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plot_mpaso_native.py
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from __future__ import absolute_import, division, print_function, \
unicode_literals
import os
from netCDF4 import Dataset as netcdf_dataset
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import cmocean
from make_plots import make_scatter_plot
# Settings for lcrc
#meshName = 'oEC60to30v3'
#meshfile = f'/lcrc/group/acme/public_html/inputdata/ocn/mpas-o/{meshName}/oEC60to30v3_60layer.170905.nc'
##runname = '20200116.Redioff.GMPAS-IAF.oEC60to30v3.anvil'
##runname = '20200122.RedionLowTapering.GMPAS-IAF.oEC60to30v3.anvil'
#runname = '20200122.RedionHighTapering.GMPAS-IAF.oEC60to30v3.anvil'
#modeldir = F'/lcrc/group/acme/milena/acme_scratch/anvil/{runname}/run'
#
#meshName = 'ECwISC30to60E1r2'
#meshfile = f'/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{meshName}/ocean.ECwISC30to60E1r2.200408.nc'
#runname = '20210614.A_WCYCL1850-DIB-ISMF_CMIP6.ne30_ECwISC30to60E1r2.anvil.DIBbugFixMGM'
#modeldir = f'/lcrc/group/acme/ac.sprice/acme_scratch/anvil/{runname}/archive/ocn/hist'
#
#meshName = 'SOwISC12to60E2r4'
#meshfile = f'/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{meshName}/ocean.SOwISC12to60E2r4.210107.nc'
#runname = '20211026.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.GMtaperMinKappa500plusRedi'
#runname = '20220223.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.CryoBranchBaseline'
#runname = '20220401.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.CryoBranchGMHorResFun'
#modeldir = f'/lcrc/group/e3sm/ac.sprice/scratch/chrys/{runname}/run'
#runname = '20220418.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.CryoBranchHorTaperGMRediconstant'
#runname = '20220418.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.CryoBranchHorTaperGMVisbeckRediequalGM'
#runname = '20220419.CRYO1850.ne30pg2_SOwISC12to60E2r4.chrysalis.CryoBranchRediEqGM2'
#modeldir = f'/lcrc/group/e3sm/ac.milena/scratch/chrys/{runname}/run'
#
#meshName = 'EC30to60E2r2'
#meshfile = f'/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{meshName}/ocean.EC30to60E2r2.210210.nc'
#runname = 'v2Visbeck_RediequalGM_lowMaxKappa.LR.picontrol'
#modeldir = f'/lcrc/group/e3sm/ac.milena/E3SMv2/{runname}/run'
#
meshName = 'EC30to60E2r2'
meshfile = f'/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{meshName}/ocean.EC30to60E2r2.210210.nc'
runname = '20241030.EC30to60_test.anvil'
modeldir = f'/lcrc/group/e3sm/ac.vanroekel/scratch/anvil/{runname}/run'
isShortTermArchive = False
# Settings for nersc
#meshName = 'ARRM10to60E2r1'
#meshfile = '/global/cfs/cdirs/e3sm/inputdata/ocn/mpas-o/ARRM10to60E2r1/mpaso.ARRM10to60E2r1.rstFrom1monthG-chrys.220802.nc'
#runname = 'E3SM-Arcticv2.1_historical0151'
##runname = 'E3SMv2.1B60to10rA02' # 1950-control
#modeldir = f'/global/cfs/cdirs/m1199/e3sm-arrm-simulations/{runname}/archive'
#isShortTermArchive = True
# Settings for erdc.hpc.mil
#meshName = 'ARRM10to60E2r1'
#meshfile = '/p/app/unsupported/RASM/acme/inputdata/ocn/mpas-o/ARRM10to60E2r1/mpaso.ARRM10to60E2r1.rstFrom1monthG-chrys.220802.nc'
#runname = 'E3SMv2.1B60to10rA02'
#modeldir = f'/p/archive/osinski/E3SM/{runname}'
##modeldir = f'/p/work/milena/archive/{runname}'
#isShortTermArchive = True
if isShortTermArchive:
modeldir = f'{modeldir}/ocn/hist'
figdir = f'./ocean_native/{runname}'
if not os.path.isdir(figdir):
os.makedirs(figdir)
year = 1
months = [1]
# z levels [m] (relevant for 3d variables)
#dlevels = [100.0, 250.0]
#dlevels = [50.0, 100.0, 250.0, 500.0, 3000.0]
#dlevels = [50., 100.0, 300.0, 800.0]
dlevels = [0., 100., 600., 2000.]
