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GWDM_AquiferMapping.ipynb
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gwdm_aquifermapping.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/romer8/2d5a618a73d6be1246cc974874a1e690/gwdm_aquifermapping.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kLC55BdBCaKl"
},
"source": [
"# Install Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "AyApLXOWCSDZ"
},
"outputs": [],
"source": [
"#@markdown ### **Installing packages**\n",
"%%capture\n",
"!pip install geopandas\n",
"!pip install rasterio\n",
"!pip install rioxarray\n",
"!pip install gstools\n",
"!pip install matplotlib\n",
"#!pip install kats\n",
"!pip install pyod\n",
"!pip install netCDF4"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "04I7TAGFCkIP"
},
"outputs": [],
"source": [
"#@markdown ### **Importing packages**\n",
"import calendar\n",
"import copy\n",
"import datetime\n",
"import math\n",
"import os\n",
"import shutil\n",
"import tempfile\n",
"import time\n",
"import urllib\n",
"from pathlib import Path\n",
"from timeit import default_timer as timer\n",
"from urllib import request\n",
"from xml.etree import cElementTree as ET\n",
"\n",
"import geopandas as gpd\n",
"import gstools as gs\n",
"import netCDF4\n",
"import numpy as np\n",
"import pandas as pd\n",
"import requests\n",
"import xarray\n",
"import rioxarray\n",
"from scipy import interpolate\n",
"from shapely import wkt\n",
"from shapely.geometry import mapping\n",
"from google.colab import files\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bhGS6o2bEhG6"
},
"source": [
"# Uploading Aquifer Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "JH0Vj3NcCxFp",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 174,
"referenced_widgets": [
"198bafafb7974c4ca0b485321cc4b163",
"5fc994b17710440f950944a1caf76d8b",
"1f60d36f66684072807c4e48bef37afd",
"75a0b454f6ab4e3da360151e230c6860",
"e2aaa8248a42436a900c3c2b7fe229ff",
"9e9014215dd84c8ea7d1a07133e2bad0",
"504925d856ec48de9644ad9a5abc4a8d",
"2c5cbc706d794847b8099176bd399c8f"
]
},
"outputId": "5086bcfd-1357-461d-f397-67074dd7b35b"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-4f710193-544b-4c24-848e-4a4964bf18d5\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-4f710193-544b-4c24-848e-4a4964bf18d5\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving deadsea2.geojson to deadsea2.geojson\n",
"\n",
"Please select the appropriate headers for your file\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"GridBox(children=(Dropdown(description='Aquifer ID', options=('GW_CODE', 'NAME', 'HECTARES', 'KM2', 'Brakish_W…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "198bafafb7974c4ca0b485321cc4b163"
}
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Uploading Aquifer File**\n",
"upload_aquifers = files.upload()\n",
"aquifers = gpd.GeoDataFrame.from_file(list(upload_aquifers.keys())[0])\n",
"\n",
"aList1 = list(aquifers.columns)\n",
"#aList1.append('NA')\n",
"a_ID = widgets.Dropdown(options=aList1, description = \"Aquifer ID\")\n",
"a_name = widgets.Dropdown(options=aList1, description = \"Aquifer Name\")\n",
"\n",
"a1Items = [a_ID, a_name]\n",
"print(\"\\nPlease select the appropriate headers for your file\")\n",
"widgets.GridBox(a1Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "tADsIEeHkbr-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86,
"referenced_widgets": [
"7bd5b9247d3e4f77a2731a11602b632c",
"3bcb39005c724208b18ab55bf5f8bf9b",
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"356d6a50c91d44269bb4a2395e29946b",
"596efc62fa134dfc957c44ae905b9625"
]
},
"outputId": "4ebfb835-1616-454a-89dc-d8a6ac2e979b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Please select the aquifer you would like to map\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"GridBox(children=(Dropdown(description='Aquifer Name', options=('Dead Sea GW',), value='Dead Sea GW'),), layou…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "7bd5b9247d3e4f77a2731a11602b632c"
}
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Select the Aquifer you would like to Map**\n",
"aList2 = list(aquifers[a_name.value])\n",
"aquifer_name = widgets.Dropdown(options=aList2, description = \"Aquifer Name\")\n",
"\n",
"a2Items = [aquifer_name]\n",
"print(\"\\nPlease select the aquifer you would like to map\")\n",
"widgets.GridBox(a2Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "Y6cTAFF1j8tV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
},
"outputId": "09a45010-240a-4d61-9d6f-bd82992f79db"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" GW_CODE NAME HECTARES KM2 Brakish_Wa Salinity_R \\\n",
"0 DS Dead Sea GW 687351.699 6874 9 to 12 MCM 1000 to 1700 \n",
"\n",
" AquiferID geometry \n",
"0 8 MULTIPOLYGON (((35.96199 31.91493, 35.95569 31... "
],
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GW_CODE</th>\n",
" <th>NAME</th>\n",
" <th>HECTARES</th>\n",
" <th>KM2</th>\n",
" <th>Brakish_Wa</th>\n",
" <th>Salinity_R</th>\n",
" <th>AquiferID</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
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" <th>0</th>\n",
" <td>DS</td>\n",
" <td>Dead Sea GW</td>\n",
" <td>687351.699</td>\n",
" <td>6874</td>\n",
" <td>9 to 12 MCM</td>\n",
" <td>1000 to 1700</td>\n",
" <td>8</td>\n",
" <td>MULTIPOLYGON (((35.96199 31.91493, 35.95569 31...</td>\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
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35.50435356882419 31.200179494690595, 35.50355416026981 31.20024099064039, 35.502487728201594 31.200246681128284, 35.501619854992974 31.20013656520887, 35.500486445835335 31.20008509097769, 35.49921915383697 31.199977371952038, 35.49801958648201 31.19998373468945, 35.4965510387874 31.199704817534712, 35.49568383479347 31.199594669550514, 35.49461871928545 31.199829461486388, 35.493153115839135 31.200009124301747, 35.49248888206353 31.200356240450006, 35.491625824719414 31.200876349418483, 35.490978236908816 31.20128988847718)))\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"#@markdown ### **Filter and Display Aquifer Information**\n",
"aquifer_index = aquifers[aquifers[a_name.value]==aquifer_name.value].index.values\n",
"aquifer = aquifers[aquifers[a_name.value].isin([aquifer_name.value])]\n",
"aquifer_ID = list(aquifer[a_ID.value])\n",
"aquifer"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "LtMFJ3UFDY8T",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 270,
"referenced_widgets": [
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"f05ddbdefe2e48deab720b0c97661849",
"f5e74db38feb4a85a92c5f986ed7306e",
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"28d0c390eabd4d2db8a8e2804f93d7bb",
"c4b15d77ec194c61b89a31d5ca911b42",
"a612b3189ccc40db99cb22f841ff91d9",
"8bb04336b8a844e69d73e18aca6119f0",
"af3ca32f286e45e69ac3610f9911dec7",
"e0e13eb38ab643f1a7c072e1efc58c07"
]
},
"outputId": "eaef92cc-3458-47c0-af25-f047088634d1"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-612d9bb7-7ae7-4c8c-a5ff-c8a1a3d46f6b\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-612d9bb7-7ae7-4c8c-a5ff-c8a1a3d46f6b\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving deadsea_wells.csv to deadsea_wells.csv\n",
"\n",
"Please select the appropriate headers for your file\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"GridBox(children=(Dropdown(description='Well ID', options=('AquiferID', 'Well_ID', 'Well_Name', 'lat_dec', 'lo…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a445b6c0eb3f49ef90876be4dac4678b"
}
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Uploading wells file**\n",
"upload_wells = files.upload()\n",
"wells = pd.read_csv(list(upload_wells.keys())[0])\n",
"\n",
"wList = list(wells.columns)\n",
"wList.append('NA')\n",
"w_well_ID = widgets.Dropdown(options=wList, description = \"Well ID\")\n",
"w_well_name = widgets.Dropdown(options=wList, description = \"Well Name\")\n",
"w_lat = widgets.Dropdown(options=wList, description = \"Latitude\")\n",
"w_long = widgets.Dropdown(options=wList, description = \"Longitude\")\n",
"w_aquifer_ID = widgets.Dropdown(options=wList, description = \"Aquifer ID\")\n",
"\n",
"wItems = [w_well_ID, w_well_name, w_lat, w_long, w_aquifer_ID]\n",
"print(\"\\nPlease select the appropriate headers for your file\")\n",
"widgets.GridBox(wItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "mWepWYSG7ynf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "682be815-cd8c-498a-89e7-64aed06921e7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" AquiferID Well_ID Well_Name lat_dec lon_dec GSE \\\n",
"0 8 9 CA1006 31.265351 35.519902 -342.0 \n",
"1 8 10 CA1010 31.135761 35.533139 -317.5 \n",
"2 8 11 CA1011 31.202619 35.532498 -375.1 \n",
"3 8 12 CA1038 31.046755 35.466592 -376.0 \n",
"4 8 13 CA1095 31.024057 35.486789 -336.5 \n",
"5 8 1 CA3043 31.750003 35.593839 -399.8 \n",
"6 8 14 CC1014 31.696520 35.807080 725.6 \n",
"7 8 15 CC1015 31.707859 35.810405 736.2 \n",
"8 8 16 CD1021 31.021006 36.012778 841.1 \n",
"9 8 17 CD1075 31.776217 35.931920 749.9 \n",
"10 8 18 CD1097 31.558877 35.735745 344.0 \n",
"11 8 19 CD1106 31.289381 36.034699 783.0 \n",
"12 8 2 CD1126 31.357977 36.079998 766.6 \n",
"13 8 20 CD1132 31.362440 36.081650 760.0 \n",
"14 8 21 CD1137 31.586803 36.014427 696.6 \n",
"15 8 22 CD1152 31.211950 35.823230 831.1 \n",
"16 8 23 CD1174 31.123645 36.010241 823.4 \n",
"17 8 24 CD1182 31.009222 36.035934 856.7 \n",
