From ac72b82b5f84c2801b1fa138c85ad3315e1c203b Mon Sep 17 00:00:00 2001
From: aaronspring <aaron.spring@mpimet.mpg.de>
Date: Wed, 29 Sep 2021 19:17:43 +0200
Subject: [PATCH] regionmask and xesmf with ICON
---
notebooks/regionmask_ICON.ipynb | 2038 +++++++++++------------------
notebooks/xesmf_ICON.ipynb | 2149 +++++++++++++++++++++++++++++++
2 files changed, 2916 insertions(+), 1271 deletions(-)
create mode 100644 notebooks/xesmf_ICON.ipynb
diff --git a/notebooks/regionmask_ICON.ipynb b/notebooks/regionmask_ICON.ipynb
index 7184958..e12c625 100644
--- a/notebooks/regionmask_ICON.ipynb
+++ b/notebooks/regionmask_ICON.ipynb
@@ -1,42 +1,26 @@
{
"cells": [
{
- "cell_type": "code",
- "execution_count": 1,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "#!conda install regionmask==0.8.0 -y"
+ "# `regionmask` for unstructured grids like `ICON`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# `regionmask` for unstructured grids like `ICON`\n",
- "\n",
- "https://regionmask.readthedocs.io/en/stable/notebooks/method.html"
+ "- https://regionmask.readthedocs.io/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'0.0.0'"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "import regionmask\n",
- "regionmask.__version__"
+ "#!pip install git+https://github.com/regionmask/regionmask.git@refs/pull/280/merge"
]
},
{
@@ -45,9 +29,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#!wget https://github.com/psyplot/psy-simple/raw/master/tests/icon_test.nc\n",
- "\n",
- "path='icon_test.nc'"
+ "import xarray as xr"
]
},
{
@@ -56,9 +38,10 @@
"metadata": {},
"outputs": [],
"source": [
- "# ICON output I want to work with\n",
- "import xarray as xr\n",
- "ds = xr.open_dataset(path)"
+ "path = '/work/bm1102/m211054/dyamond/zstar2/experiments/exp.ocean_era51h_zstar_r2b9_21223-DWS/outdata/exp.ocean_era51h_zstar_r2b9_21223-DWS_P1M_3d_20160901T000000Z.nc'\n",
+ "#path = '/work/mh0033/m211054/projects/icon/icon-oes-1.3.01/experiments/exp.ocean_era51h_r2b8_hel20218-ERA/outdata/exp.ocean_era51h_r2b8_hel20218-ERA_19970401T000000Z.nc'\n",
+ "ds = xr.open_dataset(path,\n",
+ " chunks={'depth': 16})"
]
},
{
@@ -66,17 +49,508 @@
"execution_count": 4,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of CPUs: 72, number of threads: 9, number of workers: 8, processes: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/distributed/node.py:160: UserWarning: Port 8787 is already in use.\n",
+ "Perhaps you already have a cluster running?\n",
+ "Hosting the HTTP server on port 46379 instead\n",
+ " warnings.warn(\n"
+ ]
+ },
{
"data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #e1e1e1;\n",
+ " border: 3px solid #9D9D9D;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-746b99aa-2147-11ec-960f-0800383e1321</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+ " <td style=\"text-align: left;\"><strong>Cluster type:</strong> LocalCluster</td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/46379/status\">http://localhost:8888/proxy/46379/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " \n",
+ " </table>\n",
+ " \n",
+ " <details>\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+ " \n",
+ " <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #e1e1e1;\n",
+ " border: 3px solid #9D9D9D;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">bc666bea</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+ " <td style=\"text-align: left;\"><strong>Using processes:</strong> False</td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard:</strong> <a href=\"http://localhost:8888/proxy/46379/status\">http://localhost:8888/proxy/46379/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\"><strong>Workers:</strong> 8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total threads:</strong>\n",
+ " 72\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total memory:</strong>\n",
+ " 476.84 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " </table>\n",
+ " <details>\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
+ " \n",
+ " <div style=\"\">\n",
+ " \n",
+ " <div>\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #FFF7E5;\n",
+ " border: 3px solid #FF6132;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-3a2bd33c-0541-4470-b348-3035bb11791d</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm:</strong> inproc://10.50.43.213/54799/1</td>\n",
+ " <td style=\"text-align: left;\"><strong>Workers:</strong> 8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard:</strong> <a href=\"http://localhost:8888/proxy/46379/status\">http://localhost:8888/proxy/46379/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total threads:</strong>\n",
+ " 72\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Started:</strong>\n",
+ " Just now\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total memory:</strong>\n",
+ " 476.84 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <details style=\"margin-left: 48px;\">\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Workers</h3></summary>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/6</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/45601/status\">http://localhost:8888/proxy/45601/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-likyuom_\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/3</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/43050/status\">http://localhost:8888/proxy/43050/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-wihqhzub\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/7</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/39064/status\">http://localhost:8888/proxy/39064/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-756sbgo3\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/9</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/46574/status\">http://localhost:8888/proxy/46574/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-nb80_x27\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/8</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/40657/status\">http://localhost:8888/proxy/40657/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-i38hoitv\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/10</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/44510/status\">http://localhost:8888/proxy/44510/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-_xrgb1cj\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/5</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/39702/status\">http://localhost:8888/proxy/39702/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-gaurveuv\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/54799/4</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/36462/status\">http://localhost:8888/proxy/36462/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-9pss1bit\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " \n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ],
"text/plain": [
- "({'standard_name': 'longitude',\n",
- " 'long_name': 'center longitude',\n",
- " 'units': 'radian',\n",
- " 'bounds': 'clon_bnds'},\n",
- " {'standard_name': 'latitude',\n",
- " 'long_name': 'center latitude',\n",
- " 'units': 'radian',\n",
- " 'bounds': 'clat_bnds'})"
+ "<Client: 'inproc://10.50.43.213/54799/1' processes=8 threads=72, memory=476.84 GiB>"
]
},
"execution_count": 4,
@@ -85,13 +559,56 @@
}
],
"source": [
- "ds.coords['clon'].attrs,ds.coords['clat'].attrs"
+ "# use dask\n",
+ "from dask.distributed import Client\n",
+ "import multiprocessing\n",
+ "ncpu = multiprocessing.cpu_count()\n",
+ "processes = False\n",
+ "nworker = 8\n",
+ "threads = ncpu // nworker\n",
+ "print(\n",
+ " f\"Number of CPUs: {ncpu}, number of threads: {threads}, number of workers: {nworker}, processes: {processes}\",\n",
+ ")\n",
+ "client = Client(\n",
+ " processes=processes,\n",
+ " threads_per_worker=threads,\n",
+ " n_workers=nworker,\n",
+ " memory_limit=\"64GB\",\n",
+ ")\n",
+ "client"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2016-09-01\n",
+ " clon (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " clat (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " elon (ncells_2) float32 dask.array<chunksize=(22375924,), meta=np.ndarray>\n",
+ " elat (ncells_2) float32 dask.array<chunksize=(22375924,), meta=np.ndarray>\n",
+ " * depth (depth) float64 1.0 3.05 5.2 7.45 ... 5.522e+03 5.71e+03 5.904e+03\n",
+ " * depth_2 (depth_2) float64 0.0 2.0 4.1 6.3 ... 5.615e+03 5.806e+03 6.003e+03"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds.coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
"outputs": [],
"source": [
"def rad_to_deg(ds):\n",
@@ -114,7 +631,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -122,151 +639,61 @@
"output_type": "stream",
"text": [
"convert clon from rad to deg\n",
- "convert clat from rad to deg\n"
+ "convert clat from rad to deg\n",
+ "convert elon from rad to deg\n",
+ "convert elat from rad to deg\n"
]
}
],
"source": [
- "ds=rad_to_deg(ds)"
+ "ds = rad_to_deg(ds)"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "v='t2m'\n",
- "ds = ds[[v]].compute()\n",
- "da = ds[v]"
+ "ds = ds[['to']]"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
- "source": [
- "region = regionmask.defined_regions.ar6.ocean"
- ]
+ "source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<GeoAxesSubplot:>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "region.plot()"
+ "import regionmask"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "There are equal longitude coordinates (when wrapped)!",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-10-cec9d0455ee7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# fails for equal longitude coordinates (when wrapped)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmasked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlon_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clon'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clat'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/regions.py\u001b[0m in \u001b[0;36mmask\u001b[0;34m(self, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 287\u001b[0m ):\n\u001b[1;32m 288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m return _mask_2D(\n\u001b[0m\u001b[1;32m 290\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolygons\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mlon_bounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds_global\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_mask_2D\u001b[0;34m(outlines, lon_bounds, numbers, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 191\u001b[0m ):\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m mask = _mask(\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutlines\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0mlon_bounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlon_bounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_mask\u001b[0;34m(outlines, lon_bounds, numbers, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0mlon_orig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwrap_lon_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0mlon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_wrapAngle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrap_lon_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rasterize\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"shapely\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pygeos\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/utils.py\u001b[0m in \u001b[0;36m_wrapAngle\u001b[0;34m(lon, wrap_lon)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_lon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_lon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_lon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"There are equal longitude coordinates (when wrapped)!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_lon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: There are equal longitude coordinates (when wrapped)!"
- ]
- }
- ],
- "source": [
- "# fails for equal longitude coordinates (when wrapped)\n",
- "masked = region.mask(da,lon_name='clon', lat_name='clat')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 209 ms, sys: 3 ms, total: 212 ms\n",
- "Wall time: 218 ms\n"
- ]
- }
- ],
- "source": [
- "# shapely creates a mask with clon and clat instead of cell, groupby below fails\n",
- "%time masked = region.mask(da, lon_name='clon', lat_name='clat', wrap_lon=False, method='shapely')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen({'clat': 300, 'clon': 300})"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "masked.sizes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.collections.QuadMesh at 0x2b5502be8d60>"
+ "<GeoAxesSubplot:>"
]
},
