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Add PCMDI metrics portrait plotter (#2)
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* Added PCMDI METRICS
* Added PMP portrait plotter
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@aradhakrishnanGFDL @underwoo
aradhakrishnanGFDL authored and underwoo committed Mar 14, 2018
1 parent 2e54e2d commit 9b3083f
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -33,6 +33,7 @@ E.g.
* [Radiation processor](analysis/cjs1/radiation_atmos_av_mon/radiation_atmos_av_mon.ipynb)
* [Long-term DJF seasonal mean](analysis/bw/bw_atmos_cru_ts_a1r/bw_atmos_monthly_cru_ts.1980-2014.ipynb)
* [Zonal_mean_zonal_wind_stress](analysis/bw/bw_atmos_zm_atl_pac_a1r/bw_atmos_atl_pac.1980-2014.ipynb)
* [PCMDI Metrics Portrait Plot](analysis/pcmdimetrics/portraitPlot-AM4.AMIP.ipynb)

Please refer https://www.gfdl.noaa.gov/am4.0-model for details about where to find model and OBS data used in the papers.

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254 changes: 254 additions & 0 deletions analysis/pcmdimetrics/portrait4seasv1p1p1.py
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#!/usr/bin/env python

#################################################################################
# RELIES UPON CDAT MODULES
# PRODUCE SIMPLE PORTRAIT PLOT BASED ON RESULTS FROM PCMDI'S METRICS PACKAGE
# WRITTEN BY P. GLECKLER, PCMDI/LLNL
# FURTHER TWEAKED BY C. DOUTRIAUX
# ISSUE TO BE RESOLVED WITH UV-CDAT PLANNED IMPROVEMENTS: ABILITY TO SAVE PLOTS IN MULTIPLE FORMATS
#################################################################################

# CHECK TO VERIFY VCS MODULE IS INSTALLED
try:
import vcs
except:
raise RuntimeError("Package: vcs, not found to generate portrait plots")

# STANDARD PYTHON MODULES
import glob
import json
import os
import sys
import re
try:
import numpy as np
except:
raise RuntimeError("Package: numpy, not found.")

# CDAT MODULES
try:
import pcmdi_metrics.graphics.portraits
except:
raise RuntimeError("Package: pcmdi_metrics.graphics.portraits not found.")
try:
import MV2
except:
raise RuntimeError("Package: MV2 not found.")
import argparse
from pprint import pprint

def create_plot(args):
# CREATE VCS OBJECT AS A PORTAIT PLOT AND LOAD PLOT SETTINGS FOR TEST CASE
x=vcs.init()
x.portrait()

#######################
# PORTRAIT PLOT PARAMETER SETTINGS
P=pcmdi_metrics.graphics.portraits.Portrait()
# Turn off verbosity
P.verbose = False

P.PLOT_SETTINGS.levels = [-1.e20,-.5,-.4,-.3,-.2,-.1,0.,.1,.2,.3,.4,.5,1.e20]
P.PLOT_SETTINGS.x1 = .1
P.PLOT_SETTINGS.x2 = .85
P.PLOT_SETTINGS.y1 = .22
P.PLOT_SETTINGS.y2 = .95
#P.PLOT_SETTINGS.xtic2.y1=P.PLOT_SETTINGS.y1
#P.PLOT_SETTINGS.xtic2.y2=P.PLOT_SETTINGS.y2
#P.PLOT_SETTINGS.ytic2.x1=P.PLOT_SETTINGS.x1
#P.PLOT_SETTINGS.ytic2.x2=P.PLOT_SETTINGS.x2
# Logo is simply a vcs text object, set to None for off
P.PLOT_SETTINGS.logo = None
# timestamp is simply a vcs text object, set to None for off
P.PLOT_SETTINGS.time_stamp = None
P.PLOT_SETTINGS.draw_mesh='n'
x.scriptrun(os.path.join(sys.prefix,"share","pmp","graphics","vcs","portraits.scr"))
P.PLOT_SETTINGS.colormap = 'bl_rd_12'
cols=vcs.getcolors(P.PLOT_SETTINGS.levels,range(144,156),split=1)
P.PLOT_SETTINGS.fillareacolors = cols
P.PLOT_SETTINGS.parametertable.expansion = 100

# LIST OF VARIABLES TO BE USED IN PORTRAIT PLOT
# REDUCED LIST FOR TEST CASE
# vars = ['pr','tas','psl','rlut','rsut','ua-850','ua-200','va-850','va-200','zg-500']
variables = args.variables.split(",")

# LOAD METRICS DICTIONARIES FROM JSON FILES FOR EACH VAR AND STORE AS A SINGLE DICTIONARY
var_cmip5_dics = {}
mods = set()

# CMIP5 METRICS RESULTS - CURRENTLY USING FOR CONTROL SIMULATIONS
if args.cmip_compare:
cmip_json_files = glob.glob(os.path.join(args.cmipmetrics,'CMIP_metrics_results','CMIP5',args.mode,'*json'))

# ADD GFDL JSON FILES...
# This is pretty hard coded might want to consider more magic
json_files = []
input_experiments = []
for input_dir in args.argv:
dir_files = glob.glob(os.path.join(input_dir,'*.json'))
with open(dir_files[0], "r") as f:
data = json.load(f)

input_experiments.append(data["RESULTS"].keys()[0])
json_files += dir_files


if not json_files:
sys.stderr.write("ERROR: No JSON files found in the input directory.\n")
sys.exit(1)

json_files += cmip_json_files
#Aparna edits, to work in jupyter
EXCLUDE_MODELS = list()
if args.exclude:
EXCLUDE_MODELS = [ model.lower() for model in args.exclude.split(",") ]
# CONSTRUCT PYTHON DICTIONARY WITH RESULTS METRICS USED IN PORTRAIT
non_mods = ["GridInfo","References","RegionalMasking","metrics_git_sha1","uvcdat_version"]
for fnm in json_files:
f=open(fnm)
d = json.load(f)
var = os.path.basename(fnm).split("_")[0]
for m in d['RESULTS'].keys():
# Skip non model bits`
if m not in non_mods:
if args.gfdl_compare:
if 'GFDL' in m or m in input_experiments:
if m.lower() not in EXCLUDE_MODELS:
mods.add(m)
else:
del(d["RESULTS"][m])
else:
del(d["RESULTS"][m])
elif m.lower() in EXCLUDE_MODELS:
del(d["RESULTS"][m])
else:
mods.add(m)

else:
# Let's clean it up
del(d[m])
if var_cmip5_dics.has_key(var):
var_cmip5_dics[var]['RESULTS'].update(d["RESULTS"])
else:
var_cmip5_dics[var]=d

mods = sorted([ mod for mod in mods if mod not in input_experiments ])
# gfdl_model = mods.pop(0)
mods = input_experiments + mods

# mods.insert(0,gfdl_model)

# ORGANIZE METRICS INTO A VARIABLES X MODELS MATRIX
out1_rel,out2_rel,out3_rel,out4_rel = [np.ma.masked_all((len(variables),len(mods)),np.float32) for _ in range(4)] ; # Define arrays to fill

# LOOP OVER VARIABLE
for vn, var in enumerate(variables):
vals1,vals2,vals3,vals4 = [[] for _ in range(4)]
# LOOP OVER MODEL
for mn,mod in enumerate(mods):
try:
out1_rel[vn,mn] = float(var_cmip5_dics[var]['RESULTS'][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_djf'])
out2_rel[vn,mn] = float(var_cmip5_dics[var]['RESULTS'][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_mam'])
out3_rel[vn,mn] = float(var_cmip5_dics[var]['RESULTS'][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_jja'])
out4_rel[vn,mn] = float(var_cmip5_dics[var]['RESULTS'][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_son'])
except Exception,err:
pass

med_rms1 = np.ma.median(out1_rel[vn,:])
med_rms2 = np.ma.median(out2_rel[vn,:])
med_rms3 = np.ma.median(out3_rel[vn,:])
med_rms4 = np.ma.median(out4_rel[vn,:])

out1_rel[vn,:]=(out1_rel[vn,:]-med_rms1)/med_rms1
out2_rel[vn,:]=(out2_rel[vn,:]-med_rms2)/med_rms2
out3_rel[vn,:]=(out3_rel[vn,:]-med_rms3)/med_rms3
out4_rel[vn,:]=(out4_rel[vn,:]-med_rms4)/med_rms4