"18 8 25 CD1207 31.165776 35.737651 976.0 \n",
"19 8 26 CD1212 31.632453 36.093550 707.9 \n",
"20 8 3 CD1213 31.478607 35.973060 762.1 \n",
"21 8 4 CD3125 31.534353 35.884396 667.9 \n",
"22 8 27 CD3133 31.563590 35.731270 0.0 \n",
"23 8 28 CD3307 31.372780 36.077180 0.0 \n",
"24 8 5 CD3448 31.082370 36.021645 818.5 \n",
"25 8 29 CD3469 30.996049 35.940721 901.4 \n",
"26 8 30 CD3497 31.546768 35.873368 657.0 \n",
"27 8 6 CD3504 31.551130 35.872681 649.0 \n",
"28 8 31 CD3508 31.144693 35.879363 870.1 \n",
"29 8 32 CD3530 31.468133 36.024398 731.0 \n",
"30 8 33 CD3684 31.268320 36.060640 0.0 \n",
"31 8 34 CD3685 31.232200 36.046700 0.0 \n",
"32 8 35 CD3688 31.526300 36.051000 0.0 \n",
"33 8 36 CF1074 30.833422 36.029070 889.3 \n",
"34 8 37 CF1078 30.610413 35.822486 1032.2 \n",
"35 8 38 CF3020 30.815857 35.754081 1110.0 \n",
"36 8 39 CF3042 30.787877 35.934187 860.1 \n",
"37 8 40 DA1033 31.028241 35.478376 -342.0 \n",
"38 8 7 JDW2 30.644843 35.826603 1011.2 \n",
"39 8 8 Q4 31.201908 36.036972 810.5 \n",
"\n",
" geometry \n",
"0 POINT (35.51990 31.26535) \n",
"1 POINT (35.53314 31.13576) \n",
"2 POINT (35.53250 31.20262) \n",
"3 POINT (35.46659 31.04676) \n",
"4 POINT (35.48679 31.02406) \n",
"5 POINT (35.59384 31.75000) \n",
"6 POINT (35.80708 31.69652) \n",
"7 POINT (35.81041 31.70786) \n",
"8 POINT (36.01278 31.02101) \n",
"9 POINT (35.93192 31.77622) \n",
"10 POINT (35.73575 31.55888) \n",
"11 POINT (36.03470 31.28938) \n",
"12 POINT (36.08000 31.35798) \n",
"13 POINT (36.08165 31.36244) \n",
"14 POINT (36.01443 31.58680) \n",
"15 POINT (35.82323 31.21195) \n",
"16 POINT (36.01024 31.12364) \n",
"17 POINT (36.03593 31.00922) \n",
"18 POINT (35.73765 31.16578) \n",
"19 POINT (36.09355 31.63245) \n",
"20 POINT (35.97306 31.47861) \n",
"21 POINT (35.88440 31.53435) \n",
"22 POINT (35.73127 31.56359) \n",
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"28 POINT (35.87936 31.14469) \n",
"29 POINT (36.02440 31.46813) \n",
"30 POINT (36.06064 31.26832) \n",
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"32 POINT (36.05100 31.52630) \n",
"33 POINT (36.02907 30.83342) \n",
"34 POINT (35.82249 30.61041) \n",
"35 POINT (35.75408 30.81586) \n",
"36 POINT (35.93419 30.78788) \n",
"37 POINT (35.47838 31.02824) \n",
"38 POINT (35.82660 30.64484) \n",
"39 POINT (36.03697 31.20191) "
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" const charts = await google.colab.kernel.invokeFunction(\n",
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" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
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"</div>\n",
"\n",
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" <style>\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
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" background-color: #E2EBFA;\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "wells_gdf",
"summary": "{\n \"name\": \"wells_gdf\",\n \"rows\": 40,\n \"fields\": [\n {\n \"column\": \"AquiferID\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"8\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Well_ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 1,\n \"max\": 40,\n \"num_unique_values\": 40,\n \"samples\": [\n 26\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Well_Name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 40,\n \"samples\": [\n \"CD1212\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lat_dec\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3040835496833587,\n \"min\": 30.6104133966,\n \"max\": 31.776216951,\n \"num_unique_values\": 40,\n \"samples\": [\n 31.6324532772\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lon_dec\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.19561535939745336,\n \"min\": 35.466591631,\n \"max\": 36.0935502282,\n \"num_unique_values\": 40,\n \"samples\": [\n 36.0935502282\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"GSE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 492.1913278117331,\n \"min\": -399.8,\n \"max\": 1110.0,\n \"num_unique_values\": 35,\n \"samples\": [\n 649.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geometry\",\n \"properties\": {\n \"dtype\": \"geometry\",\n \"num_unique_values\": 40,\n \"samples\": [\n \"POINT (36.0935502282 31.6324532772)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"#@markdown ### **Formatting wells dataframe**\n",
"#To avoid type mismatch, convert the aquifer_id found from the aquifer file and the aquiferid column\n",
"#in the wells dataframe to both be strings\n",
"aquifer_ID = [str(item) for item in aquifer_ID]\n",
"if (w_aquifer_ID.value !=\"NA\"):\n",
" wells[w_aquifer_ID.value] = wells[w_aquifer_ID.value].astype(str)\n",
"\n",
"wells_gdf = gpd.GeoDataFrame(wells, geometry=gpd.points_from_xy(wells[w_long.value], wells[w_lat.value]))\n",
"if (w_aquifer_ID.value !=\"NA\"):\n",
" wells_gdf = wells_gdf[wells_gdf[w_aquifer_ID.value].isin(aquifer_ID)]\n",
"wells_gdf"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "MGDfk069Dxo7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448
},
"outputId": "ef3ca9d8-5bd7-4356-9027-e23df937e3ed"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: >"
]
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
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\n"
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Plotting aquifer and wells**\n",
"fig, ax = plt.subplots()\n",
"aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax)\n",
"wells_gdf.plot(ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "MFTl7W_ID-QV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238,
"referenced_widgets": [
"34766de1f7c148999db1b50af5a2af8a",
"b966c4218b074f13b700f2d86d10dd81",
"ce2fb45e43ae41d2b65e528639f2e1b0",
"0cc05fcf2d964874a845f05958b06736",
"32830f160a9246fbab3f1ea62d9d938c",
"bfe536b3b2014e1bb22dea059dd813da",
"209c59d57abe44e8acb3d03cdf070cb8",
"d6da5ee79b8e41138a9adc6363ce4565",
"fb8566e46756433d8383e37130507853",
"b627bbce551443c984189eb6c65879d3",
"0a7c0bc21af8449f86f15e74af58e4d3",
"631157fcae94453fb09ca8d9b27058e4",
"afbf7e74b00a49d5b404676ed6bf778d",
"47d5867bc1b74561b5d60183988abe81"
]
},
"outputId": "57867c66-4bf4-426d-d750-61f023c3d033"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-7da3439e-45b0-4967-8b89-d240a499ab5b\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-7da3439e-45b0-4967-8b89-d240a499ab5b\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
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" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
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" file: file.name,\n",
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" };\n",
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"\n",
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" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving deadsea_ts.csv to deadsea_ts.csv\n",
"\n",
"Please select the appropriate headers for your file\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"GridBox(children=(Dropdown(description='Well ID', options=('AquiferID', 'WellName', 'WellID', 'DateString', 'W…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "34766de1f7c148999db1b50af5a2af8a"
}
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Uploading timeseries**\n",
"upload_timeseries = files.upload()\n",
"measurements = pd.read_csv(list(upload_timeseries.keys())[0])\n",
"\n",
"tList = list(measurements.columns)\n",
"tList.append('NA')\n",
"ts_well_ID = widgets.Dropdown(options=tList, description = \"Well ID\")\n",
"ts_date = widgets.Dropdown(options=tList, description = \"Date\")\n",
"ts_measurement = widgets.Dropdown(options=tList, description = \"Measurement\")\n",
"ts_aquifer_ID = widgets.Dropdown(options=tList, description = \"Aquifer ID\")\n",
"\n",
"tItems = [ts_well_ID, ts_date, ts_measurement, ts_aquifer_ID]\n",
"print(\"\\nPlease select the appropriate headers for your file\")\n",
"widgets.GridBox(tItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "UvCf3wWq9EfI",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "f4f162ff-12a0-4242-c978-f00c147880bd"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" AquiferID WellName WellID DateString WaterTableElev\n",
"0 8 MAZRA_A NO 1 9 1996-04-25 -370.45\n",
"1 8 MAZRA_A NO 1 9 1996-05-27 -370.67\n",
"2 8 MAZRA_A NO 1 9 1996-06-24 -370.89\n",
"3 8 MAZRA_A NO 1 9 1996-07-21 -370.96\n",
"4 8 MAZRA_A NO 1 9 1996-08-25 -371.01\n",
"... ... ... ... ... ...\n",
"5863 8 Jurf Deep Well 2 7 2021-10-25 488.63\n",
"5864 8 Jurf Deep Well 2 7 2022-09-26 488.55\n",
"5865 8 Qtraneh EX4 8 2020-02-23 386.77\n",
"5866 8 Qtraneh EX4 8 2021-12-06 386.52\n",
"5867 8 Qtraneh EX4 8 2022-09-28 386.45\n",
"\n",
"[5868 rows x 5 columns]"
],
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" <td>Jurf Deep Well 2</td>\n",
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" </tr>\n",
" <tr>\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "measurements",
"summary": "{\n \"name\": \"measurements\",\n \"rows\": 5868,\n \"fields\": [\n {\n \"column\": \"AquiferID\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"8\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WellName\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 33,\n \"samples\": [\n \"Jurf Deep Well 2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WellID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10,\n \"min\": 1,\n \"max\": 40,\n \"num_unique_values\": 33,\n \"samples\": [\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"DateString\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1972-07-01 00:00:00\",\n \"max\": \"2023-04-27 00:00:00\",\n \"num_unique_values\": 2687,\n \"samples\": [\n \"2006-03-20 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WaterTableElev\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 514.7507232867664,\n \"min\": -1738.85,\n \"max\": 1009.92,\n \"num_unique_values\": 3901,\n \"samples\": [\n 530.99\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"#@markdown ## **Formatting Timeseries**\n",
"#@markdown #### Run this cell to coerce datatypes, convert dates, and filter for the specific aquifer\n",
"\n",
"#To avoid type mismatch datatypes\n",
"if (ts_aquifer_ID.value !=\"NA\"):\n",
" measurements[ts_aquifer_ID.value] = measurements[ts_aquifer_ID.value].astype(str)\n",
"\n",
"#converting date format\n",
"measurements[ts_date.value] = pd.to_datetime(measurements[ts_date.value], infer_datetime_format=True)\n",
"\n",
"#filtering by aquifer if aquiferIDs are included in the dataset\n",
"if (ts_aquifer_ID.value !=\"NA\"):\n",
" measurements = measurements[measurements[ts_aquifer_ID.value].isin(aquifer_ID)]\n",
"measurements"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "RUoMfX_7rUiT",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "50ef21db-9409-4340-80c4-f680de666442"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"33 unique well IDs\n",
"33 wells with measurements\n"
]
}
],
"source": [
"#@markdown ### **Dropping measurements that don't have corresponding wells**\n",
"no_uniquewells = len(measurements[ts_well_ID.value].unique())\n",
"measurements = measurements[measurements[ts_well_ID.value].isin(wells_gdf[w_well_ID.value])].reset_index(drop=True)\n",
"no_uniquewellsref = len(measurements[ts_well_ID.value].unique())\n",
"print(no_uniquewells, ' unique well IDs')\n",
"print(no_uniquewellsref, ' wells with measurements')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "5oSloAWjEOb1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ad13e739-7a0c-484b-cf71-20a3a15633c4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[35.398332886716226, 30.563510619412916, 36.12764297914039, 31.918859774480342]"
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"#@markdown ### **Defining aquifer bounds**\n",
"bbox = aquifer.bounds.values[0].tolist()\n",
"bbox"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hvdh4c0JGPEc"
},
"source": [
"# Accessing Data from Server"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"id": "JPbXu32uGfMs"
},
"outputs": [],
"source": [
"#@markdown ### **Defining functions for this section**\n",
"from io import StringIO\n",
"\n",
"def get_pdsi_df(aquifer_gdf):\n",
" data_dir = \"/content\"\n",
" nc_file = os.path.join(data_dir, \"pdsi.nc4\")\n",
" pdsi_ds = xarray.open_dataset(nc_file, decode_times=False)\n",
" units, _, reference_date = pdsi_ds.time.attrs[\"units\"].split(\"since\")\n",
" pdsi_ds[\"time\"] = pd.date_range(\n",
" start=reference_date, periods=pdsi_ds.sizes[\"time\"], freq=\"MS\"\n",
" )\n",
" ds = pdsi_ds[\"pdsi_filled\"].to_dataset()\n",
" ds[\"pdsi_filled\"] = ds[\"pdsi_filled\"].rio.write_crs(\"epsg:4326\")\n",
" ds.rio.set_spatial_dims(x_dim=\"lon\", y_dim=\"lat\", inplace=True)\n",
" # aquifer_geom = wkt.loads(aquifer_obj[0])\n",
" # aquifer_gdf = gpd.GeoDataFrame(\n",
" # {\"name\": [\"random\"], \"geometry\": [aquifer_geom]}, crs=\"EPSG:4326\"\n",
" # )\n",
" clipped_ds = ds.rio.clip(\n",
" aquifer_gdf.geometry.apply(mapping),\n",
" aquifer_gdf.crs,\n",
" drop=True,\n",
" all_touched=True,\n",
" ).drop(\"spatial_ref\")\n",
" filled_df = (\n",
" clipped_ds.to_dataframe()\n",
" .reset_index()\n",
" .groupby(\"time\")[\"pdsi_filled\"]\n",
" .mean()\n",
" .reset_index()\n",
" .set_index(\"time\")\n",
" )\n",
" return filled_df\n",
"\n",
"# outdated function sarva wanted replaced\n",
"# def get_time_bounds(url):\n",
"# # This function returns the first and last available time\n",
"# # from a url of a getcapabilities page located on a Thredds Server\n",
"# f = urllib.request.urlopen(url)\n",
"# tree = ET.parse(f)\n",
"# root = tree.getroot()\n",
"# # These lines of code find the time dimension information for the netcdf on the Thredds server\n",
"# dim = root.findall(\".//{http://www.opengis.net/wms}Dimension\")\n",
"# dim = dim[0].text\n",
"# times = dim.split(\",\")\n",
"# times.pop(0)\n",
"# timemin = times[0]\n",
"# timemax = times[-1]\n",
"# # timemin and timemax are the first and last available times on the specified url\n",
"# return timemin, timemax\n",
"#\n",
"# # sarva's new function\n",
"# def get_time_bounds(url):\n",
"# # This function returns the first and last available time\n",
"# # from a url of a getcapabilities page located on a Thredds Server\n",
"# f = urllib.request.urlopen(url)\n",
"# tree = ET.parse(f)\n",
"# root = tree.getroot()\n",
"# # These lines of code find the time dimension information for the netcdf on the Thredds server\n",
"# dim = root.findall(\".//{http://www.opengis.net/wms}Dimension\")\n",
"# dim = dim[0].text\n",
"# times = dim.split(\",\")\n",
"# timemin = re.sub(r\"[\\n\\t\\s]*\", \"\", times[0])\n",
"# timemax = re.sub(r\"[\\n\\t\\s]*\", \"\", times[-1])\n",
"# # timemin and timemax are the first and last available times on the specified url\n",
"# return timemin, timemax\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def get_thredds_value(server, layer, bbox):\n",
" # This function returns a pandas dataframe of the timeseries values of a specific layer\n",
" # at a specific latitude and longitude from a file on a Thredds server\n",
" # server: the url of the netcdf desired netcdf file on the Thredds server to read\n",
" # layer: the name of the layer to extract timeseries information from for the netcdf file\n",
" # lat: the latitude of the point at which to extract the timeseries\n",
" # lon: the longitude of the point at which to extract the timeseries\n",
" # returns df: a pandas dataframe of the timeseries at lat and lon for the layer in the server netcdf file\n",
" # calls the getTimeBounds function to get the first and last available times for the netcdf file on the server\n",
" time_min, time_max = get_time_bounds(\n",
" server + \"?service=WMS&version=1.3.0&request=GetCapabilities\"\n",
" )\n",
" print(time_min)\n",
" print(time_max)\n",
" # These lines properly format a url request for the timeseries of a speific layer from a netcdf on\n",
" # a Thredds server\n",
" server = f\"{server}?service=WMS&version=1.3.0&request=GetFeatureInfo&CRS=CRS:84&QUERY_LAYERS={layer}\"\n",
" server = f\"{server}&X=0&Y=0&I=0&J=0&BBOX={bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}&LAYER={layer}\"\n",
" server = f\"{server}&WIDTH=1&Height=1&INFO_FORMAT=text/xml&STYLES=raster/default\"\n",
" server = f\"{server}&TIME={time_min}/{time_max}\"\n",
" print(server)\n",
" f = request.urlopen(server)\n",
" tree = ET.parse(f)\n",
" root = tree.getroot()\n",
" print(root)\n",
" features = root.findall(\".//FeatureInfo\")\n",
" print(features)\n",
" times = []\n",
" values = []\n",
" for child in features:\n",
" print(child[0].text)\n",
" time = datetime.datetime.strptime(child[1].text, \"%Y-%m-%dT%H:%M:%S.%fZ\")\n",
" times.append(time)\n",
" values.append(child[2].text)\n",
" print(features)\n",
" df = pd.DataFrame(index=times, columns=[layer], data=values)\n",
" df[layer] = df[layer].replace(\"none\", np.nan).astype(float)\n",
" return df\n",
"\n",
"# gio's new get_time_bounds\n",
"# the difference is that this function retrieves the closest to the latest time:\n",
"# before the max time was: 2023-12-01T00:00:00.000Z/2024-02-01T00:00:00.000Z/P31D,\n",
"# but this is not correct, so I changed to retrieved: 2023-12-01T00:00:00.000Z\n",
"# you can uncoment the next line to get the latest: 2024-02-01T00:00:00.000Z\n",
"def get_time_bounds(url):\n",
" # This function returns the first and last available time\n",
" # from a url of a getcapabilities page located on a Thredds Server\n",
" f = urllib.request.urlopen(url)\n",
" tree = ET.parse(f)\n",
" root = tree.getroot()\n",
" # These lines of code find the time dimension information for the netcdf on the Thredds server\n",
" dim = root.findall(\".//{http://www.opengis.net/wms}Dimension\")\n",
" dim = dim[0].text\n",
" times = dim.split(\",\")\n",
" timemin = re.sub(r\"[\\n\\t\\s]*\", \"\", times[0])\n",
" timemax = re.sub(r\"[\\n\\t\\s]*\", \"\", times[-1])\n",
" timemax=timemax.split(\"/\")[0] ### ------> this is the minor change\n",
" # timemax=timemax.split(\"/\")[1] ### ------> uncoment this if you want the latest\n",
"\n",
" # timemin and timemax are the first and last available times on the specified url\n",
" return timemin, timemax\n",
"\n",
"# gio's get_thredds_value\n",
"# the problem with the latest function was that it was retrieving only the latest time series,\n",
"# with the new function we use the GetTimeseries ibnstead of the GetFeatureInfo function,\n",
"# so we can get a the whole time series\n",
"def get_thredds_value(server, layer, bbox):\n",
" # This function returns a pandas dataframe of the timeseries values of a specific layer\n",
" # at a specific latitude and longitude from a file on a Thredds server\n",
" # server: the url of the netcdf desired netcdf file on the Thredds server to read\n",
" # layer: the name of the layer to extract timeseries information from for the netcdf file\n",
