{ "cells": [ { "cell_type": "markdown", "id": "8e748bfd", "metadata": {}, "source": [ "# Tide Gauges\n", "\n", "_Lecture notes from Friday, Jan 11_\n", "\n", "----------------\n", "We are now ready to work with some data, and our example will be tide gauge data from the UH Sea Level Center (http://uhslc.soest.hawaii.edu).\n", "\n", "As with most applications, we start with actually getting the data. There are many different ways to do this, but in a broad sense we can either get data and store it on a local machine/directory or try access the data directly from a \"data server\", _e.g._ , a remote web site. This latter option is not always available, so it's good to learn both methods. Also, it is sometimes useful to have the data on your local machine, for example, you can still work with it while not on the Internet.\n", "\n", "## 1. Getting data onto local machine\n", "\n", "The basic process is to visit some web site and download a data set. If you click on something in a browser, the browser will try open it with some known program. It should also allow you to \"save as\".\n", "\n", "In this example, go to http://uhslc.soest.hawaii.edu --> data --> legacy data. You should see a page like this:\n", "\n", "\n", "\n", "\n", "There are three columns to get the data: \"Data\", \"CSV\" and \"NetCDF\". You should either get one from the CSV or NetCDF columns. If you \"left-click\" on the data of interest, _e.g._, Honolulu hourly under CSV, you should get a popup that asks to \"save the data\":\n", "\n", "\n", " \n", "You now have the data on your local machine. It's fine to use a local python/jupyter instance to work with the data, but if you want to use the class machine (sunrise) you'll have to move the file over. This can be done with __scp__, recall the syntax is copy-file-from, space, copy-file-to. For example, if you download the file h001.cvs into a directory called \"Downloads\", and you want it on your home directory for class, from your local computer you would open a terminal session and enter:\n", "\n", " scp Downloads/h001.csv name@ocn463.soest.hawaii.edu:/home/name/jupyter/data\n", " \n", "where _name_ is your login name on the class machine.\n", "\n", "Recall that you can check if the file is there by opening a terminal session on the jupyterhub, then using the linux ls command: \n", "\n", " ls -l h001.csv\n", " \n", "and you can view the file with\n", "\n", " more h001.csv\n", "\n", "Now let's look at some examples reading the data." ] }, { "cell_type": "code", "execution_count": 1, "id": "bdeaaccf", "metadata": {}, "outputs": [], "source": [ "# First we import needed packages. Note these three will pretty much be the\n", "# defaults for this class (we'll always use them), and let's stick to the \n", "# convention of using numpy as np, pandas as pd, and matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "63888ae3", "metadata": {}, "source": [ "## 2. Accessing the data with python\n", "There are many different ways to access data within python, and here we'll go through just a few examples. In a broad sense we will always follow the same process: read the data in, have a look at it to see if it makes sense, and print out the type and shape to see if they make sense. It's the details that will vary from case to case. \n", "\n", "Note it is always useful to look at the data when downloaded to make sure it makes sense. For example, if you read in a dataset and expect hundreds of rows, and instead see only one, something went wrong." ] }, { "cell_type": "code", "execution_count": 2, "id": "5d509d2b", "metadata": {}, "outputs": [], "source": [ "# 1. First example: load using numpy \"loadtxt\"\n", "# the syntax is filename, delimiter, comments\n", "# in this case we have comma-separated, with\n", "# no comment/header lines, but it doesn't hurt\n", "# to keep this.\n", "\n", "data = np.loadtxt('./data/h001.csv',delimiter=',',comments='#')" ] }, { "cell_type": "code", "execution_count": 4, "id": "7d7d3100-433c-4e0c-b4f7-484b7fece87f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(175698, 5)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 3, "id": "d14738cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data are shape is (175698, 5)\n", "[[2001. 12. 16. 6. 1333.]\n", " [2001. 12. 16. 7. 1130.]\n", " [2001. 12. 16. 8. 867.]\n", " ...\n", " [2021. 12. 31. 21. 520.]\n", " [2021. 12. 31. 22. 596.]\n", " [2021. 12. 31. 23. 833.]]