colorIndices = [0, 10, 28, 57, 85, 113, 142, 170, 198, 227, 242, 255]
lon0NH = -180.0
lon1NH = 180.0
lat0NH = 50.0
lat1NH = 90.0
lon0SH = -180.0
lon1SH = 180.0
lat0SH = -55.0
lat1SH = -90.0
pi2deg = 180/np.pi
mpasFile = 'highFrequencyOutput'
mpasFileDayformat = '01_00.00.00'
timeIndex = 334
variables = [
{'name': 'temperatureAtSurface',
'mpasvarname': 'temperatureAtSurface',
'component': 'mpaso',
'title': 'SST',
'units': '$^\circ$C',
'factor': 1,
'colormap': plt.get_cmap('RdBu_r'),
'clevels': [-1.8, -1.6, -0.5, 0.0, 0.5, 2.0, 4.0, 8.0, 12., 16., 22.],
'clevelsNH': [-1.8, -1.6, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 6.0, 8.0, 12.],
'clevelsSH': [-1.8, -1.6, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 6.0, 8.0, 12.],
'is3d': False},
#{'name': 'temperatureAt250m',
# 'mpasvarname': 'temperatureAt250m',
# 'component': 'mpaso',
# 'title': 'Temperature at 250m',
# 'units': '$^\circ$C',
# 'factor': 1,
# 'colormap': plt.get_cmap('RdBu_r'),
# 'clevels': [-1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 8.0, 12., 16., 22.],
# 'clevelsNH': [-1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 6.0, 8.0, 12., 16.],
# 'clevelsSH': [-1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 6.0, 8.0, 12., 16.],
# 'is3d': False},
#{'name': 'temperatureAtBottom',
# 'mpasvarname': 'temperatureAtBottom',
# 'component': 'mpaso',
# 'title': 'Temperature at bottom',
# 'units': '$^\circ$C',
# 'factor': 1,
# 'colormap': plt.get_cmap('RdBu_r'),
# 'clevels': [-1.8, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 6.0, 8.0],
# 'clevelsNH': [-1.8, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 6.0, 8.0],
# 'clevelsSH': [-1.8, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 6.0, 8.0],
# 'is3d': False},
{'name': 'salinityAtSurface',
'mpasvarname': 'salinityAtSurface',
'component': 'mpaso',
'title': 'SSS',
'units': 'psu',
'factor': 1,
'colormap': cmocean.cm.haline,
'clevels': [27., 28., 29., 29.5, 30., 30.5, 31., 32., 33., 34., 35.],
'clevelsNH': [27., 28., 29., 29.5, 30., 30.5, 31., 32., 33., 34., 35.],
'clevelsSH': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
# 'is3d': False},
#{'name': 'salinityAt250m',
# 'mpasvarname': 'salinityAt250m',
# 'component': 'mpaso',
# 'title': 'Salinity at 250m',
# 'units': 'psu',
# 'factor': 1,
# 'colormap': cmocean.cm.haline,
# 'clevels': [30., 30.5, 31., 32., 32.5, 33., 33.5, 34., 34.5, 35., 36.],
# 'clevelsNH': [30., 30.5, 31., 32., 32.5, 33., 33.5, 34., 34.5, 35., 36.],
# 'clevelsSH': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
# 'is3d': False},
#{'name': 'salinityAtBottom',
# 'mpasvarname': 'salinityAtBottom',
# 'component': 'mpaso',
# 'title': 'Salinity at bottom',
# 'units': 'psu',
# 'factor': 1,
# 'colormap': cmocean.cm.haline,
# 'clevels': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
# 'clevelsNH': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
# 'clevelsSH': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
'is3d': False}
]
#mpasFile = 'timeSeriesStatsMonthlyMax'
#mpasFileDayformat = '01'
#timeIndex = 0
#variables = [
# {'name': 'maxMLD',
# 'mpasvarname': 'timeMonthlyMax_max_dThreshMLD',
# 'title': 'Maximum MLD',
# 'units': 'm',
# 'factor': 1,
# 'colormap': plt.get_cmap('viridis'),
# 'clevels': [10., 50., 80., 100., 150., 200., 300., 400., 800., 1200., 2000.],
# 'clevelsNH': [50., 100., 150., 200., 300., 500., 800., 1200., 1500., 2000., 3000.],
# 'clevelsSH': [10., 50., 80., 100., 150., 200., 300., 400., 800., 1200., 2000.],
# 'is3d': False}
# ]
#mpasFile = 'timeSeriesStatsMonthly'
#mpasFileDayformat = '01'
#timeIndex = 0
#variables = [
# #{'name': 'GMkappa',
# # 'mpasvarname': 'timeMonthly_avg_gmKappaScaling',
# # 'component': 'mpaso',
# # 'title': 'GM Kappa (w/ horTaper & kappaScaling)',
# # 'units': 'm$^2$/s',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsNH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsSH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'is3d': True},
# #{'name': 'gmBolusKappa',
# # 'mpasvarname': 'timeMonthly_avg_gmBolusKappa',
# # 'component': 'mpaso',
# # 'title': 'gmBolusKappa',
# # 'units': 'm$^2$/s',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsNH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsSH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'is3d': False},
