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{
"cells": [
{
"cell_type": "markdown",
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"id": "agricultural-wellington",
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"metadata": {},
"source": [
"# Python Prerequisites\n",
"\n",
"In this below cell we install sondehub library"
]
},
{
"cell_type": "code",
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"execution_count": 5,
"id": "multiple-ribbon",
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"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: sondehub in /usr/local/lib/python3.9/site-packages (0.1.7)\n",
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"Collecting sondehub\n",
" Using cached sondehub-0.1.8-py3-none-any.whl (16 kB)\n",
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"Requirement already satisfied: boto3<2.0.0,>=1.14.44 in /usr/local/lib/python3.9/site-packages (from sondehub) (1.17.5)\n",
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"Requirement already satisfied: paho-mqtt<2.0.0,>=1.5.1 in /usr/local/lib/python3.9/site-packages (from sondehub) (1.5.1)\n",
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"Requirement already satisfied: botocore<1.21.0,>=1.20.5 in /usr/local/lib/python3.9/site-packages (from boto3<2.0.0,>=1.14.44->sondehub) (1.20.5)\n",
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"Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.9/site-packages (from boto3<2.0.0,>=1.14.44->sondehub) (0.10.0)\n",
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"Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.9/site-packages (from boto3<2.0.0,>=1.14.44->sondehub) (0.3.4)\n",
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"Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.9/site-packages (from botocore<1.21.0,>=1.20.5->boto3<2.0.0,>=1.14.44->sondehub) (2.8.1)\n",
"Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.9/site-packages (from botocore<1.21.0,>=1.20.5->boto3<2.0.0,>=1.14.44->sondehub) (1.26.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.21.0,>=1.20.5->boto3<2.0.0,>=1.14.44->sondehub) (1.15.0)\n",
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"Installing collected packages: sondehub\n",
" Attempting uninstall: sondehub\n",
" Found existing installation: sondehub 0.1.7\n",
" Uninstalling sondehub-0.1.7:\n",
" Successfully uninstalled sondehub-0.1.7\n",
"Successfully installed sondehub-0.1.8\n",
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"\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.0.1 is available.\n",
"You should consider upgrading via the '/usr/local/opt/python@3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"# Install a pip package in the current Jupyter kernel\n",
"import sys\n",
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"!{sys.executable} -m pip install --upgrade sondehub"
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]
},
{
"cell_type": "markdown",
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"id": "piano-parameter",
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"metadata": {},
"source": [
"# Imports\n",
"Next we import the python libraries we'll be using."
]
},
{
"cell_type": "code",
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"execution_count": 1,
"id": "canadian-collapse",
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"metadata": {},
"outputs": [],
"source": [
"# Sondehub lib that we'll be using to download and access the data\n",
"import sondehub\n",
"\n",
"#numpy, matplotlib and scipy to help us graph and process some of the data\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats as stats\n",
"\n",
"import datetime"
]
},
{
"cell_type": "markdown",
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"id": "general-annotation",
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"metadata": {},
"source": [
"# Download data\n",
"The sondehub library handles accessing the data. Behind the scenes it's downloading the data from AWS Open Data S3 bucket.\n",
"\n",
"For this example we'll be looking at the \"S1120364\" radiosonde. You can find other serial numbers by browsing the S3 bucket for the open data at https://sondehub-open-data.s3-website-us-east-1.amazonaws.com or you can look at which radiosondes are currently aloft at https://sondehub.org"
]
},
{
"cell_type": "code",
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"execution_count": 60,
"id": "processed-winning",
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"metadata": {},
"outputs": [],
"source": [
"# This will take a little bit of time depending on your internet connection\n",
"radiosonde = sondehub.download(serial=\"S4620525\")"
]
},
{
"cell_type": "code",
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"execution_count": 61,
"id": "local-vietnamese",
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"{'datetime': '2021-02-09T22:30:33.001000Z',\n",
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" 'uploader_position': '-34.8137,138.7488',\n",
" 'software_name': 'radiosonde_auto_rx',\n",
" 'uploader_callsign': 'AREG_AUTO_RX',\n",
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" 'alt': -4.83791,\n",
" 'lon': 138.52043,\n",
" 'vel_h': 0.77782,\n",
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" 'frequency': 401.501,\n",
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" 'vel_v': 5e-05,\n",
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" 'manufacturer': 'Vaisala',\n",
" 'batt': 3,\n",
" 'software_version': '1.4.1-beta8',\n",
" 'uploader_antenna': '6dBi Vertical',\n",
" 'lat': -34.95243,\n",
" 'subtype': 'RS41-SG',\n",
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" 'snr': 11.6,\n",
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" 'type': 'RS41',\n",
" 'sats': 7,\n",
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" 'heading': 291.15022,\n",
" 'frame': 1262,\n",
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" 'burst_timer': 65535,\n",
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" 'time_received': '2021-02-09T22:30:17.135788Z',\n",
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" 'serial': 'S4620525',\n",
" 'user-agent': 'autorx-1.4.1-beta8',\n",
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" 'position': '-34.95243,138.52043',\n",
" 'upload_time_delta': -2.001443,\n",
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" 'uploader_alt': 420}"
]
},
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"execution_count": 61,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"radiosonde[0] # let's have a look at the first record"
]
},
{
"cell_type": "markdown",
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"id": "double-insulation",
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"metadata": {},
"source": [
"# Parse data\n",
"To make things easier we use numpy to make an array out of the data. This will help us when we go to graph the data.\n",
"\n",
"In this case we are going to graph altitude, snr vs time"
]
},
{
"cell_type": "code",
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"execution_count": 62,
"id": "married-founder",
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"metadata": {},
"outputs": [],
"source": [
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"data = np.array( [ [datetime.datetime.strptime(each['datetime'], \"%Y-%m-%dT%H:%M:%S.%fZ\"), each['alt'], each['snr'] if 'snr' in each else None, each['uploader_callsign'] if 'uploader_callsign' in each else None] for each in radiosonde if each['uploader_callsign'] == 'AREG_AUTO_RX' ] )"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "level-conversation",
"metadata": {},
"outputs": [],
"source": [
"filtered = [\n",
" [\n",
" datetime.datetime.strptime(each['datetime'], \"%Y-%m-%dT%H:%M:%S.%fZ\"),\n",
" float(each['alt']),\n",
" float(each['snr']) if 'snr' in each else None,\n",
" each['uploader_callsign'] if 'uploader_callsign' in each else None\n",
" ]\n",
" for each in radiosonde\n",
" ]\n"
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]
},
{
"cell_type": "code",
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"execution_count": 64,
"id": "meaningful-herald",
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"array([datetime.datetime(2021, 2, 9, 22, 30, 33, 1000), -4.83791, 11.6,\n",
" 'AREG_AUTO_RX'], dtype=object)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "weekly-charter",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[datetime.datetime(2021, 2, 9, 22, 30, 33, 1000), -4.83791, 11.6,\n",
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" 'AREG_AUTO_RX'],\n",
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" [datetime.datetime(2021, 2, 9, 22, 30, 29, 1000), -6.23293, 11.0,\n",
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" 'AREG_AUTO_RX'],\n",
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" [datetime.datetime(2021, 2, 9, 22, 30, 38, 1000), -4.91941, 9.4,\n",
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" 'AREG_AUTO_RX'],\n",
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" ...,\n",
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" [datetime.datetime(2021, 2, 10, 4, 44, 0, 188437), 277.0, None,\n",
" 'VK5KJL_AUTO_RX'],\n",
" [datetime.datetime(2021, 2, 10, 4, 43, 30, 212890), 277.0, None,\n",
" 'VK5KJL_AUTO_RX'],\n",
" [datetime.datetime(2021, 2, 10, 1, 31, 0, 73080), 482.0, None,\n",
" 'VK5ST_AUTO_RX']], dtype=object)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"data = np.array(\n",
" filtered\n",
")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "insured-probability",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[datetime.datetime(2021, 2, 15, 23, 17, 28, 754111), 759.0, None,\n",
" 'F6BYJ'],\n",
" [datetime.datetime(2021, 2, 15, 23, 18, 0, 148398), 916.0, None,\n",
" 'F6BYJ'],\n",
" [datetime.datetime(2021, 2, 15, 23, 18, 26, 819436), 1049.0, None,\n",
" 'F1JBB'],\n",
" ...,\n",
" [datetime.datetime(2021, 2, 16, 2, 1, 30, 157053), 1195.0, None,\n",
" 'F6BYJ'],\n",
" [datetime.datetime(2021, 2, 16, 2, 2, 30, 69061), 1036.0, None,\n",
" 'F6BYJ'],\n",
" [datetime.datetime(2021, 2, 16, 2, 3, 0, 177289), 965.0, None,\n",
" 'F6BYJ']], dtype=object)"
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]
},
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"execution_count": 56,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data[np.argsort(data[:, 0])] # order the data by the datetime\n",
"data # have a look at the data"
]
},
{
"cell_type": "markdown",
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"id": "confused-mistake",
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"metadata": {},
"source": [
"# Graph the data"
]
},
{
"cell_type": "code",
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"execution_count": 57,
"id": "exciting-record",
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sonde_time = data[:,0] # get the datetime from the ndarray\n",
"alt = data[:,1] # get the altitude from the ndarray\n",
"\n",
"\n",
"plt.plot(sonde_time, alt)\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 59,
"id": "interpreted-contributor",
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot SNR along side\n",
"\n",
"sonde_time = data[:,0] # get the datetime from the ndarray\n",
"alt = data[:,1] # get the altitude from the ndarray\n",
"snr = data[:,2] # get the altitude from the ndarray\n",
"\n",
"fig,ax = plt.subplots()\n",
"\n",
"ax.plot(sonde_time, alt) # plot altitude on the first axis\n",
"ax.set_ylabel(\"Alt\",fontsize=14)\n",
"\n",
"ax2=ax.twinx() # get a second axis\n",
"ax2.set_ylabel(\"SNR\",fontsize=14)\n",
"ax2.plot(sonde_time, snr, color=\"red\")\n",
"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "extended-aberdeen",
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"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
