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get_paleo.py
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#!/usr/bin/env python
###############################################################################
# Copyright (C) 2017-2019 Potsdam-Institute for Climate Impact Reasearch (PIK),
# Author: Torsten Albrecht ([email protected])
# License: GNU AFFERO GENERAL PUBLIC LICENSE version 3
#
# This script executes the ensemble analysis for paleo data.
###############################################################################
import os, pickle
import matplotlib.pyplot as plt
from matplotlib import cm, colors, rcParams
import numpy as np
#on pik cluster
#source activate python_for_pism_calib
## this hack is needed to import config.py from the project root
#project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
#if project_root not in sys.path: sys.path.append(project_root)
import config as cf; reload(cf)
import tools as tl; reload(tl)
class paleo_score(object):
def __init__(self,enum,obs,snp):
self.ensnum = str(enum)
self.showplots = snp[0]
self.printtopdf = snp[1]
self.printout = snp[2]
self.res = cf.resolution #km resolution
#runtime=cf.runtime #yrs
# do gps calculations or load from pickle file
self.calc_gps=True
if os.path.exists(cf.gps_data_file):
self.calc_gps=False
self.calc_trans=True
if os.path.exists(cf.trans_data_file):
self.calc_trans=False
self.calc_briggs=True
if os.path.exists(cf.briggs_data):
self.calc_briggs=False
self.score_names = cf.score_names
self.pd_score_names =["TOTUPL","TROUGH","ELEV","EXT","RSL"] #available paleo measures
self.score_text=""
self.appr = cf.approach
# pathnames ################################################################
#workpath = cf.workpath
resultpath = cf.resultpath
self.outpath = cf.output_data_path
self.gps_data_file = cf.gps_data_file
self.trans_data_file = cf.trans_data_file
self.raised_file = cf.raised_file
self.briggs_outfile = cf.briggs_data
self.briggs_compilation = cf.briggs_input
pism_file_name = cf.pism_file_name.replace(str(cf.fillnum),str(enum))
pismfile = os.path.join(resultpath,pism_file_name)
pism_extra_file = pismfile.replace("paleo.nc","extra_paleo.nc")
global x,y,Mx,My,mobs,Bobs,lonobs,latobs #only the one used in the functions below
[x,y,Mx,My,mobs,Bobs,Hobs,hobs,lonobs,latobs,cellarea,velobs,velstnd] = obs
if os.path.exists(pismfile):
### reconstructgion of PD uplift rates in different sites of WAIS
uplcalc = self.get_uplcalc(tl.get_data(pismfile,['dbdt']))
### paleo data (grounding line reconstructions along transects, surface elevation)
troughcalc = self.get_troughcalc(pismfile)
### AntICEdat
briggsdat = self.import_briggs()
extime = tl.get_data(pism_extra_file,['time'])
thk,topg,usurf = tl.get_data(pism_extra_file,['thk','topg','usurf'])
elevcalc = self.get_elevcalc(briggsdat['elev'],extime,[thk,topg])
extcalc = self.get_extcalc(briggsdat['ext'],extime,[thk,topg,usurf])
rslcalc = self.get_rslcalc(briggsdat['rsl'],extime,[topg,usurf])
score_choice={} #indicated which measures to consider
score_choice["TOTUPL"] = uplcalc
score_choice["TROUGH"] = troughcalc
score_choice["ELEV"] = elevcalc
score_choice["EXT"] = extcalc
score_choice["RSL"] = rslcalc
### write scores to txt file
#scores = [uplcalc,throughcalc,elevcalc,extcalc,rslcalc]
self.score_text=""
scores=[]
for pdsc in self.pd_score_names:
if pdsc in self.score_names:
self.score_text += " "+pdsc
scores.append(score_choice[pdsc])
self.print_to_txtfile(scores)
else:
print "\nThere is no PISM resultfile "+pismfile+" !\nSkip..."
print "\n###############################################\n"
##############################################################
### after Pollard et al., 2016
### Large ensemble modeling of the last deglacial retreat of the West
### Antarctic Ice Sheet: comparison of simple and advanced statistical techniques,
### Geosci. Model Dev., 9, 1697-1723,
### https://doi.org/10.5194/gmd-9-1697-2016
################################################################
def get_uplcalc(self,var):
