mirror of
https://git.victorphan.net/basketballcantho/CSIROBoeingPhase5-Vietnam.git
synced 2026-08-05 05:43:10 +07:00
Kaito0506
3729 lines
204 KiB
Plaintext
3729 lines
204 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 49,
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"id": "db6b50b8-f9c5-4ee9-b93f-ed38a89156ba",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 300 µs, sys: 172 µs, total: 472 µs\n",
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"Wall time: 484 µs\n"
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]
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}
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],
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"source": [
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"%%time \n",
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"%matplotlib inline\n",
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"from new_import import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "bfdefd7d-90d2-43bf-aa58-ebb4caef0f85",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"An existing cluster was found. Connecting to: easihub.26c21d10c6f44900aa4429853b81ac3f\n",
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"CPU times: user 762 ms, sys: 26.3 ms, total: 788 ms\n",
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"Wall time: 1 s\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
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" <div style=\"margin-left: 48px;\">\n",
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" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-e689cc55-7343-11ef-82c8-eab95fd56520</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
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"\n",
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" <tr>\n",
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" \n",
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" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
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" <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
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" \n",
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" </tr>\n",
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"\n",
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" \n",
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" <tr>\n",
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" <td style=\"text-align: left;\">\n",
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" <strong>Dashboard: </strong> <a href=\"https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.26c21d10c6f44900aa4429853b81ac3f/status\" target=\"_blank\">https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.26c21d10c6f44900aa4429853b81ac3f/status</a>\n",
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" </td>\n",
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" <td style=\"text-align: left;\"></td>\n",
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" </tr>\n",
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" \n",
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"\n",
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" </table>\n",
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"\n",
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" \n",
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" <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.26c21d10c6f44900aa4429853b81ac3f/status\" }'>\n",
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" Launch dashboard in JupyterLab\n",
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" </button>\n",
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" \n",
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"\n",
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" \n",
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" <details>\n",
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" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
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" <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
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" <h3>GatewayCluster</h3>\n",
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" <ul>\n",
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" <li><b>Name: </b>easihub.26c21d10c6f44900aa4429853b81ac3f\n",
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" <li><b>Dashboard: </b><a href='https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.26c21d10c6f44900aa4429853b81ac3f/status' target='_blank'>https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.26c21d10c6f44900aa4429853b81ac3f/status</a>\n",
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" </ul>\n",
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"</div>\n",
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"\n",
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" </details>\n",
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" \n",
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"\n",
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" </div>\n",
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"</div>"
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],
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"text/plain": [
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"<Client: 'tls://10.0.43.66:8786' processes=1 threads=8, memory=28.00 GiB>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"# Cấu hình Daskgateway\n",
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"cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,10))\n",
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"# Khai báo 1 Datacube là dc\n",
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"dc = datacube.Datacube()\n",
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"\n",
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"# Cấu hình truy cập dịch vụ S3\n",
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"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
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"\n",
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"client"
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]
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},
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{
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"cell_type": "markdown",
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"id": "657547be-5136-439b-b194-e5944c905138",
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"metadata": {},
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"source": [
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"LOAD VH, VV"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "a0a8a450-3725-48d8-837e-2f19c2e6db59",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## cấu hình thời gian lấy ảnh và tọa độ\n",
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"date_range = ('2022-09-01', '2023-10-01')\n",
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"longtitude_range = (105.5, 106.4)\n",
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"latitude_range = (9.2, 10.0) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "05c2a58f-d0e5-4b20-9320-de05897cbdf7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## cấu hình dữ liệu train và vh vv file\n",
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"train_path = \"train/ST_training data_updated_1130points.shp\" # đường dẫn shp file train\n",
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"name_vh = \"vh-0922_0923-full_ST.tif\"\n",
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"name_vv = \"vv-0922_0923-full_ST.tif\"\n",
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"\n",
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"\n",
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"train = load_train_data(train_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "33471ca1-6e5a-403e-b9af-2e7b45c1d892",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# %%time\n",
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"# ## tải về dữ liệu sen1\n",
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"# import os\n",
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"# if not os.path.exists(name_vh):\n",
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"# !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif\n",
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"# if not os.path.exists(name_vv):\n",
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"# !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "6ebfc48a-2bed-4909-b495-6bbfd3f3cc6f",
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"metadata": {
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"tags": []
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "8471dbbf-c3c6-4f03-94b0-11795b8d2062",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<h4>Dataset size: 21.60 GB</h4>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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"</svg>\n",
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"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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" *\n",
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":root {\n",
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" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
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" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
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" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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" --xr-background-color: var(--jp-layout-color0, white);\n",
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" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
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" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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"}\n",
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"\n",
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"html[theme=dark],\n",
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"body[data-theme=dark],\n",
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"body.vscode-dark {\n",
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" --xr-font-color0: rgba(255, 255, 255, 1);\n",
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" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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" --xr-border-color: #1F1F1F;\n",
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" --xr-disabled-color: #515151;\n",
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" --xr-background-color: #111111;\n",
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" --xr-background-color-row-even: #111111;\n",
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" --xr-background-color-row-odd: #313131;\n",
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".xr-wrap {\n",
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" display: block !important;\n",
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" min-width: 300px;\n",
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" max-width: 700px;\n",
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"\n",
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".xr-text-repr-fallback {\n",
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" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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" display: none;\n",
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".xr-header {\n",
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" padding-top: 6px;\n",
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" padding-bottom: 6px;\n",
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" margin-bottom: 4px;\n",
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" border-bottom: solid 1px var(--xr-border-color);\n",
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".xr-header > div,\n",