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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vLC0tad++PZ06dbrn+2owGDh06BDm5uYMHDjwno7VGPR6PRqNhrVr13LmzBnmz58v/kBrorlnus2NUqkkOTlZNKxG90B2dra4j729vRjMysnJwcXFhTlz5tCpUyeio6Px9vamZ8+e2NraNsmY4uPjSU1NRa/XizPRlJQUhg4dWqubp7F8++23VFRU0KFDBxQKBV26dKG4uBitVou5uTkWFhb4+/uL12YwGDh8+DAWFhZYWloilUrp1KkTVlZWdz2XcTadnJyMSqXCxcWFXr16YWdnJx67rKxM3NaQ35LRp327S6MmI52Xlydmg9xOZmZm49wLXl5ewqxZs8QT1uY/kcvlbc7wZmdn4+fnx7Zt25DJZGzevJnx48dXyd9MTU3lwIED5ObmYm5ujq2tLQkJCaJRcHR0JDc3F7lcTnZ2NtnZ2eh0OmbOnElQUBBr1qzhyJEjvPbaazg6OrJr1y70ej0KhYJ58+Zx4cIFrl+/jpmZGUFBQezfv59+/fpha2tLREQErq6udO/eHXd3d1JTUzlx4gT29vZioYNCoeD9999vkij/hQsXuHnzJpMnT77nY9WXGzducOzYMY4ePcr06dPp1q0b7du3r/M9q1atIigoqMnzbIuLi3nmmWea9Jh3YsyMuT3ftUuXLlV8rt26dcPDwwOJRMLOnTvRarWMGDGCM2fOoFKpGDNmTL0+76SkJOLj45FKpQiCQO/evXF0dCQmJoapU6diMBiorKzEYDCwd+9eZsyY0eKBJZVKxbVr1/D09EQul6NSqVAqlZw8eVKcARsMBtzc3OjYsSNKpRK9Xk9KSgpyuZzRo0fXeXyNRsOWLVtITk5GoVBQUVFBUFAQ3t7e2NjYYG1tjZWVFTY2Ns127Ubf8+2ZGrm5uTzzzDONM7oBAQHC6tWr6dKli/gjUKlU4gmMf7m5uRQWFtZLsEWj0aDT6bCwsGhWI21ubs7TTz+Nt7c3kZGR6HQ6evfuTdeuXamoqCAmJobi4mI0Gg2jR49GEAQ6deokVmBptVoKCwtxdXVl165dSKVSxo8fT3x8PBqNhqCgIOLj40UZwsDAQHQ6HQcPHsTKyors7GycnJyYOXMmjo6O7N27Fz8/P1xcXLC0tESlUqFQKKrcg9jYWJRKJT4+PnTo0IHr169z7do1dDodPj4+GAwG+vTp06j7dubMGU6ePEm/fv3o2bNntVShpqakpIQzZ85w8eJF0tLS+Oc//1kvXYS0tDS+++47sQy6tTH62+u7pDemZnXr1o1OnTrV6rteu3Yt9vb2hIWFIZVK+eOPP3j44YfvuhzX6XScOHGC4uJiJk2aBMDBgwcpLCwUfbMqlYpevXqJmTHXrl2jW7duhIaGNuDKWw+lUsmPP/7I4sWLsbS0ZNeuXVhYWGBvb09eXh62trbY2NiQlpZGeno6zz//PIIgoNVqKS8vp6KigsrKSvHfRUVFmJmZ0adPH/GB11wYK0y7dOnSOKPr5+cnvPLKKwQEBDB8+PAGD6CkpISkpCSys7MxGAwIgoCtrS12dnakp6cDt3yPZmZmeHh4oNVqadeuHe3atcPS0hIbGxux3t/c3Lxe5xQEgdLSUmxsbIiLi8PDwwM3NzfKyspITEzk5s2b2Nra0qVLF9zc3Pj000/Jzc0FoE+fPqKPdfbs2TUGsgB27tyJRqPBx8eHbt26oVAoSExMFINjdnZ2+Pv7o1QqiYqKIjAwEA8PD1599VWCg4MZM2YM7dq1qzFqajAYxKWYkby8PDHodvHiRWQyGebm5gwYMKDWstk70ev1HDx4kPj4eC5duoS9vX2dOa33gsFg4M8//+TChQuiL9nb27vJAypNhUajIScnB4lEwpUrVygtLUUul4sBVbVaTUVFBZ6eno36HdRESUkJN27cID4+HisrK27evMmFCxf48MMP8fDwqPV9ERERBAcH06lTJ/G1CxcukJycTHl5OWPHjiU7O7tJU95ag7KyMo4cOUJJSQnDhg3D2tqasrIyXFxcKCsro7S0FA8PDzE+czfKy8u5fPkyWVlZVR6ERvun0+no169flTL1mhAEAZ1OV+vMOSEhgU2bNvHee+81zuj269dPOH369F0v6E70ej2nTp0iNzeXfv364erqWucT3BigMjMzIz8/n4KCAlQqFSUlJZiZmWEwGKr4TRwcHAgMDMTOzo5z586RlZUlnlcqlWJnZ0dhYSGdO3cW8wyLi4tFH6tcLheDB25ubuzatYvs7Gw6d+6Mq6urWDF0+zKvvLycoqIiFAoF7dq1Q61W8/777+Ph4cFDDz1EVFQUdnZ2VFZWolKpCAgIYMiQIcTFxbF3717GjBmDs7Mzer2esrIy0tLSOH/+PBYWFgiCgKWlJZ6enqIWgXF2W9uPp7Kyku3btxMSEkKHDh3q9blER0eLn0NeXh7u7u51+lcbi1ar5cSJE1y4cIGrV6/Srl073nrrrTblftJoNJw4cYK8vDwsLS1xc3MDbj0caspqOHPmDKdOncLNzQ2FQkGHDh2wtbXFwcHhnlKjamPr1q2oVCpxReLi4oK/v79onGfMmCFOCpRKJTt37kQQBGbMmNEmi2TaOiqVinXr1onppLdnA8EtY9urVy+ys7O5fPlyle+IcV8AS0tLunfvjpeXV+OMbocOHYRnn32Wvn37MnLkyLt+mLt37yY+Ph5fX1+6deuGn59fs8ykCgoKuHTpEqWlpfTq1QsfH5+7nufOJ5RerycrK4vs7Gxyc3MpKCiguLhYNHa373fjxg30ej3W1tZoNBrReDs4ODBp0iQ0Gg1Hjhzh3LlzAPj4+KDVapkyZYr4w/jpp5/o2rUraWlpLFq0iP379+Pn54enpydw60cWGBhI586dgVtfgqVLl+Ln54ebmxt9+vQRDQPcSsH666+/GDdunOgnrs+9LiwsJCYmBkEQGD169D1LIVZUVJCVlYVKpaKwsJDi4mLxXvXo0YP27du3qQqi8vJyjhw5glKpJCQkpM5ZZW2oVCoyMzMpKyujsLBQnBz4+/vTuXNnsQhk27ZtODg44OrqSnBwMBKJBIPBQEZGBsnJyZSWlhIbG0ufPn1Ew63X69HpdFRWVtKtWzcGDhwo+orj4+MpLCxEqVSSnp7OkiVLsLKyQqVSERERQVFREX369KlTj9hE3dxZCm60j4IgcOrUKdLT0+ncuTN9+vSp8f0VFRXEx8czYMCAxs90jx8/TmJiIsnJychkMrGix9LSEkdHxyo/qMrKSlJSUkhISCAgIIAePXrcV0/d2NhYsrOzsbCwqCJGk52dzaBBg+jVqxcGg4FLly6h0WhEtwnc+rAGDRqEjY0N33//PVOmTKlRNCY6Olqchd8eOd64cSODBg0SA03Hjx8nLS2NjIwMnnrqKbZu3UpYWFgVI6nT6bh27Rrp6enk5ubSu3dvvLy8sLa2rnVWuWnTJgYNGsS99r4zGAzExMSg1Wrp2LEj5ubmODs7Y2dn16ZmtLdz6tQpsrKyGDFihBjhbio0Gg2JiYlcuHCBhx56CDMzM3788UfGjh1LZGQk06dP58yZM8jlcjw9PenSpQv29vakp6dTUVFB165dq9w3tVpdq0utvLyc0tJS8btSUFBAVFQUrq6umJubN8vqxUTDkEgkjTO6vXv3FiIiIsTSvoSEBGJiYhg0aBDl5eVkZ2fTo0cPevTogVKpJCEhgYyMDPGpIJfLefTRR+/qJ3nQuFtuo0aj4eTJk+Tn54u5wm5ubgwcOFB8SGk0GjIzM3Fzc+Pq1avcvHmTcePGVXmI7d27l8rKSgYPHoyTkxOXLl1i3759zJgxo9Z7vn37dvr27duoGV5ZWRm5ubmkpKRQXl5OSEhIrZV6xvtw7Ngx8vPzsbGxoVevXly5ckVMaC8qKqK0tJTRo0eLM/7mQK/XExUVhZeXF7179651P5VKRXZ2Ni4uLjWWZxtZs2YNcXFxhIaGYmZmhqOjI3Z2dmRnZxMXFycG0NLT05k5cybnzp1DEIRG3/e6yM7OZsWKFQQEBODv79+qOdAm/o9GG10rKyvB398fmUyGIAhYW1uL/y4tLRW1CczNzdHpdDzzzDPMmTMHmUxGTk6OODV3cXG5r2a8zc3hw4eJi4vDysoKNzc3goKCqpRv5uTkcOzYMTFqnpSUxMsvv1xlJiQIAn/++SdeXl4UFBQgkUjQ6/V4e3vTr1+NnzVwa1a0Z88exo8fX68gXHFxMTExMcjlcmxsbHB1daVTp051GiUjRUVFrFy5ErVazWOPPcb169cJCAgQxdGdnZ3Jy8tj27ZtPPzww82iI1FSUsLOnTsZOXJknbP7PXv2oNVq8fT05PTp0/Tt27fWJSTcCl6ZmZnh6urKyZMncXd35+bNm9jY2DBixAhkMhkVFRW1BmObEo1Gg5mZWZtdYfzdyMvLw9XVtXFGV6FQCIsWLQIQS+1UKhWCIGBvb4+FhQUymYyoqCgA/vrrL7FDgtGHumfPHt54441Wr4hqbdLS0khOTqakpAQvLy/S09MJDw/nxo0b7NixAwsLC/z8/Ojfvz/Hjh2joKAAuVxOWVkZOp0OJycn0X8Nt3xHlpaWZGRkcOzYMQYMGICbm1u9DFdeXh6XLl1i5MiRd903MjKS8ePHNzrFLC4uToy4Dxo0SDT0N27c4OrVq5SWluLl5UX//v2b1Per0+k4evQoRUVFjB8/vtaluiAInD9/nszMTHx9fYmLi6NDhw4EBQXVmS9bUlLC9u3byczMZMCAAUilUgoKChgwYMBdc5FNPJjs2LGDQ4cOMWXKFIYMGdL02guXLl1ixYoVGAwGKioq2L9/P6dOncLDw4N9+/ZRXl6OWq3GysqK4cOHN1l1zf1GRUUFcXFxXL9+nalTp2Jubs6WLVsoLCzE1tYWa2trlEolhYWFODo6MmbMGBwdHTEYDFVmLhqNhuXLlxMSEkJgYCAJCQmsW7eO0NBQgoKCSElJuase7+0cPnwYjUbDiBEjapwhlZeXc+DAARwdHcXAzO0dKBpCYWEhubm5FBUViSWSnp6edO7cucYZs7EVUUMxGAwkJyeLOsMhISG1GkCDwcCxY8fIysqiV69epKSk4ObmJga86ktJSYnY7cPEg0NjmgsIgsDp06cRBIGQkJDmE7wxGAysX7+eN954g5MnT3L8+PFqeYR/V1JSUrhw4QKDBg3CxcWlxrS5iIgI8vPzCQ0NxcHBgR9//JHHH3+c2NhYcVZoMBjQ6/V07NiRDh06cOXKFQoKCtBqtURHR7Nw4UICAgIaXMV1/fp1Lly4wNSpU6tti4uLE9NkBEHggw8+4Oeff27cjWgg7u7uHD9+vEFVRIIg8McffxAYGEhAQMBdiwzOnDkjpvcYDAY2btxYTfHNxIOPRqMRJSz9/f2RSqXk5eWxdetWpFIpjz32WJ3vvXr1Kjdu3ECpVIqTFwcHB4YPH958gje3V+totVoxabm+GBPpL126xP/8z/9QUFDA+fPn8fLyomvXrvetLzg9PZ3ExERmzpxZ58zpdm1ZnU7H4sWLcXFxoX379owdO7bKvomJiezbtw+AKVOmYGlpKb6/MUvzDh06UFFRwcaNG7GxsUGhUBASEoK1tTUZGRliutK7777L6tWrmTRpUrM3ffzpp5/Izs5GrVY3yOhKJBKsra3p2bNnvfbPzMwUg07GXnUajUacsRoMBlEj484sHRP3P3q9nr1796JWq8W+hVu2bAFuxSLkcnmVYh5BECgoKMDJyYmysjKOHTsm5uMPGzasilvvbqvBJpUAev/99+nVqxfbt28X1YvGjBlTazAhIyOD2NhYcnNzeemllzhw4ABJSUlMmTKFoqIidu7cKS6zjQUEnTp1wtfXt83/CE6fPs3UqVMbNE4zMzM6duwo5mXeibFFkFGB39LSUgy0HTp0iEmTJjU4Fax79+74+/uj1+vF8miJRCIm/RsN7jPPPMO7777b7PfdzMyMpUuXNsmxDAYDarW6Rj/3xIkTWbduHWPHjsXDw4OhQ4eyefNmzM3NxY4S7du3F8XOn3jiiSYZk4nm53aFM0EQSEtLo7S0FB8fHxwdHSkvL2fDhg1MmDChyu9FoVBw+fJl/P39USgUnDlzhvLycrFgyagiaNSFuNNlmpuby5o1a/jmm2/qHF+TGt1ly5bh4eEhGsmLFy+ydetWBEGgXbt26HQ6iouLmT9/PgBbtmxh1qxZJCQkEB8fT58+fcTgTvv27enRowd5eXkkJyfTp08fBEEgOTmZDRs2iALO5ubmbdIAy+Vytm7dyogRI3BwcGjQe/fu3Uu3bt1q3W4U8unbty/l5eXk5uby6KOPEhERwfDhw6sUUdRFQUEBp06dqvKacaZXUVHBkiVL2LRpU4sZ3NrIy8vj6tWrCILAwIEDax2HTCYTW++kpqZy6tQpDAYDEydOrPIZGAwGMa/VuCrz9PRk7ty5/Pvf/yYoKAhPT0/KysqQSqUEBAS0xGWaaCRlZWXs27dP/F4YXUs6nQ5BEPD29sbJyYm1a9eKkxozMzPxdyIIAidPniQ3N5dp06aRmJhIQUEBo0ePxszMTNQSro1z587x888/s337dvGcddEkIub//e9/ef311zl16lSVwIVRRwBupR7Fx8fj5uYmyrFVVFRgbW1NWFgYbm5uaDQa4uPjOXPmDGfPnuXs2bPcuHEDuLUU/uSTTxg5ciSCIHDt2jVSUlLQaDSoVCr8/f3rvbRsKZRKJXv27GHMmDFIJJJ6ydXBrXzR7du3VymWuBNBEDh37hzt2rUTMxr0ej0RERGMGjWKdu3a3fU8r732GuvXr69zn5Y2uKtWrWLp0qUkJiZiY2ODSqUiMjISX19fNm/eTGhoKJMnT65xPCqVitOnT1NYWIinpydOTk5kZGSQn5/P9OnTxf0yMjLIzMysUpySkZFBXFwcXl5eXL58mblz57bE5Zq4B4yykCUlJYwePbrevy8jgiCwa9cuNBoN3bp1Ex+uK1euZOTIkXU+bDUaDTt27ODnn3/m3Llz2NjYMHfuXB599FHeeOON1hMxN3a7TU1N5cyZM5SUlFBQUEDPnj0JDQ1l3759ZGdn891333H+/HkuXrwoKru7u7sTHBzMokWLcHR0ZMWKFTzyyCNMmjSJjz76CF9f3yppaFFRUZSVlTFo0KDmvKQGYWlpSZcuXTh+/Dh6vZ7KykosLCwYPnx4naldFhYWzJgxg8jIyFr7iUkkkmqJ8DKZjGnTprFx40ZxNVEXSqUSuFW+XRNWVlZiWXJrkZqaSteuXenVqxf+/v7s3Lmz1geAhYUFQ4YMQaVS8ccff9C1a1cOHTrE6NGjuXTpEoGBgRQXF3P+/HmGDh0qvk+n03Ho0CHGjBnDwYMHq2wz0TZJS0vj2LFjDBkyBG9v70YdY/fu3XTt2rVa5ejjjz/Onj17ajS6eXl5/P7776xdu5acnBw6derE0qVLmTNnTr1zspvN6GZlZXH06FHi4uJQq9Vi3mN+fj7/+7//y5kzZ0RhZ3Nzc3r27MmiRYsIDg6mb9++1WZ406dP57vvvuPbb7/lwIEDvP766zz22GPiUmL8+PHs3r2by5cv07179zbjcjBW7BkpKSlh79696HQ63N3dsbe3x9LSEi8vLzFwVFZWxp49expVXWRmZoa3tzcbN24UtQXqiuR36tSJwMDAhl9YC1BSUoK7uzs//vgjDg4OaDQalEol+fn5dc7k1Wo13t7e6HQ62rdvT1xcHDk5OVy9elXsHHF7GbBWq8Xa2pp27drdc9NME82LUqkkOjoaJycn5s6d2+iCEK1Wi1arrWZwS0pKiIyMZMyYMVVev3jxIj///DNbt25Fo9EwcuRIvvjiC0aOHNngMTS50RUEgbfeekt0HajVapKSkli/fr3oavD29iYkJEQ0sD169LhrnqO5uTkvvfQS06dP57333uPDDz9k48aNLFu2jODgYEpLS+nevTs5OTns3bu3WuS/rWBvb8+UKVOAWyWcxg6rarUaf39/fv/9d1FntTHVTMbWKRqNhosXL3L+/HkcHR3rbAHUFjGqtqnVagICAjh37hzOzs7Mnz+fzZs307t371r93vb29hQWFuLt7Y21tTUVFRWiSIlEIuHy5cskJyeL2s7m5uZid2ETbZsNGzYgCALDhw/n5s2bVFRU4O/vT0lJidgtuz4YK2tvJzU1lZMnT9K9e3c8PT3RarXs2rWLX375hVOnTmFtbc28efN47LHH7tr1uC6a1OiuWLGCrKwszpw5Q35+PnBriR0UFMSTTz5JcHAwwcHB99SOumPHjqxdu5YdO3bwwQcfMHXqVObPn09wcDDXr19nzpw5CILA/v3761Vx1ZoYI6e2trYkJSXh7+/PhAkTuHDhwj2XjyoUCrEc+OjRo2zevJnp06e3mRXA3QgODqZz586cOnUKZ2dnLl26xOXLlxk8eDBz5sxh48aN+Pj4iG4apVLJ8ePHKSkpEWVABw4cSHp6OmVlZbi7u3Py5EkcHR2ruHfuLEIx0bZZuHAhKpWKffv2YW9vj6OjI3/++SdSqZTk5GTee++9eh3HmCYIt74DN27c4Pjx4zz00EMUFhbyzTffsGbNGrKzs+nYsSMffvghc+fObRKhpCYxusaqot9++41OnToxfPhw+vbtK7Y8b+rmdBKJhMmTJzN8+HC+/PJLfvnlF6KiohgyZAiRkZFi+tn27dtbtD1NY3Fzc+Pw4cMUFRXh5OREYWFhkx5/0KBBREREoNVq75vKKYPBQEpKCklJSQwbNoyMjAyef/55cfvYsWPZtGkTPXr0ICgoiKioKIYOHVrN7XC7IE9YWFi185gM7v2HhYVFlU7I5ubm7Nu3j5CQECorK+sdUDO2xkpOTkatVtOpUydeffVVIiMjUavVDB06lGXLljF69OgmrRdoEms4YcIEMcLc1L2t6sLW1paPPvqI2bNn89ZbbxEZGcnAgQOZPHkyI0aMYNu2bRQVFdW7u0JrMnnyZPbu3UtpaWmTNQvMzs7m3LlzpKamkp6ezuDBg+9Z0rGlOHv2LN26dWPOnDlcu3aNuLg4duzYwaRJk8Rga0VFBYWFhWzevJlOnTrVK2PDxIPH6dOnsba2xt7envz8/HqrGvbu3Zv169eTn5/PuXPnOHHiBJaWlsyZM4fHH3+82RqZNonRVSgUTd5VtCEEBgaydetWfv/9dz777DPGjBnD999/z4EDBzh//jxyuZyQkBCGDx/eZmc25ubmYs+rhnDmzBkyMjIoLS1Fp9Ph4OAgtpixtbWlQ4cO5OTkUFFRUWVpVF5ezsWLF9usu2HixInY2NggCALr168nMDCQ8vJyNm7cKOoFKxQKXF1da9WPMPHgYyyasrS0xN/fn+jo6Hob3by8PFavXs3Nmzfx9vbmn//8Jw899FCD8+obSrOmjLUkUqmUhQsXMmHCBKZPn86XX37Jn3/+ydmzZykpKRF7g40YMaLN5fM2hsTERA4dOkRJSQl+fn7IZDJSU1M5duwYr732Gl27dqWwsJDo6GgmT56Mt7c3W7ZsQaVS0bVrV15//XXS09P5+OOPW/tS6kQikfDaa69RVlZGdnY2N27cID09HR8fHyZOnHhP8QET9z+ZmZm4u7uLWsZpaWmkpaXRsWPHOt+XmJgoytD+9NNP1bSqm5MHbnrg4uLCkiVLiI+P58KFC4wZM4aRI0cikUioqKhg9+7djVLKakscPXqUVatW4evry9NPP83w4cPR6/W8+eabfP/992IazL///W8GDBiAIAj06dOHqVOncubMGR566CEEQWDTpk11Cnq0FWxsbPjrr78oKyujY8eOzJo1i7CwMJPB/ZtjzK/u0aOH2OHl5ZdfvmveblJSkmhw//rrLyZMmNCiGi8PzEz3dmbMmMHnn38uVpY4OTkxbdo0samfXq9v8uBeS/Hrr78SHx/Phx9+KPbV0mq16PV6EhIS8PPzQxAErl69yty5c8Vc6LVr13Ls2DFyc3Pp378/a9euva/kNp944gkSExNJTExslbJc4z318PAQA8eFhYXcuHGDkydPcvHiRT7++OO7xjRKS0uJi4ujoKAAGxsbUWDIRMMx5tMa3U05OTnk5ubWGbe4fYb7119/3VPqV2O5Py3PXVAoFPzjH/9g6dKlnD9/nqCgINq3b8+ZM2d46qmnWnt498SCBQuqPTDkcjmOjo6sXr0atVpNv379KCws5Mknn8Tf359169axZ88ezM3NWbZsGVOmTLmvDC7cypAJDg7G19eXnTt3VinrBbh69Sp6vb5JzymTycS8z71792JjY8Pp06e5evUqzz//PH/88QehoaG4urrywgsv1Glwi4uLOX78OBqNhgEDBtCvXz8uX77M1atXm1257UFDEARiYmLo0aOHGDw1dveuS7agLRhceECNLsD8+fP55ptvWLlyJT/88ENrD6fJuN3gXr16lQ0bNlBcXExwcDAPPfQQ169fRy6XM2LECNRqNS+88ALR0dEMGTKEr7/+usl7dDU1Z8+eBW49XGpb8uXn5/P7779Xee3TTz9t8rHo9XrCw8PJy8vD3NwcOzs7lEolubm5xMbGUlBQwE8//YRcLsfFxQULCwumT5/OI488UuU4Go2GTZs2ER4ejp2dHZcuXWLLli106NDB1Lm3gej1erZu3UqvXr2qFELk5ubi6upK+/btq7SuN2J0KUil0lY1uPAAG11bW1sWLlzIihUrSE1NxdnZuUX6VTU3er2e6OholEolXl5eLFy4kJ07d4pKbsa2NzExMbz66quUlpby4Ycfsnjx4loj/M09S2wIly5dAm51llar1aLKk/FhIwhCtetoTh+9RqNBJpOJmR+WlpZ4eHig1+vp0KEDEokElUpFfn4+N2/eRBCEakbXqEFiNATx8fH079/f1ESynhh1jo1t7ceMGYOLiwtw6/eQmpqKj48PvXv3JioqimvXrrF48WJxNZeUlMTs2bNbfYZr5IE1ugCLFy/mxx9/ZNWqVUybNg1/f//WHtI9s3//foKCgqr4rWbPno2dnR0SiQSlUsnSpUtZvXo1AQEBrF+//q4+0KY2uE1xzAULFvDwww+LPeKKi4uRSCQIgiB22zWybt26ex1urcyfP59Zs2bVKwYwefJkUYr09pp+MzMzunbtyrVr1/D39yc8PJyoqCj27NnD2LFj22zaXmtTWVnJgQMHqKysRCKRYGlpyZQpU6pMng4ePIilpaUo3lRZWUm3bt1EHZO24lK4nQfa6Lq6uhIeHs6GDRt4+OGHycrKum+bBubn54vNLTds2MD3338vbjMG1C5evMjzzz/P1atXefLJJ3nzzTcb3VCytTE3N0cmk4k5kzUVPqhUKpKTk2stinj77bc5cOAAzs7ObN++HYArV67wwQcfUFlZiaenJ19++WWdKyALCwtOnDghtp8y9sCSSqXodDosLCzo1asX7u7uyOVy7OzsSEhIqCak0r9/f7EBaceOHZk4cSKvvvoqaWlpzJw501TYcQc6nY5NmzYxZsyYOgNjarWay5cvExoaysWLF5k0aZI4w22LBhcewJSxO3nqqafQaDT897//vW9KYO9k+fLlvPvuu/z+++8sXLiQVatWVdmu1+v59ttvmTJlChUVFfzxxx988MEH963Bhf9T/6+NzMxMNmzYgJmZGePGjatxn5kzZ/LTTz9Vee3dd9/l1VdfFZepd26/k507d7J+/XoyMjK4fv06YWFhTJs2jSlTpjBjxgyxRHnnzp2Ulpai0WhqLHKRSCRMmjSJ/fv3c+DAAQD69euHvb09cXFxdY7hQcNgMHDo0CF+/fVXUlJSqm0vLy9nz5491VZ0NWFubs7w4cPJzMxkzJgxbd7gQgvOdFvLb+jr68uECRPYtm0bffr0ua8KI0pKSti9ezft2rXj5ZdfrnEZevPmTV588UVOnDjBlClTWLZs2X1R9nw3btdMqIlDhw4xf/78OivR+vfvT3p6epXXUlNTxerJwYMHs3jxYl566aVaj/Gf//xH9CvXhLW1NQMGDABg9erVALUGACUSCTY2NnTv3h249d0sLCxs88JMTYWxm0x8fDyDBw/m1KlT4v9XVFQQFBSEj48Pv/zyC/PmzavX7F+lUuHn51fFeBsNblsImtVEi810W9Nv+Oyzz1JaWsrZs2dJTExs8nE0BxUVFbzxxhskJSUxceLEaj96QRCIiIhg7NixXL58ma+//prvvvuuSQ3uqFGjmDJlCtOmTWPmzJnArdSnxx57jHHjxvHYY4+JLdWbmrp8qGq1Go1G06jSXz8/P2JiYoBb8pFZWVl17i+Xy5vM55qRkYGzs7NY1NGvXz8KCgqa5Nj3AxkZGaILwNPTk65duzJmzBimTp2KnZ0dUqkUCwsLunfvftfPBW75b8vLy0lOThbdhrdnKWzYsKHNGVz4G7gXAPr06UNoaCj79+/n1KlTotO9LWOUHmzXrl21J35xcTHPPvssS5YsISAggOjoaGbPnt0sAZnVq1ezZcsWNm3aBMAPP/xAaGgoe/bsITQ0tNnS8S5fvsylS5fEdk1G8vLyiIiIEF0KgiBQXl5e7+N+8skn/Pe//2XmzJlUVFTc1eVkLC65E71eL0oD1hd3d3cKCwuJiYmhpKQEpVLZLJORtoqXlxchISFERETw119/oVQqOXbsGAC9evXi9OnTHDx4kDFjxpCcnFzr7/TSpUscP36cHTt2MHHiRK5du8bAgQNJTEwUsxTaqsGFBzyQdjvPPvssCxYsQK/Xs3v37mrJ9W0NqVTKSy+9JGrDGjl8+DAvvvgi+fn5vPHGGzz//PMtWsIYExPD2rVrgVvdPBYsWMDrr7/e5OcJDg7GzMyMs2fPcvjwYRQKBQqFAnNzcyZMmEBycjJHjhzBzMwMGxubu7ojjPj6+vLLL78At1wNRv9qbZw5cwalUolWq0Uul6PX67GysqK8vJyCggICAwMZMWJEvc4tk8kIDw8nJyeH8+fPo9Fo/nbi6X5+fvj6+nL8+HFycnJEw2hsbJCQkABAQEAAN27cqCJUr1Qq0Wg0pKSk0KNHD0JCQpBIJOh0umqlvW3V4MLfyOgaG82tXr2aFStWcPjw4TbdTUGv11NQUCBWOanVav71r3/x/fff07lzZ7Zu3Urv3r2bfRyLFy9GIpEwd+5c5s6dS0FBgbg8dnV1bXLtXyN+fn7Y2NiQm5srpgwZZ7UnT56kW7duoi8Vbi0r60NBQQHOzs4YDAa+++47HnrooTr3NwbFBEFAIpEQGxvLyZMnSU5O5u23326Ue8DNza3eHZsfRCIjI+ndu3e1whAvLy9SUlK4efMm586dY9y4cRgMBvbu3YtSqcTS0pLKykpxlVNUVERCQgJlZWVtKg/3brQJo6vX65k1axZubm58//33vPTSS6SmpgK3+oXZ2tqyZcuWezqHRCLhmWeeYcmSJVy6dAlXV1c2btyIh4cHoaGhbUYa0BhsuHz5Mn369CEgIIArV67w/PPPk5CQwMKFC3n//ffrbGzZVKxfvx43NzcKCgp47LHHWrRJpSAI4g9q3rx5jTrGK6+8wsmTJykqKmLYsGG88MILVFZW8t///he4JYRe355oRteNn58fZmZmPP/88xw9elQMipmoP0FBQZw/f76acZTJZFRUVJCens7MmTNRqVS89957dOjQgcLCQgwGA126dCEmJga5XI6VlRUSiYSvvvrqvjG40EaM7po1a/D19RV9c19//bW47bPPPmuySrKpU6eyZs0aXnvtNVauXEl4eDgpKSls3bq1TbgbtFotUVFReHt7izOwH3/8kWXLlmFra8vq1aurNcxrToyzMWdnZ8aOHcvFixdxdnYWSy5zc3ObTbQ+IiKC4ODgKh0CGsry5ctrfH3RokWNPqa7uzvu7u4olUrKy8vvG1H4toSvry/Z2dnVJBjlcrn4EMzOzmbLli08/PDD7Nmzh3bt2uHo6Ejnzp3x9PTEzc2tTaeF1UWrT++ys7M5cOAA4eHh1bYZ+9I3VcsduVzO77//TlBQEM888wxbt26lS5cu2Nvbk5eXR3Z2NklJSQ0OkDQFarWaDRs2EBoaSlBQELm5ucyfP58PP/yQoUOHEhMT06IG1xgZNv77yJEjdO3alVGjRhEZGQncWiaOHj26Wc4/atQoevXq1erVWsbvQklJCTdv3hQDX2fPnqVv376tObT7mtDQUE6cOEFFRUWN293d3bGxsaGiooKJEyfy8MMPEx4eTnBwcBWD21bTwuqi1We6n376Ka+//nqNN//06dM4OzvfVZC4Idja2rJu3ToWLFjA888/jyAIjB8/nu3bt2Nvb4+VlRUJCQlMmzatyc5pRKPRVImY3y7pd/bsWUaMGCFmKnzyySfExsbyyCOP8Nlnn7W48SkoKOC5554Dbrl/Jk+ezLBhw+jZsycvvfSS6Jr55ptv6n3MvLw84uPj69ynuLgYgBdffLFBAcLmELyBW8ZfEARkMhlyuZzS0lLMzc2RSCQ4OztX2ffYsWOEhobWeTxTI8xbSKVSrKysKC0trVXask+fPly9epUBAwZUuWf3U9CsJlrV6O7fvx8nJycCAwM5ceJEte3N1VjSxsZGrO56/vnn+eabb/D29iYvLw9BEDAzM6O4uPie2nYkJSWRmJiIlZUV1tbW9O/fn6+++orQ0FA0Gg0qlYry8nIEQWDUqFE4OTlRVFQk5hs+99xzHD58mE2bNhEWFsaoUaOa6Orrh7e3N1u3bq32ulFCsjG88sor7Nu37677SSSSVp/hGrkzXU+v12Nra4tEIkGv11d5MISGhtbqphIEga+++gofHx9sbGwYP358cw77vsDCwoLKyspat3fv3r2az/x+dSncTqsa3bNnz7Jv3z5iY2NRq9WUl5fz2muv8eWXX6LT6YiOjhbzQ5saa2tr1q5dy8KFC3nxxRdZvnw53t7enD9/ng4dOrBr1y7mzp3b4FlJUVERBw4cwMvLi4kTJ4rNE3fs2MGAAQMYNmyYuK9xqfrXX38xd+5cNmzYQI8ePQDw9/dn165dLFq0iEWLFvHJJ5+wcOHCprsBrYBSqcTKykoMZN1JZWUlx44dY+TIkQ1Opapv9kJD2bhxY5X/P3jwIOfPnwdg6NCh9VYKk0gk+Pj4EB4ezoEDB0hPT693mtuDipOTU4Merg+CwYVWNrqvvvoqr776KgAnTpzgl19+4csvvwRutaTp3LlzswYqrKysWLt2LY8++igvv/wyTzzxBIMHD0atVhMQEMCaNWuYN29evTUbjO2Apk2bJmYX2NnZYWdnV6OLxDhLMjc3B259CVNSUqr4lL/99lvx33UZlsZKKbY0vXv3rtbEVK/XExERgZ2dHS+99FKb1owYNmwYmzdvpkOHDiQmJhIUFFTvB3Pnzp05fvw4w4YNY8uWLSQlJTF8+HBkMhl6vR69Xn/f6oM0lCtXrpCRkUGfPn3qtf/97lK4nVb36dbGzp07G9Udt6FYWlry22+/8dhjj/HTTz9RXl7OkCFDxP5qGzduZNq0afVqqZKXl0dAQECD07mCg4M5evQoPXv2pLS0tFHXcb9WNun1erZs2cKgQYPuaeZnNFxNSU0+ZYlEwnPPPUdZWRnu7u7s27ev3gHO4OBgzp49y4kTJxg/fjzFxcVs3rwZiURCaWkpRUVFDfZl349oNBouXrxY7yrKB2WGa6TNGN2QkJAqS8rPPvusxc5taWnJr7/+yhNPPMH69evx8/PD0tKSxMREevXqRWxsLGFhYXed0VhaWlYrWzWyYcMGfv/991pzXTMzM9Hr9c0WEGqrnD59muDg4HtearfkLL9r16789ddfBAYGIpFIxBbe9SEoKIjXXnuNixcv8tRTT4mdKVasWEHfvn3ZunUrUqkUBwcH3N3d0ev1YrlwaWkpdnZ2VYpC7kd2797NmDFjSElJoVOnTnXqbKSnpz9QBhfaQMpYW8HS0pKff/6ZUaNG8dFHH3Hy5EkuXLiAnZ0dgYGBbN68+a5yg3FxcbX6+GJiYjh9+jR//fUXhw8f5siRI1X+UlNTuXbtWnNc2l1pjplVfY5ZWVlJUlISPj4+TX7+5mbMmDHs2LGDoUOHcuDAgXrreUilUjHx/8yZMxw/fpzY2FhefPFFtFotU6dOZfLkycjlci5dukRSUhK5ubkUFRVhbm5OTk4OZ86caearax4MBgPR0dH4+PiQlJRETEwM+fn5db4nNjaW/Px8Vq1a9UAYXGhDM922gIWFBT/99BP/+Mc/2LRpE/Pnz+eXX37hnXfeYdiwYURERBAeHl6rQVEqlbUWcnzzzTfI5XI2bdrEsGHD+Oyzz5DL5SiVSpKSkigpKWk1X2Zr+YL37NnDzJkz78sUKicnJ1QqFTKZjClTprBlyxbmzp1br+XykCFD6N69u2iojSlRISEh7Ny5k/j4eP7973/X+v5FixZx8+ZNJkyYIMYD7hWtVosgCM3iUxYEgevXr3P8+HGGDBmCl5cXGzduxNXVtd4xmwcp6NgiRre55P+aA3Nzc3788Ueeeuop1q1bx7PPPotCocDFxYVhw4Zx8ODBWtO39Hq9WKNf03H//e9/4+Pjw9dff01GRgYvvfQSv//+Ox4eHty4cYPRo0cTFBRU47F/++03NmzYgEQiwc/Pj2XLljXZD641KCoqQiqV3tftx0eOHMlff/3FrFmz8Pb2Jjc3t96aCk5OTqLG9O1asN26daNbt27MmDHjrse4fv16tdcaGlDVarUkJiZy7Ngx5HI5EyZMaLQuhE6nE10FBoOBxMRETp8+jZWVFV5eXmIxA9z6rfxd9SdaxOja2NiQk5PTEqdqEszNzfnhhx94+umnWblyJW5ubjzxxBN4eHjUqUrVpUsXEhISaqzHv3TpEikpKXTr1o3p06ezfft20tPTWb58OQMGDMBgMJCXl1fjAyonJ4c1a9awc+dOLCwsePHFF9mxY4eocWvkzrzRtowxNex+xt3dnX79+olaHllZWQ0yJM2pMd2QpgEKhYLhw4cDtyZItU2SajPoSqWS6Oho4NaDoFu3biiVSnx9fWsVmp82bZrYx+zvRosYXZlM1mLR5aZCoVCwatUqnnvuOT744AP0ej1PPfVUnapSAQEB/Pnnn3Tt2rXaFyolJYXp06ej1+spLy9nzpw5PPXUUzz99NOsXr2anj174ubmVusXXq/Xo1KpMDMzQ6VSiUpftzNkyBD27NkjtixpywwePJh9+/Y1S+VfS2L8nLt06cKOHTswMzMjMDCwlUfVfAb9iy++qPZaQkICfn5+SKVSLl26xOHDh+nbty9JSUns2rXrns5p7A7dnB2fW5oW8+ne/oRcs2YNtra29VpCtSYKhYKVK1fy3HPP8fHHHyMIAiNGjCAyMpKxY8dWWxrLZDJGjBjBjh07GDFiBLa2tuTm5nLp0iWx0swoLTh8+HAiIyNZuHAhM2fO5Lvvvqs19cjNzY3HH3+ckSNHYm5uzuDBg2uUpbxx4wYDBgxg9+7dbT44ZW9v3yJKac2Np6cnFy9eJDg4mMmTJxMZGYmvr+8DcW01UVvZt7Ebh5EjR440yfmMxragoABPT88mOWZr0yqBtAULFrSZMs+7IZfLWbFiBTKZjKVLl/LOO+/w6KOPsnv3bmQyGUOGDKlSg9++fXsGDx7MkSNHRL9Vv379xDY633zzDS+//DJwq+ps27ZtLFq0iMcee4ylS5cyaNCgamMoKSkhJiaGmJgYbG1tefHFF9myZUu1WeKCBQtYu3Ytw4YNY/369XfVAWhtVCpVaw/hnpHL5bi7u3Po0CGGDh3K8OHD2blzJzNmzGh0gHDUqFFYW1sjlUqRyWRs2rSJr7/+mpiYGKRSKc7OzixbtqxVfKJ39pzTarVs3LiROXPmkJyczOXLlxk+fPhd+5vt3LkTQRDIy8vj0UcfrXW/F198kY0bN963eeg10Sph4/vF4BqRy+V8++23TJ8+nU8//ZSff/6ZGTNmMGHCBE6fPs2WLVu4dOmSWEnm4uLCpEmTmDp1KiEhIVX6lvXt27fKF9LNzY2IiAhGjx7Nu+++W+P5jx49ipeXF05OTsjlcsaNG8e5c+eq7ffZZ5/x0UcfodPpmD17Nn/++WcT34mmw2AwPDA/pAEDBlBaWkpFRQWOjo4EBARw+vTpezrmnW2SnnjiCbZt28aWLVsYMWIEK1asaNRxa+p7Z+Tnn3+mW7duDRKml8vljBw5kp07d+Ln58esWbPuanAFQRC7Jt8PrrCmxpQyVk/MzMz45ptvkEql/Otf/+L8+fN8+umnhIWFodVquXbtGlu2bEEmk6HVapHJZEilUsLCwsQsgxs3bnDixAkkEomoNuXi4kJoaCg///wzH3zwQY3nbt++PRcuXECpVGJhYcGxY8dq9Rs+8cQTdOrUiccee4xXXnmFlJSUWo15a1JWVtZkOsltgb59+3Lw4EEmTJhA9+7d2bRpU51FDHfOGO/G7fdKqVTe08Rl9erV1XSQs7KyOHr0qOgGawju7u707NmTyMhIZsyYcdex5eXlcenSJdzc3B6Y3NuGYDK6DcDMzIyvv/6aHj168MUXXzBq1Cg++OAD5syZI6b66HQ6ZDIZEomEkpISIiIixDJiHx+fajKNSUlJ7N+/nzFjxrB06VKSk5Ornbd3796EhYUxY8YMzMzMCAgIYO7cubWOc/To0ezZs4cpU6awcuVKrl27xk8//dRmVhiCIPD999/zwgsvtPZQmgx3d3e6dOnChg0bmDx5MkVFRdX2EQSBQ4cOUVBQQLt27ep0D9zZJgngq6++IjIyEltbW9asWdOk41+2bBmvv/46zz77bKPe37FjR/Ly8rh58+Zd4wkODg5oNBri4uKYOnXq307u8u9zpU2ETCbj6aefJjo6Gn9/f1555RUeeeQRMjIygFuG2Wjc7O3tmT59Ovv37yciIoLDhw+TmZlZbfnm4uIC1O12WbJkCVFRUWzfvp0vvvjirkns/v7+HD9+HDc3N6Kiohg7dixqtfpeLr3JkEgkDBo06IFrP+7n58ekSZOIjIwUDYtGo+HChQts27aNTZs24ePjw4wZMxg6dGitx1m/fj2bN2/mxx9/ZN26dZw6dQqAl19+mYMHDzJlyhR+//33Ro9z8eLFzJw5U3Q/xcTE4Orqir+//13fW5fokr+/f73U3hQKBd27d6e0tJRvv/2Wb7755oHw79cX00y3kXTu3JmNGzeyevVqPv30U0aOHMm7777LggULqjy1raysmDx5MoIgkJubS1xcHPv37+fjjz9GJpNV0dC9V2rSbZg+fTrff/89CQkJBAQEcPHixTaxrHdzc6O4uPiBqjSCW5KhkydP5osvvqCyshIzMzMGDBjA6NGjsbKyAm6lWdX1gK2pTdLtymyTJ0/mqaeeYsmSJQ0eX01971atWiV2SL4bhw4d4vr16wwbNgxzc/Nq+cDGEt+7ERwcXKVk/nbNkvtFMa+xmIzuPSCVSnnssccYM2YMr7/+Ou+88w7btm3jyy+/rCblKJFIxC6wRp/frFmzyMjIqLcm69346aefanxdoVCg0WhQq9XMmTOHtWvXVut60NJ06tSJP/74A4VCgZ+fX6uOpamxs7PD1taW8ePHi6uY/Px8zpw5Q2FhIQUFBQQEBNT4GVRWVmIwGLCxsRHbJD377LNV+ont27evziaheXl5tW6706CfPHmS9PR0MRMmOzubmTNnsmHDBnHst7N48WLy8/PZt28fEyZMaNYCjwcVk9FtAry9vVm/fj3r16/n448/ZvTo0bz11ls8/vjjNRZwtG/fHo1GQ2pqqth2OiAg4J7H8fXXX7Njxw6kUinvvfdetbzGHTt2sGTJErFBZ2vOJszMzJg/f76YevegzWyWLFnC5s2bsbS0FFXDjIb2ypUrtc50a2uT9MILL5CamopEIsHT05OPPvqo1nMfP36cbt26VXu9NoN+7NgxcZ9Ro0axcePGOhuOtmvXDjs7OzZv3iyK7puoPyaj20RIJBLmzZvHiBEjeOutt/jwww/ZunUry5cvp2vXrtX2DwsL4z//+Q/BwcGUlJRQXl6OnZ3dPQW7pk6dSocOHTh8+HCNFTyTJk3Czc2Nxx57jKlTp/LLL780uENDUyKRSBg7dizbtm1DoVDUWx7xfsDCwoKHH364xm3JyclMnjyZq1evVttWW5uk28Xs74axoeid1GbQG8PgwYMpLCy8q0qYiepI6iqvUygUwvz581twOA8OcXFxZGVlATBixAh+++23GmvNjfJ+dnZ2ODg40Llz50bVpN8+W1QqlWzduhUPDw+GDh1azZCnpaWxYMECbt68SZcuXVrM2O3Zswc/Pz82bdpUJXfZYDCwZcuWNl+h2BRoNBp27tzJ9OnTm63F0KZNm6rl4DYVnp6eYiWmwWCoItbTlBhdTs8++yxbtmwhODj4rvm/bYU9e/aQmZl5RhCEfjVtv6vRrcmv09JERETUudxpDIWFhcyaNatJj1kXgYGB/O///m+N+bU6nY7U1FR+++03Fi5ciEqlQhCEWhXH6ktSUhI3b96ssU16UVERkyZNqlGpqjE0x2f0ILodDh48iL+/P1qtts6mjG2VlJQURo8eLeae1/TgUKvVzJ8/H41Gg16vJywsjCVLlpCQkMAHH3yAWq1GJpPx4Ycf0qtXrxrPYzS6Tz31FNu3b2++C2om6jK6d3UvnDx5sulH1EBqas9+rzg5ObXYtZ04cYKPP/6YiRMn8uyzz1brA2ZmZkbXrl355JNPxNciIiLu2eh27dqVvLw8tm7diouLC5WVlfj6+uLj44OjoyN9+vShoqKCnTt33tN5oHk+o7oCKiqVivPnz5OTk4NEIkGr1TJq1KgqM+i2hlKppLCwkJMnT/LGG2+wZcuW1h5Sg+nSpQsRERGMHz++1oesQqFg9erVWFtbo9VqmTdvHsOGDePf//43zz33HMOHD+fgwYN88cUXrF27ts7zGX8nv/zyS5sQEqoPixYtIjMzs9btdRpdqVTaJkQmmmsZ1lLXNnPmTEaOHMlHH33Et99+y65du/jyyy+rNWi8naYoZJBIJAwePJjy8nKKiopwcHAgOTmZY8eOiVJ+tra2TXIfmuszOnbsmBiNFwQBV1dX0Y/Yt29fQkJCRKO7adMm5syZg1arJTc3t82lo23dupUjR44QERFR74aMbQ1ra2tmz57Nxo0beeihh2rcRyKRiC4InU6HTqdDIpEgkUjEh3NZWVmNSnl3YnS1/fjjj/zxxx91tvZpKzg4ONS53VQc0UI4Ojry9ddfs27dOpRKJTNmzOD999+vdYao1WqbLHXGxsYGb29vbG1tCQ4OZu7cuRw+fPi+SEgPDw9nxYoVxMXF4eTkRPv27RkzZgxTpkyhffv24sNJLpcTEhLCpk2b2LNnD9HR0TVW97UW69atY/ny5WzatInnn3+ezZs3t/aQGsXZs2eRy+XY2dlV6Vp9J3q9nmnTpjFo0CAGDRpE7969eeedd/j8888ZPnw4//rXv3jllVfuer5XX30VJycnjh07Ro8ePdi3b19TXk6rcN8bXb1ez/Tp03nqqacASEhIYM6cOaKgx8WLF1t5hFUZMWIE+/bt49FHH+Xnn39m9OjR1cRRbt68iUQiabbSSKlUyvTp0xskbNJYahJY2bVrF5MmTcLf35+4uLg63//GG29gY2PDjz/+yEMPPcSoUaN44oknWLVqFQkJCVWyNDp27MisWbOYPHkyjz76aI2iQK3BF198wdtvv41Op+PPP//k7bffRi6XN1gPuqZ7+e233zJ06FCmTZvGtGnTOHjwYHNcgohEIiE7OxtLS8s6KwplMhlbtmzh4MGDXLx4kaSkJNavX8/bb7/NwYMHefvtt+ulCeLh4cGFCxd4+OGHqaioYMGCBcyfP/++mDDURp2BNAsLC6EtXFxdS9dff/2VS5cuUV5ezvfff8/jjz/OokWLRL/RTz/9VKvfqLWT8k+ePMlDDz1E7969xZmPVqvll19+4fHHH292Zf1p06aRm5tbJU+zsdT2GdWU93n16lUkEgkffPABb7zxBj179qz1uMbPqKKigmPHjhEbG8vBgwfFqLmrqytDhw5l2LBhDBs2rMqSNTIykunTp9/ztTUWrVZLTEwMr7/+Og4ODmzZsqVewcaG3Mtvv/0WKysrFi9e3GTjrg2JRML58+dJT08nODiY3Nxcevfufdf3/ec//8HS0pKVK1dy+vRpJBIJgiDQt29fzp49W+N7nJ2dqxWPxMfHs2DBArKzs7GwsGD58uVtUgQ/PDyciIiIxgfS2jLZ2dkcOHCAp59+mt9++w2gUX6j1mLAgAFiviPcCg5t3LiR8ePHt0grEysrK27cuMFff/3VqPcPGDCgWuVdfWhMRoK1tTVjxowRhd4zMjI4dOgQsbGxorYF3OreMXToUGbPnt2qWhP5+fns2bOHsWPHIpPJGDRoUJNndzSU+kwy+vfvT15eHmlpaVVe37x5M1OnTqVr165kZ2ezY8eOWjMPCgsLMTMzw87ODpVKxdGjR/nHP/6Bq6srJ0+eJCQkhOPHj9f53Tl69ChTpkyp8lr37t05c+YMH3/8MT/88APPPvssv/76K2vWrMHOzu6u19ZWuK+N7qeffsrrr79exS/6zjvvsHjxYv71r39hMBj4448/WnGEDePixYsMGTKEDh06tMj5jFoARlH1hjJ69Oh6qV3VpJh1r3h6evLQQw/x0EMPYTAYuHz5sjgL/uGHHzhx4kSrSVoWFRURExPD7Nmzm/zhWdO9XLduHZGRkQQGBvLWW29hb2/fpOcECAkJYfv27UydOhV3d3e6d+9ea3eM3Nxc3nrrLbFR6/jx4xk5ciS2trZ8+umn6HQ6zM3N+fjjj2s9X69evRAEgZKSEuLi4rCwsKBHjx5YWVnx/vvvs3DhQubNm8epU6fo3bs3//M//8P9UlNw3xrd/fv34+TkRGBgICdOnBBfN/qNwsLC2LlzJ++++644C27rJCcn16nB2tT88MMPZGRksHv3bry9vUlPT0cqlTJ48OC7tpuZMWMGGo3mrueoSWClrqyNxiCVSunZsyc9e/bkueee4+GHHyYjI6PFHl63o9FoiIqKYtasWU1ucGu6lw8//DDPPvssEomEb775hs8++4xly5Y16XnhVul6ly5diIuLo1evXnTt2pXIyMgaK9r8/f2JjIys9nq/fv1EUfa70a5dO4qLi1m+fDnTp0/Hzc2N6Ohopk2bhsFgQKVS8eeffxIREcHy5ct54403WLNmDevWrWvzRRT3bSDt7Nmz7Nu3j1GjRvHKK69w/PhxXnvtNTZv3sy4ceMAmDBhQpsLpNVFeXk527Zta7HzyWQyfHx8+Mc//sH48eN54oknmDlzJjt27ODcuXPk5+cjlUqxtLTEx8enyl9907FqUsxqbszMzLCxsSE2NrZFxVNKS0v5448/GDt27F2lNxtDTfeyXbt2omD+7Nmz7xqYvBd69OjBlStX0Gg0tGvXTvydNQcZGRno9Xo++OAD9u/fz9KlS8nJyRHvscFgIDs7Gy8vLz799FM6d+7MpUuX6Nu3L6tWrap2vMTERI4fP94mGlzet0b31VdfJTY2ln379rF8+XIGDhzIl19+KfqNgLv6jdoSgiDQsWPHVlfSt7e359lnn2XKlCk4OTmRm5vL5cuX2bRpU4MLICorK0UdAKPASk06FM2BUdchIiKixQzvyZMnmTRpUrPMtGq7l7m5ueI+e/fubfb7GxYWxubNmzEYDM3a/HTLli189tlnXLx4kT59+jB9+nR8fX3F/nPdu3enf//+hIeHM3v2bD7//HPCw8MRBIGlS5cyfPhwUeMabgVco6Oj+fTTT1tdxey+dS/UxtKlS+vtN2pLXL16FScnpyZRG7sXJBKJWAXUsWNH8aFVUVHBli1bCAkJqbf+b20CK9HR0SxdupTCwkKeeuopAgIC+Pnnn5v8WlxcXMRGkXcGZZqaoqIilEpls0lm1nYvX3/9da5cuQLc8nM39/fd3t6ewYMHs3HjRsLCwprtPK+//jp6vR6lUomlpSXnzp1DKpVSUlJCbGwsgwYNEvurmZubExoaSmhoKK+++ipz584lJSWFgQMHsmTJEl555RXkcjlubm5cu3aN7OxsHB0dxZhGS/NAGN2QkBBRLashfqO2hLOzc42qU20Fa2tr5s6dy6VLl9i0aVO9Zgu1KWaNHTuWsWPHNscwq+Hm5oa1tTWFhYXNmj1w4sQJRo0a1WzHr+1efvHFF812ztrw8vJi2rRpREZGNmtlnUwmEwX3+/Xrx7p165g+fTo6nY69e/fSq1evapkwPj4+HD16lFdffVXsovzrr78yevRo2rdvz5NPPomnpydLlizh3XffbZWOyvete+FBw9rampKSkjZteGUyGb1792b06NH3lVhLr169qgRbmwNbW9s2/dk1Nebm5syePRutVtvkx66taCQlJQWZTIa9vT3Tpk2rNT4gkUhYvnw5L7/8Mr169aKkpITNmzdTVlYmrtzmzZvHd999x3/+8x/UanWL+nofiJnug4BCoWD27Nls2LABLy8vUcWpLeLg4NDqfrGG0K5dO9RqdbM2QPTz8yMhIaFZjt1WkUqlBAQEcO7cObG7sSAIeHp64uPjw/Hjx9HpdNjb2zNkyBDkcjk3btzAw8ODiooKbG1tycjIwMvLC7lcjl6vp7S0tNaUt9u7ZRsNbmJiIl26dEEmk1FeXs7BgwfRaDR07dqV7t27M3r0aK5fv85LL73E6tWr2bVrF2vWrGHgwIEMGDCAQ4cOcejQITZs2MB7772Hi4tLFTGq5qBOo2swGLh27VqzDqA1aQvXlpeXJ+oHSCQSwsLCiImJYeLEia08stq5cuUKCoWCvLy8Zr+HjTn+7ffUiJmZGUqlUhRiaWrkcnmtxRh6vZ6ioqJqZdc6na5WwfHmoD73UqVSodfrG3TfHR0dq6i7VVRUcPHiRWQyGZWVldy4cYOUlBR+/vlnJBIJAwcO5PTp0+Tl5eHg4MCSJUuIjY3l4sWLKJVK3nzzTdFfW9c4zczMKCgo4NChQ3To0IGsrCyCg4Np3749+/bto3v37pw/f56OHTsSGxvLkiVLOHr0KOPHj2fy5Ml8+umnYjWkq6sru3fv5syZM1XU/hpDTZ2gb+e+0NPdv39/sxx35MiRzXLchmJlZcXp06fFJ/y2bduqNDJsS2RnZ3P06FG+/vrrKsvptvYZOTg4cPnyZfH/Y2Ji6NOnT7P6dTdv3szo0aOrVUdNmzatmr5GXbS1e2mi4dyTnu6kSZOafkRthLZwbWVlZRw5coR//OMf/P777ygUCoYOHUpsbCzjx49v7eFVobi4mMOHDzNz5kyx4KS572Fjjr9jx45qTRUHDx7Mnj17mDp1alMNrRphYWE1fm6//fZbjapiy5cvF8XkW4L6nqesrOyuM83a2LFjBz179mTOnDl17qdUKrGwsEAikVBaWoqVlVWNso2CIHDmzBl8fX3r1Eq+efMm5eXl1bJ/lEolJSUl5Obm0rNnTyQSCTqdju+++47c3NxqKaVqtRqVSnVPVX13llDfyV31dH/44YdGn7ypuLPNc1Mgk8naxLXBLcHyJUuW8Oqrr/Lvf/8bBwcHDAYDxcXFd9XmbCkEQSA6OpopU6YglUqRSCQMHTpUvIdt6TNasGBBtaW8Vqttdi1Wc3PzGu+Bo6Mjjz/+OCqVitjYWLGo4Oeff2bkyJHV+p+1pXvZUIYMGYKvry+PP/74PR9Lq9USGxtLTk4OAQEBqNVqdDodSqWSWbNmVXEVRUREoNFomDNnTr3U2/bs2YOvry8bN26853Heyf8XvKl1+30RSHvQWrbcyaxZs0hPT+fzzz+nffv2vP3224waNYrt27cTHh7e2sMDbskxBgcH1xpkaMufkSAIbN++vdl7sMlkslqNpcFg4PTp0xQVFZGSklJnEUxbvpfNjSAIoj9+06ZNjBgxghEjRoiGtLy8nD/++KNKxZ8gCBgMBkJDQzl37hz9+tW4qm8z3BdG9+/AkiVLyMjI4D//+Q9eXl4sWLAAMzMzVCpVs0dT64NOp7tvjcG1a9cICAhokftYm7B3dnY2CQkJLFq0iH379rV65WFbQ61Ws27dOqysrLC2tiYoKAgnJ6dqebRxcXGMGjWqiq6FIAiYm5vj7u7Orl27kMvl9ZKcrC8FBQU4OTk1STcXMBndNoNEIuHTTz8lOzubd955B3d3d0aNGsXmzZsZNGhQq4i3PCgkJSUxYsSIZj+PRqOplpJmMBhISEjgypUrYu+se8nnbS7XQ2s+UAVBIDIykilTpuDi4kJ2djZnz56tUWdZo9GQnp6Ot7e3aHiNDzoLCwsWL15MREQEPXv2bLL0wG+++YbJkyc3mRhVnaNqC+IQfyfMzMz47rvvCAwM5JlnnuHatWvMnTuXmzdvEhkZeddUlOZCEIT7+rug1Wrvqpp2r1RWVrJ161aGDBkivpaQkEBERARarZaZM2eiUCjIyMi4JwPXHPnRrZlzffXqVX744Qd8fX3F4Ke7uzsTJ07E29u72v7Dhw8XfbEpKSkIgkBSUpKoOaFQKOjUqVMV3YV75YUXXqh3e6Vr167dWyCtqabTJuqPtbU1a9asYerUqSxatEj8ISuVSg4ePIijo6NY8txSlJaW3lci0S2JVqslKioKqVTK6NGjq0TY4+PjmTVrVpUZV25ubpvImmkrnD59GicnJ/r27Vvv93h6ejJnzhxOnTrFmTNn0Gg0VbR0g4OD2b59e41GuzG4uLjUK3f3yJEjxMfH37VxgqkMuA3i4uLC2rVr0el0PPLIIxQWFmJpacn48eMxGAwt1jreyIkTJwgODm7RczYlNjY2d519NIaioiI2bNjAkCFDmDRpUrWUJplMVmXiYlwtNIfs4/3IhQsX6Ny5M7Nnz27wBE8mkzFw4EDCw8OZN29elQebQqFAEAR0Ol2TjbU+rgq5XI6Pj89d8+tNRreN0qVLF3777TfS09N5/vnnxdcHDhxIdnZ2i41DEASKi4ur5C3qdDpycnJabAz3ysiRIxtUnFAf8vLy2LNnD+Hh4bXmj7q4uFSRXkxPT2+Sdvc1UVPTSoC1a9cSFhbGpEmT+Pzzz5vl3A1BEATi4+PZu3cvmZmZ9yxoL5PJakwRGzJkCJGRkVy4cKFRxzUYDHU23qyJAQMG1Et5zRRIa8P079+fESNGVBH2kEgkLer2OXnyZJVeWCqViueff56bN2/yj3/8o8XGcS9IJBI8PDxIT0+vt/j63cjOzqZXr161zloFQSAjI6NK8OXChQvNqq62evXqKhV3x48fJyYmhm3btqFQKBpsRJqSs2fPcv36dQwGA35+fgwYMKBZXVZubm6Eh4ezb98+zp8/T1BQUIPebzAYWLZsGYMHD2bq1KkN7txcFyaj28bJzc2tll7UUkEtlUrFtWvXePjhhwEoKSnh8ccf58SJE3z88ccsXry4yaPpzRVJHzhwIH/++Sdz5sxpkiKJHj168Pvvv9eqf3zu3Dm6desmRtgFQUCr1baokNH69et58sknxQdDc2n91sWWLVtQqVT06NGj2fOka2LkyJFs2LABHx+fBpWAm5mZ8fbbb/P777/zzTff8PTTTzdZWb7JvdCGEQSB5OTkKt0AdDpdkz5170Sv1xMfH8/OnTtZt26dWNKanZ3NrFmzOHPmDCtWrBBbfjd15Lu5IukymYzAwEBu3rzZJMeTSqV1GlBBEKpUE2ZlZdVb/L2xLF68mJkzZ/Lnn38Ct8pRT58+zezZs3nkkUdapXXVxYsXCQsLIzAwsMXPDbdWOUOGDGHNmjUNFhdydnbm+eefZ+jQoU2qTnffzXQfxDzF2sjMzKSioqKK0c3Pz2+WdjDG7Ijy8nICAwMZM2aMOEO6du0a8+bNo6CggNWrVzN8+PAmP39LYGlpiVKpbJJjCYJQp6Zwz549iY6OFvOrL1++zMCBA5vk3DVRU9NKo1TiX3/9RVxcHC+99BIxMTEt5p4qKyujf//+rV7K3r59e+bNm0dUVBTTpk1rUMNQmUzW5I1U7zuj+6DlKdZFcnIycEur1Uh2djbu7u5Nep6CggL27NnDpEmTqvnZLl68yCOPPIIgCGzYsKHBvrG2hKenJ4cPH6Z79+6NPobBYODIkSPk5OTUWXChUCgoLS0lMTGR3NxcKisrGy0iUx9qalrp5ubG2LFjkUgk9OrVC6lUSlFRUbMqrd2OXC5vsofcveLq6sqoUaOIiIi4qxhPc3PfGd2/E0lJSQBVZrp5eXlVjHBTsG/fPsLDw6vNAGJjY3niiSdwdHTkv//9b71XA6NGjcLa2hqpVIpMJmPTpk3861//Yv/+/WJazbJly1o899fKyqrBzTXv5MqVK+h0OlETIy4ujuLi4hr39fT05Pjx4zg6OuLk5MShQ4eqbE9LS2uSVLzKykoMBgM2NjZi08pnn30WKysrjh8/TkhICKmpqWi12jqVupqa/Px8oqKimDBhQpsoZXdycsLf35/r16+36jhMRrcNk5ycjKOjYxV3gkqlavLqKrlcXs3gbt26lSVLltClSxd+//33Bs+u74ykDx48mFdffRUzMzO++OILvv/+e15//fUmGX9DMDc3p7KystFBEXNzc/G6li1bxn/+8597Gk9TiKrX1rRSo9HwzjvvMHnyZORyOZ999lmLZr64ubnRvXv3NmFwjVhbW7eocHxNmIxuGyY5ObnKrFYQBNRqdZP+cHQ6XbXj/frrr/zzn/9kwIAB/Prrr/ekLWrk9vLYoKAgoqKi7vmYjR3Htm3bmD17dqOCIykpKQwZMoS4uDhWrFjB8OHDefHFFxs9nqYIMNXWtFKhUPDll1/e8/Ebi1wuJygoiJs3bzZZddi90qlTJ7Zv396qY3igjK5er2fWrFm4ubnx/fffc+XKFT744AMqKyvx9PTkyy+/FLuLtnWMmQuTJ08WXyspKWnSIJrBYCAiIoLRo0eL5/ziiy/45ptvCAsLY8WKFY2eVS9evBiJRMLcuXOZO3dulW0RERFMmDDhnsffGOzs7AgNDeXYsWMMHjy4Qe89efIkFhYWWFlZ8c9//hMnJydWrVplKpGug6FDh7Jp06Y2Y3QFQRCzf44dO9Ys0qnHjh2rc/sDlTK2Zs2aKn7Hd999l1dffZVt27YxZswYfvrpp1YcXcPIz8+nuLi4ykzXYDA06Sz3yJEjhIaG0q5dO3Q6HW+++SbffPMN8+bN44cffmi0wV2/fj2bN2/mxx9/ZN26dZw6dUrc9t133yGTyZq1g8Pd8PHxITc3t0FBnszMTEpKShg+fDhbt27l1KlTvPnmmyaDexekUilmZma1Sl62NKmpqfj6+jJ9+nRCQ0Ob5Rx3O+4DM9PNzs7mwIEDPP3002IrmdTUVDHdY/DgwSxevJiXXnqp9QbZAGoLot1NTKO+nD9/Hp1Oh4+Pj1hltmvXLl544QXefPPNezLuNUXS+/fvz+bNmzlw4AC//fZbq4spjRkzhujo6HoZ/7y8PA4fPsysWbNQKpX8z//8Dz169OChhx5qgZHe/3Tv3p0LFy7Qp0+feu1fXl5OfHw82dnZCIJAQEAAPj4+TeIbzs7Opnv37jzyyCM88sgj93y82qjr+/3AzHQ//fRTXn/99Sp+Oj8/P2JiYgCIiooiKyurtYbXYIzpYrcbXXt7+1oj5Q3h7NmzaDQaRo4cSUlJCfPnz2fXrl18/PHHvPXWW/dkECsrK8VAhTGS3rVrV2JjY/nxxx/57rvvml1msT7Y2trWa6ablJTEoUOHmDVrFjKZjJUrV5KZmcnHH3/crEUqDxK+vr51Si3q9XrOnDnDxo0b2bp1K8eOHcPT05MpU6YwZcoU1Go1e/fureKLLSoqanBlZkFBAdnZ2c2S594QHoiZ7v79+3FyciIwMJATJ06Ir3/yySd88sknrFy5klGjRt1X6k5JSUnY2tpWyRq4evVqgzsOZGVl4eHhUeX/b9y4wfTp08nJyWH+/PmkpKSwcuVKpk2bds/jri2SPnbsWDQaDY899hgAvXv35uOPP77n890LSqVSbA+j0WiIj4+ntLQUS0tLcnJy0Gg0eHt7M2PGDCQSCRkZGaxcuZIpU6Y0a6HDg8bRo0fx8fER/1+tVnPlyhXKy8uxsbERl/yzZs2q9sCXSCT07NmTnj17cvjwYc6fP4+VlRW//fYb77//fr1nv4IgsHfv3ipiQK3FA2F0z549y759+4iNjUWtVlNeXs5rr73Gl19+yS+//ALccjUcOHCgdQfaAJKTk+nSpUuVL2FmZmaDgj8Gg4EPP/yQ0NBQHB0dkcvlqNVqpk2bJlaZFRYWsmbNGoYNG9Yk464tkh4dHd0kx29KgoOD+eOPPzAzM8Pa2pqAgAA6dOggVgHeWUll1FR97733WmG0t6irD9u9HLM5sbOzIykpCUEQKCwsJC8vj/79+9O+fXvKy8sZNmxYvQo2hgwZwn//+1/g/3rO3Z4VUxeXLl2id+/eDapGqwudTscXX3zBokWLGlzeLalrim5hYSGoVKp7HV+TYvR11saJEyf45Zdf+P777ykoKMDZ2RmDwcBbb73FgAEDao1WNnXBwb0SFBTEqFGjWL58OXBLFjA9Pb3BMyy1Wk1ERAQPPfQQKpUKKyurKlVmv//++z31k7rb59EYGvtZHD58mB9//JG9e/cil8ubVOBFr9eTl5cH1E9b9e+KwWCgXbt2LFmyhHHjxolZCxqNhszMTCwtLav1PWvo8XNzc0lMTCQxMZGhQ4fWKjpkRBAE/vjjD+bOndukn11UVBTnzp3jmWeeqfaAlkgkZwRBqLFD5gMx062N7du3i0/GsWPHMmvWrFYeUf0oKiqqUnlmMBg4ePAgs2fPbvCxjKIsOp0OKyurRleZtUUMBgObN2/m999/58KFC6jVauDWNQcHB9OxY8cmO5dGoyEuLg61Wm3y5daBUTz8/fff5/3336dHjx6EhYURFhZGjx497jmAKpVKcXd3x93dneHDh7Nx40b8/f3rPG5BQQHe3t5N/rAMCwsjIyODN998k1dffbXek4UHzuiGhISI7WwWLVrEokWLWnlEDccYRDP6bzUaDXZ2dg32SRsMBq5cuYKzszMKhYItW7bw4osvNrrKrC1QWVnJb7/9xqZNm0hKShKX2nZ2dowYMYKnnnqqxdsZmahOamoqu3fvJioqiq+++orly5fj5eUlGuCQkJAmkdjs06cPmzZtonfv3mIq4IULF5g0aRJJSUlcvnwZnU7XLI1JJRIJ48aNo3Pnzly8eJG4uDhUKlWV1kE18cAZ3QeBO4VuzM3N69165ODBgxQXFyORSDAYDHTu3JmxY8c2S5VZS5Gbm8uqVavYuXNnFWlGo4jJs88+e1/P2B9EOnXqxNNPP83TTz9Nfn4+0dHRREVFsW7dOn7++WccHBwYPXo048ePZ8SIEY0uy/b19aV9+/ZcvXqV6OhozM3NcXR0ZP369XTo0IHp06c3a3ri8ePHGTp0KK6ursTGxtapPGfEZHTbIElJSVhaWopdDvLy8uotj1dSUsLkyZOrLIGTk5N57733GDZsGL/88kubSNm6G4mJiXz33Xfs37+f/Px84NbMokOHDkyePJmnn366xdSyTNwb7dq14+GHH+bhhx+msrKSAwcOsHv3bvbu3UtERATm5uYMHTqU8ePHM3bs2AandFlaWhIYGCiWVAuCwKBBg5rjUkSM0p6Ojo7Ex8ej1+sRBEHUn64Lk9FtgxgzF4w+qNTU1DqDBYWFhWzZsgVHR8caq9aMub1PPfVUkxvcpo6ml5eX061bNzHXVyaT0aNHD2bPns2CBQvalHiKiYZjZWXFxIkTmThxIjqdjpMnTxIVFSUaYYlEQr9+/Rg/fry4dG8IWq2Wb775Bl9fXyQSiSgN0NRs376dnJwc7OzsuHnzJtnZ2fUO1JmMbhskKSmpSilhTk4OqampTJ48uUbtCHNzc/R6PQqFgokTJ9Z63OaIut/Lsr6uQFhoaCgLFixgypQppmyBBxQzMzMGDRrEoEGD+Oijj7h8+TJ79uwhKiqKpUuXsnTpUvz8/Bg3bhzjx4+nd+/ed/0uyOVyOnXqxJQpU5DJZGzYsKHJx33u3Dnc3d0ZNWoUO3bsYOrUqfj4+NQ7wHrfGd37MU+xIZSXl5OVlVWlEq2yspLU1NRav3AajQYrKyu6devWUsNsNKZAmImakEgkoovglVdeIT09XQzEfffdd/znP//B3d2dcePGERYWxqBBg2oNLIeEhBAZGYlarRY7dzQFGo2GgwcPIpfLGTJkCGvXrmXu3LkN9kffd0b3QQ+Y1NQtQiqV4uPjU+uHe/nyZcaOHYuLi0uLjLGhmAJhJhqKl5cXixcvZvHixRQVFRETE8Pu3bvZsGEDa9aswdbWlpEjRzJ+/HhGjhxZRXjIy8uLMWPGYGZm1iSqgoIgcOjQIQoLCxkyZAhKpZJNmzbRp0+fRgUA7zuj+6Bzp9CNsc1LTVKIxqhwu3btWr2e/E7uFgh78skn29yYTbRNHB0dCQ8PJzw8HKVSyeHDh9m9ezfR0dFs3boVuVzOoEGDCAsLY9y4cXh4eDRpX7Z9+/bh7e3NsGHDxLZVRi2OxmAyum2MlJQUFAqFuCz68ccfa20EmZaWRseOHQkJCWl11S74v4qw48ePVwuEhYeHs3DhwmYNhP2dmpb+XbG0tGTs2LGMHTsWvV7P2bNniYqKIioqinfeeYd33nmHoKAgMR/Yz8/vnn8bFhYWXL58mfj4eFQqFYMGDbonl6TJ6LYxjN1+jYnjffr0EWeKd9KvXz8SEhJYs2YNjz76aAuO8ha1BcIsLCwYOHAgCxYsYOrUqS0WCPs7NS018X+devv37897771HcnIyUVFR7Nmzh3/961/861//olOnToSFhTF+/HiCg4MbZSxv1zsxCiTdCyaj2wa5/UPNz8+vVRmpuLiYCxcuNEu1TW3cHghLTEwUxaltbW0ZMWIETz75pEmBqwU4evQoUqmU7t27m4TUufWb8fPzw8/PjyVLlpCVlUV0dDS7d+/m559/ZtWqVTg7OzNu3DjGjRvH0KFDG5U+2RQrSpPRbePUVipZWlrKjh07mDVrVrPnrpoCYW2PzMxMcnNzsba2pmfPnq09nDaHh4cHCxcuZOHChZSWlrJ//352797N9u3bWb9+PRYWFs0WeL5blwyT0W3jqNVqtFptjd16p0+fXqvBLSkpQa/XN6glze0YA2H79u2joKAAuH8rwmpqCf/SSy+RmpoKQFlZGba2tmzZsqWVR1p/mqO314OKnZ0d06ZNY9q0aWg0Go4dO0ZMTAwlJSXNcr6NGzfWud1kdNs4crmcysrKKloJZWVlZGVliSLQd3L8+HEiIiKQyWTY29vXWyu3rkDYnDlzeOSRR+7birA7W8J//fXX4r8/++yz+6ZhqYl7Q6FQMHz48FqD001BRkYGERERtW43Gd02TlhYGO+//z4BAQHMmzePU6dOceTIEWxtbUlLS6tRKaxv3774+fkhlUqxt7fn9OnTNR7bGAhbu3YtFy9erFIRNnDgQBYuXPjAV4QJgsCuXbtYvXp1aw/FxN8Ek9Ft41haWvLyyy+zcuVKPvnkE6ysrHj66adxcHBg+/btHDp0iB49euDo6Cg6+eVyea1L/9oCYXZ2dowcOZInn3zygawIq60l/OnTp3F2dm5S7V0TJuqiTqNrMBj48ssvW2osJoDdu3cjkUjYu3dvldeHDRtGUlISRUVFrFq1Ci8vLzQaDbt376a0tJTJkyfXamiNVW6PPvooGo1GbOjn6urK6NGjeeaZZx7oQNj69etxc3OjoKCAxx57jM6dO4tdordv387kyZNbeYQm/k7cdab71VdftcQ4TNxBQ8XXjV2P68JYi36/BcLuldpawut0OqKjo9m0aVMrj9DE3wmT0W2DuLm5NbnIuF6vp0ePHvdtIKyxVFZWYjAYsLGxEVvCP/vss8CtXNfOnTvflx00TNy/1Gl0pVIpc+bMaamxmDDR5NTWEh5g586dTJo0qTWHZ+JviCmQZuKBpraW8HArVcyEiZbmwc0FMmHChIk2iGmme5+jVCr59ddfOXbsWJ37WVlZsWzZsr9N8MyEibaKyejep2i1Wv744w+++uorcnJy6NatW40CHoIgcOnSJfR6PfPnz693dZoJEyaaB5PRvc8wGAxs376dzz//nNTUVPr37893331XpaChsLCQs2fPcuPGDVJTU5k2bRoff/xxK466+dDr9Rw9epSNGzeKAbOmZsiQIc1yXBMPJkZNj9owGd37iNjYWJYtW8bFixfx9/fn119/ZezYsUgkEkpLSzl48CAnT56kuLiY0NBQfHx8GDt2LFlZWa099CYnKSmJjRs3smnTJrKysrC1tW02o9u7d+9mOa6JhqPX69tUT8Oa6N27NytXrqx1u8no3gecP3+eZcuWcfjwYby8vPj666+ZOXMmMpkMlUrFoUOHKC8vZ9SoUVy9epWPPvqoil5CdnZ2K46+6SgoKCAyMpKNGzdy8eJFZDIZw4cP55///Cfjxo0jMzOzWTpHrFixokmPKQgC6enpJCUlUVFRIb7u7u5O//79W7QLSF5eXoMkDlUqFQcOHKCyshJzc3OGDBlSLadco9EQFxdHUVERlZWV9O3bF09PzxqPZzAYEAShRkOq1+spLS1Fp9Oh1WrJzs5m69atPP30020+t7ouoysxloTWhIWFhaBSqZpjTCbqQUpKCp9//jk7duzAycmJF198kQULFmBubg5AQkICCQkJhISEcPr0aczMzGjfvj1BQUFVfrinTp1i+vTprF+//r7z6apUKvbu3cvGjRvZv38/Op2OwMBAwsPDmT59epttxnkngiBw7do1kpOTKSwspHPnzgQGBlZRNzt//jzXrl2ja9eutG/fHmdnZ+D/jI+jo2Ojz19UVMS1a9cwMzOjrKyMiooKlEolGo0GmUyGm5ub+G+ZTIZarcbNzQ2tVotGo6GiogKtVotSqWTYsGE4OTlRUVHBoUOHUKlUVR7yUqmUnj174uzsjKWlJUePHqWoqAi9Xo+5uTlOTk5IpVIyMzOBW5rRNWnQSqVS7OzskMvlmJmZYW9vT4cOHYiKikIikVT5jhs7OhjtmaenJ3379m30/bpXJBLJGUEQ+tW4zWR02x5ZWVksX76cP//8EwsLC5588kmefvrpavKDO3bsYNSoUWzdupXRo0fX2ujxfjO6giBw+vRpNmzYwPbt2ykpKcHd3Z0ZM2YQHh6Ov79/q47PaAQdHBxqnJUqlUpycnLIyMggLy8PuKVF7O3tTWBgIGZmZrUqt+n1etLS0khPT6eoqAipVIper8fR0ZHS0lJxP61WS1hYWJ2SlBUVFaK7ydbWFn9/f/R6Pba2tqK+sFwuR6/XU1xcjEKhwGAwiMYxKysLhUKBQqHA2toaQRDuuUuFRqOhqKgIQRCqtKVqaqKjo6moqMDJyQlvb2/atWuHjY0NEolEvMY7NaqbkrqM7l0Fbw4ePNg8ozJRDUEQOHz4ML/++it6vZ5Fixbx4osvisa0tLSUvXv3IggCQ4YMoaKiAnNzcyZMmEBsbCxarZZRo0Y1eQlxS3H9+nUiIiKIiIggLS0NS0tLJkyYQHh4OEOGDGl1X55eryc7O5uDBw/i6elJVlYW1tbW2Nvb07dvXy5fvkxmZiYWFhZ4eHjg5+fHoEGDGuQuMDbCvJsAkVKpJCoqihkzZlR53WAwUF5eTmZmJufOnWP8+PF3nSHLZDJxVn07Xbp0qfe464tCoRC1MJqTsWPHAreCyhkZGaSlpVFWVgbc+hzNzMzQarVi52BnZ2dcXV2bfVxQD5/uvHnzWmIcJv4/EomEGTNm8Prrr+Pj41Nl2+XLlxk8eDByuZw//viDMWPGiEuwyZMno9Fo2LhxI7NmzRJdEG2dkpIStm/fzsaNGzl58iQSiYTQ0FCWLFnCpEmT6i0urtVqSU9PJzMzk8zMTHEGN3DgQHQ6HWfPnsXMzAy1Wo1MJsPMzIzbV3k6nY5evXqJS2hBEDAYDBQXF1NZWYkgCMjlclxcXAgPD0ehUAC3jFxeXh6HDx+mY8eODBgwoFnu0+3ExcVx7NgxRo0aJb6WmJjIhQsXsLS0xMbGBldXV+bMmdPqD6rWxsnJqdbcdONnV1JSQnx8PEeOHMHJyYlu3bphYWEhrkikUinm5uZN5muv0+h26dLFJO5cBxqNBqVSibW1dZMtk9zc3KoY2/Lyco4cOYJaraa8vJx+/fohl8vp168fsbGxuLu7i0s+hULBpEmT2L17N1OnTm2S8TQHWq2WAwcOEBERwZ49e1Cr1XTp0oU333wTb29vZDIZrq6unDlzhoKCAnQ6HU5OTlRWVorHsLW1FWcucGu25uXlVWV2qdfrOXbsGAqFgqlTpyKTyWpsfQS3Zj+XL18We2cZf2zdu3fH2tq61muRSqW4ubkRFhbWtDepDmxsbLh+/TqdOnVCr9eza9cu7OzsTDopDcT42bm5ueHn5wfcCixevXoVjUaDTqcTXRFqtVp8SLdv316UBm0MdVoKKyurezr4g4rBYODKlSucPXuWq1evigEJqVSKwWDg3XffrTZLhVu6tj4+Pg2ahWZnZ5OXl4ednR0HDx7E0dERrVZLZWUlfn5+XLt2jaCgIHF/e3t79Hp9rcaltTAWaWzYsIHIyEgKCgpwcnJi3rx5hIWFoVAoSEtLIzg4mA4dOnDz5k0qKysZPny4GMyxsrISZxvZ2dm4urrW2dVCJpNVy7Gt7Z7IZDJ69erVdBfcjMhkMoKCgtixYwdarZYhQ4a0yJL974CLi0udwdny8nLWr19P3759G91R5YFPGfv888+prKykS5cuTJgwgYsXL5KZmYmLiws3b95k6NChdOnSBalUilarJS0tjY4dO9ZpsCQSCQcOHGDAgAEEBwcjCAIFBQWUlpbi5OTE5cuXOX/+PPB/UVWdToebm5vYrcGY2mRmZoatrS2urq4kJSWJDn6ZTIa/vz+2trb06tWLmzdvMn78eFQqFTKZDFtbW9q1a0dAQEC18fn7+xMXF0dwcHCz3NOGkJWVxaZNm4iIiCAxMRGFQsGYMWOYPXs2cCvYo9fr8fLyYsCAAaJR9fb2Fo8hl8urfR5tPWWoOfHx8anxoW6i+SgqKiItLY2EhAQeeeSRe2phVWf2Qr9+/YTa+mu1NAaDQeyMW1ZWRmZmJtnZ2dQ0/ttTRwwGA2VlZRQVFeHk5IRCoSA7O5uKigqsra3x8fERb6BUKsXb25v4+Hjs7OwwGAz4+Pjg6OiIp6cnUqmU0tJSKisrKS4uJj4+Hr1eT1BQEH5+fuj1elQqFWZmZvWezRrHl5OTg6enp7iU1el0XLp0CZVKhYuLCz4+PnU+CARB4NSpU2JO7vjx40W/ozF7oX379i1aXZWRkcHRo0cRBIG+ffsSHh7O1KlTcXBwoKioiJUrVzJy5EgGDRrUYmMyYaK+CILAuXPnuHHjBtbW1nTr1g0PD496rSAbnTLWmkZ369atGAwGDAYDZmZmSCQS0bltZ2eHu7s77u7uzRIoEAQBrVaLVColPT2dwsJCMjMzxZQZKysrNBoNpaWlaLVaOnTo0KJVSwaDgaKiIjIzM8nNzaW8vByDwUCvXr1qjHrn5uby8MMPU1xc3KKBFRsbG8aPH8+ECROwsrIS06AAMeJ/rylIJkw0B8XFxURFRdGzZ0969OjR4Pe3GaMbFxdH586dkcvlVFRUoFKpcHd3F4MemZmZJCcnU15eTl5eHosXL26yc99vxMfHc/z4cTGR3LjsNj4Q3Nzc8PDwwN3dvYqvs7UoLCzkzJkzKJVK8TXjmGxsbHBxccHLywt7e/tWH6sJE3dj69atVVaLDaXRebr3glarRa1WY2VlJS7fY2NjuXHjBjKZDCsrK+RyOcePHxerYDw8POjbt+99m2falLi5uWFhYUFubi56vZ7Zs2fXWvxwLxgMBpKTk8nNzUWlUuHm5ka3bt3qdI/k5+dz7do18vLy0Gq1ADg6OhISEmKauZp4IOjevTtRUVEMGTIEGxubRhvfmmg2o7tq1SosLCzEUsLg4GBmzZrFqVOnUKlUODs7Y2Vlhbu7O9bW1igUCszNzWuUJ/y7kZuby5YtW+jfvz9arRZbW9t7KgGtjaysLA4cOCAuoSwsLMjKyiIyMpLBgwfj5eVV7T0Gg4EdO3Ywbtw4+vTpg1qtRqlUotfr651Ta8JEW0WpVJKWloZOp0OpVIqB+C+++KLJct+bzb1gMBg4d+4cN2/eRCqVYm9vz/Dhw8VtWVlZqFQqKisrqaioQKPRoFarxUT07t27i7lzfze0Wi1Hjx4lIyODoKAgHB0dxdLNO5fmer2eK1euiCsHc3Nzrly5QlZWFqGhoVWyAOBW2lpGRgZFRUXY2NgwatQo0c8rCALFxcVcv36dmzdvMmXKlGpjEwSBffv2iTmz1tbWWFpaUlpaSl5eHnPmzGnSWYEJEy1FZWUlmzdvpn///piZmeHg4ICDg0OjMhVaxb0glUrp27dvNdGJS5cuER0dLS5hKysrUSgUeHp6UlhYKFYDNbVa1P2EXC4X81NTU1NJT0+nrKyM8vJyMQXNmE8ok8nYvHkzgJjE7e3tLRrD2xEEgYMHD2Jubo69vT1KpZIdO3ZU2cfe3h5vb2/Gjx+PXq8nJycHlUqFIAgolUqKiorEz0gikVBeXk55eTkAFhYWaDQak9E1cd8hCAIrVqxg3LhxzT7Za/HsBY1GQ0pKCnFxcej1ejw9Penfvz+ZmZk4OzvXKiJi4v8wGAwUFBSQl5cnrhRKS0urpM+1a9cOf39/sQRSr9eTl5dHeno6+fn5aDQacd/bU+yM6PV6MWXO0tJSLIV0dHTE3t7+b19eauLBQ6vViqtzb29vunbt2mi3XpvJXriTjIwM4uLiGD9+fLOd4++I0SifP3+ewsJCzM3NxdJaLy8vXFxcTLNREyZqQRAEsrKySE5OFpXeJBIJTk5ODBw4sF4TjkYbXYlE0jYqI0yYMGHi/iJfEIQaZ5N1Gl0TJkyYMNG0NL6A2IQJEyZMNBiT0TVhwoSJFsRkdE2YMGGiBTEZXRMmTJhoQUxG14QJEyZakP8HAVE2ue2WzG4AAAAASUVORK5CYII=\n",
"text/plain": [
- "<Figure size 432x288 with 2 Axes>"
+ "<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
@@ -276,175 +703,59 @@
}
],
"source": [
- "masked.sortby('clat').sortby('clon').plot()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen({'clat': 300, 'clon': 300})"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "masked.sizes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen({'time': 5, 'lev': 4, 'cell': 300})"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "da.sizes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'clat'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, group, squeeze, restore_coord_dims)\u001b[0m\n\u001b[1;32m 719\u001b[0m )\n\u001b[1;32m 720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m return self._groupby_cls(\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestore_coord_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrestore_coord_dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m )\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, group, squeeze, grouper, bins, restore_coord_dims, cut_kwargs)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"group\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacked_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minserted_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgroup_dim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/groupby.py\u001b[0m in \u001b[0;36m_ensure_1d\u001b[0;34m(group, obj)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;31m# in pandas: https://github.com/pydata/pandas/issues/12813\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mstacked_dim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0morig_dims\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mstacked_dim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0morig_dims\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 193\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mstacked_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mstack\u001b[0;34m(self, dimensions, **dimensions_kwargs)\u001b[0m\n\u001b[1;32m 2070\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2071\u001b[0m \"\"\"\n\u001b[0;32m-> 2072\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdimensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdimensions_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2073\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2074\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mstack\u001b[0;34m(self, dimensions, **dimensions_kwargs)\u001b[0m\n\u001b[1;32m 3774\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3775\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnew_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3776\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stack_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3777\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3778\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m_stack_once\u001b[0;34m(self, dims, new_dim)\u001b[0m\n\u001b[1;32m 3726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3727\u001b[0m \u001b[0;31m# consider dropping levels that are unused?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3728\u001b[0;31m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3729\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiindex_from_product_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3730\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_dim\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndexVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 3726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3727\u001b[0m \u001b[0;31m# consider dropping levels that are unused?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3728\u001b[0;31m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3729\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiindex_from_product_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3730\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_dim\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndexVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mget_index\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0;34m\"\"\"Get an index for a dimension, with fall-back to a default RangeIndex\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: 'clat'"
- ]
- }
- ],
- "source": [
- "%time da_regions = da.groupby(masked).mean().compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "# workaround: add fake dimension to coords to use L470 instead of L468\n",
- "ds = ds.assign_coords(clon=ds.coords['clon'].expand_dims('dummy'))\n",
- "ds = ds.assign_coords(clat=ds.coords['clat'].expand_dims('dummy'))"
+ "region = regionmask.defined_regions.ar6.ocean\n",
+ "region.plot()"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 9 ms, sys: 2 ms, total: 11 ms\n",
- "Wall time: 7.72 ms\n"
+ "CPU times: user 40.5 s, sys: 2.52 s, total: 43 s\n",
+ "Wall time: 36.3 s\n"
]
}
],
"source": [
- "%time masked = region.mask(ds,lon_name='clon', lat_name='clat', wrap_lon=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen({'dummy': 1, 'cell': 300})"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "masked.sizes"
+ "%time mask = region.mask(ds,lon_name='clon', lat_name='clat', wrap_lon=False)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 12,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen(SortedKeysDict({'time': 5, 'lev': 4, 'cell': 300, 'dummy': 1}))"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "ds.sizes"