# Calcs rms difference of modern uplift rates from each obs site (zrms),
# using closest model grid pt (iupl,jupl).
# 1. Whitehouse et al,GJI, Table S2, whitehouse_table_s2.dat,tabupl.
# https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fj.1365-246X.2012.05557.x&file=GJI_5557_sm_SupportingInformation.zip
#FIXME: use intra-data-type weighting
dbdt=var[0] #uplift rate variable
scalemm=1.0 #1000.0 im mm
gps_data={}
gps_data[1]=['FTP1',-78.93,162.57,2.14,2.77]
gps_data[2]=['ROB1',-77.03,163.19,7.54,2.59]
gps_data[3]=['TNB1',-74.7,164.1,-0.23,0.81]
gps_data[4]=['MCM4_AV',-77.85,166.76,0.77,0.31]
gps_data[5]=['MBL1_AV',-78.03,204.98,3.28,1.09]
gps_data[6]=['W01_AV',-87.42,210.57,-2.8,1.17]
gps_data[7]=['MBL2',-76.32,215.7,0.23,4.13]
gps_data[8]=['MBL3',-77.34,218.13,0.09,1.97]
gps_data[9]=['W09',-82.68,255.61,4.54,2.59]
gps_data[10]=['W06A',-79.63,268.72,-2.2,2.42]
gps_data[11]=['W07_AV',-80.32,278.57,3.61,1.58]
gps_data[12]=['W05_AV',-80.04,279.44,4.86,1.01]
gps_data[13]=['HAAG',-77.04,281.71,3.47,0.71]
gps_data[14]=['W08A/B',-75.28,287.82,1.31,1.28]
gps_data[15]=['W02_AV',-85.61,291.45,2.17,1]
gps_data[16]=['OHIG',-63.32,302.1,3.8,1]
gps_data[17]=['PALM',-64.78,295.95,0.08,1.87]
gps_data[18]=['ROTB',-67.57,291.87,1.5,1.9]
gps_data[19]=['SMRT',-68.12,292.9,-0.22,1.93]
gps_data[20]=['FOS1',-71.31,291.68,2.14,0.4]
gps_data[21]=['BREN',-72.67,296.97,3.85,1.6]
gps_data[22]=['W04_AV',-82.86,306.8,3.42,0.84]
gps_data[23]=['BELG',-77.86,325.38,2.97,1.47]
gps_data[24]=['W03_AV',-81.58,331.6,-2.47,1.28]
gps_data[25]=['SVEA',-74.58,348.78,2.07,1.95]
gps_data[26]=['ABOA',-73.04,346.59,1.4,0.84]
gps_data[27]=['VESL',-71.67,357.16,1.06,0.45]
gps_data[28]=['MAIT_AV',-70.77,11.84,0.12,0.4]
gps_data[29]=['SYOG',-69.01,39.58,2.26,0.36]
gps_data[30]=['MAW1',-67.61,62.87,0.06,0.39]
gps_data[31]=['A368',-74.29,66.79,0.39,1.0]
gps_data[32]=['A351',-72.91,74.91,0.81,1.28]
gps_data[33]=['DAV1',-68.58,77.97,-0.94,0.49]
gps_data[34]=['CAS1',-66.28,110.52,1.18,0.43]
gps_data[35]=['DUM1',-66.67,140.0,-0.79,0.46]
#after Larsen-B break-off 2002
#gps_data[16]=['OHIG_EL1',-63.32,302.1,6.78,0.83]
#gps_data[16]=['OHIG_EL2',-63.32,302.1,3.22,1.68]
#gps_data[17]=['PALM_EL',-64.78,295.95,8.75,0.64]
#gps_data[18]=['ROTB_EL',-67.57,291.87,6.96,0.66]
#gps_data[19]=['SMRT_EL',-68.12,292.9,3.15,1.94]
#intra data weighting
lonlat_dat={}
for p,val in gps_data.items():
#coordinates of gps locations
lonlat_coords={}
lonlat_coords['lon']=val[2]
lonlat_coords['lat']=val[1]
lonlat_dat[p]=lonlat_coords
dlon=10.0 #degree
dlat=5.0 #degree
self.add_intra_weights(lonlat_dat,dlon,dlat)
#radius = 6.371220e6
#distcrit = radius * (5./180.)
iupl=np.zeros(len(gps_data)+1,dtype=int)
jupl=np.zeros_like(iupl,dtype=int)
idw=np.zeros(len(gps_data)+1)
# find gps location on the grid
if self.calc_gps:
if not os.path.exists(self.outpath+"gps"):
os.makedirs(self.outpath+"gps")
#intra data weighting
lonlat_dat={}
for p,val in gps_data.items():
#coordinates of gps locations
lonlat_coords={}
lonlat_coords['lon']=val[2]
lonlat_coords['lat']=val[1]
lonlat_dat[p]=lonlat_coords
dlon=10.0 #degree
dlat=5.0 #degree
self.add_intra_weights(lonlat_dat,dlon,dlat)
gps_save=gps_data
for p,val in gps_data.items():
#coordinates of gps locations
iupl[p],jupl[p] = tl.get_closest_point(val[2],val[1],lonobs,latobs)
gps_save[p].append(np.int(iupl[p]))
gps_save[p].append(np.int(jupl[p]))
#print lonlat_dat[p]['weight']
idw[p] = lonlat_dat[p]['weight']
gps_save[p].append(idw[p])
#print gps_data
print "dump to gps pickle file"
pickle.dump( gps_save, open( self.gps_data_file, "wb" ) )
else:
#print "read pickle file"
gps_save = pickle.load( open( self.gps_data_file, "rb" ) )
for p,val in gps_save.items():
iupl[p] = np.int(val[5])
jupl[p] = np.int(val[6])
idw[p] = val[7]
zdif = 0.
