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pywaterml.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "WDE_Analysis.ipynb",
"provenance": [],
"collapsed_sections": [
"GdsIhCI1kW42"
],
"authorship_tag": "ABX9TyPwWZM21l4tQitsz/gjbRxP",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/romer8/f79eced595079930f7432abfadb51128/pywaterml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PgIgUWTOwCqL"
},
"source": [
"# TEMPLATE OF WATERONEFLOW WEB SERVICE\n",
"please change this with the name of you WOF service"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L1jSFY-jfnqs"
},
"source": [
"### Contents\n",
"1. [Variables for the Analysis](#intro)\n",
"2. [Install Pywaterml and Additional Dependencies](#install)\n",
"3. [Add WOF Web Service](#addService)\n",
"4. [Sites](#GetSites)\n",
"6. [Variables](#GetVariables)\n",
"8. [Site Information](#GetSiteInfo)\n",
"9. [Time Series Values for Variables](#GetValues)\n",
"\n",
"10. [Sites by Bounding Box](#GetSitesByBoxObject)\n",
"11. [Sites Filtered by Variables](#GetSitesByVariable)\n",
"12. [Interpolation](#GetInterpolation)\n",
"13. [Monthly Averages for Variables](#GetMonthlyAverage)\n",
"14. [Time Series Clusters from Monthly Averages](#GetClustersMonthlyAvg)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iCZsmAefqm-D"
},
"source": [
"<a name=\"intro\"></a>\n",
"## Variables for the Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c3n1n3MVPUTs"
},
"source": [
"The following are variables to use this notebook, so just change the following, and you should be able to run the notebook and see the output.\n",
"\n",
"*Note that the VARIABLE and SITE variables are the index from the responses of GetVariables and GetSites respectively. For example, changing VARIABLE = 1 will give the second variable in from the GetVariables methods. Similarly,the SITES variable functions in the same way.*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ioWosThiPbvg"
},
"source": [
"WOF_URL = \"http://hydroportal.cuahsi.org/CALVIN_HHS/cuahsi_1_1.asmx?WSDL\"\n",
"VARIABLE = 0\n",
"SITE = 0"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GdsIhCI1kW42"
},
"source": [
"<a name=\"install\"></a>\n",
"### Install Pywaterml and Additional Dependencies"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MtWfYP3TgfR_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c79279ea-4a94-4f6b-9b94-69a9cf7232e8"
},
"source": [
"!pip install pywaterml\n",
"!pip install folium\n",
"!pip install plotly"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting pywaterml\n",
" Downloading https://files.pythonhosted.org/packages/e6/77/c5de85a2896a78216f3a9ad1a09076f4e360d076d3a5b510e0c56bb89ba2/pywaterml-1.2.12-py3-none-any.whl\n",
"Collecting tslearn\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/95/98/9a67e2869a8b1416eb6e6fd5e69c56f86869980403b18f30bdb5783ade9d/tslearn-0.5.0.5-cp37-cp37m-manylinux2010_x86_64.whl (790kB)\n",
"\u001b[K |████████████████████████████████| 798kB 5.6MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pywaterml) (1.19.5)\n",
"Collecting scikit-learn==0.22\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/19/96/8034e350d4550748277e514d0d6d91bdd36be19e6c5f40b8af0d74cb0c84/scikit_learn-0.22-cp37-cp37m-manylinux1_x86_64.whl (7.0MB)\n",
"\u001b[K |████████████████████████████████| 7.0MB 7.3MB/s \n",
"\u001b[?25hCollecting suds-jurko\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/bd/6f/54fbf0999a606680d27c69b1ad12dfff62768ecb9fe48524cebda6eb4423/suds-jurko-0.6.tar.bz2 (143kB)\n",
"\u001b[K |████████████████████████████████| 153kB 43.8MB/s \n",
"\u001b[?25hCollecting owslib\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/e1/7b/6d6c81430aea543622987de63daf38484c890029e178867b34ef9d72dee9/OWSLib-0.23.0-py2.py3-none-any.whl (203kB)\n",
"\u001b[K |████████████████████████████████| 204kB 36.2MB/s \n",
"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pywaterml) (1.1.5)\n",
"Collecting xmltodict\n",
" Downloading https://files.pythonhosted.org/packages/28/fd/30d5c1d3ac29ce229f6bdc40bbc20b28f716e8b363140c26eff19122d8a5/xmltodict-0.12.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from tslearn->pywaterml) (0.51.2)\n",
"Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from tslearn->pywaterml) (0.29.22)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tslearn->pywaterml) (1.4.1)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from tslearn->pywaterml) (1.0.1)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from owslib->pywaterml) (3.13)\n",
"Requirement already satisfied: python-dateutil>=1.5 in /usr/local/lib/python3.7/dist-packages (from owslib->pywaterml) (2.8.1)\n",
"Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from owslib->pywaterml) (2018.9)\n",
"Requirement already satisfied: requests>=1.0 in /usr/local/lib/python3.7/dist-packages (from owslib->pywaterml) (2.23.0)\n",
"Collecting pyproj>=2\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/b1/72/d52e9ca81caef056062d71991b0e9b1d16af042245627c5d0e4916a36c4f/pyproj-3.0.1-cp37-cp37m-manylinux2010_x86_64.whl (6.5MB)\n",
"\u001b[K |████████████████████████████████| 6.5MB 6.7MB/s \n",
"\u001b[?25hRequirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->tslearn->pywaterml) (0.34.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->tslearn->pywaterml) (54.0.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=1.5->owslib->pywaterml) (1.15.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=1.0->owslib->pywaterml) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=1.0->owslib->pywaterml) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=1.0->owslib->pywaterml) (2020.12.5)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=1.0->owslib->pywaterml) (3.0.4)\n",
"Building wheels for collected packages: suds-jurko\n",
" Building wheel for suds-jurko (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for suds-jurko: filename=suds_jurko-0.6-cp37-none-any.whl size=454290 sha256=affe3ec9c4092423d84e5223ab1620427fecae00fce50bf8e649b375ace9bf3c\n",
" Stored in directory: /root/.cache/pip/wheels/12/68/53/d3902c054e32115da1d45bac442a547a071a86a65db4d77027\n",
"Successfully built suds-jurko\n",
"Installing collected packages: scikit-learn, tslearn, suds-jurko, pyproj, owslib, xmltodict, pywaterml\n",
" Found existing installation: scikit-learn 0.22.2.post1\n",
" Uninstalling scikit-learn-0.22.2.post1:\n",
" Successfully uninstalled scikit-learn-0.22.2.post1\n",
"Successfully installed owslib-0.23.0 pyproj-3.0.1 pywaterml-1.2.12 scikit-learn-0.22 suds-jurko-0.6 tslearn-0.5.0.5 xmltodict-0.12.0\n",
"Requirement already satisfied: folium in /usr/local/lib/python3.7/dist-packages (0.8.3)\n",
"Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from folium) (0.4.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from folium) (2.23.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from folium) (1.19.5)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from folium) (2.11.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from folium) (1.15.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->folium) (2020.12.5)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->folium) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->folium) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->folium) (2.10)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->folium) (1.1.1)\n",
"Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (4.4.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
"Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly) (1.3.3)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "axKiaVU1pjQe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3cb74e92-842a-42ac-e642-7ee4557312fb"
},
"source": [
"import pywaterml.waterML as pwml\n",
"import folium\n",
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"import pandas as pd\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.cluster.k_means_ module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.cluster. Anything that cannot be imported from sklearn.cluster is now part of the private API.\n",
" warnings.warn(message, FutureWarning)\n",
"/usr/local/lib/python3.7/dist-packages/tslearn/clustering/kmeans.py:23: UserWarning: Scikit-learn <0.24 will be deprecated in a future release of tslearn\n",
" \"Scikit-learn <0.24 will be deprecated in a \"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0YN3fO0dsmFC"
},
"source": [
"<a name=\"addService\"></a>\n",
"### WOF Web Service"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rqGI2HnKslup"
},
"source": [
"Any WOF web service can be added to the wrapper class WaterMLOperations from the pywaterml package. In the link below, please change the <em>hs_url</em> variable below.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hfZV_E02lEfs"
