Tristan Hauser added file figures/comp_sigmasked1/field_composite_significance.ipynb  about 8 years ago

Commit id: c1a4f211b235dc1f7387981248c83d73444be533

deletions | additions      

         

{  "cells": [  {  "cell_type": "markdown",  "metadata": {},  "source": [  "## Field composite "  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "Calculate the El Niño composite field, and display only significant values."  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "### Set environment"  ]  },  {  "cell_type": "code",  "execution_count": 1,  "metadata": {  "collapsed": true  },  "outputs": [],  "source": [  "#--- Libraries\n",  "import matplotlib.pyplot as plt # plotting package\n",  "from subprocess import call # access system commands\n",  "import seaborn as sns # plotting aestetics\n",  "import netCDF4 as nc # package to read NetCDF files\n",  "import pandas as pd # packages for statistics\n",  "import numpy as np # packages for linear algebra\n",  "import itertools # package to create pair combinations\n",  "import glob # commands to access multiple files\n",  "\n",  "from mpl_toolkits.axes_grid1 import ImageGrid # plot setting libraries\n",  "from collections import OrderedDict # dictionaries with fixed sequence of entries\n",  "from mpl_toolkits.basemap import Basemap # map making libraries\n",  "from cdo import * # access external NetCDF data processing routines\n",  "cdo = Cdo() \n",  "\n",  "%matplotlib inline"  ]  },  {  "cell_type": "code",  "execution_count": 2,  "metadata": {  "collapsed": false  },  "outputs": [],  "source": [  "#--- Arrange data\n",  "# load precip data files\n",  "ncfile_anom_full = nc.Dataset('data/precip.mon.mean.safr.anom.nc')\n",  "ncfile_anom_elnino = nc.Dataset('data/precip.mon.mean.safr.anom.elnino.nc')\n",  "# get coordinates of data\n",  "lons = ncfile_anom_full.variables['lon'][:]\n",  "lats = ncfile_anom_full.variables['lat'][:]\n",  "# add extra point for grid fill\n",  "lons = np.append(lons,lons[-1]+2)\n",  "lats = np.append(lats,lats[-1]-2)\n",  "# set coordinates list as grid of locations\n",  "lons, lats = np.meshgrid(lons,lats)\n",  "# shift so that lines show grid box boundaries, \n",  "# rather than grid point locations\n",  "lons = lons - (2/2)\n",  "lats = lats + (2/2)\n",  "# load El Niño dates\n",  "mei_elnino_dates = pd.read_pickle('data/mei_elnino_dates.pkl')\n",  "# only consider El Nino dates in range of precipitation estimate\n",  "mei_elnino_dates = mei_elnino_dates[mei_elnino_dates > '1978']\n",  "mei_elnino_dates = mei_elnino_dates[mei_elnino_dates < '2015-10-01']"  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "### Calculate composite statistics "  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "_These estimates are done only for the mean, rather than for the median or mode. There is no_ `CDO` _opporator for the latter two statistics, so would have to change the script a bit to run the procedure for those two statistics. Probably the most interesting thing to try would be to look at the significance of estimates for the mode, when masking values below the suggested error thresholds. Then can consider each grid cell to have different degress of freedom, and so better constrain the estimate to where there is viable data._"  ]  },  {  "cell_type": "code",  "execution_count": null,  "metadata": {  "collapsed": false  },  "outputs": [],  "source": [  "#--- Calculate mean\n",  "_ = cdo.timmean(input='data/precip.mon.mean.safr.anom.elnino.nc',\n",  " output='data/comp.nc')"  ]  },  {  "cell_type": "code",  "execution_count": null,  "metadata": {  "collapsed": false  },  "outputs": [],  "source": [  "#--- Significance testing\n",  "# find number of time steps in full data set\n",  "nts = len(ncfile_anom_full.variables['time'])\n",  "# find number of time steps in El Niño subset\n",  "deg_free = len(mei_elnino_dates)\n",  "# create 1000 null-tests\n",  "for samp in range(1000) :\n",  " # random sample \n",  " rts = random.sample(np.arange(nts)+1,deg_free)\n",  " rts.sort()\n",  " rts = [str(i) for i in rts]\n",  " # select individual fields\n",  " cdo.seltimestep(','.join(rts),\n",  " input='data/precip.mon.mean.safr.anom.nc',\n",  " output='data/samps.nc')\n",  " # create a composite\n",  " cdo.timmean(input='data/samps.nc',\n",  " output='data/nullcomp'+('%03d' % samp)+'.nc')\n",  " # sweep up\n",  " call('rm data/samps.nc',shell=True)\n",  "# merge composites\n",  "comps = glob.glob('data/nullcomp*.nc')\n",  "cdo.cat(input=' '.join(comps),output='data/nullcomps.nc')\n",  "# sweep up\n",  "call('rm '+' '.join(comps),shell=True)\n",  "# calculate quantiles\n",  "_ = cdo.timmin(input='data/nullcomps.nc',output='data/nullcomps.min.nc')\n",  "_ = cdo.timmax(input='data/nullcomps.nc',output='data/nullcomps.max.nc')\n",  "_ = cdo.timpctl('97.5',\n",  " input='data/nullcomps.nc data/nullcomps.min.nc data/nullcomps.max.nc',\n",  " output='data/anom.np975.nc')\n",  "_ = cdo.timpctl('2.5',\n",  " input='data/nullcomps.nc data/nullcomps.min.nc data/nullcomps.max.nc',\n",  " output='data/anom.np025.nc')\n",  "call('rm data/nullcomps.nc data/nullcomps.min.nc data/nullcomps.max.nc',shell=True)\n"  ]  },  {  "cell_type": "code",  "execution_count": null,  "metadata": {  "collapsed": true  },  "outputs": [],  "source": [  "#--- Create mask for non signficant grid cells\n",  "# values above the cut off to be significant negative values\n",  "_ = cdo.gt(input='data/comp.nc data/anom.np025.nc',\n",  " output='data/over.nc')\n",  "# values below the cut off to be significant positive values\n",  "_ = cdo.lt(input='data/comp.nc data/anom.np975.nc',\n",  " output='data/under.nc')\n",  "# combine results\n",  "_ = cdo.add(input='data/under.nc data/over.nc',\n",  " output='data/under_and_over.nc',options='-b 64')\n",  "# only consider where both disqualfying criteria are met\n",  "_ = cdo.gec('1.5',input='data/under_and_over.nc',\n",  " output='data/nsig.nc')"  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "### Map results"  ]  },  {  "cell_type": "code",  "execution_count": 3,  "metadata": {  "collapsed": false  },  "outputs": [],  "source": [  "# link to composite file\n",  "ncfile_comp = nc.Dataset('data/comp.nc')\n",  "# link to mask file\n",  "ncfile_mask = nc.Dataset('data/nsig.nc')"  ]  },  {  "cell_type": "code",  "execution_count": 4,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAE6CAYAAABH32tEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFAqFQhEy/w9g7TSje4ZcgQAAAABJRU5ErkJggg==\n",  "text/plain": [  ""  ]  },  "metadata": {},  "output_type": "display_data"  }  ],  "source": [  "# create figure\n",  "fig, chart = plt.subplots(1,figsize=(8,8))\n",  "# set region/projection\n",  "_ = chart = Basemap(projection='lcc',resolution='c',\n",  " lat_0=-17,lon_0=25,\n",  " llcrnrlat=-40,urcrnrlat=-5.,\n",  " llcrnrlon=0,urcrnrlon=55) \n",  "# mark land mass\n",  "_ = chart.drawlsmask(land_color='black') \n",  "# draw parallels and meridians\n",  "_ = chart.drawparallels(np.arange(-90.,91.,10.),labels=[True,False,False,False],dashes=[1,4])\n",  "_ = chart.drawmeridians(np.arange(-180.,181.,10.),labels=[False,False,False,True],dashes=[1,4])\n",  "# read composite\n",  "comp = np.copy(ncfile_comp.variables['precip'][0,:,:])\n",  "# overlay composite\n",  "sm = chart.pcolormesh(lons,lats,comp,shading='flat',latlon=True,\n",  " alpha=0.9,cmap='BrBG',vmin=-1,vmax=1)\n",  "# add color bar\n",  "cb = chart.colorbar()\n",  "cb.set_label('precip anomaly [mm/day]')\n",  "# read mask file\n",  "mask = np.copy(ncfile_mask.variables['precip'][0,:,:])\n",  "# only consider values where mask=True\n",  "mask = np.ma.masked_where(mask < 0.2,mask)\n",  "# shade out anomalies below estimated error range\n",  "ms = chart.pcolormesh(lons,lats,mask,\n",  " shading='flat',latlon=True,\n",  " alpha=0.35,cmap='Greys',vmin=0,vmax=1)\n",  "\n",  "plt.savefig('comp_sigmasked.png')"  ]  },  {  "cell_type": "markdown",  "metadata": {},  "source": [  "If the blanking out makes the image difficult to read, there is also the option of just not plotting the 'insignificant' values."  ]  },  {  "cell_type": "code",  "execution_count": 5,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAE6CAYAAABH32tEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIARBEARBqDASQAiCIAiCUGH+H/GZY2T3Nbu8AAAAAElFTkSuQmCC\n",  "text/plain": [  ""  ]  },  "metadata": {},  "output_type": "display_data"  }  ],  "source": [  "# create figure\n",  "fig, chart = plt.subplots(1,figsize=(8,8))\n",  "# set region/projection\n",  "_ = chart = Basemap(projection='lcc',resolution='c',\n",  " lat_0=-17,lon_0=25,\n",  " llcrnrlat=-40,urcrnrlat=-5.,\n",  " llcrnrlon=0,urcrnrlon=55) \n",  "# mark land mass\n",  "_ = chart.drawlsmask() \n",  "# draw parallels and meridians\n",  "_ = chart.drawparallels(np.arange(-90.,91.,10.),labels=[True,False,False,False],dashes=[1,4])\n",  "_ = chart.drawmeridians(np.arange(-180.,181.,10.),labels=[False,False,False,True],dashes=[1,4])\n",  "# read composite\n",  "comp = np.copy(ncfile_comp.variables['precip'][0,:,:])\n",  "# read mask file\n",  "mask = np.copy(ncfile_mask.variables['precip'][0,:,:])\n",  "# only consider values where mask=True\n",  "comp = np.ma.masked_where(mask > 0.2,comp)\n",  "# overlay composite\n",  "sm = chart.pcolormesh(lons,lats,comp,shading='flat',latlon=True,\n",  " alpha=0.6,cmap='BrBG',vmin=-1,vmax=1)\n",  "# add color bar\n",  "cb = chart.colorbar()\n",  "cb.set_label('precip anomaly [mm/day]')\n"  ]  }  ],  "metadata": {  "kernelspec": {  "display_name": "Python 2",  "language": "python",  "name": "python2"  },  "language_info": {  "codemirror_mode": {  "name": "ipython",  "version": 2  },  "file_extension": ".py",  "mimetype": "text/x-python",  "name": "python",  "nbconvert_exporter": "python",  "pygments_lexer": "ipython2",  "version": "2.7.11"  }  },  "nbformat": 4,  "nbformat_minor": 0  }