# ADD SPACES FOR LABELS TO ALIGN AXIS LABELS WITH PLOT
yax = [ m.encode('utf-8')+" " for m in mods ]
xax = [ v+" " for v in variables ]

# Convert to MV so we can decorate
out1_rel = MV2.array(out1_rel)
out2_rel = MV2.array(out2_rel)
out3_rel = MV2.array(out3_rel)
out4_rel = MV2.array(out4_rel)

# GENERATE PLOT
P.decorate(out1_rel,xax,yax)
P.decorate(out2_rel,xax,yax)
P.decorate(out3_rel,xax,yax)
P.decorate(out4_rel,xax,yax)

# PLOT
P.plot(out1_rel,x=x,multiple=1.4)
P.plot(out2_rel,x=x,multiple=2.4)
P.plot(out3_rel,x=x,multiple=3.4)
P.plot(out4_rel,x=x,multiple=4.4)
# END OF PLOTTING

# SAVE PLOT
fileName = ".".join([args.descriptor, args.format.lower()])
if re.match("png", args.format, re.IGNORECASE):
x.png(os.path.join(args.output,fileName))
elif re.match("ps", args.format, re.IGNORECASE):
x.postscript(os.path.join(args.output,fileName), textAsPaths=args.compress)
elif re.match("pdf", args.format, re.IGNORECASE):
x.pdf(os.path.join(args.output,fileName),textAsPaths=args.compress)
elif re.match("eps", args.format, re.IGNORECASE):
x.eps(os.path.join(args.output,fileName),textAsPaths=args.compress)
else:
sys.stderr.write("ERROR: I don't know that format: %s\n" % args.format)
sys.exit(1)

if __name__ == "__main__":

parser = argparse.ArgumentParser(description="Portrait plot generation tool.")
# parser.add_argument("-i", "--input", help="The input directory to the PCMDI Metrics.", required=True)
parser.add_argument("-d", "--descriptor", help="The experiment name.", required=True)
parser.add_argument("-o", "--output", help="The output directory for the portrai plot.",
default=os.getcwd(), required=False)
parser.add_argument("-f", "--format", help="The output file type for the protrait plot (ps, pdf, png)",
default="png", required=False)
parser.add_argument("-C", "--compress", action="store_false", help="Compress the output.",
required=False)
parser.add_argument("-g", "--gfdl_compare", help="Option to only compare against GFDL CMIP models.", action="store_true", default=False)
parser.add_argument("-c", "--cmip_compare", help="Option to only compare against the cmip_models.", action="store_false", default=True)
parser.add_argument("-v", "--variables", help="Comma delimited list of variables to show.",
default="pr,psl,tas,rlut,rsut,ua-850,ua-200,va-850,va-200,zg-500", required=False)
parser.add_argument("-x", "--exclude", help="Comma delimited list to exclude CMIP5 models.", required=False)
parser.add_argument("-m", "--mode", help="The option to change which version of the data to compare against.", default="amip")
parser.add_argument("-s", "--cmipMetrics", help="specifies metrics location computed from other CMIP models. specify path till share directory only", default="/home/a1r/metrics_latest_v1p1p1/share/")
parser.add_argument("argv", nargs=argparse.REMAINDER)
args = parser.parse_args()

if len(args.argv) == 0:
raise RuntimeError("Sorry, but it appears that you've not specified any input directories!")

EXCLUDE_MODELS = list()
if args.exclude:
EXCLUDE_MODELS = [ model.lower() for model in args.exclude.split(",") ]

GFDL_COMPARE = args.gfdl_compare
CMIP_COMPARE = args.cmip_compare
if len(args.argv) == 1:
# only one model provided on the commandline, so lets compare against CMIP!
CMIP_COMPARE = True

if GFDL_COMPARE and not CMIP_COMPARE:
CMIP_COMPARE = True

for input_dir in args.argv:
if not os.path.exists(input_dir):
sys.stderr.write("ERROR: The input directory for the PCMDI Metrics JSON files doesn't exist.\n")
sys.exit(1)

try:
if not os.path.exists(args.output):
os.mkdir(args.output)
except:
sys.stderr.write("ERROR: Unable to make the output directory for the portrait plots.\n")
sys.exit(1)

create_plot(args)
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