},
"source": [
"try:\n",
" water = pwml.WaterMLOperations(url = WOF_URL)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "z8skY4N5tq7G"
},
"source": [
"<a name=\"GetSites\"></a>\n",
"\n",
"### Sites"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pG7eT4WqzIpb"
},
"source": [
"The GetSites method retrieves meta data for each site in the WOF web service. Data can be retrieved in three different formats: JSON, CSV, and WaterML"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SZNMaxpwrc2D",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "927a98cb-428d-401b-dca2-e3b646f78a74"
},
"source": [
"try:\n",
" sites = water.GetSites()\n",
" df = pd.DataFrame.from_dict(sites)\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
" sitename latitude longitude ... network fullSiteCode siteID\n",
"0 SR-1 40.801 -122.762 ... CALVIN-HHS CALVIN-HHS:SR-1 1\n",
"1 SR-10 37.956 -120.519 ... CALVIN-HHS CALVIN-HHS:SR-10 2\n",
"2 SR-12 37.033 -121.133 ... CALVIN-HHS CALVIN-HHS:SR-12 3\n",
"3 SR-15 37.614 -121.745 ... CALVIN-HHS CALVIN-HHS:SR-15 4\n",
"4 SR-18 37.001 -119.705 ... CALVIN-HHS CALVIN-HHS:SR-18 5\n",
"5 SR-20 37.584 -120.267 ... CALVIN-HHS CALVIN-HHS:SR-20 6\n",
"6 SR-22 36.994 -120.93 ... CALVIN-HHS CALVIN-HHS:SR-22 7\n",
"7 SR-3 40.583 -122.533 ... CALVIN-HHS CALVIN-HHS:SR-3 8\n",
"8 SR-4 40.717 -122.417 ... CALVIN-HHS CALVIN-HHS:SR-4 9\n",
"9 SR-52 37.2 -119.917 ... CALVIN-HHS CALVIN-HHS:SR-52 10\n",
"10 SR-53 37.2156 -119.9903 ... CALVIN-HHS CALVIN-HHS:SR-53 11\n",
"11 SR-6 39.533 -121.517 ... CALVIN-HHS CALVIN-HHS:SR-6 12\n",
"12 SR-7 39.458 -121.638 ... CALVIN-HHS CALVIN-HHS:SR-7 13\n",
"13 SR-8 38.7 -121.167 ... CALVIN-HHS CALVIN-HHS:SR-8 14\n",
"14 SR-81 37.7017 -120.4211 ... CALVIN-HHS CALVIN-HHS:SR-81 15\n",
"15 SR-ASF 37.53 -122.36 ... CALVIN-HHS CALVIN-HHS:SR-ASF 16\n",
"16 SR-BBL 39.808 -122.329 ... CALVIN-HHS CALVIN-HHS:SR-BBL 17\n",
"17 SR-CFW 39.05 -121.317 ... CALVIN-HHS CALVIN-HHS:SR-CFW 18\n",
"18 SR-CL-IVR 38.89 -122.238 ... CALVIN-HHS CALVIN-HHS:SR-CL-IVR 19\n",
"19 SR-CR 38.225 -121.021 ... CALVIN-HHS CALVIN-HHS:SR-CR 20\n",
"20 SR-EBMUD 37.9 -122.2 ... CALVIN-HHS CALVIN-HHS:SR-EBMUD 21\n",
"21 SR-EL 39.239 -121.267 ... CALVIN-HHS CALVIN-HHS:SR-EL 22\n",
"22 SR-HHR 37.948 -119.787 ... CALVIN-HHS CALVIN-HHS:SR-HHR 23\n",
"23 SR-LB 38.5153 -122.0808 ... CALVIN-HHS CALVIN-HHS:SR-LB 24\n",
"24 SR-LI 35.646 -118.401 ... CALVIN-HHS CALVIN-HHS:SR-LI 25\n",
"25 SR-LK 36.412 -119.003 ... CALVIN-HHS CALVIN-HHS:SR-LK 26\n",
"26 SR-LL-LE 37.894 -119.962 ... CALVIN-HHS CALVIN-HHS:SR-LL-LE 27\n",
"27 SR-LS 36.061 -118.922 ... CALVIN-HHS CALVIN-HHS:SR-LS 28\n",
"28 SR-LV 37.838 -121.726 ... CALVIN-HHS CALVIN-HHS:SR-LV 29\n",
"29 SR-NBB 39.393 -121.14 ... CALVIN-HHS CALVIN-HHS:SR-NBB 30\n",
"30 SR-NHL 38.155 -120.814 ... CALVIN-HHS CALVIN-HHS:SR-NHL 31\n",
"31 SR-PF 36.833 -119.325 ... CALVIN-HHS CALVIN-HHS:SR-PF 32\n",
"32 SR-PR 38.25 -120.85 ... CALVIN-HHS CALVIN-HHS:SR-PR 33\n",
"33 SR-SCV 37.118 -121.549 ... CALVIN-HHS CALVIN-HHS:SR-SCV 34\n",
"34 SR-TR 37.875 -120.603 ... CALVIN-HHS CALVIN-HHS:SR-TR 35\n",
"\n",
"[35 rows x 7 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wGdgtYByuvb0"
},
"source": [
"The following code is a helpful code snipped to plot the different stations in a follium map"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "ld6QSYA2j_Wk",
"outputId": "93580416-6ee3-4c99-9d54-d389eb9db67c"
},
"source": [
"try:\n",
" m = folium.Map(location=[40.0150, -105.2705], \n",
" tiles = 'Stamen Terrain')\n",
" sitesLocations = []\n",
" for site in sites:\n",
" sitesLocations.append([float(site['latitude']) , float(site['longitude'])])\n",
" folium.Marker(\n",
" location= [float(site['latitude']) , float(site['longitude'])],\n",
" icon=folium.Icon()\n",
" ).add_to(m)\n",
" m.fit_bounds(sitesLocations)\n",
" m\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write( decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f158aa103d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0UKt9Ty7xQfP"
},
"source": [
"<a name=\"GetVariables\"></a>\n",
"### Variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z-Ntb2bWxVR-"
},
"source": [
"The GetVariables method retrieves metadata from the different variables in the WOF web service."