nrms = 0
zrms = np.zeros_like(iupl,dtype=float)
weidat = np.ones_like(iupl,dtype=float)
totupl = 0.
for p,val in gps_data.items():
if self.appr =="a":
weidat[p]=idw[p]*(val[4])**(-2.0)
elif self.appr =="b":
weidat[p]=1.0 #approach B : no uncertainty, no intra-data-weighting
for p,val in gps_data.items():
zdif = dbdt[iupl[p],jupl[p]]*scalemm-np.float(val[3])
zrms[p] = (zdif**2)*weidat[p]
if self.appr =="a":
totupl = np.nanmean(zrms)
printline= '\nUPL (misfit uplift GPS stations)'
#print np.sum(idw) #sum of weights should equal unity
elif self.appr =="b":
totupl = np.sqrt(np.nanmean(zrms))
printline= '\nUPL (rmse uplift GPS stations in mm/yr)'
if self.printout:
print printline
print totupl
#return (totupl)
###############################################################
if self.showplots or self.printtopdf: #FIXME: use basemap
###map of PD upflift rates compared to PGS locations #######
fig12 = plt.figure(12,figsize=(9, 9))
plt.clf()
ax12=plt.subplot(111)
ax12.axis("equal")
ax12.axis([0,Mx,0,My])
ax12.axis("off")
#map of uflift rate
dbdtm=np.ma.array(dbdt,mask = mobs == 4)
#dbdt.mask = mobs == 4
relbd=20
ticks12=np.arange(-relbd,relbd+5,5)
cs1 = ax12.contourf(dbdtm*scalemm,ticks12,alpha=1.0,cmap=cm.PuOr_r,extend='both')#cm.RdBu_r
## colorbar dbdt
#cbaxes = fig12.add_axes([0.15, 0.25, -0.04, 0.5])
cbaxes12 = fig12.add_axes([0.15, 0.23, 0.3, 0.02])
#ticks = np.arange(int(mn),int(mx),4)
#cb = plt.colorbar(cs1,cax=cbaxes,ticks=np.arange(-30,35,5))
ticks12b=np.arange(-relbd,relbd+10,10)
cb = plt.colorbar(cs1,cax=cbaxes12, orientation="horizontal",ticks=ticks12b)
cb.set_label('rel. bed elevation change [mm/yr]',multialignment="left")
cb.outline.set_linewidth(0)
#bedmap2 contour
cs7 = ax12.contour(mobs,[2.5],colors="w",linewidths=1.0,alpha=1.,)
#GPS locations
#cmrb = cm.get_cmap('RdBu_r')
cmrb = cm.get_cmap('PuOr_r')
colors=[cmrb(1.*i/len(ticks12)) for i in range(len(ticks12)+1)]
for p,val in gps_data.items():
colorind=0
for ti,t in enumerate(ticks12):
if val[3]>t:
colorind=min(ti+1,len(ticks12))
ax12.plot(jupl[p],iupl[p],'ro',markerfacecolor=colors[colorind],markersize=20)
ax12.text(jupl[p]-1,iupl[p]+2,str(val[3]))
#if p>1:
# ax12.plot([jupl[p-1],jupl[p]],[iupl[p-1],iupl[p]],"r-")
### print plot to pdf file
printname = self.outpath+"plots/gpsmap_ensemble"+self.ensnum+"_"+str(int(self.res))+"km.pdf"
if self.printtopdf:
plt.savefig(printname, format='pdf')
#plt.savefig(printname.replace(".pdf",".png"), format='png',dpi=300)
###GPS upflift rates lined-up and compared to PISM results ###############
fig13 = plt.figure(13,figsize=(13, 10))
plt.clf()
ax13=plt.subplot(111)
ax13.axis([0,len(gps_data)+1,-15,15])
ax13.set_xticks(np.arange(1,len(gps_data)+1,1))
ax13.set_title("GPS data at locations around WAIS")
#rates with standard deviation
for p,val in gps_data.items():
ax13.plot(p,val[3],"bo",label="gps obs")
ax13.plot([p,p],[val[3]-val[4],val[3]+val[4]],"k-")
ax13.plot(p,dbdt[iupl[p],jupl[p]]*scalemm,"ro",label="uplift model")
#draw a line and legend
if p==1:
legend13 = ax13.legend(loc='lower left', shadow=True, fontsize=11)
else:
ax13.plot([p-1,p],[dbdt[iupl[p-1],jupl[p-1]]*scalemm,dbdt[iupl[p],jupl[p]]*scalemm],"r")
ax13.text(1,12,"Ross west \n(1-4)")
ax13.text(4.5,12,"Ross east \n(5-8)")
ax13.text(8,12,"WAIS divide \n(9)")
ax13.text(12,12,"Ronne \n(10-14)")
ax13.text(17,12,"Peninsula \n(15-20)")
ax13.text(22,12,"Filchner \n(21-24)")
ax13.text(30,12,"EAIS \n(25-37)")
ax13.axhline(0,linestyle="dashed")
### print plot to pdf file
printname = self.outpath+"plots/gpsanomaly_ensemble"+self.ensnum+"_"+str(int(self.res))+"km.pdf"
if self.printtopdf:
plt.savefig(printname, format='pdf')
