{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 7.0 Fire/ENSO teleconnections" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7.1 Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is much public and scientific interest in monitoring and predicting the activity of wildfires and such topics are [often in the media](http://www.bbc.co.uk/news/science-environment-15691060).\n", "\n", "Part of this interest stems from the role fire plays in issues such as land cover change, deforestation and forest degradation and [Carbon emissions](http://www.google.com/url?sa=t&rct=j&q=fire%20carbon%20emissions&source=web&cd=6&ved=0CFEQFjAF&url=http%3A%2F%2Fwww.kcl.ac.uk%2Fsspp%2Fdepartments%2Fgeography%2Fpeople%2Facademic%2Fwooster%2F30yongwoosteretal.pdf&ei=4FPCTuvZE9Gg8gPZybyxBA&usg=AFQjCNG81fTRoCcK1nhKnk3u0b8az24bGQ&sig2=EjJYm2S-_2gHu2vgt4ByvA&cad=rja) from the land surface to the atmosphere, but also of concern are human health impacts. The impacts of fire should not however be considered as wholy negative, as it plays a [significant role in natural ecosystem processes](http://www.fl-dof.com/publications/fires_natural_role.html#firerole).\n", "\n", "For many regions of the Earth, there are large inter-annual variations in the timing, frequency and severity of wildfires. Whilst anthropogenic activity accounts for a [large and probably increasing proportion](http://www.google.com/url?sa=t&rct=j&q=fire%20frequency%20july%204th&source=web&cd=7&ved=0CFIQFjAG&url=http%3A%2F%2Farctic.synergiesprairies.ca%2Farctic%2Findex.php%2Farctic%2Farticle%2Fdownload%2F2806%2F2783&ei=K1bCTqv1MYS28QOR9ekG&usg=AFQjCNFKillAOZMXrT5xpFhckMKvqW50Vg&sig2=r3J6454VcvI1xpC3Sf3RKw&cad=rja) of fires started, this is not in itself [a new phenomenon](http://www.google.com/url?sa=t&rct=j&q=anthropogenic%20fire&source=web&cd=2&ved=0CCcQFjAB&url=http%3A%2F%2Fwww.as.ua.edu%2Fant%2Fbindon%2Fant475%2FPapers%2FHamm.pdf&ei=rFXCTu-PHsay8QPdy-2MBA&usg=AFQjCNGUMrfnDTwRDBxFB-wioZokBt8EtA&sig2=Zt1nfHoKktbka-pEZs6NGw&cad=rja).\n", "\n", "Fires spread where: (i) there is an ignition source (lightning or man, mostly); (ii) sufficient combustible fuel to maintain the fire. The latter is strongly dependent on fuel loads and mositure content, as well as meteorological conditions. Generally then, when conditions are drier (and there is sufficient fuel and appropriate weather conditions), we would expect fire spread to increase. If the number of ignitions remained approximately constant, this would mean more fires. [Many models of fire activity](http://www.nasa.gov/images/content/492949main_Figure-2-Wildfires.jpg) predict increases in fire frequency in the coming decades, although there may well be [different behaviours in different parts of the world](http://science.sciencemag.org/content/334/6057/787.full).\n", "\n", "\n", "[![](http://www.nasa.gov/images/content/492949main_Figure-2-Wildfires.jpg)](http://www.nasa.gov/images/content/492949main_Figure-2-Wildfires.jpg) \n", "\n", "\n", "Satellite data has been able to provide us with increasingly useful tools for monitoring wildfire activity, particularly since 2000 with the MODIS instruments on the NASA Terra and Aqua (2002) satellites. A suite of [‘fire’ products](http://modis-fire.umd.edu/index.html) have been generated from these data that have been used in a large number of [publications](http://modis-fire.umd.edu/Publications.html) and [practical/management projects](https://earthdata.nasa.gov/data/near-real-time-data/firms).\n", "\n", "There is growing evidence of ‘teleconnection’ links between fire occurence and large scale climate patterns, such as [ENSO](https://www.ncdc.noaa.gov/teleconnections/enso/enso-tech.php).\n", "\n", "[![](http://www.esrl.noaa.gov/psd/enso/mei/ts.gif)](http://www.esrl.noaa.gov/psd/enso/mei/)\n", "\n", "The proposed mechanisms are essentially that such climatic patterns are linked to local water status and temperature and thus affect the ability of fires to spread. For some regions of the Earth, empirical models built from such considerations have quite reasonable predictive skill, meaning that fire season severity might be predicted [some months ahead of time](http://www.sciencemag.org/content/334/6057/787.full)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7.2 A Practical Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2.1 In This Session" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this session, you will be working in groups (of 3 or 4) to build a computer code in python to explore links between fire activity and Sea Surface Temperature anomalies.\n", "\n", "This is a team exercise, but does not form part of your formal assessment for this course. You should be able to complete the exercise in the 3 hour session, if you work effectively as a team. Staff will be on hand to provide pointers.\n", "\n", "You should be able to complete the exercise using coding skills and python modules that you have previously experience of, though we will also provide some pointers to get you started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2.2 Statement of the problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Using monthly fire count data from MODIS Terra, develop and test a predictive model for the number of fires per unit area per year driven by Sea Surface Temperature anomaly data.**\n", "\n", "The datasets should be created at 0.5 degree resolution on a latitude/longitude grid.\n", "\n", "You should concentrate on building the model for the *peak fire count* in a particular year at a particular location, i.e. derive your model for annual peak fire count.\n", "\n", "\n", "Reading datasets\n", "------------------\n", "\n", "You should use the datasets described below.\n", "\n", "If you follow the naming conventions below, you should have:\n", "\n", "----\n", "\n", "`fdata` : shape e.g. `(186, 36, 72)` i.e. `(nmonths,nlat,nlon)` of fire count data\n", "as a masked array.\n", "\n", "`fyear` : shape e.g. `(186,)` for the years for which the Sea Surface Temperature anomaly are available.\n", "\n", "\n", "`fmonth`: shape e.g. `(186,)` the associated month\n", "\n", "\n", "----\n", "\n", "\n", "`cyears` : shape e.g. `(69,)` for the years for which the Sea Surface Temperature anomaly are available.\n", "\n", "\n", "`cdata` : shape e.g. `(12,69)` for each month (the `12`) and each year for which the Sea Surface Temperature anomaly are available.\n", "\n", "----\n", "\n", "\n", "Derived Information\n", "---------------------\n", "\n", "To do the modelling for this exercise, you should access:\n", "\n", "`peak_count` : the peak fire count per year\n", "\n", "`peak_month` : the month in which the peak fire count occured in a particular year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2.3 Datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We suggest that the datasets you use of this analysis, following Chen at al. (2011), are:\n", "\n", "- MODIS Terra fire counts (2001-2011) (MOD14CMH). The particular dataset you will want from the file is ‘SUBDATASET_2 [360x720] CloudCorrFirePix (16-bit integer)’.\n", "- Climate index data from NOAA\n", "\n", "If you ever wish to take this study further, you can find various other useful datasets such as these.\n", "\n", "#### Fire Data\n", "\n", "The MOD14CMH [CMG data](http://nsidc.org/data/modis/data_summaries/cmg_sample.html) are available from the [UMD ftp server](ftp://fire:burnt@fuoco.geog.umd.edu/modis/C5/cmg/monthly/hdf) but have also been packaged for you as [![DOI](https://sandbox.zenodo.org/badge/DOI/10.5072/zenodo.61299.svg)](https://doi.org/10.5072/zenodo.61299)\n", "\n", "\n", "\n", "\n", "If for any reason, you *did* want to copy or update them, use the following unix command:\n", "\n", "`cd Chapter7_ENSO/data;wget 'ftp://fire:burnt@fuoco.geog.umd.edu/modis/C5/cmg/monthly/hdf/*'` \n", "\n", "(though you may need to update the password). The data need to be placed in [data](data) and are in HDF format, so you should know how to read them into a numpy array in python." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/MOD14CMH.200011.005.01.hdf data/MOD14CMH.201507.005.01.hdf\r\n", "data/MOD14CMH.200012.005.01.hdf data/MOD14CMH.201508.005.01.hdf\r\n", "data/MOD14CMH.200101.005.01.hdf data/MOD14CMH.201509.005.01.hdf\r\n", "data/MOD14CMH.200102.005.01.hdf data/MOD14CMH.201510.005.01.hdf\r\n", "data/MOD14CMH.200103.005.01.hdf data/MOD14CMH.201511.005.01.hdf\r\n", "data/MOD14CMH.200104.005.01.hdf data/MOD14CMH.201512.005.01.hdf\r\n", "data/MOD14CMH.200105.005.01.hdf data/MOD14CMH.201601.005.01.hdf\r\n", "data/MOD14CMH.200106.005.01.hdf data/MOD14CMH.201602.005.01.hdf\r\n", "data/MOD14CMH.200107.005.01.hdf data/MOD14CMH.201603.005.01.hdf\r\n", "data/MOD14CMH.200108.005.01.hdf data/MOD14CMH.201604.005.01.hdf\r\n", 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"data/MOD14CMH.201203.005.01.hdf data/MYD14CMH.201301.005.01.hdf\r\n", "data/MOD14CMH.201204.005.01.hdf data/MYD14CMH.201302.005.01.hdf\r\n", "data/MOD14CMH.201205.005.01.hdf data/MYD14CMH.201303.005.01.hdf\r\n", "data/MOD14CMH.201206.005.01.hdf data/MYD14CMH.201304.005.01.hdf\r\n", "data/MOD14CMH.201207.005.01.hdf data/MYD14CMH.201305.005.01.hdf\r\n", "data/MOD14CMH.201208.005.01.hdf data/MYD14CMH.201306.005.01.hdf\r\n", "data/MOD14CMH.201209.005.01.hdf data/MYD14CMH.201307.005.01.hdf\r\n", "data/MOD14CMH.201210.005.01.hdf data/MYD14CMH.201308.005.01.hdf\r\n", "data/MOD14CMH.201211.005.01.hdf data/MYD14CMH.201309.005.01.hdf\r\n", "data/MOD14CMH.201212.005.01.hdf data/MYD14CMH.201310.005.01.hdf\r\n", "data/MOD14CMH.201301.005.01.hdf data/MYD14CMH.201311.005.01.hdf\r\n", "data/MOD14CMH.201302.005.01.hdf data/MYD14CMH.201312.005.01.hdf\r\n", "data/MOD14CMH.201303.005.01.hdf data/MYD14CMH.201401.005.01.hdf\r\n", "data/MOD14CMH.201304.005.01.hdf data/MYD14CMH.201402.005.01.hdf\r\n", "data/MOD14CMH.201305.005.01.hdf data/MYD14CMH.201403.005.01.hdf\r\n", "data/MOD14CMH.201306.005.01.hdf data/MYD14CMH.201404.005.01.hdf\r\n", "data/MOD14CMH.201307.005.01.hdf data/MYD14CMH.201405.005.01.hdf\r\n", "data/MOD14CMH.201308.005.01.hdf data/MYD14CMH.201406.005.01.hdf\r\n", "data/MOD14CMH.201309.005.01.hdf data/MYD14CMH.201407.005.01.hdf\r\n", "data/MOD14CMH.201310.005.01.hdf data/MYD14CMH.201408.005.01.hdf\r\n", "data/MOD14CMH.201311.005.01.hdf data/MYD14CMH.201409.005.01.hdf\r\n", "data/MOD14CMH.201312.005.01.hdf data/MYD14CMH.201410.005.01.hdf\r\n", "data/MOD14CMH.201401.005.01.hdf data/MYD14CMH.201411.005.01.hdf\r\n", "data/MOD14CMH.201402.005.01.hdf data/MYD14CMH.201412.005.01.hdf\r\n", "data/MOD14CMH.201403.005.01.hdf data/MYD14CMH.201501.005.01.hdf\r\n", "data/MOD14CMH.201404.005.01.hdf data/MYD14CMH.201502.005.01.hdf\r\n", "data/MOD14CMH.201405.005.01.hdf data/MYD14CMH.201503.005.01.hdf\r\n", "data/MOD14CMH.201406.005.01.hdf data/MYD14CMH.201504.005.01.hdf\r\n", "data/MOD14CMH.201407.005.01.hdf data/MYD14CMH.201505.005.01.hdf\r\n", "data/MOD14CMH.201408.005.01.hdf data/MYD14CMH.201506.005.01.hdf\r\n", "data/MOD14CMH.201409.005.01.hdf data/MYD14CMH.201507.005.01.hdf\r\n", "data/MOD14CMH.201410.005.01.hdf data/MYD14CMH.201508.005.01.hdf\r\n", "data/MOD14CMH.201411.005.01.hdf data/MYD14CMH.201509.005.01.hdf\r\n", "data/MOD14CMH.201412.005.01.hdf data/MYD14CMH.201510.005.01.hdf\r\n", "data/MOD14CMH.201501.005.01.hdf data/MYD14CMH.201511.005.01.hdf\r\n", "data/MOD14CMH.201502.005.01.hdf data/MYD14CMH.201512.005.01.hdf\r\n", "data/MOD14CMH.201503.005.01.hdf data/MYD14CMH.201601.005.01.hdf\r\n", "data/MOD14CMH.201504.005.01.hdf data/MYD14CMH.201602.005.01.hdf\r\n", "data/MOD14CMH.201505.005.01.hdf data/MYD14CMH.201603.005.01.hdf\r\n", "data/MOD14CMH.201506.005.01.hdf data/MYD14CMH.201604.005.01.hdf\r\n" ] } ], "source": [ "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "\n", "!ls data/*hdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are **really** stuck on reading the data, or just want to move on to the next parts, you can use [`python/reader.py`](python/reader.py) which will create a masked array in `data`, and an array of years (`year`) and months (`month`):" ] }, { "cell_type": "code", "execution_count": 341, "metadata": {}, "outputs": [], "source": [ "run python/reader" ] }, { "cell_type": "code", "execution_count": 344, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# refine the data mask\n", "data.mask = data.data.sum(axis=0) == 0" ] }, { "cell_type": "code", "execution_count": 345, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data shape (186, 360, 720)\n", "data type \n", "mask shape (186, 360, 720)\n", "year and month shape (186,) (186,)\n" ] }, { "data": { "image/png": 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IzJqlwk5RykBDsYqijFdU2JXEwAAwe7YKO0UpAxV2iqKMV1TYlcTAgDp2ilIWfX3AjBmy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\nDaHYNWvsLstfiepOrCj3LWhjL1kCDB0anZtSDk1N4Y7dypUm1uNY8iy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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the reader script loads the \n", "# firecount data into the data array\n", "# shape: (nmonths,nlat,nlon)\n", "print 'data shape',data.shape\n", "\n", "# data is a masked array:\n", "print 'data type',type(data)\n", "# so has a mask:\n", "print 'mask shape',data.mask.shape\n", "\n", "# also: year and month\n", "print 'year and month shape',year.shape,month.shape\n", "\n", "# display the firecount data\n", "plt.figure(figsize=(10,6))\n", "x = year + month/12.\n", "y = np.sum(data,axis=(1,2))\n", "plt.plot(x,y)\n", "plt.ylabel('global fire count')\n", "plt.xlabel('time')\n", "plt.title('global fire count')\n", "plt.xlim(x[0],x[-1])\n", "\n", "fyear,fmonth = year,month" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data pre-processing\n", "=====================\n", "\n", "A. Sort the data size required\n", "----------------------------\n", "\n", "This dataset is at 0.5 degree resolution and we want to perform tha analysis as 5 degrees.\n", "\n", "**First, confirm for yourself that the shape of the dataset is consistent with the data being at 0.5 degree resolution.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use the dataset here, we will need to shrink the dataset by a factor of 10 then, to a 5 degrees dataset.\n", "\n", "There are different ways to achive this, but one way would be to reoganise the data:" ] }, { "cell_type": "code", "execution_count": 352, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 352, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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c15mSxJsjDblkBxVKCrLWTpRrvOcDV4mISpVrn7QK2xzkU0t1qfDl9FJTdbfI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E+5wnqdURRMHdDQxZ73pjOl2qWhA952hiHmWaF3zFi0izWOsUFTe6TLs14n7Wz2ivP08Yl1Ovuq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\nObSTp73rDZTTM1UDLIiIbhSHB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.imshow(data.mask[0])" ] }, { "cell_type": "code", "execution_count": 358, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 358, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GxK7qfU93DVo/ojPvu+PVv2AFAAAwWRHbJW2XJD30UDP53KKmSdInJd0SERfOW3a5pNMl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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.imshow(rdata.sum(axis=0)[0],interpolation='none')\n" ] }, { "cell_type": "code", "execution_count": 420, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(186, 36, 72)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 420, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# how does this work: do it in stages to try to understand this\n", "# NB -- all we are trying to achieve is a reduction in the \n", "# dataset resolution by summingover each sun 10 x 10 cells\n", "\n", "rdata = np.array([data[:,i::10,j::10] for i in xrange(10) for j in xrange(10)])\n", "# sum over 1st axis to achieve reduced resolution summation\n", "rdata = rdata.sum(axis=0)\n", "nslots,nlat,nlon = rdata.shape\n", "\n", "# set a min count\n", "mincount = 1000\n", "\n", "#build a mask\n", "all_mask = rdata.sum(axis=0) <= mincount\n", "# repeat it for each frame\n", "rmask = np.repeat(all_mask.T,nslots).reshape((nlon,nlat,nslots)).T\n", "\n", "plt.figure(figsize=(10,5))\n", "print rmask.shape\n", "plt.imshow(rmask[0],interpolation='none')\n", "\n", "\n", "fdata = ma.array(rdata,mask=rmask) \n", "\n", "plt.figure(figsize=(10,5))\n", "plt.title('log fire count for %d month %02d'%(year[10],month[10]))\n", "plt.imshow(np.ma.log(rdata[10]),interpolation='none')\n", "plt.colorbar(fraction=0.02)" ] }, { "cell_type": "code", "execution_count": 423, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(186, 36, 72)\n" ] } ], "source": [ "# or even make a movie\n", "lf = np.ma.log(fdata)\n", "vmax = np.max(lf[lf>0])\n", "\n", "root = 'images/'\n", "\n", "print lf.shape\n", "# loop over lf.shape[0] (time samples)\n", "for i in xrange(lf.shape[0]):\n", " print i,\n", " fig = plt.figure(figsize=(10,5))\n", " plt.imshow(np.ma.log(fdata[i]),interpolation='nearest',vmax=vmax)\n", " plt.colorbar(fraction=0.02)\n", " file_id = '%d month %02d'%(year[i],month[i])\n", " plt.title('log fire count for %s'%file_id)\n", " plt.savefig('%s_%s.jpg'%(root,file_id.replace(' ','_')))\n", " plt.close(fig)" ] }, { "cell_type": "code", "execution_count": 425, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 425, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make a movie from the files\n", "cmd = 'convert -delay 100 -loop 0 {0}_*month*.jpg {0}fire_movie3.gif'.format(root)\n", "os.system(cmd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](files/images/fire_movie3.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A. Sort the information required\n", "----------------------------\n", "\n", "What information do we need to derive from the fire dataset?\n", "\n", "There are several ways we could approach the problem, but a simple method might be to first find the 'typical' peak fire month per geographical location. We could call that the 'fire month'.\n", "\n", "To calculate that, we could find which month in each (full) year the maximum fire count occurs in.\n", "\n", "A useful method for that will be [`np.argmax()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html) that gives the argument ('index' if you like) of the maximum value in an array.\n", " \n", " \n" ] }, { "cell_type": "code", "execution_count": 435, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the maxiumum value in the data array is 0.977845430318 that occurs at sample index 34\n" ] } ], "source": [ "data = np.random.rand(100)\n", "amax = np.argmax(data)\n", "\n", "print 'the maxiumum value in the data array is',data[amax],'that occurs at sample index',amax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have defined the spatial distribution of fire month, we can calculate an annual dataset containing the firecount (for the fire month) for each year and location." ] }, { "cell_type": "code", "execution_count": 426, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 426, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.imshow(fdata.mask[0],interpolation='nearest')\n", "plt.colorbar(fraction=0.02)" ] }, { "cell_type": "code", "execution_count": 297, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015]\n" ] } ], "source": [ "# find complete years\n", "\n", "# the list of years\n", "good_year = []\n", "\n", "for y in np.unique(fyear):\n", " if (fyear == y).sum() == 12:\n", " # then we have a full year\n", " good_year.append(y)\n", "\n", "good_year = np.array(good_year)\n", "print good_year" ] }, { "cell_type": "code", "execution_count": 501, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 501, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3jRIJJZS75myguEMAAAAYfSVu3zRoJGcAAGD0jdJpTQAAgNojOQMAAKiQkjc+\nHySSMwAAMPo4cgYAAFAhNUrOct0hoNQA3CEAAAC0GcodAt6cMxf5Qn3uEAAAAFBflNIAAACokBqd\n1iQ5AwAAo4/kDAAAoEIopQEAAFAhHDkDAACoEJIzAACACtk37ADyIzkDAACjj1IaAAAAFcJpTQAA\ngAohOQMAAKiQGpXSmDHsAAAAAPpuf85HB7avtT1p+59T2s+3vcv2ncnjvWVC5cgZAAAYfeVOa14n\n6S8lfSajz7ci4sJSoyRIzgAAwOgrUUojIm62fWaXbi4+wpE4rQkAAEbfgZyP4hbZ3mL772y/uMwb\nceQMAACMvrTTmvub0oFm2Xe/Q9IZEfG47cWSviTp7KJv5ogoG1D2AHb0ewwAAFAfthURPTsNmGO8\n0LE5c5EnOseWnNb8ckS8JMd4/ybp5RHxk+nGKnHkDAAAjIPypTSslOvKbM+NiMnk+QK1Dn4VSswk\nkjMAADAOSpzEs32DpIakZ9n+kaQ1ko6RFBFxjaQ3236nWssOnpC0vEyonNYEAAADNZTTmrmzs8HG\n1knX1Zq2Z9m+zfZdtu+2vSbZfrLtr9n+vu1/sH1i/8MFAAAYbbmOnNk+LlmBcJSkb0u6VNJvSvqP\niPjftv9Y0skRcXmHfTlyBgAADuPIWbZcdc4i4vHk6Sy1rlMLSRdJWpdsXyfpTT2PDgAAoCf25XwM\nX64FAbZnqFXD4/mSro6I29tXJkTEdtun9DFOAACAEp4YdgC55UrOIuKgpPNsP0PSF22fo6cfH0w9\nXjgxMXH4eaPRUKPRmHagAACgnprNpprN5pCjKHdzzUGa9mpN238i6XFJvyupERGTtudJ+mZE/FKH\n/lxzBgAADhvONWfbc/aeV/1rzmw/+9BKTNvHSnqdpK2SNklamXRbIenGPsUIAABQ0mhdc3aqpHXJ\ndWczJK2PiM22b5W0wfbvSHpI0rI+xgkAAFDCCJ/WnPYAnNYEAABthnNa8/6cvc8e+mlNbt8EAADG\nQH2OnJGcAQCAMTBipTQAAADqrRoX++dBcgYAAMYApzUBAAAqhCNnAAAAFcKRMwAAgArhyBkAAECF\ncOQMAACgQh4fdgC5kZwBAIAxwJEzAACACuGaMwAAgArhyBkAAECFcOQMAGrNnii8b0TxfQH0S7kj\nZ7YvkPQhSTMkXRsRH+zQ5yOSFkvaK2llRGwpMhbJGQAAGAPFj5zZniHpKkmvkfTvkm63fWNE3NfW\nZ7Gk50fGlhSlAAAGf0lEQVTEC22/QtLHJS0sMh7JGQAAGANPlNl5gaQHIuIhSbL9eUkXSbqvrc9F\nkj4jSRFxm+0Tbc+NiMnpDjajTKQAAAD1sD/no6PTJG1re/1wsi2rzyMd+uTCkTMAADDqHpImzszZ\nd9pHunqN5AwAAIy0iHhuybd4RNIZba/nJ9um9jm9S59cOK0JAACQ7XZJL7B9pu1jJP2WpE1T+myS\n9DZJsr1Q0q4i15tJHDkDAADIFBEHbK+W9DU9VUpjq+1LWs1xTURstr3E9g/UKqVxcdHxHBG9iTxt\nADv6PQYAAKgP24oIDzuOquK0JgAAQIWQnAEAAFQIyRkAAECFkJwBAABUCMkZAABAhXRNzmzPsn2b\n7bts3217TbJ9je2Hbd+ZPC7of7gAAACjLVcpDdvHRcTjto+S9G1Jl0paLGl3RFzZZV9KaQAAgMMo\npZEt12nNiHg8eTpLrcK1h7ItJhYAAKCHciVntmfYvkvSdklfj4jbk6bVtrfY/pTtE/sWJQAAwJjI\ndfumiDgo6Tzbz5D0RdsvlvRRSe+LiLD9p5KulPT2TvtPTEwcfn7SSSfpD/7gD8rGPbKazaYajcaw\nw6gs5icb85ON+cnG/GRjfrJlzU+z2VSz2RxoPHU2rdWaEfEzSU1JF0TEzraLyT4p6VfT9puYmDj8\n2LVrV+FgxwEf3mzMTzbmJxvzk435ycb8ZMuan0ajcUQugGx5Vms++9ApS9vHSnqdpPtsz2vr9huS\n7ulPiAAAAOMjz2nNUyWtsz1DrWRufXLn9c/YPlfSQUkPSrqkf2ECAACMh1ylNEoNYFNHAwAAHIFS\nGun6npwBAAAgP27fBAAAUCEkZwAAABVCcgYAAFAhA0vObF9g+z7b99v+40GNW1W2r7U9afuf27ad\nbPtrtr9v+x/G+a4Ltufbvsn2v9i+2/alyXbmSJLtWbZvs31XMj9rku3MT5vk7iZ32t6UvGZ+ErYf\ntP295DP0nWQb85OwfaLtjba3Jt9Dr2B+nmL77OSzc2fy52O2L2WOemMgyVlShuMqSW+QdI6kt9j+\nxUGMXWHXqTUf7S6X9I2IeJGkmyS9Z+BRVcd+SZdFxDmSFklalXxmmCNJEfGkpFdHxHmSzpW02PYC\nMT9TvUvSvW2vmZ+nHJTUiIjzImJBso35ecqHJW2OiF+S9FJJ94n5OSwi7k8+Oy+T9HJJeyV9UcxR\nTwzqyNkCSQ9ExEMRsU/S5yVdNKCxKykibpb00ymbL5K0Lnm+TtKbBhpUhUTE9ojYkjzfI2mrpPli\njg6LiMeTp7PUqlkYYn4Osz1f0hJJn2rbzPw8xXr6vwHMj6TkVoWviojrJCki9kfEY2J+0rxW0r9G\nxDYxRz0xqOTsNEnb2l4/nGzDkU6JiEmplZxIOmXI8VSC7eeqdXToVklzmaOW5JTdXZK2S/p6RNwu\n5qfdX0j6Q7WS1kOYn6eEpK/bvt327ybbmJ+W50n6se3rktN219g+TsxPmuWSbkieM0c9wIKAahv7\nInS2j5f0BUnvSo6gTZ2TsZ2jiDiYnNacL2mB7XPE/EiSbP+6pMnk6GtWocuxnJ/EK5NTUkvUumzg\nVeLzc8hMSS+TdHUyR3vVOl3H/Exh+2hJF0ramGxijnpgUMnZI5LOaHs9P9mGI03anitJyb1Ldww5\nnqGyPVOtxOyzEXFjspk5miIifiapKekCMT+HvFLShbZ/KOmvJf2a7c9K2s78tETEo8mfOyV9Sa3L\nT/j8tDwsaVtEfDd5/TdqJWvMz9MtlnRHRPw4ec0c9cCgkrPbJb3A9pm2j5H0W5I2DWjsKrOO/F/9\nJkkrk+crJN04dYcx82lJ90bEh9u2MUeSbD/70Coo28dKep1a1+UxP5Ii4oqIOCMizlLr++amiPgf\nkr4s5ke2j0uOSsv2bEmvl3S3+PxIkpLTcttsn51seo2kfxHz08lb1PoP0CHMUQ8M7PZNti9Qa/XL\nDEnXRsQHBjJwRdm+QVJD0rMkTUpao9b/XjdKOl3SQ5KWRcSuYcU4TLZfKelbav2DEcnjCknfkbRB\nYz5Htn9FrYttZySP9RHxftvPFPNzBNvnS3p3RFzI/LTYfp5aK+tCrVN4n4uIDzA/T7H9UrUWkxwt\n6YeSLpZ0lJifw5Lr8B6SdFZE7E628RnqAe6tCQAAUCEsCAAAAKgQkjMAAIAKITkDAACoEJIzAACA\nCiE5AwAAqBCSMwAAgAohOQMAAKiQ/w9ipG2I+WjG8gAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# so for these years, find the max fire count\n", "# and the associated month\n", "\n", "# the list of years\n", "good_year = []\n", "month_data = []\n", "\n", "for y in np.unique(fyear):\n", " if (fyear == y).sum() == 12:\n", " # then we have a full year\n", " good_year.append(y)\n", " # this gives the month in this year where firecount is maximum\n", " year_data = np.ma.array(np.ma.argmax(fdata[fyear == y],axis=0),\\\n", " mask=fdata.mask[0])\n", " month_data.append(year_data)\n", "\n", "# now find the e.g. median of these to get th erepresetative fire month ...\n", "month_data = np.median(np.ma.array(month_data),axis=0).astype(int)\n", "\n", "plt.figure(figsize=(10,5))\n", "plt.imshow(month_data,interpolation='nearest',vmax=11)\n", "plt.colorbar(fraction=0.02)\n", "plt.title('fire month')" ] }, { "cell_type": "code", "execution_count": 547, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 547, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gmll3M+sMDAc2LnagmXUC9gXuzN21ITC34JA3cvdVrWptQ7JwIRx0UNhA/uCD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\nSVMiJ1KDVl8dDjoIbr01diTJee21UPX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now generate the fire_count data for each year in an array fire_count\n", "\n", "fire_count = np.zeros((len(good_year),nlat,nlon))\n", "\n", "for iy,y in enumerate(good_year):\n", " year_data = np.ma.array(fdata[fyear == y])\n", " for m in np.arange(12):\n", " r,c = np.where(month_data == m)\n", " fire_count[iy,r,c] = year_data[m,r,c]\n", " \n", "fire_count_mask = fire_count == 0\n", "fire_count = np.ma.array(fire_count,mask=fire_count_mask)\n", "\n", "# plot a few years data\n", "for y in xrange(2):\n", " plt.figure(figsize=(10,5))\n", " plt.imshow(np.ma.log(fire_count[y]),interpolation='nearest')\n", " plt.colorbar(fraction=0.02)\n", " plt.title('log max fire count %d'%good_year[y])\n", " \n", "# plot for one location\n", "# the biggest one ...