# #{'name': 'gmKappaScaling',
# # 'mpasvarname': 'timeMonthly_avg_gmKappaScaling',
# # 'component': 'mpaso',
# # 'title': 'gmKappaScaling',
# # 'units': '',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsNH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsSH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'is3d': True},
# #{'name': 'gmHorizontalTaper',
# # 'mpasvarname': 'timeMonthly_avg_gmHorizontalTaper',
# # 'component': 'mpaso',
# # 'title': 'gmHorizontalTaper',
# # 'units': '',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsNH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsSH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'is3d': False},
# #{'name': 'Redikappa',
# # 'mpasvarname': 'timeMonthly_avg_RediKappa',
# # 'component': 'mpaso',
# # 'title': 'Redi Kappa (w/ horTaper)',
# # 'units': 'm$^2$/s',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsNH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsSH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'is3d': False},
# #{'name': 'RedikappaNoTaper',
# # 'mpasvarname': 'timeMonthly_avg_RediKappa',
# # 'component': 'mpaso',
# # 'title': 'RediKappa',
# # 'units': 'm$^2$/s',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsNH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'clevelsSH': [0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800],
# # 'is3d': False},
# #{'name': 'RediHorizontalTaper',
# # 'mpasvarname': 'timeMonthly_avg_RediHorizontalTaper',
# # 'component': 'mpaso',
# # 'title': 'RediHorizontalTaper',
# # 'units': '',
# # 'factor': 1,
# # 'colormap': plt.get_cmap('terrain'),
# # 'clevels': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsNH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'clevelsSH': [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
# # 'is3d': False},
# {'name': 'temperature',
# 'mpasvarname': 'timeMonthly_avg_activeTracers_temperature',
# 'component': 'mpaso',
# 'title': 'Potential Temperature',
# 'units': '$^\circ$C',
# 'factor': 1,
# 'colormap': plt.get_cmap('RdBu_r'),
# 'clevels': [-1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 8.0, 12., 16., 22.],
# 'clevelsNH': [-5, -1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 8.0, 12., 16.],
# 'clevelsSH': [-5, -1.8, -1.0, -0.5, 0.0, 0.5, 2.0, 4.0, 8.0, 12., 16.],
# 'is3d': True},
# {'name': 'salinity',
# 'mpasvarname': 'timeMonthly_avg_activeTracers_salinity',
# 'component': 'mpaso',
# 'title': 'Salinity',
# 'units': 'psu',
# 'factor': 1,
# 'colormap': cmocean.cm.haline,
# 'clevels': [27., 28., 29., 29.5, 30., 30.5, 31., 32., 33., 34., 35.],
# 'clevelsNH': [27., 28., 29., 29.5, 30., 30.5, 31., 32., 33., 34., 35.],
# 'clevelsSH': [31., 31.5, 32., 32.3, 32.6, 32.9, 33.2, 33.5, 34., 34.5, 35.],
# 'is3d': True}
# #{'name': 'GMnormalVel',
# # 'mpasvarname': 'timeMonthly_avg_normalGMBolusVelocity',
# # 'component': 'mpaso',
# # 'title': 'GM Normal Velocity',
# # 'units': 'cm/s',
# # 'factor': 1e2,
# # 'colormap': plt.get_cmap('RdBu_r'),
# # 'clevels': [-2, -1, -0.5, -0.25, -0.05, 0., 0.05, 0.25, 0.5, 1, 2],
# # 'clevelsNH': [-2, -1, -0.5, -0.25, -0.05, 0., 0.05, 0.25, 0.5, 1, 2],
# # 'clevelsSH': [-2, -1, -0.5, -0.25, -0.05, 0., 0.05, 0.25, 0.5, 1, 2],
# # 'is3d': True}
# ]
# Info about MPAS mesh
f = netcdf_dataset(meshfile, mode='r')
lonCell = f.variables['lonCell'][:]
latCell = f.variables['latCell'][:]
lonEdge = f.variables['lonEdge'][:]
latEdge = f.variables['latEdge'][:]
z = f.variables['refBottomDepth'][:]
depth = f.variables['bottomDepth'][:]
f.close()
lonCell = pi2deg*lonCell
latCell = pi2deg*latCell
lonEdge = pi2deg*lonEdge
latEdge = pi2deg*latEdge
# Find model levels for each depth level
zlevels = np.zeros(np.shape(dlevels), dtype=np.int64)
for id in range(len(dlevels)):
dz = np.abs(z-dlevels[id])
zlevels[id] = np.argmin(dz)
#print('Model levels = ', z[zlevels])
#print(np.min(depth), np.max(depth))
#print('z levels = ', z)
#figtitle = f'Bottom depth {meshName}'
#figfileGlobal = f'{figdir}/DepthGlobal_{meshName}.png'
#figfileNH = f'{figdir}/DepthNH_{meshName}.png'
#figfileSH = f'{figdir}/DepthSH_{meshName}.png'
#clevels = [0., 10., 50., 100., 250., 500.0, 750., 1000., 2000., 3000., 5000.]