]
},
{
"cell_type": "code",
"metadata": {
"id": "v3DOPKYXsJ7i",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2d1227ca-5a33-4d28-d61b-46a7591be954"
},
"source": [
"try:\n",
" variables = water.GetVariables()\n",
" df = pd.DataFrame.from_dict(variables['variables'])\n",
"\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)\n"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
" variableName variableCode ... timeSupport speciation\n",
"0 Reservoir storage STOR ... 1 Not Applicable\n",
"\n",
"[1 rows x 15 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MoeHbfokzByc"
},
"source": [
"<a name=\"GetSiteInfo\"></a>\n",
"### Site Information"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JeVHdm8gzTx1"
},
"source": [
"Retrieves the information of a given site. GetSiteInfo() function is similar to the GetSiteInfo() WaterML function.\n",
"\n",
"<em>Please note that the site used in this example is configured to use the SITE variable at the beginning. If you want to change the site, you need to change the SITE variable at the beginning of the code</em>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "v4J-ZB9esVlp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dd2fe775-7c88-4ddb-f22e-4b3ce9c8f5e5"
},
"source": [
"try:\n",
" site_full_code = sites[SITE]['fullSiteCode']\n",
" siteInfo = water.GetSiteInfo(site_full_code)\n",
" df = pd.DataFrame.from_dict(siteInfo['siteInfo'])\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
" siteName latitude ... beginDateTimeUTC endDateTimeUTC\n",
"0 SR-1 40.801 ... 1921-10-31T00:00:00 1993-09-30T00:00:00\n",
"1 SR-1 40.801 ... 1921-10-31T00:00:00 1993-09-30T00:00:00\n",
"2 SR-1 40.801 ... 1921-10-31T00:00:00 1993-09-30T00:00:00\n",
"3 SR-1 40.801 ... 1921-10-31T00:00:00 1993-09-30T00:00:00\n",
"4 SR-1 40.801 ... 1921-10-31T00:00:00 1993-09-30T00:00:00\n",
"\n",
"[5 rows x 36 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UyYmqDND2pDj"
},
"source": [
"<a name=\"GetValues\"></a>\n",
"### Time Series Values for Variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l3I2khI92hFu"
},
"source": [
"Retrieves the values for an specific variable in a site. GetValues() function is similar to the GetValues() WaterML function.\n",
"\n",
"<em>Please note that the variable used in this example is configured to use the VARIABLE variable at the beginning. If you want to change the variable, you need to change the VARIABLE variable at the beginning of the code</em>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "I4mlk7O1mn_s",
"outputId": "71354318-9e24-46eb-9293-a1b904a82432"
},
"source": [
"try:\n",
" variable_full_code = siteInfo['siteInfo'][VARIABLE]['fullVariableCode']\n",
" start_date = siteInfo['siteInfo'][VARIABLE]['beginDateTime'].split('T')[0]\n",
" end_date = siteInfo['siteInfo'][VARIABLE]['endDateTime'].split('T')[0]\n",
" variableResponse= water.GetValues(site_full_code, variable_full_code, start_date, end_date)\n",
" df = pd.DataFrame.from_dict(variableResponse['values'])\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
" siteName siteCode ... sourceCode timeOffSet\n",
"0 SR-1 SR-1 ... 1 +00:00\n",
"1 SR-1 SR-1 ... 1 +00:00\n",
"2 SR-1 SR-1 ... 1 +00:00\n",
"3 SR-1 SR-1 ... 1 +00:00\n",
"4 SR-1 SR-1 ... 1 +00:00\n",
"... ... ... ... ... ...\n",
"4315 SR-1 SR-1 ... 1 +00:00\n",
"4316 SR-1 SR-1 ... 1 +00:00\n",
"4317 SR-1 SR-1 ... 1 +00:00\n",
"4318 SR-1 SR-1 ... 1 +00:00\n",
"4319 SR-1 SR-1 ... 1 +00:00\n",
"\n",
"[4320 rows x 25 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yETx0fg33IoJ"
},
"source": [
"The following is a code snipped for plotting the results of the GetValues function using the Plotly library.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "diZKW5aB5qQv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "62ed3101-0c33-4a3d-de5c-ff3c72f64de1"
},
"source": [
"try:\n",
" ##PLOT THE TIME SERIES\n",
" timeStamps=[]\n",
" valuesTimeSeries = []\n",
" for index in variableResponse['values']:\n",
" timeStamps.append(index['dateTimeUTC'])\n",
" valuesTimeSeries.append(index['dataValue'])\n",
"\n",
" fig = go.Figure(data=go.Scatter(x=timeStamps, y=valuesTimeSeries))\n",