\n", "y,r,c = np.where(fire_count == np.ma.max(fire_count))\n", "plt.figure(figsize=(10,5))\n", "plt.title('log fire count for pixel %d %d'%(r,c))\n", "plt.plot(good_year,np.log(fire_count[:,r,c]))\n", "plt.xlabel('year')\n", "plt.ylabel('log fire count')" ] }, { "cell_type": "code", "execution_count": 549, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15, 36, 72)\n", "15\n", "(36, 72)\n" ] } ], "source": [ "print fire_count.shape\n", "print len(good_year)\n", "print month_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In summary, we now have the fire month stored in `month_data`, and, for each year (in `good_year`), the fire count for that month in `fire_count`.\n", "\n", "This is the core information we require for modelling.\n", "\n", "We now consider the associated climate datasets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Climate Data\n", "\n", "The climate data you will want will be some form of Sea Surface Temperature (SST) anomaly measure. There is a long list of such measures on [http://www.esrl.noaa.gov/psd/data/climateindices/list](http://www.esrl.noaa.gov/psd/data/climateindices/list/).\n", "\n", "Examples would be [AMO](http://www.esrl.noaa.gov/psd/data/correlation/amon.us.data) or [ONI](http://www.esrl.noaa.gov/psd/data/correlation/oni.data). Note that some of these measures are smoothed and others not.\n", "\n", "When you see a new dataset, you should investigate the formatting and other issues before trying to interpret it.\n", "\n", "Whilst learning coding, it is often worthwhile doing the 'low level' reading and formatting yourself. We provide 2 examples of this below. \n", "\n", "There are simpler ways of reading in tabular ('csv') datasets, so we show two of these as well.\n", "\n", "First, lets explore the dataset format:\n", "\n", "Suppose we had selected AMO and we want to read directly from the url:" ] }, { "cell_type": "code", "execution_count": 550, "metadata": {}, "outputs": [], "source": [ "import urllib2\n", "\n", "url = 'http://www.esrl.noaa.gov/psd/data/correlation/amon.us.data'\n", "\n", "# as a backup, in case the noaa server is not responding,\n", "# you can access the data from the data directory or\n", "# https://github.com/profLewis/geogg122/blob/master/Chapter7_ENSO/data/amon.us.data\n", "\n", "req = urllib2.Request ( url )\n", "raw_data = urllib2.urlopen(req).readlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now examine the data format and work out how to access the information we want." ] }, { "cell_type": "code", "execution_count": 551, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first 2 lines:\n", "[' 1948 2016\\n', ' 1948 -0.009 -0.021 0.034 -0.064 0.002 0.061 -0.033 -0.016 -0.046 0.014 0.140 0.069\\n']\n", "years range: [1948 2016]\n", "last 5 lines:\n", "[' 2016 0.243 0.167 0.200 0.189 0.356 0.421 0.444 0.468 -99.990 -99.990 -99.990 -99.990\\n', ' -99.99\\n', ' AMO unsmoothed, detrended from the Kaplan SST V2\\n', ' Calculated at NOAA/ESRL/PSD1\\n', ' http://www.esrl.noaa.gov/psd/data/timeseries/AMO/\\n']\n", "fill value: -99.99\n", "data name: AMO unsmoothed, detrended from the Kaplan SST V2\n", "\n", "data info: Calculated at NOAA/ESRL/PSD1\n", "\n", "data url: http://www.esrl.noaa.gov/psd/data/timeseries/AMO/\n", "\n", "length of data_str: 69\n" ] } ], "source": [ "# look at the first 2 lines of the data we pull from the url\n", "#\n", "# The first line gives the dates range\n", "#\n", "# The data values appear on subsequent lines\n", "print 'first 2 lines:'\n", "print raw_data[:2]\n", "\n", "# years: integer array\n", "year_range = np.array(raw_data[0].split()).astype(int)\n", "print 'years range:',year_range\n", "\n", "\n", "# look at the end of the data:\n", "print 'last 5 lines:'\n", "print raw_data[-5:]\n", "\n", "# so the last 4 lines of the data contains \n", "# dataset information\n", "fill_value = float(raw_data[-4])\n", "print 'fill value:',fill_value\n", "data_name = raw_data[-3]\n", "print'data name:',data_name\n", "data_info = raw_data[-2]\n", "print'data info:',data_info\n", "data_url = raw_data[-1]\n", "print'data url:',data_url\n", "#\n", "# we notice from inspection that \n", "# we want data from rows 1 to -4\n", "# so raw_data[1:-4]\n", "# set this as data_str, the string representation of the data\n", "data_str = raw_data[1:-4]\n", "print 'length of data_str:',len(data_str)" ] }, { "cell_type": "code", "execution_count": 552, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nyears, nmonths: 69 12\n", "first year: 1948\n", "1948 (0), 1949 (1), 1950 (2), 1951 (3), 1952 (4), 1953 (5), 1954 (6), 1955 (7), 1956 (8), 1957 (9), 1958 (10), 1959 (11), 1960 (12), 1961 (13), 1962 (14), 1963 (15), 1964 (16), 1965 (17), 1966 (18), 1967 (19), 1968 (20), 1969 (21), 1970 (22), 1971 (23), 1972 (24), 1973 (25), 1974 (26), 1975 (27), 1976 (28), 1977 (29), 1978 (30), 1979 (31), 1980 (32), 1981 (33), 1982 (34), 1983 (35), 1984 (36), 1985 (37), 1986 (38), 1987 (39), 1988 (40), 1989 (41), 1990 (42), 1991 (43), 1992 (44), 1993 (45), 1994 (46), 1995 (47), 1996 (48), 1997 (49), 1998 (50), 1999 (51), 2000 (52), 2001 (53), 2002 (54), 2003 (55), 2004 (56), 2005 (57), 2006 (58), 2007 (59), 2008 (60), 2009 (61), 2010 (62), 2011 (63), 2012 (64), 2013 (65), 2014 (66), 2015 (67), 2016 (68), (array([ 8, 9, 10, 11]), array([68, 68, 68, 68]))\n" ] } ], "source": [ "# so to read the data we loop over each line\n", "# Version 1:\n", "#\n", "# we could do this in a loop of this sort:\n", "\n", "# We want the data in an array of shape (nmonths,nyears)\n", "# so initialise this\n", "nyears = year_range[-1] - year_range[0] + 1\n", "nmonths = 12\n", "first_year = year_range[0]\n", "print 'nyears, nmonths:',nyears,nmonths\n", "print 'first year:',first_year\n", "\n", "cdata = np.zeros((nmonths,nyears))\n", "cyears = np.zeros((nyears))\n", "\n", "# loop over all lines in data_str\n", "for i in xrange(len(data_str)):\n", " \n", " # load the string data for thatr line\n", " line_data = data_str[i]\n", " \n", " # split it into an array of (13) floats\n", " fline_data = np.array(line_data.split()).astype(float)\n", " \n", " # the first entry is the year\n", " this_year = int(fline_data[0])\n", " \n", " # give the user info on which years we read\n", " print this_year,\n", " \n", " cyears[i] = this_year\n", " \n", " # the rest of the line is the 12 data entries per month\n", " year_data = fline_data[1:]\n", " \n", " # the year index is this_year minus first_year\n", " year_index = this_year - first_year\n", " print '(%d),'%year_index,\n", "\n", " # store that in cdata\n", " cdata[:,year_index] = year_data\n", " \n", "# now, generate a mask of the fill values\n", "cmask = (cdata == fill_value)\n", "\n", "# where are the duff (fill) values?\n", "# (why would this be?)\n", "print np.where(cmask)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the dataset has no gaps in year, so instead of working out `year_index` in the loop, we could more simply use the index `i`.\n", "\n", "Actually, we could read the data in in a more 'pythonic' manner with a much simpler loop:" ] }, { "cell_type": "code", "execution_count": 553, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shape: (13, 69)\n", "(12, 69)\n", "fill values: -99.99 (array([ 8, 9, 10, 11]), array([68, 68, 68, 68]))\n" ] } ], "source": [ "# so to read the data we loop over each line\n", "# Version 2:\n", "\n", "# form float array of all 13 data columns\n", "cdata = np.array([r.split() for r in raw_data[1:-4]]).astype(float).T\n", "\n", "# check the shape is how we want it\n", "print 'shape:',cdata.shape\n", "\n", "# separate the data and year values\n", "cyears = cdata[0]\n", "cdata = cdata[1:]\n", "\n", "# make a mask for fill values\n", "cmask = (cdata == fill_value)\n", "\n", "# put cdata in a masked array\n", "cdata = ma.array(cdata,mask=cmask)\n", "\n", "print cdata.shape\n", "\n", "print 'fill values:',fill_value,np.where(cmask)" ] }, { "cell_type": "code", "execution_count": 554, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(12, 69)\n" ] } ], "source": [ "# so to read the data we loop over each line\n", "# Version 3:\n", "# use numpy.genfromtxt\n", "# see http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html\n", "\n", "cdata = np.genfromtxt(raw_data,skip_header=1,skip_footer=4,unpack=True,usemask=True)\n", "\n", "# In this case, need to specify and fix fill value\n", "cdata.fill_value=-99.99\n", "cdata.mask[cdata.data==cdata.fill_value] = True\n", "# separate the years column and the data\n", "cyears = cdata[0]\n", "cdata = cdata[1:]\n", "print cdata.shape" ] }, { "cell_type": "code", "execution_count": 555, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1948, 2016)" ] }, "execution_count": 555, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vp5/sP/eCBfIG+H//J0v90aZjR2DiRAlQAvF4JD3S32ofIMF0uXLyhxdtUlMlxfO998yn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\nT5K2+eab5l9nFM1xejWViHI/vo0QUdSaMUMqeka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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15,3))\n", "plt.plot(cyears,cdata[0])\n", "plt.xlabel('year')\n", "plt.ylabel('January AMO')\n", "plt.xlim(year_range[0],year_range[1])" ] }, { "cell_type": "code", "execution_count": 556, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(12, 69)\n", "keys: Int64Index([1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958,\n", " 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n", " 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n", " 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", " 2014, 2015, 2016],\n", " dtype='int64', name=0)\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ "0 1948 1949 1950 1951 1952 ... 2012 2013 2014 2015 \\\n", "1 -0.009 0.159 0.117 0.109 0.178 ... -0.053 0.140 -0.050 0.004 \n", "2 -0.021 0.166 -0.027 0.005 0.189 ... 0.016 0.127 -0.031 0.008 \n", "3 0.034 0.045 -0.099 0.019 0.237 ... 0.037 0.170 -0.069 -0.117 \n", "4 -0.064 0.105 -0.124 0.175 0.198 ... 0.092 0.148 -0.082 -0.060 \n", "5 0.002 -0.018 -0.052 0.179 0.187 ... 0.176 0.113 0.010 0.056 \n", "6 0.061 0.007 -0.036 0.296 0.389 ... 0.312 0.058 0.074 0.041 \n", "7 -0.033 0.078 -0.049 0.428 0.379 ... 0.387 0.202 0.233 0.144 \n", "8 -0.016 0.109 0.027 0.310 0.410 ... 0.443 0.206 0.346 0.190 \n", "9 -0.046 0.076 0.016 0.256 0.368 ... 0.460 0.267 0.322 0.312 \n", "10 0.014 0.110 -0.084 0.266 0.361 ... 0.341 0.359 0.304 0.336 \n", "11 0.140 0.114 0.086 0.181 0.257 ... 0.177 0.140 0.077 0.198 \n", "12 0.069 0.124 0.095 0.181 0.347 ... 0.153 0.048 0.070 0.241 \n", "\n", "0 2016 \n", "1 0.243 \n", "2 0.167 \n", "3 0.200 \n", "4 0.189 \n", "5 0.356 \n", "6 0.421 \n", "7 0.444 \n", "8 0.468 \n", "9 NaN \n", "10 NaN \n", "11 NaN \n", "12 NaN \n", "\n", "[12 rows x 69 columns]" ] }, "execution_count": 556, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# so to read the data we loop over each line\n", "# Version 4:\n", "#\n", "# use pandas to do the whole thing from the url\n", "# pandas is a useful package for dealing with and plotting tabular data\n", "# that you may like to consider\n", "import pandas as pd\n", "\n", "# see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html\n", "# and http://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", "c=pd.read_csv(url,skiprows=1,\\\n", " header=None,delim_whitespace=True,skipfooter=4,index_col=0,engine='python').T\n", "c[c == -99.99] = np.nan\n", "print c.shape\n", "# now display the dataset in a table\n", "#print c.info()\n", "print 'keys:',c.keys()\n", "\n", "# display option\n", "pd.set_option('display.max_columns', 10) \n", "# show table\n", "c" ] }, { "cell_type": "code", "execution_count": 557, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 557, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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M2Ik8+OCDGI1GHn30UV5++WWMRiMPPfTQSffn51dKTk4OFksFfn7paO7RXwwJ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\nV5ocpNfTs2IRjg9GD97++ul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4GyuZfK6tjS6r1VW0JDcXlUyGX3Q0A90ORnzq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gg5rqHhoVRXiH+ZuIDMTNojRiPJlVIWWIbN7EWTn4rY4iJqlCoilSqk4oEtyntCneSCuMhEi5cn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\nsng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W4cCnvJ4dtQFotfxnI7Z+JHz44YckJyfj5eVFfHw82/vFU/yQuCikUlEUvxFFcZAo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PAiODi818v673//y9athzh48AAdHSW9HlPWVIbQpMHV2VYvLDl0CDuDI/Jg+/OWTH5+4HNc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\np5/YWVXFbcHBHBkyhG9792bOmf4nEEKI34AEb0KIC9qJfW9jht6Aw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40VJSXcEhTU4nio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nkq3b8s8kOWPCe5n1mlRV7yfgIGAv4JaBuhuAg1r5M8Aprbzj4HIT3ucvwHta+RLgsOneNqd1ynoj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\nAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = c.T.plot.line(figsize=(15,5),title='ENSO AMO')\n", "ax.set_xlabel('years')" ] }, { "cell_type": "code", "execution_count": 558, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1948, 2016)" ] }, "execution_count": 558, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uKuzcKY8bb3R7JgTYW+DllVfkbsrMmbm3WpZZjRtH/z4/Y7VPKfmHvWhRaxur\niYgo+uzdK3v5a9YMf4z27aVC9tKl9s0rFOEWdvFWrJgUm3O6iXe0qFdPCtssWuT8uYwifuGu9gHh\n7/PbtElWOKtVC36sU+meVtM8gcAB3EgAs5RSzZRSxTIfiQBmZH4tqk2bJr1TnC6rT+Zce638wVpd\n+t67V1IoZs6Udgp5XbQ3ctcamDhRAj8D0z2JiHKfRYvkZruV1QilgK5dpUCKG+xY8QOsfQ9ijVKR\nK/Ly1ltS28Hq9V84+/xmz5Y0TzPbx1q3luI+dnM08MvsozcQwGAAOzIfgwC8qbX+2Nppncc0z+hS\nsKDkJK9fb22cL74A7rsPKF3annnFumgv8LJhA3DmDJCQkPVcrVoM/IiIchsraZ7eunSRTJEzZ6yP\nFQqjoqcdgV9e8+CD8jNLTXXuHCkpsqjz3HPWxwon8DOT5mlo0EAKBR04EPLU/PJ4ZItTgwbWxgl4\nT0Jr/YvW+mat9cWZj5u11tOsndJ5x4/LxXCbNm7PhLxZTff0eIBx44AnnrBvTrGuTh3Z43fqlNsz\n8c07zdNw1VWs7ElElNuEW9Ezu8suk2yWyZOtjxUKqxU987JKleR65JdfnDvHG29IPQU7sr2uv172\n+JkNzNLSJFBs0cLc8fnySZGXuXPDn2N2KSny+2m1Umygdg7vB3pYO62zZs6Uu05ulfcn3+rVs1bZ\nc/Zs+aW/4Qb75hTrChaUN7Dk5ODHusEI/Lwx1ZOIKHc5dUpu6NWvb894bqR72pXmmVc99phUZvV4\n7B975UpgwQJre/u8xcdLnGC2gmxysrTrCiXoatXK3nRPq/37DIFW/HoAuAnAXgDLAazI9ohaRhsH\nii5WV/w++UR65dGForWR+6ZNwJEjMj9vDPyIiHKXZctkxadQIXvGu+MO2Rry99/2jGeGHa0c8rL7\n7wfS04Fvv7V/7NdfB157DbjoIvvGDCXd09jfFwqjwItd1U7t2N8HBA78KgD4BEAbAJ0B5Afws9b6\nS631l9ZP7Yy0NOnr1q6d2zOh7K6/Xt7I09NDf+2+fcC8edIvhi4Urfv8Jk8G7rkn5yb3SpWkD82J\nE+7Mi4iI7GVXmqehYEGgUydg/Hj7xgzGjoqeeVlcnLTj6NsXOHnSvnEXLJDV5G7d7BsTCK2fXyj7\n+wzVqwMFCti3tWXp0pw30sMRqLjLYa31WK11cwCPAigJYINSqrP10zpnwQIptZpXyujGkmLFgIoV\nw1vtGT9UZWvyAAAgAElEQVQe+M9/3G3UHq2itZH7pEkS+GUXFyflvlNSIj8nIiKyn5XG7f507Sr/\n9mdk2DuuP0z1tK5RI1kZGzLEnvG0Bl59FRg4UIIoO9WpA+zeDRw6FPi4kyclWy3UwkVK2ZfueeaM\nNK6vU8f6WEELziql6gF4FsBDkFYOUZ3mOXUq0zyjWTjpnizqElilSvKGGMmUmGC2bZPWG/7eKJnu\nSUSUO2RkyM1Hu/smX389UKaMZPs4jRU97TN0KPDpp1J4zqrffpMtIw8+aH2s7PLlk9/ZYPv8/vhD\nKmkWKRL6Oezq57dihbRFK1jQ+liBirsMUkqtANAHwO8A6mutH9Nab7B+Wuf8/DPbOESzcAK/OXOk\nihOLuvgXbY3cJ08GOnSQDdS+sLInEVHusG4dUKGC9WqDvkSqyAsretqnQgXgpZeA3r2tjePxyGrf\nm2/6v5awyky6Zzj7+wwtW0om4rlz4b3eYNf+PiDwit/rkPTO6wG8DWClUmqtUmqdUmqtPacHlFJt\nlVKblFKblVIv+znmfaXUFqXUaqVU0IXO2rXtmh3ZLZzKnp98Iqt9Zppm5lXR1sjdVzVPb1zxIyLK\nHZxI8zR06iR1G44edWZ8A9M87fXss5Ka+Ouv4Y8xaRKQPz9w9932zSs7MwVewtnfZyhdWq53rF6f\nRSrwqwKgBYB2mY87Mh/Gx5YppeIAjIYUkLkGwANKqauyHXMrgGpa6xoAugMYG2jMO+9kgBDN6tYF\nVq82X+53/37pg9Kpk7PzinXRVODln3+A7dvlDdUfBn7khq1b3Z4BUe5jV+N2X0qXlp7M//d/zoxv\nYGEXexUsCLz3njRbP38+9NenpwP9+gFvveXsNX29enK9cviw76/v2yfbVurVC/8cdqR7RiTw01r/\n4+sBoBKAl+w5PRoA2JI5dhqACQCy79C7C8BXmXNaCqCEUqqcvwG5vy+6lSkjBVq2bzd3/PjxUiCE\n6ReB1asngdSZM27PRNI8775b8uf9qVFD9iSmpUVuXpS3bdsmNxz8/QNPROGxu6Jndl27Al984dz4\nAFs5OOG224ArrwRGjQr9tV9+KUUaW7a0f17e8ueXfX4LFvj++ty50ojdSqpp69bWAr+9e+Xarlq1\n8MfwFrS4CwAopeoqpd5RSu0AMBiAXffqLwOwy+vz3ZnPBTpmj49j/qdpU5tmRo6pV8/cPj+jqAt7\n9wVXqJBs/F2+3O2ZBE/zBIDCheVN3ewNACKrZs+WIhQzZ7o9E6LokJ5ufW/4rl1AaqrczHPKLbdI\n9s9a2zYZ5cRUT2eMHAkMGyYrZ2alpkoVT6dX+wyB0j2t7O8zNG4sN+bDuel4/ry0sbjvPvu+F37v\nySulrgTwQObjEIDvAajM9g5Ra8iQAf/7ODExEYmJia7NhXwzCrwECw7mzZOVvvr1IzOvWGe0dbj5\nZvfmsHu3tGlo0SL4sUa655VXOj8vf1JTpVqWk3erKTrMni3paNOnM3WcKD0d6NIFmDBBUjUbNw5v\nHGN/n5MX6PHxwMMPy6rfyJH2j8+Kns6pUQN47DHglVfM92QcO1auE+3oWWdGs2ZAz545n9da9vf1\n62dt/AIF5Lps3jxpS2ZWerpUM82f39yqaVJSEpJMNCYMkIyFTQAWAGintd4KAEopizV6ctgD4HKv\nzytmPpf9mEpBjvmfAQMG2DU3ckjduvKHHQyLuoSmcWPg++/dncOPP8o+2/z5gx9rVPZ0qwrv+fPy\nJjxvHnD8eODUVIptGRnA/Pmy2te2rfyDyp835VUZGcAjj8gKxOefS1bNihXm3rezczrN0/DII5KS\nN2yY/f3cduyQvYTcUuKM11+Xf++XLAkezJ08Ke0g7GiBYFb9+rL/++hRoFSprOc3bZK/CTtSLI19\nfmYDP49HAuYTJ6RNnZm/zeyLXQMHDvR5XKBUzw4A9gGYr5Qap5RqCcDuS/BkANWVUpWVUgUA3A9g\narZjpgLoAgBKqUYAjmmtD9g8D4ogM6meBw7IHwnvzJsXDY3czaR5Gtws8JKWBjzwgFz8X3YZsH69\nO/OgyFi+XH7OCQnS9zKaWp8QRVJGBvDoo/Jv7JQpspJ22WXAu++GN56TFT29Va8O1KoF/PKL/WNz\nf5+zihUD3n4bePrp4IX93ntPUisjWZ2/QAG5flq48MLnjTRPOxYfjEbuZq7PtAZ69ZKtMD/9ZE/v\nPm+BirtM0VrfD+AqAPMBPAegrFJqjFKqtR0n11pnAOgFYBaAvwBM0FpvVEp1V0o9kXnMrwC2K6W2\nAvgYwFN2nJvcU7Gi3HEPlPM9frz0gStRImLTinmVK8t///nHnfPv2yf9nMzmw7sV+GVkSIpTaqqk\nOTVpwkAgt5s9O6sc9+23S7onUV6TkSGrCHv2SM/jwoXlovajj4B33pGCW6E4cUKadFupeBgKp4q8\nsKKn8x56SG60Bkr3PHxYUhr9LFQ5qlmznP38rLRxyK5WLbnuDVZZWmvpgZicLDc5wmkaH0zQ4i5a\n69Na6++01ndA0ixXAfDZby8cWuvftNY1tdY1tNZDM5/7WGv9idcxvbTW1bXW12utQ+wCR9FGKUn3\n9NfPzyjq8sQTkZ1XrFPK3X5+P/0EtGtn/u6UEfhFcoXSSJ84dEiqjxYsmLVSSrkXAz/K6zweKRLx\nzz+SOuZ9QVmlilxsPvlkaO/HS5ZI0Gf3ioQ/994rqzJ799o7Lgu7OC8uDnj/feC112RrhS/Dhkkq\npF3VK0ORmHhhgZe0NPncTL0CM5Qy19Zh8GDZkjBzpnOpx6aqehq01ke11p9orR0usEq5XaB0z/nz\ngYsuAho0iOyccoPGjd0LYiZNktYbZl1yiWzaP3jQuTl583iAHj0kfeLnn6USKsDAL7c7dUpuMhlF\njxo2lNXpnTvdnVdelprKVi6R5PHIPr5t22QV4aKLch7Tu7dUzpwwwfy4Tvbv8+Wii+TfmK+/tndc\nrvhFRkKC3HgbNCjn1/buBT77TPYDuiEhQQrTGUFpcjJQtapcp9jFSPf0Z8QI4NtvJTgsXdq+82YX\nUuBHZBejsqcvn3wi/0ixqEvo3GrkfvCgXFy3aRPa6yKV7qk18Oyzspcve/rENddI6tORI87PgyLv\n999l875xsRsfLwVeuOrnnmeekdRCcp7HIyt5mzbJ77yvoA+Q4hGffAL06WP+vTBShV28GemedmWK\neDxSZIwrfpHx1lvSo2/jxgufHzxYsnEu89uszVkFC0rwZ+zzs6ONQ3a33CLppOnpOb82diwwerSk\nl5bz26ncHgz8yBX+Uj0PHpQ7Ig8+GPk55QY33CBpK2fPRva8U6bIxXThwqG9zqjs6SStgRdfBJYu\nBWbMkI3m3vLlkzf8ZcucnQe5wzvN08B0T3ctXMjvfyQYRSLWrwd+/RUoWjTw8Q0byorayyY286Sn\ny3tmuG0gwmWcz64bnKzoGVlly0q653PPZQXv27YBEyea+71zkne6p537+wxly0padfZrja+/Bt58\nU85ZqZLv19opaOCnlHpaKVUq2HFEoahRQ/ZZHT164fNffgm0b8+iLuEqUkQ2EfvbP+mUUKp5eovE\nil+/fsDcuZIz7+/3iumeuZevwK9NG+CPP4AzZ9yZU1527Jik2a5bx1V2J2ktVRRXrfJ9w8uft96S\n4xcsCHzcmjVSUMzJlDRflJKqpHYVeWGaZ+T16gXs2gVMmyaf9+8vGTkXX+zuvIwCLydPyt+NE2nM\n2dM9J0+W/bWzZkVub6OZFb9yAJKVUj8opdoqxQQ8si4uDrj++gvTPT2erN59FL5IBzGHD8tq2q23\nhv5apwO/wYNlNXL27Av782TnZlEccs6ePbJvKXvVwVKlJOtg/nx35pWXLVsmmQk33yx3uMl+Rmp7\ncjLw22+hrWYVLy5FOJ54Ajh3zv9xbqR5Grp0kZuNp09bH4utHCIvf35p29C7t7TamTNHVgDd1rCh\n3AiYPl1qTDhRUbN166wCLzNmAE89JavxkUw1NlPV83UANQB8BuARAFuUUm8ppVyou0O5SfZ9fklJ\nkirYsKFrU8oVIr3P7+ef5S6Wv70jgdSq5VzgN3y4bJSeOxcoUybwsQ0bSvAarMcQxZY5c6QqW3x8\nzq8x3dMdRhPntm0lKCF7aS0X1IsXB85yCKR9e8nKGT7c/zGR6t/nS4UKcu5Jk6yPxYqe7mjdWnr1\ntWoF9O1rfkXaSYUKyX7wwYPt399nuOkmYO1auW7q0kVuTNet68y5/DG1x09rrQHsz3ykAygFYJJS\nKsDbAlFg2St7Gqt9XFO2JtIrfpMnh5fmCQBXXCGNhO1OuRs1Sn6f5s41t1G6XDlJWUpJsXce5C5f\naZ4GI/CLZDsRkhssDRtmBX78/ttHa+CFF2Q1btYsoGTJ8MZRSgpNjBoFbN7s+zyRruiZnV09/Zjq\n6Z5335X3gh493J5JlmbN5HfC7v19hkKFgBtvBDp1An74IfJ7ZAFze/yeVUqtADAcwCIAtbXWTwK4\nAUAIxduJLuRd4OXff+Ui4KGH3J1TblC1KnD+vOTQO+3YMbnIuP328F4fHw9Ur25vwDV2rKSRzJsX\nWoUw7vPLXbQOvEH/6qsl5Xz9+sjOKy/TOmvFr3p1yRJYu9btWeUe334rv/OzZgVObTfj8sultH6P\nHjmD8x07JDuiShVr57CiXTu5QN+2LfwxWNHTXVWrynWf0VopGiQmyg2T7NsD7PTqq1JdvHlz584R\niJkVv1IAOmit22itJ2qt0wBAa+0B0M7R2VGudvXV8g/ImTNS1OXuu8O/Q0lZItnIfdo0efOykqZh\n5z6/X36R4gRz58qFSygY+OUu69ZJFUN/F6dKMd0z0rZulWDv0kvlc6Z72mv2bKBnT/sKrvTqJX3N\nvvrqwueNNE83s3MKFJDq3+PHhz8GK3pSds2aSWEjX9sD7DyHW0EfECTwU0rFA7hfa/2Pr69rrR0u\nxE65WYECssdrzZqs3n1kj0g1cg+3mqc3OwO/L78EBg6UO4mhYuCXuwRK8zQw8IssY7XPcOutUuCA\n7JH9+2tVvnzAuHFSdfDQoazn3U7zNHTtKoFfRkZ4r2eaJ2UXFwdce63bs3BWwMBPa50BIEUpFeK9\ncyJz6tYFRo6U5pl2/oOV10WiwMuJE1KQ5447rI1jV+CXni5pTm3bhvf6OnVkReLkSetzIfeZCfya\nN5cbT4cP23POdeuCl8HPy7IHJs2aAStWyHsJWXP4MLBvn/2BTL16srL2/PNZz7lZ0dNb7dpA+fLh\nV4dlYRfKi8ymev6llJqrlJpqPJyeGOUNdetK404WdbFX/fpyEerkBdXUqXLX12rPRbsqey5dKn2l\nKlQI7/UFCkiLkeXLrc+F3JWaKqsSLVoEPq5QIdnTMXOm9XNqDXTrJhXhyLfsgd9FF8lNqrlz3ZtT\nbrFsGZCQ4EyK2qBBcpNv3jzZ171jh9woiwZWevpxxY/yIjOBXz/IXr5BAEZ4PYgsq1dPLr5Y1MVe\nRYvKytfXXzt3jjFjgMcftz7OlVcCW7aEn65j+O238Ff7DEz3zB3+/FMu6MzsGbYr3fPXXyUdbskS\nWX2mC505I4U0spcuv/VW7vOzw+LFzmXNFC0qVT579JDelwkJ0ostGjzwgPz+hHPzkD38KC8y08fv\nd1+PSEyOcr9GjaSlg9UKZJRTz57Ahx86Uy591SqpGmo1zROQi4oyZYB/fO4kNo+BHxnMpHkabr9d\nfnesBGtaA2+8AQwbBlSqJOmjdKGVK+Uiu3DhC59nWwd7LFnibGn4O+4ArrsOePLJ6EjzNJQqJW0n\nbr45qzG2GUZFz1q1nJsbUTQy086hkVIqWSl1Sil1XimVoZRiRj7ZQinZ40X2S0yU729Skv1jf/ih\nFOPJl8+e8azu8zt4UPpN3XijtXkYRXF4ERrbQgn8KlaUYM1KwP/zz3Ih2b490LQp9/n54q/wiPH+\nv5Gl4sLm8UiqZ8OGzp7n/fdl5bZpU2fPE6qHH5ZCY507y79NZvzzj1T0tLpVgSjWmEn1HA3gAQBb\nABQG8DgAk39a/imlSimlZimlUpRSM5VSPv/8lFKfKaUOKKXY7YcoBEplrfrZ6ehRadpuR5qnwWrg\nN3u2FOooUMDaPCpWlGB2xw5r45B7Dh+W1OFQ0t6spHt6PED//lJNNi5O9r0uXBjeWLmZv8BPKaZ7\nWrVxo2RNXHKJs+e59FJ5n3aqubUVN98sKd4ffST/7qWlBT6eaZ6UV5kJ/KC13gogXmudobX+AoDF\nhCoAQF8Ac7TWNQHMA/CKn+O+ANDGhvMR5TmdO8uG/N277Rvziy+A224DypWzb0yrgZ8daZ5AVg9E\npnvGrrlzZUUilJsAVgK/H3+Ucxlpz8aKn1Orxp9+KgU2Yk2gVgPs52eN02me3i69NHoLsVWtKsHf\n9u3yb9TRo/6PZUVPyqvMBH5nlFIFAKxWSg1XSvU2+bpg7gLwZebHXwK429dBWuuFAAL8+RKRP8WK\nAZ06SZ9EO3g8cke1Vy97xjNYqezp8UhVxjY23R5i4OeOM2fsCZZCSfM0NGwopfB37gztdRkZsto3\naFDWxXDlytKeZsuW0MYyIzUVeOaZ8MvXu2X3buDcOf/9NVu2lOIkp09Hdl65hd39+2JZiRLAtGnS\ni61RI9kC4AsrelJeZSaA6wwgHkAvAKcBVAJwjw3nLqu1PgAAWuv9AMraMCYRZfPUU9KE9/x562PN\nmgUUL27/RYaVFb9Vq2SvRpUq9syFgZ87OncG+vWzNobW4QV+8fGy6hTqqt8PP8jfQ/bVZqf2+S1e\nDJw9CyQn2z+2k5YuleDa30pRsWLSgsaJ/ch5gZMVPWNRfLz0B37+eflb9NUuhKmelFcFLc2gtTZq\n7Z0FMDCUwZVSswF4J4QpABrA675OFcrY/gwYMOB/HycmJiIxMdGOYYli1tVXy4ra5MlS+tqKDz+U\n/RN2p/qULy8rAocPAxdfHNpr7UrzNNxwA7B+vayuFCpk37gUWHKyBG0PPwzUqBHeGFu3yt6ecCr1\n3X478M03UrXQjPR0YMAA+ZvI/vdg7PN77LHQ5xHI7NkSIMVa4GdmRaptW2DGDPk5kHnHj8ue5Ouu\nc3sm0eeJJ+S95IEH5G+1Rw95nhU9KTdKSkpCkom7Z0oHya1RSm2Hj6BMa+0nacMcpdRGAIla6wNK\nqfIA5mutff4ZKqUqA5imtQ741qaU0sH+f4jyosmT5Q6olaIT27dL/6adO4EiReybm6FhQ+Ddd0Mv\nFd60KfDaa/YHfx98YL1KKJlz9Chw+eXSEmHuXAkAwrm58NFHUt1w/Pjw5lC5MrB/v7nf76++kv12\nv/+ec67r1wN33y2BqJ0SEoBXXpGm1UePSjGZWNC0qaTE3nKL/2PWrgU6dLD/e5bbzZkjqcZ//OH2\nTKLX1q2yB7dVK/k3ZtcuKQaza5fbMyNyjlIKWusc/5Ka+WejPoCEzEdTAO8D+MaGOU0F8Ejmxw8D\n+DnAsSrzQURhuOsuuStspb/YmDGyGuNE0AeEl+557BiwejXQrJm9c2G6Z2StWwfUrg0895zcWJgy\nJbxxwknzNJQqJc3F588PfmxamlTxHDzYd4B69dXAkSOyb9AuR44AKSlAu3aS2uxv71K0SUuTHn4J\nCYGPq11b9nky8AsN0zyDq15dvk8pKbKi/OefTPOkvMtMA/fDXo89Wuv3ANiRjDEMQCulVAqAlgCG\nAoBSqoJS6hfjIKXUdwD+BHClUmqnUupRG85NlKfkyyd998Jt7XD2rFTzNJsGF45wCrzMnSsrhNmb\nQlvFwC+y1qyRVLX8+YHRo4HevSUICEV6uuwRC7SqFIzZ6p5ffQVccYX/Gw5xcfJ7aec+v3nzsqqV\nNmggK5uxYO1a2X8brF+aUlnpnmReJCt6xrKSJeVvu2ZN4JFHWNGT8i4zDdzreT3qK6V6wMTewGC0\n1ke01rdorWtqrVtrrY9lPr9Pa93O67hOWutLtdYFtdaXZ7aTIKIQdesGTJwYXin477+XvUXVq9s/\nL0M4K3527+8zMPCLrLVrgeuvl49btJC037ffDm2M5GRJF7XSZsQI/ALtGDh/Xlb6Bg0KPFbTpvb2\n85szJyuoTUiInX1+S5eaX5FiP7/QaC3vU043bs8t8uWTJvRffAE8+KDbsyFyh5lUzxFej7cB3ADg\nPicnRUT2K19eLqzC2f/04Yf2t3DILtTAT2vnAr/q1aW0/J499o9NORkrfoYRIyS1OJS0Pytpnoar\nr5bVuvXr/R/z+efyuxpsL6rdlT29//9iKfALJTC55Rb5nqWmOjun3GLLFqBoUemtR+Y99JDs4ybK\ni8ykejb3erTSWnfTWqdEYnJEZK+ePaUAhsdj/jXLlkm1TScCLG/Vqslme7MXfRs2SNnumjXtn4vR\nyH3pUvvHpgtlZEhp9dq1s56rWBF46SXpWWe2Xtfs2dbSPAH5uQdK90xNBYYMCb7aB8iF5ZYtUnXR\nqr//ltRXY19SvXqyL9KOFi1OC6XHXKlScgOAhUrMYf8+IgqVmVTPgkqpTkqpV5VSbxiPSEyOiOx1\n441SnCWUBtAffih7++LjnZsXIPu7rrjC/CqPsdpnd2sJQ+PGTPeMhG3bJD2zePELn3/uOakkO3Vq\n8DFOnpR+jjffbH0+gQK/ceOAOnVkj10wBQpIevTixdbnZKR5Gr/rxYrJ30qglclocPiwVEkNZT9V\n27ZM9zSL+/uIKFRmUj1/BnAXgHRIA3fjQUQxRilZ9Rs92tzxhw7JhXfXrs7OyxBKuqdTaZ4G7vOL\njOxpnoYCBaSlxnPPSXGhQJKSJBizo+Js8+YypyNHLnz+7FnZd2hmtc9gV7qnrzTWWEj3XLpU5hnK\nTSMWeDGPFT2JKFRmAr+KWuuOWuvhWusRxsPxmRGRIzp1AhYtkvYOwXz2mfQjC7WperjMVvY8fVqC\nshYtnJtLQoKUoU9Lc+4cdGFhl+xuuUVWzYYODTyGHfv7DIUKAYmJOVedxoyRi+y6dc2PZUfgl5Eh\nFT1btrzw+VgJ/EINTOrVk5VCM+9Pednp09LSI5TfRyIiM4Hfn0qp2sEPI6JYcNFFQJcuwNixgY/L\nyJCL3Z49IzMvwPyKX1KS7KHKnh5opxIlJJ1u3TrnzkH+V/wM774r6cbbtvk/xs7AD8iZ7nn6NDB8\nODBgQGjjNG4sNw/OnQt/LqtWSWGmyy678PkGDaI/8AtnD1pcHNCmDTBzpjNzyi2WL5e/m4IF3Z4J\nEcUSM4HfTQBWKKVSlFJrlVLrlFJrnZ4YETnnqaekOmGgQirTp8veq/r1Izcvs4Gf02meBqZ7Oi/Q\nih8AVKoEvPCCpHz6sns3cPCgvSsft98uv2Pp6fL56NHSsy9QgOpLsWJSfGj58vDn4i+ove46KR5z\nOko3Xng8suIXTqsBpnsGxzRPIgqHmcDvVgA1ALQGcAeAdpn/JaIYVaOGpFT98IP/Yz78MLKrfYBc\nJG/aFLzqKAO/3OH4cdlHWrVq4OP69JG0tmnTcn5t9mxJg7Sz+FDFihJwLlkCnDgh7SX69w9vLKvp\nnv6qlRYsKFU+V60Kf2wnpaQApUsDZcuG/trWrYH582OjaqlbWNGTiMJhpp3DP1rrfwCcBaC9HkQU\nw3r2lODOl82bgdWrgfsi3LGzZElZJQnUP2/rVuDUqcCrRHZh4OestWuBa6+V9L5AjEIvzz6bs9CL\n3WmeBiPd8/33JRAJpTKlNyuB35kzks7ZrJnvr0fzPj8rgckll8hNoD//tHdOuYXRuJ0VPYkoVGba\nOdyplNoCYDuA3wHsAMAkDKIYd9ttwIEDvtPQxoyRSp6FCkV+XsHSPWfOlD1ATrVx8FarlnyPDh92\n/lx5UbA0T2+tW0s65/DhWc95PNLqwKnAb/JkYNQo4A0LDYxuukkCmIyM0F+7YIH8Pxcr5vvruTXw\nA4Bbb2W6pz87dsjNkkqV3J4JEcUaM6megwE0ArBZa10FQEsAvAdOFOPi46U/X/ZVv9Onga++Anr0\ncGdewSp7RirNE5DvUUICG7k7JVhhl+xGjpSVv7//ls/Xrs0qwmO3hg2lpUO7dsCVV4Y/TrlysoL1\n11+hv9bo3+dPNAd+4VT09MZ+fv4ZQXUkbn4RUe5iJvBL01ofBhCnlIrTWs8HEMFyD0TklMceA6ZM\nuXBF67vvZJWicmV35hRoxe/cOeD3351Z4fGH6Z7OCWXFDwAuv1z2+xmFXpxK8wQk6P/4Y2DIEOtj\nhZvuGez/r1YtaZB+9Gj4c3PCqVNSeKZOnfDHaNBACvcESvvOq5jmSUThMhP4HVNKFQWwAMC3SqlR\nYAN3olyhTBngzjulXx8ge0dGj458URdvgQK/hQtlr1Wk+goCEvgtXhy58+UVHg+wfj1QO8RmQc8/\nL78f06c7G/gBwD33AJdean2ccAK/AwckpS8hwf8x8fGSCmqlaqgTjFYDBQqEP0Z8vPxs2dYhJxZ2\nIaJwmQn87gRwBsCzAH4DsBVS2ZOIcoGePWVPX0aGNHY/