#plt.savefig(printname.replace(".pdf",".png"), format='png',dpi=300)
#plt.show()
return (totupl)
##############################################
def get_troughcalc(self,fn):
rhor = cf.rhoi/cf.rhosw
dkm=10 #km steps
#raised_data = pickle.load( open( raised_data_file, "rb" ) )
point_names=["Ross","Pine Island","Ronne","Amery"]
### define transects as lon,lat coordinates
points_ross=np.array([[-112.5,-79.8],[-119,-80.8],[-152.8,-82.35],[-166.2,-82.3],[-175.1,-81.4],[-178.2,-79.9],[-175.9,-77.8],[-173.6,-73.7]])
points_pine=np.array([[-89.1,-75.4],[-96.7,-75.4],[-99.8,-75.2],[-103.7,-74.7],[-106.3,-74.2],[-107.4,-73.3],[-107.8,-72.0],[-107.0,-70.2]])
points_ronne=np.array([[-91.5,-82.8],[-65.7,-82.0],[-62.1,-81.4],[-58.84,-80.77],[-56.5,-79.4],[-55.5,-77.9],[-53.9,-76.3],[-49.4,-75.1],[-42.0,-72.7]])
#points_ronne=np.array([[-91.5,-82.8],[-65.7,-82.0],[-62.1,-81.4],[-59.3,-81.45],[-56.5,-79.4],[-55.5,-77.9],[-53.9,-76.3],[-49.4,-75.1],[-42.0,-72.7]])
points_amery=np.array([[63.5,-76.8],[66.4,-75.4],[65.8,-74.4],[66.1,-73.8],[67.6,-72.85],[68.6,-72.1],[70.2,-71.1],[71.5,-69.9],[73.7,-68.7],[70.6,-66.4]])
#points on 20km grid
#points_ross=[[89,119],[96,116],[121,103],[130,99],[136,93],[138,85],[135,74],[130,51]] #ross, gl [121,103]
#points_pine=[[60,141],[61,131],[60,126],[59,120],[57,116],[53,113],[46,110],[36,108]] #pine island, gl [60,126]
#points_ronne=[[101,139],[101,158],[101,161],[100,164],[92,172],[86,177],[80,184],[78,193],[77,210]] #ronne, gl [101,158]
#points_amery=[[204,172],[213,172],[218,175],[221,176],[227,176],[231,176],[237,175],[245,175],[252,173],[263,183]] #amery, gl [227,176]
#find closest points on grid
if self.calc_trans:
if not os.path.exists(self.outpath+"gps"):
os.makedirs(self.outpath+"gps")
transects=[]
for xi,tr in enumerate([points_ross,points_pine,points_ronne,points_amery]):
transx=[]
for yi,lonlat in enumerate(tr):
ip,jp = tl.get_closest_point(lonlat[0],lonlat[1],lonobs,latobs)
transx.append([jp,ip])
transects.append(transx)
print "dump to trans coordinates pickle file"
pickle.dump( transects, open( self.trans_data_file, "wb" ) )
else:
#print "read pickle file"
transects = pickle.load( open( self.trans_data_file, "rb" ) )
#raised_snaps=[20,15,10,5,0]
raised_snaps=[0,5,10,15,20]
raised_snaps=[0,20]
raised_option="A"
raised_mask={}
### scale to 15km grid
for snap in raised_snaps:
if snap>0:
raised_mask[snap]=tl.get_data(self.raised_file,["raised_"+raised_option+"_"+str(snap)+"ka"])[0]
else:
raised_mask[snap]=tl.get_data(self.raised_file,["bedmap2_"+str(snap)+"ka"])[0] #mobs
### get points along transects first ########################################
trans_points = tl.get_points_along_transect(x,y,dkm,transects)
glp=np.zeros([len(raised_snaps),len(trans_points)])
glp0=np.zeros_like(glp)
gldiff=np.zeros([len(raised_snaps),len(trans_points)])
sea_level=np.zeros(len(raised_snaps))
######################################################################
if self.showplots or self.printtopdf: #FIXME: use basemap
#clean figure window
if 16 in plt.get_fignums():
#fig16.clf()
plt.close(16)
plt.close(17)
fig16, ax16all = plt.subplots(2, 2,figsize=(13, 7),num=16) #sharex='col', sharey='row'
#fig16.canvas.set_window_title('Figure16')
fig17, ax17 = plt.subplots(figsize=(9, 7),num=17)
#not fill in legend
rcParams['legend.frameon'] = 'False'
Bobsm=np.ma.array(Bobs)
Bobsm.mask = (Bobs<-2500)
cs1 = ax17.contourf(Bobsm,np.arange(-2000,0,100),alpha=0.8,cmap=cm.Blues_r,extend='both')
cs7 = ax17.contour(mobs,[2.5,3.5],colors="w",linewidths=.9,alpha=1.0)
cbaxes = fig17.add_axes([0.15, 0.25, -0.04, 0.5])
#ticks = np.arange(int(mn),int(mx),4)