" # Edit the layout\n",
" fig.update_layout(title = variableResponse['values'][VARIABLE]['variableName'],\n",
" xaxis_title =variableResponse['values'][VARIABLE]['timeUnitAbbreviation'],\n",
" yaxis_title = variableResponse['values'][VARIABLE]['unitAbbreviation'])\n",
" fig.show()\n",
"\n",
" df = pd.DataFrame(dict(\n",
" data=valuesTimeSeries\n",
" ))\n",
" fig = px.box(df,y=\"data\", points=\"all\")\n",
" fig.show()\n",
" \n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
" <div>\n",
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
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" if (document.getElementById(\"fbd36807-38c0-4f83-8ad1-ebf793e14c00\")) {\n",
" Plotly.newPlot(\n",
" 'fbd36807-38c0-4f83-8ad1-ebf793e14c00',\n",
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1028.26, 798.2, 717.52, 798.2, 717.52, 803.33, 772.14, 605.32, 772.14, 605.32, 772.14, 762.92, 590.32, 762.92, 590.32, 762.92, 757.89, 581.27, 757.89, 581.27, 757.89, 774.35, 607.25, 774.35, 607.25, 774.35, 909.05, 764.51, 909.05, 764.51, 909.05, 1394.36, 1420.97, 1394.36, 1420.97, 1394.36, 1738.57, 1850.0, 1738.57, 1850.0, 1738.57, 1899.26, 1857.13, 1899.26, 1857.13, 1899.26, 1797.28, 1647.66, 1797.28, 1647.66, 1797.28, 1644.64, 1428.02, 1644.64, 1428.02, 1644.64, 1591.35, 1334.25, 1591.35, 1344.35, 1591.35, 1559.21, 1106.66, 1559.21, 1116.71, 1559.21, 1537.79, 893.46, 1537.79, 903.49, 1537.79, 1522.97, 677.47, 1522.97, 696.05, 1522.97, 1511.38, 658.73, 1511.38, 677.3, 1511.38, 1513.75, 649.08, 1513.75, 667.64, 1513.75, 1539.41, 681.18, 1539.41, 699.74, 1539.41, 1630.76, 809.83, 1630.76, 828.37, 1630.76, 1850.0, 1085.29, 1850.0, 1103.82, 1850.0, 1865.91, 1088.35, 1855.95, 1088.35, 1855.95, 1777.73, 961.17, 1767.8, 961.17, 1767.8, 1753.01, 924.59, 1753.01, 924.59, 1753.01, 1734.48, 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" {\"responsive\": true}\n",
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" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"\n",
" })\n",
" };\n",
" \n",
" </script>\n",
" </div>\n",
"</body>\n",
"</html>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7_TeQpmbMEgx"
},
"source": [
"## Extra Functionality Methods"
]
},
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"cell_type": "markdown",
"metadata": {
"id": "0vjseuQkMOWe"
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"source": [
"The following are extra functionality from the package, and it includes the following methods:\n",
"\n",
"1. GetSitesByBoxObject : Get all the sites from a bounding box from a endpoint that complies to the SOAP protocol. The GetSitesByBoxObject() function is similar to the GetSitesByBoxObject() WaterML function.\n",
"\n",
"2. GetSitesByVariable: Get the specific sites according to a variable search array from a endpoint that complies to the SOAP protocol. The GetSitesByVariable() is an addition to the WaterML functions because it allows the user to retrieve sites that contains the epecific site/s.\n",
"\n",
"3. GetInterpolation : Interpolates the data given by the GetValues function in order to fix datasets with missing values. Three ooptions for interpolation are offered: mean, backward, forward. The default is the mean interpolation.\n",
"\n",
"4. GetMonthlyAverage : Gets the monthly averages for a given variable, or from the response given by the GetValues function for a given site.\n",
"\n",
"5. GetClustersMonthlyAvg : Gets “n” number of clusters using dtw time series interpolation for a given variable.\n",
"\n",
"Please note that some functions such as GetSitesByVariable, GetMonthlyAverage, and GetClustersMonthlyAvg takes time depending in the number of sites that are in the WOF service\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HZ4vWfkLwonY"
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"source": [
"<a name=\"GetSitesByBoxObject\"></a>\n",
"\n",
"### Sites By Bounding box"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6rORLbDkwvj7"
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"source": [
