def test_portrait(self):
try:
import vcs
except:
raise RuntimeError(
"Sorry your python is not build with VCS support cannot geenrate portrait plots")
import json
# CDAT MODULES
import pcmdi_metrics
import pcmdi_metrics.graphics.portraits
import MV2
import numpy
from genutil import statistics
import os
import sys
import glob
# CREATES VCS OBJECT AS A PORTAIT PLOT AND LOADS PLOT SETTINGS FOR
# EXAMPLE
x = vcs.init()
x.portrait()
# Turn off antialiasing for test suite
x.setantialiasing(0)
# PARAMETERS STUFF
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 = .12
P.PLOT_SETTINGS.y2 = .95
P.PLOT_SETTINGS.xtic2y1 = P.PLOT_SETTINGS.y1
P.PLOT_SETTINGS.xtic2y2 = P.PLOT_SETTINGS.y2
P.PLOT_SETTINGS.ytic2x1 = P.PLOT_SETTINGS.x1
P.PLOT_SETTINGS.ytic2x2 = P.PLOT_SETTINGS.x2
# P.PLOT_SETTINGS.missing_color = 3
# P.PLOT_SETTINGS.logo = None
P.PLOT_SETTINGS.time_stamp = None
P.PLOT_SETTINGS.draw_mesh = 'n'
# P.PLOT_SETTINGS.tictable.font = 3
x.scriptrun(
os.path.join(
pcmdi_metrics.__path__[0],
"..",
"..",
"..",
"..",
"share",
"graphics",
'vcs',
'portraits.scr'))
P.PLOT_SETTINGS.colormap = 'bl_rd_12'
# cols=vcs.getcolors(P.PLOT_SETTINGS.levels,range(16,40),split=1)
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
vars = [
'pr',
'rsut',
'rsutcs',
'rlutcs',
'tas',
'tos',
'sos',
'zos',
'ua-850',
'ua-200',
'zg-500']
vars = []
# LOAD METRICS DICTIONARIES FROM JSON FILES FOR EACH VAR AND STORE AS A
# SINGLE DICTIONARY
var_cmip5_dics = {}
mods = set()
json_files = glob.glob(
os.path.join(
pcmdi_metrics.__path__[0],
"..",
"..",
"..",
"..",
"share",
"CMIP_metrics_results",
"CMIP5",
"amip",
"*.json"))
for fnm in json_files:
f = open(fnm)
d = json.load(f)
var = os.path.basename(fnm).split("_")[0]
vars.append(var)
for m in d.keys():
mods.add(m)
if var in var_cmip5_dics:
var_cmip5_dics[var].update(d)
else:
var_cmip5_dics[var] = d
vars.sort()
mods = sorted(list(mods))
print "Models:",mods
for bad in ["GridInfo", "References", "RegionalMasking",
"metrics_git_sha1", "uvcdat_version"]:
if bad in mods:
mods.remove(bad)
else:
print "Not removing column %s (not present)" % bad
# ORGANIZE METRICS INTO A VARIABLES X MODELS MATRIX
out1_rel = MV2.zeros(
(len(vars),
len(mods)),
MV2.float32) # DEFINE ARRAY
vn = -1 # VARIABLE INDEX
for var in vars: # LOOP OVER VARIABLE
vn = vn + 1
vals = []
for mod in mods: # LOOP OVER MODEL
try:
rms = var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xyt_ann_GLB']
if P.verbose:
print var, ' ', mod, ' ', repr(rms), ' WITH global'
except:
rms = 1.e20
if P.verbose:
print var, ' ', mod, ' ', repr(rms), ' missing'
rms = float(rms)
vals.append(rms)
vars_ar = MV2.array(vals)
# COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
med_rms = statistics.median(vars_ar)[0]
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn, mn] = (float(var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xyt_ann_GLB']) - med_rms) / med_rms # RELATIVE ERROR
except:
out1_rel[vn, mn] = numpy.ma.masked
# ADD SPACES FOR LABELS TO ALIGN AXIS LABELS WITH PLOT
modsAxis = mods
varsAxis = vars
# LOOP THROUGH LISTS TO ADD SPACES
for i in range(len(modsAxis)):
modsAxis[i] = modsAxis[i] + ' '
for i in range(len(varsAxis)):
varsAxis[i] = varsAxis[i] + ' '
yax = [s.encode('utf-8')
for s in mods] # CHANGE FROM UNICODE TO BYTE STRINGS
xax = vars
# GENERATE PLOT
P.decorate(out1_rel, xax, yax)
# P.plot(out1_rel,x=x,multiple=1.1,bg=0) # FOR PLOTTING TRIANGLES WHEN
# USING TWO OR MORE REFERENCE DATA SETS
P.plot(out1_rel, bg=1, x=x)
# x.backend.renWin.Render()
# END OF PLOTTING
# SAVE PLOT
src = os.path.join(os.path.dirname(__file__), "testPortrait.png")
print src