\n" ] } ], "source": [ "# We now have a variable called \"data\" that should\n", "# have all the sea level data in it. Let's print\n", "# the type, shape and look at the data just to make\n", "# sure. NOTE: type is an internal function, so the\n", "# syntax is type(data), while shape is a method of\n", "# the object data, so it's syntax is data.shape()\n", "\n", "print( 'Data are ', type(data), ' shape is ', data.shape )\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 5, "id": "306461d1", "metadata": {}, "outputs": [], "source": [ "# data are now loaded into a variable called \"data\"; \n", "# note the object is a numpy array, and there are \n", "# 167,321 rows and five columns. Here we either \n", "# check the web site or just assume the columns are:\n", "# year, month, day, hour, sea level\n", "\n", "# Next, we assing the columns to individual variables.\n", "# NOTE: since our object \"data\" is a numpy array, we\n", "# use the synax of [row,column], with \":\" meaning all\n", "# so \"year = data[:,0]\" means the variable \"year\" will\n", "# be set to \"all rows\", \"first column\" of \"data\":\n", "\n", "year = data[:,0]\n", "month = data[:,1]\n", "day = data[:,2]\n", "hour = data[:,3]\n", "sea_level = data[:,4]" ] }, { "cell_type": "code", "execution_count": 6, "id": "daf43b85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Finally, to check what we have, let's make a quick\n", "# plot of sea_level\n", "plt.plot(sea_level)" ] }, { "cell_type": "code", "execution_count": 6, "id": "ccb3a3fd", "metadata": {}, "outputs": [], "source": [ "# 2. Second example: load using Pandas \"read_table\"\n", "# the syntax is filename, delimiter (in this case\n", "# sep for separater); again in this case we have \n", "# comma-separated\n", "\n", "data = pd.read_table('./data/h001.csv',sep=',')" ] }, { "cell_type": "code", "execution_count": 7, "id": "d20aa8cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data are shape is (175697, 5)\n", " 2001 12 16 6 1333\n", "0 2001 12 16 7 1130\n", "1 2001 12 16 8 867\n", "2 2001 12 16 9 553\n", "3 2001 12 16 10 308\n", "4 2001 12 16 11 177\n", "... ... .. .. .. ...\n", "175692 2021 12 31 19 584\n", "175693 2021 12 31 20 493\n", "175694 2021 12 31 21 520\n", "175695 2021 12 31 22 596\n", "175696 2021 12 31 23 833\n", "\n", "[175697 rows x 5 columns]\n" ] } ], "source": [ "# Again, let's check what we have:\n", "\n", "print( 'Data are ', type(data), ' shape is ', data.shape )\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 8, "id": "b353fc40", "metadata": {}, "outputs": [], "source": [ "# This time we get a \"pandas dataframe\"\n", "# We will see this is a much better format\n", "# with which to work. More in the next \n", "# example" ] }, { "cell_type": "code", "execution_count": 7, "id": "26d81348", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data are shape is (175697, 5)\n", " 2001 12 16 6 1333\n", "0 2001 12 16 7 1130\n", "1 2001 12 16 8 867\n", "2 2001 12 16 9 553\n", "3 2001 12 16 10 308\n", "4 2001 12 16 11 177\n", "... ... .. .. .. ...\n", "175692 2021 12 31 19 584\n", "175693 2021 12 31 20 493\n", "175694 2021 12 31 21 520\n", "175695 2021 12 31 22 596\n", "175696 2021 12 31 23 833\n", "\n", "[175697 rows x 5 columns]\n" ] } ], "source": [ "# 3. Third example: load using pandas read_csv.\n", "# This is very similar to read_table, but\n", "# presumes a comma separated data set\n", "\n", "data = pd.read_csv('./data/h001.csv')\n", "print( 'Data are ', type(data), ' shape is ', data.shape )\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 10, "id": "6c77765c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 2001 12 16 6 1333\n", "0 2001 12 16 7 1130\n", "1 2001 12 16 8 867\n", "2 2001 12 16 9 553\n", "3 2001 12 16 10 308\n", "4 2001 12 16 11 177" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Again, we get a pandas data frame. Let's just\n", "# look at a few features. For example, we can\n", "# look at the top of it (data.head()) or bottom\n", "# (data.tail())\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "a01d0901", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year month day hour sea level\n", "0 2001 12 16 6 1333\n", "1 2001 12 16 7 1130\n", "2 2001 12 16 8 867\n", "3 2001 12 16 9 553\n", "4 2001 12 16 10 308" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Notice we have a nicely formatted table, with\n", "# a row index (starting at zero) and column \n", "# headings. However, since this data set had\n", "# no headings, pandas uses the first row as the\n", "# headings. This is actually read data, so we\n", "# want to put our own headings. read_csv lets\n", "# us do that:\n", "\n", "# define a list that contains our desired headings\n", "column_headings = ['year', 'month', 'day', 'hour', 'sea level']\n", "# now add that to the read_csv\n", "data = pd.read_csv('../jupyter-gesteach/data/h001.csv',names=column_headings)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 10, "id": "c79218ac", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# So now we have a nice looking table. NOTE: since\n", "# this is a DataFrame, we access the rows/columns\n", "# differently than with the arrays (e.g., remember\n", "# before we had data[:,0]). Now we simply address\n", "# the column by its heading. So, to make a plot:\n", "\n", "plt.plot(data['sea level']);" ] }, { "cell_type": "code", "execution_count": 13, "id": "6068aace", "metadata": {}, "outputs": [], "source": [ "# 4. Third example: load using netcdf. We now will\n", "# use the netCDF4 package to read a netCDF file.