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".xr-header > ul {\n",
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".xr-obj-type,\n",
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".xr-obj-type {\n",
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".xr-sections {\n",
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".xr-section-item {\n",
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".xr-section-item input + label {\n",
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" color: var(--xr-disabled-color);\n",
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".xr-section-item input:enabled + label {\n",
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" cursor: pointer;\n",
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".xr-section-item input:enabled + label:hover {\n",
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".xr-section-summary-in:disabled + label {\n",
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".xr-section-summary-in:disabled + label:before {\n",
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".xr-section-summary-in:checked + label:before {\n",
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".xr-section-summary-in:checked + label > span {\n",
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".xr-section-inline-details {\n",
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".xr-section-summary-in:checked ~ .xr-section-details {\n",
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".xr-preview {\n",
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" 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-index-preview {\n",
|
|
" grid-column: 2 / 5;\n",
|
|
" color: var(--xr-font-color2);\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",
|
|
".xr-index-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",
|
|
".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
|
|
".xr-index-data,\n",
|
|
".xr-attrs {\n",
|
|
" padding-left: 25px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs,\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-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",
|
|
".xr-no-icon {\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: (time: 33, y: 8874, x: 9902)\n",
|
|
"Coordinates:\n",
|
|
" * time (time) datetime64[ns] 2022-09-06T22:46:14.500000 ... 2023-09...\n",
|
|
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
|
|
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
|
|
" spatial_ref int32 32648\n",
|
|
"Data variables:\n",
|
|
" vv (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
" vh (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
"Attributes:\n",
|
|
" crs: EPSG:32648\n",
|
|
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><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-de4f45a4-3b04-4c2b-8596-a959c85ef519' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de4f45a4-3b04-4c2b-8596-a959c85ef519' 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'>time</span>: 33</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f879a0f3-7c3c-405b-9199-b74b88d5abc7' class='xr-section-summary-in' type='checkbox' checked><label for='section-f879a0f3-7c3c-405b-9199-b74b88d5abc7' 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'>2022-09-06T22:46:14.500000 ... 2...</div><input id='attrs-4634af50-36ef-4221-beed-c94c8d305011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4634af50-36ef-4221-beed-c94c8d305011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ed653b5-cb8a-4e10-81ac-d880078f9a72' class='xr-var-data-in' type='checkbox'><label for='data-2ed653b5-cb8a-4e10-81ac-d880078f9a72' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array(['2022-09-06T22:46:14.500000000', '2022-09-18T22:46:13.500000000',\n",
|
|
" '2022-09-30T22:46:14.500000000', '2022-10-12T22:46:14.500000000',\n",
|
|
" '2022-10-24T22:46:14.500000000', '2022-11-05T22:46:14.500000000',\n",
|
|
" '2022-11-17T22:46:13.500000000', '2022-11-29T22:46:13.500000000',\n",
|
|
" '2022-12-11T22:46:13.500000000', '2022-12-23T22:46:12.500000000',\n",
|
|
" '2023-01-04T22:46:11.500000000', '2023-01-16T22:46:10.500000000',\n",
|
|
" '2023-01-28T22:46:11.500000000', '2023-02-09T22:46:10.500000000',\n",
|
|
" '2023-02-21T22:46:09.500000000', '2023-03-05T22:46:10.500000000',\n",
|
|
" '2023-03-17T22:46:10.500000000', '2023-03-29T22:46:10.500000000',\n",
|
|
" '2023-04-10T22:46:11.500000000', '2023-04-22T22:46:11.500000000',\n",
|
|
" '2023-05-04T22:46:11.500000000', '2023-05-17T11:11:32.500000000',\n",
|
|
" '2023-05-29T11:11:32.500000000', '2023-06-10T11:11:28.500000000',\n",
|
|
" '2023-06-21T22:46:13.500000000', '2023-07-04T11:11:34.500000000',\n",
|
|
" '2023-07-16T11:11:35.500000000', '2023-07-28T11:11:36.500000000',\n",
|
|
" '2023-08-08T22:46:16.500000000', '2023-08-20T22:46:17.500000000',\n",
|
|
" '2023-09-01T22:46:18.500000000', '2023-09-13T22:46:18.500000000',\n",
|
|
" '2023-09-25T22:46:19.500000000'], dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-02420cd0-1560-41c9-85bb-0b4d408a63d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02420cd0-1560-41c9-85bb-0b4d408a63d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd70eac-3005-4e6c-8896-eb1882e3a72a' class='xr-var-data-in' type='checkbox'><label for='data-fdd70eac-3005-4e6c-8896-eb1882e3a72a' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-d267fb88-0d6e-40ee-b8c9-bd4eb7544b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d267fb88-0d6e-40ee-b8c9-bd4eb7544b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3479e1c-0a73-43c4-95f9-95ea989c69fa' class='xr-var-data-in' type='checkbox'><label for='data-c3479e1c-0a73-43c4-95f9-95ea989c69fa' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32648</div><input id='attrs-572341f3-5f80-47e1-af75-fe0dae39a453' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-572341f3-5f80-47e1-af75-fe0dae39a453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a15f6176-323d-42fa-bb63-707b72a93881' class='xr-var-data-in' type='checkbox'><label for='data-a15f6176-323d-42fa-bb63-707b72a93881' 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>spatial_ref :</span></dt><dd>PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32648, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e732f0dc-338e-489e-b86f-cd339c416f76' class='xr-section-summary-in' type='checkbox' checked><label for='section-e732f0dc-338e-489e-b86f-cd339c416f76' class='xr-section-summary' >Data variables: <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>vv</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-ab47b529-dbf3-4d65-81cb-d58079e4b9a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab47b529-dbf3-4d65-81cb-d58079e4b9a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18984c3b-81df-4b46-8cf5-bef62d3533ba' class='xr-var-data-in' type='checkbox'><label for='data-18984c3b-81df-4b46-8cf5-bef62d3533ba' 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>units :</span></dt><dd>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 10.80 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (33, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 825 chunks in 1 graph layer </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"194\" height=\"172\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"10\" y1=\"24\" x2=\"24\" y2=\"39\" />\n",
|
|
" <line x1=\"10\" y1=\"49\" x2=\"24\" y2=\"64\" />\n",
|
|
" <line x1=\"10\" y1=\"74\" x2=\"24\" y2=\"89\" />\n",
|
|
" <line x1=\"10\" y1=\"99\" x2=\"24\" y2=\"114\" />\n",
|
|
" <line x1=\"10\" y1=\"107\" x2=\"24\" y2=\"122\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Vertical lines -->\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"107\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"107\" />\n",
|
|
" <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"108\" />\n",
|
|
" <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"109\" />\n",
|
|
" <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"110\" />\n",
|
|
" <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"111\" />\n",
|
|
" <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"112\" />\n",
|
|
" <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"112\" />\n",
|
|
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" <text x=\"7.474299\" y=\"135.016210\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,135.016210)\">33</text>\n",
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"</svg>\n",
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" </td>\n",
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" </tr>\n",
|
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vh</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-1f098b16-f9d1-4241-ade9-7afeb57307a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f098b16-f9d1-4241-ade9-7afeb57307a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b28c1234-2f53-4687-ab6f-0725a9d38072' class='xr-var-data-in' type='checkbox'><label for='data-b28c1234-2f53-4687-ab6f-0725a9d38072' 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>units :</span></dt><dd>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
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" <td>\n",
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" <table style=\"border-collapse: collapse;\">\n",
|
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" <thead>\n",
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" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
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|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
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|
" <td> 10.80 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (33, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 825 chunks in 1 graph layer </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
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" <td>\n",
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" <svg width=\"194\" height=\"172\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
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"\n",
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" <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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"\n",
|
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" <!-- Vertical lines -->\n",
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" <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
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|
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"\n",
|
|
" <!-- Vertical lines -->\n",
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" <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"122\" style=\"stroke-width:2\" />\n",
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|
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"\n",
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" <!-- Colored Rectangle -->\n",
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" <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,122.49050867486044 24.9485979497544,122.49050867486044\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
|
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"\n",
|
|
" <!-- Text -->\n",
|
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" <text x=\"84.948598\" y=\"142.490509\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >9902</text>\n",
|
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" <text x=\"164.948598\" y=\"68.719553\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,68.719553)\">8874</text>\n",
|
|
" <text x=\"7.474299\" y=\"135.016210\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,135.016210)\">33</text>\n",
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"</svg>\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9f0ce76-0767-48b9-973c-40f9d48d74a1' class='xr-section-summary-in' type='checkbox' ><label for='section-c9f0ce76-0767-48b9-973c-40f9d48d74a1' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-937fc008-7447-4879-a638-0bdfcf9e3cbe' class='xr-index-data-in' type='checkbox'/><label for='index-937fc008-7447-4879-a638-0bdfcf9e3cbe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-09-06 22:46:14.500000', '2022-09-18 22:46:13.500000',\n",