cb = plt.colorbar(cs1,cax=cbaxes,ticks=np.arange(-2000,0,200))
cb.set_label('bed elevation [m]',multialignment="left")
cb.outline.set_linewidth(0)
ax17.axis("equal")
ax17.axis([0,Mx,0,My])
ax17.axis("off")
for j,points in enumerate(transects):
for i,p in enumerate(points):
if i>0:
ax17.plot([points[i-1][0],p[0]],[points[i-1][1],p[1]],color="#777777")
###########################################################################
#### find snapshot index for raised data comparison
for count,snap in enumerate(raised_snaps):
time_raised=snap*(-1000) #years
### load snapshots
#snap_add=str(time_raised).zfill(8)
snap_add=str(time_raised)
if time_raised==0:
sfn=fn
else:
sfn=fn.replace("paleo.nc","snapshots_")+snap_add+".000.nc"
#sfn=fn.replace("result_","snap_")+"_"+snap_add+".000.nc"
m,B,H,h = tl.get_data(sfn,['mask','topg','thk','usurf'])
#print np.shape(H),np.shape(h),np.shape(B)
rm=raised_mask[snap]
##################################################
if self.showplots or self.printtopdf:
cs8 = ax17.contour(m,[2.5],colors=cf.colorscheme[count],linewidths=1.2,alpha=1.,labels=str(snap))
if snap>0:
cs9 = ax17.contour(rm,[0.5],colors=cf.colorscheme[count],linewidths=0.8,alpha=0.8,linestyles="dotted")
###############################################################
### loop over four troughs
for l,po in enumerate(trans_points):
Bav=np.zeros(len(po))
hav=np.zeros(len(po))
Hav=np.zeros(len(po))
mav=np.zeros(len(po))
hab=np.zeros(len(po))
rav=np.zeros(len(po))
sea_level[count]=h[0,0] #sea_level
glfound=False
gl0found=False
### loop over equidistant points along trough
for k,p in enumerate(po):
i=np.int(np.floor(p[1]))
j=np.int(np.floor(p[0]))
di=p[0]-np.floor(p[0])
dj=p[1]-np.floor(p[1])
#interpolation
Bav[k] = tl.get_interpolated_value(i,j,di,dj,B)
hav[k] = tl.get_interpolated_value(i,j,di,dj,h)
Hav[k] = tl.get_interpolated_value(i,j,di,dj,H)
rav[k] = tl.get_interpolated_value(i,j,di,dj,rm)
#mav[k] = get_interpolated_value(i,j,di,dj,m)
mav[k]=m[np.int(i),np.int(j)]
hab[k]=rhor*Hav[k]-(sea_level[count]-Bav[k])
#find point on transects closest to raised data
if k>0 and gl0found==False:
if rav[k]>0.0 and rav[k-1]==0.0:
glp0[count,l]=k-0.5
#print snap,point_names[l],i,j,k
ax17.plot(int(j),int(i),'bx',alpha=0.7,markersize=5)
gl0found=True
#check for grounding line
if k>0 and glfound==False:
if hab[k]<0.0 and hab[k-1]>=0.0:
lam=hab[k]/(hab[k]-hab[k-1])
lam=max(0.0,min(lam,1.0))
glp[count,l]=k-lam
#print snap,point_names[l],i,j,k-lam
ax17.plot(int(j),int(i),'rx',alpha=0.7,markersize=5)
glfound=True
#no reconstructions available
if point_names[l]=='Amery' and snap in [5,10,15]:
glp0[count,l]=np.nan
#reconstructions highly uncertain (Check Pollard)
if point_names[l]=='Ross' and snap in [10,15]:
glp0[count,l]=np.nan
gldiff[count,l]=(dkm*(glp[count,l]-glp0[count,l]))**2
#print snap,point_names[l],dkm*glp[count,l],dkm*glp0[count,l],np.sqrt(gldiff[count,l])
##################################################################
if self.showplots or self.printtopdf:
ax16=ax16all[l/2,l%2]
#Normalizing
kmdist=np.arange((-glp0[0,l])*dkm,(len(po)-glp0[0,l])*dkm,dkm)
kmdist=kmdist[0:len(po)]
#ice shelves
ax16.plot(kmdist,hav,cf.colorscheme[count],label=str(snap)+" kyr BP")
ax16.plot(kmdist,hav-Hav,cf.colorscheme[count])
#bed
ax16.plot(kmdist,Bav,"k",linewidth=1.0,alpha=0.6)
#grounding line position along transect
ax16.plot(kmdist[np.int(glp[count,l])],-1900,color=cf.colorscheme[count],marker="^")
try: #if available in data
ax16.plot(kmdist[np.int(glp0[count,l])],1900,color=cf.colorscheme[count],marker="v")
except:
donothing=1
#print "No glp0 value "+point_names[l]+" for -"+str(snap)+" kyr"
#ax16.fill_between(kmdist,hav-Hav,Bav,color="#000099")
#ax16.fill_between(kmdist,Bav,-3000,color="#996633")
#ax16.fill_between(kmdist,3000,hav,color="#e9e8e5")
if count==0:
ax16.axis([kmdist[0],kmdist[-1],-2000,2000])
ax16.set_yticks(np.arange(-2000,2000,1000))
ax16.set_xticks([-250,0,250,500,750])
ax16.set_title(point_names[l])
ax16.axhline(sea_level[count],color="k",linestyle="dashed",linewidth=0.5)
if l>0:
ax16.text(-250,-200,"mean: "+str(np.int(np.sqrt(np.nanmean(gldiff,axis=0)[l])))+" km")
if l>1:
ax16.set_xlabel("distance to PD GL in km", fontsize=11)
#print glp0,glp
#labeling
ax16=ax16all[0,0]
legend16 = ax16.legend(loc='upper right', fontsize=9)
ax16.text(-600,-200,"mean: "+str(np.int(np.sqrt(np.nanmean(gldiff,axis=0)[0])))+" km")
ax16.text(-600,-1900,"PISM GL position", fontsize=9)
ax16.text(-600,1800,"RAISED GL position", fontsize=9)
if self.printtopdf:
printname = self.outpath+"plots/le_trough_sideview_ensemble"+self.ensnum+"_"+str(int(self.res))+"km.pdf"
fig16.savefig(printname, format='pdf')
#print gldiff
#print np.sqrt(np.nanmean(gldiff,axis=0)),"mean over snaps per basin (Ross, PI, Ronne, Amery)"
#print np.sqrt(np.nanmean(gldiff,axis=1)),"mean over basins per snap (PD left, LGM right)"
#approach A
if self.appr =="a":
sigma_trough=30.0
rmsgl=np.nanmean(gldiff/sigma_trough**2)
printline= '\nTROUGH (misfit GL in 4 sections at LGM and PD snapshots)'
#approach B
elif self.appr =="b":
rmsgl=np.sqrt(np.nanmean(gldiff))
printline= '\nTROUGH (rmse GL in 4 sections at LGM and PD snapshots in km)'
if self.printout:
print printline
print rmsgl
return (rmsgl)
##############################################################
### AntICEdat
##############################################################
def add_intra_weights(self,dat,dlon,dlat):
relative_site_weight={}
count_per_site={}
dat_weight={}
for ids in dat.keys():
count_region=0
count_site=0
lon0=dat[ids]["lon"]
lat0=dat[ids]["lat"]
site_num=str(ids)[0:4]
for ids2 in dat.keys():
lon2=dat[ids2]["lon"]
lat2=dat[ids2]["lat"]
site_num2=str(ids2)[0:4]
#fixme: average over four shifted lon-lat rectangles?!
if np.abs(lon0-lon2) <= dlon and np.abs(lat0-lat2) <= dlat:
count_region+=1
if site_num==site_num2:
count_site+=1
relative_site_weight[site_num] = np.sqrt(count_site)/np.sqrt(count_region)
count_per_site[site_num] = count_site
#print count_per_site,len(dat),relative_site_weight
### weighted per point
total_site_weight = sum(relative_site_weight.values())
for ids in dat.keys():
site_num=str(ids)[0:4]
dat_weight[ids] = relative_site_weight[site_num]/total_site_weight/count_per_site[site_num]
dat[ids]["weight"] = np.around(dat_weight[ids],decimals=7)
### check sum of weights = 1
#print sum(dat_weight.values())
#for site,weight in relative_site_weight.items():
#print site,count_per_site[site],weight,weight/total_site_weight
return dat_weight
################################################################
def read_xlsxfile(self,data_file,sheet_name,sheet_range,col_num,col_types):
import openpyxl as px
wb = px.load_workbook(data_file)
ws = wb.get_sheet_by_name(name = sheet_name)
col_id = col_num[0]
col_ids = col_num[1]
col_lat = col_num[2]
col_lon = col_num[3]
col_val = col_num[4]
col_valu = col_num[5]
col_age = col_num[6]
col_ageu = col_num[7]
col_type=col_num[8]
col_proc=col_num[9]
col_reg=col_num[10]
col_ref=col_num[11]
data_dict={}
##### get the data
for rownum,rowval in enumerate(ws.iter_rows(sheet_range)):
data = {}
site_id = rowval[col_id].value
site_type = rowval[col_type].value
#print site_id,site_type,col_types,site_type in col_types
#if site_id!=None:
if site_type in col_types and site_id!=None: #isinstance(site_id,int)
if rowval[col_ids].value != None:
site_id=str(site_id)+str(rowval[col_ids].value)
lon0 = np.float(rowval[col_lon].value)
lat0 = np.float(rowval[col_lat].value)
data['lon'] = lon0
data['lat'] = lat0
try:
data['val'] = np.float(rowval[col_val].value)
data['valu'] = np.float(rowval[col_valu].value)
except:
data['val'] = None
data['valu'] = None
data['age'] = np.float(rowval[col_age].value)
data['ageu'] = np.float(rowval[col_ageu].value)
data['type'] = str(rowval[col_proc].value)
print sheet_name,rownum,site_id
ilonlat,jlonlat = tl.get_closest_point(lon0,lat0,lonobs,latobs)
data['j_grid'] = jlonlat
data['i_grid'] = ilonlat
di,dj = tl.find_interpolation(ilonlat,jlonlat,lon0,lat0,lonobs,latobs)
data['di_grid'] = np.around(di,decimals=6)
data['dj_grid'] = np.around(dj,decimals=6)
data_dict[int(site_id)] = data
return data_dict
################################################################
def import_briggs(self):
# get briggs data compilation from xlsx file
if self.calc_briggs:
if not os.path.exists(self.outpath+"briggs"):
os.makedirs(self.outpath+"briggs")
EXT_data_file = self.briggs_compilation+"/mmc3.xlsx"
ext_data = self.read_xlsxfile(EXT_data_file,'marineCores',"A4:U36",[0,20,3,4,20,20,14,15,19,19,2,17],['M','G']) # 20 is none
#print ext_data #[2101]
RSL_data_file = self.briggs_compilation+"/mmc2.xlsx"
rsl_data = self.read_xlsxfile(RSL_data_file,'rsl',"A3:AF111",[0,8,6,7,17,18,26,27,31,11,2,29],['D'])
#print rsl_data #[91011]
ELEV_data_file = self.briggs_compilation+"/mmc4.xlsx"
elev_data = self.read_xlsxfile(ELEV_data_file,'paleoThickness',"A3:R147",[0,8,6,7,12,13,10,11,17,17,2,15],['D'])
#print elev_data[11011]
dlon=10.0 #degree
dlat=5.0 #degree
self.add_intra_weights(elev_data,dlon,dlat)
self.add_intra_weights(rsl_data,dlon,dlat)
briggs_data={}
briggs_data['ext']=ext_data
briggs_data['rsl']=rsl_data
briggs_data['elev']=elev_data
print "dump to gps pickle file"
pickle.dump( briggs_data, open( self.briggs_outfile, "wb" ) )
else:
#print "read pickle file"
briggs_data = pickle.load( open( self.briggs_outfile, "rb" ) )
"""
if self.showplots:
plt.rcParams['contour.negative_linestyle'] = 'solid'
fig15 = plt.figure(15,figsize=(13, 10))
plt.clf()
ax=plt.subplot(111)
im1 = ax.imshow(mobs,origin="lower",cmap=cm.Greys_r,vmin=0,vmax=4,interpolation="nearest")
ax.contour(lonobs,np.linspace(-180,180,37),colors="k",linewidths=0.5)
ax.contour(latobs,np.linspace(-90,90,37),colors="k",linewidths=0.5)
for data_type,data_vals in briggs_data.items():
if data_type=="ext":
colorplot="ro"
plt.text(10,10, '%s' % (data_type),color="r",fontsize=11)
elif data_type=="rsl":
colorplot="go"
plt.text(10,30, '%s' % (data_type),color="g",fontsize=11)
elif data_type=="elev":
colorplot="bo"
plt.text(10,50, '%s' % (data_type),color="b",fontsize=11)
for ids,each in data_vals.items():
j=each['i_grid']
i=each['j_grid']
ax.plot(i,j,colorplot,markersize=4)
#plt.text(j+1,i+2, '%d' % (ids),color="k",fontsize=4)
ax.axis("equal")
ax.axis([0,Mx,0,My])
ax.axis("off")
plt.show()
"""
return briggs_data
################################################################
def get_elevcalc(self,dat,tex,vars):
Hex=vars[0]
Bex=vars[1]
tex=tex[0]/cf.seconds_per_year
snaps = np.shape(Hex)[0]
s_struct=100.0
mean_misfit=0
for ids in dat.keys():
#print ids,dat[ids]
i = dat[ids]['i_grid']
j = dat[ids]['j_grid']
di = dat[ids]['di_grid']
dj = dat[ids]['dj_grid']
sigma_h = (dat[ids]['valu'])**2 + s_struct**2
dat_age = -dat[ids]['age']
dh = np.sqrt(2*sigma_h)
dat_wgt = dat[ids]['weight']
H_int = tl.get_interpolated_value(i,j,di,dj,Hex)
b_int = tl.get_interpolated_value(i,j,di,dj,Bex[-1]) #PD: sl and bad adjustment removed
