysIqYbSaAqmlCs0gkTz2GNIOaMw7aWZnaCnOldJonFc2EzRPKHnLSRFuNTeyarjSQB1SkQ6xNch+E2zJSDItpkTv7p2dsM7V7jbGNNr1hB52H5P1tbU+Fu18HZAqWIwNMx0PaSV3tK13q9208vcZi1fjkXGzU+9tM5NXdkd76Zr68HvfHp0tWgEpLJF1yTaW5BvEv7p/Ak4aOh7/eS1ysMdopIxNxgM5YwZk27Fx3VzO+fJk7ZGttGKte0eb3u8Mf4vC/CbG/n5g6yNQupPtEypmNcFngnGfM7TG7DWPT5iDd2YvS4wjNdqsBGYFQBlBuY6QNYVqtKXpeJnBlib4zDqwW9ksP5XxB2diQssSclWMXc2qqc9jv2tH4nePTIriI62v7U+ZxmpHchay6CH7PRPEUMbfOruUPpllGGOM4+OgxEjaYVQrHfjnlrUWknMGF04k0Binp8iFAYz55gIZN4yWHa8MjCAiTqM7m2jbwsPMx8dykc/PDsZnK6PdVtGcpuOLVtaGG7nBI+Nm47bXp4d/20yjmweTwq5Lc3Lbs23zlPOXJ9/HTSOm48ibR4dh//tgPo68eQyOunlMGGZt0gkTnmnDfAfg1XXJ722G5VlMfqYkDg2SNq75SF701+UiJ4e1YQboYScFaZ7RqUY74wFPjr26kNGWDFA2GtPasdBCbZ6ljEr6nzHDRXKQlmtGKIhK0dimLXu9urka7c6EQbSYKsawEs1wdFBytUK2kMY9ZFlvapNwm6l2x2N0xzIKyyfRLcqQ+ex/3ev461Mf4JHxrnWPpIsy2kKFbObmkjV1ThrGDDSkaBAZ5DgaPW0JAODhcRHdlNFmztCyrntkTZBMEdPyp11lWLqmHte9MsV68o9dHWB9LemJTMf5QTYprddUN1zvXTCGZcnqOuxx+Ws48e5x4W9sT2EOqdKYZ+t8kcKoG9Q2FHD58x/j7RlLwrC3pi/Bn59439qvGbLf0XZpYXsHYO/HJlxaXYRzuOCeXVYLoRJntKvc8gw9sU2RMWTmb/YON1vjXvloIT5bUoNn353n/GaVTcZZ2j1yNibS6KHjmZ1TSBizNHDngu8NZ7dtXRr5c1qSVhnK9lIrLMw3TqMrpAxIPAREO1901w7qNdwz3agwAYxZdsuhFhZkjJa04XFyoj6Qa3tLKB5aEspot1E0F5+9oqYB37xiGHa55JUwLAgC/PWpD3Dli59gztIaAOnateZidrNCbmpZn8lKazPj2G2Z8FhsNpqpi6InzNIk9uw3czj3abSzgkmLM6dlzAA54NvSZ7esdX1H26Ah1M4JeuhBM1uY7/doY3MPAwy286nKNdpZ46U9k9PcSCqikqsDRIFMhU62uXQpjB2yGUPuw3rEdJxptJlWit2TtEoLBTJuWq5xSW8zFpdZv5ifC5YJdtGiVdbB8jpOCn/ns2WcoDIYwyrDDDOwbE20VnJ/DqVPaXWU9ZDE1mtbGOD+zrTu2TXaor/IGGW+B9IsLM594n3cMHwajkxl/Nyxku7l2c/YUI00YSTkpwTb44Z9sghr6gt4c9oXXEAUjmWZT+lT9gETkBQ8a13SULl91Azc+eZM/Oz2sWHY5PnZnla0tM8Jwgtr6lQwr02UKmKhxfpavrfNzgVGoy2LY5rdUtncDDoel40f06/MIRtngN1xkibk8fkMYGbUSVYSaQyrobHeEi64ZZhwxkiyeIxGn+MydpZiY50JLJipt9vXRKgCFuYxO3e04W57m7+d9EHgrikeg3cmxHDbm41bOZdkenQoKKPdwfHpItcbt9wI1pSf70iTMjPHTutyMvjombpwFV74YD5Nk0YOOwB0IYd0ttmllWfWerkgN0Y4wTaSrGAHd3vDcMO45LqyciuBzWi75ruc0U7Oh4FtbI3RaJsDcFZHajYNyfGYlrS1YDdnMi1p9xIjDXIUxp6/M1/l2pJ2XYLxPWmm40zz0UDoYUyxaQr2JJ5spjRnT2mHWNP/TKMtnUulPZ1kwBjptN+lRreeWHJQk87ApTvrupVmlp1Vo23dGQ5pSKbHNGnaeEvTaI+bWdK2ShN7/o42YQasMeEevpPW67S08TgO3CGO/uU7+EDkBZ2Z5bJ1S4axNvP1ddIaKa94GaQ52jOwn950hVPMU7uPSUtiBuVaaNqKmW9LizZ2LojuaLtpmVaRhcl6WfUhY4XRwLX7Iozcp44YyeS03jwN3XQdhhPG2qeB+GjIwkgWg8B6M13WJ34PnWmfgdj5CmyPY+PMLitKz4UTjFnO6nyMlUHLJunZOCtpzklakLBMZch1Q2i00bGgjHYHBzPplBuBMXNj5ncSjdHONgUFj+nXQf8ehTMefAejhdfhrGAHAGZ2ysyBGOyN22i0kw/Fcbw3ZzlmL6mh5VWqWWYLfJozE76xrLtlzvImz0zHM57S06KlmY4mJWcbN3OQloa0mrTE27I+TVIcbEz4UEVMvGR754km1oRJSxVzuLKcfSU8NVTK0x3j3HTcPVTXk7v3TPMtKWDMRbhmiohM0CDjsLlZIG3O6La0xeQwx9YZpvmV2tk0Rtz0CTd5dtcKZr6ZBkaC9QRZyh3tUAjGmFiRhrWZ+c6YIpvG5HZK03gbZDbBlmuU59AvfwP8h+es1ljmu6zK52UP82l3tM1XWb+s3uJLeXlJpGlqMz4dZzHahqkm85Uxc5WYPMu10OwRzGx9TV2yRtsIC9m1Nco0OgyUu06x9OZrXYMrTLbmDBmPiUIeaw/ndLN9OEkTz85hLJ699rj0hOkIPX6Nrx3mMzFPFzrYvyWhlM6NmJUpjqdl95+92nSiq87uYZyPxyzvcsu6yLV2XZ5BWwPKaLdRNNcdbTlczUYgpZlmn2DeblsTtvmm+/sHn1fuXIw7AGLasOSyDeyNuxy/ApP3OUtr8OObRmOfa0aIfNwDUFbI2Fz7ZH8CwswLbrx1ATnODNPE6JZoDDnsIMo0lQzMm7zsl7g2wCccSGtHZvLa3LCZsKSDt/yeTItkdkwfsvVDtlPYjuSAn/Xpm1I57iGWabTtMeUvxz3KcUaSrYk+KwRrztEDYvmTMl9RPOZhOimeRGM02rJvjJUBExqwdc/nDG1tfSF8FiwOzsRGYfJn0yeSsYmeSYviJTOxoi5kjLK06Vp3UgOq0XbnOhujadeWku6vMtPYeDnx32SekvlaUHboaM23ML6bNk2jbdrEZ4LLwIRFWf3C2Bptd+1hnrHZ1SoZJ7NGm+zhad7tjTUJGxPx+S3NbWWYj0YWT1oRmTnAlDJpz21GY4yNR3+6eFiaoDc8u8iw8qcUREZlxNuMO5CLh/H6+McEE75ksVQLgsA9N5ByaFuQcOYMLYA7v9i50JQT9wnEGHXejlE9zdxnVgCsNaR23l7v42nbN5TRbqNgDlUaAzlgI9Mm4XzIOLixJMCuqVUn8tbKujR3XRdmtUzSTg/pKZsV+41rmpLp/pAIC6zFKSF5sRjg6pc+wSsfRW+Hp5lvJW2KLabRZmawzVg292zpP9gysPfI80wbCuDKFyZjj3+8hoUra518KjMdXzfIqmlkd9p94MyOXD9K3xssxtY9uDFmJ004xUxZ5drE7rqa7G3v3e4aR+cMOTyxQ5ZPOMmYvMgRk4hH1g+miWNlszvabL2WyGr+z0zm08zyJW37XzcSe141HDOJGTDb4nwabTN+OpGxxy15onyKCe3INNqW9ULKNpym0eam9S49UXzxPRbfl5YxIfF0cVAtp5h7zLlnyPgRwQ+z+JBzywh2GNPoAxNysLvFDPZYdsdJCGtMgMaL5rAMc/My56o8Fd5E8eoLbh3MmppFG8rf0Wb95daHCR3ridCRCZ2otpdaBrhnChPHpxlm+0LaecYksZ73iuUry3HKpjSy+cXffy7lETjfqVCVtYWTF2foGY1ZTMeDgJ+cqQZa/G2W2IDQ7RMGmDAzz2l7k7RSiJGDXyDX3qGMdhtFs2m0LcbPdexkprt1N5BIZpl55rpEVpPXSpDVdJxpgRnStMVpTAO7b5Zmwm/wyscLcPPI6Tj9/omUVn6ISzikkY2yEqxY24Bf3jsB/3uf3583sCT7dIN3y66EHCZJDZwvyXlabwKTg7I8kN42agaWrKnHzSPcN7PT2pExCM0N9g4uQ9rYkbAYoHJb1BGHZuwdbW4G665RPrBnkrp0cp14sfuY7LkxVm/bu7Ap19WapGmPZJ5sftHrDVbabPEKRKCXptFO1boaWkkYo7GBMOQAML+sGR02eaFTBmOkfFSbdDJNPTVvNzS64yxN+BL1tZ/GOKNItbeiFqElB5mHaQJP87M1dug9WXddl+kZ2BiXWk4mYGRMg/mWj6vDwPs67pgsad2jpuMNUqDjT8sE3pLEHInHLA3sOMltkSfrHutXYzlgCSJyrnDCp9EOAs64leK6dDMBQa1ktPOuIIKbUMOJF5Aww1AzDTJjJH3l0D2J1IVd9XKmAjsXEEbUd0ZiaQGkmvqz9T8sm7WPY77N53UW7TVjdiNa4uPH9f7t7y83rYERPPLnxjz1E2mrwvQdC8pot1E0F1srNzazIFFvwIQBkowiM7Fel1gXZrVpjpSSNDYMadqwNKZBHhpYeUllz1tONKgp2qfkQ78Mi77PWVqDy5//GPNXcPNPgxuHT8VrkxfizIfeSYwnpf0NeZfZYcxXEpgponsIMQeobG3LHEAVUu5oM61iWhXWhTApDjbXGRrL9DeQtcKEsTvaTGsqi0tltImHWam9XVvWeLExzhzxsUNoqoaUziNOb5JpJPeWzQ7zIj+i7Wkgd+LTGG3qvZusH0wTS7VmKdYZoWM3MU7SzK5lPkzjn/TGPdOwyRxN/ZjHYlmGpPGJiXPxtYtfxhjLPwjltEMkeaJmjATXDJIwyLByHs4B2C2HweRpCcvInGH7R5KjSSawcMxTE2ijjLYQBiStZ+wqi5xvZpwxYXkWjTYbK3litcOYd/aShdQEptFTJAwiSNykNYXdGWfjhFtTuHPdZSRZO/q8Tvv2azuej568ZU3jzplSXsRsuegXWMTPCj4TbKsMOmdMXWJtwcIC3o4O3ZQej1l2OTC+rlJGHSZuglYafJyETHpSWlq/qC45cAFYR4Ay2m0UWZ2spIEfgN1NiG0Y8uDG3hhtTlz78hQMHT0z/Dvt4MaQxiDwO9queaKdZ1J50XcqPU6khpvBsc18bX0BV7wwGRNmLXV+86UNEsNSNk/xfcjQcbjzzZk47b4JiXWRT6Ylgd3RpuaJVv+7dY1MjOzwItngo0OIE0RhSciJloIx2vQOb8oA8JmOL1pZax0emgLLLDshT1l+/CA9bdHqmOYvimAO5+yJLlkevSdpGG2L3uRGa0jR0Js5xUy1bYa1nAfRUgVknOTJdQIJ39qTxPilWcQkMbaWyWs5cK1YT1LvaFOnYm7bppZNaXTbInozPaIx7f4zYxDYXVf2TF6asCS0VCHjzafR/tPj72FNfQG/FGth2rYY+Xhw548sOUlbnCYsDcewsxYG9u/kN/ndcpBF2ps52DTZMMsZdm87rtFOOlCzM0fWpwAtoRoxHWfm7X6TZzc8XD9EVOYMja0pZs2UbRZpEd1yGePvo7GYQiPbX9mTgUlrFBcGufTE6S5kvPNLBZ4w5WSjJ0ubBeF/bp52m7l0cweLcMLCJZPMzSRmV8bLwnwzJlYy35ZHb8KoSyGItK5Iokdqvk1dQrNzktZbP/NHLqKzg/HZymi3B0xfvBqn3TcB789dXnHaBnLYlYd4dhfPTDi5qTHGv7kmw7RFq3HjiGm45L8fe6SCzVNO2vNea8khPel+ODdPdA8mPjDHLswT8S2vT8fto2bg6Fvfcn6T4KZf5CAh04QMh8wn+j5jcelu5YefJ79fmvVqAbujzTbPfEr/+ySfzFMm05InC1DcNrPvIGdjtNOksqxvJs1ehm9eMQxDho5LTJsVtoAgYnLem7Mcf3/mAywrP0tkMabi+1PvfI4D//U6fvfIpDCM9aG8d5j0jrZsuoai2/9pAgbmDI1pr9I0mknawjTBT5J2JY6A/B69M5uWJ0g8M4fdw5wU3KU66SLv/7K7t+wOs32Yd9ubNYXJp446LhJkQR4I4cRlvkToU5Cxg7JDd/krEyDJMtIE3uxnGcQ02kzTyBkJsl4nMH3s4B7PM56PjMhMx1nbFgjdVNNIBBbuHW3/GsneNbe8fCesFZblTMHtV9NJ9jpR+mQMixvXrTfTaLP5as5V8iWLanpH2y3XhLMwf9luHS2LGGLdQ8dZxjMFUBojlPGLj9Eg8N/bJ2tK6hmHnGdMela2rx3j5Xg13+40ilk/8fYJAjgEsfaJ02LycuoCLkgImWLLozdjeEVc4ZjPbbNoLEdMtQyL0rre+7n3dhOUC/9LPzu1Nyij3YbgY8x+ee8EvPrxQhxx0+iK85QHm8jEUGxC4QHYXcDqiUnOukBNvfvGJGP809DUQ1GdYbQtRzr+/OzDJduYEsmhGm3GDE4jb6GzNuH3X+XvLMy/oVaCNOdLBpJRone0iXaNIee5yxMEnPmOx00az/YBv/SZ5lQsidH2lcWECQ+MnQ0AGD1tiZe+SmA5ABNrwY9vGo0Hxs7G1S9PscqXdAMlIQ8AvPBB5HSPXUdhntobiPbJOuDl3TBJx+wlNbhj1AzLzJG/4ev2DT0gkvt0EoRHC/ufMRxsnDh5EiaIChMTGC2+zrhlS40k09TahLF2TAkLD7tu2cycVMLMe0kjswyxTRxFnuWo9Em0VE28O/8jS5VkwU2aYVmaVj5PhInJazPZP8jcZGuZYzqesIbS8UPmK7tbzixDqKdtcpUhTpPJ86N5K7DP1SPw3/fmhb8xjTbz8s1ga7TdeSQoiNHDvSqb36IwN0/OsMIJq0/QaLO1h2tneS2Yjxdq3SHSh34tiOKAMe5Uy0361WVO+ZNPLJ6sk6SbtY8VL1xHY/TAdz84W9mu1t0uT6blFiju4HNCApcev1Y5PZ5kgOPrqrs2SO13VI479rhG24RVCcsMWj86twyNOYt570hQRrsNwcdcGI+taYwbAzMdZ459rI2y6KZdlwNfajFq610zL1Z0Y+ixvdi62hCm0U5iOq3DSsKB2ofrh011wtjGxjzQy3KYFi+J4YC1OdjxgeQDmg9ppqoGXKMdhTHnY0lghxDvpijbJyFP+Rt73ouNiaQ72s6hKGT82CG2aRPtsyVr8M7sZeHfcg4zGqcvWu3QyJwGSbD76kyzIwV67I52nmi0ZT4/uOEN/OOFybjihclhGHN8Y2m0CSMaPr1HmWV2CHXD0pxC+fqNa4bsT0mvdZAsunXhaUt/SNPxtBcPGLNUIHsA017ah11/m0mYPpJa0zQa2bhgT3nZDvLctTCpv1gZlax/bH7YY89tH1o2W8PDz+T1OoxXwdLBtJe2tUHpM/X9byKQMd3KzjM+0/GzH5qE2UtrcPbDk8LfJFNt8pIMQ0OCFJx5HefCUPtvxiAmCbesdY/MV5C04TlMvv4SK6v0h1sXH43U2oCUzRjjeiIgYAx5ZIrO9i2LHBSKnDl1GS3+VrNTDgtLWFvjoMKJIONZIWCCBLd9wv5Kmesm3K134BxMioGbOHCjeeonGGDLdNxNLzXaVcJfgFMOojqb+SmZamk67hujPhpzueZ7bamtQRntNgR58GjsFe0Vaxu8b/6yu9dJ5k7Me6ZEBft6IiTjX0vebzVFp2k5K9G61xFHOpFkV+SZkEe6qbY/LduI4mmSmC75G2N26EaQUAY5GwAAunXOtkRkNh2XmgZi+sck6QzRwSSenjsKKcVNHs8G9iYbOHQz0uIHSBmPbYrxfJjpX2Ow7zUjcdTNYzCrLJxLcy7Ws2u1VT7AnQZJyCzZIY1dRzHrjG2yaMaee6ACgJW1JU32G1Mj51PM8Q2zxkkzb2YMaxIDzPKTtHrfUo/RZeIyjYQTj5UdkDYrf017j9y3pySZhDIBLKdR1sWuEyCePiJ7E2M4fPSwvmYm7/Z65gYmvVHOtOY+MCsquZ+xZ7Iictxxb61LZA1PskCKk8rWuKj93LLrhKCGrUeMqQ4ZcnLFIO1+dym89LlGWLUZMDNxJsRgYO9o220LN6z8t89MOKughlqWEWGJ5ak/loevDB+NUX2y0c3OD1ZdKFPt9mEkfMvSjnw8+jzRs7qQ6UH3a9Y8LlPtcSrm6X87rVs2FdSReOZvp33IHXbG7NL8iMZeMs9SERIEEV3S143j0AzumbpYjMqJmHeXoQ8ojUyIFdVPPu+lpuOKdQZ2/7ASfLZkDXa55BUMuTu63ynNN83Bhj1flPWAty4gJenGnJp7wM3mIMprqksO6QUiTPA5BksCP7j70zKpuXunpfyFHJAlXfVMQED6kG8Objz5vUeXTt46SGT1Si/70JgOc8Y22emNb0GmG2rsM15mHHLDYk6TWFr2xBJjnuTfbHwwhr0xmLKwdN2ACdBW1UaO63p2LfWv3TbRX0zCzN4ZtzU7ruCHWQYUiEabzTdmBlrK35TnzleZS6jltpgLNyxJIEOFU2QexcHSFwL3XmLgfEk5uFtnOZeetLWS1ZuZ4jLGVtLItX1mDssx4Y6T6H4/p5W1L3+PmvVXtjBruJkyCNPoQ9ozaRGTJ4pJaLM05iKrYCOeLqSHCp1Kf9Tlyf5I9g9WNltnGEPu02iz+cPeTJfRspqOJ70JzpgBxtjGy07yVyDjJTHkeXJtgd3vZWuFS6NNg79s+xNIc4ZG1h5SLqOHMaxMiMEYzlI57jzMqtzgJtjp7VhiOON18Wifw1yitJIG+SsTRMRHbwAyh+GWTYUYxWSBjDQ4lO+wM8dnkUbbY20Q2PFK/RWkp01pW+t5r+Rlt91BGe02BDm2GqPZemLiXADAm+L5EabRZvcpqfSZbLLrAjaj7Zp5sc08Cb549Slmp8z8O6lI9pxWdodbsbzIhpN0yJPCkrqCedIobZM1BwFGt02LQffO1f5KCLBn0hiYtQXbPLmJsZtvvH+YRJqa0Hkp5P2f5nWamo4Lmlj+NoNZ/q2JGm2DKmHWFZZbrsJqceeZawqjNGkabVYXNv8iz79RWEDSsuo3EAFZ6Q+3PGYmzp4R4wxr+Tey9vBDenKYL30QEGFQQjmUSRNp2cG0Pp88jvjh2y2bvVuedtg1X9k+I8tlz3NZe6CVp78cdvc+zVSfMw2kDCSD3dFOvx7h5s3oThQGEQ2pj0GsIibYRdK29h1t8+leM2OCaMuqgF1F8DGNHtqBmEabMIOJztDI/Of3jeNrs+/tYM7QZQ1jbUbf27bKdfPz0RhaG6SsSWweMWuBJMVBmtARKK2hfG8mYUU3LJ5nOE6seHDiRfuZnWdAaPQyfojHS7JykGW44yxJw+7Sw+5jM6GBJz+H7ui71GiX8izBulMdrhdRelbvMK2JZ2m0k9J67puXQ/V5L0WLQI6trHdU00Cf9yLawnRJKKGnmeaCdApm7hmyu4Fpd/oMfFpBdrigXjrJYQ4APvx8BS589kN8sbquHN/Nu0jolvlVeTSxhSIxoaK1KEG+Jxo9cyPpMfS7dWH9am9g0V89umRktC0zPz/lDeyOtvjdnJ3kIZ1phYymlZki+ZgYJmAAgAUrai2HW7KBzLzIp1yjYPcAfZssS8826UohNSRsnLH7qyvX5imNycIS9/Btv1Htjnt6wI/R5SvXtxYyZjBp7WLzX+bMTRazHfp8DAMzW6YaiYwHbbpGZdRo+zx6s7XQ/J5mvWCGHLtPKfeZaExE5YaaNEFqA5n3pTJNeaQtUgSHrL8ixt/NL80RpwxigiimyU/TFibSbY0d86OM51tnXBqZp39+JcBd98zPbA6nPV8Wku1dC5PbmY2frO9oJwo7yF7B9hQnPREcMWFSkiCbzVEmbM8SZhiltGMaW8+Sr565c5Df0XbXM3avOU5eMWBWDnbepTq4a3OS8C1eThC4ewt1kEbiSebSKYe0Dxe+EHocLbcbD6RPOd3Jjsvc571K3yMz8SjH6O419xIfmphbTLpdTsDaDKwPorS5nDBlR8dCNrtQRcvAWowrH2osCWW0mXdRotGqRLLfFKSZjrNDVhKyWJhzTaX9W+l79McPbngTALB0TT1u/MVu9gEPJC2R7PuchrH+NmEsBfPem8ZUcWdG5d/IZgXYmuokSNPxunwBXTrxdIWiOx7TDv15MkZNo8Sl4SAHpXA8E83XvOVrsedVw9G/ZxdMvOAgk4VIG1g0xH8P6xXYdQKEJ1nP4ZJt0k1htGvzktFmz8XAKXdl2YycbfrVOX4HlWlN0jTISb4g0ixIfJorEzVNWEaditEwOw/5e3ZrERu+w5dzKIrFl2moJpaFifzS7uazNYAxmGnWC0laLltYRq4TJDCc8fCQHjqeXRrTte5sfSzHJwIHH+IH2Fwul6o5pcxyLL6MxzSSkqwouzix7jgN2BiP0yXK4VppOGGpDDnJT5bJ5r3sh0go64YxsD6075ubzzg93Huz/PTRnWTeTPeUlGtrJn/GpDl7nEeD7NIDJ4y+/mHSEsFxVsbfpYcwWh4GUX6W6IATRqYRTWtiufswo9Gniec02v0fxRelWp8yvbu2kHLA9xS6LrH2NtpiuU4JGuUZxfR1LolZFmFWvFwpVJ47nDOtL78wMCfyTFl42xlUo92G4C7xTcirPFDrCpKxKS/wYnVINquCE5aF3konidRo8zvaKNPtSs0ZsggpwrvA8oBANNCsLtMXu17gkw547D1RJjV3DiHl6spF0hxYpfde7jWU0ePWKc16IavAR8oP2PvgBvbTcmV6kNxm7I6dz8SILfDU3LYcNmZ66RmtJeX3pEvxZTxDa3I7GgEC62vGxMbpYSaYlUI6M4q8xbllyCKMGbnvkMbA5qaMnidm4mydAWlHVqpP+EA1kOT6hxlyqUwM06oRgVVWZs6bnhyKA0ZP4nx148myzTzzXUVIcyBnvrGn09KYC5NYWljwayIu3RKUySPMSZpwglkVRPOM0ePm54MUQzUQYVJSm7H1KHu85DU8nleYJ2G+k4RlTLDBBFFZLcN8d7TTrgXVEYddSUJJJhilVxHiJsYB31Pkp0xv7QG0v+w8ZB3Y03JuH3q013DhOz+k9TVnEG367bR2uYwhY8xpochNh33tLUOTxi0TWMfLKfVrPIwx0IQpZvmJPBD7zuZ/PIMSg8nKtsG1ynycRE7KTJHyKS6RXjhds5yhwcT1M8tWnvJ+dznMvPIawK0fq4ukO5eDPu+lWPewziqNGGnWHT3zfBXRKsgFnmkK2MKb0Q9ZOV32uAC/o80WMPY0lES0OcQXMP+CTBkokqdEaE6dctgx3+Wmn6Tl9G048jBntJZMo83vYrmVYTQyZjf+PQmyHPY+uEHa28GsX5g2LMkZmrMxmU8yxunzPLItynQwIQ9zekTfjiV9HafdfGuKRlvOI+6Eyf60w9LHXtJBgmq0pfaJOEijByoy3pgJsSybma3aa1fSIU2GuXVJYqpZWh+zE0/vX6NE2iJrH5AwNy3z8i2NE5hVArv2wLSXacyp+Sod1/EnCMkaJUBN2ckaJ+lhY5z2AWFCzB/WQTttOkqzbLPnpgkxyJzzzcM4jWycsTFhh7v9RbXkKfsjwOK5YzRRABLr7KQxIPuaWT8lXVFK85/iE4wxBpHVMcmJbJogOzzPpCg3TLjTZkTzmXR+4AJ4Nx6ssNhvnjDzN6fHrkuA2Hwr55llzaRjOUGQkKTdZWXZdckWFi8mXI2yzE146l0Okwwn1c47GvIov2rylnW1pdGOfojeb2dhLuElK4DAiWeiWabj8f4nGcbj5IhvmY4AZbTbENikrSi9SLS2vuDkw7yOm8WeabnZ5pAFlTILdWkabZJvUhlMSi3zKYUlbXZyMXfLMYw2PTQSRtJivjwabbYxRflEqCN32JnpeCQ0cOlhTDW73x3/ngRZdrJG25Xis3z4EzlRvOiOtpveXeDdgkxIjhyU2cZNNXZk7Mi55bVeIAcEfv+1wnlEromkaV19B10TJUcsFdLmDHtPtsAY/xgN8Xyi/LgzNCaAYwdb5nU8ooHVBU4YOzw5c7iYkYEOCMNB6YnyTaLRXSm5djVOa7w+2e9oy7oEdgIRxrwqp2nnfUI+xqgxpjMpTz4XZNryp7xigmSwvsmTdqQ0ynzIHpVUFxbPqxmk61kUFpB6R30oyjZXocgcTdOQhoy7Q6NJQ/IURLL1LJ9gOs76wBIahdZU8Xr410LOdLr0srUiXgbA90J3/SB7XJGfcVja8jcRxsaUW3bWtdDEYWHOWh5whty9TsDXTICPUTaHs5hqM8aP1aXENPK2ZXOL+Rei2l27GMi714yJDdMWCY2CFsnsGpqqYs7QQma5wrewAxmvStSlHCi13K6gJVmIkYOtje9IUEa7DaGp9xLkfewaw2iLLJnWLdkZmrsY2/RyOrJqQQ3YHW3GxNpvMLtl+MyJzV/skEa1cx6m08B44mbSVbboM60S3eA9G468U18XMtVRvHoSllXrFmkfbfjuY/kg2zFJo22b+ZJxVg5jGu0Ca0d6MOGbIjtISEbSzBnZjqHZMTuESnrKo68SZ2gymDE7afds45DtzhltOOX6Drqs343wLo3xY2MqiP1mly3ikeHmE/wk5xmFRWuczMftGzZOkhhEl0433Df23MOzS2OBtG1WrVlo0eShNU2jzdauIBZffmeMW6pX5YQ104lr5gdZK7IyeexQzAUt6X1tYB+qTTmV0xNdMWBpQcLYGLVpS2IG0jSVfEyw8egPs+eWm59FI1xwjWaEJGdoqQLGGF3yB+96ndqOrA/98exra/ZnlJ7da+faax/DSoVJdC7IcgPr09ACuOeUAHw9c2nkY9QrIIqVYsqKgzlDdJk8D7Pr0ONqi0tvR7v5yfJMXST9dli8HH5OMUE5IaR3723z/g+ZZ/LEluMMDXY50ow+yaFZUQTa8cpppYCA9X8sPxmllJ0+76VYx7AXusrTr6mPDtoR0xBlZDRD6SZ9cMMqkDBVSrtkEOoStLO2gMBvOu69B0bCmLbM6gdCr3l7mB84Mm6oZIFnGxNg38c2bSUXsTrCVJlv3JmJLNf+LV521r70WRtMXbgKy8T9Z+50zy23gbZZFJaLxYeI42tHKzgcU1FgTX3ZA3fGw7fl5bsKJJ5Lt4xjH7Td+iRZBjDUEYEMG8vsXrK7oZY+JbNv8pQyLnb4iiwDsrWjfYAlFfOACTzo4ZIwZEzowAQjSVpc90DuP6THD98+YSA/NLr1s9KSuoQCK495bZqzSeYzIKsDOba2hu1A+4rXj8VNv+pD1nDSh9xqCw5oGBGwAggtVdKcBSYJ2pigwepX0tnMhF7GZdfCWJ52mFsXun6QNY4x7tE6YZHonUvxslm8xOe9iACdjW+HQfTsH65A16WHnaW4kKf0Sc8eCQyUVT75221bO2+Tn/yUv9trJpx4bG0tpefMMjvjxOkuFn2aZt+YssuIhwWkvc3fTpsFrP8rW9fZPGIWMW5/JWt3QwU0KZvVBSLMtposhUqFQoCobauE4se0BfMmHqfdKSdGN9Vo07pIjXbOeyWwvUMZ7TYEezGufKCtrY+eKGJaxXriIIc9u5Mk9WbIog1LgsVUsY2g/Jmm0faVnySRTtOGMSuD7mXTcbZ5ss2BOz1xN3OfgCCNgWogjmIoU03q5Gca3H5IAjtUTVu0Ggf9exS+e/WI8DfufM7NJ40RyHkcjQWkLtzbeukvOU+McEpGTLonyx2fuWXE505oykyYCulULk2jPXtJDWYvqQn/riMa7bR7++xAJf9m/ZV2Jzoqz/3OnKGxQx+Dz3zPGithf4k1jtU7YY1jli9pGkDzN9NSxMsuFOFqhQL7U+bP79OSuqQwsYwpkOmY46sCuR+fdvgO12ualtDtodGec+664DPL95Ujc0yuixvmA9X4ppi3UgFKwj7MTcfduvgYllSv/AlhaW9hJ+2trC8rcoZG5qHsw8TrY3RMJNNtwvm8jtOWLU/WX6au9rnH5Ju+ppQYsvj6ke2OdpJwm/e/LMP+TYbH27FANNoB3H4uBglWDmTOgNAN1gfOuYAw9J54Lt2ulpvtXQ6t8I9v37ISZ2KDqCjBhAKMHJOnrVUup4Xt18Ykt7TfRistzlbOmBKCEWmdaWJVSVN0p5LEWiCI6tyRnaHp815tCBU5YSFYS5iGwF6tAGTfzNMOwGSdc9JlQZrkkt3FTNpkfQcOJom373Kxepc+JdPTw5iOk/ZhdGc1J3bpNhuyy9iwNrM3czeMaYC8G1PCRsLAtIojpywCEHm2jufH6E7SrrEuz6qRkPmUyiyhRliBML8GSe3NvW9n32RlKJtLSYz2mro89rlmBABg2j8OQ6fqKvuONn1bPSzFyc8Ze/QgWfpMt0pwx1SSdtZqh4ThxvoaiI89Qk/RrTdrCs40+tdC5xgRuGHsUEwP7lkPxR7BSJwe890WWPL1Mzrkw4mb9qwdm1smoi1gTFi3SB+44S7dyQxUtjHKwIQG/rhu2UxITK9hkXJYO1rlMf8ORJBowiuxVGJzhj/bBSeM7T2sbXxMVdrazuZHkrCdWe2kMd+luHAaUprVuvSw8ZxtjFPfErF6FAO27vGwuBCDm3+z+WHC7Pz88dy2YOOM0UjXx1gYF4zbNMjvdO9xMyUCgoBaAdB13bNek+M13c94nu5cMEEhwyoY26pcDoUg4AxwEISlGGZVmrzncrkwPUg5smzmTC2kEW48anZO6lz0tKMJyyG6gpfx2NluoBrttoSMBwEf5IJjzI25RjMKY46L+OaQVC7fzLPCXlDdDcx8tR1kZTMbk+nZgpx2Z9TEWyOYxcgZGlv0XbrTnkgxf2ehO0igMe0OKlv12SHUl2eSttF2AlfCyrUNTjybHDL2zHjMeHBjmsF4XbhJcOlTCqdMH9v3e015bn5cACLqktK23AQziteQ97f3R/NWht9rynWw72j7x2Oadk3+zQ7fqe8EU2bA/k3+zsYyg1+jnU0Qxepijcfwk9RFxKPMJQwD7a6F7HDhr2dK25IDedaxx57G8pXD5hwXRPppTBWAJKTNQiPXJtmfvrTMdJzPS/94dOvj0pV49YD0dRrdPD+3XBPu7in+duRCNeYMz13r051UJa8z7GoatVTJ2DfWvA7r4qaN58CFYJxplJ8yf7tsf39ZpuOx32R6yiCStdDVhke/RWH+vk4TjLL9w8TJSne8y5gQg649sU/5B91H7WIo4xcE7rjz0cjaW35KejPVEZ73usvfbW1xKbRaBLplMAY4CsuBX7djTHVktp7s3Z7d5Q6f98ra/4Coc06doSnWPQLPdx8Wrayld3BL300+7mZlH4rdjSBpM06jOzPxAswDLvPezZ5Tovll2MxNlHyK2bopUmoLq8gTXYmaPdm2Pu1DwA9Kki6ZZ9aDEj0UZdoA3bKTzpzsELOyNu/ES9sUsx44fZtaEhPDyrGsQBpcB4JJh0vmGZ9uvLHeLpJ6ByRufYJGe+qiVeF389SbHKMmrV1nRqMbJmmngiMyX3l/wYnH7l0y5pLBPcy59LBxwZ7iifqLzX+3LnwOxulz5xGI9oHNdW4RY9Mqf7faOxQGuHVhVhxAepuF62NKvKS0TBCVdnBn9QeiOqbT7S+H0cjXTE4DQ2M0p1np4WsKKTfhgO9qOe18AAgrCVlO6Q8mJE5rb3Ydjc1VX55xGqx4JC1DmhaX7W8mnJ0fGEMWT58sDEqLx+kpBpyBZuu1zyyfjnEiQM8qLKHXtWJhBboW8vrFo2UdZ3SfiRLEyiVrLnz3pNPbOzrHkTHljBXWX8nxKLNMTL+tupS/W1rlcuJcLkovx4m8Ux1qpYnpuUUjbBqloKWaaMgjGlmd7fbW570U6xzsAOPDyCmL8M0rhuF3j74r0rjp2SYtkSebIl/o/LT4GFsf3p2zHItX1UV5k7TssMOeJWNgEzyeZ3iQSGGWzMIgPbonM9XJm2eSRtvnKIYd3Fi/sjZL04aZdC4z6N/EahsKeHLiXHyxOupDdiBbWUs02myMp7QP7StPO/JNze2bsC4ZTcdZX7NrB0yw5dBDwtkYrU9whiYdzBlNttSQJL2tbh0O4KGRrQHw15syEuxwSeYbPSgRsPt0cRrp+EnSXor8Ik28S4V1CA0Jjs8Zz0E5ll2hyA54btlJWry0Pkwat244+530F4kXeuVn44SYHfO55dbPRw9dr+HWK+2QnvSEJVsnfNpTGcrahyxxlDHOLmhxxy0rw+TF9hRXu2bTIOlI3TMpjS49Yds4TCNff+RvkrasZyS2JvD79LG2CNw1yMcoyU+ZJyubt4VLsMvYZNUWu+sMF+jatJboYGsKiUfGKAAERVJ2ka2FxBlawIWTJVqT5xEXvrjzo/S327GsX2kY2ZX4OczThyyM1FtSLplYA/nklwkP4xVJmBg7uVwuYmJFZzGmupqYrSd5Mi+1WRCWU8ovq3VX1C1SGJBmSdTeoIx2K2LCrKW48sXJkSdpIh3z4eaR0wEA/31vnkjjpk/bCLI7Q3MJ8knSkzbASbOX4YibRmOPf7xG42dltBijbZL4nrmA1RYmH2J2SvK0yiOHXVYXZp4WEFoMPT6BBdtIbI0E25jcvvEfyJLujLr9cO3LU/DHx9/Dz257y6FB5r9aaLT5OINTv6x0swODCfeZorFyahvYe91RzEJgx5d0M2dPWWmMh7OwpDvaMss64gwvet6LlEuer/NrWJPrmHSIlQ0exH9Dcl8z+A671vwI+zAKSxp7jEFMXwvdtKZsdnCjGrJ4vCRtaMqVENov5C53geRTSufW0XxrnPm3SZus0S7QdSvKm63D9ngu55nisInJZJPW+qzMpVuOoccNowJYwtika0j9NDA9V7bDLimHzBk2h6kQk4WR8S3jsDNDIWWdSlgq7H6N5SG/x/Mo7YXp85W3mUtj0j7KPLqz/nLM/+HZwx2hAenDhPUju0CftY8b5va1bw2P0+2WHQkT0+aWOwfDsu2iqYa1yOIRuukeHvuM4jKLCD72THaGYZVlMG2xdJ4ahbnPbtmm41HJ8t1rEyo14k7Z4v+qBLNz3mbujA2sOsccvnUgqDO0VsTRt5YYlX49uuBX+26d+cAJRJNGgmsv3Iz4Rhn9nvXpE99vSaSPmb4kMX24IZODjTy4mbtjVPsUZxrIBmjSFaw2c+MZOmwzSJduxgDTDYNsaqYc57ARdqFLt2xlqhmK0WrRw7TXZIP3pX/powUAgOmL14S/cZPgCA2FIqqrqmMHIP8hhDGhtjbLbW8T7pPspzG2SZomFsaeZ0mbW1aclPHjewM5ljT0xyCj19Nn8sw26baDu+nHaAXvm0TGz8qvPN+oNoyNbxe+jTvt4F9I7FeZ1v5Mq4u3X2Nh7JDmcz7IyuGMlrVolj/S5pHb9naebtmpAhTSjtF8JfWjc9AtNx43ibFpzF3n5LHs0igFDjlyuJX14IKIbGFsr0gbtwHceCZ/Zk7sE6qxNktdC0nZvH6lT58pO5v1TGDB6AaAm0ZMQ48u1Th5ry0T6iLbzKXRxM1sTh6b22y9Zn2YyCCSNc5hYosu4wb4FQxZ5wITlrA7/s56Bjb2PF7HydmD5Sc/ATnXJS32py9eSA9hvrP0f5Gu1wGhx21vE4fuASSiCbEY4HKg/fRVKbDEFAc2oyyfGhVpDQMt+9Q2UQ9i6WHnWbD7q1pqnwM7v2LgES6QNcokls978RWh/UIZ7TaAz5evBRCbtCkDLUc4bap1YdoQkSZ0hkYO36mLsYdWtikm0Z12+GKbOVtkfW/wJXmxZJsLY1hTPX9XKM1mixCTZvvpdsP4wY31oVUMxDopwvzpu1S7hjCMgeos4tXli+jWuZoyu/ZhxQ3jDLlNp8wzy0GSmWpyrQoZE4H9m0WPSOvTAISMn6QxnjncuknIPOmTb0SjnXR4ZgfJePrke3IkjExinlaWCy9884OPCxGParRJH7D5nziPXHri6xs7cLLDJRvPWRky37z2xfN9t+gkY5ymTXAWyDVNbj6s/9zv2dqCmtabcUI0pHCrTJkd2Q5VRNtUyr/0Pe0JOzZG2Z7LwMctZ4CKRa7ljJeQNI+YwDv91QGWH1kTINuCrL0pe5cJW7CiFte8PAUAcMK3t0Cn6ip7f2Vrs+eaSBDY48SUk2UOJ64fVlr7U9aLaVgd4QSy0mjnbZft9itrW5ZntifI+J7C2sxn5cAFI27b8vU6lic48+32v0sPiPA+XD5YH5J6M4WAS6N0NGbSRnWvskzHUY7n3se2NN/lvH1vVMvnuEyezJs4y9P2MF4KzYl48TEawGW+5ZjI5bjDto4ANR1vA+jTvTOA+MEkOU2O6rQjMIYuSWOb3WuopIHTmnRWqCactr0BZltQfRsBoyerdi5pw2nI+CZs2gE4CH+L05jRfJsxDQnMYJomICzbcwBi6TtVu33InKFVC7umUFAh2ycU8ti0lMJSxi0Zj2FdyOFJfsp0spwCOXwlzQX2/nta/9tx2RiP4iU9oy3zNM7QZL3rC+yw4qaNwtwxEQ9POiCmHS7Nd0vLScpOYjTISC6lEXkywQjXaLuZJjNAbjyufYqHuXUqPdHC68LHD1mjnJTp65tPqMSYNxPG3yh34zHhJN8/CI3xA1nC2ONhIm1C2fZcZ+uMW67J3zsuyVhJnzPu4MsuLLXLkmUzJ1VsL/Stj6xsxuzaApmEfiXj1qt1hQu6N8s2K48b+doCu0bD6fGtzYwhI2FFtx2j/cNOGw/Lei3HxMmyx/kYW4CP8dS5SQRoPnqCwKWb+aFg84iN2yThDd/DRX5ww0yAQw/c+tF5FPD90btnxosO3FDWZkURJu86mzDmdZzd5Y4Y8nQmNvQSLuhjT35ZZuLlwCqpvTbxyNvaSXWGmFs52Ix6R4Iy2q2Emvro/mrvbobRjn5P0mYB0QSRYMxX2sGfO0PLtlFGHgL5xk3pJow223AKpOy0wy479Mu/WVuwe4tsMc/LN2FDDTmjMVsYW6SzmX4ZGrLVj5yJsh0kEjbkTmTwsft0Mk9uymzTVfpeLjdF8OPr6wDu+POzNZ42E/GY5oP2K/tG6ifrkVafRKZT/FZbNh23q5nU/24Z7FAUpzFijGRt2Tix85BpfIxfvFwGv8ZGtjnpQ9q2Nv1W2tS10J3/Jk/OpMXrweP5aExnoJPqDCdePDxpnWLOx9LKTmSUKY28XxlzytaKNJP5rIJj//roOqqM5yPpsPKE2xZs7QFcuhPnYIZ1z7ensHhxGvn5gdFj18lK46wpfs/YbN7LdSbpOTYp0K2jTiD9dLOzAgsLqAbZDmOvG/C2demJ9r10eoKArdeBI5hl9Wbnj+QzRRZ6soXJ8mXZ7n5tPgMnMKvwzllfwa7mBYgP0qIbVOp/Ug8v3ZnqnXxv2zLpLofJ83ao5RZ3osP5ILTUUtNMTceJl+8qweTH55i8msfujEttOJ1HsXaR61HJYRvC9B0Jymi3EpasjrwGd+nkdkNjNNrcyVEUlrSZ0QM53ayisFzst6hsP/HcdNxNy8LYYZeVxd6j9h1sufbBbQz51FLWA5CvFaiZX+DWJaLRThsvmz5flEAjXfQ9fcjy7EzHq6y3m5Y65yICC2aCb76y+2Jsg6f7qZOnuyEnaaX5Zs7iJae10oO1GUlfDHDeU+/jobdni/gRjEY7rR2TBRYWiSkHxGzlUGdPoowk0z8Gf79KuhmNbI0zbcvSinik7CKrDJIOEmSMkng+GoPUeC49SfNX5g0IKwAi3OIaTRnPzY8zX9nmmymHmeXH65Oo5Urpr6R9jzGxbN+LZRmWw15goONRpqVvz/tppOMsFoaA7ynuPPL3a5qQOMlJFdMWsnuyPtj+U8h4NvFEnqG/CpEPT8tp9M9Xl26298TrxPoryQ9NvDmCwA1kTFoxINYC4Rh3+yZtXvvmAvcmzsYjaVtyqKUm1Kwd6TiDE893bmbjLCDxmQCFzhl4+oqEU0adlF1qi1KgZTpejsccjVGv4czrePifPU5CrbRkvqmTM5fxZ/exmSfysH4kTAqdfW99dwQoo91C+GJ1HSbPXxn+vWJt9PSRYUJ8C46EmSSUYWWaKqKxkZMqSaOdxnD4pE9JjDbXaIs6EBqD8DcZz18Wa0f/Id1tH7ZwWx6mEzawbIwtp5HdaSl9uvSkm1C6NEbtGFsAiRlcQPI0cTpXuX1I7yuLxPXEpI8xCPT+Mhl7phzWrz4NGR8/sg4mLHkusAMnOygzbZ8vblKewz9ZhIfHzcH5T3/gxAe4M7Tk8cjGLW8zJhhJ1yq4NLKy+RiDF751RpZdIPVONBPOrA1NnusmnDFpzvwv+tcjLiyRZUT5RmnZWm//5tYBTnjaWhExrKQtyJUAVh7rF6r5IG3r5umGsXqz9aOQ2P922fE1xV6P2VqRnCdbH6nQkY0JUoahkR1iWduyMeqrC9sfLSYto28S87dPo82QtqYYeqSjOvOKhL0X2vGt/OIMdCytiesTREokad3TBPpMOG3+jpcUEHoA/x1tLryV+WUbt4bOJAbRoFDMthYGgdsH8rd4XVj/p831sJx4+9B1mAsx4rt46UzphsVp9JVdCrPjMa0yBEtva5XLYdRpGnt2S97HjgpiWm7K0FvlRHnK+lhls3ZA1IqRiblo7ZytTe9IUEa7hbDHP17DYf/3BqYuXAXAHoTMIVk8jkHEaCczrOFkYosVScPC+IE6CjNadd+hsVAMMOKTRVgi3luWZl5Jd9WY6XiWjUD+JvP0aRW5Yxe3nHSv40k0ugtyJhoT7gyzDSerpjHLoSFJg5D1jrbMti7BCzYXdrj9wjbFLO1ITf2pEMMNS3onWMYLYr/JNL6xZ6WnY6r0hxTKxX8DZNsSesg8KhLC3U2f9U1CnikHN1Y/qrEjAqJ4Hkn1CdOJMMZ8+8ZUPMwnFKEHsiJvx3j/l8ZUfIy6dUmaH7KgqK9ZWlGutdaxcSroYYfGhHZkNGYVyLBDsXfOkDxtErPRyOvM6YkziPGxEC873RlaAo3UU7tNC4PUXkVFu6bjQRAfeUi8ZpZ2BuBjj9NKta68OhZdpfzZel36lK8/REJHkjZtfMPsKS4d2c4U5TxShDe0HdnaDA+TBl42F1i5/S9psOhOma8mDi87Tjcv2z3jZhM6UmFiLL6vLiauOz/ctqXCeyZcIPMooof1Vzy9u39AtEWkaY7oqSJm3jmqaXZpkk9ncdPxKEwy5CFDLzyRh2ktjbbJDyItqXM5zFw/lO2dg1TeoUNBGe0Wghk4Y2eUnreSk6yBaPtKcUg+5VDmCi3N1LtyjQ07mLhUscUYAB4eNxsn3zMeP7jhzfA3qQw1zqTS34lNOJgQetx3Jysz1WQHiQZyR5seLmVdvE9NsLBs99eiw2UUSJ9jI8xgyFSRsl0GwS4PQHhPrXNGr+MyLXsTmh8k7c8SvYFThomTZVNMEoxwJh9OWNpcSDq4+TSxaUIZ851br0SIvI67aTlTwOLF53DWeeinW+bIDkpcGFT6TPN27aOd0kM0p4xG3+GShZUOnG6cLPHY4ZIJRgu0fvanTXdyXaL5FqSO8cQDOTkAM6ahMXSb8HgPMHr4+kHGGelrdkpnfRAvy6HBotv+tMom7Z0+9pL63x1nccKLARwrKTZGk4TEaVYOvP89ayH4vU0fmBAcpG2ltVl0jUbSE8tDfGdOQFn7sPnKGHJfOYweam0SK6hA1gq6fhSZUMW/r6fNmSD2mwzPtp555rUzJlzwc4/9acr1hbnrh8/8343njElP//vWcF62k6UTMUDUB5bGtxwvdFwm6KmuEmnLgdLhsBmTOXHZVNZZ5mlQTSwW+RveSXe0+RoVmsaH5Ub9ksvlvMq79g5ltFsYBbIYh6bjsbhsEzJBme86k99lPEYPO9SUwgPHKUgS3S99WHpvef6K2vA3qYmP6u1uqKnPypj6EXoYo+U7XLBDE9sAbQ/T9qcvP0Z3qRzfcyGcRm4Smlw2Z1hNWnczd+hh46KcA2W0SZvJtMnvOsMNk/UjDJmhx6XbrR8IPWEU0o4yMBJiuHSzQ1+2Q1qMBpFVVkZbxqtrcE3HkzRprFx3DrP0pU9+TcBNS8sh48leo0p/5cn89+29kkZm/k+fjGN0V2gFwgQo/FBM4sUrwdYjE0afaGRrQsa0nvHIBZlueublnZpBW+1N4hG6TbiPIeNMLFn3Uu/ek7EXy1eWw/rFLdtdA5LmkVXvpHkkafT0oe+uqyu8dfdM1rbhPCLtyISqmcyyA/KSgm9SwzP2SD9IIW4tWQt5Wrf/TTp67iFhWQR/SWbZ3NEgoycWFsvPhLFzj1u/hDHK5gwdK+nzlZ0pArC2JWOZrYUx+q0wcn5054enHeN1IWnZWh8EfHxbhAmaMq1xRcFU51yGtVqYk8fD5N5TJRhledU00XSchMkxXpVwH1uOCct0PMwPJEww1OVAS6OdtDC0Qyij3cJgmyx7iqcU1x1sgRiUYTyqsbE/Zf4yV7Y5MA2p+TsbI+mnP2dptInJa8KGyqYeW4yTFrB4OfStTiueTauMl1WrxNqRhblKXzd9ojY9JcynPQjohmznU0pf+uxc7QpLqGMXkWlDwa1LVk2zj4nlhxC/RhtIzpPfsYvn4TsAu3Ux+TMmzZtexiuPB/qUn4jIPO2y8RjQuvC25UxD48N42W5dTDx2Z6wi5kvGY8wgOxQjIT+nbHfO+IQq9ACcYUwkMW50Hom07LCa1te8LZLXV97ebjw2P5KFGPEwVrZNl8yfrYUg9DDzZMbs+MzurbU5MU+3bSW4ACXbmmnC47my/VoelH00eOlOmOu0X4kXbOZV24e0ccaEcpF1j4gXi1/Kj5dPz1xw27ZQdJUO7IoKXdfJWsjGrUlJza1jseLCoKSweDnULDtpzyXMN6PHPeN44jnMrr+v6Xxj6zVrM0aPZ52x6SFziwi22HrEA3ibBYjqJM3ETTwrrBxom46XAi2NdjmiZGIt0/Eqcke7yi3HvvJpwuDGI3e5q8kzYJaAAKYuoi38y0K7hDLaLYA0Js2n0WaDLZw4YlTWE4Y1SdpvhRkmnRwkUiX7hBG0y3bpl6gnTuCyay5K3+27c25dot/iNJY/qebDzdNyhkY3Jju+j24TlwlV3PYux5dh5M5oklVC2nUC87evD9nd605Co722rEGgAgsRVl8okLLddmRCoyhfl8YskmKfFigeRg+NjJ6Ew6VLMz8oyU8fjaZN7Q0o4RAiw8LP5H5hhxVAMkuMbrcc6lNB5sfmQhjPpVEenqtyOSu+S7vdjvEDFF9TGI1RHk5dMo093tdZmJ1A/JZEY2L/p41lkp8VFyRuyhina2aCwCIb459tT0kKk2BjPLnOyeujT3iZOM5SwoLYbzIs1QwePqYhIF6+/YK/NMExa0c2bs3+xsY907ADfI1jwg12npHnKy50dOn2nVGyM6ycCQXS171kZtdGQOYCC0OQ7WpV0rzO0j7w1Nu1IstYF1IGW3N9a308om+9Lv3N6I7H433N2pGltegy4YQp9/ZrmUb5vrWJJ50Pm6TyOS0TTzocNlOj9HSW62Fcaq8NqsNyIholY2zap1qoqk1qZt7OPJEzZj6HnHCG1rGgjHYLwDAjADe183sdZ5O2BKnjolJcepBwF0/m5ZltTOZvzszF6TZ5utNFTvIGosmPGGB38bQONR5zYk63e7DlTEfglG2+sue9+Gblpo1TyCXAnAF283T7kApQSDyfYIR5CE06NMqxZwQQaU7l6skYTfTUTcKYYx+mffIeLqx4bttGB6Vs7U3TEnpcDQChJ1aepFEifCVAVLxA29H+9IVFdKfPjyRBlFV20Q0LYp8yH3pwJ4cBr2WAXM+IQzK6fiTM/7S+NnGyHL7ovC7yg5dbNuuDhHGbEs/E8TEI6Vouf7y0MUXHo+9KCFmvs2pYk7SutjDAptVXFxOXresyH5lX2vxgbUs19h4Bqp8ZcPs1ztgGbDyS/kp+gYGEwUb8rGDisHe9AXtPYd7omb+TUCgn72iTFxiStKFUUEvWa7b2xNuRXzOyy7PpSe5/gDMcZgw4NMb3R2TTaIfJssxNcEENKzu+yAVEN8y03AHYudcdZ+Z7mlLGxGWvurAzqrs2u0LVEnPJ11GnPmTOsZKlQK86J5nYUmC1eMrL5EfNsgVXJ4X19I520pNfgWs6Lve9yEw8ah8ZFmfILRqlYA2CRs9LRu0dymi3AGoFox15GI8Q3f8lk5EsanHUJ0hxbXNANw9++OaLVZxh9R/m3DwNpKaqgTFf9FBUDku5b+h/LsZ/SM+qictTr+Nu2jQNoMmfSs09DJm1AYY0sn6wy5C/AfywwugzedHNM5Yn0z6YEmRIxAy6dNvlmrSsbUmbsTHqOaykCUG4d2p3LrDDJSvDSyNjRAM4YZG2J9qVzKGSjdE00+ioX9y55R4ubBpkmP28jzsmojQuPZLwcHjLOpcT8zva7rx2y2YHN7eOSRrtNEbSxMly+PaZJ/rWdSr4s9onrJSbNqUuaWVT4Sb1Jp9cDhvLWc2OTZ5O2yY4zrPKif3mK4etKWxdN3GZo6l4nkkMPV2Hafu4daFz0wnzMUDxsIT+p2PP7a80r+rmb24ZwNvRWuPomsLWZhNf3tF2/b6wvvYKVQJ3f+R0+88UbEzYa5xNv6SX9Q3bz9ga5yyZ7hJK1/UkgX4mgQ6jh7YjH8vUMijD/DcFcMtCRk96HzK6Wf2KhMbkPnTLYecfEyKZXRNoCZwDO14g2pa97GOZjotyQ9NxQYr1lJeJR0y96dvaodY8alv2BFk1uUcOQL2OKxoP4wkTANbU5QHYhxqfRrvIJn05oEGkj0zH3YWSLvqyDCqF5QuGmx+c/GQ4W+jlpDIbJDuksQ1VZheEv2XZHLhE0i3HrXfY3pYzNLbpu2WzDT6iJ0ZOwA/pglR/2QnMDhN2sH7lJobuxgRkeRLNLTtkYrO+t0uEKq5E2m3bAG6ds2o0+Jxx65KY1i6atj87rLDxEzoyEfEayJyhgpawHd225XM4nZ7k9iH0sKeKiIAo6xUMty3ZnKtQY2u1GWnH8DN9TSkUk+/dWWkdoar9CfiYS7ImhPQn18WkZ4fnePokzRcfE6JtSb8kCayyCtDi9eECBlaOSzeDbw5zJpbNYfvTpju53mzcM0bCpGNMAxtTmQSRif3vxkvbMyMabQRF//Nels+ZcmbM6RqsNit9T9Vo0/Fo/ybLziYsI4IEsg+ztSepfqxv2Hh06SHWC3D7P9mSC05YHL5zCp8f8bT8PJspjNBlvqatCaW/fQLPxvV/QMrwjamAhZEMZJg0oTbRchYDXAqtJmG26XgQpq0ipuPMnLxall2ueJXQSkc0RvUrkjAnrVijJI2mbS2v44x5aMfo1NoEfBkgTcdr6ssbgfidOQUDzATnC6XRBgNSox3Fo9J+EkbNtMjmYP6mi6yzebp5GkhNVX3e3aTZRsCk5kyT6mMug4AdOO18JB2snIai1Gi7i3lWaXZEj3vgYGZ+cRoZ3ZwZDJx4zGTRlMM2DdaOgH2wicztU+guumVzab//IOCMPWom7Ge00pjq6MDhxktldkgZJm4mQUssH1mOvL+Yp07lCD1ww3id3f7zxU1kGpjmk2qVkBwW+7RpcedMPD27BsHalh+AWVhg/RaGFz0HxEz9Tw6yGctOHMtw68KEAT5NHGf83HhW35j5SoUqMq396Qsz5bBDcbw+yWGSnnhjR+HWT751hq7XAeKPbDIGKqne9hbnn2/xgebVSpO1kJlqu+OWzWs4YWzsRXMrvc0CBJbmTOYv1zjmcyRpzZVnilryvFfSFSVnjyN1KZJ9hu2P0XN8yXQnnz1i9NC+Zvt14LjMLJK5zl5gyPr8ofmb0xjvf6aJz26B5jv3snlN93BCo9vXHgaa1S+DoNVrweiZr0kCtCqh2Y1Mx+08S/GkOTnK8aQztNJnDtJ0PJpz8TDAd886qmOcWZZ9XZWgvS5dJ7DpBmyFgmq0FY2GNB1fbTTaYiSxZ65KcfwMovWuMz2QkcWTaq/hpPUeLtliRSYEO8QayI2/gWri3bTRIcSNRzcMop11DyuBQw9nLsq05rMdQqjUnG5CMXqK3NRO5uMLY+/tso2btZnJk4X5tEr5YnJbsHZMdNjGmG9RrvnO3hlnTAw74PnylHHDsZCV7gwHE3YfN+thl/VhnrxHThkO6sys/EkYMs4MxsetG5e3hf2bTMMOSkgZO75xG/4di+vGM33jlkPjkTUlixksFaCQ8cicNSYL6ly6rSzIOEkyjfVrudyy05lvQwKjMblfTdmMsfUdYrkw0U5boselMY2B9u97GRmEBLq5wFPkxzy1ww2L6LFpDGL5GXqyCQ2i3wz4G+6B9ZtMw9ssHsbmUelTvqyQ9a6zocdyNhumjeLxK09wwvx0Z3eQFs8zSfCTdl4zf9PLMM5EIv0P/3hMozFJSRAvh6654OORxqNrgptW0m/TTdaZjFplfs+azXVSF8TCYp+yPpnqLcKqEky15Ri1TcwDKx4QzYfS/Wfbw3hVLhcytrK9I+ad3LMWfch8lCZpr6Vw2jIdDwSNpi3ijdjOoYx2C6BWmI6HB2UxkJhm18RxFwKyubAFJ9QgWtk5YUmaPbq5EAm5jyFjc4Ux2oyeNM1HonaNbIA+GrmGTWzIcNubL/p+erJsOMXAfd4r6RBr9SvpQ3ZIZwILk1eWA5n5y37/M1s/JEr7SV9z7TOjGzEkacNkNLcdk7QmafeaA1YGfGOPt3c8vWnnglAXmisjTIBml+G2mVeLE3DG36v5tMz/yzSmjPsgFt8OI2WQA6LbtnzssUNR6TeZ1v5N0kGZHaYZJPRkYzj8NLK5kMaw8r5204Zlx9eZjEweP6STdTRGg6Q3i0CHt62bnlnJZKXR0EnHKFtn2FNVZB75GATWPrDCSDt61hR5iDXwtVkmJp20LbVUMnlQXynp/UoZDhNANNpsjDMGkb0IksrsmtqwtZC0I18fY3WueB1OpjEqK/nvMC8Sj415t2yXRrY2mzhZNNXsjMP7n5QBPr5Ln3a5cRqTBHrsQEodpNF2dNcEb7/QdcadC0lnLst0vBzNvhNdCqyW2mfY8QDJxEqmuhwGm3kHSR/PU573bW/iJp5JF60zIY2yLpZ23tCIkHsnXdWuoYx2C0BqtBnT6L+j7T9w2M6C2MYEEmY2zyiQm47zzZOZ6iZpucm+Y5uOh/V2N5y0t0g5823XKUzPZMLkcJHE+LFDCI9nFezEM3llWXiThCAgdHOJvZuWboB2EGcQygFMyMP62mK0iZYipCdGSzy/UJNCzJvpwZ1KqX2MCJywgNLN4rl0c9Nhl26nDxIEFvIgmU98Ek+0bew3mSaLJs4nIHBptD/98Vg7uusRGxM+enxz2CcgonOYCLHonCH08LBsbetqXJLWYUaP29d2GW5+5u9s64xpH5vueLykec3WKGrKTplYTiPzOZAugLXrFJUT0PGY5aDN1vCSsLSCfkUyjT5BrU9Qw2hkwttMllNZ/QOQPjBxK1mbZTA7kyQJk/NFdz+yC2F1ccNMOBPycQERYmEJdMfSxmn1CsHJOhOAj1G6pjjxSF8n9Gu8oACEHkKjS42HRjKWfRryeK7h2LME/1G+cTDBCKMxTSAX5u9tW9KHGepYWlNKkE9nmfyYVpkx5NLEnJllm/pJL98+h2RRnn7HZ6X2CWx6JN2p98jL5Qq7FtZ/7RnKaLcAJKMdPacVDSSzUTBzlziY5Dv5YOsuTPai78Zj5rKmbHrgcBbj8NTg0G9p58h9U2pCG6Nf/pFFc1HygOjSXfqNlGMt5v6NKa29fYfdgNEYkMWFNCOrY9JTLPTAaZdSCiebQ/zQGJUn8iQHbXZgDQ/kVrluPM7E2vFles40wokXL5teRyDjvkDGSWVt627TcsORdMfLjgQMcq1IKJvOIzeMDTPWZt45k7rOkMYIQOKRaCSeKSfetnxueg5FiI8z068iLMEzPtdeuv3KNCQsLTt4xVGpwILNGVaOT4BCD7EyT8I0MvqT18x4Op9ZrhsvXp/EPY6Ne0JzFuarEkbLPcybz2zz1QqDG8+Uk5Uho3e0yXg0vxnwFxjs+L60ABAUQQQobtmMqWZls/3DoIFotNP2I/+8ZmsPX4/idWbPLLL1LGn/YGcu2v+OkCejJjYzPayly+kzjHt/O8bygxsPdH10+yuIfcpy3T2Azw8qfCP7tctUu+d13xmAKXrc1FG+ACzts4lXJcPKEdk9afuOdhDmVxUzHZdva1vPexFnaJHX8YjGUCst+kuassfDApG2mmrdbWdqHQnKaLcA0g4hzJlZKU6SiaF7uCiSMJk864bDyjBx2ILKmMZ42Qb2/V7/Ap+mkQg3JpJfZaaIdrx4GGvbZGbQLSPeDuygxGlkhwaTp+xrt1/ZgdNQ4m6iPq2rSyNg9yGrNyubS/ttugCfNqOcBxE1Z5kzbnm8DzNrbDLOLfM3E/z4xiPra2mWzZ73YodQ3geGbrdfef/bYSBtwce9Sw8PYzTydqSHOWQ7AJu/uGkkCcu4FrI+dNqWCPmod3KyzvgECU5d6BrF5zo3b85WTlYhWAA3zP9mtofZ8Qqi3MmddUw5h2U3wMpDBmeZH0nCqaxroZXeU2cmTOLMtxsmNWQyvziN1EKH7TPgoIIxEt/8Ldc4LpS16QKi/SBP/NUwGun5iPWrQyPp62I2p4Js3YvTL+ngfePOBdKtVIDiX2fiZaTvhaW/uZCY0RMHN8v2jROeoZXe02aMHrbHedeeDOsRPZsT4X0prhvGy5FMdcScmsCcpUE28dy60DvaQlscMt8A1SCzZ7tCB2miJXJEm868jktnaFF+vifIRL07EJTRbgHYm0P5kxxg3HR8EYmnT7qrkraZJ5mIce2DGxZfVn2HvFJ5kh53UwyZCykNpwdBElZOE3eaJctKojFJg8Q035S5yHDYBVng2QbPGPWIniiMMqfhmMhyuOAbCduEANtUj5tGwgmjTsXg1iW5HV26+SE9HuafC2nmm6D1c8et+UoZP6JVioObGJb+on4NrLqweZSNbvM3Z4A43dwKILkdo/aRZbBxy5H18FQsBu4d5MD+tGlMpsc3hxlTVShmZb7ccWJqTg+7rA9lSlKXpDnDDoIyTSl/NmdceriFFR9nnJ7KhI6sHNkYtA9dtyghfVzz6dLjmIQTAQpb182ftB1jZfjC4mCa4QC8fsyBpJuWjbPSZ4GE8esW6fODll3ufxnOnaGx+VH6ZM45WTsyARPd95z12rf3eNbHWLx42ZweN21IY6a+5gyrK3T0z2t2lqJnBTIsszGS7JzhieeZR2ztYWtFlis8AWlJX79m2TP960yyY8DqqkhbjFiYFGyEWmDRPtVVLvOds7TFJkw85VVObIdFtEXFcGdopnDmGZ3d27a14XZaQJxvcmo6rmgC0g4H/gMwY76yLYqJkn2BJCdVfCNhG2qcbpsuCWtTJJ6R+eHSpT+RKaYLr4dGWceEzZz2ITlwWGU4X2Re6RsTZegyb+blT8bExumkZfsPErbzGVa2O37o4SncmOSmb8LghLnvhPrevHQ3TydPwuSztmXrfVRXSbdv7LntzQ6hgfgtjFd02zsftmOUtkDaOxyhJCybkIcfDuLpo3GWPA/DfrUOeHCQtPawwxxlYp255eaZxNhw4SSnM14OPRQ7YX6hGheCyjLsT1OuWxdOp09YGqedrR+sLZL6lQt5stDD1yMgzuS55UTjHiTM7bMsAgJ2SPcJRtl65JTDTIwJjT66qbCELDQB2PNefM7EaUwUjKaMRx+N8kAe5emmTbJqYkw+Nx0nNGZl8hAPI4wb/BZfdG7See2OZZcZ9JwLs7a3bw0n5zAqvCVrKTunuAImboJN10d6pvS0rZWfS3cpLltz+bmH0k3SMgGau5rYdMm/+ZgqwWZY7TBZF6NVlu1jeScvh+Vk+jLh9n1shGlNPHm2CnlgWbZ1z7qE6I52QOMVQ7rdtKnPe6FjQRntFkCahoR5Ty39zRdexMK5VtFdmPhhjmwEns2TLUx8s3LpMWDPadkMi0sjk1wmHp7JwpvpAOTZrBx6SNpoM3fLYJs0Z3Y4jWlaV84gkLA4sQn0BIEr2Td/MfN/28QwVp4VL1tYtisBnoOJXZUof9pfUSB73st85do+m5Z4fqYeXKMZp5HVO0YXfM7Q4IAdnqk5aOw3md532GFzOG0u0Hh0XsMJM3/HqfYdyFh7uOW4RNI2IzSG9LC1MMvhMqH/ZTAToETrevKaABYG33hk1wRcenjZ2frQx8T6xplv7FHGmMZz10JWRdJkmYUTNB5hbEt5u+tHQONJ+tw6++gJSDvyMVqhybN1VoAbRuaMicMFOohFdOkOGW1q6WQlLcdnzjnd/s8mLPO0GXUqZ4dxXylOEbRto/Wf0EPYDqqxJXF855n0fdiN56OHr2ckDGwOuoTTurB+jX1G4UyxwtqCr81xlOqSnpaNpzBPdk4ph+UIw5ojmuE0T+QG1lNe5R+qhLbYzBfrbe1igC5owOa5RRaza/KVWndKI+x4gWgP6TQNsfwk7aXnvTqm6Xin1ibgywAuuURimEkXH2/0nUiSnpr50QWVb0IBOUjwzdz/niQDf4PZpadA6kcl3KQdHTM/uii6efIDTGUHDkZPtk2aS1JlPpLetI2Sjwk7X1mf+EgrFP1tYT3vlXBIszUkpuzkjZI5wjFfHe0MoZEd5hiD4BO0+OOx/ocTjx0a4mFsXrPkpkw5Z4z2hjISqXPd/rToYWHEmZFTdoxW+Z1q56wyTBiZr3bRlLGRB4EwHmEko7ng9mFaOyZrhlmYTY9vzYwPCrZ+8DHK5ocZ3yQeW3vsIEojF+jZn7LMrMIApxzaXzwsTk/SHpC2Zhr6mDUN60OmGWZzhqV16Yn9KOuVsh6Zcpw+BN/j2F7oLlKMRv+67s5CPn588zBel3ibFUg5SYIIqdGmVkmx32TZzJzYWXvINYHSnInHY2suW69Ln2lm+YZmujbTsJwVFpC5zvrLdxaK1yVOv8yRCu9IOm7l4Obn3cPp+kjyJDSasKpctA6yPSVzXajAgbU4389MmGVaXQ6NnIW5WmBLq8wY1nIdC4h7E7eZ2LgztEs63YOfdxoBjAc+rPobgmBLyiyHYZbJeynPr6z5ALvkFmBpsHNEd8Lb2oBwhhb+57Zte4dqtFsAaQsv38DKA5NIOOPpWRg/uJU/RUkFktakp5J0cnDzH4rgoMA2RUp3cl2yatd8NJpNNt4+PgsCdrCl2sJYGfEwE5dqOWPxEoUqcOvNtNwsnru5uIXTTaj8p3VHOxw/dlofPdRUm9bPjZekHbDTp/dhdNh180o7uCdaGhD62Hj0W6pEYewd7eiFAjce0yozRtJpH88c9jNfdrx4OWzcJwkDsrQPY2y888gZy245nEb442Uohx3c+drDD4LxchhDliyoTZ/rTOuaJFRLE25GTJo7OdmcySbkYW3r5sn8A7Ax7mdiGFPNjsWs/7NeZfCPezb24iVTRqvoGY8krXtHm/eBS2NUVhxs7MXB+tW3v/o02nQ/yxgGEuZYaNE2c2mkPl8I+8TPdg459Jzimx8BOI0uPXBiJgk7ClZfk/HoFhHSHv+NzQVGZ1YBEdc+R3nEacyyDkOsKeypKrtspyqc8XeK8Kd1wsU8rBa24yZeZCYepbXNyUuBgl8N26ZKMNCR6bhrJi7NyYvFoMRkl/Fwl38gEJOfehMPyS7NhQOrJmLI5NPxdJeLsGv+/ZDG6K1vNy0Q84Iu2qwjQRntFgAzT5XjiG0sgFmY4huTuyAnajkpoyXikUO6iZtFi5NVW2zApLhpkvRkrakIM3UhUny2UPI6u2F+upM3fbY5mPAsm0tEC2sLNx7XNMEJZAcgRiPT4gCImfSZT9YWUbxo3JNDSKrQwJTLxiOcMNa28TxZ3ySNPaqdTRm3EY3uePSNM8bYSI12nmi0kw5zaQKriMZYWNKTeLTNZDxTgSgs6fBC54ITlx/cef/H6E6ypsg8h0k5pD5Z1hSfaWO8nKQDub0m2J8yTTbGljENbvqkuZAm5PHtCX4hqEujL8+Alu2mdcYPCfQKmDO2I9t7XHpsukq0uG1ryHPGikcQwJhYLiCI003oSVzX3fWRCW+ylM32nuzvaNuf8rvMMSBhJpyOiTgIjSwt83eTZEXY+LMCGyeM8qR5xMJIe8dz9NHD4hGBNx/fbDzH/3Y70b828zkjGVETlmW+ldKRujjx+PgJQPpV1ImZaidpr2U8qdEOz6i5yCRcaosNGxt5Io/C+tR97tC95crxId2WIzbYNAJAUAxwRqdny+EBfpx/2a2LoBu5nPWEWUiP3tFWNBbMoQw1gYulY4sa36TdPJOYIrroe6SzWejxLYpssjANtL0pBk688LcYLaZ8SQuj3ZrgIj3b1PxaPHcTYvEcmj2HCz9TLcP8hwvbDNbdKOnmGfu06YmFMUkzOQwm0SjLZlrXSPvghqUJWiK60zdANr+SzG2tg5L5jdwXLJKIdH4gHuYGsoOW2SSZ13HWZtnMYBnj5zmQx+jmh+8oX1lGPB5LG41Hdy5k1Qw7h3RaP3fsJWlxqJaTtiNbM9naw+oXz8+mVdKRzlzy9danGcp0mLfqlBAWzpm0NcGNZ/5229s/Jliedjz/2GN9yOYmazOuGWZtGyuD0Erbx+exGKzN+FxwWsMzznwCNLo+Wu3t0sjC4r8JcjwMkB0WKR5kGCvb7evk/ShOH1FksLnO9kISj/u7cddmdjYLnC8JNIL3a6YzjqA/Tg9jYrnllduLbjm+c48d5hViOSVEv7lgfUjWuPJ3dt9Y0p1lXvuERvxs6Bd42PT4mOpSaMjYijEh36M2WeYQMbFhmNReh2GRlnv7pSMAANOCTfF5zx0BAFutfDtiloUjtkhAEFkG7FI/EbtVTQtp2b34PorlAc7qlxP1kQ7bpIChI0EZ7RZA+kbghpm/nQWHxGVS8+jwJQ4chFFimxpQ2gS4BpEsamQjiJdjvqc50jJfUyWupAwmrS3VhZt5OffXqGS1nGcjtLgmbhYmhi3GEV12vHg5VEqd6NE9nj+pOdmkk/K0mU6XHjbOAtaOCR70uabJqUyCgCClbHO4iOUn82B0yHzYfHU1Nu5Yi8azm6el0S665STdDWTmsjSMjFvfgZwJHSwBGu3D7Ote2sEkojugV2tY/Uz8KD8/jVkY+mIAp7PZgczLSJKDaZyeZC03Wwvtcvza2WzrejxPzpC5ZdO12bfPeMaZM8fowdZti6RxFoePWWZjz10L+RruMORlza4Mpmsh6f/S34zx88VzxxSjkdXZ0OqEkT2cj1E3T9avLt1uPKbRpoIasi+Ecx2M7mz0sPUjS/2C2Kekh53NsgqT3XXGc35APB4/U8bpSVpT4uBrs7+cNLrpWuhpb0ZXEGRzVCfnjP1OdPr8l2Hi6jQX0oi/JbOb1Df2k1iBFVYk8YoWPcR7dw6WN3JAeBKPxStFC7D7kmcAAPcUv4/JGxwCAOhTNz9Mz5yuRW9mB9ip/j0AwOQNDkH+/9l783jPjrJO+Fvnd2/fXtLpPekknXR2QhYSAgGByKIICoMCDjqjw+A4M7gA4wuMr6K4j+OMIm4woI6CiiAuwyIgCILsiyyBBEIgCVl7y9LpTu/3/k69f5xTVc/yrfM7zcynx/R76/NJ7u3nVtXz1FNVTz1b1YkN1uEAVh+7B4C43y3G19GIPJ6OxqBS4U+msvwY2gkoXJkrsHI4M4XDwlKf0cFmKRxZWDF6qFHtx8GVUBiYxz1tI+YmQd03HaKHRvxn8DGNYdy9tHFKH1M4hnhrS807S5UQMv8ed+kjlezt56eb+3VstJB9Tkv+lH1SGgVsSuaVKe50XuPAvLpDlqfB1lLt1JqiCrAek/y7Xo+6XwUn+8NN1cCakkYsV0I9bkZ3akcjtmMiO2SuhxQ3mqpN+mPr1kJqspAZiPWoIqFR8rHy7VhmxHT3mskeHiEzawqnxc0/iUfWLZET6d/j5DVzEBRaZT1Jg6zH5CPbMzwy5GXuWBkl+55Fj1tnkZ0BtUf3PI2Ut+4crZ8N0dTrYLPb0/UDfp6x9cj6S3+ztOm7vKnf4f2fxjFWzrC0c0fPwFnBs7tkW0936svzkRuSbM/YMrT2JKLUdtY1ukTPOGcQmVcyliIL/Rxy3pr2kRuNo5yJlEY2B3xfWrq7f/s9gwqNPlWbG+m2tdQpmhAw7fcfHYuApboAv9bhjFjx92KkR8TeTJbf1k78nohQqTSqywvjaczBGd/dS+QB54ed2HRsB47EebwL1+KRKzsDecPRHYhzCY+/o72iPYpXzv0ZNh2e4DnH3gUAuHP9NZjsuQEXh7ux+dAtAE4zjg0UGkMAzPpefnV8uXzThUXsqKCbsXEL1Ao2dgj5PpkCfDzRQqakM0WJGYNLbcTcxB7mXnjmQ0gqc+lvJBquU9H7+k6oMWVu/Phk311b/Tf5OzOW2aExRgHiiqTn2TDMj8UfQrV0OX9Q2j4zbMYjZ/zhIj8+eqBW5tUK6dTX8fHWzyHgYTSyowxJrpjUooV2JMxZltb20ozvxLJXemsREu50qBlffi3bPpkROrRuqTz6JvcHm39m7LD1w2SUpUv1yehx1Iwzdvl6TLzw+2OmUpwME0tPZQ5ZZK8+17I/j4fVK/xm9TQe9hBbTQ5XHbrkPNOyR5ElyGQ5TGyuY3ZmKtykHjvDq9E5KuMYjWy/knk14+hIZPNvxyLGJMYi/yb/Tsh2hNfkDOePhg29Os50Be50lDjIGgUQ28pZAQMj5uCQEavXvaZB0stoJCKusm4tbysOZlbPrAEu1y0hAjs9zyx/KmuZtKX7iPTH6OKO1YrO1f9eHh/jvGXHR+pvEgKm/blj21onf6kLV+Q+nDQEplLCO6COkEc9FpRzQD2G1vfXqNTxvj90KeGPab4KALguXoijWIl9K88CAGw6egdWNkcUPREF97fseAO+Ze7vgGNlXLvXXoYVcTMuxt045di9AE7LaedSV5CfG8vrjtB4spTl1PETUGYZOzUDqBadk20kbPTdINEli5ClOkyB5kqaptsKBtk/czowhZzxTEevdX1Zz3/ShCvKLmJLFBimFLGDcsgDzPpkCucY5XsIt1KKzd8kjN+7YnR7WB03W2cFxh4BZMrF0Foe+4kdd3gyGFG+hiJkdMzs/QOi2I4xYofSYFkWCDX8Z9Bt22kax9Etf5ax6Hr8Kkv/UzonSH/p35yPfv5ra9S29fQM7SOP2yt+x+Ess/UqxpOjEXVYJDBv7BCZQgxbmoIv+rDd83Xm5cysMyq1H3v2sHryp+yfXVsaI5uZI6JWj61bf6bU3wyw/dVp9DA/rzXDxtPI1nL6m8SR6Jdta3TTNUVx+7Gws1D2LX+fdeayrCsm10t7P9eMbu5oq8lw0bYlMCLDGSzBx8iZGmzU2hniLeOPbt79m82/qVeb/1FGem1/0LrkfEWBTURk152HhD/SIC9GI5dlEiQNY4qn77XcvS71ZPQ5456VOt4Dg3gMrdAYyB3tDp4M7U+3lyAE4P5V5+H29jSsaI/gmqXP9eMud6dT+/P2fhK2PLDmfNyLdQCA1Yv3AeCPuMn09uTETDR/R/NZbLrnM8CxQ67/h2pZNrRPQOEHRoHV7kmTc4ArZKNh5GBimikqCgIRnjWBA+hXCZfogcMOVEJXglEFhfVn63hGtky5pMqBp5EpnLXDnB5MjI+OQoFH0ZNgYv2QQ5rNP6o0Dqc2Fdx+7fI5TId0Kez72MxZwg0gMhYMHdIjYISPNOoOBtM/a3RnGmccyLU+03jpuwayHnlVm82V/ZvEzdY9u05yPNFQSWWGMHlEFNax339mRgMbn6arMv8V+cFgVHmiBnQle6E6/2wvDM8rkxM1GmsGa32vM9g4OczlhG038jEkIpuHUp6pA43ICouIGjbwYx7rDKJKP5HDtQgOlxW+bs1549feyIg2oYvKQtI29en34bjMsuHrMXp88m/q70TOcKcKHMyVytqryXAu9/y+Zmczd8p5PLMlT00WjvtsY01XGO3cJm25PBrnsGTyutTlsstWTGD1PelRe11HYmt0W7z6dXOLp8DUq9yprTLou98nwthN9MjItzSgE9je227Q4kk3/iL+89xb82NojwpfAwD8U3tJF2luGnyqvRQAsL29U+FJ/D4Fh7D5UHn8DAA+NL0SoZng3rgOALD62P2eZ33dgCDuaBcaA4Dfnn8tnvTJfwfs34GTpSynjp+AIvdYMorYgcEPId3XcJRTwFoPY3iKATT7oGwjuPKtSSwbR8DS97NnKYg5ikcPfV9vTNSEeaRr46ulwU4JbqoUM4PeHXZ8rm0ZjLqRB6mUcoH6OuEGq8ftH4tzaOjcOLrA1yM1yIlDJit4ZO3VoqmqHoniZeWS8FGNj+zX4b1l8dQ+78TXHsPDrkfI5syJUZuXmoLg558pO2RNVPZR+pvEkfqwNFJlzvKn5UY+V+Y83QqhosfT7Y2vSvq3rlZVECnMtSVzSPjIZGZuO2J/1BwjNWNwlvOOGTGURoHL4XZrahyNzKEz6Pgj64LNA8NjU8d5pLpyPpL5tzSWvWBlbsWAJvPF6OFpvn4ski4J419MKTAmMxN2t/Zo1J3LzBo9jO5ZTl6m99RwR3jZE+HXbYQfM9/Dnp5h5/S4ubaEM90FFdlTk49UNsPUZW6eGm7HRy/46BxEdj563tbooetejCV/1xmeRvbIoaRRG5y+ooSJz2N7HVnshXz/WZxx9F6zUKhTW/k96rKefOp4urf96HATHr7rnXj4HHB4uhlH2u/D9mYPAOD69rxc77a4FQBwVtsZu02jnQFPbL6ESVzCLe0Z+K9rfhqPDV/Gb9//LXhRCLg3ngoAWLN4vxqf4lkoDo/yubGA1fEQ1oSjXZ1Tz8TJUpYj2iegcG/m8OGQ/l1LMWQHCffiS5jHMyV3xhOdTMhSg5Uo37bQiCYR8IMGEOHZlMDoWMhh7h/XYvdpfJ+D/CZ4xsCoMCYKAuMtO8xpvfxzhCJBDhLG3+GoW2lLvzFq+uja6j5qsPTvMXSzfZSw6/1R+rA0zjIkanu469PS7SsOZVOwT+LxOfBj4Q40Q6TUJAQeHp3zNFYVZdPW0+jXTmpI9weZV77X/foG7Jry8qjMq+eZx+2BhI29E2O2PGL84dkUdZnp1964B9t4tgBbU4yekXQP7A/u8NT1pvS77vqn/F2tPXhY+jd7vGxM9gIzbLhjnBlpA7LH0khkBXcwsn1ke+N0D31nnsu94XqpLl17ZG9Z591SdsrrthYPOx+HHHo8c8LT7fZHWztnPKyGm8rmdrhegrN9TdeJhameS9tx+4jLZjpugn+sjkNhIHsw15+9zqp4+nrSWKbOsoF12wjLlgZVBKxRD4gRnqU+g4T1bYNvS9PJyR1t/SmvAkMI+Jbmxlz/B9q/xdbDtwAA9s5twT6ckj9m/Y3e0N7W7gSg75EjAk+dfB4A8P72Ubh1ci7eueq7cQgr0YSAPXE9AOCKve/HY8KN9H63fLBNRuI3TTuj/+j8OmDFapwsZdnQPgGFPeIl9131E1sDSixTvmYpkvQQIvXSv53AackhTYQ+o3F8OhgZXz4Ihg/ZoRR8ppCPOTyHDvMxiqQULpoeW8+fDkWxEfQQPh4vb5ki4Q/4epSLKg2R1ZM0ln6H6KEGa/8PfqfejCVyfvsxexqHcFMaCb+5M0DjBoPRtvqnxMnmQI27MtfMEGWGVs0AGmO48f3qecvWt20nYSwSQ6N4ZCzyp/ydyULGs2/WIIusP5A1amiQdHBngGxb2ddEnjEaa4ZE1weRKbPq0c8A+npd+3Fft+BOXr8PaXSuOq9MIefRtHEOi8r3hB0OTZcEjnImkrHwPTNyzFl/YDS6bvU8RPOT1TH49b+HHuIr8Cl5z4XrOEP1Zp8Lnjs9hM61HYumQcJmBVuqZzOZsNr8j4Hx+Uyy2cseW2rt/Zqq7C1KI5uXmjyaTRN3ZIpUbfKCtuqL7Zkexl7fzvUMfTSSy+ghqezS+M79KQdBVG2Bskcackc79O95P675Sq5/Fvbg3INfAgDcteKCvp6OaG+LOxzuEKd4SnMdAOAfplebsQDXx/Mzjt9Z8Vp1vzvVC8LSls6ATdN7AQCHV56Ok6ksG9onoKiDgESQqaKMmiKQ+iFCkRwucoczYcU+IZNo4YqEp6dmIEjw+DvadZjWS/p6LLVNk1g5hCrCj7St4pH0gJfY6raFHjbXfg4sHq5IeFiiSPZZVX4I/TGyR+XI3AwY+Xrdez5yZ4DGJfumSuPIdWsL563fC2OjeKmdw82MGPi5HnJYzTLomYNpKKLt5nrkXq9HKfxYLD32b5LGUQoiuPLFsmkc3gHFltJtmzMY28PEqKrNa22NSnAR637tzYriJhrZHDInH5OPFs/Y7JVSHzPrTQkfq+uMzH9HP4ER/lgO1TILGGyUod3WHuL0+99SU9sLNdlM5QeTR6Pqlb9ZGmedw1UjFpU3HuCLpWeJyrO0P4Zl4dgrGDV6auuMR1j9mCUN8ne2Rvke9og8jETiSdtIZX1dX6NBGdO+vjc9zAKZE1zikJ/d4mcCaVvZX0P8kcbpKMeYGMpQOrhdOxKP52OBKcPfwCDG15BHxSZkLEGkjk9zW+DqY5/DNf3DZ6k8Yu/7AQB3LVyQcYQA3BY7Q3cdDmAdDigaL5veiA3hAI7Mr8fn40WGPyUaDgANWp1u38OT4d/BC91blnYBAA4tG9rL5XjL7MgO0Rj6uk6RJAYCOygZHqaQsZTeVInf+fPKiped/vDl91oJjSQawg6mwTEToc8UpTGvWA/jia4em0MmkGkk9jhwU94SjzRXgMmBTHlmYO4XoZCRrA1lDBJFKRLY0Iuzfr64gTAmWkx5axwiHW2kXkUJ6cDj1tSoNTrAC72v/Xqs74VKRJvsawZj3zJnSlGdRtVj/38r4/h+HWP4d+18f5Yetj9YlDvVYcrcqEgs5W1t7cyWZ8zYxXHQPTbKTY0GQ6uEjZGPNDOApoT7eU2023pdH2P3h25fXWfMWHZ3tMca39E9uskWJEsnT3g8LyqOKGZckP3PZZTpk8jw8qtfE3wvzB4L4yM7A3jEN9UTYzmOhyEZPTV5NiZLiskUtmfq9PDMMjrXZKGMq1dbE5xu5hkZZeTPMFgF2c5oZHf5Gd1VPJW114qIbwertfXzUqKu9XRwq+ux9O9iF5c+2ae82KNr8vGwhEelsgscCZyi3BccvgE/s+8XMRdafHnzd+LD00cAAE4/0qWO3zEvI9oBR7CAB+ZPAwBc2dySaXzm/r/E7x75WQDAnZuegCkm2oDuXzL/1fBCAMDeuNa8Oi5obLQzIATggmNdavvedQ/HyVSWDe0TUGYJWfbdSNuutNf9yN9npeqNTctOtIyNXjOjEdCpvtxLrX9KOrj3cnjM5RNSXgD6vmreTN5WGfnmp6RvTNph989xB5OtSZ0Tx0kjS8H19Pg5GDT82CFI5mZsmt8sZb5GIzck6xE7zke2Z4bpruMZ9x1tpoTwdw08bm6Q6b9JOFO0LD0s8t1WlBBWL/3N0hNJPWpAmz7Z3uJ7eEgBJuuWOn4I7lE880yr1WPrVtIqCWHzytYtX3uzYUzW/++cH0NrbxQ9ZF5ZZgh3WmtaU1tbT9ZVMFYP/D0P1h8/R72st/SwT12mcdC9SdYPdwb4tt90RgMzbAfXHuOZ5wVzoHo8np7B6z9UFnoaxxiIrG1NhsufGreksdYnkbnE6Jz1YJekkc8BHMxRTuRMppHxkY1lJD0Ji44Az6a74CF1iYzLEW1h+NmK1SyX/nd935jgEEAW/VaPnPXAQAx/dW+7B04IUD6Glg1W8f/Em+/Y+xeYwxQfmV6B95z307gxnqNo/9rCZT0t5TNgN576BADAv2g+lXE/88G/zG3u3PLknpxynqV6n8clAIDTwl4VDU9FfoJM7oMLj9wAANiz4WqcTGXZ0D4Bhac7lb+XaK9pVxFWXZ+yf4+HKW55oQsYM4BSuzFKbO1A7cYl8bSaBkWPpNuRSA0grgi67nLdUdEHJj1z357uUUaMHQy6eRoT0WbM4IYWXD0a+Up4qFFtUY98dZwpEiR7gV1RGJtWlw7TMamITFmhsKw0+nlVSpqhSwLHOKdq6+x4XieeMp4pfmtaJW1jDRtbphXDxkbnxhqNkTCydi+RK0/jZM/wXOv+LIzONSqykNCYaLJ0+/3PlbSu3zgKRnRiopwSPrbcEWXXI8j6Yfs//UvvGT6vzLCpG2SGHNJfJASNNb4TnZQXI+aaOp2IkVZ3tJqzi/yW6o5a43TdM7pJtkBlLJbGIQfaGJ7F/D9Jj5dRS/kTH142Mxg9CxUtfixALzep7GEwNr7ZeDiN3T/Y+cr0hzHn9WjjG2RNDNx/Z3vGBS4YzwXd6q6yrSf4qAxOgoIGTMDH6PdROff0d7QZ3bpIB+qE3DeWOGRj9dkuh7tULfe+Sz11r9k4IiTdE/kYWgSuCjfjed/4efzO9L9gA/ZnR8Z5R74MAHj10vOwGFbib6ePz+0OxxXYO9kEQD9S9tXe0H5c85U+kh+xqj0IALiz3YK7t1zb4RU6QaJxNzYCADaGAzh1+kA/FjHX6mX0Dri+3YvTlnaijQH3rLsCJ1NZNrRPQJllVGdFwAlUppikn16YM28vV5QKkKVpZdxEgR6lFI08kIci2mD8IfWo4e4KN2JoKnJlLFNCNz8UdeGKpG8LooSy+WIHII3OmZ8FDY+6ehg30uRPDdNtLYxdURiOznrFZlQqIvxpzo3B3GCQHraWmTKf8RBFuTavtl41Yidxk+hczfFj23Z1CD2Uj3ARMu6wYnxIfYxbJ5xnts+a8mVgbC0Pfq/dC0i3Z1BTWC1urnw7qUIEBZNx3BFV34MM99hH3Nj+l/g0jNBN2o5xJtZwjzV2u59R1bM01vYrkymoyL2xDjRukDsUjsaq04kQXotA0+wejKjnUXBnQG1/VM7dUVFgyrMBWUFgLOuCOadoJN/SAy+PuOypnymz9gfjbfk3mUO2Z6jxzeZ/zJogcga80D1MeAFBdwnEesEugw4qxZjQyJYZ00lrPEOmR38T2uLhjq0OJr9v7edKz4tMUU995vaCZ+p728k5EbzxHWRA2xjfALD22B68dcWv4PIH/gFPiF/A+xZ+GmG6iAvCDqyd7sMS5vCVuB3TNuLL8Vy8av7H0KLByxd/VH1vO3X5jVVXYAkNzm7uwfrFXTgv7MIcppiiwbcd+03E+fIquJ3rB7EGuxa6R9EuOvLFzIdEt4xop7bbj3Vp7DfHM3Fsbi1OprJsaJ+Awg2TAqvdk2aHdNrcCp4FvMfDFCX9sFcdNz2EiACrRrTpoUhwzzAQWUQKpL+akVaLaDPPbN2z7+nmypfB03LFZIzxTQ9u8xOofHuaVURdqWIqhz88CT1M2ck8E2uPOHRK5IusW0cNe3W8FtH2SghT+hyNZD2murMyMTJusiZ8SidXGuyayPNKHpAbGzVhShFPoTSlosDU9jqD8XUr5r8qe7ixzBWqYWVH4p4Vna3PKxl3y9NOx+zrWmTX0+PJGWvsprrcaKzPmaOHyD1Zoqkv24yRuRGoZHewthy7PStqBjkTZ2OyACL4w5A0WuxIHJprzzM/h9x5N9rhTeSerzcQdVeyK8mjsfSM4a2f1yV2VpD9wTNV2D6qyfWaTNH0RLDx8TPK0W3oB4a/jsLXKMHD2tp6VPbAZSUVsUfOFNcnMdSZjiPaBmnYsjVhjEY+L7WzZiwfRWRYGPQ0kYfNQQ9jUepCi147DYtUk9TxwXqCHPYNb/lt7Yv3fQILYTH/+7TwAM45eD2e3nwWAHD9wlU4hvm8/t614un4b9d8DO9pvyXztksd7zo92qzGTeiM5Yfd/2G8YPI+AMBXwkVYxJyYV7HXBUG3r74UAHDG0dsd3QkXUPbDlmn3ENrtcauXow/xsmxon4BCFSVI2IBQqyoNUph7wT2UQqUi2lmh8gfOGGEn02pkPTku+TuNcs/8Zva4/tJBzhVbC6sZynwsjG6u5Jv2qEQfbD2mADGDNXoYp5GvKRrdBT9wWERK4pN0zFrj5f68qEdTnvu1R5RvbsQwmB8LOzyrNM7gBWub6o2BdW0N3S2/TtD9ZOve17N4bdsMJ/NPo1yED+wTa2x9WxqH0sl5eiuBaTR0jbL9VjOK+gairQPlf7OoJFXwSNtRiiCZryzXyQqa5QxKfY2CtTyyZ+lJeNi/NZit+Pr5QWUh4XfNGLR7gcn/Gu0ez9j1w2RrZS9U5prLUT9G+tUKg5vLuEqGzZi1nOuL9UjkeqKH6wqeRi5b/XqU+IDaA2mexrFZDmkco/bwSLqHMlCU3CPneoKP2gtUV4Aj3LZLVcY4+YZ4NmSIyvYJJlO1HU2innrsq7IeXamtM+pU0Xiq8prKVjIW2raUEm0uUN0+9jBBT6KRECnvOpexlDvV5xzs7jd/5IwfxifDIwEAL7rj/8Hjmw7+5YWr8niAZFQ3GQ1Qoszo+fCh8BgAwJNu+2380NzfAwD+fPLsHrcYt+NtxANz3cvhG5d2u3ryLnhaf5v7enfFzXzxPoTLsqF9Agr/jrY/HMYc5kUhk/3rvmVf3EgrbackxTzhYREEZgzUo8DDuAejOIYWOSZZj6f5+LGMSREa5jebQ1OXKcSVA5k6UGqKJIORg3sWzxKcKaLsYHNGFVH8GX+Q+TNy/gkvaCSORJVsqUVsmCfd0l1TJCOpV3MGjE5FY4qE24N1ns12Ovm2mUZ4mC1UAa4ofXW6h3kbzd9kXbYP2X4dE0FOcNufo4fsrUwPnVdWz8I4v2uKJNlGFcNvNu6a45DhrkW07drzOFIfs2VzjWe2dNkLcBXrNOp1X5PhfE0xmN+vbh+1lS9wECOGyRNJl4K5uiON5ZFyZmyEfEj2jDK0wOefTLfLFuBfnii0WnrGOiLZ+cH4bQdDZTiI3BP9FhyivoGxT8HxdPQx9XiGjdtfZA/XMirrqefHB5M2I3WM9r+rR8qosetXT3TmbQfjjtEOWO4184wo9qhgojIQo1jVE0BlK0fdPsG7et4gT22bdhHfe+BNeHJzncad6EExjs84cjMAYNfqi/G/Jt+Z+7p20t3P3jl3NoAyBwE+qixTx2ME3oDvxoemV6JBYcrnmssV3fK8lpH4vf2r5esW7+nrlbmWdCc+bJ7uAdAZ2kxOPJTLsqF9Aoo2islBQKJ9XR0enZU/ZTtuxMj+QGC+P/s32Z5HnwyNg5/oYoenPITYFqsf+lP6ONNsWF0xIQcqQNN3R0cLyGjG1gP4WpmlhETz09JpEY1TvjQ+SQejRzt0CJEVGvnBzYzG2mE3Bkb2YU2RpHvD010zoHi65Gx+swfkWPS6rAmNl9HeKRKz123NEeVoJGuHRkjM3+TvYwzWljC8xke+L/1YLD21CDJXWFm9cTKT7nUm1/lyrOwFvkbHRLQZjWD8oTLB76PamcL5M3bP8H0tSO1x8jPKVVQ9638xOTP2U5B8LBalJ6TqvCV9dv2OWFMERiPs4HJF0pXqWVj699jsMK8r+HrsCyUla8/LQqZneO7Uzg8mP3xhU1ibVk6374vPq/+3X1MjHWg1GVWlm8mF2XhmZXLodGu2j6KuR+RbdR+MrCvnKz8qRl50n+UY1Q+SJbpFe9GOpn83fow5Ut16Xvzowf+B7z/wJrxxxa9jIR51iLpPeQWswWFsPXobAGDPmovxick1sGX3/FkA9Oe0klFdXiyXL78D0zDBW6bfpvrZj+5udnmBPQq6S+O9c1sAABsWd/U8K+OTuEtEu6t3d9xC5/qhXOb+bxPw/4fCjZDo/s6VC640WAOhmmJO6SjQujeTePbAhdrYSIOka2gsHpbwi0Of8DGNZ4yRxsZXUw4sHp4uWTt8K15TqnAGV682rxIRx8FppBENeMJr0Vn5U/Y/K1I9HayncVPlgh2eqHjxieFXcxpIcP1hwJrRQOgmWixVTGBh3LAF7EN8x7c/qNJgYOMdG/zTcF55Tn/UbS0sErrTv0dnoLA1Qde8p1vSkPqzsPTvUfPP8PgVUZUJqV9b2DqzNdlZEaPPSqGOGjKv1FnC5BuhkcFSn2P2TI2e2v6wRFZpNPCaA5anjs6G1bJFmLGT+hBkO1iC+/Uzkre1eq6/oZfRPYzTQ+QHjSDPlq1UHyK4jzuibZlB92ZlX9PzWtcbdjr7uabrh80ho5GMxK+92jqprEfXpy+Rwodf79YvaJt6gpZisHLe8og205G4XuBSx5n+wNayGIv8xFbM/YUy95IRyZAU2S8Tkf6dI+zqMbSC58nNdXj60b/P3b3sM0/CefNPwKpjE2D3Y7AV2xHC+QgBePncX2EOS7itPR37F85AaHbhZxd/GL86/8cAgN0rzsHuuW0A7s80hhBKun4Pa0IxttMefn/7KMejrr3gWRqycC7sWdFF0DcevRvzWEJEU+qhOBOS3rVpSUS0yVw/lMtyRPsEFHY4jFHw2N05VA614xP6Goet16EZn2LGhKzsW+FUBw6hMd/bZfU0ffJvkkaufM8+4GnkMtNPDnNX1yOvH9wWxgT8yIyGSBwJkiaDx6dB8kPWZguQKewOQKtQ5b9JHIlnfk0wRWLsevT8YfNfmStUlF1Gj+EF+/xVZB5ypoRUaKwZO7MyQ9jdcrZO8lgYfyiM7HUy15ZfZX8M87bmaOGOCF8PTPki5zPb6+VvUVck9NA9U1FYebTY9zdmPTLHaMHt6RkTVaQpzxW6GT3EVnGFZVgAfH+wiCb7BBlft1ym1GTmOIdnbS/YPjlsTLZI2cMSlsZicXOnE40MEuOCOQ3o+IjTWdKlaBwhzzw1FdlM5mCJrJ/ofim/M7knK9aU9kiEFzPS2prMpKaoIZHMazXjg8o9LmfGzsEY+V87+EbrKdE3ls4JnUJtKSywoUfTyFRlPFT3YXuuR8SiyqUtdyYjj0VGqTUMKP2pV7UrY0xFvmSe5muuPYKfn/tTAMCeZkuu+5zJx/Gd7Ufwnbe/Cu9c+DmsnB7A08I/4Yfn3gsA+Ej7CISmu/98Q3tubven234pI0o4uvRt/YkteXc67YSIBh+4/NcBAG9a/YLMWvYwXMlKiLh/shn742pMMMX5YYfabyEE9R3tBRzD+ul9AIC74pbKznroluWI9gkoTHly3kyicNQOeCYIaoqS0iPJ4Vk18umhWIsqeCEr++5+ZzBHIlW+81AI3VS51CAuUMkBz5V5j4cpoQnOcI9SyBERoSPaoP35g5sexpV5pYc0HUvtsxlMCbEwv8aX0nfU6Yv3nhdj5qY211xZ8fVSv5buMdFUpmRRY6flCjDTBJhSbOmhV0/I3mLjS3AeAbD1xr3HALa+6Z7xY2H7OtEzLoJYM4D9WNjakbR29Pi9leDj0iq5DLc9ckO59CtxWFjGbWms7A/2joCRMkD0L+PTtFxwBbjrYva8Tuk+YvzmsDFRVxrN6tcoxcPOBcIznxlQi4aPgFUcBJkohWekDK/IOHquO9lTn1c57sLvcfJ6jMykbYnznzrlY52PWj7W6K6PW8FqfKycKUx3AYFxh2d9f4RQaGHOALru2VhIW0Mipa+DjdtHEDBpkHlZWOhR37duU9vCa+sMyrgJXjqP/c9ADERNs+dj4oWOSPc0NgCm3e8lBZvfYWYPw5V09ELPo+58I85vduEY5vHz638NF+9+L142/9eKrtPCA3jZ556K6aRI9HdNvwVXd1+pxhfjhXjN6b+Ct95xCh676gJg8UhHY1vwltTx0q9ILMiE3771aXjsZ1fjzM3bER84VsadxtcTrh5IQ8BNcRuuCV/Dw8Kd+EY8N8+1vB8eY8QZoTOyj4aVeACn0PX0UC7LEe0TUFhUeYzRUE+Bma2s1FL/JA3AQLosUbSZYOKRFE1D6s/CatFZ+RMQBy95ndzSPSUnIDtcqOcSnN81usccOAwPn+uaQlU5FM3B7WmJrl7B7eeaKxiEj+5gIp/dieYnamtC/xzCQ9PGavUII71ykeYwymqWbEfrEG42XxE1Jw9r62GSVmCYj5LyqkNvJB87eF3hkP1VZU8kvJXrtraPiKZE13isRHtGKs+eHk2XhLMlPs55N874ZvJxyPDn0TAvK6izZMT30ZncQ2X/Wxrr+7qyVug6Y3zkPLNZKTWlfww9XjoCqNFDZIqXR3w9yZ8Jb49qNo0UxvCMdDqDOw0A7XSoyUeWgcJl8ziZkgwBpQMknqn+Eo2E7hm6R/q3WytUrleczq7pgMPbtLUwCXd4evDQ693ciVE5UypteUYdkTPsDCCyMIEaYpzKigmmjbQOOBEXoOtnF5kbAkvtlUHv1kQZn/pudQ+U0flUVES7X6gqMlwZY6YnPwLXjXqCKS69+28AAK9f9R+xc3ImXjv9Hrzj8t/DM4/+Kv7V3G/hM1u+N+OchK6fPzztZ/GZ+HB1//m61Y/DnfF0ZfiX6HUxdgvdetyFZ8BubMQUDdLMykfc8lwb58JNbZc+/rDmTiVn5KNwbYzYFu4FANw3vxVAoHP9UC7LhvYJKDRqRpQiJ2grhxVPB/KHiPypcUsUqZ5DTQyykYpk1Phk/yyiDcIfS4ukVeFw0QeuFDM+2jLkpWbRQouoGk2xiCoKBxMtXqlh88rXRPeTwAkv2Lj9Q3M8KjBm7bHB1fYCVQYJz7gjatz8s3llUc6MmyoyjB7b1h/6DAeP4vZ/Y5+/E/XYOwtDa3Ss0cCMamrgmXpTQuNQ1H1IScvtWToxauvENKZyNPEsymq0RIxfe64tk0dsv5E+hvbwOGV33Oe96NqOWkFMbW3hkXiy/8H3Iai85gYQTREF6DrzdPL5GneW8jXKnBg8mu7bOrrJl0NK+xF0k1mMhOHcYcHnStLa/U4IB19nVaORwOwZXuSH35t6PZYxWRiThWNkBeNjXadgfLQ4/F5o2WByn57G1FYadLbwdRZhKa85X+BqVmRXZZ2xAEXqd/jV8YJfpY7ntmXMNqvE4lF9mnpyD8t71rS//nf2KS/mNJgE7wyQadlyrTTyjnfqU1i2MQLXNjdg9eL9uC+uxd/NPxWIEVNMcNemx+PL8TzcjHPwj1t/WNH9C4svwKfXfFvGbe8/y+i1XIslfVvUy3e0BS+a+txImEo7jxE3xd7QDneafSTpAc7qDe2981txMpZlQ/sEFCVkcwTZ12HCgXpSWVty6Hf1GUwcYASW4ewQGjgIJD0SHwCR8ipxs0PI10t08JR5D7OF8QxU4eBpUbbfakSbCm6uKPE59PQwpc/hpko2qdcD2HE1SiGna7S+9nTaaV3hoMrOmLmpKBff7P6oKhzg96zZHvZ7hsx1yx0yNUOCRWdkc+YgKFGT2WuP85HJBB5JYWPx9JT6ju4RNPKrA57GWoS9ZhRHU8/S3f2bZ22MkYWsP0mjfFDG1q2ldHIFmPGCtx0Tia+lwbP5lz8lvfyMIzSOoQfcAE71ZVsmh6tO1JFjpOtsxJk56DgmdHJeWBiRZ/R9iMo6ITgdKVmGe3k9xhFRi14zGv3eigpfByLrLNMj8aZ6bihcxo1Yj1VdYeS6tbjrEW0mC8u/dYS0vq6GxlKT9ZZG9u+MZIQ+I8fC7jUXGqURm9oKI1ZGtMVEy9etubPM05NgKmJLDt1sdA4Zl2Is0v/BDFu5v9hjaDrSHPFdzacBAH87fRyWMOcNckQcmt+AC478GQ7NrQMA/EN7de5P3g+fijFnmKDRGuS1iLZyMBh6JC9qEe1LmjvVvKbX0oFuDs4J3Te0986fntufTGXZ0D4BhSpP5EDmB7zva5QClA+r6GBK6Av8tlcHI0KtFsUDtBDj3mdyKCb+GByMRuaImLZcQaTRFUc3Uw4IH83f5B9GzSs5CFjhh2JUPzscpG1SgAk9VFEiuMfwezCaDk+jXnucRjZunlpPlA06h6yu52NtXjkveNTNG508Esv38Oy1R/cRuf5RxmLwkLXHDrUIX6/7t59/ZqxI+iUOLns8PYyPoxxtIHwEGSOjh6zbQo8u1NCi8prFgAqu/OIsVZ4r+6Nm+LnWftOwKFctxZzJ9TFOlZoDhQld5qihzpLW3yPne4vzm/XJpEJVPhL+jHGWc4Ms7xDVllFUjVQTuqnc4pvL46jtYVPPwhKAzwOhke0twu9U39JDjW8iH+l6HCHDmeypnXF03ZI10dE/WzYz/UGehezOb8ZDFinZbohEr4vib7YuPYdtn1T2MLpr+mzU9QQttYj24D1rIh8lbDL0CrqiW7Tt/z4hY6Hp7UHS6I1qOTcqZT4CVzTfAAB8or2M0pOcWFNM8OeP+BM8N/4G7opb9Ce6hFOm45dIE1cwTbfMmaitvTbzseBw9fo+UkR7W7gXa3AoczJA474idGPeufL8zLOTqSwb2iegsGgo987zA0f1xQQlUQ6GFFu1iKP+m8Q9JIRUvYrgZkY+Sx2dZSDUDiae3soU5XHRUK6s9n9T98D030rdcQc3VS4rDgJbWNStltKbcNk+GY1MJ2bOF6Y0jslooEYjeWG+Ph7GM7L2mJJeOeA73J5GNg9jrlYw5ZspRdW1V5n/WREbFvmsRWeZCkwVk8hkVG19WxSMRr+amYxKdcfsa9pvjd+VdkNGsIZ5/oyb15qc6YDsIRxLR21vz8I9bbk882uZp5OPMTij+SnpY3X9PIzkNyrGzgh+M1oSbEyfNZliR1PNAqgojaOvUYzhhaOmfp5xR5Ifi6Wn0MjWikfEx2Kr+YfmqAOWLDSmSw1Hvsled//2G/t4Ah61vcr1ByaHCZ4sKwqeMXuratiSepKuVKguNZYX4t/q0SyDW+qZE+lI6IHyG9UlEite9CZ7mH5FRQAmAzJX7plcT37fmoyFvjoOqFe1U10V5e6BMi17Li7iwnA3AOArcbs6K6SDIOF5OepvPgABAABJREFUcNVZ+Dq2V/lTeCM/5VVokTR2sKCdGI5nZf6Zs0NmGiAC+3AKDqzoXk2/CHcpgzzjbiOuam4GANy++rLMi5OpLBvaJ6AwRdkWGoklgrJtuTCvGTsMxh8Vs7i58ByjfLP0eB7RLv1aeqSAH/JI0291jqA7kqNprLJbM2KYcwJECaEGGeAPtZH01A59TiOPfDFFiz2axHkLWmi0mPydKywjcJO2tXp1Z4DmY1ef1HV7jkcvxtDIjBiQNZrImBnRHlKKR6ypmsIxJvMBTPEyNNh+NT1+H3Y0kr3+TcojHuHgNFYdNQbG1hSFEZ4pRTIpbsQci+6XITy1aKEfC3VOkLkeY8wlgFp7Apenh9ENV89Hr8ftIx65TnvYwtgarTw2ytYJWY/HFeVke5jRbSmkcnj846V0v1b2MHWCOxprsNlzw84Puk6S8a30Av1T0UtgY3WcMTTWMppsYTLweAxbiUen787e6xKX7s+3ZeSPXeNUfxAytyGRXY0jqvFJ571OHUfuT/Vp6YafRCkzpYE4JKMagTvPgTIufT0WLVbRb2Ww6vZtjHhc/AIWwhIOLWzBXXFLpxeIcWfc/QBD/p+Y/0pKeKo4FfOSccvU8TxmT2OCK3oECxv1sFv3h3tXXwAAuLi5K7eVEfbVSw/g1HAYALBnxbl5jCdTWTa0T0BhUTxqIJp2NUWSRSRqMn9WBDEfnuRg41EOSyOJNJKDkh2Aw15zDxujaE8rSvEYJSuSilThyEOpHxqyLlOKLLBqIFTokVCuwERXr9DD5svirhkNsw9uHnXHKFgNz/EYQKMUBoGrwLgCRJUvsD3D6OZzYEtkePuWPF2ywAa/UU7X/WzeJposPePmStPf4eW8bYUiofAY3FO2tyr0uD1M5qCmfHP+uC4rinbFicX2ev+7flCmso8I7XQe6LryY2EG2RgjBJU5KL2kajXe8jOOyz0PG+PEAOFjwU3WD6PHwfgZN8YZWLuW05MqyfZAcDlcN7T8JI7i98D+UDRW9jDNqGN7k7UFOWfA16PFzZyl7FG5CA9LNLLznp+PTNbbsdQfhaNOZ8LHIZ6xB6kk7lFZNwxvqjWwdoei6W3r20rcMjLMSupPvzqeYD51PIhwMb26FMuYyrVmYezKlGdLi6LHOzYm9K5yaZ8NWwRlGFvHARtjBHBV/CoA4O7TnwIgmHr9mEVQTt51LrhlWjYcLBu7Ytxt9G312iv13JoQ5wJ7IO2+NRcB6B5EY3fBNyzuBAA8MNmMdrIitz+ZyrKhfQIKOxz8wTZW4fCN2xbuG4PDB5OsVzs8uYIwLn3PG+TTjDuqepIGwCuGHUzX1/RY3Hws4yJNXHlO/Tq6yKHIaB+rSHB+a1jdYPE4OI1MSefe+THzWrur6GgamGs353QvjFRMmDFAJobeAyR7pkoPeXyIpf/TCALB0bZD1z/IniFj0ezmcobNP98zfkFKBUbSU1PcRqVLRi/lotQuSqejjMuqM4DAcseOHoeaGzakHuOZLR093R+G7l2y+S/tdV2+N8etR3ptJY79jKSmdZBuVOaL8HZs1hYdH3wZfS4wGomM6zLQbNuaYTtbNrOU5wQfs575NbPKHDgaaw4rXZmNJeEed7Wi4nQaAUv/rEX4ZVtDdulrpNwbL1Nm70vCRlTIoXMDIXPZ/V41Fgfj2SsORUU2y38GFYlFT0+dbslH+RJ1oqfcN645HXuYsGKLkRZU1HVIJ+V3on1UWtFtjGrZXyCOUfnquByzMmxNewj+5M9gR+AM3AMAOHDKeYU/iWfCSG/F+Nw961BePE9O+C4LoKs3bQuR2UiPnrcQ8lFFry1vo+RtbprH98Dq7QCAC8IOYfgXPBuP7QIA3Dd/eplXKsUfumXZ0D4BhRmXLhLbEq955Q7qqJTF/LNyAs+ox5RdJphqd+Kmrac71ZdtLYwdQvVUK5I6SPjIvevjlLQo/jaLHsYHphiAHtLE2IWvx9rWIlx6BAXOnRm+T393juOuGYhqXkFgUf8c6rMlC7KmkNtCFSCy0FoP6v89Lhqm+s31eB3fXz2Ko+WHx1M+7yXWKMGb+vRKeSVCNkbpJ7joHjZ/S6VmAHF+k/U4xgAmwNrai5bwvr2PxoyUPYN700YFCN2eHIDyZxwvgJpxSdoy+TiCxpozkGalkGhYzQAad3WgJgO4Eexxe2AXNZu9X8dmi7Ao52B2j1uj45wqtfNobCYODJywIdcZs1bo3EQS6SQyijsYE0zQSPjI5GgHrzhGYIEVWeirDZyFcq79/Oe6ZK5TtXL/lQcE6PwT+cbPVr7OPO7SpX7krH5+MAN60IhF6a/66Sx511mTDWnQy3vf2WiUj6GZ8UWxHll6+qSQU9oyZwD0PfLcPkeGPT1tjDgTewAAh1efpXBIeuQhZfF0sGHjeyrGnOule9sw85Dp9pF8FvmWayKVfWvOBQA8cXI9LrvtTzKihOeMo7cCAO5Zcbb7CsfJUpYN7RNQmKJMU7CJUGQHAU8l8/1ZPEkIzDqYUh12uIxJB+QGcPmbrDeGntpBWfV8kwOHeXZHRVwYPeClplS7awJEUeLKTiW6RhQYRtT4aAhfe0wxcUYaXY+et8zBFM1PCR9Ht29bi3JRh4elZ0ABGs1HO5bamqD9cRrl98zzHib7Q7auGzt+nVVTXkfAGN3MGcCyaTqa+VUY9z4AyyCgc81kGV/L6W9uPBYGzjOmxHIZ7ve/VdJ4vUKTbU8NkTH7ujaHGlR3oJHxdX/z+5pHmojMNbip4q56Tv3DlQgyGINbfeuVGJieP5V9TQ6aMfKIyb16BopfF1LRHqrHDGDGnpqTt/ubrleLKHNHP+Etofu4AgcCXK7hCRjhY132MFlfOz9mt63pQpbuCqvpepRnXIkq+17kPmKvZSsaR6xHi4e+oC2NZTeWMuZho7qsCWmEpv4mxIiV33ru+q3vYbnXU6fM+J40zLgs9Nh6hT/2MTTBLxL9Zi+wS0fEWcnQXnNWocfUk3OoUsczjsLf/HkvyYtEI/yjaToSL+aQpMGrsaC0T/USbN/q8zJ/Hnvzbznc5x7t0uXvWPlw9Q3vk6ksG9onoDiliGiDdaPB1xtj7OQ/EQE/Kg06ek8zPQArNLLDytETPT2lrwJkh2xqNzU01u5oj4G1VBFIh7SgJ1paZZ9MuTB4Kq9iUiXdzcu4V+druJnyFcmaqsHGKPOo4JA/NWz2eKopymPmwJNNFTJL1xBuHnX3bTOd5t9jlLQEUUZMVhqjqpnw+zEw3AbPcawTyluyRl2p0cP6pPRUnBME0ag01v7nWOcd3zMj8FBZWMbMlCfZth+Rh5MBcRk3ex/VHW1kLIwWQw7b66U9w+3X1JiILZMz7LHQ3L7/nUXYFI1svkbUi/l/up4Hyr8VHB3Mz9e461EVp9OY/eoppHKG6ShpHI7llI9sjdeDBB6H542nkchM8zfVpyebrnGu9xAaic4kf/p/aDiVAT1sooxdgjtqmVJd3wRvGpOuW/Cwe9bKoKfysfudRkhpKnqx0jJMWCj0W881mZJpHEhvFjCWEs74GIRRnehhn/cKij/w9Ah6U70V7SFsxIMAgCO9od3pilHVixKPSB1X/EEao68n29r73fI1cDmn7Jvi6r65mUNZ79jCBvz64vflvlbhSMYd0OK83tC+ffXDlyPay+WbL1xJ1zD62A89zLmSXktZYkr60L0UhZsoCPTQh6lGDiZ2J3bI26uVEH+gFno8L/iBYepV79P6sVjc2QCCr8vny5FdVRp8f56PFK9rXcPDFQ7bQxdVnL3OWH+1l/Fh6KlFccamE4+NaNdS21J92bajx/KCR1g4brYe2Vh8GbP2WIrpUAR5SCErMM/wqgJDeFsrs/Z6/jfdh7P3MMPP6GYyMxI+lvoewvv07Ubtf6JcUjoqPGuJvG4j6AObzPCjsoYa5LPXsuzX4vZ7htPj+iR7nc4rlWV+rjrapTIo4Z7Gymx7yMh5Yesk0QrzOxujP4bHO0vHykKeQVChxxTuDKjsD9IfHwuZgyofJV5Pd22vV50To/Y1kzN83SZcBUdFFtI+y5pSBiLhdyrsdWrV38i5lucru3s7+L6EGM1EfhIrJpgwqvs20iDPEWQSLVbG4Aw5LL89ndvL6LWrRwx/MTp9H7uDC3FS9kHQ0WIbEW8JLzYs7gYA7IurMV1YJ/BA0y3mX74mPmRUJzhgPgNG6iWghKnvhyPKamr+ZXQ/wSZNwP+YPhv742oAwFnh3hxhPzfsxpr2AI7EeexcOF9FxE+msmxon4DCjSUDI2FmeqeNCjU4IIvYxfw30ueYw5wI6SkhKILc7+3/Pcv7TOmONbprCrmhp3J4ulI59AHwh50Yf2yXRGmoXhNgSh+pR42+ihHi6WFrqqa4eaWBtWXzMuaOXT7gKR8JbkfjyOhshW6Lu66QwZUY/R+qyq5tS3EwZT7RSug2bS0s/YOt87EyZUz6VpU35m+1uaZOwhqNlGe+P6o8k3qpD0v7GOWbO7xqDktTD2VOZ30bV/6U8LGyh/KH0U2QsMwn7hDkxht3yhJZ6FAfx6eqPNlVWZhKIFEXSTeXhQw2UkZVaNF7WP/UUTfGWw1jRj5bE/W9bmnUPyUeW4j0qdBd1pl+FdmOZZwcTQCdbcYFX02+1uZG08OcTkT2kXOPOcvKvmbrp4NJA7HwbPiaSV478lVtO380m87TaOljacL6e9T1vcC/wVzohhmzbKteHZf3n2c+Itn3SYxqRrf6hncaHzGKVYQ9DwY+qmxpdPs6ijOgA27qX9++M542+FichOn72IU2d/daGv5taZuKzD4IBuZ5oelhDos0blnujpsBANvCPZm+K8MtAIAb4nmIzbzi2clUjsvQ/rVf+zVcc801WLt2LU477TQ8+9nPxk033aTqHDhwAC9+8Yuxbds2rFq1Cg9/+MPxute9TtXZtWsXnv/852Pr1q1Ys2YNrr76avz1X//1//5o/pkWdiiO8T7XlEt2wNM0XxgByM+gijIw0kOOyoFBvsEsf8rfucLBDunZfJxWDpIxaZBcWSl/szAevWAKh8FN2441Gnkkhd9XHKlI4njw+HnxkW8/PtmHbCt/SoL8N7xrhgRbJ6QtmRf5s0erfhZ4xekEWyo00jnwPGPfDk70y3rd3/x6lMiHDEkaxSNyxo6lFgmtKWh0X49SdtlBy41Yp1BjWOmT9SyNBQtrT9Y9Vcr9v7mS3sHK/cXK+AiRbA7rkTgiz0acM7UsKbarazLOQusORkbPbPnPxmflROKvdPwO3WFlYxntaCFjqcFg4LYv+fgQdTqMoBFV2WNhYyRZwkPWPMkO4/Mqo7MDadAYN9epLyYfaeYV/TY7H6er52AVw53tN+j2w5kqXWlEuDDDVGSY0Wh4W92vnje0CP6wh6/UWwcD888cBMxoVAZ5qlf7RrXCU5czMh1dRsQBzZ+GpkEP9AdhnJrxpDEX0oVhrCK+mp4zj90OALgtbqWffcyZAdBzYF8Tl/e2Syp7UC/HJ8KHPw0mZCa5w66zscq4S9Hj2xU3AABOCw9kR8RVzc0AgC+2F+iH3Sry56FajsvQ/vCHP4wXvehF+NSnPoX3v//9WFpawtOe9jQcPHgw13npS1+K9773vXjTm96EG2+8ES996Uvxkpe8BO94xztynec///m46aab8M53vhPXX389nvvc5+L7v//78YUvfOH/3Mj+GZUxaVVTKrjHGdU8KpQOIQkTncDAqTLAcI9Qngg9PILoD0p2MKV2TLngqeN+LIy3XuHkY3E0Rv1T1vW6DjfyqFLkQdWD2/VH/sINBN9LVYml/Xkaxxlu+qekgo7bQHmksuacSAJe9EfXt8YzZPiPiSpRfqOyZzQGMAMv70KyZzSs+zlmH83imR4LqTeyraRV0jYqxZSsRxrlQoVu2MKM2EQkGY/DM96B4mUr2ZlEoa7hlT+H6fFQZgDRtcz444dC5yrBmSwdH5Xm68fCmBNjVnbWxCiSEkYNemo08PXDo8qWHi6XU7+qrairFPIxZxzDg3FzLWkqOHif9msiEpejkeHogeyerKzHIsO0P1jnvZePpX1FBpg+aVSbZhyOo9viYgGG9O+Y+SMNvw6o03dtW28MyraSHrq+2ZjF3KiXuh3MrzMIfrNINXsgTdMNNeZED5AeGhsaT1kH7B7xXJjiXzSfxIalPXleJT050iyiuK2hGyhrQkWBVQS5tIcZj9zDqc+zjnXR3a+02yvR4qLQyMyHgifmeokelSaeYLKtMaqbxven6BZfiWD6lbSzLc/uiesBAFuwL/PsqqYb83XtBSozgJ0BD+VyXIb2e9/7XvzQD/0QLrvsMlx55ZV4wxvegDvuuAOf+9zncp1PfvKTeMELXoAnP/nJOPfcc/HCF74QV155JT772c+qOi95yUvwmMc8Bueffz5e+cpXYv369fj85z//f25k/4wKjz7MPjyrkQZ7Fw/8sOp+RgGLuV9blx1CXIDBFX5Q+vF5evRP+fch4Snr2hT1muLGDQRdqp5d97co/q/7tIXhJsdS1QByNNV4S+dl+EBWNFK67TojBlA79iVyv/bSOh6j0EewNcrn0B3cdHyenqIAE9yMZ2S/2lJLtRzTX/q3VG4LHz1Mtq8qnIROiptIFaak1+bK4mZ7PeOGgTnMNX4PO1/UoV3h7ShHXWUv2MKN6oJbpfilNdq4aoIWTaumZwQMfq7r4/O4ufOtsp4tjMgAfs7wtTeurV881qCyKZQAf/l3CE9LBsjWFBjPGN3p/DD7Q/57MiC7qnjI+Th67YyYF0CvJ32vdXZ7abhpR4LHM8phSeoWI9bPIV/j3e/s80Wl3vivrTCHlaRL/Y3NV/+7il4nGDFES1vJW9GflYWCJtWW0ceMd7HOGD3M+JowI11Fw6OhG45ugBuNTCZpnhVeAMAW7MWvXv9kvGbF7+H59/6W562YQ3Yvmb4wLuASBjme1J7Ma+rynGRox3P01QFDYz11XOK2EXaxX3P0ujyGJmEsFV2lhOex1LMFEt9k2z1YDwDY0ke012MfLgu3AQCuixf039Yua+pkKv9bd7T37dsHANi4cWOGXXvttXjnO9+Ju+++GzFGfOhDH8LXvvY1PP3pT1d13vrWt+L+++9H27b4i7/4Cxw9ehRPfvKTKZ6jR49i//796r+HUqHKoKtTieyMMhrJQWd+apg/+NkhRNOERxyoEVyZ7/qVh2J0sKKQM9wej6ObfAaIHy7jlRBLT4HBF4p79qHPlB2MNWxi7SAn9LB5BaPHd8iUPrpOWH+k3+p6JHiYUs2NvAKQ36ccNa/mbwqPpacyW86wjp6XtYitLTH/Lbp6kVSUFEVWL+Fma8rVG1ZMVT2Cw+IusG9WpoyThRKHfhTI1tE/LdzhIbCxstDT4xU3ZqyUeZ1ND2tfU/zZHmZzSKNzFTkzNurK9hbvj9QbmGvZNoFkCqV0yA4aHfDAWgRxbESbnVEWjc1KmwzJrqrssbgrsoe09dNQuRohkAynKDPju8CYEaPrsbHA1eswEdhonqV9mGBjZRzjLVm32Zk8Yh4E8tqnnErbOs/YY2gskjo0vq5uoXvwBW2CWxmD1CAvEVLbHwTdctwqgjz4zoLkmYxUR3z35JO53iMOfwZhelSPhdDIYIk/mT5r7Ab9YJu9jx1ReDsJAQs4hjMW7wQAfKU9d4RjI+Eh6d/SESH4oD8Pp19vn0bfVu11cpVFvYKf6pGIdsKbIto/NPf3mMRj+BdH3o35MMXdk224M56m6OHK9UO3fNOGdowRL3vZy3Dttdfi8ssvz/Df/d3fxaWXXopt27ZhxYoV+M7v/E78j//xP3DttdfmOm9961uxtLSETZs2YWFhAT/yIz+Ct73tbbjgggsorl/7tV/DunXr8n9nn332N0v2/5VCo4DmVOR3i+GANE2cCJuspEVdr8NP8HiqqUJmC1PmGI00opl+lzR6UK7LDjb2MrYt9dRodvjZeoxuTuVYb3h3sNm2/ID3Sh93gLBIk6Z1Fo2EP05ZqUUAZ88/U4rYGq3irik71PjSykGiydLY/SQ00v01e89RGFFi64qprRdzfU93AebVSPY6dcCN4bevxueghsPAo/tF0EnGPWp/1GjMSk1qWzfw+Bx6GTBG8aeZSiJ9W6bfWRiTwUX2MHrIevQd0HF7XLw/HlXmdDI4lSms/UjZM0a26miPv78I6JTXMfIR8OfM6P1PYOVven/IavpO7Gx5VHWC0b0+e15rc63uug84A7jzpsDYY1+ybe38sGNJ+CUO+TeFmwivBNOPa9X3G0t5lvQwGKOHZUnIevyzVEJWGBkn6R58uIqNj5zhCZ5KGImbXYVhd6/Z96hr38zm34nOVA6uPetAeWxzo6p72qGbe9yCF6kt+U641CnYJ7FYlFvuV+ZAmzQBDwt3okGL++Kp2IP1gheFHkmjNWITng53uR8+FfQ4GqVRndvyO9p63dfnWtJjo9yfbS/Ofzv38I24dOkrAID3rPkepIR1S8/JUr5pQ/vFL34xvvSlL+Etb3mLgv/u7/4uPvWpT+Gd73wnPve5z+E3f/M38eM//uP4wAc+kOu88pWvxN69e/GBD3wAn/3sZ/Gyl70Mz3ve83D99ddTXK94xSuwb9++/N+dd975zZL9f6UwQ4un9HkBz+8l+rYeZ+xxycM85j5sGXUw1RQ8Oj6vCFhaeUSb7zCm/jD+1D6T5trSA74e+ZSKWjR/032O4A8zqsgIqQJE6jFlpYMzLz6n27dlB3K97ky6kwIUx/DRr/LafUpqpLEDnipFfI1SnhlYNV3StqVzWDNi7PgKLlnPEsnS4JlMSO25cUFglLeWbrIzCR/ZXq/jZmuK4eEysyhFQ8plqW9pHzWHkb+Lwee6p4ekiesolccrfyp6RsiuWpYL3deO7nGyo0bPWBg1bCu8GCNvpSyU34mVcyU/2zOuTy5zeSozWxN+zyRcEib/rVNZPW4qjyhuXerz58dSO1NSKRGtmv5A6El7QUTD2D6qZmgpWvqfrYR53pZ/kzFmA9HTyAxWZjQWHDyjidMzvMaloyUBWYbOUPo2xJqS/blAC/z8ZXoynu6n3Assoq2zRXq6Rbq0i+wKGS4fPpP9JWjRwYKS7UOOSHsGXBB2qLqnHblV10NZt9KwRYZ5Q1K2n+Y72jqlm59Jpe7DmzsAAF+N5wAmfVvKswzLnPCGcRcZNnJPWLHKQWBTx0NJ31bOSZqpAMcz6YgobbufX47n4RunPgYA8Lydv4GrFq8DANw0d4mjuxY4eqiWuW+m0Ute8hK8853vxEc+8hFs27Ytww8fPoyf+Zmfwdve9jY885nPBAA84hGPwHXXXYdXvepVeOpTn4pbbrkFr3nNa3DDDTfgsssuAwBceeWV+OhHP4rXvva1eP3rX+/wLSwsYGFh4Zsh9Z9F4Z79EYd56+sdT/q2/JnqpT4cbkqj6bOCx9UDdyRIGhQ9qh4hvIabCVmihIw2bD3aQWNnTHuA8LamRFCFg4zPHZQ8za+qnDK6mSJAeMYMpeNKHTdtLaxGd03ZHZqDWYYWG1/6m67rETE+ViM7bC+MXN+5PvTvdM8IaOa3WxYkollZ+KMMstr4MG7PtJEpfpU9M0JGddHiokh0bcdHtKupld+sfBR8nBB6hh5DqzpLQOaGwrhjpKiuAo/F3ZIxE+NC4rJ9MphFxMddUZ5HnJmSxlp0ZughLqbi1WTFqP1fucokfxZaCoQZeaXuuK+WVOXoiLHU5lq2VXdvmU5h2uo72r4/RTeVPVxAUvlIzylLj3fKyjXRBGDaw3J0rgEw1WdcE+pO2ho9kcIKz+m9ZhUNjQXW6jNYv/KdaDy+szDBkcfIDFFv+PGou4T1e1MYkshty+/+BW2d5l/ORk83VJ/FcAvtEs4JewAA754+Bs+cfAZbDt0K4HJhSMKtCen4bUjUXRrVKn1bjEeulQJLfAQeHm4HANwYt/cwOf/6/JBtq9dj+p/yU17M6chSx+Xd8lTYo3LMGcjusEvYF7c8C+ft/wxOO9YFS4/Eedw22Q7gAGSpHH0P2XJcEe0YI1784hfjf/2v/4UPfvCDOO+889TfFxcXsbi4iKbR3U4mE7T9jB86dKhDPFDnZCvsoKRKNTmQxyt9TKGqKLusT0LzOOXSS+kYyYNtA4cii2hTeoiiNSrFrHq42P64cpnqexhRLsbwFqD8oWeGUw4YHyoHJfi6GFKg9aMXBDehx19F8IPmfNR/s3AL40pjfQ6G72gPRBBHGJiM7oRL1SFqLOuvlrLa/c3/rlPHPW+LQe5xj3nYJ6JSj8goO5r0LzXXBJbo8wo0XKlF54aMBpk2ODrSROpSRx3hBSOdRvHEHpYKDJf/ZI21nJ5RmSWkTyofyZiHMmfGvJ/B93Dl7BrRtlvLTK53v8uIjYzOBGI0HD+NNT7atp5hTO7ZvaU/DeT316jMErB5rY3FtuNzraNcx9feGYiVehzG1xhM3SLDWZ/1+eLR2ZL5YO+lwqyz1B97qJbSw/Zmpc8CY3TLtrEK004Rg5dKVytLvVGlI5rQeEhbec6oembMqW6GZSO24C24uZxSjpGevtPjvZgPUyw2C/hUeykAYMOxHYQ/sccjx9LzkdAYDH8sjEW+5SOykxBwbtgNALglnqnwSN5Kk0nyJzstWniYWBPZ+BbrW3+iS6eYS8N9YsYn6ZFnl36VPdUrsOs3PBV/vvTt+d9fjWdjsY/3SmcAk2UP5XJcEe0XvehFePOb34x3vOMdWLt2LXbt2gUAWLduHVatWoVTTz0VT3rSk/CTP/mTWLVqFbZv344Pf/jD+NM//VO8+tWvBgBccskluPDCC/EjP/IjeNWrXoVNmzbh7W9/O97//vfjXe961//5Ef4zKCyNeoyyU09Z0zAuPEu/tvCD29M8RlGqHRjjUsc9jYVuSyOnh/PW1IM3BvkdMm5wWHqGD0/P2zFGNVUuCI2o8CEV5V2vrIuheW1CyHPv1g7pj6fQsbVcaBIjcfQnuF/3Iw0tgXuWIsiUdEoPWWeR7kO4QvnIcICt+a7I9TM2M6Q6FpA1SuURccg4LJU1xvZ1ZK3rc+PWGYkM1iKkCdQQpViPhfGiss6+WZ6JPcyUw6CUUIYDtIyJaFavwpj0Pi7/PU6ujmueF9hxRE5HrdGKbKbyqC9BpLyqV8cL3ePkwjhHy2h5S3aS5bd6xIl9/xkGhtp6JG3pXjDyunJJUn0mTUU0x+6jvu2MCCt3oHB5PTaiPbR+dIQ9KhplZqFy3vb92Igro5HLLg8r53CiUUZyZUQTM2Fyv6lUdEJj/exK4/Zj0sZlVHgg25LILpt/+pAWfOp4EwKmkkanc0WCJ2JrvAcAsH/F6bjzyBYAwIaj2tCWuPknyPxYVHp7NHyA3r86MpzqBpzdR9rvbLdkWNeWOX5EpFoZ0AW3dTBK50TbFn6bo4DesZbj0RkIcg51PUUPNOy/Lv0AnrXyCzh16X789fRJgg/DmS4P5XJchvbrXvc6AMCTn/xkBX/DG96AH/qhHwIA/MVf/AVe8YpX4Ad/8Adx//33Y/v27fjVX/1V/OiP/igAYH5+Hu95z3vw0z/903jWs56FAwcO4MILL8Sf/Mmf4BnPeMb//oj+GZYxd5CYIQl24BCBTg9eIuBLVNnWHalcElWJGsBkfMxAYGmwzGhINLPNx6Nzs/lDIQwHo6dyeLI+23acgcjv2HHlwvdX1sSkCWinha9j5lUf8J3SwOeaK0W21NaELSXdbpxSNSaKM/YBmFqUm42Je81rMMYzj2fcHk77NQ7DBH7WXtFIoqG1lHfXtlLPbxm+tziNI/cwyPyDzJWYa5Y2LHFIugb7JLDa41O2sPt9kmcT+bkXupdYn54XHQ9n79c2RjQkdZztBcpbZkxQPMexr21/jLcYt/9lPRlVYtEZmt5eldeet/z9FEYjl5lD60d969vR44F8X9dkjwVyfg8ZjoAx6EasFUl3EAbQ6LXnyPaysPrOBhmjnH8WYR+6ty3XmUwnZ1erLI2SJj2eQjd7VVt+qirVs5FUCZPOyeFrNPrfkyZgatPR4ffMzG9hOxrF3hSGW2pLjbzgU54DtEHG156n57R4LwDgwYWtuCt2Bu36Y7sUPXKdsesEkkZqxCayEeDuSQt6lKMGEdtC5wS4o6dLZmOlUoumWzzSOaHoQZ2PhT59FzzjbrThLmkExF4gjh/0eNK8HMQq/NF5v4X53V/En++5GpeJNVH8NOzke+iW4zK02SFry9atW/GGN7xhsM5FF12Ev/mbvzke1A/pQg+2EcYgvTtXOzCIItD9jB7G6jpY5J70UQdqPaItwbkdgdHDlypuul71Dhqh0fPWA6Os72Cz6ZEHt6JnxFjGKpxyzKE3lDPNY/gj1uOsA2xUNgVby+STNukfXAEyfRLRW+dtVxqSnqbHUptrtjd9XW4MWjyekWRaKB9TUanjiY+j9sy49cPp8W1B5pWPOdFd/pB/H7PnIoviDUfICj01pZjTSGUuM5bG7GGGR/SmlGKjmLD1XejxuBmMysyBvS6J5Lyty6hZmTPUeJsxX6Ipcb6MW6NaFvrITgcfiBbn/xkaDe7OIJ8tw2sZVrOunSijiuxXmsnl1iORPZHtf7Ki2P6H/UyaN7SG+pQyczCrgMpmvpblT6DMM9cfPEFZVlDD1huIw+8s8LUsf0rahxxRzNHCrjzQ7y3LRxdTf0IWepmi6c530wXdcr5kNLVvTeoJ/pAsAPqqNjFi5Qvj6k500nFQ+8xeqlvo3orO0D6wcDp2xk0AgFXtAazGEUzCKaVt4m3moz9TUl3AGNXS+E4jlLwVzok08FOX7sVCWMIUDXb0dE2Epd2aOZS4EeT98AQKYtyFnjSeEnUPynGQx5JwsOwV8kBaR4+HyTlIWmmawz2rzsfNq09DxN5Mt3wEjh6GD+Hyv/Ud7eUyrrDDd9SdNjClqPL4lMPpD5yacskUINCDmylKTFklh3n/b6V8Z+OrwMrBRHA7PhLsZCxUwSPjo3cNEx8ljRlm6RmpNJB6tcggUxh4JKUr8rCic6gUCdHeHCQ0dZQofbXIZ7XI9QjORz7XvmJN2S1GTIGx+Xek1fYHqV+NxJA+KYzsYVszVaEG6wi6x2Z3jDUkmZMGFRzyp6TteI1TRTejkew3qiiTsXR/I/KD0DP26sDgnWHyuRj1kuwM46vAyP5oK7hJf3x9z55rFjXL7UfhqUXT/Fnh15QfTEvOQjkn8jM3yoiVCjTdH7PlNZdRbP9XZAKRP9SI9dVG00P3utco6v0R3DyKV5PDuq361B25y6toJOukljpOeWT/TWVuwc2ucDTiLLSPYUnHhrxP6/ZrND/zeJhjVe4vQaMxtFQ0dOCuM4t8VzM2FO4hZwJIvcJb/lmyMjprkGn5WPpm9591arSk3YxH0CkdmaeniPbKM3AQK7GIeQDABjxIo/P6QcJCj82UCgFOzgQxbvVJPMWLDr6uT1+/b7IFS+m+slyPfXOp2ilHRBq35A+JXqf/l7vcPnUcwY+vq1uPzks4/462j7pLk16njtfl3kO5fFOvji+X4ytUwBMlZoxiOzqiHcvfJF75N/GHkQe3r1iLIE/tneh+1/H7VBiEAUBk3xkn9bix66XxLCVd4rD0RPdL6dMWCiOHAwg9iL49i1y0rU4dL3X53Ng0c3mgTsTBNCZdshohd+sx5j5kPfk31V53SZW52lwnkEwRZV7vWpof7ZPuD8YfP+7RCnllPbH9wVLHR6VWo5a2TmAjx8yMBkm/HIPjRVuJSBCZyZRYKjP739VjNpV9PeS0krSPnS+/hUWEhDm2hAefsZHxll49GCnPWJ+1FGzq0O1B6YpJwcPqzt7XdJ1V1t4YBzOiVYBZymOZCDqH7Exh+8OjJg4LD+OnAtTXJKRRRefa0VNzoPixUKOP9UcWj07LT7DKOTwgr5mRVnCQdVtZE4l+Sx9zHPmxlHpsb7LUcVYvDOzhWuo4lc1R9skM21IPjI+ICibba7qHaaF9khfPx0fdpeEeVT0dffZ0d9HrrujvVuuAgh1QAsmxpIj2wYXTAQQ82JyKje192BAexFRlFhG60/jQ4pfn3oB1OIBD09f2MJ9uLaPF6l0D4SFI0GRo75lszfXUfEHzW+ER0XQZqc7Ra2HtegM6qLVSxmLaouY04PNVaEyyIuQsCWl8I9h63mlwspRlQ/sEFB7lGCd4mWEzJkKalXRTr/vpD6ExirbtL9cjlWqRBjmeaH4qGl2XHlHNiLEVRxsSlUN/NI2V9tSgszD2KTcQGglieugnOAo8odWHUFT8GUodZ9EnnrLIFI7UbxQwv0ZTD2PT8m1hd74YH0FgZT368fi2Y43lSn+Et2w9dX+Lqp6lvZY6zgx6prBWHRZsf5juhiKfKuW9onD2JHkaHR7XrLo/htIq60gT7eyQJ7IHvh53gpam+rvuica6MwDwyk7qb4wcrsrCYOtVxkL6s3KiJ37UXqg5UNjZNa6/ylmW9j/kY2iSjwNygcqZ2qehLG/9BHKnGq/LIl+1/To6m2LMWYjKPnIUFhqlscPW3ixZyL4pn0rLxlJZE139YfmY8QzszYYYQOxbzXPiGetcj9zblnjlT0u7ozEZVfSb0sHVGzL85Vwr55LBa9fosNOhtFep4z0wvTlBo+4CyUSltyccZczDqeOzItoFKIZdDO1VZwAA9mdD+wD2krcy2CfNvvOO38SjJu8HAHxsx/sAbO+M2B63lDPMaGQ6yaZD3wAA7GyKoU3vv7O0dRFNz/yBjGinsZB6wcjxBIOeL+mwVBlDKnqt16OsW/osXyOSRjU3vnFSleXU8RNQxhnLNeXCH5Q1pdPWkz9lHaJbVJR0ckjTA8wrO6OiVNlAKPDofhG4qUHH8DAaYerNPnAg+48e9k1HbMDmoDRmj56U/rjilhprL6P3Pkvdmyn5Upgz3P69gXGRNOr4UfSXUk9bZzA2B1pZ4Q6CGh8J7TU8dI36sbB6YyMcgDa2mMFaHgDyNFIDERbmGc7qVbMXyL7s/uaFDze+CD1j1lTNiOl/15HL2fIowx3Mryk215ghM9WdVkMjc1glemDqMkOtLpvJ+EatveF9bdMGx8hXsq2r0WLbYZXfbHz97yqqpJRd2addFxUYI96TQ+uNkf+J9lT4o1Kpv4qDecwcYqS8jlAR9lT0941Ln1TODNCjUlEJjWOCE6nIe/blnGHy3vY5HKkuBqJwjGVD0u+FIafjmEwHvb/S2GSEldXzUdcJoYeNT+MVa6/6sjp6WWpxe3nG9YzStrzoLtd8oSlHZ8mjYo0wbLv1bHgrrtFIeX16vA8AcHDhNADAg806AH3quHKqwLWNEdiCvXjknndkPNd+6RXYFu7hD5IRB0HXJ3K9xN2tD3wBAHDj/MMFL9gcCP6o1HFvGLv0dvIYmnRiSLy+reiv9etE96n1z1QyL0S9xqwBSeMYeftQKsuG9gkooyLQTPCipuxawVI7PIkCA4Z7nHEq02pcpw63h3mHQ8GlEKNyUI6hu8ZbSyMT0JX+LD2R0V1rT3nGFGByWKESASRKlj0cUnvbpxTw7BXkoYddGG62Rtk1CLb2CmzE/gBXyIfmoESFxu2P1JsfD1s/hJ5UWbUlzgmq7PJon+2S7hlBp6eb8dau+9ockPERnNU9o+g+Dt7GWkRrBN0C7/A9UH6YU7knxijv4o1LoxcKsEq/7PvLkRTCCGgjQin+th6jpyLXHYWkv66dHx+7okL7JPKVrRW6N8EjtuOuRpR6AfAplBjOImAwZTX0pZpZRuqxtmztMSWWZT+Ukco+a/vIAks9Ge0bs99Sn4A1gJicqTid0vhUhNVPIrvSwdZEwuVhlnDQh+8yPSTqyqK4+vFCPS+jdZwe4En0hi2VZ+qMk4Ztv+4Z3WQ9Zbxm/7P72Oyb0lJ2sTXqHQSlLUsxnzAjTRmNGaQfNGQywIwltItYGw4DABZXbAAAPNisBQBsDA/qu+6G7oT76ubraPKHxbry2/Ov1Ua1mAP2erddKws4hi37bwAA3DB3ea6n5tDMdddnknHMMCb1KB9J6jhxdkijmGbdiD6HXh2v01Ngynl3EpVlQ/sElJpCZ//tDKiKAc0Fy2wlJGGldR3N41LUacQO/uAeeyeWGZZ1Gj2Meq6p8lSZA6IcWBojPKxW+HwNe+zZQyGpzMo0CDW4UqrQ46njrkWaxt2nhWNuPerKjCBiVJG9UFNYvXIxfBg7Gkco9NK5IWnk2SZkvmydCizhKjC/ZxKcz42nhbXlc8DqkbkmbR3duQ9dlzoO6b5mYya4hWEzfA2CH+U8e4EowGxNUXrk3hIw2x84L3WUE6XuiPVcM/zGXj0aWifs0y6WoDHrp5pZRA0/Um9obwUTOTW0M3nGzxS+9sbII8pbgjfhYTRmRZukk+Y+UeOZ3gvaESmQEFrYtE6FtVOajxu3nFdmSBS6y/9Lf/xcT7RKutmQZmVOMEe0PAuzg4mczdI4aM34FC5Du6OxhecPkxViLIMyRfCbRZ8LLZo+6RCEGaNcF5QXyvjW9eQ5LF/VLjpKYdpUPNhl31mQvO3knuWZGHePZ2HpQG6ztOJUAMD+0P3cEA5QncsatueG3QCA97aPwcG4AAB4WLgTjeEPIJ/60ntdPfYVgR+avA+TuIRdcQN2hNNzPckfmPUICKeRMOh1tklQ9QLg6LFrNNNtHC2K33KdkA5YOnl3z95Gr2sOFI/nZCjLhvYJKN9sRJulvLIDtRbtZXjlzyHcdcXN42EH2LT1YxmlKFf+VqWRHaisLXMukLZU2zXgbOxQJW82niofjWDjCgdXYKRQK7hLRe0N14eQ9qTX8XDlu7YmbOGMre8FDasaX0whMwfl8eyZhN/RM2Ie5KEv61nCx95VlfXt7zwqzeixfY2XR7YxhzEHSMHvxzBmf/CoogPV1qObf4KX8CG3R2pfaHSpiK7H+jpLhaXVDTnVau35WcEdFjwyTOi2BjB7fFK01Y/eMLor63HEXDOZUr+C4XGriHZWQsvfh5wbo2lk9QiMX1HhG509NCRpZI87srucBU9prPtLa69ON8gcSBpl2mntmsDQHLKU50I3/7IGlUew+zjt9tnylTmOJB76+vZgFlihST1cRRYpPbsERGXjWFkhxsIefGQvN7OH3ZRRE0ldQSMz1PmdcQEboFFmBhXjW/In7WFvsNrPV1me67npfq6a7gcA7I+rgEn32ng2tEXqeC2DpI0R20P3ze1bcDauPvr7AIC14TDWxge9IUkitnKMMQJnYxdePPd2AMBbp09BKx7PYNcR1B12QWOCZn6jFkHWBnmXJq6N5RCKQS4/pybTxDONM2DSgHaOiOBT2SXsZCvLhvYJKPzwtYofUyS5ojRKeaocfoweGtsZqygRGI32xeO8t23JqSrFpE8/lIpRNWIOiGGTfqUKaz40vOAepEdQM/hdzppSIw5P+ZBOKg05hAa/HUpWBTXSamOpKEV83Gxd2HrjYDKdeEKUA4m4lhrt+cs+++Zrsn3IFe3KHiZGaBpT7o99jxx87Y5PJy6zra8TjNhbTHTERKvfM37cLFLFcNdknG3rlWcmj2q0ZwLAFWgeLYKrp2hMbRutWEhYot0WbnxxOcXW7ZBxwWgcbCv+r6McRDaDrT22P1zTXNf/m60JwjP5Pdlc1/Ox7mzz65GeXQYvh42TjwAwnRYgOz949LLQXZRYgcfVk2eFlI8Gh5k/iQewaacjHZnSkCQGWWk7LtOAyceanKk6A8zelHhYFtiE7H/p7Mh3VcW+npLJrp3tVvYwo1HSyOS1PP8tPXLPaGdHIYa9/j34AjuVj2RNCdiQkZ5o6uoxA9Hwx+CG1MN62MqlBwEA+7Emf+lBp45nljkdLuHeHvYAAO7AVhzFChxcsQUAcNb0bnXWAHUnX14/bYv/Ovl9rA2Hcd+pl+K3l57LnaoQ81/5/FkaNjO+mXOiFRawtZVVPYkDvti08w5WfqdzGOU6S/VAYLXD+aFZlg3tE1CoAj3i8K170u0hxJTVesSOEDh4KA7iIQdYodS0JRVriteoCDs8cn6AcQWI0cKVS42GwVKf9lBkvGWOFna3WB7wBXfFeBKH2pBQk4dQUAetwU0WJJs/fgfRr/n6Hd2aM4Hhrq+9ohSJsSjlwtPtYD2zxxgIo40Y1p/4v6SHNa5FSMek69eUU7duW69QgY6vJicYFl037xnCW3d3srqH/QL0c1hRnt34eERbGkvMsFUpnUYh4zKqonASemwWkB3fLIN+lPOmKo8sjO/hLDvgFTyPm+HpoJpnXp75uRneb4HwUUWV1P3n3OVoZ4CXW+Q8nQFTZwJZyzT7Qcwrixayqz5D3zemhrsynrhz0r46LCNfLCW4Kq+F8m3pzvXg54Bn6EXRIvUXLajUHdgf7LOWQ1lgZYw8BXeWnGGyQpIdlKBJfbL5F9XMHMj9OhHrKeGoOTtUdDeNh8wrwxPImqLfqCZOTJ06XtZtljM57TgofSYVwrJM98qlfQCAB+IpmZ59YR0AYD0epOvePva1velSx+9Cl+K9Z+2lAIBLFr9C0rfZ57TKGLfv/wwe29yIo3Een3jUqxHRmMh3WXs2ewXQkXP3CUOhA8ozwb5EHgSs1CkwOaezjOoC0zyT/abxpD5ZyrsQkSdVWTa0T0AZo5DXokVjDFt+MPHoY8KvcROlqvX1yNlZjQowZWWMl9rSquraPkl646y7WLJ/rnCStqZu5os77IgRi0oKPzsojaLEFLKakl0UYP/dScAedklwp3ELhWxGaqxTGig9tWiY75TNAwiMOSLkvW3t4Y6qnlSUFF6Lk5NInQm1/TX+mgiBOXoiuYIRKY2UHjIPqNCdIOxl7IKbyInKWCyNtUyV2n4dlRpP1kQ3ln59ywiJ3Vvw/VncQZ76RtmRsiIQpVH3l2j0bWfRw5Qvnh3EeOYnUfKHjVlWHFqjOlrM6R7mRYIRGml//Kyw2SsJd4ej9oiPd6Dk/hisdsa5tlx2JNjgZ8UcjbK9kc2izyGjil+jKTSyjJ/aQ5zW2JG8le2Z8SajVA7PSP2B8Sz9W0W080+yxp1BP+xgoOOTr0aLdZZg9i434B2JknZHoz27xJh0RozGrZ2BdYeMHDMzTBVuAZSGEl9Thh4jK1LbVLQcTbCCbypodNFiaZCJ80xF9808rJ52Ee19cU3m4/7QRbQ3hQe148/IKAAIS8dwJrpXy+8M3efB7l53NQDg0mNfctFZbbB62XPFvX8HAHjL9Ck4vGZbprvUQ4ZZfnf8Kfy2NnAwtNt6+pEz0zb4tHOGo6vrgRKkdNKsswkYNJ4gHn5g++OhXJYN7RNQ/OFbU2y8ouSrcQVmFk5ZjyoXAwebVDhpv0z5NnWmLTE44Q2gjk5mqFeUImLYeOWyojwZGE/z40Za+hugD5xyABZ6hhQyJuCZV1iNz9GtD0+WkjM2dZy9bCpx03XiSaKGYBqTp50pRQR3orvx/B4eS6U/NhiQMbJxV2BDa09FkCw9Fbw8Zd3XrTvqPD0WSLMpyPhA12Mtnq0V9RrdVdlT6VPTzZRirwhKvExZ6eAe9+Cr/ChKg4qGMWWetKX0kMd+tPI1ZNCzGCk7ewacYLPqSTkj6GF3UKuGDRuLw10Z3+C69YZ2gDcQEzzjpvuayaPZ5wxzbDAaq3OlLJsZ8swYEkrWk29UswwrFvmWRnFq3Bk2eq5D/l8at29vzxllNAx8R7uDjTgTIPCn33NWkqlL5lrSo+h2sNIvc06M/eaxpqei25k+WQYCCN16XsWY4cfiIvZGxxl68V6vqdKpww1CD8g6Q6koo6uSngRXV0KILNVZCVBjXNnf0X4AazLd9zcbAQCnh/vV3mTOknXHdqAJEUcna7C3v9t917pHAQAedfQzODvuyDzric77Q6WON8AclnDhAx8HAPzt9HFUjg6959HVTbzwad0db4KB6QBFbkuMdG8Az87aYHTT++HR42ZZMlxCPnTLsqF9Aoo3JuvKqYXxKLCGsQhHzVOc+rB9Mpq9IVkxitiBQXDXjAaPm8DIH7iSxg0OqwxKAS1p5AcTp88equqgHVCKIQ4w+/3FWn+F7ko0S8yVfWRC0yMVCW8gsDtWGpfBTR96qcOoAsTqOqWIRxCoogQyPtafwcsUN/u38u+x0TC/JhAr0WdnmNSzUmrKmywtWUCR4O43FwCbNsjWmW/KrjIkXIoWQrdM9ZY00r3peCHWxMAdS0BE4hoPA2Z8OkfsObY/9B3N+lgUDiQcghdUoRIKJ9N2clsM7hlJjy3VCCI1dryixPtkcq/QyFKZS7WRe0HMtb1PmWlktwuDnK/ZZwpzTs3iD6snjQvmYJ4K5XnYYBFrlKU3D0a05dkjabT1NMwqxfJFd8kzmrZOopcqa4ssPq5TcDmj3oIwP1PhesGw42cwhRqFRukYZ2m+9GoFoVLuGZZZRLMSSIYNm2sWQWbOdz2e4Xkd/jxoGZ/OxNF0y61F5XXwKdiNNMiMTKrBVES7h+0O3fe0N4YDWIUjom0/FjGHm47c1bVfeVaWKXtWX4R74joAwHcvvb+jW+xBnk0D/PH8b2DV9EHcG0/FF+JFKksi1yNXFKrXEcy5wAzjDqb1wiD+X+iTqfoCRuQoSyeXECEq3N1radArmJUzJ0lZNrRPQBmnSHoYU6a7yv6frL9q6jhVBnyfrfjEQpVuYoxJYZxKLbWRjZAaam0tCuDbjkkTBlOKpHJADjXSRV83tZcCdUiRFMo3UTiGjF2qwMIIJiKsJiqCQA5KojRQY8fgra29arTYk34cSn5X6Cuv9B4YyvhG8LF+v8+Ph7dn9bwCNCutVrUlsBqN7AEgW5ghQaNuqNHjJ4utidSvhTl6iFFVzXJgPEOiW/Rn9lGqa2HMiJVrhTnbdOooTD1PpJ5/35+OAOixJDyp6PZWphDjlM2N4C1TTCVeui8zjDv0FD1sfxjZQ88esZaD2sNdkamk9P5iRkwe9lFRk9r54en2zs2K847QnQiXcquWbt9RraNKQ+cCe8+DpvQmWS+uW2kjHbo/eSaI8fDXjn0EkTmT+Pk6zMdUuO6hf8p/8DOXrFOzfmQ9lkZPje8BIx0YyPhg+4vMq5PNbE2IsbOINpPrLBruxpP6lJ/tgm4vHVHq3Rey7i1M7Rl4QysIh47K+BEyt9Dox5jmdVUytHFK7u9gsxqHmjUAgC3TPWIseq4BYOOxZGifXaKzaPCGpe8EgBLRFhkf1pCcYIprd7wBT5xcDwD4++mj0KJRDo9U2BnA7rCH4NO/m4ZFuaVMSTh46niqGcXEsOj1hADZQ3pyDum8CtmsHTUnT1k2tE9AsWuGR4YqipI9hIjBWYvOubWaDyEDrim2/e/6hUgyFnKo2SK9mQoHVThIB4TuWemyQ1HpqqLU/z6k1HZwftj5Q4jwrPWeXYqbGbukv9Q+4Q0GJmlkacKoHYoOB5nrKszT3f2NrTMLY+PmyhxXgMy80LXH95ukVeE2MG4MDq/x2emSHsayF8bSyGEVBc8oX2z++f7nSrH8WYNlPCPWeBct8vWYEZMKe6WV3eXt2gtMtk/RLzeM63tGtSXRJ2YgMhyybk2W2rug/CqMUEIHnSpwxaXVGiM2w1HjBTNEhw169srz0KNQXZ8aBwCo78lKJd3RWOgevP4zY38oum1/II1hH1Lya4peW+gXuVxT7NwbevisUD+Q+uk+xcMNeuUYIXOYSkljHXsmDKxRAc+ZU7oq3TNVJ1YPo3yUkdi8HgsZNorf4YErTHeRe0GnqJt5haTRrxP1HWQ7/6I/lToucFgDsWtfDCWuKxheMD5C1mNrJyg9BdAGo4pyJxoFL4beJljdp453Ee1C9/1z3cNmm6b3ZFiW62IONx7rPu21b9VZytF3QzwXALAt7uxpLGO2Tr7fnn8tvvWuP8h9/tH0GYpGlWFFzgDtiCj8dka1+iJAgZGKJEuK3+WedR9bdOloTHCg8lichOUWZNM8hMuyoX0CylDKY6nDDhJiDIAoQZHgYIaA+alwE5rZPbBRijI9RBgf/IHa1fWw+uGrYWxzU4NDKTCFngQsgqUeSXcKkDp8C4xlFgxFuYYeV2IGHlCElX6kxh+UIAeJPKyYJ12OmSkrfjmOm9MyHtueKEqtp1FFuehjJhJG9oLD2ytpbk0RXhAaKc/E72yd5Hpsv7J9KepbGi3y2t6k0bm0HoXyZRuz9OSoqNK0UQcK2cMsjZ5F53nE1uw3FLKZUa2ML9Edf/iIGUtyb0VdD4zfBIeAa3r0WCTMtR/gRYFxuWf3P3eW1PZlz0dwhx77Hq2msa838H4C5Hok2pxOO9b9AfJ1YpIuKeUj2D4sMH2H1ZLoYRBreeZ1IiIj85oAd/ywNGH9oKWuV5U9GSbH3OMm61E/ZiR5W+pRmWtxg/CCnc2M3yDrm0if9Dt1eFo8atxinVg+Ml2IjUXNdcFDvyaA2tqzfbL593hkf9rJo9djam/pznqP4AVdPxIPoYfd+2YG8ERGyAUia5A1IWSHqb4z7M/SIefUqukBAMADOEXR/WCzDgBwanywjMXoZgCwdql7CO3Qwha1Vr7Wdg+Znd3ejbNwj9ozxYAGLg534lmTTwEA/nLpSfjlK/4et8SzNM+k7iqj1+LsKjI38avyLWwjNqVjLMNQeQwt4RAZBLPuY7P2ZTj8k2Elco78L+ncOpnKsqF9AgoV8AZWv1vM2uqatYg2i+KlPmpEKu+jE+bM8K8YsQS3O29IvTQeBhtlkNUUG2doRXJgRCdk2VgybssfpZALTOzQT20Jbim+uELtC/2kjaiovxOpD8BZ6YmSFsoLA+OPGRX8uk+2P7iTyEYLdHQm4S7tSrpbwTEUcatGi2tGg4P53SB5OyEKjMRNeVNhJKXRwZisGFY4h749XjOU2b5MuGxdH3WtObJm40Fl/mWEpODp61UeKVJz4+SeXHulP7//K7I5761SL+OV9JirOh1MKl+pvd+H9chwVG2rMg6kLR1LoYXKmYEoOZcztb2ux6yMOaF409RxdUe7jBHwKZ1sTaUy9AJ/BMs28RFJGmmG5o2NIOkIMnz72hkHgtvITOnw1m90QNVLfXYVfdTNXssoBpSfV3bGDb1hUpU9tTNF/KFc//Gy2cN0ZDDDLY0oa3zwTrTQcdQXD8ihyeS1XnsF4ue6tGWvzsuzntFjHXpSBtPvLcPIHiR6Eh7dHtBzyN7PYPfDbRT4dNyP7937P3H19AasxSFMp9NCY+YYMd6VbtfB1kz3AdCvjscYcXDSPWx2Sru/jM/MNQCc2hvah1dszuOetsAubMINC4/EBC2eM/mYyQIpY3l08zUAwG3rH4v/d+lHcDiszn3LwIGFJZoAa7wXPMwI9oa2v2XdEIO8gxEDmNzRJmiV4a94YfqEmesCM/VOkrJsaJ+AwhQOK2W5YsIOeCa0K4YNwWF/S/RYASiVxsHHuaJXOGYpnENj6ep6GONPJHxUYyGeQkljKkN3iJjCaftk32tmD4XIsTjcqOAeWCfKa56FsfcKyz5phIzygqxROtfE1ULmP0cfyByOieTyqFnpVz2uQpR5GN5KJV3STWkkMLYqZimNUllhWQ6sQ5ouXaFxzD1SgPGbKUrDDiLRG+VD6sPR49ZFRT56qv2aaOHohtrDvk+pcKr9wRxeLHIq9oxVWLXSV9/XzOCUNDIHQQfP5DhFm4h7oxTPkjMJd6GlZtgAvUJF7mhrRxaZa0sP2R9qrysl3fK7FMnHIgsrd7SHaKRzzT/RSPd/pifh9WeCNbImRjbLNM9unZn2IPMv5kuuW3/vX8r/+pg1PSSSFsx6Jn3m8ZHzka1lSeGo7J6oaZIw2ucAjEWQtQNd7w/FM3a/u/HrUfdZOSvY2dUXbXwTGeXG4ve67JNlr7G5tn0mRFo+JvlK+hQwEP4U3FKmAK+YfzOese8v8DtHfw7Xr/wPeMH1/xZzWNIRWzHuvI9aPzenLt0PALgnrlPnwoGm+8TX2t7QluOWPDt12rU/uGJz5lDaH59c/RQAwHMmH0Nsl3KbhfYgLg+3Ik6X8O8m7wUA3Lf6gn7Mck30xjORe0AtiFLm0H0LO/jHywJ4lNvXE5+HFWsxEEuRPs5ZRBddU/Jb6NlhIeVealuxDR6qZdnQPgGFH8hWyjLFpBZJY/37tkw5kD91n1b4cqXRllp0xRvAvj2LfOb2DA9r7xQlHkEaMp74IVs3OFL7VPRnJfQBKBVyTqNXviTd3hgsJx2PhnmPosYjxi3m1UaG6gqQhvF0YrYmQAvrc9Y6Y3fQ5JjzwSsUPBu54Hij+skiflKBZhHbofGx9HY1PjZytrfA1tTwGh9bTypF3hlA5lUoNVIx7WicTU8tk4cve09PKmxelfGVInFEgdG0k/EIM3/oBWW2TpgCLA13qavI4eUoJ3GWSXk9K1U7IWePONGUzgED2K4Tec82laHPAzInSPWxsFaPTxk2aswaLzCc3SOV+Qi/RlnqqKYxwfwilTD2zetahlU2WMVlTHYOl/UjI3Z+/tm6ZXzkjhZpaIt6RsGX6aCsvaY7k+11CsMLy7MMI/qM/Fv5veCSxWZjFJiZL9G2UeeHoRtyXgvdtl5qn8rguAX9+ksGaV6lxWpwQ45P0lj2QqZH6AoJhyzcKS/wJBqpo8XLD1mPGbHKydeDNxy4Bc+efELRdfqhr+OR4eZOx0F97UbIPRwARKxfuhcAsAsb1V440PQR7T7iHaNt2/W4rjfUjyxsdhH/f1rzRDyINbig2Ymn3/yrACJCCPi+na/CuxZeiZ/93LW4qLkbALB31XbH27yHVQaBXz/SMC51hWWLxFs4WNWoZvXMXg8exWAknTlMeZ9lf9m2la3+kC3LhvaJKGTVMCWfGSdEVyEGZyX65NrG3EetT2YgsgN+GM9YGrlxEYXiJhUlH5ke5tnwnUYi6CrKJStTJhQV7kyh64Qp5FIJGbofLhUBdo+HeRQ1PdKo7mD6jl1qW4l8MuPL0kjWBOj8dXNN21OeabpVCh2J2HGlzyvAEgfglapunUXTnitKzDHiIp+V/equiYCt2/I3DWdGMF8/QxkoQ84pdk8+4Vbjg+cNMGRU1+lR46Ntu9/po4IkTVyndJa+BhVE5UzwRprmRfeTrT1G4+wIold2tNMpAZlxUfgo6yFqmJp/kmLM7nwGMcb6PXJLj3eCUWNQtC2i1Y+ZnlGwfDQ0CubyPcxkhZQ99fFJGDOoAlk7cjzq0SOlhOq9Wb8eVYfVjRC9t1gWSKdUd38oqePc8GOPeDVk/TRqQepSlf8OFvPfHIzITYdb7HV69URQmPcwkQlDvO3qFlpYur7ah2aN8/vYw2uUOw29riCdAVJOMEOJvkMjr2Y5+Vg5Mw2NgF1THey7bv4FAMAtC5fivyy8LNf9jsnn3F3eIu8TTPNnPQ5gHosAgD1xg3KWPtgb2mv6iLa+o9/93IJ9WBkPYxoDDq483UVijzZr8NrVPwYAuOLed+PJzXWYj4t45P4PQpaPtZfj5i3f0Y+5wIccG7LItH79UrepB/YYmq/XND4qreZffLfc9ld9IA1e5qp3MaSME7qmbeulwkO7LBvaJ6BQhYMcJL6hN2xGG7tMiYjpp4UTQ0vQzWC5LSF+lqEk27JxMwE/9kCWgnL2PbAirFJbblB5GiVMfjorlSwwIrxhU1G+s0JFDjA5vqzMkcM85P9xPjLvs1Q4hh5iq68zQuPAmD1c/4EZZG0sgp/fLZc09oqEODAyzwYiCuV+H/o+vfE1ZKizfU0dP4yPbnfU50DSqOrClzEZMXTPQNItaHT7oxaxY7QMyx5JD4002jFD7mGiCBIDmBkSqr0YI/vaQvG9kGiYMiTEmI2ckXKPKcDyrpoyGgk9zHgv/Bk28ofeOpDROXZ1SN2TFIiVwcLOH7N+aDqxgCnD39Jt1jxTELkyJ/hj6Oa4vWFTdW6zeg6HjjSye5eSbtsnM4zZNRrJn6FvGTO6AVQfPrM0MkctdQbAz3VtX1tpVodpaNFxdJ/UmSwOvlnXqFhK+JBhyzIsAH3tKeEu+8g7IpQjKkc+5Rnn16O+WuXpkW8YFP4kWgJspLGr62UPh2Xm5NKQdUJf1Q4d7jNwH7YevAnTGPD6rb+MD614Ev79sZcDAJ47+SgCopLXNPtFjHtr2AsAuC+uxTHMq/W4v1kPADh1aW+GWefdRU33aa874mloJyuVbpfG/I8rnoQ3Lj0NAPDGFb+Bdz7wnDy+W095JJ579Bfxw9NX4tj8qZkXmT+N3v+Jblv0Pky8JWnigUWgg57wvkcGcYYyfFNPncTT/S6OM5eOruUHHIzpDw/lsmxon4DCDRavhHjFnx0YzNhhh/7A/U7bHl5ws3utMdaiYbY/bzaMjbqnuqnMTP9mfHSKLY/s2UNRG1SpP9a6bsQWnnk8jMZZdyedcqEUmAJnn75gd6wg1hm/MyroNmOOYC9Wkzkgbdm6zeMhMMs0dndWzdeAUtzVNevboxD7w/KHGAOiTz0WywtmxFT2kYURflsaJbzqOLD0WJha935NyIwPuHqMNwNpngN7eOyeUTC334gjCdq7noq+Eyfx9O1VhMSvidLWK8/MIKMPKYkTmBmDWgmV47bymp8fPpuCyBkxZtkfyLxIQ4s69IjxlscHz1swQ1IaF0RusU9+KYNVIPZ85Aa9jlTX1yOTo3l8ZJ0INlJYEHjkXLG1y+4Cs5fRm8FMJbZn/JgBqRR7p4p89EidZ4QemRqb1xlxTqVS02eYTMgDML+yaz1FTrHznpzDJH176BNS7ApGR4uUM3LcddzMCcKyZJhhIjPikOew/J2mt4t0CutU6WgsMDtudWVigI9QspXI5p7ORzc3AQCuj+fh4IqNCCHgI+2VWArz2Bz24/FHPqr2te3TOoS3hi7te0/c0I878Szinv7zXhsWd2VYHnbPi/ND9+mum+NZ/d7s/my/bvDB9pGw5V3Tx+KPLvg9fD5eDJltqNbEgJNHls6GLXtJ8kzVYzCBW8FIPSvXpWxV9Wq4kdpLPGKtoGbQByGnuP7wUC3LhvYJKGMiudQQpQaikCyqP4KDVSS4ZylfQymvszz7qp4bC99Q+vEQodCbHtp2+EAejmgPH5TswLDjsTRqJX1AkRB9DqZfxcp3mZMSKr3mQijSlM7Bu2VC0SYGq8LteDGgACmYj3BZPMxYKli8ElKNmvRtAuEjUyIs3V45JVEFwp86L6wS4vdwNa3awaKiUcLHzFcHM/tIGgjZACp/p1cj5AuvhjeM7toY2za6O4Mgez0y3opaMqXTGjZAUYqocgmdgTK0pqRh45RvQg+Xox4HoI1qm07YKAPRR5tZFoA2YiHGotcjU561g0jUS/wSihuT12wNcCVd0pPJ9tF0ZUiWeZGRnWwMyIiddBxDK3jy/KHGkiBIzkPhoy56nYgxk/FJGZX215Qoz5rn0vDTfKTrTPyfXaOSY86Gv1yP6tvjmsbGGA00Sg4xD+COKCnXM14m9wB/FuYRlj+keRrK+lMR3yz3GB8LHn72+LbKEWVoknC29urZDxbGZRRYPeh6kh7tiO7pgz43Ydor/YPuV0k3WXtGHkl6kiF6SXMHAODL7Xl5ty5iDvev6D6H9X0H/kxnqrC9kOcLOL2PaO/qDW0Zid/TdIb2+sU9mGCq92aP+6zQ3e++K27hRmMvez7dPhx3rrkMsnyivZxGhrXT2vABcAZsosfWrRnLtrnELfF6A9oHaqjR39dyeMw5ZWFD0WvpSLD7/6Felg3tE1D8QVIxJGDrVRRla3BWDDJvZ/NDSBoI5SA4HuXCHmp+mzAFn6XfZtr7whQ/jVsXeqdJCP1AxqKjVEV4poqMRnbHjitu3LueFRN1yBIayZhzCjVLB6t4BYsSKg8hIdSMosXWWcTIjAZm9IEXyTP9cjjp046lppiIQ7arVyhgkYI6HqYg1GmUj+bo8Vka2Z6p7HW2r+HngTqixJpi9Fh4R6Pfbyw9UaVaWhihr4yH0JNwE6NKj48QLZSdUlfvI8DPAcDT2CS/uOLmYVJhyN+zV3vLywTLb0CnDsPsYf3ZFClnBCsYexIe4iDSUfxo6pW27CEtHdH0PKuljlPnhFtTUfAMgh7dVl63UdEeMS9WabSKoFv3YtyK35aPI528Uk7INaoNm+53GUGW9bw885HhTgcg6yzRIx2/dt1Cjq+MR0e0PI064m9xe+NUOuU4v2V/0KUiM2HqMtmWcbs9488P0PU47PhhbwvofV3oYCnquSpZK+zsiZD7tYybZVMwuWf1OglTcy3pVpk3fozeeedlpjyP+HfvAyYh4tJwOwDgxngOIIzB953xQgDAadPdmMOS4EWPh+3XEHA69gIAdsWNql6MwAOTjTgWJ5hgitOxV51dyRA9I3Sf9trZty88Q093x7ejWIE/vPgP8DtLz811Pt5eJq6yCJlC7u2zz/vJItur74w7Q5jc0Sb1fNK5N5wTzOEImnbdp5TPhXbARuJtW2+knyxl2dA+AWWsAu2VS546Sts6hbpifMMrZMq7xu5tkQii7JQZVQzHmDF3daXiJup61BUnRv3AYd7swUfBwDe9hPF0sNSeR+JgaEzjUXRHj5t5igEe0Z6y14AF7TyFTuLSdPO700ShFv+XfTHRqdaZOih1bZWWL+/JpUNRrFvLWx6RYHFzo7CQKIdWTpni5+m2ytPx3Etk+9rX5Pu95nTyRjpXLl0kTvyfZxAUwmv7emj9qPuLthA5o+8bl/FlxYulJ0qDXETsBhVgoaTLOXSGDfw+YnsrCgZJXUNFPjIsEVNgKnKuDCgzry2fVxcNBV8n1iBDrj2gkAdbs2LEEIM+EJ7xVF1RL9Eo2k/NvHR4Cm+H07ILcuYMGHoMjb3HoPZWxYjN80poZAZU/oOpBzdfnGepzMqw0HKrzEMq6lOAGVba+jkk6wweN1vLsp6EyZ+F7oqjjsk4e/a0pUdqILJ9LWRPlgmNzkpIhUeqy5oq8rXgcTKXjoXLQru+JT1Uf4D/JF4ND/sUJHMQqDVh6nV4up8L0wP4y/BTeMrkiwCAG9tz1L7+ytprsT+uxjwWccXiDUiIZOS0AwmnSlNSx3djg+JFjBFTBNwdNwMAtoV7nNwLATijb78zbuoM26QvCseo3O9vXXoyPjf/KHzg9H+P2+NWIXvEY2bkPrY13G2pnQu2NMHDpZyRMFqPGumeFoZeOjLlvX+boYVAXAnKcXNylWVD+wQUv2jYMU0U/0hqESVWyGJdjyjujCB2mGtFWx6y5ADsf1fRB6dpMy6QAWLg+7bu8PX0qANVOQ2igVUilxZGjIOEB65u7TA3dBMaaXTWjY63BWQ0TKb+DCtF2hnA5toi52Nx1cgatfSULtk8sGhRWY9DnxXRii1T5gtzK0P0aZBS+R5I1WV0Q+6jRta04xuOkKWScFI8BOjXc2VN5XoJjzfS+J1PYswxPkDzVtNt+9Qef2Agiutwe1hqD2glZta9bR6BTriJ8UX3P3PyiPUo6SGPeLF0YikYdDaOLlH8X8szBmNj0TJT4gmQToeCfJgXZdz0s12Nn8OxjzDJyA67vyj7k/NPjQFj7KiMmCG5jgJjc00N/xDEY0hpgKA0DkVYmSykZziT9UIK01fwBT2JxqYxxmCaQ2k0Ot4yGjluu5z5VaaofhY40X3kulfOEitTiNEo1zIJOjRszPDnsDRitJwSdJOzh8oUeNxeXnNZOLVXdVB42BlkXgeQDsZyFiLDMt1qrutrb0JwP+2238AluD3DvxbPRhPE1YoI/EN/D/onD/w61uAwpIO5JmdK6vjGPEbJs7viFgC9oY1SAgIaRDw8dKns34hbTeZMFDULb3dgM355/a/gQ1v/XU93WWMs2ivvjCdes3RtKxd6EDWMx3zKi0XDG2IAc+Nb/zRkqvFIJzE9z0Q79enWk6gsG9onoAwZWkOwlgBZHI4ZgzSNNR1Mtr1SyJHrlLuTmk5L49BBINsNGU8WnspMA4GM0Y/FGwj9Hzzd9vAD3/T83lXhJPPsc+PEj4XRU0guADYvzJCQdWuefW9c1hRJS8/wegoDayfBnAGlaBym2yl41fHpAz6iYvgTniljmeDRYzEwMe4JoUe1ddTw9c0KS63WxqlX3EIYqCfXLYnsa5hf89U78AbeMt4yGVdzGph5ZXdiE7wjUjsOUj1pDKSis180TPJCf44vav6ArR2tzDuFHJ7GECR/SCSWnh9Emc//g0o7RcbjDTf6dYOKQ69h86CME7tWyiam+5UYNjIiNfSIU423qVDDRsCYocWcC6U/LzuYYZv6TPQwGoXo4edZNk5FPSLjhsfi+SDl9ZTQoyKfIlXf7k2WJqzvNddx12Qrk5lpnAoO3762fih/8lg87gnZ6/yTqDZKbuZ1htyj1xbYHpY6BZMzua3Yw3kOJKzQqB1MnhdDdM/OIuxhJkjwwsnf4rJ735thX1r3bXgQq1Ukto3A65eehSkarIv78Yq5Nyt6mKOuCcgR7XRH2+oKxdC+1+kjF4QdODUcwhEs4KvxHMUflkXIslKUzCS8pW80wBfldBB9smizM2IFjbburLYNwZHGZs1ySaPWSe2Zy9Lbtdw7mcqyof1/odSiD+7AiGTBCUGg6rmDkkWAU/16e5fiARsZZjRawesHI+sxvLLUUsd5n74tM7TcQUAEtFT6lbCYQePQq5Ey/Z89FMMUE31Hb/ZcAeLOTgMkcSWbMgWB3bFiirakxacn+0lg3x3OdV2fYm5IWiZPjWM8SzjEvEolxCgmbHypfSo6TdjQI/4vx+eVwWElJNdjMLZn4NdEB/fzoKZmQGmcpSjNiiCDtXUUal5IunOfA1dUuNOg4OFGg1h7RImRdwO5AiRopMZyUcgs7qEU7Ag9r8yolgp5orHwws+XXHvSiE316Oe9CB+HjDTAGF9SNoOM0a0fITMHotcsuwfKkCh48/yL8djXgLs+S0X2KTh2z1YZ9GauE52OHruWW79GFY2BK+SNYCSNkkdPI5ePqR4cbOh8TH1aelTkU+4Pt/aYrPCGJFsT/G0JJvMKLlmYHJY90AiyWvdEPjp+8/UdxWQnXsirDGqNm7WijTyyRtVcE1lI5XVfT15byPd7Nc86ssvE0tRxSHnmaWRv2wjVg+6FjUfuxM/MvwUA8AlciXOPvBlv2f5LXT1Rt40RN8Vz8JqNrwAA/MvJRxDblvBRO/lSRHt3uqOtstKiiWgLHScEXBB2AABun9uOJcyhaWqGZPc7+0Qfd2KIdW9klGwri+UFkKLkpl4wASXTVtZzxnfwxjOrF9wvoi40jczBEBjd4hyuBRQeqmXZ0D4BxSvQPOJrK7YRLoWSKbH0XjJRVtO/GW5nfDGhD3YAlg61QPUHJTsQ2XaiadnkqJUHvG47dAj5A2NYKWaYNW8HU8cpbgITE8sUyTI+YVwKOtkdK5mCLwVdzlQgnn11B42sHycA6VwXmIyGTYmHSc+DVELKQWlx05Q1EX6m/IYds1dMEu5UeFQq8cKv54RLFknjbANoeJ0lBBRv9I6MqsMqwRq/9pSinOaQKnhiLIaPXNHla0redZavPA855QLFTeZaGjZK2S10dh16Y0f1qfaMp0cq366toKf2kJqjB1ohL/XI2s2vv3OZMqi4M6Vf0GLnWo0x5P9VlEspU+ryUX7yieFRzjcDs07VYNrKlMVZ0eLAZE/w9dhXB9j5yLOkygDZvU2maMszks5/5iMxtNh5RNZOotPSOBUZbfZM0fuIrHHBM5nKnDOn1N7ya9SKQn4eEUGY6hqYzqYoc5NKQxwRNFI94LDS7zYUg4Wl4Esa2Tore4E5J9he8PyWTGDXaPRjqj0OgXtakYVlrnV/eiylIte5Cu4LDn4h//4HzfMUbnYl5POrnoApGqwMi1hx5B7PR7FW5uMSNof9AIDd9tXxnsS7xB3tTHvPi7PDHgDArv51cik/GI3qnEHiTzlngpGZlhddtSL/FVzyItbrBoFnEAYWDfcp5umfEp5wWirFsaCd1v1P6fAeMtL5zn7olrn/2wT8/6GMiUrRCCJRvflrp/qQnUILY0ePbS+wSAOzFX1aPAp3//vQp49YamvXXuO2BgPzmjLcTSgHnVfIhfBMQkBEGoYOKzbmNEZJdx63gaHWZw/j3/D2B1MeM6SS3gneaYzqYQ6nXBolPZVZkUq2bv29ZK4A2TkAaVvGXZST3KmhkSk2oEa6V55l9FGu0VRz0gS00yjgus/afNH0b7FnkpHtDNZUGZ3COe3HwfYlM575XXe/v6TCStOOJX+YUS3WmR0fS21U9cQDWUO086senBd+f7B5KUTqKHCiMdEpPvkGoTwR5VuSQu9Zq/1aFK1cz8yBoien4Eao12kTPSQSL/cmm8NyBniHjpYffX9CtjLnC31UDlruFRq9vGYZKNqA1jzjabBMPpa2QTCI0SiNLO6oKcy15wJ16Nh6bdRfdBA0wsxVRyMy3e7VYfF/ul9BsqRAXh0HPB8ZjJ1bkPIt0PU45ExgMlfuTZqVIHQPJsvG6FGZbncmCVmh9mZZF6nTPIc0C6DghuG3XDtqf8jMmUx7qcz4yN8wSPTA08PkdSGRO3kITDqnqK4QvYxj57A991bjCF7Z/DmedsPX8eT5dVh15yX4VHgkvhzPxbmHvgQA+MxZL8D1uy4BcKzyXkH/s5nDPc1p2NruwsoH7wDipMrH9dPu01zHMI/7sTbTk0qMJqJt5PVZvfGdDG2WqaB0LppNU5eZgHZ45Yoo+rAEu30YSPQ6EGOZwKQDTcFgYUUiyfWd8Ds8/e/sHrnO2tKNA7TMPZnKckT7BBS7ZrhRzQwWpnCW/5f+KsYFMW0Z3AqXDKOHNOnPKUq1sRC84t9FwBcoO+xye9TotjBxgAnFBKberGiGLDzqXiprY1krHCyCxKOmXrlAhWf6sytCWYZV0vl85f4G5lAapxLGZCI74GtKkb8fThQO0mfnLOnHwqLzJPLNsgrYC6i1ukMZFuxeIlOoZGQnDKyz2v6n65E44Ng9W7YXqg/7mLZU4SR7Jo/bjgdcLniFsyIr7DoRuNkdyyAUiXJ3jhgNQbdPhaeJE2NHKIISd25r5KiiRymSXiGTa1T2aR/DqspwK2fUHHq5PiudmD26KGlkkTg9bk2PTm/2+019ysusMxkN149PFXqGPvkVUXg+9PgUj9hWZHgaM5H1UimVdzntuac+nSXnUN6pN/TMMr4GHWj5fzWDjOyPoNeodZZGSo9fU7WrXsyZ6N+c8fIk/821lXK41HHyEZ7Gbj120Ll+EqKQ4fkxK3O2ZoNMePSYfjWUgq11kro8kqOelZUydA2CGUWOFxYm559k/DQB+J7Jx/EDk3/A5qN34ImT63HN7r/C78y/Bg1anH/4egDAnesePSivJT27J1sBAKsO3ukcUdKBsu7YbgDAnmYL0k6z+yMZ2meG+zDXLipZcQa6T3vtCVtyW3cfW8lwOFi51ufloORjKsH8zPCgZUiC2Zry/JAwb5CTtsGB8r8VnSH9YLi1DiGdAVI2M3rsmXKylGVD+wQUt2bIAcEN4Ipn17ZlRhr4YqXtmYIIKSi94ibHZhUJ6pGuwYjAmWXspKIUf6J8Dx3wTAkFwQshtBVuQSS7e82MGG1UaeFbiwzyCHIR8O5+H6TQL4e2PAicAkTWD9h8MXoiXD1mcCbctlQNLccfFlXwyq68o80jZHl4rj/AvniP3D4VpVwOKH78cZ26okTXLXwZVi79H9zdSfh1XzXI2D5yBqcfHzCUveBpTjB2j1iOoyimpb9UmNyTB7c0GuyeoWl+0A6YIfnBUsLlurcKPsBfX2WGDY0gij5Z+vfQi9ec7lkGmRhKgok+uZJeWWfQdOs9I+qldTaY8aPb5te7BT02yg1BI2r8oTTqvaDfgqiPmab0QqeYNmSu5Zrg8kOPRclRE7GT9Mix6E9VJX7npvoRL7tGJT1SDjNnMnU6SPnh5R7VjwyQ6Ud2jAoGg7u67q1sLrxQDgJ3zljZo/UZGUGkMlc6asieGSuP+DUBsfZavW4ljdIAUnoYNaCH5jUqutNdZ1kubHbg1pX/BpsXdwIAdp96OdKGVS91WzkTAnbPnQEAWHXgDqEDFt4k2PrFXV3fYXMZi9ivbYzYjQ04PHcq5kKL8+IduXUAsDk8AAC4t/80mEy31m98WFlInHwCN7vWZ/89JqWcpnoHlk5OXiIPpC18vRzRDrJenXbuLBkz7tK4puM8VMuyoX0CihP6qCvRskjPXGlLDhdymLftcaSYgilpkQiwYWeA/R6go9EdnuWQZR5AOR5ZV/dJlJ1MdxH6EIK78EEflDTtnJov1ttbCGLKJb2DmvhNDn1mAGcMUR5gnmfSuKil+bkUQ6lcEqNB4zf/BldqUpGylKXbp3FKeliEtRXaFzMk2GdlWLRv6I4lzFiGHrnjRqNfj0pxa3RdSQ/H4aPUbA+m9q1J1645oux6VPf+iREjFWVLoxQoLPLZwRONfj23Lfh6pLIQ1Xp6Xr0CJL/1LJ1OXT3khUrvGyrcfixauZy9txTu4B2M9Fu2RoHheMzag1x7wwaCjXIpQ5KljgfCRyF7Imlfjc4BGsbkKPxcI0p6ZITdy0Kt7Kbm0hgseCDaF7oNjWQ9goxZGzuZjWWvVvko9yHps8czJw3EgbVX+ySa5cOsz3tJg0yOxfGH4RF0K4enmVcaYBD9KRg5pGqOunLPWvah51q2o49rEbr1tRz0/UmDVcgjgbfMTaKxEKCvGSU8pR6TzX4t+70FQWM1ewFBwdJ4LGy2Ay3JI+BJTZce/p6zfgK/tPh83LbusZDl6+1ZWJxbOxgZlo6fPX1Ee/XBO7lM6duniPbuPiJt9Y40M3vWXAIAeFh7i+LtJuwDANwb1hP+JOZ4h66V14mHWV8T8pYZ1JZWiyeDgnbgJv6QptQodrgDqZf/Vv6Q1p1v79dZ6hewstlSCbU/TqaybGifgGKXjFQ4cp3IDhKiaFMlNMIp+ARv194jikKas/RNlWI8JPRJ2zKW8n9JN4pcogJeRhWZ0ZHKhBgDWunrYVIpirbtOKU/1c14iJGv721C9wnCW9QUE49X9lcEt4QZxd0IT64gJCVE8tEoIZHfGeYKUMEt66YyNr1VP5Cl23IFmEW+a4qg3jMAd/JIupnRkMddwW0VGzrm2jqxe6bl65EdTFXj1IxF80e0NXyArKeudOh1kuAJNpSVwtYKG7dspyNARYmx45NKCDPIJMxGuQGbMt+vFckzU0/KFOWoM3trNj1ecZPK0yxFWynulmfRzyGTo2w9AiaK42jk959pdod6xC3JwtQfk4U1BxEKf7JThdNTaBHy2pybLIIsecTuEOq59nRbA1jzDGTtiUgTk3HwMkVlNFD5WPjAZAI1oJWBGAxM05OKXGdwvPVnro18prbOIUfOf5DzMdHj3oIAZvDH08hkONv/7Hyk6zEYXYHpGowX0DRqAxqinpeFkp7MiyyPCoxmxBFnqY5yM32m1EtVN+//Mi5q7gYA7Fp5Ad4w/S58+Lz/B7L8wfSZWl6LPZyjwEI+7ukj2isP3u3PM8GLU492hvYubHbjk7JwzymdoX1xe2tZOzFiEx4AANwX1+XxOZkCuYchYN3v9J0NIv9TSf2zB82sYdtlY/l6zKi2xbfUZ4+nR7b1MPs3nVpf1n3GzWgiZ9fJUJYfQzsRxSqXZBExo1pGFWxd270zbGINj1AaQhGIVNklipYU+ul+rfP2Eqq5oawPFq4gDijp5FCcdR9XKt72AKOGmxhNCKWfmmc3FSXMmRMjtVX3iE29/D8xZqG4MyWNHahWUfIKQsUgE7Bp384tKbLOmGHa0VjqpEfAND3s4GYKkFikzhnAFBgfxa/dnWReWOqZxYz1SFJepfPFOTbEfXPh1qXZIdaASne+ueGv9ybjLXNssLRKGg0V9Ix5NMs5DgSM4c5jEet+8BM7xsBLD42xPcO+E1xzBg06S4TRmBVyoXhZmOePUchBFBOhPUkFekLSJRkfmZNP0kgNDiTcmWzjvNOyB9aQyHwsNMLh4fym+0jywoylaeQcChqJgahlcx03y36hETuSyaXOGUN3ap9gCay/y1t45vioZFypJx01dnz8E5YCh2kraeyibv14Wk/jzEwF4izXKe9ERokxJ9lPMwOpfGS61HDEX8oPZFh9PVaNdNHWyhTpGOeZHGReo5d7bQvBs3KmOFko+JOM2Dbq17ILf1I9/qlDOa8WxjKnIOjZvPc6AMCOuBG3rLkSwN24f82FeOmxH8Mi5nDk4u/BB766By9H2a/yc6W5R7GH9zfrAAArjj1A5ELBfWof0d7ZG9rSMJX8vndtH9GOt+KTfduV031YicXu71gHoDVZUmkv1PZwOmckb41cBzGK008CZwa0r8eN71HRa0FjKqmdbJ/riKoZJPXXRHf/O+MPo5FmxT6Ey7KhfQKKXTPsgOiUHXOQRF+PHyJeOVAKVROyULAGVDokrdCHgkncSQB2UlsKteGHtIaNNClEaqnjqSSDl6U30gNM1lNGrBhL4pnAkfpT9k/wh76OAtkDUHrDEy9ItEjQLYWX88xHSWMlGmYPSqm4mfnP/SLByr/lwT2FVqhKO8m10hnDwVLH7Tq1Y9SOEaNwSAWYREioAjTweSZAzyu7TzV0lYHh1muiH4txOBQ+6DFL3kj6JF65h+3hJOUC25v8c1NyTZQ1ZesNptDCfgYoICleXp4VfqhP/pB6ct1n/gg+WFjI/9PGV4YpA1HXU+OGH2PnZDR7PRbsQ49mpfYWt5wDH4nnhoi8ouAjvnA0Vh1bzAhJ/SkD0UlxrTSmetIxQoyv2qfOUus8liD6y2MuvClsFEosuQcocTAa9Rmg9ysztKrRayvL4GVZoj3TY+ca2onhnY6ej9zBzM8juocTvyDP10QjMxCEKi/2K8+SqfOidgddOmRSHdk2ORZZIELyIpNI57CiNxm5RzMGo18nmXYMzWtmWS4zjW9bD9HDxBzK/ZZxpP0Ro3ofIBWdORMUTNZlV4IkkWxNbdx/IwDgrUtPwTQ2me63x29FjMC3ibFYngHc8D/YdC+Izx/b5+iR58epR7s72juxKeNQOkDfdu+a8wEA2+LODHvYnvcBAG5tt+JwXABwGDJ9m9291sENaJiQ4SzLoYwj5L9ZuAFR41s6xlRbWk8DWX/5b0HXkz8ZTEyh1qcreLR7wO/rh3JZTh0/AYWmQTkl3S+t0XeVKocVO+CVMTCQ+hPhlTRmAMmhqRdi2fjs4Qd9qPG7Qciwobs49I6mPJiyokWUPnnQMcUEpa0VsrrPymE3oHCoaEiuVxg0pDBIAS9fHR9S3KQCLRVbrnyVPi3uVGQ0TBkmpq2kR+PhDp0EHFLItfKVFFOucOb+CD3S0Jafd1KfpUmwUCaHGo3E2PHKU2U9ujmo7H8BHEoz1/OaIZ5nYr8OXQlhWRwsQyK1T7CyZwiFZK9HEPkoDUm2/yvrO+HWn0PRSqOMctTWaObP0Her1bjr+6jD3deDd5alv3SwgkPxkeEZgMl7zW5NEDlKna+YIWek4ibxNGxu5P7Qe4at73qqrZhrRw9T0nXmlMs2kPtQGNX8DruuR9+WIIayotHUTfXEETDOiJXn2UA2DeS8Zu1Prye7ZxKdkm6FW+xXvX6sLBxn+KssOZq94tuqQmA8jb5UZlfKdEaLoUecKexKSHEwivchhI7D9qYcD8tKWXt0Ny4PtwJtizXTB7GAY5jH1PVXO+utAcSvsgC1R0DPwj2YWzqYYUMOlCgQrd/XGdo3xHM1fxxu9uUACD6Wegcn0tA254Kgau2R7qG1nf23suWjYPJMObDqLADAOhzAiqUHAURcftdbAQB/PP0uRbe8NlVohKERmeEyUwl5jfsx5xLSD/0XZpSzCDRIPdGt+DepE3w9HtEu+CUt8m8Sj41UMzzy/FiOaC+X4y5uzUR/QEgjr8B8RJPfDfUKTITezFn8KIVF4O77Gkzfgz+EUnvVH/j4HBukwljxXMrDQQpz+61wtkGHUrXZoTYr8pkFZYyoGh1CkKT2/N63na+imGgFptA47ZW2KAjKAn5ahLlLlxRCjUVDtMKR+BMRY/ePCVlTqVjDbRojtOJe6spILo2c0yiQVNI03Ur5phFt5PFZxa2M2xpAUrnoD3g51yRamMcnCFfKXKKbGAgT8kDakCJpjVW5znzd8jtTGoejT9IRNVTPG0AAT6tj8ky2V+uRjIUpyj7qLuaPwJmckYqSymgYMqpjZZ1ZGkVbljmx4eid+FH8Nb7QbMO0fYSjh32XVbJw+GFAKVPSmJlRxBVlu7cA6Zwghq3hrV/P3rkJ5Qzy8nEoZVXylinpTYBSqrt6AmbgmW5ypjCZmeeVGhxS6S/0pMIMjrGfm4M0WMkeFjajkD318Sn5D+TIp0pFt/socJ6xV+f5WPJQhGwudNt91K0TMeaWZ8h0uImOJH5nZwo7w4cySKT8V87b5ECj+8M4kw09TJ4lp8P5YQe+/zP/Ac9fOITb7j0f5y7dCqwEjn51Fb4WfhoRjyH6g14TVqboSHz6zb+WDQAbj+3EBxdejsUb1uLt+GXswibjqPP7NQJYiaM49cFbAAA3tOfhWnWHuVw/zNiNIdoY3SW1PdCnjjdxCWtwBAexyqX1r8cBzLeHAQA74qbcVu6PNO52fg3ui2uxKTyIK/d/GO3kfqw/fDsOYBXeNr0Wa9Q9a6NfQa/7xG/roLZ6mG1bZgB57BrOXhgfa0Czl8i5UcxwSLrkv2TdQH6zfbD3M2Qrdg6fDGU5on0CijM6hXJRYBWl2tYj648ZF+xemcTV1fVKgz6EtEDWCpmo18NYZFeNz0BVOnlFMA1HAaXBQg5Pcjds0NgVhOu0aiIoldHoBctgpKFmDA7QyJQ5KaT152JKn129AKpAk/mfkDlkNGbeEOWJKdkdXCgXJFpMFW32UjdZewo3MdzsHVtAv/zJxiO/w23HSHkhZrYcahFWeZJ7fUL2mzaejcJoYIMv9UYy1wI3i0rLe4DO0GLzStYJACwJZU7tjzRusV/t3LBrJnLP0JfRK8bXkCddp7H1+4jta0GPNrTMOoMwGqiBWPpOCsd3f/3n8OP4S/zhildj4fA9jkampLEMiy7CptczNXbAYD6Kqx1bhW6puGtDzRiIqJwfdq5bT4++8wlHt5Tr7K6jvausYBKu5AeR13J8TjZ7JwYUz7xsZS/ySj5O5VhSl2UIWn5kumVFvfYQyXpUzhc/13rteRpnZXKxuWap/oPO+9p5bXEoqVAK4w8zBqHq1XUKuc5Ytlkt48NlKjRB7cNUgqLR0IOO5y+cvAsrpocAoDOy+7LQHsa/mfuAWaOiv4LF6QpSa9FOHg0DgEv3fhALYQmnLO3F900+jPV4EI/a+RacH3b0Toc0Fj2vl4Q7EdDinrgOu7GBO2oGcOtoaKm31CzgSJwH0BnUetwd/rPCvQCAe+KpOIwVBQcKf+S83tieAwD4/p2/jv82/z8BAO9pnoSDWEWdssWpUnkYMsNQYK4tMaj7SuMM4/H1GB5rlHd1fH/yp4LNqJfp6X/X55nHc7JGtJcN7RNQnIHZephU5uSB6vvixg430hJsRl14w4bXG1aemLGrxueGLIwLSCPWK0XqAJTKDj2QTT0xPnpfUCn45aCUPOvo8emSErc2JPOwXQRRagLsRVamUAXCb3WXk0RDdGqTVL4snorC6RQJ9up4TXH3So26EiDap6KMBgPThqgYi+GjHp+kx9QT9FSdAdRYkgqrLpHQw/YRmGKiFEmvCMp/Sxjbw3Isuc8k6aXCqdKJNW8Vz9TbAmVN2Xosy8WmRnsFuGIMmLEoPpLURvqpMpR1JiPs9LqFoDEV9ngd//6zl3taPhp+o5v/Bi22HLolw07d/7XMM69wVB4pEs6grDQK5FnG0asMgkbnIKgYEi2f1w6m93VZe16+6gwUc6aAy0xOY1mP1rCZmb0Af3bJfaQMIErj7PG1pK3kI1C0ULkHrREiadTrTK7Rej39+FhqK/iQqfFn3Ow0Xy9fVfaCypwwMlfJBDEHBMYzw+CKumbCsjYaX2+mQ9/sYXVm1l7v7mHs0UX67ocYtzTyL4h34zmTj/uB9uUR4VbNs0qWgz1zZ10zk7LwYQ98JP/+rZMv4dXzr8OTvvFb+JP5/45Je9TvjwiEuIS3L/w8AOCG9lwAwawfo6eQtccM2yQLH8ApAICN4UHFx8S3bb2hfXfcTN4MMXpvCPilpRc43v715LsIPeb8AHfy2T0snc7aIBeCQfSloXBj7OoGJVdSO19PdCzrGQtQrgmLU+rECadKHU9RbkOzWlPsjr9ob9PyT5aybGifgOINzJrR2f3OjAvZl48MFzErFQZmSCgfp1K0oOpyoc+EOVckGI1uzFEfBFnhEHXYnSamIMx62TQrEgRHQyI2LM2v04mCqif7ZFFuN8ZMdzlwMt1OkfQGq4qaivYsYqe/o13G4pQ0pUgwGgtvvPgjSihRaiSNHdwf5lIptutRGeTq01KprejPrhOlAAvKyf5gqePsmoBUWHN/ou5ghoXg2eD3UsH2TERkKfhE6WTGEksxHm8Ae5nQCsJVCn7mbWWdEeOUZSCkIvEw5wJzRCH4OZT6xlAatOOFkSlt9MY3qHz0azm1PzPci7l4LMMuvfn38+9ccdMKnuoz+jUlrzLQe+1EIWdrgr2zEAKTPeZVZStnIGHIbbk80jB+dUQbad6QyGRTxVavx8zGXGjGj8zEGTDShhzRlo/Z8TPjTrR+LNCeFWQsAKdnYHxg8wqxZ8Q+0unx9jyTe5PIQjr/IG0TrBDOsq5kqab/j1hnmj89boGFPT5Xk3veyVNz1JdxI9dNuIF/3bwPC2EROzdcg99c/JcAgD9b+QN4wpHfAQBcEHZg5fSA1xWUDOeGX6Jc6igWtgV7cdaBGzK91zRfw7dNrgMAnN3cg6v3f5DuzScd/cfc5uPt5T1/MojLOGN8N1KmQNe7u793fXbY09XN2VhdzbOEoT3782UBX4/b8ObwzAy77tx/jzvC2R3d5P2EEtwIdA8POXTZ2ZP5Yvgj+WVAag4zrGGPphGY4DfDb/8tdWJqVIca3czhyVPZbZbUyVKW72ifgCIPkpTaateR8oaHAPSHtk+hGGGwdtVyYfdSCx59iOnHmczhKfplG2Jok0ihz2jhKUJayDIFwR6+9OBOYwRP6ePpxJ5nAUAkh0MqLBpmlUGgE9D5peUBI18qSuz+qjKqhSJZIiSSljKHLjoHEZ0hvNXppH4OhxQY9Y4kMRD4i97RrWemsEp6JsRBMBQB6nDDFZYmrhwoKuW5HJZ5XzO6jYHY1sbiHC18z0gYi7CUutx5Q6M9dg7BjR2//73yLHkWxCJlKeqJH7a9lyE1BxGhu29Bo66BOVAClV1appS5tniGvj2tDFtjsD4s3KlGuPmBL2IBxxDCaqHsCgUxtaVRTsYLMf9if8CtRym3kCtK3KlIWegUTuh5aC3Pquseuh6T9RDzyhwt8PMqYcqQyPTIqKRf93Je5ZnU4fDz32Wq9TBqXPIzpaxRSWNQMD9uwzNBD0tvV1c1Wj0WWU/SoyLsmMFHR49Yo8yIJY76mgMl8YG2JcoG1xW8LKzt61E6heqv4NaZSuW8B/h6VDTC0xhjxKXhdgDAzdueg9/buR13nf4UfHW6DXfjIPauOAMbju3E+Ytfx35sqdMjpOF5938Un1v4Rdy859H4x/AfIe+by2tmCfbUyecREHFLewYuaHY6fl954KOI8VsM3cDVi58HANy/5TF4451P17wQ60d+e9rCZJ+23u3xdDwKX8f2ZGhL2dPGbGjfFbdgCrmWkevZeX11+Lc4dMZjcN1t9+LqC/4dmp23KboT7ZI/Ug9ThqSB8bbsTnRQPzMcxDilsIqRbmANxU3wEnrSr7JqjnKTCL2P5PPCghsnQ1mOaJ/AwqJXqSilkURnWb1UWASQede7ukJJJ0ojE9IZRg4XiVsZxZZuAkvjSW254PWHnTa+kyIpD2l/WHElVMM63OzwZAKVK5epaKOhVySU0mhpZIqS563idy3VitAoDb9iQBcYvcvb98PSZSVvfD19KEnaC3+8wqEei6LKl1UQfX80XVLQVI8qJV54BZFGEDE8X0MGq+VPqujojlrhsP1JGtWdaqk0JjykPVVilWKaYBDj8zjoPiLrMQo4+0KBjkjohVZ/3b7ng4g0qnpm7QkQfbiIZTRIatgdbe5UKXiZQj5tI65pulTxd4buAzdNnOLicJeSe9OBx5UkPbOjhXVZKI1qZuwo/iiBbegJkhdlUfF17ydRX2XQY1FGOoFJhS8KIJeFZd37KwFSziDXczIOfD0moHot244ZVvYEBwsAzg07sf2eD+Y2g1F3sDPOz3+mE8Y5Sc5h/V33nu6W0N2S/SFxy0wee87I+SfnjDJs2ZqAL2qtNHKuTXupAzRs/j2M8VY/hlb2ujVYWURT4/HjnrTH8PBwGwBg76mXAAi4be48xNAx9e7VlwIALlr6WsWho8+4BRzDd97yX7ApPIjHHvwQLgp3KXqkodVGYDP24ccn7wQA/M30iYrPH73opwAAjzj4Cfzw4l9o/sQWj1i8HgBw62UvxlIf09PXEfi6tzBUYLe3pwMAzgndt7InZl2cRVLHlSEpZXPP8CkaXHfKE/Gu9nFomsY774K8etR1I2WKck5aWChGaCRtU0n/GmMs12EaKDPLJB6bvi0j9rI/mLqFxiBggdIt67PU+oKHX+E6GcqyoX0CiosMR69IqiiVUoBNPXq48EPI3XODNnaY0siiaUOvDkvczLMv6XZUy0MNfDNqA8gqg/6w04eVoLvvm6W2Mf7Qh2uUYVvqWSVE4Zb0DEYQawYiDI3akGBGp4uGmEPf8UI5HXoskWc02AyLCGJwiP/36AFY5cLDFI1O+ZqhSFLeosDMmAHrXU+HJ5tDTyPfM34vSNzqWkdr2kLSmNGR/a8h+h5xB5vrNVvtdJB0W3qkAjxQT8CGlENAp9qxuaH0UDlT+ODkUVX5LjJlKEuG3y3mcw03bj7Xrh48z1LdRzc3AQC+0FyKj00vAwBc0txRoZG/BsycPPw18QJzkWZIfhO6CX80zNerrfsxTkedqVJgQ+dZCOV+3/DdyVpEy69R+sL4LIcuUn8Jh5CjKIWeKeJu+ar2AP5x4eV4xpf/M57afM7QI/Yr+SawhNl75OzsgZSPbJ0J+Sjphlo/ZA6dwSrWBIHph8K8fPT3qWt6RWlfttys/doZob9413/An+16NjZiv8KzYnoEF4c7qYOYOVAg5pV9WnBWwCPx56JDX8SacBS743o8uPb8wrO+4o5TekN78SbXn8QTejqvba7HmqW9+e+Pa77S10OpJ9r+1vxrcXZzD6ZhDu9pH4OPb3wOAOD3l56Jr571XHy17VKr//30rfiO5rPZeXPWsduwKd6Po3EeD25+ZMbHrrPJbDzLny7qamR4H8W9PXaG9rm9oa2uo0XgTJk6LsbH5N4sea2uBPQw9rCbMujtHMh6QrhKuZDqpbHLUjOCaaK4BbFaoSdA1fIGcKGj/CHxRhnfwdbyfep58Lil7XMylWVD+wQUeriYhaQMv4GDhKVLSc+8Pui6ohW80lYb/ppGfbe0fkjLQ00bjZpGNhZ7z8mnl8g7fwy396TbSEyul9sW3KlwA0HSWT8omcIp28u0w4m6o9vXa6RgMbgjeawlCkNCegBbAlM0emVX2O5ABM4PO/CEPW/BAo4ZJT2TQx0/eczMoALcQZngAP90Vm2dlTWacFe+rdzXPGv/F/HuFa/An87/Gjb03/KcyLmWh3mC0RQxMUYxlqLElnreYB2OxPAUU982FYnD9WmUb57pImSFVDhJPR7Z0XhB6qXxdDCtmHiDhRuDqQymRkdCo2gvFRu5HlkEEQQ3ze5QVxRsPS9HQeQRAEymR/CI0D2E9qXmUtwSzwQAnBt2VQxEMa/EoSeNUyaH+WviXmbqtxMKb60CRGWhUIrVeiR7eHB/gCjAYl6YAixlnErBBhQswTNuC1NOR7/uM2qxHtm5zta3nH91T7b/w1TM9b+96xdy3f+54jexBodVeru76gPG7zKH6uVvYsQyo4F915vvaz9GKsPz//Rc2ywytSZklkPGIcbH5pUY1fp85XrP1c3XsW3xNqzEUbx1xa+UvR6B5+97Hf5+4adw3p73Z3zM8aPu3tLoJRTM0SP2EgBsO/p1AMAn20sxaSaC7q7erlNERNvwp6OnwAKA84NO/U7RcnVNoG9/6d1/hW+ddHezP3D+K3BbPANvO/3FeOGxl+J3lr4XYbIC33Xs1/ChU78HAPCa+d/DliNdf087+A4AwEfaRwDzKwXPEj1CLxDGaVpU3CAXbQHcEU8DAJzT7NZ87P8rEe0t+nOTIpLu5ZE8U6TM7XHD38eW6fYyyl3qJRyFt8oR7aziIP6voTx13NQTa0/BXL2RbZHohyuS9vQbu3vNMgPc+IINrJ08ZdnQPgGFKhIYgCmlSC84eXCX/kkKFHzkIrVPhSs2QzTKQ7qMbUgp0jTqP0gIUy4hDytZlzgOhu+RAk65EIc5MxDYN2/FOcBT6JQBJBQtRo9dE0rh1AeGhSnvc+ZZgTmvcCMEqjAQpUMnoMUb5/87nnb3a/CSubcpZUUq7tbTqBT3ipPHpjxKnrF723VDy85hZW/1sKt3vBmXNbfjiZPr8eTrXgYg8miY4CNTgPg9cm8s1+ZLGiyWRqWYEgXYlhij2cOensEsADHACVt78kVvN69+LSd4B4OAeUVCjkd/OkcoWkjrjMx1Gp+KxGvcypEI5ogSTh6hANk0T0mPdHjN+hybu/8OqWgXPKcdvQ0rwhSH5jdgRzgdd/TRme1ht5J7+hXjfj3KOaRvPLA1AQdjWQk0LVuNMdZhAUqGe9xcxqWiHimD5rfEw2AWd6Ex+HoCp42cUwdB+oOsp84jCFj9PGJnHLvLuT7ux0UHPwdZ/vPcX2o+uvOMG7ZDMDt/lj/qelTjYUOZV1oWJjxCZsYpNmGfcd6JebVtIedKwMQK0l8P0bA6fwofHxm+nvu6qLkbj931lsyfbzv0XgDAt97wczgVB7AaR0Y45TsYe/eFZXLpcXfAzYudYXxb3ErncM+aSzCNAZvb+3DqsT09f/z+6IgCzgu7AAA3tdsAAJeEOzLPcrW++bfe+lsZduPp3SNhU8zh79trcAgr0YSAiAZ/telH8bVwLhbCIl54/b/G3674GTz10N8BAP58+u3mrEjrp5IdCAuTDh2912+LWwEAZ+B+LOCYClCsjIexqX+NPD2a1vHVOoPsPtJ6IUtvh6Pby+tGGLHsJXq5B5mxm/BbuDV2rTyzuHM9cOPbRZUrxrf8KX9XhrZzGBTso8Yt+jjJ7OxlQ/tElKHUuEbseqs0Si9u7kueYKWaU0K08uQFncQdiZCVQmgodVRHSODqSbo5TGghWVgVEE/VFTAk3PKQ7euxT5XJQ4gqc5o3qc80ZpZCxxQWpuxqpc8rc/mQpXenEy1awWcHwcOmX8MZuI8+UiT5I+l5eLwF5zTdd3xfPPcOLCwdoIoSk4CDUUV4A0ErnKUf9jm24ZR3Odea3wEtznqgKKtrj+zA1eHr2qkivdzGOdH1mcYtlDmhSCKaOST7mkXsIBTl4Ye0ND12zPKwikJYqO//Gv7IMdI9Q5RV9lq6UoqJIsmdQRUDOssPgdspxTMyA4hDpuOPXXuBwITxbYyOxEEr97q56XHPcKAAnp6tR28HANy3+nzINMjtYbfi2VTgtUaaHreYG/LGx6y0fOcgUHJL7GEaGULm49C1Di57NB7ftozZwiQvAlXmpKNFjGVg7Sk5rNZtgpUzatS6NWPO9IhtbTMVLm5vRYPu8alU/t3c+/D0Ha9DQGuMxlSDyJTq2WNolDwDOePIOYMgHag1uafPVwl78m2vxudW/hieeeTdZJ1wB2GZFz9/tq7N2tFZGyjtc1vg/GYXZPn2O38P28MuLMQjGTbXHsGXVr4QX1n5w7ho3ycUXmA4einXnnbosXF3P7cs7gDQ3UdWulnfdmluNb4Wu/Ttc47epNpKPMmourjp7mS/t30MAOBC3IUGLXUQ7F/RyaT3Tq8B+mg6kz2LYQV+Z/KCDL+iuS3//tn2YkUPS63WbwFo/khHrf229v1Yi2OTNWhCxLZwj9rDW9FFs4/NrcWDWJ3xqyANotvrUfJMwGeljjtHlKg39tpSpjHTauBBSrlEt4+I0z6p8V2/j23bJlwSr61fp3vYqJZ4mMP7ZCjLhvYJKPSeVP83ZgANfY86KvGg+wfsgtZKTeozFZ3yGD3MKpcodGvFhLQ1NMq2mroeh6CdpXSyFFOe3s4jsWXMPW6prDDFjfBMCjDmIFBeavIATI4WwSvkUAYCo5srplYwXXD0RvzugZfjT1f8N6UUI4+bR5WuQrnjBQCXHvqspyf6iDZT+qSDQDKNRnFoqjbP0IDD48eS1sQl4U6sXNqHg3EB75g+HgDwg3MfoE4nOa9aEdAKp6SRppMClG5/rYMo6S1cf3IPKgSetcooH474zjJipfJdp1uO2Y4P0AZZorKOW8s96ogQ8lE5CMl+lTQWZQeZaXIOU1tqXBBjaVDuzZBRUnE7Y/E2AMDe1echhIBdcQMAYHPYr/YHS6HT69HzlkX22PsgfO0R+c+iTxDOAHJthUZdIdd96s/Pv5QzQw7LDndfryl12SNe2diVBjmi63N0RhQ1bEt/9cwZgQfciL2w/QYA4Ma4Hb/x2E/g/dNHAQC+dc+f43HNV9BdFdN0y/nX8iPiVBzAHJZ6mNzXcOOjBkJt/iHbM54ZGvv/5rCEK3f+FQDgJUdejw1H7nD08PvYXs7I3aEMUSv3SHur42wLnaP51Wt/Eh+dXo6AiJ+f+zP8yLE3gpXn3PXrCGi5riDPBGK40cxCCWsCAlqcc+xmAMAt8UxFt8xWvK69AABw7uEb81hSueS+D+DfTt6Ha/b8DX4hvg6P7h9gfPv0CTgWFrAqHMP2sJuux9WLewEAv90+zznaEo1pfP8UHoHnHv1FxZ9fWvXTOIDV1UAP3wumnpDXSob3v+xf1UXmt4fdSuaeHzuHwsFTtiuamqD1cIj575mrzw951vQ4faZbIPwpb5OwAAP7rnfhSx6mK6OM2EAi1YDD07C2YAZ5ql/+kH5VeIL+m8bliRyie/mO9nI57sKFfve7UtzZ4WnN06gFsm1LUzpJeqGEUwW4dngOKO7s0Nc0RgPTB6U1GnWUQkbOPY35Dlpbo5spSpruDremJfXZEySMRgEih6dKgxykWyjkhG4faSqNdfShAz7lwLsBdKlvGx68yZKtHB5yDq8NX4QsFx2+jjtVzByy6INctVK5kE1tVFG1lzxTBoLhDzwfu3YRT2y+BAD4p/YS/MnS0wAA3zv5GDZP92R8UzGJYwwJxQsyNzLyVQwJwW9lACflQiiNbq5hd7+L7A85wehegDcGdCQuEzMoE5QhIaKFBVYUQZ2VYHlG5AeG5aO+yqJpTH32rV0kT+1X6eTJwxb1cn8edxvLJ/pKtoCM7KX+jOLW93rGsdsBAPevPh8hAPfFdQCATdiHEKNSGlPbEk0V61EoJvTufV5TELBEj5iwVE8Z6X5/2HR5RSNkPS9LZ+1hZtiyjA/2yrN8nEs6yxJQOlCufPBDeHxzg97Dcr76vul3xgf4KOvRdNlA1hmRPRf0hvZX2nMwxQQvXHwp9sdVAICHhzt63lj+8DP8rGO34rMLP4bn7XyVGx8zOAvfKgZQT+SpcR+e8MA78Ctzf4yVR+8Z5I91jH7v5KOQ5ewDXzT0SJlZ+rMyQfJR8UKc90Pnhw1unNunVe+ZnIY3TrvPUX375At47lKXNv6Wpafg85f+NH5n6TkAgHVL9+IZzWdwxX1/lx0Z6tvK6XE+lTmVKc90s8zCJgQ8q/kk1rQP4lBcwFfidiXXkfkd8OV4LgDgzGO3qv42YR+ec+sv4Jfn/wTPuPM38Vx0L9jvnT8d34hbsXNF1+5h4U7nVFmNI1g57VKvd8VNal8XGgssAvh8vBhvu+w1+Jvpt+InN/w2Pjr/+DzuVHSkWvcZBO1TcScaKHOY6qUumaEdI3BxnxL/4KkXQxed+TJLHgXbOvD9ATsWL1oBKcPJ/k8l1WGGrb+jPc5YbhpvuMvzyOJh9Fi83d88jN3xZsY7q8eyCE+Gsmxon5BShAsw3miQirvsyS5BqZio6GyrD5vUZyr8YCs0OiVE1GSKO3MkDNOtnQvsMRvmkRb2WEbE0/I9bulRHlKK+L0i73G1NKbCFFEWxeP36RJ/iMGBMtcaR/fztMW7M+yM+z8taBS8TXh6RE1cxGPRPXryj9t/AgBw1cGPI7RTR09r6SGKqTWeXHq7aZ/KUIqycpYwZc6kmD5j0o3979tH4/PxYuw49SoAwBWHPu14Jg94ZgBXs0CEsuNolHudKMBuz1CHhT9w5DqRfGTGoNwzct3zNGGyHg2NEB6QWXtGPuwUyHpm6Zv6wSYoGtUdfbLO5CN3Volp0NLHZ+TjOtaIdQ97JV6Qzc6cKizyLRXtMxdvBwDsXXM+UhokAKwIU5wSDjqHnrx3Rx0/0oEi1qhLeY6knjyPSNq5VIpk+qaN9nT8LuOGwa3lWeGjl3HRtdXXG/Lw9TpDob3QiDxuALhs+lW84K5fxJtX/FeEdnFwz/G9JelJfEztOI3FMcIiw95ZmiLaX4nb+7lp8IbpdwEALm++ofeRkB/2TZYYge/a91dYEaZ49N739I9cVhz6Ul4P7IW2F7r/de9P4nm7fgvPn/sA/sUXXohV8bDhD9dx5uNR/MTc30CWrYdudmPx2TRe3ko+ajzeic55VmAX3PeP2Br2okWDHZMz8aH2kbjhtGcpOl+z9Gzccv6/wW8tPS9/JeC1K34Xz7z5F/H/zr1V0SP3DEvBn4rPNlrjK9H4g3P/AAB4x/TxWMKc/ta7GF+6g7xu6V41vic2X0ID831IAH919s8CCLh7oXvF/JLmDuUsbULAxv5+89E4j4NhlQuCSLrlmXTXxm/Byxd/DDfPX8ydcuQKR9nD/l6zdNSyLKl9q7q0+c7QRqbnfHR60MF1F6qxq/OIOkaFE9zQnsZso65Sf5gVvc58yGwkxryg1cFsXbE31Rhdn0HZBgk2ygDu/ynbs3TyBHN9Ch0wFXmeSUQsi/BkKMuG9gkoRbkUigk5FLmibPvy6aQs9RPgj3DQO7FCkxxKMaVKkVJ2/YEh8TLjO/1fCsDaYzbu4FYKIjMQ6gqefjRJ0GTmRdHjFI5eWMH3WbzPtUi1oTsSuiF425A5gJyHDnj6UjG0tz5wXR5hqnfVLa/HX01+FleFmzEXl/DE5os4/Uj3MNP+uApfPON5eCCuwfrpfbjg8Jc8PY7G6A5UmTYqadSf0+pg1YfGMn/6thXFzRqIALB6uh9XhE5ZTWmXydDe2hs4kmcy5VW9GmsMiQS3NCrjAh4WB9rqh8YMH1FzbJQxS7hPUecKJwzdwLDyPfMF5WwAF1jtVVV7r5mnDhdeDD00h8jnP7VdOT2Idyz+GG5d+W/wjK//AuaxRKNzjQDS1FgweoizTGxiJbfEwgih+4zQaUtd9Gzvmi6ifRQrcHSyBgCwsd3n1qOimym7gkHDKc9exkk5wwzJIJRdn74p6QkKxqLSgMXN5aNdZ1omiDOOGoOFxguOfBkvmrwdqxb34YpwK37vyM/kto+87Y/w3OYjqk+a/i1ls8hecJ/oE4TzT5B5h0Uj+DhtIxZwDGe3nQy/sd2e632iN+ye0XwGq5b2V2Shxt3GiPXTezMdj2u+YmQmXFslr8k500ZgE/Zj2/Su3O+GQ7fhKfinfoxknQmn2rctfQxnhvvx4MJW/PTifwAAnHXwBtc2laGzBwJHojPT7WQXc26Xub5i518DAD6w5lnYF9ajRYMPXPzz+BdH/wve2zwJP7/hv+NubMltb+2/EpDKv558sHsgTeBw+ozaR4JuscZTmcciruq/SvCH02f2fZYxyz28u792sn7pPjXmS5o7cn8H5jfi8ZM349wjb8bNq68CAOxKhraKaHf/W4cDAIAHsAYB5XvSyslnnC+AcYKm8QGeF/Dyg50pdb2wgz2w8iwAvaEt5nobumsAh0nquH50McG9ziWdAanQsRBZKPc602dZplLGkQxWBtNVqVHNK3IDmvZHYF0X5Q/B/E3Xc6hH0c14e7KUZUP7BBSnaDPlQvxfH/rsUNSLUD7Moh97guqvg3EBzxSyUq9+mNO7eBGw20Q+ACZh0mhkKVRZrVGKJAo9RHH34xNKHzXSCj57903D/KE402gUNPHPLqWx1AxW3Z+6ty8O82kbsRaHsG66N9Ow5ujuQiO6u3GP+sbrcWVzC1489zY8fuef4E9X/He85K6XAwC+HrehnSx0j58AuOLAJxQ9yiiySq2kO/9PHyTqQSr4+WIGHf1mulx7zkCIuOTw59GEiAfWnI97sB4AsH+he6F04+LujI/dndQ0lnGXMZK9QOYmR1gjiIIn1h6JSOp94JVDbewURH5vSgMKon2aw358reR3GbOMxHb9DUeV5EFcV6g8L9x9U8DJs2rqONmvaQ6fcO9bcSa6qwKX3/dePHvyMaU8zVSU5D6066zV0eZMt5XXUuHs98L5YScatHggrsHh+Y0Z96H5jQCA9djnHIJyjXLHz3BUminAQwa5xs0cjAWPeihIjtvypwLjTmctZ0D2h+VPjnyltmjxI7t/CT85/5d4ye0vwb+afAgTEeG75vY/wKtXvB5bcZ+6ekIdVg7mjW92Jx7gd8ZrDyRdHO7CBC0OTtZjNzbksXw6XoJ7Vl2AhbCIC/Z9Upwpko+GPzHinMVbMx1Par5Io/NSRtWNhh7WRlzUdI6AvfOn401L3w4A+PXmNVjAMf0gquFFBPCwtjMebz396fjo9AoAwJmHbsIqHKEPuYrprxhkoh7ZC0MPuSYhvgpH8sOZ71vzLLXObojn45fmfwI3zF/R87v72/XxPMiyNhzGS+beRr9GIrNAYPZRwmNhWx68EQthEfuadbg1nqHqJdITbFfs5Map0729M7Grd2HoHlJ709K3408u/SMcCwuKZztXdob2w8Idaj02IWBD6AztvXGtGic978m+lgsg9SnHaDOGOv4Q2Qz+acHEiX0ru9Txc8IetZ7P6u/bHz3lLEV/11/hoV1ndt1K3VDWA6wzubS3ePgXL7zMtEXKkPSbM8rh2zeMblKPRcNZpLmkspsODT3pN9sng3F64Ob6ZCnLhvYJKO6QbtkG5ymmNEphFqFOGyxw5aXMtJTfWRos87hOlOJGjIaszEHA/E7xdHPFxAp9W5c5IoYOVKnM56gbOxwglSw4mDcarPJU2iiD3OFh0bDSdogexTNowZTumKWy8theVe/McF/+2yObm3HtXX8IADil3Q8A+FLbpbF+un04AGB7fsVUKmQ9jZKPhm4ZxWevASPwdcbXo4ANOHTk/D/q0McAAHed9qTc974VnaG9YUka2v4A5DTmJjpilxR6gZvd285txV7391e9UQzBBxr5Nu29rODrzPFMTCKTM8ypwtaoUoDl91uZocX2MMM9pLirsQjcEWjQ4ooHPghZvqW5UaXL2TS/c8JuhKUjPUz7/8saT/+uXW/Q/FHOgL7PK5rO+Pl6PAuhKTIuG9rtA27tqQcElWwuc+PXnpzDxMcI79jgDhR5RmXcSmkMrt6QvOYONOZ0lm8dyLPQy2b2eGGMEXNYws/c97PYMO1k3tZjt+dU3C+se2q+8wwAFzQ7tBN0cC8UPtr9ylJR0xgzjUbuyTMlRuBbm+sBALtWXQAgiHMh4NYN1wIALtr/CYIbbj1ubu/F2l62A8CF4e5eRvVtiZOv2wtadnV0F9iFoTO0dy5cgA+1V+X+H9ncbGRP4lmBndveCQB4YM35uBtbsB+nYIIptoV7tYOIOBiHzusAvfbsuamvo0hYxGOamzCJi7grbsaOyTa3F9gafdv0WvzPM36xi3g/7FcBAM+ZfAxxupjrlf2a2nr9AdAO+FS27rsOAHDTisuRWtGMqKa7drKIOQDAadib613QG9rvah+H/SvPcGtvx0L3iNp5zW6cd/tfZj4GABvQpY4/gFP6s7DMYaab7HV2jYYZmHIvTIUFTXnG9kwPe2ChM7TPDnswF7pD5/F3/zE2hW7dHztlG2SRDpm2ss703gyqvUodVw60UB2fdET7tj6tOrWT52lqx4xlHxH3dFPjF8Z4royZ9VGM7+D+blvL+Zcw7zQIdJ2dDGXZ0D4BxSsS4lAkBqJ+XEnXk0I/9y+U79mp40RQEiWW3n2qKbvO4KjQSOnu+wMXqEooMqPBKA36UEx4mOFWCn8MTSqcMdez/GHp5BoP966DwJzyJE6whvBbKcAx4rzQfXPzvmYTAGDl4gO5HkJ3GKWSvjEpy99OH4cmAF+Knaf77KM3axrhvycsjbQJ4W1HQKEx/bPcAyzVtFe4PtfF7vdZAJP2KK483N3Dvmvrt+e+H+gj2luO3Y359HCNWE/8MGcKZ6HR0lNot/sjjW9or0sHit9vzEGkFeAiU1ifej1rmGxL0zepo638nTmYsuxp9L7Oe2GAF3VFW+OOZCwJz+OaL+O0I7fhAFbjPx17EQDg6vA16lRpEPH8fX+Ajyy8FE/5ys/2A+EyjslrSo+6ElLWz5pwFD8+eScA4MPTKyEf8To4vwEAsAH7XcaHUuaUki7knpNxMsrt5Vkt6i75mGBuXuWeUfe2Cw4/Xyxrp8azRI+X69IZMBVjLvQA3958AVcc+wIA4J7+oTkAmKLB28/6z7jm6OuwY+0jAHTfFmZOB22kmfmHN2zZXk/wzJ80r+KBzbwPp1P8m7n3AwC+uOHpGXcq39j4BADARfs/DcT0fobYM4bubzumHU3bw24jWwV98jxDGnehWzp+Lgpd2vjuldvxD+3VOLiiuyN8VbgZVMcReLbF7ox68JRzAQB7mq7tWeEe6rynnyqka9SuvcQL2Sefryc0Xer6x6eXIwa9X2U92XYJc/jCKU/CDfF83L7lSdgfV2Nr2Isr9ry95xngzr0go5xlDDbjCwBOPdw5M+6Y2+7q6WyjDtF9oXPSnR72ogmhu4LQn/e3tGfSCOuh+Q3Yja7dY274le7V9X6vrwsHAQB74ylUNwNq54KUR1J2pfbIvJC6S6pnH5DrjG97DhfY/hWn4Wicw4owxbrF3ZjDEh6/440AgL+ZfivalWXvJ/rU+TpwNuvH6/oSKmNxc13OcfZeiYrO27vTQf/MzCk/xHgqhq0BBmiZVNraivWItoSHUt3BbJFrT8IY3TYz4GQpy4b2CShFkUj/Jt5eWOFpFckCs6uwfsCzDV7a6oNEH2I6xVwo5FQJ0bjlwZ1pEQpMKvbAkBH/DqiV4oKn8IIbp9rgiOL/PNon6DQHRhp3Tw6PaJu2sk8VaRoyGiUv4NcEe/RIKt8xAg9rumjBV+e6iPSK9jAWcCwreOnOlywfm16GV5/5avzA4s/h8/FiNE3Ajtgb6vEwVuOIwmGjIdpIS4zQ43MpdMHzseMZUbQSnpZnfGTlu4c9cvolrIqHsTNuxN71V+S+71nYjj1xPRbaw3hCHzViKeGURum5hqCRrSmrXMo5JHudZTnI/e/ugYqNpbMFSBRQOEGk7LGRT2o0EuWJGUCKPyG4vaCMLwjnBjEQudwrMiHPNTHIJT0xApeF2wAAn24eiX9sr0JEwHnNbpy+eLdbjy869kd45sH/BQA4754PIaBVyqEyWIUs9FFX5iBAJjwAeFl4E85tduPBcAreOH26WmcH5zYA6CLaViFTCp66olLGbHHL7AetpOuxMH5L/nRGbOkT0PtDKpJyrbjUeiXD2T4qY2ZOFZvSLXkhlbY2Rnz/5EMAgI+d+gw85ehv5r89GE7Bsbk1OIoV2dC+ONxlDLrZ/OFXWXQkTtKTeWbOYbnOth76Ks4M9+MgVuOGjU8FoK8j7V7bybPV0/14I34BK7BonMR6Lzxx8eMAgN9bejYA4MxwHxryAJycA0kP/9RhxEV9RHvPwnkAAr689TkAgG3SWFb7Ok3iFJtxPwDg8KouHXp32AIAOCvcN8OpytdozpyBXWdWxnmnU1JTru0N7Y+1l3PjC37dSv5M51bhrdMnAwBOO/i1PGZ37gle8IBHgaVrX/f1jghHj6Hx3t7Q3hruRxO67LZJiDjUnIJ7sM7MYeHZK8NLcv/f2nwp8zFlx+2OG8xez9WVwTq017t1r8dt5yvXow7G7u9yrhNsCQ3uit0a2nBkBy4Jd2A+HsPROI+XL/6oegg40y1ATL9m+hUEjDlGigPNn3vsfGT6o+1rTFq2vNajYKZP6YjM9ahRzQ13SYOE6Yg2fEXo+RdEOm9AbX+cDGXZ0D4BZSgSSx8fkoc5NEzozlSZl2tXvmxZNniB8dQfL0Q0HtLWjkXV7PG2XnmSBhmEYJoyYQxyAFYVN0MPMSRYimAH93xkj3jlQ1EdIqVNSbf2CgdNO5XjI59j484O/YhX/qTVwmNxLE4AdA/XdAow8O2TzwMA/t/F/4hrj/42Pnbef8Irl34YX1t1FT7Vp4s3IeAwVuJoWNm1D/uoMj90T1oqbpKXhWVFmvPMCZKVAFlP0JN5FnAW7sF/OvL7AID3TR+NSTPJbVo0ePf0sQCA/zj3ng6m0ps9jXY9NkG83CmdZSxVlxmx9NM3Wtmo8WFC5x+ZjxF+f1Xv4wqeOTwkqjgUaerweEUyf9841CJ+hRdsTQ06DeR+M/11uGN2Ot3SbMd+rMEdazoj5fv2/bEyJLZgL56z+O7cNiDi/LDTKaZ2XqWksUa+Hp9wjAXgInR0/d2qZ+EAVisZlyLa6+O+wlvl5EEZd6aXyUdNC2Aig2Q9WmO3w+P3sP6clpZ7Um3ShkSB0/l3criyHt0caHmd2q89thtPar4IAHj/hn+FA1iNT53yHQCA1658YaZ01ymdzHtEcwv9hKGMsHtnqeSN3Ee6reKPUGKlXE9zeNrhzhF609zDMG30fVoACHPz+Ke2+1zRI3ET/mrFL2HS45P7ddIEPK35p/x6+YemV+HIZA3mQovt8e6KU63QmKiUmVxFXkec23RG2H0rzwFQrjxsDvsHz9dTpvswhxZtDDi60Dlzd4XOkDwz3Dt8nYDIhFTX8lbinpVZdGp8EJc2twMAPtFeRvWwKID6ikrZC1+LXYryuqO7CD1CV7AwcAfa6qNdNPrePjsN4IGaxIpUb2vYixACntJcBwDYsfJCJCnh9KsQ8PnmCvzRUvei/fawJxuD6XvbX2gv1M5S4jSSmSr68bk8Qm9givbTQeOUGF9GD7s9ng4A2HD0TlzcZ1t8rr0IgE+rZp+5SnhSkY/42bpS55K8sFd9QOiWc8DuqltaAoMZgiQfJUzKn1wPDEbGFzxM/uxo62HKggyObv0X/W8HU3yMOJnKsqF9AopVlrXBWuqww8UbMcMK/qyIhIq6sk+5EI8S/XyRINyNRdBTeDDLSAcRYCYdzEVsSHpizfAjinumx9zvlG0lTIqHoSh3oj3hLhFEzx/GM3YHVTsnyGHXLuGS0CnxX1lxOfZgAwDgjHBfFvDn96nl17UX4q54Gq47+9/itngGXY/7mvUAgC3Yx9fegJOmNevJq896DjNM8Wdortk+CvjDFa/GGbFTUN7XXuMOzz9ceiammOAJzZdxWbiNetdnfYNXGl/u/ryar/relGuCpx0zmFQ4y/4YdDoRZU4aEmoOGW+dnBneM/KgVJ9sQeLPsELGvuE8FLFlaw/o5jDthVubzhj4220vBQA86vDHcVp7T6bnqZPPo0HEzSsuyUbMI8KtRrksY+UR7VKvNWORazkg4HR0d4Y/N3915lnCc3CyHgCwIe51UZNqemJhLnUG2DWls4ikUPF8lKmRdr/W5XUZt+NZ6/ewopHwbCgSX+PP0/f8ESYh4saFR+DeFd3nf960+SfwPUd/GR9e8cRcb+fqhwHovk3dYFr4mPGU8ZV1z2jMXKTnsDTIbPp/I+Zw85HbAQB3TrbR8zoA+LXFH8DRprtffmVzKzYfvhVAxLP2vxn/cf9rcQoOYXV7AD8396bc7qvxHOxZ3a3ti9pblTPgvLATZ2KPdIs6oyGIMTZLx7AVewEA9/X3Y5ODaFPYp6LFqaS26b78vVgHTOYBALvQRSPPDPeZszDRKHgLu3b4/MtzQd8ZTnIvwSLOCV3k+ODCabgP61Rbuf8LjO0P4O4+qrru6M7Ms2D42AT/sJccj4StPtIb2qEY2vRaTw/b1WcGXBLuQBMCnjK5DgDw+bXfluv5/dr9tzeeAgBYjwcRAGxa3ImrmlvQIuAzwgGfeFbGN6CnAELOSGdCP5aa8Z1hZXyunmjdCkN749G7cU7T8e22HsY+acV0ZPlAXrHnySexgp/X6rsfZizSaSD1DJ9WneoHAQvqb4oeA7X9ZXosHmLkN0Lv8fRovF39Akx4Rz2GFsjdcvDz42Qoy4b2iShiowFcCVHGF/GkK6EftXBgEakET23tpgf4nVoWYSP7Vke5jeIuI2mFFmJwSKVPjEcLJtJeHL6ZRqV8WyXdK5fMsAVkqq6k3QtFKaCdN1Ph9uNuyVzrhznK+Fyar1QEhGBau3Q/5sMUUzS4v9mM29ruoDk37EYIAaunB7C+v3d1Z68U0GyKHs/+Xunf5KIURUmzY2EHbxDjnuYIcllTMjWSrWdqSArcCfnK9mCOTNw/2YxPtw93B+oObMaX1z0RAPA9k49TJ4+KXov1k/gNtaYMjagYEtZAJEYRfZglkrUMzVupPDln0kyDBQ6mlcsB2SOkgrqL56Jh3DBWUWADi6IiW2fFjmT8BkK7mF/cvbXZDgDYsfIifHx6GSZo8bTF7u7qNEZc03QP/l2/8tH4Uts9DnRNcxOPSMHIFOKcgJUzcnyIOK1PnU0KtFQa98930b2N7f3eqBZ4koyahIgV7ZGCx851K+eQnDNqrqHqWdzs/GDRB+toAfQVHraH3doDMViVnCk06AeJgC3Yi0fv+3sAwF+u++FM+JGwCl+MFyoF//4VZ+BYnGAhLGFD/2kkmpWS/2fOvTQ+Y4TIeoAwlsmakkr66Ue+AQC4e1IecLJOlc/Hi/ELl/09butfon7GjT+F21b+IL5//xvxjCPvxr+f/B2efcvP4+ymcyb9/rqfwCGszNH7X5j+HkLbvVFxyb6P4UMLL8e7V/wMwtIxhcfh7infuLgDTYg4FFbj8Nw6AMDBPqK9CfuNjNNn14XHbgQA7Iwbc72UOn5muI/KhKFsGMlbqT9I+aE/x6f7BIAzesfXoT6VnclMuR5rmTy3xe4NkM1H78C/nHxYyb3t930Mvzf/u/jBo3+Jde39qq0cT4KtxSGsPNbV2xlOF/XkXtA0/tPkKgDA9819GNv2fAiXhO7TXreuvLSjMRAjr///A+gN7XAQIQCPfaDL8Ln5lGuwE5vMfkPfnzGAmT5Tlj131BlYkzYxrNxDTzdyW2SeIRva64/cne+l35kMbWfgGflBZLhyEDijM3g+inosSDTkSKA4+oazDFuLR1FpYYEb9Mz4dvUquCWtsp5DHQgeglvSuBzRXi7HXbxCLgVlqlM2uHwVMxX2UAxLJ5WLd+ibjl1dLRwkPeoe4ECasLw7qQwls08iiOIOTfegckkMGyh6CiJv0NeUS3ngaNzbjt6Cv1vxU/jXk3/Q0WsmPCkfi+DOMGEs+0fFOprmsYRn3v07eNX867FuupcbsUVzz7RvONalrD0wtwWxmcPt/cG/vU/z27zY3as7OL8Rh9Glhes77BrP/j6ivTnsU7htxA7UQRChnDz5oNRKseMjUnvvVAH8HKLfCw1aPPtrr8j1fuq016NFYx5N6hpft/5pAIDvnnwCbTt1NPIHXLyiLJVvnllSaB5j2DDHD83OEEoWgt5LqbCIPzW0WHr7gHLJrgMAJipg5roRsFZYVTKq5KP7LHo5nKki6dlw+HYshEUcbVZjT3N6P8aId7ffAgB45NJ1PW7g6vB1AMDNC5fiH9srAQDPnnwca3DErQmgm+tzw058//2vxynxgKJHfk1iQrKFVi7uxYr+Ib57+owTufb2z3XG98Z2r3PKynoLR+/FK+b+HDev+EH85+u/G48MXzdRJa/sWjnjYXoOEn8sbpbyyFOjiSyM8AYrMci00wiFRuOI8rgDnjf5MCZo8U/txbhl5WU0ipcdf5jgznga8P+x995hmhVV/vin7vu+HafD9Mx0T08OzMAMQ84IAq6CiogZc04rYlpd17jrmuMqRgyLAVERAxhARZCggDBkGBgm5+lJ3dM9nd73vfX7o25VnVN17u3W7w+fdZ4pH+me01X3nDpVdeqkqgLQnclHug9Le498JISuf81gjEb3H0nR1pg7YgzRNeXDiMz0PKRr+Fp9JgAz12l5QekWLB643f37T80mZX7ttHMcbMX+mwEAZ2z/HgCgQw1jdt+NjkbpLK+lZ3rVOLB2lmc5eoazuwWmR8eMbB8N8OQR8xrEb+unuHG1qeOz1W7u2AxkIRA7aRiNZO4xmSI6dPw8sy9xDDfNZDgA2QkaZs4AZmx2Kn+W+nOVy7xjAvvx/EffiQtKd+A1Y1fgXwf+h9HN6TH/XpQ5CXfoqRhS/nb8kjAfLTn3qKPwYNOJAIAnr3wbOtQwxnUJ2xtMRg/fzwjPFNCfRbSnqkEoKKwYvBUAcHfXM0nbbM0I+6Mke8L9zHJN0gFo6nhoaEn6lQKVuZoY2psxR5l347doMx50vCx91LAN71SI6ObNTQaKa0voCS5xC2l0uENDWTCKVfCTwYS6ASjXQRDWpGvG90+6xTys5WGUvS7qHtEoX3wWwch36fo4GMohQ/sfUKR0SVvEKI6gVEueWWawChuBnPJKFSBej+LOP8sd0xMqTzITfP9EPpDFSC+PcM1Jeyl6zSNsvt+Wxlhxl5V02+93rH8TliWb8Z/l74uefeqtpZtIghQXlW7CrNE17nuhUU1YFkVsn1e6Faft+ileULoF51d/R5wqrilzENhPdmbvQ++t9EAB7u3Nw9UWKAXMHX4EALCrZYnraxixpbCBklGepmG/nIpK6ZHmLShvfXsDUzIfmeNIUnY5zK6Ft5SuwYIBc9P4t8svxpBqI7htH83Px9tPRVWXMFPtQ+NIX4Y7NhpAxrouXLgjKW6QFHLE9URjR1CeaD0xAgx5XfsL5AQjH/F8lAwJo6yGMGpICnIGfC2EMIZbMFjEZ9IIv8PLnug8o5GmeQfMXQXbWpfDXumapuayIwA4qvYwjlLr0DS2BwuSnUihsL5pGW5Nj8JAYy9a1BhOUI9GBofFfV3D+/D0wZ/hYv2jDLfpnSRn6Ph3jJjzg1v1NIxnz/HQ/uwv2ee99kHpkI9+zZy18ct4U9lEnZrSA/h85evQOhUjsdGcIuuoyKFjeEbljIphAM5L/oq33fVUfKL8bSRISbTH80x+EcLTiADGjw6Q+c1kBeePzUA5JXkUAPDL+hmBTKF7ru+fVdKtI1J0Ok0gj0rCfBRTx4nBER7h6sE+TKkPoKYTrC8vjtY17bOGxrfSZ+G7tXPdN25vMoa3jWQDwGubv4h6Ns+2tx+DMW1+7x1dhx7sxewDD7u6Z973HhynHudOebIPW9iM8ewitFKv6+Ngo3FWtKsRNKcHHI2h7Jlb2wDAnIW247IDxiCaib3ujXO6xzHHT8rHgPOHH/WIxoEAaTr5rMww8xFtYY8jc4LrCl4vSBTwzdr57m+N6TCUUu5eFFuOH7sLK9Q6ObiRwVYkhk/r0l75VQ7ETp6aSvC/097FcN2QnuCe/YKi/KEyBdiXRbQ7cABtehAzxwz+x1vs8Za8c8l0P4v3FFukdciDP75itH+Qeuz2blJvU+Ys6xzdiu7sWIN9WzxOT4ZvjHhd0z2K0k0/EBrQVMeRnJNSOrkt9OgIJ5LjdlHu4AuJUvF5bAjRawGPZFQnKodn4HB3lITRaOsH7aVvCvTw7FCNg6kcMrT/ASVStEXlMlZCeEqnb4ugHo/sEoWcLHD4qlF7UQkRNgKtfeqQdJGO5AG2Rb7ghG9gUYQEgTB3ik32zZT020U+ivGIfQ48qYvVVpSzyFOTqiLNOs0u66BKP9kwLi79Ep+ufAsvWv9BWMNUMnZi54RRBOxtyQAwK93hGktRKirAplaN0biv3INEKTyQmie6jk7WQUFjwbC5aXtrdtMu/6YDuTEcUB0A4ihFHCkg/CaGkleK4w1VUpSjs7wBjaGn2cLKegxvLf8SgHnO43ulF4iRL4tHlyrYA9O3prFdjo/RBk9oFM/Ea3p7dwYjdSUDQbqdWr75m6xhcBycHhXx0bQn9IS8yHEGgPSRAB0eC5OjijF/qEIlpv9bg4wZHIQ/gSIpRXGYMk80i/lDxtDe3HYMi8Rs0j24r+U0AOZJn+W7rgMAbErmYiSZAkBhfYe5MO8C3Mp4Owu7MRN70Fzbj2Zl0mxPxCO4MLkNb7r9HNzf+AZ0V7ehpTaACmpMsbTj1TG8EQCwPp0pGl82ot2IcbRn79iyo0PKZLwctu9W0LIo2YEVfb8iBjQ8v4P1KhqSmLyB6Newxiljf8bHKpejuT6Il5ZvxH+O/w8qepy1Nd+ExyM5SwIjRhM88pNfIPRQ/ijMz87crkln8wi7a0uia9ob2jPGtwi88DjCuzsYH93aFDKsEMq9UEk35k6PMgZCHzpRTyrCPkOdasAYGvBftVfj6pOvwlvG34Yvdr4fj5YPdzh/Vj8Ta5MFfr0mCp+qvQQA0D22Aa8rX4ewLE82clno1rDv94xaFtGu+HeZq0kz9mZR0Y7xnY4/Stfw8tIf8Iy1H8OTkgfRmfYDANbqWe57e9CJOkooqxRT032uz6Ljh9DjYCSdWNJ9JEcdlc29WUR7tHmmq1fkiJKOVgBmbD5Rexn6K8bomz32OBJovLZ0vatjjd63lX8RvBzA9+GnJSsBALemR+cGZSJHVArsTaa789aAyRxgN3U7x7GdE6Z9vzaO6S41iOV146ham/ZiMDs+ZvQw22ffX3G/Fo4t8X3BMSzSxdj6oHqhnffsmJn/3mbdjVQrNKQjWJiY+bcr2+PDiLbB6/9JjXfXF4Y7bC7oM4KBmInroM/Sme843mv/xaPFvN8g/45oJHoBbR/3Jed7AcyOE4Xb3ydNY0SPFN3n8vVgKocM7X9AkQytKN2WKkVM4eCKNo2kUeEXwgDZiOEKEBdqQN55M6uQSVHFvAgAMty+L7ZQ5YdGZ+W0IQ8rUhC54sY3WbYxCYpb2P7s7MZaWxqHtmZ4Y+FJhUOlNoR/q1wNAJg2thmvLV0PmiaeCIZWuHlaRREAuvWuYoWTiM6umo9oQwEP6QWoqgb0qr14Y/WHOKb/BgDG+Aj7LG3mAy51fD+PxGb12Jvg0bwNzzlZnnmmhbjp+NOi2DcR4AGWVFejSVVxoDIN/1Z9M+pIxOiD9yord1Fc0+guh1tyoESwYEuU5r2kkBddfEbXRxTFIeuIRmzpjd7SGEoXjUnGjnTruLyubVt+7s7zgc5Iq8z5/klKjLShcvnIYbSudIadjvX8LKK9pe0YP/eygVnXaM6qLk824pSdPwYAXNt0gePjvd3PBQCcp/6Czuxd1obqfvy28X34TeP70bP/QYfnMGzBpyvfQmN9CB1qGB/b8058ZcvzcVXDf6MlHSQ0mjL1gLlVer3u5fIjk+3jqgHrs7sVltZWR/xRAE5NHkFTfQi7dDuOrF0BnfVwxa5fRxkIktORZyp43oSZBqauxxuuhSPGHsB7+j+GGWrA1f+X+m14yuC1Gd3x/gEtr+FoTyHzhGdy+bXpHDUk4l/WNfMWMMxlSJxu2hc/f+zZ2mlZpJatGcKL0OFF13qRAUR5IRkclsYZqh8AsEt35hs2RFewjOxvW4LfpqdCK4UPTflvfLj6Kjw+7Rx8sfY81peSUlil5wMAlg/dgTdmGRE0Ctuj9gYOb89bW+aMrwMA7CjPZXuhfWKpc2w7GjGOmaPr8Nr61fhY5XIctetX+GHDJwEA6zEbw2hyfKwhwUDFtO2q20sKyT6jhPkoOYPA9xli22TfjIMWqdaYbVPHW7KINvyakzJs2Jwi55Ut8q3N5tK52cOr0a13YVmyCSlKOHb0Mvxr+1eQQuHc0kq0D65xfQgNzqMSw2P73Jivl/UFsRwFAK0UHkoXuH//NT0iiroD9IkuM6d2arMfTscAjq6Zp85WpksL92swuU55ZmnkekqoA7DxKtBTo3kvfG8cFWxGN2jZpTsNPYExF6Zvx89ncqdjqJPQCLQ8J/z35Cwg/kHa1sGsYUvq2jqSAR1a0BKeJJFSwoV6kpGu/N88DsVoDX8Py+Qi2nwPOJjKIUP7H1Di6FWx0egVk1i5BFE46JlfH5GiCnm8wTPFjWxCtkgRX5C2sbKbl8o+cT3aP+rZS2PUpm70TeqcEBR3EuWGo5vCCH+yNqnWODlLP7Sle+O1nsZQCSFtn7z5G6zdv5d/jKkjmwRlN97MU62BNMWSZItrPzPti5wvpo90XM0fumomot2fpY6PoAkPdZnUwlfUfgYAGNclbGlZ7vkTKHMU1m8vQwO9DI2cp2TKJe8fc7QgR7l0fUHUF6pIFmUlqLSGF438FACwrfMEAIo5nfgm7XlmN+FGa2iTucfoCeYj3whipZpH9zO8KJ63kkygjo0wXRrIcaCRFMOiLBmmFIm89fyK5yhxThGZIhnVlG4xK8Wlt8eyJ39dB23ZvDWwBlTRVTX3EvS1HO4mmqVnS6O58OzZpdvRXt2FAd2CGxqe6vq9o+Vw3JEuQxkp/uPRF2BN0ytx8V//BZ3qAKapQRyz9uugpUlV3e/t6QAA4LhkDV6/9hJUUHNzebHaiiM3mZug700P48/XkHl2d2qikstrjzi62zCMJfW15iK35G4AwB/qJ6Cuyvjaip8AAHqHHnGTgO4fCMZQk/8WOVUMz6mS7ud4GTW8q+/9AICduhP/c9Qv8KnqiwEAF+39Bk5Sj4pjk4pzT44CexotLbJMoRGp7nQXyirFuGpAHzrZ+qB9sSXVGuszQ3vm6FqCNp578X6t43qEj5MxBi09SinnsNilOwBJSSf7DHXelojH4oBqxffr5+G65Z/FZt3DZXOicHe6FPvQhrL2c/aLtefjc9UXAgB60M8Ub+I/MzSiH4eNmXPkjzWuIHLKZIsAwIyRNfhg+Qr855bX4w3pVQjLXTgy4m1/2RhJ0zJnMeejbxsdwQLnd1HGID+OZP5WT31EeywztPOcQbaEukLGHgfb0mTW75yRVVhQ3wgA2NW0AP1ow7bSHDzQdBIAoHfPHeSj2fdSjWkYwDQ1CA2Fx/XsnIwfxDRme/PX6hdCI8E3689CH6YGjhpuxNi+7EEbaqoBidI4s2rO0a/US0VdUTpOQuU6d+jFekrhHQ8CHpNNYWB14Xu2Lyv1EY7WsaQZB9DM6KaspqBYnyX8IX30fJB0QOL4ExwRYVZSSJCUvh3Snpc6TnH79jkR7RCmlD1ZVfg9ycj3xjeiIp1Dj/oNqS+xrniwlEOG9j+gRAo0NQYERaLwTBuZgC76nMaCl+EFVeb8B0JBENJjixxBJnQLEQkQYeXbata/MME8NvwVU2xCZYkbMSD17PekjUkYg4AXS7Ongf5YPw4A0Nr/GMLConhZ2yP3/t79fX37SWhSVRzTd40cVYzoBo6oPoRZaq/7xiy9E1OGDS38NnnbP89fa2jvq3Q7eu6Y8SJUs/e09zbMwturb8UovVyFGGS2WP4MqE4ANnVcUDgLoqbcSUON2HieSc9AiREJ8l+DR+O/+t+PE2srMaYruGf+67MafmCZUkRS6OxFVM2jPnWcKl+WD9FG6f6THy22QO5g8n0M+0dTGx1vsp88Jc//XbyEhfGWyArBQAiVdPn8u7yuvUyZIEohOIhYCm4SKCYBjdL6CJVvyUjrwn4AQFWXMFJpj7J21jQd5c8tArgxPQ71pOIoT7XGr+unIq9MHzARn5QoCdWkidWpa4XekTV4f/mHAIDW2j5c3fARJLqOGkq4LT1KjIZorXGXzgztqjk/W081vtfwKXx58B340up/wSvKJjPl9+lJUFAYaOpFTSco6ypmZDea0zXDlDxwY1fxxZXx0ffLH0fgY32MWouGLEX8HdWLMVDpwbfq52NfdtzkuaVbgwwLi4bMM9FoiOWMvGdKjjGFWal5WmlXeRY0kih6bfngHUQa96ZLUNcKXWNbMV/tEJ0OUpQ71zkFDjPtPY1hJD7J5F43+gEAfVlEO1pbVCGXeCatGWo0KKCGMj6gLsHO7Mmzx6edg2E0YSd5CpIZCMF5/JeVb0AZNaxK56GvPIv1767MQTR74B68sHSz+9t19ZPwvRN/jtvry7GuvBiXq+cEdAP9WUR7ao1GtLnsofTkyUJJn5GcoFbuleqj6MkyCUZbZjt6bBFTx6HYNy1vLe4NTdnb7P034H2DnwAA7Gxe5Ohe02D+3j3wgG9L+LgkMZkVw61zMIpGObOQzT3PRwC4PT0S15x/Fz5Tf5mjPcZDeaagkWCo0ThKelKjR9xNItp0PkrnpMVsQ83XnFtLNJU90gE8zzkegd+EZ4B34ADA9saF7nfxMrRgTzJwD5P0K1sorC44wfjzZXzvsWs9oifEIUaLs/rSc2MSjYKxPJHTwdAjRZ8Fegj9IY1FPKPfjOmJda6DpRwytP8BJd4ASVR6ksql9LaudLkSU5QIjtBTxAQYiYZFl0KBb9xShCT0Hk+kPPHb0mMhK6eOI9oo6e2kcqSR8owrnCEfLS9K1WHMz56IuDk9FgDQOLxdpIfysQFVtNQGAAAnjX4Vj3WdDQCYNrIxJ3rJjS+tgbnVDQCA1VOfjFvqRwEAFuz4Pesf4w/8mR8bDeivzHR92da0GOeMfwEva7kMl664Gtelp3CDLlD6AD82NnV8mhpgYy1HFYK+EN5aHgGc33HqJ5w0puOVZ+QvVVuwomrSeL9Uey762w7LeOPHX5H21KDfBdO3prHdrl60wRMYSwdD3Edp3strwfMhXsOSTIBrLEdx6BqOx5BdKsXuVLD05Ctu4V0Ars+Wt4rOXWENEyWEOQkjI8aRLUafmLIb0E2L5cM0ZVK296ENSiWRcjlWasXjFX+W9ab6cYHxBfy0fha+hwuwLUsDlcrXOt+NP+iT8Of6kfjtcT7KPZS04Sf1cwAAryn/Dq+v/hBPGvgNpqohHGjqwZsqn+CRJsUjJCuzt7wXVx8HoDFtZCOOT9Yw3DVVyS6UApBUsDW7YXeWNjJAvp3YTz55n7HzhMoZSyOVFcCxGT2PNhyJ29MjkWqNOkr4asPrAABHJJvFiHaofBvccYYWjYbZOTEFQ2gf3ULoQUaPX6+9dX8jtuOt64uXmVQBHkSLeyv4TaVfsXnGjscEPMs9yiTsw7IxSHlLUsfRySJfUgotzSySjWoiKxzdBvZnHIePL/wBFo/+AL854tMAgI3ZcYVFyXYmM3kmj3lvHACuqp8FlSS+XqpdJsa8fXe4LI+Lkw/hX6vvwGDLPLyk+kG8a+qXsS17N5vOiX0Vg39q5iwGqGPD857qMyFveQTRt2Hp/8HYzBjdAADYrdtRbZoq8DaWo6IOAC9/NjYtQ6oVEmg0wPDh3uylCwWFR5uOAQAs7LsBR6oNfJ/RGkuUmeMH2hYzmg09th9ekEoOb11uiva9hM4pIQNpoLHX4RksT8M63RtcSMZ1RR4hj/c95iwnden5cDsysjPAt03cERdLoa9o+3cXfKbeyvanEZ7xvYLuR7S9mIGihOguhPUBPoauL8E2RdtSWFRP+b/ReubnxO3Dtr5lyIv4nLSULq+Cn5RI2h+XaTjZfocUqpi3B0s5ZGj/I0qkxMrKbqhcMIHKFE5bDw4WngM2eGIBVvh8Eajn0cBOUaswe+hBKKSg7gBODxwe291IoWL15LaWRnrLs/2v6MUlfJRwSzRKfCRocFT/jUiUxq6GuXgQxhPdPLzN0xgpQGbTmJFFJMZ0GbvQiX1N5lmNrrHNQvRBGn+N7rpRlPc3zsJvU3Mp05zdtzGeGf7QcVVowzBatbnttb+hh9G4Rc/A9tJM11YcawrLgP1Jh6FfDaGMesTH0Him32PjAjL3BGVFvMVUME4BPoY0vf+b9WeJl4qx8SI8251FcFrGfOq4wyKsGSkdjCq2dAzDC3fyztNF57bp/BYuGpTWNRA70IDgjC4x6Gw97yBABJPGQEoxV6Dzx+P2PPPfYwZCID946njMC57JUWRcmP93KRPR3qPbgnnmOIaN5QXumzenR7OoQqo1xtCALyavxDeWfgefrb4I1y14L84e+zw+WjVRorVpL+5oOgNvS9+Nl1U/gF2dx6KuzRfWNhyOL9We577/mvrVuHDvdwAAG2aeh0dLxpCWoiH1FNike5BCoQljmI79OHLgT+5bQyWzJv/U8yqMo+LotueMl+gNjGeMj8wYzJfhYQaC4ZkvWmsckWX8PNh4PKu3pmTk5TK1CeXqfteGzT2HJ3+tS4r790sfx9seehGekdzJo2EkxXRubSMAYHt5juNtZLAKsuf7daOYH52sFw1WZvgzGe6/aem2lHOemZ8VPYa317+PY9Qavn8omjreyRx61Nh13wOIHKbjGvI2PutuDaA6SkgyQfOoNhHuOWo3mtPhaI9LMoXcZno9pudG+sNqPQfjWfYUAKxuXIE71DEANcgQr2FAY1+WOt6VXejJ07yp3ON9ZjBSl2VTiPuUAfZkt2uv0bOL5yOVo/DGjjcavXEynLTiL6k3+r5euwCPTjk1oxt4vHEFNqQ9SJDi1aXrmTNIa42lmaE91LEk6kvRUQ/aP5qqS43l2Pj2sL5W71R8tOMM6OCuk0jvUdxRJwY8aHsE7cm8kO4hkG/05nqP5RkA7FDd+Ov8N+Gy2vm4teMC15fSBBZOGvCR0wO+kGGN0xBG5BFb1/n1OCwAkr9RvCHMwiVnQhGNDobY2E0Eeuz3aX/sr5zGGCb1xf5bcpizrNiDqBwytP8BxRnGRFA6wVQUnYX34hV5zSUjFKCpX4JQIwKDzmnqkTw9eQg/afwoXvrg63FNw4eg0los4EV6yFleFvnWuW2ltBGmFBGjmMLEyCdi/rgLgCbYCOZlt3M/1HkOtmWRosaRPpRRi6IhnhZ/a+yu7Gxgf7NRXqaObYVKa4wXCIydMmq4AH/C0qpJUR9omo1H0vkAgCmj2zK6XfeZ8aQU3DmzvXoKqkmz6HEPI8iUP1L2wiDakGbiwZ49NXX5uIpRM3ADyLVlN6CGNBKFgyg2/KkqP3+sQvLjxheghrLovAEbL78Wdivz9EfzuL8MLSFGp2tL+NiAKmZgH1e+gn6z+UzWlqsnnJ0uzALJMdJplCJUYCge0bCB5JTLSztFUM8XquywS5MiRYnIGcKf6ExbSI+gSEZ8JHTbOWVTx/fqdlEpVgq4vuUCPJLOx49nfwADmCIq6UopIEnw1fpzcPf052CD7sV36ufjj2dehQvGP45x1ejpVgrnjn8GtzaehR+3vx470YXvLv+2u9jMlo29T48dTKyPGlWUsScxcmeu6sOiIXMD8VenvBX/ufSXOHH06/hj96tdWyjg9kyxP13f62m3/JYiX47u/HFhNCq6PjQWJOYM/I7KnIy35m+bkznYVpmPZjWOeTtvdN+hqeMWObt0M9r3eDZFK0awIlkPBY1/LV8bRD78eC2pZjcmNyz1/IGlGxG/7Zx4VBun6GK1DWmaxg4mxGtBdk4J95DAr4Uz9v4Cr9DX4prGD6PjwAZXUQHotvuH7uBGrHC5kuSIEh3woAY5HDDch/djCg40mDnXM74pNoqUOZq0MNmJFAkeSedH0ccqylivfVT0923Pi/cFMveoPrK3Yg3t7aQvQVt4uvleSOSMpYdm9yR8ntB+Txsze8jadFZwuSfHTWUPdbaGKdi2Px+tvQL3dj0d7+/8LD5dewnTuZJE4Rt1YwjOTXZFY/3kxKSUD04zr4NI+7W8V3DHjxtusreH/KG4t05Z4eo91nFG3L9I1nOHVZRFSBhJjUnJUA8DB7yefITL88z37e6Fb8Qnay9DFd7hExpzUeo4cZbYIs0pWyhvHYzoGVynVLn1KMzW9zhURLsK/ub7E9NTSqR0dKFtIhi7Sohoqxi3rSNGtMM+5vEswnPoea9D5f+hxMp3jrIDv8Btu1iA5SjkGUxULtimmC886TfTVOND5Ssc/OhkPeYdeEjcKC1FPGU1oDEHxoWx5w8QGF9k3YkRbdJvOU2cC1SmhJBFP2PMpMf1NS3EPtWOMV2BgsZMtS+fRuVT/3bqqVAAhhp7MKYrKOkaevRuzlviiEiUwkfLl+Pj+BqW18y5zP7mec7IbxnbhTJqwW3AdAzNOWogu+SLCHhdsFkB/EKikLd1VcJg0g4A7lkWqoQUHR0weCxvpU3WMzK8sG+u2onFO38Pnb2pSlOr6Vkny29rNJeEsabjZSOsSvmIdutYn68nGGR+7ml8pXIpfoe34LDM8DHOGzunfD3HCzrHCxxMzGgkCpnHAdYW4N7+kLesLptnFgbB2InXh6XJ4rGF3vLsjOoJ5ExCFqJ31PB6APCUTV/BNyufR6k2GstCwelADQ5khkhXljq+FzyiTRW8bZX5eOb4J3F353mkbUx32BcAGOxagWE0RcrlWj0bn29/LzZWFgIAdrQdhXPG/wevLn0KaxqPxEvGP4C9HStiBwMxoCyeXSVjoB+ZbEDXuDE+NpfmAaqE3ejw6zWbt3/KjriciIfRiHGm7ErzMZp7GhG/AZpZpMj6ABZkLyPsLM92eABAJQkeaT0ZADBtcJX7Dh1/KQo80V5xeBZJBcw+9NR1n43GpjEdwfzaegDA6soRDkcoCym/7Rhs0t2oqkY0q3FMG90oOHniTAxqNIqOYzanzM85o6tdP47b/N2Mt4bOGfAynK4jKUImrXUmU5gTw643Yd8jYz3QZJwm02vbPH9SjePVahw9uhKf2/JSAMCjlWXoRxs3djIc76++DsMN0/GZ6kW4u/VM0bEOgbc7GuYDMHtvgjToiyORnWu3hae3B2uL9FHK+JmePem2QfcEeliGm2WG8P2e9ts6SyzsMT0Pv5z/ITzWuILRY/fCx1OzbmZjNxvrjpFNmJ/0YUyXsbf3TIfb98XjiHQcnV/X/juG+Wjoo51n4Yu15+GXTc/FuuyJQzn7zfOVGpehDsDugkC8Fiw8LKHDm89737don5HWOrhOEtaj7fMc2UWp4/Sbcbo1IZI0Fuxa912Kg/4tr57Hw6HUKcdgQdvQ6WBgsnPC/s3TI8AEuu1fJJ7FteT1ejCUQ4b2P6CIikSwwJkCVLB5ciXd/IzPOhs4NRq8UiwJXk+rhbXU+rEsMUZnXZnLg6aObyNeZS/UQnryos/hJmv7DXABFp5fs3hsKY7EkGghpTFsq6lh44WvNbR3NS0AUMI2bQy5WdjNlW92BhU4PIuwbtYzMiWrhI3aeOrfgh8zGpkiCY2XlG9yfavpBNvbj8EetGEMFSRI0YN9XAGmm6xSmMaieJM3GorGX2uNPSVznq6nvt21jVKjCW/lFGwKg6MnVBoVgJKu47cN78d5q96Hs3BPhse3pd+0qZZ7MkNbithJymCigE3KpNq2ju9BJwYZz3ikMVPIhjfg3NJKVFDHi7Z+MrtNOsfwy3BLEf+GdBT/Vf4uXlv7CUrpeNy/0KlG6BGVS0K3dM5ejmjHWQmSo+WY+oN475pX4EnJg8ETO1SxCXim/DjkRd0lWZggxSfK38Yp26/AuaWVOGWvcIEg5a2ivOWGiDW097i1EMgUYjRKERJ+g7qn0RZJsWXOu4Duh5Ml+PTsS3F7dqY6XHN0bVpn0Momo+i+rHQDOrN02r7STHFdJ8qk/u6vzEAzxnFS8pi8VxC8dAyWqw2YhZ2iYSPtFS3VfW7t7cwi2pQee6v7tKHH3XcKs7EgOEbJWi/rGt5fuRK0HLfzavyb+iHmqx1uvOaOrkYJKbbrLuzOMgIkBZ/y29KSqhK2tZsI4qL9d8ZOWcGQoH1hmUp074OZ25X6EACgd2yt+1t35ohQCmjUo+jJLsHcobtkw0TFdJv2wp5CYGlAo5RBAHhDe8b4Nodn+b4b8fPG/8J7d7/f1bu++ZkObxKsj5X6cFx11g3m5mtBFoowAHvKvRjRDajoccxXO0X9iPJCSXPU/YfzR3JOWJ5NHzfZYht1D+EjMWKz+iyQQXQFGk0NcYv3A2RjaO9U6FV7kCB1fewZMo72h/UCoKE1ols6epb3aoWUbeScZXT+uD4qfLH2Alze9gbopJy1Jd8TZI/rMwS5HvHMFH7uG6xQun2fY4NcXNeUxpS2D9qqUMZ5PA4mnGFn3wvphmxAByDTF6HPIZ2yUa3Y3yhYikDLsLBxXOhxAo6Zj4Mi9Yvp5vYHbS9d2MYzIg6ecsjQ/geUOI1aOtcspIkSxVRKEZPOM9A5zc5YOVpsvTiSQr/Znil4u3QHHu0xqU5d49uJt5f0z/UlVky4EcIVGIPbt42EMcimRtadeFsyOdcK901Cn6TgESVdKaAFo5hSN0pkf+NsQMFFlmepPUy5cBHSjMYTEhOtsJcZKQXshYkIH6cfy3BbevxYzxjiT4ntQifqlSnQSLAL0xzuvDNEiQKmuijeFMdLzltiiAqbUN5mvqU83+CvbnS8jRROphTJNEabtKA0KqWwaOQBtKkRAMDT1Z0RPX4zB7qxz/Q5ux1dmntM4SAb/AG0YEPqo4ZUiWVRkww4/8D9rl+d1T68tnRdjlEdOxioQn7apsvw6vLv8Yb6T/C+Vc/By0t/wAVbvoAvVb6C5Wo9GrJzrTwCyHFQGikfpQuApFRWyTHC02CB2diFr1U/jJ7xTfhhwyfRPt7nvk2/Fx5HoGoSTx3Plwv1VOOUZBVeWvapxkuG7ooMBDbPxIshDX7rdNqn25iRT6PAzukopCx6WZrjxCiKDObIZipzacZQyEe7Pm5p/hcAwLJkM0qoo6pL6E+mkjma8TCxNCqsbTPPBp2RPMgMP1v4nDB/mDNwD37d8AH8Jvk3zBhd52i0Rbo/YeaoMaD7KrMxmrRkdMPV29pkLiWcPrTaEUD7TNewpdHNcaqkZ8DFu2/AiZls/fGCj2KXNjL1tepa/Krhg0hqowCAowZvBZDdluz4E++FVOmja33tVBNBPGH3taJzKtyvmZNHWOvWUfOZyjfxnvvPx9OTv2JmdiYYAKYNr0cZNROxH30ADaqOXaUebMO0THkO5gnBI6YTg+73jrUxjXQfJvJ6b7OR9XPHVjv+nLDvt6DlmvrpuKXyZEdPyEcgcHgSOQzYcQ3XB5CqBKu1MfSXqs3cMS6k9NK1WfQGM+DXK+eZ+Tmjam743qBnis7Sck46eeQkFvYPC2c0ZrBd6ISGQlml6MKga9x9wKyt+9PFhd8T9StwfcbRKBiNhUEZ5My9YB+lc5TLZmEvzPtmYGjlGaKyEctlJjXS5ONInl/SmWju0PH7mWSoR7FhyThViGFsh7QwXx/B75RO+2scbY7pSQQPgZSqLRnViUL05BccPRPRKFQEnz+0vZQFEDrvDpZyyND+B5Rwkw6NAQcLFRMdpNsGbeXzxnnPTwQbjqSEwk/+jqo5v7pdd2F/s0l1mjq+XaY73ODJZsUiKcHmwGgkDJoo5TnPqAKYbikb+XTzY0q6P+u8X7dgvNzKvM9zVV/gueY0LlDm3OKqdJ7T5T9ZfYnhW6b8c8++aT9tyKcTAsC19dMcjTuUNfJ3i5dCWeXCRvH22QugLG8FA4hvQlk9wjRqNG52hraJ8tP07VIBvykeKvP5+WcV1esd8bcrL8ZW9k2q7DRo/yzLnszQlm5apgpHPdjgH9ILAMDd/CpmU2RcW3TgXgDAMMwzTk9KHsqJzsVni22Ua5naiGO2/Mj1r7W+Hx+rXI5Tdv8cF5b+gmsr78cFq/6dtxVwGFisrEhHAvjZ+wL5AT8f5+y7A39uejtoOWrXr93vhRexETmj6uM+C0RYwwlRgFeo9QzfEUN3Yub4Rosl4oU3bIlszQSai2ijHRDkB5sTlLdhPUGZc3UtLBhrPicozbFCXnSufX+pC1vLcx3O+/Ri6KQUy3XCn3Xt1tB+iNULDZskrWHO2Do0YxTHbfkBEqXRpKp4x2OvxOnJQzhpy+V4X/lKlFELsgBMmTdsIrGbG5dEc09Boa9hHsZ0GY31IcxR5sgMOzMcKORszRD5uHjsEbyt9HMs23I1AODy2nl4sOMcnDr2Vdw9y1xK166GsXDoHjSgihMGzOsMP6ufyaN4wRylPKN8XDXjGQCA7tH1aMsulkyUuZvhqNGVUGmd8ZEbX1n/mOxRmIedeEHpFpT1OL7R8EUAwP3qCFR1CQnqmIEBKADzxu0t7iscdeE88U6V0JCERe7pESLxkpOXypQNnSaLYumBu1HWNSRIsZA4GL/d/X68vfpW1DOVMW99SFlN1OiLdSEzRx/PDO3D1RaRbsYLto6y75H+SI6IEDYLu9GaGlmxSXezewTCOyzYnkJknHyxV6xz0SNPSinUUcJwZSoAYAZ5PrMzu1V/vZ4ptKX2S3wkKMrQk2RFwAtqaOU922VhXj56ftMxCCPDVAfkOoDHTYbW8TEsssEaf4+ua5Y6Lhp9cZF0UjCeexpjA1H6aI6DQIDZ71Ic9G+GPoKLtY/xlCTeqritlDouXa7mU8djGullc3k00r8RTDKNgow7GMohQ/sfUMKNLRUMP1FJI5okg2VF0bZBdNbiMbCcdGKrcAobk41o79BdGMwM7Wnj2yJvP92EeDq5gUqpTdLmaTyknG7aR240+N9jxTaO2FHeSga5jbpZxXCrnu74s1GbyOf8pI85J5hyAX9Oejc6so1JYY02fGtRY2jBKEsxVWkdVzV8BKc//F8AgD+kJ+GHzS/F12oXuno7lUndnp1F0x3PAqNxKsi5VMTCim2UwlhLNz9Da+wuGUO/s77XgkiEhPC70BgkZ7nAeQZwA3jOiI/uz1H+/LRta1vP77sRjaqKHUk3tquegG64wZaUHbt5PpwuBACsSDYEigRfH+0YwoosWvZlZRT85clGc2mSEJ3xFxL5PmsNvKv8UySo4zf1k3FO6XuQytz9KzFP7cyJAPl67HIdaVyJYeNgRFaE56TpGB678bsRXbMH7ye4i4xGuJF+9kOX4C9Nb8OTRv8UKcWMHq1NVgGAv857g/v7SQf+5PCYLwoX7gBMuTSp4/YYBV8L7BZ0wdCKHC2I1xEgj7V4JERIMZZkbp5i+4u2lzqc19ZPz1nXXq6vnXIiADOfO8d3xPRk9V45dgU+2fdmrGp6LRbuvRW0XNnwCZy+4at4U/nXWNX4Gpyw9fuEcPPrkuF7AQBrmo8OnAmGvjRpwKrscrHTkocj/lCjox1DOGP4RjToUVavnI7gI7vegXdVrsbM/nuQaoVv1c5Hqs1t2bcufAd+DROBnjOyCk9LVqK1PoC9pem4JT2GvxIQKN/SJaBKAWOVDndMaDHMmfBSovCpyrfwXwMfxEv0bwyM7eG+L0A8H49PuBMVAL5WfgX6sucFn1m6E5/dczHOGrgWALC5PM+1hYrnaFEGmkZMDwSjmjunPV07W5ZiQLegKR3B6fW78PuGf0djOoJB3YxXz70eK9ufGuCWz92WpLUg8CdcM/bc8qJkW05f5GMdRWeQAfl4THM6hL80vQ0AMKBbMIpG0XkvHm+AYOQz3I47kXObrvWhislWm6H6HaxzzDiXN6ObyUfTVolyRgnjzzKLRHlt6yFeC4j7AkjBm/i4FSAfKQSjJ+aZq0XG1cOCSg63gUup6HVhL6RIhE8yWBqMf0xjDBMj31L/gu/ZdrSuN74JzBnfAR7I35T6GFbMo0d0JAQ0WqBkfEvGewhLhLGm3zt0RvtQ+buL9OxO0ZunVKBKKZQshZoaEgiFkF8szOuZwaS0U3t+aauejqHM0F448iBa61l0lgjoiYxYW6TNkwl4Rw+lO94opZR5aVN0m1WaYvr4VjRiPDhPR+iEiRybPk+DTTvenJ2znqd2BsqO7Z85e9uqxgAAu3WH83oeQBPGExMFnaH6vQBLU7ztwKU4OXnM4b9DL8eVzS81tyBn9XZkqeOz1W7mma0HY+gugNLt+eMqKGkTKW79yVQAQFt9H+GtqcfSpYVURMkrLF1c48YKGouH7nHtu9SgcU4wBcjUPWzrLwEAv294GnQmwsRL7ti890aRAvCwng/ARLSpd51dFKPreHXp92jQ41iVzsW1yVMwnjRhutqPWQP3kbXpcUhOtQXVdXha6R5oKHy+9iIMqlb8tfOZrq879FT3+wq1nq3V0HADQsVNMZihxxAkOVVEYzCDzUA/evvNGPxb8l58bp55H3r6sD9byp1gfAxb00GUUMdrS9dj7oC5NO5lQ98V5x41WI5UGwEAfe0rcGntOQCAEw/cHNMdKel+tI38UOjOntnbrTu4UU1Ttd27rIISKsJ8n6UzurJjlCic8LDw3LdCrFwoKNzdcgZ26k6MJK34bf0UpqxIUaWhShfu1Ob28aP2/d7RE67Xs8e5cf1QugD/XX8VwlJRdZyy7QpHt+Flivmjxhm2puXoXKfjTfXjAABvKv0abRhmkUHHRwX8vvG9ePv+z+KC2u8zGs3fjhh/hNFybXoatmE6G5sNyuxJXWPbcFHpJgDAza3nIUUS3NTNaaQ8o0o6lHIvPbwE1+G85C687Jan4Hkl87ziO9Pv4VuVz6G3316GKN1XwR3eJyhjaA9UZuDK2jn42ex/x0Ol5diZrfcPVa7Awto6TK2bfWdLKTO0RcMkPlrlaAeXj6L+INDIonhIcF9q0v7/a+RTOCwx+//96SKopCTK8FC2mm8SfofrQwtHK7L/bM+cHD3oZ476vJTeeO7Rte6aiIr7st3Xu9//kJ7I+QO6x1m6gz3F9tHNs3g/o/Wki9QOUENbAY0Yx7RRkzm2Ie0VnYGsz6H+GGTo2crUwSjJvej4j4qNaoCONaK2pFpwdIDygu9TVO5RHJKRl2fwUtwJw+HrhRHtiQw8S3sujUqiW4rOx/XMGMTfoz9t25AmqV7uN4XbxCW6JV4kYj0V0WNRijRyEt3ezOkRounkG4ci2ofK31RkAzFWGkFhVMATxT+vLdsI4HdA7sUPBXcMM980P7vHjdBfq2c5QxsAnj56HceNHAcBUQbjvnj+cI9kvClCWHjSOSDRkMhgF478Ah/d9Apc0fCJIBrmBSqUOQsN+HPZSsFdaDY/izSGG7xSwJTMEK2qBgyhmTg7FPaX/RlvK/RPHvojnjb+R9Dy13Q54ZmptyW7tGuB2hFE8f1oJ4pEtHWbOK5MSaOGkuuLh1E+WkO7vbbP4RXvGwjnMryyQxWEkN8U96fxRbTV9mC/bsZ40gzApOvzuwmAHuxFz567AAA3VM7x0VnSZ8ZHp3AQhUUpPJwuAAAsTrajRR8QFbIXbvsM3lUxqas/qZ+DMdWE+6eeCwA4ZvtV8dyDEMXRGm8Z/gYAYOOMc7BOz4LWwNUz34WXj78P3zvy2zh17Ku4um7OPi5W28QIELuQjChKIR9NXd5noNipYpWiV5evR6JrWJ/24GZ1IvoaTDrnlPFdaMEoa083xbrWeHnpD/j0mmfjO/UP4vTkIYejSY+Km6e7BKY+gkXKKPV72o7A46nBObu6EUeq9WL0Oi+CWFIpZmfOsk26O1MuOG666cspxvkwgzujhxjQRedf6fEfMh3lSBwZ15pqxAVjH8d/z/kW9qDDxgqCer6x1sBv9JMAAIfvuzmiRymFaRjATG0yRe5Mj8B4qRVfrz0b368/Hf9x5J9wztjn8dUn342zq5cCMBdiTsV+t4YXqh1o1sMY0Q3Y2bAgN6r0w/pTMdgwA4cl2/CZymWR8gwA7SObMDN70urI+uqMZ6biwtpaVvf7NbPm6mRstsDIxpP3/w5PLj2IFAq3tJ7neG65FSrkjN9MoQauyN7Tfqa6HZc1/A+aq/sYHU8r3YPTVn8GdpXTbwLe0dKEMTzjgbfhpaUbAAA/n/3veH/tDbh7mrnrxBqVYVlVWRbTSIy50NgFgnPtFkZu9I8umgM5/hM47+5Jl0Q03aOXOKczpcfwjI8/EDvvDG4iuyIdx1C+K4vyd6t9gaPe/85fW7DtEcG43IthK3Ze637/eNVkjhQ5oikekP2VzsdoXSPmDzV2+hvNU2in4BEkSuHk5FFU9Dh26KnYpPzFh+GrHECwz7Cn3KiMs3RTYznWC2wpulSO8TaNcUyUYcHXYdwfRx+RjwxWUI87WuI+S8a8/QYt7M4hod++vZwSLhm2Uon6J9GZ/S4Z3xJ/wpKXlh9XlZ/ykvoX4rKyg5OtHP6wfZhZoEh9Vo/sZwdTOWRoP8GFefuERUGFJ8AFoAZNY7H1pNTo4HxO9ve6YOzIHmlKo4H2VE363Ho9E2PZeSIA6K7v5LiJscs2+MhzHRvktq6lMU6XmtgY9Lyw/IkjTWeP3QQAOClZjaZ02OEgMhqJUi6ivU1Pc7ht6niP6keTHos3eChMqfUDAAZLU+FM7KzejqZFAEx0w/LMpsVu113YPuMMfKv2TDyKeVGa5wYYB8fiZJvMs2xgu5S51XYf2sRxpfRICj7bmMhZtf6kEwDQmu7PbtqO06pY1CSJaTS4AiUt2CibMIan4g4AwPfq52GfvZhH7YrG9VmlO6CgcVe6FDuSbjnjA77jkrKjAOxBB/Y3ZE6U6vpIkWjFME7qN06lGsr4ef1MJAq4a/rzAABL9/wRC/XmiBfhhUQt6SCOrJsI3d2Hvd31paYquC09CjvajgJg3nIFgKeW7omUUIqD8pZunnkOPQ/zv8eRJgM6LDN4f1A/FxoKw8kU7M4un1qY3UEgjyHwitIfAABH4XE8tXSvQ9WeDuDZB36GRowHl4qZn52jW1FSGvt1C8aaZ+CRLNMAMGnQ0o3O3EGAjB6FmdiDiqqjigp2YmqmNMbKpR/rDJEgZ8z6UAwW8rbo5Qga7afr0M3HIiM/W5t9mIrd5R4Gi+rBskfjj+kJSLXCrAOPoBv7AkeNH9+dpZm4aPzD+P7Zt+E36almn1EJ1uteqERhG7qxJXM2LlbbHO6jlLkwbZWeB52UZTkDc3zm50s/AwA4N7kbjZnMpeM/Yx+ZI5mj0BnaVWNo/7F+HP5y7Gdwj17K+p0oYHvC3yj/67QLsafSG/HHcoin9AYwZeA3p0djV+M89t23jL+N/Xva0GrMxu7CDLRLyr/AvD0mEn5z/Wg82noKLPYkgXPyAcD9DcdjXDXgpvox6FftjB6AGojxkQfLC4c7nHuEbtHJH8jrlRmfbXms5XhcUXuawVGwf7BsGqaTWL2AyKNw/8jgfboTgInw0nXN5Z6HiRFfsuZCemy9I9UGdA89ijFdxltm/wz7sgtL6dGj0HkLyAbvRFlboZyhsPunnQ8AeJa6DT1j6/Ff5e8BAP5QPyGQ6xaFHwQqe9h9FQQ3vQPD1ovkB7gsdW3DPROS7FHRnACIDsgi7Cr6JjWMffFODFtyjcaC+SgFZej3bJ8kOKUxUcJ55SRuzc/PUxo5UIriQqBHuZ+KVFMRncEnXCkJeJIkNmzliHZskntDW7F6IT3ujLbAi5BndO+iNSWH18FQDhnaT3Ch08Wl26bS7ZvSeariaLEXavmRJtNWEJSQBZNSCh0YQk92I+cqPR8qUfhg9TUAgI7sTeXwfB6jkcBpZMfT7ZcYdxAEAl4Dh408gEVqm6xcUAcDWbY0ijMV+zG7vtX9rWf4cU8PfFulzFlowJ7RNoKyH1MwXm4DAMzSO2LPvvLn2QfKXaQvpmxuMemcryr/Dg2oAgC6M97+W/XN+POpl+HjtZeLirtNj5yp9qFZD7s+hKmj7tZxexlawebJ0ioFxY06UIYwBVVdAgBMwwDzmvOjDLat+4zo+KHKLqVnmdqEUvbdz9deiIFGY3TOVX1s3i/DOnyoYtJZr62fLuLmEW3qTPC4nROk5XAAwILqGscLOwbLtX+e6D0Lf479aDXGT+tS3FI/Cgoap+n7DC9JdB/BHH/+gasAAJvSGRiaMt/TmH3bKnN/TQ0txyTr8OSBa7LvccXNFikFnxoxkgLEZUpssEID87Jz8et0r4tIbNAmcrhQ2SfeKB9N+2mjG3F4sgV55RWD38YbSr8R6ZmanUvcqLuRKIW1ejYGSmYddWJIdCZyB4H/pqW/r9wDjYTPPep0DNYHTZeTUjXZUyMib7N6QCyvCUxK/wbDbUE0xVw7mMUjKcBam9uM79Pmea1zS3fz7J5E4bDE8NpetEazRcL7PKzjZ3Gy3SnkRyXrAQAPpgsLnHcGuK11Gbbo6Sgpjd6hVRHPZuy91/1uo+yWnsU1cznY9+rnYsscf8SCylwb0QaAula4btYlE/DHC4Uwy8GmmGsk+M7C/8Gr6h/CuWOfxk+fcjN+m56KXzY+G1WUHL5jk7WBvM5wAJg2thlvLv0KAHCjPgGXVC+B8+dke9xP62djX0MvflI7G5+a/kn856Kr8Kbqu6IxMDT6tiHdBu7lo5jKHsijlFi74V0nd6TLsLF5OepI8K/jb8elsz+PPkyFpABLxiDgHbX8DLvlj+CA19bQngoAaFcjaNKjUWYIAHb2HgX8qUup46nGC0t/wm8azVNlv09PxGDSRuiOdRfJcWzXgvmmJ8c7dIhhG+wp1MmzueVI3JcuRkXV8c7Vr8TixMjXb9QuEPdwasyLeiGorikcPfAsIzwj8yz1LX09QX8g35OywOQz43mOCDK4FgZeFGJjmeuKMT3SDfO0LaVTqiftAZ4eKQosOANy+iIZ6SE9eQarBJOcAZQ/lG6pbfx9uX8h5XZt0LqOt5IJLYyDBJNk3MFQDhnaT3DJPUdsYcI5IClNWLohOM+7FQl45UWGnE7u2ycKTqFal85Ef+bxtR7nrtRcjCWlDvMUSts/ilezega334TCzfOF9V/hHZvfjusa/gNdg/5iGelMHEvpJBvli0t/QhPGXNve4ccEug3q2SCXoSkvGAdbTDrrrHRHvGEA6Bg3Uf595W6Hx9a7o+tCAMAMtR+d49sAaEyr73J4eJofbzuIVneWq6fqDZk6M5a1eSIE5tZx5Hl7g7bS5gfws7OpSrAbHRn9A0whLzLcGG42rlQhz+jRGsuTjQCANe2nAFDobzJK/jzVx5TYN6ufu+//tn4Ky5yQzs5Kqf6mmoHuaD0CALB89L4o+rBUbwAA/KZ+Mg6oVt8XAHekxnlyNFZnPPN8DOf9sqq5EOqq+tnBbcCa1btPH+b69ow9V6ABVdH5wnlL+MgizXyt+36HTjDPM+gU85SZx5t0tzO+16f++ALF3aBH8TZ9JT5SvhxH7zXR7NUtJ6CKssP5nuob3e9PLd2DOjm3YPvdOebfsrX03NVuUoXpvQayTOGOFnuB3s6kN4MJ6d9kLRTPUQ+jZ+KLjqjwoxXZH8kYKvtd8BRcWwrXKxlX8a6D7D+/r5szp+8uX2UuryRr4TBlDe15nG4EjjEFd5HjYWqro9kZ2npRxldLT0y31sC92Znf3sGHGN1z1C7M3nGD6/c8vR23Nb4N5z72YZyf3IHZqZkTD6cLxIimUgqDyhtJfZiKeqlRWOtySq+dFFIabH+lG3foI7Faz0WtyTh8vtn8BjxZXY4f1syza8uTDaJTBRpYPHw/Skpjb+thuDh9D/ajNUpl34VOfGnFz/De2huhFHCg3IlxVMRMLl2w1g1uhzpywFPZrIR6/KiPRg1lXLbwy3hd+zdxXXpK7ri674XGYMCLUC+gznaqUGtoDKIZw+VOAObFCYlG6UIythcG6xUAO2b23vKPHfzLtedO6Iik6pV0DEvKkikKeNB9L9UaN6dHg5Y/zngVtmKG7GiBvF75kSmKJ9T3PExKZWcXCBKZa0DCGAhylNJD9y7J6KTjZQvlmYMJbWk9KRVdykDybZX7BsdD++gRTcZADNvb78vnpOO+2L+F3+KwuJ6jJ6BF5K2EW9n6tK3Es7ieHWce0Va8QRFu5tLxzaRsnIOhHDK0n+BCJwwVYJKyGxrVLD2t8MIlHiEPPfvS+S66WYUbjk2h3qh7nEFuPc5T031ZX4hAjbzPwgaGeJNl9Ci++QLAaTVz8UyjqmFx3x/ib1JeCFHyI3Zdj/dWzOZ6IPNe/8vG/8E5yb04CQ9D69TxooQUM5VxImzT00D0SAw2mwjQrHQHodtvsh1ZRHtvZmjTiNRoMgWrsxtV28b6MBWDaMxu2d2upwXOl9Co0tjbsgAA0JM9dwTwyE4zxtCoTKR8b5A6TiMkXiny/A77QuHWaLCpw9PVgDiGeeMq3bTKAhJkPh6pNgAAdrSY1MXdTQsBAIerzZ4XaR1nqfsAAGsXvgR70CEbjWTN0M2uTjpuP/lI5zkAgGNH70QrRhgvltSNUbEqnR8ZNg9nT4Mt1NsC3HGUa1bd1PljenxOpkrGL5TwieNuwnbdhY76HpyT3Ata6Bkn6bKnPN7aIjrByFjPTrehVY2hljRho+5x63q9Nkbr8cnjOC15GK947BL8R/lKPH3rl/E6/AKvKv8BT9v1XQDAhpYj8W/l92O3bsdvF/wHflo/G2/r+T5SJDg2WYtZ237v6LEybmrVGPdb9AxHz/6ykTX06Rvt/hMo37Z/UJgL862dpZlZn2WjOop8KmLECtEw7tjyeEPHmGR8h2srokdRh2e8DqVUVMmQtAbCT+pnY1/TPHSqA/j01lfgMG3mcUkpLM5Sx7eW5mTtKd3UiFFYo42z65hkrVlH0FieXVpnI9pJSCN1WKT+zO+swQccjQ2o4orKJ9BQ3Y9tugtbEkPLHLUbR/T9Fl9tMOfD9yTTsAcd7PkYxh8ibK6tn8ZkLs0WEPkojgHlBRh/UgDDaMa6LLtjvupzjijbHjA8nJe9nLB5+pmysRPhRjQfLZzCeAaS54l0q7a85+avf/rNtNSAvmz9TGQ0huPPcUvRa+nYm22v0DfFZPUsV+ujeoweNl583oa8sPNnyuh2TM9eJLj+6EuxWs+d2GFBcLNjeME6ZLoUPY4SzMeErHWttXOKAeaIwY3TXuzbWrw5+2gcYADb92K5l0N3ALN0hrDwXDogyy12Qa+vKRpvsSGal24dwuL+MaeakOZP29I+hn0GQudNWE82oGM8cgRZ6h/AbVNF6od0SwZ9jEeuF1rALo0+MJYluqN6SQzzdCMqUZRbxfUiOUzm1T97OWRoP8FFT3ITSqVNiCnu+RslNdyVIODpNBfPFaVcUPbAGNM79VQntG1Ee6reB0DneNI93sj4EpRQRg+hUmuNEuo4MvW3ch+79YfuQiYpVTM0OADg3Ec/6H6/tvMV7vfLGz6LH5Q+ioX773Jtp6MfFVVHHQn6MJUJcxvR7k23x8oTgI4xY4DvLc1w9NENbHt2e3jbeJ8767pLTcM4KsFYcz5qDfQ3GdzTslvgzTd9nzv0AABgFA3mmRLE40qVIp7GBs9Iy0fqvYa5vRkwhnZK5iNPRUTWbzqunkb5cjYPW5FFynY0G0N7Z4tJfz0i2eSo6az1oVFVMa5LePio9zkCRUUyJ4Jg+ufHta9poUlvRYrDsnRVW88aKI/o+VFUabM24zwHOwFo4uThEf+p2I92bZQ7GrH1yiXn2bhqxK11c2b7cLUloFuI2Cg/XnXGW95ngGeg2EIjw0tT0//+jiNQR8k5sW5IjwcAPKV0H37U8HEsGlqJN5d/jZN2/xJh6Wuch7tKx+LEsW/gnukXAgD2VHpwTZtRII969H9QRi3rt2nTmWWDbM9u+geAgcREEmegPydzAh5G5pk1tHeUehzPopRHMh+5zOQ8CxXbjN2Rgm/o8byNMj4CPOHb9Yr0x6VvquJUXUlR1tp8cx/acc1RX3G0faB+GRRSlFTqMke2luc4PK4EvPhT/VikUDg5eQyzR1djRf0RtKkRjKomrNGzmVLELuwixuDd2XGI2fvvQwl11FON05OHsCDZiWp5Cl45/h/4euubsR+tuDc9DNVSsyNnXXkx4yPrd4bnJeMfwHXtL8IXai/McIf8iY/H0HXEx5WsD2Ftaq2xUZPsjhxjZ96oSZPf3bFCpkca12iOSjC4DuZd9iSe2y2qhxzeWjmT+raFvE1jeiQdQBPc7O6FrF5flmF0JNZHTizKi3Ce2T5LDgtLT+8Bc0/GQ+kCbO1+Mmtr29v+WTg/4kb5oxh/zP5asF6Fey3qWuNhvRDvxLtw3exL8KrqezGStGRtSSQ+pfPE06JJvw0bOczLMwJzffF0R7IQsZGfKCqjYp1Cck6GOmlkZ6kIBKGaaEiK6wO+IjW+JaPYN6B0+9+LUscFk1F2JPwN/bN9CmmknyyKxEtOg7hezEdbiYKtkc5wK/83W3xEm9NCaaXtpUh+NK4I5RQOmnLI0H6CC1NsyYYcbUK5G5OFecUNwWZFjXQ6zYvSahSpyTccoCe7DXYnulxbeytoBTVMxWAQTbdtrZCVn/GIjG9Qwc0NiWPUWjRnhjUAVNIxvKT0R/5NydCCicR2Yb9ruyGZhz+0PRdvG78Y1ey5LQBYvP+vjmO9WRR/b2kGUiRGgGWEDjRZQ3sHUTgcu9GVPcuxnSiwzmkAYHtqDIf2sR1YnD2bsinhUSVbl8JSrTFUMUZde22P5xnZzNtT088B4SIdvmEox1tXj/Db4lXBfNyVOVi60Q+QsaZGmi08/SpWOKjctFUr44Muor2lzaTS7WpahCHdhC41hFmZgtQzthFAdma4VIm+J55fJFtbXmTwwew97cPqax2sEeOYlxpD95F0fpQ2uFVPh4ZCixrDNOwnT6yAzfszkwcBAOuTeRhGE3c6hQpnhnuttmdjt7lvWrqlc3JeKY7HMDwSAoSOCD9Pjq2Z9N59nSscb7UGVuu52DT9LEjlTnWUe3sYADY3LfNRYDL+v2m/CPv0FLQNb8bR2YVaFrfNBtmOaY7uARrRFo4EWBwlXWWRWGtob89u6+fzOVae+KU3nGd5xjeVPZ63BEbWkoV5GoV0OaKQ1QUaJaVYeqmBjuuBltl4xfh/ADCX061vejku+u0xmK72YxAtWFNewui2/PU0mvF4oNOkSj+r7zK8dPRHAIA7ms9CHSVRpoAp+BoP6wUY0K1orA/huaXbcN7Ob+Ej2aVP2+aejzV6Du6rHIvzyv+L547/Nx6Y9ypHzwMNx0Y00nOkiQJuT4/EjzvfgDE0cOVbWAuMj4F8RCj3cpzgNsq/RG1BSzoYOUYbMYZZY2Z+7+5YIRrL4f7BYCLdfk5IZ1A53Rk9bG+2csY1YXuXLfQliwg3lT0sYmu/J+k49BZ0r9CEe5wm7vJdbebWdRPRJmuB4OnEIM7e+1Mckz2dxs5yCvyxdM/O9pEH0oWR05nTDUdP3hG3Iv2KOdUC2UxIdGNwA07FnT0vAaByLwv1MCILBbr5/srXApUfE2b3RG2JPiPsPfkRbboX8yIZfjJMMmx9oXuz64uO6/m2Svxb3hEVOXqdL8MpHrFeUNF+nxmsjkYP9MY3/4AUPU6SOCodm78Ut2L1Qpj0m3UYy1H3ALOSeCHvhZLD42AohwztJ7iIChlyPM1hPdGwkdOvJO+hZGjxM9q8HrJ6NoXaRrEVgCrKGEqMMdet+sVbng1I4zR9L5rHdnt6wAUvv+3U4uWb0FvK1wAAHp5yGv67aqLRryjdgAQpuxkbDLffcA5PNjsc75zyKaQArk2fhCtOuBrfqD0LALB04DYopFAK6NWG3j0u/duLFW9ob494VtJ1dI0aXNuyS4ZoX7T2F0p1jGzEEcrUtYY2e7ZJc/5oAEMN5vbf9upuV49e7NOWDhgas7P0PPKZVVPyhuqN0HhO2NFal6UOH52syyKxmtGtCT2yA0XekO3cnTG8GiWlsRXdONBoeF9VFfwpPQYAML//TgBA97jh2zo9K4hocoM1XAvxMQrF1oLtX2+6M6PRKNIlpBgutWMHuiJH1BgasKfJnHM9K7mfzUeqfL+ybI47/Ll8isNt+SCt/5TQY5+84mvT99vyMM6wUNFap3g0gE+Xv4FfNHwYLeN7cWnly/h53zNxQd3Q2p8Z2tAe932H+5uX/9r1LPf7FcmFeNX4f+DPHRfglePvxZ6mue5vdAzGkmasSg2/5mfnwO14Ta1lUWhM9+stuwxtuhpgW7HtzcwDj2JD00tx6/hFeMbObzk8czJDe5s7ox0bg8xgjZwyMh8lJZudA3Vrhhg7JINAmo8OL6FHUtzF530E/YOqtUoBt6ZHY3/2agAt3yi/AmNoYHSzPio/V67vfgOquoTDD9yNY6v3AwBuaHlGhiQ2GqjMTLVGigR3amM8fa5yGZ66+weYnxjHyu6eM1xfasqc69/edbL5qbpxY6Mx8sULqQjPpHR7OtZy2wCGwIgJDGj77w26F/1tS9Goajir9pfIwbwc61FCat4/b+yOjSX3H742w/lI6ZHOBtMnGnk2RbAXgjhQRKNRGEPF5ZSFyToFhwF870LAH8pbyVm+J0sdP1xthqpXHW6HJ9V4d/kqPLfvq/hx8iGsUOvkDBTPHodn4aA5hvbXdBlChyWjUVMa/d+ldegd1HnZJpyPNMLKnrp0bTNaoOS1Rde/NEcLxkZB2guleh53zdZL8tZWvAalIBGdU7bkGqKB8SWlauc5eWIdN25r92rJyLeFriPJMBbIkS9sC+oBseFuu8t44WAeJEWQbTue+u3p5HTLMIqPt+c4Qpgz0hMBFnc8KlQWejyhnJrwM/805ZCh/Q8sPqUrJwqsvWAD7EYpbJ7BRsCjJn7+Ss9KSAoHnc8KGkdnqbyr0zlMIA6UTQp0t+pnbS0900Y2YmXjm/E1/Qk8ddWHAMi3U+elA7pNKE1xWmI80Ld2PR8/rp+D0VIbFiQ78eLSTTnpaRyPvfjnhvpxGMIUV2+4pRdfqT0Hg7oZM8Y24Si1HgrAzOwitN2lbjcurt/NxijuSftQQt3hBoDD9AaU9TgO6EbsylLHoXiE1V0stOM6vK5snoy6r7TC4bFF6stgFtFuIxFtqpC1ZanJ/dnlQFwhz1fS2WbOnoEjSojWuD27+Ovk5FHoNI5SSHcGGBrdr7E3nGzwHWPmttWtaiZRTID7souUZhwwkYvpVVNvQ5CCHRqs0VqQFESiNGzJ0sB7rKGd+svZdjQvAaDEOfrINPPm7jNKd4lKWiUdxYmJof3X5fNce0u3T0/1fEo1XER7kdoOgPM2Mr5AFaXslzwlJAO2ju/BC0u34LhkDS645QI8u3Q7aNk980zX1rYebF+CBaNXYsHolfjZrPfipNGv4itHXoU7k+PwuJ6DH/W8C7ekx2QKB6fR0r4pe4/e3gxeShQaUEVH9gb9DvjU8f3Z++1T1RAqepzw1pzxffkDr3TfftqeK9CMUTRWB9EB88zdVuVTx+F4FvNRcjrKFxdR3sbKJZfhISwwjAWFMz7r7P8rperWBQWY4rFK2d3Npzs8u6ediDPGvoRflM7LcfIwogAAeyq9+Fn9TAf+fPUFWF05wtNTIGcs3TelJyAsg7oZ+3pOc/Us3bu6jsdTxz6DN0z5MobUFIHG/HXNz2jabuSlPPM+h05nb5ySthlw8xzjaHrP+Nfw4l1fRA/2uvE7OjFZMQ+ki6GShBjVBE9IY4A7hMmOFj9Y4v4qwCSHrnTMhOkPjO543tOLxhyM7s3CUY+4HpEzLXMwqJvRqKroHN6Q0eP34XoKJ5sB4Hml24KMD4k/QBk19I6YozF3pYfLUVzfhTgSDzlgwiPVGUww/CVnAM1KibKABCskdoL79h4W84IfKQrGUAlHVFSgAyB/HXk+Et7kBIQkwzYAMZnC6oUw6gQt0nGl72Xtis5oS/u9bw/CDY87LHQdTVSP/mQw0sD+KnyCwfx56hB3Pi/k9gJuUtG/o03qIa5n4ZNLrVeBPnTwWNqHDO0nuHABncHIfznMFHrGKjrzRzYwydihXiH5vFBWD9QA8tjbxnegW/WjhjIe0IuY4r63YhTYxYq865wJ1Bnoxwvvfw2mZU9Nze3/K6aDXmYUXwDFaCSwtrGd5mImlLC29XgMowl/nvt6AMDbyz9zk5boP3zD0SlelqWZr9OzIoNsCC3uludliUn77oW5CXxXMsPzMWsz1DADY7qMMuroySLflu6zqn8GANySHo1a9gQM5W2qNR7V/G1WALi9dFIuL6hiMtRgnBud4zuglM7q+T631zND20W0OW4HC5QD2j/ZIDfjZWmfqobQpgeYYmvqEd6KZ9qKFXJ7vn2HmsE2c3vh2Iwhc7nQ9OxJtE26hzuYyLz3fMx+UQIvmAIEZ2jPSPscf05OzN0A27Izg1Jk57EOY4A8KXkIjdYYJH20t2kPoRU7lclKkLNAyAafamzS3aijhFY1hsPV5pwonu9zpDQSXoSp4wopzt3xTQdrqA2ClofT+ahmNy1rcKeM563GLkzFvua5sfNG5LcZf2doJztdXZs5M6or6Ee7+96gakMtu728M7t8UWf9OVqtRViOUuvRkWWV9OlOjKCR0GNppMrTxDyjipKU0svPpcLzDNL6oEo1p50q1Vz5tvTkG7Ese4F8086z7059G87Ct7Fg9Er8+czvY4uewZyTbM2wNFHPi0trz8OeSi/+0HQuvlx/Ll/DkpwJlPRf4Cz8ZdHb8cf6cfj0/G/iKWOfw3PG/xtpg781nNK9Rs/BMJpy9gpPd5Eh6jODyJoRFHJpTkB7uOTQ2z77PEfP2fuvxWcrl7l6x2Rz8750sWhoWf4CwXEdi1qae0KfpSh+uMcZWJzxw3hB6OFnwbleoEAdx7wf5nvxWqDBBCmCzIIJFk+S4BE9HwAws/8+V8+vwzRzQJpybulupKm90JRE/Inhp5TCXLULJdQxrBuxDdOCl1DC9a+jPc7CLSwaL1YPnp5wz3X/kcdV4rfrB/hYed56WkQ5TOZ9hJv2mx1RsjAq9+J5EmbiWP443rD5zDuUZ3yLkW/E9aK1rgTDX/qe8jRJcNpv5Bj+8dhMLsVcipB7I1bFMFZPRfWkJ7byosnSGCQON4El/G/0m/TbUoQ9F7fUb4Fn4XgdRHb2IUP7iS5pjhAKNyEtCU9MBIsVAeoVki7wkLzmVJlrHzEbWV+px5x9g1/w6xtNGuBxyRq+8WrgdeXfoqk26G6pBoAjkw3udxuRsrilYuE9o0Zh2ZLMgk6Mwn1v9/NQ1SX0qH6XbkoVCfrFHr0LR2Sp4z+pn82zBbKKj2aprCclj6GkqzhaG+NqU3m++56lp4bEGWS99ew94UwYP616IwDgl/UnicalNeaur5/k6PvllIswnhkSeZu55Vlf8yKM6xJaa/1YoHaxegrA1DRLec/O0DPcQtSEpbuFmycRxXaejaEBA5mDZVZtWxSJZQYZ3ayEcaF4bB/bM0N7Z9LtKmttjD4A6BjdinYMYUYW0d6UvbdscYROJ447jxdeQdiqjRHck/YB0Dhh50/xgtItAIC1bSblW0o739G8BDt0F1rUGBYfuJfQY0rnqDnjva3UK6YTW3qSgO4aytiWXQr3itIfJjD8VLSuE6JRUdlT0VV8rnIZTtr3G+SV79bPY4obnIHo6xSmt0IwELP/b9JmDtmIdqIUZpF360F4q5VyN4f3ZEcGrGbBH1QAAQAASURBVEPPOkEAYBDm8qAjkw1oHzb1NugeJuPCiA2d5UVKoyLaQVE01NXN2BXLa+IERazgsbqi4k/wwLGC0J3VE6JrKRLszRxwksMz751gyottmI7PHH4Vvtr2DgBKNhoEOeOPtyS4f96r8Lrqe7CpcQnW6VlYq2dzhTyMzgn7I6dRiGiRunWiKNsipWpz3sZ7Kb+wy0BH2+bhntQ/xXda8gjKuoqXl/7gskPu14sDw4jsw1FfYqNBMiSozJTeieaZRbbP1BHt27g9IKFzKp7j/C6AsF5MI8XDDf+4HnsGivTlhrq5fPHwXdfZL7pvtlZ3o1MdMN+Cwhy1G1P2r3F4pciwUv5pwg16JjQS9oSl7V+RbhZ/M5QLhL/iGMZ4ilK6pTOtIHMHAc8yEMnkksZLiEqTvtA7NWzbWra4xCwgxHMZCF/MiXlBuiMYp7STFhbrixJu0zSQ4RCMYsFgtX20ha4jKfItGcsxTHCqCm4DKQLto+60HqJ6HuaBPjU+5G1MoyWFn7NWMYx8w+OJ60nfs/2JUOfMCQqjZ/z/2cshQ/sJLnSqsLOcAYwZDUwYazRjFCXbQutIyKZkd+AbARzM0UOVvsjQAqaMZ09Vlab7tlm9jQ1GwViitpKzWCaCfGHpLwCA91dfh9/pUwEAh6ktgUJl6fa/e8XNC6b5w+bt4cdKS90CraoKHtcmhXvugYctI8XU4SV6o+nDlKVYq2fzMcjqrUrNedIXlG7B61Y+B4dnt0w/UDmW8NHzbGNmKMxM7XvCQCtGME3vA2DORErReQt7e/Vi/OrMX+LY0cvw4ymvkpVdkt6adQ811YiH9UIAPjWRKtozbaQXM913REVA2ISijZJuatoLur32jHp9m6srXXrClS/PgyKFs2PcGNA71Aw2n/djCjamJgr63vJPML1m6m0khnaeUkT5I543zEo99YZ2sx7Bh8s/wDM2fyGjpxub2o9lfaH901D4Y3Yj9zF7r/c8y+r27r8fgLmIzyu7VLm0hrGnx+K5c/rzAJg0ySLHmKUp7B+dt5aw19/7PDy/dCsA4KPVl+OYUR/Z/lnT8/D2yofx0/pZQTTM40iCbxrexvSExo4dgyh1XCnMyRxH2/S0TKnxODaVFwAAZo2vtawFtMZ5JfNSwAerr8EPkgsBAP9Z+QGe8rC5iX6T7pEjNpJxKkSpRAWY9pnIPd9ny+UcByoo7li5FGlEyEd/OWNdoFE+J+mRS2c5884/+37HsMKzvCC8DS4uMzBCo3CvAXU4+Ps8PK/4nOI00guA5LG2MMHBDLpmYnqoAy1RwEvHP4BnNXwHI6oFFVXH6eu/jI9VLnd0PpAuBKRxZQaQp0dyEEiZOLYwpwosH/1/pdRxLh9jo0qSH/yISryuixxWKXFuU7nn6BbkdUkpXFN/EupaYdb++3GcepzhmTVk9v3tjYuwEuZI0/xNP/c0kjGkdC9TJmvNXmYXyShPVn4WgDCnpPeoJccodfJ7mOeDpIdxKRHuPTGN9AgGCD18DYf9VhGNkv5IjXzJ+SJlKrDMGcSOg1xDK+x3jmEbHltQ7j8TP89FqrJv2iLtuSFNEUz4t0y3jFdJMGpAZ79Tk9U7DRDVE438AOa/KbUnuIU0cfkdbU4/wxWeYRfqUVkI8Hn1z14OGdpPcJE86WYD9MIACCODvt5hIw/ivsY34iV7zZMtuWlDFgcE5TtvQ7X1iPBsHTWG5O5kum+b1dtTNsZmr9rDFMmu+m70qr1IVQm3pEdjLcyZZGOQez64DTVvA4O51fzEfuPNfri8jCkmN6fmVuoT91zj+hLxAsACbaJb/W2HOSA1EACwdO4p40bhfyhdgP7sAiGebq+doe0i2lq71NexUiuG0SSm5NlxHkMDDrQtRj/akKqEpDdSXshG7GOpMXTtufOUKAjdtewWc8RvB9M+h8oBCIxuVpLRsDe75GpWfWuUJq4D3kZzj8Eof8zv9nmnnUHqOAB8rW4MqZeV/4iKrqKmE2zT09lm5RVgz0dJgWIOHaIMjKHBXb712rIxmLfpLnxkygeBJLvdnGy81Ii5Kn0KAGB5/03own4WVZi3xxwpuKt8fGFWCne0GODOFnMr9AK1I1DSQuVJUubjdb1YbUNb5kC7s/N8fKf+TAxgCv582rfw5dpz8IPml+OvybEA+HNYkoEoXcTG004FepRPHe9R/WjDMJJEsUhTGIXZmBnavaPrMxjQkg7hmMTc6nx9/WTcjPj876Pp3MJUbzPHQ4UzZ30UOaeEc4nMIHMGORwjwzNo/pu+vYMTXoS4J3pbmcrmMEuGj6sghwk8z4DyuMFgimi7RdHZPHpopFEyEFNx7sXGYCqsGX6HSdg/WbmT9uZEKYyiEXvUVGxrmA8AOHbrlaBlP6aIhhY3TuO5J8lr6nwVHZbe+nK08+wFPv7ABMYyo9t+T94rIp0iaBuPKx1/zy/anz5Mxe9Sk/31+vJvGJ5Fg3cDADa2HImHYPb2w9d/HzOwD+wMcnAEY3mWWfdwusDhifkY001plJwT3JDk34SKz15D0sOAaK3TvdkW2r/Qeedg1OiExeNhoSykEdsJ33AXZIJbw+zogMfO9g/eHUjRXTafXb04Qkr7OPFZd87LvDPMkoEn4VbC2CRKToMW+xfCsn/zqDLvY/gNj9fCFIEp9t0i3OJ5bAGPCn4C1PgWvifQHPVF5AX/3qEz2ofKpAudKvwsn4UR4SlslBft/ToaVQ1nD1yDGehnmyc1yCQvHMUdbpR5Qn/KmDW0p7l2tt7eskmfnqqG0JCOOrqXjZtnjPa1LsYoGrEmizwflmxjt1MTzO73MGVxqdqCztpu7NfN+FPlLLZ5/qhuDJt5Q/ehFSPcYUGYO0sbo2KoZa7DESocj+s52NMwG7T8rH5mjoIYn+XVWrtn0IYajBFB07J9FIf022khsbOE8YJu5BruMrXF2U3UDg90bGiTcaXpYFFEiuGRlWJL4z5raKfbXHuf+kcuPQFNy/Z4bOERDgWFFJ3VzNBOuskGb35eVT8Lfe0rXPs6SuZpIYq7kI95Diau+FkHEgDsbpyD08e+gvWlhbKDgCg7D+mFuD9dhJKu4QWlm7N1CJygHkPXgTWoa4W7y8dFc4/yTDIk9jWa9dOlhlAe3ee4K4+hAqDRPrwFCina1TBO330VpmPA4Xhy8gAAYJfuwE963+Pw7Zl5Bj5fexHG0BAb/mRtsXRSYe+TnwHiRlE/2tzt/Scnq9BYP4C3Zi8LbNA9bFw04AztmdlzSRoai7K3zvc3zcJudOBxtQA/nXEJo+W29KjCC4Cyqcf5yOYE5S2CtnJWAZfhXom19VgKpaAAhW9rc9ns69mmkkLObkFndwGY4h068pphDhSEfZTSiSXjS5I9MW8NbrqGuQw3N2jzegACR6aFxRai7GBGRDdPg473KemICh3rdQ1HuLrr0x7cVj8S35n+XoInf04Vvv5AaJCMWNFII/1hadmWj9LzXgRZuF4ZnhxYmE1BDTKKm+optkgZSLbeD+rmosljknVsvJbtvw0A8Ej7k3GV8mflLyz9BYSNEb/tvvmYnutoZ30hEk6D8szTKDsnKEzGbdrC1Ysi3+R71PEnGXhMpgjrwxu2Qqq3tF5FmCAzIURIyb+p7Mk7jiJFcmPbNCfFOITBAyVHZKh70PaScRrWkS7E9O3zHARCXyYDU/ynxRHSKEWqJcNW+l4ujQW4J0oJLyUxTBXxdpK8YPM5xUFTDhnaT3ChTpniNGq6mWeQVGN6bYere3bpPmd8AWRCa/5Nt3GnGmXU8NrtH8E1w6/G6clDxZuDAjoPbAAAbC3Ndl+ygmVYTcEBNAMwl3OZthrnjJingTbPOAsAf2+UCj+qcHr+eOSJMunmALBKz8d40sQ2gk26B5vTGSjpOo5PHmcOC88KjV5tItQHWmZb9kSbeRVlfH7pFVgx+m1cesZdeHrjD3B5/emiN9PcTm0i/N31PkdPL0xEeyh7lqroHUwDt5u5vFGGyqV1oNjo+zFqDQDt8HTWd6GiqxjXJWzXxDES4iabOVeoff88XqIoZd/pbzb4Z9W3RylrmjCX4RbP97muIlHAdAygrKuoa4XdalpEo0aC3570XVxeOw91JHhP9Y2MP5r8V4oMi4oNENHzcMvJru3W5iMiuqUsAJ2NzQ/r5hmil5RuBNIUlXQcX6x8DQBwU3os9pMb7/nFNVxxpzTWyi3Ynr1PPWV4k+etCsfQ9OfZyV/wrw88H39qeBd+rt6N87deirub/hWNVWOkW0P7m7XzocmOlwR9YTCaHK34nAz5yAxE15dYAdrSkaXaJ2txZN+vHR33pEvN9wk9G0sLAADdoxuQIIXWwJKaucV9d/uRDsefpj7PXPa17MP4Yum1eFTPy3WWWQrjSIx0r0Weccn5EPLMw8g6EuYe5VgciZOMAarg++/Zwm5apgZiMPdSIofFKE4wxy0sXEfSWBP9N0iNjWnkZ0s5LE3znX8OlrXhzs1MnglyuOjITGjE2CK/mOHrPdx0HABgvDQFLxv/AF5e/QD+MuVpjjsSnjiNWn56MbpbQBgDmlXAnXcZDBD3mSKnirR/SDKTzkcppV/KzpDOltO6FvZguhApEsxRu3H0vj/iN8m78POGD6Ozavbera3LsDPpxtdrFwAAZqvdbO6FTlXrEN+uu0S9BwpB5l22p0jrA76P7EK7QKZI64hnBnjkkrEbFuX+IzuDKB5JNjM5w5wlsZwJj3qI55IZ3fFYG3o88ZOKXpP5Q/snGo2BLITAW/G8sTNiw2/GfJQi1RKNCnJfRBhv6umRYATozl6z/VuoJ3zPfjPss5RGL58Fj/EUXYYW8ofKLk+PwB+EqePMqPmnLocM7Se6SIa2YGhx77OBdegBtKX7Xft/Se4VU5tCZcXO1cMHbsOHyz/ACUN/QhcG8F/l74lefOrZ7TywHgCwOXvnGaALTGNdsgAAMHvIRLFn1nfgmOp9SLXC2rnPBWDeXk6RoEMNuzPMtM+0cKVRYVFiUrPXpv4tXErjfXoxAGCF2sCcDpSPs2A25JHmWTFvydquoYQhtEAphUE1BYBihr/bkFN/ltdGtFOt0ZOljtv3n/nmGWwEAY22UFljN7bwTeh70iWoqxJmq92Yr3a6b/aMmRT5zbob4zpxfCyMpFCDKthQmUJFzoHZSOSsdBs02Xwdja7X3DkRfjNU3GZnl2HtQBfSpCwraaUKPlJ7Jd4y5xe4Nn2Spx2BEZOjFIW4pZTX2zr829Crppwa8VH0mmf4f1U/DWOlVixMdmLJ8D14ur4Fc5NdONDYjXdWL87JVJHnIzUaNqQmQ6HtwMYIdxhVfEbprwCA+UkfeuGfgTtl50/RiHGcmqwCYG7Gz43ESjC6NgOlkW6ekoFAzzXbMdjTvAgAsFDtwIJ95k6Hm5ufinv1EjZ3NIAdpZkY0Q2o6PEshR5YUn0cgDe06fpfM/f5+GnFKN7SxXeFhgTZ9OW5Q2BZW/lCKjka5tYH4QUIzK1D0Rj0fYkNMtI/Ro/HK2VT+HRk34an0asIFo7/RDwr+h7DTXhWIsaO40+O88bPPYLb1hPkGYvYOblHsQi4iZzxdPs5el/zqXjz+Dtw9alXYxumcxpVDm5w/shp2YLxBYG3pC2VKaHconQDebLZ447XcEw38ugOjDmAv47i6GERdrInARhCCza1m2MhL9r4n1istuH4xGSzHNCNGC51QAHYkTkju9U+eZ9RCmVdRZcyz/716U5ej84T5MtCWui8p86SeI4qYR35WcqdwSHdkpzgqeO2lOgxKipzw3FQMX9s3TzcNTrPBEOZOsYcPRLPEBt5YvQakvGVB+N0U703PDrGjUawv8nfjOsX9kXl9CWEEXlN69m/FdFoMUh94ZFmWy/GE8/nuK7onHD4CI1JXC/ulad9cvzh/D5kaB8qky40yuHmpOZeZYALeFtvvt7GvnVm8gDKuhpFFWnkwk7W05KH8ZpN78Mry39w7ZcmW7Fg669cPa9cmJ8L1Ta0jO9CVZewseTTrGi9lSVzTnp+v1HunzpuntG6LV2B4RZjkI3rBmeczU03+/4VKo3m/zPQDwDYoe27unyTfiA1yvqpySMs6kYjUvZN7OHWzNAmNalQYSld5FI6gAv4VGt3xnRqug+92INUAzMzT/mBhhlZPf89Reixhad+ZWPIDESucCCbE8Nowt6m+QCAmxvfhQX778aFyW1406Z/AwA8oucHmyzHTZWDOjVCXL18GADsb56DVCu0YgRT6nsZjdHbwaHyxTb97IMZbLYy47RVT8+U+aCew6MwrJodjN9smsFomt/faFQNVqbhm+2X4Ae1p+K+tnMMXvqsDFPSuQIzgiasmv50AMAJ+2/AS1MTqX1swcsxiBaW0jlR6jiNfKzXxtBuH97o6LYE0bNzrfVBnJE8BKnMH7wHT04eQLMax1DDDDym54pKBHP8CeeNxeguMwYkxd0Ptu2jvVTv/OROzMoui/td87NsNcbvOhKszo6gHJFdZrSkZgztvR3W0JZvtpXeGWeROFsvXG8I5RGXPTQClGeQpcK61gIvXFsI2QJ0jopGIzHSwGEg32NHVKjzztEdyx4e5fT9i8c/R85ICn7wPYpbXB/gjqj4fOhkU2MFRwtiGrl8jPnI1iuNfELh+vRkjLX4iyhDxzGjETmGTViP8oxGFRG0JaOgNZcfno9WV3Ak8qfOBF6gkLcUN69HjTnKR/mMNp17GYwsxHUdp0Aqu3QnVGLGv093AjD3PvCUZ0cOWmtmv6qhjH1oY2tYPKMNbrDGRU5vhjiuGS8ILDKqIYwBhAhgDj3iGkasX+Wds5acahEfaWcEevLG1dPI6bQwKfIp8Ta2DwUZBbpeCZHguMXUaPDxktYmrzdxXyS6pTFUiv8kZHPDVuqLNarJ9/IuJBOGMCd6zX9SOjgeFdPjYIhKTI/kfAnnc/ydf9ZyyNB+gkueNzwyEK1VRer1ZNHT2+vLMVCehlY1hhXVBwTPtTforaA8UflncPaXpmKzMkbnmQ99ECerVaAeVytYzlH3AAD+kh6JA7rFf49sQtbQXrL7j5iK/TisbjzNv09PZH3Z07wAADC3tjnDwSOfedFC+w73HrSLXsbfpyeafiQPomV8L2lvKnam+9CEcdS1wmhzr+dPwFuGm27S9N1JeNz7MQWPVMx54WeW7jAGfU5Em+y7cgQRZPyTWLCws4EZbHXXOa7e69a9HV9qMOnJVVRwWe1ZXFGOlMs4YkPHQHIuUJ7pUqOL6M8ZMcaOd/IEziSC54zkQfz7tnfgzQe+hs+UL0NSHyV4gNnZrdNb9XSuSBIJG0cPAqNBMJYkw4adlQ/wKKVw45Tz8aHaa1FVFc8zkT9w/bZlfZeJtJ+2/3ochs0Y0C1YN+/5jpZISYesnNInv7yhvSnrn4oUCQXg2OE/o02NYGfTIpwy+hU8VX0TX17+YwDAggP342OV/wUArOl5OgAlzkfTl3wlXUV1+cbNnDyhgUj6t2PKsgyHRkP9AEZ0AzZkZ7GZnMkm3/2pyV75WsOlePXwd9GtdyHVCns7jyQ0ImovKdDSpTneiBUMN6JWyGcaZeUylOuAHN23hSt4ti9SSq+Q+ik4O+w3DS3EEUX6LB1b0FIfhdRR2WEl8RG5bYHwwkdf18JkQz1WgOtFhihtmwr8EeeOq8Yyi8LjWlojojvmT9BvFeNhRpFk+EvGINlI2VjrgG665+YYZCIvonqxEWOMi3AMZD6GN0RHPAuypABgS+uR7ve1ejbGdQkA8IBe5JwYO/VUAEAPTETbylfqLGvLLjodKE2FnTXSGNB9qziyLBtQkmwO9y4l8IcaadKN3hSvZMTmz71QxvlxpWsmvEuGZXyQvTU2TOXshfB26RA3g4XfFCxtKq95W0u377Mt9WA+0dZ5xiDd76nsks6WTyp1HHLaucSHkB6JRjGdvKhehEeOxNPvAMR4JzD7K4VJb3jbP0tOBzkFP55T9DvUmf3PXg4Z2k9woVOFnQMMNkVqsFilqCc7a7xFT8fjrcbAXFp/PFaeyPesMJ+uBhze/531EazNUr4B4PzSHaIicLQyRvOd6bJA0bECSONhtRS7dAcS1PGa8vXozZ67Wqd72Wazp2UhAGBufZPjhOixJwpMohSmKZMqv1u3B/VMxU2Yib6WJUiUxvzBe6IsgOn17HItTIUqNTh6JAW4MIUKKopy/6XpyQCA55T+jDRN8w1tpih5fPKzKzE91CC3sLt6X4qw3N51Id638Co8pBeJKYYschFt5vEYINhEaBbATemxAIATDtzE+xJkbFg8c/rvwhUNn8TSsYdw/uhv8KLyzXjxo29DJwZRQQ2zxjeyiDaE+Uh5IUeaZGWXXTQkKDGIxprP8bAvUnSebgI7Oo7DmK64f3+m9mLUG9oz/vCMgZBupgAR3Ou1cRJ1kIh2rLgrLBo1aeGPtZ2KnejC3qQLexrnsmgPAGyddlpEN4vY6RDmqgVKfqx8S8/FSM6b/eXpuKx2vvvuz+pnYgRmjSYJV0w0gN+mPqr1orGrAZhIf70yxVXyRod03jCmx9JkYKR/Tmn0sLh/vl5u5JMYxq4vBZEqCMYyVeaKU5Hl9cHPFmcyhTh5PN3+d86zkI+SIRGnYEvymtIonmvXnii+F3o8sWOEyDPRGeT7AoqHtDO8iOeElKlgnAF8b9akLnfyEf5YXqQUJsmZsH+UtxTGx5pmNDBZSI8tBOs65IV0/MPvFb5eJKNAxyDun7Q+pPGncArb1LrCPcH5PVyA11Xfg790nI8PV1/tnDd9mArApo7rmLdQ6Bw1WYG7y70RH1hmiKOROyfi1SoYVUqQe2QNMwMafKxdZfDorIQ5hFjafX9o+wBGxpX1JaSb9E/SFWhbeu+DLSXJ0FaTTLdWcS8pjVI9KVWfZWeAj7n/NWaGiCcca0iMFF6TEL5HswooLKZRBbT632lrMcotwPL6UnRGmxnQAQ15dHvHxiTGUICFqfAHj5l9yNB+wov0vBcQe5ol42tmdnv2NkxHf4PZKKalu6N6GvHmMD97r/bHtbOxvvVofKvxVQ53r9rLFfwUaMS4i2jfrxcxzywInqoq4xvZJSQnqtXO0N6ke5jhbyPas2ubfP/gvxkbtgY2DQMAgL3W0A6EuQKwucOc31o4uDJSJOZk+DbpHu6NtPWEs5Oki/I5pwz5X5qfjHE04KhkA+YdeBBLs4vb+lsXZPXst+IImaHR45VS2cOIBFVMapU2XKL/3f17+ej/4pez3o3hcgcAcvMnEfCSsis/DRUbEjSaXkqUyyQ4ffD3eLjxNZiaXYZH563powKg8azVH0RY5g3dj5eUbsKrxn6ED216DV6VHWvYlqWOh1FTg5vTSPkIxAYiIKe3FqXbUl7w25IzLEIEiEbIa5U2fKj2auwu9+LbeC6urD+FZyUE52RpH3mk2uOxFwpOPbAeLRhlijulcf7YowCATS3LPd2JwldrF4KWvW1HRHT7NFgqP/zf+VrgdFMnBptnLqoUt61rjR/Uz8UGPRObuk7Hp2svyU2N1hq4I12GO7tfxPqRIpHXEQgeFmHjaykhBE32EictzJPQEeHpgcPtaBaMb9q2KHVcipBJ0dm8SKzkQJHWjGgguLEW1keOnCnMAmEvMFgapeM/3BBF2EeisFJHSziuCZXhvmKOcSnIngKe8XkS85Hq40VRbmm9cRkedY+9JsH1h1jO2LolASbJR8kY5GnHwvgLEW3u+An6B84zOs9sqekSXj7+flxx2BfwC3UObk2Pxg973o1+tDnc1pnYqGpo1wdER1THmNmjd2aGNuh6EyLINAiSH8nlhcsuDqe8CGWcgcXvzEtGcSLADFyee6JsFoyvWC9ARA/NSqGdk+iRz2jHuKls5vWCtgJuKZNHkuG2mZgGHeERYJANaCmiHTZWlAACk74XVnUwwYgVDXLS1teLcYcjI/MiHw/9pr91PGwp8TZmrjjHgyj5oTPah8qkCzPm8hQJcIXDTuwF2mwSa9Ne7M+ekJqe7vGKO7lkJEwJ7s0umvpNeioUgO2lmXjt+LsBmHeww83ls5XL0KzGMVbpwN3p4cHGS+kG7kiNUn966RE0oIYqytimpzGFc092pri3ttXzgSpuTgHyvGmv78XcLJ3YpI6zpC7XdnOneWdz4dA9xIgxtQ6vmgjfvelhTCmSzk5KZ5WK0oSHkk7c12gMztP2XoMmVcWQasX+7FZubszx7xm4nwDOiKUKR5AGFc6Tm9Xx+Grt2bhi9ocxjCbmDBCNQdFAjI1qTceF4nZ0AyvTJRhHGQDQqsawfPf1UT07WlMxiNbaPkjlsGQLnjV+HYNt1dPZPKPPhUip46JyKTxfI55LA3JgvC2ocSEYZOFzSlfVz8EH5l+BS/ESaCSFNxYbuKR8+3ob9ExsSHtQ1uM4M3kwU2w4LxoxilnZ81ebmpf570Hhe/Xz8Lvu1wMA/poejmqjuTiIZyBk3yP0iMaXZJyAywXHR2E+UsV2i56Bc2tfxHXHfAWDaGEZLSDfM3Qq/HHBu3DB2Mfc335Uf4p4q7ZkLHHli8KK1oynJ5yPPDWWrmvCB8mIZc9SxQqZiuoJThUVG1pSurSl3fEng9ELqcL9g/UbdJ6RbwbyjK8ZzeoA8l0QeTyb6A6DsD1VWIvPlsZri8L8vI1lJuUPjXIy55Qw1hI9gv3D0pYjY8cSSvtMZJmUGQbITh5ZPpJ9JuQPhHHN5VlMY5EjQsq6oDSG9xrsQQfWdZwmyBRDzzgqGK10AgCmZS+AMHoAdA1vAAD0lWaytlGfLV7w+RwbX7LBGkby5fGi/InbcmdQgJfUo4Wv4SLZLMfIo32P1KulMd2UD7HpFhtPnnYBFvUx5rfs7EDcP1GnVOwnpU82RDlQwp2omJNS/yDwDOIYxjQWRZrpJ+WItorq2TqSozcPt5QFQGElkbcqqmf7ESc60FXnIfQ7h85oHyqTLsxAsBsGhE0xUDjeWb4ax2oTrVqt52Kwwb7jvDva4OmmDwUsTjfg8MQY6duyS8UU/C2dC9ROlHTNL/q0irOS+wGYS5zG0CCeF9MZrse0v5EcACqoZZEmT8/+BvM2cWe6DyXUmYLHNjuiuB974M8oqxTbynOxXs/kihJRVre2H4+6Vpg+thkzdHY5V8bIZdVHAAAr06XcILP8ZpfCxBuT6LEnmtLahqUAgBP2m2js2vISqOyj0hkr6TbgVHshIr07KUU+MxGGz9ZejHva/8XTGChAtj+sLesLorZidAU8DXYETfjvxne7v7dnZ9/o2UA7VxYqE+3eqqfhd50XYVBNweerLwAAHKk2ogUjoGWtnsXmhJTeLCrp8OMqXSrHDS0yNraPgrdfiu7zLICs3iRT2Sksj8ZQ0UoyDfjW9CgAwAnJatDzdHaeLamtRQkpduip2FeaQWg09f407SK8ZfxtePX4ewWFauJL5WSFPJYLXNFGwLNYsYUw92iEQ2u/bhKl8KBehOe3XYGPNv87Lq+fJ/KWrWGyrgFpXCUaVdA2lj0Ui3TedDJ8lCI2IW8h8VswBhUhcmIj1rad2PEj0R3JYVGxjdewQjxPDG5hfQiReLo2+cVOyIXZIkWa6fgXnfmmNILIQsl5K6WOU3oKx4vwrPC8OaFbipBKxz9MewHGUpl9+5AeaviJxkVAt7SnAF7O1IU5SutKzlKliMNDmHsHGrJnN9NdkYJeUhrz+u8AADzWcKSnMTRsyfoHlSmKzyFDt2QMyhlsjj/CPBGdRpq2C/gNFYLibwrvdRe9CS3KPUmuR9RwWc+/F9MoOSwQzSg+NhQWt/R/KHb8xd/ITW9WMX8FEMNDeyJG7KO2kowqoJEALb20uXgGXXgGLK8vkz3DLsJc9JnQ6BiOqEhOjDx5fcXrTsFVbzoN01ob4g/9k5ZDhvYTXKRbxyVvuIbfINpqu/H28s9du3W6FwONJvWpN93hGoup41B4d+0y13a7nuaUnXW6F2OlKWhTIzgnvd3Rc9auK9GhhjGgW/HI4tc7Gs33/Lqxm0gNZWzrPDHqK1UuD1SmoqYTJEgxHQNMwRM3ZAXMqJrzVA80nQgN/1wVracUUK1MwYN6IQDgJPVIBldoxxDm1s3la/ekSwh/5AuACj37Sn5j8t7m05Bq/43HKkeIqS5S5JOmS/oIveefrSop6VDeyKtTBSgydmRlJ948pYhdXkTbAG8pnYpvTjPp69OHHyf1+GY3Nzu2sCntwS+mvQmvmnGVO297RLIZJRiNoE934ledL8MWPSPY9D1PZN5mfCREslRE5kBRHMb4Y7noi5QGqaV6VJFkl5xZenxdt9aTHBoVh9lq96aHAQCOTx5n26zFvaRqLjy8P10spoOOq0b8Nj3VZD84WnKMHcH4ktIgKS+kOe4VIP/PaA0T3HVifbP16mg0P/erdtxcOQMaiR+XyMnD6ckzBjxu2zbP0RavrfDeBoDL8AkNVvBCeSHxR7whWjoTLTyxw/YFInvCdW1wW3piJ6EUvaR9ZDCBZ+F6o20lA9FkKhQo0IQX3FkGVih/Qmcg73O81g09HhbxTNjDYxoDGOJ5Ro1qybnADU7zOzfIqf4Q73FhlhQQnrOW9gWOW1aK47a0L/T5O+l5Ly6vLR6Bj5B1AFt195TDAQBH1R4ma8HUO049huZqP/p1q7vIVMxeITjYGiZw2u+oCDKTzlH2BF00l2MjndajeIXjz/k8k9Zm8FFR7iHecxOBEXTuUFgUxc3+IJ0ZltPJw6ZxJJauLSnlPZzzKmhLf7L+CITLdCOCTZbusEjRa1uPpWU7uj3QpYkTKy6ROg15bCReiCnqrr6Hye9oi6hznDyycwIATl7YhZMXdqGpUsLBUv4mQ/uTn/wkTjrpJLS1taG7uxvPec5z8Nhjj7E6Q0NDeOtb34o5c+agubkZy5Ytw9e//nX39w0bNsBGhsL///SnP/3/p1f/l4qgcNLnpqR0ubn9d7nm32y/BOOooL9xDmo6QStG0DZuDBl6lpduQnPT7a79MJqMoAMwikY83n0eAGCJXg+lFBpQxdP6vgsA+BpeCCRlR48tdCFb+F+WfRC/rp+CFAr/0/I2h9uWFCXszC4r6VV7Wf+oykE3u2lVQzdP8wrrmf89mD3ztRSbMz4CRyjz+xY9A3vRLke+JtjM64J1QXm7tWEB/pz6G1EfrxxOlD64eqEiCBSf+QvxiDSGCgKZU0VKI4+wx84FbjxlNLr/UIeOxuomE2Xt3f8AZmKPoS+gpyd79mwHpjqFbK2ehcHyNNev+9qejJPHvoZfdL4mwx1HgCh/pDOoNKWTpyISesIxhGQMECeGkPonnXNjl9zRNZzV5efsYwVYuqiOjhcA3KOXAACOUutRRjUaw2XVhwEYg1zqc9GbxwA3Ggsv8RL4AyWsYaKE0KhyeCYy3zAh4xoYDVQBpuvaOawAYbwkIyZPsY3HOhoXxHMnpGciI7YoDXJCwz+iW14zihDunHdM9uTLGbA5XmA05szbUEZxWS8YyvCOOh+5pMeoZBkXZU7Af5Nhjta/FGmO+Q1MJpumQF4La47JI2J82X6L71tLRrVjDh8DSRbKx4csPYLRKclMMga+UKNa6nOxDJezKYQ1I3yTOiw2dZ4MADiq/lBE95NwPwDgpvRYVK3jnsooglfKSpFSmWl/PCckZ5kgZyge4b4CaZ+hOKTUX1mWxnOFznGJbn6BqLAXhmtLmBMSjty6Hg2DhjA6XhSP5Ii0WGjGBsAd3FIU19EYAHPP6AewRKQx53tBRRepJpWVQKMUVba/U65LhrKnO6Qnrmt/ZQZ09g+akeci2qSipSOeu0L2A+IxEJ00B0n5mwztm2++GRdffDHuuOMO/OEPf0CtVsO5556LAwcOuDrvfOc7cf311+OKK67AqlWr8M53vhOXXHIJrrnmGgDA3LlzsX37dvb/j3zkI2htbcUznvGM/39793+gkD2Rb4qBoZVqOGDXyAYAwNXqabihxdzSq8sN2JA9+XNk/43mm0x5Mt9rre7F1OxCsbfPv8bRYfHsbjFP5izUWzClvg+vL/0GZdQwpsu4Uj1D2Hi50Lb9OdC2EG+tvh1nN/8cv2t4GsNh6mr//IbaG0X7pM18emBoI/ieZWSigMf1bADAYrXV4T4sMRHxdZidwbK2oPx2nyzezMnmEBr515HbkB+vHCH2BQEfAYiXT0kbJUsxJt5+Mb1ZEPBuwxEiZPx8p7B5EuUgpFFrMzZ3p0uhoPGU0n1R1Ewphe7spuudeirho8It3ebm9HGUcdtU8/wVv9k0VtJCZSWEUWPJ8UwwOqSLbyTFXYpyyCm0xYaW7FSJaeQpuNyQ3KBnYrjcgUZVxZzRNWw+tmIER43fB8C8YS+/lyoo7pQ3wrrOO28aGjaJisdGithRRoqp2oQ3DrWgkOc/kwb/TYu7KIIkwVhfOG/y6knRWSAvfdfzQlJ2WIZGQGOdOSwyGB2D0PgCl3u2SE8+5R1RicdVWh/yPJOiYdJ6k/dCT7fvj/zusTcGsraJrKTLRr7HbYtkVMup7L6ePNaeP7ZIEdJiJ4bsaEHAW9pd/jwogQvrQ6KHr49wXFVsIAjjKqX0AyjMAqFwngYd80Iy6HdMMffFLKytd5lSlrdHYD0Ac4yM7xPCnBDmozHy8vvtYTkOi6B/lN/USeN5lrWNdmbOB1ry9Zm4nrg+BLkXrnXRkIR8blsyluh6p/2ReRvC8o13QyPZewQ5Q3/avtjvhv2J9SE5PT6iRWoLmWfxuHK6zO8qolFKCZfTvD3+kJ6wyGfBFfsp1Qfo814CjQJyMSthErw9WEr5b6l8/fXXs39ffvnl6O7uxsqVK/HkJ5unj26//Xa86lWvwtlnnw0AeOMb34jLLrsMd999Ny688EKUSiXMnDmTfecXv/gFLrroIkyZMuX/oSv/Nwv3KJLNN/sppX9PH1oNAFiP2Szy/dP6WXhf8iOc1vdjlHCyeMPj7KEHAQCPpXMwXJoC4AATQn0tJkp2Sn0ljnv0pWipmHerN+oeQFD6mVKE+FyaFgQEYDZFeya8V+01qXYQNhwiKG1Ee0epJ8Id0vNoai4ge1pyNxaq7VA4Cickhm/e0M4UJcEACnGHyhdVJMOLq35SPxvP7NqOe/c1YLDU6ftcZHDACxsdfBOubsZHmmIsKWTEqCryHkrPwMgGAm1NeGbHOuGwP9WPwYnJapyV3I8r6/8SGQizsov4+vRUNvdum34RPrN+IWZ1z0DvlHkAtoopdNL517qgpNE1Y8dG6+ILpGif2bnEAMYUSUEByo1yBQq5dMkhbU835DB1HFDYPmUFFvf/GQtHH0Gielzb55VuRbMewa6G2Xh4dAHmi8qzxyuNPzViLZETXRYovzPteRZFSEBhcLCQj2wNspRwP9bekPAMd7xFfKlUkggGFOmLdDt90VvGiaJKqDAfiYyT74KIFQ5qfOdF92O6LYwYNoKBKD5BluMMYNkLlm5h/NlZzqyeZABxYyXgA8VL1gd1TklpqzxLgvPM4qKFOYNondCQpDgE+UidaqLDQsj4kgxRKNmpYnEz31TQVpqj1LlA515u1lYmIOV1TWC2rXBEwfEGdI4KsofyLMABhFF3Xi+mO+aFLXub5mJEN6BZjaG7th1Ag8NzODYCAB5J57vGYnYX+V745FvUb9Ke1aOyFKHck3Db7yGeE2TucNwxTFzDcbUCY5DLOCp7PN44iptnKMl056QJS3yM6BaiwHSOEjkh9QXgPJKM09z+SLiD71lgPE9s7QlwKPsJ/wcV/I3+XTJ2pXTySfXP4iZ/8LDienatloWItjj/IpblR9gPxvL/dEZ7YGAAANDV1eVgZ5xxBq699lps3boVWmvcdNNNWL16Nc477zzxGytXrsR9992H173udbl4xsbGsH//fvb/f5YiXQDGnpAg55e0BnqxB3P3/BkAcD+WMq/id+vnYQgt6KjuwqtLv2OLzgrz6SPrAQAP6QXiBRdbphxlbjNGipb6oGu/D218UxPOdrHoA7mgxMJKwWUm1tCeqfYVXHBjYK16CC3pEABgp5rp6Q4UTtuXu/Xh2Fsx9d5T/gmaqvtxQfIXAMAf0pMznlniPeH56WkWRo0Gzlun1KKE78/4N3y+9iImeHk6MKeb0kOjc5Tnvh7hYxrPn8L3LRWdZxYmKBcTtSW0hxGSP6XHAACelDyECmoslfFZ+lY8s/RXAMAm3Z1tQr7fG/VMDCRTi5U0wWjg57Y9kdwTb/F4WKykeaVBigJzZ0lAozBPKI1aC44oTRwWdL0WKLt0c98yxRxTmD/yMDOqlqmNAID72v8FGglPvwznskUEnhnAInbw9NginucvOuuM2LDl6dseFhtfnA/SeVNn2Nh65O4F0WDNG39wGI3EidFZppDzsQLkCLsY5VShCpuzrglMUp6lSLP8bnFMI03Ll8eayi7J+JZoJH2J6snOW7ZWwz1Fc0dLOF5UsZVSteGq5ThQA5ilCcg3qm1hBn7BWFuawn5H81EwQqjMpB+L9hkQ2Qo693wzcT+TnLfCmqHyUSBH2B9jZ5eFh0WcewRYZzA+f5JE+fPqOsFmnV0UW9+Z8UejCWPoyW4iX6NniQ4LeX+kNAoGJhCNF9VTii6BpDKzULYiHn+KgxYxA0laC4o7hGz/3F6RxnTTPk/GBqJ760TtxYg4GQdbElFmCk55aZ+xEpsZkhYm0S/glugR+TNxX6QxlOjxkWZKN6J6DhbQF7a1eCR+h0WKaBelmFOHmadx4rmbN0cP1vJ3G9paa7zrXe/CGWecgRUrVjj4pZdeiuXLl2POnDloaGjA05/+dHzta1/DGWecIX7nO9/5DpYtW4bTTz89F9cnP/lJdHR0uP/PnTv37yX7H14kT7HRLbiyM3/kEUyv78SKZD0SpFiVzsWDWMKiimNowK/K5wIAjk9WB0ajqTd11Nw2viGdKRsxSPCv1XcwGlMk+EbtAraxsDN2RCkKlV2wvnB6dmSp4zOVOcvr9SQi4DMae+ommr1Lt2NENXq6I2VFOZqvnXkJAOCs5H4s3PILNKg61pUWYiUOz3hm28YZBABXTmXDj/OCbiSFRjrBw1OM4fkozAtfLx5XAwrpQdSY012kPEuKKVVC4nsE0gz2sF6AkYYuTFGjeE/5J4w/L9Im66WvcT5uSo9lNGqJZ4JnnymrAR8MPdn3EKTgcVbwbwoOBtnI94jlcc0UKvYetafbNp/4HHmsaEnG5ZZWI1vnjzzCjIFFibnZfVdj9rQcVRCJ8u1oDPpieEHGVcDN03+zfhNlPjIGCSOlNGFJEZQu4qOywrGMGIgSb+nY87UVj38YaQKbJ4450ZyQUglNcysf+dvztkjzx7VVXL6GMDnjg85lj9t/ExHMRxW1yDPpzgApEp9KvBWcPPzMJ+K2BBbfV0L4w3DH9Eg3dTM+CFqxGMULcFB6xOwFxHSHvBCdxOI+E5IYCzOegh/PiTznlHRjfuFTkIJ8dDQFNFLcrm0w/gBfC74/VBb6PoawvP2V8narNjePd9X6HGxO9kzoWLkN+zGFZbRE448YZumRjaUARvmYFvDW/Sd0gOSvdYZXkD3STe1A3plYDkwk3EJbeW3FNNpKkzMwJT7K9cIPsvUqOCfpegvpkYzY8JsT0S2fGUdUYpA8LvYbnkYl0K0mVc/+Ppkz4/af/DI1/m1Wj7S3a7pcIvUEemx7ae6GoEMRbaG89a1vxQMPPIAf/ehHDH7ppZfijjvuwLXXXouVK1fi85//PN7ylrfghhtuiL4xMjKCK6+8sjCaDQDve9/7MDAw4P6/efPmv5fsf3jhxqVXJGjkY77agfdsvhjf6X8dDlPmnPFjei5Tiqyi9EByBABgttotpl91jhrebNQzuSfVeYA1Vun5eHnLN3DXtAvx9LFP4cMLrsBN6XGyEUJgNO2QKW6kLyDtd2hz+dVMtS+LXAjfzGDzqhsAAGv17JxoGOlLhufhtidhXdqLVjWGY1Z9DgBwf/noOO20gEaHRxDSRUrIRIZS2JbxjETiJK8581ILSj5P84wlaqjsULqLn8MhSjriCKudtxoJtk43jrM3ln+DBQMmgr24/884GiZ9/zsLPo86SmwMi5Q5ScG3cIvbw+JxDfkWfZMZjRZG68Ubd3hWjTIsT/kOo3OY4NwupUdKb9/SYs4gTqvuQDvMXRhN43txtDLvZ+9pnMPaJgnlWWyQsduphTRYKlPqgvGlxTXjYVIWiKTY+rH29HiOkSwAIerOzm2z9RGOv59ohWe0oYR1TebEBNE5fnRAM5j5pm2bo4Rkv0v8kVPwiUEVztEJ6KbrOt94D74prM28LABvzPn+Rc4uUNlDnRP+e9Jt4jT1XMo2KOItg4VzGfG5VNvHPBjNppn8WXcyXkI6saNR5dAtGemUj9lvEz/RRz4aOgNA54+nR1TcEc7HmEYLDwtNE2fzOaTbk02cYHwNb8sM7Wm1nQ42L3v5YrB5NvueOG+FfcKgkaOp4YDRo1l8rcfr1Ue+XVei/QiIUARrhuKWx1qaU4K1HGU5gIyhw5HEco+uawoLaSqivUj/4HhimC1FY+jpIX3JKon8jUkUcMc1RdmjYj6I6fKWnvCDAW77KcX6Yn8qArMVQ7qFcc3q8kh1zB/7dypX7O8Ulu/EyHsKLqyHg7b8XYb2JZdcgmuvvRY33XQT5szxbyqPjIzg/e9/P77whS/gggsuwNFHH423vvWtuOiii/C5z30u+s7VV1+N4eFhvPKVryzE19jYiPb2dvb/f5aiBQUmfL7mOLXG/A6N47NzxmvS2WIq4nbVDQCYo3azyZumGo0Yx8yhVQCAVXqe6JG2sG2lXvx63r/jUT0Pu9kt39n3RIXBUA8KA1XSCT2aRLSxNzirGm/mi6qm3w+ki3I2IUEJhcKnay8GLZtKc4UUUxJ9YOfpEBXpDKoWNkD5zCc8LFBgHPEIItqIhXmeoiQqA6JAtfU8NFKeSD1SixmcYaSBOlruW3oJVqdGiXnB+g+iA0M4efuVAIAra0/Bvkq3xxPNKeliN7nPCOYt5Q/LVKDaDu1PwAseiYkVf/FSMTGyR+ix80xLrwnIqbr1gmMGlGcjqgW7dAcAYBaMInncrl+iRY1hY2URtjYfEdBI+syeZ7J8iBXglMzHknDelI6OnAVC6IZibWP5gVzni3SnAh2ryIEGHtGezFpgcpjAbGMp9VMLbRkekW5hPgsKp7iuc2iM5zJy+GhgPJ3cf0/KAvGohWg6HX+BxmLnZDzWVD5Snkl3L7B1KMwzKVpsi2zESpkKfJ7ZEhkhoFlSQtYFIO73RU4MSSGnckbqCz+KQOWMpVGae/G84GvTExTSnZcui4A/XM74/kkmDP2etMdJjjppPqY6O6IEYGZ1i6u3SJkMucFmk/0oZmzQ/ZHwkXZSTMGON05xXxDHP9x7cg3lGK/ERykzgK4PSqRAtrBXSHJ0chF2++/JGlWSQS5m/ETrWtIpuBwG+Tdt72mMvxnpDxKNOf2ejHNBlFEBXZQ2WrcY5os7tz0JWWjbN5BN3xnLKuaZFE3nZ7Tjvli4mBHDqx2KaNuitcZb3/pW/PznP8eNN96IhQsXsr9Xq1VUq1UkwWGQUqmElIYusvKd73wHz372szFjxoy/g/R/jiJFM6jyXVIKM7JbmgHgaaV7AABr9SyWLmnn81bMRAqF6Wo/2qp7XbtUa5yd3I9yOoY+Nd3dyg1YAZbVKzAaeCoaVQ6IAhQY/jz6QDcNYDvsGe29XFEWFP+F48bQfjBdyHFLNDrmAjekx2NL5tEGgI3JXB9VZPxGRCM/P2/x2HpxBElU0hieAgUGAR8tLGcjsYUbUBKNiNrSiG9Yryg9jXnmCZzdoJ39Zbx5Ji4Y/zg2pTPQUh/EWcn9mLv/XgDA1+sXcEPS9Tt2ENBoSKxQIZontq79nnReleEQpH48rrJShACWsHr+7zTin4ZzD3Lkixmx4przMHsGcbY2hvaSfnMXwe+mPBdIShHdhYYbYQW9w0A6JykZwdQRIaWj08wZ+70wskPnqPS+ObvDws49YEInxmQUIPk8fjyuHBbzgX/T13MOC1JPulnf0QjqdPKwcAyltSDxkUbNqNHAnHdMqQ54IczxXGMnkDO0dxNFmuQsAP+9yRos0kV1rp3Ib5keH2mkcibsXzAfi9YM4Y/kQCtMHVcCjLRl9QgfPY3+d8kol50gE9EYz3vXlka0A7ku0W1x2yI5kyRdgetSfmwe08aYnj2+HoB5teWDlR8CAPqnHJbbl6JzyRYup2CHsBwnSASL92GgeD9ieHNotIU5QSchC8V1TeQjrSfP0ViW0Z+0yJHh/L5wGqW2nO7coyMBbsk4tf8OYbkyReCPRLdoXAZ15ah0/F0JVni+W5q3Ql8AoLnBv1fto9xxn6TU8VJCjfT8tpPbm+N2B0v5m24dv/jii3HllVfimmuuQVtbG3bsMOcEOzo60NzcjPb2dpx11ll4z3veg+bmZsyfPx8333wzvv/97+MLX/gC+9aaNWtwyy234Le//e3/f735P1yocAgNrYVqR1R/rZ7FvOZ2UQ2rZuxsWoje0XWYe+ABAPMBGGH+3NJtAIAbG84CRlSQNhZs+tJGADAaHe1u96MRxAxEYYEh0ZdFtJtUFe04QLzmZHPRGmXUMG98LQDgfr0Y7Tmea0cjUS7qKOGMsUvx8ydtxk133IVVpSOgMczo4Ua+L/x8sMUTb1YTnXVGBIsVL/q96FmqQMDw9N0ihVUw0hHjzlOeQsRsk9WCAgxqNAJjaMDDegHmYRcuKv0JCerYhF5s1j04SXQ6eD6Ecw8qVqgobil9kztQZKW6SFGSDAmJRn7ZT9ZWihYSw79E5oS0PgqNfKuka+0iNsdjjTO0u8ayexgalhCDw+MQI81k7jlekDGQaMzPauHznthZRIklbaP+xeNPo2aa1GU3Ubun7hAVafzzInHhuBqFyssK8z0/AYqOW7i6GeHyuV2PO1zrbPzFKB7pS2gokR5rYawkozHsY6KAOsMdv3sujZe0hvNgkSMhkGWWSpapIhhVEx8ziWWhpODFhpYic8LXpfTYwo4tBLCojw5G13rMi5BIBRVdXEXH39eLcdi6YRHXMPg4OHomYYiyecIM93gNRrQo/9M4JyW5ly8fkwC2KnuBpKe6Gacmj+CFG65yuPrblsQ0CnuhtK6pU84TL8wzUYbHBjRdsXUhgkydC3JkV6bRFjEY4ckWZKEnvNjxU/y6ia/Hf1J4+E0pHV1sm/c9YT/KM4Cl6Gwsh3McDJJMCWmG0DaHbvF4U1DXOSxYXfM7dxrwv9E2YiQ+0vfMz6ZyKYJJ6egUJt06bj8vZyBIMAQwaYYfHOVvimh//etfx8DAAM4++2z09va6///kJz9xdX784x/jpJNOwste9jIsX74cn/rUp/Dxj38cb37zm9m3/vd//xezZ8/Gueee+/9PT/6PFklxtwbCfLUDR+28BsuTDaxNXZWxQc9khg292Xht6wkAgGV7yLn3tIrTk4cAALeVn2RAwvMc8nm6eEOl77fattSQEM+lUoUjNYbYUKkDANCrd/E0T8KfY9RaNKCKA6UObNLd7NwmHM+8dhAqnACwbf6z8eX685AiiSKIoWMjVESZwiEoXxN63G2fiQIjKZd5qbHRJiRsnqFy4doWCHMpfU9KoWPtbf/AaTR0x0bjRm2enHpS6WEAwJ3Jsa49gvaSkV/4pqtSEYzRQ2nMVap5SUjHpaiy5MTgimmoPOWNK4GRMbSFeuK9Qubppmmrq1MTsVmarkM7htBaHwAA9FVmeSOkIB2Ujr90SRGPIMc00nGQo8BUUeYwqHj8ZaMRjN/R3EPM7xD3ZKJPkJRGRmPclhnK8ef4mqEGlIUz2SXQGBosiHnL+CimweYbJkDgvJOie0VOOYY7bivdllx44RbpYegss22ldS1F7SXnpi3CaRLz71Cuk7/LjqjYmE81d1rbwmRctH+oeG3mGCyROp8j68NxoXTTQiN+Mh8tmvi8umSEUDwszTu62VqgO/gpZd5Izm2aog5C4050YUdpJkpI8eOGj2H2qMmO24yZ2DbjDE4j+Bg6GoXVLcmPXDkTrmFx74llM1RORFvAUeS0MN+M6fG44x4mDLf/Xuzkkede+MHC88+hzM3Zr6MoZyKZth411VOlyHdIT2FEW6SbwwzPJLpjmDSGojwK4NJ71NL5ZymCnB/RlvhtAI2V4qi0bcfwiGe0C8Yfk4EdvOVvimjTTSivzJw5E5dffvmE9T7xiU/gE5/4xN+C/p+ySG9Ht9b24kuVr+DC0l+ANYjcHXumHo3xkQpKmkZI7PeAezrPwxl7forF/X9GA16JcVQwe/hRtKsRjJbbsa68CMAwiWjkPadjYLReUfSAKsCScSG9g7m30osp9QHMxi5R4UhTjSclxkhbM+UE4IAKNt5QEYg3+IxyhMWRExg7CnHqn2hUBcrchBepFEafCR8ZHlmY28LTSTN6Cs9TSbgF54Kw8fKxji/x0sSytYJ2rZ7FvnFncpzBk8a8pdHr8PyjwZ0/1mLERoMZk7EikuOlhv1m3G/5eZZ8GOUFNRqoEauDerZu1MeIZ8ax9YBeBABYmq7FAmWi2jv0VFST5kkqqx4oZQakTM6QNSwpp0LquJQSzg1gJdQLaKRGiHCBHHNYSGnQwmKg9DgYBGWH8KzwbKhS4ruqkoGY5wyQI2S2Xky31LYuzHnRCJGexANXqsN5QcmT06Dz5R6Vj9LFfkVnVQE5op0k8Xq1cArLNQYQwqjjmHwv6B+lh6o9dI+D9usjAOU6PESZEszIySrpCRnAXBrJN6O5IowDk4VFqcx58zbqS/6zQjZ6HT8P6GGUEUUvRzzQcBxmjlzn6q5Ne/Gypq/i4nKTQGPQF2EP9n2U9o+4P9Gegpy20brO0RUiYnLmc9Ze64kd8EV7IXd483r52RQxzNYP8cjOpLAvk4zEKwWlDL2Srkhx0J+Gtnwa4/UlR6BFp1PEC4FnSphnyv+N1iN/YvRKfZHOTovrNYDZ9k2VOKItfZPuffZYt3xGexLyTJjkh85oHyp/dwmNywQpnr/5U8bIDsrq0mEY1yWsn/cC0xaaKJJ+Q93adBh26Q5U0lG8uHQjftnwIbx9k3nqakvnSUhVKatbZMQImwMTvLae/y81EHlatldEbbHf3NtgDLG5aieLaNv/phpYmphU2K0tyyLcskIeKxcsfdfB/KZGDduJUhktTL48RnGYEhRywl02/hld9NIs2t73xf+72DiZaPNEVE+KFNC24plY+k5w4Pi5Lz3MtT9Q6cKdpeMjPLaIygVTsqT+8XogbdkFewEu358YJis2fE7QPkprhs49v46oM8DSGK8ZIDRE4rGhxtJD6QIAwBy9HUcn5rbxjbpHVIppxF5e1wKN1KnCLlIi7QvmHl2vovEdfA+YwEhPBQejpk4+RyI3YhCWYmcSqxfNCQkmzTGy1iFHtIvPXkpKbM76RwGNwjlwNtbCJXd8D4iNatloRH5bWk84By4dJ6DGLjUQecQ3WB85iv9klGJGD1XSg7UO4XshrMg5AcJHyahmjkhhPkYQhUInT172gvTZwiwiUFkYzzP/KRX1RTzTCghtkdUP6CETSLrvRDJibbevb3k2w/HX9AgxCwzIyWhBXGQneFxZlo95PDOFj78wvwvmLccdv10uGqyC3GNO0EK6BZkpjLUzGkXwxOs1SYQ5ruJ+0zkq6YoBOWIadGz4yU7Qyczn/LPcAizgg0SPRLf9dUKDPMnrX76e2VwpPqNtm0l8nExEO2xrac7jxcFYDhnaT3BhhoBSuLj0Sywb9Eb2QJO5tGxjwxJc3PoFHDH2PWyee6FpS5RL7+3XSJHglvQoAMB/V76HY5O1KMFoMZunnhoZkgBPRQW4IJA3tVjwUqOaGRIBDtreG9p9otc81RoLsxtC9zTODXALCp64efKNV6axwGCFpEh6ZhQ7IsjmWRQNBeE3ae9wkSJFwwg5Ykov5YNoFIV0C8oF/Rp1Tkhn3S2MRrQTXSdOHt+5aJ6RDYelfob8JtwVLyTSMh7fn1iYS8oOy1QoSE8F2fSLbqcGgih3NvfyLsiS1iZ1OvSjDXsbegEA/142x3Q2pDPzFQ74thbGjnpYXlA+WrqFNcznM2kv1QPnhZV7gGwM8lRkOBolw19yWND+hMpOQsbL05yXOs75CCpniFAQFQY2/vnzgtbluBGVyBkY4eHt+BEV83veGe2JblaP52NOZkgwJxQhkmUBCHIrPAdu4UU0Fj7lJcpCxEXYP+g6YvQIzZPgZY2IxkKngyrkN6V7Mqm/UiTVwsMi7aVMnk1wHKH4uEX+uIjRUGXRB3MFOTJFmD9hvS2VhfjPhVfis9UX4Y7O8/Gp2ku4ETpBWvZEPKN1i1LH+f6azzNZXtPvhbRIszGQmxS3IPeiGZS7tkK684zvWBYCeQ6vsD/SOEj1ip0GUrYhbRvS441TYbyjf+fJvfxxpUU6167CoxUCbjl6raJ61iaQ3sGeVKp+9rNJTB2PeSZdhsbf0Y77Ev6N/jvvjP/BWA4Z2k9wYWdx0iouKN3u/vbO8X/FT0++Gh+ovhaf7/mUuQQISRAN40q6NWxvrB8v4tvaeWJs2MALFn4GLdjoyOYgRYqYUU1TYzOg9CzVvkZjiM1BH/ueEzD1cfcUx+4mc6mJ9FxIkTMAkA1/bnxT3LaPrqKoIPoNzGsmokLlYB6vFLEDCD0MNZcw+RecTKw00H7z80uTELyBIuCNLz73AD/+KRLc3vksAMBt8y8WlC/Sl8ILYKhiGgMndLSIm2KsKVF+S4qJfAt2TLd0kVbejcWSYcQcNSIv+Bzd0WQyB9qVuehvtZ7D51mh8RTPWyCQKURhEWkM5p54jAJShITwjDrAhDVD1zql3dEjpLcXvUefp+CFc4KmHcspnbZezvcIfZIDrtAxhuKsCwYL2iakz2KKOb3Yi8oUyeEh9Ju/4R3TLeLO/i7P5XzeAuHN6JLxRXgRyUyBZ6Iy7S8ao9liklyXNEYx2yiv3+HeReUM44XQl5DuvDkhyJ68PcHKbEZ3RKMS5r1AD6E7ZbIjXIMF4xLOM7YOfd2ifY86Qfc2zMJX68/Bj2e+BwOYwngh7TOMNyLP8lKMBTkTtkXcb7pX0P0/OraQw2/zjZhGcbwFQDwOdAxJvbCp2GeZjxJyWcblGLEhHiA6M05x8dcS4u+F5EhnmPPONeemjoe05Ogek5Hhtg6PFsd0F9ejMBW19bhjGgGeOi6l1qugPv07vXXcOTEm0W+IPIsqHTTlkKH9BBciv5AmFTxn/KP4Zc8leMbYJ/GL9EzochN+WH8qBpP2WNllXuoMlBmN16cnYbzUEuEbap4tGH6C8kQ3VPG9XdpWOdz+0iS/YfhosV8ptv2+RhOxn6f6mPJs2/ceeATNahyDSTv2ZRFtOWKjo7Z14Qwij7pmPKNXKZGdVlbmijzpsdJo+RZ+z/6XKt4sskOVd/AijRe9hENSQljb7PfCaK8geKnirkl78ZwsaXpV99vxorEP4eGZF+ZEEDjPpHQ32XATxgUeSJZHprAEfUTOBi98U8STFWkzL0q/BGLHGACI79sKcyo0OgFgd7Y2AOPc+EH9aUxRKrwoCojWuq1r61E55frNIrkC3cE6FNcMhKgSpPEvnmfSujZ4PN2xAiTDRKdTNAbCuAjfs3Ud3fA0ytkPsbJTdPyD0iimVcLj9jgsjDh+pCMBENYcm+P59IDNUUekl1sFjkjkyFF+x4dFk+NMEOkR+FMg9ybKaJoodVxy1DHnlsPjvyntM1FXyLhQWDgKfPzpfiR9U5hTwnr1uELnjdA2+13KzrFF2t8sIJq7eetQFdWL+ydmd1CHdw7PJZg0DiIvBOaKxrujMeatdESN4oAIl3UAOVWXF/GeDcROedmJJTshaB8pXDKg5W/GNIpts98luUfrUbrs9wzMA3OzBRQfa/v7ZM93i7CIt5YejjePRqkeJuifrRJF07Mq/Iy2hMf8lNLE2RntPP5gcrL5UOr4ofJ3lzCqNIwm3NL1AqzS8zN4Vg9CtFD7Zy78JDRmYx0l/Pror+G+dBFHWCoLxoBgQEPYUBVXbC3MY45TTNM0TwE2wIFGk/I6R+2C1nWPO6u7cMi8G/5Y0zFQmXdMVHbFaChVJD2NDiYo7nKkKVYG6UZSpzwL6ynK22KPO1XGii4usbhCenz7fMVGwQ/YhGdLhY3JgbT7DzOy/Pj7xlWU8Ve9DEqVcuYZ+WRQpLRcriiFc1SR+R3QLm6A+Ru8lN7K12uAW1pHdKxZGiz5HuGF67eAWzr3bw2WPU3e0H5gyhkYR4X1Vzp7LV0UIx1FsM47V1eaPxEvqLIj1BNePJAMN2ldSvcs5DksJJ45PiSC8iUoQHwMLRuEdSQoSlQx1ZBlKX2WTMSNsEwuGmLhQOho4XRb3Ajg9Jv0/Hw8/oKiBD+nCm8xJ/Nbvhk/7gsA1DKmSbJUlnvCWBN6pHrSuBZlHwAcr3SxpBy1F/YFwbntaJTkVo6sD+WRoTHPGMwfQ57+b36XHAn+e5IslOdJyEj7Tzd3pTVD+BjvAXT+xG0lxyjVo0QDURhscc0JciZ/nsV4wvXKdS5br+AG7ZhMYd7HhKucfhfNUVty07JzxzV/rnB64n7I8zZuK/U5lq0qokeMFgu/2X+xs8qEprjeZMa/gEZKheI/8+iWos/ujLaAJ4TZdk1lGpUWvpn9LqWOc+M7ptF+U5bNMj0HYzlkaD/hJV/oA9wYDJ/Y0UBkxNJ6uzqPxnPGP4Yd2XvVW/R0tsFLt9NKN2MTfdz9N5UUIC0/70U3CFssnsHGHqRaoVHV0Dje77+Z/Vw4dC8A4LHmY117KbVNVg6p0UCYlhV3aywBM14wD3nwTSKYpLPlonEhGBJ5xq63a/K8feF4URpt25zNXFB2RQUoLIqOq5C+q+l8JGMtOWUm5FnQFkJbybAlbXm/ZeWkSInlSn7AM8QKUJIIERJCuZQGa5wTxQ6dKBpGeGHrPdZ+BtYki7AyXYLfT31x/vckBRh5PMv6RxYNXwvkm7D9tryI1yZUvI5EZY7gliJNWstyT5p7dK7Eip8U7Sl2MBXeDyHMJwTz28+pODoHkcacNSwaA0JfwrUOROMPBM97CdkmYnpz0SVu0rgS3sp7iq9nC4toi1H34jVjixT5mijVXxpXSrek+Em0i/IsDw8snnwHkbReJ0q1DR1oUlRJTt8N+yIYAxI9gMBveZ7kRT4jAxp036N95DQypxFzWARzmay3oswXaZ54uLQ2wz5OzjHGMsbo3oNgvSHmrf0+xSPB8nDLcypnDMK2f8MclegBJuecAKRoet68F2gMM37cN4KKmBwfQ6dMYVr2ZOaEgEMc1wyDaOxKl48xmvnfKDxPB6Tp3x5PXI9+U7513PJ24rGW5JmUAXKwlEOG9hNcdIHCAARGtYP5DcPBBKPR1nvR+IdxX9Mp+M/qq/jGJESBJUVSunFSiuIx3JRw5ONBUsEetAMAWkb7XEWlFEqo47ChlQCA9U3LY54RGqkCa5FIN8RKETtKOKVROq8ujxdVQjhueVOj0VBSj+CVDFFKi+Sdt4j4ue8QN40KxH3xOHKUJ9LWz7OMbsQwIJxn4cYtGW6Q+Sj0T8q6kOSxtAVKfUzIB4rXQs64CvSExrfDg8ARRXkhjGEqTHwLG27owlvbv4Tnj38EG5qOcL0N07Lphiq/BhAbVKFTLVZYpbUgjKFAD1WexGMrxJgvOo6iyewTU8eleaEQne+TFX/pZusc52SMwvHBOAPi9Spl6FB6JqOQxS15SidNy0YwR01d31qcjwVOHrquKTWhbKZtC8/dKr9/5F3YFj91N0FEW5SjxbJZqjfR/RfUGcCXqyTPwnXoYdJ79J6eyb0JTw3E0HCLv2m/wmHRGOasj+iL0hgIhpIYxQuktXQ+mDllBFkRZyDEY0iPoxQ6nYU+W/qktTm5erJzO9bD5GCCiAhcCigBZttL+55IIyxu2nYSY5gjCy0uWo/9sYBGCbfQVJRHYj1l4f4v0nns3GyBgB77azxXJudogTDPCs+MC33hbZXQNoaZf+dHtFsafOp4W1OZ/Y1/M4aJEW2R1oAeqV7OnD8Yyt/0jvah8rcXt4coKtSIcsEuFeOKpAY9qxjXsxN1k+7B52d8FLf278ap8BJMUrTqVMkKBLyBZ7hFJUI6/xynmWtwwb1DT8UMNYDmsZ0Aetz3vlT5qsO7s2EBZgh0h32hSpZEd57xLY2DZNBTtK7f4ruzXlGSlfmsHrswJxtD0Ih2zkaScVKKKk54dm4SQl88+8R44+cZfRMawXxk9BDc0sbNz+2av9cF/hRGPgVlzuKRPKJFZ9VopAnheOUoZNH4C/0DvMcXmsqArK7W7Gy6nT/88ik79ygv4vkYzQlCshSxly64oinv9txeHXlvnPuB9ZdKSeNfbHAWj4uf9+6su5968twL+u7rhXhylMZw3ubIx6L0y7x1XZxZkJNCKSjPEs/kVNsMJhxlYHXFsaHGW77RORFMOqMv3jBP5TWRo3WhP8zZGo2rrKdNSgGGLKPimcJlDMtqCXmGWF5bOg3M45UU06h/Er+lZgV1ZRiXC+J5XPF7k9t7xEiasj/JvoKQj5xOSqP9LiA7ouVMHHkt2O/nzR3JYJHP6E5ivEg9To8w90RZxr+ZF0GW5q7KGYeiLEL/PRnHZM5oK8Sw3PZCPSnVH0pBBQfR8hwJlC5bj/6kxEnjxVPHhQ4ih7eQYPEs80a+/5v0RJfUF/s7N4ojFORvAT2ZvL3wuFl4aNsALjppLiqZ4qIE/kyUOi6lwVu8k9VJD9ZyyNB+gosUIWXKBVUkMxiNrobKJVXmDNxsLMXefg+QL/uBhwkCg25+4c2//CIlIxRTzVPMd+guHIUNWUTbvP/bqkbwrNIdAIBRXUGt1Cgo6ZJxSemOFWCaBsujihNEFQoUCSnKyTfzcMORvdQUpj3TcjbArK5gfElOEEa3IOiKDATfFn6sIWRYUJj4zM3klEsQ3HKKuaUxdopAoNuAZWEe1QvGwcLi4wRy+yg6m6dQk/koXpwmpAmK574F3kq38svnyON5q4V5G46Vc/IIfWQKedCeGVWEj3FEG1FhsofKPfaGewZj8sy3n1x6YwwUFSA2Bh6vrNRmNBIhLivkeZEhYQ0La1NS+hGOgfIOIhotZk/LTTgfbb/jucfoiejme5fvn/A9YQ7wd7TNTyl1lF4M6WFy2indax094rq2NPrOhJcHWdptKTzrLsyfPCfGZGSzbHDIfTF/MmuY1RX6Eo2NsBrynFPhF8UoY87coT/9+pLmiv+ufNY9doxQeSs5A4tS6EP4pGRK3noVvhfNCcR8yJsT9CdIO6lu5PyfgB7WVlhHUWtx7qmYxuwfef0J6Zm0nhK1jeef/adkiNKafj7G/Wb1hO/Z9jErYgeq5MRyqd4TpGqL57Gz3zmN0hgggtF23W1N+NKLjxPpovWoUW1lNTe0YzxdrQ3ZMbOQZ5JczxFmB0E5lDr+BBd2PnMCA1GK2NiaJbJhMGU5UGKBeLNieCTjktXj9IfGDlWgJFisnAI7szPkrWO7He4evcfheP74R2RlBfH3+Cbr64UXD1EYTcHnG1s8NhRvpBQR4SDykeGwY42oHvfWF58tLnrSREEarxx6JgMjfJDGmjoIKL/4ePE5LvOR1qN0B23df2RHS9gh2eERqResj/6bphSfS6WKqac7jEgDfiOiKfhQsbMEIn9iPBYX4JVGc2acr39uAPv+SWfxIuPC4gjmKVW+mGJbsDYlPvJocbzJOj6Q/1J+yVF7z0h5Pgcw5K2ZeE5NNioEQjeTM/9P63ByfQn5nQj8NnCypwhrToLltff1QhqpTNCsLv1e3hrOy1QR16HADJGP0SeLHaOSU4yW3CfIJDklwOx/WTbNhBTKtFA5SksiMC03AimsddmxEbRFzNtEITonK0fIFesUPVISHingjizfv2LnpCcycgbSTYX2RVjrcc2CtSnwVjSAw3mWs39IOBDQmUuj0B9xfSBnXQffkxxbRTyjX1DR3/y/Jzen5IyfsKK0XmWj0zJN4KPEH8mQFPDIxrfE21j2IKgbrg9ANqodaQRo9Q6Wvg+5FBm21Okppo5nyMv0eS9Y3P4706c0iDTQ+248nlxy/unLIUP7CS403S3ceAG/eHm02LcPL5AJlbnQwOTnlzyOUCHDBPU81TJu/3wRezwr2tiSRDlDu2Wsz+GZhn4AwM6GuXhYLzCKAE1RBhdWE6bkKV4v4hk1GkPlAvHuQnEXnyFDVCgOHhXy9LC6BQJZSmWsazqGAn4BIKdfhZsD/158bIFkDJDNt+i8shx18zSy570CGKV7wudZcpSBIq+y/Ga257e4mQd9pkqfnKmixXkmKZJShF2KfKWMRtvWMyLqC4WFHQqKSA/rt0PjivQmcFGEPf/SJE83dSZayiVHZJGzLD9lLR7XyUS5cuUjGX/ptYZC3EQOc3om05fYCJls6rfF5NYCmc/RsRep3zl0i+sjnPM59IRGmscVUi2PYcTvnG8VGQ1FxxsA3pbKKddeOFsuO6IpLwT5L8JCWkSXY4ExGMAQ72fAJNsK/JHwmjkWj5WlP699Sh1HobyGxMdJyi3E60gQ9fk0Ii91PGwvRbnjPTxfJsRtbf0QFukuYn8kB1PeeIXfE8awgGeMRpnEXPkqGtWSniJ+b+J5JkaBEcMsbvpNaQxsu8ntFTlOg6C9j7qrCeqpqJ61e/kY5NAti9vsOzHuiVLH/RltD5vR1hjBLO2S/nmwlkOG9hNcwqgghQFUuaTpkr6y3VychylS5gIFAfFmJUXIpE1WEsZEl2epkd4olo1Ymm65A10AgCnjfQ5Pl+4HAOwvTY3wFHmpJYPD4OH1KIwZiIgvPqK4XbcZ7vyNm37PQyjPELUN3zyVBHdoxFKFg38z3qRjAzHuYN5ZPCnyxVPwY9wsIgoO4/32dfx5YyHyRS9Xg9QWUZF5UbzBU2MgSnnO2fSlOYEABgDSc3OUHunCHlmRjPtd91O5eP0XrXUVK5Humw4P+QA4jNItPbHDUzWD/il5zYDMHemGYef4oWd5BZ7Rb8qKMq+XJPEWL9l8koKHAK+mdQXcRcaO/15ePWF+B7KM1iu6zAwIo9+C0Sg4Sxju8INKwE1pZPcfROQwpU3qI4XF92LEAk1OWcxROO2cLzirCoROHkR1JXlfvDfnGf5hyTGUcmSh6KgJ6xHjRLp8jNabmBrZGSTtM7ahbLAI8yecj4REid/80sVM9hQ5wCRacizEPANK6mPMb7rHFawtyE6IiByZRBmipHmmIueWtC/IPJNlQojdjt1k0q0lmTn5OwPyjHReV7wMzcGK9wopfdu2l/YZme64XohbOTy0bcxHO3ZSPWZ859D9t0a0JYcFvXVcelps+pTGiEbbPuLPQWyNHsRd+79RuJHGNwwQWG7quDNss3+DR7RDJV8yBug35YuL4GDRxgIulMKoO7G9mWCiHmkb0Z4yvsvh6dIDAIDBzNCmfeHPXAT9Y332dNG3x23JvSE25Bkk4Slc4kOUFYnfUts60XZDRQDIiUooOlc83bZICkfQPKBHVi6idgEtkbHDnCo0mhYrDdJNzdTBFI0B4RmPxKqgXgHtgiIhKtqBMUDHpvA4Aeg6Im2zX6UblFmfSb9ZxDaC2dpypEk6tiBdZiSfabTfyjca43PW0oVW1GHh++LXB+mzBBMmqeON9I42kTPSWV7xmIA076X1CkkBEmBKlhMUIsmZQtwCzOKP8Eh0Z78Xyf/8iAZdXwSvsC9MzmlQvP4nOv6Rd5mhLM9UUG9yBitdW/x7ptDxy0tlt6WwjyJMMMgnOUdNWnbYP3kV584VURZaGilMGhuBZwKOyRlp/KdUV86IgYPBzdvYoSc5NvQEe70k6/NoLJo/tL1sNPJ9QVxbBfJASgmWjOXJyjNxbKK5N8kL4LJ/s7o5jFQqj+6w3mTTsmVHlP0b7UtIo8BuTyOtlyNHc2VUwXrzbWMaEgGPLBs5XXltiyL2eUU6ez3hZWjRL0BHc0XElSfrD9ZyyND+BxW+iXg4vVXXFil1nBkhRLl0qbrCRll4uRKtJxi2jHZ4GsOUTv7eMsXt6XZntMd3u3rTszPa/aUu973Ycz2RMk8NG16P8szSbmGuPUu3D/uMGDf5pnSZldw2rhfegFrkDaWGTXweVzYkpY1pIgMhpJv22z8tx8+6R4pNzryNFf8cBS8a6xw+SgpwgD/Ew2AZkKWJh3NPMiQIQL4h3NeVonP8bGGRISLcyk46yJ0TWVshZZVmXVgsLptCmHe8ppy+KWWBSA49/mQgXzMQxoVHyOl6NT+1+w9X0CSlmtIizhVhsUtjHc+diaNP1Nkiqw3xWpfPzk5MN1sfgiwLExLE9Q5bt8CZKLUlsp7DwraCMS/wG8g3bGOe50XYY+Ck2iLOcpLHLoxox3JYNgZjGutCW0/25PqXx8fc8ZK+GcpCoZ4YfcLk1kxe/+hP+lV5D+CyS55n8VqgBiLfMyfui2SQZRTmZDpNYgzJCEq36vPvIQbaBvmggm9K+5EsUyZzdtrUi7+HoK6XPSqoKxunk4p8TzCXfVvFaKC0cZhif+P0CH0R94/J0B3PCfGGcUsP+YKjm1SUjOqSNCeEtuH3wyId4+ERbfOzXKI0xmvG6kGSYySOsOeS809fDhnaT3CRUl7zUkzDM7G0rjdsfVSZbr4+rZJHhgxu394Z5CqmRz6LI0ea+LM7RUaVQp/uBAC01PpRQQ2Awvx0CwBge3lO1pZHyUO6pbPX0iVcvJ7/O08xte3jTTr8Hu8LnNAqPNfOxsV/L+yfwZ2nVGV0U8NG5I/QdlKbUDHdpo+hk4crO7aqZHRKjh8pWiS9Jy3eiizwMaRdVhDiDT7CQ+Ze4WVo5HupsN6kuxdCGi2cK1qKwfLGOrqPAZ63cqp+rDwXRpXBZUCRw4sqsSwSH+CmvChcb75pMM/8urZfFNN3VbxRS/v235L5MJlISmik+4vqJFmat+ZiekTlO6jHDImCaHGu0ZAzLyQH2qTkB4TvieNSpOLFdSMDXMlKY6TE5q1BCRbRHX8PCG5vn2REW7oFm2Y6TeqiKXGfkPmoJskfGhFPBTnDvicgESOxBevaFp/Kmk83O1KU/awXyCNFkMvZHf5jRQ6iEOnk9tK8d8+L1ptfIOJYRzD+09YznwjxSA4PSd7nXMQatY1nWaEDhcE8fo5nsnSrnEyO+HsxjTFuOeKLCObxeKD9u4R78nqYPK5ipoJA92RhSvhe3jqUSknEoyKYeOs4+Y5NLZdks8Tvg7UcMrSf4MKNYlPC1GGAK5JMccuA0nuydFMUz3cK7+2KTw0VeVfJ90Kj2vQPjO4wupcoYB/aMK5LAIDpGIBSwPx0MwBgW2V+hEd6BkiMXgubsRRdY31UQr+lzRdk0xeMRin66NvGzg7aH0ojhE1ewXsUpXGd0BiMYMLGrRDVDBWlMKrI6Ablo28fG3meF8XPUnl+S215/2KBLCrvknLB5qiwZgoU7fzUaD4nAK6QUxol41Qydooi3/ScdMRbYc1Ia932J6YxZ1zF9RE1jyJIVCZMNoonGdXcESn0UeUobpOYPxMZX4Ww4HvFzoR8wyimJ+S37JCz1YqM4lyjgYx2kXEiGbu5PIvkYw6/pbmnZOMrqoc83HH/RPmYszZDuidKHWcZAyqGxUaep4g64KVJJc+JuC/ShBINcGlOsbEmMrdgL2SwAIWcYizLUdID8ZuSk086UiYdowm2V6EvISzvzogcuTfJvVSWFZxGM8+CegXrgBlQFoagblDPf3MSNE4SJuH19QmNWS1JJkn8mUyKOnL6EjvkLG6JZ6SuBENsDBYewUEAE+dU3F5yTiR+sCMYrWf/PFF6u/A5Vlcq4mVoxFq0BnY59IIEeEqJ8C438uRCLjn/9OWQof0EFzk66/9uJyxPl/QzjqZvAjxVG0RhlTdzv1lJCnC0MeVtxvZ7QOQMSHXOO8HMIFfYhU4AQI/ah87xHZild6CuFTZVFjkaY4NF8lzHygHlGTUFxcgXlDgO0qIX03IDnk20yXKnQTBWrq4ghMDxyGnHOcbgpDZuOcJB69EIAhBehiacqYagFAnjRZXGOpujtp7F4f9LiJQ3fWnzzVW0kdHt2zreCpkPEg4pfbvOnpaTNZOIntz1ar9Jxzqgkc0ToR5bMxJMIDFXAYrrRfOe8JutNwsrcBAloZwJnDxa87sgbNECHvpNWSETcEe8KJ47/nu8M9w5EdaV5YxoxIhrOJ/fUuYU/Z6jNYeeoswrsS8QLlIibfkeFdOTe9mcSGMsHyV5NqmzqqJSLOwpQp9d+wL+FB2toEumiLeSI0p2Tsn7Nf1J4UURrKIb5hOR3znzFnG9XCO2YE+SHFZ6InmGoK3IM3l/lGB5NIa2Rd6FXUVzVHK0unqQeRvSI0Uv3TcRFnneT/aY2eQcKBFSz0dhr5hMynwidIauI/q9PLn3d0e0g37bX0WeCREqaS2EpEvzTDq3TWny9czv4s3fjBR5nhTJAyl6LaWEs+cOhYxcF9GOvh/PiUNntA+Vv7t4fSPeRIAwMmzgdPK6KJC77CtICbd4hI2pLsDks3MTRGHcRhdf2MYUYEKQj0iZn1v1dADAU0srccTg7QCAe/QSDCZtDneUOk4WoxR9ls5jS7dYM/4oyou4Hm1rwXIqc75iItEojYGFR7jhhSKPDHK6gTwvdQyTNitpI5BS5rmxE/ebR9jDfqsYRhr7VGR5rCeTDpz3R1HJI4q2fOFOkXER9zkhjORHQmJKc8ch5CP8ODBHXUS3ME/EevTYAoUJNOYYO+EkF886/n/s/Xm87UlV3w+v795nuPO9Pd3u29zbt5se6LnpgbmhW+ZRUNEoiGCUQQYVFCMOgQyG/IzDT/JEHhKf8IqPGp9fVH7we5LwBBVB84BGjBhHjMZIUERF6YHuvsM5vz/2ru93Va33qqp9zt7nnHtufV6ve/c+tWtYNa1aq9aqKmxvmG9URqKkrSd8L37yy7aZuPMwBp/RBCse5udYdol/QPvq+RGVXUE38WaBuU4CDAl4Ol+PxngjkvM0aZM1hZUQ2hbx5rvjlusoIjYe05kGDHNmSOsJfum6ILiW2g1d3a/ZizipPtCO3nnjSVmQZ2atIEu8jpcG8saWwzvSqjjjsRM79lAGENuOQjyc6qLKiGmy/I3COT2H5BRgehJRR7RDtIvoCmlDvrZsk6UzD8th7Obr8wQdHL7aVwI8Xmj7JreG93FGvPaE+LouhsbQ10meXUI3WZWHuti0aSBfXmbDwldWdm1+FI/yS5GzaI9hnFE5+tZxbaTo8xnZcRvKtvzMJeecR1O0F4z0KScRdoNeX1+Pzj8GDBbtEJF3Q9mCONAxWOJsWrporKdZMRFtLaYbvTXrTM+W/8zZp4mIyD2jT8p1931cRER+8ewdkaA8KF+Z3exIILdKtaZRT2RSbkko0jVJhSeJ2kylFYG0SVhn2yaE0yI0XHI39GvINPtWrxA8RYKEiAHkYcGK35DGegEMEeObX+N4kaC0FrdPTLdt21BOzSUu+gKY+BxpEoZlOwJeIgj29U5pFFqk2QIdoMdeSqMejzlPlXiu+2MnjaspJ76QO2YSh3WGRrOoJjQOG3XTMS/JMQxJ6iO2vwTnIQh4Tl3SFsL+g7C+HJNnLT111iK+UIiPsoT4aZ51Fx/5cy79G/uFWsibHzTnqBwzr0lwc9azzBjNewsN+U7i2jyzHg3Cc72mfsw7mJ9oWnU40iOBbs18bOd4Yzz923qL0NjxaIR1T3TbDptv3N4+X9dlUNswhdzmaaDXhzbPIWLsqm/Lpv439HQ+jTUbLXQZmkA8j2fqz0kZls8MedXwGRg/I6990vwym05RGV0f39Ylzx+7JH5ctuXhpFyKxHXsrc0Qbxwp+bZwPDs9smVsxFIcu47bcoL8v7o0EHnqjDqrOkXujDZ5fOxWLG03AbsdkbV3ClKA18W6S4oKG/eXpq1HLpS4kz6dtuugFcXum2laEcPVRE0AUKii9J2yvCcbBB9fu1FERG4Z/YnIfX8iIiIfXnusXAQKK715mbvZWmMd2nYSN9TFLtwssAxhsXtqvJhrBYjSyhDNKkoSusYuGpL0IbaP1k5U4TULGG4QOAI1uQfpekcXV03DonPESVoce9DetmV40Z+lPiRc6ih5ASi2uvb04NyyQFdGsXEn8yilcciU5/rkN61c4AsDM1jsInqIL4DwlVOUckpMyXXcbjB2Impz0t0kRIHT1rdG2fHPltN4HP6P8gQaqy8KqqiLQBgJxUOelvA6K7mzxdjPBb8MatshT0OOKWcEZSNfgAZyLY04r217h98mca2FNVM09j9uBgA38/vf9pWmNUfPaDSEkWIbl23rUuexwXxiQqvfN8SH6RJI3LxVLqzUtp4r+6hLvK6ELLF1z01R/bRy4ckpkzKYj+pPTTfOpfTvynno8z3Of9R15n6QGhqRlwLdnlt+ZFGZJvYUN7b4Ao1plm4f+PwjotvkF8oZKkAW9sElfBh89Eb1oJDr+ukyuF410Er+MF/V79PAJRXxi6fOmnhE9ySO7e1m0W7YMOKN4nhxENHKisgw+YYRZ87JOhZtvpBqGqYWF3SXBeU7wHN518wmZ+UMk+fT65fIQ6MDfZq/7C6W318/EV2all4UFWiK6R7i5c4Bi8TuL9H7v0HpcNzM0zqytTAnmOSZcWr5JGHXntEVp79MUhZ20kV/5NBNSqwK0mMqRM271ivhSQtK6bhVNJZuO0alrwOBRer6gca4L4QkY0LYGs5u2XUWH9rQ0X1dfjM7bcchsXcrvw6raUeXbtNmcD+E0BiNNzHWE16olW8tfOfmIVrTQMCTDhQbmgmdM/ZskIgzdmssMW7aNBrya0uR5/KIwiDUG3kchan8sq7RMMa8uJ6QX8MX/DariKfiD2lDOybjmZQGHFMe3T6PCiALkHTUV11Ea5ynHXtonU/yxLsOhOLZxFn3dludHtFFrgk/00QS36M1U5dBYZPP4YeBRh6ncXrySnMU28oxIUl4B61GimRfDs4Fv/91WNX8B56C/Tr9A89jJ+VIJ3jvQ3X9YIyG+AHkbt2ni+a6H4/ap2azZITl2D4MX5f101lpHqouY3Jvp46ZAeQ6Hrm8g+b4xdNnp/GHeINFu4Kvk0CyS9AU7YUDLJKO4kauuuaJpShPUDrFLkyRkA4Lany7rK1BGm9Cz/AdlappWH/Zm4zk/ce/bfq9k5/c+zIR6QpKlVaKA91gVZRhctfcOm7PguetoeTKnr2dGtvRWh8DjSRwYDli681CrJ9fTE150Q80BuAFQEooMq7jKp+stZjqLFY4oJ1wj3ZPqJZ+rGi603nkCUoTxDdoS5wWxsRQR+ovX5DgZ7J02pieVGGN652MeZzrvADm+iuuS5of8BkdGKXtqUQ3+vi5wpDnOqTXZdswugwtT4/KD8PEgHiAOyZAACbrbJ2bNwuhInkLYjZ9V/fcmLv2IO+x8MaU3Rhh61UazVfS0zqzohziE50iKZ+COBVtRoGYnxAvhDHVRR8qOD+H6ULLLD3unLH01JydTX+jDTQ8o91v3g551Wx4Ij1IYz5soId4T1pOrWcAjL3pHyOiRyxoA47GCrWFvVSQZQVNQ0S3CuzjAY/jTTUY44bsujUKXat7GqEuhbQeH8V2pPkG5dCYGhRoVTbQTeefqzeCC9BjgM6Hj6Ggh6YWbaKxJJvtZmu2SHMdXziy1mdRC4vk3cz1WW7Y2GUFiMoBASj7RI7YeCLsTtwpOvUTZgGfPPJMedenLpKX3XGxfOizh0TkvuytwSwIDEybLZIqvdpG4huY7eZEVG8JZQ9pJS1b0UN058KG36jsaTloObWbBkPaOsXfE6iJRnTBF1EX9Nl2jN6EnqZdh7QR3dPvZ3NCOixqgXYThooEbaAMZWfP96n86Jx86VxqTsFIMUraUfd/LBSHcTLklbZ3qHcNjSM1xge665S0EJ4mJt6Ts1yuKWao6xy7x3cSKeQofNWNnxLv0WEpqC5DOVCSbZ5KZblOkaSNO5TwoNwQpcqVtUA33TpP8eLwWgsbCP7Q3n5Ymh+19wSjrovmjFBc7C9nnGXWmRyRvKEL9VPxZ6Exf67ZU2xoDkNYnNThgjx+RKXXd9gY/gh8pr4duz66jqc/J/GG8lO6qUrEF/D+hA2OPb+v69+etuuwrQpbHxX9ktCY0DLJwxZeM2eEeE8nIuvpOGMFWNOafjd1wb6meGkxbHWnNU5/6u+6d0KYdsumIyFsDZ+GgZIuIrIyHskp/TyKg8iiPf2qZX56vvSLp85EtIrkbh3vsB12K5pFe8Ho1wsZJlP0FE8fpm7aVYMuKFr9O9pRnlrQtjvS2jWaLHZ4ZhgZ6kBjmt+kHD+unuRn19blz+Ri+eu9V+JCSc+z9GWohrTCHCyyKY36rGugp3RGu7Np07ZlgdwuGR4b8dLTZWgkxOKTNiBc+hTENPrKVyjbWqXJM2A4EgCKLQkl6tbQnHVFMExQQQAZBhVRTwilQvr6RWMvzCNr5TZlw/yyNNp21Glz78x3KmJu8006Z/fbE6qSMD6DxhdxpXPd9eKYfp+8o209efRGJM5DEvzTckjZAaHI27Ca5BuHEjRPiusI87VGIIf2FspP2PItTjjxHttmXl9DmxGNMLdyylYxfWddF932pvoR/6Exn+QZvlO9Db+Hsj1+RP3i9WGc1mboKgNUjmo0shYH0Bn9EF4K8/qFaEQFWuz80G0bb3jaulhlh/OTpJw+LIrXmbDJ33WbScMGYZQY0+rPmB5NY4b3wHj0FL8S3ZQ2UB0F5+imPjRle1ZuQybzFAhLyx4s2pbwuK9zaeNy+Bx53dzsxIYFLEXKckxDKFckUYDxLPfwXV9elkN87rszZdPYe+j0mvltPN0s4HnINO5GNEV7wSBXTX1RVJgR+pIzet5rsKTFl5z1AqdWRCWEZazcamCvg/IUEAm1zvnO7DNJYmnEeMRMlVCcs5rHdRm+06VyHbSZbwXuTDkhWuS2XiXA2o2EvmwTNuQZuTJLIoQQg6eyIczbpUYhVHsvRBsMcd+EcBG+KCh+DielsbP90rHA4LkcsoCQluO1T7mcUTdYCshNnDaDLEiQsONH+jxt/5Nbbu0TdN49C7rsuovzvHFv2zEgPt5iih54nNjL0DTfi3lKKU/7d82YICU29H0qkHt14XltYjI9ZpzQuHUsjRAWaE0LquIBNEa9tkaBKk2bs5DZcFY6y/Noorgl+QnzFCi5p3UIoZK9MQBWd0jMfchW5XT9oDExWMhsORa0WQp8wV0roA9q+ZtQHfX6mtkEFz3/h5rw+gF0Q34TemxYB2GkQNk6Am/VzFllxRs3cR5odQ90w6slTKMNY+8FGGdUF4cesgynGFWOZ6iKs/6zVTmlIYSVLNXEM6l+Lj1C7W3nZn9ZmIoc6kHKrk7cK8BwGZpnLV5dHhtaCSOaDArsOn7GxnPWntQjxts02i1oivaCQQqHPlekF7pUuAzhIslzWhLSD3nGZ0YnofRuLT2dVHxHO9Bdcf7Z0jPE5fO9vvIlOl5kIU/ptkqIS6Oqo17MU24SKycUVticMFVxRB1apDtNY6jLQOKaUvK8xSUO8y4UsQs80ei6jpu2GPI8C2G6Lrizn+YH9fHYMVvYeFFk5T0tx7PsQdrp99xlX245ThjOmWRMiAx1jto2HbcQT/efrY+tI1k06u4CUHVRE4kFcunjDdbrKd36cS9FY7y5Uaa7Cz9U0E1K0YTWOJ5n7eOxl+RJvMIbjxnFJEfPIFzSfE/7IeSS5AntU+MG7W8k2jARuxnJ3g+O4icWOT4T0Qj5abr0H3XWOZrX9byn7kw8t62mPy6nruwU3Nd1G7XeeoQ0ajkltwkezX9ldJj+rl88qZlHIRIpJ6WwgR6bZZWnArTZ0ImGxKicIYzmdZolh9VdIEjtaMsmRZLcm9M4OXpIIfd4D+U3ySPOz9CYxNdhZMWt6Wv2porz8egP9EaXoUHZvUUbxmh0vlulObtWdhtP8wzf9POa7Dpuz2jnnvfquvjv3YymaC8Y6O6oFgctAEfPJCWI3Xelj2fdbe3EoLIj119VTs6yR8+SeeFaGezLUUK+JHmS8tUN0bLWLInqF9MeoD0DUqUjjduXI3HbiniubTZt1YIqcX8NYcPCFrnWp30ojvWxWiC3geWxZzcn6FKgs9p1HNLmLE3kBaLrktLUCxwmrl3sPDevKgEYGq1WgBXxBJu8UEQu6vR0GtUle468A4FTuM1DHhp8SU1+3NMmTRRv+n1d0dlfpriu+Ye2XunNtpTGWsWW+p/ThrKoflFcUhDExtX9qtOa/IBuzw3W9lXXl1WKKzRGoWzB9qmzfArx+oxAzucfy3VhBSG/oavLTekJ32ryZCXP0k1nS5GnQLnErweBnNrR0sNrQBKEc4bGI/V/18eHotw86SlInSfylFHMH11+ZOge6NfxDI0UNqWnxkvGO8riH/WI6Q5lpTGpv3LHBHQYjSluM8gvpTH5zQtzaRw5/CwlEfmR3aQbaIR+xTBKm89vWjSuj+QunZYzbN7ociZ/6Oe9kB+FtNFTXJMw7XaujU8PTW8GL4EuQ4t/H75fdmiPiIg85eqLTfzhMjR/Dntl7Ca0y9AWjPj86mQwrWlBuV8whv0i7+zkkGfGLVstviTYkoJIrtF9uTKEReefYYsGheqkjmk5WeVLLUw5N3gS+ifhus0UPWmewoKbZOkOET33qyQ/sYtViMuC5FRo0Bb/VLGFsllopDJIOOSxx5sqVtkRlaceo6YdSQkBekJc+rvrur5Th8WcFsAK4UJIEGABiKyCtAvP45EFltz5pahtp79nn68RnXYIo7EcPvWGk4oa0cj9BWMqTSt0RtsWEvUVePfEm4GaJ+VoFBPoWgtz9KTo9Ffua1dhgTYjeqq8M5x55JHLPIloTOMxr8D64fiGciFMf+bpoXK8TTWY/6iE2nj6MwojGiHPus077i/mKVSGTZvSHcqpmsNAtz4yE8Wr6ReVh31vGeJOg/QThsQrAujYG63XOq2n2MbBnQnLziMcZ2LDcM7YsEm+wy8d0eONR6LRmYc1d1OMui66WDamR9FIdLs0ejJSSrcdo6OuM89o0zEROlPNFm2ag10UP6oLzKMawwpb/OPfJmVOPpdB2V1SVu5g8cZz2yqevpj44dMbsGhPv2o5QVvM3/eGJ8t/+G+fla+66/gkvson0KvDRlBn2wO7C03RXjD0kzSDsCvTsGTxjNyjY+At351VyPQEX4+E9G4aJkO8UCwo6T3UAu1ZtIeopAyEstejMHPO2mO800Byg6UL4NL0FGby7IjZOu//dqEdfUsaChwg6Ax5lmmvVQYmi2ddWl5kqV+H7+uwMUI3Wa8Vx56lu8q1ERZuz6WLL1Jh63WVqyYI87WCe58e+9DmSa7e6YLnbqBMv8feK+k8Gj61102IH9NYKeR3bFXo+YfhCTHdpFT39dPeJ4rG7HvNNDdxLtCmAW/8TWhK8+M5Q5tE1P9pIPIEd+zZ/Nw5AwJrjaKlx2NUF1O2Y0EmXgh1IRp5M8FRGoyLKfPmOv5oeUovfGfGeDGMhHoce+XxiPytG35Lg+vmcN0mMY173LwdBXpEzqq0itQoT1zbkcZJKCnk0dG8jDyj85vkMfyAyoDXtjAPRazLs84jSgtjPo07zA8dj+kR5As8D4m+Wl4vSTgptiEGzRnir7lNZx2WqoveGA35Whptv5KyG7d3F30GjEfOJpbYurjlJGlDvmm8ZXUTebiVnNy4l9XOGL0AVAK9zb3uyP/HDu+Vb7j7KkW/zofmluWtu92i3VzHF42cJVUxkfX1+Ox1ish9N7rlO8lTTe9IMR6l8UhYZetMCNUTFhUJzHOoUOyqHU9eZPCqfXIXO3W6kCgtCzVEOQtFMd1xe6swXDxNITOFsVCcxiNrGAvPtgxf2E3hnnUPypt+Rzu5LEwvlCXrI9FjBQZL6yBw1OWZBrrWIiu/wKLvWYrEgAUWTx2YgDbVshcI6vlRuHws0J+WWqM0jMjNT7i/DN+DMrS1ny7XSy9xs7zUtqJ3TKDu/KvlR4NQVDG/xGlH4lMmsaMgwqVHVAadpyc6eX7UWpochZPKwHmUtmHXxzflGMWGNkaYn3Gb2bI9Id1b52x6G2bTVW4agMLhrjNmjPJEd3luZV/beE57u/3amTAY+FZ2wTzFxNPzlS6L1WltqROgqzbWBaiu4SnUr86YmNCj2qxXTjoTz5QtvHFYxyu8NY7nB69BlkZud8tfcS2F/HnsQSFJ2axA23i0+TL0S5z/GNYU3gSz4yen+C+NbZhWtMN3zYfDtKELlWdB9DxYkneav0mr6kKXodGGjCef7BY0RXvBiBx/g6CsL+vo462jtTCAlZ1hMtNzU/EbjJP/yapMSmxAJzY/Ed5F0+nPqjoOliqtpFkas4t+pKSHtJZGTJuE46JYUe+ovTOWNHEWT8+aUWXFEZueBZu6BdUT3PRnQMS08bZtvVEzHeOR1wbESwrhM40sUOvPmG7bvrWXlLCwYzuhXiH35kedAJRuWGlqyMMiKrsXODXdaRm02Nkwl24YowJjWYdljzyo/GhDL20HwwOc8Vxt2TNpeQ5KEpfShvQ1QmwntZf40fyvnEfd8FtaNgxxh1dQO9q03Bb2byOn9TRSXFt2ipFLo60zH81CcqLCPD7jz+uKuR5xSJ8egXge7xChDQZnrNCaYjZQ6ngZHeEazj8DjZn2ycoFKl7kOp7yBCG+xxtEKT05q3KaK87NNLGwIuiuzUkpzHs6oCbQk4RB+di2EMb3cYT2UW3WWXr6Mk2evN6TRRvXcEhr+X/Xx8/RiGth/1tcLtZlZAM9S3xKz0CDLWdJn9GehukL0oJreXRR8nTsR2e0lfn/FU88KSIiL3/CFYYGjTG0mfd7Np9wRlt0fmLCyPtjN6G5ji8YfDZ4Gtb/l1q0p4siWHbSPNPzjyG9jZekVZnSrnCuXBG7kA90J3VUi1A/4WlBJSE9opHcYH1rlmtVpIVNqN52d13Xhfo1wDuL6wlFVQslpRfOs6YtJgtv2g7DIpSeiQ2Ib61O+4H61Yah4kf0QJ0D4ktYOhPmpZ+0GZRj6PEEAUt4bs7E4bDAQvxJW0y+x26Q07CsFXeoDHmv6DIGmtbjMFBOqsaoUxdULjJ9RS8HpJcc8pEJ6kVbDgl9OavZkLbr4+swt1+h7Kp5jePWJqYy+rhJvJBv+kNN+9C476BjSeFgbxHaXBzi27hp2XVveCPfy/DhNG1Kz0BjEtcmz4SXeRwpEmjtgxK8dnTpqVk/KvPLWvHEtiPyD7O+UlvQ5u2Qn36BBW+sNuV2fVkJ2XX9j2PKuyAN2tHr187G4TPDUEczN3nTmo8o2AqSe3pKO/GZYW2mcV+eCzT/J8cQynQjPUB3QHRTd7b/48TjkeclZXmUzkfnFYdNPrWyHOJFFu2lURRfQ497rRt8zwtvkOfdcpncefICmyipk66LSHpHk8f14n5d6s+RK9o6G+bntjvQFO0FQ1+JTwpHf/5B8sqb5woy5Bn+9iyD07T4JrQ6T4uLQ0K3+Ltyw0Jpd6Rzls8QN84PlF2d39o6phvikXDAAhkJsekmhi5b51d1Zo/KnX7SRTM1C5OAooUCMNENY0wvLjVPuQWc7ZXBgUZ66irngYBCv+RpjCKqcoZgR9A25ZAQ4pSdmR9DvGEhSY9HoaXJCA2kVIfSUsuwpdHMGUfhGHJMwiAuH1GoieeM5Tia6Iur4rsgxKC2X70Lu6rOWDp9LTa4z2M92QywSoyz+Wf+5vFYJ6yyYoLlUHpS3xRf1/FGXTy4kY86fWCKgPHo0zhDv6LwXW5bonXIq4LfYxgrDbVzpmYjMufyWssLa8atp6SRlVt/ahpz7UNHlKic+Hm/ydfsG9w0B6F92L3VoVtq32tnbwrcxJKYdhp54Xstb07hyikwRm288Ft+nLnzmsKQn9l2nGzopLzH9wxAV2YhunUY9LVTl3HHmxjUjmme4XvkJj4tYWk8MvGiM9pw/nndXBMXe4itLo3lydPbwXOgDYZa6PHY35wOddZh7Yx2w6ZAO7PkOj65dZwt2CKxq4a2aNnLQzxBsovSiuhFTfq0uPiZtP4Od3/J2TrXsacnxAMXc52fWXjdRZaZbJUAJMz0QyDdbKrjWbo9a7plxiF+Gs79YOmusewILEJ07mooS0zcAHrfNPeM1Ghkxz3XxUpFvAtvaerMFx3Xtk+NRcK3clI7Urnpwj2UH6f3xmM85/Tcyrngx/PDpo1LSRZ9sQt3Tw+E8dgrj0dXwJO4fgLlisjkGRgb6gjuFfNDPHp47JHQQOOZ5ivlWaP4Ux/6ZSRhMB5zcWsUW+mgLpQfpU3omsQL4xHmF6Sr9l6BQmv4dY7P0FzKjeccPR2E1vJ13KRLPmO6a9YZr1/LYyLngot3QWCek7CzICukcUViXthvjK9ZWSEAvc3Ubymq6KY8gW72dMNROokfWRUto3HlB8iR+msE7cP9D9y2LztOm8bvkt90epqHdd4CxLcslbRZ0l/OFT2JNf0N+LrufzpPnSvbU/w19WSp7unBy9CGsJWpRTt3XnqjiCzQ0z9qT3pravp3tKHOS+QWu0tx/tR0m6B0wX6w0VNc+kkbZtzD33QGKbJ84YJh44UyYiu3v6B6b1TryKlFU4ehVRoU6Ci/KT2l57nswmIXgvQ3XZfcgpOrNypzzsLruRySgFh/9tqGVQl9mJYX7ugivtzlXErgpDaLFD+8SKlGYLCL57AopnG99gHBAYWJ+G9PqXZd1igMhQt/rJxVF811fZgvhOpFP77EjxfjqN6dpTvNMwDdRGWG8UjzLbDCokWb52vNWd6uY68CnMMmg25KUxfFG/JI05fz9LxpahQgPAc4MlXJCrt1fciboHiMAoRQUvA0XZo29u5Jw+o2UNzNsqp+GeJHiQXa3OH3teVsnK874ykJp/bu04OcQWPKa580v7QctgxzHnoq0AWkRGP+aB5vgHgWZJrXHC/Ns64PhdYPyo/mR/JJdOm/aVzQ0ZqaeZ2zaEf9imuz5Zkhfc0mCB+joPmfWYdV+AgUP9pgHgHdXf9bTM/EdZzqEkN7ug1hk88VpWgPiqilMbZow63jtdpwAZHreMi6Mm/d3sOt4/r3yae24m/kwrZzCU3RXjC0ta8P05eChQVD9DM7dpbqgboOSjVdPhWgmVrxArEM09ZnNGgXLbJKaat9T6NV6On5oii/NK3QIgsCYzf85tUnCqO6EN01ixUssizURKSa8DiMmDkLyjW762Sxcy8u0RdrwLGFaFNF4rBJnHjs8eUqnuCeBPXCjl2EaoRG7GtSJJyyayxNWbdDU7ZTxy7MD3s3QTRn0vxUuTkPGRJ2O/NF00hjF8IqBCrqA1KKQngKz7ujpr8mcS2NPF9TWoZ807xgGm7KAk1jGV1ETV18xbbubgpvTFXMrdr6TT9rrYV1LtP1Yd5xBPqb5seG+Qy2kLf2AK8Hfu2tHdi2Ne3j0FOjXKJyEugB6yxultCYAiVmWFPU8bEuCYP2oQqSojXwxyGsV9Kq50IFnyHeqvLN0UNrfyi77s4JO5ZpM2AEimSOHt7IhrKBn1kl3zmiAPnRujXJYwjrldIO4oFy2UG8lO6x1/8wZ2yekz/CeWv9u1ZE96yMRWSwYouod7QXYdEmb4pK6OhjmDO9FX+TT5CdS2hntBeMQXkeBmCwUmm3uHV1GxoxRXxHGxiq6w7Ulz3b+SW9YOjJQFMvKgeszbTTnFsUdRha4iHM0AR1xHhWuugZ9HpuE6NzlC9YRLwFnhfuioXSSVt1CUsn0oH1SH8GoOu4qg5dkKbT2r62q6wncHi71HhxCdQHBWhsnzQMBCUUDjhs8tmJuWisYtHSJevlB+dMRugrbb7pz4huiIsCOYTlzowH4LEFiCfibehxvdMc2ANh6JMhHvPMqtuJu/gzKhvGD417UoxRdoIwHI+Q1I1L494RqlNSNmOxTfMla08I5/KBP9YI5DivWVHWdGVpFKecqv7Ke7RE4RU3iYe/0OUVeGF9f0Gb0fgW7teIzySfpbJzbUvyQ+wRA+Okgm66sMk9x0+1gUGB7SjMM9OyQzqyDBq6auchlS28RtGakIZjWJd+CX/yBkrNsRXk60I80/YMHBlWc8bSTf1v6AZzJdWF5mH4towW7SHswOp4Gjak1WkC5qWuxreOz5Y7WbTjuRX/JjIY5nYrmkV70VDCbh8E548mFm3rlhsQKTvanTQpjsOGRTGnpNMiFPKcpI3zNPFUOLnHU71zt45rJrsWKXNJWhFcRMQJrzr/Js6CjAsTJEZBkOJBuFS6DsI4ofxYWOFxMinb0h5A44eUQSonZ4n1lSKPRksfW8PS9BwPlQtYuHFuOR1LAoepN1gLRNGtN8bSM4jsVjdQmVfIB/qHtE7/d1zvuratUyRgykR0mnJq5iFkqXmUptHG4zCHTGdMQdlUTkW9XcsOtgP3dW5NifJMwryy6463+Io7CZw1FjKq92hUNzdrFY5BKLaoPSZg1oDKNtPlR2WkdAB9Q5slEaEcpqd+IyJVMHKKRM3mVBpvCCO6J9/pVQZ6glKnpTBDY/Lp1cXNE+J5axxtGqXfem+a6Ncuoj+uz8b7leY/b2ym48yGDW1rw3i+Ej0V7e3OwbgfggIZj8fJ93jzvos+o3yTv8ejuvahcR/oWIE3s7VFe9/KxCZK72hraK/TzQDf0a51HVffhwvbVN7TCi431/GGeUErz4MSqi8km4StryeWU2cx0OmJmYsjuBnrLKzSJAALMN6h7DT9wBLJdZxuoo5vrLb0kGuTmLoAM4SFqU9OdSThu2LxJGEFXbpxARvyNXR3aVzbYbSLq9tWx0NXRBD6qGz9N40fLdiQcBnS0237Uby4WB4TfXxaFFO66wRtdi+uF6iswBB/TuI5NLphMa/Qlvj4mTTbhyG/6BbslEagx+1/8fqrPB4pP11+AFmAQzilpTbf+EU6nuBmw/TnhJb0C/7ZB9aWg3wryQ7brLNtMw+lsbodaTym8XrNL4ko3LZVlyZRXQTohjBLjC5b8xlLY6BnFt6lgedfO+f5sip+FOa1ojuhP0cPziOptAxDOUObaXqYF1Zvlqg8yNOtv4iz440ECgvxdbmG7sw6k5OFYrohDNKm5SDvccdjJZ8RgbblcebeL6Nq1CWf+vfS5os4YZ47Oa0f/h04ww9kYcW5TmEwJkQmyrvnlaCDaSM7pFuJXMcngdp6vX+qaOt4WhGfN7RFO5R555UXiIjIgdW8I7RunmHzIt+O+q3v3YjmOr5gkBVvsEgNYZNbx6dxOzEM2XVbIoYKcUK8rOu4sHKJgnI3nSjR+91DXHLpWlNamo1HO7uWSG+B9wQOm76uju5iJWm8SoVceFGTJNxbhLAfSJCA1dxTQmoWh0Bn0tXYr14/kDeFHVOeIMmrJ1q0nTmioY9r6HLqFf8yjWR9GBSJinIUjaQsZy/7gTbw3G91njpttXUWGqhKwAN6aDyKZKyumXGm01JYunfuzfW0Z1Bp6NssTe+99WzzRMGfeErqyg50s+KmEiThKd3+vQ/p352MRusmjPuf25HmMAq2aR+OnDGa0kh90NWN72G+WhqxfWvWCncuVParyY82UONPTa/dlKE1yRZOdREYJ6Qgkgu2Rw+WLV69J9/pJRMtcwHZ2C+aVp02tsRxvjimbFX8dQbmelqOtxk8+Uzz9O4ciQPHuLFh+fASWGxxHUb+GH/q37tC29KdGJNwGpC+x442/A6XoVFdLN3U/7ipkiwqkZs53NOUlhO7jsuU7iHB/qnrOD3vpTEvw7Au+9CeZREROXpwj/zadz+jqGhHHgRo0bZpdrtFuynaC8a60p7NQqCY38SirRUWyxQD4oumYnSS3ynMPQ00XeniIAiTKX3MpEM5QzwTJkP94lusbX51woohz1UatWutrgsvgDYeCpLVQl+ZRk9wF2ofgbaw4tRUWCEBhmJSvSf9Gr3fLsp1fF3HE5N2EIpUWFoy9OtAkf076q/Mol/l3gph7ObnCVlpfv7iQnGJxtRNXI/H4pxBYZXmdSIUJb9pGk370AU5QmXzPRIpKK0XN+Qb/e3whQoWN+XN0DMmPzs3cTyKM86QHuKtnudMTX7c/0xj59xrkJbjXIa0zmPKlEF9IHHds21raGQLonVldupSU+c+D6IR+gxCbHvwRksNL8QxYcocaIyVxmkYuHrX8jPLH5luXV4UBsRuZr1P1xRRY+Ks3ryvmDMprfF3W5eaDS+ew/VjQsRT/Ox4rPJqg77GIyGQ1vMq0jSEtCk91WGhLiMbRptJ3tijsLHKdAx5jqEd8RgEjO9J/p1x2dZK+VlTH03j5A86o63D9q8G13Fn8M4Z+tLbg3sGNfHowT3FtJrCvcuTDYLYim/rsNsvQ2uu4wvG4CQ+TNDBtSmesmrNQKYYoF3PiZmnibWFJHY7TeOxIOgKu1ROJk98b1m7k+OiaBcCdpdnIYTOv5JwSu6J2D7pAiYkCDoCMKRNaRzW9zQuLch1GwSCAgcLIRENQGtczuR77lK62DrrH3noqIyMslN7ng7HlJkgMHKpLjDOvPOrmtaQ1qWH+iGZr3qMxl4yNE4qykhojQIxPY/dOF7u7dA4bc058BDO9Ni41e62Jp0zvoHu8FsaWNWvwmXXzoU0FL2AnLmu6Y9oBCEf6aZ+xHjU3sxoSLFBV21SRCoUBPacYR6F66hJF2hMwml+zNS2ti41StrQZoZUbNsaq6tAm6GXBPJCSw+5y/bpcL7acsy4V3WJ732ZhOWO1nnrqEdjB3Wx84hkBaoL8wl7mRbNDwjTmSR51pQ9OVts6aF4lp6UZs1nqB2HMFJiB+s1hEE5NRu64U9t+eXLubroN11m3N42rchUUXd5binPCfTzXiFM07NvxVq0yQg8L3X1i6fO9t9LFuwUWmc+OLWG0+W15xOaor1g0AVgZ8HVdn19PTqjnU5cvXjFFvE4HgocHViaBdLaYl0hYvJpC+e4admOdR9EbWayNXQzU/SE7xqXLqH2doS0OoubXUk84YmFnVoh3elrb4xVxNXjLOcSrv+MPDGwDGgzELJEeOebrU8pmEZW0oBGkxu3TVp2VmkgBWH6nd6JDSAXOh5n/tiLaNT5JmC+YONUta0zh9kTxwRlhao0Lfc/9TXMI6B7km8XxdOfOi5Zhqgf6vprBot2Sosq38Q1DeRb2CzdlqDacaI/o7CIF3o8vH5dqBlT2TkMvBk9nVJ6KumWzrHOQ1+ZpL3gr/mepZHq4tJNPNfjzaZ+lp6AujlT6Vqt+J6WZwKy8lEn7msbZdfxLv5RxfVkpJRu3sDhtKX5IUwOzGBu2/GojjcP7r/QPnodNl943NIA6MdOQov+DHA3Rp3xqJW8rKUa5Ac6T23kJucoS1ohPqM9+b68NIQtwSVne5atok2Ylwf2F0+dydJTmzYo6R20w/mE5jq+YMQW7Qm0W3W/M6vSuEJ1n75kBU7CVNm5GznTcjxa8gulzS9kSefDy+d2/bpEYZ6gBDTiwkRCaEYBorLy9NACa9PTgjOh2xMkIYxcBEm5gPqF+BxXuY6rcRa7jsOiOA3LPTdFl5mE9Gn9UhqHhVLTbMMGemyeGIY0OgRFQVYC8ujxFPredTxz1INp9MYtL24kXHD7pGWzQlbv3pyGQWXEUfohHvIPp18pvxpehvPVEQZFKi2f4vQX0lnmRzTXe4Hc0OMI5Oiqbee1oY/KIL7X00NjT4X1ZdE4q6PHKEXuXLdl2LKd+UFhyK+99axmHoGleRRo1O6pQ5hOS+jgt06oLZ2NhCSMnk4azqpqesSEeXmWwuiMto5Z4zmD60fPwmle81gxNOJct/FqLvFCBdEpn/kMeSrU8ZneCpyUkdKDFn+YRyMIo3hdEj9HN3kGhQxKFu1Q3zE0RsQLoQ9CnkRjSjuIBX3YynjchwVXbW1JPgSW4UXiwUfOliM5eOCRQdEOF6md7xbtpmgvGPpm7EEx8S/wEJky32RCxYr2kGeVYkzChcCCQUI/KgIzCBwy0EOuw2fB9StLd6WgNAiXsNhV5Cko7NjERLcn4FMZfb5JWN2ZcRKiHGt6Gsuh0dAT/gYhBgU4Z6yU6O6EBE5LOG0G4De3HW1lvE0Qj3aNUWfnKpXtKWQoVKs8su/MS+UcprGn8kjpRgUBy4ZyoG036vEh4lkk66yXeAZVeOzVuZgO9Ou0+tOLF/KkulS1oxcPKpgGDcqOrbfhZ+r/XDmeAo1KI5xpTYsOX/lyJVMS0lOlVDnqjle/juhJU+O4d8Jq1jgnHo3R4XM4jqY/Nb11sgIrMdaqSPzfbzM+EmCT1/Cu0WigZzh6Rh4kzDNxHiQ00gYBbSSEsknJM8rXqLL/YdxzmO3rNH5UThKHn6WydI+hLWh+kDxDc50UcrIW0+bCUJcK/hjoV+eax7gZNP2MLk2LadBpagwZg9t7Z8NgnOmz10HB3rM8lp9/yz0y6uLbxnNYn5Pz+EOnN6FoP3zGhI2hzucTmuv4wjEol+H/+IKjSazooikQEHRAZNE20WqFEEspMbCQnkghZscMcLoogkvX8Ca4t2tuhYua3dou+YxptHHxjDYwT15ky2kn6csLPC1MIbyqbFq4nbLds8WmHE+pSsPYWkCKW43FBvs/K3DotFyZTogeb2PDxkuB/ZITODC9JTKkjW96twTVCFRenX0aaayk6Ws35bh98GiEWKRunoEery/iPDvYBKkdozyPwm+Sfod2NMYHyBPDhMuuUtyctAmJfRk4h2t4D/Qh8yNq787QgwqZdCbMLdvpw9q0KYielP4Ab+PHTGtob7QqOkqDoaSWz0C8SbDT16bNWB6xdRniD2Ed0NiZeD09NTKFCim6ieOYSMOm4wx4TUn5dsuhulTKCqTE0maZN691HlFcGLd8N00Sr7doq7Jxvtqyaa6PoL1xA90ZtzjfRHDcisQW7Zz7NvMeXa6tn0jeoh2T5PO9ZaVIa0v2NUcPyKMvOSBbjefefJmIiDz64v0zp33wlFW0Y55g0xAP3k1oFu0FgxaC2IW2m8ZLn8myAkLXJe9tdxyv6oxlpoyaMJG8JY7S66e8AkWzusHTAu8JjH3GhkYbWOcO5lkL0wXMEWAdGklwR2Gnoq9x0YV2RGsfLOYD7bYcvgSuIi31ayXdmn5dboifBlZZ99O04llDHasZCCuaroieKqGhtpzaOeNbZzWVtGEx5AljHPkH8J4auqHOk3I4v5ox5Vu00vx4882jhcZkSXDL0U2lVfNmky6zgUb1pr6hPPGm5QoaqVxVfhqqg7wzkW7ZwKNq0orY+qW06u+e5SwO4w0dXI9SxQb6kK1mtH7Ysdcln/EPHl+Io9Vs1LJSHf+mf2ePMSg7VYDVOCsbHWBMkBJqaOziH514gW7qa56vdWkD/WnZHcVDfp1mCpbqUd3aHBRVlFOAHjq3jWedo7XH1iW7sSEJVPogH4dyolvH8TI0S+Ms6+MI1lebmvkZbQYc2JNXy1782Mvltz/zBbn72ovNb/M6o33HFRfIL37bPXLs8N6Z0z4Abud0Tl6DnirbTWiK9oKRP5c6TDp9vT0xkiD4r0v+XBKfDSPBhBWJGrg70lSODHVZW4/jTsL0U0VpWktPR+X2/9n8aRHjs8BAd81CKZXWFaAbFxxS0vqy0zB4qgYEMn9MxGF4xkq8tuBFuvaiIU5bI4RMQrQr0iC4WcGk7nyX401Rq0g4wipaH6Afqs/8pWGY1sbkPrACxyBb2HlIvIK4BVu+qX7ltrWxdJ5AD/EPKBs3pyr4aIhTsny4AjCEseu453WT8g9f+YrTduY3mjMh3OZR6QWg/s/lR+2Dc6bnRzTGLYWmbUedjNYqeVSaHwj5w5piM6hbC+sUm8mYqOCFKn4aRsJ8rddOXTzHip/SQ+3Yf9p5yHMB+Nn0e3TfDdBD/Up10Z8xjQP89ZE3rKq8gDLzg87Zx+3ozA+UNW0oWWKRR4Fy2rdCgR52wYcwmOvDWAYeZ+ge+kbfiyTCFm2aH6QMxl5yIX5c9qgT43mFVnIYZyH/M2cH4bh0y/ePfPXtsr6+jnxwntioJf2myw/J//XJP4vCaH2Mf19sXbYbTdFeMPozE7gYD4x3bZ3D+zCZDMa19fXB9RwF4OH/uCwbDxemisUBeKxLjxYG1/AyNJUW6akQOFDwmpZvLDG0cPPiW6t8157v9OhmgdPWh8N8ISSlydBjFrCYriztSI8jkFMYpcUwW7+QRxpa044h3zTTKrdsyI8UJVykPaVBSGjgMe1ZkEo0Uh/o+DpeGtanr1EkOmfDwoTxfJvF86FqTI04nmlvmOuBTvob2ywS0q1AFf7meVTDZ+qtz54CxMpOmqejQMEiUKWQOWWkZRM9PQ+HPKtcwp2ya8bZwGeoXylPCxPm8D1cZ2qejKT5Cn2d9QwwJNZtROY2q2l+RBbNERHu8ACIp2WKs7mNegqzZGM7Et3910pemJZWzQuBf+RoRJkEPVDieN6RB6OQw9qVax9ah9NyJ7/puRV+s2lrLMjaKh17TsYK9PC9QHdCa/xdhzFf7w0gNA9VvJDykTODFXi14jz2LEr2wRmf59osvv4pV8ryeCT3XDdY3LkdB2zVJW/bhaZoLxjxm7cxJmGT0LX12KJtmLRapNfUll3t7rMZ3MBQRTzLrrNYEZN2hIEJ3cMGwYateJA2hMf52UXfy5QWZDwzigIH0NLVW7mJPMoXFf/q/qf27frfBlerISwtu07RorFSqUA7dHvnKXmB1wu3FQ7ccsQT5m3ZpKRTkPnmCA0C42zS11A2FU3tKBZsVU7zdPqfwrx2hAXTzC18VsYpF/qP8qS4LEhWbgZ0At4iQx4pomo7fe22I5VdMdeF6ueqfcn8mH6vtZwSjTRuKQwzhLInYeV4NFYmcWCsVI1bVr4VCXHYBnku8RRSvkmxFeLBKr4k39ENGtZI0wXEP4ivA922PK0AiQoLvNkUUz0/Qti6kiks2ZUW5IRWkfzt1HUeEd6YsIlJNtN06TKrj+aYOHZNwcvQhONpGjRtND80aDyiwgn18zanR07bpjSEr3TrOI3HMSQeYdgQRE+faZq5jnFdJjSqd7S9ibVB7N9iRXt1aSzfcPdVUVjp1nG68X03oSnaC0avExcW4/TWcV8YWI9u7+Z4aRgtao4QWrnQidQJg1rYzbq8Y36elbsiTOVRrI/UK8YkmOCt09QvQItI2dWuL9sUzdZUssS57psSj9EQlgLHT0U89ywvxTN0Q1hoMzgHRota9eVM2D52PLKQxYOPFv20HDqP6So7FXOT3ImZT4RPuwDyplwatEnvBZOfjedZ7OMvfjnYtpAvCcCepTHEH9J20aeOx/1q+SPedSAQVjH2uB26iK7oO0xiss5hO9JFdTXtrWiV5DspjVSfusvQnE3ZqrSWHuIz4W9cS025bLE1FMK41VbpdGNUZ0Bjj9p7KLuOV9Ac9jxsYr7XRZ8ifIt1+JvD7NwM2cUXrJbnm6DcE9M/CbNzxrsMizaTeNyzV0oKmpuhTN6wKNND6+sYx6htx2HToYvi6U9NR7TRMoJ4XaiLbW+8fKxmbkVlx4+r4zvaUE6J9wzzqIZHTT9HVEfbjvc+5hJ51o2Xym3HD8tmsA6HtG/ZZJ7zQMmi7d2RsVvQFO0FQ++4siAw+R6f0WZBYlhc9FvYsLjAmCVlsEZp8BZZccJzQkNk3a9YZHVZOp6JY9cVV2nkskkg81wj7eJpeYRvVRp1+piAz8xRiIE2q3KhhPYhVys+vyQmrM+Txg8KOxQvzQ/aEQUYf7Ei4ZIU2xp3e08oonbwLkfqonidCQvhde1oqXSPN1AZEBboz9E9+bvWTbhOyRdIixtEVIbTjgLzkDcDHNfYin7NKax1Z7TBgijE4/JrRY5uTWdKT41bpj+vLT3YX0LxbBlDzuFbjvdQb9fMYW++1Y1lS8+U99C9GLhulucCbYyNcF4PYy9ZPpAXIt01fQ1lk4Wd00ZkTdKiomXpnqTjevOyMAnNXobmzOs0HnoBJL/p7zQ/ajZLyUvOm+um7P5ZKgij+VqjfINnUUivQU9ijanNoP8pHj4XBnM9p8T6Rgtdj5BelR02DTTv6ee17X9dDPF/yk/nGc0Fautp2NJ4JP/q6+4yv88KrWb/7Dc9WX7qV/9U3vb86zed72YRGUagHZrreMNcwALW8P9aeut4RohZK7ij17plp6m9hbfGrc5Nr+LHbwJbxlQlaKMCZIWVvoYVgkTnMe6asrEuGSGk6/odB2xHh99gOcL1qxKUoX34cjZFd4Ja5cu0oyuYVtANC3JOKLajwlOq4kA+OmBz9ARl/alTsVBE9JTDMD+3/217609NL45xI+xUKt/QPh6P8sYo0o2CbU2elZtTUWlDPBEWLuO+tv2v42p4G2hcdrl+nlI0+YQ5U8uH0zyF+4vHTtr/NF+JRjHxhjzTML6pn86q8oYM91W1kk80pum8MNOHPt/TG6PYjqBceJZY3GASC14fbZuRZZDmgsdnuHBui1BW9nkvKIOUtJyVk2iq2cgWb53B8Vjmzf1fKpCU2L4cqndC4cR1PKXR91TQGeTcm0uXuGWPExBPmGHcRhsRlmxU8omeWl5fsmgzP7Nh84I2aN958gK58+QFcy5hY0AjiPP7bkR7R3vBiBaCDMOI38vNCzE5F2zQa2YQbP2FNo03CQeBg+ooge6hLiRcoiIBtFS52vYBQGPFgkx1QWFeeFGrOQeWY7y1Lo/sYkh9bcMmMe2iUVoo+zwthY5waWPSWUWkm6KJyLi0KCa/5ehhwZ0FslqBOi3bt2hTX9uZSGO03oXWX9DRHXCDikSgqRTPs6KgyzqUG+KXaHSt5GnaDtob+BH361CWjUdzmMop8xTynHGPZUDakG9UiFjrrKfksTBveU/NmkJ9iPwR+FEIr6k3WxCBHzl0pzTqfG2eaXpS8h26gcaNutsPYZrezqQN4cwLLd119w3Ycnp61Dgj62ygecPeRh2vPWw1j+OFfkIFCtZH3HSomDPu+ljBZ/hcM/er7xGTtrdzPCZJG84Ps4Kpx54Ny9GtQ4f5b/Pj43ExKC62I1jnQ2586zjxdU2LDdP06GCyiO92BTOgdAZ7tz/v1RTtBSPcOu4plyEotmjnFZZ1pbDyOaCKhQDLABrFE0w9AQjongbqOtadX7JhIU9DozNPa4WLqrpQ2Y5gYsiBhZuEopxiSy7q6OZX0Y79Uodl2wWQxkCdQO6NCUsjC3MshbBQZKKhIFE7F6yYVD9uPXrQulfRPuKM2zQxWudoHtG8cn7zrNJppsgrpHKTz2lvqgsUjTSOgCBqb1Yu+TiJpiGOn8aqVcigK6i9vblF+UHaCT00r8u8C9cFbDPID+kGeoA/koU00M71TsOY79Vs6PZ8ZmSCYNzbxuA1xeN7lod7ymDJYodux147OvMDWE8dD6+mx5/DtA7z5h3x9Qpe39l4w3laHdZFv6W/6/wmZRF/pLJtHr5FW8+P+FMk727LmxhxHLZog2ILLuo49sSGkUKeO7eN+VWtHzYurcN03jzQQ0ovu6Lb/NjgkdBD42xOGtg33H2VHFhdktfde/V8Mpwz9KYjPfXdXMcbNoXYom1BSigtBlpAOLvmW7SJkfuLPpVRsxjbb+GvtHy9+5hrC1QaMIwUDlvpEFIj7AgIO7q9o7QmZa0iOfym08a/srAiMssGAbetDbOLRs41mYWYCsFGuC7U1+yeaPMb8khp1YunXWTdPGsFIK9tHUWCPRUgPcyjKguSkx81GgqCSZ6+BYHbkd3bbSCOiQrhyfMgCL+ZPBMaPfd23JxK0hIvJOsFucaSchHiVtUR6oL9T3wmwwt1prljAvg0kMnTOUds6uKEQdkiqeDpzWHPylXHU2g9849WlHkzPiMnVnikMVXLewxhotsxz2c6h3B/fogJM/waVHKihxQyr27euOf+shFxsxxlihjogp2Z6xAN52Fa1LjjDUvirWmeZGHNKXM1fMalJwkbnvfSNE7zhY0oortUF+LrtIE+xPXGHtEDeRbpgbkFNI4dOaPWFZ42bTeC733hjfJdz79hxyqsmiy6sG1eGw47Fbu8etuPQbkki8swzdZomyeNO02vLwCps9iBoG3lDWczAPa4HWbuKQ0hnn3CrILu2jBnQa1hZJ6yRAo9Cqa4eDr0RAzehnX9b0BPxeJJHg1efmk4Lg69IpGUg+MHFInRLH1owzy6eUFV5WYUCRK2al1wUaCCMJdGVAbi9P6mSjkMlWKhdrDCxRDfCjE1c10oHpSBSjqMW49HEbC/vLZIwuiohzj5BfrTMDwSQjRCWJ3F39sgsP2v6dL0slsl8a60bO95SEsjhnntiGG2bdFjqKLN0jIGGivGI/JmhxcKlO2MKbamQ7869JRcY4fviq+Du2zIi3lFef6ThTS3npHyzeOeeEDSPmSJhQnbQXuzQtZFnx6NeAY9pI2LQb43og2ZzvEWSugZwizd9bwnnlujEXsQpGmXxtQ+od6WRknKSeOh4p7E19+rvPawb2ye2cvQVLwcHy25mEdl67DMZsA8sFOVbBGftpsuPyQiIl92+/GtJGfL0SzaC4Z68rpKyOrD4Q3XEHNdZ5pgNCLnL5rQ9lkZqRRgfGYOearFM7otHeirtiBask2g72LKbc6Cv4kGSgjvpFt6Qjn5Rdp1HXeUGHKD48Wc66GD8bxRZ75k6cF4FUqjt3h6Yw8XVKyL7a/chpemOwUK6QLtndAV5Qf9UOd1YeEr/jPMYVj0SWnIbaDF5dTOYUcw6dLXCex4Eie85tiCdGCxdeZHGg+PKCS/xXUpjz322vDna0wP8/VAz1lz6WKXxALFD3kpjx+0nqOyw4O5ZImjtg1xmD8ST6ng18Lj26exrg+tRZvmpufl0CVzoYs+NR2kfOuw4QKouvlRs2Z681p/hroYenr33bRobwPOkChp6UiPYGBP19n+tQ2JPjXdpGjR/K9zb/YsyExjVE5f9hDKlmbu63R9HUN+gW5amyefVI7Kc2Tj4Rvcob3BVZ/GTtqHngdSmp76K3cZGr31zLKi6gNvLGfKof7a7fDWgJ/8xifIr/6Pz8vTrz+6xRRtLZqivWCsZy4u8wRqVk6GRWMtejKshnF71qKaxYoW1MA8LfG4Oz/9fnZN0+jH0+Vs2GWZebS7cNe7CdtATwiOo5F4ENOqf+eFu2JRHHnv8tq0aTm0mNMC5pU9CbPxUOAQGw9dUU286dgb2Xjs+lVXdq1yWtsOKf3ueKR5SEojKtWeN0VKN3s5pPSQoDyUE4f57ta2bKu4sYUs5DG86859ldIdwnWYe0kNjCrX7TgtI8QfJRETgjwX/FSxoTnYlw2DjxT/mvahOexb4py2KNQlhNVYyNDjA+ewpTvQXLuhYzcSeC00YxTap+//SnrqvJLya9e6iqd/i+Npfm3bEc8be/MIeAWNM3HipWVrxXag2xl7nQ2tXYereXPyKcLnbIfv+fHoj1E7j0YwPwRpnNJTcsvuzzrr/md60k2nESjAIX2adlBAdftA2TCv2eU99L9t25r5PwL+SOnHkAHVe2gzmkdiQBd3kcHD1qczcXewEXqu0O2gPceP7FuR59x02TZQtLVoruMLRmzR5oUpBS0kIQ+R+NZxPidp0wF/BwGYFyuPqdUtOENYbtPBTWviOFa8NJ76LS2D8uTzybwAGhrJ+8BZ4EvnkjwhhC3VldYZaO+c4IbuiWnZAv1VGLfZtDaZSIbu0oI8iAawAIJQxOeSbQVrBTyXHhSAID21relXz6JNaT0aSbiwYxcViRrLcFe7iWWFkNw4yV1MpOtQOxeoHLKQp3nWzusQt0v+TummeG4YNCTlSRujRHdIR5sldESh6siMcHtbGm0YKYghUxrPPO4tPbX3H6RlU/8P5dj01D64gUY8ShweBzTGbTb9JCUtKsPON7cuTttSO3rlkAs2bf5VueXDSuz1tc9nbFjJMwDP8oo/JmrWFNr4G/pLtxm1VSg7n3Yoe8CYBsU0A1qjdN5u2f2nHbcCaSmM5ZG0bTObdyrMOz+t4+v8x7ixMcQL8mu67g356O+2bD5awXntNmhvgXW8Dm13oynai0Z0Rjv+iRcRu0CkC1PuHW0R3rGvOWOry1C5+UoshKXQruz6bDkpDVXnJKGsjugGxu3GxfSO4IZtO8PiCQtJKazP1dSF3WVJ+CaFLPwWQMJXL0hAo7PQkC7w3Ik2P2d+QBmaVl0HWjzRFZEEZWpb8gwwCodT55RGCPPy1HXSf9da0mrrl5ZDvEjTTuktjVA/iOdvROXpySo70P8kNHqWuDQ/79xuSSDvkt+iuEQjlE38rEZxy52djKI6fIZ5hRj4HhYUxn1d3LCAsPA38b3q4ygVaw8J2p35MtSD+OvYbMA6FwjCEa5Avy7DhPVjzw5IUtJwfkDJNXyYvUBsOdRm+XFvCarlZ1XrdUKryKBgla2zYuCv12Iq6I1Hd34AT0HLcMV6ncqf3m3Z1Nf9Ge2E7rScnEJesxGZhnnjhO8HyIxx5F+6LkRjCLNpvfPGket5ko+mQ6c+fyza203B9qIp2gvG8LyXZRgCQt8kuCsy/RBeZS0SUL4pjBY6p1z9qb+zIjH5vlbYdKgTYEgIhbadom43tM7SQH3IrqgsCITyTVgkFHl02wWQFFayfNO5fRIk+QxRTKumHYUi6i8Tjcaj7VddfhRRYsadE4oMPd7lbCTMYZilD8ey2LhIj9NmNW6nk3i2bBRCnf7XwSQchPBaelJMFIn4BxY47VxI+WBMo00fhcH47ukmGiv442CJsQJV1aZKwnt6gZPO8qY0imchs3WJCFNh7GJKNJbnpjf2iIenqLcqAkPq6YGwtByM11kPJExr6QngTWuYM8bjg+N5Fu24fVSCPh60bRpfxSvlNwlnBbpmvc5bJVW8zBltWmfsW+/OZmkcbZLW4VE6OO9ar+dMoFvPdZtfGj8tJ45jEyON0Ga4xkF7D30wBLpHB4Cv063sxONyz4DhGf2kXEN3n4dtx5o5k250RUAa8/wxuDx7b0Jj+0RWcjt+cODsQlA7nk9oivaCoZ+0sguTx6ATJpR89uHAFIlxh7JMWlNuvUsW0RziWxongdrlncsply20eILSR4w70IkLPNJdF1arFKf0sLLjCSEchsI8Kkpx2mHxHMLIpWtY7JKyVb9m6fHaVmzYLNYwtnzptGExt+lr5lEIj9Py28o2CNqxD4L+sjk6Y7Tc3iPKz6lfQqJqs3J65AvYXx5PsWFp4UhzL1BRvw5h/hlUp81w7GHR3GYopAH5NNdNJKBxZCNObl+2feDSWAgLf8MQr/SmcDZ+a9oRvgWw9RriVI5RHFbUEJKkd+Ywpe86K5AjD8e5FXhzZ8KozchNnN3O9fxg/jhy+HDtWpjSyK7jds6E9FapTnOsXwsnt3zbua5pLdFY9l6x+fV1MWOUZQWSZya/AU9R8VBh7fOlvrZpaZPPtiO1maWH1txZ21sKaUM4hWm6IroLPCVvdRcbpsaoVhppI2MMfYgy13mE81DPbor2oqEetEKh33NbJIGTlQ4rhKD1qnrRp7RAn/ACb+QVJfivrSnrfsVCCWusKzylcNvMqWPdWXe2NJGg5AqXhcWzb8cqIZbPTmIfgHJh6MkJ5KjYlIHudpgfWzNsXWK6NMHRAg+76xN6QDEmJbazZaOnAo3H5DOm24bVeKWI168mTLBjvL6i8Vg17kHZmcnLAXmZJzTm4w15Dn97QhopCKhoi6XRu0AoDatVdmhMDGWnYc6t6mLjmbKBxqznTMXc5PEIYSImLY77PkzR6DxL5eVJm1ikuBnPmcwcFksivghCG0zm1nGIJ8L3Q+hPXTbza8X3SOFAZUdMWPgbxyO0N609lsaYhiheXIxTTt1Grcc0yXtBxGszO2dKCqu3Xo9hE8x7lszdLIt4Shd9xmE2bVQu5OedX6ZmXIK46L0ANAw8E/ofeaYmxtbFC6dxRjeMUxbhd20BZ+W7i/IVic8b0/qjNzJoc+t8VLTPRzRFe8HQFu0ahSyE4242LDh1FltSJGdxOzcU9r/peCKsLIegteE6YccSm5bjLMYYzwo6AnFJOGW6eQFEd8CaPiDhCZh57lxSCurXWuvcCBfPmAaiQ9NOY4qFokTggLSuEguCiUhycUlCv47HrsM1496hJ4nn0T35zc6PtAwdX6fneZS2I9TPKcvoSTTOuvi3iB7j/g3zsJJuQeXCF2xiociZ10mensVOQGkcwZgQGt8JDTosrrelkfraE9K9DZTaMVpTTq4dsWxsb9vXdEGe5V1d9KG/xm1mx0QfB/oG10JYFzbqEeV7yeSftdSBrFRb/qg/PXriPOLvumx6YslbZ2gE0IaFt/mWlkPjzFOAOhjkLOOwd0/NnBFox/470J3ml/7m8XC2unq83ikHwkqbZST3DPQOga5XgVOXNC6Ns9p4NCY4Lc9/asec1R6mTLFs5kdxvpMwm0an0zeUQzegLLvbsX4e+o43RXvByJ3R9lzHQ3z73S7ctYw7J+ir3FB4pgVMf+rMcgtOdBkaLUKkAJGQZejJCUr+gjlrOYH2tGxTBtVlYN2mDCqr2gUb6EbLcBKP3lZFK6BDYydQNtDtjdEqZTfTL7wRBQt8kqdvvUzj2dTsls+XMGla9feSB0HIkxRj27TUthzmueCTIIGCCdBIVkCaH9iv0P8pPSzMhc/8uO+S+FE8bJ8kLClXZ0oCVdmibfu6F4rjUgSGHvYhnVWsLWdoH+BdUO2adhSvr505HLdjZ+ihtp2k8zZqbVjNWohjom8zO6Z4zbV5GtdxbAtec9NyOhU/gBUEy3sGXmjblqzuaSf6a7OYMFMO0O1vWDgbdQ6vsPRAWuDXKT38vJdAWBf9Vq5LSqNn3OABQPTozqE3nKkPyEOCNl96elJZAcuxPIXWXFKAcxv6vGkU00h8L6/4i0FpLhAPz3ls2HDqQ78tzics0ZMhuxznX423GLFFG5i+t1gR86xZ4FEAdi4FAf5eY5Hqks/4u11IQp7ReXWoOAk7lsQOzhVZDAt8TEvIw+Rpyqb2BulC6pXiSVygUefluDyjgIh0+66IFFYUyMTGG+jZYBjmx0psClRigMas63CS5wgFIO/Jt3J+Q7/aRbbkDjrkCfMQyuEzfxVpqa8zbUZ8qq4P2SJh8ks+9V8RnwFhJfxdxTMhrNbSxC6moc1K4zEXVu5DtuKI344gDKKAWMNnYADg2KO6uGoEtxl600AnIp+p6ldno81R5vgt47RvLKMYdXDruHhWd5t2UrbiH2Cdy8kKJY+44dxtTb86G3qwHplygO9l3dbFhlVtltE4c9JO4sd1sTTaugxzJj/XhzztOLEjxeG3adnQtqTkD/OI6tJlw7xysgp9oc2IZ+KN59OvS2rSeB4t6C0ANA4WbZqfRKMih3g9zPPO+T7q68O8gVOdH/CeR9vNaIr2gjG8o+1Zi+1CF+JL8p0EFlysqBxcmLjsOD/LpnKubbkFcLBo11kVWZivE7KyO4pQ0c0oLGnYeMT5peXguSunrydp7d9mEYJLk4aydTxbDp/54sWK3ffIXc5ujMjcBY74Nx1GQmNakD+myvE8V31Ld/zpxQtxaIxSObVusN5ZsFgoCmG2zdASQ/OjZg47gvuEhjxPCV/ZEqvmOvmDimMNAY0V62y+DN/rLdo2jK2uSRjMNzqCwYpE/KljoPt3FR+248nl1zPQqANJAA5x8em9lB4IrT6iQjwlsw7XutHXbKrQGO9UfBMGdJcUW79tPQ+UJExsm+UsfmwZtu1Dxwzq+ZFJ6cs40D7IexL6bJiNF/Kk9rHThmWhQH9Ko446gsJxg0BMNNeiLVAOKazYh5l5zUqspXsZrcoJiRl+RAr9vddfIiIi1x49YAkTp/+TT6JDl5si5DWGcU9r7vmEpfOw0kvbTcCuh7ppO4W3457G93eAyXXQMn0R4XiUHzB9T2nUWQ67j2meQ4KzxVvH7aJY67LmblgA82SlOiHI5JhZzM2Cypb4kIelUadlGukiFemc3V7TZt3wOe0DdpezI8db7EBfdZUvk6dAPGfchvjqeL+hNWdJQWEnzR/7lYRLK/SlCl5MDwghBeUr0IPKBZRTVT/hvkrhKhKYp2dBon6l+tXwFE3XuhuP6PH733sfvVyXWq+EnBXPr19aflq2Nwc9vlegu4vj67g1awBt8nmbE978oDlMgnuNO/GE7rp5hJuGIiYsxE/Bc9OG0a3jNQoiuZiGSLVKLClFdATDuI6LpQf5OvFMGFM4F0Br7NN6fCEtOwmbdQOFFDKcMyPbZiVvgRCHLbF1/HHyxQbq5GOgZ1B2qf9VWsfaG/7Sp2j5hZKEVvHGGbWtmHjh2xK8yUUbjHb+x5+6zKMH98gn3/5s2bcyNvF1/mXDEQzGiE77ndzRV5eU1Z4G+C7H+WjRbor2gqEVBFrMXauJDvMEcoGFBDSgDjLuoCwSYALtRCOsA6wYT7/3ruNSpzQICn3ssmbTTj6I8Zq2qBUkxKnflM71zDvhAbxrbhdu7K+KNuMxYbOsdWXt06KAUFF2rQAESqxeANeSCzTYgmjD7JDiMUWDgugm18/wW3w0IpkfkgkzY6VuA61eAGarUPhNpyV6qvvVUVh4Htl4koTzfLXxiB6yAPXxTNt6CqJNq2nVYXwkZAgk5clvbx57LHCKCQt1ShFbZy3dXjn+2IMwiBc+zfwozeHkc4jrtY8Y1HgBULyBRpofNeNn41ZXPm8a4sd10XTp7yO4QTkee130m05ft6FHdFt60vK8urh97dBIPK7GxZg9omxbEA/PtnftnEnDhNaUQI8u25ZD/VpLY854I5I8WwXHDHhDx+Y5zqRFi7ZytT6zxpdmsbeALUdvdB3euxzFH0Nfl9ztaVxrUB21oh3C9joK//kC8Kbf9Wiu4wvGoFw6woHYsMmnFYpqhC/O09lJrVrAxIBdnjm+51pLYTWu2tJ5VgoTrS9/CLMLUwivsZp4Fv80z7HT3il40bd0h3AaK+ieaOJZQQIXaXAnc8/tAo3S2TajnX1SyMhiR2Oqr0u0U5wRBIzwZWnEMSXQ/xSGQqxPN/d1UjbND7GBaFXs7KzpnDJSuqOykjyr+rUTay3Guc6W+IH+oQxDmzc/TF3smB/otmXT3PKE0KgPccMijNG4XC9e3bpQd1O/9O1o6a45b0wbrYJrl3PrOI5HVmII8Tyycz3EwU3HJK/RyKkNjVuHJm6NyjwN3c7lbE4fxpsOMFZgLuQUBJ3WVbRq5RRIm9IQ0QO/81wA3mPGPXgWOfCsxTqULbY2YicmCMfCQCOFWXrcTZ5oY8z2P51LpnlNHgRkxffqQ897sedliGfHHnkQEE9YVhbts66izfJDSk/qrRHFBwWYrM8aPO85/xBOee5Z1op23TjeTWiXoTXMHevajAvCKgrUkiz8wKxCXCuQsRBKZ7FYuYAwd0GN80MakR5mlLjAQ11AT8IFOqVxEELLgtYYXLVZAA70a8YtLkpvj9NiPvnbFk79OoI2I7dV2mkmgcyjJ40XaCShkdpsFhc6tsTZ9iblghUEQ6IjAFm608qwFwAIQPjN9kGWnrhod/5Xz6OURq/NxNmIqGhHkUqe0M8joLFAd6Cd3DyNsDuCMOLDne9tRAJiyUpKdRnkX5sYxwStH2Z7oTPldMlvUTwYK3WW2NoNovgzLlu3Y+DXZT5KgnY34jar2WhhfhTosWHsvm3pTC03Xjk0HvWnLof6kOiOxxm1rQ3T5ZToJr5ea02lZ5fceS1UTv2aQnKP/gz5pTQS3bxp5PDwzpuvCT20+ZLQoGlEC3JSxixhNf0/hoIGGmGMRnRDX0/DSBHG57AS8Brn15HA8kNpbjEt9J0t2pPPvUrRPrB6/jkVn4d6dlO0F43hMjRHACbtKYnrsQtcSEDZ8QSgKuESw4ayDI0gNBj6aVEkpYEWIRAuBYQnQ6wizZbjCd8pjdSOIMTkLMMFBu4pO571ksKo/wP9Q362nNod6Vw51LZVGz/ORkJKOAm2WTc2qAuN+5RubxPMUyTYlZ3aUZcR5xHRA+OZPAhqNrEE5lFKl6YD5zsKkpCfyd/fTEzj6c84zNJbEsg9N9ZJeGfCaiz2OdfYUl8T3bnNoLowXlN03lH+OPby/GgSlyxxlfOD6gjjHuc68NaQV91msrPmmg6kGRPTH4dRPFsOKRJUsrcJRptOJXdimsOk2JIiMNBOcyGlOyMXgBJDG8x1R30EznTSWsHyg2ctjtox6xqt4sE8gqA+fd0aDv2f4XvxHLbzg8YjjRPXop3hbaicEt1R2unnyIaR7BG/Uc3wvLZSGnMW7TF4G5Us2uhhRTKw+j6OtMpJqLZo71sdy/mGXL/sVjRFe8GIzu1SBLNghE/F1BymGPKN/3YWK9QQLLMqCbCajpLwlMvTABccu8i6NNYIK7DIhvCadhQQyGkBRGEX6fEXMHbLJrrjMHbBhnJgTLEV0FmQhduclJNaBcEV0g01XpvF5aZ0e+7kndhdVqrfaMQbG5P4mh6i29LjWkOE+pqV/BpFgue/HRM0lvuygR4iqM6dmHkZFD0N68z30maAp0ji/KidR8h/Q3zbjiUeTmM5hKdFscePJYi6kMqZyW3dpYd4FPNH5IWofNl2ZG+B9G/eLLH9SvPQ5kllc5gzHgWeoaT5AeMsq/jROIt4nE93Ka1HTyc8j9y2jfKrrIs3r0GBHjnjrLYu6bdBkYzT6s8oTPLxfHr8jVpap3C+qrR8qVyuD4YwfGJLbLvquFR2SX6gvs56bECdCTXzOnfpFnsqiAnz0hAdVMcxtI/GeWnRzvTrbkVTtBeM2KJdFgSQwYsNC3/XCN8E1823SngGhtPTBAugWRQrlQZhYdffSbdh8W6vZfrhlyoFsf/Pgi2seXrQ5dlVbJketrqV22c4lwYCUIHuEG5aMTOe03jUkJ6rLi1mtZZPsgpZehzBzdAHdYbMOyB8ENL0eDTR+r9rlC92O2UaXQEvKTcNG/K17VPjTUMeLSQoz6oApUi9Xzq3MtbK5Slk3hEM5iliwkpeF1llF9vb0pOmzV0+xP2alE18huhBBYhvdE/Lps0pmq/pbxE9pmxvLXR4CoSRZVCAxtJRlhCXle+URnL/n+ZpjWEmraE7o9houK7DRA+1bWZ+lNZh9OSBtEPZKT2O/ADt6Fm0dTDNa7biZto7JkfYS0YMclZgopvbEeYRWIZ1PFKedXqim9bhjbRPzlU/osUGub+FuRJZtL3FQth6zfM/TxDxEZ2ntmiHttBnz7Ub+fmCZtFumDvWi09aJWFiGZPnduwvirDA1yhKqED7ylNpV7APh0URXZFN6SwoWbrjz0B3So8ne5PSQK6j3Ie04MTlRWUCPSjMYdk2rMZamFvscJzBgoOCduWYqqXRW/RZsLV14QXeCgKkVNkxWmtB5nGvaY3j2+/UZjTn0h16dEX0FCUYOylBtBHFXg6OYAv0eJZYz1pcUhC75Df9Aytu1GZx0hGc7+WLhijMlsN1iX+L06ZV4SMztKk226YjxKP1A9aZmg1Pz9Jo6BEKs/M1Zy0mfmSQ4ddo3YV4I6KR2hF4innea0Z+XXIJprLZqjjQlIYRjcibnflKgbgRpaKhJVaV5WQb0W77FeKh676lh96U7pLPye/Q/05ZE36d0m29HNK8dV7sBaDohjBa1/NpmZ64LrZfUX7o46tyQAEmD4LcZlCOpjSuDoeXwjDv3PyIyizQQWvFGFzm19WV7jXtv9twPr6j3RTtLUInLKx6glIsAJug/m87T3mhYqWhLBQFF8GioASMN/yNrpoYr0Yw8c8BFl2RzBe/bLo0CZVGyDJnvSpaw0C5GGgUE0oCeRoNFxJwHcspAyTY5HbodbzadvTGMikI9GxG3AdRFlFasnyUFDcRmd5i7MwZ7OshHr7V6rnvwfwgsFDMionnQhkJSv040WE8Hj2PDztOSCmCcZKhp+S9EP6O+VaG7rRfK+eMNxd0fBH2Fsm5eac5djD2sG0pDOvdRb9p2mrfFN+4qy7xQk1VHMZnOWvLtv3qra9FBTqJn+Y75MdzeNSx8pWC2ju3cViyXg88E/gjzI/atdmdr1Ge8W9xXWzZOCaq6CHe4/M4dv/Pt+OICKIghyfU8hksBseoLYfWGaIbN19gc0HnWQJ7IFkqc54BKJtFtPjl1/C9nKJevnUc+HWWhxNV8Q3bofedy9TPG+T6ZbeiKdoLRnxGGxivszgQQyUmjQtlQTjo4zk0k3DJCxPQDcJOjZCGim3n3YxeXvSH8uP8hNJDP3iLIgkCIb5Om9LD/WppJCGr/xv7MI1HQpGVBnpLRonGILgBp2CBLAnr6sbeOKfEVguS+QXeE+YE6OmEhRB3vkIfsnU2P050WXE5nnBJAoH921vcimM00ApKAwk7NV4O1N7ZORPNYUtj+JvmEc5rE8bxdD6TMvyxxxYpqku5/2ke4VoB7V27WTbUi3hhxZiCscc0puVxW2BYtq9hnFW2Y6A/BW8G2DDimcQrUosae/cQ3ws0aroTGtTvFK+0Wca8NfAZolFM2KQc3Ycw7nt69LinulgaQzivHzbMpIY65vh1PIfj3/TvVGezNtsglHEG+qEdddrMGkceX3Ff23ieRdshD2i0YbkNnSgM2nZWyy7xcJ23SOEytEp5jcqICfFpmtBj+cfa+vmtaS+dhw9pN0V7wVifntImxksCMAp4uUURlM4at+xZrbO8u6qZiGVW4W8S0mrqosuPCocyNA36O7nfEcOsUmwhXm7hLgkctAB2kDb8nZI96joZjy3dViiKaRUZFqQ4zC6+RHdPT40AhAoiL5QUL6WR2ofCsi50cTFI4wga3JvDhsZauiFeiFPDKyZhlu465RvaAoQvVzD0BOAKejz39hA/R6M/HmP+4SlU3LakNMY0xGEUbwjLvctbVxcxoPlGLrRZ4Rv5Y1KO2DyxXx3Jwbd8Qr9CY8TzyKHRWc+oX12eAvwjmQyTskdpiDev63hm3ZzxaeTxo9N20af+vWTF8+ip5XvZDUYVRvMjv87Ysr05EtNN5dhxhq7jMI/YgyuUFWPC9yjM8sI0PbUjlRM2cjqiW48JiEd1TuOkiGnM0A35EY20CR7T4hNDRqs0PPuONoy9jbyjXQI9V3Zk3/IGcto9OB8t2ufflXdbjHV9G5qBFQRqd2HD33QOiIqqWaxyTHHyqd8ELyvf4e8aplW7oLKLGCyUKo80kAQJdP2rajMrSPQKK6hfsUBu6cnt7BPd5oydWBpzglZRsUk+c/TQeCYFoetgnJBQDIWHr0WvC2jv3DxChQwWc2+BL3p8wDjpv5v28TbQBAB01/RBSoMOi+LZuvR/V8wjbzx6QhSNvfhiH4ceJ7EpGxoRXcdR+A6fQyBePlZ7LCP51L/kNmCD6yFb53N058PC38Rzia/bOwNg7ankKdl5bcqpnMMCc5iUE5VvWnaUnuY6jEfiCaEc2tjwlC8upxAGjZt73qtmo4UVxKQ80W1WihfoAd6D/WrpScPY6h7y7mS4ljamQX+nC8TqPTEsjSnlta7jvCkHfAbXmfg3Lz+WUezfEf3wSkCJp+Rcx8kIEpXnk+LLOFA2gdYUWsPjMolT5+mler/2nqvlt/7XF+RLb7vcpW8343y8DK0p2gvGoGezy2KNVZkWsOHvJD0KSr6gHYdp5rve06g/o7SwwFcrMcjILD1VaUEoYuXLxgtx2WpSXoQ6ap/MAsiuX6LCbLwQiei2FgnyDLDl4JlhajNY4EO6mnZE4QLngt/eNBc2Yi10rZxC/c+LuWsBgPmB1tAkv7QuOXrIijub4D4oaSgU09iDdgxxTb9C2GjE4yR8riulMS0b5/DAfLJle27ZLAyZ7LICYtxmQ9umZaAVVyCsYuxFY0pfsAn9kpZNSpWrDJJSNcNc8D0DbGXQYkd8NC4GafQsiF2XKFgZobqk+GEYjdHpZ5W1mPhjZtyX+Edu3MKeAY6furmu22w9DoM+RH5d0deTPoQxDvKMK+NgvYcwvlTMDytZvkVEOnATJz7T9b91cbykPjz2LM9kmWJKtyKcNvnp75hW4imlsJh+r+waL6wcqC3qXcctDZQSw6I0Noa2aIf8D+1Zlv/3NzzBpW23I3cb/G5Fcx1fMNaVYMuKiRV0wm8BJITqPKK/PeUUErpMlsomATEp2YaxcIF0d0w3XtaT5tXTavNHSzO1GSyKhm7qrz6+XsTsgkM0kqu2QP+HdGTJTxcTz9qTlkP9SgJZmoeuD4fB2AN60rAxntG3dPcub7CAlQQlErL6/HEexSChj8Y9n7HM0IibSUkxwu7Nxp14ZBUOT1lO6elyYUk6snJNaEzzJ08MajNDGgrpNI9CnizgpfOVvRJsWFxeTJeNN6IxGs11S3fn9AvNraEPY7oNbUBxB23h9auOr/9m5Qva0fAty+Ow/5P4EY06zCHcazOiG5LHeet4UHaRrwP/D7Fr+GNWESUaI03S0kPxQhm4UYuNk9IY562jlLyN0OVZlZ8WS+1jWtaycFaMxbZj+J2Ur9JmWRpf002ynbvpmMTzaNTxxnD8i9Y4KoM22tM4KSJ6cm7rouN10acIK/7I/zPU1MyZnEU7ukw1470Ul0n8No9SHc9H5N43361oivaCkT2jjYLp8NsQL/7Ucavcm0FI78QyMmLIOYs2MXOyNFjhgm2uxPRrrCsoFAktspbuIc84cHI5V5lGUoxpUaQFkIQVEtwncXhjpEaxyVm0aumusqY6C1GNQkBWqpQuXQZae7SgRJe3OfmT0kDKhXQ8ByflET22DqUxMYnDAjkrl4ZEFFbTcmLvlUCjFZR8Gr15CHymar5OaYAzsdE3GLfhbxKy7FzgtN5YLlts/DBsb6AxBQnkOYt/iaf0vDC60dvm576tK6R81XpT+DSy54xKi3WxaYc4NszO10w7RnRbntkJxIP+DxHIos2bKlSXmk0Hf0zxGFXxYONnoCcOw9cWMuOZx72lO2pJj884bVazOTX0qx7jNow2HWrHIymcIc907LEXSIZ/QLxSe6NhJKFfx/M2ogg0P+J2jGmNy7F1GeOYqKQl+TG34cnp7Xd6yaRED+WjQWe0z1esLE0my72PuWSbKdl6NNfxBSOyaJsFzBeKBRgTCTu5Xb8hfVkw1YWSAFTapUYXwVAOlEIMnsPStJkLRYDpsTBn24xdEVN6cmfVgEmDwMELKiyKCX2dQzctOOQanX6js6U54QIFsiqlyjlPVyGYDIJNfuHOW7TzgkCgm9ZkpNGEDXno/Caf+XlEdQnlmnYUuxNMc0ac9h4+43sWYj4Tf+o60MYI0Uht5nntjDqRsyHeyJZDY4/aNo2vy+C2iN2JaTOQ3ElZ0LbxBou2HXsC9atpR+ILWlg+O11o0GunsuyeRuDDaTuiGyzxVWiLDiqDc6Yvi3imLZvG3lqX9nVCgyqzqHwjX09+U2XYW8fr6RYRPhMrEIbtqPOz8TxFi7w7iD8O9FCb2bZgi3+ZHprpvOnsr83FsjNjtFQXjz963h3pfdMwNZ3+iunX32uVal7/E3pgDtNvOPYgKfG9nPEmKs+lhPiR5Qu5d7RJqUYeFZWRIQhoSsupkdV3M375O75EfvfP7muKdsP8ER6nR5dupSDqZ8Amv1mw1U0gjBZKWzbI6NGnGyaWRlJ2c+XgRTqY1tY5BQohfb42zC7cjiBh6OG0ppycUKTC+Jx0ELJmqHcyfswCj0+VWHp44WZBApVBGnsQNnLCqP9TGsP5LlJiaBeadtJtH7LgRu7EVknv+vhD2ml8eBOYFVs7X1mxtfFSUNvWzoW8lcKWQzSmwHESlbceh9FcAE3CHClJ6j3kZ/twbR14T0J3P2dGkGfUPpZGejqHrX3xb3FdUnpsOVkPhErhmzdV47I7se2Iik1n+VZKQ4nuklKUW6OqrMW0dmXyLG7KQlra5AvhNG4tP7Ll4Gay0JiwdCNfd2j0+HrOG8iUU+gv3Ozoxy2VDWFEh9OQvJls24yUL6qfRO1ogvpw2sQwTztBX9Nclww9PD+IR6m0VAb8ndYpjVficfmNcWhHr8DCTzRncnWJrOkhfkEp5iMDQxg92sWbrecnLj20Ry49tGe7ydgWNNfxBWM9uXwoAi6oNsxzT/IEoFxZQzxfICehqOQa6wkmI1oUxXsfu4JGZ+FNaaQVkOgONPPOvi0nTUtCTG4h4TNflmxus7S/7GKZVVhpoQVlsCQoBdpJiLV028p0kB9ZFWuFWBLcuV+8uhA9lu6sIFks2w8zAHo0/UPZ9uZnHCfEZ6gt+nkUl6E/dTh1l1EQYW6hcAp9OOQxfB9DO9LfnmvshB6bvzevSQAmpaHklpsdJ8jPHN5cmJssfFMY0RPCyjyX+DpuWOTmjA4LvIzWmWjTwPYLDacc3Wk5aT66nJhGihfnq8PMixAdzCMhZSfEt7yrNIfZAw36v6cxKhr5MPH17CYPlj2EDUcUgD8CT6ENC4wnNiylkTaYAz2xoh1/atpKYyKN04eNSOaqmx85y3C1TKH7wGnvnDKIvCsqx/YrenL1Sr7O2xacp8WOiZSg7PNeaNFW+VP/ZegRETm7ZlVt4sMN5x+aor1g9LeO46KfEYAKTGgSh4UFYvq42w/x0nxz1sIal2ch4YLogXi6/LhUXqx4wyIhRmx7dsJ9w7v9Nm1aDgrapOyGto2ES5AE+rJtmCmnA7GozxIWWhB22DIoBkSPDXNc/5y8SIiJvmUVCRJCbDxTNoZlPD6StDpvry682x//puPSRhR7BlgaSQhN6R5+w1aO8nPT0vwgJc0pkwR/UhBKVpO+nIp4PK9tGPLmhK44HpSNioTNkPgZzaO0nA7S55Q0nOsFKx5TOAmp8pzq9K8xbWw1sqWiEhKV4fe1Wbcq5zD1zcBviR/ZdrQXVfIxCq+v4zqKAc+ZEKbLtZnUbjiEuHWbdz7dPK/zdQlxqQvtpqzv/UZ8WBPZQX/VKo3eRiRtBoyAGVL7oAcCyQ/Qd+x2bvuFNjtSOmzexGds2eg5h/H8skRErrnkgPubtzbrM9G5262pLhs5M67joaINmzcN5x+a6/iCoV16SXgOv+mHwCZhKp7DmDzBxjKJOussudXlLCSoxKCwk5bjKNDAiNA90WGy5QU+/ozzTBccKMdpx8knLJRQoWhnnwQTiBfKccuWYfzkrXM2DIVY6FcW8C24HWmMcrpoJgCN4dwV3xBr8yspSuFvGhO2vVW/rmeetCEFAscoz5kO8uBx77m3M93xBkpd//PYsXVJ84/KMX0d0xClxf4iGokX6nid+kWFj0S6s1FQds7o9FnLsJ7XYLFBC5AqP62MP9eBRhh7pXKoD4nGUHbVHPaeNBKeh5EbNfVrnxZ4D83/guKu8yqeN03y1mlKSoO7XlO/At08LizdNN+Ir+fe0TYK4gjW4U4M4TS9eJz5bUa80JQjtp64YWGTYh2pv1jRij9dupPfcjSOOpE1qF+aZ045pT4kvh5bkCk/y6NMAQlog2EMZXM5dtzqsHXleP2z3/Qk+ZlP/C/5e8+93qXF6+tldTA7d7v1uOC1xSnz/OUMKNoluaDh/EBTtBeM9egMol14RRzBTeykR2XAMG7nTDQoFx49tNsrBXpGcJvnEBcEjhohhBQgsWktrSzMeQrC5Ld4kfWUL25HXlxATioKF468wf3qLGz5DZ1pvBGEoaJkf6OwlI483SDg6cVuPTm3C/H4KIMVBHTqvHuiDfNcOkfdcIkX9lcfz9KDSmNctK/4Az20GURCqFs2CALxGI3p9+rS0wiKvzdOSm1BCovrldDFmw4krIZ0JLin+WVdR1W83Bu85ZvoeQB4NHrp4/mhMunL7qJPPyyhS2VVMx6ZX+fotjwFlQtKS+MWaUzpifMOdOvforCoHEtPbtOZns6aZdMRLYhJ2jTeQGN+vvZWd+hr2/+ZfgW6cR1WYXQRZ/bYgiQAvkcWZOprgb7un+NE7w5dFxhnDp/xNu9cIwGUU9q8Ya8tG0bjaQzrfxonBV3ORxZb2nTEea3y00fX7zx5odx58kKXjkni5M/p31rRzrqOR+1o5yslLVmk10DRPn1mCAu3bjecf2g9v2Bkbx0HBsgChyNICDPulB+QoMT5xeXpOKXNAG+HlIQvcZQBslLVKuRpfWihpEuKQj3Y+mQbrabNWBi08XJPjbA1JF0UbZ5xaUyzCFvT0cqBizmPR5N2GsfQ182m2JQUVrQWZoWQ8pgiy84gXBaEoqQ8Xa/Szn5Iz2MUhCLoetvegR47r0uusbVjOfxtu5qPDuh8vDxz1nTaTCIa7TizbTahOx0Ttm/YQmZpJOEyv9Fiy3b5TEF5z7VtrUBuhVg4rkNKg8BYzowzVgZ0fv7cGkFn0/pqxijsRFCeGEbzCOJ5Y5Q2xnjOUNnTtsBnC+P8fHportu8iDdTmKYrCoviTf4qPZ3lyg+g5LteUs66hzTCnBlH9FBdYlr1d5zDNZsGCa36e6nNyFMBXZ6hPPK6ob81ltTgo42aEdITaICxrNLSRWI5eC7vK0u2bwhj4KPsEWXL0Lhg/3L/nSzaf/vQqf773pWxS0/D7kZTtBeMwSGczrlNQAsgRUTGTQsOMduKBXVW1zgUbI3wTeXYpxe83d6ahXdoxzitpdvWL/xdWoxDegpLyx6+1wkXJQE4xPUFsny8wcKmwnI7zbqMhP4oLC6Gx3gH7Sh2nOCZOJVHwLg//24XVFbISCiydLOV047lNIOc0hALaZn2rpibAnOBlGq27Nj2qXcdD2HldkTFBmhM84nyRHooHvA4yLuqfUa2bUkQpfE4yoxHtLDjJo2th6s0RHXJlA3MubQx6nn8sHWOeL2/qULtGMezdcnOrcIYdctJ0ujA8lyw4576ZVCKU3poYvtKA66vBYXFWxNSGnPzgzZavA0U3IBHevJzIccLbdmel5SlO/w2lGPnJl2GxvzRdjbxBGqbUHYNb873v6WnNBfym9M89ghkvaaNP4FytOzhKfmzwJNxNmbRnn5G/Z8v80dffoc89sQReeeX3dqH0Rntv/3iaZeGhvMHzXV80ejPcgITowU+s4tPPNAINqSIgtJAygXlWWtp8PPiRbFqR9qL5yz6LISoumTc22lBxU0DCEvj5pRvWuxKQtFAI4XaRYNjeYJ2nu7ac6khnDdGyvGyLq9RvDp6aq3c4W9u7zQe0WPIdujROYdvm5szpNgy3UN8U1Zn6bE5JmFOO446a5kgoRgtpzSHE/pDGZZGyxdIWA1/z7KpwmPKzpnYXTLOQ6flTRrql/jv3Hyl8VjaVGFlh/me5zrMG6P1/LF+vkaVsfFw3Iqg1R2VGMt88BvOo2m/gBKSumV7llhPHyhvOqQUcv1w7YHn60JeNTyl5wsFhW5wo9f0+PFyit6Q3pMLOH5pE50UrZyBgc5yx/QxkyI+Q7ILjR+am/zUZY4naB41jQfPzXmILf62X1kuJBpt2Kwm7ZTMkGekaGcqw3KPDfPKfP4tx+T5txyLfj+ztmbS/M0XT5mwhvMPzaK9YOgrzuqEp/hz8t0TJFjZKSmSQ1obpsuLyi7SGNepD6cnLTpWvkixIYXDlC2hbIin0nrCt3S8ENhobLFN80T3tIQGEVGuVsDUsV/LZXPb2nrzBTm2bGrH/JnICrqhJVHQTujX8UrW0KyFlNo7pQc4IwtfMD+S+HE8oAcGebUVJ60HpE1piMpO0qbxcmM5LZ0VG+BHVHbym/6j1N7h75q+RqtrWq44vCIz10mpLvEU2sQK+ftKA9GY5yk55bu0qdbTA32Ibevwa6pD0RpKNArEg3kZ4nprXFGJRY+G3BzWYZM/qs5oi+XX/PRiHX/k+sE4cfva8UqaYQ5Tf2mlB48tjcSE9WWn7UN8BtaUXN9QW9Ct0/S2Nlu+aa6ndMN4TNKIDP1P7s1RfsBUqP+Jl9H6TzRrjNWADHnhpaSaRupr4I+zwpNx4svQ/PTYrzCPcmWmAIO2nDk7q1N8w25EU7QXDH3rOCnAImUFsf9eK3yDcIFCiKGHmOL0M9q5tHAtSE4YWz4r6CaBIytIwIKT0J5aZ7w3JrkdbTm174TmrNy2bN8ymC7yJKykNNa6VqMQ6wgSXh/WhA3uZDAZFLJKTEl4zvQrtbc79mi+djaeCNCIdYnL9rwSap4L8qzcKY1SGZabR+yVYOkxVpO+HJofnYlX8gxJ48f5WRqpzUxemflBvAI30GAi1QjkqHxl2of4cTwew6edH0L0VIwTnDNJGSL1SmP6my4Tz4HWzmsnfy47n2ffjroMohvCQnrexGA6kOdG7UNtYdPOYkGe9Kvle/7aY8OIN49BgKC0JOOQxd/cKI3j0c5XVPz6MWrbbEUrbtC2PCaYP1I7El+gowd5j5bSmLDxiG8RzRp0U3fpeUCUzWATa31Gk7Y3Z1aU2X05PZ8I8fX3guiBfLGEf/ySm+XiA6vy/V9xazlyw65Fcx1fMDQDIUFg8kmMkphskp7Cuspzsk6YX7aVOEqufwM9JHDQQtmlQe6uZ9d19nZqEopgUTTucp1XF0uP22YqLGcN24hiS2Xov1OrFAnAHo06kJ4BQSsOlBvipDSOOz63m4LkrJzLKwlPZJGoUUzGo86cryLrXK2rJtUFBVPXigMCmdi5MOoEXf8cWc6ME7cukeBmaaS5NdCXtmMtPbacnHszpaU5k2LSrzGNxI+yZ7RBsC0q35l2TBso14fFOQwV53lk6Xb5HrSPe0TJ5T0wD4kX0hiFOUyKLc0jWjO9PNkLIM9Tcu2YWtTQ4t/BmiJ+vWt54ZJSOPjSzeknbN7V0Rh+s3OO2kzrPEv93Mq3Yygn7dcJjel4pHjhN6q3jUcWzeUlqoudY+whQXzU0m3z8unR1SaDAM0FTEubgQIbGAp0K7d+tzrkpTdVBvf2IR/q//VZXcdTuqcBWrnes+RfPkZny0seBDlrv4fbThyR//LdzzDjteH8wkwW7Xe+853yuMc9Tg4ePChHjx6Vl7zkJfIHf/AHUZwHHnhA3vjGN8rx48dl7969csMNN8i73/1uk9fHPvYxefrTny779++XI0eOyL333isPPfTQ5mqzA1F367hNR4wSd59rlEGxAkfObafIuPs8bBpUTmDhrllQhQSlboifhtVan4xA5tSlzoXSLho5ejRoFz/nIkhCEeVt3aBto+VvLK0UGqgPU+VrRK66nVuXYr+C9SGUuQSCQMmyE8LRzU9sPF2ezitWJGxd0CIBAnVIRy74hk8AjWkZuhxUTorCvG0z4gkhrq0Lu8uasmFM1VpncnQTz0zBmyo2/XCuFehR8fhJG2pHy6NCfWroIV6Y9e4QWxdSQqyixZtObLFl/hi1BfKemAb9O7l5RqXMwKOylmocZ5YeSotW3Jp5LeS9EH+KShfX258LJb430+Z9UobOi3hcXM70U2U6hvWIxmgIp41xthY7NBb4R+gnViTBXRrfYKYyVLwk3zRP4il467hKG56LKllncTOIPMiAPo0x3DpeWodzm1O66HndOr6kFe1lX72h8+8lee3yI3tmpHJKKy06DecVZlK0P/KRj8gb3vAG+fjHPy4f+tCH5MyZM/LsZz9bHnzwwT7Om9/8ZvngBz8oP/ETPyG/93u/J29+85vlTW96k7z//e/v43zsYx+T5z73ufLsZz9bfu3Xfk3+y3/5L/LGN75RRrlDFecoolvH3YWgLCCI0ALo7D6j+G3DSFFKYxJDpkXWdUUDajxFy6ZlwcSW4wtPyDxhkSZlzvZBzkIS18WmJHrizyheYZHVZXrjKl829GtCl0ZMo108Q7kk2JqFpmMlLc2T2pbqTMJczvJZc3YOlUaYm9RmNPZ6ZSApN007lB2Hee7EVlACV20e9i498bweytdliIix2LEATIoblZ0bj9COUXv789+WzR4W/tzSRVsOmdvQYYs2hOH84LGHFmgsO582d0abFD/bPpavjyAM14rktzhM8z2gG/oFFRPIz0tPG2OsIMTl6e+UtmaNw82NDK9A6z4ofqSkkULGG1HAU4AfeXSPocO0YhOU19LRqhCHxhnLCkwjW5s1iba/8Mxvdl6LDauYW8PYy48zmjPLY5sWvUD6r0OYfzyOwydpdLw4H0237n/yDKJ31NdnNmmndNsoq8u+RZvmR7yBMnz/sa+7S172hCvkFU86ORuNDQ1TzOQ6/sEPfjD6+73vfa8cPXpUPvGJT8jTnvY0EZko0a985Svl3nvvFRGR17zmNfKe97xHfv3Xf11e/OIXi8hEGf/mb/5m+c7v/M4+r2uvvXYz9dixiCza5ldfKJIC8+zzBMZtmE638R3gnHJBQggxQKN4QhgJ5FG+CT20G04uayNgnuQFwLuZ0D4JPTlhl9oRF5yor8Wgy/0IwTWKzRjcxLPum0CjJcexDNvuB8XW5ol0o9A4+QPd2GBM8EZLGsbKRZp8aIu8UMTWcG7ISdk2rEq4dOaWLdsWXa2wOvNoNOpk/aylx/MCwbFOcwbcPLm983PQs9iOus5YVLIWZJjD8bwWE4Z1hvE0+Zs8kOJ8PHp4SNX1q3eUwXPVrbJoo7LjzwWaM6QAsQfRkFb3Z9fF63CafgSCf1ZZEgvqF2PRhmfkNEsKNOe8X0obh1Q2bfKQ8h3CiadYXmjzzCmisfIV56Hj4bjHsVdPYxxm+5A8EEId9LvM4Ru5GDMftQR4NBKvKG2qrU4t2sjjRIfB3HIs2p1KczZRfsmiTd4my9E5aVsOud7nnuIiEA8XETmrbv7OWbSj/urDIubcf3vmjZfKM2+8dCb6Gho0NmVC/sIXviAiIhdeeGEfdvfdd8sHPvAB+cxnPiPr6+vy4Q9/WD71qU/Jc57zHBER+dznPie/+qu/KkePHpUnP/nJcumll8o999wjv/Irv+KW88gjj8h9990X/TtXEM5ok2DCO5fEZMWEhTwNe+osg5+sVTWLVViE8osnL1Y27SQuKANYF7aGeAsBsURingJ1oQWYhXmi29ZPp9F51Spa1cqOodkXluhvpgfCoPXoMjxWBhMaO+pXOrdvxxRZznMKEF/MosNi+iMaiW4TFtMalZOk1XTptJhfEk7tSHNmNHJuMTYlTdsMLzSEvqb6dZCC+FFFO5Jgk+3/whym+VGrCIS4aZuR+zcJwGQNqx171N59maYT/fnKFlYqu0Qj88fafmXPkIj8KP8O4pEVbwxhKX0icR9oHYHH1ACy7mXbUceDd9TDV3PreJKfpS4pG/IszQXanMjxo5q5gMc/oC06obDJH7GiPYri63jk3cFeMkA3LexS3qgb3tGO8xMpW7TJy6W2beJwGPcFhT7QVmrH8FVvRPfu8kYmtTyuTwPu7bypot+ytipG6Yx7DdLYoZiz6oWtlcxlaJqGPVPLN21ONTTMAxtWtNfX1+Utb3mL3H333XLzzTf34e9617vkxhtvlOPHj8vKyoo897nPlR/90R+Vu+++W0RE/viP/1hERN7xjnfIq1/9avngBz8od9xxhzzjGc+QP/zDP8Sy3vnOd8rhw4f7fydOnNgo2VuPzBntQUkrMGlnURQnT7NQdpXP7iTlTcoEGiHeQGNKoqNoVQiXnmu8VzYLbioMLE0h/RKdPzI0+mJSSdil9qkVvn23fBs3za9EI7rBYdtaeqoEclQGSSGP847LtvHIUh1ZtKefpadYQp5m4YYxim655ku9UJw9bgGdiC7P0I7+ZlC+D/vv0Rjt0iA8Yxvi1ihkuX4V6H+07Oj8MhsyqIQYevxNFXYTpTCVlpR0sRFzG3rumdgS/8jMYbSGwlimTbC0rydrjx2ks/R1HBf6FcetDeuta56ipeP2m6BDWL2F1c5X3hifhiUDLTcXbCkVfKbPg/ieLtfG89Y4dMsGPkO8q9aiTZdh0Xic1Ic3ZWt4Zs5wUOLDgdxluHW8pAAjj4I+KKUvvevdK9rFNXPy117lSt173Yw6HLu0aUH1pmMCeh3uLdrQ/17eNfDW5rX1dTdOVJ76be9KOOtu69fQMA9sWNF+4xvfKL/1W78l//bf/tso/F3vepd8/OMflw984APyiU98Qn7wB39QXv/618vP//zPi4jI2tS147Wvfa18/dd/vdx+++3ywz/8w/KYxzxG/vW//tdY1tve9jb5whe+0P/79Kc/vVGytxz9GW1U0kggi3/T32lxISWmSnCDeCEKW7QEwjRjsvEmcWFxwbqwgkDKbkpjzj2xpACHv2m3lprHXszitw9ZvnSuvdspWR+SctMy9N+eYpXmqUELG9UFLXt9vjZ9let4J6ZxUTmxTeY87xM+bTuyq2VSdkbRwrkJRG5ks8xVtDoeo7YbSQjl+a8/6Tf9V61HAyn+NXMYhV1QWEg4xTEP/UqKkifsUhi6NwO/Jl7IT37FeXh0hHBvDpc2S3IbETpizhpOG5H+5o0uG8Zthl/zRqTlfHz5nKXFE9ZpnMU8zp4Zrt+Imn4CDyfvHm9TdD2Jpz8jeqBe8WZyF9XJSzsoczGNnYgZexOewnSn8VJ6kO8hD7djIsQpbU73NKZhCQ1e+sGibftQu0GHM790FwDxdbZ8p2MK+mbaJ/HG0eS7ttIOirbOz5YR6Nm7Mijap9X7znThG1u0bR1LYXS0plYeycHr6/T1EA96voYNiHiNm40ejVnr0rD7saHnvd70pjfJBz7wAfnoRz8qx48f78Mfeugh+a7v+i553/veJy94wQtEROTWW2+V3/zN35Qf+IEfkGc+85ly7NgxERG58cYbozxvuOEG+dM//VMsb3V1VVZXVzdC6rZjXT9BZRbe8Fm3wLNAnubpnLEyYb7iT6gVbGusIeniGTKzdalTGnKLCym7hs936fkjm1/4m+qi847T04KsabTtTYJJTkEk+NYwEnbsoshn+aj/QfhKSKLnvUiRZFdGEEIyCkJ067jasU/jVQmSSgBah7CYwrTatm3ZymHDQlyikRXbJDFtvqHgF/+mv2NYQQkJ4enTWeQtUDvGc9ZCnuuWRp1jyT2x6waXY74LwOZDfCYnXJboDnFqvFeGr5YegXhFbxqH73meCpO4Q6Oh55QlRymnQE+pXyE/6ceJww+7YRazomXzHPqQ+jrPU8JXc0a74zljCZ7mDa7MvGFh49F5Y5rXtAlGm3whTXrWvZRnqB9ZtHUpdNwqxInL6KL4UVzgjx49tF6XLkMLLsYjqHRUF0/OUL+n559xwwuUWK0sh1vHy+fkJ9/3rQzi/iNnBh/r0UhEzvrpA8iLrLQ5MfT1ELaIM9qBnrRdPej6Ub9uRFf+qW98grzj//od+SdfdsvsiRt2NWayaK+vr8sb3/hG+bmf+zn5xV/8Rbnqqqui30+fPi2nT582t4ePx+Pekn3llVfK5Zdfbp4F+9SnPiUnT+6+W/3Wh2vHQaCyAhkJEt7lGp0IWFitEpPm1/9NYUncnIBYihfiWro7OL9G7uRWaRiMM5rpxzToiLGlKcoiSs9PUth4nsCJCj3EKyk2OWuf0xR4+3Mcz9I4uO8NYTkXShIkSZDAMQ70eAtl1IPQrzkhnW/ateMEFQlTjzifOCxOa8PE1CWnSLBwCXMdhGJSvr3+x7GnFeiErlCGSzdsllgBzbfi2dnK8xqF4kplh+rsgeZryZ2QLhXKefzwUQZLhx2PNj3zFOBHSXlxPJtfWnontq/DX+m5Rs+lM0oPYbmzpaV5TZsdMQ2maO4vSkN8WGdO86jnUSkv5M0yj17aqKXxTMo3uo5HZdh2DHmirOCk53XK0k3jVqAPrDdFrPj7m1PAw0mhh3JoozZ8W4bnoiheqV8iWikM5jApttr9eyVj0RbKT9H9yJnh1kq2aFsqadOabx0f8gt500akhnfsw4O3Ub9WadGOXMfJU2FGekREnnzNxfKf3nyP3HXlheXIDecVZrJov+ENb5Cf+qmfkve///1y8OBB+exnPysiIocPH5a9e/fKoUOH5J577pG3vvWtsnfvXjl58qR85CMfkR//8R+XH/qhHxKRyQB+61vfKm9/+9vltttuk8c+9rHyb/7Nv5Hf//3fl5/5mZ+Zfw23GdHzXk6ckrWw6+PF6dAFkpQYKD0nPJFQXXT9c2giN0iKR9Y5drWziye1bE4gTwnqJFlE4BxfKMdzoSsKaUDP8K637X+qS41bpi4r/bv0Hi0pCFk3RhLSgPY0jAVOEtxsffi5OStI9Is+CtS2bF9BsGWXNnlomKHyBYJpSEe8ws5/6uvcxUXUhxAPlIaYRpt2Qp9j0aYB4NEDdBe9QJL4IvxOfMmiPeq6/owflZ0/l6rzienSYSXPIJHJOHPnMAi2OIejtJO/lpSlCecWjIkQTmEi03Y+G/Lku0BsOQkR4rS30Fw3SZG/aZDCEvHhzGZJ7bymMWrf0SaL/9BfqecM81wxYQJtq9OGt4VLZ36HfOO/0Xsh+U3nScewoue9xkOdTf1IToExQesw8cK0PsTjckpjpGgvkUIWf+pyddusy7pJo7+XNo56i7ZStJeX/LFH80NDW7RJLjh6cFUeeORMlGaJxg9sHEaXroW+hvmmkW5KleCN0XrX8eF7sGgXloeGhg1jJkX73e9+t4iI3HvvvVH4e9/7XnnVq14lIiI//dM/LW9729vk5S9/uXz+85+XkydPyvd93/fJ6173uj7+t37rt8rDDz8sb37zm+Xzn/+83HbbbfKhD31Irr766s3VZgciet5rBuWipKSFKCSQkeBuXK1AeMJFCCyfPT0V55oprOvKO9ehzBrBTaB9ckI6lU03cpq6dFyXtGwS8lEw6RU3ojEvZHllT8qy/a/zEVEuXQW6s5bYmBzc5BmPSPHzLcg0gmpd45Zg0Ue6zSLNmzzDZyxcUr8SvdiHugznOSV0wXWUHbZ8p/SE+EMY3bScPYMc0SImXk9P1SaGGLDAacdo7lxyJChnNkU8UJ46sMN4MV0irLh1yW86LW06eXO49jZp2oRlazjxTDuAPN6TKh3+Ggdzk+JBGexhQXOdO5j6QbfjGBha9lwz5UftOErbgtdmkYnl79T0yuScosUb8Lbsoqt2qEsiF+DYSz71HzE/I75n6cm55ZfkGRoTfTyPbign2pygY0bT79oNuncxxg1rW4auc3wLfifDmmLH7NA3tt6r6smqFbwMzc4F4rePnB4UbVKg3/OKO+U7f+6/ybc+c3h2ly3aql5hLOvnvUbW6j4Pi3aKQM/FB+uOmOqxF9zxxzDOGhrmgZkU7ZpH5S+77DJ573vfW4z3nd/5ndE72rsV+nmvdO6SAoS72dNPWlxyFh79Ny1CrrWYBA4SgNHyJSqeDXPLhgYiwY0Wz0F40mVTXUL8fFuQMBfCPUts2RpiJRNS/If6UZjXFhKHm/aGxRw2ULjNSJCw8cLfNow8LFhJS+nJjSm0AJSUGGf1pE2ePirOBaIR5kIhv5yyg5tlzuLfdYMgNxpRWhijeDkX0W3r7HpYjERGaxKHqT7sNx3DRgQ9NwZ1xE0+mCBLcDawZA2PaFUCMJ2zZqt7F8UXGcYhKbbxU1U8j0bdpC0j2vpyLN0liy9vRPjx0lYiBTHAs+6Zs7xJfpPfbB+iMF+wIJaMYZynmDCmUQVm2jamx46dEO4NQTonS/wjKifTh9Etz2NITPNIPB6u48YKIq7DMB6jjY1Mm6EXmUtLEg/aW9Ol89dh/VNX0TibfCfXcXxurtD/6bvuufqQok6u48vADEIK8uTS0K7jdDb92ksPys9+05OjNKQME4+LjBYZbxENOnOfgzdGX3/v1fInf/WgfOltl2fTa7r3whntyqPeDQ1V2NQ72g1laIGDLNIijqKl4tUuQqEcUnZ4B9iG6fJcGoHIrCXWaNCwaSA2rFb5onJ4AbMLfPibnpxIQ0bQttRhTCPRE5Lm6SYBT8fxxlUfDwSyob52UYzHXvw5oZHLpbARjfvOjgn27vDzjxSokV3gc+7kZGGtEdK4vyie7TBS/Ok8rTtGRcBCBnPOUd50edF3HLc0/8tlsPXatk8H7ZgXimnO2DKi91uhbYreiYX+4g0d24foLdArQDos/k3nSbxH563pwDaDfuWzzqoM50KqEawfpARn+xB5oQpL4utAWo+oDA+ljRry7slZr6u8AMRa7lAG6AINVhSL41oa0XMmqZP+TmOUNo4tjT4/K/V1Tvli3pqgE9xAR3kGwnSaOD3NBduOeBla8ZK6ON+0XjDEkVeQQo+XoUG/0OaUhnYdJ4s2YYn4XrTBGMpWZ7THtr+ojNIm6D//mttl38pY3vuqx4kIyA/TTA/uWZZ3f+2d8rxbjmXz08XRZWjak6GhYbPY0K3jDbOjk86sIjlXq5JgMs2Ud3aNEkOCO1sa9aemrWQ55931+Le4bCtw5Cxx/dnJIHAAHWh9igRTS2PIUy8Yoay0zuPOd6tDZamgSOSsIaUw/bepD4yJFPRsV273OY7nlWuKkfEINoOkMxa7rIUV6bHxSgoQje8Q1ygXeDEc5OmM8bQuqIQkecRhMD+McBHy7o1hkXISbmAlwZYEbXQdh772xt1kU0UQ8RyO89HfcZOv4KlCSmjuyZ4Qnp7n480kS2PUjhm3U9rk0QpVyvdzx4wGLwCbZ3F+TD/5jHZ+roe/PQWR2pf6mjdgJR8GaXPrkQePxj4MvR9iGnQaUoqI/5PCahVEf5yS+y/OQ6Anngv2duphfiT0mPwEkduoK23A9me0C+0Y4lTdBSPAH6F9cmHUB/ScFll2aSx7qDUI6Lke6ND0hO80t+Kbv+3i5J3RztGOSjWsH7TJQ2uhBhk6NF502+Xy/FuOodwyyTOb3OCMet4seCroZloi16GGhg2ijaYFY11druMqtiTsRPG8hZuFojRsPHIU21RUhsWTLTuWHnQ7d1SbrqPFyrfiFAXyLv1SUICBKWtGv5a4PQ7l+O5pJeUU6clY0WusJu64SPKkduwFLaBboM3qPAPYosljL6Wxss06G0YLfN76WKaRNksGwRbGPQpP+fqRhZzmf4hlvUCg7CRv/R2fU4oz9OmhMQEWds/SQOkjHlc513MWKToH6gmRJJeh5RzKofkRKTaZOTMGa1hKD1m0Q9ySS2hOIau9sXyWIzN0rhH7GvqVFMlSvwqUUdBrUHmIxgrOddsWQ/fbcUQbunQW1VMGY2+c+LeYHpNlcdNxieYgWPFDnGjs2OKicJ6bYsKwvaH/UYEm3mPoJsMBlWPbgtfCyWe4cGwSL44vYo9IiAhuumrQ2kXrgjaqBsVPl73cP+8FaaH/RUROXLhXRESec9OlfRhtCBLoHhuynC+Bkt85fK8vt0JTpvfL+/QlJpDg4J7Bxkiu4+SW39CwUTSL9oKhXveyixYsqP1PIIWVFKoQRrvmRnCDDHK7+LRzXRKeSCjqaTSXsFA8vVCuq+81CpldkMlSEPLSi+x6cvlVlkao4/BMFghplcIlCTumbEeg82jU6BdKtSjyO9pEt9OO4lgkoP/JfVvnrfMnoWhUbEcb5m4QjLiv07jo1UFzgTaduvg3lx5vznS0QeDTqM8bz7oZxAog1D39uxPrqRDVuz+kbeihviaX3v5rVOfJZ+mctM6b3lvlcaZ+78uzdJcuOaPxqKGtwJONUaatfF6Zxt7kD9qIKllNA+xzjDY8zjO+vV2nznl3cHvbtDSWPZC7PXk/FNuR6BnZsDDH0fJpeGEX0aXLRu+OwhwmxZZu+aYxOsmrw7Hj1QP7EMao1lvw1mmPN3fJJmhPt6XHXZshjC3IQ7zgok0eKOT9VrLYpnWqoZH6MFK0wTMg5KTp1mk+8Ia75bc+8wW5+5qL8fcc7bree+BJLNxUgfUaN74KbZaCZLhZcHDPsvzc658sK+MRbmKsNNfxhjmiKdoLxvCOdt1FWnzms88iQgd5TtLHYXzzs80P3Q5hYeuSzzgt0B0XYxdPpy5kvRy+g8AR5eenJaaMN4Sm7UPtCFbl3LndSJiHhQkVN0/YmX5edGAFaUpjRvSMwoKsFsWMkI5KIwk76djrnHsETJi/sJGlCS9hGduwaAF3xuOoI8WG+jX+nPxu6R+EJxij2LY6Xhxfk85zvUwjjseRDcu59MbWNVtGoMOOPKi3+aJ5ihJ20GJj6xxikBUn2kBTujVddlMqh/gM9VfuokFPqE3HlMdnSu9w09gL3/TZyTyPSvlwZ5+q6kKeth+ofdCboMBnyCuFuERJyCZLWFQOKn7hszCH0Wrq00Vva4t43jg2Xjw34/ge3aTsDF4XNi3VJUXuPDZ7ctDbysS34gLT9cO1aItde3I0atCm3KE9yyLCLth6LpD3W2E4Rr8Pmzy6LWxYuL1bK9D9reNi+yuyKqtdjgv2r8g9110S0UPeFATdduH2c74MTbvb+zxBw3uaz0Mae1aLtojIHVdcEOepsmiu4w3zRFO0F4zo1vGEF4Q/SYmNhdDM4gJ5WgGY4vmu2huxDMeWq/g7KWRV1nlSJCjPrFAEYWZBZoGcNkaobd1ygMZoUUSB3JY9CIeWHhGRSw7ET1p4VkVCbAWMy4vpseXa8+rsWm8FWX/TCZUGKhvajN7LZaU4pZH7ekKppjozHpP83LIpHpRhWgzGHirVQCN5WOSE6o2cAw7hNVb37BldFbi6FFz6ID8oA8/iRhto+atkSfCnTTBWJHR72zBKa/st9tqJabP0IM/FeRTo0WFUl5BfjK6zPKXWkt8r3zCHS4oW8f+cpdkDlV10ZYaNKAEa2XuB54eOn9JORxzKZ+8tH6Z+WQLGzvfD+DxPZKLYnZqe7e37Kyrb1rGvX9S2cfyofhJjNPLGSRyPj3XFecfpbR9qGg9MXYu7rpMnPvpC+dz9j8j1xw5O4gFPiflRfkTqn8lzJueV8JjLDsrxC/bKxQdWnbk+jQ+bjh7Gzr0RKfSdFnSBGPHM+lvHS7M4BskZm4Xu/+Y63jBPNEV7wYguuAEFb/JZJ5CRuxRaqhMaSAAWSDsIF3X0oPAcLRihqIRGpy41C2XtZkD4VqIx0EcCuW1HW5dBgbZ0s3Cp6YnpCrSk+ZEApNNfkrwdaeP5gl/tUxy0a459mOQ/GjkW7RqhKKPEkoAYL/Biwrw7A8gSO4KoJCDm5wK0Y+VcJ7mhxiMmPz+GsNxb7xqsFHN82tCjTbBhblq6dRhd9pPbkKGxrMOKL7YUeApu8oCgnD+jbcNMnqA00Nyt3UwaBGArUNfwR7JoB7CCCPREdEMYbmJNyyjMmZKQTRserAwAjToMN3n9tARv/SBliHSP8lluG49cx9Gbqs8jqnSPKy/eJ7/9mfuSuLZs4s3RRVMj66nijb1xMhdyFm1ajyQJJ4W+n68qoj7D+29f/URZW+f1cXBY5LlMoHVBW1BJ8Q+W4ZWlkfzSt9+Lbazzo/Htwbs3IkV0gdgSuY7HtE6+25vRqYjNuo7PqKcjRtDeDQ3zQNu2WTAGL+TcRVpDGAohsIB5eZJbNgkinVimytZCWgDDYmWZbFwXW7+QF9XFbhoAPbQZYL7osi2NaWukCxEtniEva5GwdSTltEt+E2FhZzjzlxcudf43P+pwHA5CiAd6EokEZawfyIw1F9zQopqzVOtAssSQcDFYQ23b4pruzYVCGCVnIbRubnnW4k7RnyuHrG7sYm4nQ959l8q187XGMyB3rlXHG56vydcFLUBgVV4rWLR12WiVhnjoBRDGKAih3oU+aZt7l2Zp5DbGSGFF92Sc63bsecI6XSCFihaEkZIWldKJiZfGt4ksSrw0f0Y7Xz8O8wny1o/cuWVNW3w8ZvK5R72tTBsWSzBOcrfye0177dGDih7bPuRGH76Wz8THNOi4RA8ZGNKBkO9DnbYzNB5YXYp+9xTXtfX4LgKiLf1dfw95rYztJlh8oZ1+MmvkHqcIeS+Prfu2h1qL9umzw03ly/2zXXZeU1vRWhjTUJjEBeSOntVCt2mzaDfME82ivWDET7bEv9Gikb29O03fMZNNWQ5frgM3BGcEGlRYgXnWPMUhqFRbur3F15Zt6aewnIKoQYtnSG/DLI05emhBjtvdduLQjgnd079feOsx+aO/fEBuO34E65NTECPhO7Mols4ghwJKimT4mwWlVCGzdFObkRKzumSf7HDdE0FBHPqLxr0OswIHTVdqs5xga2YMtBmnt23OHhaWnqFsKzyV6PbkJFb8gFfAnKF3YnGcgIBHGzIFPRvHOCuIti1Y4czTiDxe8ht6ug615x97ZQe8O2puvKexF8B5WnpgevBGLczX0vNcztAzeesyyfuFb51XaYmvAxE5euzmVKDBpqJ5HSsAk7D9SikkXoiKLSja0tfPtreIyGWH95hyYn4d/6bTj4ieqrHXCW+0pfHsOpOWp9NrhJ/1BYkHp2e0CbrstanuSbzeg/458I3o6b2+D4d4SxllmdbruP/zSmPtGW2taIdy6G3taFMANwNt3iWvlBLmoGdHKLnbNzTMgqZoLxjr+iZZ0pSFlbSS+/bwmxU4SBm0QjorkmmetVb3nGBiafYWypRGW3be0kiCkgoDazHROZzRjsNHIxKUrBAzhsagtiVljgX86WdMTpT+W595nVuf3CKk6zgoC3mB09+wSC6uAUU5pK8ZeyTYcr/aBb5XtAtzJuRJNKZlh1io+BXaJzc/OCwZZ0Jv8E7zRktlfuyR94JAe3fmi45HQlZKt01fW++hD4d4/XfoVzp3G7mOlzRthUHJy9NNFm26iC3U33ORjOo4gssip3Roq/zAZzSNMI/EtkXunKxdpjrxDDzlixxjGtx4tPmSK0PRUBLS2QJtf6e2oA2ikgfBLBbtEPfMmh2btHaRUrRvZWzCIiVtZDesem8qaBtvDQ/zUUS1ReTpYvOklqi1aLM1NHwm86Oj4z9Ux8l33dqhnIdOne3DtEU7hS572JSv639dnsjAN5bJoq3yyVlYS94r5TPadbSfPmvHKHnoxBsE/lvfcT5ZEovYrKKeYmWpWbQb5oemaC8YWjAiBXjywxBGcmTtLi656YkEwc2mTZFVYgsCh13mWAkN+ZvbV3OKBAoDVuCgRb8kAE3aMA5XPRaFj2Axp4U/a6nUwg4I5GgpyCi2BO8uAIwLCmtJ4PTcm208Gxby94QiVlit8IUXTan+758foT6Hsr1NHrRoQ16lYwI4JmhuZec69yvlGW2gjGxY7uKjssIhbrzUMpfzpojqAtbQFdgswc23cB4fecIQNoOerRR1ONcMlrjYNda6vJNlT9c2HWeGz0AdluGsK4+zQJctr+SpQoqpBj3lxO7EQCOVHY1RG5ZL66G0RuJ9BbQWZp5tpI1opiWZw9NPbS3sy4N2XAKF7EBk0bb1WwI3X7J84xquvofjOJN6hPT2bHF8jGLyqcdt7qZtXSDfdRB/ajo9/hh7SVh6QrrrLp1cNHbRgdVqV2byfiu5MZM3xTK8mKHzySnLJT5cumiMNp0IOEahrzVobqFFe5MW5HkboJcKXgANDbOgKdoLhr7B2igcvTKoA61gEr5SenShS5iOV3bKU9l9L/7Uv3cQhkqxUfw6GXXrSRgtlNAWKJCBAJxxYyvtPq9nXce7JMyWk6OR3epUPLKaJnmkZacwCnlJEp2CF0UcRTiaAABxdUlEQVRLNyl4IZyF4jherh1pQSYBOLa6ToXQ6FkRq6TpYxyG7qQu+Q0PK8TgGCW6wfoM0z+7EZb+jd4PqAyQMEd023lE7aBB5YpIb4ktvhMM9SZFm4+oTIVLJaySUl06o60t3qQYk1JFc5jOh5Nig54DEuZHTFuo97raBhzGnl0DiPfwDeFQlwLP1CDX0TIv9MNIuSy9R+1tONLv1A+k+PEmIdXPH8sEbzP59Bk7NqkdtUIWvu1LzhOL8H0FUd6ZMUpzVURkz7JVPpZLHg3QN4EefNc9otGGDZb0hM90GYs21Ics2itLI/nwt98708Vc/fNeUbn5NLR5twyXoWl+tJyxsNL6McvzVChDAs6sWUVbtxW529Nb36MozZLc//AZeeYNl1bTS5i3YtwuQ2uYJ5qivWDodxbTqcvCRfgtLzxTnp4C5L9lHIfmbh0vWSlYKbaCSaDP232O6IGyUUHAPIjBx/GJNhGlkAE9Nr5ts6wiESkxNh4JeGRd0eGGImhvCk/DyIJA/c9u1aFf8/mFPI0LJQq7mT6ENtOLfn/TLqy/qYA/2fixgpst29ab2pYuBcJdfFLcQZEM+XtCeumyH2pHOpeaq0tJKY4saKOud4Xt80S6dTm2Lr0FDfp/BGElF8mSQTuycvVWcltOVG/aYAQPgpwba5pnGJMaZInr4+t4qJxM6wLWy9LcKukc5EVAlko6948eFoW2HejXafM04njWinZmrpc3A2wZOasgHQkRKVsLwzetUISsDqyOTbzSRgLeOk7rjPpDu9OGOsZni31esQ5HHkpn73P836wfnb/ulejRec16CRa5juM6qxpyCazXutyQl94YXM4okrR+zHLGuNZ1/BRtBimyLthnFW32Xhh+/5XveLp8+m++aC50nRWbvUwtxb6Vpho1zA/NP2LBCBYIWghYqZp+RvGsYBLilAT8kCdZi63gTsIFlN2ZL0pB0HTHddL01Fj32UJCglscX6cpW/FAiIO0IjJ9qsq2bQp2Haf62XI8d3ui1bVopxsoOcGPrJcFgZNcKMPf1C8psq7aSX4pjWMYE70SowSYIJBG76P3b9qDYOHMTYH6RO0LNOaVapO0qLiH/D2LdhxseUD9XAifMLeKxzKG38uXV9GYsv2fcx1nHpUXtrTg+rybLxMRkZc94Yo+7CwItuTqTYItWRBja3gIG3LzZGc+7jAJQ0W70Bbh6xK4+dY++eWB+1pUGNDYf3YmDC3IKsNhPc3T5aED5b3avRXma05BxPLTn6Z/nwJFm/phGS7N2r+Svwytf2JJ5Y3vaFO5Kg25jvPZYku3Bt2fgJuBOJbtHJzQ6W+Wli9D29hYEpF+947WTA+0CbIMt8lrA3LuMjSa697rBoSI32c0gpIue2TfismPLM2a3sP7ljetZNfQVovXPO3Rcu9jLpEnXX3RfDJsaJBm0V44tOt4lWt0/5sNo4Wkg4gpz8Fbx8UuOv3OvgrLCRKkIJQE00loJ+m6EQn5yTvWtRYEVmJA2HUUhIAgUKRtRi6dtKjmnsnaoy6uqXX9JStVGicO57/5HL2m0Rc48YZYHFMqLQhzlE6Xg30DghsKF3qBh3OJvZIC/eL1a+kyLBqPAjRSO+Y9Q2KCRqN0bnG9h762dUFLY1QXf/6z5dLGE5kI0I9k4uLcFBtvtXf/1+XE9Os0RYu2UlJ/8Ktuk6+660QkUJ1dA6sb9SH0Fz1fRG02hj4Qia3tOdf8dbDLFzciJIxlW/YKWiR12jxYoLdjvLh+9GPChkXPKa3Fv9WAXOsjTwW0ugGfAcU/N74JHi8sntEOYxz6az+5jsN4pM0FemeevA9E0svQxKQP30rrK7mtI08BFzTiHWm6NIzm8BocE9kI1hJZZVJuPj/aYChZtHO8LZ6vNr8SiN8TvvVZ18nH/8dfy9c98co+7MFHhgvkDu+dWLSvumi/POWai+TI3hW+TX/jze2i9nhcCd/1/Bvmkk9Dg0ZTtBeM9Fyg0iN7sJuwXYTQop38rePr/GlhSlnTCDJAqyuUA2siCv1EX5RnFI+ENJ8eEubo3VFPQQgIAgU9f0bKpUgsAHe9Ymz7cN/yMOXIOkvKDilkOdS0d0BkiQSFLOtVQGNKC3OOQs6KhI2bs5CRJS224gSheEhLRwI8a2jJ1dfSmJ8fXL8QHwTOpBy8LBC9JGjO2DE1yGF5YZeUnRyPSuMOdMA81HWBtsie0Y7mjC2XoNnuvpUl+ZLrj0a/k2Bbqnc/h8FKRXyrxtKUU7Thcuokrc2dNiJCWOwOHH7Lj3kNap+iRTtbDvH1IW2vDM4gWOuYI+ovmB+1m7e8WerTlg7RkI5udKYNj8h1fPpJz3vhc1rA63W8MP6p/0SS97r79TV/W3bviWFqV3Y7p3mUlYWctqVxpmWwzVhDh6OBdq570F4kw4YHndEe0mTH1MiWPUudvP5O8agje+WXv+PpUdj9D5/uv/f3oow6+clvfGKmvE00eEPDOYjmOr5gaIu2iLPow4Ia8yK7YIQ4pUuPRCbKRsractbZWoGDXXBhwQG+6rl5oSKiw8DNj86WDUIaCBwZOkT4/d4Q13N5xrCozSZ/7F+17ne1FpKSpSAtq//bc3VI4tLNn7nNjjR26ljvucET6TlXzUixAaGRlJgguEQW7eA6XlOXcEYXXDXZA0FsWFSXyrmV/KbppDCRZPwkv0XxorDcvLbximXoto+sMzZunwMoJ3pjZNjwIhot7+mgrzVKz3tpi3bvjVEQYvuxRwoCveHuKbFwPlxjUBAKz0BVbkSEeCtj4Ecwvj2UrJJDV8Ocw3GvwoAe76LKHGiMly2+tuycJZ4suwSz7k0/z8IOCinvy2TRVl5SPf00JnS5QPca7ETqfqNNGdq8ofmxj2gE5VIjLzMl7YhrcyhHxwthtv83ArRoF9IsRWVPPpeXbH+VLm9M44vwJtZM6YvUx7j1+JGZ4ovMNncbGnYDmkV7wUiff4hco3OCu8qDrLghLxSeExrQoi2WGdNCSYybhXRYzMFtMKRLj+7E5Ux31mlH23xhGrPnswqCpKdodx3spKOAEH/qePtW8k+x5Cz2ngCUIv0lt7DRTyVF0nMdl44VYLvxY0utdW/GNuvHyRCP3jImi7ZXl94jonSG1dQkr8TGdEOYQ0/X+TSWBC2cR8gruij+JM3kcwx0U7+IlC/dIh7XJfFF1NnSQvvUWkhKcitZi+M3gX2+p/kZni2F8shSq9OppUJ5zlhwO6r8wDofvtKzQrSmeKC5hJ5FNCYiq7ud/55SlZZbAo0VVLSgX6n6xKOWQQmdhbaV8cic06bNiWgTa/qpLdqPnJnkUXp2jS4kG27Q5nEbuY6HflX0aItmmsEtjzosX/P4K+TEhXv7n/jGeltn4rfIH8WGiZS9nzbjdkwvWeAN2Op3omcZLrk7W61o6++z14XGSi2uOXpA/sM3P1UuObhanebI3pXZCmloOMfRFO0FI7BKLcSEUy3DokHCnGV+tICj0E8COSxMKXLvW5LVvaQg5iyf3s4+CY2xwmoVhJxiU/tGtYbnOj4e2cvQAmJXNCtIhjC9sx8EpFL9chZ7gqeQYRoIZNdPasekDztWuNIictaLWqsruxMPYUtj3ixJwzxhnqzktOmA9IgNI6ExxMQyDM18/CNNn90E032TU9LjUiyNMFt1vy2PbDi3j6UxEkj7c/ZDWG4Tq4SNvNXKfZOvX6A78rCAM5NkqU3zPNu7804CaTMAFX9oFFJs6Cbp0gZBXLaNS3wK373HeZRPm3qI1YDvpsjzKd74s2X3GxawIUew697k73/zdx8vb/2ZT8o/esnN6jcbj/pwr3LpfujUWRMvpV+ErfjkLaC/a9fxPh/1+7BBPQTqzbJ3fvktUVqyhpdodC3aYvkAe2PZdtwM1pL5KZK/uCwte7gMzY6f0jGRNA+R2RVlkbI8VMKNlx+qivfPXnqr/PkXHq6O39CwW9AU7QVjPTn3NFkwY+Yc8XwS5sHSlFU4UgWxs27ioaBRNzB0dtWy5ZD0xUqIFfrD39Yy7CsIpYWWBHdyec3tmmtkXcfFhonEliZsi+mnVrQv3L9iaBzS5sNyC6J1HfdBQigqbpC/6UNJx44VVunvSVj8GYWpXAfhS5djspNrjh4wZfFlPzGtKe14My5a4izdEtFoiSTl220zyCNndYsUP4iXsxZJNM4gXmaui6Tj2Z/DtHlTmuu5PtAgY9BGhOta7w68dVzFw7JVEFm0o03ZEBddx3VaGxa+Ry6y068rS9aSRnX2wBtjdiwswZjANQ7Ws9jyvznX8aG/iG4dFuiy8WgzYAmOmBA8j6gnXX2R/Mrfe3oS1+a5TMcyVKZfPD0ZMWg1Be+F6DI03MQYvp+4YK+k0KNxuFNhCMvNOXrmCo+E6EQev4ZN8PBX6T37zYDcu0t5R94UoR+gLdYqNW0yjMyCkjw0L3zlXScWl3lDww5GU7QXjP6JhkFr6IGCTf+ZF/D4CRgbT0SkG/kWO3JlZ8G/MsxQZZW5iaLtLIobEvJjGqL8IoHKpmWLNt86TmsYhZFiE75r1/Hw7mRMNwmh8af9Iw9qs/Q3jbKixGm7rsPxbeIBjSxo2fSspA3ff+27niFfPHW238TQSDe9dNpUOMqNKRK0I9E0q5BbutE1NgkadfQc3+STbrcuWYHprgPeVJmWUTkmRNLNCVsfopFvPKe0/vwogXhmMU3hnDXdME7eAnj7rkN5XE58rwBatPWcGVH7TP6gYxArdJlV9ARZvs2i+QFHCgY+TGPCjlvexB0CSRksedjieIa+4c0gmw/Fo1ujkZYZxiA1faTQQ5pg0Sa6NY3DPQJDRDpvrHHRgVV5/xueInvVZrEejyv9KwFDDrnbr+mMtq4zufSjcWIaJQ3reTsooujevQGsoet4vo8jpXpE8zDkXadox8dbZgfdJdHQ0DA/tMvQFgz9jraIo4iiIgFhtNsLK2paxrizIl3WBbtg8RtkooLwDYJXSOe5wdItpiW3ZWwzEJ5ybtkaq3Dbcfjbe0e71nV8dXmo3wXBoh31dRxf07jRnedZznOL8GYHCUBkQSDlIi2DFnO2VPvxyCogInL00B658uL9Jl2UBtubacQbtGlMgdKACmdURkKXSmvnq+3HnMW32jIMdBPvIWHOu5F9CZQOss7Ftxj7ZfNFWpbPlDAuuHQStCISLIIx/7DxSHFj1/HhO92CTHOcLnmL22waBjx8DC8w0AVXxOs9xO+rQ/pescmPM0pL879W+dDAzS3gH7wZVKB7+n25oAAP5ae0ZeJCm8VneW3iL546IyITxS3QdOzInimNStHGti3TdNuJI3LdpQdVGvX2fDgyUalol97Rzo1HvF/GaVtqx3lZtO976PSULj3/8wwJbx0Hj4hnXH+piEh0rp1QWjNLGMP58IaGhvmhWbQXjPSCkaIbNAnaIHh1FBbyTRYRtJgl+eh4FIZu2bAoRhbN3jpry69x1cU6Ao39BgMpA5WLucblR/amVZnk1XWylgi6OWskLVr6vdQj03cnS+8gU1/NcjvokKdPqwa525fGSQjvxMbz+lqDLCykpJFbLlmvCf15wdKGleTHPfUNWWdrz07z3LL02M2A6SfMmZJnQc6TGXlUQVGiPtKgM957QdEmXljug7q5sCGLNigNpf5aojPa0CaRVVbxlZynw5p9bjnZLLEMpOeF0K90NnQWwb18Seb0N5oLYImnNU5jHX4rdSsp9Hi5H/EFlQ9tvnWZdmRayryQEDZESi7qD/YW7U5+6+3PkbPr672HFrmdE8jK7UHfLUBrc+68Ml9IZscErylxXl1nN8Fpk5DkjM0gXD4X1blk0abNW4Uj+ybr2RUX7ZNf++5n9O9Te0DPkOj3bHK8yb6hoWF+aIr2gjG4uk0+SUAoKTE5ARjdUxMaRiN4uzsSLiY/jsECnd0MoLroMiAsxPX4OZ1VGsPboSjgRWXYRXYMApWm40dffof8n//1M/Itz7zW/BbKScUOqkbOTTicHRbRF3blhdWcS28NsoIf1IDd92GcJflO+jU/Tujvctnqd7AW1t54ugwXpHmCBdZ7FP8moi16tt5k0eZ5VNdmxguk37yjMBpT+XGGZ++n30lY9W6rRTdpyFO7oNKRCbbEx5+G3gw2exlabr6WzvxSm9Ab7zodzdsN3TpOCuL0U1u0ccOy0GS8MWLbhzcaiG5LY4Q5uY6X3pnGzQ6aw9PP6PzzDH6CueY9o97WDt4fpXfYg+u4SDy/RGL35JxFV8c7cxZ2dhRo4ydyHc80BvPH4Xd669ubG53YsRr+xFcU5qRop3SJlD1n6BjF5x881YddpDaOjx7cUywbva4USvNjJeN10NDQsHk0RXvBWE9upSShqCx8WwGYFiZPSB911hJLbyvz2VKbJ9HIrqhWgAlFpkLvoJzYXXcUYpP89G86ba11VkTk+bcck+ffcgxynv41EhklgsWQHFw6Id7Rg3vkP735aXJwj37mq64u4tBdQi4mZRO/O+orO2nSdAOFzqp6ZQ5lQ3+p3+ls3cXVirbNz5O3am/bz52pJxdqGnv45m06Z7raW8dtvfAMquP+b+JllDTXdbxgvQ3fyaJdagt21Qfh0oSULU3jUWfeM46UBroFHZ5oC2NUt9kyCN90UZ8Op3nC72hbeol/UP21oh3qTq7fHtDbAObCuPBMXs5rS2O40DAP7/kmaltSNHIeWlTnaN2aydvIj3vq7KA07wGrtH5S7Bvuvkr+X7/yP+Qtz7rOzU+/1VyraJ8+m9fSyOKtWbQuM0XpmBgdPfJu1Z9YtMWETfJRtIX+2sAxEo0Dq0vywCNnhrJU6bNYtEMd//L+R4a8ZljbReo3Gl16NtkWDQ0NebStrAWjX4iCcgLCLrpGqzzIupYTalIxZHJGO5XcbcycazUKK5C2ZKUI+ZPrlwjfjFr7hi/RTReh1N7ebV116Yz25G/aNea+Ebnu0oNy7PBw7mpM0iWQRZaUGoRk7/jSm+TA6pJ8x3Mfk82ndnMi1XlT92avbXNtXro0KZT5RWW5uajSdTxnkayjJ9TbKjEsfNt4NK+pX62LKW+gpXE7yhMUrZyHzRKc5WVlztZFxLHewlyILNpAd94LwNJdQsmKhe70KmwZmA9tRJDyTec2o+LoDW8gCPkM0IsbaKpfw7NhsfXSXhZYalqaP/TUFR8JgnlY4B/pxrUHXS+KeVYpkKvTDR/aYMANRhiPy3B7ew1yUU+dGXZ1aZPncrWGfO8Lb5Tf/0fPlZsfddjNr9a9XfO30yWLduFyvtylYyWLdu7iQ8sLLc+kzbs+bJPa6ZunGxovuu1yQ/cst46Hfrjz5AUiwhtyJeA8EpGbH3UootFD7hz9uYB5eyc0NMwbzaK9YKTvaNOiUVzMId7w1QoH6SKEim2m7DU4d4UKdKUAbNf0zpyXDH8twTmySLEhCyIqLCAAg7KTE4rShXvcdXK2Sz0DfJAFsVQOv2/c2bJmWFtCXo+57KB88u3PZsVeoXQZ2tCOcdpJObbOnoJIIEuUjh0Et6sv2S/XX3ZQLju8B993JayA63hpjaZzm3zjf16YC8pH0eMDxmjI37VoFwYGKoPQtvjEkti64Kah+k7CNfEubdEO36PLtTKbPLNslgSUFe1OUo1XP7GzhB4Rts1C/Wex8vv0xODL0GzZHfyu2/vh0xMFKlK0p37AnuBOoLbQVkyaC7VeScTjai7sEuFnyzS0ArkKzznm1gpqn2WoX0iTc93N1UNbk0Mffv7B031YemSmxAdrXcc1ylfP5WPkFEfi9UsRjfFvImoTK/WIAz8C2hglD4SN4OuffKU84aoL+4vhSvcxaJBF+8vvOC57lsdyx1ThngXeEZ73vf4p8oWHTsvFB/IeX+e66/hG7t5oaNhKNEV7wRjeqJ58xjKxFRq7+Kfod3YHHcKojLDT6yk7kXAxzeDjf/z5Pmy4fMoy81gm6qJPj+4+HYVJusttFZvB8JFvs15BnMF1PIVRdjrfEl97dpKg82RhPi5rEraxxcU8Y1WIQy7GuSMB2AdJxJwcUrpAbLildST/8VueOpOb3dLItm0xfTQPp3QVzuP25SkhM3wnyx7X2bZZ2nesQMe/6e9L1K9QP6IHbx13TJ9s0dbpbdnDDfxWCC0dW6gdAXWKdgxtsRsuQ7P0RLfgE2+GsnVxpEATuWxB1DT6a4q2XD3w8BlDV2/RdgR3At+2bwX3aNxn0tI40VhPnjvzsKoVbcjnlFJiQ7vQ82e0xo1g8Om21V20PBrJqYxVOFePR87YdGfVoehZLXm1Fm2NUiwaj2ejzak6izat//1t8BE9vkxh1qMwN4EXbtYKOhp1kfeA3tgpea/QrePjUVe0PLu0wJorMunvkpId4p3LmNNLbQ0NC0NTtBeMdfNGNQnFosJocZn+VhTIbRi5eUb0qDBafG84dsj8RjSyi2kIi/PtoKywgMbCwPST3HcjBSHQY7WG0uVaufWWNidIAapJnxOoaDecFCAKq0EuLv1W2lRx+7Xj8eQJQAEkXHnlxMLZjIImWa4Ki7Q+E0vvxHLfWGFObxD08TIWZDOuOpjDSXn6u67XGKxAOc8ZOm5RcpfX2gWd+Ys3JyafwaoqInJwdWn6G9Co8slZuUVEjuxblr/94ml55g1HDQ0lSxMrtlYh401Q29e0ARKXNwTy0RObqHRGuz8e4fwe8MCpMybsTH9Gm2kkrCzpOk6+09vcY7jZmPqwxOPo8i2mK2/Rpku+6EKz0lEGOvNrlOHhlMtMeOSMTfhldxyXf//f/lyefeNlM+e3XLixnFDq//ROgzSs1qLNRzDCmMj3wSQOb07aciaf87p1PECPt83eOj4rZvFAIZzzivYc2rChYZFoivaCMdw6DgoiCdoguAuEoUs3CMC0WOmyaRH7p19+i/yrX/5j+devehwLl0l5XjnepTB0cQktinRzeAeFY71DWPFiL59JkyXWKjt1gkRuLShZ8QZvAZ+2HHJxD6xaFoDnkgsbLSKT9iV3eSsUxX9Hmyu0meTQNisG9856wUQLjfg2O8yj8E0LVKGOWvDEy7Vo00h4kyf8SdZmOnqA79ZSfmR1h3byLvMiF1ZyCX749KBI8IWP07DCeNSV+Mhbv0Q+8zcPyY2XH8rTAKDfzyrFli5nGpTqIaz3AioI1DqIPGJoqJcU8r6PoR01HnzEKtrhGSE+osQIT0fpIukJKr6vgMatptuWRxsNGl//lCvlvf/5T+S7n39DNh6dPY6fvwrzWkWgzQBIq28LXxp3IoO390ygi8gOrC7JT7/mSRvKT58jL7VjQKn/6TI0PWfyt47bTXXadB1RH6R0ivXaC7EW+Y52wOp4mAfknaKhN53mQUfpuEUJuQvrzgU0Rbthp6Mp2gtGuhChuxydSwRlgM9yq7T9p40X4prnxhRtIf+vfvwV8tWPvyKim88Mk/AkNizhg50Ac5z+Ge26Tz9L5/tybn7ezcgpjTUYjzrpzrKyQ4JL7U20kUUK3ZvDp22bGuTivuwJV8iHfvcv5Fk3XtqHseVTVJht76xHQ1Jm2g+6zyNrAOS5KUUbz2jn86NLd0kRjcdjiGetHEtR2NRCTvnx9IjDekVUhxE9QRm0QmzsBGLjkVdJ2Hvw+uIgbN7Q5sRDp63FrvxetyE74nGH9y67786Wxg4NBdJHSlbOnMt7VJ6qBfEPGps/9HceK6/4sV91LzQk93aqV3AdFxH5msefkN/4n38r3/vCG038oqK9bC3H5KJMt46jAlXYqD0NFlSt2Pz9F94ob/ySa+Qi5TJLSjXlQ3SjxwfMD51Wb85thl+dAov2ZrAS0ViXZiM6DG1OEugpT7yJHOSZkjeVjkveb4u0aJM7vcYy8eFNgIw3syC3GXIuYM5d2dAwdzRFe8FILdokIJLrKCnQJcsXCaaptWtd/5EQlLfuKnpgAaNzoOjSPU1vFQmriLBFK6ZBV6F0dnIJzi9mz2inNHZ217xXtCF9yXWU4tHN2DgmZlhcci7W+1aW5P/z2thCEo8ZqzQM/W/bkTaISu72Wsdgi7Zqn02squhWW0izBlITWd3wYh+waNN4rNkAqvW8oDYnaxH1YT+PCi6dw83PYsJERA7sIUV7+B6KvPuai+XnfuMzsmfZznn9vRRWK1zWPO+VglxjiWfSMQGaRyIi1192UH7/s/fLS24fzmSW7ngIeOyJI/LJtz/bVWDojgeKqZ8meueX3xr95nn8jDqrREQW7WlcclHmM9qWxohuIBz7I0rTRUq2SHxEIeA0nH9eptv21e849qD/tUV3M/zqFNC4GUSbAZUW7dJGJFq0S5rmFCQrxPdIhN+GNCQLBTqt8m3nIW38zAN67NCaoRFtOs2BjJj3zJ5+Ized7yTkNnMaGnYCmqK9YKwlz5Gg1bXSpbNs0bYMxwgF4cw4uiP79UBLQ0FI9xbFiZtXuihOPnGXOzZ9RL/pMkkR1fSsgKKVV7Tz7Z3GSUGKDUHTwGe0aUzULy6zrkPxu6M2j9wYpU0eb1MlQJ8XLZ2p34yFaIUuiipkR0JjPGZAqZp+JeVro67jteNnCLO/kwKtrSuhSKpLrFT75YrwcQRS3l7y2EfJ3uWx3HbiSJbu0jirFVb3A10ejQH4TnBh02mc4UciIu9/41PkL+9/RI5fsC9LD1ldRaxguQauuiOv8Cn2rfg3VHv9qtePAL1JEkAuwfT8Gd8lYtcPDbJOl5TBh8EyXHIdZ6U6/ky/B0SXgW1Ck8pdorYR6D4oKYMBJW770juPy3/+738tt6iLwWjOlOjJ8cxorkO/hDgpreSNEyzP8347esnxaOC4to6bQfz2+OzjTR8pOBfRbh1v2Ok4t2fYOYD+Ge1+kbaCLS/moNgq4JloFEKt4qLjRL9nmD4JF6Swlt68DuFW+ZogPicX563rQJTSpVCansGibetCqFGAQhySLcjKWSoHz2jjmKjHrJeGkcWm6FVAG0lOX6Xk6LajTSf9+2YE1+UZN1pE6hWtqK+nn3qjpb91XI1vegaK5vDkb59OEliX4Nw7PSuzDJez0bjVNKabhyKxRfbgHuu6jZepjTp53i3H5PIjezEeKmmdpbHUh//spbfK1Zfsl+/7sluy8aiNSSEpjZ9xgcbVpbFRsol/0IVdhDPwBNkYjuCIiPzrV90ljz1xRL7/pbEVW4M2VdLwAG3RDogFdzv2ck80xntYtsBaa6nGI2DRPlNwHce1lNZr4MQbeUaLcAjm0bxA9SeU1o6XPPZR8n++4Sny0695Yh9Wb9HWZ8ZDmJ0z5LVnFG2xw4XS75mOza9+3Ak5tGdJXnrn8SpaZwF5C1xx4TDf8QjPJqCH2EY8KM75y9CaRbthh6NZtBeMcH4s54pWetKIb0ANAvAQr+s/HeVaOY/nXNkJJWt6LxRHAp6nnFrxhFzHBiXG0kFCGm0kRG8Z94pWnZAexRvZ9k7jpFhyhN0UkUWbNgP6eCpsgWtL9WVouAGSTyuSXxiLVo4NWCJe9eQr5YO//Vl5xZNORvkSbSlIaCSlGi/xg7G3HIVN26fQ3inNKWhDKBZYA41WkaB36+nIQ+Q6DuVqFF3HOVlE/4Qe246eoJ3DV951Qr7yrhPFeCRvoue4KpoUqTEcCdjIfD1DFwQAaDNxGdpMROTp118qT79+uI+BEF9yl77lXLZoLxfGD24wiw2j8UVtUhoLdHs3W7RJyRvAl2na8i49vKf/vhnL6fe+8Eb5/IOn5FVPvnLDeXg4W3l9e2ncdl0nj1UeKZO8K8etaptw9ITOaMcvKEz7JRl2kwtWmVhd19XpRY0XHViV3/jeZ2WfH9sotJ79vtc/Wf7Fh/+7fPcLbuzD6FWHzYC8AGbBIjd0tgIPnZrvXQYNDfNGU7QXjGHN8RUoEkLiRcMqeaTY8NM+LHCRlbT+jDYJklOhuMI6Nx51VvnqBX9LD18+BfFwI2IIW+nPyapyM+ssbSR4Lu+Eeou2EpRH9DxPEPrq+mqz4HeZiR4VBuObXP/SvEzZ0If0/NkseMeX3iRvf9GN2I6lZiTrBCrGaFW2dGvBbri4SkxazzpDIIWOBFZqWzqXGm8k2LShSWjDQUTkmksOVNHIdbHf0aV3Bot2Lci6VPJoII+W/tbxjtPU4nSlMqTbIriOU//XwptiFIwWbbi9u3ihJbnbQ4nUJqXq4RltUNhLl6ENv+XLPnZIKdqbsLZdfmSvuT9jXqj1St/IuK09/63bJijn5HWj5yV5fIlM+sHjmfrNdP2++iKUbJF4o+H2Ky6QH3vl42K6NqkYp6BjK7PgWTdeKs+7+TK59fiRTdOyHXgAXlBoaNhJOLd9Rs4BpBcHldygshbbguW7jw/Kd1pOl3ymv6cope0F/ArlcnJxCdOMl6GV2mf6Gb9RPAFdPkVlEKht0tghOYkWdFaRwGe0rTBPdV4E6Pw7ezTYsJLLu8jQ10+++iIREblMCaY0xufx7qh3LruUH986rfOdfNLFV2O4YTxSvvGGaGecZWgkV1byDKGzgeTSS32t+yXwNDqqISLypKsvku95wQ3y3q8fBEyqI6G00TIC4Xtee04vvG1yOdl1lw4bBSWLNitidg5vhMaNWLTpmMCsRccX3wl+D8Az2rB5Q/TQ3CttgpVugSc8DLfbP0odVwiIFW1LA3vyWN79dPWG+2aOuiwS9ZehzZ537flvPc4um3oBlI6JBJ6abop1YsdL+FsfwdgKN+nSGXXdpvNwHddZbGRjZzzq5N1fe6d8071Xb5qWhoYGi2bRXjDCmkOu3nn3bwhDJUayYbQbrPOqVTpIqKa0fBlanO+og7JAGUALW0YAYmv4EK+/+Xls60IggcrbIKDnearf0SbXuGhMQNi8tAtAyVU3N5ZLbv463Y989e3yLz/6R/I16ik5dnm2wuy8UJKD0XUc5iGek4ZNHv2UyjK5GPd5pHMmo5xCm9HmBMYruI7jueX8RdwiIvKNT310TH+l4kdzLrq9PSjaMM42i7c86zq56fJDcvc1F/dhpfeGQ9n7VgfLLlmVNkJj7Tlack+mt6xr4dFKl3MVLdqw6ZTfTFZhlfSW6kft+L0vvFHWReRlmveoNguXZtHGIXn3iIj88t/7EvmL+x6Rmy4fLgab96Vbm8WjjuyVz/ztQ3LvdZdUxd/IOlO65C9Az+snXHWRCQvDSG9WjGGciEw37+GImkj9PJoXSnsYF+xb6b/P+9bxRXq67TR0XbmtGxp2ApqivWAM1h+ZfuaF9NxOOik2+JyS6HjDdxJspGBByOVTekKI6iLCgmiIswySFlm5yPKxDGdQ9cITBECikcCbGJ0bJ0WtJZbOaEfxoS6LXE9JqaZbg6Oz1kCj7+Y3+fuSg6vR2bW0HHQ7nYOiXbu5JMKKNl66V9ggCEp1vGlgXceJRvrb/a1XoG0/kLcJvltfGGfr5L9R6BZPObHxbNnkTkqbE5vFnuWxvPixj4rCSrfOB9IuPrAq//glN8vq0gitZlt11CNAKyezvq1bIlVbsS89tGp+Lz7vBfmj91LlXN+IMnjJwVX5519zexSmLzELt7LD1HL58LHDe+XY4dhSPu+Nwc3iF77tHrn/4TNyyUHbb4SVDViAX3Tb5fLHf/WgPP7KC7Pxuq6Td7zoRnnw1Fm55fhkc2IJNmnijWjHot35PJPO4y8SpTPquu3n7Tq+0zZ2FokX3Xq5fOCTfyZXXbx/u0lpaMiiKdoLxnBxECmXIDSSZRiUGLSGk5LuKHud2Lg5ph8ptpCW6e6i+DrcKF/TT7JKU1tQ2UtgqSZlJxZCfXjCsd5JrbU05kDKFwl4tRbyzaK08cOXAlFY/JmGEyj9PIQRr4ySkM6XoQ3fsW/AfTsIiMuR67htszTfgU6fRi7busFGmyCdjScwZ4i2yqPDEWoV4/j+hCk9eK69vg83AzrH6W3UfO0TT7r5LFLfIvfT6G3cGcvWddIWoztPXiCf+J9/I99w91V92K3Hj8ibn3mdPOqCQcEse0nYcrrkczZ687//wxffJH///b8j3/X867PxdJvtXZmIRnp65DxDPMybd20We5bHsmfZf9ot4Ie+6jb5vn//e/IvXn7HzGWMR5285VnXVcV91VOuiv6OL10M57b1Le42nsiE76UtHaJsuaJdMLNqRXseG3CbPaN9ruL7vuxmuenyQ/KCW49tNykNDVk0RXvB6M/rkMIy/SQhZAXOG0cW7d7FmARYFpTJqkRWcgK7vA+BYfGms3ipIDwewRntXvC3tJcuOQvflrHNtKJtz8nWKsre2plb1mot2vqn4SKlvGBaWqBvOHZIfu/P75P9mfdyPZBbLlpsC0oxKeQ6nFC6DG8eiBWlfFy8DI02xmBik8t7fBnaNG0kXNoyJllmxk9EW1yepjFq2ykZ+ikm4kfUPjQVSj3kedaYfGBM6Vusl8AzYJGbTt/6zGvlY3/0V9Hxho1ZXedOWg+0aAMvrIXHW/7lK+6Uj/3xX8tzbrosCv+WZ14b/U2XisUvMIS1a0gzbKDO3q+laF/3pCvlBbcck4sO5K24uux9y8GibemJp3q+9EVduDVPLI87czncl99xXL7s9kctdBOLoPlW2OPEjWi0aKe0Tv6uvetgXiidUT+pnvqah8eDzmKneVAsEgf3LMtr72nnyht2PpqivWAE6w8qiKScTBcHurm1eEEWKM/e0w99erFhBLIqH1TP+ITvpYvLQjl0bjstp1fewBWRhDS6QVkLeP0Z7UoFWOfnXXASkvM72tayQyDBnaxm4Knt4j1fe6f8wH/6A3ntPY8uxMzTQ4rkmOgB7wN6TzhNlyt75FgvNgs95kuX0ZDQROeNycq9DJbYJRAao+olR036MiuVU1SqoR9613GY11FdoH3o3HJJIK/dJCodW+jbbAar4mZw6aE98ktv/ZIoTBdXW/ZW3akQsJkLBHV2uq8vOrAqL7z18mJ6HlO2v+gSv1mU2CG/crySki0SWz73hg1KyNrbvCacC4rPnuWxnD5rb27eaiVbhDe3l5yFbzzqeo+jrrNd1Vu0t/iMdukytKOH9siPfPVjZWk0mstGzCwb8A0NDVuPpmgvGOmbs6xA2YV7Gdzviq7jfXwr6JjwGS/YIkv84b3D+4uHpoo2P1UWY3JxSZJ/uDQHaKSwND+RxCUcrOHLcCFVbl3S+fmK9iQDOrdKVhyCd2FdSmPtc2EiIldctE/elZxBrEVRKM5s/MReFzZtms6WreKBC/Y8EHkQFM60Hd67LH/94KkojG/Gzt+W31+GpipIruNhFKXjZda5OY4uELLtuNJ7d8CZyMJ4pKlQ6qFaATBW8iefUZsFd/JtFC6Jh5dw9SWLO0dIfUTrRy02q2BpLwm60I4u06Q1rtqiPafu15dmrcJlaLxZni/8XLBo710ey/0P74wnkvS4XVmatne08zN8HXednJXhBYS0K0LfnD6zNa7j4aK55958WTFuehfEZkAvqzQ0NOwcNEV7wegvQwuKrfptEIpVWC9cWiUtdt9Ocxm+6jJ813EbN2c5JOHiyN7h9syDeyZKN1+GFuc7wkVxSoOuN1g0Kc+cRXsULUKzWbQjNzZnrc7JWbVn+YrCHChAi9Qt2FVfTFg8XmBTBNLS317ZwwbDfIVVXXzpuZd/8fI75C3/xyflrc8ZzhzSPCq9Hz+4jg+B5GHRnzSZoc3oQkS6kCo+6mGPUYSf9QYTXYZEm06l8QhHwTleyaLdu9vXlz1veM87Ef79N98tn7v/Ebnm6MGZywkWu4Or+WWaXcfnwys2Yguk40NjuDOAPDE24gY7L8urVsjofpFhHtkwD19++6Pko5/6S7nmqH1bfqdg7waOFy0Kum3JCyiKOxKRs0M6K2dMPs9s5FKJDeD/+6a75bf/7AvylKsvLkeeI3S1571WNjQ0bB5N0V4whkuzwmdeAB4E5bxFG60CSXwR3/LTgbCTVwZVOdPvR/Zpi/bke+xWa2kUCWe0ubBVON9HrtXktrwEOxaRtbiPp/NDMkxdvAtOcnJW6abd/jdQGtDaAwLsIoDu+2jlFhsGmxj2PJ1PPVmG520UWgKXbg83HDsk//FbnhqFFV3Hp4F6yCyB0EjzdV3YdbzWot3fOg7tqMd9eJZpGS4G1HNiZcm2D4WV3HzJm6YEsoZu9WVoBF1aSRe86fLDctMGy/l3r3uS/NP/+Pvy9194YzYeXoa2iVvHN4tVPT7CeKTjP2KiRbSuwjgjzMvhhS7N4gtE68fyix97uVxx0T65dicr2hWXo20VdNvSRqQGebp1ndqsnIY97+Zj8vO/9zk5foF9O32euGD/ijz12rpn0+YJOtbV0NCwc9AU7QVjbX1wbZp8GX7L3TqOF3vVuvRqC5djvc25YBMorXYdD2e0Sbm071va/EPcFbycSUwYCUBkTY/qP6J4dQuT92RHSM9ntOuUgeJlb7BpsEh32THsqvA58gLdTv5ZizZexDZfTVuPseUNuNrRxWe0waSPE9BlaJRfKiSKlK1mkcICis1w1n0oO2fR1v1Pys7+FbtslGiMxm6mO/HJQNVmgbbxFm06EfRUX+Q8vOOKC+T/eO2TivEWatHegEn7nuuOytWX/ImsLo3lov2Ts9HHj2glB3hKWCsUrbShQ5jXRgKd5dX00CZvScnvuk7uuOKCeZC3MNTcQr4dKFq0yXAg6vjNNOzLbn+UHDuyR248dmhBlG4vvHt4Ghoadgaaor1grKXvaKvfBkXSLhiRG3QQ0slaCPmRy+okHxUXBJtagSxkeVhZtIP7WekytK5jpTMIS8HSptPTk2HkyrwM503JTZzOcm8UueRkvSSUBM5ho6Wu3M2C6KHL8Gjc1uyoZzd0yJo+58rGT2zNrsTTmCq9PZ0TGnXa3gMGNpc8RMoAWIGJxt6iDZcu6nik7OxbnV0wj69uqOtPeolgGc61b+cFQDvh8qH94Fo+r7PB+GZ6AVdctE9+4dvujcJOXjScUf+L+x4WkXRdsLxej81n3XipfOh3/0Kuu9Rahue1D3frow6bMOTDhbl+rmEnWbQ1wiYfPfklwmtSp0za2hPsyVvszr2V8OS9hoaGnYGmaC8Y6XM9eC4NlAt6doddqPOLvmvR7mxY/RntyeehPcvyVXcdl1Nn1uTo9G3IkougJ5iiRRsUrZwL/hKc0aaLxsYQb6PorZfFW8f9gkhR6uD3rXILI0WSNjYwTAm9nohevTkRFK05X/CyOt6cYEnCN3kvkLs9HQnR+dFlaCVljt64J+8OrXutLtt3vfv4Ki2d0SaLdgkbUU7o2ELYJNuq570Ieq53c1LyNoM3Pv0a+c///a/kq+460Ydt5tbxRWBlaSRXXLhP/vTzX5Q7T14gInwcqUvSBPzAS2+Tf/eJT8uLbrO3ns/Lov3cmy+TH/zK2+S2E4PCHeWN83r723az2ElntDUCb/LWTnqtpUbW2G2oleEaGhq2B03RXjDSy43I7YzOGi2DSYsvGtPRrOLuCVxkbc67jqtyVLzvf+ltUTy+DKvD3+P8J+ErcGNt6W3mQbmAc+3giry8SUGpE+uehreOQ38RSGmg99HJxXgRwPexKzc7atozJxiTNf3vPO6EvPuX/kiedeOlxbxrsLwEmu0MoDFOrsyPufSgPPOGo3LxgVWMl+Yhoi5PBCXEA3k6aItmsF5rz9jh3Xs7Z2IvGFv4PhDMS/1ee78ACcp6o6XfLNtWi/bQkDvhTOTFB1blQ2+5JwqrvR+ihHnymf/05qfJfQ+dlqOH9ohIejzC8nW9yXN437J841P5qcJ5dUHXdfIVdx5386Y1d8vPLSwAr7vnavnF3/+cvOCWY9tNSgTyptHjke+cERO221HypmpoaNheNEV7wQiCc79Iw9nL+Fzq5JNuHY8tsSSwh/gDSEHScSKZIcOkyU2LgDuqFYtfCF4pXALHl3NNFejImm4F8kN74QmyDYDc0wi1VrfaDRDdB4t8GTRW3LroM6IHFviatq3ddAj1vfjAqvzXv/+subnF6TG2EddYdJl3Nkt+7JWPc/MJ51cJ3sYWw/IKnSK4FtPzRfi6gbNRl+YXUVDaDJipPiHNVNFWAzJ4BGynQO0J+zsJdIxmu7FneRyfB6b1SIUtV1+Gtrj6lV5/2Kn9Pwsef9WF8uvf80y5cN9KOfIWonRRJXlUTXjLehS227GdF0M2NDSU0RTtBWOtt2hbBYqfbZoqjeBCTdbp0k3kZNHVcWqfqqm9aTV+qig8bca/x/lPPksWbVJEh5ttrbv90ngkP/ryO+Th02fl6MGJJUUL7uuVJpvUE+FsX47fFkug+JfyDnUtKbG1dG8EdBkWXbhCYyu2uucvkCN4XgAloWsW6HGykWbk98Pr5oeIyL/6urvkrx94RK682L6tHOih55A8nFLPEoX+0jcoBwv0GRVG72jnjlto7N/QGe26TSeNwTqvFP+x5QlbrUjqIbNT5drtfN6rFiWL5GrlnF9kH9CGTs2Fj+caLj7gb/ptF0o8P5al4s/0+27GLPd5NDQ0bD2aor1grIGyGZCzhq2QAIxWM5Wf2HjeebKgLOg3cZczt8rQeTqMpyKaG9fFV7JCnFjRnnySsI8Wbaeuz09c4rQQerZSkrRtF3sqFG8dz+TddZ284NZj8vkHTsk1lxyA+EGJHUK2ynX8kqkApssOynDpvW0P9Z4TixcbNtKOsbvw5HvtMQERqXKBp+MkHh46fWYoe1r4I2fzyjddNEb8SG9KfcPdV8m//60/l2+427rwlrqKjkJwvOE7Pu8FZ7S381jivK1m8zpjuQTPtu00xLPIbtTV3jq+WIu2/U6W1Ib549GZjUiRGtfx86NvdpuHRUPDbkNTtBeM4R1tslRCWDijDa5/9DYuLTbeG6qklGuMM5dOkfs2QQvFa2u2rJIcGV+GZq1XuWekyAuAsBHLsHfOPNBIudDFXh7+xcvucMsjS8piXceHci4Kina0eRPcd20f1CgKsz51tkhsxHWcNp1qj1bU0jPLhX1fPHXWhGkrd2jvM/B8Ufycnp1v2qj0vS+8Ub7nBTdg/5U2A+J3xusaiDbaxnDr+Pa6js837408N1fKZzPPNy1yQy/2iJl86trXerEstvvt2txtcux13WLb9VzHj//dx8snP/238tybL8vGizZqgqeb1PPN3YLddgt+Q8NuQ1O0F4z0ciNSdunW8dL7tuE7ubZpIV0rEqV3nXMX+5D1gaCZfrBoRwJVYfWjd3vpDd4RhNXetBttBlRKPLGljb+nmMX9N1ceKbGLdB3XuOjA5Nyebs/QfqRwRhd7Sfx7Dclb/T5yyROBQBd7kZV7Iwi68CybNF98xCrajyhFO+AMuG/o+XZ22hix63g8Hz1aSgJe7UYdpdEKV/Ba2c6bnzXPmPctv/M6IqHpuuKifZvIaXF8hjaBIz5TuemwSMvlCKY13XXSMD887bpL5GnXXYK/aXmGNjy833czmut4Q8POxg54nGR3wz7vNfyWe4N3hc4bg0JOCvBSpJDZ8nQ6nX7W570IVLYWqEq39JKiHSs2oOSRF0BWAR5+PAtWvlIavHwEslkCujcCGieLVLMfUhbSC6YX5OAzaTMoO7W132oFitqxNEbJVW/ez03Ncg70i8p1POAUKNpPuWbylux+dWu4tnY+cnqSpnQZGqEUa5Yz7H08UGzCmfKdctPuvIV5ek5tI/jbh073309csHFFe5H7efFmYuDrQ2DtpsMiLZelSyAbtg+kYC7S22SnYhRPpIaGhh2GZtFeMNYSizZfcjXEJysOPiHUK+42v/iyL523LUenzwnVtWefNI1p3UXKFu3rLj1o84zotuX09VYRD+9ddsvQ9K9ZfQSxEfeszZwjJVdkXW6tJX4juOHYIXnioy+UkxfuR8+JpQ247+qb2nOgd9QXCe0Z8B3PfYx8/wf/QL7/pbdm08Ru0JNP7736jdITbVgV0pDrOFm0rzl6QD787ff2Xgoi8cbWI2dsPvOy2JLnTTmN3Q5kT56tlS4Xuck1L4v27SeOSNeJXHnR/uqzzlsNdMFWXVntOr5IRRu+b/b1h26D6RpixG7idhDslNv2F412RruhYWejKdoLxlpi1cWFG3Yk6azeEpzbpM3MWEjPu1pp5JTgWgtbpMSCYlWS2w/uWZbf+N5nRcJheYPBKn6Pu/LCfEEZGgkbEeyj94ZnXP+icdL3terXRV6GNurkp1/zJBOWfifvjAiKxtrqH9ozbJBsiUVb0fj6e6+Rr33iyYgGAllnY++FTdATypjheMNDoGhftJ+f6rkquWBIjylSzqufVCv01ebem1YXNuKt41uLRR7biN543wSO7FuR//q9z5J9K5tb4hd76/jwfeDhQ1jt2Nuy5736zfLh9w2/WtAOaW8apc27bmfuL80d7XK+hoadjaZoLxj9ZWiB6YOCSO6y+tmd4N5cet4r5B3tuKv1fDPWqRFY8UpInzYTqRO4L0yUBH1OlLwAQo7XHj0gr3rylXL1JfvxrV+mcXbXcRK0b37UYfm1P/l8FHZcuWzOKleVXG23Wk6jM9qlM/8atWPmkPJEyFyCPzekzVhSskXKF9XNQ6ec5R6BJ199sfzsb/wvOXZ4Tx/2Lc+4Vv7X3zwkX3nX8eqiHz5tFfarjx6oSluqcu1lcaV8lvDW8d0jXM7zGbsjc3gXeV5v1xOiM9rTT32Sp/6M9hyJyuQ9L9fx3TNatx7+UTiyaJ8f2OzlfA0NDYtFU7QXjOEtabBoByEdrDN7lgeBKwjAY7h1nCzkUfnqe7TzOaMAtRH3pFB3slzMAm0ZJmFHu8G/40tvminvjTzvRUm+7dnXycE9S/K8W4abUo8eHN4m/ewXHp6JrtJu/VZdhhZAxxtmOUfcVTpMHtwzsKStsWjP3o50bGFeZ7QDNfERjHyat3/pjXL9ZQflBbcOz9hdsH9FfuyVd81UtrZo//J3fIl84aHTcumhPZkUA8pHB4bvs/ar3jQbLuLT83+m7DaNRU69eZ3Rnhc2c2N5CfGRosmnHve11vhFHh0gPhyvBc0yvZWILtjU3zv7+27agKvF+Vfjhoadj6ZoLxhribJJN5bSk0arS4OA84XpxTb05m3x7KN23wXFqJYxb2TXNHWbF9mYVf3Aqla+wuecFJta1/HC7/tXl+TNz7ouCtObGVdePNuFRLEQARbtmXLbPKKz/OOwsTH8Ttbn9Q34jtdYlOeJjbQjXcS3keerkJ5w8/cMN8wf2rMsr36afdt6VmiL9okL98mJGdKWaryZy8uOHd4rf/+FN8qB1aV+Ts37tu/txrVHD8gffu4Becntj9puUiLoDd95g+bRgdUl+Sdfdousy3r2no04n62xutORmeMbuGiuqebzAck+K+NRfxHkeahny8EtXj8bGhrKaIr2ghEWVTpbPALrDC0OQdGmc7IlS7NWdug5ploFqKT4EfAytA0sfgeUlZNup92MzF1/4Y4qZAZJ6cPffq/89889IHeerDszTuXh/skWS2v05NMs525rY2qLNl3yNW9ccmC1HCkBuUGTt8lG0Fu0o7sONpzdTKAz2rUo8YTaW8e9fP7u3Vcl+Q3ft6p9AhZhxfx3r3uS/Maf/o087Vp+1mi7sEiLtuYKuttf9oQrNpjL/KE3EI/snbjir4xHcu3RA/K3D52WF912+cx51r500ZAH8ZSVpZHII/b33Y5/9OKb5C/ue0Qec5m9TLahoWF70RTtBWNtLXlHW/0Wvo8K1jCyaPMtmxbeGe1Zb+ScxU04YLBoq3w2adGmM9obEbVe+aST8m8+9j/l6dcfrYqvy5tFTLrq4v3mAqoalDwVFnnrOIHf0c63+3rkTVFXjhbsT5/duOJXwr/6urvkp3/tT+U7n3f9zGnxeS8VVmuJI+yd1n+Wy9DmBTqjXYtS9+Im3yYQ3/y8tXNhEd1xZN+KPP36S+ef8SZBzy3OC/NySljo815qZB/ZN5nXo1En/+Fbniqnz65t+rK5ho2DNvB32tGLrcIrnnTldpPQ0NDgoK0SC0buHe1eaSwosWTRHpT0vDVcC+mbsQJvRFBONxlSGmoRndut9AIo4R1fepP83buvkisurHP9i59MW7xgX2qnrd6t15tB5E1RwiwbO6+959Hyh3/xgNxxxQXVaWbFs268VJ5148YUG3KD1u1z8YGNX0JFrqhbtaeyGYt2CfO+sIeO22wVzqcLoxd7Rns+fbhVZ7SDoi0y8YSa58V1DXWINm8jjwhl0Z7ifLJoNzQ07Fw0RXvB6G8dDxbtzi4OpRt0Qx6RshfyUPFKykwkkMy4Bm1EKHp4+i7vrLeOp9CXIfUXrG3y1uGu6+TkRWVL8/d/xa3yT/7j78k/f9ntAw0zlzY7SjXa6jOqaNHWVv5Co8zSRW973g2zkLbloGn0xUcGa/DFG3BHDzh50exnPueFzSjapTlY601TO050fot1b7Y4j/Ts6K6QeWNeXg6L1KdOqTkxj1vcG+YH4sPaA6Pp2Q0NDTsBbUt2wVjL3To+/YyVxuH3H335HXLyon3yA195m4iIjNVzJ3T+meRX79ZxoicHusSthHDGNiJxA4ufdh0PbwZvhJ6N4Ksed0L+6/c+K7KuboVFaxbFZStQuh+A2iS2Puwe0A3jn//iqT5sI4rfV911XC7YtyyvvWfzl5rNim9/9nUyHnXyjhfduPFMCh08d9dxlcki3ZsJW33j/3ZikZeh6XGwmWfEZj0GNQuCN5mIyP6Vrd3QaRhw0+WHRETkpXcOlwWSl0yzaDc0NOw0NIv2ghFkMr4l3J7v1L8//5Zj8vxbhid74rd1J5+ls9P63hVyHa91u9uIq/YXHzljytiIgqgF6UumT2ZFrqMLlrPTNtqSM6GqSJLrt1rRjoVi+5ZxOf3uEXo+/+CgVB+eXpB0+4kjm8rzf/uKW+WffNm6LG2DO+obn36tvOZpV0dC6rxRutxvVuiht9WK9vmEvVvkOr60CSa+SFZ46/HD0nUiV1y4b1fxsHMNP/O6J8uf/PWDcr267IueDR07RouGhoaG7UJTtBeM1PJMym5sIfNXh/ht3WApH4BJlZYWx51tFSpd2Eb44mlr0d6om/cvf8eXyEOnz/bue7HgvvvOaJZuat9yizZsbMxyEftuknkuUC6kYePn2ksPyr//5rur351O0XVd/2zadmCzSvYslOd4QG0+2+k6/uhLDmxpeduJG6eWxEVA9/V4E2N/kZbLg3uW5b+94zltM2ebsXdlLDcci8ci9fsjpwdX/7Yx0tDQsBPQFO0FI70MjZ6kqLUWj0GzKSmcWgHaxBHt+OKRyjSpNV9kY7eOi0ze9dUg993dhNKzbZtxtdwISuOW3Gm3+jborcILbzsmn3/wlDz1uouj8JsuP7xNFG0f3va86+VHf+mP5B+++OZsvHm9ez/ksX2u43eevED+97/zWLlyA68JnCv4mdc9SX75D/9KXvHEkwsrQ/fh8gYs2s+56VL5//3OX8grn3zlHKmy0EeXGnYOiI88cmbxT0I2NDQ0zIK2giwY/eVdQdHWrk1T2aJ0GdoQ3wqrpWegYkD6UhLIe2Zr+CZuO/fz3Dg9m8VWX4ZG9dv6W8fBE2OWW8d30WbI6tJYXv20rT9LvRPx2nuullc/9dHFDbQD6uWAeXugLPLCLg8vuf1R5UjnMO668kK568oLF1qGHjIb8dD5f37tnXL/I2fk0J6NP6fXcO6CRszDpxf3ckJDQ0PDRtD8oRaMtcSqOwbhQruM1oobh6Zv9Zbie28Zb6myrOs8J40r2nSYS4712OrnvajJttrNmDY2ZunKtd1p3G6QOi+Vi/arG5sz0S/YP/vNzqsLvLCrYXHQGy7LG+BnXded00p2O0O8OZAM83CzaDc0NOwwNIv2grGenNGObm+eBi5VWrQ1jkwV7VL8NfeMdvxZwiyun6tLo+ipoM2e0S7Rsxvf0d1xZ7TRm0K7jts0OuzUAt9obtj50Ofac0P3eTcfk695/F/N9IZ6Oz97bmKzFu1zHdtx8eFuAi374VWShoaGhp2CxukXjN6iDedagyVoI+eNDweLdiG+a9GuK6YHPQ3m4Se+8Qly+eE98p5X3Dkt17rLbxbeTe1bgS1xHY8UbVvBeXkG1KJ0u30Jp842Rft8xoXKUl268PGdX36rfOVdJ6rzDje/N5xjUMNgM7eOn6tYPg83F+YJWhe/7kmTOwWeecOlW01OQ0NDA6JZtBcI7WIc1lTt8huUpWW1s13r0t0r2gW1J7oMLTqjPdsiT29WenjclRfK//9tz1DlDpiX9Xm2s+nnHkp9tdUWoNijwZa9Wy8+26k418b8Zeo29nkN3e947mPkf/zlg/LERy/2LHHDYqB5ynbeuL9daBbtzYFGzLc/5zHy5Ksvlsdf1XhCQ0PDzkBTtBeI2JpsLdpjsmgX8gxu2U+5ZnLjcafWanbfVa7j4I5cezHRZi4f09HnpSDGbbbVruOLVypLruPbe0bb/o5jb3HknPc419SSw/uW5X/7ilvkzNq67FuZz7Lz+nuvmUs+DdsDPYa3+hWFnYCNnEtvGEDr0OrSWL7k+qNbT0xDQ0ODg6ZoLxBnlfZBSnVwHddCxlpBifv4254hf/vQ6f65q1mW6th1fBMW7ZlSLuY8dXxT+1yyrMZWKJClM/FbfS69dDN+U6q3FufiG7F/53FXbDcJDTsI+hK989G6ux235e8mnIs8sKGh4fzD+be6bSHOnB3Uj6BM67O1/WVoSsgoKdoX7F+Rq9T7rdFbxqDuBBdzkdQduUh+BK3MzroTH7uOz1aum2fBlXmR2JLL0NT3nfCOdmmjZSus/A0DzkMDYMMuw/lq0f5nL71VLty/Iv+Pl92+3aSc01g5DzdnGhoazj00i/YCcWZtuAAquPrSO9qxRXu2Mjwr9b98xZ3y7o/8kXz/S2/VEUy6Wh1VK1p7Z3X9XITr+DZehrYVKF1cN97Gy4MuP7K3Kl7TvReHrT4u0dAwb+g15Xw6o/2Vd52Ql955vFlkN4mV9tpAQ0PDOYCmaC8QZ9e0RXuyKERPmnTWnfzsjJq2Z6V+9k2XybNvuiyJy+lqoOnetzKby9tmLmGroWc3iisli/12WID+05ufJg8+ckYuObhqfqNRe+fJCxZP1PmK3TjoG84rLOLujnMFTcnePJpFu6Gh4VxA41QLxOmz9tZxrXSO6Yz2rIr2DLdv083h3/vCG0VE5JvuvTqbVrsv712eUdGGzYXNQnsG7PazbiSEPuayg1tOx3WXHpTbvfeN1bD9+bfcI+940Y3yDXdfZaKdTy6ii8D3vOAGERH53//OY7eXkIaGTUJ7vCyfh897NWwOy82i3dDQcA6gWbQXiGCdXhp1vZIb3SY9CmHKoj2jv+0sF5x18P0p11wsv/sPn1O8CVgr2rNatOO3w2dKWpXnnuXdveDqfvu51z9ZPvS7f1HcGNlOXHP0gFxz9AD+1tz9NodvfOqj5eVPOCl7Z5yDDQ07Dfo+kvF55DreMB+strWkoaHhHEDjVAtEOKPtnT8j6+6sFu3S7dQa8ZNRWnEu77fotLM+z+OVuxlor7E9M1rYN4tXPPGkiIg856ZLt6Q8bb2/44oL5O899/otr3MJpVEbNgbe8aKbFk/MLkdTsht2A/QxqWbRbpgVbdO2oaHhXECzaC8Qg0WbFwRyCZ7Zor3BuLPqu2eUUDSroF+6QXsj0PmsbrFF+3teeIM844aj8oSrLtqS8s4FW0/p1vHveM5j5OufcqUcPbhniyg693H1Jfvlj/7ywe0mo6FhIdCbyufbGe2GzaOd0W5oaDgX0DjVAhHOaHtCBCmds986Xi+gbOZJrIdOne2/z3wZ2gIuvdFPp221dXd1aSz3PubollkWd8PFOV3XNSV7RqSXGTY07CboTeV2d0PDrGgW7YaGhnMBzaK9QASLtvfuNCmds7uOD99LCtlmRJmHzwyK9vLMO8nzf4rr9Nnh6bQ9u/wytHNBBm0vec0f3/rMa+XM2TV5xg1bc0ShoWEroV3HR+cCk2vYUWgW7YaGhnMBTdFeIMIZbd+ibcNmft5LW6lniDsr9m3CaqzrP69bx08pRdvbyNgtmJe7/SLxP//6i9tNwq7D6tJYvvsFN243GQ0NC8HajMekGho0mkW7oaHhXEDjVAtEcG/2zmiT4jur63icX/73w3uXN5z346+6UL7x7qvk+7/i1pnTauV6XkrjqTODor0bXKtz2MnGnpc94QoREXntPY/eZkoaGhrOJai90oaGmdEU7YaGhnMBzaK9QIQLxGY5l7yZXf5SKRcdWNl43l0n3/PCjVnXdP3n5SJ46nyS0nbwRsL3veRmecEtx+TW44e3m5SGhoZzCLN6bzU0aDTX8YaGhnMBTdFeIPpbx2dwbd6M8FGy7F64f+OK9mYQKdrzOqN95vwR0nayRbvrOnnKNRdvNxkNDQ3nGK6/7OB2k9BwDuPRl+zfbhIaGhoaimiK9gLRv6M9g6Y06/NeGqVSLti3/Yr2vG4dP3X2bDnSLkF3Tjzw1dDQ0FCPKy/eL+97/ZPl4gOr201KwzmIe667RL7nBTfIDccObTcpDQ0NDS6aor1AnOmf96p3cZr11vFZcPOjtmdBii3a81Eaz6fFdSdbtBsaGho2ituvuGC7SWg4R9F1nXzjU9vdIA0NDTsbTdFeIErPe+XSbAQl1/HjF+yTn/2mJ8nBPRu/FG0jWFqAov38m4/JP/3yM/LYK47MJb+djPb0TUNDQ0NDQ0NDQ8O5haZoLxB0GVpJz1zkreMiIneevHDjBWwQowWc0R6NOvnqx18xn8x2OHbwXWgNDQ0NDQ0NDQ0NDYB2beMCcebs7Ge0b7p897lELy3gjPb5hHPhHe2GhoaGhoaGhoaGhgHNor1AzPK81we/9anyK3/4V/KKJ53ccHk7VR3TiuJuf/N6EWgt1tDQ0NDQ0NDQ0HBuYSaL9jvf+U553OMeJwcPHpSjR4/KS17yEvmDP/iDKM4DDzwgb3zjG+X48eOyd+9eueGGG+Td7353FOfee++Vruuif1/91V+9+drsMAxntMvNfP1lh+Qbn/roqrgedqoOO4vrfINFs2g3NDQ0NDQ0NDQ0nFuYSav7yEc+Im94wxvk4x//uHzoQx+SM2fOyLOf/Wx58MEH+zhvfvOb5YMf/KD8xE/8hPze7/2evPnNb5Y3velN8v73vz/K69WvfrX8+Z//ef/vPe95z3xqtIMwi0V7Htipz0Dp+m/i9bLzFk3PbmhoaGhoaGhoaDi3MJPr+Ac/+MHo7/e+971y9OhR+cQnPiFPe9rTRETkYx/7mLzyla+Ue++9V0REXvOa18h73vMe+fVf/3V58Ytf3Kfdt2+fXHbZZZskf2djI2e0N4OdqpBFirZYTXun0r1T0CzaDQ0NDQ0NDQ0NDecWNnUZ2he+8AUREbnwwuEm67vvvls+8IEPyGc+8xlZX1+XD3/4w/KpT31KnvOc50Rpf/Inf1Iuvvhiuemmm+Tbv/3b5f7773fLeeSRR+S+++6L/p0LCBbtpegd7cUpTTtVH4s2GsCivVUbEecqWvM0NDQ0NDQ0NDQ0nFvY8GVo6+vr8pa3vEXuvvtuufnmm/vwd73rXfLqV79ajh8/LktLSzIajeTHfuzH5O677+7jvPzlL5errrpKLrvsMvnt3/5tedvb3iaf/OQn5UMf+hCW9c53vlP+wT/4BxslddsQzmiPZ3hHezPYqa7j2iJLnuPtJvI8juxb2W4SGhoaGhoaGhoaGhpmwIYV7Te+8Y3yW7/1W/Irv/IrUfi73vUu+fjHPy4f+MAH5OTJk/LRj35UXv/618uxY8fkmc98pohMzmcH3HzzzXLttdfKXXfdJb/xG78hd9xxhynrbW97m7zlLW/p/77vvvvkxIkTGyV9y3Dzow7J6+65Wq6/7OB2k7KtWIrOaFtVe3nUXpkjvPPLb5Ff+x+flxfeemy7SWloaGhoaGhoaGhomAEbUrTf9KY3yQc+8AH56Ec/KsePH+/DH3roIfmu7/oued/73icveMELRETk1ltvld/8zd+UH/iBH+gV7RR33HGHLC8vyx/+4R+ior26uiqrq6sbIXVbcefJC+XOkxcmoYu7DWynuo6PCpehbZXF/1zD1zz+Cvmax1+x3WQ0NDQ0NDQ0NDQ0NMyImRTt9fV1edOb3iTve9/75Jd+6Zfkqquuin4/ffq0nD59WkaJhXI8Hsva2pqb7+/8zu/I6dOn5dixZrnbDM6FN6ppm6Gd0W5oaGhoaGhoaGho2E2YSdF+wxveID/1Uz8l73//++XgwYPy2c9+VkREDh8+LHv37pVDhw7JPffcI29961tl7969cvLkSfnIRz4iP/7jPy4/9EM/JCIif/RHfyQ/+ZM/Kc9//vPl4osvlt/93d+Vb/u2b5Pbb79dnvKUp8y/hjsMz7rxUvm3v/ZpuWDf8naTsi1Ai3ZTtBsaGhoaGhoaGhoadhFmUrTf/e53i4j0T3cFvPe975VXvepVIiLy0z/90/K2t71NXv7yl8vnP/95OXnypHzf932fvO51rxMRkZWVFfmFX/gF+ZEf+RF54IEH5MSJE/KCF7xA3v72t8t4PN58jXY4vuQxR+Vnv+lJcvUlB+ae97mgrtLzXkvtjHZDQ0NDQ0NDQ0NDwy7CzK7jJVx22WXy3ve+1/39xIkT8pGPfGSWYncVuq6Dc9vzynsh2c4VNITO98viGhoaGhoaGhoaGhp2FzZ863jDzsM5oGdHeP8bniI/9at/Kt/+nMdsNykNDQ0NDQ0NDQ0NDQ1zQ1O0dxHOhcvQNG47cURuO3Fku8loaGhoaGhoaGhoaGiYK9rh2F2Ec0HNrjl+0NDQ0NDQ0NDQ0NDQcC6jKdq7CeeApt3U7IaGhoaGhoaGhoaG3Y6maDdsKZpBu6GhoaGhoaGhoaFht6Mp2rsIo3PgjDY979XQ0NDQ0NDQ0NDQ0LCb0BTtXYSdr2Y3i3ZDQ0NDQ0NDQ0NDw+5HU7QbthRNz25oaGhoaGhoaGho2O1oivYuwjngOd4s2g0NDQ0NDQ0NDQ0Nux5N0d5F6M4N5/HtJqChoaGhoaGhoaGhoWGhaIr2LsK5YNFuaGhoaGhoaGhoaGjY7WiK9i7CTla0j+xbFhGRp19/6TZT0tDQ0NDQ0NDQ0NDQsFgsbTcBDfPEztW0f/Hb7pX//rkH5HFXXrDdpDQ0NDQ0NDQ0NDQ0NCwUTdHeRdjJFu0L96/I46+6cLvJaGhoaGhoaGhoaGhoWDia63hDQ0NDQ0NDQ0NDQ0NDwxzRFO1dhNEOtmg3NDQ0NDQ0NDQ0NDScL2iK9i7C/tV2EqChoaGhoaGhoaGhoWG70RTtXYB//JKb5a6TF8jr77lmu0lpaGhoaGhoaGhoaGg479Gtr6+vbzcRs+K+++6Tw4cPyxe+8AU5dOjQdpPT0NDQ0NDQ0NDQ0NDQsMsxix7aLNoNDQ0NDQ0NDQ0NDQ0NDXNEU7QbGhoaGhoaGhoaGhoaGuaIpmg3NDQ0NDQ0NDQ0NDQ0NMwRTdFuaGhoaGhoaGhoaGhoaJgjmqLd0NDQ0NDQ0NDQ0NDQ0DBHNEW7oaGhoaGhoaGhoaGhoWGOaIp2Q0NDQ0NDQ0NDQ0NDQ8Mc0RTthoaGhoaGhoaGhoaGhoY5oinaDQ0NDQ0NDQ0NDQ0NDQ1zRFO0GxoaGhoaGhoaGhoaGhrmiKZoNzQ0NDQ0NDQ0NDQ0NDTMEU3RbmhoaGhoaGhoaGhoaGiYI5qi3dDQ0NDQ0NDQ0NDQ0NAwRzRFu6GhoaGhoaGhoaGhoaFhjmiKdkNDQ0NDQ0NDQ0NDQ0PDHNEU7YaGhoaGhoaGhoaGhoaGOaIp2g0NDQ0NDQ0NDQ0NDQ0Nc0RTtBsaGhoaGhoaGhoaGhoa5oimaDc0NDQ0NDQ0NDQ0NDQ0zBFN0W5oaGhoaGhoaGhoaGhomCOaot3Q0NDQ0NDQ0NDQ0NDQMEc0RbuhoaGhoaGhoaGhoaGhYY5oinZDQ0NDQ0NDQ0NDQ0NDwxzRFO2GhoaGhoaGhoaGhoaGhjmiKdoNDQ0NDQ0NDQ0NDQ0NDXPE0nYTsBGsr6+LiMh99923zZQ0NDQ0NDQ0NDQ0NDQ0nA8I+mfQR3M4JxXt+++/X0RETpw4sc2UNDQ0NDQ0NDQ0NDQ0NJxPuP/+++Xw4cPZON16jTq+w7C2tiZ/9md/JgcPHpSu67abnPMG9913n5w4cUI+/elPy6FDh7abnIYpWr/sXLS+2Zlo/bJz0fpmZ6L1y85F65udidYvOxeb7Zv19XW5//775fLLL5fRKH8K+5y0aI9GIzl+/Ph2k3He4tChQ41p7EC0ftm5aH2zM9H6Zeei9c3OROuXnYvWNzsTrV92LjbTNyVLdkC7DK2hoaGhoaGhoaGhoaGhYY5oinZDQ0NDQ0NDQ0NDQ0NDwxzRFO2Gaqyursrb3/52WV1d3W5SGhRav+xctL7ZmWj9snPR+mZnovXLzkXrm52J1i87F1vZN+fkZWgNDQ0NDQ0NDQ0NDQ0NDTsVzaLd0NDQ0NDQ0NDQ0NDQ0DBHNEW7oaGhoaGhoaGhoaGhoWGOaIp2Q0NDQ0NDQ0NDQ0NDQ8Mc0RTthoaGhoaGhoaGhoaGhoY5oinaDQ0NDQ0NDQ0NDQ0NlWh3Se9c7KS+aYp2g8FOGqANMVrf7Ey0ftm5aH2zM9H6Zeei9c3OROuXnYPPfe5zcv/99/d/t77ZOdhpfdOe9zrPcerUKfnhH/5h2b9/v9xyyy1yzz33bDdJDVO0vtmZaP2yc9H6Zmei9cvOReubnYnWLzsTZ86ckde+9rXyC7/wC3L06FG59tpr5Uf+7/buPCyK+4wD+HcAQa6AB0c5BEpA5UHwiKIijaZRkxobEa9qoxgjKh5NjFXzRKPSmHi0HtWgqVEBQXk0EY0RW7yoUSPRGI+gxHqicogHHhzCsm//IExCU5M2bphh9/v5y50d93l/fHd25t2d+c3y5WjZsqXWpVk8vWbDX7Qt2K5du+Dl5YVt27Zh/fr1iI6OxqxZs1BZWal1aRaP2egTc9EvZqNPzEW/mI0+MRd9MhgMiI2NxZkzZ5CcnIzf/e53OHXqFAYOHIizZ89qXZ5F03U2QhZr8ODBMm7cOBERuX37tmzZskXs7Oxk6dKlUl5ernF1lo3Z6BNz0S9mo0/MRb+YjT4xF33Kz8+XoKAg2bBhg7qssLBQvL29ZfLkyVJUVKRhdZZNz9nwF20LUlNTo/770qVLOHz4MKKiogAAzZo1w6BBgxAXF4fU1FQcPHhQqzIt3sWLF5mNDnGb0S9mo0/MRV94DKB/3P/r161bt3Dt2jV07doVAPDw4UN4enrijTfeQFZWFg4cOKBxhZZLz9mw0bYQs2fPxuzZs9XH/v7+MBgMuHfvHgCgoqICADBnzhyUlZVh165dePDggSa1WpqsrCycPHlSPQgKCAhAdXU1s9HY119/DYPBoD728/NjLjpRVFRU77Gfnx8/z3QgJSUF58+fVx8zF/3gMYA+cf+vT++88w7mzJmD9PR0dVnbtm3h7u6O1NRUAICVVW0LNXHiRDg7O2PXrl14+PChJvVakr/97W9Ys2ZNveY5KCgInp6e+sxGs9/SqUFs27ZNPDw8JCIiQlauXCm3b98WERGDwSDjx4+XsLAwdd2qqioREVmwYIH4+vrKnTt3tCjZYqxfv148PT2lXbt24uzsLPHx8XL9+nURERk3bhyz0cjatWvF29tb2rRpI+Hh4ZKUlKT+/ePi4piLhtasWSMdOnSQrl27Sv/+/SU7O1tERCoqKvh5pqGamhqJjo4WRVFk8eLFUlFRISK1GTAXbfEYQJ+4/9ennJwcadWqlXTs2FGef/55cXZ2lpiYGLlw4YKIiEybNk2Cg4OluLhYRET9rEtOThZXV1f1MZnexo0bxd3dXbp16ybt27cXNzc3mT9/voiI3L17V6ZPn67LbNhom7EHDx5I//79JSEh4b8+/9FHH0nr1q1l2bJlIiJSWVkpIiIlJSVib28vn376aYPVamk++OADefLJJ2XTpk1SUlIiaWlp4ujoKCdOnBCR2mzatGnDbBrY1q1bxd/fX5KSkmTv3r3yxz/+URwdHWXFihVSU1Mjn3zyiQQHBzOXBlZYWChDhgwRPz8/Wbt2raxevVpeeOEF8fT0VNf58MMPuc1ooKamRkREJk2aJJ07d5Zf/OIXcvz4cfV5fpZph8cA+sT9v35NnTpV+vXrJyK1n22nT58WPz8/GT9+vJSWlsqRI0ekY8eOEh8fLyIiRqNRRET2798v7u7ucvLkSc1qN2dpaWkSHh4uq1evFhGR69evy8qVK8XR0VHu3r0rIiK7d++Wzp076y4bnjpuhuSbO7ZlZ2fjyJEjePPNN3Hnzh3MnDkTCxcuRFpaGgDg17/+Nfr06YO//OUvKCgogJ2dHQDgyy+/hJubG5ydnTUbg7kyGo2oqanBvn370K1bNwwbNgwtW7bE8OHD4e3tDaPRCACIjIxkNg2o7u/+6aefwsfHB6NGjcIzzzyDRYsWYfTo0UhNTUVmZiZ69+6Nvn37MpcGdujQIVy7dg2ZmZl4+eWXMW7cOCxduhR2dnbYvn07ACAqKorZaMDKygo3b97EkSNHsGfPHtjb22PlypUoKSkBAHTr1o2fZRrhMYC+iAj3/zolIrh79y4+//xztG3bVl0eGhqKGTNmICcnB+np6YiIiMBLL72EpKQkZGRkoLq6GkDtPiokJATt2rXTaghmqa6fqa6uRkREBEaOHAkA8PLyQvv27eHt7Y0zZ84AAHr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We then plotted global average surface air temperature.\n", - "\n", - "### What's next?\n", - "We will see some more advanced examples of using the CMIP6 data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Resources and references\n", - "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/search_and_load_with_esgf_opendap.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ] - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/77/_sources/notebooks/foundations/google-cloud-basic.ipynb b/_preview/77/_sources/notebooks/foundations/google-cloud-basic.ipynb deleted file mode 100644 index 3892575..0000000 --- a/_preview/77/_sources/notebooks/foundations/google-cloud-basic.ipynb +++ /dev/null @@ -1,2906 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Google Cloud CMIP6 Public Data: Basic Python Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This notebooks shows how to query the Google Cloud CMIP6 catalog and load the data using Python." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "\n", - "- **Time to learn**: 10 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:03.359512Z", - "iopub.status.busy": "2023-12-18T19:54:03.359002Z", - "iopub.status.idle": "2023-12-18T19:54:04.146059Z", - "shell.execute_reply": "2023-12-18T19:54:04.145310Z" - } - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import zarr\n", - "import fsspec\n", - "import nc_time_axis\n", - "\n", - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = 12, 6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Browse Catalog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data catatalog is stored as a CSV file. Here we read it with Pandas." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:04.150216Z", - "iopub.status.busy": "2023-12-18T19:54:04.149550Z", - "iopub.status.idle": "2023-12-18T19:54:06.089502Z", - "shell.execute_reply": "2023-12-18T19:54:06.088913Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id member_id \\\n", - "0 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "1 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "2 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "3 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "4 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", - "\n", - " table_id variable_id grid_label \\\n", - "0 Amon ps gn \n", - "1 Amon rsds gn \n", - "2 Amon rlus gn \n", - "3 Amon rlds gn \n", - "4 Amon psl gn \n", - "\n", - " zstore dcpp_init_year version \n", - "0 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "1 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "2 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "3 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", - "4 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The columns of the dataframe correspond to the [CMI6 controlled vocabulary](https://docs.google.com/document/d/1yUx6jr9EdedCOLd--CPdTfGDwEwzPpCF6p1jRmqx-0Q/edit).\n", - "\n", - "Here we filter the data to find monthly surface air temperature for historical experiments." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:06.091892Z", - "iopub.status.busy": "2023-12-18T19:54:06.091676Z", - "iopub.status.idle": "2023-12-18T19:54:06.144555Z", - "shell.execute_reply": "2023-12-18T19:54:06.144001Z" - }, - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    635 rows × 11 columns

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    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id \\\n", - "973 CMIP NOAA-GFDL GFDL-ESM4 historical \n", - "1766 CMIP NOAA-GFDL GFDL-ESM4 historical \n", - "8074 CMIP NOAA-GFDL GFDL-CM4 historical \n", - "22185 CMIP IPSL IPSL-CM6A-LR historical \n", - "22298 CMIP IPSL IPSL-CM6A-LR historical \n", - "... ... ... ... ... \n", - "522952 CMIP MRI MRI-ESM2-0 historical \n", - "523274 CMIP MRI MRI-ESM2-0 historical \n", - "523712 CMIP CMCC CMCC-CM2-SR5 historical \n", - "523721 CMIP CMCC CMCC-CM2-SR5 historical \n", - "523769 CMIP EC-Earth-Consortium EC-Earth3-Veg historical \n", - "\n", - " member_id table_id variable_id grid_label \\\n", - "973 r3i1p1f1 Amon tas gr1 \n", - "1766 r2i1p1f1 Amon tas gr1 \n", - "8074 r1i1p1f1 Amon tas gr1 \n", - "22185 r8i1p1f1 Amon tas gr \n", - "22298 r2i1p1f1 Amon tas gr \n", - "... ... ... ... ... \n", - "522952 r7i1p1f1 Amon tas gn \n", - "523274 r6i1p1f1 Amon tas gn \n", - "523712 r3i1p2f1 Amon tas gn \n", - "523721 r2i1p2f1 Amon tas gn \n", - "523769 r1i1p1f1 Amon tas gr \n", - "\n", - " zstore dcpp_init_year \\\n", - "973 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist... NaN \n", - "1766 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist... NaN \n", - "8074 gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo... NaN \n", - "22185 gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor... NaN \n", - "22298 gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor... NaN \n", - "... ... ... \n", - "522952 gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica... NaN \n", - "523274 gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica... NaN \n", - "523712 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor... NaN \n", - "523721 gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor... NaN \n", - "523769 gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E... NaN \n", - "\n", - " version \n", - "973 20180701 \n", - "1766 20180701 \n", - "8074 20180701 \n", - "22185 20180803 \n", - "22298 20180803 \n", - "... ... \n", - "522952 20210813 \n", - "523274 20210907 \n", - "523712 20211108 \n", - "523721 20211109 \n", - "523769 20211207 \n", - "\n", - "[635 rows x 11 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ta = df.query(\"activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'\")\n", - "df_ta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we do further filtering to find just the models from NCAR." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:06.146885Z", - "iopub.status.busy": "2023-12-18T19:54:06.146683Z", - "iopub.status.idle": "2023-12-18T19:54:06.158212Z", - "shell.execute_reply": "2023-12-18T19:54:06.157749Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    56143CMIPNCARCESM2-WACCMhistoricalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...NaN20190227
    56326CMIPNCARCESM2-WACCMhistoricalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...NaN20190227
    59875CMIPNCARCESM2historicalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190308
    61655CMIPNCARCESM2historicalr4i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4...NaN20190308
    61862CMIPNCARCESM2historicalr5i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5...NaN20190308
    62691CMIPNCARCESM2historicalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2...NaN20190308
    63131CMIPNCARCESM2historicalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3...NaN20190308
    63266CMIPNCARCESM2historicalr6i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6...NaN20190308
    64615CMIPNCARCESM2historicalr8i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8...NaN20190311
    64914CMIPNCARCESM2historicalr7i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7...NaN20190311
    64983CMIPNCARCESM2historicalr9i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9...NaN20190311
    66341CMIPNCARCESM2historicalr10i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190313
    200772CMIPNCARCESM2historicalr11i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190514
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    422459CMIPNCARCESM2-FV2historicalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...NaN20200226
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    " - ], - "text/plain": [ - " activity_id institution_id source_id experiment_id member_id \\\n", - "56049 CMIP NCAR CESM2-WACCM historical r2i1p1f1 \n", - "56143 CMIP NCAR CESM2-WACCM historical r3i1p1f1 \n", - "56326 CMIP NCAR CESM2-WACCM historical r1i1p1f1 \n", - "59875 CMIP NCAR CESM2 historical r1i1p1f1 \n", - "61655 CMIP NCAR CESM2 historical r4i1p1f1 \n", - "61862 CMIP NCAR CESM2 historical r5i1p1f1 \n", - "62691 CMIP NCAR CESM2 historical r2i1p1f1 \n", - "63131 CMIP NCAR CESM2 historical r3i1p1f1 \n", - "63266 CMIP NCAR CESM2 historical r6i1p1f1 \n", - "64615 CMIP NCAR CESM2 historical r8i1p1f1 \n", - "64914 CMIP NCAR CESM2 historical r7i1p1f1 \n", - "64983 CMIP NCAR CESM2 historical r9i1p1f1 \n", - "66341 CMIP NCAR CESM2 historical r10i1p1f1 \n", - "200772 CMIP NCAR CESM2 historical r11i1p1f1 \n", - "385224 CMIP NCAR CESM2-FV2 historical r1i1p1f1 \n", - "386297 CMIP NCAR CESM2-WACCM-FV2 historical r1i1p1f1 \n", - "420771 CMIP NCAR CESM2-WACCM-FV2 historical r3i1p1f1 \n", - "421251 CMIP NCAR CESM2-WACCM-FV2 historical r2i1p1f1 \n", - "422013 CMIP NCAR CESM2-FV2 historical r3i1p1f1 \n", - "422459 CMIP NCAR CESM2-FV2 historical r2i1p1f1 \n", - "\n", - " table_id variable_id grid_label \\\n", - "56049 Amon tas gn \n", - "56143 Amon tas gn \n", - "56326 Amon tas gn \n", - "59875 Amon tas gn \n", - "61655 Amon tas gn \n", - "61862 Amon tas gn \n", - "62691 Amon tas gn \n", - "63131 Amon tas gn \n", - "63266 Amon tas gn \n", - "64615 Amon tas gn \n", - "64914 Amon tas gn \n", - "64983 Amon tas gn \n", - "66341 Amon tas gn \n", - "200772 Amon tas gn \n", - "385224 Amon tas gn \n", - "386297 Amon tas gn \n", - "420771 Amon tas gn \n", - "421251 Amon tas gn \n", - "422013 Amon tas gn \n", - "422459 Amon tas gn \n", - "\n", - " zstore dcpp_init_year \\\n", - "56049 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori... NaN \n", - "56143 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori... NaN \n", - "56326 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori... NaN \n", - "59875 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1... NaN \n", - "61655 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4... NaN \n", - "61862 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5... NaN \n", - "62691 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2... NaN \n", - "63131 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3... NaN \n", - "63266 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6... NaN \n", - "64615 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8... NaN \n", - "64914 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7... NaN \n", - "64983 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9... NaN \n", - "66341 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1... NaN \n", - "200772 gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1... NaN \n", - "385224 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica... NaN \n", - "386297 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his... NaN \n", - "420771 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his... NaN \n", - "421251 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his... NaN \n", - "422013 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica... NaN \n", - "422459 gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica... NaN \n", - "\n", - " version \n", - "56049 20190227 \n", - "56143 20190227 \n", - "56326 20190227 \n", - "59875 20190308 \n", - "61655 20190308 \n", - "61862 20190308 \n", - "62691 20190308 \n", - "63131 20190308 \n", - "63266 20190308 \n", - "64615 20190311 \n", - "64914 20190311 \n", - "64983 20190311 \n", - "66341 20190313 \n", - "200772 20190514 \n", - "385224 20191120 \n", - "386297 20191120 \n", - "420771 20200226 \n", - "421251 20200226 \n", - "422013 20200226 \n", - "422459 20200226 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ta_ncar = df_ta.query('institution_id == \"NCAR\"')\n", - "df_ta_ncar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will load a single store using `fsspec`, `zarr`, and `xarray`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:06.160444Z", - "iopub.status.busy": "2023-12-18T19:54:06.160259Z", - "iopub.status.idle": "2023-12-18T19:54:18.085463Z", - "shell.execute_reply": "2023-12-18T19:54:18.084769Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historical/r2i1p1f1/Amon/tas/gn/v20200226/\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:    (lat: 96, nbnd: 2, lon: 144, time: 1980)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
-       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
-       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
-       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "    time_bnds  (time, nbnd) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    tas        (time, lat, lon) float32 dask.array<chunksize=(990, 96, 144), meta=np.ndarray>\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            674885.0\n",
-       "    branch_time_in_parent:           10950.0\n",
-       "    ...                              ...\n",
-       "    tracking_id:                     hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
-       "    variable_id:                     tas\n",
-       "    variant_info:                    CMIP6 CESM2-FV2 historical experiment (1...\n",
-       "    variant_label:                   r2i1p1f1\n",
-       "    netcdf_tracking_ids:             hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n",
-       "    version_id:                      v20200226
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 96, nbnd: 2, lon: 144, time: 1980)\n", - "Coordinates:\n", - " * lat (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n", - " lat_bnds (lat, nbnd) float64 dask.array\n", - " * lon (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n", - " lon_bnds (lon, nbnd) float64 dask.array\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n", - " time_bnds (time, nbnd) object dask.array\n", - "Dimensions without coordinates: nbnd\n", - "Data variables:\n", - " tas (time, lat, lon) float32 dask.array\n", - "Attributes: (12/48)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " DODS_EXTRA.Unlimited_Dimension: time\n", - " activity_id: CMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 674885.0\n", - " branch_time_in_parent: 10950.0\n", - " ... ...\n", - " tracking_id: hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n", - " variable_id: tas\n", - " variant_info: CMIP6 CESM2-FV2 historical experiment (1...\n", - " variant_label: r2i1p1f1\n", - " netcdf_tracking_ids: hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...\n", - " version_id: v20200226" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get the path to a specific zarr store (the first one from the dataframe above)\n", - "zstore = df_ta_ncar.zstore.values[-1]\n", - "print(zstore)\n", - "\n", - "# create a mutable-mapping-style interface to the store\n", - "mapper = fsspec.get_mapper(zstore)\n", - "\n", - "# open it using xarray and zarr\n", - "ds = xr.open_zarr(mapper, consolidated=True)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the Data\n", - "\n", - "Plot a map from a specific date:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:18.089846Z", - "iopub.status.busy": "2023-12-18T19:54:18.088773Z", - "iopub.status.idle": "2023-12-18T19:54:19.550349Z", - "shell.execute_reply": "2023-12-18T19:54:19.549564Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds.tas.sel(time='1950-01').squeeze().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The global mean of a lat-lon field needs to be weighted by the area of each grid cell, which is proportional to the cosine of its latitude." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:19.553021Z", - "iopub.status.busy": "2023-12-18T19:54:19.552803Z", - "iopub.status.idle": "2023-12-18T19:54:19.556685Z", - "shell.execute_reply": "2023-12-18T19:54:19.556101Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def global_mean(field):\n", - " weights = np.cos(np.deg2rad(field.lat))\n", - " return field.weighted(weights).mean(dim=['lat', 'lon'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can pass all of the temperature data through this function:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:19.558937Z", - "iopub.status.busy": "2023-12-18T19:54:19.558745Z", - "iopub.status.idle": "2023-12-18T19:54:19.598545Z", - "shell.execute_reply": "2023-12-18T19:54:19.597990Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.DataArray 'tas' (time: 1980)>\n",
-       "dask.array<truediv, shape=(1980,), dtype=float64, chunksize=(990,), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    " - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ta_timeseries = global_mean(ds.tas)\n", - "ta_timeseries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:19.600792Z", - "iopub.status.busy": "2023-12-18T19:54:19.600567Z", - "iopub.status.idle": "2023-12-18T19:54:21.114580Z", - "shell.execute_reply": "2023-12-18T19:54:21.113979Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 406 ms, sys: 249 ms, total: 655 ms\n", - "Wall time: 1.49 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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    <xarray.DataArray 'tas' (time: 1980)>\n",
-       "array([285.53603312, 285.63958225, 286.27324086, ..., 288.15781771,\n",
-       "       287.18662389, 286.87765827])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    " - ], - "text/plain": [ - "\n", - "array([285.53603312, 285.63958225, 286.27324086, ..., 288.15781771,\n", - " 287.18662389, 286.87765827])\n", - "Coordinates:\n", - " * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time ta_timeseries.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:54:21.117354Z", - "iopub.status.busy": "2023-12-18T19:54:21.116635Z", - "iopub.status.idle": "2023-12-18T19:54:21.399950Z", - "shell.execute_reply": "2023-12-18T19:54:21.399254Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Global Mean Surface Air Temperature')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ta_timeseries.plot(label='monthly')\n", - "ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean', color='k')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.title('Global Mean Surface Air Temperature')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "In this notebook, we opened a CESM2 dataset with `fsspec` and `zarr`. We calculated and plotted global average surface air temperature. \n", - "\n", - "### What's next?\n", - "We will open a dataset with ESGF and OPenDAP." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Resources and references\n", - "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/basic_search_and_load.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ] - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/77/_sources/notebooks/foundations/intake-esm.ipynb b/_preview/77/_sources/notebooks/foundations/intake-esm.ipynb deleted file mode 100644 index 90caa30..0000000 --- a/_preview/77/_sources/notebooks/foundations/intake-esm.ipynb +++ /dev/null @@ -1,2022 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Intake\n", - "\"CMIP6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load CMIP6 Data with Intake-ESM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "[Intake-ESM](https://intake-esm.readthedocs.io/en/latest/) is an experimental new package that aims to provide a higher-level interface to searching and loading Earth System Model data archives, such as CMIP6. The package is under very active development, and features may be unstable. Please report any [issues or suggestions on GitHub](https://github.com/intake/intake-esm/issues)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "\n", - "- **Time to learn**: 5 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:55:03.594745Z", - "iopub.status.busy": "2023-12-18T19:55:03.594528Z", - "iopub.status.idle": "2023-12-18T19:55:05.895146Z", - "shell.execute_reply": "2023-12-18T19:55:05.894357Z" - } - }, - "outputs": [], - "source": [ - "import xarray as xr\n", - "xr.set_options(display_style='html')\n", - "import intake\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Intake ESM works by parsing an [ESM Collection Spec](https://github.com/NCAR/esm-collection-spec/) and converting it to an [Intake](https://intake.readthedocs.io/en/latest/) catalog. The collection spec is stored in a `.json` file. Here we open it using Intake." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-18T19:55:05.899543Z", - "iopub.status.busy": "2023-12-18T19:55:05.898828Z", - "iopub.status.idle": "2023-12-18T19:55:10.132212Z", - "shell.execute_reply": "2023-12-18T19:55:10.131408Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n", - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " .applymap(type)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. 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-       "  * time                (time) object 1850-07-02 12:00:00 ... 2014-07-02 12:0...\n",
-       "    time_bnds           (time, bnds) object dask.array<chunksize=(165, 2), meta=np.ndarray>\n",
-       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
-       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
-       "  * member_id           (member_id) object 'r10i1p1f1' ... 'r9i1p2f1'\n",
-       "  * dcpp_init_year      (dcpp_init_year) float64 nan\n",
-       "Dimensions without coordinates: bnds, vertices\n",
-       "Data variables:\n",
-       "    o2                  (member_id, dcpp_init_year, time, lev, j, i) float32 dask.array<chunksize=(1, 1, 12, 45, 291, 360), meta=np.ndarray>\n",
-       "Attributes: (12/52)\n",
-       "    Conventions:                      CF-1.7 CMIP-6.2\n",
-       "    YMDH_branch_time_in_child:        1850:01:01:00\n",
-       "    activity_id:                      CMIP\n",
-       "    branch_method:                    Spin-up documentation\n",
-       "    branch_time_in_child:             0.0\n",
-       "    cmor_version:                     3.4.0\n",
-       "    ...                               ...\n",
-       "    intake_esm_attrs:table_id:        Oyr\n",
-       "    intake_esm_attrs:variable_id:     o2\n",
-       "    intake_esm_attrs:grid_label:      gn\n",
-       "    intake_esm_attrs:version:         20190429\n",
-       "    intake_esm_attrs:_data_format_:   zarr\n",
-       "    intake_esm_dataset_key:           CMIP.CCCma.CanESM5.historical.Oyr.gn
    " - ], - "text/plain": [ - "\n", - "Dimensions: (i: 360, j: 291, lev: 45, bnds: 2, member_id: 35,\n", - " dcpp_init_year: 1, time: 165, vertices: 4)\n", - "Coordinates:\n", - " * i (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n", - " * j (j) int32 0 1 2 3 4 5 6 ... 284 285 286 287 288 289 290\n", - " latitude (j, i) float64 dask.array\n", - " * lev (lev) float64 3.047 9.454 16.36 ... 5.375e+03 5.625e+03\n", - " lev_bnds (lev, bnds) float64 dask.array\n", - " longitude (j, i) float64 dask.array\n", - " * time (time) object 1850-07-02 12:00:00 ... 2014-07-02 12:0...\n", - " time_bnds (time, bnds) object dask.array\n", - " vertices_latitude (j, i, vertices) float64 dask.array\n", - " vertices_longitude (j, i, vertices) float64 dask.array\n", - " * member_id (member_id) object 'r10i1p1f1' ... 'r9i1p2f1'\n", - " * dcpp_init_year (dcpp_init_year) float64 nan\n", - "Dimensions without coordinates: bnds, vertices\n", - "Data variables:\n", - " o2 (member_id, dcpp_init_year, time, lev, j, i) float32 dask.array\n", - "Attributes: (12/52)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " YMDH_branch_time_in_child: 1850:01:01:00\n", - " activity_id: CMIP\n", - " branch_method: Spin-up documentation\n", - " branch_time_in_child: 0.0\n", - " cmor_version: 3.4.0\n", - " ... ...\n", - " intake_esm_attrs:table_id: Oyr\n", - " intake_esm_attrs:variable_id: o2\n", - " intake_esm_attrs:grid_label: gn\n", - " intake_esm_attrs:version: 20190429\n", - " intake_esm_attrs:_data_format_: zarr\n", - " intake_esm_dataset_key: CMIP.CCCma.CanESM5.historical.Oyr.gn" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = dset_dict['CMIP.CCCma.CanESM5.historical.Oyr.gn']\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Summary\n", - "In this notebook, we used Intake-ESM to open an Xarray Dataset for one particular model and experiment.\n", - "\n", - "### What's next?\n", - "We will see an example of downloading a dataset with `fsspec` and `zarr`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Resources and references\n", - "- [Original notebook in the Pangeo Gallery](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/intake_ESM_example.html) by Henri Drake and [Ryan Abernathey](https://ocean-transport.github.io/)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ] - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/77/_sources/notebooks/how-to-cite.md b/_preview/77/_sources/notebooks/how-to-cite.md deleted file mode 100644 index 41afd45..0000000 --- a/_preview/77/_sources/notebooks/how-to-cite.md +++ /dev/null @@ -1,7 +0,0 @@ -# How to Cite This Cookbook - -The material in Project Pythia's CMIP6 Cookbook is licensed for free and open consumption and reuse. All code is served under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), while all non-code content is licensed under [Creative Commons BY 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). Effectively, this means you are free to share and adapt this material so long as you give appropriate credit to the Cookbook authors and the Project Pythia community. - -The source code for the book is [released on GitHub](https://github.com/ProjectPythia/cookbook-template) and archived on Zenodo. This DOI will always resolve to the latest release of the book source: - -[![DOI](https://zenodo.org/badge/507993770.svg)](https://zenodo.org/badge/latestdoi/507993770) \ No newline at end of file diff --git a/_preview/77/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/77/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 3225661..0000000 --- a/_preview/77/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ /dev/null @@ -1 +0,0 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) 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@@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

    Short

    - */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/_preview/77/_static/copybutton.js b/_preview/77/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/_preview/77/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_preview/77/_static/copybutton_funcs.js b/_preview/77/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/_preview/77/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/_preview/77/_static/css/blank.css b/_preview/77/_static/css/blank.css deleted file mode 100644 index 8a686ec..0000000 --- a/_preview/77/_static/css/blank.css +++ /dev/null @@ -1,2 +0,0 @@ -/* This file is intentionally left blank to override the stylesheet of the -parent theme via theme.conf. The parent style we import directly in theme.css */ \ No newline at end of file diff --git a/_preview/77/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css b/_preview/77/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css deleted file mode 100644 index 9b1c5d7..0000000 --- a/_preview/77/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v4.5.0 (https://getbootstrap.com/) - * Copyright 2011-2020 The Bootstrap Authors - * Copyright 2011-2020 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - 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ul{list-style:none;padding:0 0 0 1.5rem}.bd-sidebar .nav li>a{display:block;padding:.25rem 1.5rem;color:rgba(var(--pst-color-sidebar-link),1)}.bd-sidebar .nav li>a:hover{color:rgba(var(--pst-color-sidebar-link-hover),1);text-decoration:none;background-color:transparent}.bd-sidebar .nav li>a.reference.external:after{font-family:Font Awesome\ 5 Free;font-weight:900;content:"\f35d";font-size:.75em;margin-left:.3em}.bd-sidebar .nav .active:hover>a,.bd-sidebar .nav .active>a{font-weight:600;color:rgba(var(--pst-color-sidebar-link-active),1)}.toc-h2{font-size:.85rem}.toc-h3{font-size:.75rem}.toc-h4{font-size:.65rem}.toc-entry>.nav-link.active{font-weight:600;color:#130654;color:rgba(var(--pst-color-toc-link-active),1);background-color:transparent;border-left:2px solid rgba(var(--pst-color-toc-link-active),1)}.nav-link:hover{border-style:none}#navbar-main-elements li.nav-item i{font-size:.7rem;padding-left:2px;vertical-align:middle}.bd-toc .nav .nav{display:none}.bd-toc .nav .nav.visible,.bd-toc .nav>.active>ul{display:block}.prev-next-area{margin:20px 0}.prev-next-area p{margin:0 .3em;line-height:1.3em}.prev-next-area i{font-size:1.2em}.prev-next-area a{display:flex;align-items:center;border:none;padding:10px;max-width:45%;overflow-x:hidden;color:rgba(0,0,0,.65);text-decoration:none}.prev-next-area a p.prev-next-title{color:rgba(var(--pst-color-link),1);font-weight:600;font-size:1.1em}.prev-next-area a:hover p.prev-next-title{text-decoration:underline}.prev-next-area a .prev-next-info{flex-direction:column;margin:0 .5em}.prev-next-area a .prev-next-info .prev-next-subtitle{text-transform:capitalize}.prev-next-area a.left-prev{float:left}.prev-next-area a.right-next{float:right}.prev-next-area a.right-next div.prev-next-info{text-align:right}.alert{padding-bottom:0}.alert-info a{color:#e83e8c}#navbar-icon-links i.fa,#navbar-icon-links i.fab,#navbar-icon-links i.far,#navbar-icon-links i.fas{vertical-align:middle;font-style:normal;font-size:1.5rem;line-height:1.25}#navbar-icon-links i.fa-github-square:before{color:#333}#navbar-icon-links i.fa-twitter-square:before{color:#55acee}#navbar-icon-links i.fa-gitlab:before{color:#548}#navbar-icon-links i.fa-bitbucket:before{color:#0052cc}.tocsection{border-left:1px solid #eee;padding:.3rem 1.5rem}.tocsection i{padding-right:.5rem}.editthispage{padding-top:2rem}.editthispage a{color:var(--pst-color-sidebar-link-active)}.xr-wrap[hidden]{display:block!important}.toctree-checkbox{position:absolute;display:none}.toctree-checkbox~ul{display:none}.toctree-checkbox~label i{transform:rotate(0deg)}.toctree-checkbox:checked~ul{display:block}.toctree-checkbox:checked~label i{transform:rotate(180deg)}.bd-sidebar li{position:relative}.bd-sidebar label{position:absolute;top:0;right:0;height:30px;width:30px;cursor:pointer;display:flex;justify-content:center;align-items:center}.bd-sidebar label:hover{background:rgba(var(--pst-color-sidebar-expander-background-hover),1)}.bd-sidebar label i{display:inline-block;font-size:.75rem;text-align:center}.bd-sidebar label i:hover{color:rgba(var(--pst-color-sidebar-link-hover),1)}.bd-sidebar li.has-children>.reference{padding-right:30px}div.doctest>div.highlight span.gp,span.linenos,table.highlighttable td.linenos{user-select:none;-webkit-user-select:text;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none}.docutils.container{padding-left:unset;padding-right:unset} \ No newline at end of file diff --git a/_preview/77/_static/css/theme.css b/_preview/77/_static/css/theme.css deleted file mode 100644 index 2e03fe3..0000000 --- a/_preview/77/_static/css/theme.css +++ /dev/null @@ -1,120 +0,0 @@ -/* Provided by the Sphinx base theme template at build time */ -@import "../basic.css"; - -:root { - /***************************************************************************** - * Theme config - **/ - --pst-header-height: 60px; - - /***************************************************************************** - * Font size - **/ - --pst-font-size-base: 15px; /* base font size - applied at body / html level */ - - /* heading font sizes */ - --pst-font-size-h1: 36px; - --pst-font-size-h2: 32px; - --pst-font-size-h3: 26px; - --pst-font-size-h4: 21px; - --pst-font-size-h5: 18px; - --pst-font-size-h6: 16px; - - /* smaller then heading font sizes*/ - --pst-font-size-milli: 12px; - - --pst-sidebar-font-size: .9em; - --pst-sidebar-caption-font-size: .9em; - - /***************************************************************************** - * Font family - **/ - /* These are adapted from https://systemfontstack.com/ */ - --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", - Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; - --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, - Liberation Mono, Lucida Console, monospace; - - --pst-font-family-base: var(--pst-font-family-base-system); - --pst-font-family-heading: var(--pst-font-family-base); - --pst-font-family-monospace: var(--pst-font-family-monospace-system); - - /***************************************************************************** - * Color - * - * Colors are defined in rgb string way, "red, green, blue" - **/ - --pst-color-primary: 19, 6, 84; - --pst-color-success: 40, 167, 69; - --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ - --pst-color-warning: 255, 193, 7; - --pst-color-danger: 220, 53, 69; - --pst-color-text-base: 51, 51, 51; - - --pst-color-h1: var(--pst-color-primary); - --pst-color-h2: var(--pst-color-primary); - --pst-color-h3: var(--pst-color-text-base); - --pst-color-h4: var(--pst-color-text-base); - --pst-color-h5: var(--pst-color-text-base); - --pst-color-h6: var(--pst-color-text-base); - --pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: '\f058'; - --pst-icon-info-circle: '\f05a'; - --pst-icon-exclamation-triangle: '\f071'; - --pst-icon-exclamation-circle: '\f06a'; - --pst-icon-times-circle: '\f057'; - --pst-icon-lightbulb: '\f0eb'; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - -} diff --git a/_preview/77/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/77/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 3225661..0000000 --- a/_preview/77/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ /dev/null @@ -1 +0,0 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) 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rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - this.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? 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Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
    " ], - col: [ 2, "", "
    " ], - tr: [ 2, "", "
    " ], - td: [ 3, "", "
    " ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " -{% endmacro %} \ No newline at end of file diff --git a/_preview/77/genindex.html b/_preview/77/genindex.html deleted file mode 100644 index b738b99..0000000 --- a/_preview/77/genindex.html +++ /dev/null @@ -1,389 +0,0 @@ - - - - - - - - Index — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    - - \ No newline at end of file diff --git a/_preview/77/index.html b/_preview/77/index.html deleted file mode 100644 index 960bb69..0000000 --- a/_preview/77/index.html +++ /dev/null @@ -1 +0,0 @@ - diff --git a/_preview/77/notebooks/example-workflows/ecs-cmip6.html b/_preview/77/notebooks/example-workflows/ecs-cmip6.html deleted file mode 100644 index 73f7d35..0000000 --- a/_preview/77/notebooks/example-workflows/ecs-cmip6.html +++ /dev/null @@ -1,4183 +0,0 @@ - - - - - - - - Estimating Equilibrium Climate Sensitivity — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - On this page -
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    CMIP6 image

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    Estimating Equilibrium Climate Sensitivity

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    Overview

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    Equilibrium Climate Sensitivity (ECS) is defined as change in global-mean near-surface air temperature (GMST) change due to an instantaneous doubling of CO\(_2\) concentrations and once the coupled ocean-atmosphere-sea ice system has acheived a statistical equilibrium (i.e. at the top-of-atmosphere, incoming solar shortwave radiation is balanced by reflected solar shortwave and outgoing thermal longwave radiation).

    -

    This notebook uses the “Gregory method” to approximate the ECS of CMIP6 models based on the first 150 years after an abrupt doubling of CO\(_2\) -concentrations. The Gregory method extrapolates the quasi-linear relationship between GMST and radiative imbalance at the top-of-atmosphere to estimate how much warming would occur if the system were in radiative balance at the top-of-atmosphere, which is by definition the equilibrium response. In particular, we extrapolate the linear relationship that occurs between 100 and 150 years after the abrupt quadrupling.

    -

    Since the radiative forcing due to CO\(_2\) is a logarithmic function of the CO\(_2\) concentration, the GMST change from a first doubling is roughly the same as for a second doubling (to first order, we can assume feedbacks as constant), which means that the GMST change due to a quadrupling of CO\(_2\) is roughly \(\Delta T_{4\times\mathrm{CO}_2}=2\times\mathrm{ECS}\). See also Mauritsen et al. 2019 for a detailed application of the Gregory method (with modifications) for the case of one specific CMIP6 model, the MPI-M Earth System Model.

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    For another take on applying the Gregory method to estimate ECS, see Angeline Pendergrass’ code.

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    Prerequisites

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    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Dask

    Helpful

    Climate sensitivity

    Helpful

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    • Time to learn: 30 minutes

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    Imports

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    from matplotlib import pyplot as plt
-import sys
-import numpy as np
-import pandas as pd
-import xarray as xr
-import cartopy
-import dask
-from tqdm.autonotebook import tqdm  # Fancy progress bars for our loops!
-import intake
-import fsspec
-from dask_gateway import Gateway
-from dask.distributed import Client
-
-%matplotlib inline
-plt.rcParams['figure.figsize'] = 12, 6
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    -
    /tmp/ipykernel_98/1804016931.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
-  from tqdm.autonotebook import tqdm  # Fancy progress bars for our loops!
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    Compute Cluster

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    Here we use a dask cluster to parallelize our analysis.

    -
    -
    -
    platform = sys.platform
-
-if (platform == 'win32'):
-    import multiprocessing.popen_spawn_win32
-else:
-    import multiprocessing.popen_spawn_posix
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    Initiate the Dask client:

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    client = Client()
-client
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    Client

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    Client-03fec84d-9dde-11ee-8062-92cdf4efe03d

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

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    Data catalogs

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    This notebook uses intake-esm to ingest and organize climate model output from the fresh-off-the-supercomputers Phase 6 of the Coupled Model Intercomparison Project (CMIP6).

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    The file https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv in Google Cloud Storage contains thousands of lines of metadata, each describing an individual climate model experiment’s simulated data.

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    For example, the first line in the .csv file contains the precipitation rate (variable_id = 'pr'), as a function of latitude, longitude, and time, in an individual climate model experiment with the BCC-ESM1 model (source_id = 'BCC-ESM1') developed by the Beijing Climate Center (institution_id = 'BCC'). The model is forced by the forcing experiment SSP370 (experiment_id = 'ssp370'), which stands for the Shared Socio-Economic Pathway 3 that results in a change in radiative forcing of \(\Delta F=7.0\) W m\(^{-2}\) from pre-industrial to 2100. This simulation was run as part of the AerChemMIP activity, which is a spin-off of the CMIP activity that focuses specifically on how aerosol chemistry affects climate.

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    df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
-df.head()
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    1HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrsdsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    2HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrlusgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    3HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrldsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    4HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpslgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
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    The file pangeo-cmip6.json describes the structure of the CMIP6 metadata and is formatted so as to be read in by the intake.open_esm_datastore method, which categorizes all of the data pointers into a tiered collection. For example, this collection contains the simulated data from 28691 individual experiments, representing 48 different models from 23 different scientific institutions. There are 190 different climate variables (e.g. sea surface temperature, sea ice concentration, atmospheric winds, dissolved organic carbon in the ocean, etc.) available for 29 different forcing experiments.

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    Use Intake-ESM

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    Intake-ESM is a new package designed to make working with these data archives a bit simpler.

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    col = intake.open_esm_datastore("https://storage.googleapis.com/cmip6/pangeo-cmip6.json")
-col
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    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -

    pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unique
    activity_id18
    institution_id36
    source_id88
    experiment_id170
    member_id657
    table_id37
    variable_id700
    grid_label10
    zstore514818
    dcpp_init_year60
    version736
    derived_variable_id0
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    Here, we show the various forcing experiments that climate modellers ran in these simulations.

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    df['experiment_id'].unique()
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    array(['highresSST-present', 'piControl', 'control-1950', 'hist-1950',
-       'historical', 'amip', 'abrupt-4xCO2', 'abrupt-2xCO2',
-       'abrupt-0p5xCO2', '1pctCO2', 'ssp585', 'esm-piControl', 'esm-hist',
-       'hist-piAer', 'histSST-1950HC', 'ssp245', 'hist-1950HC', 'histSST',
-       'piClim-2xVOC', 'piClim-2xNOx', 'piClim-2xdust', 'piClim-2xss',
-       'piClim-histall', 'hist-piNTCF', 'histSST-piNTCF',
-       'aqua-control-lwoff', 'piClim-lu', 'histSST-piO3', 'piClim-CH4',
-       'piClim-NTCF', 'piClim-NOx', 'piClim-O3', 'piClim-HC',
-       'faf-heat-NA0pct', 'ssp370SST-lowCH4', 'piClim-VOC',
-       'ssp370-lowNTCF', 'piClim-control', 'piClim-aer', 'hist-aer',
-       'faf-heat', 'faf-heat-NA50pct', 'ssp370SST-lowNTCF',
-       'ssp370SST-ssp126Lu', 'ssp370SST', 'ssp370pdSST', 'histSST-piAer',
-       'piClim-ghg', 'piClim-anthro', 'faf-all', 'hist-nat', 'hist-GHG',
-       'ssp119', 'piClim-histnat', 'piClim-4xCO2', 'ssp370',
-       'piClim-histghg', 'highresSST-future', 'esm-ssp585-ssp126Lu',
-       'ssp126-ssp370Lu', 'ssp370-ssp126Lu', 'land-noLu', 'histSST-piCH4',
-       'ssp126', 'esm-pi-CO2pulse', 'amip-hist', 'piClim-histaer',
-       'amip-4xCO2', 'faf-water', 'faf-passiveheat', '1pctCO2-rad',
-       'faf-stress', '1pctCO2-bgc', 'aqua-control', 'amip-future4K',
-       'amip-p4K', 'aqua-p4K', 'amip-lwoff', 'amip-m4K', 'aqua-4xCO2',
-       'amip-p4K-lwoff', 'hist-noLu', '1pctCO2-cdr',
-       'land-hist-altStartYear', 'land-hist', 'omip1', 'esm-pi-cdr-pulse',
-       'esm-ssp585', 'abrupt-solp4p', 'piControl-spinup', 'hist-stratO3',
-       'abrupt-solm4p', 'midHolocene', 'lig127k', 'aqua-p4K-lwoff',
-       'esm-piControl-spinup', 'ssp245-GHG', 'ssp245-nat',
-       'dcppC-amv-neg', 'dcppC-amv-ExTrop-neg', 'dcppC-atl-control',
-       'dcppC-amv-pos', 'dcppC-ipv-NexTrop-neg', 'dcppC-ipv-NexTrop-pos',
-       'dcppC-atl-pacemaker', 'dcppC-amv-ExTrop-pos',
-       'dcppC-amv-Trop-neg', 'dcppC-pac-control', 'dcppC-ipv-pos',
-       'dcppC-pac-pacemaker', 'dcppC-ipv-neg', 'dcppC-amv-Trop-pos',
-       'piClim-BC', 'piClim-2xfire', 'piClim-SO2', 'piClim-OC',
-       'piClim-N2O', 'piClim-2xDMS', 'ssp460', 'ssp434', 'ssp534-over',
-       'deforest-globe', 'historical-cmip5', 'hist-bgc',
-       'piControl-cmip5', 'rcp26-cmip5', 'rcp45-cmip5', 'rcp85-cmip5',
-       'pdSST-piArcSIC', 'pdSST-piAntSIC', 'piSST-piSIC', 'piSST-pdSIC',
-       'ssp245-stratO3', 'hist-sol', 'hist-CO2', 'hist-volc',
-       'hist-totalO3', 'hist-nat-cmip5', 'hist-aer-cmip5',
-       'hist-GHG-cmip5', 'pdSST-futAntSIC', 'futSST-pdSIC', 'pdSST-pdSIC',
-       'ssp245-aer', 'pdSST-futArcSIC', 'dcppA-hindcast', 'dcppA-assim',
-       'dcppC-hindcast-noPinatubo', 'dcppC-hindcast-noElChichon',
-       'dcppC-hindcast-noAgung', 'hist-resIPO', 'ssp245-cov-modgreen',
-       'ssp245-cov-fossil', 'ssp245-cov-strgreen', 'ssp245-covid', 'lgm',
-       'ssp585-bgc', '1pctCO2to4x-withism', '1pctCO2-4xext', 'past1000',
-       'pa-futArcSIC', 'pa-pdSIC', 'historical-ext', 'pdSST-futArcSICSIT',
-       'pdSST-futOkhotskSIC', 'pdSST-futBKSeasSIC', 'pa-piArcSIC',
-       'pa-piAntSIC', 'pa-futAntSIC', 'pdSST-pdSICSIT'], dtype=object)
-
    -
    -
    -
    -
    -
    -

    Loading Data

    -

    Intake-ESM enables loading data directly into an xarray.DataArray, a metadata-aware extension of numpy arrays. Xarray objects leverage Dask to only read data into memory as needed for any specific operation (i.e. lazy evaluation). Think of Xarray Datasets as ways of conveniently organizing large arrays of floating point numbers (e.g. climate model data) on an n-dimensional discrete grid, with important metadata such as units, variable, names, etc.

    -

    Note that data on the cloud are in Zarr format, an extension of the metadata-aware format NetCDF commonly used in the geosciences.

    -

    Intake-ESM has rules for aggegating datasets; these rules are defined in the collection-specification file.

    -

    Here, we choose the piControl experiment (in which CO\(_2\) concentrations are held fixed at a pre-industrial level of ~300 ppm) and abrupt-2xCO2 experiment (in which CO\(_2\) concentrations are instantaneously doubled from a pre-industrial control state). Since the radiative forcing of CO\(_2\) is roughly a logarithmic function of CO\(_2\) concentrations, the ECS is roughly independent of the initial CO\(_2\) concentration.

    -
    -

    Warning

    -

    The version of this notebook in the -Pangeo Gallery -uses the abrupt-4xCO2 forcing experiment, but fewer abrupt-2xCO2 datasets are currently avaiable in Google Cloud Storage, which significantly reduces run time. If you want to run this notebook on your own computer with the abrupt-4xCO2 experiment instead, change co2_option in the cell below. You will also need to take half of ecs, as described in the Overview.

    -
    -
    -
    -
    co2_option = 'abrupt-2xCO2'
-
    -
    -
    -
    -
    -
    -

    Prepare Data

    -
    -
    -
    query = dict(
-    experiment_id=[co2_option,'piControl'], # pick the `abrupt-2xCO2` and `piControl` forcing experiments
-    table_id='Amon',                            # choose to look at atmospheric variables (A) saved at monthly resolution (mon)
-    variable_id=['tas', 'rsut','rsdt','rlut'],  # choose to look at near-surface air temperature (tas) as our variable
-    member_id = 'r1i1p1f1',                     # arbitrarily pick one realization for each model (i.e. just one set of initial conditions)
-)
-
-col_subset = col.search(require_all_on=["source_id"], **query)
-col_subset.df.groupby("source_id")[
-    ["experiment_id", "variable_id", "table_id"]
-].nunique()
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    experiment_idvariable_idtable_id
    source_id
    CESM2241
    GISS-E2-1-G241
    GISS-E2-1-H241
    GISS-E2-2-G241
    IPSL-CM6A-LR241
    MIROC6241
    MRI-ESM2-0241
    -
    -
    -

    The following functions help us load and homogenize the data. We use some dask.delayed programming to open the datasets in parallel.

    -
    -
    -
    def drop_all_bounds(ds):
-    """Drop coordinates like 'time_bounds' from datasets,
-    which can lead to issues when merging."""
-    drop_vars = [vname for vname in ds.coords
-                 if (('_bounds') in vname ) or ('_bnds') in vname]
-    return ds.drop(drop_vars)
-
-def open_dsets(df):
-    """Open datasets from cloud storage and return xarray dataset."""
-    dsets = [xr.open_zarr(fsspec.get_mapper(ds_url), consolidated=True)
-             .pipe(drop_all_bounds)
-             for ds_url in df.zstore]
-    try:
-        ds = xr.merge(dsets, join='exact')
-        return ds
-    except ValueError:
-        return None
-
-def open_delayed(df):
-    """A dask.delayed wrapper around `open_dsets`.
-    Allows us to open many datasets in parallel."""
-    return dask.delayed(open_dsets)(df)
-
    -
    -
    -
    -

    Create a nested dictionary of models and experiments. It will be structured like this:

    -
    {'CESM2':
-  {
-    'piControl': <xarray.Dataset>,
-    'abrupt-2xCO2': <xarray.Dataset>
-  },
-  ...
-}
-
    -
    -
    -
    -
    from collections import defaultdict
-
-dsets = defaultdict(dict)
-for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):
-    dsets[group[0]][group[1]] = open_delayed(df)
-
    -
    -
    -
    -

    Open one of the datasets directly, just to show what it looks like:

    -
    -
    -
    %time open_dsets(df)
-
    -
    -
    -
    -
    CPU times: user 2.72 s, sys: 94.1 ms, total: 2.82 s
-Wall time: 13.7 s
-
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:  (lat: 160, lon: 320, time: 8412)
-Coordinates:
-    height   float64 ...
-  * lat      (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
-  * lon      (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 355.5 356.6 357.8 358.9
-  * time     (time) object 1850-01-16 00:00:00 ... 2550-12-16 00:00:00
-Data variables:
-    tas      (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>
-    rsut     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>
-    rsdt     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>
-    rlut     (time, lat, lon) float32 dask.array<chunksize=(600, 160, 320), meta=np.ndarray>
-Attributes: (12/47)
-    Conventions:            CF-1.7 CMIP-6.2
-    activity_id:            CMIP
-    branch_method:          standard
-    branch_time_in_child:   0.0
-    branch_time_in_parent:  365243.0
-    cmor_version:           3.4.0
-    ...                     ...
-    tracking_id:            hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6
-    variable_id:            tas
-    variant_label:          r1i1p1f1
-    status:                 2019-10-25;created;by nhn2@columbia.edu
-    netcdf_tracking_ids:    hdl:21.14100/a0bd7d8f-4785-4687-b510-fd87808051b6
-    version_id:             v20190222
    -
    -

    Now use dask to do this in parallel on all of the datasets:

    -
    -
    -
    dsets_ = dask.compute(dict(dsets))[0]
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-/srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    -
    -
    -
    -

    Reduce Data via Global Mean

    -

    We don’t want to load all of the raw model data into memory right away. Instead, we want to reduce the data by taking the global mean. We need to remember to weight this global mean by a factor proportional to cos(lat).

    -
    -
    -
    def get_lat_name(ds):
-    """Figure out what is the latitude coordinate for each dataset."""
-    for lat_name in ['lat', 'latitude']:
-        if lat_name in ds.coords:
-            return lat_name
-    raise RuntimeError("Couldn't find a latitude coordinate")
-
-def global_mean(ds):
-    """Return global mean of a whole dataset."""
-    lat = ds[get_lat_name(ds)]
-    weight = np.cos(np.deg2rad(lat))
-    weight /= weight.mean()
-    other_dims = set(ds.dims) - {'time'}
-    return (ds * weight).mean(other_dims)
-
    -
    -
    -
    -

    We now apply this function, plus resampling to annual mean data, to all of the datasets. We also concatenate the experiments together into a single Dataset for each model. This is the most complex cell in the notebook. A lot is happening here.

    -
    -
    -
    expts = ['piControl', co2_option]
-expt_da = xr.DataArray(expts, dims='experiment_id',
-                       coords={'experiment_id': expts})
-
-dsets_aligned = {}
-
-for k, v in tqdm(dsets_.items()):
-    expt_dsets = v.values()
-    if any([d is None for d in expt_dsets]):
-        print(f"Missing experiment for {k}")
-        continue
-
-    for ds in expt_dsets:
-        ds.coords['year'] = ds.time.dt.year - ds.time.dt.year[0]
-
-    # workaround for
-    # https://github.com/pydata/xarray/issues/2237#issuecomment-620961663
-    dsets_ann_mean = [v[expt].pipe(global_mean)
-                             .swap_dims({'time': 'year'})
-                             .drop('time')
-                             .coarsen(year=12).mean()
-                      for expt in expts]
-
-    # align everything with the 2xCO2 experiment
-    dsets_aligned[k] = xr.concat(dsets_ann_mean, join='right',
-                                 dim=expt_da)
-
    -
    -
    -
    -
    -
    -
    -
    -

    Do the Computation

    -

    Up to this point, no computations have actually happened. Everything has been “lazy”. Now we trigger the computation to actual occur and load the global/annual mean data into memory.

    -
    -
    -
    dsets_aligned_ = dask.compute(dsets_aligned)[0]
-
    -
    -
    -
    -

    Now we concatenate across models to produce one big dataset with all the required variables.

    -
    -
    -
    source_ids = list(dsets_aligned_.keys())
-source_da = xr.DataArray(source_ids, dims='source_id',
-                         coords={'source_id': source_ids})
-
-big_ds = xr.concat([ds.reset_coords(drop=True)
-                    for ds in dsets_aligned_.values()],
-                   dim=source_da)
-big_ds
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:        (year: 250, experiment_id: 2, source_id: 7)
-Coordinates:
-  * year           (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0
-  * experiment_id  (experiment_id) <U12 'piControl' 'abrupt-2xCO2'
-  * source_id      (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MRI-ESM2-0'
-Data variables:
-    rsut           (source_id, experiment_id, year) float64 99.36 99.8 ... nan
-    tas            (source_id, experiment_id, year) float64 287.0 287.1 ... nan
-    rsdt           (source_id, experiment_id, year) float64 340.3 340.3 ... nan
-    rlut           (source_id, experiment_id, year) float64 240.2 240.5 ... nan
    -
    -
    -
    -

    Calculated Derived Variables

    -

    We need to calculate the net radiative imbalance, plus the anomaly of the abrupt 2xCO2 run compared to the piControl run.

    -
    -
    -
    big_ds['imbalance'] = big_ds['rsdt'] - big_ds['rsut'] - big_ds['rlut']
-
-ds_mean = big_ds[['tas', 'imbalance']].sel(experiment_id='piControl').mean(dim='year')
-ds_anom = big_ds[['tas', 'imbalance']] - ds_mean
-
-# add some metadata
-ds_anom.tas.attrs['long_name'] = 'Global Mean Surface Temp Anom'
-ds_anom.tas.attrs['units'] = 'K'
-ds_anom.imbalance.attrs['long_name'] = 'Global Mean Radiative Imbalance'
-ds_anom.imbalance.attrs['units'] = 'W m$^{-2}$'
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-ds_anom
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    <xarray.Dataset>
-Dimensions:        (year: 250, experiment_id: 2, source_id: 7)
-Coordinates:
-  * year           (year) float64 0.0 1.0 2.0 3.0 ... 246.0 247.0 248.0 249.0
-  * experiment_id  (experiment_id) <U12 'piControl' 'abrupt-2xCO2'
-  * source_id      (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MRI-ESM2-0'
-Data variables:
-    tas            (source_id, experiment_id, year) float64 0.06312 ... nan
-    imbalance      (source_id, experiment_id, year) float64 -0.04953 ... nan
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    Plot Timeseries

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    Here we plot the global mean surface temperature for each model:

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    ds_anom.tas.plot.line(col='source_id', x='year', col_wrap=5)
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    <xarray.plot.facetgrid.FacetGrid at 0x7f11d6f93490>
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    -../../_images/2c585d68371e9a955e97d040a88bec5f8faebf52b6e7a93ff47dfc82898f2983.png -
    -
    -

    We can see that the models cover different time intervals. Let’s limit the rest of our analysis to the first 150 years.

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    first_150_years = slice(0, 149)
-ds_anom.tas.sel(year=first_150_years).plot.line(col='source_id', x='year', col_wrap=5)
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    <xarray.plot.facetgrid.FacetGrid at 0x7f11d675cbb0>
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    Same thing for radiative imbalance:

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    ds_anom.imbalance.sel(year=first_150_years).plot.line(col='source_id', x='year', col_wrap=5)
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    <xarray.plot.facetgrid.FacetGrid at 0x7f11d675d330>
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    Calculate ECS

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    ds_abrupt = ds_anom.sel(year=first_150_years, experiment_id=co2_option).reset_coords(drop=True)
-
-def calc_ecs(ds):
-    # Some sources don't have all 150 years, drop those missing values.
-    a, b = np.polyfit(ds.tas.dropna("year"),
-                      ds.imbalance.dropna("year"), 1)
-    ecs = -1.0 * (b/a) # Change -1.0 to -0.5 if using 4xCO2
-    return xr.DataArray(ecs)
-
-ds_abrupt['ecs'] = ds_abrupt.groupby('source_id').apply(calc_ecs)
-ds_abrupt.compute()
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    <xarray.Dataset>
-Dimensions:    (year: 150, source_id: 7)
-Coordinates:
-  * year       (year) float64 0.0 1.0 2.0 3.0 4.0 ... 146.0 147.0 148.0 149.0
-  * source_id  (source_id) <U12 'CESM2' 'GISS-E2-1-G' ... 'MIROC6' 'MRI-ESM2-0'
-Data variables:
-    tas        (source_id, year) float64 0.5582 0.8175 1.095 ... 1.646 1.616
-    imbalance  (source_id, year) float64 4.026 3.355 3.579 ... 1.022 0.7031
-    ecs        (source_id) float64 3.526 2.659 2.932 2.501 3.827 2.235 2.552
    -
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    Also, make sure that we set a couple of the variables to be coordinates.

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    ds_abrupt = ds_abrupt.set_coords(['tas', 'imbalance'])
-
-fg = ds_abrupt.plot.scatter(x='tas', y='imbalance', col='source_id', col_wrap=4, add_colorbar=False)
-
-def calc_and_plot_ecs(x, y, **kwargs):
-    x = x[~np.isnan(x)]
-    y = y[~np.isnan(y)]
-    a, b = np.polyfit(x, y, 1)
-    ecs = -1.0 * b/a
-    plt.autoscale(False)
-    plt.plot([0, 10], np.polyval([a, b], [0, 10]), 'k')
-    plt.text(2, 3, f'ECS = {ecs:3.2f}', fontdict={'weight': 'bold', 'size': 12})
-    plt.grid()
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-fg.map(calc_and_plot_ecs, 'tas', 'imbalance')
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    <xarray.plot.facetgrid.FacetGrid at 0x7f11b3d0fdc0>
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    ds_abrupt.ecs.plot.hist();
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    ds_abrupt.ecs.to_dataframe().sort_values('ecs').plot(kind='bar')
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    -
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    <Axes: xlabel='source_id'>
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    -
    -

    We’re at the end of the notebook, so let’s shutdown our Dask cluster.