" # lat: the latitude of the point at which to extract the timeseries\n",
" # lon: the longitude of the point at which to extract the timeseries\n",
" # returns df: a pandas dataframe of the timeseries at lat and lon for the layer in the server netcdf file\n",
" # calls the getTimeBounds function to get the first and last available times for the netcdf file on the server\n",
" time_min, time_max = get_time_bounds(\n",
" server + \"?service=WMS&version=1.3.0&request=GetCapabilities\"\n",
" )\n",
" print(time_min)\n",
" print(time_max)\n",
" # These lines properly format a url request for the timeseries of a speific layer from a netcdf on\n",
" # a Thredds server\n",
" server = f\"{server}?service=WMS&version=1.3.0&request=GetTimeseries&CRS=CRS:84&QUERY_LAYERS={layer}\"\n",
" server = f\"{server}&X=0&Y=0&I=0&J=0&BBOX={bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}&LAYER={layer}\"\n",
" server = f\"{server}&WIDTH=1&Height=1&INFO_FORMAT=text/csv&STYLES=raster/default\"\n",
" server = f\"{server}&TIME={time_min}/{time_max}\"\n",
" print(server)\n",
" df = pd.read_csv(server,skiprows=2)\n",
" df = df.rename(columns={'Model-Calculated Monthly Mean Soil Moisture (mm)': f'{layer}'})\n",
" df[layer] = df[layer].replace(\"none\", np.nan).astype(float)\n",
"\n",
"\n",
" df = df.rename(columns={'Time (UTC)': 'time'})\n",
" df['time'] = df['time'].apply(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%dT%H:%M:%S.%fZ\"))\n",
" df = df.set_index('time')\n",
"\n",
" return df\n",
"\n",
"def sat_resample(gldas_df):\n",
" # resamples the data from both datasets to a monthly value,\n",
" # uses the mean of all measurements in a month\n",
" # first resample to daily values, then take the start of the month\n",
" print(gldas_df)\n",
" gldas_df.index = pd.to_datetime(gldas_df.index) # Gio's addition Convert the index to a DatetimeIndex\n",
"\n",
" gldas_df = gldas_df.resample(\"D\").mean()\n",
" # D is daily, mean averages any values in a given day, if no data in that day, gives NaN\n",
"\n",
" gldas_df.interpolate(method=\"pchip\", inplace=True, limit_area=\"inside\")\n",
"\n",
" gldas_df = gldas_df.resample(\"MS\").first()\n",
" # MS means \"month start\" or to the start of the month, this is the interpolated value\n",
"\n",
" return gldas_df\n",
"\n",
"def sat_rolling_window(gldas_df):\n",
" years = [1, 3, 5, 10]\n",
" names = list(gldas_df.columns)\n",
" new_names = copy.deepcopy(\n",
" names\n",
" ) # names is a list of the varibiles in the data frame, need to unlink for loop\n",
" # This loop adds the yearly, 3-year, 5-year, and 10-year rolling averages of each variable to the dataframe\n",
" # rolling averages uses the preceding time to compute the last value,\n",
" # e.g., using the preceding 5 years of data to get todays\n",
" for name in names:\n",
" for year in years:\n",
" new = name + \"_yr\" + str(year).zfill(2)\n",
" gldas_df[new] = gldas_df[name].rolling(year * 12).mean()\n",
" new_names.append(new)\n",
" return gldas_df, new_names"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "q0wgNQGlGVXL"
},
"outputs": [],
"source": [
"#@markdown ### **Downloading PDSI data from server**\n",
"pdsi_response = requests.get(\"https://github.com/BYU-Hydroinformatics/gwdm-data/blob/main/pdsi.nc4?raw=true\")\n",
"with open(\"pdsi.nc4\", \"wb\") as binary_file:\n",
" # Write bytes to file\n",
" binary_file.write(pdsi_response.content)\n",
"pdsi_df = get_pdsi_df(aquifer)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "Bf5EpzZLG791",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "499c3699-f491-4548-fa00-7691731b0577"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1948-01-01T00:00:00.000Z\n",
"2023-12-01T00:00:00.000Z\n",
"https://apps.geoglows.org/thredds/wms/geoglows_data/soilw.mon.mean.v2.nc?service=WMS&version=1.3.0&request=GetTimeseries&CRS=CRS:84&QUERY_LAYERS=soilw&X=0&Y=0&I=0&J=0&BBOX=35.398332886716226,30.563510619412916,36.12764297914039,31.918859774480342&LAYER=soilw&WIDTH=1&Height=1&INFO_FORMAT=text/csv&STYLES=raster/default&TIME=1948-01-01T00:00:00.000Z/2023-12-01T00:00:00.000Z\n",
" soilw\n",
"time \n",
"1948-01-01 220.360140\n",
"1948-02-01 224.065520\n",
"1948-03-01 241.779020\n",
"1948-04-01 236.698290\n",
"1948-05-01 194.676800\n",
"... ...\n",
"2023-08-01 42.785736\n",
"2023-09-01 32.274204\n",
"2023-10-01 26.108322\n",
"2023-11-01 28.802877\n",
"2023-12-01 38.053270\n",
"\n",
"[912 rows x 1 columns]\n"
]
}
],
"source": [
"#@markdown ### **Downloading soil moisture data from server**\n",
"# SERVER2 = \"https://tethyswa.servirglobal.net/thredds/wms/testAll/psl/soilw.mon.mean.nc\"\n",
"SERVER2 = \"https://apps.geoglows.org/thredds/wms/geoglows_data/soilw.mon.mean.v2.nc\"\n",
"# SERVER2 = \"https://www.psl.noaa.gov/thredds/wms/Datasets/cpcsoil/soilw.mon.mean.nc\"\n",
"LAYER2 = \"soilw\" # name of data column to be returned\n",
"soilw_df = get_thredds_value(SERVER2, LAYER2, bbox)\n",
"print(soilw_df)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "6ncaaUXSHyW_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9c1e71f0-9cae-45d9-9e87-2e9633d145bf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" pdsi_filled soilw\n",
"time \n",
"1948-01-01 -2.235825 220.360140\n",
"1948-02-01 -2.349896 224.065520\n",
"1948-03-01 -1.048602 241.779020\n",
"1948-04-01 -1.489158 236.698290\n",
"1948-05-01 -1.657278 194.676800\n",
"... ... ...\n",
"2023-08-01 NaN 42.785736\n",
"2023-09-01 NaN 32.274204\n",
"2023-10-01 NaN 26.108322\n",
"2023-11-01 NaN 28.802877\n",
"2023-12-01 NaN 38.053270\n",
"\n",
"[912 rows x 2 columns]\n"
]
}
],
"source": [
"#@markdown ### **Resampling the data grid size and adding rolling average**\n",
"gldas_df = pd.concat([pdsi_df, soilw_df], join=\"outer\", axis=1)\n",
"gldas_df = sat_resample(gldas_df)\n",
"gldas_df, names = sat_rolling_window(gldas_df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oQDvbJCQIaoB"
},
"source": [
"# Temporal Interpolation"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "mQ5d6JG8I00d"
},
"outputs": [],
"source": [
"#@markdown ### **Setting parameters for temporal interpolation**\n",
"min_samples = 5 #@param {type:\"integer\"}\n",
"gap_size = \"3650\" #@param {type:\"string\"}\n",
"pad = 365 #@param {type:\"integer\"}\n",
"spacing = \"1MS\""
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"cellView": "form",
"id": "fQE3F7EuI77v"
},
"outputs": [],
"source": [
"#@markdown ### **Defining functions for this section**\n",
"\n",
"# def smooth(y, box_size):\n",
"# \"\"\"moving window function using convolution\n",
"# y: 1D vector of values\n",
"# box_size: size of the moving window, should be an odd integer, will set if not\n",
"# this function pads the data so that the edges are equal to the\n",
"# orginal value\n",
"# \"\"\"\n",
"# if int(box_size) % 2 == 0: # make sure box_size is odd integer\n",
"# box_size = int(box_size) + 1 # if even, add one\n",
"# else:\n",
"# box_size = int(box_size) # if not even (e.g., float), make integer\n",
"\n",
"# box = np.ones(box_size) / box_size # convolution kernal for moving window\n",
"# y_pad = np.pad(\n",
"# y, (box_size // 2, box_size - 1 - box_size // 2), mode=\"edge\"\n",
"# ) # padd for edge effects\n",
"# y_smooth = np.convolve(\n",
"# y_pad, box, mode=\"valid\"\n",
"# ) # convolve, 'valid' key work trims pad\n",
"# return y_smooth\n",
"\n",
"# def plot_anomalies(df, x='date', y='amount'):\n",
"\n",
"# # categories will be having values from 0 to n\n",
"# # for each values in 0 to n it is mapped in colormap\n",
"# categories = df['Predictions'].to_numpy()\n",
"# colormap = np.array(['g', 'r'])\n",
"\n",
"# f = plt.figure(figsize=(12, 4))\n",
"# f = plt.scatter(df[x], df[y], c=colormap[categories])\n",
"# f = plt.xlabel(x)\n",
"# f = plt.ylabel(y)\n",
"# # f = plt.xticks(rotation=90)\n",
"# plt.show()\n",
"\n",
"# def extract_query_objects(region_id, aquifer_id, variable):\n",
"# session = get_session_obj()\n",
"# aquifer_obj = (\n",
"# session.query(gf2.ST_AsText(Aquifer.geometry), Aquifer.aquifer_name)\n",
"# .filter(Aquifer.region_id == region_id, Aquifer.id == aquifer_id)\n",
"# .first()\n",
"# )\n",
"# bbox = wkt.loads(aquifer_obj[0]).bounds\n",
"# wells_query = session.query(Well).filter(Well.aquifer_id == aquifer_id)\n",
"# wells_query_df = pd.read_sql(wells_query.statement, session.bind)\n",
"# well_ids = [int(well_id) for well_id in wells_query_df.id.values]\n",
"# # well_dict = {well.id: well.gse for well in wells_query_df.itertuples()}\n",
"# m_query = session.query(Measurement).filter(\n",
"# Measurement.well_id.in_(well_ids), Measurement.variable_id == variable\n",
"# )\n",
"# measurements_df = pd.read_sql(m_query.statement, session.bind)\n",
"# # measurements_df['gse'] = measurements_df['well_id'].map(well_dict)\n",
"# measurements_df[\"date\"] = pd.to_datetime(\n",
"# measurements_df.ts_time, infer_datetime_format=True\n",
"# )\n",
"\n",
"# session.close()\n",
"\n",
"# return bbox, wells_query_df, measurements_df, aquifer_obj\n",
"\n",
"def extract_well_data(name, well_df, min_samples=0, ts_col=\"ts_value\", date_col=\"date\"):\n",
" if len(well_df) >= min_samples:\n",
" # if (well_df['date'].min() < start_date) and (well_df['date'].max() > end_date):\n",
" # elevation = well_df['gse'].unique()[0]\n",
" df = pd.DataFrame(\n",
" index=well_df[date_col].values,\n",
" data=well_df[ts_col].values,\n",
" columns=[name],\n",
" )\n",
" df = df[np.logical_not(df.index.duplicated())]\n",
" return df\n",
"\n",