|
|
" '2022-09-30 22:46:14.500000', '2022-10-12 22:46:14.500000',\n",
|
|
" '2022-10-24 22:46:14.500000', '2022-11-05 22:46:14.500000',\n",
|
|
" '2022-11-17 22:46:13.500000', '2022-11-29 22:46:13.500000',\n",
|
|
" '2022-12-11 22:46:13.500000', '2022-12-23 22:46:12.500000',\n",
|
|
" '2023-01-04 22:46:11.500000', '2023-01-16 22:46:10.500000',\n",
|
|
" '2023-01-28 22:46:11.500000', '2023-02-09 22:46:10.500000',\n",
|
|
" '2023-02-21 22:46:09.500000', '2023-03-05 22:46:10.500000',\n",
|
|
" '2023-03-17 22:46:10.500000', '2023-03-29 22:46:10.500000',\n",
|
|
" '2023-04-10 22:46:11.500000', '2023-04-22 22:46:11.500000',\n",
|
|
" '2023-05-04 22:46:11.500000', '2023-05-17 11:11:32.500000',\n",
|
|
" '2023-05-29 11:11:32.500000', '2023-06-10 11:11:28.500000',\n",
|
|
" '2023-06-21 22:46:13.500000', '2023-07-04 11:11:34.500000',\n",
|
|
" '2023-07-16 11:11:35.500000', '2023-07-28 11:11:36.500000',\n",
|
|
" '2023-08-08 22:46:16.500000', '2023-08-20 22:46:17.500000',\n",
|
|
" '2023-09-01 22:46:18.500000', '2023-09-13 22:46:18.500000',\n",
|
|
" '2023-09-25 22:46:19.500000'],\n",
|
|
" dtype='datetime64[ns]', name='time', freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ddafd822-800b-43be-aa92-d518873acbd2' class='xr-index-data-in' type='checkbox'/><label for='index-ddafd822-800b-43be-aa92-d518873acbd2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
|
|
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
|
|
" ...\n",
|
|
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
|
|
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
|
|
" dtype='float64', name='y', length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-607eaa7a-a89e-40d9-8181-aeb2f9653fe7' class='xr-index-data-in' type='checkbox'/><label for='index-607eaa7a-a89e-40d9-8181-aeb2f9653fe7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
|
|
" 554865.0, 554875.0, 554885.0,\n",
|
|
" ...\n",
|
|
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
|
|
" 653785.0, 653795.0, 653805.0],\n",
|
|
" dtype='float64', name='x', length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ecf46046-39ae-4eff-9342-2b18e07e5d47' class='xr-section-summary-in' type='checkbox' checked><label for='section-ecf46046-39ae-4eff-9342-2b18e07e5d47' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
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],
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"<xarray.Dataset>\n",
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"Dimensions: (time: 33, y: 8874, x: 9902)\n",
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"Coordinates:\n",
|
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" * time (time) datetime64[ns] 2022-09-06T22:46:14.500000 ... 2023-09...\n",
|
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" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
|
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" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
|
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" spatial_ref int32 32648\n",
|
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"Data variables:\n",
|
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" vv (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
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" vh (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
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"Attributes:\n",
|
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" crs: EPSG:32648\n",
|
|
" grid_mapping: spatial_ref"
|
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]
|
|
},
|
|
"execution_count": 38,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
|
"ds = dc.load(\n",
|
|
" product=\"sentinel1_grd_gamma0_20m\",\n",
|
|
" x=(105.5, 106.4),\n",
|
|
" y=(9.2, 10.0),\n",
|
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" time=(\"2022-09-01\", \"2023-10-01\"),\n",
|
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" measurements=[\"vv\", \"vh\"],\n",
|
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" output_crs=\"EPSG:32648\",\n",
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" resolution=(-10,10),\n",
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" dask_chunks={\"x\":2048, \"y\":2048},\n",
|
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" skip_broken_datasets=True,\n",
|
|
" group_by=\"solar_day\"\n",
|
|
")\n",
|
|
"notebook_utils.heading(notebook_utils.xarray_object_size(ds))\n",
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"ds"
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{
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"metadata": {
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"outputs": [
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" font-weight: normal;\n",
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|
" 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",
|
|
".xr-no-icon {\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 'vv' (time: 33, y: 8874, x: 9902)>\n",
|
|
"dask.array<dc_load_vv, shape=(33, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
|
|
"Coordinates:\n",
|
|
" * time (time) datetime64[ns] 2022-09-06T22:46:14.500000 ... 2023-09...\n",
|
|
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
|
|
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
|
|
" spatial_ref int32 32648\n",
|
|
"Attributes:\n",
|
|
" units: intensity\n",
|
|
" nodata: nan\n",
|
|
" crs: EPSG:32648\n",
|
|
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vv'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 33</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-54624dbe-34c1-4064-b45f-6dd0247611ca' class='xr-array-in' type='checkbox' checked><label for='section-54624dbe-34c1-4064-b45f-6dd0247611ca' 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, 2048, 2048), meta=np.ndarray></span></div><div class='xr-array-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 10.80 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (33, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 825 chunks in 1 graph layer </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
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" <svg width=\"194\" height=\"172\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
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" <!-- Horizontal lines -->\n",
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" <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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"\n",
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"\n",
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" <!-- Horizontal lines -->\n",
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" <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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" <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,122.49050867486044 24.9485979497544,122.49050867486044\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
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" <!-- Text -->\n",
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" <text x=\"84.948598\" y=\"142.490509\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >9902</text>\n",
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" <text x=\"164.948598\" y=\"68.719553\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,68.719553)\">8874</text>\n",
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" <text x=\"7.474299\" y=\"135.016210\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,135.016210)\">33</text>\n",
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"</svg>\n",
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" </td>\n",
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" </tr>\n",
|
|
"</table></div></div></li><li class='xr-section-item'><input id='section-a308d95b-4c80-42ba-affe-9cd806b67e01' class='xr-section-summary-in' type='checkbox' checked><label for='section-a308d95b-4c80-42ba-affe-9cd806b67e01' 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'>2022-09-06T22:46:14.500000 ... 2...</div><input id='attrs-080d1ac1-212a-4823-adb7-f30d86555a8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-080d1ac1-212a-4823-adb7-f30d86555a8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc4e375-387c-40b5-9f46-db4086bbd466' class='xr-var-data-in' type='checkbox'><label for='data-0dc4e375-387c-40b5-9f46-db4086bbd466' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array(['2022-09-06T22:46:14.500000000', '2022-09-18T22:46:13.500000000',\n",
|
|
" '2022-09-30T22:46:14.500000000', '2022-10-12T22:46:14.500000000',\n",
|
|
" '2022-10-24T22:46:14.500000000', '2022-11-05T22:46:14.500000000',\n",
|
|
" '2022-11-17T22:46:13.500000000', '2022-11-29T22:46:13.500000000',\n",
|
|
" '2022-12-11T22:46:13.500000000', '2022-12-23T22:46:12.500000000',\n",
|
|
" '2023-01-04T22:46:11.500000000', '2023-01-16T22:46:10.500000000',\n",
|
|
" '2023-01-28T22:46:11.500000000', '2023-02-09T22:46:10.500000000',\n",
|
|
" '2023-02-21T22:46:09.500000000', '2023-03-05T22:46:10.500000000',\n",
|
|
" '2023-03-17T22:46:10.500000000', '2023-03-29T22:46:10.500000000',\n",
|
|
" '2023-04-10T22:46:11.500000000', '2023-04-22T22:46:11.500000000',\n",
|
|
" '2023-05-04T22:46:11.500000000', '2023-05-17T11:11:32.500000000',\n",
|
|
" '2023-05-29T11:11:32.500000000', '2023-06-10T11:11:28.500000000',\n",
|
|
" '2023-06-21T22:46:13.500000000', '2023-07-04T11:11:34.500000000',\n",
|
|
" '2023-07-16T11:11:35.500000000', '2023-07-28T11:11:36.500000000',\n",
|
|
" '2023-08-08T22:46:16.500000000', '2023-08-20T22:46:17.500000000',\n",
|
|
" '2023-09-01T22:46:18.500000000', '2023-09-13T22:46:18.500000000',\n",
|
|
" '2023-09-25T22:46:19.500000000'], dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-8ac2f2d7-f37d-4090-8be1-2042b5196efb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ac2f2d7-f37d-4090-8be1-2042b5196efb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58e22520-ccff-4511-9501-a56537d85800' class='xr-var-data-in' type='checkbox'><label for='data-58e22520-ccff-4511-9501-a56537d85800' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-f7de8dbf-dd9d-4067-89e6-4f5f5a1511ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7de8dbf-dd9d-4067-89e6-4f5f5a1511ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd1dace-6234-4f1c-bfd3-e5c903a30d7d' class='xr-var-data-in' type='checkbox'><label for='data-4dd1dace-6234-4f1c-bfd3-e5c903a30d7d' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32648</div><input id='attrs-53391cc4-74d9-4954-a631-c9cb7be69d2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53391cc4-74d9-4954-a631-c9cb7be69d2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-474c58c5-b2f0-4064-8ff1-ac9e473231e3' class='xr-var-data-in' type='checkbox'><label for='data-474c58c5-b2f0-4064-8ff1-ac9e473231e3' 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>spatial_ref :</span></dt><dd>PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32648, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d6e8f08-e72b-41cd-b386-2d5ed69950c4' class='xr-section-summary-in' type='checkbox' ><label for='section-8d6e8f08-e72b-41cd-b386-2d5ed69950c4' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e71d029d-225c-46fb-b84f-9fddf98b5e6b' class='xr-index-data-in' type='checkbox'/><label for='index-e71d029d-225c-46fb-b84f-9fddf98b5e6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-09-06 22:46:14.500000', '2022-09-18 22:46:13.500000',\n",
|
|
" '2022-09-30 22:46:14.500000', '2022-10-12 22:46:14.500000',\n",
|
|
" '2022-10-24 22:46:14.500000', '2022-11-05 22:46:14.500000',\n",
|
|
" '2022-11-17 22:46:13.500000', '2022-11-29 22:46:13.500000',\n",
|
|
" '2022-12-11 22:46:13.500000', '2022-12-23 22:46:12.500000',\n",
|
|
" '2023-01-04 22:46:11.500000', '2023-01-16 22:46:10.500000',\n",
|
|
" '2023-01-28 22:46:11.500000', '2023-02-09 22:46:10.500000',\n",
|
|
" '2023-02-21 22:46:09.500000', '2023-03-05 22:46:10.500000',\n",
|
|
" '2023-03-17 22:46:10.500000', '2023-03-29 22:46:10.500000',\n",
|
|
" '2023-04-10 22:46:11.500000', '2023-04-22 22:46:11.500000',\n",
|
|
" '2023-05-04 22:46:11.500000', '2023-05-17 11:11:32.500000',\n",
|
|