h_int = H_int + b_int
h_thin = np.copy(h_int)
#print b_int,np.shape(h_int),H_int[-1],h_int[-1]
#misfit of surface elevation
misfit_int = (h_int-dat[ids]["val"])**2/sigma_h
misfit_all=np.zeros(snaps)
misfitmin=10000.0
h_at_age=0
h_lgm = h_int[-1]
# exclude periods of growing surface elevation
for l,t in enumerate(reversed(tex)):
lmax=len(tex)-1
if h_int[lmax-l] + dh < h_lgm:
h_thin[0:lmax-l] = 0.0
break
else:
h_lgm = max(h_lgm,h_int[lmax-l])
for l,t in enumerate(tex):
#misfit over h and t
if h_thin[l] > 0.0:
misfit_all[l]=misfit_int[l]+((t-dat_age)/dat[ids]['ageu'])**2
else:
misfit_all[l]=np.NaN
if t < 0.0:
#time interpolation to datum
if t < dat_age and tex[l+1] >= dat_age:
t_before = dat_age - t
t_after = tex[l+1] - dat_age
h_at_age=(h_int[l]*t_after + h_int[l+1]*t_before) / (tex[l+1]-t)
#print dat_age,t_before,t_after,t,tex[l+1],tex[l+1]-t,h_int[l],h_int[l+1],h_at_age
misfitmin = np.nanmin(misfit_all)
mint = tex[np.nanargmin(misfit_all)]
mean_misfit += misfitmin*dat_wgt
#print ids,mint,misfitmin
"""
if self.showplots:
fig16 = plt.figure(16,figsize=(8, 4));plt.clf()
ax3=plt.subplot(111)
ax3b=ax3.twinx()
#vals and uncertainty
ax3.axhline(dat[ids]['val'],color="k")
ax3.axhline(h_lgm,color="b",linestyle="dotted")
ax3.axhline(h_lgm+dh,color="b",linestyle="dashed",alpha=0.3)
ax3.axhline(h_lgm-dh,color="b",linestyle="dashed",alpha=0.3)
ax3.axvline(-dat[ids]['age'],color="k")
ax3.axvline(-dat[ids]['age']-dat[ids]['ageu'],color="k",linestyle="dashed")
ax3.axvline(-dat[ids]['age']+dat[ids]['ageu'],color="k",linestyle="dashed")
#ax3.axvline(mint,color="b")
ax3b.plot(tex[:],misfit_int[:],"m-")
ax3b.plot(tex[:],misfit_all[:],"c-")
ax3b.plot(mint,misfitmin,"ko",markersize=4)
ax3.plot(tex[:],h_int[:],"g-")
ax3.plot(tex[:],h_thin[:],"r.",markersize=2)
#ax3.plot(tex[:],h_int[:]+dh,"y-",linestyle="dashed")
#ax3.plot(tex[:],h_int[:]-dh,"y-",linestyle="dashed")
ax3.plot(dat_age,h_at_age,"bo",markersize=4)
ax3.plot(tex[:],Hex[:,i,j]+Bex[-1,i,j],"r-",alpha=0.2)
ax3.plot(tex[:],Hex[:,i+np.int(np.sign(di)),j]+Bex[-1,i+np.int(np.sign(di)),j],"r-",alpha=0.2)
ax3.plot(tex[:],Hex[:,i,j+np.int(np.sign(dj))]+Bex[-1,i,j+np.int(np.sign(dj))],"r-",alpha=0.2)
ax3.plot(tex[:],Hex[:,i+np.int(np.sign(di)),j+np.int(np.sign(dj))]+Bex[-1,i+np.int(np.sign(di)),j+np.int(np.sign(dj))],"r-",alpha=0.2)
ax3b.axis([-31000,0,0,100.0])
ax3.axis([-31000,0,0,3500.0])
plt.text(-30000, 50, '%d: score: %.3f, %d,%d' % (ids,np.sqrt(misfitmin),j,i))
plt.show()
"""
if self.printout:
print '\nELEV (mean of minimum deviation in surface elevation and date)'
print mean_misfit
return (mean_misfit)
################################################################
def get_extcalc(self,dat,tex,vars):
Hex=vars[0]
Bex=vars[1]
sl=vars[2] # in 0,0 corner
tex=tex[0]/cf.seconds_per_year
snaps = np.shape(Hex)[0]
retreat0=-35000.0
s_struct_glr=250.0
s_struct_omc=500.0
mean_misfit=0.0
count_ooc=0
for ids in dat.keys():
#print ids,dat[ids]
i = dat[ids]['i_grid']
j = dat[ids]['j_grid']
di = dat[ids]['di_grid']
dj = dat[ids]['dj_grid']
sigma = (dat[ids]['ageu'])**2
dat_age = -dat[ids]['age']
retreat=retreat0
retreat_fine=retreat0
mask_int=np.zeros(snaps)
H_int = tl.get_interpolated_value(i,j,di,dj,Hex)
B_int = tl.get_interpolated_value(i,j,di,dj,Bex)
for k,t in enumerate(tex[0:-1]):
#for k in xrange(snapshots-1):
mask_int[k] = tl.get_mask_value(H_int[k],B_int[k],sl[k,0,0])
mask_int[k+1] = tl.get_mask_value(H_int[k+1],B_int[k+1],sl[k,0,0])
if mask_int[k] == cf.mgr and mask_int[k+1] > mask_int[k]:
retreat = 0.5 * (tex[k+1] + t)
### interpolate to 100yr resolution!
tsteps = np.arange(t,tex[k+1],100.0)