ezZwepnTrroK2LjR99cimeZpaNgQWLYs\nvD1a5N+2bXLjoUSJ0F5XsKAUXHnmGQnImzd3Zn52uukmCfx0CGXR5s2TRvL58gU+Lhr7+dkVmERD\nuueZM8DOndKPcfZs4P/+T9KN+/cHJkyI/HxSU+WGyQ03RP7cRBT7/P6TopQ6iZzVO42M8jeUUtsA\nvKa1nuvU5IjIeQ0ayAX4jBnAt99Kj79gVRadVLGilNA/fjxnUPDbb8C4cZGdzyWXSEn6TZukfD7Z\nI9Q0T29t28qK0v79Ugk22lWrJiucO3YAVaqYe43Z1cyEBAlGosmSJcADD1gfp00bSe1NTw8eAFs1\nebI8Dh268OHxyPtj9sfFFwOvvSbpqC+84OzcvK1cKTfHihSJ3DmJKPfw+1aqtfZTRwxQSsUDuBbA\nt5n/DZlSqhSA7wFUhlQKvU9rfTzbMRUBfAWgHAAPgHFa6/fDOR8R+derFzB4sLRxGDPG3bnExUkp\n95eyBkMAABrsSURBVJQUCUoNe/YAe/cGTn1zirHPj4GffUIt7JLdp59K4BcLlMpK9zQT+Gktgd/L\nLwc/NiFBgqNoobUUdhk1yvpYFSrIXuOlS4EmTayP58+xY0D37sCwYXLjyTvAK1LEfxGVbt2AFi1k\n7/Frrzk3P2/c30dEVoR1X19rnaG1XgPgAwvn7gtgjta6JoB5AF7xcUw6gD5a62sANAbQUyl1lYVz\nEpEPHTtK6t1990XHCoqvfX4zZ8oKiJ2Nus1igRf7rV1rLfC7+OLYCsRD2ee3ebMEG2aqiVapIoHH\n3r3W5meXnTvlv5dfbs94kUj3HDUKuOMOKXbVpo2kUVauDFx0UeDKmRUrSrGpb7+Vlh+hpPKGa/Fi\n7u8jovBZSujSWn9s4eV3Afgy8+MvAdztY/z9WuvVmR+fArARwGUWzklEPhQqJC0cXn/d7ZkIX4Gf\nG/v7DAz87Gcl1TMW3XSTFCcyY/Zs2Wdrply/UkD9+tGzz2/JEtkXa1erAaf7+R0/Lnv2Xn01vNdX\nqAAkJUl15L59nQ/+WNiFiKxwcScPymqtDwAS4AEoG+hgpdQVAOoAYEctIgfcdlv0NATOHvilp0tP\nszZt3JnPddcB27fL3kOy7sQJ4OBB2fuWV1x3HbBvH/Dvv8GPDbUpfTT187M7MGncGNi6VX5fnPDB\nB/LeV6NG+GOULQvMny8/t969nQv+du+W4i556e+GiOzl6HZppdRsyP68/z0FKRjja13B71tlZjuJ\nSQCezVz582vAgAH/+zgxMRGJiYnmJ0xEUSF7Zc9lyyR1rEIFd+aTP7+UzU9OBlq2dGcOucm6dZKm\n6Ubarlvi4yWIWbgQaN/e/3Hp6bKC9Mkn5sdOSAA+/NDyFG2xZAnw9tv2jZc/v+yjmzkT6NzZvnEB\nuQExapT5ldhALr4YmDtXshKeekp+HnYXyWLjdiLyJykpCUlJSUGPUzoSSem+TqzURgCJWusDSqny\nAOZrrWv5OC4fgF8AzNBaB9wurpTSbv3/EJF9UlNlr+HJk3Lh98YbwPnzwNCh7s3pxRdlTpEq4pCb\nffQRsGpV5Cu0um3IEODIEWDECP/H/PmnBA6rV5sfd98+4NprpQqlm0HBuXNA6dLSg7BoUfvGHTcO\nmDULmDjRvjEB4K23gL/+kj16djlxArj9dllBHDfO3psbL7wAlCrF9yAiCk4pBa11jn8R3Ez1nArg\nkcyPHwbws5/jPgewIVjQR0S5R6FCUjjh77/lczf39xm4z88+Vgu7xCozBV5CTfMEZCW8SJGsvxe3\nrFkjAY+dQR8A/Oc/EhCbLY5jxsmTwHvv2b+vuXhxeb/asQN4+GFZwbULK3oSkVVuBn7DALRSSqUA\naAlgKAAopSoopX7J/LgJgAcBtFBKrVJKrVRKuXz5R0SRYOzz+/dfae1w443uzscI/JhUYF1eK+xi\naNAA2LABOBVgw4LZ/n3ZJSRISrSbnCo8UrKkpE4+9hhw9qw9Y370kaRt18qRZ2TdRRcB06fLCmyn\nTkBamvUxz5+XVXI32tkQUe7hWuCntT6itb5Fa11Ta91aa30s8/l9Wut2mR8v0lrHa63raK3raq3r\naa0dLuxMRNHACPxmzwaaNwcKFHB3PpddJiuRbq+qxDqPR/b41a7t9kwir1AhoE4d/yvHJ09KiudN\nN4U+djQUeDEqejrh7rtln63XNv6wnToFvPuus1WMCxeWSp9nz8qK5blz1sZbs0aKuhTz22GZiCg4\nN1f8iIj8MgK/aEjzNDDd07rt22UfWKlSbs/EHYHaOvz+u6wKFikS+rjREvg52Wrggw+AL78Eli+3\nNs6YMUCzZs73gSxUCJg8Wfb5tW9vbbWSaZ5EZAcGfkQUla66StLiZs50r41Ddgz8rFuzJm/u7zME\n2udn9O8LR/36kgpo556yUBw8KIVratZ07hxly0phnK5dJfUxHGfOyBj9+tk7N38KFAC+/17SVe+8\nUwpXhYON24nIDgz8iCgq1aolKxilSgFVqrg9G9G4MQM/q/JqYRdDkyayF8/Xvq9wCrsYSpYELr30\nwjYokbR0qaR52t3CILtOnaTf6LBh4b1+7FhZdY1kqnG+fMDXX8tK94MPAhkZoY/Bxu1EZAcGfkQU\nlS6+WB7RkuYJyB6jDRvsKzCRF+XVwi6GkiWBqlWBlSsvfH7PHmD/fvkdC1eDBu6le0YqMFFKgrf3\n35dWDKE4cwZ4553IrfZ5i48HvvoKOHYMePrp0IpEHTgAHD3q7GoqEeUNDPyIKGrVrw/cdZfbs8hS\nuLBUFx071u2ZxK68nuoJ+N7nN2eONCq30vfNzX1+kVyRqlQJePNNqfIZyurZJ5/IHN268VCwIPDT\nT5K2+eab5l9nFM1xejWViHI/vo0QUdSaMUMqeka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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to just dump it as a numpy array if thats all you want to do ...\n", "cdata = np.array(pd.DataFrame(c))\n", "cyears = np.array(c.keys()).astype(int)\n", "year_range = np.array([cyears[0],cyears[-1]])\n", "\n", "fig = plt.figure(figsize=(15,3))\n", "plt.plot(cyears,cdata[0])\n", "plt.xlabel('year')\n", "plt.ylabel('January AMO')\n", "plt.xlim(year_range[0],year_range[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2.4 Data Processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fire information we have is the peak fire count and the month this occurred in, for each location and year.\n", "\n", "The climate data gives the monthly ENSO-like variable for each month and year." ] }, { "cell_type": "code", "execution_count": 569, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------- Fire -----------\n", "(15, 36, 72)\n", "15\n", "(36, 72)\n", "------- Climate --------\n", "(69,)\n", "(12, 69)\n" ] } ], "source": [ "# relevant datasets\n", "\n", "print '------- Fire -----------'\n", "print fire_count.shape\n", "print len(good_year)\n", "print month_data.shape\n", "\n", "print '------- Climate --------'\n", "print cyears.shape\n", "print cdata.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now want to form datasets matching the ENSO index for N months prior to the fire month to the peak fire activity.\n", "\n", "We can prototype this for `N = 0` to test the idea.\n", "\n", "We appreciate in our design that `N` months before any month may lie in the previous year, so have to build a case for this:" ] }, { "cell_type": "code", "execution_count": 615, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15, 36, 72) (15, 36, 72)\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# what offset to use\n", "N = 0\n", "\n", "enso = np.zeros_like(fire_count)\n", "\n", "for iy,y in enumerate(good_year):\n", " for m in np.arange(12):\n", " r,c = np.where(month_data==m)\n", " # which month in the climate dataset\n", " climate_month = m - N\n", " \n", " # account for possibility of month being in\n", " # previous year\n", " if climate_month < 0:\n", " climate_month += 12\n", " climate_year = y-1\n", " else:\n", " climate_year = y\n", " yy = np.where(cyears == y)[0]\n", " # pull the climate value for this month and year\n", " climate_data = cdata[climate_month,yy]\n", " \n", " # store in enso\n", " enso[y-good_year[0],r,c] = climate_data\n", " \n", "plt.figure(figsize=(10,5))\n", "plt.imshow(enso[0],interpolation='nearest')\n", "plt.colorbar(fraction=0.02)\n", "plt.title('enso value for year %d at lag %d'%(good_year[0],N))\n", "\n", "# these should match up\n", "print enso.shape,fire_count.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, now we have the ENSO index dataset for lag month `N` and the fire count data.\n", "\n", "Lets look at a plot of this two for a particular location:" ] }, { "cell_type": "code", "execution_count": 616, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "slope, intercept, r_value, p_value, std_err 1668.5039503 15592.6186334 0.0838460646882 0.766401291507 5499.72415493\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.stats\n", "# see http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html\n", "\n", "y,r,c = np.where(fire_count == np.ma.max(fire_count))\n", "\n", "enso_ = np.array(enso[:,r,c]).flatten()\n", "fire_count_ = np.array(fire_count[:,r,c]).flatten()\n", "plt.figure(figsize=(5,5))\n", "plt.plot(enso_,fire_count_,'+')\n", "plt.xlabel('ENSO')\n", "plt.ylabel('fire count')\n", "\n", "# linear regression\n", "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(enso_,fire_count_)\n", "\n", "print 'slope, intercept, r_value, p_value, std_err',slope, intercept, r_value, p_value, std_err" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see there is a rather weak relationship between the two here, but that may just be the particular sample we have selected\n", "\n", "Let's make a method to help wiith this:" ] }, { "cell_type": "code", "execution_count": 622, "metadata": {}, "outputs": [], "source": [ "def enso_data(N,cdata=cdata,fire_count=fire_count,good_year=good_year,\\\n", " month_data=month_data,cyears=cyears):\n", " enso = np.zeros_like(fire_count)\n", "\n", " for iy,y in enumerate(good_year):\n", " for m in np.arange(12):\n", " r,c = np.where(month_data==m)\n", " # which month in the climate dataset\n", " climate_month = m - N\n", "\n", " # account for possibility of month being in\n", " # previous year\n", " if climate_month < 0:\n", " climate_month += 12\n", " climate_year = y-1\n", " else:\n", " climate_year = y\n", " yy = np.where(cyears == y)[0]\n", " # pull the climate value for this month and year\n", " climate_data = cdata[climate_month,yy]\n", "\n", " # store in enso\n", " enso[y-good_year[0],r,c] = climate_data\n", " return enso" ] }, { "cell_type": "code", "execution_count": 637, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 0.312752086836\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 637, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y,r,c = np.where(fire_count == np.ma.max(fire_count))\n", "\n", "fire_count_ = np.array(fire_count[:,r,c]).flatten()\n", "\n", "r_2 = np.zeros(12)\n", "for N in xrange(12):\n", " enso = enso_data(N)\n", " enso_ = np.array(enso[:,r,c]).flatten()\n", " slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(enso_,fire_count_)\n", " r_2[N] = r_value**2\n", "\n", "print