{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ab8655e8-3a16-4481-9ffb-0e4bcfeada62",
"metadata": {},
"outputs": [],
"source": [
"from requests.auth import HTTPBasicAuth\n",
"from requests.packages.urllib3.exceptions import InsecureRequestWarning\n",
"import matplotlib.pyplot as plt\n",
"import requests, json, os\n",
"from dotenv import load_dotenv\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d351a581-662d-44c7-881d-e827821fbbd7",
"metadata": {},
"outputs": [],
"source": [
"requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n",
"\n",
"#create a file envViewer.txt with \n",
"#USER='XXXX'\n",
"#PASSWORD='XXXX'\n",
"load_dotenv('envViewer.txt')\n",
"user = os.getenv('ISTSOSUSER')\n",
"password = os.getenv('ISTSOSPASSWORD')\n",
"\n",
"auth = HTTPBasicAuth(user, password)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "94dd302a-071d-4c7f-a211-9fcfdd8835dd",
"metadata": {},
"outputs": [],
"source": [
"url = 'https://apps.hatarilabs.com/istsos'\n",
"service = 'temperature'\n",
"procedure = 'genericSensor'\n",
"off = 'tempnetwork'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f83f18d4-32e5-44ff-b83e-bafee3d444f3",
"metadata": {},
"outputs": [],
"source": [
"beginTime = \"2023-06-01T00:00:00-05:00\"\n",
"endTime = \"2023-06-02T20:00:00-05:00\"\n",
"# Preparing \"io\" object to send\n",
"observedProperties = requests.get(\n",
" '%s/wa/istsos/services/%s/operations/getobservation/offerings/'\n",
" '%s/procedures/%s/observedproperties/:/eventtime/%s/%s' % (\n",
" url, service, off, procedure,beginTime,endTime),\n",
" params={\n",
" \"qualityIndex\": \"False\"\n",
" }, auth=auth)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9ee581d9-e2cd-4cf7-b702-851a5db38fa5",
"metadata": {},
"outputs": [],
"source": [
"observedList = observedProperties.json()['data'][0]['result']['DataArray']['values']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f95a8df8-6eb2-4c24-a1c5-c57b155b7b48",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[['2023-06-01T10:49:04.650563-05:00', 23.625],\n",
" ['2023-06-01T10:50:04.170496-05:00', 23.625],\n",
" ['2023-06-01T10:51:05.050523-05:00', 30.5],\n",
" ['2023-06-01T10:52:04.490570-05:00', 29.125],\n",
" ['2023-06-01T10:53:04.810532-05:00', 28.0]]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observedList[:5]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4772aabd-eb5d-4bac-8c03-d6c481d905bb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Date | \n",
" Temp | \n",
"
\n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [Date, Temp]\n",
"Index: []"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observedDf = pd.DataFrame(columns=['Date','Temp'])\n",
"observedDf"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f8d48408-5eea-4e9b-a1a7-249f9c49b119",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Temp | \n",
"
\n",
" \n",
" Date | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2023-06-01 10:49:04.650563-05:00 | \n",
" 23.625 | \n",
"
\n",
" \n",
" 2023-06-01 10:50:04.170496-05:00 | \n",
" 23.625 | \n",
"
\n",
" \n",
" 2023-06-01 10:51:05.050523-05:00 | \n",
" 30.500 | \n",
"
\n",
" \n",
" 2023-06-01 10:52:04.490570-05:00 | \n",
" 29.125 | \n",
"
\n",
" \n",
" 2023-06-01 10:53:04.810532-05:00 | \n",
" 28.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Temp\n",
"Date \n",
"2023-06-01 10:49:04.650563-05:00 23.625\n",
"2023-06-01 10:50:04.170496-05:00 23.625\n",
"2023-06-01 10:51:05.050523-05:00 30.500\n",
"2023-06-01 10:52:04.490570-05:00 29.125\n",
"2023-06-01 10:53:04.810532-05:00 28.000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observedDf['Date'] = pd.to_datetime([i[0] for i in observedList])\n",
"observedDf['Temp'] = [i[1] for i in observedList]\n",
"observedDf = observedDf.set_index('Date')\n",
"observedDf.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e51f8234-4f60-4683-976b-88f5589baf65",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"observedDf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad4f0734-f78f-43df-904b-755dca781ad2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}