fnm = os.path.join(os.getcwd(), "testPortrait.png")
x.png(fnm)
ret = checkimage.check_result_image(
fnm,
src,
checkimage.defaultThreshold)
if ret != 0:
sys.exit(ret)
if P.verbose:
print var, ' ', mod, ' ', repr(rms1), ' missing'
rms1 = float(rms1)
vals1.append(rms1)
rms2 = float(rms2)
vals2.append(rms2)
rms3 = float(rms3)
vals3.append(rms3)
rms4 = float(rms4)
vals4.append(rms4)
vars_ar1 = MV2.array(vals1)
# COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
med_rms1 = statistics.median(vars_ar1)[0]
vars_ar2 = MV2.array(vals2)
med_rms2 = statistics.median(vars_ar2)[0]
vars_ar3 = MV2.array(vals3)
med_rms3 = statistics.median(vars_ar3)[0]
vars_ar4 = MV2.array(vals4)
med_rms4 = statistics.median(vars_ar4)[0]
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn, mn] = (float(var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xy_djf_GLB']) - med_rms1) / med_rms1 # RELATIVE ERROR
out2_rel[vn, mn] = (float(var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xy_mam_GLB']) - med_rms2) / med_rms2 # RELATIVE ERROR
vals = []
for mod in mods: # LOOP OVER MODEL
try:
rms = var_cmip5_dics[var][mod]["default"]["r1i1p1"]['rms_xyt_ann_GLB']
if P.verbose:
print var,' ', mod,' ', `rms`, ' WITH global'
except:
rms = 1.e20
if P.verbose:
print var,' ', mod,' ', `rms`, ' missing'
rms = float(rms)
vals.append(rms)
vars_ar = MV2.array(vals)
med_rms = statistics.median(vars_ar)[0] # COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn,mn] = (float(var_cmip5_dics[var][mod]["default"]["r1i1p1"]['rms_xyt_ann_GLB'])-med_rms)/med_rms # RELATIVE ERROR
except:
out1_rel[vn,mn] = 1.e20
# ADD SPACES FOR LABELS TO ALIGN AXIS LABELS WITH PLOT
modsAxis = mods
varsAxis = vars
# LOOP THROUGH LISTS TO ADD SPACES
for i in range(len(modsAxis)):
rms4 = 1.e20
if P.verbose:
print var,' ', mod,' ', `rms1`, ' missing'
rms1 = float(rms1)
vals1.append(rms1)
rms2 = float(rms2)
vals2.append(rms2)
rms3 = float(rms3)
vals3.append(rms3)
rms4 = float(rms4)
vals4.append(rms4)
vars_ar1 = MV2.array(vals1)
med_rms1 = statistics.median(vars_ar1)[0] # COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
vars_ar2 = MV2.array(vals2)
med_rms2 = statistics.median(vars_ar2)[0]
vars_ar3 = MV2.array(vals3)
med_rms3 = statistics.median(vars_ar3)[0]
vars_ar4 = MV2.array(vals4)
med_rms4 = statistics.median(vars_ar4)[0]
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn,mn] = (float(var_cmip5_dics[var][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_djf_GLB'])-med_rms1)/med_rms1 # RELATIVE ERROR
out2_rel[vn,mn] = (float(var_cmip5_dics[var][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_mam_GLB'])-med_rms2)/med_rms2 # RELATIVE ERROR
out3_rel[vn,mn] = (float(var_cmip5_dics[var][mod]["defaultReference"]["r1i1p1"]["global"]['rms_xy_jja_GLB'])-med_rms3)/med_rms3 # RELATIVE ERROR
for mod in mods: # LOOP OVER MODEL
try:
rms = var_cmip5_dics[var][mod]["default"]["r1i1p1"][
'rms_xyt_ann_GLB']
if P.verbose:
print var, ' ', mod, ' ', ` rms `, ' WITH global'
except:
rms = 1.e20
if P.verbose:
print var, ' ', mod, ' ', ` rms `, ' missing'
rms = float(rms)
vals.append(rms)
vars_ar = MV2.array(vals)
med_rms = statistics.median(vars_ar)[
0] # COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn, mn] = (float(var_cmip5_dics[var][mod]["default"]
["r1i1p1"]['rms_xyt_ann_GLB']) -
med_rms) / med_rms # RELATIVE ERROR
except:
out1_rel[vn, mn] = 1.e20
# ADD SPACES FOR LABELS TO ALIGN AXIS LABELS WITH PLOT
modsAxis = mods
varsAxis = vars
def test_portrait(self):
try:
import vcs
except:
raise RuntimeError(
"Sorry your python is not build with VCS support cannot geenrate portrait plots")
import json
# CDAT MODULES
import pcmdi_metrics
import pcmdi_metrics.graphics.portraits
import MV2
import numpy
from genutil import statistics
import os
import sys
import glob
print
print
print
print
print "---------------------------------------------------"
print "RUNNING: Portrait test"
print "---------------------------------------------------"
print
print
print
print
# CREATES VCS OBJECT AS A PORTAIT PLOT AND LOADS PLOT SETTINGS FOR
# EXAMPLE
x = vcs.init()
x.portrait()
# Turn off antialiasing for test suite
x.setantialiasing(0)
# PARAMETERS STUFF
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 = .12
P.PLOT_SETTINGS.y2 = .95
P.PLOT_SETTINGS.xtic2y1 = P.PLOT_SETTINGS.y1
P.PLOT_SETTINGS.xtic2y2 = P.PLOT_SETTINGS.y2
P.PLOT_SETTINGS.ytic2x1 = P.PLOT_SETTINGS.x1
P.PLOT_SETTINGS.ytic2x2 = P.PLOT_SETTINGS.x2
# P.PLOT_SETTINGS.missing_color = 3
# P.PLOT_SETTINGS.logo = None
P.PLOT_SETTINGS.time_stamp = None
P.PLOT_SETTINGS.draw_mesh = 'n'
# P.PLOT_SETTINGS.tictable.font = 3
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(16,40),split=1)
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
vars = [
'pr',
'rsut',
'rsutcs',
'rlutcs',
'tas',
'tos',
'sos',
'zos',
'ua-850',
'ua-200',
'zg-500']
vars = []
# LOAD METRICS DICTIONARIES FROM JSON FILES FOR EACH VAR AND STORE AS A
# SINGLE DICTIONARY
var_cmip5_dics = {}
mods = set()
json_files = glob.glob(
os.path.join(
os.path.dirname(__file__),
"json",
"*.json"))
for fnm in json_files:
f = open(fnm)
d = json.load(f)
var = os.path.basename(fnm).split("_")[0]
vars.append(var)
for m in d.keys():
mods.add(m)
if var in var_cmip5_dics:
var_cmip5_dics[var].update(d)
else:
var_cmip5_dics[var] = d
vars.sort()
mods = sorted(list(mods))
print "Models:", mods
for bad in ["GridInfo", "References", "RegionalMasking",
"metrics_git_sha1", "uvcdat_version"]:
if bad in mods:
mods.remove(bad)
else:
print "Not removing column %s (not present)" % bad
# ORGANIZE METRICS INTO A VARIABLES X MODELS MATRIX
out1_rel = MV2.zeros(
(len(vars),
len(mods)),
MV2.float32) # DEFINE ARRAY
vn = -1 # VARIABLE INDEX
for var in vars: # LOOP OVER VARIABLE
vn = vn + 1
vals = []
for mod in mods: # LOOP OVER MODEL
try:
rms = var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xyt_ann_GLB']
if P.verbose:
print var, ' ', mod, ' ', repr(rms), ' WITH global'
except:
rms = 1.e20
if P.verbose:
print var, ' ', mod, ' ', repr(rms), ' missing'
rms = float(rms)
vals.append(rms)
vars_ar = MV2.array(vals)
# COMPUTE MEDIAN RESULT FOR PORTRAIT NORMALIZATION
med_rms = statistics.median(vars_ar)[0]
mn = -1 # MODEL INDEX
for mod in mods:
mn = mn + 1
try:
out1_rel[vn, mn] = (float(var_cmip5_dics[var][mod]["defaultReference"][
"r1i1p1"]["global"]['rms_xyt_ann_GLB']) - med_rms) / med_rms # RELATIVE ERROR
except:
out1_rel[vn, mn] = numpy.ma.masked
# ADD SPACES FOR LABELS TO ALIGN AXIS LABELS WITH PLOT
modsAxis = mods
varsAxis = vars
# LOOP THROUGH LISTS TO ADD SPACES
for i in range(len(modsAxis)):
modsAxis[i] = modsAxis[i] + ' '
for i in range(len(varsAxis)):
varsAxis[i] = varsAxis[i] + ' '
yax = [s.encode('utf-8')
for s in mods] # CHANGE FROM UNICODE TO BYTE STRINGS
xax = vars
# GENERATE PLOT
P.decorate(out1_rel, xax, yax)
# P.plot(out1_rel,x=x,multiple=1.1,bg=0) # FOR PLOTTING TRIANGLES WHEN
# USING TWO OR MORE REFERENCE DATA SETS
P.plot(out1_rel, bg=1, x=x)
# x.backend.renWin.Render()
# END OF PLOTTING
# SAVE PLOT
src = os.path.join(os.path.dirname(__file__), "testPortrait.png")
print src
fnm = os.path.join(os.getcwd(), "testPortrait.png")
x.png(fnm)
ret = checkimage.check_result_image(
fnm,
src,
checkimage.defaultThreshold)
if ret != 0:
sys.exit(ret)