np.argmax(r_2),r_2.max()\n", "plt.plot(r_2)\n", "plt.xlabel('lag month')\n", "plt.ylabel('R^2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the highest correlation is achieved for `N=8` for this location." ] }, { "cell_type": "code", "execution_count": 643, "metadata": {}, "outputs": [], "source": [ "# do this spatially now\n", "# so\n", "\n", "r_2 = np.zeros((12,nlat,nlon))\n", "\n", "for N in xrange(12):\n", " enso = enso_data(N)\n", " rs,cs = np.where(~fire_count.mask[0])\n", " for i in xrange(len(rs)):\n", " r,c = rs[i],cs[i]\n", " enso_ = np.array(enso[:,r,c]).flatten()\n", " fire_count_ = np.array(fire_count[:,r,c]).flatten()\n", " # regress\n", " slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(enso_,fire_count_)\n", " r_2[N,r,c] = r_value**2\n" ] }, { "cell_type": "code", "execution_count": 653, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 653, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lzgEAANqoBaU03i/pXkkHJtq/ERGnN9uJNIXkzHafpC9Ien9EjFXuJdtD5qaZ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\n2BERj4vzk3KKpB9FxBZxjlp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P47ZMZl/Qr5LZUKXbV1zjdNZXnB9t2fwm7ZfMfvvk55LZ5cMOSGaH/HJKflB7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\nshYtWwtAqu3uyA2Wnmq0vYa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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lag_month = np.argmax(r_2,axis=0)\n", "max_r2 = np.max(r_2,axis=0)\n", "\n", "plt.figure(figsize=(10,5))\n", "plt.imshow(lag_month,interpolation='nearest')\n", "plt.colorbar(fraction=0.02)\n", "plt.title('enso optimal lag month')\n", "\n", "plt.figure(figsize=(10,5))\n", "plt.imshow(np.sqrt(max_r2),interpolation='nearest')\n", "plt.colorbar(fraction=0.02)\n", "plt.title('enso/fire -r')\n", "\n", "\n", "####### NEED TO EDIT BEYOND HERE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2.4 Code to perform correlation analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea here is, for a particular (or set of) SST anomaly measures, work out which ‘lag’ month gives the highest correlation coefficient with fire count. \n", "\n", "By ‘lag’ month, we mean that e.g. if the peak fire month for a particular pixel was September, which month prior to that has a set of SST anomalies over the sample years that is most strongly correlated with fire count. \n", "\n", "So, if we were using a single SST anomaly measure (e.g. AMO or ONI) and sample years 2001 to 2009 to build our model, then we would do a linear regression of fire count for a particular pixel over these years against e.g. AMO data for September (lag 0) then August (lag 1) then July (lag 2) etc. and see which produced the highest $R^2$.\n", "\n", "Before we get into that, let's look again at the data structure we have:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# climate data\n", "print 'cdata',cdata.shape\n", "print 'cyears',cyears.shape\n", "\n", "# From the fire data\n", "\n", "print 'peak_count',peak_count.shape\n", "print 'av_fire_month',av_fire_month.shape\n", "print 'min_year',min_year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, if we want to select data for particular years:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# which years (inclusive)\n", "years = [2001,2010]\n", "\n", "ypeak_count = peak_count[years[0]-min_year:years[1] - min_year + 1]\n", "ycdata = cdata[:,years[0] - cyears[0]:years[1] - cyears[0] + 1]\n", "\n", "# check the shape\n", "print ycdata.shape,ypeak_count.shape,av_fire_month.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to consider a little carefully the implementation of lag ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# we will need to access ycdata[month - n][year]\n", "# which is a bit fiddly as e.g. -3 will be interpreted as\n", "# October for that same year, rather than the previous year\n", "y = 2001 - min_year\n", "m = 2\n", "lag = 5\n", "print m - lag,y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# so one way to fix this is to decrease y by one\n", "# if m - lag is -ve\n", "Y = y - (m - lag < 0)\n", "print m-lag,Y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import linregress\n", "\n", "# examine an example row col\n", "# for a given month over all years\n", "\n", "c = 24\n", "r = 19\n", "m = av_fire_month[r,c]\n", "# pull the data\n", "yyears = np.arange(years[1]-years[0]+1)\n", "\n", "R2 = np.array([linregress(\\\n", " ycdata[m-n,yyears - (m - n < 0)],\\\n", " ypeak_count[yyears - (m - n < 0),r,c]\\\n", " )[2] for n in xrange(12)])\n", "\n", "n = np.argmax(R2)\n", "\n", "x = ycdata[m-n,yyears - (m - n < 0)]\n", "y = ypeak_count[yyears - (m - n < 0),r,c]\n", "slope,intercept,R,p,err = linregress(x,y)\n", "\n", "print slope,intercept,p,err\n", "plt.plot(ycdata[m-n],y,'r+')\n", "plt.xlabel('Climate Index')\n", "plt.ylabel('Fire count')\n", "plt.plot([x.min(),x.max()],\\\n", " [intercept+slope*x.min(),intercept+slope*x.max()],'k--')\n", "plt.title('Fire count at r %03d c %03d: R^2 = %.3f: lag %d'%(r,c,R2[n],n))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# looper\n", "\n", "data_mask = ypeak_count.sum(axis=0)>100\n", "\n", "rs,cs = np.where(data_mask)\n", "\n", "results = {'intercept':0,'slope':0,'p':0,'R':0,'stderr':0,'lag':0}\n", "for k in results.keys():\n", " results[k] = np.zeros_like(av_fire_month).astype(float)\n", " results[k] = ma.array(results[k],mask=~data_mask)\n", " \n", "for r,c in zip(rs,cs):\n", " m = av_fire_month[r,c]\n", " # pull the data\n", " yyears = np.arange(years[1]-years[0]+1)\n", " R2 = np.array([\\\n", " linregress(\\\n", " ycdata[m-n,yyears - (m - n < 0)],\\\n", " ypeak_count[yyears - (m - n < 0),r,c]\\\n", " )[2] for n in xrange(12)])\n", " \n", " n = np.argmax(R2)\n", " results['lag'][r,c] = n\n", " x = ycdata[m-n,yyears - (m - n < 0)]\n", " y = ypeak_count[yyears - (m - n < 0),r,c]\n", " results['slope'][r,c],results['intercept'][r,c],\\\n", " results['R'][r,c],results['p'][r,c],\\\n", " results['stderr'][r,c] = linregress(x,y)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(10,4))\n", "plt.imshow(results['R'],interpolation='nearest')\n", "plt.colorbar()\n", "plt.title('R')\n", "\n", "plt.figure(figsize=(10,4))\n", "plt.imshow(results['p'],interpolation='nearest')\n", "plt.colorbar()\n", "plt.title('p')\n", "\n", "\n", "plt.figure(figsize=(10,4))\n", "plt.imshow(results['slope'],interpolation='nearest')\n", "plt.colorbar()\n", "plt.title('slope')\n", "\n", "\n", "plt.figure(figsize=(10,4))\n", "plt.imshow(results['lag'],interpolation='nearest')\n", "plt.colorbar()\n", "plt.title('lag')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which we can now predict:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# prediction year\n", "pyear = 2012\n", "\n", "# which month?\n", "M = av_fire_month - results['lag']\n", "Y = np.zeros_like(M) + pyear\n", "Y[M<0] -= 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# lets look at that ...\n", "plt.imshow(Y,interpolation='nearest')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# climate data\n", "scdata = np.zeros_like(Y).astype(float)\n", "\n", "for y in [pyear,pyear-1]:\n", " for m in xrange(12):\n", " scdata[(Y == y) & (M == m)] = cdata[m,y-cyears[0]]\n", "\n", "plt.imshow(scdata,interpolation='nearest')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now predict\n", "\n", "fc_predict = results['intercept'] + results['slope'] * scdata\n", "\n", "plt.figure(figsize=(10,4))\n", "plt.imshow(fc_predict,interpolation='nearest',vmin=0,vmax=10000)\n", "plt.colorbar()\n", "plt.title('predicted peak fire count for %d'%pyear)\n", "\n", "plt.figure(figsize=(10,4))\n", "plt.imshow(peak_count[pyear-min_year],\\\n", " interpolation='nearest',vmin=0,vmax=10000)\n", "plt.colorbar()\n", "plt.title('actual peak fire count for %d'%pyear)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = peak_count[pyear-min_year].flatten()\n", "y = fc_predict.flatten()\n", "\n", "slope,intercept,R,p,err = linregress(x,y)\n", "\n", "plt.plot(x,y,'+')\n", "plt.xlabel('measured fire count')\n", "plt.ylabel('predicted fire count')\n", "cc = np.array([0.,x.max()])\n", "\n", "plt.plot(cc,cc,'k--')\n", "plt.plot(cc,slope*cc+intercept,'k-')\n", "plt.title('fire count predictions')\n", "print slope,intercept,R,p,err" ] } ], "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", 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