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pycpt_functions_seasonal.py
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pycpt_functions_seasonal.py
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#This is PyCPT_functions_seasonal.py (version1.6) -- 4 Jan 2019
#Authors: AG Muñoz (agmunoz@iri.columbia.edu) and Andrew W. Robertson (awr@iri.columbia.edu)
#Notes: be sure it matches version of PyCPT
#Log:
#* Started simplifying functions, wrote readGrADSctl function; added functions to create the NextGen files for det skill assessment and plotting --AGM, Sep 2019
#* Fixed bug with plotting functions when selecting a subset of the seasons, and added start time for forecast file in CPT script -- AGM, July 1st 2019
#* Added VQ and UQ from CFSv2. User can now select the seasons to visualize in the skill and EOF maps. Fixed bug related to coordinate selection in CHIRPS, TRMM and CPC. -- AGM, June 13th 2019
#* First Notebook seasonal version -- AGM, May 7th 2019
#* Several PyCPT sub-seasonal versions (through v1.2) --see logs in that version 2018-present
#* First iPython sub-seasonal version (Jupyter Notebook) -- AWR, 24 Jun 2018
#* First similar version (BASH for CFSv2) by Muñoz and Chourio for the OLE2 -- 12 Dec 2010
#To Do: (as June 8th, 2019 -- AGM)
# + ELR proceedure is not reproducing results obtained in R or Matlab
# + Simplify download functions: just one function, with the right arguments and dictionaries.
# + Check Hindcasts and Forecast_RFREQ
import os
import sys
import warnings
import struct
import xarray as xr
import numpy as np
import pandas as pd
from copy import copy
from scipy.stats import t
from scipy.stats import invgamma
import cartopy.crs as ccrs
from cartopy import feature
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.ticker as ticker
from matplotlib.colors import LinearSegmentedColormap
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import fileinput
warnings.filterwarnings("ignore")
def lines_that_equal(line_to_match, fp):
return [line for line in fp if line == line_to_match]
def lines_that_contain(string, fp):
return [line for line in fp if string in line]
def lines_that_start_with(string, fp):
return [line for line in fp if line.startswith(string)]
def lines_that_end_with(string, fp):
return [line for line in fp if line.endswith(string)]
def exceedprob(x,dof,lo,sc):
return t.sf(x, dof, loc=lo, scale=sc)*100
class MidpointNormalize(colors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
def discrete_cmap(N, base_cmap=None):
"""Create an N-bin discrete colormap from the specified input map"""
# Note that if base_cmap is a string or None, you can simply do
# return plt.cm.get_cmap(base_cmap, N)
# The following works for string, None, or a colormap instance:
base = plt.cm.get_cmap(base_cmap)
color_list = base(np.linspace(0, 1, N))
cmap_name = base.name + str(N)
#base.set_bad(color='white')
#return base.from_list(cmap_name, color_list, N)
return LinearSegmentedColormap.from_list(cmap_name, color_list, N) #perceptually uniform colormaps
def replaceAll(file,searchExp,replaceExp):
for line in fileinput.input(file, inplace=1):
if searchExp in line:
line = line.replace(searchExp,replaceExp)
sys.stdout.write(line)
def readGrADSctl(models,fprefix,predictand,mpref,id,tar,monf,fyr):
#Read grads binary file size H, W, T
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+id+'_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
Wi= float(line.split()[3])
XD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+id+'_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
Hi= float(line.split()[3])
YD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+id+'_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("TDEF", fp):
T = int(line.split()[1])
Ti= int((line.split()[3])[-4:])
TD= 1 #not used
return (W, Wi, XD, H, Hi, YD, T, Ti, TD)
def PrepFiles(fprefix, predictand, threshold_pctle, wlo1, wlo2,elo1, elo2, sla1, sla2, nla1, nla2, tgti, tgtf, mon, monf, fyr, os, wetday_threshold, tar, model, obs_source, hdate_last, force_download,station):