#colormap = cmocean.cm.deep_r
#dotSize = 0.25
#make_scatter_plot(lonCell, latCell, dotSize, figtitle, figfileGlobal, projectionName='Robinson',
# lon0=-180, lon1=180, dlon=40, lat0=-90, lat1=90, dlat=20,
# fld=depth, cmap=colormap, clevels=clevels, cindices=colorIndices, cbarLabel='m')
#dotSize = 5.0 # this should go up as resolution decreases # for ARRM
#dotSize = 25.0 # this should go up as resolution decreases # for LR
#make_scatter_plot(lonCell, latCell, dotSize, figtitle, figfileNH, projectionName='NorthPolarStereo',
# lon0=lon0NH, lon1=lon1NH, dlon=20.0, lat0=lat0NH, lat1=lat1NH, dlat=10.0,
# fld=depth, cmap=colormap, clevels=clevels, cindices=colorIndices, cbarLabel='m')
#dotSize = 25.0 # this should go up as resolution decreases
#make_scatter_plot(lonCell, latCell, dotSize, figtitle, figfileSH, projectionName='SouthPolarStereo',
# lon0=lon0SH, lon1=lon1SH, dlon=20.0, lat0=lat0SH, lat1=lat1SH, dlat=10.0,
# fld=depth, cmap=colormap, clevels=clevels, cindices=colorIndices, cbarLabel='m')
for month in months:
modelfile = f'{modeldir}/{runname}.mpaso.hist.am.{mpasFile}.{year:04d}-{month:02d}-{mpasFileDayformat}.nc'
#modelfile = f'{modeldir}/mpaso.hist.am.timeSeriesStatsMonthly.{year:04d}-{month:02d}-{mpasFileDayformat}.nc' # old (v1) filename format
f = netcdf_dataset(modelfile, mode='r')
for var in variables:
varname = var['name']
mpasvarname = var['mpasvarname']
factor = var['factor']
clevels = var['clevels']
clevelsNH = var['clevelsNH']
clevelsSH = var['clevelsSH']
colormap = var['colormap']
vartitle = var['title']
varunits = var['units']
if varname=='GMkappa' or varname=='Redikappa' or \
varname=='gmBolusKappa' or varname=='gmKappaScaling' or \
varname=='gmHorizontalTaper' or varname=='RedikappaNoTaper' or \
varname=='RediHorizontalTaper' or varname=='GMnormalVel':
lon = lonEdge
lat = latEdge
else:
lon = lonCell
lat = latCell
if var['is3d']:
for iz in range(len(dlevels)):
figtitle = f'{vartitle}, z={z[zlevels[iz]]:5.1f} m, year={year}, month={month}'
figfileGlobal = f'{figdir}/{varname}Global_depth{int(dlevels[iz]):04d}_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
figfileNH = f'{figdir}/{varname}NH_depth{int(dlevels[iz]):04d}_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
figfileSH = f'{figdir}/{varname}SH_depth{int(dlevels[iz]):04d}_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
# What I have below could instead be:
# dsIn = xr.open_dataset(modelfile).isel(Time=timeIndex, nVertLevels=zlevels[iz]) # this would go outside the var loop, right after modelfile is defined
# fld = factor * dsIn[mpasvarname].values # this would go here in place of the next 5 lines
fld = f.variables[mpasvarname][timeIndex, :, zlevels[iz]]
fld = ma.masked_greater(fld, 1e15)
fld = ma.masked_less(fld, -1e15)
fld = factor*fld
fld = np.squeeze(fld)
if varname=='GMkappa':
kappa = np.squeeze(f.variables['timeMonthly_avg_gmBolusKappa'][timeIndex, :])
kappa_horScaling = np.squeeze(f.variables['timeMonthly_avg_gmHorizontalTaper'][timeIndex, :])