\n", "# netCDF files are very common in ocean/atmo data,\n", "# and contain metadata within the file, so it's\n", "# a great format to use. \n", "# NOTE: you'll have to repeat the above download\n", "# but this time get the netcdf file and not the CSV." ] }, { "cell_type": "code", "execution_count": 39, "id": "0f70d553", "metadata": {}, "outputs": [], "source": [ "from netCDF4 import Dataset\n", "fin = Dataset('./data/h001.nc')" ] }, { "cell_type": "code", "execution_count": 40, "id": "11d8e873", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['sea_level', 'time', 'lat', 'lon', 'station_name', 'station_country', 'station_country_code', 'record_id', 'uhslc_id', 'gloss_id', 'ssc_id', 'last_rq_date'])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We now have an object call \"fin\" that has the\n", "# data inside. Since it's a netCDF we can do \n", "# different queries. For example,\n", "# show all the metadata\n", "#fin.variables\n", "# show the variable names\n", "fin.variables.keys()" ] }, { "cell_type": "code", "execution_count": 16, "id": "074078af", "metadata": {}, "outputs": [], "source": [ "# The data can be extracted in a similar way\n", "# where we specify the input variable name.\n", "# For example, we can set the dataset variable\n", "# \"lon\" to a variable longitude:\n", "time = fin['time'][:]\n", "latitude = fin['lat']\n", "longitude = fin['lon']\n", "sea_level = fin['sea_level'][0]" ] }, { "cell_type": "code", "execution_count": 17, "id": "148d3ac5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mmGdCPhVhLPrSDTs49aEZ9LznY379UuSOy2jLPESbTl9WZrj17W8jHuMktHlnpW2EyFcrNldqtw0a9NjncT+XX8QyfyAW175YyLlP+n99e7fcOWERYyY6X0Fd7rDmkReCS3b8ryh6P5ebEkr6xpiNxphSY0wZ8C+gn3VXMdDadmg+sN4qz3coT6stu/Zzx4RFCbVJ2zk1tX/zw1bGf76Cl2f+wH+/XZ/w7Lz1cW4Nd1mESSTxGPLEF+WjIFJt3ba9MQ2vCzXosc8rLLF7xiOfld+eYTUf+VG6d7nasH2v40S9aFdwVcmLM38oH3gR+nmdtdK9EW3J+PBbbzroE0r6Vht90AVAcGTPB8BwEakpIm0JdNjOMcZsAHaKSH9r1M6VwIQk4k7IvR8u4aWZP1QaHxxOPAOMLhw3k/snLePVGGrdD09dzqad+8rXXo82+sCtEUBeXU66KdpkJz+K9+otXn9669vyfoPpSzdywt8/YWiYWn2iTWShwtWiveC3PWhjZV/KIZ2qRTtARF4DTgOaikgxcCdwmoj0JNBEsxr4DYAxZrGIvAksAQ4BNxpjgu/46wmMBKoNTLZ+Uir0DV5qvTmijYrZse8g9WuGf2kine205V/BqIkVfl9Zspt+Y6Zz7rEteerS43gkSrtt6AigRGXqhyPTxfKyT0tiHZkJ89ezesseLu3Xmr9YY/rDrVn0wfz1XNg73/G+eCS6Aqtytn3vQRrWrp6W54qa9I0xIxyKn4tw/BhgjEN5IdAtruiSlMgkqqJNOznz0c954MLunNWlRQqiOuzDBRt46lL9AFV1X6/aSv92TSIek+wIsW/XbuPbGOaiHIpxEEGmCK1Qhep25xRfrlAKFSthW3btT1vSr/IzcuP1vTXWeMaykrDT2//77XoKRk30xToa8TAErnJmrdxSfrXzzQ8/8dLM1WmNw40lHDbtrNj3cbDUv5cxj3z8na/7HKoyvyZ8L1XppJ9sh220zQ28XjFvSZzDILfvOchb3xQzfPys8jHnF477qsJ8gHRwY9mA0DVt/L6Wu31vW5U63/zgj07aeKVq72YnVTrphy7d+9+QyTXGGM79xxeVyv3k9vfDr7sSrrMunC2797PGakZYk+IlG1IttM3a7wvAJbqEsIrPheNiX7PHa/ZxIslusBSPKpv0b359XtTZc8bAonU7+N3r8yIe56X/zHKvBmsMbN0TSD7xDg1VSrnLq4EVVTbpvz8/fO39wKGyCsMgjYEPF1Q+PrhnaiSbduxLahicW5uhxyq49s1rc9Zw8gOfpPW53bBz36GUD4FUqiqrskk/ktvfX8S5//iSDTsO13ZDN3QAuOaF6Am53/3TuSmGzbPDcWMz9Fht2X2AvbZdsdI1GStULOvTh/PE9O/Zstu7FQozUbZ1Znq11HSmyMqkH2z//WhR5Ulaibxd0pm4q4JE9t+1s29grsK72FqT/kzbzGU3+GXTlnAmRLjKV1ma9IPu/dA/swqVctsca/epH3e4238Tbo0jv9i2RzvNI8nqpB9JrEs1KBWPdDc92NcqAti6O771d+at+YmCURNjmvjlF28nsLGJF7y6XtKkr1QVdvv7iyr87rQQWyTBfRA+83hOSjwWrfNmTZtMoUk/RFr2cFQqTdzYx0BVLZr0lUqjTBtYEtwWMZOad1RkmvRDZNhnUilXlJUZtu8NNP3MX7uNn6wZxJ8uDzTvTF+2iaJNye9Apg7zqlUh6iqbSqmq76Gpyxn36Yry3zs2r8fUP5xa4ZgzH/2c1WPPSXdoVZZ25PrAwuLt3PBK4hOtlMpUkxZW3MXpu4272HdQZz5XRZr0be74YFH0g5RKgl+bD53W8+9z3zQPIlGppknfZt6abV6HoJRvOC3foB26mS9q0heR50Vkk4gsspU9JCLLRGSBiLwnIkdY5QUisldE5ls/z9jO6S0iC0WkSESelHg2oFVKeSJ0tNF5//yfN4Eo18RS038BGBxS9jHQzRhzLPAdMNp23wpjTE/r5zpb+ThgJIHN0js4PKZSVV6mLQaWbYu1ZYOoSd8Y8zmwNaRsqjEm+G6YBUTcaVlEWgINjDEzTeBd/xJwfkIRK6USVpphXzrKfW606V8DTLb93lZE5onIZyIywCprBdgXxCi2ypRSabT/oL93GMsmXl31JTVOX0T+ChwCXrGKNgBtjDFbRKQ38L6IdMV5HkLY/7GIjCTQFESbNm2SCVEpX9F6tgpa9qM3k90SrumLyFXAucBlVpMNxpj9xpgt1u1vgBVARwI1e3sTUD4QdtFrY8x4Y0wfY0yfvLy8RENUSikVIqGkLyKDgb8Aw4wxe2zleSKSa91uR6DDdqUxZgOwU0T6W6N2rgQmJB29UhlGm9SV16I274jIa8BpQFMRKQbuJDBapybwsTXycpY1UucU4B4ROQSUAtcZY4KdwNcTGAlUm0AfgL0fQKms8PrX7m10nwgdKK2iJn1jzAiH4ufCHPsO8E6Y+wqBbnFFp1QVE7rcgVLppjNylUojbd5RXtOkr5RSWUSTvlJp5HVFX3RvuKynSV