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    client.shutdown()
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    Summary

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    In this notebook, we estimated ECS for a subset of CMIP6 models using the Gregory method.

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    What’s next?

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    We will plot global average surface air temperature for a historical run and two branching emissions scenarios.

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    Resources and references

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    previous

    -

    Search and Load CMIP6 Data via ESGF/OPeNDAP

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    next

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    Calculating ENSO Using Intake-ESGF

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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    - - \ No newline at end of file diff --git a/_preview/77/notebooks/example-workflows/enso-esgf.html b/_preview/77/notebooks/example-workflows/enso-esgf.html deleted file mode 100644 index 00b0375..0000000 --- a/_preview/77/notebooks/example-workflows/enso-esgf.html +++ /dev/null @@ -1,13361 +0,0 @@ - - - - - - - - Calculating ENSO Using Intake-ESGF — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Calculating ENSO Using Intake-ESGF

    -
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    Overview

    -

    In this workflow, we combine topics covered in previous Pythia Foundations and CMIP6 Cookbook content to apply the Niño 3.4 Index to a broader range of datasets. As a refresher of what the ENSO 3.4 index is, please see the following text, which is also included in the ENSO Xarray content in the Pythia Foundations content.

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    Niño 3.4 (5N-5S, 170W-120W): The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more.

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    Niño X Index computation: a) Compute area averaged total SST from Niño X region; b) Compute monthly climatology (e.g., 1950-1979) for area averaged total SST from Niño X region, and subtract climatology from area averaged total SST time series to obtain anomalies; c) Smooth the anomalies with a 5-month running mean; d) Normalize the smoothed values by its standard deviation over the climatological period.

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    -

    The previous example in the Pythia Foundations content detailed a single simulation. In this example, we aim to apply this computation more generically across a variety of datasets.

    -

    The overall goal of this tutorial is to produce a plot of ENSO data using Xarray and intake-ESGF. The plots will resemble the Oceanic Niño Index plot shown below.

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    ONI index plot from NCAR Climate Data Guide

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    Prerequisites

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

    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    hvPlot Basics

    Necessary

    Interactive Visualization with hvPlot

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Calculating ENSO with Xarray

    Neccessary

    Understanding of Masking and Xarray Functions

    Dask

    Helpful

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    • Time to learn: 30 minutes

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    Imports

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    import hvplot.xarray
-import holoviews as hv
-import numpy as np
-import hvplot.xarray
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-from intake_esgf import ESGFCatalog
-import xarray as xr
-import cf_xarray
-import warnings
-warnings.filterwarnings("ignore")
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-hv.extension("bokeh")
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    Access ESGF-hosted CMIP6 Data

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    We will use the Climate Model Intercomparison Project version 6 (CMIP6) dataset, which is available from the Earth System Grid Federation (ESGF) data servers.

    -

    There is a toolkit, intake-esgf, we can use to interface with the data servers, making it easier to search for our datasets.

    -
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    -
    cat = ESGFCatalog()
-cat.search(
-        activity_id='CMIP',
-        experiment_id=["historical","ssp585"],
-        source_id="CESM2",
-        variable_id=["tos"],
-        member_id='r11i1p1f1',
-        table_id="Omon",
-    )
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    Summary information for 2 results:
-mip_era                [CMIP6]
-activity_id             [CMIP]
-institution_id          [NCAR]
-source_id              [CESM2]
-experiment_id     [historical]
-member_id          [r11i1p1f1]
-table_id                [Omon]
-variable_id              [tos]
-grid_label            [gn, gr]
-dtype: object
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    Load into a DataTree

    -

    Once we subset for our data, we can load the data into a datatree, which is a nested structure of xarray.Datasets, which include the climate grid cell statistics as well!

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    <xarray.DatasetView>
-Dimensions:  ()
-Data variables:
-    *empty*
    -
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    The nodes in the tree represent two different, grids. We would like to stay on the native model grid, using the gn node of the datatree, which represents the model native grid data.

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    ds = tos_tree["gn"].to_dataset()
-ds
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    <xarray.Dataset>
-Dimensions:    (time: 1980, nlat: 384, nlon: 320, d2: 2, vertices: 4)
-Coordinates:
-    lat        (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19
-    lon        (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8
-  * nlat       (nlat) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384
-  * nlon       (nlon) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320
-  * time       (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00
-Dimensions without coordinates: d2, vertices
-Data variables:
-    tos        (time, nlat, nlon) float32 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>
-    time_bnds  (time, d2) object dask.array<chunksize=(1, 2), meta=np.ndarray>
-    lat_bnds   (time, nlat, nlon, vertices) float32 dask.array<chunksize=(600, 384, 320, 4), meta=np.ndarray>
-    lon_bnds   (time, nlat, nlon, vertices) float32 dask.array<chunksize=(600, 384, 320, 4), meta=np.ndarray>
-    areacello  (nlat, nlon) float32 ...
-    sftof      (nlat, nlon) float32 ...
-Attributes: (12/45)
-    Conventions:            CF-1.7 CMIP-6.2
-    activity_id:            CMIP
-    branch_method:          standard
-    branch_time_in_child:   674885.0
-    branch_time_in_parent:  219000.0
-    case_id:                972
-    ...                     ...
-    sub_experiment_id:      none
-    table_id:               Omon
-    tracking_id:            hdl:21.14100/b0ffb89d-095d-4533-a159-a2e1241ff138
-    variable_id:            tos
-    variant_info:           CMIP6 20th century experiments (1850-2014) with C...
-    variant_label:          r11i1p1f1
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    Calculate ENSO

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    The calculation is covered in more detail in the Pythia Foundations book, here, we apply the calculation to our datasets!

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    def calculate_enso(ds):
-    
-    # Subset the El Nino 3.4 index region
-    dso = ds.where(
-    (ds.cf["latitude"] < 5) & (ds.cf["latitude"] > -5) & (ds.cf["longitude"] > 190) & (ds.cf["longitude"] < 240), drop=True
-    )
-    
-    # Calculate the monthly means
-    gb = dso.tos.groupby('time.month')
-    
-    # Subtract the monthly averages, returning the anomalies
-    tos_nino34_anom = gb - gb.mean(dim='time')
-    
-    # Determine the non-time dimensions and average using these
-    non_time_dims = set(tos_nino34_anom.dims)
-    non_time_dims.remove(ds.tos.cf["T"].name)
-    weighted_average = tos_nino34_anom.weighted(ds["areacello"]).mean(dim=list(non_time_dims))
-    
-    # Calculate the rolling average
-    rolling_average = weighted_average.rolling(time=5, center=True).mean()
-    std_dev = weighted_average.std()
-    return rolling_average / std_dev
-
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    enso_index = calculate_enso(ds).compute()
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    <xarray.DataArray 'tos' (time: 1980)>
-array([       nan,        nan, 0.06341499, ..., 0.79205155,        nan,
-              nan], dtype=float32)
-Coordinates:
-  * time     (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00
-    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12
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    Visualize ENSO

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    Basic Visualization

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    We can create a basic visualization of the dataset using hvplot!

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    enso_index.hvplot(x='time')
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    WARNING:param.CurvePlot00970: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
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    Identify El Niño and La Niña

    -

    Including the indices as we showed above is not always the most helpful. We need to add additional context to help the reader understand when we reach El Niño and La Niña, which are helpful thresholds for the wider community to use.

    -

    A typical threshold to use is 0.4, which means El Niño occurs when the ENSO 3.4 index is equal to or greater than 0.4, and La Niña occurs when the ENSO 3.4 index is equal to or less than 0.4.

    -

    We apply this using the following function.

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    -
    def add_enso_thresholds(da, threshold=0.4):
-    
-    # Conver the xr.DataArray into an xr.Dataset
-    ds = da.to_dataset()
-    
-    # Cleanup the time and use the thresholds
-    try:
-        ds["time"]= ds.indexes["time"].to_datetimeindex()
-    except:
-        pass
-    ds["tos_gt_04"] = ("time", ds.tos.where(ds.tos >= threshold, threshold).data)
-    ds["tos_lt_04"] = ("time", ds.tos.where(ds.tos <= -threshold, -threshold).data)
-    
-    # Add fields for the thresholds
-    ds["el_nino_threshold"] = ("time", np.zeros_like(ds.tos) + threshold)
-    ds["la_nina_threshold"] = ("time", np.zeros_like(ds.tos) - threshold)
-    
-    return ds
-
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    enso_ds = add_enso_thresholds(enso_index)
-enso_ds
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    <xarray.Dataset>
-Dimensions:            (time: 1980)
-Coordinates:
-  * time               (time) datetime64[ns] 1850-01-15T13:00:00.000007 ... 2...
-    month              (time) int64 1 2 3 4 5 6 7 8 9 ... 4 5 6 7 8 9 10 11 12
-Data variables:
-    tos                (time) float32 nan nan 0.06341 ... 0.7921 nan nan
-    tos_gt_04          (time) float32 0.4 0.4 0.4 0.4 ... 0.6829 0.7921 0.4 0.4
-    tos_lt_04          (time) float32 -0.4 -0.4 -0.4 -0.4 ... -0.4 -0.4 -0.4
-    el_nino_threshold  (time) float32 0.4 0.4 0.4 0.4 0.4 ... 0.4 0.4 0.4 0.4
-    la_nina_threshold  (time) float32 -0.4 -0.4 -0.4 -0.4 ... -0.4 -0.4 -0.4
    -
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    Configure a Function to Plot the Data

    -

    We will use the hvplot.area functionality here, which enables us to shade the area between values. We use the newly added variables in our dataset to help here.

    -
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    -
    def plot_enso(ds):
-    el_nino = ds.hvplot.area(x="time", y2='tos_gt_04', y='el_nino_threshold', color='red', hover=False)
-    el_nino_label = hv.Text(ds.isel(time=40).time.values, 2, 'El Niño').opts(text_color='red',)
-
-    # Create the La Niña area graphs
-    la_nina = ds.hvplot.area(x="time", y2='tos_lt_04', y='la_nina_threshold', color='blue', hover=False)
-    la_nina_label = hv.Text(ds.isel(time=-40).time.values, -2, 'La Niña').opts(text_color='blue')
-
-    # Plot a timeseries of the ENSO 3.4 index
-    enso = ds.tos.hvplot(x='time', line_width=0.5, color='k', xlabel='Year', ylabel='ENSO 3.4 Index')
-
-    # Combine all the plots into a single plot
-    return (el_nino_label * la_nina_label * el_nino * la_nina * enso)
-
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    plot_enso(enso_ds)
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    Apply to Multiple Datasets

    -

    Now that we have the workflow, let’s apply this to multiple datasets. We focus here on two different instiutions:

    -
      -

    • The National Center for Atmospheric Research (NCAR)

      • -

    • Model for Interdisciplinary Research on Climate (MIROC)

      • -
    -

    Both of these modeling centers produced output for CMIP6.

    -
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    Setup a Function for Searching and Combining Datasets

    -

    We can use the query mentioned previously to configure our search. Here, we parameterize based on the institution id (ex. NCAR, MIROC).

    -
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    -
    def search_esgf(institution_id, grid='gn'):
-    
-    # Search and load the ocean surface temperature (tos)
-    cat = ESGFCatalog()
-    cat.search(
-        activity_id="CMIP",
-        experiment_id="historical",
-        institution_id=institution_id,
-        variable_id=["tos"],
-        member_id='r11i1p1f1',
-        table_id="Omon",
-    )
-    try:
-        tos_ds = cat.to_datatree()[grid].to_dataset()
-    except KeyError:
-        tos_ds = cat.to_dataset_dict()["tos"]
-
-    return tos_ds
-
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    Apply the Search and Computations

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    ncar_ds = search_esgf("NCAR")
-enso_index_ncar = add_enso_thresholds(calculate_enso(ncar_ds).compute())
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-enso_index_miroc = add_enso_thresholds(calculate_enso(miroc_ds).compute())
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    Summary

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    In this notebook, we searched for and accessed two different CMIP6 datasets hosted through ESGF, calculated the ENSO 3.4 indices for the datasets, and created interactive plots comparing where we see El Niño and La Niña.

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    What’s next?

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    Resources and references

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    previous

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    Estimating Equilibrium Climate Sensitivity

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    next

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    Global Mean Surface Temperature

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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    - - \ No newline at end of file diff --git a/_preview/77/notebooks/example-workflows/esgf2-arm-comparison.html b/_preview/77/notebooks/example-workflows/esgf2-arm-comparison.html deleted file mode 100644 index 1785c7f..0000000 --- a/_preview/77/notebooks/example-workflows/esgf2-arm-comparison.html +++ /dev/null @@ -1,4123 +0,0 @@ - - - - - - - - Compare Data from ESGF and ARM — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ESGF logo -ARM logo

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    Compare Data from ESGF and ARM

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    Overview

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    This notebook details how to compare CMIP6 data hosted through the Earth System Grid Federation (ESGF) to observations collected and hosted through the Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility.

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    The measurement of focus is 2 meter air temperature, collected at the Southern Great Plains (SGP) site in Northern Oklahoma. This climate observatory has collected state-of-the-art observations since 1993.

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    Prerequisites

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    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Search and Load CMIP6 Data via ESGF/OPeNDAP

    Necessary

    Familiarity with data access patterns

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Dask Arrays with Xarray

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    Familiarity with lazy-loading

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    • Time to learn: 25 minutes

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    Imports

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    import os
-import warnings
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-import act
-from distributed import Client
-import holoviews as hv
-import hvplot.xarray
-from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-import cf_xarray
-import metpy
-from pyesgf.search import SearchConnection
-import xarray as xr
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-xr.set_options(display_style='html')
-warnings.filterwarnings("ignore")
-hv.extension('bokeh')
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    Spin up a Dask Cluster

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    We will use a Dask Local Cluster to compute in parellel and distribute our data, enabling us to work with these large datasets.

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    Client

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    Client-37a80ab4-9dde-11ee-8146-92cdf4efe03d

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    Connection method: Cluster objectCluster type: distributed.LocalCluster
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    Cluster Info

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    Access Data

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    Our first step is to access data from the ESGF data servers, and the Atmospheric Radiation Measurement (ARM) user facility, which has a long term site in Northern Oklahoma.

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    Access ESGF Data

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    A tutorial on how to access ESGF-hosted CMIP6 data is included in the Foundations section of this cookbook:

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    We use the following block of code to search for a single earth system model simulation, the Energe Exascale Earth System Model (E3SM), which is the Department of Energy’s flagship coupled Earth System Model.

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    conn = SearchConnection('https://esgf-node.llnl.gov/esg-search',
-                        distrib=False)
-ctx = conn.new_context(
-    facets='project,experiment_id',
-    project='CMIP6',
-    table_id='Amon',
-    institution_id = 'E3SM-Project',
-    experiment_id='historical',
-    source_id='E3SM-1-0',
-    variable='tas',
-    variant_label='r1i1p1f1',
-)
-result = ctx.search()[1]
-files = result.file_context().search()
-opendap_urls = [file.opendap_url for file in files]
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    esgf_ds = xr.open_mfdataset(opendap_urls,
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-                       chunks={'time':480})
-esgf_ds
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    <xarray.Dataset>
-Dimensions:    (time: 1980, bnds: 2, lat: 180, lon: 360)
-Coordinates:
-  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00
-  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
-  * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
-    height     float64 2.0
-Dimensions without coordinates: bnds
-Data variables:
-    time_bnds  (time, bnds) object dask.array<chunksize=(300, 2), meta=np.ndarray>
-    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(300, 180, 2), meta=np.ndarray>
-    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(300, 360, 2), meta=np.ndarray>
-    tas        (time, lat, lon) float32 dask.array<chunksize=(300, 180, 360), meta=np.ndarray>
-Attributes: (12/54)
-    Conventions:                     CF-1.7 CMIP-6.2
-    activity_id:                     CMIP
-    branch_method:                   standard
-    branch_time_in_child:            0.0
-    branch_time_in_parent:           36500.0
-    contact:                         Dave Bader (bader2@llnl.gov)
-    ...                              ...
-    e3sm_source_code_reference:      https://github.com/E3SM-Project/E3SM/rel...
-    doe_acknowledgement:             This research was supported as part of t...
-    computational_acknowledgement:   The data were produced using resources o...
-    ncclimo_generation_command:      ncclimo --var=${var} -7 --dfl_lvl=1 --no...
-    ncclimo_version:                 4.8.1-alpha04
-    DODS_EXTRA.Unlimited_Dimension:  time
    -
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    Clean up the dataset

    -

    We need to adjust the 0 to 360 degree longitude to be -180 to 180 - we can do this generically using the climate forecast (CF) conventions.

    -
    -
    -
    lon_coord = esgf_ds.cf['X'].name
-esgf_ds[lon_coord] = (esgf_ds[lon_coord] + 180) % 360 - 180
-esgf_ds = esgf_ds.sortby(lon_coord)
-
    -
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    -
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    -

    Access ARM Data

    -

    We use the ARM data API, which is included in the Atmospheric Data Community Toolkit (ACT) to access the data.

    - -
    -

    Load the Data Using Xarray

    -
    -
    -
    arm_ds = xr.open_mfdataset(files,
-                           combine='nested',
-                           concat_dim='time',
-                           chunks={'time':86400})
-
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    -
    -

    Subset and Prepare Data to be Compared

    -

    We need to subset the climate model output for the nearest grid point, over the SGP site.

    -
    -
    -
    lat = arm_ds.lat.values[0]
-lon = arm_ds.lon.values[0]
-lat, lon
-
    -
    -
    -
    -
    (36.605, -97.485)
-
    -
    -
    -
    -

    Xarray offers this subsetting functionality, and we specify we want the nearest gird point to the site.

    -
    -
    -
    cmip6_nearest = esgf_ds.cf.sel(lat=lat,
-                               lon=lon,
-                               method='nearest')
-cmip6_nearest
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:    (time: 1980, bnds: 2)
-Coordinates:
-  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00
-    lat        float64 36.5
-    lon        float64 -97.5
-    height     float64 2.0
-Dimensions without coordinates: bnds
-Data variables:
-    time_bnds  (time, bnds) object dask.array<chunksize=(300, 2), meta=np.ndarray>
-    lat_bnds   (time, bnds) float64 dask.array<chunksize=(300, 2), meta=np.ndarray>
-    lon_bnds   (time, bnds) float64 dask.array<chunksize=(300, 2), meta=np.ndarray>
-    tas        (time) float32 dask.array<chunksize=(300,), meta=np.ndarray>
-Attributes: (12/54)
-    Conventions:                     CF-1.7 CMIP-6.2
-    activity_id:                     CMIP
-    branch_method:                   standard
-    branch_time_in_child:            0.0
-    branch_time_in_parent:           36500.0
-    contact:                         Dave Bader (bader2@llnl.gov)
-    ...                              ...
-    e3sm_source_code_reference:      https://github.com/E3SM-Project/E3SM/rel...
-    doe_acknowledgement:             This research was supported as part of t...
-    computational_acknowledgement:   The data were produced using resources o...
-    ncclimo_generation_command:      ncclimo --var=${var} -7 --dfl_lvl=1 --no...
-    ncclimo_version:                 4.8.1-alpha04
-    DODS_EXTRA.Unlimited_Dimension:  time
    -
    -

    We need to convert our time to datetime to make it easier to compare.

    -
    -
    -
    cmip6_nearest['time'] = cmip6_nearest.indexes['time'].to_datetimeindex()
-
    -
    -
    -
    -

    Next, we select the times we have data from the SGP site, specified earlier in the notebook.

    -
    -
    -
    cmip6_nearest = cmip6_nearest.sel(time=slice(start_date,
-                                             end_date)).resample(time='1M').mean()
-
    -
    -
    -
    -
    -

    Calculate Monthly Mean Temperature at SGP

    -

    We can calculate the monthly average temperature at the SGP site using the resample method in Xarray.

    -
    -
    -
    arm_ds = arm_ds.sortby('time')
-sgp_monthly_mean_temperature = arm_ds.temp_mean.resample(time='1M').mean().compute().rename('tas (ARM)')
-
    -
    -
    -
    -

    We need to apply some data cleaning here too - converting our units of temperature to degrees Celsius for the CMIP6 data.

    -
    -
    -
    cmip6_monthly_mean_temperature = cmip6_nearest.tas.compute().metpy.quantify()
-
    -
    -
    -
    -
    -
    -
    cmip6_monthly_mean_temperature = cmip6_monthly_mean_temperature.metpy.convert_units('degC').rename("tas (CMIP6)")
-
    -
    -
    -
    -
    -
    -
    -

    Visaulize the Output

    -

    Once we have our comparisons ready, we can visualize using hvPlot, which produces an interactive visualization!

    -
    -
    -
    esgf_plot = cmip6_monthly_mean_temperature.hvplot.bar(title='Average Surface Temperature \n near the Southern Great Plains Field Site',
-                                                       xlabel='Time')
-arm_plot = sgp_monthly_mean_temperature.hvplot.bar(ylabel='Average Temperature (degC)',
-                                                    xlabel='Time')
-
-esgf_plot * arm_plot
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -

    Summary

    -

    In this notebook, we searched for and opened a CMIP6 E3SM dataset using the ESGF API and OPeNDAP, and compared to an ARM dataset collected at the Southern Great Plains climate observatory.

    -
    -

    What’s next?

    -

    We will see some more advanced examples of using the CMIP6 and obsverational data.

    -
    -
    -
    -

    Resources and references

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

    previous

    -

    Regridding with xESMF and calculating a multi-model mean

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

    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

    -
    -
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    - -
    - -
    - -
    -
    - - \ No newline at end of file diff --git a/_preview/77/notebooks/example-workflows/gmst.html b/_preview/77/notebooks/example-workflows/gmst.html deleted file mode 100644 index b603b4e..0000000 --- a/_preview/77/notebooks/example-workflows/gmst.html +++ /dev/null @@ -1,1638 +0,0 @@ - - - - - - - - Global Mean Surface Temperature — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - On this page -
    - -
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    - -

    CMIP6 image

    -
    -

    Global Mean Surface Temperature

    -
    -
    -

    Overview

    -

    This notebook uses similar techniques to the ECS notebook. Please refer to that notebook for details.

    -
    -
    -

    Prerequisites

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

    Concepts

    Importance

    Notes

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Seaborn

    Helpful

    -
      -

    • Time to learn: 10 minutes

      • -
    -
    -
    -
    -

    Imports

    -
    -
    -
    from matplotlib import pyplot as plt
-import xarray as xr
-import numpy as np
-import dask
-from dask.diagnostics import progress
-from tqdm.autonotebook import tqdm
-import intake
-import fsspec
-import seaborn as sns
-
-%matplotlib inline
-
    -
    -
    -
    -
    /tmp/ipykernel_526/1335193511.py:6: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
-  from tqdm.autonotebook import tqdm
-
    -
    -
    -
    -
    -
    -
    col = intake.open_esm_datastore("https://storage.googleapis.com/cmip6/pangeo-cmip6.json")
-col
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -

    pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unique
    activity_id18
    institution_id36
    source_id88
    experiment_id170
    member_id657
    table_id37
    variable_id700
    grid_label10
    zstore514818
    dcpp_init_year60
    version736
    derived_variable_id0
    -
    -
    -
    -
    -
    [eid for eid in col.df['experiment_id'].unique() if 'ssp' in eid]
-
    -
    -
    -
    -
    ['ssp585',
- 'ssp245',
- 'ssp370SST-lowCH4',
- 'ssp370-lowNTCF',
- 'ssp370SST-lowNTCF',
- 'ssp370SST-ssp126Lu',
- 'ssp370SST',
- 'ssp370pdSST',
- 'ssp119',
- 'ssp370',
- 'esm-ssp585-ssp126Lu',
- 'ssp126-ssp370Lu',
- 'ssp370-ssp126Lu',
- 'ssp126',
- 'esm-ssp585',
- 'ssp245-GHG',
- 'ssp245-nat',
- 'ssp460',
- 'ssp434',
- 'ssp534-over',
- 'ssp245-stratO3',
- 'ssp245-aer',
- 'ssp245-cov-modgreen',
- 'ssp245-cov-fossil',
- 'ssp245-cov-strgreen',
- 'ssp245-covid',
- 'ssp585-bgc']
-
    -
    -
    -
    -

    There is currently a significant amount of data for these runs:

    -
    -
    -
    expts = ['historical', 'ssp245', 'ssp585']
-
-query = dict(
-    experiment_id=expts,
-    table_id='Amon',
-    variable_id=['tas'],
-    member_id = 'r1i1p1f1',
-)
-
-col_subset = col.search(require_all_on=["source_id"], **query)
-col_subset.df.groupby("source_id")[
-    ["experiment_id", "variable_id", "table_id"]
-].nunique()
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    experiment_idvariable_idtable_id
    source_id
    ACCESS-CM2311
    AWI-CM-1-1-MR311
    BCC-CSM2-MR311
    CAMS-CSM1-0311
    CAS-ESM2-0311
    CESM2-WACCM311
    CIESM311
    CMCC-CM2-SR5311
    CMCC-ESM2311
    CanESM5311
    E3SM-1-1311
    EC-Earth3311
    EC-Earth3-CC311
    EC-Earth3-Veg311
    EC-Earth3-Veg-LR311
    FGOALS-f3-L311
    FGOALS-g3311
    FIO-ESM-2-0311
    GFDL-CM4311
    GFDL-ESM4311
    IITM-ESM311
    INM-CM4-8311
    INM-CM5-0311
    IPSL-CM6A-LR311
    KACE-1-0-G311
    KIOST-ESM311
    MIROC6311
    MPI-ESM1-2-HR311
    MPI-ESM1-2-LR311
    MRI-ESM2-0311
    NESM3311
    NorESM2-LM311
    NorESM2-MM311
    TaiESM1311
    -
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    -
    def drop_all_bounds(ds):
-    drop_vars = [vname for vname in ds.coords
-                 if (('_bounds') in vname ) or ('_bnds') in vname]
-    return ds.drop(drop_vars)
-
-def open_dset(df):
-    assert len(df) == 1
-    ds = xr.open_zarr(fsspec.get_mapper(df.zstore.values[0]), consolidated=True)
-    return drop_all_bounds(ds)
-
-def open_delayed(df):
-    return dask.delayed(open_dset)(df)
-
-from collections import defaultdict
-dsets = defaultdict(dict)
-
-for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):
-    dsets[group[0]][group[1]] = open_delayed(df)
-
    -
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    dsets_ = dask.compute(dict(dsets))[0]
-
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    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:832: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/indexing.py:560: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
-  array = array.get_duck_array()
-
    -
    -
    -
    -

    Calculate global means:

    -
    -
    -
    def get_lat_name(ds):
-    for lat_name in ['lat', 'latitude']:
-        if lat_name in ds.coords:
-            return lat_name
-    raise RuntimeError("Couldn't find a latitude coordinate")
-
-def global_mean(ds):
-    lat = ds[get_lat_name(ds)]
-    weight = np.cos(np.deg2rad(lat))
-    weight /= weight.mean()
-    other_dims = set(ds.dims) - {'time'}
-    return (ds * weight).mean(other_dims)
-
    -
    -
    -
    -
    -
    -
    expt_da = xr.DataArray(expts, dims='experiment_id', name='experiment_id',
-                       coords={'experiment_id': expts})
-
-dsets_aligned = {}
-
-for k, v in tqdm(dsets_.items()):
-    expt_dsets = v.values()
-    if any([d is None for d in expt_dsets]):
-        print(f"Missing experiment for {k}")
-        continue
-
-    for ds in expt_dsets:
-        ds.coords['year'] = ds.time.dt.year
-
-    # workaround for
-    # https://github.com/pydata/xarray/issues/2237#issuecomment-620961663
-    dsets_ann_mean = [v[expt].pipe(global_mean)
-                             .swap_dims({'time': 'year'})
-                             .drop('time')
-                             .coarsen(year=12).mean()
-                      for expt in expts]
-
-    # align everything with the 4xCO2 experiment
-    dsets_aligned[k] = xr.concat(dsets_ann_mean, join='outer',
-                                 dim=expt_da)
-
    -
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    dsets_aligned_ = dask.compute(dsets_aligned)[0]
-
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    source_ids = list(dsets_aligned_.keys())
-source_da = xr.DataArray(source_ids, dims='source_id', name='source_id',
-                         coords={'source_id': source_ids})
-
-big_ds = xr.concat([ds.reset_coords(drop=True)
-                    for ds in dsets_aligned_.values()],
-                    dim=source_da)
-
-big_ds
-
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    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:        (year: 451, experiment_id: 3, source_id: 34)
-Coordinates:
-  * year           (year) float64 1.85e+03 1.851e+03 ... 2.299e+03 2.3e+03
-  * experiment_id  (experiment_id) <U10 'historical' 'ssp245' 'ssp585'
-  * source_id      (source_id) <U16 'ACCESS-CM2' 'AWI-CM-1-1-MR' ... 'TaiESM1'
-Data variables:
-    tas            (source_id, experiment_id, year) float64 287.0 287.0 ... nan
    -
    -
    -
    -
    df_all = big_ds.sel(year=slice(1900, 2100)).to_dataframe().reset_index()
-df_all.head()
-
    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    yearexperiment_idsource_idtas
    01900.0historicalACCESS-CM2287.019917
    11900.0historicalAWI-CM-1-1-MR286.958154
    21900.0historicalBCC-CSM2-MR287.996260
    31900.0historicalCAMS-CSM1-0287.084974
    41900.0historicalCAS-ESM2-0287.263682
    -
    -
    -
    -
    -
    sns.relplot(data=df_all,
-            x="year", y="tas", hue='experiment_id',
-            kind="line", ci="sd", aspect=2);
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/seaborn/axisgrid.py:854: FutureWarning: 
-
-The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.
-
-  func(*plot_args, **plot_kwargs)
-
    -
    -../../_images/a44b9396df9b5f62bf48b66c1619e8dc0badb779cf2093f525344d61882832fd.png -
    -
    -
    -
    -
    -

    Summary

    -

    In this notebook, we accessed data for historical, SSP245, and SSP585 runs from a collection of CMIP6 models and plotted the multimodel-mean global average surface air temperature for each run.

    -
    -

    What’s next?

    -

    We will use CMIP6 data to analyze precipitation intensity under a warming climate.

    -
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    -

    Resources and references

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

    previous

    -

    Calculating ENSO Using Intake-ESGF

    -
    -     - -
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    next

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    Precipitation Frequency Analysis

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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    CMIP6 image

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    Precipitation Frequency Analysis

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    Overview

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    This notebook shows an advanced analysis case. The calculation was inspired by Angie Pendergrass’s work on precipitation statistics, as described in the following websites / papers:

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    We use xhistogram to calculate the distribution of precipitation intensity and its changes in a warming climate.

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    Prerequisites

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    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Dask

    Helpful

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    • Time to learn: 5 minutes

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    Imports

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    import os
-import sys
-from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-import xarray as xr
-import fsspec
-from tqdm.autonotebook import tqdm
-from xhistogram.xarray import histogram
-from dask_gateway import Gateway
-from dask.distributed import Client
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-%matplotlib inline
-plt.rcParams['figure.figsize'] = 12, 6
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    /tmp/ipykernel_803/106887996.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
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    Compute Cluster

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    Here we use a dask cluster to parallelize our analysis.

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    platform = sys.platform
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-if (platform == 'win32'):
-    import multiprocessing.popen_spawn_win32
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-    import multiprocessing.popen_spawn_posix
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    Initiate the Dask client:

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    client = Client()
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    Client

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    Client-c0ce0756-9dde-11ee-8323-92cdf4efe03d

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    Connection method: Cluster objectCluster type: distributed.LocalCluster
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    Cluster Info

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    Load Data Catalog

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    df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
-df.head()
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    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    1HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrsdsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    2HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrlusgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    3HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrldsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    4HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpslgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
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    df_3hr_pr = df[(df.table_id == '3hr') & (df.variable_id == 'pr')]
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    run_counts = df_3hr_pr.groupby(['source_id', 'experiment_id'])['zstore'].count()
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    source_id         experiment_id     
-BCC-CSM2-MR       historical             1
-                  ssp126                 1
-                  ssp245                 1
-                  ssp370                 1
-                  ssp585                 1
-CNRM-CM6-1        highresSST-present     1
-                  historical             3
-                  ssp126                 1
-                  ssp245                 1
-                  ssp370                 1
-                  ssp585                 1
-CNRM-CM6-1-HR     highresSST-present     1
-CNRM-ESM2-1       historical             1
-                  ssp126                 1
-                  ssp245                 1
-                  ssp370                 1
-                  ssp585                 1
-GFDL-CM4          1pctCO2                2
-                  abrupt-4xCO2           2
-                  amip                   2
-                  historical             2
-                  piControl              2
-GFDL-CM4C192      highresSST-future      1
-                  highresSST-present     1
-GFDL-ESM4         1pctCO2                1
-                  abrupt-4xCO2           1
-                  esm-hist               1
-                  historical             1
-                  ssp119                 1
-                  ssp126                 1
-                  ssp370                 1
-GISS-E2-1-G       historical             2
-HadGEM3-GC31-HM   highresSST-present     1
-HadGEM3-GC31-LM   highresSST-present     1
-HadGEM3-GC31-MM   highresSST-present     1
-IPSL-CM6A-ATM-HR  highresSST-present     1
-IPSL-CM6A-LR      highresSST-present     1
-                  historical            15
-                  piControl              1
-                  ssp126                 3
-                  ssp245                 2
-                  ssp370                10
-                  ssp585                 1
-MRI-ESM2-0        historical             1
-Name: zstore, dtype: int64
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    source_ids = []
-experiment_ids = ['historical', 'ssp585']
-for name, group in df_3hr_pr.groupby('source_id'):
-    if all([expt in group.experiment_id.values
-            for expt in experiment_ids]):
-        source_ids.append(name)
-source_ids
-
-# Use only one model. Otherwise it takes too long to run on GitHub.
-source_ids = ['BCC-CSM2-MR']
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    def load_pr_data(source_id, expt_id):
-    """
-    Load 3hr precip data for given source and expt ids
-    """
-    uri = df_3hr_pr[(df_3hr_pr.source_id == source_id) &
-                         (df_3hr_pr.experiment_id == expt_id)].zstore.values[0]
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-    ds = xr.open_zarr(fsspec.get_mapper(uri), consolidated=True)
-    return ds
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    def precip_hist(ds, nbins=100, pr_log_min=-3, pr_log_max=2):
-    """
-    Calculate precipitation histogram for a single model.
-    Lazy.
-    """
-    assert ds.pr.units == 'kg m-2 s-1'
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-    # mm/day
-    bins_mm_day = np.hstack([[0], np.logspace(pr_log_min, pr_log_max, nbins)])
-    bins_kg_m2s = bins_mm_day / (24*60*60)
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-    pr_hist = histogram(ds.pr, bins=[bins_kg_m2s], dim=['lon']).mean(dim='time')
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-    log_bin_spacing = np.diff(np.log(bins_kg_m2s[1:3])).item()
-    pr_hist_norm = 100 * pr_hist / ds.dims['lon'] / log_bin_spacing
-    pr_hist_norm.attrs.update({'long_name': 'zonal mean rain frequency',
-                               'units': '%/Δln(r)'})
-    return pr_hist_norm
-
-def precip_hist_for_expts(dsets, experiment_ids):
-    """
-    Calculate histogram for a suite of experiments.
-    Eager.
-    """
-    # actual data loading and computations happen in this next line
-    pr_hists = [precip_hist(ds).load()
-            for ds in [ds_hist, ds_ssp]]
-    pr_hist = xr.concat(pr_hists, dim=xr.Variable('experiment_id', experiment_ids))
-    return pr_hist
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    results = {}
-for source_id in tqdm(source_ids):
-    # get a 20 year period
-    ds_hist = load_pr_data(source_id, 'historical').sel(time=slice('1980', '2000'))
-    ds_ssp = load_pr_data(source_id, 'ssp585').sel(time=slice('2080', '2100'))
-    pr_hist = precip_hist_for_expts([ds_hist, ds_ssp], experiment_ids)
-    results[source_id] = pr_hist
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    def plot_precip_changes(pr_hist, vmax=5):
-    """
-    Visualize the output
-    """
-    pr_hist_diff = (pr_hist.sel(experiment_id='ssp585') -
-                    pr_hist.sel(experiment_id='historical'))
-    pr_hist.sel(experiment_id='historical')[:, 1:].plot.contour(xscale='log', colors='0.5', levels=21)
-    pr_hist_diff[:, 1:].plot.contourf(xscale='log', vmax=vmax, levels=21)
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    title = 'Change in Zonal Mean Rain Frequency'
-for source_id, pr_hist in results.items():
-    plt.figure()
-    plot_precip_changes(pr_hist)
-    plt.title(f'{title}: {source_id}')
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    -../../_images/54d11012a2b31d23c2cf6d720caf493580f808a43dfe10fd86f967c6e043911c.png -
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    We’re at the end of the notebook, so let’s shutdown our Dask cluster.

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    Summary

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    In this notebook, we used CMIP6 data to compare precipitation intensity in the SSP585 scenario to historical runs.

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    What’s next?

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    More examples of using CMIP6 data.

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    Resources and references

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    previous

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    Global Mean Surface Temperature

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    next

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    Regridding with xESMF and calculating a multi-model mean

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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    Regridding with xESMF and calculating a multi-model mean

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    Overview

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    The main goal of this workflow is to calculate the mean change in ocean heat uptake (OHU) associated with the transient climate response (TCR) for CMIP6. TCR is defined as the change in global mean surface temperature at the time of CO\(_2\) doubling in a climate model run with a 1% increase in CO\(_2\) per year. The amount and pattern of heat uptake into the oceans are important in determining the strength of radiative feedbacks and thus climate sensitivity. See Xie (2020) for an overview.

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    In order to use as many models as possible, we will need to load the model output in its native grid, then regrid to a common grid (here 1°x1° lat-lon) using xESMF. From there, we can take the average across models and either plot the result or save it as a netCDF file for later use.

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    Prerequisites

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    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Computations and Masks with Xarray

    Necessary

    Load CMIP6 Data with Intake-ESM

    Necessary

    Intro to Cartopy

    Helpful

    Understanding of NetCDF

    Helpful

    Familiarity with CMIP6

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    • Time to learn: 30 minutes

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    Imports

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    import matplotlib.pyplot as plt
-import matplotlib.colors as colors
-import numpy as np
-import pandas as pd
-import xarray as xr
-import intake
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-from cartopy import crs as ccrs
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    Access the data

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    First, we will open and search the Pangeo CMIP6 catalog for monthly hfds (downward heat flux at the sea surface) for the control (piControl) and 1%/year CO\(_2\) (1pctCO2) runs for all available models on their native grids. The argument require_all_on='source_id' ensures that each model used has both experiments required for this analysis.

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    cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
-col = intake.open_esm_datastore(cat_url)
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    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
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    query = dict(experiment_id=['1pctCO2', 'piControl'], table_id='Omon', variable_id='hfds', 
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-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
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    0CMIPCSIRO-ARCCSSACCESS-CM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...NaN20191109
    1CMIPCSIRO-ARCCSSACCESS-CM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...NaN20191112
    2CMIPCSIROACCESS-ESM1-51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/1pct...NaN20191115
    3CMIPCSIROACCESS-ESM1-5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/piCo...NaN20191214
    4CMIPAWIAWI-CM-1-1-MR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/1pctCO...NaN20181218
    5CMIPAWIAWI-CM-1-1-MRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/piCont...NaN20181218
    6CMIPCAMSCAMS-CSM1-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/1pctCO2...NaN20190708
    7CMIPCAMSCAMS-CSM1-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/piContr...NaN20190729
    8CMIPNCARCESM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...NaN20190320
    9CMIPNCARCESM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/1pctCO2/r1i1p...NaN20190425
    10CMIPNCARCESM2-FV2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/piControl...NaN20191120
    11CMIPNCARCESM2-FV21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/1pctCO2/r...NaN20200310
    12CMIPNCARCESM2-WACCMpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/piContr...NaN20190320
    13CMIPNCARCESM2-WACCM1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/1pctCO2...NaN20190425
    14CMIPNCARCESM2-WACCM-FV2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/piC...NaN20191120
    15CMIPNCARCESM2-WACCM-FV21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/1pc...NaN20200226
    16CMIPTHUCIESM1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/THU/CIESM/1pctCO2/r1i1p1...NaN20200220
    17CMIPTHUCIESMpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/THU/CIESM/piControl/r1i1...NaN20200220
    18CMIPCMCCCMCC-CM2-SR51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/1pctCO...NaN20200616
    19CMIPCMCCCMCC-CM2-SR5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/piCont...NaN20200616
    20CMIPCMCCCMCC-ESM21pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/1pctCO2/r...NaN20210127
    21CMIPCMCCCMCC-ESM2piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/piControl...NaN20210304
    22CMIPCCCmaCanESM51pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CCCma/CanESM5/1pctCO2/r1...NaN20190429
    23CMIPCCCmaCanESM5piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CCCma/CanESM5/piControl/...NaN20190429
    24CMIPEC-Earth-ConsortiumEC-Earth3-VegpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20200919
    25CMIPEC-Earth-ConsortiumEC-Earth3-Veg1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20200919
    26CMIPCASFGOALS-g3piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/piControl/...NaN20191126
    27CMIPCASFGOALS-g31pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/1pctCO2/r1...NaN20191126
    28CMIPFIO-QLNMFIO-ESM-2-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/piC...NaN20200921
    29CMIPFIO-QLNMFIO-ESM-2-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/1pc...NaN20200927
    30CMIPNOAA-GFDLGFDL-CM41pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/1pctC...NaN20180701
    31CMIPNOAA-GFDLGFDL-CM4piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/piCon...NaN20180701
    32CMIPNOAA-GFDLGFDL-ESM41pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/1pct...NaN20180701
    33CMIPNOAA-GFDLGFDL-ESM4piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/piCo...NaN20180701
    34CMIPNASA-GISSGISS-E2-1-GpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/pi...NaN20180824
    35CMIPNASA-GISSGISS-E2-1-G1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/1p...NaN20180905
    36CMIPNASA-GISSGISS-E2-1-H1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/1p...NaN20190403
    37CMIPNASA-GISSGISS-E2-1-HpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/pi...NaN20190410
    38CMIPNASA-GISSGISS-E2-2-G1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/1p...NaN20191120
    39CMIPNASA-GISSGISS-E2-2-GpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/pi...NaN20191120
    40CMIPIPSLIPSL-CM6A-LR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/1pctCO...NaN20180727
    41CMIPIPSLIPSL-CM6A-LRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piCont...NaN20200326
    42CMIPUAMCM-UA-1-01pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/1pctCO2/r1...NaN20190731
    43CMIPUAMCM-UA-1-0piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/piControl/...NaN20190731
    44CMIPMPI-MMPI-ESM1-2-HR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/1pct...NaN20190710
    45CMIPMPI-MMPI-ESM1-2-HRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/piCo...NaN20190710
    46CMIPMPI-MMPI-ESM1-2-LRpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/piCo...NaN20190710
    47CMIPMPI-MMPI-ESM1-2-LR1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/1pct...NaN20190710
    48CMIPNUISTNESM31pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NUIST/NESM3/1pctCO2/r1i1...NaN20190703
    49CMIPNUISTNESM3piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NUIST/NESM3/piControl/r1...NaN20190704
    50CMIPNCCNorCPM1piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCC/NorCPM1/piControl/r1...NaN20190914
    51CMIPNCCNorCPM11pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/NCC/NorCPM1/1pctCO2/r1i1...NaN20190914
    52CMIPSNUSAM0-UNICON1pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/...NaN20190323
    53CMIPSNUSAM0-UNICONpiControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/piContro...NaN20190910
    54CMIPAS-RCECTaiESM11pctCO2r1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/1pctCO2/...NaN20201130
    55CMIPAS-RCECTaiESM1piControlr1i1p1f1Omonhfdsgngs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/piContro...NaN20210213
    -
    -
    -

    Conveniently, NCAR contributed some data to CMIP6 that has already been regridded to a 1x1 lat-lon grid, which is the resolution I am interested in for the ensemble mean. We will use the coordinates from this Dataset when we create the xESMF regridder.

    -
    -
    -
    rg_query = dict(source_id='CESM2', experiment_id='piControl', table_id='Omon', variable_id='hfds', 
-             grid_label='gr', member_id='r1i1p1f1', require_all_on=['source_id'])
-
-rg_cat = col.search(**rg_query)
-rg_cat.df
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0CMIPNCARCESM2piControlr1i1p1f1Omonhfdsgrgs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...NaN20190320
    -
    -
    -

    Now, make the dictionaries with the data:

    -
    -
    -
    dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
-list(dset_dict.keys())
-
    -
    -
    -
    -
    /tmp/ipykernel_631/4245233592.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
-  dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
-
    -
    -
    --> The keys in the returned dictionary of datasets are constructed as follows:
-	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
-
    -
    -
    - -
    -
    - - 100.00% [56/56 00:15<00:00] -
    - 
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:167: SerializationWarning: Ambiguous reference date string: 950-01-01. The first value is assumed to be the year hence will be padded with zeros to remove the ambiguity (the padded reference date string is: 0950-01-01). To remove this message, remove the ambiguity by padding your reference date strings with zeros.
-  warnings.warn(warning_msg, SerializationWarning)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/coding/times.py:167: SerializationWarning: Ambiguous reference date string: 101-01-01. The first value is assumed to be the year hence will be padded with zeros to remove the ambiguity (the padded reference date string is: 0101-01-01). To remove this message, remove the ambiguity by padding your reference date strings with zeros.
-  warnings.warn(warning_msg, SerializationWarning)
-
    -
    -
    ['CMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn',
- 'CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn',
- 'CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn',
- 'CMIP.NCAR.CESM2.1pctCO2.Omon.gn',
- 'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn',
- 'CMIP.NCC.NorCPM1.piControl.Omon.gn',
- 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn',
- 'CMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn',
- 'CMIP.THU.CIESM.piControl.Omon.gn',
- 'CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn',
- 'CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn',
- 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn',
- 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn',
- 'CMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn',
- 'CMIP.UA.MCM-UA-1-0.piControl.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn',
- 'CMIP.SNU.SAM0-UNICON.piControl.Omon.gn',
- 'CMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn',
- 'CMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn',
- 'CMIP.CCCma.CanESM5.piControl.Omon.gn',
- 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn',
- 'CMIP.NCAR.CESM2.piControl.Omon.gn',
- 'CMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn',
- 'CMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn',
- 'CMIP.CCCma.CanESM5.1pctCO2.Omon.gn',
- 'CMIP.NCAR.CESM2-FV2.piControl.Omon.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn',
- 'CMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn',
- 'CMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn',
- 'CMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn',
- 'CMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn',
- 'CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn',
- 'CMIP.CAS.FGOALS-g3.piControl.Omon.gn',
- 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn',
- 'CMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn',
- 'CMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn',
- 'CMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn',
- 'CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn',
- 'CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn',
- 'CMIP.NUIST.NESM3.1pctCO2.Omon.gn',
- 'CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn',
- 'CMIP.THU.CIESM.1pctCO2.Omon.gn',
- 'CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn',
- 'CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn',
- 'CMIP.NCC.NorCPM1.1pctCO2.Omon.gn',
- 'CMIP.NUIST.NESM3.piControl.Omon.gn',
- 'CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn',
- 'CMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn',
- 'CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn']
-
    -
    -
    -
    -
    -
    -
    rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
-list(rg_dset_dict.keys())
-
    -
    -
    -
    -
    --> The keys in the returned dictionary of datasets are constructed as follows:
-	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
-
    -
    -
    /tmp/ipykernel_631/2341675588.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
-  rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
-
    -
    -
    - -
    -
    - - 100.00% [1/1 00:09<00:00] -
    - 
    ['CMIP.NCAR.CESM2.piControl.Omon.gr']
-
    -
    -
    -
    -
    -
    -

    Define some functions and organize

    -

    First, let’s make a function to get the diagnostic of interest: the change in ocean heat uptake at the time of transient CO\(_2\) doubling compared to the pre-industrial control:

    -
    -
    -
    def get_tcr(ctrl_key, expr_key):
-    ds_1pct = dset_dict[expr_key].squeeze()
-    ds_piCl = dset_dict[ctrl_key].squeeze()
-    ds_tcr = ds_1pct.isel(time=slice(12*59, 12*80)).mean(dim='time') - ds_piCl.isel(time=slice(12*59, 12*80)).mean(dim='time')
-    return ds_tcr
-
    -
    -
    -
    -

    Note that the time slice is 20 years centered around year 70, which is when CO\(_2\) doubles in a 1pctCO2 experiment (\(1.01^{70}\approx 2\)). Just for convenience, we will also define a function that creates the xESMF regridder and performs the regridding. The regridder is specific to the input (ds_in) and output (regrid_to) grids, so it must be redefined for each model.

    -
    -
    -
    def regrid(ds_in, regrid_to, method='bilinear'):
-    regridder = xe.Regridder(ds_in, regrid_to, method=method, periodic=True, ignore_degenerate=True)
-    ds_out = regridder(ds_in)
-    return ds_out
-
    -
    -
    -
    -

    Finally, the following function takes the list of keys generated by Intake-ESM and splits them into two sorted lists of keys: one for the piControl experiment and another for 1pctCO2. This will work nicely with the get_tcr() function.

    -
    -
    -
    def sorted_split_list(a_list):
-    c_list = []
-    e_list = []
-    for item in a_list:
-        if 'piControl' in item:
-            c_list.append(item)
-        elif '1pctCO2' in item:
-            e_list.append(item)
-        else: print('Could not find experiment name in key:'+item)
-    return sorted(c_list), sorted(e_list)
-
    -
    -
    -
    -

    Let’s make the lists and look at them to make sure they are properly sorted:

    -
    -
    -
    ctrl_keys, expr_keys = sorted_split_list(list(dset_dict.keys()))
-
    -
    -
    -
    -
    -
    -
    for i in range(len(ctrl_keys)):
-    print(ctrl_keys[i]+'\t\t'+expr_keys[i])
-
    -
    -
    -
    -
    CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn		CMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn
-CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn		CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn
-CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn		CMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn
-CMIP.CAS.FGOALS-g3.piControl.Omon.gn		CMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn
-CMIP.CCCma.CanESM5.piControl.Omon.gn		CMIP.CCCma.CanESM5.1pctCO2.Omon.gn
-CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn		CMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn
-CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn		CMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn
-CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn		CMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn
-CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn		CMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn
-CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn		CMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn
-CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn		CMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn
-CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn		CMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn
-CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn		CMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn
-CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn		CMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn
-CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn
-CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn
-CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn
-CMIP.NCAR.CESM2-FV2.piControl.Omon.gn		CMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn
-CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn		CMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn
-CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn		CMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn
-CMIP.NCAR.CESM2.piControl.Omon.gn		CMIP.NCAR.CESM2.1pctCO2.Omon.gn
-CMIP.NCC.NorCPM1.piControl.Omon.gn		CMIP.NCC.NorCPM1.1pctCO2.Omon.gn
-CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn		CMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn
-CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn		CMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn
-CMIP.NUIST.NESM3.piControl.Omon.gn		CMIP.NUIST.NESM3.1pctCO2.Omon.gn
-CMIP.SNU.SAM0-UNICON.piControl.Omon.gn		CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn
-CMIP.THU.CIESM.piControl.Omon.gn		CMIP.THU.CIESM.1pctCO2.Omon.gn
-CMIP.UA.MCM-UA-1-0.piControl.Omon.gn		CMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn
-
    -
    -
    -
    -
    -

    Note

    -

    If you look at the hfds anomaly for SAM0-UNICON, you will see negative values around the North Atlantic, especially in the Labrador Sea and Denmark Strait. These are areas of deep water formation and ocean heat uptake. By the CMIP convention, as described in the hfds attributes, a negative hfds indicates an upward heat flux from the ocean to the atmosphere, so by physical reasoning, this data should have the opposite sign. We could do this manually, but for simplicity, let’s just remove the model from our analysis.

    -
    -
    -
    dset_dict['CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn']
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:             (member_id: 1, dcpp_init_year: 1, time: 1800, j: 384,
-                         i: 320, bnds: 2, vertices: 4)
-Coordinates:
-  * i                   (i) int32 0 1 2 3 4 5 6 ... 313 314 315 316 317 318 319
-  * j                   (j) int32 0 1 2 3 4 5 6 ... 377 378 379 380 381 382 383
-    latitude            (j, i) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>
-    longitude           (j, i) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>
-  * time                (time) object 1850-01-17 00:29:59.999998 ... 1999-12-...
-    time_bnds           (time, bnds) object dask.array<chunksize=(1800, 2), meta=np.ndarray>
-  * member_id           (member_id) object 'r1i1p1f1'
-  * dcpp_init_year      (dcpp_init_year) float64 nan
-Dimensions without coordinates: bnds, vertices
-Data variables:
-    hfds                (member_id, dcpp_init_year, time, j, i) float32 dask.array<chunksize=(1, 1, 148, 384, 320), meta=np.ndarray>
-    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>
-    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>
-Attributes: (12/63)
-    Conventions:                      CF-1.7 CMIP-6.2
-    activity_id:                      CMIP
-    branch_method:                    standard
-    branch_time_in_child:             0.0
-    branch_time_in_parent:            99645.0
-    cmor_version:                     3.4.0
-    ...                               ...
-    intake_esm_attrs:variable_id:     hfds
-    intake_esm_attrs:grid_label:      gn
-    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1...
-    intake_esm_attrs:version:         20190323
-    intake_esm_attrs:_data_format_:   zarr
-    intake_esm_dataset_key:           CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn
    -
    -
    -
    -
    get_tcr('CMIP.SNU.SAM0-UNICON.piControl.Omon.gn', 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn').hfds.plot()
-
    -
    -
    -
    -
    <matplotlib.collections.QuadMesh at 0x7f8d6879dcc0>
-
    -
    -../../_images/465d198e4e41dc12713238ec174a10c784e6c9e5ccd1308ff889af055d1e5e32.png -
    -
    -
    -
    -
    ctrl_keys.pop(-3)
-expr_keys.pop(-3)
-
    -
    -
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    -
    'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn'
-
    -
    -
    -
    -

    We will also remove AWI-CM because it raises a MemoryError that causes this notebook to fail to execute via binderbot. Feel free to add it back if this notebook is being run locally.

    -
    -
    -
    ctrl_keys.pop(1)
-expr_keys.pop(1)
-
    -
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    -
    'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn'
-
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    Regrid the data

    -

    First, we will define the output grid. It does not matter what the data actually is, since we just want the structure of the Dataset.

    -
    -
    -
    rg_ds = rg_dset_dict['CMIP.NCAR.CESM2.piControl.Omon.gr'].isel(time=0).squeeze()
-rg_ds
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:         (lat: 180, lon: 360, d2: 2)
-Coordinates:
-  * lat             (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
-    lat_bnds        (lat, d2) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>
-  * lon             (lon) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
-    lon_bnds        (lon, d2) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>
-    time            object 0001-01-15 13:00:00.999998
-    time_bnds       (d2) object dask.array<chunksize=(2,), meta=np.ndarray>
-    member_id       <U8 'r1i1p1f1'
-    dcpp_init_year  float64 nan
-Dimensions without coordinates: d2
-Data variables:
-    hfds            (lat, lon) float32 dask.array<chunksize=(180, 360), meta=np.ndarray>
-Attributes: (12/61)
-    Conventions:                      CF-1.7 CMIP-6.2
-    activity_id:                      CMIP
-    branch_method:                    standard
-    branch_time_in_child:             0.0
-    branch_time_in_parent:            48545.0
-    case_id:                          3
-    ...                               ...
-    intake_esm_attrs:variable_id:     hfds
-    intake_esm_attrs:grid_label:      gr
-    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piCont...
-    intake_esm_attrs:version:         20190320
-    intake_esm_attrs:_data_format_:   zarr
-    intake_esm_dataset_key:           CMIP.NCAR.CESM2.piControl.Omon.gr
    -
    -

    Here we create a new dictionary to store our regridded data. The for-loop goes through the two sorted lists of keys and tries to regrid each model. This allows us to avoid removing a model and rerunning the code every time there is an error.

    -

    To summarize,

    -
      -

    • Get the diagnostic of interest and try to regrid to a 1x1 lat-lon grid

      -
        -

      • If that fails for any reason, print the error

        • -

      • If the regridding is successful, add it to the new dictionary

        • -
      -
      • -

    • Repeat for all models

      • -
    -
    -
    -
    ds_regrid_dict = dict()
-success_count = 0
-model_count = 0
-
-for ctrl_key, expr_key in zip(ctrl_keys, expr_keys):
-    model = ctrl_key.split('.')[2]
-    try:
-        ds_tcr = get_tcr(ctrl_key=ctrl_key, expr_key=expr_key)
-        ds_tcr_hfds_regridded = regrid(ds_tcr, rg_ds, method='nearest_s2d').hfds
-    except Exception as e:
-        print('Failed to regrid '+model+': '+str(e))
-    else: 
-        ds_regrid_dict[model] = ds_tcr_hfds_regridded
-        print(model+' regridded and added to dictionary')
-        success_count += 1
-    finally:
-        model_count += 1
-        
-print('-'*40+'\n| '+str(success_count)+'/'+str(model_count)+' models successfully regridded! |\n'+'-'*40)
-
    -
    -
    -
    -
    TaiESM1 regridded and added to dictionary
-
    -
    -
    CAMS-CSM1-0 regridded and added to dictionary
-
    -
    -
    FGOALS-g3 regridded and added to dictionary
-
    -
    -
    CanESM5 regridded and added to dictionary
-
    -
    -
    CMCC-CM2-SR5 regridded and added to dictionary
-
    -
    -
    CMCC-ESM2 regridded and added to dictionary
-
    -
    -
    ACCESS-CM2 regridded and added to dictionary
-
    -
    -
    ACCESS-ESM1-5 regridded and added to dictionary
-
    -
    -
    EC-Earth3-Veg regridded and added to dictionary
-
    -
    -
    FIO-ESM-2-0 regridded and added to dictionary
-
    -
    -
    IPSL-CM6A-LR regridded and added to dictionary
-
    -
    -
    MPI-ESM1-2-HR regridded and added to dictionary
-
    -
    -
    MPI-ESM1-2-LR regridded and added to dictionary
-
    -
    -
    GISS-E2-1-G regridded and added to dictionary
-
    -
    -
    GISS-E2-1-H regridded and added to dictionary
-
    -
    -
    GISS-E2-2-G regridded and added to dictionary
-
    -
    -
    Failed to regrid CESM2-FV2: lon and lat should be both 1D or 2D
-
    -
    -
    CESM2-WACCM-FV2 regridded and added to dictionary
-
    -
    -
    CESM2-WACCM regridded and added to dictionary
-
    -
    -
    CESM2 regridded and added to dictionary
-
    -
    -
    NorCPM1 regridded and added to dictionary
-
    -
    -
    GFDL-CM4 regridded and added to dictionary
-
    -
    -
    GFDL-ESM4 regridded and added to dictionary
-
    -
    -
    NESM3 regridded and added to dictionary
-
    -
    -
    CIESM regridded and added to dictionary
-
    -
    -
    MCM-UA-1-0 regridded and added to dictionary
-----------------------------------------
-| 25/26 models successfully regridded! |
-----------------------------------------
-
    -
    -
    -
    -

    CESM2-FV2 fails because of some issue with the dimensions of the coordinates. If we remove ignore_degenerate=True from the regridder defined in regrid(), there may be a few more failures because of a degenerate element: a cell that has corners close enough that the cell collapses to a line or point.

    -

    Now we concat the results into a single DataArray:

    -
    -
    -
    ds = list(ds_regrid_dict.values())
-coord = list(ds_regrid_dict.keys())
-ds_out_regrid = xr.concat(objs=ds, dim=coord, coords='all').rename({'concat_dim':'model'})
-ds_out_regrid
-
    -
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    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray 'hfds' (model: 25, lat: 180, lon: 360)>
-dask.array<concatenate, shape=(25, 180, 360), dtype=float32, chunksize=(1, 80, 144), chunktype=numpy.ndarray>
-Coordinates:
-    member_id       (model) <U8 'r1i1p1f1' 'r1i1p1f1' ... 'r1i1p1f1' 'r1i1p1f1'
-    dcpp_init_year  (model) float64 nan nan nan nan nan ... nan nan nan nan nan
-  * lat             (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
-    time            (model) object 0001-01-15 13:00:00.999998 ... 0001-01-15 ...
-  * lon             (lon) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
-  * model           (model) object 'TaiESM1' 'CAMS-CSM1-0' ... 'MCM-UA-1-0'
    -
    -
    -
    -

    Plot or save the data

    -

    The following function extends lon by one grid point, giving it the value of the first point. This fixes a bug/feature of Cartopy where a vertical white line will appear at the “seam” of the plot. For example, if you have a dataset with longitudes [-179.5, 179.5] and make a plot centered on the Pacific, there will likely be a white line at 180. This is only for improving the look of the plot, so if you are doing further analysis or exporting to netCDF, skip this.

    -
    -
    -
    def add_cyclic_point(xarray_obj, dim, period=None):
-    if period is None:
-        period = xarray_obj.sizes[dim] / xarray_obj.coords[dim][:2].diff(dim).item()
-    first_point = xarray_obj.isel({dim: slice(1)})
-    first_point.coords[dim] = first_point.coords[dim]+period
-    return xr.concat([xarray_obj, first_point], dim=dim)
-
    -
    -
    -
    -

    Now we can take the ensemble mean and plot. Thanks to the work leading up to this point, it’s as simple as using Xarray’s .mean().

    -
    -
    -
    cmip6em_ohutcr = add_cyclic_point(ds_out_regrid.mean(dim='model'), 'lon', period=360)
-# cmip6em_ohutcr.to_netcdf('cmip6_ohutcr.nc') # remove add_cyclic_point() and uncomment to save
-cmip6em_ohutcr
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray 'hfds' (lat: 180, lon: 361)>
-dask.array<concatenate, shape=(180, 361), dtype=float32, chunksize=(80, 144), chunktype=numpy.ndarray>
-Coordinates:
-  * lat      (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5
-  * lon      (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 360.5
    -
    -
    -
    -
    fig = plt.figure(1, figsize=(12, 5), dpi=130)
-ax_mean = plt.subplot(projection=ccrs.PlateCarree(central_longitude=-150))
-mean_plot = ax_mean.contourf(cmip6em_ohutcr.lon, cmip6em_ohutcr.lat, cmip6em_ohutcr, transform=ccrs.PlateCarree(), 
-                             cmap='RdBu_r', levels=np.linspace(-35, 35, 15), extend='both')
-ax_mean.set_title('CMIP6 ensemble-mean $\Delta\mathrm{OHUTCR}$')
-ax_mean.coastlines()
-ax_mean.set_xticks([-120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
-ax_mean.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
-ax_mean.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=True))
-ax_mean.yaxis.set_major_formatter(LatitudeFormatter())
-plt.colorbar(mean_plot, orientation='vertical', label='W m$^{-2}$')
-
    -
    -
    -
    -
    <matplotlib.colorbar.Colorbar at 0x7f8d27e24e20>
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
-  warnings.warn(f'Downloading: {url}', DownloadWarning)
-
    -
    -../../_images/9bd675273770bca0bbcbd7f912d92a651919b5bc1ada8ecf21a2055325153455.png -
    -
    -

    Notice how the heat uptake is highest in the subpolar oceans, especially the North Atlantic. From this multi-model ensemble mean, we can see that this is a robust feature of climate models (and likely the climate system itself) in response to a CO\(_2\) forcing. For more background and motivation, see Hu et al. (2020).

    -
    -
    -
    -

    Summary

    -

    This notebook demonstrates the use of xESMF to regrid the CMIP6 data hosted in Pangeo’s Google cloud storage. The regridded data allows us to use Xarray to take a multi-model mean, in this case, of changes in ocean heat uptake associated with each model’s transient climate response.

    -
    -

    What’s next?

    -

    Other example workflows using this CMIP6 cloud data.

    -
    -
    -
    -

    Resources and references

    -

    Hu, S., Xie, S.-P., & Liu, W. (2020). Global Pattern Formation of Net Ocean Surface Heat Flux Response to Greenhouse Warming. Journal of Climate, 33(17), 7503–7522. https://doi.org/10.1175/JCLI-D-19-0642.1

    -

    Xie, S.-P. (2020). Ocean warming pattern effect on global and regional climate change. AGU Advances, 1, e2019AV000130. https://doi.org/10.1029/2019AV000130

    -

    Parts of this workflow were taken from a similar workflow in this notebook by NordicESMhub.

    -
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    - - - - -
    - -
    - - -
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    previous

    -

    Precipitation Frequency Analysis

    -
    -     - -
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    next

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    Compare Data from ESGF and ARM

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    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

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    - - \ No newline at end of file diff --git a/_preview/77/notebooks/foundations/esgf-opendap.html b/_preview/77/notebooks/foundations/esgf-opendap.html deleted file mode 100644 index 974d00e..0000000 --- a/_preview/77/notebooks/foundations/esgf-opendap.html +++ /dev/null @@ -1,4582 +0,0 @@ - - - - - - - - Search and Load CMIP6 Data via ESGF/OPeNDAP — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    CMIP6 image -CMIP6 image

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    Search and Load CMIP6 Data via ESGF/OPeNDAP

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    -
    -

    Overview

    -

    This notebook shows how to search and load data via Earth System Grid Federation infrastructure. This infrastructure works great and is the foundation of the CMIP6 distribution system.

    -

    The main technologies used here are the ESGF search API, used to figure out what data we want, and OPeNDAP, a remote data access protocol over HTTP.

    -
    -
    -

    Prerequisites

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

    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

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      -

    • Time to learn: 10 minutes

      • -
    -
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    Imports

    -
    -
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    import warnings
-
-from distributed import Client
-import holoviews as hv
-import hvplot.xarray
-from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-from pyesgf.search import SearchConnection
-import xarray as xr
-
-xr.set_options(display_style='html')
-warnings.filterwarnings("ignore")
-hv.extension('bokeh')
-
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    client = Client()
-client
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    Client

    -

    Client-3fa20d94-9ddf-11ee-8528-92cdf4efe03d

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

    Cluster Info

     - -
    - - -
    -
    -
    -
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    Search using ESGF API

    -

    Fortunately, there is an ESGF API implemented in Python - pyesgf, which requires three major steps:

    -
      -

    • Establish a search connection

      • -

    • Query your data

      • -

    • Extract the urls to your data

      • -
    -

    Once you have this information, you can load the data into an xarray.Dataset.

    -
    -

    Configure the connection to a data server

    -

    First, we configure our connection to some server, using the distributed option (distrib=False). In this case, we are searching from the Lawerence Livermore National Lab (LLNL) data node.

    -
    -
    -
    conn = SearchConnection('https://esgf-node.llnl.gov/esg-search',
-                        distrib=False)
-
    -
    -
    -
    -
    -
    -

    Query our dataset

    -

    We are interested in a single experiment from CMIP6 - one of the Community Earth System Model version 2 (CESM2) runs, specifically the historical part of the simulation.

    -

    We are also interested in a single variable - temperature at the surface (tas), with a single ensemble member (r10i1p1f1)

    -
    -
    -
    ctx = conn.new_context(
-    facets='project,experiment_id',
-    project='CMIP6',
-    table_id='Amon',
-    institution_id="NCAR",
-    experiment_id='historical',
-    source_id='CESM2',
-    variable='tas',
-    variant_label='r10i1p1f1',
-)
-
    -
    -
    -
    -
    -
    -

    Extract the OpenDAP urls

    -

    In order to access the datasets, we need the urls to the data. Once we have these, we can read the data remotely!

    -
    -
    -
    result = ctx.search()[0]
-files = result.file_context().search()
-files
-
    -
    -
    -
    -
    <pyesgf.search.results.ResultSet at 0x7f3e8648dcc0>
-
    -
    -
    -
    -

    The files object is not immediately helpful - we need to extract the opendap_url method from this.

    -
    -
    -
    files[0].opendap_url
-
    -
    -
    -
    -
    'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc'
-
    -
    -
    -
    -

    We can use this for the whole list using list comprehension, as shown below.

    -
    -
    -
    opendap_urls = [file.opendap_url for file in files]
-opendap_urls
-
    -
    -
    -
    -
    ['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',
- 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',
- 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',
- 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc']
-
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    Read the data into an xarray.Dataset

    -

    Now that we have our urls to the data, we can use open multifile dataset (open_mfdataset) to read the data, combining the coordinates and chunking by time.

    -

    Xarray, together with the netCDF4 Python library, allow lazy loading.

    -
    -
    -
    ds = xr.open_mfdataset(opendap_urls,
-                       combine='by_coords',
-                       chunks={'time':480})
-ds
-
    -
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    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:    (lat: 192, lon: 288, time: 1980, nbnd: 2)
-Coordinates:
-  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
-  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
-Dimensions without coordinates: nbnd
-Data variables:
-    time_bnds  (time, nbnd) object dask.array<chunksize=(480, 2), meta=np.ndarray>
-    lat_bnds   (time, lat, nbnd) float64 dask.array<chunksize=(600, 192, 2), meta=np.ndarray>
-    lon_bnds   (time, lon, nbnd) float64 dask.array<chunksize=(600, 288, 2), meta=np.ndarray>
-    tas        (time, lat, lon) float32 dask.array<chunksize=(480, 192, 288), meta=np.ndarray>
-Attributes: (12/46)
-    Conventions:                     CF-1.7 CMIP-6.2
-    activity_id:                     CMIP
-    branch_method:                   standard
-    branch_time_in_child:            674885.0
-    branch_time_in_parent:           306600.0
-    case_id:                         24
-    ...                              ...
-    table_id:                        Amon
-    tracking_id:                     hdl:21.14100/e47b79db-3925-45a7-9c0a-679...
-    variable_id:                     tas
-    variant_info:                    CMIP6 20th century experiments (1850-201...
-    variant_label:                   r10i1p1f1
-    DODS_EXTRA.Unlimited_Dimension:  time
    -
    -
    -

    Plot a quick look of the data

    -

    Now that we have the dataset, let’s plot a few quick looks of the data.

    -
    -
    -
    ds.tas.sel(time='1950-01').squeeze().plot(cmap='Spectral_r');
-
    -
    -
    -
    -../../_images/738d2d0a9c31b9f6a43a423296b183e605a4bfd976944c3650d01bec46981d18.png -
    -
    -

    These are OPeNDAP endpoints. Xarray, together with the netCDF4 Python library, allow lazy loading.

    -
    -
    -
    -

    Compute an area-weighted global average

    -

    Let’s apply some computation to this dataset. We would like to calculate the global average temperature. This requires weighting each of the grid cells properly, using the area.

    -
    -

    Find the area of the cells

    -

    We can query the dataserver again, this time extracting the area of the cell (areacella).

    -
    -
    -
    ctx = conn.new_context(
-    facets='project,experiment_id',
-    project='CMIP6',
-    institution_id="NCAR",
-    experiment_id='historical',
-    source_id='CESM2',
-    variable='areacella',
-)
-
    -
    -
    -
    -

    As before, we extract the opendap urls.

    -
    -
    -
    result = ctx.search()[0]
-files = result.file_context().search()
-opendap_urls = [file.opendap_url for file in files]
-opendap_urls
-
    -
    -
    -
    -
    ['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r11i1p1f1/fx/areacella/gn/v20190514/areacella_fx_CESM2_historical_r11i1p1f1_gn.nc']
-
    -
    -
    -
    -

    And finally, we load our cell area file into an xarray.Dataset

    -
    -
    -
    ds_area = xr.open_dataset(opendap_urls[0])
-ds_area
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:    (lat: 192, lon: 288, nbnd: 2)
-Coordinates:
-  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
-  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
-Dimensions without coordinates: nbnd
-Data variables:
-    lat_bnds   (lat, nbnd) float64 ...
-    lon_bnds   (lon, nbnd) float64 ...
-    areacella  (lat, lon) float32 ...
-Attributes: (12/44)
-    Conventions:            CF-1.7 CMIP-6.2
-    activity_id:            CMIP
-    branch_method:          standard
-    branch_time_in_child:   674885.0
-    branch_time_in_parent:  219000.0
-    case_id:                972
-    ...                     ...
-    sub_experiment_id:      none
-    table_id:               fx
-    tracking_id:            hdl:21.14100/96455df2-979e-4cd4-8521-ddf307c6bc4a
-    variable_id:            areacella
-    variant_info:           CMIP6 20th century experiments (1850-2014) with C...
-    variant_label:          r11i1p1f1
    -
    -
    -
    -

    Compute the global average

    -

    Now that we have the area of each cell, and the temperature at each point, we can compute the global average temperature.

    -
    -
    -
    total_area = ds_area.areacella.sum(dim=['lon', 'lat'])
-ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area
-ta_timeseries
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray (time: 1980)>
-dask.array<truediv, shape=(1980,), dtype=float32, chunksize=(480,), chunktype=numpy.ndarray>
-Coordinates:
-  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    -
    -

    By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly.

    -
    -
    -
    %time ta_timeseries.load()
-
    -
    -
    -
    -
    CPU times: user 1.21 s, sys: 269 ms, total: 1.48 s
-Wall time: 20.7 s
-
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray (time: 1980)>
-array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,
-       287.06284], dtype=float32)
-Coordinates:
-  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    -
    -
    -
    -

    Visualize our results

    -

    Now that we have our results, we can visualize using static and dynamic plots. Let’s start with static plots using matplotlib, then dynamic plots using hvPlot.

    -
    -
    -
    ta_timeseries['time'] = ta_timeseries.indexes['time'].to_datetimeindex()
-
    -
    -
    -
    -
    -
    -
    fig = plt.figure(figsize=(12,8))
-ta_timeseries.plot(label='monthly')
-ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')
-plt.legend()
-plt.title('Global Mean Surface Air Temperature')
-
    -
    -
    -
    -
    Text(0.5, 1.0, 'Global Mean Surface Air Temperature')
-
    -
    -../../_images/13e9d14914e27075b8c7f7bb77c6c3fdd54365121628340a39755a1d587fc3a9.png -
    -
    -
    -
    -
    ta_timeseries.name = 'Temperature (K)'
-monthly_average = ta_timeseries.hvplot(title = 'Global Mean Surface Air Temperature',
-                                       label='monthly')
-rolling_monthly_average = ta_timeseries.rolling(time=12).mean().hvplot(label='12 month rolling mean',)
-
-(monthly_average * rolling_monthly_average).opts(legend_position='top_left')
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -

    Summary

    -

    In this notebook, we searched for and opened a CESM2 dataset using the ESGF API and OPeNDAP. We then plotted global average surface air temperature.

    -
    -

    What’s next?

    -

    We will see some more advanced examples of using the CMIP6 data.

    -
    -
    -
    -

    Resources and references

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

    previous

    -

    Google Cloud CMIP6 Public Data: Basic Python Example

    -
    -     - -
    -

    next

    -

    Estimating Equilibrium Climate Sensitivity

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    -
    - Unidata -
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    - -
    - -
    - -
    -
    -
    -
    -

    - © 2023. - - By Ryan Abernathey, Henri Drake, Robert Ford. - - Last updated on 18 December 2023. -

    -
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    - -
    - -
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    - - \ No newline at end of file diff --git a/_preview/77/notebooks/foundations/google-cloud-basic.html b/_preview/77/notebooks/foundations/google-cloud-basic.html deleted file mode 100644 index f942b0b..0000000 --- a/_preview/77/notebooks/foundations/google-cloud-basic.html +++ /dev/null @@ -1,2856 +0,0 @@ - - - - - - - - Google Cloud CMIP6 Public Data: Basic Python Example — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - On this page -
    - -
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    - -

    CMIP6 image

    -
    -

    Google Cloud CMIP6 Public Data: Basic Python Example

    -
    -
    -

    Overview

    -

    This notebooks shows how to query the Google Cloud CMIP6 catalog and load the data using Python.

    -
    -
    -

    Prerequisites

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

    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    -
      -

    • Time to learn: 10 minutes

      • -
    -
    -
    -
    -

    Imports

    -
    -
    -
    from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-import xarray as xr
-import zarr
-import fsspec
-import nc_time_axis
-
-%matplotlib inline
-plt.rcParams['figure.figsize'] = 12, 6
-
    -
    -
    -
    -
    -
    -

    Browse Catalog

    -

    The data catatalog is stored as a CSV file. Here we read it with Pandas.

    -
    -
    -
    df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
-df.head()
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    1HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrsdsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    2HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrlusgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    3HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrldsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    4HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpslgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
    -
    -
    -

    The columns of the dataframe correspond to the CMI6 controlled vocabulary.

    -

    Here we filter the data to find monthly surface air temperature for historical experiments.

    -
    -
    -
    df_ta = df.query("activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'")
-df_ta
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    973CMIPNOAA-GFDLGFDL-ESM4historicalr3i1p1f1Amontasgr1gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...NaN20180701
    1766CMIPNOAA-GFDLGFDL-ESM4historicalr2i1p1f1Amontasgr1gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...NaN20180701
    8074CMIPNOAA-GFDLGFDL-CM4historicalr1i1p1f1Amontasgr1gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...NaN20180701
    22185CMIPIPSLIPSL-CM6A-LRhistoricalr8i1p1f1Amontasgrgs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    22298CMIPIPSLIPSL-CM6A-LRhistoricalr2i1p1f1Amontasgrgs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    ....................................
    522952CMIPMRIMRI-ESM2-0historicalr7i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...NaN20210813
    523274CMIPMRIMRI-ESM2-0historicalr6i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...NaN20210907
    523712CMIPCMCCCMCC-CM2-SR5historicalr3i1p2f1Amontasgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...NaN20211108
    523721CMIPCMCCCMCC-CM2-SR5historicalr2i1p2f1Amontasgngs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...NaN20211109
    523769CMIPEC-Earth-ConsortiumEC-Earth3-Veghistoricalr1i1p1f1Amontasgrgs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20211207
    -

    635 rows × 11 columns

    -
    -
    -

    Now we do further filtering to find just the models from NCAR.

    -
    -
    -
    df_ta_ncar = df_ta.query('institution_id == "NCAR"')
-df_ta_ncar
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    56049CMIPNCARCESM2-WACCMhistoricalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...NaN20190227
    56143CMIPNCARCESM2-WACCMhistoricalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...NaN20190227
    56326CMIPNCARCESM2-WACCMhistoricalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...NaN20190227
    59875CMIPNCARCESM2historicalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190308
    61655CMIPNCARCESM2historicalr4i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4...NaN20190308
    61862CMIPNCARCESM2historicalr5i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5...NaN20190308
    62691CMIPNCARCESM2historicalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2...NaN20190308
    63131CMIPNCARCESM2historicalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3...NaN20190308
    63266CMIPNCARCESM2historicalr6i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6...NaN20190308
    64615CMIPNCARCESM2historicalr8i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8...NaN20190311
    64914CMIPNCARCESM2historicalr7i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7...NaN20190311
    64983CMIPNCARCESM2historicalr9i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9...NaN20190311
    66341CMIPNCARCESM2historicalr10i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190313
    200772CMIPNCARCESM2historicalr11i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...NaN20190514
    385224CMIPNCARCESM2-FV2historicalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...NaN20191120
    386297CMIPNCARCESM2-WACCM-FV2historicalr1i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...NaN20191120
    420771CMIPNCARCESM2-WACCM-FV2historicalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...NaN20200226
    421251CMIPNCARCESM2-WACCM-FV2historicalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...NaN20200226
    422013CMIPNCARCESM2-FV2historicalr3i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...NaN20200226
    422459CMIPNCARCESM2-FV2historicalr2i1p1f1Amontasgngs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...NaN20200226
    -
    -
    -
    -
    -

    Load Data

    -

    Now we will load a single store using fsspec, zarr, and xarray.

    -
    -
    -
    # get the path to a specific zarr store (the first one from the dataframe above)
-zstore = df_ta_ncar.zstore.values[-1]
-print(zstore)
-
-# create a mutable-mapping-style interface to the store
-mapper = fsspec.get_mapper(zstore)
-
-# open it using xarray and zarr
-ds = xr.open_zarr(mapper, consolidated=True)
-ds
-
    -
    -
    -
    -
    gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historical/r2i1p1f1/Amon/tas/gn/v20200226/
-
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:    (lat: 96, nbnd: 2, lon: 144, time: 1980)
-Coordinates:
-  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0
-    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>
-  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
-    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>
-  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
-    time_bnds  (time, nbnd) object dask.array<chunksize=(1980, 2), meta=np.ndarray>
-Dimensions without coordinates: nbnd
-Data variables:
-    tas        (time, lat, lon) float32 dask.array<chunksize=(990, 96, 144), meta=np.ndarray>
-Attributes: (12/48)
-    Conventions:                     CF-1.7 CMIP-6.2
-    DODS_EXTRA.Unlimited_Dimension:  time
-    activity_id:                     CMIP
-    branch_method:                   standard
-    branch_time_in_child:            674885.0
-    branch_time_in_parent:           10950.0
-    ...                              ...
-    tracking_id:                     hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...
-    variable_id:                     tas
-    variant_info:                    CMIP6 CESM2-FV2 historical experiment (1...
-    variant_label:                   r2i1p1f1
-    netcdf_tracking_ids:             hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...
-    version_id:                      v20200226
    -
    -
    -
    -

    Plot the Data

    -

    Plot a map from a specific date:

    -
    -
    -
    ds.tas.sel(time='1950-01').squeeze().plot()
-
    -
    -
    -
    -
    <matplotlib.collections.QuadMesh at 0x7fb85ee074c0>
-
    -
    -../../_images/04bc4411c28a9526620e7ba874a15f1142d5c6962ac138dd8f48059174a7fc62.png -
    -
    -

    The global mean of a lat-lon field needs to be weighted by the area of each grid cell, which is proportional to the cosine of its latitude.

    -
    -
    -
    def global_mean(field):
-    weights = np.cos(np.deg2rad(field.lat))
-    return field.weighted(weights).mean(dim=['lat', 'lon'])
-
    -
    -
    -
    -

    We can pass all of the temperature data through this function:

    -
    -
    -
    ta_timeseries = global_mean(ds.tas)
-ta_timeseries
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray 'tas' (time: 1980)>
-dask.array<truediv, shape=(1980,), dtype=float64, chunksize=(990,), chunktype=numpy.ndarray>
-Coordinates:
-  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    -
    -

    By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly.

    -
    -
    -
    %time ta_timeseries.load()
-
    -
    -
    -
    -
    CPU times: user 406 ms, sys: 249 ms, total: 655 ms
-Wall time: 1.49 s
-
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.DataArray 'tas' (time: 1980)>
-array([285.53603312, 285.63958225, 286.27324086, ..., 288.15781771,
-       287.18662389, 286.87765827])
-Coordinates:
-  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    -
    -
    -
    -
    ta_timeseries.plot(label='monthly')
-ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean', color='k')
-plt.legend()
-plt.grid()
-plt.title('Global Mean Surface Air Temperature')
-
    -
    -
    -
    -
    Text(0.5, 1.0, 'Global Mean Surface Air Temperature')
-
    -
    -../../_images/0720750ddd4a9c9c83fc38ca9696d867699466d378258d79d2bb69335cd4efda.png -
    -
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    -

    Summary

    -

    In this notebook, we opened a CESM2 dataset with fsspec and zarr. We calculated and plotted global average surface air temperature.

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    -

    What’s next?

    -

    We will open a dataset with ESGF and OPenDAP.

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    - - \ No newline at end of file diff --git a/_preview/77/notebooks/foundations/intake-esm.html b/_preview/77/notebooks/foundations/intake-esm.html deleted file mode 100644 index 250ff98..0000000 --- a/_preview/77/notebooks/foundations/intake-esm.html +++ /dev/null @@ -1,2171 +0,0 @@ - - - - - - - - Load CMIP6 Data with Intake-ESM — CMIP6 Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Intake logo -CMIP6 image

    -
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    Load CMIP6 Data with Intake-ESM

    -
    -
    -

    Overview

    -

    Intake-ESM is an experimental new package that aims to provide a higher-level interface to searching and loading Earth System Model data archives, such as CMIP6. The package is under very active development, and features may be unstable. Please report any issues or suggestions on GitHub.

    -
    -
    -

    Prerequisites

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

    Concepts

    Importance

    Notes

    Intro to Xarray

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    -
      -

    • Time to learn: 5 minutes

      • -
    -
    -
    -
    -

    Imports

    -
    -
    -
    import xarray as xr
-xr.set_options(display_style='html')
-import intake
-%matplotlib inline
-
    -
    -
    -
    -
    -
    -

    Loading Data

    -

    Intake ESM works by parsing an ESM Collection Spec and converting it to an Intake catalog. The collection spec is stored in a .json file. Here we open it using Intake.

    -
    -
    -
    cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
-col = intake.open_esm_datastore(cat_url)
-col
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -

    pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unique
    activity_id18
    institution_id36
    source_id88
    experiment_id170
    member_id657
    table_id37
    variable_id700
    grid_label10
    zstore514818
    dcpp_init_year60
    version736
    derived_variable_id0
    -
    -
    -

    We can now use Intake methods to search the collection, and, if desired, export a Pandas dataframe.

    -
    -
    -
    cat = col.search(experiment_id=['historical', 'ssp585'], table_id='Oyr', variable_id='o2',
-                 grid_label='gn')
-cat.df
-
    -
    -
    -
    -
    /srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-/srv/conda/envs/notebook/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
-  .applymap(type)
-
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
    0CMIPIPSLIPSL-CM6A-LRhistoricalr8i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    1CMIPIPSLIPSL-CM6A-LRhistoricalr5i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    2CMIPIPSLIPSL-CM6A-LRhistoricalr26i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    3CMIPIPSLIPSL-CM6A-LRhistoricalr2i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    4CMIPIPSLIPSL-CM6A-LRhistoricalr6i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...NaN20180803
    ....................................
    168CMIPCSIROACCESS-ESM1-5historicalr11i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/hist...NaN20200803
    169CMIPEC-Earth-ConsortiumEC-Earth3-CChistoricalr1i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...NaN20210113
    170ScenarioMIPEC-Earth-ConsortiumEC-Earth3-CCssp585r1i1p1f1Oyro2gngs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti...NaN20210113
    171CMIPCMCCCMCC-ESM2historicalr1i1p1f1Oyro2gngs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/historica...NaN20210114
    172ScenarioMIPCMCCCMCC-ESM2ssp585r1i1p1f1Oyro2gngs://cmip6/CMIP6/ScenarioMIP/CMCC/CMCC-ESM2/ss...NaN20210126
    -

    173 rows × 11 columns

    -
    -
    -

    Intake knows how to automatically open the Datasets using Xarray. Furthermore, Intake-ESM contains special logic to concatenate and merge the individual results of our query into larger, more high-level aggregated Xarray Datasets.

    -
    -
    -
    dset_dict = cat.to_dataset_dict(zarr_kwargs={'consolidated': True})
-list(dset_dict.keys())
-
    -
    -
    -
    -
    /tmp/ipykernel_1546/2728409572.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
-  dset_dict = cat.to_dataset_dict(zarr_kwargs={'consolidated': True})
-
    -
    -
    --> The keys in the returned dictionary of datasets are constructed as follows:
-	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
-
    -
    -
    - -
    -
    - - 100.00% [27/27 00:58<00:00] -
    - 
    ['CMIP.IPSL.IPSL-CM5A2-INCA.historical.Oyr.gn',
- 'ScenarioMIP.NCC.NorESM2-LM.ssp585.Oyr.gn',
- 'ScenarioMIP.NCC.NorESM2-MM.ssp585.Oyr.gn',
- 'ScenarioMIP.DWD.MPI-ESM1-2-HR.ssp585.Oyr.gn',
- 'ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp585.Oyr.gn',
- 'CMIP.CMCC.CMCC-ESM2.historical.Oyr.gn',
- 'ScenarioMIP.MIROC.MIROC-ES2L.ssp585.Oyr.gn',
- 'ScenarioMIP.CMCC.CMCC-ESM2.ssp585.Oyr.gn',
- 'CMIP.EC-Earth-Consortium.EC-Earth3-CC.historical.Oyr.gn',
- 'CMIP.MRI.MRI-ESM2-0.historical.Oyr.gn',
- 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3-CC.ssp585.Oyr.gn',
- 'ScenarioMIP.MRI.MRI-ESM2-0.ssp585.Oyr.gn',
- 'CMIP.CCCma.CanESM5-CanOE.historical.Oyr.gn',
- 'ScenarioMIP.CCCma.CanESM5-CanOE.ssp585.Oyr.gn',
- 'CMIP.NCC.NorESM2-MM.historical.Oyr.gn',
- 'ScenarioMIP.NCAR.CESM2.ssp585.Oyr.gn',
- 'CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical.Oyr.gn',
- 'CMIP.NCC.NorESM2-LM.historical.Oyr.gn',
- 'ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp585.Oyr.gn',
- 'ScenarioMIP.MPI-M.MPI-ESM1-2-LR.ssp585.Oyr.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-LR.historical.Oyr.gn',
- 'CMIP.MIROC.MIROC-ES2L.historical.Oyr.gn',
- 'CMIP.MPI-M.MPI-ESM1-2-HR.historical.Oyr.gn',
- 'CMIP.CSIRO.ACCESS-ESM1-5.historical.Oyr.gn',
- 'ScenarioMIP.CCCma.CanESM5.ssp585.Oyr.gn',
- 'CMIP.CCCma.CanESM5.historical.Oyr.gn',
- 'CMIP.IPSL.IPSL-CM6A-LR.historical.Oyr.gn']
-
    -
    -
    -
    -
    -
    -
    ds = dset_dict['CMIP.CCCma.CanESM5.historical.Oyr.gn']
-ds
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:             (i: 360, j: 291, lev: 45, bnds: 2, member_id: 35,
-                         dcpp_init_year: 1, time: 165, vertices: 4)
-Coordinates:
-  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359
-  * j                   (j) int32 0 1 2 3 4 5 6 ... 284 285 286 287 288 289 290
-    latitude            (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>
-  * lev                 (lev) float64 3.047 9.454 16.36 ... 5.375e+03 5.625e+03
-    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(45, 2), meta=np.ndarray>
-    longitude           (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>
-  * time                (time) object 1850-07-02 12:00:00 ... 2014-07-02 12:0...
-    time_bnds           (time, bnds) object dask.array<chunksize=(165, 2), meta=np.ndarray>
-    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>
-    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>
-  * member_id           (member_id) object 'r10i1p1f1' ... 'r9i1p2f1'
-  * dcpp_init_year      (dcpp_init_year) float64 nan
-Dimensions without coordinates: bnds, vertices
-Data variables:
-    o2                  (member_id, dcpp_init_year, time, lev, j, i) float32 dask.array<chunksize=(1, 1, 12, 45, 291, 360), meta=np.ndarray>
-Attributes: (12/52)
-    Conventions:                      CF-1.7 CMIP-6.2
-    YMDH_branch_time_in_child:        1850:01:01:00
-    activity_id:                      CMIP
-    branch_method:                    Spin-up documentation
-    branch_time_in_child:             0.0
-    cmor_version:                     3.4.0
-    ...                               ...
-    intake_esm_attrs:table_id:        Oyr
-    intake_esm_attrs:variable_id:     o2
-    intake_esm_attrs:grid_label:      gn
-    intake_esm_attrs:version:         20190429
-    intake_esm_attrs:_data_format_:   zarr
-    intake_esm_dataset_key:           CMIP.CCCma.CanESM5.historical.Oyr.gn
    -
    -
    -
    -
    -

    Summary

    -

    In this notebook, we used Intake-ESM to open an Xarray Dataset for one particular model and experiment.

    -
    -

    What’s next?

    -

    We will see an example of downloading a dataset with fsspec and zarr.

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