{
"metadata": {
"Author": "Nikolay Koldunov",
"Category": "Visualization",
"Date": "2013-06-29",
"Tags": "basemap, matplotlib, visualization",
"Title": "Use of Basemap for Amazon river discharge visualization",
"name": "Amazon_river_discharge",
"slug": "earthpy-basemap-amazon"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task:**\n",
"\n",
" Show how to work with river discharge data.\n",
" Also show cople of ways to visualise this data with Basemap. \n",
" \n",
"**Solution**\n",
"\n",
" Pandas, Basemap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook was originally created for [Marinexplore Earth Data Challenge](http://marinexplore.org/challenges/1-earth-day-data-challenge), in order to show how data for the submission was processed. I think it might be also interesting for those who begin to use python in geoscience, because it demonstrate couple of ways to handle csv and netCDF data and plotting capabilities of the Basemap module. There will be no extensive explanations though, moistly the code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I want to show a small example of the work flow that is more or less typical during research process. Often you see some interesting feature in your data and want to investigate it in more detail. If you not lucky enough to work with the model data, this would require dealing with multiple data sources, and possibly multiple file formats. Having all data sets in one place in consistent format becomes very handy for this type of applications."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I begin with the visualization of river discharge data, that I did for one of my projects. It is performed in [Tableau](http://www.tableausoftware.com/public), and use [Dai and Trenberth Global River Flow and Continental Discharge Dataset](http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/index.html). This data set is also available on Marinexplore. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import HTML\n",
"HTML('')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
""
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This visualization is interactive, so if you choose several rivers on the map it will show a bar chart with distribution of river discharges. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is an intresting story related to this visualization. After my first attempt to plot the data it was obvious that something is wrong - Niger had discarge close to the Amazon, and there were also cople of rivers in the top that I never heard of. I check my scripts, they were fine, so the problem was with the original data set. I wrote to the autors and now the error is fixed, and there is red letter warning on the [Dai and Trenberth dataset website](http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/index.html). Funny thing is, that for four years, that this data set is used nobody notices the error, and it took only one simple visualization to find it. So visualization might serve as simple, but very efficient way to check your data for errors. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I also want to show some simple statistics about my data, so I create another visualization with only 20 biggest rivers:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"HTML('')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look at Amazon. I knew that it's a biggest river in the world, but I never though it's domination is so overwhelming. This huge river discharge should be visible somehow in salinity field of the ocean. In order to test this I decide to look at satellite salinity data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But first we would need to plot our river discharge data in pyhton. [Text file](http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/coastal-stns-byVol-updated-oct2007.txt) with anual mean river discharge values from Dai and Trenberth have been converted in .csv format with vertical bar as separator and is available [here](https://dl.dropboxusercontent.com/u/107639/notebooks/amazon/rivers2.csv). We are going to open it with [pandas](http://pandas.pydata.org/), and plot with [basemap](http://matplotlib.org/basemap/users/examples.html). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('/home/magik/NOTEBOOKS/marine/rivers2.csv', sep='|', na_values=[' Null', ' Null'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is our data in pandas DataFrame:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" No | \n",
" m2s_ra | \n",
" lonm | \n",
" latm | \n",
" area(km2) | \n",
" Vol | \n",
" nyr | \n",
" yrb | \n",
" yre | \n",
" elev(m) | \n",
" CT | \n",
" CN | \n",
" River_Name | \n",
" OCN | \n",
" Station_Name | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1 | \n",
" 12462 | \n",
" -51.75 | \n",
" -0.75 | \n",
" 4618750 | \n",
" 5389.537 | \n",
" 79.0 | \n",
" 1928 | \n",
" 2006 | \n",
" 37 | \n",
" BR | \n",
" SA | \n",
" Amazon | \n",
" ATL | \n",
" Obidos, Bra | \n",
"
\n",
" \n",
" | 1 | \n",
" 2 | \n",
" 10291 | \n",
" 12.75 | \n",
" -5.75 | \n",
" 3475000 | \n",
" 1270.203 | \n",
" 97.1 | \n",
" 1903 | \n",
" 2000 | \n",
" 270 | \n",
" CD | \n",
" AF | \n",
" Congo | \n",
" ATL | \n",
" Kinshasa, C | \n",
"
\n",
" \n",
" | 2 | \n",
" 3 | \n",
" 11474 | \n",
" -61.75 | \n",
" 9.25 | \n",
" 836000 | \n",
" 980.088 | \n",
" 75.8 | \n",
" 1923 | \n",
" 1999 | \n",
" -9999 | \n",
" VE | \n",
" SA | \n",
" Orinoco | \n",
" ATL | \n",
" Pte Angostu | \n",
"
\n",
" \n",
" | 3 | \n",
" 4 | \n",
" 10374 | \n",
" 120.75 | \n",
" 32.25 | \n",
" 1705383 | \n",
" 905.141 | \n",
" 99.6 | \n",
" 1900 | \n",
" 2000 | \n",
" 19 | \n",
" CN | \n",
" AS | \n",
" Changjiang | \n",
" PAC | \n",
" Datong, Chi | \n",
"
\n",
" \n",
" | 4 | \n",
" 5 | \n",
" 10245 | \n",
" 89.75 | \n",
" 24.25 | \n",
" 554542 | \n",
" 670.943 | \n",
" 44.3 | \n",
" 1956 | \n",
" 2000 | \n",
" 19 | \n",
" BD | \n",
" AS | \n",
" Brahmaputra | \n",
" IND | \n",
" Bahadurabad | \n",
"
\n",
" \n",
"
\n",
"