+pdPI666usp0lfqTQymvWVxzTpK5VG2pGrvKZJX6k08jrn6zh9pUlfKaWyiCZ9pdKoTNt3lMc06SuVRprzldc06SuVRbRJX2nSVyqNtKKvvKZJX6l00vYd5TFN+kqlkdcpX4dsKk36SqWRVvSV1zTpK5VGugyD8pomfaXSSGv6ymua9JVSKotETfoi8ryIbBKRRbayxiLysYh8b/3byHbfaBEpEpHlInK2rby3iCy07nvS2iBdqayiNX3ltVhq+i8Ag0PKRgHTjTEdgOnW74hIF2A40NU652kRybXOGQeMBDpYP6GPqVSVpzlfeS1q0jfGfA5sDSk+D3jRuv0icL6t/HVjzH5jzCqgCOgnIi2BBsaYmcYYA7xkO0eprGE8rurrlYZKtE2/uTFmA4D1bzOrvBWw1nZcsVXWyrodWu5IREaKSKGIFJaUlCQYolJKqVBud+Q6tdObCOWOjDHjjTF9jDF98vLyXAtOKa9pTVt5LdGkv9FqssH6d5NVXgy0th2XD6y3yvMdypXKKl6P09fvHJVo0v8AuMq6fRUwwVY+XERqikhbAh22c6wmoJ0i0t8atXOl7RylsobW9JXXqkU7QEReA04DmopIMXAnMBZ4U0SuBdYAFwEYYxaLyJvAEuAQcKMxptR6qOsJjASqDUy2fpTKKl5voqLjpFXUpG+MGRHmroFhjh8DjHEoLwS6xRWdUkopV+mMXKWUyiKa9JVKI23SV17TpK9UOmnWVx7TpK9UGmnOV17TpK+UUllEk75SaeT12jtbdx/w9PmV9zTpK5VGXjfv5OboSP1sp0lfqTTyekZux+b1vQ1AeU6TvlJp5PXaO0pp0lcqjcrKvI5AZTtN+kplEb3SyCyp6PjXpK+UUj51oNT9S0NN+kqlkddDNpXSpK9UGmnKV17TpK9UGmlFX3lNk75SaeT1Jioqs6Ti7aJJX6k00pSvvJZw0heRTiIy3/azQ0RuFpG7RGSdrXyo7ZzRIlIkIstF5Gx3/gtKKaViFXW7xHCMMcuBngAikgusA94Dfgk8Zox52H68iHQBhgNdgSOBaSLS0baHrlJVnrbuKK+51bwzEFhhjPkhwjHnAa8bY/YbY1YBRUA/l55fqQyhWV/FTlKwPp5bSX848Jrt95tEZIGIPC8ijayyVsBa2zHFVplSWUNr+sprSSd9EakBDAPesorGAe0JNP1sAB4JHupwuuNHQERGikihiBSWlJQkG6JSvtGtVUOvQ1BZzo2a/hBgrjFmI4AxZqMxptQYUwb8i8NNOMVAa9t5+cB6pwc0xow3xvQxxvTJy8tzIUSl/KFRnepeh6CynBtJfwS2ph0RaWm77wJgkXX7A2C4iNQUkbZAB2COC8+vVMbQ1h3ltYRH7wCISB3gLOA3tuIHRaQngff36uB9xpjFIvImsAQ4BNyoI3dUtinTrK88llTSN8bsAZqElF0R4fgxwJhknlMplTjtSFY6I1epNNJVNlU8xHH8S3I06SuVRpryldc06SulVBbRpK9UOmlVX3lMk75SaeT1HrX6naM06SuVRmXub3mqVFw06SulVBbRpK9UGnndvKOUJn2l0kiH6at4+HlpZaVUDDTnK69p0lcqjbSmr7ymSV+pNNJlGJTXNOkrlUZlmvSVxzTpK5VGXqf8FPQLqgyjSV+pNNKKvvKaJn2l0sjr5h39zsksqbgy06SvVBppTV8FHd2snifPq0lfqTTyuqav/KNlw1qePG9SSV9EVovIQhGZLyKFVlljEflYRL63/m1kO360iBSJyHIROTvZ4JXKNJr0ldfcqOmfbozpaYzpY/0+CphujOkATLd+R0S6AMOBrsBg4GkRyXXh+ZXKGF5vjK6jd1QqmnfOA160br8InG8rf90Ys98YswooAvql4PmV8i+t6CuPJZv0DTBVRL4RkZFWWXNjzAYA699mVnkrYK3t3GKrrBIRGSkihSJSWFJSkmSISvmHNu8or1VL8vyTjDHrRaQZ8LGILItwrNOVpeMnwBgzHhgP0KdPH/2UqCpDk74KiuWtIClYZjOpmr4xZr317ybgPQLNNRtFpCWA9e8m6/BioLXt9HxgfTLPr1Sm0ZSvglKxbHIsEk76IlJXROoHbwODgEXAB8BV1mFXAROs2x8Aw0Wkpoi0BToAcxJ9fqUykdcduUol07zTHHjPuvyoBrxqjPlIRL4G3hSRa4E1wEUAxpjFIvImsAQ4BNxojClNKnqlMkyuDp9RHks46RtjVgI9HMq3AAPDnDMGGJPocyqV6fq1bcLcNdu8DkP5QLg2/RPaNWHmyi0pe16dkauUUj4y5oJuKX18TfpKpZHXG6Nrl4J/hOvIbdu0bkqfV5O+UunkcdbVEaP+Ee5vYR+mmaMboyuV2TTnZpd+bRsnfG6dGrn+G6evlIrP4G4tvA5BpVHTejUSOu+la/rx8S2nuhxNgCZ9pdLouDaNoh+kqoxzuh+Z0HmndMyj1RG1XY4mQJO+Uiqlzjm2pdchKBtN+kqplOrUvL7XIaTd0O7Rm/Ea1q6ehkgq06SvlEpKnRrht8U4v+eRWb2Gf6Qhuhm39o5Smah149S0k2az605tH/a+x4f3SmMk/iE+/qrTpK+yip/HqZ/TPfVt31t370/5c2S7Fg1q0d7a9DyvXs0K9z1wYffy26kYjhkLTfoq6zx+SU+vQ3B04tFNUv4c89duS/lzhPKqGcMrn916Gr8742he+3V/jm9X8W/asmHgSvPiPvlehAZo0ldZ6MgUDIUb2LlZ9IN8IBXNDkfUidwh6VWN1is1q+VSLTeHE9pX/hIf0KEp957fjTt/1rVC+b+v7puu8DTpKxWPXm2OcCxvGCXx+UWjuu7HWata+I7cSIrGDHE5kvjUqOZ++quRW/kxj2pSp/y2iHBF/6OoW7PiAsddj2zgeizhaNJXSVl41yCvQ3D0zOXHpfX5hnar3B5/Rf+j0hpDLPzUwVjNIUHa/fGsjo7lD1zYnUuPb5P08393n/tfOgkvqJfGP4smfRVVpFpI/Vr+rOEOdkjCAKd1ykvJ853ZpTlf3Hp6hbI7ftal/PatgztFfYz8RnWiHuNH0RJdoq07l/RtXb6Mgf3vVqNaTtgvBD8K999vUjexJRqSpUlfZZV7hnWjgzWyItGrAfsIjHD+NKgj1W012WtPbhv1nKOtuJy4NavVL83rI/rFVlM/rVOgr2RoyMimJiGjYvziL4M7Vyrr2qqh47FDPFqHKZk9cluLyAwRWSoii0Xk91b5XSKyTkTmWz9DbeeMFpEiEVkuIme78R9QqefHZopE5eQIjerWYPXYcxjcrSUDOjStdEz9WuE3lBvetzWX9G3D/ReET/z5jWpz0xkdKpTVTLDdO+iflx7Hg784NqnHgNQk/ZOOrvwaRnN/jBuF9Gx9BADtbGvM92rt3vpFwcd3w1UnHMWvBrSrVN4uzPr49SK8z1IpmZr+IeCPxphjgP7AjSISvJ59zBjT0/qZBGDdNxzoCgwGnhaR5D4JKi2G92vD6XE0iyS7BvivB0SvFSfi6csq1+xfvvb48tvRRlC89uv+XNynNUCFNuUJN56UVFzV07hxbira9PMb1WH12HPC3n9My8rNg8ERPZHmJtSsnstlx7dhxp9Oo09BY5beM5iiMUMocHGTkUQWwHv+6j6O5aeFGcF19YkFjuVdj3S+Aki1hJO+MWaDMWaudXsnsBRoFeGU84DXjTH7jTGrgCKgX6LP75VrTkpNQvK7eIbdPXuV84ciVr92qC25wWlkhZ19x6KmDs0HJ7RvUuF1+N3ADrwxsj89rNpicATPucfGt7LirNEDmf7H1CyjW0kavl9CvwC6OiT9oJoRRtA0rF0dESn/u9SukRu18zdeo4d2dlz+ONKX2BmdmzPl5lMqlH108wBO7+Sc9P3WFOXKKygiBUAvYLZVdJOILBCR50Uk+FXaClhrO62YMF8SIjJSRApFpLCkpMSNEF2xeuw5nm93l27v3nAiAB0jLJp1ZMNaFX5vXDe5N3mzBrWiHxSD0A9uPH+5KTcPYNLvBpT/PmpI5bbaW87qWGHyTYNa1Vlw1yBuPTt6p61dvVrVaJ9XjxYNanHmMc3DHxjyH1hx/1D+dWV8X7Bu53z7a/zkiF4VXrOgSH/Pzi29XYytem5O2Jp4JJ1a1Ofrv55Z/nu1JC9vQ2fuplLSSV9E6gHvADcbY3YA44D2QE9gA/BI8FCH0x0/h8aY8caYPsaYPnl5qRlt4YW8+v76xo+mSd0a5Ze/vY+qfBkcXD2xQchqgc1C/p+xrAu+euw55T8AJyfQThwUOoomqEOEjlKo2N7dpF5NuthGLUVaX8auQa3q5CSYAHJzxPEqKThSJXQuQG6OcFaXCF8SDlI5UWpYjyMrvGZ2HZs7v/a/Ormdc79KzfS1d4d2Escqr35NHrukB0c3q0frxsmNvErnBLakkr6IVCeQ8F8xxrwLYIzZaIwpNcaUAf/icBNOMdDadno+sD6Z5/dCMmu3tG6U+EzQZ+Os0bktNJEDlFkvxvNX9y1Pih/clFz7dlC8NdigWtVzwn4Ao80crVU90MUUbLd3wz9G9OKJ4T2TeoxhPQPNRcH4AEaeklgTmFeDd8L1JeTkCH92uDJaeHdqx3nYR261y6vHknsSe74LeuUz7ZZTE+6of++GEx3//6mU8NepBL6angOWGmMetZW3NMZssH69AFhk3f4AeFVEHgWOBDoAcxJ9/mxzZpw1OggMARx32XEUbdrF9a/Mjft8ew2oh8MohxZWs86RR9Rm1JDO5U0g67ftjet5ft6rcitf7QjL9UZSFuFLOVonZo3cHJbdO7hC2/+7N5zI3gOlCcUC8LMe0dv3o1Ukgv+nYPSndMzjtqHHJBRPuiqUoU0mDWofTjWDu1Ycqnhs/hH8bmAHnpz+fTpCC8QQZh6H3e3nHMN9E5e68nwvX9uPzbsqL3bXq00jeqV5N7VkrqFOAq4AForIfKvsNmCEiPQk0HSzGvgNgDFmsYi8CSwhMPLnRmNM4p+mNFt27+CkH+PuYd342VNfuhBNbD787cnUqp5LhwQ3sbhrWMX1QTq3qM+yH3cCgWGJT4ZZNrd29dgSducW9fkopEMsaUl2udQKiT3V2xsO63FkxM5MOHxFdWL7Jlzevw03nn50ws+Xrpp+6Hvnn5ceR7/7pwPOS0HcclbHtCb9UPYKQSqW3x7QwT/N1MmM3vnSGCPGmGPtwzONMVcYY7pb5cNstX6MMWOMMe2NMZ2MMZPd+S+kXucW9cuTgUmifad7fkMeuahHwue3z4s+VM2+nkhoAotXbkjb9H9+dTzD+waaPv44qCONwswobFS3BhNuPIm8+jW5wFaL79wi9Z12LRom3gnsxcSlJ0f0itqeG7zyqJabw33ndy9fqTERXi1+5lbnfKrYh81+8sfTAHfH8PuJzshNs0Q+c5FmaoaKtZadiKb1ajL2wmP58Lcnc37PSKNzA81BX//1TB67pCcPXXQsvY9qVGFMPECXCEP5QrWMMZlfeUL8E8nGX9Gb9nl1OaKON9Piown98g3VKI7F3vwxITdyFO9cf2LSz9A2zrH81XJzuOn0o3nn+hPLZ1J7NXkq1arm/yqCpvVqsHnXgYTPT6Sef+fPuiQ1oaRWdX99N3cLM608nBPbN+XE65tSWmZo3bg2fz67M62OqBXz5JT7L+jO7v2HGDPJnfbVUIO6tmBQV2+mxEczuGsLmkepJX98y6mc+uAMdsfQ91DTZ+8lJ04jxeKVyP6zfwrpULVf1L94TT+uer5qdEH6/x3gok7N63NelBpqNLF0zIX65UltyyduJFLTj3Y5PyiGTt5FcY6GGNHPvREsQbk5whe3nsGwHkfS+6jGUZufnhjek9dH9k9qRcVP0jXpyUX2se/PXNE7ak2/ab2adIqx6axJknMo3FA3wU56r3RuUZ9TrGGliYzp95usSvoj+rVOutbcu00jfnVyW/q1bRzT8YkOrbN75OJAP8CjF/d0vP83tjHk4b5U6sU57tkPG1+c17MV/UN2HuqR39BxXHdQWUifS7u8ejTIwMv0t647gefimNkc66xPH/xZuWWQOytkBvuXoPIQ3yZ1ayTdhxS8wjq/VytEhNVjz6nUQZ2JsirpX+XCt3ROjnD7uV3Ij3HMfejU7AYJLEUcPKdH6yNYef9QHrukYmew/XLYrT1gY5lQ5YW+BY0jzlp1+v+XT2ryQcKLVd+CxgyMNDs3xMO/6MFvYqhg+OElqFPD+Uv4jZH9mX3bwJgfZ+yFx5bvWBb6/3rk4h68d4PznBGn4cdOGtetwfL7Bsf0umaSrEr6btZeIy1LYBe6ZdoZnZtxz3mJ1xZycoQhMYwxTlayMwzdFmziyM2ViLVVp++8V3/Vn7uHdU2onTdTNKxTndFDj2He386KeJybnZMvXhPf0lkX9c7n0YvDj147vl2TqP0XoVpZla/QWeFH1KkRdq5HPAvk1ayW64urXjdl3nWvT4wc0I5tew7SI79hXBOfRIRL+rbmjgmLy8t6tTmClSW72b73YIyPcfh2MJFddnwbZizbxJ6D7kx9qFfTX+2ulx7fhvXb9vLbMzrwzjfFYY8Lbd6BwBeYG1d5blk99hy+KtrMujgnscUi3DDaoGiLzsUj3pFiDyUxXDmc24YewwntmoRtbm3TuA5rtu5x/XkzWVbV9AHa58U+/DHIacXFnBxh1JDODOneko9urrzIVCQ1q+VynG2v1Reu7sdnfz6t0sJlsZg1OnA5POaC7nw1eiDVcpL/k151wlFhVwz0Sq3qudx+bhfq1awWMdn8ond+GqNK3IlHN+UiF5d7iFVVq7XWqp7LkATXzslWWZf0z+7agvo1q/HE8J60ibEJ48kRzjNPgzq3iH9T4762mknDOtU5ok6N8kvVaOyzB0MvYf9wVmDzjl/FsFOTkxYNajFqyDG+Tg75EWZMNqvv70lA6RBpWWA3/6zJTFRMhVd/dXylsruGdXE4MrtV2aR/Skfnac91a1Zj4d1nc17PVrzwy8ibZgQ1TsFelsnsRhVpCN+Ivm249/xu/DmGPVkB/nx2pwrb/826bWDC6954LdKGHCog3DunTwJj490YT++mEx1WZj2jc/MK/Qj2ET/ZStv0PZLfqA7z/naWYxu03dvXnVCpLDdHmHPbwEpL7UKg2SncF4pT+2ZwHZe/vLMw1tB9y8cXJ77h1FQJJLQctNsbmqRK8CN2Qa9WjL0w+S0nM11m/NWSNLBzs7i2+0vE5N/H164PgU63aOOr+xQ4d1A1a1Ar7uVcX7424zYqcxRutUx/NTb4y+md8njm8t4MDrMZd78w77NMc1aX5mHncWidICAravrPRdn71A1O+4D6zVFN6tKwdnXHUULz/nYWB8vKPIgqfv3aNubqEwto27Qu3Vo14MJxM4HUrjuU6c44pnnYhA/wh7M6Ui1XeHxabCtd3nKWOxOs3JboPgzZJCtq+uG0alQ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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NOTE: in the above we added an extra constraint\n", "# on time [:] and on sea_level [0]; this will become\n", "# clear later, but essentially we are getting all\n", "# time and all sea level, but the file stores these\n", "# with different dimentions (check this with type)\n", "\n", "# Again, we make a quick plot. This time we can use\n", "# time as the x-axis and sea_level as y. Notice we\n", "# don't have problems with missing values!\n", "\n", "plt.plot(time,sea_level)" ] }, { "cell_type": "markdown", "id": "2bef2164", "metadata": {}, "source": [ "### Repeat without downloading\n", "It is important to note that both the CSV and netCDF files can be imported into your python script without having to download the data. Insead you can just get the link to the data and use that (note the server has to be properly setup to do this, like the Sea Level Center does). To get the URL, _right click_ on the data of interest and get a pop-up like the one below. Select \"copy link location\" and paste that url into your code.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 17, "id": "a8e63d7c-d3a8-480b-8574-7ead028cf6ee", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "url = 'http://uhslc.soest.hawaii.edu/data/csv/fast/daily/d057.csv'\n", "df = pd.read_csv(url) " ] }, { "cell_type": "code", "execution_count": null, "id": "2d957721-e5b5-4a84-a12d-bf9473d2e501", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "id": "534e608b-2f73-4d11-a62d-6a531f962a5a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 1905 1 2 1263\n", "0 1905 1 3 1264\n", "1 1905 1 4 1269\n", "2 1905 1 5 1294\n", "3 1905 1 6 1311\n", "4 1905 1 7 1330\n", "... ... .. .. ...\n", "42758 2022 1 27 1439\n", "42759 2022 1 28 1432\n", "42760 2022 1 29 1427\n", "42761 2022 1 30 1430\n", "42762 2022 1 31 1452\n", "\n", "[42763 rows x 4 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 19, "id": "975274d0", "metadata": {}, "outputs": [], "source": [ "# define end-point URL to the data set of interest (see above)\n", "# Note that the URL contains the site number (001 below) and the\n", "# resolution (e.g., daily). So, you could actually change this\n", "# URL without having to go back to the site and right-click/save\n", "\n", "# get daily data from Pohnpei (UH id 001)\n", "URL = 'http://uhslc.soest.hawaii.edu/data/csv/fast/daily/d001.csv'" ] }, { "cell_type": "code", "execution_count": 20, "id": "a821828f-892a-48b1-9f96-035d017f6772", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 2001 12 17 652\n", "0 2001 12 18 650\n", "1 2001 12 19 654\n", "2 2001 12 20 662\n", "3 2001 12 21 644\n", "4 2001 12 22 605" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read data and have a look\n", "# Note this will come in as a pandas dataframe\n", "data = pd.read_csv(URL)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 21, "id": "1db47583-c836-4ee9-9823-37a253e0b7e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year month day sea level\n", "0 2001 12 17 652\n", "1 2001 12 18 650\n", "2 2001 12 19 654\n", "3 2001 12 20 662\n", "4 2001 12 21 644" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The data set has no column headings, so pandas just uses the top data line\n", "# Let's change this to be year, month, day and sea level\n", "column_names = ['year', 'month', 'day', 'sea level']\n", "data = pd.read_csv(URL, names = column_names)\n", "data.head()" ] }, { "cell_type": "markdown", "id": "9aefea13", "metadata": {}, "source": [ "## Working with the data\n", "At this point we have read in the data and setup some variables. There are a couple things we'd like to do:\n", "\n", "1. Convert the time variable to something more sensible, and\n", "2. Remove the missing data which appears as some big negative number\n", "\n", "Fortunately numpy and pandas will provide the solution. First, we can use pandas to_datetime to \"fix\" the dates." ] }, { "cell_type": "code", "execution_count": 19, "id": "19da5849", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year month day hour sea level\n", "0 1993 5 14 1 1092\n", "1 1993 5 14 2 939\n", "2 1993 5 14 3 804\n", "3 1993 5 14 4 710\n", "4 1993 5 14 5 697" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We have an index in the DataFrame that indicates each row (reminder this starts\n", "# at zero). Since we have one hour per row, the index amounts to \"hours since\", \n", "# and the first time value is May 14, 1993. Thus, row one is \"zero hours since\n", "# 05/14/93\", second row it \"1 hour since 05/14/93\" and so on.\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 20, "id": "57687b92", "metadata": {}, "outputs": [], "source": [ "# there is a function in pandas to make dates easily; pd.to_datetime()\n", "# it takes a \"date\", an \"origin\", and a format so if we specify the\n", "# index and \"hours since may-14-1993\", each row will have the proper\n", "# time (note this is just one way to do it).\n", "\n", "# a) to get columns, e.g., \"hour\", we use square brackets: data['hour']\n", "# but now we want the row, or index; to get this use data.index\n", "# b) datetime will read seconds, so we convert from hours to seconds\n", "# (*3600)\n", "# c) finally, we specify the start date as the origin\n", "\n", "date = pd.to_datetime(data.index*3600.0, origin = '05-14-1993', unit='s')" ] }, { "cell_type": "code", "execution_count": 21, "id": "2fcdcf80", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Now we can plot with a more \"sensible\" time\n", "plt.plot(date,data['sea level'])" ] }, { "cell_type": "code", "execution_count": 22, "id": "fbf1dd96-84ea-46fb-9eba-21b471989176", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaysea leveldate
0200112176522001-12-17
1200112186502001-12-18
2200112196542001-12-19
3200112206622001-12-20
4200112216442001-12-21
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" ], "text/plain": [ " year month day sea level date\n", "0 2001 12 17 652 2001-12-17\n", "1 2001 12 18 650 2001-12-18\n", "2 2001 12 19 654 2001-12-19\n", "3 2001 12 20 662 2001-12-20\n", "4 2001 12 21 644 2001-12-21" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Another way to convert the date, which is currently\n", "# year, month, day in separate columns to a single entry. This\n", "# involves two steps: make a new variable year-month-day (e.g.,\n", "# 2001-12-31) and then add this as a new column\n", "\n", "# 1. make new variable\n", "measurement_date = data['year'].astype(str) + '-' + data['month'].astype(str) + '-' + data['day'].astype(str)\n", "\n", "# 2. add to dataframe\n", "data['date'] = measurement_date\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 24, "id": "0ee577df-7a0f-4d4c-84c7-4e93276f3f67", "metadata": {}, "outputs": [], "source": [ "# Now we use the Pandas function \"datetime\" to make\n", "# a date that is more recognizable; note we specify\n", "# our format: %Y=4 character year; %m=2 character month\n", "# %d=2 character day\n", "data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": 25, "id": "2c376b7d-5867-4161-8d47-3bad5ce09321", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaysea leveldate