"def interp_well(wells_df, gap_size, pad, spacing):\n",
" well_interp_df = pd.DataFrame()\n",
" # create a time index to interpolate over - cover entire range\n",
" interp_index: pd.DatetimeIndex = pd.date_range(\n",
" start=min(wells_df.index), freq=spacing, end=max(wells_df.index)\n",
" )\n",
" # loop over each well, interpolate data using pchip\n",
" for well in wells_df:\n",
" temp_df = wells_df[well].dropna() # available data for a well\n",
"\n",
" x_index = temp_df.index.astype(\"int\") # dates for available data\n",
"\n",
" x_diff = temp_df.index.to_series().diff() # data gap sizes\n",
"\n",
" fit2 = interpolate.pchip(x_index, temp_df) # pchip fit to data\n",
"\n",
" ynew = fit2(interp_index.astype(\"int\")) # interpolated data on full range\n",
"\n",
" interp_df = pd.DataFrame(ynew, index=interp_index, columns=[well])\n",
"\n",
" # replace data in gaps of > 1 year with nans\n",
"\n",
" gaps = np.where(x_diff > gap_size) # list of indexes where gaps are large\n",
"\n",
" for g in gaps[0]:\n",
" start = x_diff.index[g - 1] + datetime.timedelta(days=pad)\n",
"\n",
" end = x_diff.index[g] - datetime.timedelta(days=pad)\n",
"\n",
" interp_df[start:end] = np.nan\n",
"\n",
" beg_meas_date = x_diff.index[0] # date of 1st measured point\n",
"\n",
" end_meas_date = temp_df.index[-1] # date of last measured point\n",
"\n",
" mask1 = (\n",
" interp_df.index < beg_meas_date\n",
" ) # locations of data before 1st measured point\n",
"\n",
" interp_df[mask1] = np.nan # blank out data before 1st measured point\n",
"\n",
" mask2 = (\n",
" interp_df.index >= end_meas_date\n",
" ) # locations of data after the last measured point\n",
"\n",
" interp_df[mask2] = np.nan # blank data from last measured point\n",
"\n",
" # add the interp_df data to the full data frame\n",
"\n",
" well_interp_df = pd.concat(\n",
" [well_interp_df, interp_df], join=\"outer\", axis=1, sort=False\n",
" )\n",
" return well_interp_df\n",
"\n",
"def norm_training_data(in_df, ref_df):\n",
" norm_in_df = (in_df - ref_df.min().values) / (\n",
" ref_df.max().values - ref_df.min().values\n",
" ) # use values as df sometimes goofs\n",
" return norm_in_df\n",
"\n",
"def input_to_hidden(x, Win, b):\n",
" # setup matrixes\n",
" a = np.dot(x, Win) + b\n",
" a = np.maximum(a, 0, a) # relu\n",
" return a\n",
"\n",
"def predict(in_values, W_in, b, W_out):\n",
" x = input_to_hidden(in_values, W_in, b)\n",
" y = np.dot(x, W_out)\n",
" return y\n",
"\n",
"def impute_data(comb_df, well_names, names):\n",
" # for out test set we will impute everything\n",
" imputed_df = pd.DataFrame(index=comb_df.index)\n",
"\n",
" for well in well_names: # list of the wells in the aquifer\n",
" train_nona_df = comb_df.dropna(\n",
" subset=[well]\n",
" ) # drop any rows with na in well (measured) data\n",
" labels_df = train_nona_df[\n",
" well\n",
" ] # measured data used as \"labels\" or truth in training\n",
" tx_df = train_nona_df[\n",
" names\n",
" ] # data we will predict with only over the test period\n",
" all_tx_df = comb_df[names] # data over the full period, will use for imputation\n",
"\n",
" tx = tx_df.values # convert to an array\n",
" x1 = np.column_stack(np.ones(tx.shape[0])).T # bias vector of 1's\n",
" tx = np.hstack((tx, x1)) # training matrix\n",
" ty = labels_df.values\n",
" input_length = tx.shape[1]\n",
" hidden_units = 500\n",
" lamb_value = 100\n",
" W_in = np.random.normal(size=[input_length, hidden_units])\n",
" b = np.random.normal(size=[hidden_units])\n",
"\n",
" # now do the matrix multiplication\n",
" X = input_to_hidden(\n",
" tx, W_in, b\n",
" ) # setup matrix for multiplication, it is a function\n",
" I = np.identity(X.shape[1])\n",
" I[X.shape[1] - 1, X.shape[1] - 1] = 0\n",
" I[X.shape[1] - 2, X.shape[1] - 2] = 0\n",
" W_out = np.linalg.lstsq(X.T.dot(X) + lamb_value * I, X.T.dot(ty), rcond=-1)[0]\n",
" all_tx_values = all_tx_df.values\n",
" a1 = np.column_stack(np.ones(all_tx_values.shape[0])).T\n",
" all_tx_values = np.hstack((all_tx_values, a1))\n",
" predict_values = predict(all_tx_values, W_in, b, W_out) # it is a function\n",
" #pre_name = f\"{well}_imputed\"\n",
" pre_name = f\"{well}\"\n",
" imputed_df[pre_name] = pd.Series(predict_values, index=imputed_df.index)\n",
"\n",
" return imputed_df\n",
"\n",
"def renorm_data(in_df, ref_df):\n",
" assert in_df.shape[1] == ref_df.shape[1], \"must have same # of columns\"\n",
" renorm_df = (\n",
" in_df * (ref_df.max().values - ref_df.min().values)\n",
" ) + ref_df.min().values\n",
" return renorm_df"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "MAc1hoUEI3wM"
},
"outputs": [],
"source": [
"#@markdown ### **Collect well data that qualifies for use in interpolation**\n",
"wells_df = pd.concat([\n",
" extract_well_data(name, group, min_samples, ts_measurement.value, ts_date.value)\n",
" for name, group in measurements.groupby(ts_well_ID.value)],\n",
" axis=1, sort=False,)\n",
"wells_qual = wells_gdf[wells_gdf[w_well_ID.value].isin(wells_df.columns.values)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "2i764sqEJI1F",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6bd78f16-4005-4e52-8059-c8f5bdfffd80"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"33 wells with measurements\n",
"4 wells dropped\n",
"29 qualified for use in interpolation\n"
]
}
],
"source": [
"#@markdown ### **Cleaning up wells dataframe**\n",
"wells_df.drop_duplicates(inplace=True)\n",
"wells_df[wells_df == 0] = np.nan\n",
"# wells_df.to_csv(\"wells_one.csv\")\n",
"wells_df.dropna(thresh=min_samples, axis=1, inplace=True)\n",
"no_qualifiedwells = len(wells_df.columns.values)\n",
"no_droppedwells = no_uniquewells - no_qualifiedwells\n",
"print(no_uniquewellsref, ' wells with measurements')\n",
"print(no_droppedwells, ' wells dropped')\n",
"print(no_qualifiedwells, ' qualified for use in interpolation')"
]
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(1,2)\n",
"aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax[0])\n",
"aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax[1])\n",
"wells_gdf.plot(ax=ax[0], markersize = 30, marker = 'o')\n",
"wells_qual.plot(ax=ax[1], markersize = 30, marker = 'o')"
],
"metadata": {
"id": "LmXcDkzBhgJe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448
},
"outputId": "c4a8f84a-16b4-47b3-a343-0a022c512d15"
},
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: >"
]
},
"metadata": {},
"execution_count": 44
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "KRiecuiHJObc"
},
"outputs": [],
"source": [
"#@markdown ### **Running simple pchip temporal interpolation**\n",
"well_interp_df = interp_well(wells_df, gap_size, pad, spacing)\n",
"well_interp_df.dropna(thresh=min_samples, axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"id": "mOXbYEhnKHM2"
},
"outputs": [],
"source": [
"#@markdown ### **Running machine learning algorithm to fill gaps**\n",
"# combine the data from the wells and the satellite observations to a single dataframe (combined_df)\n",
"# this will have a row for every measurement (on the start of the month) a column for each well,\n",
"# and a column for pdsi and soilw and their rolling averages, and potentially offsets\n",
"combined_df = pd.concat(\n",
" [well_interp_df, gldas_df], join=\"outer\", axis=1, sort=False\n",
")\n",
"combined_df.dropna(\n",
" subset=names, inplace=True\n",
") # drop rows where there are no satellite data\n",
"combined_df.dropna(how=\"all\", axis=1, inplace=True)\n",
"\n",
"norm_df = norm_training_data(combined_df, combined_df)\n",
"norm_df.dropna(how=\"all\", axis=1, inplace=True)\n",
"well_names = [col for col in well_interp_df.columns if col in norm_df.columns]\n",
"imputed_norm_df = impute_data(norm_df, well_names, names)\n",
"ref_df = combined_df[well_names]\n",
"imputed_df = renorm_data(imputed_norm_df, ref_df)\n",
"\n",
"imputed_well_names = imputed_df.columns # create a list of well names\n",
"loc_well_names = [(strg.replace(\"_imputed\", \"\")) for strg in imputed_well_names] # strip off \"_imputed\""
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"id": "NcVDS0wWDmrb"
},
"outputs": [],
"source": [
"#@markdown ### **Merging measurement, pchip, and machine learning dataframes**\n",
"#formating column names to match so that the dataframes can be combined\n",
"well_interp_columns = []\n",
"for i in range(len(well_interp_df.columns)) : well_interp_columns.append(str(well_interp_df.columns[i]))\n",
"well_interp_df.columns = well_interp_columns\n",
"\n",
"imputed_columns = []\n",
"for i in range(len(imputed_df.columns)) : imputed_columns.append(str(imputed_df.columns[i]))\n",
"imputed_df.columns = imputed_columns\n",
"\n",
"#replacing the machine learning values with pchip values when available\n",
"replaced_df = well_interp_df.combine_first(imputed_df)\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"id": "5Rei8iiYcSVf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86,
"referenced_widgets": [
"cef2cf3472ea4f918a0e98122c2a1228",
"aeb7ae478ec5490d94167ed4fe56918a",
"b5b99105c4cb467e98b758bd4b86abed",
"77730c1050b64351a1aee0b9f4b4b6b8",
"0ae7b554e564400ebd9e9a76348b96ea"
]
},
"outputId": "cd31054f-eb00-488e-f49f-fa7195a3c20f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Select the well index you wish to plot\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"GridBox(children=(Dropdown(description='Well Index', options=('1', '2', '9', '10', '11', '12', '13', '14', '15…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "cef2cf3472ea4f918a0e98122c2a1228"
}
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Run Cell and Select the Well you Wish to Plot**\n",
"pList = imputed_df.columns\n",
"p_index = widgets.Dropdown(options=pList, description = \"Well Index\")\n",
"\n",
"pItems = [p_index]\n",
"print(\"\\nSelect the well index you wish to plot\")\n",
"widgets.GridBox(pItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"id": "11LdQpfzWhH5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 828
},
"outputId": "7fd60c63-2cb0-4b46-ae5c-4801a33b1754"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7cdc5fa56800>]"
]
},
"metadata": {},
"execution_count": 49
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"#@markdown ### **Plots measurments, pchip, ml timeseries to show complete temporal interpolation**\n",
"well_no = imputed_df.columns.get_loc(p_index.value)\n",
"\n",
"#formatting dates for plotting\n",
"imputed_date = pd.to_datetime(imputed_df.index.values)\n",
"interp_date = pd.to_datetime(well_interp_df.index.values)\n",
"wells_date = pd.to_datetime(wells_df.index.values)\n",
"replaced_date = pd.to_datetime(replaced_df.index.values)\n",
"\n",
"#ignoring NANs for plotting\n",
"well_interp_df.dropna()\n",
"wells_df.dropna()\n",
"\n",
"#plotting the three dataframes: measurements, pchip, and machine learning\n",
"fig, ax = plt.subplots(2, 1, figsize=(25,10))\n",
"#ax.set_xlim([datetime.date(2001, 1, 26), datetime.date(2021, 2, 1)])\n",
"ax[0].plot(imputed_date, imputed_df[imputed_df.columns[well_no]]) #machine learning\n",
"ax[0].plot(interp_date, well_interp_df[well_interp_df.columns[well_no]], color = 'red') #pchip\n",
"ax[0].plot(wells_date, wells_df[wells_df.columns[well_no]], marker = \"o\", linestyle = 'none', color = 'green') #measurements\n",
"ax[1].plot(replaced_date, replaced_df[replaced_df.columns[well_no]], color = 'purple')"
]
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"wells_df.dropna()\n",
"fig, ax = plt.subplots(1, 1, figsize=(25,5))\n",
"ax.plot(wells_date, wells_df, \"o\") #measurements\n",
"ax.plot(interp_date, well_interp_df) #pchip"
],
"metadata": {
"id": "W6RS71etVkKO"
},
"execution_count": 50,
"outputs": []
},
{
"cell_type": "code",
"source": [
"fig"
],
"metadata": {
"id": "2DkkusTYQHUe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 433
},
"outputId": "0bc3eeda-1093-4b4c-c5a0-47ad138534a7"
},
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Figure size 2500x500 with 1 Axes>"
],
"image/png": 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gvx/kbYwfzJ/bvpmsc5/X4vHR6KTa2nvV3r5Dzc17ptUYwVppfLxVY6MdmfW0jlwnrcamvWpoGJYxVul0RBMTrRofz7wmxluUSsWD/RaPj6u5pV8tLbvV2DigSKRyT8rS5abTcSUmGzWZaJTnReQ6nhw3LcdJy3Uz27h0XKV1s1ZKjUX00I+XBdtkJmKe1TFveUKmOVVTo4jpNiSZ63xm1cFwkb6SuViv6Vys38cyFH5/ztYxk8vz3r7FWt39bFnepcdnxYZDVkqORjS+q14yJd8/Fd9nZ8q+z+dn8+9zf0z++8taSb6RtdnvK2tkc9Vl6XYt2T5BfpW+E0K2ZX4dbdV0oUI6qtU27/QuEX1f79G4GitOq71anGbLuLBJ1ZZQ86UvU/KuynxVJ01jnaaz+nNRQc4gy9JyVCuXlSna/lOlDXtfui+mXYaQSrN0N1bdxrb0bfiHv/LhkS9FPqRdWLLccPF4a/LnpbUfy3OnWhlyQfr8sIqC95Lk2NywVaPG1aQRNWlEUaXkyJeRL1e+HHkykjy5SsuVL1dpReQpopSiSiqmpOLy5VSob6of2HaqL7OSyVN99sLzq9zqtWwLTlGesPS5udxEixw/XnFe30nIiw/PuFm8m26QHx2f4dy1yP4unLMTQCM32SLHqys4CiVrrPzse0eSTP4z5ztJednGZqXHdP7qQf53WFhjFZWsWX5+G3yOHPmKKC1XXvaVVkTpolrAyNf2pKPkZJOOtW1Kt24rWFZhYxkpMrBcGyeb9fJIXE7ak2NNftm5053sf0f5r/3cqY9R/iVlPq+5ea4diGrSGr2xJaUFrs002rHSxsmI7k5EdHIsrbPiKck4MkZyrJExjkw2x3zeRk7+1EsmN81myunIBOVyJFlZjcnoKjupDyquFhk51tGKEU9xW/xZyLFWcuJW3Re/UI5j9MdHvyXt+mEmbeG5TnAA5LdDqZ7N56tx14ly6qwWffxFmdvlW6vHv/Mt7VjxnfzHN/ffZlam48G36fwF/6l/Pevr0w5Y7/nhLRp/IJYtbybjka67tePEq8oKGlTJPe/VSfet1kTJfJk0mUQNxydDA++3PHWLLv79xZK16t4bV33C1UTcU39HUtZIV5x5xYwD79bz9MAnXq9dr9hUVv7cR6Xzv0/Ssrf9u/b+eEtoPgvesrIoEP2H//yA0m03heYZGcwE3p+5+wZtGfpkaLqFP1uu2O1PFS/MGEW6u7Xs1ltkXFfW87T5714sd/egJKPBtmVKxFoUTw6rbfBxWVn5nW1qu+IiPTp8SU3Liiw6WXWnvS+7uPKjr3R9S1nP06aPvUZ7z9kcuryOG4/TSV/7L241PwsIuk8DQXcAAPbdwMCfde99bw7e9+5cpsceWzOPJXpuMMZUfFWb5qVSmhwdUXGrhfxw0UXFolM9KzfmKd6aDH7sW2vKgrvWOkonorJyshf5DsYI2NQyQX5PufX3s8/3RGXGeKqvH1Z9w7AibipoDJFpWJC9o0L2fzod0+Bgj5LJyoEax0kpEkmGTg8Ti42rvn5YkWgiEzQvaKhgZTQ+1qbR0Q6lUrX1wChdv1xDi1zDh9xnLTEck58qbahhpjxccpfdnIivWFO6JJaUv2oTi02orm5M8fhYttGJlYwt+J+9eGQK64R8A4l8Q4lK41Swr/INLILLgzbTGMYq2yjGOtl6wxTNN5usjHwvIs+LyFoTXBjLraPJHlPF28AWpMsca5FIUtHopCLRhJzsuMJ5HMeX62aONcfxyhr/5OpHa51sA55MPZmpL13VjVnVj2fydeVLxlfaRpTyY/KsK8fJBAycbGMhR5m7ifi5GtgULCu4DGtyuy9gsnV84YXSikKCpY6sHONlPhPGk2sy/yXJz9b/vrLrKVe+deRZR1ZOMN3KKYgt26IAlrE2uEia2cQ2uHDqhFxuKA2eGWPlZC9eG/lyTO5idWa7+NZRShGl/ag8FV8wGhxt0DP97cULsNmL2bnAjrX54LjJXvI1ZuqgS6h9Oe4rbJPS724pW+bsBXxrJetn3xe1Fgj+W2Mk4+TXK/s+P5xbZyezfQq3S9Fy8tsseF+UZ/Y4Ldh2puRcI78qtuhf6LlKbn2rzBMsozBdtfOfEKFBnSl36TT3uQl9M408Zjhftct8IXna0O8sU/Rvep5D50wHXAu0fTdXDT+mxTiyjiM5s/17Yh4uZc/qIit/D4Q21ah66b60zgxZRmk9XCF5aB1acflTbZBq+7tK/V24rNLvhZCsalXLd8S0VcxyPx2f+y2kk1/ObzvP1qQb18t33a7W9HAw/oGWY/XXhhN07OADetHAn8NzMkZeQ3MNy6z9u6diXVd1vhoyNWUDtSn9LjFWaWPkRa06mjvlxmKKxGNy4lG58bjcWFQmHpUTi8qJR2Wy/yVp5I6tsm40+Knly9fg4j/JOgn5mZ/YQdOO3CvtuWrZeZocY2Szvwv8bAsLmz23s35K9Sf2yDhOZpwU3Enxj8/+Uc5YWh3DMUX8/CfGc33tbU7Jb4zojMPOyFyvsgrml/LDhXlmR0iySu/dq6HUg5nHDQXz5n5vZn/k+BE1DK+UvErbMnuNLOqobnl75rqY72lP7y1SVEWHTFAuKyklLVy0Vnt3/UG+k5KMzZ/+5l5Wkh+VeTwuz4nI2JQi6XHZzGoqfuwqmeYmpYYHNbnlUSVjjZqsWyDPdXMrKGM91U3uUTQ1ruSRruSkK9YE1krGujI7YvIdV/GGjsz5e8Exk9+mknGN3K56+dZX2qaV9tKZ/37mlUpNKmkn5FnptPZBvaxzqGSBUmSiTSd1XKnWM15coUSYDoLu00DQHQCAfdfb+ys9+NBFBe+fp8cePSP7Lnc6m1Ha6zkzrvS9r2g0oUg0oWgkUTQciSYywZSCYccU9PjNnnAfd+y/yRjpgc0XFudd9Caz3OOP+5ba2k4pKUPlH1mVxheOqzWAPhO+7+nbF5yn0b27y6a5seOU+5mRe+V/cGTe17VPqucF/dmgk18UgMoMZ8Y9e2enjLE67Iy+zA82a+RnfwxlWvUb2ew2N8GPvuyPpaB7YrYcuUGp4EdE9kdWLhArv6DXts0EeGwu6JVtGJALhvlONp9cmfOrbZxM4MZxskEb42d6KXqOfBvJNCzIbRdjZbJBVJO9fXzmfzoIwmULrUyvRiPfupnbznsReX5EnheVn66Tn6qTn45L2Tsu5HuG57LI9ChXUKbMy2bX3ZpcENGXYzwZx5PjprI9rVMyjp8N/EWyy3Yzw35xoDez+Y2sH5H1IxpJZn60u5Gk3EgqOz0XNHWDhhYyvqzxZI0vx01JJp2Nq+R7YxRG+0z2fXKiRWODizQ21CPfq9y7phpjfMWjSRnjyTi+fN8okayXtfmgmusmFYtNKBqfUCw+pkgkIeN4kvGVStZrcqJFE+Nt8rK3dayV66YUyW7j/HFmggYoubslPKeCBgAAAACwn92SXK6Fw0/p1LH7Chru5RpnZn+7u1Ellh4730UFDkom8ohefMZfK05rf+YtWv22y/dziQ4+tcaRI/uxTAAA4CAWj3cVve/peUI9PU/MWv7RaIdSqQHV1nI885z2JUteIkl6anurEom+kHkzaRcvfrFKn+l+IHr24QcrBtwlKdJwloyJVp3fT0k77qx9eU//vva0z1XGSWUaIOSC69ZIcoIA+gHD+JL8IDhf2mBC1sj3YpJffoqfnsHi0ialWj5vEUktskpHJpSKjigVHZVk5VhXjnUV8WKZhg+5smYbXESTrYomW2RKeq02ycpzx2UdT266Xo4tPqYLnsAnI6k++/JNWl5kXJ47ETQgsMbLNijINOhw0/WKpJsUSTWWLbcSm2u8ks3Pc1LynJSsScs36ewyfPmymf8mc5NKPze+4L1jHdWlm1SXapQTNCowBX/L+bLyjSffTchzJ+S7yWCZspJrozJFDW1KZaZ5Ji3P8YIGJ9bkG55YWfm599kGIL4pzs8zaaWdtDwnrdzdCzLbJ3snAxkZ68jIkZWfKbPjyTe+PJOWbzwZ68iRI8c6mWHrFgw7MnJlgttD5nrWZ4Zli29KmRlns+8Lt6SCcVa+ktFRJaJj8p1Utue1CaYba+TIUdSPKupH5Vq3aPvk9n9me/jyldmfwSv7PtfnO7MORp7xMtvKeMEyHJt/FZY511CprGdISI91M8O7ChSWwc1udzd7DObWpXQ9bfa49bONg/yCW6gWbueyMhaOt1WmVRif2z+F+ynHN77S2eM4V75qctve2My+yQ3n5s2tU/BZmImZ7I4ptknwPnvsOwWfm9xLUlDuwuPVM17xPjS27Hj1g3pJZdumcHsVHtPGmqLtVXjMmIIGZsF+LNifVd+XrGtY2rLjI7vMSsPGFvfWrmXfTitNyD6f7jFUa/qgMeMUeVT6bFXaN5kZjcq2eFC35N8X1rr5eUzR/io39YeiWh1Rmkf13MKnFq9TzaXJtw+ttqwKZQ7fHhXmD3tnS0cV9x/MDxUfi8XHki3/W6Ghc+a8pizHCnmULH8GdZ5fcI5R2y+4fZdfUvF6Fk9TQaPg7FhTmC7/t3I+BdOz3+NFvSsrzFe2TYNnl6jkEJvyRvbKNH/NNoYuyLjs0dSmYBaVf7pym8BUGFf4iSgtj6l03BYsYMrv6NJ6XAXnaGHjwz5pJZ/1qp9flW+Domk11BtVz2Wqrna1c/5KZZk6TfUlVahWZkE63SZrHUUiwzLBbyErP92ktBw9dFSfnugMf458XbpeL9l5RPDesVaOLzlWyrW/z/Uuzu2OzLlH5n3uf6mKx1yNx2NYmvznuvqcwbIqFcFm1s34mdvpO1Zyg/U1wX9T8H+mp6XlC5+lfPZ5QWaKJKbiYE5tdzKocWVN2cBBZXFsMnTaxJw+IgSlCLoDAIBZ0db2AsXjPVWC2zOVCYovX/4pbd78IYX+oilIL0nPX/4vQRD9+csv1QObL6gwb3naA93o4EDoND/1pKRcz1yj4Ba6BS8jI7fOU7wtFVxElXUU3CbaGiVH4vJSmdtAulEpUp+5xbq1TjCPDebN/c/9IMpdcMj9ynWy17PyF3SD21jbgrJZaarnqO+zbKCydDnWr95QYcps3aTc6Lic6ISM4xVcvMpeLLe5bZe7hb8rZZ/Znhl2CoarnJ5bR8reASAYVbVgvlJOQl72NtL5oLfy77NcP6qIH5Nr88uP2Oltl2gqrvpUW8VpvnxNRkfkG09NkxFF00bR1JhiqScVSU/IGleeE5EXqVMy2qxUtEm+U69IelIRb1jGT2duiWwyjzzIPfog83JljSNjXDm2RdFUcYtj46cUTw4rmhyVUUJSQtKe/HYwrnwnKs+Jynei8t3MsHVyAe1MQFlWckOelTnfHC8h34mq8NZ0jpfMbBvnuVG3zSnry/FTcnxPjp9ULDWqaHJEseSIYqlhxZIjcvyU0pF6pSP1mW2Zm9W48ty4PDeuVLRRyWiTUtFm+W5UvnEl48j4KbleSo6fVP72qEEOQV6523kb68mxvoz1Mi8//6iC/ILDP93lt0ctnTd8Wu6CZe4mGY5v5djMuLRrlXaLL2YWVucmmzZ3YdQpuDCYK1Mwb74irBBUqBjOCfKpvOb54IeCOsDN1gtu8F65cXKD+kEq6GGVe3qrVfY7Mnv79cxTTbPDuVe20MEzXgvGl6QLvv+MyueX8p/NogfB5/7njhlbNt4Upq04b8EyCt7lmqTYorHlafPrUppDcdryM6eCshQ8m1nBlsp935ZOy+UUNv1A5VQ8p7JF4538tNwt+AvHl85vpnHOU7Tvi48ZkwsLlJVP01sGAEzF5p6AboP6p1zB2LIHXavye+qq54z/aJ7UkGv1pj0xLfZcHfbML9Td/3tds/K16l3Uon/6s6dXbQ8PaCajcT14/Dn7scTPLZkbUiclm5K1niRPsp6kdPZ/gZo+X5WGQ6abamlKx2cbw5SVoXLa/DlKpbSFPx5KXmXnmJnzIVtYF1VMX9xYuFw+rSnMO7hckh9vZOW4mSbhvu/L+oW/K3K/APxsnZi5a6FMOnuWmCln5m4PuQeM+dlHcWUeQ2aUlmO9zCPJbFpGfmapBeeXmSVlH/3oZK735X7npH3JN0ZWUXX2hF+/8Rq6Qqdh9hF0BwAAs8IYt0pwe8a5SsoExbu61ssc5+rRxz6nRKK3II2jwpPqeLwnSJ/T1bVexx93ddm8ldIe6Jra2kOnpcZurC2TUSnWPKzFZ/Qp1pTvA50cjejZO7s1sac4aNm6dFiHv2SnovVT/XiZHutXvsZic1GeXIA8COgrH+TPDgc9bYtuN58NdsvP/rdFMQXrG1k/Kuu7wW3Yre8W3f5d1spkI0r5Z2Vbyfoybi6AlHm2tnGLfwCHrddU03J8z0hys0H6gv8FDR8KG0lkAvGZ7eBEknLchEwkKeOkp/2IUus7SgzXa8efF2vk6XZZWbUsGVX3KbsVa6jeX976buZW++nMbd6Nk5Jx03Jjo4rUDQePD2i7zlXDX2rpZV4xPDTlPIXBeMnI9SannU9xXhH5jpsN8Lnys0E96+SDern/wbRgfD6tsb7iiUHFEkOKpsdkbK63af5He3F+xXlb48g3kexyXVkZxZLDiieH5dh0tryZaY6fCgIxqWijEvE2pSP1+TI6BeviROTnluFEsuuQ3z/WuEpH6pSO1Mlz4gWXUjOBy+CChO/JsZ6sceQ5sUxQ2ollGjO4sWxwOS3XT8v4KTk2LcfPv4xNZy5GFD5bOvhfYXwQdCrcY1KutvCcqEaaj9BQy9FKxlvlu3H5riQ1KBlvm8ERUeVYcaJKO1FJDbOaLw5C1b4AKlRUMzmTKg3Jz7Za8j2Qw+fPSWHHjZn5tja+FzT8cayXqX+Lxvn5RkHZ6Y7NDweNV1TaEKiwEUm+WUoRW9hvrfg7JTy/wmn7np8p+u4ozq/4+dkF3y1l3zdzkV+2xEbKNRItbAxkCwIctiCIkRuuOK4gMJJpKKKS8dktVJh38D6XTkEZwo+6kPFVDtLpP2M7LH1BA6jstsutQ3B0VFif4m1TaXsUNLoqysspGJ8Pu5Qur7BBVnEahQxX3r8V85qPQHXRc4cP0rre5n/vmqAeKT7HVMlnunicLZk3N66gnrIl+QXj8vmWLzs3rmDZJT/0wu+RUBpYnSp96Tz5T2qmgbaRmx5XNG3UNJFU+3BKTjpzbLRk34dJxFJyvGTl5YUVJ7cdpvhhO/XxWOP6TnNypqGjLRwRUqrC78XcqEqlNso8pDwTQM2vdqXvktI5K5Wj8nxTl2OqtNlAdU1lKHtTPEeuviuoB8vHZX6XF48r+F6c5Tqx+loemFom9iiue4tHWsmdbJNJrJifQh2ieKa7eKY7AACzqb//pikD45FIm4ykVHqw6rh4fFFZUNxaT4ODf1Ui0a94vEutras1NHRv8L6t7QWhvdZL562W9kBV7Znu02asmnrGFWlIKz0e0Whvg0Jvx2msmnrG1LR4XJKVl3CVmogqPeZKJtMbPj1eaTitaL2n1GSkJG1Eo331auqeUKQhVSFN2HzTWcZM8qpWtqnKXDpvukq+6ZDyVZt/psO5/F1F66baDiHHQdGxUpxvtDml9uXDitZ7FQ+dskNpQjIV4vehjTBKxqcmHA1tbdHI9kbJSM2Hj6n16JGi5acmHBkjReoq/0S2U1+3mbJs6QlXO/7SpeGnmmrPaAotR46q5wW7FGucooHDNMufn7HGfKZo8VBL45GailOST3IsoqGtTWpdOlpxG8xkudZKXrJZvheX9SPyvZi8ZLPSyRalk81KJ1rkJVvke1G50XG5kQkZN38x0BhfTmRSjpuQGx2TGxtRJDYix800bJHxZf2o/HRM1o9lrwsWbrzshSNrsneSzd31wpW1br5hTdHjLKbaubk8p5nez9y6UmnJJDOjbcTIulYmZeRMSmbSBF/XxmbKHcSQXMlGJBvND8tRybW8kLLk8ioobe79eH+dGjony9bEFnW5N/m8jZWjzKMjjDwZ48lkh2W8zDj5mfG5HjHKzJ/Zztm8jB/0QJG1mYZXkqRcTxRbMC27wU22sYkpuPBtM42zihqkSDJ+5obKQawq98Hycw2npOyzNySZ/LbOXUQsChRl/gd3iAkytbnkwbJt4WRly1y4I4IdUnqT3ErBwOz47BNYjBeTdZLZ8uV675t8+VU6LAV3aMneIrgoXW5fSAX5VShS2PvpmkG9GQR5TaZXUtC4r3BcLqCSfaRIrrGeKTq+svsi18BPBcdMboOb4iVLyt65QfntXLTDTbZusUGembEFH+Lsckzus2E8WcfPNCzM74qiNo0Vt9MU3wvzZjrlCku7v9ftQCnHvqilrM+l9QlTdr/22eUmm1U/sEIT7Y/Ii41OURZV3J6ZGLFRx9ZXqn5oWcVA6kTrE9p71G/zXzG5NIVfWyU9Vm32+0oyath9ksYW3i/ZTKMNW1ZxlNZhxQHjikFDq4K6r3i8KdvmxemKGl4GDa8VLNOY/Dy5sEfrk2vVMrIqn2NiRKltd2jAdbRt0f9Sh2t0VNxRPFvYibbHtGfpr0vWq4pckefgmM+dbvTft0BjfQ1q7B5X18l7inZlrgxW0p6Hz9FRy9fokY196nClI2NGsaJ9Y4uHjSPjZBrjOm5UctzMNZLstvCtlZMdfmtLq7a7EX1zeEgrUyk9kcj8/vr/FrTpj/G4PjI2qlcnim9xPelb7Ur76oo4ijvZHeXGZWNNcuNNMk71PqF7ZTX60kUaPrxB3/vJ/Vrbm9Qik9SKmK+4U/mHwaRv9dikp11p6eVvXynr7dSffvV/tPiMPpVutqLPVul5bHZTtT3yT3pmx0rtSlm97K3HqOuoTPxox5+/p6fNz8oLkJ3vvseiurUloo+e+lG9cNHpFdLZ4BjNnErkv8uHfvsXTT5eGHi3Gm97WLuW/2dZtZRrK9J790I1jb5GKxvai+bLpMnkHT8qpZaXry5alqzVrm1b9cf/79r89qlQ9RlJL3r9m7Xg8CNKypx9Y/PHl7U2n4e1Gtgxqq13/17xo28tzztb/sQTa5Tqn9Cy5pMrbKtMosdHNmnZ2jVqX3SYBnvH9Mi9d6pt6R+D499k6yU/+39o2xnqOfxIDWzbqPjSZzO/B0z+Do65uz4mnlqskdGjZZxW5c6ncwfGkpUdaums19hgUtv+tlvWmOzT/Yp/81kjWX9CDfU7FekcUv5c18k3EpBRek+7Jia7ZI0UNUk1x7rkG5Np/C6T7Qmf2T+jqb1q7GxVQ3OzcncYyfwuyXQAGd0zqNTIdsXb98pd/rTMKTsKtlnm39AfXqCeE9+nM157Zvl2xbTUGkcm6C6C7gAAzLZaAuOSygLglcY914Li+8Njf7lTv7rii/NdDBysgltBm+Jx1aK8FYPymYB+46JxdR4/UBQAT004GnisRamR2BQNGkobIdTSKCCbRlJTz7hajhxR+/OLGwUkR13tebhdiaFYDQ05cmUYzzY6kUZ3NGh0Z2N5OWZDsC6p0EYNcxX0rqT2fTXdRiFV9m3JNphJg5SWI0bL9vt8mq19Vig5GtHA4y1qXzZcdNeSuSjDdJdVa57P3tmtoa0tal1afveVSg1uShvTzMV2rbUB0FTjp9pmpfN5Y/Uaf7JbDUfvlNuY2Ic1COdNRJXceoRGt7cr3j2gplXb5cTDe6TlpMfqNHH3iRp+qlXxrgHVNUkTo5mLfo1HDCh21Ha59fl8vImo0rs7FFm4t2i8PxnVyMNLlOrvqDjfc1F6rE7Ddx2jqBNX/ambFKmw7+biON0fec/HcqZrOuWa7md4rhwo5dgXtZS1NE3h57+u0QR1SG3DUrTeV2rCneZ8+7acnU+PadGSRtU3zc7y3USLUn5S6fiQ/PG4nD2HayI9LmOsmhZNKB0flFuXVKw5XVY/hm3zoH7e1iYjqc5t1IQ3FgzH3QYlvAnZxU+o7bSHK36/hOXtjzVo7O4TNLytVfVH9Kn1tC1yGydqOg7SY/VKbTtc0aO2V6wX02N1Sm1bUjZ9Xz7Tlc4TcucaI1sX6RXPJuTGW2Q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},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cZlgqfszLNZI"
},
"source": [
"# Spatial Interpolation"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"id": "lFIun_lQlTdv"
},
"outputs": [],
"source": [
"#@markdown ### **Setting Parameters for Spatial Interpolation**\n",
"raster_extent = \"aquifer\" #@param [\"aquifer\", \"wells\"]\n",
"raster_interval = 12 #@param {type:\"slider\", min:1, max:24, step:1}\n",
"output_file = \"results.nc\" #@param {type:\"string\"}\n",
"aquifer_name = \"DRtest\" #@param {type:\"string\"}\n",
"units = \"Cubic Meter\" #@param [\"acre-ft\", \"cubic-ft\", \"Cubic Meter\"]\n",
"start_date = 2001\n",
"end_date = 2019\n",
"#let user pick the entire date\n",
"#then if they pick an inappropriate date, give them a suggestion for the date"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"id": "37y1_38NMNWv",
"cellView": "form"
},
"outputs": [],
"source": [
"#@markdown ### **Defining functions for this section**\n",
"\n",
"def create_grid_coords(x_c, y_c, x_steps, bbox, raster_extent):\n",
" # create grid coordinates fro kriging, make x and y steps the same\n",
" # x_steps is the number of cells in the x-direction\n",
" min_x = min_y = max_x = max_y = None\n",
" if raster_extent == \"aquifer\":\n",
" min_x, min_y, max_x, max_y = bbox\n",
" elif raster_extent == \"wells\":\n",
" min_x, max_x = min(x_c), max(x_c)\n",
" min_y, max_y = min(y_c), max(y_c)\n",
"\n",
" n_bin = np.absolute((max_x - min_x) / x_steps) # determine step size (positive)\n",
" # make grid 10 bin steps bigger than date, will give 110 steps in x-direction\n",
" grid_x = np.arange(\n",
" min_x - 5 * n_bin, max_x + 5 * n_bin, n_bin\n",
" ) # make grid 10 steps bigger than data\n",
" grid_y = np.arange(\n",
" min_y - 5 * n_bin, max_y + 5 * n_bin, n_bin\n",
" ) # make grid 10 steps bigger than data\n",
"\n",
" return grid_x, grid_y\n",
"\n",
"def krig_field_generate(var_fitted, x_c, y_c, values, grid_x, grid_y):\n",
" # use GSTools to krig the well data, need coords and value for each well\n",
" # use model variogram paramters generated by GSTools\n",
" # fast - is faster the variogram fitting\n",
" krig_map = gs.krige.Ordinary(var_fitted, cond_pos=[x_c, y_c], cond_val=values)\n",
" krig_map.structured([grid_x, grid_y], chunk_size=1000) # krig_map.field is the numpy array of values\n",
" return krig_map\n",
"\n",
"def fit_model_var(x_c, y_c, values, bbox, raster_extent):\n",
" # fit the model varigrom to the experimental variogram\n",
" min_x = min_y = max_x = max_y = None\n",
" if raster_extent == \"aquifer\":\n",
" min_x, min_y, max_x, max_y = bbox\n",
" elif raster_extent == \"wells\":\n",
" min_x, max_x = min(x_c), max(x_c)\n",
" min_y, max_y = min(y_c), max(y_c)\n",
"\n",
" # first get the coords and determine distances\n",
" x_delta = max_x - min_x # distance across x coords\n",
" y_delta = max_y - min_y # distance across y coords\n",
" max_dist = (\n",
" np.sqrt(np.square(x_delta + y_delta)) / 4\n",
" ) # assume correlated over 1/4 of distance\n",
" data_var = np.var(values)\n",
" data_std = np.std(values)\n",
" fit_var = gs.Stable(dim=2, var=data_var, len_scale=max_dist, nugget=data_std)\n",
" return fit_var\n",
"\n",
"def generate_nc_file(\n",
" file_name, grid_x, grid_y, years_df, x_coords, y_coords, bbox, raster_extent\n",
"):\n",
" temp_dir = tempfile.mkdtemp()\n",
" file_path = os.path.join(temp_dir, file_name)\n",
" h = netCDF4.Dataset(file_path, \"w\", format=\"NETCDF4\")\n",
" lat_len = len(grid_y)\n",
" lon_len = len(grid_x)\n",
" time_dim = h.createDimension(\"time\", 0)\n",
" lat = h.createDimension(\"lat\", lat_len)\n",
" lon = h.createDimension(\"lon\", lon_len)\n",
" latitude = h.createVariable(\"lat\", np.float64, (\"lat\"))\n",
" longitude = h.createVariable(\"lon\", np.float64, (\"lon\"))\n",
" time_dim = h.createVariable(\"time\", np.float64, (\"time\"), fill_value=\"NaN\")\n",
" ts_value = h.createVariable(\n",
" \"tsvalue\", np.float64, (\"time\", \"lon\", \"lat\"), fill_value=-9999\n",
" )\n",
" latitude.long_name = \"Latitude\"\n",
" latitude.units = \"degrees_north\"\n",
" latitude.axis = \"Y\"\n",
" longitude.long_name = \"Longitude\"\n",
" longitude.units = \"degrees_east\"\n",
" longitude.axis = \"X\"\n",
" time_dim.axis = \"T\"\n",
" time_dim.units = \"days since 0001-01-01 00:00:00 UTC\"\n",
"\n",
" latitude[:] = grid_y[:]\n",
" longitude[:] = grid_x[:]\n",
"\n",
" time_counter = 0\n",
" for measurement in years_df:\n",
" # loop through the data\n",
" values = years_df[measurement].values\n",
"\n",
" beg_time = timer() # time the kriging method including variogram fitting\n",
" # fit the model variogram to the experimental variogram\n",
" var_fitted = fit_model_var(\n",
" x_coords, y_coords, values, bbox, raster_extent\n",
" ) # fit variogram\n",
" krig_map = krig_field_generate(\n",
" var_fitted, x_coords, y_coords, values, grid_x, grid_y\n",
" ) # krig data\n",
" # krig_map.field provides the 2D array of values\n",
" end_time = timer()\n",
" time_dim[time_counter] = measurement.toordinal()\n",
" ts_value[time_counter, :, :] = krig_map.field\n",
" time_counter += 1\n",
"\n",
" h.close()\n",
" return Path(file_path)\n",
"\n",
"def earth_radius(lat):\n",
" \"\"\"\n",
" calculate radius of Earth assuming oblate spheroid\n",
" defined by WGS84\n",
" Input\n",
" ---------\n",
" lat: vector or latitudes in degrees\n",
" Output\n",
" ----------\n",
" r: vector of radius in meters\n",
" Notes\n",
" -----------\n",
" WGS84: https://earth-info.nga.mil/GandG/publications/tr8350.2/tr8350.2-a/Chapter%203.pdf\n",
" Taken from: https://gist.github.com/lgloege/3fdb1ed83b002d68d8944539a797b0bc\n",
" \"\"\"\n",
" from numpy import deg2rad\n",
"\n",
" # define oblate spheroid from WGS84\n",
" a = 6378137\n",
" b = 6356752.3142\n",
" e2 = 1 - (b ** 2 / a ** 2)\n",
"\n",
" # convert from geodecic to geocentric\n",
" # see equation 3-110 in WGS84\n",
" lat = deg2rad(lat)\n",
" lat_gc = np.arctan((1 - e2) * np.tan(lat))\n",
"\n",
" # radius equation\n",
" # see equation 3-107 in WGS84\n",
" r = (a * (1 - e2) ** 0.5) / (1 - (e2 * np.cos(lat_gc) ** 2)) ** 0.5\n",
"\n",
" return r\n",
"\n",
"def calculate_aquifer_area(imputed_raster, units):\n",
" y_res = abs(\n",
" round(imputed_raster[\"lat\"].values[0] - imputed_raster[\"lat\"].values[1], 7)\n",