"The GetSitesByBoxObject method retrieves meta data for each site from a given lat/lon bounding box in the WOF web service. Data can be retrieved in three different formats: JSON, CSV, and WaterML"
]
},
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"cell_type": "code",
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"base_uri": "https://localhost:8080/"
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"source": [
"try: \n",
" Bounds = m.get_bounds()\n",
" BoundsRearranged = [Bounds[0][1],Bounds[0][0],Bounds[1][1],Bounds[1][0]]\n",
" SitesByBoundingBox = water.GetSitesByBoxObject(BoundsRearranged,'epsg:4326')\n",
" df = pd.DataFrame.from_dict(SitesByBoundingBox)\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
" sitename latitude longitude ... network siteID fullSiteCode\n",
"0 SR-10 37.9560 -120.5190 ... CALVIN-HHS 2 CALVIN-HHS:SR-10\n",
"1 SR-12 37.0330 -121.1330 ... CALVIN-HHS 3 CALVIN-HHS:SR-12\n",
"2 SR-15 37.6140 -121.7450 ... CALVIN-HHS 4 CALVIN-HHS:SR-15\n",
"3 SR-18 37.0010 -119.7050 ... CALVIN-HHS 5 CALVIN-HHS:SR-18\n",
"4 SR-20 37.5840 -120.2670 ... CALVIN-HHS 6 CALVIN-HHS:SR-20\n",
"5 SR-22 36.9940 -120.9300 ... CALVIN-HHS 7 CALVIN-HHS:SR-22\n",
"6 SR-3 40.5830 -122.5330 ... CALVIN-HHS 8 CALVIN-HHS:SR-3\n",
"7 SR-4 40.7170 -122.4170 ... CALVIN-HHS 9 CALVIN-HHS:SR-4\n",
"8 SR-52 37.2000 -119.9170 ... CALVIN-HHS 10 CALVIN-HHS:SR-52\n",
"9 SR-53 37.2156 -119.9903 ... CALVIN-HHS 11 CALVIN-HHS:SR-53\n",
"10 SR-6 39.5330 -121.5170 ... CALVIN-HHS 12 CALVIN-HHS:SR-6\n",
"11 SR-7 39.4580 -121.6380 ... CALVIN-HHS 13 CALVIN-HHS:SR-7\n",
"12 SR-8 38.7000 -121.1670 ... CALVIN-HHS 14 CALVIN-HHS:SR-8\n",
"13 SR-81 37.7017 -120.4211 ... CALVIN-HHS 15 CALVIN-HHS:SR-81\n",
"14 SR-ASF 37.5300 -122.3600 ... CALVIN-HHS 16 CALVIN-HHS:SR-ASF\n",
"15 SR-BBL 39.8080 -122.3290 ... CALVIN-HHS 17 CALVIN-HHS:SR-BBL\n",
"16 SR-CFW 39.0500 -121.3170 ... CALVIN-HHS 18 CALVIN-HHS:SR-CFW\n",
"17 SR-CL-IVR 38.8900 -122.2380 ... CALVIN-HHS 19 CALVIN-HHS:SR-CL-IVR\n",
"18 SR-CR 38.2250 -121.0210 ... CALVIN-HHS 20 CALVIN-HHS:SR-CR\n",
"19 SR-EBMUD 37.9000 -122.2000 ... CALVIN-HHS 21 CALVIN-HHS:SR-EBMUD\n",
"20 SR-EL 39.2390 -121.2670 ... CALVIN-HHS 22 CALVIN-HHS:SR-EL\n",
"21 SR-HHR 37.9480 -119.7870 ... CALVIN-HHS 23 CALVIN-HHS:SR-HHR\n",
"22 SR-LB 38.5153 -122.0808 ... CALVIN-HHS 24 CALVIN-HHS:SR-LB\n",
"23 SR-LK 36.4120 -119.0030 ... CALVIN-HHS 26 CALVIN-HHS:SR-LK\n",
"24 SR-LL-LE 37.8940 -119.9620 ... CALVIN-HHS 27 CALVIN-HHS:SR-LL-LE\n",
"25 SR-LS 36.0610 -118.9220 ... CALVIN-HHS 28 CALVIN-HHS:SR-LS\n",
"26 SR-LV 37.8380 -121.7260 ... CALVIN-HHS 29 CALVIN-HHS:SR-LV\n",
"27 SR-NBB 39.3930 -121.1400 ... CALVIN-HHS 30 CALVIN-HHS:SR-NBB\n",
"28 SR-NHL 38.1550 -120.8140 ... CALVIN-HHS 31 CALVIN-HHS:SR-NHL\n",
"29 SR-PF 36.8330 -119.3250 ... CALVIN-HHS 32 CALVIN-HHS:SR-PF\n",
"30 SR-PR 38.2500 -120.8500 ... CALVIN-HHS 33 CALVIN-HHS:SR-PR\n",
"31 SR-SCV 37.1180 -121.5490 ... CALVIN-HHS 34 CALVIN-HHS:SR-SCV\n",
"32 SR-TR 37.8750 -120.6030 ... CALVIN-HHS 35 CALVIN-HHS:SR-TR\n",
"\n",
"[33 rows x 7 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jYrhXF_5P9Fn"
},
"source": [
"<a name=\"GetSitesByVariable\"></a>\n",
"### Sites Filtered by Variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Psyx8nz3P9Fp"
},
"source": [
"The GetSitesByVariable methods retrieves metadata from the sites that contains certain variables in the WOF web service\n",
"\n",
"<em>Please note that the variable used in this example is configured to use the VARIABLE variable at the beginning. If you want to change the variable, you need to change the VARIABLE variable at the beginning of the code</em>\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Kvi1i7o6P9Ft",
"outputId": "c78e9dcc-2ce7-42ec-80fd-31064548132a"
},
"source": [
"try: \n",
" variablesTest = [variables['variables'][0]['variableCode']]\n",
" sitesFiltered = water.GetSitesByVariable(variablesTest,sites)\n",
" df = pd.DataFrame.from_dict(sitesFiltered['sites'])\n",
" print(df)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
" sitename latitude longitude ... network fullSiteCode siteID\n",
"0 SR-1 40.801 -122.762 ... CALVIN-HHS CALVIN-HHS:SR-1 1\n",
"1 SR-10 37.956 -120.519 ... CALVIN-HHS CALVIN-HHS:SR-10 2\n",
"2 SR-12 37.033 -121.133 ... CALVIN-HHS CALVIN-HHS:SR-12 3\n",
"3 SR-15 37.614 -121.745 ... CALVIN-HHS CALVIN-HHS:SR-15 4\n",
"4 SR-18 37.001 -119.705 ... CALVIN-HHS CALVIN-HHS:SR-18 5\n",
"5 SR-20 37.584 -120.267 ... CALVIN-HHS CALVIN-HHS:SR-20 6\n",
"6 SR-22 36.994 -120.93 ... CALVIN-HHS CALVIN-HHS:SR-22 7\n",
"7 SR-3 40.583 -122.533 ... CALVIN-HHS CALVIN-HHS:SR-3 8\n",
"8 SR-4 40.717 -122.417 ... CALVIN-HHS CALVIN-HHS:SR-4 9\n",
"9 SR-52 37.2 -119.917 ... CALVIN-HHS CALVIN-HHS:SR-52 10\n",
"10 SR-53 37.2156 -119.9903 ... CALVIN-HHS CALVIN-HHS:SR-53 11\n",
"11 SR-6 39.533 -121.517 ... CALVIN-HHS CALVIN-HHS:SR-6 12\n",
"12 SR-7 39.458 -121.638 ... CALVIN-HHS CALVIN-HHS:SR-7 13\n",
"13 SR-8 38.7 -121.167 ... CALVIN-HHS CALVIN-HHS:SR-8 14\n",
"14 SR-81 37.7017 -120.4211 ... CALVIN-HHS CALVIN-HHS:SR-81 15\n",
"15 SR-ASF 37.53 -122.36 ... CALVIN-HHS CALVIN-HHS:SR-ASF 16\n",
"16 SR-BBL 39.808 -122.329 ... CALVIN-HHS CALVIN-HHS:SR-BBL 17\n",
"17 SR-CFW 39.05 -121.317 ... CALVIN-HHS CALVIN-HHS:SR-CFW 18\n",
"18 SR-CL-IVR 38.89 -122.238 ... CALVIN-HHS CALVIN-HHS:SR-CL-IVR 19\n",
"19 SR-CR 38.225 -121.021 ... CALVIN-HHS CALVIN-HHS:SR-CR 20\n",
"20 SR-EBMUD 37.9 -122.2 ... CALVIN-HHS CALVIN-HHS:SR-EBMUD 21\n",
"21 SR-EL 39.239 -121.267 ... CALVIN-HHS CALVIN-HHS:SR-EL 22\n",
"22 SR-HHR 37.948 -119.787 ... CALVIN-HHS CALVIN-HHS:SR-HHR 23\n",
"23 SR-LB 38.5153 -122.0808 ... CALVIN-HHS CALVIN-HHS:SR-LB 24\n",
"24 SR-LI 35.646 -118.401 ... CALVIN-HHS CALVIN-HHS:SR-LI 25\n",
"25 SR-LK 36.412 -119.003 ... CALVIN-HHS CALVIN-HHS:SR-LK 26\n",
"26 SR-LL-LE 37.894 -119.962 ... CALVIN-HHS CALVIN-HHS:SR-LL-LE 27\n",
"27 SR-LS 36.061 -118.922 ... CALVIN-HHS CALVIN-HHS:SR-LS 28\n",
"28 SR-LV 37.838 -121.726 ... CALVIN-HHS CALVIN-HHS:SR-LV 29\n",
"29 SR-NBB 39.393 -121.14 ... CALVIN-HHS CALVIN-HHS:SR-NBB 30\n",
"30 SR-NHL 38.155 -120.814 ... CALVIN-HHS CALVIN-HHS:SR-NHL 31\n",
"31 SR-PF 36.833 -119.325 ... CALVIN-HHS CALVIN-HHS:SR-PF 32\n",
"32 SR-PR 38.25 -120.85 ... CALVIN-HHS CALVIN-HHS:SR-PR 33\n",
"33 SR-SCV 37.118 -121.549 ... CALVIN-HHS CALVIN-HHS:SR-SCV 34\n",
"34 SR-TR 37.875 -120.603 ... CALVIN-HHS CALVIN-HHS:SR-TR 35\n",
"\n",
"[35 rows x 7 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7tayhz1L4ShT"
},
"source": [
"<a name=\"GetInterpolation\"></a>\n",
"### Interpolations"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V0xGv07qmeAP"
},
"source": [
"Interpolates the data given by the GetValues function in order to fix datasets with missing values. Three ooptions for interpolation are offered: mean, backward, forward. The default is the mean interpolation"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jW9hBijwoqUn",
"outputId": "47e5ee26-56c9-4a6a-b8e2-fbdd08a43e8f"
},
"source": [
"try:\n",
" mean_interpolation = water.GetInterpolation(variableResponse,'mean')\n",
" backward_interpolation = water.GetInterpolation(variableResponse,'backward')\n",
" forward_interpolation = water.GetInterpolation(variableResponse,'forward')\n",
" df_mean = pd.DataFrame.from_dict(mean_interpolation)\n",
" df_back = pd.DataFrame.from_dict(backward_interpolation)\n",
" df_ford = pd.DataFrame.from_dict(forward_interpolation)\n",
"\n",
" print(\"INTERPOLATIONS\")\n",
" # print(df_back)\n",
" print(df_mean)\n",
" # print(df_ford)\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"INTERPOLATIONS\n",
" 0 1\n",
"0 1921-10-31 1886.546000\n",
"1 1921-11-01 1880.000467\n",
"2 1921-11-02 1873.454933\n",
"3 1921-11-03 1866.909400\n",
"4 1921-11-04 1860.363867\n",
"... ... ...\n",
"26263 1993-09-26 1590.880000\n",
"26264 1993-09-27 1586.275000\n",
"26265 1993-09-28 1581.670000\n",
"26266 1993-09-29 1577.065000\n",
"26267 1993-09-30 1572.460000\n",
"\n",
"[26268 rows x 2 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXUBcAuomsNP"
},
"source": [
"The following is a code snipped for plotting the results of the GetInterpolation method using the Plotly library."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "DOxyWtwyo0sl",
"outputId": "3e80963f-30a4-4924-e8dc-5e6053da5bb1"
},
"source": [
"try: \n",
" ##PLOT THE TIME SERIES FOR INTERPOLATIONS\n",
" timeStamps=[]\n",
" valuesTimeSeries = []\n",
" timeStampsBack=[]\n",
" valuesTimeSeriesBackward = []\n",
" timeStampsFor=[]\n",
" valuesTimeSeriesForward = []\n",
"\n",
" for index in mean_interpolation:\n",
" timeStamps.append(index[0])\n",
" valuesTimeSeries.append(index[1])\n",
"\n",
" for index in backward_interpolation:\n",
" timeStampsBack.append(index[0])\n",
" valuesTimeSeriesBackward.append(index[1])\n",
"\n",
" for index in forward_interpolation:\n",
" timeStampsFor.append(index[0])\n",
" valuesTimeSeriesForward.append(index[1])\n",
"\n",
" fig = go.Figure(data=go.Scatter(x=timeStamps, y=valuesTimeSeries, mode='lines',\n",
" name='Mean Interpolation'))\n",
" fig.add_trace(go.Scatter(x=timeStampsBack, y=valuesTimeSeriesBackward,\n",
" mode='lines',\n",
" name='Backward Interpolation'))\n",
" fig.add_trace(go.Scatter(x=timeStampsFor, y=valuesTimeSeriesForward,\n",
" mode='lines',\n",
" name='Forward Interpolation'))\n",
"\n",
" # Edit the layout\n",
" fig.update_layout(title = variableResponse['values'][0]['variableName'],\n",
" xaxis_title =variableResponse['values'][0]['timeUnitAbbreviation'],\n",
" yaxis_title = variableResponse['values'][0]['unitAbbreviation'])\n",
" fig.show()\n",
" ##ONLY MEAN INTERPOLATION WHISKER AND BOX PLOT\n",
"\n",
" df = pd.DataFrame(dict(\n",
" data=valuesTimeSeries\n",
" ))\n",
" fig = px.box(df,y=\"data\", points=\"all\")\n",
" fig.show()\n",
"except Exception as e:\n",
" print(e)"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
" <div>\n",
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
" <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
" <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script> \n",
" <div id=\"6d8e85aa-6a92-4b5e-b55e-51370068e87e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
" <script type=\"text/javascript\">\n",
" \n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
" \n",
" if (document.getElementById(\"6d8e85aa-6a92-4b5e-b55e-51370068e87e\")) {\n",
" Plotly.newPlot(\n",
" '6d8e85aa-6a92-4b5e-b55e-51370068e87e',\n",
" [{\"mode\": \"lines\", \"name\": \"Mean Interpolation\", \"type\": \"scatter\", \"x\": [\"1921-10-31T00:00:00\", \"1921-11-01T00:00:00\", \"1921-11-02T00:00:00\", \"1921-11-03T00:00:00\", \"1921-11-04T00:00:00\", \"1921-11-05T00:00:00\", \"1921-11-06T00:00:00\", \"1921-11-07T00:00:00\", \"1921-11-08T00:00:00\", \"1921-11-09T00:00:00\", \"1921-11-10T00:00:00\", \"1921-11-11T00:00:00\", \"1921-11-12T00:00:00\", \"1921-11-13T00:00:00\", \"1921-11-14T00:00:00\", \"1921-11-15T00:00:00\", \"1921-11-16T00:00:00\", \"1921-11-17T00:00:00\", \"1921-11-18T00:00:00\", \"1921-11-19T00:00:00\", \"1921-11-20T00:00:00\", \"1921-11-21T00:00:00\", \"1921-11-22T00:00:00\", \"1921-11-23T00:00:00\", \"1921-11-24T00:00:00\", \"1921-11-25T00:00:00\", \"1921-11-26T00:00:00\", \"1921-11-27T00:00:00\", \"1921-11-28T00:00:00\", \"1921-11-29T00:00:00\", \"1921-11-30T00:00:00\", \"1921-12-01T00:00:00\", \"1921-12-02T00:00:00\", \"1921-12-03T00:00:00\", \"1921-12-04T00:00:00\", \"1921-12-05T00:00:00\", 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\"1922-01-15T00:00:00\", \"1922-01-16T00:00:00\", \"1922-01-17T00:00:00\", \"1922-01-18T00:00:00\", \"1922-01-19T00:00:00\", \"1922-01-20T00:00:00\", \"1922-01-21T00:00:00\", \"1922-01-22T00:00:00\", \"1922-01-23T00:00:00\", \"1922-01-24T00:00:00\", \"1922-01-25T00:00:00\", \"1922-01-26T00:00:00\", \"1922-01-27T00:00:00\", \"1922-01-28T00:00:00\", \"1922-01-29T00:00:00\", \"1922-01-30T00:00:00\", \"1922-01-31T00:00:00\", \"1922-02-01T00:00:00\", \"1922-02-02T00:00:00\", \"1922-02-03T00:00:00\", \"1922-02-04T00:00:00\", \"1922-02-05T00:00:00\", \"1922-02-06T00:00:00\", \"1922-02-07T00:00:00\", \"1922-02-08T00:00:00\", \"1922-02-09T00:00:00\", \"1922-02-10T00:00:00\", \"1922-02-11T00:00:00\", \"1922-02-12T00:00:00\", \"1922-02-13T00:00:00\", \"1922-02-14T00:00:00\", \"1922-02-15T00:00:00\", \"1922-02-16T00:00:00\", \"1922-02-17T00:00:00\", \"1922-02-18T00:00:00\", \"1922-02-19T00:00:00\", \"1922-02-20T00:00:00\", \"1922-02-21T00:00:00\", \"1922-02-22T00:00:00\", \"1922-02-23T00:00:00\", 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