" '2023-05-29 11:11:32.500000', '2023-06-10 11:11:28.500000',\n",
|
|
" '2023-06-21 22:46:13.500000', '2023-07-04 11:11:34.500000',\n",
|
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" '2023-07-16 11:11:35.500000', '2023-07-28 11:11:36.500000',\n",
|
|
" '2023-08-08 22:46:16.500000', '2023-08-20 22:46:17.500000',\n",
|
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" '2023-09-01 22:46:18.500000', '2023-09-13 22:46:18.500000',\n",
|
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" '2023-09-25 22:46:19.500000'],\n",
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" dtype='datetime64[ns]', name='time', freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c8f68f6-6172-4fc0-984b-df03ca3da249' class='xr-index-data-in' type='checkbox'/><label for='index-7c8f68f6-6172-4fc0-984b-df03ca3da249' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
|
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" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
|
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" ...\n",
|
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" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
|
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" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
|
|
" dtype='float64', name='y', length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e1b3335e-68f5-4734-81fe-f358b078b753' class='xr-index-data-in' type='checkbox'/><label for='index-e1b3335e-68f5-4734-81fe-f358b078b753' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
|
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" 554865.0, 554875.0, 554885.0,\n",
|
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" ...\n",
|
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" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
|
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" 653785.0, 653795.0, 653805.0],\n",
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" dtype='float64', name='x', length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-456d5aa3-c6ab-497c-bc69-e3315daf98f9' class='xr-section-summary-in' type='checkbox' checked><label for='section-456d5aa3-c6ab-497c-bc69-e3315daf98f9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
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],
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"text/plain": [
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"<xarray.DataArray 'vv' (time: 33, y: 8874, x: 9902)>\n",
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"dask.array<dc_load_vv, shape=(33, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
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"Coordinates:\n",
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" * time (time) datetime64[ns] 2022-09-06T22:46:14.500000 ... 2023-09...\n",
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" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
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" spatial_ref int32 32648\n",
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"Attributes:\n",
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" units: intensity\n",
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{
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" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
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" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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" --xr-background-color: var(--jp-layout-color0, white);\n",
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" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
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"}\n",
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"\n",
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"html[theme=dark],\n",
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" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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" --xr-border-color: #1F1F1F;\n",
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" --xr-disabled-color: #515151;\n",
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" --xr-background-color: #111111;\n",
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" --xr-background-color-row-even: #111111;\n",
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" --xr-background-color-row-odd: #313131;\n",
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"}\n",
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"\n",
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".xr-wrap {\n",
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" display: block !important;\n",
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" min-width: 300px;\n",
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" max-width: 700px;\n",
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"\n",
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".xr-text-repr-fallback {\n",
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" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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" display: none;\n",
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"\n",
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".xr-header {\n",
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" padding-bottom: 6px;\n",
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" margin-bottom: 4px;\n",
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" border-bottom: solid 1px var(--xr-border-color);\n",
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"}\n",
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"\n",
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".xr-header > div,\n",
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".xr-header > ul {\n",
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" display: inline;\n",
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" margin-top: 0;\n",
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" margin-bottom: 0;\n",
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"\n",
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".xr-obj-type,\n",
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".xr-array-name {\n",
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" margin-left: 2px;\n",
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"\n",
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".xr-obj-type {\n",
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" color: var(--xr-font-color2);\n",
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"\n",
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".xr-sections {\n",
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" display: grid;\n",
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".xr-section-item {\n",
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" display: contents;\n",
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"\n",
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".xr-section-item input + label {\n",
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" color: var(--xr-font-color2);\n",
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"\n",
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".xr-section-item input:enabled + label:hover {\n",
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" color: var(--xr-font-color0);\n",
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"\n",
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"\n",
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".xr-section-summary-in:disabled + label:before {\n",
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" color: var(--xr-disabled-color);\n",
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label:before {\n",
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" content: '▼';\n",
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"\n",
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".xr-section-summary-in:checked + label > span {\n",
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"\n",
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".xr-section-summary,\n",
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" padding-top: 4px;\n",
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"\n",
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".xr-section-inline-details {\n",
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" grid-column: 2 / -1;\n",
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"\n",
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".xr-section-details {\n",
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" margin-bottom: 5px;\n",
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"}\n",
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"\n",
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".xr-section-summary-in:checked ~ .xr-section-details {\n",
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"\n",
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".xr-array-wrap {\n",
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" grid-template-columns: 20px auto;\n",
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"}\n",
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"\n",
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".xr-array-wrap > label {\n",
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" grid-column: 1;\n",
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" vertical-align: top;\n",
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"}\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-index-preview {\n",
|
|
" grid-column: 2 / 5;\n",
|
|
" color: var(--xr-font-color2);\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",
|
|
".xr-index-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",
|
|
".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
|
|
".xr-index-data,\n",
|
|
".xr-attrs {\n",
|
|
" padding-left: 25px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs,\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-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",
|
|
".xr-no-icon {\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 (band: 13, y: 7680, x: 8687)>\n",
|
|
"[867310080 values with dtype=float64]\n",
|
|
"Coordinates:\n",
|
|
" * band (band) int64 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
|
|
" * x (x) float64 5.597e+05 5.597e+05 ... 6.465e+05 6.465e+05\n",
|
|
" * y (y) float64 1.099e+06 1.099e+06 ... 1.022e+06 1.022e+06\n",
|
|
" spatial_ref int64 0\n",
|
|
"Attributes:\n",
|
|
" AREA_OR_POINT: Area\n",
|
|
" TIFFTAG_XRESOLUTION: 1\n",
|
|
" TIFFTAG_YRESOLUTION: 1\n",
|
|
" scale_factor: 1.0\n",
|
|
" add_offset: 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 13</li><li><span class='xr-has-index'>y</span>: 7680</li><li><span class='xr-has-index'>x</span>: 8687</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6c3da6d2-7f48-40c6-b7da-08ded3b5c7f1' class='xr-array-in' type='checkbox' checked><label for='section-6c3da6d2-7f48-40c6-b7da-08ded3b5c7f1' 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>...</span></div><div class='xr-array-data'><pre>[867310080 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-80a47ad8-9c51-4fdf-ad2f-bd5f3523bad5' class='xr-section-summary-in' type='checkbox' checked><label for='section-80a47ad8-9c51-4fdf-ad2f-bd5f3523bad5' 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'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12 13</div><input id='attrs-27957191-ccb6-40b2-8109-01785361633d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27957191-ccb6-40b2-8109-01785361633d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c4df72b-1d70-478d-bcb7-fc53c85a6924' class='xr-var-data-in' type='checkbox'><label for='data-6c4df72b-1d70-478d-bcb7-fc53c85a6924' 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([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.597e+05 5.597e+05 ... 6.465e+05</div><input id='attrs-6f776fae-8b95-4a59-9321-377f3d3903dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f776fae-8b95-4a59-9321-377f3d3903dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4ef37b8-bf8f-4cca-967e-22093264adf5' class='xr-var-data-in' type='checkbox'><label for='data-f4ef37b8-bf8f-4cca-967e-22093264adf5' 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([559675., 559685., 559695., ..., 646515., 646525., 646535.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.099e+06 1.099e+06 ... 1.022e+06</div><input id='attrs-c9d264db-1770-41ff-ae5c-cd7ff9ec4e5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9d264db-1770-41ff-ae5c-cd7ff9ec4e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da25a8d-0625-43f2-bc87-b79ba811cd8d' class='xr-var-data-in' type='checkbox'><label for='data-8da25a8d-0625-43f2-bc87-b79ba811cd8d' 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([1098695., 1098685., 1098675., ..., 1021925., 1021915., 1021905.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-1dd0cc70-7ad1-46c6-a7b3-2bef71fc3233' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dd0cc70-7ad1-46c6-a7b3-2bef71fc3233' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95505948-fcd3-4625-aaaa-c5d4e75ef3c4' class='xr-var-data-in' type='checkbox'><label for='data-95505948-fcd3-4625-aaaa-c5d4e75ef3c4' 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>crs_wkt :</span></dt><dd>PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 48N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]]</dd><dt><span>GeoTransform :</span></dt><dd>559669.999999998 10.0 0.0 1098700.0000000002 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-594342f6-c021-4d07-9774-f5403b324c2c' class='xr-section-summary-in' type='checkbox' ><label for='section-594342f6-c021-4d07-9774-f5403b324c2c' class='xr-section-summary' >Indexes: <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-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad9668e7-5485-4630-9467-a5210754591c' class='xr-index-data-in' type='checkbox'/><label for='index-ad9668e7-5485-4630-9467-a5210754591c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64', name='band'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-923ba1f6-3e99-449e-b004-d0b39c051b12' class='xr-index-data-in' type='checkbox'/><label for='index-923ba1f6-3e99-449e-b004-d0b39c051b12' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([559674.999999998, 559684.999999998, 559694.999999998, 559704.999999998,\n",
|
|
" 559714.999999998, 559724.999999998, 559734.999999998, 559744.999999998,\n",
|
|
" 559754.999999998, 559764.999999998,\n",
|
|
" ...\n",
|
|
" 646444.999999998, 646454.999999998, 646464.999999998, 646474.999999998,\n",
|
|
" 646484.999999998, 646494.999999998, 646504.999999998, 646514.999999998,\n",
|
|
" 646524.999999998, 646534.999999998],\n",
|
|
" dtype='float64', name='x', length=8687))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-228bb996-0f9b-4471-9cce-c74867bbabc1' class='xr-index-data-in' type='checkbox'/><label for='index-228bb996-0f9b-4471-9cce-c74867bbabc1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1098695.0000000002, 1098685.0000000002, 1098675.0000000002,\n",
|
|
" 1098665.0000000002, 1098655.0000000002, 1098645.0000000002,\n",
|
|
" 1098635.0000000002, 1098625.0000000002, 1098615.0000000002,\n",
|
|
" 1098605.0000000002,\n",
|
|
" ...\n",
|
|
" 1021995.0000000002, 1021985.0000000002, 1021975.0000000002,\n",
|
|
" 1021965.0000000002, 1021955.0000000002, 1021945.0000000002,\n",
|
|
" 1021935.0000000002, 1021925.0000000002, 1021915.0000000002,\n",
|
|
" 1021905.0000000002],\n",
|
|
" dtype='float64', name='y', length=7680))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3640100d-043c-4a55-8351-38e863d99413' class='xr-section-summary-in' type='checkbox' checked><label for='section-3640100d-043c-4a55-8351-38e863d99413' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
|
|
],
|
|
"text/plain": [
|
|
"<xarray.DataArray (band: 13, y: 7680, x: 8687)>\n",
|
|
"[867310080 values with dtype=float64]\n",
|
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"Coordinates:\n",
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" * band (band) int64 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
|
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" * x (x) float64 5.597e+05 5.597e+05 ... 6.465e+05 6.465e+05\n",
|
|
" * y (y) float64 1.099e+06 1.099e+06 ... 1.022e+06 1.022e+06\n",
|
|
" spatial_ref int64 0\n",
|
|
"Attributes:\n",
|
|
" AREA_OR_POINT: Area\n",
|
|
" TIFFTAG_XRESOLUTION: 1\n",
|
|
" TIFFTAG_YRESOLUTION: 1\n",
|
|
" scale_factor: 1.0\n",
|
|
" add_offset: 0.0"
|
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]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
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],
|
|
"source": [
|
|
"dsvh, dsvv = load_sen1(name_vh, name_vv)\n",
|
|
"dsvv"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "26f1d256-48cf-40a2-8de4-6ec7ea220bca",
|
|
"metadata": {},
|
|
"source": [
|
|
"LOAD SENTINEL 2\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "68e134d5-1b22-4b10-bdcb-d65d178e30d2",
|
|
"metadata": {
|
|
"tags": []
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},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Most common native CRS: EPSG:32648\n",
|
|
"No datasets require offset correction\n",
|
|
"The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n"
|
|
]
|
|
},
|
|
{
|
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"data": {
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"text/html": [
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"data": {
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"<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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"<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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"</symbol>\n",
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"</defs>\n",
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"</svg>\n",
|
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"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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" *\n",
|
|
" */\n",
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"\n",
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":root {\n",
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" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
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" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
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" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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" --xr-background-color: var(--jp-layout-color0, white);\n",
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" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
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" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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"}\n",
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"\n",
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" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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" --xr-border-color: #1F1F1F;\n",
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" --xr-disabled-color: #515151;\n",
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" --xr-background-color: #111111;\n",
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" --xr-background-color-row-even: #111111;\n",
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" --xr-background-color-row-odd: #313131;\n",
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|
"}\n",
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"\n",
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".xr-wrap {\n",
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" min-width: 300px;\n",
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"}\n",
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"\n",
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|
".xr-text-repr-fallback {\n",
|
|
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
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".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",
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|
".xr-header > ul {\n",
|
|
" display: inline;\n",
|
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" margin-top: 0;\n",
|
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" margin-bottom: 0;\n",
|
|
"}\n",
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|
"\n",
|
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".xr-obj-type,\n",
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".xr-array-name {\n",
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|
" margin-left: 2px;\n",
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|
" margin-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-obj-type {\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
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|
"\n",
|
|
".xr-sections {\n",
|
|
" padding-left: 0 !important;\n",
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|
" display: grid;\n",
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" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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|
"}\n",
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"\n",
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".xr-section-item {\n",
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" display: contents;\n",
|
|
"}\n",
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"\n",
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".xr-section-item input {\n",
|
|
" display: none;\n",
|
|
"}\n",
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|
"\n",
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".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",
|
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".xr-section-summary {\n",
|
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" grid-column: 1;\n",
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" color: var(--xr-font-color2);\n",
|
|
" font-weight: 500;\n",
|
|
"}\n",
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|
"\n",
|
|
".xr-section-summary > span {\n",
|
|
" display: inline-block;\n",
|
|
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"}\n",
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|
"\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: '►';\n",
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|
" 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",
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|
"\n",
|
|
".xr-section-summary-in:checked + label:before {\n",
|
|
" content: '▼';\n",
|
|
"}\n",
|
|
"\n",
|
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".xr-section-summary-in:checked + label > span {\n",
|
|
" display: none;\n",
|
|
"}\n",
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"\n",
|
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".xr-section-summary,\n",
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".xr-section-inline-details {\n",
|
|
" padding-top: 4px;\n",
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" padding-bottom: 4px;\n",
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"}\n",
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"\n",
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".xr-section-inline-details {\n",
|
|
" grid-column: 2 / -1;\n",
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|
"}\n",
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"\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",
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"\n",
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".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",
|
|
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|
|
"\n",
|
|
".xr-preview {\n",
|
|
" color: var(--xr-font-color3);\n",
|
|
"}\n",
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|
"\n",
|
|
".xr-array-preview,\n",
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".xr-array-data {\n",
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|
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|
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|
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|
|
"\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",
|
|
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|
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|
|
" 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",
|
|
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|
|
"\n",
|
|
".xr-dim-list:after {\n",
|
|
" content: ')';\n",
|
|
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|
|
"\n",
|
|
".xr-dim-list li:not(:last-child):after {\n",
|
|
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|
|
" 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-index-preview {\n",
|
|
" grid-column: 2 / 5;\n",
|
|
" color: var(--xr-font-color2);\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",
|
|
".xr-index-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",
|
|
".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
|
|
".xr-index-data,\n",
|
|
".xr-attrs {\n",
|
|
" padding-left: 25px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs,\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-data {\n",
|
|
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|
|
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|
|
"\n",
|
|
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|
|
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|
|
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|
|
" 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",
|
|
".xr-no-icon {\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: (time: 151, y: 8874, x: 9902)\n",
|
|
"Coordinates:\n",
|
|
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
|
|
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
|
|
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
|
|
" spatial_ref int32 32648\n",
|
|
"Data variables:\n",
|
|
" blue (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
" green (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
" red (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
" nir (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
" scl (time, y, x) uint8 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
|
|
"Attributes:\n",
|
|
" crs: EPSG:32648\n",
|
|
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><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-a493d1c1-2bcb-45cc-a4d2-70e2ce67e12b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a493d1c1-2bcb-45cc-a4d2-70e2ce67e12b' 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'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bf6d0366-e257-4032-98e7-767d2c80d0ca' class='xr-section-summary-in' type='checkbox' checked><label for='section-bf6d0366-e257-4032-98e7-767d2c80d0ca' 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'>2022-09-02T03:35:23.960000 ... 2...</div><input id='attrs-11925f60-891a-4214-823f-6a752e9104f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11925f60-891a-4214-823f-6a752e9104f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373c6025-dc82-45b9-a9d1-157d35346e69' class='xr-var-data-in' type='checkbox'><label for='data-373c6025-dc82-45b9-a9d1-157d35346e69' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array(['2022-09-02T03:35:23.960000000', '2022-09-04T03:25:18.037000000',\n",
|
|
" '2022-09-07T03:35:13.646000000', '2022-09-09T03:25:27.617000000',\n",
|
|
" '2022-09-12T03:35:22.247000000', '2022-09-14T03:25:18.529000000',\n",
|
|
" '2022-09-17T03:35:13.648000000', '2022-09-19T03:25:24.773000000',\n",
|
|
" '2022-09-22T03:35:18.931000000', '2022-09-24T03:25:17.431000000',\n",
|
|
" '2022-09-27T03:35:12.140000000', '2022-09-29T03:25:23.829000000',\n",
|
|
" '2022-10-02T03:35:19.048000000', '2022-10-07T03:35:09.133000000',\n",
|
|
" '2022-10-09T03:25:22.746000000', '2022-10-12T03:35:17.151000000',\n",
|
|
" '2022-10-14T03:25:15.533000000', '2022-10-17T03:35:11.148000000',\n",
|
|
" '2022-10-19T03:25:18.808000000', '2022-10-22T03:35:13.746000000',\n",
|
|
" '2022-10-24T03:25:15.878000000', '2022-10-27T03:35:10.923000000',\n",
|
|
" '2022-10-29T03:25:20.127000000', '2022-11-01T03:35:15.818000000',\n",
|
|
" '2022-11-03T03:25:14.204000000', '2022-11-06T03:35:10.096000000',\n",
|
|
" '2022-11-11T03:35:15.334000000', '2022-11-13T03:25:16.056000000',\n",
|
|
" '2022-11-16T03:35:11.542000000', '2022-11-18T03:25:18.155000000',\n",
|
|
" '2022-11-21T03:35:12.396000000', '2022-11-23T03:25:16.067000000',\n",
|
|
" '2022-11-26T03:35:11.063000000', '2022-11-28T03:25:16.297000000',\n",
|
|
" '2022-12-01T03:35:12.009000000', '2022-12-03T03:25:14.366000000',\n",
|
|
" '2022-12-06T03:35:10.293000000', '2022-12-08T03:25:17.072000000',\n",
|
|
" '2022-12-11T03:35:12.184000000', '2022-12-16T03:35:10.130000000',\n",
|
|
" '2022-12-18T03:25:15.584000000', '2022-12-21T03:35:09.988000000',\n",
|
|
" '2022-12-23T03:25:15.763000000', '2022-12-26T03:35:11.128000000',\n",
|
|
" '2022-12-28T03:25:15.151000000', '2022-12-31T03:35:10.753000000',\n",
|
|
" '2023-01-02T03:25:15.152000000', '2023-01-05T03:35:09.844000000',\n",
|
|
" '2023-01-07T03:25:15.343000000', '2023-01-10T03:35:10.149000000',\n",
|
|
" '2023-01-12T03:25:12.066000000', '2023-01-15T03:35:08.140000000',\n",
|
|
" '2023-01-17T03:25:12.454000000', '2023-01-20T03:35:06.593000000',\n",
|
|
" '2023-01-22T03:25:14.446000000', '2023-01-25T03:35:09.824000000',\n",
|
|
" '2023-01-27T03:25:13.987000000', '2023-01-30T03:35:09.963000000',\n",
|
|
" '2023-02-01T03:25:13.595000000', '2023-02-04T03:35:09.008000000',\n",
|
|
" '2023-02-06T03:25:15.471000000', '2023-02-09T03:35:10.646000000',\n",
|
|
" '2023-02-11T03:25:15.100000000', '2023-02-14T03:35:10.240000000',\n",
|
|
" '2023-02-16T03:25:28.099000000', '2023-02-24T03:35:12.118000000',\n",
|
|
" '2023-02-26T03:25:14.146000000', '2023-03-01T03:35:09.182000000',\n",
|
|
" '2023-03-03T03:25:17.841000000', '2023-03-06T03:35:14.717000000',\n",
|
|
" '2023-03-08T03:25:12.032000000', '2023-03-11T03:35:07.230000000',\n",
|
|
" '2023-03-13T03:25:20.837000000', '2023-03-16T03:35:15.940000000',\n",
|
|
" '2023-03-18T03:25:14.467000000', '2023-03-21T03:35:10.279000000',\n",
|
|
" '2023-03-23T03:25:18.795000000', '2023-03-26T03:35:14.570000000',\n",
|
|
" '2023-03-28T03:25:14.536000000', '2023-03-31T03:35:10.140000000',\n",
|
|
" '2023-04-02T03:25:19.945000000', '2023-04-05T03:35:14.925000000',\n",
|
|
" '2023-04-07T03:25:16.518000000', '2023-04-10T03:35:12.000000000',\n",
|
|
" '2023-04-15T03:35:12.743000000', '2023-04-20T03:35:10.328000000',\n",
|
|
" '2023-04-22T03:25:18.572000000', '2023-04-25T03:35:13.748000000',\n",
|
|
" '2023-04-27T03:25:16.567000000', '2023-04-30T03:35:12.151000000',\n",
|
|
" '2023-05-02T03:25:18.866000000', '2023-05-05T03:35:14.433000000',\n",
|
|
" '2023-05-07T03:25:16.583000000', '2023-05-10T03:35:11.284000000',\n",
|
|
" '2023-05-12T03:25:19.200000000', '2023-05-15T03:35:15.683000000',\n",
|
|
" '2023-05-17T03:25:17.911000000', '2023-05-20T03:35:14.193000000',\n",
|
|
" '2023-05-22T03:25:36.378000000', '2023-05-25T03:35:16.657000000',\n",
|
|
" '2023-05-27T03:25:20.078000000', '2023-05-30T03:35:15.642000000',\n",
|
|
" '2023-06-01T03:25:20.268000000', '2023-06-04T03:35:16.047000000',\n",
|
|
" '2023-06-06T03:25:20.180000000', '2023-06-09T03:35:15.534000000',\n",
|
|
" '2023-06-11T03:25:21.573000000', '2023-06-14T03:35:16.859000000',\n",
|
|
" '2023-06-16T03:25:20.769000000', '2023-06-19T03:35:15.908000000',\n",
|
|
" '2023-06-21T03:25:20.640000000', '2023-06-24T03:35:15.972000000',\n",
|
|
" '2023-06-26T03:25:19.927000000', '2023-06-29T03:35:15.752000000',\n",
|
|
" '2023-07-01T03:25:21.763000000', '2023-07-04T03:35:17.227000000',\n",
|
|
" '2023-07-06T03:25:21.702000000', '2023-07-09T03:35:17.102000000',\n",
|
|
" '2023-07-11T03:25:21.515000000', '2023-07-14T03:35:16.582000000',\n",
|
|
" '2023-07-16T03:25:21.326000000', '2023-07-19T03:35:16.050000000',\n",
|
|
" '2023-07-21T03:25:22.444000000', '2023-07-24T03:35:17.845000000',\n",
|
|
" '2023-07-26T03:25:21.503000000', '2023-07-29T03:35:17.083000000',\n",
|
|
" '2023-07-31T03:25:36.753000000', '2023-08-03T03:35:16.494000000',\n",
|
|
" '2023-08-05T03:25:21.570000000', '2023-08-08T03:35:16.313000000',\n",
|
|
" '2023-08-10T03:25:22.024000000', '2023-08-13T03:35:17.448000000',\n",
|
|
" '2023-08-15T03:25:22.075000000', '2023-08-18T03:35:17.812000000',\n",
|
|
" '2023-08-20T03:25:21.568000000', '2023-08-23T03:35:16.224000000',\n",
|
|
" '2023-08-25T03:25:22.592000000', '2023-08-28T03:35:17.571000000',\n",
|
|
" '2023-08-30T03:25:21.212000000', '2023-09-02T03:35:16.627000000',\n",
|
|
" '2023-09-04T03:25:21.856000000', '2023-09-07T03:35:17.134000000',\n",
|
|
" '2023-09-09T03:25:20.648000000', '2023-09-12T03:35:15.326000000',\n",
|
|
" '2023-09-14T03:25:35.755000000', '2023-09-17T03:35:15.269000000',\n",
|
|
" '2023-09-19T03:25:24.089000000', '2023-09-22T03:35:15.439000000',\n",
|
|
" '2023-09-24T03:25:18.524000000', '2023-09-27T03:35:14.112000000',\n",
|
|
" '2023-09-29T03:25:19.046000000'], dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-660c3976-0adf-4c08-b108-deac67ef63c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-660c3976-0adf-4c08-b108-deac67ef63c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-196225e4-c0e1-428f-8f0f-a079703cca99' class='xr-var-data-in' type='checkbox'><label for='data-196225e4-c0e1-428f-8f0f-a079703cca99' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-2ddc258d-4c5d-431e-bd6e-3e6150469840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ddc258d-4c5d-431e-bd6e-3e6150469840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde2c981-d440-4da1-b5e5-ef77bc1de791' class='xr-var-data-in' type='checkbox'><label for='data-dde2c981-d440-4da1-b5e5-ef77bc1de791' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32648</div><input id='attrs-8b625ad4-8feb-433e-8878-bb78a4323330' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b625ad4-8feb-433e-8878-bb78a4323330' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db0d5894-52ce-4eb6-887d-46f523916add' class='xr-var-data-in' type='checkbox'><label for='data-db0d5894-52ce-4eb6-887d-46f523916add' 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>spatial_ref :</span></dt><dd>PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32648, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac60caa5-547f-4d65-8544-ae731534ef84' class='xr-section-summary-in' type='checkbox' checked><label for='section-ac60caa5-547f-4d65-8544-ae731534ef84' class='xr-section-summary' >Data variables: <span>(5)</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>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-05b9d27e-9d7e-4e8a-b078-2bde08e8e244' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05b9d27e-9d7e-4e8a-b078-2bde08e8e244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-300b8a23-4ece-4b66-8229-115781c1091f' class='xr-var-data-in' type='checkbox'><label for='data-300b8a23-4ece-4b66-8229-115781c1091f' 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'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 49.43 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (151, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 3775 chunks in 8 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"196\" height=\"173\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"26\" y2=\"16\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"10\" y1=\"24\" x2=\"26\" y2=\"41\" />\n",
|
|
" <line x1=\"10\" y1=\"49\" x2=\"26\" y2=\"65\" />\n",
|
|
" <line x1=\"10\" y1=\"74\" x2=\"26\" y2=\"90\" />\n",
|
|
" <line x1=\"10\" y1=\"99\" x2=\"26\" y2=\"115\" />\n",
|
|
" <line x1=\"10\" y1=\"107\" x2=\"26\" y2=\"123\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Vertical lines -->\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"107\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"108\" />\n",
|
|
" <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"109\" />\n",
|
|
" <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"110\" />\n",
|
|
" <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"110\" />\n",
|
|
" <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"111\" />\n",
|
|
" <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"112\" />\n",
|
|
" <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"113\" />\n",
|
|
" <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"114\" />\n",
|
|
" <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"115\" />\n",
|
|
" <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"116\" />\n",
|
|
" <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"116\" />\n",
|
|
" <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"117\" />\n",
|
|
" <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"118\" />\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-ae1f77f8-8c8a-40db-821f-cee6380bd061' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae1f77f8-8c8a-40db-821f-cee6380bd061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31069dfd-8cd8-42a1-92ca-42abe6643850' class='xr-var-data-in' type='checkbox'><label for='data-31069dfd-8cd8-42a1-92ca-42abe6643850' 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'><table>\n",
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" <tr>\n",
|
|
" <td>\n",
|
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" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
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|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 49.43 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (151, 8874, 9902) </td>\n",
|
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" <td> (1, 2048, 2048) </td>\n",
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" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
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" <td colspan=\"2\"> 3775 chunks in 8 graph layers </td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>red</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-81575054-55f2-440f-8cb4-63fb9459f42d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81575054-55f2-440f-8cb4-63fb9459f42d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4100c1e0-134e-4e8f-95fe-4f39bc4ff179' class='xr-var-data-in' type='checkbox'><label for='data-4100c1e0-134e-4e8f-95fe-4f39bc4ff179' 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'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 49.43 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (151, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 3775 chunks in 8 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
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" </td>\n",
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" </tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nir</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-e7b945b7-b0f3-4545-a395-0e4f1ac8c710' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7b945b7-b0f3-4545-a395-0e4f1ac8c710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59e8a64-0c41-4188-a3e2-228f4892141f' class='xr-var-data-in' type='checkbox'><label for='data-d59e8a64-0c41-4188-a3e2-228f4892141f' 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'><table>\n",
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" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
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|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 49.43 GiB </td>\n",
|
|
" <td> 16.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (151, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 3775 chunks in 8 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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|
" </tr>\n",
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" </tbody>\n",
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" </table>\n",
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" </td>\n",
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"</svg>\n",
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" </tr>\n",
|
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray></div><input id='attrs-8558eafb-e87c-4565-8e4b-f5a7f14f61b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8558eafb-e87c-4565-8e4b-f5a7f14f61b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bacdcb01-f715-4755-90b0-6ffa5b3d5eb8' class='xr-var-data-in' type='checkbox'><label for='data-bacdcb01-f715-4755-90b0-6ffa5b3d5eb8' 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>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>flags_definition :</span></dt><dd>{'qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7], 'values': {'0': 'no data', '1': 'saturated or defective', '2': 'dark area pixels', '3': 'cloud shadows', '4': 'vegetation', '5': 'bare soils', '6': 'water', '7': 'unclassified', '8': 'cloud medium probability', '9': 'cloud high probability', '10': 'thin cirrus', '11': 'snow or ice'}, 'description': 'Sen2Cor Scene Classification'}}</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 12.36 GiB </td>\n",
|
|
" <td> 4.00 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (151, 8874, 9902) </td>\n",
|
|
" <td> (1, 2048, 2048) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 3775 chunks in 1 graph layer </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
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" <td>\n",
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"\n",
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" <!-- Horizontal lines -->\n",
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"\n",
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" <line x1=\"75\" y1=\"16\" x2=\"75\" y2=\"123\" />\n",
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" <line x1=\"100\" y1=\"16\" x2=\"100\" y2=\"123\" />\n",
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" <line x1=\"146\" y1=\"16\" x2=\"146\" y2=\"123\" style=\"stroke-width:2\" />\n",
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"\n",
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" <!-- Colored Rectangle -->\n",
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" <polygon points=\"26.20332950438934,16.20332950438934 146.20332950438933,16.20332950438934 146.20332950438933,123.74524022949538 26.20332950438934,123.74524022949538\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
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" <!-- Text -->\n",
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" <text x=\"166.203330\" y=\"69.974285\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.203330,69.974285)\">8874</text>\n",
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" <text x=\"8.101665\" y=\"135.643575\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.101665,135.643575)\">151</text>\n",
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"</svg>\n",
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" </td>\n",
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" </tr>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fb87126-1d38-477c-a0ba-040fc9fc0764' class='xr-section-summary-in' type='checkbox' ><label for='section-8fb87126-1d38-477c-a0ba-040fc9fc0764' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-021656b2-7a57-47ac-a5c2-97c9b84a10b1' class='xr-index-data-in' type='checkbox'/><label for='index-021656b2-7a57-47ac-a5c2-97c9b84a10b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2022-09-02 03:35:23.960000', '2022-09-04 03:25:18.037000',\n",
|
|
" '2022-09-07 03:35:13.646000', '2022-09-09 03:25:27.617000',\n",
|
|
" '2022-09-12 03:35:22.247000', '2022-09-14 03:25:18.529000',\n",
|
|
" '2022-09-17 03:35:13.648000', '2022-09-19 03:25:24.773000',\n",
|
|
" '2022-09-22 03:35:18.931000', '2022-09-24 03:25:17.431000',\n",
|
|
" ...\n",
|
|
" '2023-09-07 03:35:17.134000', '2023-09-09 03:25:20.648000',\n",
|
|
" '2023-09-12 03:35:15.326000', '2023-09-14 03:25:35.755000',\n",
|
|
" '2023-09-17 03:35:15.269000', '2023-09-19 03:25:24.089000',\n",
|
|
" '2023-09-22 03:35:15.439000', '2023-09-24 03:25:18.524000',\n",
|
|
" '2023-09-27 03:35:14.112000', '2023-09-29 03:25:19.046000'],\n",
|
|
" dtype='datetime64[ns]', name='time', length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-180a596a-926b-4e2f-ac52-534a1e58fbbb' class='xr-index-data-in' type='checkbox'/><label for='index-180a596a-926b-4e2f-ac52-534a1e58fbbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
|
|
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
|
|
" ...\n",
|
|
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
|
|
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
|
|
" dtype='float64', name='y', length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0578e02-d010-431c-b7c1-6fef80dd5908' class='xr-index-data-in' type='checkbox'/><label for='index-d0578e02-d010-431c-b7c1-6fef80dd5908' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
|
|
" 554865.0, 554875.0, 554885.0,\n",
|
|
" ...\n",
|
|
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
|
|
" 653785.0, 653795.0, 653805.0],\n",
|
|
" dtype='float64', name='x', length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f98a136f-c577-4d8e-acb3-889b5634225e' class='xr-section-summary-in' type='checkbox' checked><label for='section-f98a136f-c577-4d8e-acb3-889b5634225e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
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],
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" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
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" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
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" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
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" spatial_ref int32 32648\n",
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" blue (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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" green (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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" red (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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" nir (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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" scl (time, y, x) uint8 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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"Attributes:\n",
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" crs: EPSG:32648\n",
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" grid_mapping: spatial_ref"
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]
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"output_type": "display_data"
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}
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],
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"source": [
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"data = load_data(dc, date_range, longtitude_range, latitude_range)\n",
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"notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
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"display(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "0c1ac39c-7a78-425d-afbc-b04a7988f012",
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"metadata": {
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"text/html": [
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"<style scoped>\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>bits</th>\n",
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" <th>values</th>\n",
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" <th>description</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>qa</th>\n",
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" <td>[0, 1, 2, 3, 4, 5, 6, 7]</td>\n",
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" <td>{'0': 'no data', '1': 'saturated or defective'...</td>\n",
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" <td>Sen2Cor Scene Classification</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" bits \\\n",
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"qa [0, 1, 2, 3, 4, 5, 6, 7] \n",
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"\n",
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" values \\\n",
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"qa {'0': 'no data', '1': 'saturated or defective'... \n",
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"\n",
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" description \n",
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"qa Sen2Cor Scene Classification "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"{'0': 'no data',\n",
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" '1': 'saturated or defective',\n",
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" '2': 'dark area pixels',\n",
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" '3': 'cloud shadows',\n",
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" '4': 'vegetation',\n",
|
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" '5': 'bare soils',\n",
|
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" '6': 'water',\n",
|
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" '7': 'unclassified',\n",
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" '8': 'cloud medium probability',\n",
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" '9': 'cloud high probability',\n",
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" '10': 'thin cirrus',\n",
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" '11': 'snow or ice'}"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 2.06 s, sys: 84.8 ms, total: 2.14 s\n",
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"Wall time: 2.14 s\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d9ad365b4b3d46ca9d0d596113c0a9ce",
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},
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"text/plain": [
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"VBox()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%%time\n",
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"# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n",
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"result = mask_clean(data)\n",
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"progress(result)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cd57a554-a804-4d0f-ac69-136e34d089db",
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"metadata": {},
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"source": [
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"CALCULATING THE MEAN VALUE AND FILL TO NAN POINT"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "973ac9b2-14fd-45e4-934c-b201ac51ec9e",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6878e9bc108249bc9c0152ce6b16d463",
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"version_minor": 0
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"text/plain": [
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"VBox()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n",
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"ndvi = ds1[\"NDVI\"]\n",
|
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"average_ndvi = ndvi.resample(time='1M').mean().persist() ## tính mean cho từng tháng -> time = 12\n",
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"progress(average_ndvi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "5af8393a-e667-4c93-ae32-ca69ee9d9da7",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(13, 7680, 8687)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dsvh.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "5b81db73-d533-483e-bab5-44d2c3eff64c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
|
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"average_ndvi = average_ndvi.compute()\n",
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"average_ndvi = average_ndvi[:, :dsvh.shape[1], :dsvh.shape[2]]"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "504a37be-62cc-4121-b0bf-46639cb82fe6",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 3.5 s, sys: 1.2 s, total: 4.7 s\n",
|
|
"Wall time: 4.69 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"filled_ds = average_ndvi.bfill(dim='time')\n",
|
|
"filled_ds = filled_ds.ffill(dim='time')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2b6caa44-5793-4bac-ab67-d1e75d19aab3",
|
|
"metadata": {},
|
|
"source": [
|
|
"FIND NAN POINT AFTER FILLING AND FILLING AGAIN WITH LINEARREGRESSION ALGORITHM"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "0c47e46d-2c74-4b96-91b8-fcd5e46e7699",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Number of NaNs: 20800\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"nan_mask = filled_ds.isnull()\n",
|
|
"\n",
|
|
"# Print the NaN mask\n",
|
|
"# print(nan_mask)\n",
|
|
"\n",
|
|
"# Count the number of NaNs\n",
|
|
"num_nans = nan_mask.sum()\n",
|
|
"print(f'Number of NaNs: {num_nans.values}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "ca51b1a5-2499-457b-bb99-65a950092270",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import PolynomialFeatures\n",
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
"from sklearn.ensemble import RandomForestRegressor\n",
|
|
"\n",
|
|
"mask = ~np.isnan(filled_ds)\n",
|
|
"X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)\n",
|
|
"y_train = filled_ds.values[mask]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "c78559a3-a503-4dac-a21d-63b414f932a2",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
|
|
],
|
|
"text/plain": [
|
|
"LinearRegression()"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model = LinearRegression()\n",
|
|
"model.fit(X_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "6a987dee-50b3-46d0-8fcd-d8fefe44ecd8",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)\n",
|
|
"filled_ds.values[~mask] = model.predict(X_pred)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "146aa63d-217f-4c36-be36-e84a1ff3bf52",
|
|
"metadata": {},
|
|
"source": [
|
|
"MATCH LABEL TO DATASET"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "62024ea2-5b65-4aad-b1ea-66adb843f136",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 2.93 s, sys: 0 ns, total: 2.93 s\n",
|
|
"Wall time: 2.93 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"\n",
|
|
"# Takes 1 minute to complete.\n",
|
|
"loaded_datasets = {}\n",
|
|
"for idx, point in train.iterrows():\n",
|
|
" key = f\"point_{idx + 1}\"\n",
|
|
" try:\n",
|
|
" ndvi_data = filled_ds.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
|
|
" vh_data = dsvh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
|
|
" vv_data = dsvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
|
|
" loaded_datasets[key] = {\n",
|
|
" \"data\": np.concatenate((ndvi_data, vh_data, vv_data)),\n",
|
|
" \"label\": point.HT_code\n",
|
|
" }\n",
|
|
" except Exception as e:\n",
|
|
" # loaded_datasets[key] = None\n",
|
|
" print(e)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "9d80765f-c25c-465e-8c99-582276477561",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"label_mapping = {\n",
|
|
" \"Lua tom\": \"0\",\n",
|
|
" \"Lua\": \"1\",\n",
|
|
" \"CHN\": \"2\",\n",
|
|
" \"CLN\": \"3\",\n",
|
|
" \"TS\": \"4\",\n",
|
|
" \"Song\": \"5\",\n",
|
|
" \"Dat xay dung\": \"6\",\n",
|
|
" \"Rung\": \"7\"\n",
|
|
"}\n",
|
|
"label_encoder = LabelEncoder()\n",
|
|
"\n",
|
|
"# Fit and transform the labels\n",
|
|
"labels = train.Hientrang.values\n",
|
|
"numeric_labels = label_encoder.fit_transform([label_mapping[label] for label in labels])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "dc42088b-3aae-457f-bed8-206692121a37",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = []\n",
|
|
"x_new = []\n",
|
|
"lb_new = []\n",
|
|
"for k, v in loaded_datasets.items():\n",
|
|
" X.append(v)\n",
|
|
"for i in range(len(X)):\n",
|
|
" if X[i] is not None:\n",
|
|
" x_new.append(X[i][\"data\"])\n",
|
|
" lb_new.append(numeric_labels[i])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6abe9a1b-55a6-48b0-a4ee-a1b2c7f209cf",
|
|
"metadata": {},
|
|
"source": [
|
|
"BUILDING DATASETS "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "e4e234b9-88e0-4f72-bf61-02cd6c6a6cfe",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train, X_temp, y_train, y_temp= train_test_split(x_new, lb_new, test_size=0.4, random_state=42)\n",
|
|
"X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "717baa23-76b6-45e0-8e7b-42d7a79fe773",
|
|
"metadata": {},
|
|
"source": [
|
|
"TRAIN MODEL"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "131df37a-b1c0-4c16-9086-0ba0104fd803",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Best Parameters: {'classifier': RandomForestClassifier(n_jobs=-1, random_state=42), 'classifier__criterion': 'gini', 'classifier__max_depth': 15, 'classifier__n_estimators': 300}\n",
|
|
"Accuracy: 86.0 %\n",
|
|
"CPU times: user 8.96 s, sys: 4.88 s, total: 13.8 s\n",
|
|
"Wall time: 26.8 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"from sklearn.model_selection import GridSearchCV\n",
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"from sklearn.naive_bayes import GaussianNB\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"from sklearn.metrics import accuracy_score\n",
|
|
"\n",
|
|
"# Define the models\n",
|
|
"rf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
|
|
"knn_model = KNeighborsClassifier()\n",
|
|
"nb_model = GaussianNB()\n",
|
|
"svm_model = SVC()\n",
|
|
"\n",
|
|
"# Create a pipeline\n",
|
|
"pipeline = Pipeline([\n",
|
|
" ('scaler', StandardScaler()), # Apply scaling\n",
|
|
" ('classifier', rf_model) # Placeholder, will be set by param_grid\n",
|
|
"])\n",
|
|
"\n",
|
|
"# Define the parameter grid for each classifier\n",
|
|
"param_grid = [\n",
|
|
" # RandomForest\n",
|
|
" {\n",
|
|
" 'classifier': [rf_model],\n",
|
|
" 'classifier__n_estimators': [100, 300, 500, 700],\n",
|
|
" 'classifier__max_depth': [6, 8, 10, 15],\n",
|
|
" 'classifier__criterion': ['gini', 'entropy'],\n",
|
|
" },\n",
|
|
" # KNeighborsClassifier\n",
|
|
" {\n",
|
|
" 'classifier': [knn_model],\n",
|
|
" 'classifier__n_neighbors': [3, 5, 7, 9],\n",
|
|
" 'classifier__weights': ['uniform', 'distance'],\n",
|
|
" 'classifier__metric': ['euclidean', 'manhattan']\n",
|
|
" },\n",
|
|
" # Naive Bayes (GaussianNB doesn't have hyperparameters to tune here)\n",
|
|
" {\n",
|
|
" 'classifier': [nb_model],\n",
|
|
" },\n",
|
|
" # SVM\n",
|
|
" {\n",
|
|
" 'classifier': [svm_model],\n",
|
|
" 'classifier__C': [0.1, 1, 10, 100],\n",
|
|
" 'classifier__kernel': ['linear', 'rbf'],\n",
|
|
" 'classifier__gamma': ['scale', 'auto']\n",
|
|
" }\n",
|
|
"]\n",
|
|
"\n",
|
|
"# Use GridSearchCV to find the best classifier and hyperparameters\n",
|
|
"grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
|
|
"grid_search.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Print out the best parameters and classifier\n",
|
|
"best_params = grid_search.best_params_\n",
|
|
"print(\"Best Parameters:\", best_params)\n",
|
|
"\n",
|
|
"# Make predictions on the validation set\n",
|
|
"y_pred = grid_search.predict(X_val)\n",
|
|
"\n",
|
|
"# Evaluate the results\n",
|
|
"accuracy = accuracy_score(y_val, y_pred)\n",
|
|
"print(f\"Accuracy: {round(accuracy, 2)*100} %\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "bd2d4a3d-061d-4238-8fd9-ab4aaf062a2e",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy for test data 86.0 %\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"## check accuracy score\n",
|
|
"\n",
|
|
"y_pred_test = grid_search.predict(X_test)\n",
|
|
"test_accuracy = accuracy_score(y_test, y_pred_test)\n",
|
|
"print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "c5cd8314-c444-4bd4-a608-41e8a925ac51",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['model_train/model_new2.joblib']"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"dir_save_model = \"model_train\"\n",
|
|
"if not os.path.exists(dir_save_model):\n",
|
|
" os.mkdir(dir_save_model)\n",
|
|
"joblib.dump(grid_search, os.path.join(dir_save_model, \"model_new2.joblib\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "f11593ca-dcb4-4018-8142-2e933812878e",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"client.close()\n",
|
|
"cluster.close()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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|
"mimetype": "text/x-python",
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"name": "python",
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|
"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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