"""Function to download (or not) the needed files"""
if fprefix=='RFREQ':
GetObs_RFREQ(predictand, wlo2, elo2, sla2, nla2, wetday_threshold, threshold_pctle, tar, obs_source, hdate_last, force_download,station)
print('Obs:rfreq file ready to go')
print('----------------------------------------------')
# nday added after nlag for GEFS & CFSv2
GetHindcasts_RFREQ(wlo1, elo1, sla1, nla1, tgti, tgtf, mon, os, wetday_threshold, tar, model, force_download)
#GetHindcasts_RFREQ(wlo1, elo1, sla1, nla1, day1, day2, nday, fyr, mon, os, authkey, wk, wetday_threshold, nlag, training_season, hstep, model, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
#GetForecast_RFREQ(day1, day2, fday, mon, fyr, nday, wlo1, elo1, sla1, nla1, authkey, wk, wetday_threshold, nlag, model, force_download)
GetForecast_RFREQ(monf, fyr, tgti, tgtf, tar, wlo1, elo1, sla1, nla1, wetday_threshold, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
elif fprefix=='UQ':
GetHindcasts_UQ(wlo1, elo1, sla1, nla1, tgti, tgtf, mon, os, tar, model, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
GetObs(predictand, wlo2, elo2, sla2, nla2, tar, obs_source, hdate_last, force_download,station)
print('Obs:precip file ready to go')
print('----------------------------------------------')
GetForecast_UQ(monf, fyr, tgti, tgtf, tar, wlo1, elo1, sla1, nla1, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
elif fprefix=='VQ':
GetHindcasts_VQ(wlo1, elo1, sla1, nla1, tgti, tgtf, mon, os, tar, model, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
GetObs(predictand, wlo2, elo2, sla2, nla2, tar, obs_source, hdate_last, force_download,station)
print('Obs:precip file ready to go')
print('----------------------------------------------')
GetForecast_VQ(monf, fyr, tgti, tgtf, tar, wlo1, elo1, sla1, nla1, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
else:
GetHindcasts(wlo1, elo1, sla1, nla1, tgti, tgtf, mon, os, tar, model, force_download)
print('Hindcasts file ready to go')
print('----------------------------------------------')
GetObs(predictand, wlo2, elo2, sla2, nla2, tar, obs_source, hdate_last, force_download,station)
print('Obs:precip file ready to go')
print('----------------------------------------------')
GetForecast(monf, fyr, tgti, tgtf, tar, wlo1, elo1, sla1, nla1, model, force_download)
print('Forecasts file ready to go')
print('----------------------------------------------')
def PrepFiles_usrNetcdf(fprefix, predictand, wlo1, wlo2,elo1, elo2, sla1, sla2, nla1, nla2, tgti, tgtf, mon, monf, fyr, tar, infile_predictand, infile_hindcast, infile_forecast):
"""Function to download (or not) the needed files"""
readNetCDF_predictand(infile_predictand,outfile, predictand, wlo2, elo2, sla2, nla2, tar)
print('Obs:precip file ready to go')
print('----------------------------------------------')
readNetCDF_Hindcasts(infile_hindcast, outfile, wlo1, elo1, sla1, nla1, tgti, tgtf, mon, tar)
print('Hindcasts file ready to go')
print('----------------------------------------------')
readNetCDF_Forecast(infile_forecast, outfile, monf, fyr, tgti, tgtf, tar, wlo1, elo1, sla1, nla1)
print('Forecasts file ready to go')
print('----------------------------------------------')
def pltdomain(loni1,lone1,lati1,late1,loni2,lone2,lati2,late2):
"""A simple plot function for the geographical domain
PARAMETERS
----------
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
title: title
"""
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
#name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
fig = plt.subplots(figsize=(15,15), subplot_kw=dict(projection=ccrs.PlateCarree()))
loni = [loni1,loni2]
lati = [lati1,lati2]
lone = [lone1,lone2]
late = [late1,late2]
title = ['Predictor', 'Predictand']
for i in range(2):
ax = plt.subplot(1, 2, i+1, projection=ccrs.PlateCarree())
ax.set_extent([loni[i],lone[i],lati[i],late[i]], ccrs.PlateCarree())