+ "import dask"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 39 ms, sys: 3 ms, total: 42 ms\n",
- "Wall time: 39.3 ms\n"
+ "CPU times: user 22.4 s, sys: 4.48 s, total: 26.8 s\n",
+ "Wall time: 24.9 s\n"
]
}
],
"source": [
- "%time ds_regions = ds.groupby(masked).mean()[v].compute()"
+ "with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
+ " %time ds_region = ds.groupby(mask).mean('ncells')"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -801,1051 +1112,236 @@
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
- "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 't2m' ()>\n",
- "array(296.05936, dtype=float32)\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'to' (region: 15, time: 1, depth: 128)>\n",
+ "dask.array<concatenate, shape=(15, 1, 128), dtype=float32, chunksize=(1, 1, 16), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
- " time float64 1.979e+07\n",
- " lev float64 1e+05\n",
- " group float64 8.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t2m'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1f023ceb-1023-40da-87a5-4365f28d161b' class='xr-array-in' type='checkbox' checked><label for='section-1f023ceb-1023-40da-87a5-4365f28d161b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>296.1</span></div><div class='xr-array-data'><pre>array(296.05936, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-464898fa-1910-4d8c-a83b-36e3d022ba0f' class='xr-section-summary-in' type='checkbox' checked><label for='section-464898fa-1910-4d8c-a83b-36e3d022ba0f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.979e+07</div><input id='attrs-92ef134c-a627-4679-a51c-90671568dd26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92ef134c-a627-4679-a51c-90671568dd26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02361442-886f-49ce-a49d-1ebcf8a5fcfa' class='xr-var-data-in' type='checkbox'><label for='data-02361442-886f-49ce-a49d-1ebcf8a5fcfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(19790131.75)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05</div><input id='attrs-f1010d26-c8d6-4e66-a03f-d54b943ff138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1010d26-c8d6-4e66-a03f-d54b943ff138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e807bb27-7fdb-4d22-8740-b024df32c5f3' class='xr-var-data-in' type='checkbox'><label for='data-e807bb27-7fdb-4d22-8740-b024df32c5f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(100000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.0</div><input id='attrs-b5c7aec1-056e-4487-a1d5-fa8c32f5f870' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5c7aec1-056e-4487-a1d5-fa8c32f5f870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0747ddd0-ea59-4d4c-9030-e8fb670aa1b1' class='xr-var-data-in' type='checkbox'><label for='data-0747ddd0-ea59-4d4c-9030-e8fb670aa1b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(8.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7c10375-bfc4-4f98-aea3-0a7923c7b45c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7c10375-bfc4-4f98-aea3-0a7923c7b45c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+ " * time (time) datetime64[ns] 2016-09-01\n",
+ " * depth (depth) float64 1.0 3.05 5.2 7.45 ... 5.522e+03 5.71e+03 5.904e+03\n",
+ " * region (region) float64 8.0 19.0 38.0 46.0 47.0 ... 54.0 55.0 56.0 57.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'to'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>region</span>: 15</li><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>depth</span>: 128</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-20d37872-c8e1-4faa-8eec-8a7cf6d0767d' class='xr-array-in' type='checkbox' checked><label for='section-20d37872-c8e1-4faa-8eec-8a7cf6d0767d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array<chunksize=(1, 1, 16), meta=np.ndarray></span></div><div class='xr-array-data'><table>\n",
+ "<tr>\n",
+ "<td>\n",
+ "<table>\n",
+ " <thead>\n",
+ " <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr><th> Bytes </th><td> 7.50 kiB </td> <td> 64 B </td></tr>\n",
+ " <tr><th> Shape </th><td> (15, 1, 128) </td> <td> (1, 1, 16) </td></tr>\n",
+ " <tr><th> Count </th><td> 626 Tasks </td><td> 120 Chunks </td></tr>\n",
+ " <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+ " </tbody>\n",
+ "</table>\n",
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+ " <text x=\"93.209562\" y=\"68.622179\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >128</text>\n",
+ " <text x=\"173.209562\" y=\"35.915871\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.209562,35.915871)\">1</text>\n",
+ " <text x=\"11.604781\" y=\"57.017398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.604781,57.017398)\">15</text>\n",
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+ "</td>\n",
+ "</tr>\n",
+ "</table></div></div></li><li class='xr-section-item'><input id='section-50998131-8db7-4858-ade6-bd0a351e5686' class='xr-section-summary-in' type='checkbox' checked><label for='section-50998131-8db7-4858-ade6-bd0a351e5686' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-09-01</div><input id='attrs-dad80220-98a4-46bc-8abd-7064267589e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dad80220-98a4-46bc-8abd-7064267589e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba88400-522d-4e21-aaba-3fc9674fd717' class='xr-var-data-in' type='checkbox'><label for='data-0ba88400-522d-4e21-aaba-3fc9674fd717' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(['2016-09-01T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.05 5.2 ... 5.71e+03 5.904e+03</div><input id='attrs-6f769b5d-7357-4142-a9e8-be47aef143e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f769b5d-7357-4142-a9e8-be47aef143e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57457e7d-62fa-493e-9f52-451bca0ce7c7' class='xr-var-data-in' type='checkbox'><label for='data-57457e7d-62fa-493e-9f52-451bca0ce7c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth_below_sea</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([1.00000e+00, 3.05000e+00, 5.20000e+00, 7.45000e+00, 9.80000e+00,\n",
+ " 1.22500e+01, 1.48000e+01, 1.74500e+01, 2.02000e+01, 2.31000e+01,\n",
+ " 2.61500e+01, 2.93000e+01, 3.26000e+01, 3.60500e+01, 3.96500e+01,\n",
+ " 4.34500e+01, 4.74000e+01, 5.15000e+01, 5.58000e+01, 6.03000e+01,\n",
+ " 6.50000e+01, 6.99000e+01, 7.50500e+01, 8.04500e+01, 8.61000e+01,\n",
+ " 9.20000e+01, 9.81500e+01, 1.04600e+02, 1.11350e+02, 1.18400e+02,\n",
+ " 1.25750e+02, 1.33400e+02, 1.41400e+02, 1.49750e+02, 1.58450e+02,\n",
+ " 1.67550e+02, 1.77100e+02, 1.87100e+02, 1.97550e+02, 2.08450e+02,\n",
+ " 2.19750e+02, 2.31450e+02, 2.43550e+02, 2.56050e+02, 2.68950e+02,\n",
+ " 2.82250e+02, 2.96000e+02, 3.10250e+02, 3.24950e+02, 3.40100e+02,\n",
+ " 3.55750e+02, 3.71950e+02, 3.88700e+02, 4.06000e+02, 4.23900e+02,\n",
+ " 4.42400e+02, 4.61500e+02, 4.81200e+02, 5.01550e+02, 5.22600e+02,\n",
+ " 5.44350e+02, 5.66800e+02, 5.90000e+02, 6.14000e+02, 6.38800e+02,\n",
+ " 6.64400e+02, 6.90850e+02, 7.18200e+02, 7.46450e+02, 7.75650e+02,\n",
+ " 8.05800e+02, 8.36950e+02, 8.69150e+02, 9.02400e+02, 9.36750e+02,\n",
+ " 9.72250e+02, 1.00895e+03, 1.04685e+03, 1.08600e+03, 1.12645e+03,\n",
+ " 1.16825e+03, 1.21145e+03, 1.25605e+03, 1.30210e+03, 1.34970e+03,\n",
+ " 1.39890e+03, 1.44975e+03, 1.50230e+03, 1.55660e+03, 1.61270e+03,\n",
+ " 1.67065e+03, 1.73050e+03, 1.79235e+03, 1.85625e+03, 1.92225e+03,\n",
+ " 1.99045e+03, 2.06090e+03, 2.13370e+03, 2.20895e+03, 2.28670e+03,\n",
+ " 2.36700e+03, 2.44995e+03, 2.53565e+03, 2.62420e+03, 2.71570e+03,\n",
+ " 2.81025e+03, 2.90795e+03, 3.00890e+03, 3.11320e+03, 3.22095e+03,\n",
+ " 3.33230e+03, 3.44735e+03, 3.56620e+03, 3.68900e+03, 3.81585e+03,\n",
+ " 3.94690e+03, 4.08230e+03, 4.22220e+03, 4.36675e+03, 4.51610e+03,\n",
+ " 4.67045e+03, 4.82995e+03, 4.99470e+03, 5.16490e+03, 5.34075e+03,\n",
+ " 5.52245e+03, 5.71020e+03, 5.90415e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.0 19.0 38.0 ... 55.0 56.0 57.0</div><input id='attrs-fdf87d0c-e1f3-41e7-a03b-8efa8eafda74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdf87d0c-e1f3-41e7-a03b-8efa8eafda74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa4fda46-3ce5-41b2-b7f0-646d87bed04d' class='xr-var-data-in' type='checkbox'><label for='data-fa4fda46-3ce5-41b2-b7f0-646d87bed04d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 8., 19., 38., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56.,\n",
+ " 57.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0ab2b2a-1c2b-4fdc-b87b-b72109847eb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0ab2b2a-1c2b-4fdc-b87b-b72109847eb5' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
- "<xarray.DataArray 't2m' ()>\n",
- "array(296.05936, dtype=float32)\n",
+ "<xarray.DataArray 'to' (region: 15, time: 1, depth: 128)>\n",
+ "dask.array<concatenate, shape=(15, 1, 128), dtype=float32, chunksize=(1, 1, 16), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
- " time float64 1.979e+07\n",
- " lev float64 1e+05\n",
- " group float64 8.0"
+ " * time (time) datetime64[ns] 2016-09-01\n",
+ " * depth (depth) float64 1.0 3.05 5.2 7.45 ... 5.522e+03 5.71e+03 5.904e+03\n",
+ " * region (region) float64 8.0 19.0 38.0 46.0 47.0 ... 54.0 55.0 56.0 57.0"
]
},
- "execution_count": 23,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "ds_regions.isel(time=0,lev=0,group=0)"
+ "ds_region.to"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 52.8 s, sys: 30 s, total: 1min 22s\n",
+ "Wall time: 40.2 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_region = ds_region.compute()"
+ ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## manual check"
+ "- `R2B8`: 35s + 24s + 17s\n",
+ "- `R2B9`: 35s + 24s + 39s"
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 16,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Region: Caribbean (CAR / 8)\n",
- "center: [-72.5 18.5]"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "region.regions[8]"
+ "ds_region = ds_region.assign_coords(names=('region',region.names))"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(-90.0, 12.0, -55.0, 25.0)"
+ "[<matplotlib.lines.Line2D at 0x2b6f49e44760>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e44f70>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e44e20>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e442e0>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e44b20>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e44370>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f49e44af0>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed66f70>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed664c0>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed66ee0>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed7edc0>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed66c70>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed66d60>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed66490>,\n",
+ " <matplotlib.lines.Line2D at 0x2b6f8ed662e0>]"
]
},
- "execution_count": 25,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
- }
- ],
- "source": [
- "region.bounds[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
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- "}\n",
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- "\n",
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- "\n",
- ".xr-icon-database,\n",
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- "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 't2m' (dummy: 1)>\n",
- "array([295.85605], dtype=float32)\n",
- "Coordinates:\n",
- " time float64 1.979e+07\n",
- " lev float64 1e+05\n",
- "Dimensions without coordinates: dummy</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t2m'</div><ul class='xr-dim-list'><li><span>dummy</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b7cde308-bb35-4efe-aa14-be78c5e077c3' class='xr-array-in' type='checkbox' checked><label for='section-b7cde308-bb35-4efe-aa14-be78c5e077c3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>295.9</span></div><div class='xr-array-data'><pre>array([295.85605], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-d726e346-4616-48b5-8dae-e985cbc5477b' class='xr-section-summary-in' type='checkbox' checked><label for='section-d726e346-4616-48b5-8dae-e985cbc5477b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.979e+07</div><input id='attrs-958f9323-5338-4376-8e18-e62dc3f0f76b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-958f9323-5338-4376-8e18-e62dc3f0f76b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5c10797-5d4c-46ea-acbf-aa939e048883' class='xr-var-data-in' type='checkbox'><label for='data-c5c10797-5d4c-46ea-acbf-aa939e048883' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(19790131.75)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05</div><input id='attrs-4f6537ed-543d-478a-a88b-9619790c8e84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f6537ed-543d-478a-a88b-9619790c8e84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5778814c-b355-4965-9818-33a63f37f3ea' class='xr-var-data-in' type='checkbox'><label for='data-5778814c-b355-4965-9818-33a63f37f3ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(100000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b45c2071-9ef6-4214-a0de-4b73c468a653' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b45c2071-9ef6-4214-a0de-4b73c468a653' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
- ],
- "text/plain": [
- "<xarray.DataArray 't2m' (dummy: 1)>\n",
- "array([295.85605], dtype=float32)\n",
- "Coordinates:\n",
- " time float64 1.979e+07\n",
- " lev float64 1e+05\n",
- "Dimensions without coordinates: dummy"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ds_car = ds.where((ds.clon<region.bounds[0][2]) & (ds.clon>region.bounds[0][0]) & (ds.clat>region.bounds[0][1]) & (ds.clat<region.bounds[0][3]),drop=True).compute()\n",
- "\n",
- "ds_car[v].mean('cell').isel(time=0,lev=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## fails for large grids"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "# download from https://owncloud.gwdg.de/index.php/s/dxysIXU4N7o7TtW"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Frozen(SortedKeysDict({'depth': 128, 'time': 1, 'ncells': 3729001}))\n",
- "convert clon from rad to deg\n",