fld = kappa * fld * kappa_horScaling
print('varname=', varname, 'month=', month, 'zlev=', int(dlevels[iz]), 'fldmin=', np.min(fld), 'fldmax=', np.max(fld))
#fld3d = f.variables[varname][timeIndex, :, :]
#fld3d = ma.masked_greater(fld3d, 1e15)
#fld3d = ma.masked_less(fld3d, -1e15)
#fld3d = factor*fld3d
#print(np.min(fld3d), np.max(fld3d))
dotSize = 0.25
make_scatter_plot(lon, lat, dotSize, figtitle, figfileGlobal, projectionName='Robinson',
lon0=-180, lon1=180, dlon=40, lat0=-90, lat1=90, dlat=20,
fld=fld, cmap=colormap, clevels=clevels, cindices=colorIndices, cbarLabel=varunits)
#dotSize = 5.0 # this should go up as resolution decreases # for ARRM
dotSize = 25.0 # this should go up as resolution decreases # for LR
make_scatter_plot(lon, lat, dotSize, figtitle, figfileNH, projectionName='NorthPolarStereo',
lon0=lon0NH, lon1=lon1NH, dlon=20.0, lat0=lat0NH, lat1=lat1NH, dlat=10.0,
fld=fld, cmap=colormap, clevels=clevelsNH, cindices=colorIndices, cbarLabel=varunits)
dotSize = 25.0 # this should go up as resolution decreases
make_scatter_plot(lon, lat, dotSize, figtitle, figfileSH, projectionName='SouthPolarStereo',
lon0=lon0SH, lon1=lon1SH, dlon=20.0, lat0=lat0SH, lat1=lat1SH, dlat=10.0,
fld=fld, cmap=colormap, clevels=clevelsSH, cindices=colorIndices, cbarLabel=varunits)
else:
figtitle = f'{vartitle}, year={year}, month={month}'
figfileGlobal = f'{figdir}/{varname}Global_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
figfileNH = f'{figdir}/{varname}NH_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
figfileSH = f'{figdir}/{varname}SH_{runname}_{year:04d}-{month:02d}timeIndex{timeIndex:02d}.png'
fld = f.variables[mpasvarname][timeIndex, :]
fld = ma.masked_greater(fld, 1e15)
fld = ma.masked_less(fld, -1e15)
fld = factor*fld
fld = np.squeeze(fld)
if varname=='Redikappa':
kappa_horScaling = np.squeeze(f.variables['timeMonthly_avg_RediHorizontalTaper'][timeIndex, :])
fld = fld * kappa_horScaling
print('varname=', varname, 'month=', month, 'fldmin=', np.min(fld), 'fldmax=', np.max(fld))
dotSize = 0.25
make_scatter_plot(lon, lat, dotSize, figtitle, figfileGlobal, projectionName='Robinson',
lon0=-180, lon1=180, dlon=40, lat0=-90, lat1=90, dlat=20,
fld=fld, cmap=colormap, clevels=clevels, cindices=colorIndices, cbarLabel=varunits)
#dotSize = 5.0 # this should go up as resolution decreases # for ARRM
dotSize = 25.0 # this should go up as resolution decreases # for LR
make_scatter_plot(lon, lat, dotSize, figtitle, figfileNH, projectionName='NorthPolarStereo',
lon0=lon0NH, lon1=lon1NH, dlon=20.0, lat0=lat0NH, lat1=lat1NH, dlat=10.0,
fld=fld, cmap=colormap, clevels=clevelsNH, cindices=colorIndices, cbarLabel=varunits)
dotSize = 25.0 # this should go up as resolution decreases
make_scatter_plot(lon, lat, dotSize, figtitle, figfileSH, projectionName='SouthPolarStereo',
lon0=lon0SH, lon1=lon1SH, dlon=20.0, lat0=lat0SH, lat1=lat1SH, dlat=10.0,
fld=fld, cmap=colormap, clevels=clevelsSH, cindices=colorIndices, cbarLabel=varunits)
f.close()