0200112176522001-12-17
1200112186502001-12-18
2200112196542001-12-19
3200112206622001-12-20
4200112216442001-12-21
\n", "
" ], "text/plain": [ " year month day sea level date\n", "0 2001 12 17 652 2001-12-17\n", "1 2001 12 18 650 2001-12-18\n", "2 2001 12 19 654 2001-12-19\n", "3 2001 12 20 662 2001-12-20\n", "4 2001 12 21 644 2001-12-21" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "f21afb74-024a-490c-9354-c49c35a90409", "metadata": {}, "outputs": [], "source": [ "# We notice that missing values are set to some extreme\n", "# negative number (turns out to be -32768). We want to\n", "# remove these to make our plot look better. Note there\n", "# are several ways to do this, but here we just replace\n", "# all negative values with \"nan\" or \"not a number\"\n", "data[data['sea level'] < 0 ] = np.nan" ] }, { "cell_type": "code", "execution_count": 27, "id": "3b184866-14d7-492f-9062-186d15a1f50e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,8))\n", "plt.plot(data['date'],data['sea level'])\n", "plt.grid(linestyle='dashed')\n", "plt.title('Sea level at Pohnpei, FSM')\n", "plt.xlabel('Date')\n", "plt.ylabel('sea level (mm)');" ] }, { "cell_type": "code", "execution_count": 22, "id": "fe8ae411", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Now how to do this with netCDF data? Here we can use\n", "# the same funtion, and it's a litte easier. The netCDF\n", "# variable \"time\" is defined as \"days since 1800-01-01\"\n", "# so we just use that:\n", "\n", "date = pd.to_datetime(time,origin='1800-01-01 00:00:00',unit='d')" ] }, { "cell_type": "markdown", "id": "c508b834", "metadata": {}, "source": [ "## Do some analysis\n", "A quick thing we can do is try plot the data between a certain range. Let's pull data for Honolulu and Midway, then make a plot over mid May 1960 to see if we can observe the tsunami generated by the Chile earthquake." ] }, { "cell_type": "code", "execution_count": 30, "id": "76a55d4b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import datetime as dt\n", "# read data from URL; specify URL here\n", "HI_URL = 'http://uhslc.soest.hawaii.edu/data/csv/fast/hourly/h057.csv'\n", "MW_URL = 'http://uhslc.soest.hawaii.edu/data/csv/fast/hourly/h050.csv'\n", "\n", "# define column headings\n", "column_headings = ['year', 'month', 'day', 'hour', 'sea level']\n", "\n", "# read data into DataFrame called \"data\"\n", "HI_data = pd.read_csv(HI_URL,names=column_headings)\n", "MW_data = pd.read_csv(MW_URL,names=column_headings)\n", "\n", "# convert time into a reasonal date format\n", "HI_date = pd.to_datetime(HI_data.index*3600.0, origin = '01-02-1905', unit='s')\n", "MW_date = pd.to_datetime(MW_data.index*3600.0, origin = '02-09-1947', unit='s')\n", "\n", "# plot\n", "plt.plot(HI_date,HI_data['sea level'],label='Honolulu')\n", "plt.plot(MW_date,MW_data['sea level'],label='Midway')\n", "# set the x-axis to run from specific start/end\n", "plt.xlim([dt.datetime(1960,5,24),dt.datetime(1960,5,25)])\n", "# set y-axis limit\n", "plt.ylim(400,2000)\n", "plt.grid()\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 33, "id": "fe2e2b45", "metadata": {}, "outputs": [], "source": [ "# fit a linear trend to the data\n", "# \n", "# here we take advantage of the functions in numpy called\n", "# \"polyfit\" that will fit a polynomial to data. This uses\n", "# least-squares to get the best fit, and you can specify\n", "# the order as well (1: linear, 2: square, 3: cubic, etc.)\n", "#\n", "# One important note: polyfit can't deal with missing \n", "# values, so we need to somehow fill these. We will do\n", "# this by filling with the mean. In otherwords, if a\n", "# value is missing, we fill it with the mean value. This\n", "# is not scientifically advisable, but we use it here as\n", "# an example.