- "convert clat from rad to deg\n",
- "convert elon from rad to deg\n",
- "convert elat from rad to deg\n"
- ]
- }
- ],
- "source": [
- "# R2B8\n",
- "path = 'R2B8.nc' # without depth\n",
- "path = '/work/mh0033/m211054/projects/icon/icon-oes-1.3.01/experiments/exp.ocean_era51h_r2b8_hel20218-ERA/outdata/exp.ocean_era51h_r2b8_hel20218-ERA_19970401T000000Z.nc'\n",
- "ds = xr.open_dataset(path, chunks={'depth': 'auto'})\n",
- "v='to'\n",
- "print(ds[[v]].sizes)\n",
- "ds = rad_to_deg(ds)\n",
- "\n",
- "ds = ds[[v]].isel(time=0, depth=0).compute()\n",
- "da = ds[v]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "({'standard_name': 'longitude',\n",
- " 'long_name': 'center longitude',\n",
- " 'units': 'degrees',\n",
- " 'bounds': 'clon_bnds'},\n",
- " {'standard_name': 'latitude',\n",
- " 'long_name': 'center latitude',\n",
- " 'units': 'degrees',\n",
- " 'bounds': 'clat_bnds'})"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ds.coords['clon'].attrs,ds.coords['clat'].attrs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "ename": "MemoryError",
- "evalue": "Unable to allocate 50.6 TiB for an array with shape (3729001, 3729001) and data type float32",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-30-17dfc24d4e4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# fails for large grids (3M cells instead of 300 before) with MemoryError when creating meshgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmasked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlon_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clon'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrap_lon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/regions.py\u001b[0m in \u001b[0;36mmask\u001b[0;34m(self, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 287\u001b[0m ):\n\u001b[1;32m 288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m return _mask_2D(\n\u001b[0m\u001b[1;32m 290\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolygons\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mlon_bounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds_global\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_mask_2D\u001b[0;34m(outlines, lon_bounds, numbers, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 191\u001b[0m ):\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m mask = _mask(\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutlines\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0mlon_bounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlon_bounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_mask\u001b[0;34m(outlines, lon_bounds, numbers, lon_or_obj, lat, lon_name, lat_name, method, wrap_lon)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_mask_pygeos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"shapely\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_mask_shapely\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;31m# not False required\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_mask_shapely\u001b[0;34m(lon, lat, polygons, numbers, fill)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parse_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpolygons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0mLON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLAT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_LON_LAT_out_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0;31m# add a tiny offset to get a consistent edge behaviour\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/regionmask/core/mask.py\u001b[0m in \u001b[0;36m_get_LON_LAT_out_shape\u001b[0;34m(lon, lat, fill)\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m \u001b[0mLON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLAT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 470\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[0mLON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLAT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mmeshgrid\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mmeshgrid\u001b[0;34m(copy, sparse, indexing, *xi)\u001b[0m\n\u001b[1;32m 4371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4372\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4373\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4375\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4372\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4373\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4375\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mMemoryError\u001b[0m: Unable to allocate 50.6 TiB for an array with shape (3729001, 3729001) and data type float32"
- ]
- }
- ],
- "source": [
- "# fails for large grids (3M cells instead of 300 before) with MemoryError when creating meshgrid\n",
- "masked = region.mask(da,lon_name='clon', lat_name='clat', wrap_lon=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [],
- "source": [
- "# workaround: add fake dimension to coords to use L470 instead of L468\n",
- "ds = ds.assign_coords(clon=ds.coords['clon'].expand_dims('dummy'))\n",
- "ds = ds.assign_coords(clat=ds.coords['clat'].expand_dims('dummy'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 8.39 s, sys: 282 ms, total: 8.68 s\n",
- "Wall time: 8.67 s\n"
- ]
- }
- ],
- "source": [
- "%time masked = region.mask(ds,lon_name='clon', lat_name='clat', wrap_lon=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen({'dummy': 1, 'ncells': 3729001})"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "masked.sizes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Frozen(SortedKeysDict({'ncells': 3729001, 'dummy': 1}))"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ds.sizes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 3.64 s, sys: 611 ms, total: 4.25 s\n",
- "Wall time: 4.25 s\n"
- ]
- }
- ],
- "source": [
- "%time ds_regions = ds.groupby(masked).mean()[v].compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
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- "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
- " *\n",
- " */\n",
- "\n",
- ":root {\n",
- " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
- " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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- " --xr-background-color: var(--jp-layout-color0, white);\n",
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- " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
- "}\n",
- "\n",
- "html[theme=dark],\n",
- "body.vscode-dark {\n",
- " --xr-font-color0: rgba(255, 255, 255, 1);\n",
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- " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
- " --xr-border-color: #1F1F1F;\n",
- " --xr-disabled-color: #515151;\n",
- " --xr-background-color: #111111;\n",
- " --xr-background-color-row-even: #111111;\n",
- " --xr-background-color-row-odd: #313131;\n",
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- "\n",
- ".xr-wrap {\n",
- " display: block;\n",
- " min-width: 300px;\n",
- " max-width: 700px;\n",
- "}\n",
- "\n",
- ".xr-text-repr-fallback {\n",
- " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-header {\n",
- " padding-top: 6px;\n",
- " padding-bottom: 6px;\n",
- " margin-bottom: 4px;\n",
- " border-bottom: solid 1px var(--xr-border-color);\n",
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- "\n",
- ".xr-header > div,\n",
- ".xr-header > ul {\n",
- " display: inline;\n",
- " margin-top: 0;\n",
- " margin-bottom: 0;\n",
- "}\n",
- "\n",
- ".xr-obj-type,\n",
- ".xr-array-name {\n",
- " margin-left: 2px;\n",
- " margin-right: 10px;\n",
- "}\n",
- "\n",
- ".xr-obj-type {\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-sections {\n",
- " padding-left: 0 !important;\n",
- " display: grid;\n",
- " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
- "}\n",
- "\n",
- ".xr-section-item {\n",
- " display: contents;\n",
- "}\n",
- "\n",
- ".xr-section-item input {\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-section-item input + label {\n",
- " color: var(--xr-disabled-color);\n",
- "}\n",
- "\n",
- ".xr-section-item input:enabled + label {\n",
- " cursor: pointer;\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-section-item input:enabled + label:hover {\n",
- " color: var(--xr-font-color0);\n",
- "}\n",
- "\n",
- ".xr-section-summary {\n",
- " grid-column: 1;\n",
- " color: var(--xr-font-color2);\n",
- " font-weight: 500;\n",
- "}\n",
- "\n",
- ".xr-section-summary > span {\n",
- " display: inline-block;\n",
- " padding-left: 0.5em;\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:disabled + label {\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-section-summary-in + label:before {\n",
- " display: inline-block;\n",
- " content: 'â–º';\n",
- " font-size: 11px;\n",
- " width: 15px;\n",
- " text-align: center;\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:disabled + label:before {\n",
- " color: var(--xr-disabled-color);\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked + label:before {\n",
- " content: 'â–¼';\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked + label > span {\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-section-summary,\n",
- ".xr-section-inline-details {\n",
- " padding-top: 4px;\n",
- " padding-bottom: 4px;\n",
- "}\n",
- "\n",
- ".xr-section-inline-details {\n",
- " grid-column: 2 / -1;\n",
- "}\n",
- "\n",
- ".xr-section-details {\n",
- " display: none;\n",
- " grid-column: 1 / -1;\n",
- " margin-bottom: 5px;\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked ~ .xr-section-details {\n",
- " display: contents;\n",
- "}\n",
- "\n",
- ".xr-array-wrap {\n",
- " grid-column: 1 / -1;\n",
- " display: grid;\n",
- " grid-template-columns: 20px auto;\n",
- "}\n",
- "\n",
- ".xr-array-wrap > label {\n",
- " grid-column: 1;\n",
- " vertical-align: top;\n",
- "}\n",
- "\n",
- ".xr-preview {\n",
- " color: var(--xr-font-color3);\n",
- "}\n",
- "\n",
- ".xr-array-preview,\n",
- ".xr-array-data {\n",
- " padding: 0 5px !important;\n",
- " grid-column: 2;\n",
- "}\n",
- "\n",
- ".xr-array-data,\n",
- ".xr-array-in:checked ~ .xr-array-preview {\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-array-in:checked ~ .xr-array-data,\n",
- ".xr-array-preview {\n",
- " display: inline-block;\n",
- "}\n",
- "\n",
- ".xr-dim-list {\n",
- " display: inline-block !important;\n",
- " list-style: none;\n",
- " padding: 0 !important;\n",
- " margin: 0;\n",
- "}\n",
- "\n",
- ".xr-dim-list li {\n",
- " display: inline-block;\n",
- " padding: 0;\n",
- " margin: 0;\n",
- "}\n",
- "\n",
- ".xr-dim-list:before {\n",
- " content: '(';\n",
- "}\n",
- "\n",
- ".xr-dim-list:after {\n",
- " content: ')';\n",
- "}\n",
- "\n",
- ".xr-dim-list li:not(:last-child):after {\n",
- " content: ',';\n",
- " padding-right: 5px;\n",
- "}\n",
- "\n",
- ".xr-has-index {\n",
- " font-weight: bold;\n",
- "}\n",
- "\n",
- ".xr-var-list,\n",
- ".xr-var-item {\n",
- " display: contents;\n",
- "}\n",
- "\n",
- ".xr-var-item > div,\n",
- ".xr-var-item label,\n",
- ".xr-var-item > .xr-var-name span {\n",
- " background-color: var(--xr-background-color-row-even);\n",
- " margin-bottom: 0;\n",
- "}\n",
- "\n",
- ".xr-var-item > .xr-var-name:hover span {\n",
- " padding-right: 5px;\n",
- "}\n",
- "\n",
- ".xr-var-list > li:nth-child(odd) > div,\n",
- ".xr-var-list > li:nth-child(odd) > label,\n",
- ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
- " background-color: var(--xr-background-color-row-odd);\n",
- "}\n",
- "\n",
- ".xr-var-name {\n",
- " grid-column: 1;\n",
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- "\n",
- ".xr-var-dims {\n",
- " grid-column: 2;\n",
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- "\n",
- ".xr-var-dtype {\n",
- " grid-column: 3;\n",
- " text-align: right;\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-var-preview {\n",
- " grid-column: 4;\n",
- "}\n",
- "\n",
- ".xr-var-name,\n",
- ".xr-var-dims,\n",
- ".xr-var-dtype,\n",
- ".xr-preview,\n",
- ".xr-attrs dt {\n",
- " white-space: nowrap;\n",
- " overflow: hidden;\n",
- " text-overflow: ellipsis;\n",
- " padding-right: 10px;\n",
- "}\n",
- "\n",
- ".xr-var-name:hover,\n",
- ".xr-var-dims:hover,\n",
- ".xr-var-dtype:hover,\n",
- ".xr-attrs dt:hover {\n",
- " overflow: visible;\n",
- " width: auto;\n",
- " z-index: 1;\n",
- "}\n",
- "\n",
- ".xr-var-attrs,\n",
- ".xr-var-data {\n",
- " display: none;\n",
- " background-color: var(--xr-background-color) !important;\n",
- " padding-bottom: 5px !important;\n",
- "}\n",
- "\n",
- ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
- ".xr-var-data-in:checked ~ .xr-var-data {\n",
- " display: block;\n",
- "}\n",
- "\n",
- ".xr-var-data > table {\n",
- " float: right;\n",
- "}\n",
- "\n",
- ".xr-var-name span,\n",
- ".xr-var-data,\n",
- ".xr-attrs {\n",
- " padding-left: 25px !important;\n",
- "}\n",
- "\n",
- ".xr-attrs,\n",
- ".xr-var-attrs,\n",
- ".xr-var-data {\n",
- " grid-column: 1 / -1;\n",
- "}\n",
- "\n",
- "dl.xr-attrs {\n",
- " padding: 0;\n",
- " margin: 0;\n",
- " display: grid;\n",
- " grid-template-columns: 125px auto;\n",
- "}\n",
- "\n",
- ".xr-attrs dt,\n",
- ".xr-attrs dd {\n",
- " padding: 0;\n",
- " margin: 0;\n",
- " float: left;\n",
- " padding-right: 10px;\n",
- " width: auto;\n",
- "}\n",
- "\n",
- ".xr-attrs dt {\n",
- " font-weight: normal;\n",
- " grid-column: 1;\n",
- "}\n",
- "\n",
- ".xr-attrs dt:hover span {\n",
- " display: inline-block;\n",
- " background: var(--xr-background-color);\n",
- " padding-right: 10px;\n",
- "}\n",
- "\n",
- ".xr-attrs dd {\n",
- " grid-column: 2;\n",
- " white-space: pre-wrap;\n",
- " word-break: break-all;\n",
- "}\n",
- "\n",
- ".xr-icon-database,\n",
- ".xr-icon-file-text2 {\n",
- " display: inline-block;\n",
- " vertical-align: middle;\n",
- " width: 1em;\n",
- " height: 1.5em !important;\n",
- " stroke-width: 0;\n",
- " stroke: currentColor;\n",
- " fill: currentColor;\n",
- "}\n",
- "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'to' (group: 15)>\n",
- "array([24.687952, 13.716603, 27.133892, -1.622877, 18.418442, 27.311337,\n",
- " 21.645702, 17.663412, 27.302595, 21.3692 , 27.422678, 28.171144,\n",
- " 29.063435, 24.740253, 6.294363], dtype=float32)\n",
- "Coordinates:\n",
- " depth float64 5.5\n",
- " time datetime64[ns] 1997-04-01\n",
- " * group (group) float64 8.0 19.0 38.0 46.0 47.0 ... 54.0 55.0 56.0 57.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'to'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>group</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-48a2fb28-80f1-4b7f-a6bd-96fc51a068a9' class='xr-array-in' type='checkbox' checked><label for='section-48a2fb28-80f1-4b7f-a6bd-96fc51a068a9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>24.69 13.72 27.13 -1.623 18.42 27.31 ... 27.42 28.17 29.06 24.74 6.294</span></div><div class='xr-array-data'><pre>array([24.687952, 13.716603, 27.133892, -1.622877, 18.418442, 27.311337,\n",
- " 21.645702, 17.663412, 27.302595, 21.3692 , 27.422678, 28.171144,\n",
- " 29.063435, 24.740253, 6.294363], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a5f11a53-d8fb-4a13-8780-253ec5418323' class='xr-section-summary-in' type='checkbox' checked><label for='section-a5f11a53-d8fb-4a13-8780-253ec5418323' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.5</div><input id='attrs-14c86b9a-7886-4e47-985f-6c692e40ea50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14c86b9a-7886-4e47-985f-6c692e40ea50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bc35931-e1f2-4847-837b-1d3c8cdb1096' class='xr-var-data-in' type='checkbox'><label for='data-7bc35931-e1f2-4847-837b-1d3c8cdb1096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth_below_sea</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(5.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1997-04-01</div><input id='attrs-9f2c0455-cfe2-45ec-b794-372bd6ed80a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f2c0455-cfe2-45ec-b794-372bd6ed80a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93bc2135-f673-420d-8353-2f8beeb63dfb' class='xr-var-data-in' type='checkbox'><label for='data-93bc2135-f673-420d-8353-2f8beeb63dfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array('1997-04-01T00:00:00.000000000', dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>group</span></div><div class='xr-var-dims'>(group)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.0 19.0 38.0 ... 55.0 56.0 57.0</div><input id='attrs-3a615e3e-f651-49a2-a901-19284b0c2b88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a615e3e-f651-49a2-a901-19284b0c2b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9145b16d-b7ab-4471-b149-197d9d7be6dc' class='xr-var-data-in' type='checkbox'><label for='data-9145b16d-b7ab-4471-b149-197d9d7be6dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 8., 19., 38., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56.,\n",
- " 57.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8496e72c-cefb-4129-ad0d-4f72be1b0608' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8496e72c-cefb-4129-ad0d-4f72be1b0608' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
- ],
+ },
+ {
+ "data": {
+ "image/png": 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\n",
"text/plain": [
- "<xarray.DataArray 'to' (group: 15)>\n",
- "array([24.687952, 13.716603, 27.133892, -1.622877, 18.418442, 27.311337,\n",
- " 21.645702, 17.663412, 27.302595, 21.3692 , 27.422678, 28.171144,\n",
- " 29.063435, 24.740253, 6.294363], dtype=float32)\n",
- "Coordinates:\n",
- " depth float64 5.5\n",
- " time datetime64[ns] 1997-04-01\n",
- " * group (group) float64 8.0 19.0 38.0 46.0 47.0 ... 54.0 55.0 56.0 57.0"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "ds_regions"
+ "ds_region.swap_dims({'region':'names'}).to.plot(y='depth',hue='names',yincrease=False)"
]
},
{
diff --git a/notebooks/xesmf_ICON.ipynb b/notebooks/xesmf_ICON.ipynb
new file mode 100644
index 0000000..0f5aa07
--- /dev/null
+++ b/notebooks/xesmf_ICON.ipynb
@@ -0,0 +1,2149 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# `xesmf` for unstructured grids like `ICON`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- https://github.com/pangeo-data/xESMF\n",
+ "- https://pangeo-xesmf.readthedocs.io/en/latest/notebooks/Using_LocStream.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#path = '/work/bm1102/m211054/dyamond/zstar2/experiments/exp.ocean_era51h_zstar_r2b9_21223-DWS/outdata/exp.ocean_era51h_zstar_r2b9_21223-DWS_P1M_3d_20160901T000000Z.nc'\n",
+ "path = '/work/mh0033/m211054/projects/icon/icon-oes-1.3.01/experiments/exp.ocean_era51h_r2b8_hel20218-ERA/outdata/exp.ocean_era51h_r2b8_hel20218-ERA_19970401T000000Z.nc'\n",
+ "ds = xr.open_dataset(path,\n",
+ " chunks={'depth': 1})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def rad_to_deg(ds):\n",
+ " \"\"\"Convert radian units to deg.\"\"\"\n",
+ " import numpy as np\n",
+ " #ds.coords.compute()\n",
+ " with xr.set_options(keep_attrs=True):\n",
+ " for c in ds.coords:\n",
+ " if 'units' in ds[c].attrs:\n",
+ " if ds[c].attrs['units'] == 'radian':\n",
+ " print(f'convert {c} from rad to deg')\n",
+ " ds[c] = ds[c]* 180./np.pi\n",
+ " ds[c].attrs['units'] = 'degrees'\n",
+ " elif 'bnds' in c:\n",
+ " print(f'convert {c} from rad to deg')\n",
+ " ds[c] = ds[c]* 180./np.pi\n",
+ " ds[c].attrs['units'] = 'degrees'\n",
+ " return ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = rad_to_deg(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = ds[['to']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_2d = ds.isel(depth=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xesmf as xe\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "ds_out = xe.util.grid_global(1,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xesmf/frontend.py:496: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
+ " ds_out = xr.apply_ufunc(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xesmf/frontend.py:496: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
+ " ds_out = xr.apply_ufunc(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for method in ['nearest_s2d','nearest_d2s']:\n",
+ " re = xe.Regridder(ds_2d, ds_out, method, locstream_in=True)\n",
+ " ds_re = re(ds_2d)\n",
+ " ds_re.squeeze().to.plot()\n",
+ " plt.title(f'method={method}')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> `nearest_s2d` looks fine\n",
+ "\n",
+ "> `nearest_d2s` shows a fine pattern but values are off"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## generate weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 45.9 s, sys: 1.44 s, total: 47.3 s\n",
+ "Wall time: 47.3 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time re = xe.Regridder(ds_2d, ds_out, method='nearest_s2d', locstream_in=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 9 ms, sys: 5 ms, total: 14 ms\n",
+ "Wall time: 23.8 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'weights.nc'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time re.to_netcdf('weights.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1020K\tweights.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "!du -hs weights.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# reuse\n",
+ "re = xe.Regridder(ds_2d, ds_out, method='nearest_s2d', locstream_in=True, weights=xr.open_dataset('weights.nc'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### single level"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 6 ms, sys: 4 ms, total: 10 ms\n",
+ "Wall time: 6.38 ms\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xesmf/frontend.py:496: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
+ " ds_out = xr.apply_ufunc(\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_re = re(ds_2d)"
+ ]
+ },
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+ "ds_re.to.data"
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+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 63 ms, sys: 32 ms, total: 95 ms\n",
+ "Wall time: 92.8 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_re = ds_re.compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### all levels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
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+ "text": [
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+ " ds_out = xr.apply_ufunc(\n"
+ ]
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 26.9 s, sys: 27.8 s, total: 54.7 s\n",
+ "Wall time: 47.3 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_re = ds_re.compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# R2B9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!ncdump -h /work/bm1102/m211054/dyamond/zstar2/experiments/exp.ocean_era51h_zstar_r2b9_21223-DWS/outdata/exp.ocean_era51h_zstar_r2b9_21223-DWS_P1M_3d_20160901T000000Z.nc"
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+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
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+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
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+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
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+ " color: var(--xr-font-color2);\n",
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+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
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+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
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+ ".xr-section-item input {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (depth: 128, ncells: 14886338, time: 1)\n",
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2016-09-01\n",
+ " clon (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " clat (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " * depth (depth) float64 1.0 3.05 5.2 7.45 ... 5.522e+03 5.71e+03 5.904e+03\n",
+ "Dimensions without coordinates: ncells\n",
+ "Data variables:\n",
+ " to (time, depth, ncells) float32 dask.array<chunksize=(1, 4, 14886338), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " CDI: Climate Data Interface version 1.8.2 (http://mpimet...\n",
+ " Conventions: CF-1.6\n",
+ " number_of_grid_used: 42\n",
+ " uuidOfHGrid: 7d4df6e2-1b06-11e8-9572-8146aa8bc243\n",
+ " institution: Max Planck Institute for Meteorology/Deutscher Wett...\n",
+ " title: ICON simulation\n",
+ " source: git@gitlab.dkrz.de:icon/icon-oes.git@7ab1ae25a1b38c...\n",
+ " history: /mnt/lustre02/work/bm1102/m211054/dyamond/zstar2/bi...\n",
+ " references: see MPIM/DWD publications\n",
+ " comment: Helmuth Haak (m211054) on m20000 (Linux 2.6.32-754....</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-aeff7633-f997-4b66-b70d-879ab49cbc64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aeff7633-f997-4b66-b70d-879ab49cbc64' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 128</li><li><span>ncells</span>: 14886338</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3199430c-c4d0-4061-96db-ea0c4236dd61' class='xr-section-summary-in' type='checkbox' checked><label for='section-3199430c-c4d0-4061-96db-ea0c4236dd61' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-09-01</div><input id='attrs-1bc7fe4e-3b4d-415c-8213-13f40db119bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bc7fe4e-3b4d-415c-8213-13f40db119bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac3f181f-e0d0-44e6-b960-04371713cddd' class='xr-var-data-in' type='checkbox'><label for='data-ac3f181f-e0d0-44e6-b960-04371713cddd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(['2016-09-01T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(14886338,), meta=np.ndarray></div><input id='attrs-9f7e4399-47fa-4e95-84c8-26e89fe061d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f7e4399-47fa-4e95-84c8-26e89fe061d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6892eb4-aa35-4a69-9c94-d783720b93fd' class='xr-var-data-in' type='checkbox'><label for='data-d6892eb4-aa35-4a69-9c94-d783720b93fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>bounds :</span></dt><dd>clon_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
+ "<tr>\n",
+ "<td>\n",
+ "<table>\n",
+ " <thead>\n",
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+ " <tbody>\n",
+ " <tr><th> Bytes </th><td> 56.79 MiB </td> <td> 56.79 MiB </td></tr>\n",
+ " <tr><th> Shape </th><td> (14886338,) </td> <td> (14886338,) </td></tr>\n",
+ " <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
+ " <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+ " </tbody>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(14886338,), meta=np.ndarray></div><input id='attrs-69620b5b-ad5e-467b-b808-b616c25dda11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69620b5b-ad5e-467b-b808-b616c25dda11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0528bf3-efef-4871-8ef3-d61af6e82e68' class='xr-var-data-in' type='checkbox'><label for='data-d0528bf3-efef-4871-8ef3-d61af6e82e68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>bounds :</span></dt><dd>clat_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
+ "<tr>\n",
+ "<td>\n",
+ "<table>\n",
+ " <thead>\n",
+ " <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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+ " <tr><th> Bytes </th><td> 56.79 MiB </td> <td> 56.79 MiB </td></tr>\n",
+ " <tr><th> Shape </th><td> (14886338,) </td> <td> (14886338,) </td></tr>\n",
+ " <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
+ " <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+ "</svg>\n",
+ "</td>\n",
+ "</tr>\n",
+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.05 5.2 ... 5.71e+03 5.904e+03</div><input id='attrs-21c05560-2775-43b7-8feb-62758d377378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21c05560-2775-43b7-8feb-62758d377378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2aebc5-f2d4-452d-b50b-36364059badb' class='xr-var-data-in' type='checkbox'><label for='data-ff2aebc5-f2d4-452d-b50b-36364059badb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth_below_sea</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([1.00000e+00, 3.05000e+00, 5.20000e+00, 7.45000e+00, 9.80000e+00,\n",
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+ " 2.61500e+01, 2.93000e+01, 3.26000e+01, 3.60500e+01, 3.96500e+01,\n",
+ " 4.34500e+01, 4.74000e+01, 5.15000e+01, 5.58000e+01, 6.03000e+01,\n",
+ " 6.50000e+01, 6.99000e+01, 7.50500e+01, 8.04500e+01, 8.61000e+01,\n",
+ " 9.20000e+01, 9.81500e+01, 1.04600e+02, 1.11350e+02, 1.18400e+02,\n",
+ " 1.25750e+02, 1.33400e+02, 1.41400e+02, 1.49750e+02, 1.58450e+02,\n",
+ " 1.67550e+02, 1.77100e+02, 1.87100e+02, 1.97550e+02, 2.08450e+02,\n",
+ " 2.19750e+02, 2.31450e+02, 2.43550e+02, 2.56050e+02, 2.68950e+02,\n",
+ " 2.82250e+02, 2.96000e+02, 3.10250e+02, 3.24950e+02, 3.40100e+02,\n",
+ " 3.55750e+02, 3.71950e+02, 3.88700e+02, 4.06000e+02, 4.23900e+02,\n",
+ " 4.42400e+02, 4.61500e+02, 4.81200e+02, 5.01550e+02, 5.22600e+02,\n",
+ " 5.44350e+02, 5.66800e+02, 5.90000e+02, 6.14000e+02, 6.38800e+02,\n",
+ " 6.64400e+02, 6.90850e+02, 7.18200e+02, 7.46450e+02, 7.75650e+02,\n",
+ " 8.05800e+02, 8.36950e+02, 8.69150e+02, 9.02400e+02, 9.36750e+02,\n",
+ " 9.72250e+02, 1.00895e+03, 1.04685e+03, 1.08600e+03, 1.12645e+03,\n",
+ " 1.16825e+03, 1.21145e+03, 1.25605e+03, 1.30210e+03, 1.34970e+03,\n",
+ " 1.39890e+03, 1.44975e+03, 1.50230e+03, 1.55660e+03, 1.61270e+03,\n",
+ " 1.67065e+03, 1.73050e+03, 1.79235e+03, 1.85625e+03, 1.92225e+03,\n",
+ " 1.99045e+03, 2.06090e+03, 2.13370e+03, 2.20895e+03, 2.28670e+03,\n",
+ " 2.36700e+03, 2.44995e+03, 2.53565e+03, 2.62420e+03, 2.71570e+03,\n",
+ " 2.81025e+03, 2.90795e+03, 3.00890e+03, 3.11320e+03, 3.22095e+03,\n",
+ " 3.33230e+03, 3.44735e+03, 3.56620e+03, 3.68900e+03, 3.81585e+03,\n",
+ " 3.94690e+03, 4.08230e+03, 4.22220e+03, 4.36675e+03, 4.51610e+03,\n",
+ " 4.67045e+03, 4.82995e+03, 4.99470e+03, 5.16490e+03, 5.34075e+03,\n",
+ " 5.52245e+03, 5.71020e+03, 5.90415e+03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e7bcee3-a6cd-4258-97d8-69eab5e980e5' class='xr-section-summary-in' type='checkbox' checked><label for='section-4e7bcee3-a6cd-4258-97d8-69eab5e980e5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>to</span></div><div class='xr-var-dims'>(time, depth, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 4, 14886338), meta=np.ndarray></div><input id='attrs-eaa3a2a6-350d-46ee-b68e-9dc565c483bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaa3a2a6-350d-46ee-b68e-9dc565c483bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d299af4e-ee17-4e2c-86be-35d8faf8cf57' class='xr-var-data-in' type='checkbox'><label for='data-d299af4e-ee17-4e2c-86be-35d8faf8cf57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>long_name :</span></dt><dd>sea water potential temperature</dd><dt><span>units :</span></dt><dd>deg C</dd><dt><span>code :</span></dt><dd>2</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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+ " <tbody>\n",
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+ " <tr><th> Shape </th><td> (1, 128, 14886338) </td> <td> (1, 4, 14886338) </td></tr>\n",
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+ " <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+ " <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">1</text>\n",
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+ "</tr>\n",
+ "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fe62c1f-96f7-47f6-935e-f539097341ae' class='xr-section-summary-in' type='checkbox' ><label for='section-8fe62c1f-96f7-47f6-935e-f539097341ae' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>number_of_grid_used :</span></dt><dd>42</dd><dt><span>uuidOfHGrid :</span></dt><dd>7d4df6e2-1b06-11e8-9572-8146aa8bc243</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-oes.git@7ab1ae25a1b38ca792f5f8344826e87207b28368</dd><dt><span>history :</span></dt><dd>/mnt/lustre02/work/bm1102/m211054/dyamond/zstar2/bin/icon at 20210827 211905</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>comment :</span></dt><dd>Helmuth Haak (m211054) on m20000 (Linux 2.6.32-754.33.1.el6.x86_64 x86_64)</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (depth: 128, ncells: 14886338, time: 1)\n",
+ "Coordinates:\n",
+ " * time (time) datetime64[ns] 2016-09-01\n",
+ " clon (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " clat (ncells) float32 dask.array<chunksize=(14886338,), meta=np.ndarray>\n",
+ " * depth (depth) float64 1.0 3.05 5.2 7.45 ... 5.522e+03 5.71e+03 5.904e+03\n",
+ "Dimensions without coordinates: ncells\n",
+ "Data variables:\n",
+ " to (time, depth, ncells) float32 dask.array<chunksize=(1, 4, 14886338), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " CDI: Climate Data Interface version 1.8.2 (http://mpimet...\n",
+ " Conventions: CF-1.6\n",
+ " number_of_grid_used: 42\n",
+ " uuidOfHGrid: 7d4df6e2-1b06-11e8-9572-8146aa8bc243\n",
+ " institution: Max Planck Institute for Meteorology/Deutscher Wett...\n",
+ " title: ICON simulation\n",
+ " source: git@gitlab.dkrz.de:icon/icon-oes.git@7ab1ae25a1b38c...\n",
+ " history: /mnt/lustre02/work/bm1102/m211054/dyamond/zstar2/bi...\n",
+ " references: see MPIM/DWD publications\n",
+ " comment: Helmuth Haak (m211054) on m20000 (Linux 2.6.32-754...."
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds = xr.open_mfdataset('/work/bm1102/m211054/dyamond/zstar2/experiments/exp.ocean_era51h_zstar_r2b9_21223-DWS/outdata/exp.ocean_era51h_zstar_r2b9_21223-DWS_P1M_3d_20160901T000000Z.nc',\n",
+ " chunks={'depth': 4}, parallel=True)[['to']]\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convert clon from rad to deg\n",
+ "convert clat from rad to deg\n"
+ ]
+ }
+ ],
+ "source": [
+ "ds = rad_to_deg(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1min 46s, sys: 38.2 s, total: 2min 25s\n",
+ "Wall time: 1min 9s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.backends.zarr.ZarrStore at 0x2b32ea0e70a0>"
+ ]
+ },
+ "execution_count": 85,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time ds.to_zarr('/work/mh0727/m300524/ICON/R2B9.zarr', mode='w', consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.open_zarr('/work/mh0727/m300524/ICON/R2B9.zarr', consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.coords['clon']=ds.coords['clon'].compute()\n",
+ "ds.coords['clat']=ds.coords['clat'].compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 46.5 s, sys: 1.67 s, total: 48.2 s\n",
+ "Wall time: 48 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time re = xe.Regridder(ds.isel(depth=0,drop=True).compute(), ds_out, method='nearest_s2d', locstream_in=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 8 ms, sys: 7 ms, total: 15 ms\n",
+ "Wall time: 21.1 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'weights.nc'"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# same weights\n",
+ "%time re.to_netcdf('weights.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of CPUs: 72, number of threads: 9, number of workers: 8, processes: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/distributed/node.py:160: UserWarning: Port 8787 is already in use.\n",
+ "Perhaps you already have a cluster running?\n",
+ "Hosting the HTTP server on port 44834 instead\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
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+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-14f1b91c-2148-11ec-8ea0-0800383e1321</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+ " <td style=\"text-align: left;\"><strong>Cluster type:</strong> LocalCluster</td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/44834/status\">http://localhost:8888/proxy/44834/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " \n",
+ " </table>\n",
+ " \n",
+ " <details>\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+ " \n",
+ " <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #e1e1e1;\n",
+ " border: 3px solid #9D9D9D;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">149725d3</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+ " <td style=\"text-align: left;\"><strong>Using processes:</strong> False</td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard:</strong> <a href=\"http://localhost:8888/proxy/44834/status\">http://localhost:8888/proxy/44834/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\"><strong>Workers:</strong> 8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total threads:</strong>\n",
+ " 72\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total memory:</strong>\n",
+ " 476.84 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " </table>\n",
+ " <details>\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
+ " \n",
+ " <div style=\"\">\n",
+ " \n",
+ " <div>\n",
+ " <div style=\"\n",
+ " width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #FFF7E5;\n",
+ " border: 3px solid #FF6132;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+ " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-388fa3a1-983c-4bae-abf6-96bfd118f288</p>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm:</strong> inproc://10.50.43.213/52896/150</td>\n",
+ " <td style=\"text-align: left;\"><strong>Workers:</strong> 8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard:</strong> <a href=\"http://localhost:8888/proxy/44834/status\">http://localhost:8888/proxy/44834/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total threads:</strong>\n",
+ " 72\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Started:</strong>\n",
+ " Just now\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Total memory:</strong>\n",
+ " 476.84 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <details style=\"margin-left: 48px;\">\n",
+ " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Workers</h3></summary>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/157</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/44011/status\">http://localhost:8888/proxy/44011/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-zewvtjl1\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/156</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/44777/status\">http://localhost:8888/proxy/44777/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-ii5eoh8t\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/159</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/34923/status\">http://localhost:8888/proxy/34923/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-3uovzety\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/153</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/40630/status\">http://localhost:8888/proxy/40630/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-fbivlomx\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/152</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/41643/status\">http://localhost:8888/proxy/41643/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-mertc3g2\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/155</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/44310/status\">http://localhost:8888/proxy/44310/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-aqh14vzx\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/154</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/45036/status\">http://localhost:8888/proxy/45036/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-lm_ga_bd\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " <div style=\"margin-bottom: 20px;\">\n",
+ " <div style=\"width: 24px;\n",
+ " height: 24px;\n",
+ " background-color: #DBF5FF;\n",
+ " border: 3px solid #4CC9FF;\n",
+ " border-radius: 5px;\n",
+ " position: absolute;\"> </div>\n",
+ " <div style=\"margin-left: 48px;\">\n",
+ " <details>\n",
+ " <summary>\n",
+ " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
+ " </summary>\n",
+ " <table style=\"width: 100%; text-align: left;\">\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Comm: </strong> inproc://10.50.43.213/52896/158</td>\n",
+ " <td style=\"text-align: left;\"><strong>Total threads: </strong> 9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Dashboard: </strong>\n",
+ " <a href=\"http://localhost:8888/proxy/35751/status\">http://localhost:8888/proxy/35751/status</a>\n",
+ " </td>\n",
+ " <td style=\"text-align: left;\">\n",
+ " <strong>Memory: </strong>\n",
+ " 59.60 GiB\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align: left;\"><strong>Nanny: </strong> None</td>\n",
+ " <td style=\"text-align: left;\"></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align: left;\">\n",
+ " <strong>Local directory: </strong>\n",
+ " /mnt/lustre01/pf/zmaw/m300524/pymistral/notebooks/dask-worker-space/worker-vmuegy45\n",
+ " </td>\n",
+ " </tr>\n",
+ " \n",
+ " \n",
+ " </table>\n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " </div>\n",
+ " </div>\n",
+ " \n",
+ " </details>\n",
+ " \n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ "<Client: 'inproc://10.50.43.213/52896/150' processes=8 threads=72, memory=476.84 GiB>"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use dask\n",
+ "from dask.distributed import Client\n",
+ "import multiprocessing\n",
+ "ncpu = multiprocessing.cpu_count()\n",
+ "processes = False\n",
+ "nworker = 8\n",
+ "threads = ncpu // nworker\n",
+ "print(\n",
+ " f\"Number of CPUs: {ncpu}, number of threads: {threads}, number of workers: {nworker}, processes: {processes}\",\n",
+ ")\n",
+ "client = Client(\n",
+ " processes=processes,\n",
+ " threads_per_worker=threads,\n",
+ " n_workers=nworker,\n",
+ " memory_limit=\"64GB\",\n",
+ ")\n",
+ "client"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 702 ms, sys: 450 ms, total: 1.15 s\n",
+ "Wall time: 740 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "# use pre-calculated weights\n",
+ "%time re = xe.Regridder(ds.isel(depth=0), ds_out, method='nearest_s2d', locstream_in=True, weights=xr.open_dataset('weights.nc'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# with persist, compute() only takes 3s, without 50s\n",
+ "# %time ds = ds.persist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 28 ms, sys: 14 ms, total: 42 ms\n",
+ "Wall time: 35.7 ms\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/pymistral/lib/python3.8/site-packages/xesmf/frontend.py:496: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
+ " ds_out = xr.apply_ufunc(\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_re = re(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " </thead>\n",
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+ " <tr><th> Shape </th><td> (1, 128, 180, 360) </td> <td> (1, 4, 180, 360) </td></tr>\n",
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 14.7 s, sys: 1min 29s, total: 1min 44s\n",
+ "Wall time: 6.26 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time ds_re = ds_re.compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- with `zarr` dump: 5s\n",
+ "- from `nc`: 50s"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client.close()"
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
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+}
--
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