" ) # this assumes all cells will be the same\n",
" # size in one dimension (all cells will have same x-component)\n",
" x_res = abs(\n",
" round(imputed_raster[\"lon\"].values[0] - imputed_raster[\"lon\"].values[1], 7)\n",
" )\n",
" area = 0\n",
" # Loop through each y row\n",
" for y in range(imputed_raster.lat.size):\n",
" # Define the upper and lower bounds of the row\n",
" cur_lat_max = math.radians(imputed_raster[\"lat\"].values[y] + (y_res / 2))\n",
" cur_lat_min = math.radians(imputed_raster[\"lat\"].values[y] - (y_res / 2))\n",
"\n",
" # Count how many cells in each row are in aquifer (i.e. and, therefore, not nan)\n",
" x_count = np.count_nonzero(~np.isnan(imputed_raster.tsvalue[0, :, y]))\n",
"\n",
" # Area calculated based on the equation found here:\n",
" # https://www.pmel.noaa.gov/maillists/tmap/ferret_users/fu_2004/msg00023.html\n",
" # (pi/180) * R^2 * |lon1-lon2| * |sin(lat1)-sin(lat2)|\n",
" radius = earth_radius(imputed_raster[\"lat\"].values[y])\n",
" if units == \"acre-ft\":\n",
" area_factor = 1 / 4046.8564224\n",
" elif units == \"cubic-ft\":\n",
" area_factor = 10.764\n",
" else:\n",
" area_factor = 1\n",
" area += (\n",
" radius ** 2\n",
" * area_factor\n",
" * math.radians(x_res * x_count)\n",
" * abs((math.sin(cur_lat_min) - math.sin(cur_lat_max)))\n",
" )\n",
"\n",
" return area"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"id": "wcBFa86OLMF2"
},
"outputs": [],
"source": [
"#@markdown ### **Setting up grid for nc file**\n",
"coords_df = wells_gdf[wells_gdf[w_well_ID.value].astype(str).isin(loc_well_names)]\n",
"x_coords = coords_df[w_long.value].values\n",
"y_coords = coords_df[w_lat.value].values\n",
"\n",
"x_steps = 150 # steps in x-direction, number of y-steps will be computed with same spacing, adds 10%\n",
"grid_x, grid_y = create_grid_coords(\n",
" x_coords, y_coords, x_steps, bbox, raster_extent\n",
") # coordinates for x and y axis - not full grid\n",
"replaced_df = replaced_df[\n",
" (replaced_df.index >= f\"04-01-{start_date-1}\")\n",
" & (replaced_df.index <= f\"12-31-{end_date+1}\")\n",
"]\n",
"# skip_month = 48 # take data every nth month (skip_months), e.g., 60 = every 5 years\n",
"years_df = replaced_df.iloc[\n",
" ::raster_interval\n",
"].T # extract every nth month of data and transpose array\n",
"\n",
"file_name = f\"{aquifer_name}_{time.time()}.nc\"\n",
"# setup a netcdf file to store the time series of rasters"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"id": "auxwnaz0MhaP"
},
"outputs": [],
"source": [
"#@markdown ### **Generate nc file using kriging**\n",
"nc_file_path = generate_nc_file(file_name, grid_x, grid_y, years_df, x_coords, y_coords, bbox, raster_extent)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"id": "e26WwYjhNNFe"
},
"outputs": [],
"source": [
"#@markdown ### **Assigning dimensions and clipping nc file**\n",
"file_path = nc_file_path\n",
"temp_dir = file_path.parent.absolute()\n",
"interp_nc = xarray.open_dataset(file_path)\n",
"interp_nc.rio.set_spatial_dims(x_dim=\"lon\", y_dim=\"lat\", inplace=True)\n",
"interp_nc.rio.write_crs(\"epsg:4326\", inplace=True)\n",
"clipped_nc = interp_nc.rio.clip(aquifer.geometry.apply(mapping), crs=4326, drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"id": "RqTj_U06NlRE",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "da5f3d41-dadf-446d-ca8b-ef07c6ebc46b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"6884411607.249258 Cubic Meter\n",
"<class 'xarray.core.dataset.Dataset'>\n"
]
}
],
"source": [
"#@markdown ### **Calculating drawdown**\n",
"area = calculate_aquifer_area(clipped_nc, units)\n",
"if units == \"acre-ft\":\n",
" vol_unit = \"Acre Feet\"\n",
"elif units == \"cubic-ft\":\n",
" vol_unit = \"Cubic Feet\"\n",
"else:\n",
" vol_unit = \"Cubic Meters\"\n",
"# Calculate total drawdown volume at each time step\n",
"drawdown_grid = np.zeros(\n",
" (clipped_nc.time.size, clipped_nc.lon.size, clipped_nc.lat.size)\n",
")\n",
"drawdown_volume = np.zeros(clipped_nc.time.size)\n",
"for t in range(clipped_nc.time.size):\n",
" # Calculate drawdown at time t by subtracting original WTE at time 0\n",
" drawdown_grid[t, :, :] = (\n",
" clipped_nc[\"tsvalue\"][t, :, :] - clipped_nc[\"tsvalue\"][0, :, :]\n",
" )\n",
" # Average drawdown across entire aquifer x storage_coefficient x area of aquifer\n",
" drawdown_volume[t] = np.nanmean(\n",
" drawdown_grid[t, :, :] * 0.1 * area\n",
" )\n",
"print(area, units)\n",
"print(type(clipped_nc))\n",
"clipped_nc.attrs = {\"area\": area, \"area_units\": units}\n",
"clipped_nc[\"drawdown\"] = ([\"time\", \"lon\", \"lat\"], drawdown_grid)\n",
"clipped_nc[\"volume\"] = ([\"time\"], drawdown_volume, {\"units\": vol_unit})"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"id": "sw8LQkQUOCsX"
},
"outputs": [],
"source": [
"#@markdown ### **Saving to notebook**\n",
"clipped_nc.to_netcdf(aquifer_name + \"_\"+ output_file)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LJQqMoirIt8-"
},
"source": [
"# Visualizing Results"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"id": "JSKToMJ5HDJ3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 493
},
"outputId": "76f4c18b-5166-42ba-a577-6f9cd2710624"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'DRtest Drawdown Curve')"
]
},
"metadata": {},
"execution_count": 62
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2500x500 with 1 Axes>"
],
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},
"metadata": {}
}
],
"source": [
"#@markdown ### **Plotting the Drawdown Curve**\n",
"nc = xarray.open_dataset('/content/'+aquifer_name + \"_\"+ output_file)\n",
"time = nc.variables['time'][:]\n",
"volume = nc.variables['volume'][:]\n",
"ts = nc.variables['tsvalue'][:]\n",
"\n",
"fig, ax = plt.subplots(1, 1, figsize=(25,5))\n",
"ax.plot(time, volume)\n",
"plt.xlabel(\"Date (years)\")\n",
"plt.ylabel(\"Volume (\"+ vol_unit+\")\")\n",
"plt.title(aquifer_name + \" Drawdown Curve\")\n",
"### don't clip it so we can see the whole curve from machine learning"
]
},
{
"cell_type": "code",
"source": [
"#Export drawdown curve to CSV\n",
"drawdown_df = pd.DataFrame({'Time': time, 'Volume': volume})\n",
"drawdown_df.to_csv(aquifer_name + \"_\" + \"drawdown.csv\")"
],
"metadata": {
"id": "X9UWqEfIINb2"
},
"execution_count": 63,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"!pip install basemap\n",
"from mpl_toolkits.basemap import Basemap\n",
"import numpy\n",
"from os import path\n",
"import glob\n",
"from PIL import Image"
],
"metadata": {
"id": "I2m7vkhpXuMB"
},
"execution_count": 64,
"outputs": []
},
{
"cell_type": "code",
"source": [
"leftbound = bbox[0]\n",
"bottombound = bbox[1]\n",
"rightbound = bbox[2]\n",
"topbound = bbox[3]\n",
"Frame_Cushion = 0.25"
],
"metadata": {
"id": "J0_gvTEGeMgv"
},
"execution_count": 65,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Set up the time index for your GIF\n",
"starttime = numpy.datetime64(f\"{start_date+1}-04-01\", 'M')\n",
"endtime = numpy.datetime64(f\"{end_date-1}-12-31\", 'M')\n",
"startindex, endindex = -1, -1\n",
"for i in range(len(nc['time'][:])):\n",
" check = numpy.datetime64(nc.coords['time'].values[i], 'M')\n",
" if starttime < check and startindex == -1:\n",
" startindex = i-1\n",
" if endtime <= check and endindex == -1:\n",
" endindex = i"
],
"metadata": {
"id": "ZCDKuRweO5ON"
},
"execution_count": 66,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"if path.exists('/content/images_gif') == False:\n",
" os.mkdir('/content/images_gif')\n",
"\n",
"fig, ax = plt.subplots()\n",
"aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax)\n",
"m = Basemap(projection='cyl', llcrnrlon=leftbound-Frame_Cushion, llcrnrlat=bottombound-Frame_Cushion, urcrnrlon=rightbound+Frame_Cushion, urcrnrlat=topbound+Frame_Cushion, resolution='i', epsg=4326)\n",
"EsriBasemaps = \"World_Topo_Map\" #@param [\"NatGeo_World_Map\", \"USA_Topo_Maps\", \"World_Imagery\", \"World_Physical_Map\", \"World_Shaded_Relief\", \"World_Street_Map\", \"World_Terrain_Base\", \"World_Topo_Map\"]\n",
"m.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service=EsriBasemaps, xpixels = 400, dpi = 69,verbose = True)\n",
"\n",
"for i in range(startindex,endindex+1):\n",
" im = plt.imshow(numpy.rot90(ts[i,:,:]), cmap=\"Spectral\",extent=(leftbound, rightbound, bottombound, topbound) ,vmin=numpy.amin(ts),vmax=numpy.amax(ts))\n",
" plt.title(aquifer_name.capitalize()+\" \"+str(nc.coords['time'].values[i])[:10], pad = 15)\n",
" if i==startindex:\n",
" cbar = plt.colorbar()\n",
" cbar.set_label(ts_measurement.value+\" in \"+units, labelpad = 20)\n",
" plt.savefig(f\"/content/images_gif/image{str(nc.coords['time'].values[i])[:10]}.jpg\")"
],
"metadata": {
"cellView": "code",
"id": "9HFQb2dbVS6O"
},
"execution_count": 67,
"outputs": []
},
{
"cell_type": "code",
"source": [
"frame_duration=300\n",
"frames = []\n",
"imgs=glob.glob(f\"/content/images_gif/*.jpg\")\n",
"imgs.sort()\n",
"for i in imgs:\n",
" new_frame = Image.open(i)\n",
" frames.append(new_frame)\n",
" frames[0].save(aquifer_name + '_animation.gif', format='GIF', append_images=frames[1:],save_all=True, duration = frame_duration, loop=0)"
],
"metadata": {
"id": "GkNIO8kOV4De"
},
"execution_count": 68,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
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"name": "python"
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"_view_name": "GridBoxView",
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