# Put a background image on for nice sea rendering.
ax.stock_img()
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
ax.set_title(title[i]+" domain")
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
plt.show()
def plteofs(models,predictand,mode,M,loni,lone,lati,late,fprefix,mpref,tgts,mol,mons):
"""A simple function for ploting EOFs computed by CPT
PARAMETERS
----------
models: list of models to plot
predictand: exactly that
mode: EOF being visualized
M: total number of EOFs computed by CPT (max defined in PyCPT is 10)
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
fprefix:
"""
#mol=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
if mpref=='None':
print('No EOFs are computed if MOS=None is used')
return
nmods=len(models)
#plt.figure(figsize=(20,10))
fig, ax = plt.subplots(figsize=(20,15),sharex=True,sharey=True)
tari=tgts[0]
model=models[0]
monn=mol[0]
nsea=len(mons)
#Read grid
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFX_'+tari+'_'+monn+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFX_'+tari+'_'+monn+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
if mpref=='CCA':
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFY_'+tari+'_'+monn+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
Wy = int(line.split()[1])
XDy= float(line.split()[4])
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFY_'+tari+'_'+monn+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
Hy = int(line.split()[1])
YDy= float(line.split()[4])
eofy=np.empty([M,Hy,Wy]) #define array for later use
eofx=np.empty([M,H,W]) #define array for later use
k=0
for tar in mons:
k=k+1
mon=mol[tgts.index(tar)]
ax = plt.subplot(nmods+1,nsea, k, projection=ccrs.PlateCarree()) #nmods+obs
if mpref=='CCA': #skip if there are not predictand EOFs (e.g., PCR)
ax.set_extent([loni,loni+Wy*XDy,lati,lati+Hy*YDy], ccrs.PlateCarree())
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFY_'+tar+'_'+mon+'.dat','rb')
#cycle for all time steps (same approach to read GrADS files as before, but now read T times)
for mo in range(M):
#Now we read the field
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize) #this if for each time/EOF stamp
A0=np.fromfile(f,dtype='float32',count=numval)
endrec=struct.unpack('i',f.read(4))[0] #needed as Fortran sequential repeats the header at the end of the record!!!
eofy[mo,:,:]= np.transpose(A0.reshape((Wy, Hy), order='F'))
eofy[eofy==-999.]=np.nan #nans
CS=plt.pcolormesh(np.linspace(loni, loni+Wy*XDy,num=Wy), np.linspace(lati+Hy*YDy, lati, num=Hy), eofy[mode,:,:],
vmin=-.1,vmax=.1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'EOF charges'
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
#tick_spacing=0.5
#ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0., linestyle='--')
pl.xlabels_top = False
pl.xlabels_bottom = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
if k<=nsea:
ax.set_title(tar)
#if ax.is_first_col():
ax.set_ylabel(model, rotation=90)
if k==1:
ax.text(-0.2,0.5,'Obs',rotation=90,fontsize=9.2,verticalalignment='center', transform=ax.transAxes)
nrow=0
for model in models:
nrow=nrow+1 #first model is in row=2 and nrow=1
for tar in mons:
k=k+1
mon=mol[tgts.index(tar)]
ax = plt.subplot(nmods+1,nsea, k, projection=ccrs.PlateCarree()) #nmods+obs
if mpref=='PCR':
ax.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree()) #EOF domains will look different between CCA and PCR if X and Y domains are different
else:
ax.set_extent([loni,loni+Wy*XDy,lati,lati+Hy*YDy], ccrs.PlateCarree())
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
if k == (nrow*nsea)+1:
ax.text(-0.2,0.5,model,rotation=90,fontsize=9.2,verticalalignment='center', transform=ax.transAxes)
#tick_spacing=0.5
#ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0., linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xlabels_bottom = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
ax.set_xbound(lower=loni, upper=lone)
if k > (nmods+1)*nsea-nsea:
pl.xlabels_bottom = True
#if ax.is_first_col():
ax.set_ylabel(model, rotation=90)
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_EOFX_'+tar+'_'+mon+'.dat','rb')
#cycle for all time steps (same approach to read GrADS files as before, but now read T times)
for mo in range(M):
#Now we read the field
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize) #this if for each time/EOF stamp
A0=np.fromfile(f,dtype='float32',count=numval)
endrec=struct.unpack('i',f.read(4))[0] #needed as Fortran sequential repeats the header at the end of the record!!!
eofx[mo,:,:]= np.transpose(A0.reshape((W, H), order='F'))
eofx[eofx==-999.]=np.nan #nans
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), eofx[mode,:,:],
vmin=-.1,vmax=.1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'EOF charges'
plt.subplots_adjust(hspace=0)
#plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
#cbar_ax = plt.add_axes([0.85, 0.15, 0.05, 0.7])
#plt.tight_layout()
#plt.autoscale(enable=True)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label(label) #, rotation=270)
f.close()
def pltmap(models,predictand,score,loni,lone,lati,late,fprefix,mpref,tgts, mo, mons):
"""A simple function for ploting the statistical scores
PARAMETERS
----------
score: the score
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
"""
nmods=len(models)
nsea=len(mons) #number of seasons and columns
#plt.figure(figsize=(20,10))
fig, ax = plt.subplots(figsize=(20,15),sharex=True,sharey=True)
k=0
nrow=-1
for model in models:
nrow=nrow+1
#print(model)
#kk=-1
for tar in mons:
#kk=kk+1
k=k+1
#mon=mo[k-1]
mon=mo[tgts.index(tar)]
#Read grads binary file size H, W
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_'+score+'_'+tar+'_'+mon+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_'+score+'_'+tar+'_'+mon+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
# ax = plt.subplot(nwk/2, 2, wk, projection=ccrs.PlateCarree())
ax = plt.subplot(nmods,nsea, k, projection=ccrs.PlateCarree())
ax.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
if k == (nrow*nsea)+1:
ax.text(-0.2,0.5,model,rotation=90,fontsize=9.2,verticalalignment='center', transform=ax.transAxes)
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
#tick_spacing=0.5
#ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0., linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xlabels_bottom = False
if k > (nmods)*nsea-nsea:
pl.xlabels_bottom = True
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
if k<=nsea:
ax.set_title(tar)
#ax.ylabel(model, fontsize=11)
#for i, axi in enumerate(axes): # need to enumerate to slice the data
# axi.set_ylabel(model, fontsize=12)
if score == 'CCAFCST_V' or score == 'PCRFCST_V':
#f=open('../output/'+model+'_'+fprefix+'_'+score+'_'+training_season+'_'+mon+str(fday)+'_wk'+str(wk)+'.dat','rb')
f=open('../output/'+model+'_'+fprefix+predictand+'_'+score+'_'+tar+'_'+mon+str(fday)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
var[var==-999.]=np.nan #only sensible values
current_cmap = plt.cm.BrBG
current_cmap.set_bad('white',1.0)
current_cmap.set_under('white', 1.0)
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
#vmin=-max(np.max(var),np.abs(np.min(var))), #vmax=np.max(var),
norm=MidpointNormalize(midpoint=0.),
cmap=current_cmap,
transform=ccrs.PlateCarree())
ax.set_title("Deterministic forecast for Week "+str(wk))
if fprefix == 'RFREQ':
label ='Freq Rainy Days (days)'
elif fprefix == 'PRCP':
label = 'Rainfall anomaly (mm/week)'
f.close()
#current_cmap = plt.cm.get_cmap()
#current_cmap.set_bad(color='white')
#current_cmap.set_under('white', 1.0)
else:
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'_'+score+'_'+tar+'_'+mon+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
var[var==-999.]=np.nan
vmi=-max(np.nanmax(var),np.abs(np.nanmin(var)))
vma=-vmi
#define colorbars, depending on each score --This can be easily written as a function
if score == '2AFC':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = '2AFC (%)'
if score == 'RocAbove' or score=='RocBelow':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'ROC area'
if score == 'Spearman' or score=='Pearson':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=-1,vmax=1,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'Correlation'
if score == 'RPSS':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=vmi,vmax=vma,
cmap=discrete_cmap(20, 'bwr'),
transform=ccrs.PlateCarree())
label = 'RPSS (all categories)'
if score=='GROC':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=100,
cmap=discrete_cmap(11, 'bwr'),
transform=ccrs.PlateCarree())
label = 'GROC (probabilistic)'
if score=='Ignorance':
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), var,
vmin=0,vmax=vma,
cmap=discrete_cmap(20, 'jet'),
transform=ccrs.PlateCarree())
label = 'Ignorance (all categories)'
plt.subplots_adjust(hspace=0)
#plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
#cbar_ax = plt.add_axes([0.85, 0.15, 0.05, 0.7])
#plt.tight_layout()
#plt.autoscale(enable=True)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label(label) #, rotation=270)
f.close()
def skilltab(score,wknam,lon1,lat1,lat2,lon2,loni,lone,lati,late,fprefix,mpref,training_season,mon,fday,nwk):
"""A simple function for ploting probabilities of exceedance and PDFs (for a given threshold)
PARAMETERS
----------
thrs: the threshold, in the units of the predictand
lon: longitude
lat: latitude
"""
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+model+'_'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+model+'_'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk1.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
#Find the gridbox:
lonrange = np.linspace(loni, loni+W*XD,num=W)
latrange = np.linspace(lati+H*YD, lati, num=H) #need to reverse the latitudes because of CPT (GrADS YREV option)
lon_grid, lat_grid = np.meshgrid(lonrange, latrange)
#first point
a = abs(lat_grid-lat1)+abs(lon_grid-lon1)
i1,j1 = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
#second point
a = abs(lat_grid-lat2)+abs(lon_grid-lon2)
i2,j2 = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
df = pd.DataFrame(index=wknam[0:nwk])
for L in range(nwk):
wk=L+1
for S in score:
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+model+'_'+fprefix+'_'+mpref+'_'+str(S)+'_'+training_season+'_wk'+str(wk)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
var = np.transpose(A.reshape((W, H), order='F'))
var[var==-999.]=np.nan #only sensible values
df.at[wknam[L], str(S)] = round(np.nanmean(np.nanmean(var[i1:i2,j1:j2], axis=1), axis=0),2)
df.at[wknam[L], 'max('+str(S)+')'] = round(np.nanmax(var[i1:i2,j1:j2]),2)
df.at[wknam[L], 'min('+str(S)+')'] = round(np.nanmin(var[i1:i2,j1:j2]),2)
return df
f.close()
def pltmapProb(loni,lone,lati,late,fprefix,mpref,training_season, mon, fday, nwk):
"""A simple function for ploting probabilistic forecasts
PARAMETERS
----------
score: the score
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
title: title
"""
#Need this score to be defined by the calibration method!!!
score = 'CCAFCST_P'
plt.figure(figsize=(15,20))
for L in range(nwk):
wk=L+1
#Read grads binary file size H, W --it assumes that 2AFC file exists (template for final domain size)
with open('../output/'+model+'_'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+model+'_'+fprefix+'_'+mpref+'_2AFC_'+training_season+'_wk'+str(wk)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
#Prepare to read grads binary file [float32 for Fortran sequential binary files]
Record = np.dtype(('float32', H*W))
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
f=open('../output/'+model+'_'+fprefix+'_'+score+'_'+training_season+'_'+mon+str(fday)+'_wk'+str(wk)+'.dat','rb')
tit=['Below Normal','Normal','Above Normal']
for i in range(3):
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#We now read the field for that record (probabilistic files have 3 records: below, normal and above)
B=np.fromfile(f,dtype='float32',count=numval) #astype('float')
endrec=struct.unpack('i',f.read(4))[0]
var = np.flip(np.transpose(B.reshape((W, H), order='F')),0)
var[var<0]=np.nan #only positive values
ax2=plt.subplot(nwk, 3, (L*3)+(i+1),projection=ccrs.PlateCarree())
ax2.set_title("Week "+str(wk)+ ": "+tit[i])
ax2.add_feature(feature.LAND)
ax2.add_feature(feature.COASTLINE)
#ax2.set_ybound(lower=lati, upper=late)
pl2=ax2.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl2.xlabels_top = False
pl2.ylabels_left = True
pl2.ylabels_right = False
pl2.xformatter = LONGITUDE_FORMATTER
pl2.yformatter = LATITUDE_FORMATTER
ax2.add_feature(states_provinces, edgecolor='gray')
ax2.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
#ax2.set_ybound(lower=lati, upper=late)
#ax2.set_xbound(lower=loni, upper=lone)
#ax2.set_adjustable('box')
#ax2.set_aspect('auto',adjustable='datalim',anchor='C')
CS=ax2.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati,lati+H*YD, num=H), var,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
#plt.show(block=False)
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
cbar.set_label('Probability (%)') #, rotation=270)
f.close()
def pltmapff(models,predictand,thrs,ispctl,ntrain,loni,lone,lati,late,fprefix,mpref,monf,fyr,mons,tgts):
"""A simple function for ploting probabilistic forecasts in flexible format (for a given threshold)
PARAMETERS
----------
models: models to plot (array)
predictand: predictand used
thrs: the threshold, in the units of the predictand
ispctl: logical to identify if it's a percentile threshold
ntrain: training period (number of years)
loni: western longitude
lone: eastern longitude
lati: southern latitude
late: northern latitude
fprefix: file prefix (predictor)
mpref: MOS method
monf: forecast month of initialization
fyr: forecast year of initialization
mons:
tgts: target season(s)
"""
#Implement: read degrees of freedom from CPT file
#Formally, for CCA, dof=ntrain - #CCAmodes -1 ; dof=ntrain for now
dof=ntrain
nmods=len(models)
tar=tgts[mons.index(monf)]
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_Obs_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("TDEF", fp):
T = int(line.split()[1])
TD= 1 #not used
#plt.figure(figsize=(15,20))
if ispctl:
thrso=thrs
thrst = thrs * 100
fig, ax = plt.subplots(figsize=(10,nmods*3),sharex=True,sharey=True)
k=0
for model in models:
k=k+1
#Read mean
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
muf = np.transpose(A.reshape((W, H), order='F'))
muf[muf==-999.]=np.nan #only sensible values
#Read variance
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'FCST_var_'+tar+'_'+monf+str(fyr)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
vari = np.transpose(A.reshape((W, H), order='F'))
vari[vari<0.]=np.nan #only positive values
#Obs file--------
#Compute obs mean and variance.
#
muc0=np.empty([T,H,W]) #define array for later use
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_Obs_'+tar+'_'+monf+str(fyr)+'.dat','rb')
#cycle for all time steps (same approach to read GrADS files as before, but now read T times)
for it in range(T):
#Now we read the field
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize) #this if for each time stamp
A0=np.fromfile(f,dtype='float32',count=numval)
endrec=struct.unpack('i',f.read(4))[0] #needed as Fortran sequential repeats the header at the end of the record!!!
muc0[it,:,:]= np.transpose(A0.reshape((W, H), order='F'))
muc0[muc0==-999.]=np.nan #identify NaNs
muc=np.nanmean(muc0, axis=0) #axis 0 is T
#Compute obs variance
varc=np.nanvar(muc0, axis=0) #axis 0 is T
#Compute scale parameter for the t-Student distribution
scalef=np.sqrt(dof*vari) #due to transformation from Gamma
scalec=np.sqrt((dof-2)/dof*varc)
if ispctl:
thrs=t.ppf(thrso, dof, loc=muc, scale=scalec) #If using percentiles, compute value using climo
fprob = exceedprob(thrs,dof,muf,scalef)
ax = plt.subplot(nmods, 1, k, projection=ccrs.PlateCarree())
ax.set_extent([loni,loni+W*XD,lati,lati+H*YD], ccrs.PlateCarree())
#Create a feature for States/Admin 1 regions at 1:10m from Natural Earth
states_provinces = feature.NaturalEarthFeature(
category='cultural',
# name='admin_1_states_provinces_shp',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(feature.LAND)
ax.add_feature(feature.COASTLINE)
if k==1:
if ispctl:
ax.set_title('Probability (%) of exceeding the '+str(int(thrst))+'th percentile')
else:
ax.set_title('Probability (%) of exceeding '+str(thrs)+" mm/month")
pl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
pl.xlabels_top = False
pl.ylabels_left = True
pl.ylabels_right = False
pl.xformatter = LONGITUDE_FORMATTER
pl.yformatter = LATITUDE_FORMATTER
ax.add_feature(states_provinces, edgecolor='gray')
ax.set_ybound(lower=lati, upper=late)
CS=plt.pcolormesh(np.linspace(loni, loni+W*XD,num=W), np.linspace(lati+H*YD, lati, num=H), fprob,
vmin=0,vmax=100,
cmap=plt.cm.bwr,
transform=ccrs.PlateCarree())
label = 'Probability (%) of Exceedance'
ax.text(-0.2,0.5,model,rotation=90,fontsize=9.2,verticalalignment='center', transform=ax.transAxes)
#plt.autoscale(enable=True)
plt.tight_layout()
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
cax = plt.axes([0.2, 0.08, 0.6, 0.04])
cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
# for i, row in enumerate(ax):
# for j, cell in enumerate(row):
# if i == len(ax) - 1:
# cell.set_xlabel("noise column: {0:d}".format(j + 1))
# if j == 0:
# cell.set_ylabel("noise row: {0:d}".format(i + 1))
ax.set_ylabel(model, rotation=90)
cbar.set_label(label) #, rotation=270)
f.close()
def pltprobff(models,predictand,thrs,ntrain,lon,lat,loni,lone,lati,late,fprefix,mpref,monf,fyr,mons,tgts):
"""A simple function for ploting probabilities of exceedance and PDFs (for a given threshold)
PARAMETERS
----------
thrs: the threshold, in the units of the predictand
lon: longitude
lat: latitude
"""
#Implement: read degrees of freedom from CPT file
#Formally, for CCA, dof=ntrain - #CCAmodes -1 ; since ntrain is huge after concat, dof~=ntrain for now
dof=ntrain
tar=tgts[mons.index(monf)]
#Read grads binary file size H, W --it assumes all files have the same size, and that 2AFC exists
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("XDEF", fp):
W = int(line.split()[1])
XD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("YDEF", fp):
H = int(line.split()[1])
YD= float(line.split()[4])
with open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_Obs_'+tar+'_'+monf+str(fyr)+'.ctl', "r") as fp:
for line in lines_that_contain("TDEF", fp):
T = int(line.split()[1])
TD= 1 #not used
#Find the gridbox:
lonrange = np.linspace(loni, loni+(W-1)*XD,num=W)
latrange = np.linspace(lati+(H-1)*YD, lati, num=H) #need to reverse the latitudes because of CPT (GrADS YREV option)
lon_grid, lat_grid = np.meshgrid(lonrange, latrange)
a = abs(lat_grid-lat)+abs(lon_grid-lon)
i,j = np.unravel_index(a.argmin(),a.shape) #i:latitude j:longitude
#Now compute stuff and plot
plt.figure(figsize=(15,15))
k=0
for model in models:
k=k+1
#Forecast files--------
#Read mean
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'FCST_mu_'+tar+'_'+monf+str(fyr)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
muf = np.transpose(A.reshape((W, H), order='F'))
muf[muf==-999.]=np.nan #only sensible values
muf=muf[i,j]
#Read variance
f=open('../output/'+model+'_'+fprefix+predictand+'_'+mpref+'FCST_var_'+tar+'_'+monf+str(fyr)+'.dat','rb')
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize)
#Now we read the field
A=np.fromfile(f,dtype='float32',count=numval)
varf = np.transpose(A.reshape((W, H), order='F'))
varf[varf==-999.]=np.nan #only sensible values
varf=varf[i,j]
#Obs file--------
#Compute obs mean and variance.
#
muc0=np.empty([T,H,W]) #define array for later use
#Since CPT writes grads files in sequential format, we need to excise the 4 bytes between records (recl)
f=open('../output/'+models[0]+'_'+fprefix+predictand+'_'+mpref+'FCST_Obs_'+tar+'_'+monf+str(fyr)+'.dat','rb')
#cycle for all time steps (same approach to read GrADS files as before, but now read T times)
for it in range(T):
#Now we read the field
recl=struct.unpack('i',f.read(4))[0]
numval=int(recl/np.dtype('float32').itemsize) #this if for each time stamp
A0=np.fromfile(f,dtype='float32',count=numval)
endrec=struct.unpack('i',f.read(4))[0] #needed as Fortran sequential repeats the header at the end of the record!!!
muc0[it,:,:]= np.transpose(A0.reshape((W, H), order='F'))
muc0[muc0==-999.]=np.nan #identify NaNs
muc=np.nanmean(muc0, axis=0) #axis 0 is T
#Compute obs variance
varc=np.nanvar(muc0, axis=0) #axis 0 is T
#Select gridbox values
muc=muc[i,j]
#print(muc) #Test it's actually zero
varc=varc[i,j]
#Compute scale parameter for the t-Student distribution
scalef=np.sqrt(dof*varf) #due to transformation from Gamma
scalec=np.sqrt((dof-2)/dof*varc)
x = np.linspace(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)), 100)
style = dict(size=10, color='black')
#cprob = special.erfc((x-muc)/scalec)
cprob = exceedprob(thrs,dof,muc,scalec)
fprob = exceedprob(thrs,dof,muf,scalef)
cprobth = round(t.sf(thrs, dof, loc=muc, scale=scalec)*100,2)
fprobth = round(t.sf(thrs, dof, loc=muf, scale=scalef)*100,2)
cpdf=t.pdf(x, dof, loc=muc, scale=scalec)*100
fpdf=t.pdf(x, dof, loc=muf, scale=scalef)*100
oddsrc =(fprobth/cprobth)
fig, ax = plt.subplots(1, 2,figsize=(12,4))
#font = {'family' : 'Palatino',
# 'size' : 16}
#plt.rc('font', **font)
#plt.rc('text', usetex=True)
#plt.rc('font', family='serif')
plt.subplot(1, 2, 1)
plt.plot(x, t.sf(x, dof, loc=muc, scale=scalec)*100,'b-', lw=5, alpha=0.6, label='clim')
plt.plot(x, t.sf(x, dof, loc=muf, scale=scalef)*100,'r-', lw=5, alpha=0.6, label='fcst')
plt.axvline(x=thrs, color='k', linestyle='--')
plt.plot(thrs, fprobth,'ok')
plt.plot(thrs, cprobth,'ok')
plt.text(thrs+0.05, cprobth, str(cprobth)+'%', **style)
plt.text(thrs+0.05, fprobth, str(fprobth)+'%', **style)
#plt.text(0.1, 10, r'$\frac{P(fcst)}{P(clim)}=$'+str(round(oddsrc,1)), **style)
plt.text(min(t.ppf(0.0001, dof, loc=muf, scale=scalef),t.ppf(0.0001, dof, loc=muc, scale=scalec)), -20, 'P(fcst)/P(clim)='+str(round(oddsrc,1)), **style)
plt.legend(loc='best', frameon=False)
# Add title and axis names
plt.title('Probabilities of Exceedance')
plt.xlabel('Rainfall')
plt.ylabel('Probability (%)')
# Limits for the Y axis
plt.xlim(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)))
plt.subplot(1, 2, 2)
plt.plot(x, cpdf,'b-', lw=5, alpha=0.6, label='clim')
plt.plot(x, fpdf,'r-', lw=5, alpha=0.6, label='fcst')
plt.axvline(x=thrs, color='k', linestyle='--')
plt.legend(loc='best', frameon=False)
# Add title and axis names
plt.title('Probability Density Functions')
plt.xlabel('Rainfall')
plt.ylabel('')
# Limits for the Y axis
plt.xlim(min(t.ppf(0.00001, dof, loc=muf, scale=scalef),t.ppf(0.00001, dof, loc=muc, scale=scalec)),max(t.ppf(0.9999, dof, loc=muf, scale=scalef),t.ppf(0.9999, dof, loc=muc, scale=scalec)))
plt.subplots_adjust(hspace=0)
plt.subplots_adjust(bottom=0.15, top=0.9)
#cax = plt.axes([0.2, 0.08, 0.6, 0.04])
#cbar = plt.colorbar(CS,cax=cax, orientation='horizontal')
#cbar.set_label(label) #, rotation=270)
f.close()
# def readNetCDF_predictand(infile,outfile, predictand, wlo2, elo2, sla2, nla2, tar):
# """Function to read the user's predictand NetCDF file and write to CPT format.
#