\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "30293023", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# read data from URL; specify URL here\n", "HI_URL = 'http://uhslc.soest.hawaii.edu/data/csv/fast/hourly/h057.csv'\n", "\n", "# define column headings\n", "column_headings = ['year', 'month', 'day', 'hour', 'sea level']\n", "\n", "# read data into DataFrame called \"data\"\n", "HI_data = pd.read_csv(HI_URL,names=column_headings)\n", "\n", "# convert time into a reasonal date format\n", "HI_date = pd.to_datetime(HI_data.index*3600.0, origin = '01-02-1905', unit='s')\n", "\n", "plt.plot(HI_date,HI_data['sea level']);" ] }, { "cell_type": "code", "execution_count": 32, "id": "db4d8047", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# replace missing values with mean\n", "# find values equal to -32767 and replace with nan\n", "HI_data2 = HI_data.replace(-32767,np.nan)\n", "\n", "# replace these nan values with the mean\n", "HI_data3 = HI_data2.fillna(np.nanmean(HI_data2))\n", "\n", "# compute the linear trend\n", "B = np.polyfit(HI_data3.index.values,HI_data3['sea level'],1)\n", "ssh_trend = np.polyval(B,HI_data.index.values)\n", "\n", "plt.plot(HI_date,ssh_trend,'r-')" ] }, { "cell_type": "code", "execution_count": 33, "id": "6dc452b9-bf31-4778-a863-77c5cddf6de8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.75693716e-04, 1.27824609e+03])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "B" ] }, { "cell_type": "code", "execution_count": 38, "id": "174523c5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show the trend as text on the graph\n", "# the syntax here is: 1) slope is the first\n", "# value of B, i.e., B[0]; 2) this is in\n", "# milimeters per hour, so we change this to\n", "# centimeters per decade; 3) we use the\n", "# matplotlib function \"text\" and place the\n", "# string at x=1910, y = 2100\n", "trend = str(B[0] * 24.0 * 365.0 * 10.0 / 10.0)\n", "plt.plot(HI_date,HI_data2['sea level'])\n", "plt.plot(HI_date,ssh_trend,'r-')\n", "plt.text(dt.date(1910,1,1,),2100,'trend is:' + trend + 'cm/decade');" ] }, { "cell_type": "code", "execution_count": 36, "id": "b96d4299-a473-4428-ac72-c3ad8e51cfec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.539076951910953" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trend = str(B[0]*24.0*365.0*10.0/10.0)" ] }, { "cell_type": "markdown", "id": "3ef35d34", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "code", "execution_count": 40, "id": "677faa98", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Sea Level (mm)')" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# read data from URL; specify URL here\n", "HI_URL = 'http://uhslc.soest.hawaii.edu/data/csv/fast/hourly/h057.csv'\n", "\n", "# define column headings\n", "column_headings = ['year', 'month', 'day', 'hour', 'sea level']\n", "\n", "# read data into DataFrame called \"data\"\n", "HI_data = pd.read_csv(HI_URL,names=column_headings)\n", "\n", "# convert time into a reasonal date format\n", "HI_date = pd.to_datetime(HI_data.index*3600.0, origin = '01-02-1905', unit='s')\n", "\n", "# find values equal to -32767 and replace with nan\n", "HI_data2 = HI_data.replace(-32767,np.nan)\n", "\n", "# replace these nan values with the mean\n", "HI_data3 = HI_data2.fillna(np.nanmean(HI_data2))\n", "\n", "# compute the linear trend\n", "B = np.polyfit(HI_data3.index.values,HI_data3['sea level'],1)\n", "ssh_trend = np.polyval(B,HI_data.index.values)\n", "trend = str(B[0] * 24.0 * 365.0 * 10.0 / 10.0)\n", "\n", "# plot\n", "plt.plot(HI_date,HI_data2['sea level'])\n", "plt.plot(HI_date,ssh_trend,'r-')\n", "plt.text(dt.date(1910,1,1,),2100,'trend is: ' + trend + 'cm/decade')\n", "plt.grid()\n", "plt.title('Tide gauge record at Honolulu')\n", "plt.xlabel('Date')\n", "plt.ylabel('Sea Level (mm)');" ] }, { "cell_type": "markdown", "id": "336130ef", "metadata": {}, "source": [ "Now repeat the same thing, but use netCDF data" ] }, { "cell_type": "code", "execution_count": 39, "id": "8734f1bc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['sea_level', 'time', 'lat', 'lon', 'station_name', 'station_country', 'station_country_code', 'record_id', 'uhslc_id', 'gloss_id', 'ssc_id', 'last_rq_date'])\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# import netCDF4 package, only \"Dataset\" and \"chartostring\"\n", "from netCDF4 import Dataset, chartostring\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "fin = Dataset('./data/h001.nc')\n", "print(fin.variables.keys())\n", "latitude = fin['lat']\n", "longitude = fin['lon']\n", "time = fin['time'][:]\n", "h = fin['sea_level'][0]\n", "date = pd.to_datetime(time,origin='1800-01-01 00:00:00',unit='d')\n", "plt.plot(date,h)\n", "#print(fin.variables)\n", "name = chartostring(fin['station_name'][0])\n", "place = chartostring(fin['station_country'][0])\n", "plt.title('Sea level at ' + str(name) + ',' + str(place));" ] }, { "cell_type": "code", "execution_count": null, "id": "0c676b35", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }