{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_s1960-2018\n", "p_fdr = 0.1\n", "_s1960-2014\n", "p_fdr = 0.1\n", "_s1960-2010\n", "p_fdr = 0.1\n" ] } ], "source": [ "import sys ; sys.path.remove('/mnt/bcpu-ns9039k/ingo/jupyter/Modules'); sys.path.append('../../scripts')\n", "import norcpmTools as nt\n", "from netCDF4 import Dataset\n", "import numpy as np\n", "\n", "# plot ACC \n", "fields = [['sst','sst']]\n", "fields = [['TREFHT','TREFHT']]\n", "resOptions=[[5,5]]\n", "leadRanges = [[0,0],[1,4],[5,8]]\n", "expOptions = [['HIN1','HIST']]\n", "memRange = [1,10] \n", "product = 'hindcast'\n", "biasCorrect = 1\n", "for field in fields:\n", " fieldMod = field[0]\n", " fieldObs = field[1]\n", " if fieldObs == 'sst':\n", " obsName = 'ERSSTv5' \n", " obsCoverage = [1950,2018]\n", " levRange = [0,0]\n", " landFill = True\n", " elif fieldMod == 'TREFHT':\n", " fieldObs = 'TREFHT'\n", " obsName = 'HadCRUT'\n", " obsCoverage = [1950,2019]\n", " levRange = [0,0] \n", " landFill = False\n", " for leadRange in leadRanges: \n", " for res in resOptions:\n", " lon = np.arange(res[0]/2,360,res[0])\n", " lat = np.arange(-90+res[1]/2,90,res[1])\n", " lon2, lat2 = np.meshgrid(lon,lat)\n", " mskfdr = np.where(lat2 < 80, 1, 0)\n", " tagRes = '_{:d}x{:d}'.format(res[0],res[1])\n", " tagField = '_' + fieldMod\n", " tagLead = '_LY{:d}'.format(leadRange[0]+1) if leadRange[0] == leadRange[1] else '_LY{:d}-{:d}'.format(leadRange[0]+1,leadRange[1]+1)\n", " # extract data\n", " for expOption in expOptions:\n", " if expOption[0][0:3] == 'ANA' or expOption[1][0:3] == 'ANA':\n", " modCoverage = [1950,2018]\n", " else:\n", " modCoverage = [1950,2029]\n", " if product == 'analysis': \n", " syear1 = np.max((modCoverage[0],obsCoverage[0]))\n", " else:\n", " syear1 = np.max((1960,obsCoverage[0])) if not expOption[1] == 'PERS' else np.max((1960,obsCoverage[0]+1+leadRange[1]-leadRange[0]))\n", " syearn = np.min((modCoverage[1],obsCoverage[1]))-leadRange[1]-1\n", " syears = range(syear1,syearn+1)\n", " tagYears = '_s{:d}-{:d}'.format(syears[0],syears[-1])\n", " print(tagYears)\n", " if obsName == 'GlobColour':\n", " obs = np.flip(nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes),axis=1) \n", " else:\n", " obs = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes) \n", " if expOption[0] == 'HIST':\n", " fld1 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=[1,30],levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'PERS':\n", " fld1 = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes,persistence='mean',ensave=False)\n", " elif expOption[0] == 'HIN1':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'HIN2':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'ANA1':\n", " fld1 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False))\n", " elif expOption[0] == 'ANA2':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) \n", " #\n", " if expOption[1] == 'HIST':\n", " fld2 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'PERS':\n", " fld2 = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes,persistence='mean',ensave=False)\n", " elif expOption[1] == 'HIN1':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'HIN2':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'ANA1':\n", " fld2 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False))\n", " elif expOption[1] == 'ANA2':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) \n", " #\n", " tagExp = '_' + expOption[0] if expOption[1] == '' else '_{:s}-{:s}'.format(expOption[0],expOption[1])\n", " filePrefix = 'ACCsmith19biascorrplain{:d}'.format(biasCorrect) + tagField + tagExp + tagYears + tagLead + tagRes\n", " titleString = expOption[0] if expOption[1] == '' else expOption[0] + ' - ' + expOption[1]\n", " title2 = tagLead[1:] if product == 'hindcast' else '' \n", " r = nt.corrSmith19(fld1,fld2,obs,biasCorrect=biasCorrect,nboot=5000)\n", " if biasCorrect == 1:\n", " fld = r\n", " nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=filePrefix,lbLabelBarOn=False,\n", " title=' ',title2=' ',landFill=landFill,plottype='ACC',printFDR=False,printLOC=False)\n", " else: \n", " fld = r, np.ones([r.shape[0], r.shape[1]])\n", " nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=filePrefix,lbLabelBarOn=False,\n", " title=' ',title2=' ',landFill=landFill,plottype='ACC',printFDR=False,printLOC=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 5 6]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np \n", "A = np.array([1,2,3,4,5,6])\n", "print(A[list(set(range(6)) - set([0,1,2]))])\n", "np.log(1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 == True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }