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plot_maps.py
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from __future__ import division
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
plot multi axes ####
Dec 2021: adapter for Py3.x
"""
__author__ = "Saeed Moghimi"
__copyright__ = "Copyright 2017, UCAR/NOAA"
__license__ = "GPL"
__version__ = "1.0"
__email__ = "[email protected]"
#Wed 24 May 2017 03:12:39 PM UTC
#import matplotlib as mpl
#mpl.use('Agg')
import os,sys
#sys.path.append('/home/moghimis/linux_working/00-working/04-test-adc_plot/')
#sys.path.append('/home/moghimis/linux_working/00-working/04-test-adc_plot/csdlpy')
import matplotlib
if os.name == 'nt':
matplotlib.rc('font', family='Arial')
else: # might need tweaking, must support black triangle for N arrow
matplotlib.rc('font', family='DejaVu Sans')
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
import datetime
#from collections import defaultdict
from pynmd.plotting.vars_param import defs
from pynmd.models.adcirc.post import adcirc_post as adcp
from pynmd.plotting import plot_routines as pr
from pynmd.plotting import plot_settings as ps
from pynmd.tools.gtime import roundTime
#from pynmd.tools.gtime import find_nearest_time
from pynmd.plotting import colormaps as cmaps
import pandas as pd
import glob
from dateutil import parser
#sys.path.append('/scratch2/COASTAL/coastal/save/Saeed.Moghimi/opt/pycodes/csdlpy')
#import adcirc
from atcf import readTrack
try:
os.system('rm base_info.pyc' )
except:
pass
if 'base_info' in sys.modules:
del(sys.modules["base_info"])
import base_info
defs = base_info.defs.copy()
try:
os.system('rm geo_regions.pyc' )
except:
pass
if 'geo_regions' in sys.modules:
del(sys.modules["geo_regions"])
from geo_regions import get_region_extent
import matplotlib.tri as Tri
#from dateutil import parser
#import glob
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import (LONGITUDE_FORMATTER,
LATITUDE_FORMATTER)
#====== subplot adjustments ===============
left1 = 0.1 # the left side of the subplots of the figure
right1 = 0.8 # the right side of the subplots of the figure
bottom1= 0.1 # the bottom of the subplots of the figure (ntr==16 bottom=0.05)
top1 = 0.9 # the top of the subplots of the figure
wspace1= 0.05 # the amount of width reserved for white space between subplots
hspace1= 0.15 # the amount of height reserved for white space between subplots
###############################################<<<<<<
try:
ftypes = base_info.ftypes
except:
ftypes = ['png','pdf']
ftypes = ['png']
def find_nearest_time(dates,date):
"""
Return nearest time indexs
Inp: dates: timedate vector
date: timedate pint
Out: index
"""
dtsec = []
for tmp in dates:
dtsec.append( np.abs( (tmp-date).total_seconds() ) )
return np.argmin(dtsec)
def plot_map_old(ax,tri,val,extent,vmin=None,vmax=None,dep=None,cmap=None):
try:
#if cartopy
ax.set_extent(extent)
except:
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
if cmap is not None:
cmap = cmap
else:
cmap = cmaps.jetMinWi
if vmin is not None:
#print ' tricontourf '
dv = (vmax-vmin)/50.0
levels=np.arange(vmin,vmax+dv,dv)
#val [val < vmin] = vmin+1e-3
cf1 = ax.tricontourf(tri,val,levels=levels, cmap = cmap , extend='both')#extend='max' ) #,extend='both'
plt.colorbar(cf1,shrink = 0.4,ticks = [vmin,(vmin+vmax)/2,vmax])
else:
cf1 = ax.tricontourf(tri,val , cmap = cmap)
plt.colorbar(cf1,shrink = 0.4)
if dep is not None:
cond1 = ax.tricontour(tri,dep+0.1 ,levels=[0.0] ,colors='k',lw=0.5)
cond2 = ax.tricontour(tri,dep+0.1 ,levels=[50.0],colors='k',lw=1, linestyles='dashed')
if True:
con1 = ax.tricontour(tri,val ,levels=[0.25 * val.max()],colors='k',alpha=1,lw=2)
con2 = ax.tricontour(tri,val ,levels=[0.5] ,colors='r',alpha=1,lw=2)
con3 = ax.tricontour(tri,val ,levels=[0.25] ,colors='g',alpha=1,lw=2)
#plt.clabel(con1, con1.levels,inline=True,fmt='%1.1g',fontsize=6)
#plt.clabel(con2, con2.levels,inline=True,fmt='%1.1g',fontsize=6)
#plt.clabel(con2, con2.levels,inline=True,fmt='%1.1g',fontsize=6)
xtext = extent[0] + 0.6 * (extent[1] - extent[0])
ytext = extent[2] + 0.05 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.25 * Max(Surge) [m] ', color = 'k',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
xtext = extent[0] + 0.75 * (extent[1] - extent[0])
ytext = extent[2] + 0.15 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.5 [m] ' , color = 'r',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
xtext = extent[0] + 0.6 * (extent[1] - extent[0])
ytext = extent[2] + 0.15 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.25 [m] ' , color = 'g',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
def plot_map(ax=None,tri=None,val=None,var=None,lim=None,dep=None, pres=None,no_axes_label=True,plot_mesh=True, scale_bar = True, u=None,v=None):
if lim is not None:
extent = [lim['xmin'],lim['xmax'],lim['ymin'],lim['ymax']]
try:
#if cartopy
ax.set_extent(extent)
except:
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
if var is not None:
cmap = var['cmap']
else:
cmap = cmaps.jetMinWi
# set scale for vertical vector plots
pos_ax = np.array (ax.get_position ())
aheight = pos_ax[1][1] -pos_ax[0][1]
awidth = pos_ax[1][0] -pos_ax[0][0]
#only for vector plot
fig = plt.gcf()
#fwidth = fig.get_size_inches()[0]
#fheight = fig.get_size_inches()[1]
fwidth = 1
fheight = 1
wscale = aheight*fheight/(awidth*fwidth) * (lim['xmax']-lim['xmin'])/(lim['ymax']-lim['ymin'])
#
xsub1 = pos_ax[0][0]
xsub2 = pos_ax[1][0]
ysub1 = pos_ax[0][1]
ysub2 = pos_ax[1][1]
cb_dx = 0.9
cb_dy = 0.3
cb_size = 0.3
cbax = fig.add_axes([xsub1+ cb_dx * (xsub2-xsub1), ysub1 + cb_dy * (ysub2-ysub1), 0.01, cb_size])
vmin = var['vmin']
vmax = var['vmax']
step = 0.2 #m
levels = np.arange(vmin, vmax+step, step=step)
#contour = ax.tricontourf(tri, zeta0,levels=levels,cmap = my_cmap ,extend='max')
cf1 = ax.tricontourf(tri,val,levels=levels, cmap = cmap , extend='both')#extend='max' ) #,extend='both'
cb = plt.colorbar(cf1,cax=cbax,ticks = [vmin,(vmin+vmax)/2,vmax],format='%1.4g',orientation='vertical')
cb.set_label(var['label'])
if base_info.vec:
vecp = ax.quiver(tri.x, tri.y, u, v,
units='xy', scale=10., zorder=3, color='k',
width=0.007, headwidth=3., headlength=4.)
if dep is not None:
cond1 = ax.tricontour(tri,dep+0.1 ,levels=[0.0] ,colors='k',lw=0.02,alpha=0.8)
#cond2 = ax.tricontour(tri,dep+0.1 ,levels=[25.0] ,colors='k',lw=0.2, linestyles='dashed')
#cond3 = ax.tricontour(tri,dep+0.1 ,levels=[50.0] ,colors='k',lw=0.2, linestyles='dashed')
if base_info.plot_mesh:
ax.triplot(tri, 'k-', lw=0.1, alpha=0.6)
if pres is not None:
indp = np.argmin(pres)
#ax.plot(tri.x[indp],tri.y[indp],'ro',alpha=1,ms=8)
#location of Burbuda
#ax.plot(17.57,-61.80,'ko',alpha=1,ms=8,zorder=3)
#cond1 = ax.tricontour(tri,pres ,levels=[pres.min() * 1.1] ,colors='k',lw=1)
try:
if val.sum() !=0:
ind_max = np.array(np.where((val == val.max()))).squeeze().item()
#ax.plot(tri.x[ind_max],tri.y[ind_max],'m*',ms=14)
else:
print ('val is zero !!!')
except:
pass
if False:
con1 = ax.tricontour(tri,val ,levels=[0.25 * val.max()],colors='k',alpha=1,lw=2)
con2 = ax.tricontour(tri,val ,levels=[0.5] ,colors='r',alpha=1,lw=2)
con3 = ax.tricontour(tri,val ,levels=[0.25] ,colors='g',alpha=1,lw=2)
#plt.clabel(con1, con1.levels,inline=True,fmt='%1.1g',fontsize=6)
#plt.clabel(con2, con2.levels,inline=True,fmt='%1.1g',fontsize=6)
#plt.clabel(con2, con2.levels,inline=True,fmt='%1.1g',fontsize=6)
xtext = extent[0] + 0.6 * (extent[1] - extent[0])
ytext = extent[2] + 0.05 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.25 * Max(Surge) [m] ', color = 'k',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
xtext = extent[0] + 0.75 * (extent[1] - extent[0])
ytext = extent[2] + 0.15 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.5 [m] ' , color = 'r',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
xtext = extent[0] + 0.6 * (extent[1] - extent[0])
ytext = extent[2] + 0.15 * (extent[3] - extent[2])
t = ax.text(xtext, ytext , r'0.25 [m] ' , color = 'g',fontsize=7)
t.set_bbox(dict(facecolor='w', alpha=0.75, edgecolor='w'))
plt.setp( ax.xaxis.get_majorticklabels(), rotation=30 )
if scale_bar:
pr.scale_bar(ax, ax.projection, 10)
if no_axes_label:
plt.setp( ax, 'xticklabels', [] )
ax.set_xlabel('')
plt.setp( ax, 'yticklabels', [] )
ax.set_ylabel('')
def datetime64todatetime(dt):
tmp=[]
for it in range(len(dt)):
tmp.append(pd.Timestamp(dt[it]).to_pydatetime())
return np.array(tmp)
def plot_track(ax,track,date=None,color = 'r'):
"""
plot storm track
"""
ax.plot(track['lon'],track['lat'],lw=3,color=color,marker = 'None',ls='dashed',alpha=1)
if date is not None:
dates = np.array(track['dates'])
#ind = np.array(np.where((dates==date))).squeeze().item()
ind = find_nearest_time(dates,date)
ax.plot(track['lon'][ind],track['lat'][ind],marker ='o',color='k', alpha=0.6,ms=8,)
def get_diff(varname,ind0,ind1):
fname = base_info.cases[base_info.key0]['dir'] + defs[varname]['fname']
nc0 = netCDF4.Dataset(fname)
ncv0 = nc0.variables
val0 = ncv0[defs[varname]['var']][ind0]
#
fname = base_info.cases[base_info.key1]['dir'] + defs[varname]['fname']
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
val1 = ncv1[defs[varname]['var']][ind1]
return (val1 - val0)
def get_val(varname,ind1=None):
#
fname = base_info.cases[base_info.key1]['dir'] + defs[varname]['fname']
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
if ind1 is None:
val1 = ncv1[defs[varname]['var']][:]
else:
val1 = ncv1[defs[varname]['var']][ind1]
nc1.close()
return val1
def get_uv(ind1=None):
#
fname = base_info.cases[base_info.key1]['dir'] + 'fort.64.nc'
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
if ind1 is None:
u = ncv1['u-vel'][:]
v = ncv1['v-vel'][:]
else:
u = ncv1['u-vel'][ind1]
v = ncv1['v-vel'][ind1]
nc1.close()
return u,v
def read_track(fname=None):
if fname is None:
fname = '/scratch4/COASTAL/coastal/save/Saeed.Moghimi/models/NEMS/NEMS_inps/01_data/tracks/ike_bal092008.dat'
track = readTrack(fname)
keys = ['dates', 'lon', 'vmax', 'lat']
for key in keys:
tmp = pd.DataFrame(track[key],columns=[key])
#dfh = df
if 'trc' not in locals():
trc = tmp
else:
trc = pd.concat([trc,tmp],axis=1)
trc = trc.drop_duplicates(subset='dates')#,keep='first')
trc = trc.set_index (trc.dates)
trc = trc.resample('H').interpolate()
trc.drop('dates',axis=1,inplace=True)
dates = datetime64todatetime(trc.index)
return dict(dates=dates,lon=trc.lon.values, lat=trc.lat.values)
#################################################################################################
if False:
if base_info.storm_year == 'IKE':
#read IKE track
track = read_track()
include_gustav = False
#read Gustav track
if include_gustav:
gustav_fname = '/scratch4/COASTAL/coastal/save/Saeed.Moghimi/models/NEMS/NEMS_inps/data/tracks/gustav_aal072008.dat'
gus_track = read_track(fname=gustav_fname)
if base_info.storm_year == 'ISA':
print (' oopppss ..')
isabel_fname = '/scratch4/COASTAL/coastal/save/Saeed.Moghimi/models/NEMS/NEMS_inps/01_data/tracks/isabel_aal132003.dat'
track = read_track(fname=isabel_fname)
## back up scripts
out_dir = base_info.out_dir + '/maps/'
scr_dir = out_dir + '/scr/'
os.system('mkdir -p ' + scr_dir)
args=sys.argv
scr_name = args[0]
os.system('cp -fr '+scr_name +' '+scr_dir)
os.system('cp -fr *.py ' + scr_dir)
#
# read base_run only_tide time info
fname = base_info.cases[base_info.key0]['dir'] + '/maxele.63.nc'
nc0 = netCDF4.Dataset(fname)
ncv0 = nc0.variables
depth = ncv0['depth'][:]
depth [depth < -10] = -10
#dates0 = netCDF4.num2date(ncv0['time'][:],ncv0['time'].units)
#indd0 = np.array(np.where((dates0 > base_info.tim_lim['xmin'] )&(dates0<base_info.tim_lim['xmax']) )).squeeze()
lon = ncv0['x'][:]
lat = ncv0['y'][:]
elems = ncv0['element'][:,:]-1 # Move to 0-indexing by subtracting 1
nc0.close()
#
fname = base_info.cases[base_info.key1]['dir'] + '/maxele.63.nc'
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
nc1.close()
#
#construct HSOFS Tri mask
#lon,lat,tri = adcp.ReadTri(base_info.cases[base_info.key0]['dir'])
tri = Tri.Triangulation(lon,lat, triangles=elems)
#
print (base_info.cases[base_info.key1]['dir'])
#
track = read_track(fname=base_info.track_fname)
if base_info.plot_nwm_files:
#read channel points locations
geo_info = netCDF4.Dataset(base_info.nwm_channel_pnts)
geo_ncv = geo_info.variables
lat_nwm = geo_ncv['latitude'][:]
lon_nwm = geo_ncv['longitude'][:]
# read channel connections
geo_poly_nwm = pd.read_csv(base_info.nwm_channel_geom)
#for region in ['hsofs_region','ike_region','ike_local']:
#for region in ['ike_region']:
#for varname in ['elev']:
if base_info.plot_adc_fort:
fname = base_info.cases[base_info.key1]['dir'] + '/fort.63.nc'
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
dates1 = netCDF4.num2date(ncv1['time'][:],ncv1['time'].units)
indd1 = np.array(np.where((dates1 > base_info.tim_lim['xmin'] )&(dates1<base_info.tim_lim['xmax']) )).squeeze()
nc1.close()
for varname in base_info.varnames:
for ind1 in indd1[::-1][:]:
print ('-------------------------------------------')
for region in base_info.regions:
lim = get_region_extent(region = region)
date = dates1[ind1]
# find matching index for new netcdf file
print (ind1, region ,varname)#, date)
try:
pres = get_val('pmsl',ind1)
except:
pres = None
pass
title1 = base_info.cases[base_info.key1]['label']
if varname in ['elev','u','v']:
if base_info.dif:
ind0 = np.array(np.where((dates0== date))).squeeze().item()
val = get_diff(varname,ind0,ind1)
title1 = base_info.cases[base_info.key1]['label']+' - ' +\
base_info.cases[base_info.key0]['label']
else:
val = get_val(varname,ind1)
if base_info.vec:
u,v = get_uv(ind1=None)
if varname in ['rad']:
radx = 'radstress_x'
rady = 'radstress_y'
#
defs[varname]['var'] = radx
valx = get_val(varname,ind1)
defs[varname]['var'] = rady
valy = get_val(varname,ind1)
val = np.sqrt(valx*valx+valy*valy)
if varname in ['wind']:
radx = 'windx'
rady = 'windy'
#
defs[varname]['var'] = radx
valx = get_val(varname,ind1)
defs[varname]['var'] = rady
valy = get_val(varname,ind1)
val = np.sqrt(valx*valx+valy*valy)
#if 'max' in varname:
# defs['maxelev'][]
val[np.isnan(val)] = 0.0
fig, ax = pr.make_map()
#fig = plt.figure()
dx = abs(lim['xmin'] - lim['xmax'])
dy = abs(lim['ymin'] - lim['ymax'])
fig.set_size_inches(9,9*1.4*dy/dx)
#plot_map (ax,tri,val,extent,vmin=vmin,vmax=vmax, dep=dep , cmap = cmap)
#sys.exit()
if not base_info.vec:
plot_map (ax=ax,tri=tri,val=val,var=defs[varname],lim=lim,dep=depth, pres=pres,plot_mesh=base_info.plot_mesh)
else:
plot_map (ax=ax,tri=tri,val=val,var=defs[varname],lim=lim,dep=depth, pres=pres,plot_mesh=base_info.plot_mesh,u=u,v=v)
#track = readTrack(base_info.track_fname)
#ax.plot(track['lon'],track['lat'],'r-',lw=2,alpha=1,ms=10, markevery = 5, marker = r'$\bigotimes$',mec='None',mew=2) #marker = r'$\S$'
plot_track(ax,track,date=date, color= 'r')
#find val max in the bounding box
[ind_box] = np.where((lon > defs['lim']['xmin']) &
(lon < defs['lim']['xmax']) &
(lat > defs['lim']['ymin']) &
(lat < defs['lim']['ymax']))
ind_max = np.argmax(val[ind_box])
lon_max = lon[ind_box][ind_max]
lat_max = lat[ind_box][ind_max]
val_max = val[ind_box][ind_max]
ax.scatter(lon_max,lat_max,c='m',s=15,zorder=3)
#ax.set_title(base_info.storm_name + ' ' +base_info.storm_year+' '+base_info.cases[base_info.key1]['label']+' - ' +\
# base_info.cases[base_info.key0]['label']+'\n '+defs[varname]['label'] +\
# ' Date: ' + date.isoformat() +'\n Max. Val. = %.2g' %val.max() + '[m]')
ax.set_title(base_info.name + ' ' +base_info.year+' '+title1+'\n'+ ' Date: ' + date.isoformat() +'\n'+' Max. Val. = %.2g' %val_max)
date_str = date.isoformat().replace(':','-')
del val
out_dir1 = out_dir + '/' + region + '_' + varname
os.system('mkdir -p '+ out_dir1)
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
filename = out_dir1 + '/adc_fort_files_'+region+ '_' +varname.capitalize()+str(10000+ind1)+'_'+date_str+'.png'
plt.savefig(filename,dpi=150)
#plt.show()
plt.close('all')
os.system('cp -fr '+scr_name +' '+out_dir1)
os.system('cp -fr base_info*.py '+out_dir1)
#else:
if base_info.plot_adc_maxele:
varname = 'elev'
for region in base_info.regions:
print ('Maxele diff ', region)
lim = get_region_extent(region = region)
print (lim)
fname = base_info.cases[base_info.key0]['dir'] + '/maxele.63.nc'
nc0 = netCDF4.Dataset(fname)
ncv0 = nc0.variables
zeta0 = ncv0['zeta_max'][:]
dep0 = ncv0['depth'][:]
lon0 = ncv0['x'][:]
lat0 = ncv0['y'][:]
elems = ncv0['element'][:,:]-1 # Move to 0-indexing by subtracting 1
zeta0[zeta0.mask] = 0.0
tri = Tri.Triangulation(lon0,lat0, triangles=elems)
date = None
##
fname = base_info.cases[base_info.key1]['dir'] + '/maxele.63.nc'
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
zeta1 = ncv1['zeta_max'][:]
mask = zeta1 < 0
val = zeta1 - zeta0 * 0
val = np.ma.masked_where(val<0,val)
val[val==mask] = 0.0
val[np.isnan(val)] = 0.0
val[val.mask] = 0.0
fig, ax = pr.make_map()
if False:
vmin = base_info.defs['elev']['vmin']
vmax = base_info.defs['elev']['vmax']
step = 0.5 #m
levels = np.arange(vmin, vmax+step, step=step)
#contour = ax.tricontourf(tri, zeta0,levels=levels,cmap = my_cmap ,extend='max')
cf1 = ax.tricontourf(tri,zeta0,levels=levels, cmap = cmaps.jetMinWi, extend='both')#extend='max' ) #,extend='both'
# set scale for vertical vector plots
pos_ax = np.array (ax.get_position ())
aheight = pos_ax[1][1] -pos_ax[0][1]
awidth = pos_ax[1][0] -pos_ax[0][0]
fwidth = 1
fheight = 1
wscale = aheight*fheight/(awidth*fwidth) * (lim['xmax']-lim['xmin'])/(lim['ymax']-lim['ymin'])
#
xsub1 = pos_ax[0][0]
xsub2 = pos_ax[1][0]
ysub1 = pos_ax[0][1]
ysub2 = pos_ax[1][1]
cb_dx = 0.9
cb_dy = 0.3
cb_size = 0.3
cbax = fig.add_axes([xsub1+ cb_dx * (xsub2-xsub1), ysub1 + cb_dy * (ysub2-ysub1), 0.01, cb_size])
cb = plt.colorbar(cf1,cax=cbax,ticks = [vmin,(vmin+vmax)/2,vmax],format='%1.4g',orientation='vertical')
dx = abs(lim['xmin'] - lim['xmax'])
dy = abs(lim['ymin'] - lim['ymax'])
fig.set_size_inches(9,9*1.4*dy/dx)
#fig.set_size_inches(9,9)
plot_map (ax=ax,tri=tri,val=val,var=defs[varname],lim=lim,dep=depth, pres=None,plot_mesh=base_info.plot_mesh)
#sys.exit()
plot_track(ax,track,date=None, color= 'r')
#find val max in the bounding box
[ind_box] = np.where((lon > lim['xmin']) &
(lon < lim['xmax']) &
(lat > lim['ymin']) &
(lat < lim['ymax']))
ind_max = np.argmax(val[ind_box])
lon_max = lon[ind_box] [ind_max]
lat_max = lat[ind_box] [ind_max]
val_max = val[ind_box] [ind_max]
if False:
#plot maximum surge location
ax.scatter(lon_max,lat_max,c='m',s=15,zorder=3)
out_dir1 = out_dir + '/' + region + '_maxelev_'
os.system('mkdir -p '+ out_dir1)
ax.set_title(base_info.name + ' ' +base_info.year+' '+base_info.cases[base_info.key1]['label']+' - ' +\
base_info.cases[base_info.key0]['label'])#+'\n '+defs[varname]['label'] \
#+'\n '+' Max. Val. = %.2g' %val.max() + '[m]' )
if base_info.plot_nwm_files:
#need to be based on date or/XX
chan = netCDF4.Dataset(base_info.nwm_results_dir + '/short_range/' + 'nwm.t02z.short_range.channel_rt.f018.conus.nc')
chan_ncv = chan.variables
f_ids = chan_ncv['feature_id'][:]
streamflow = chan_ncv['streamflow'][:]
df = geo_poly_nwm[(geo_poly_nwm['xlat_mid'] > lim['xmin']) & (geo_poly_nwm['xlat_mid'] < lim['xmax']) & (geo_poly_nwm['ylon_mid'] > lim['ymin'])& (geo_poly_nwm['ylon_mid'] < lim['ymax'])]
mask = (lon_nwm > lim['xmin']) & (lon_nwm < lim['xmax']) & (lat_nwm > lim['ymin'])& (lat_nwm < lim['ymax'])
lon_df = lon_nwm[mask]
lat_df = lat_nwm[mask]
#ax.plot(lon_df,lat_df,'o',color = 'k',ms=0.5, lw=0.5,label= str(df.loc[fid]['feature_id']))
all_ind = []
for fid in df['FID']:
[ind] = np.where(f_ids==df.loc[fid]['feature_id'])
all_ind.append(ind.item())
df['streamflow'] = streamflow[all_ind]
df_sorted = df.sort('streamflow')
df_sorted [df_sorted['streamflow']<10] = np.nan
df_sorted = df_sorted.dropna()
lws = np.linspace(0.25,4,len(df_sorted))
il = 0
for fid in df_sorted['FID']:
lontmp = [df_sorted.loc[fid]['xlat_up'],df_sorted.loc[fid]['xlat_mid'],df_sorted.loc[fid]['xlat']]
lattmp = [df_sorted.loc[fid]['ylon_up'],df_sorted.loc[fid]['ylon_mid'],df_sorted.loc[fid]['ylon']]
ax.plot(lontmp,lattmp,color='b', lw = lws [il] ,label= str(df_sorted.loc[fid]['feature_id']),alpha = 0.6)
il += 1
#q_lateral = chan_ncv['q_lateral'][:]
#velocity = chan_ncv['velocity'][:]
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
for ftype in ftypes:
filename = out_dir1 + '/maxele'+region+ '_maxelev' +'.' + ftype
plt.savefig(filename,dpi=600)
#plt.show()
#sys.exit()
plt.close('all')
if base_info.plot_nems_fields:
#varname = 'sxx'
for varname in base_info.varnames:
if varname == 'pmsl':
file_piece = '/field_ocn_pmsl*'
if varname == 'sxx':
file_piece = '/field_ocn_sxx*'
defs[varname] = {}
defs[varname]['cmap'] = cmaps.jetMinWi
defs[varname]['label'] = 'Sxx'
plist = np.sort(glob.glob( base_info.cases[base_info.key1]['dir'] + file_piece))
for ind1 in range(0,len(plist)-1)[::-1]:
for region in base_info.regions:
#for region in ['hsofs_region']:
lim = get_region_extent(region = region)
fname = plist[ind1]
date = parser.parse(fname[-22:-3])
print (date)
if ((date> base_info.tim_lim['xmin'] ) &(date<base_info.tim_lim['xmax'])):
print (region, fname)
#
nc = netCDF4.Dataset(fname)
val = nc.variables[varname][:]
#
### NOUPC pressure
if varname == 'sxx':
defs[varname]['vmin'] = 0
defs[varname]['vmax'] = 5000
val[val > 1e10] = 0.0
if varname == 'pmsl':
#defs[varname]['vmin'] = 92000
#defs[varname]['vmax'] = 99000
defs[varname]['vmin'] = 92000
defs[varname]['vmax'] = 105000
### plot stuff
fig, ax = adcp.make_map()
dx = abs(lim['xmin'] - lim['xmax'])
dy = abs(lim['ymin'] - lim['ymax'])
fig.set_size_inches(9,9*1.4*dy/dx)
plot_map (ax=ax,tri=tri,val=val,var=defs[varname],lim=lim,dep=depth)
plot_track(ax,track,date=date)
title1 = base_info.storm_year + \
'\n'+varname +' Date: ' + date.isoformat() +'\n Max. Val. = %.2g' %val.max()
#base_info.cases[base_info.key1]['label']+', '+defs[varname]['label'] +\
ax.set_title(title1)
date_str = date.isoformat().replace(':','-')
del val
out_dir1 = out_dir + '/nems_fields_' + region + '_' + varname
os.system('mkdir -p '+ out_dir1)
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
filename = out_dir1 + '/'+region+ '_' +varname.capitalize()+str(10000+ind1)+'_'+date_str+'.png'
plt.savefig(filename,dpi=450)
#plt.show()
plt.close('all')
nc.close()
os.system('cp -fr '+scr_name +' '+out_dir1)
os.system('cp -fr base_iny '+out_dir1)
if base_info.plot_forcing_files:
#varname = 'pmsl'
#varname = 'hs'
for varname in base_info.varnames:
if varname in ['pmsl','wind']:
print ('Plot HWRF input file ..')
file_piece = '/inp_atmesh/*.nc'
plist = np.sort(glob.glob( base_info.cases[base_info.key1]['dir'] + file_piece))
fname = plist[0]
nch = netCDF4.Dataset(fname)
nchv = nch.variables
uwnd = nchv['uwnd']
vwnd = nchv['vwnd']
pres = nchv['P']
dates = netCDF4.num2date(nchv['time'][:],nchv['time'].units)
if varname == 'hs':
print ('Plot Hs WW3 Res file ..' )
fname = base_info.cases[base_info.key1]['hsig_file']
nch = netCDF4.Dataset(fname)
nchv = nch.variables
hs = nchv['hs']
dates = netCDF4.num2date(nchv['time'][:],nchv['time'].units)
for ind1 in range(0,len(dates)-1)[::-1]:
for region in base_info.regions:
lim = get_region_extent(region = region)
date = dates[ind1]
date = roundTime(dt=date, dateDelta=datetime.timedelta(minutes=1))
print (date)
if ((date> base_info.tim_lim['xmin'] ) &(date<base_info.tim_lim['xmax'])):
print (region, fname)
#
#
### NOUPC pressure
if varname == 'sxx':
defs[varname]['vmin'] = 0
defs[varname]['vmax'] = 5000
val[val > 1e10] = 0.0
if varname == 'pmsl':
val = pres[ind1]
if varname == 'hs':
val = hs[ind1]
#defs[varname]['vmin'] = 0
#defs[varname]['vmax'] = 16 ### plot stuff
#defs[varname]['vmax'] = val.max() // 5 * 5 ### plot stuff
if varname in ['wind']:
valx = uwnd[ind1]
valy = vwnd[ind1]
val = np.sqrt(valx*valx+valy*valy)
fig, ax = adcp.make_map()
dx = abs(lim['xmin'] - lim['xmax'])
dy = abs(lim['ymin'] - lim['ymax'])
fig.set_size_inches(9,9*1.4*dy/dx)
plot_map (ax=ax,tri=tri,val=val,var=defs[varname],lim=lim,dep=depth,plot_mesh=base_info.plot_mesh)
plot_track(ax,track,date=date,color = 'r')
ax.set_title(base_info.storm_name + ' ' +base_info.storm_year +' '+ base_info.cases[base_info.key1]['label']+\
'\n'+varname +' Date: ' + date.isoformat() +'\n Max. Val. = %4.4g' %val.max() )
date_str = date.isoformat().replace(':','-')
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
del val
out_dir1 = out_dir + '/forcings_' + region + '_' + varname
os.system('mkdir -p '+ out_dir1)
filename = out_dir1 + '/'+region+ '_' +varname.capitalize()+str(10000+ind1)+'_'+date_str+'.png'
plt.savefig(filename,dpi=300)
#plt.show()
plt.close('all')
nch.close()
os.system('cp -fr '+scr_name +' '+out_dir1)
os.system('cp -fr *.py '+out_dir1)
if False:
indd = range(0,len(dates)-1)[::-1]
for ind1 in indd[::-1]:
date =dates[ind1]
date_str = date.isoformat()
print (date_str)
fig, ax = adcp.make_map()
fig.set_size_inches(7,7)
extent = [lim['xmin'],lim['xmax'],lim['ymin'],lim['ymax']]
ax.set_extent(extent)
ind_time = find_nearest_time(dates,date)
color = 'red'
#ax.plot(track['lon'][ind_time],track['lat'][ind_time],'ro',alpha=1,ms=8)
#ax.plot(track['lon'],track['lat'],lw=3,color=color,marker = '*', ms = 2, ls='dashed',alpha=1)
plot_track(ax,track,date=date)
out_dir1 = out_dir + '/atmesh_only_track_'+'_' + region
os.system('mkdir -p '+ out_dir1)
filename = out_dir1 + '/'+region+ '_' +varname.capitalize()+str(10000+ind1)+'_'+date_str+'.png'
plt.savefig(filename,dpi=450)
plt.close('all')
"""
varname = 'rad'
fname = base_info.cases[base_info.key1]['dir'] + defs[varname]['fname']
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
radx_2d = ncv1[defs[varname]['var']+'_y'][214]
vmax,vmin=1e-4,-1e-4
dv = (vmax-vmin)/50.0
levels=np.arange(vmin,vmax+dv,dv)
tricontourf(tri,radx_2d,levels=levels,cmap=maps.jetWoGn())
latp = 29.2038
lonp = -92.2285
#613979
radxx = ncv1[defs[varname]['var']+'_y'][:,613979]
plt.figure()
plt.plot(radxx[2:])
radxx = ncv1[defs[varname]['var']+'_y'][:,613978]
plt.plot(radxx[2:])
fname = cases[key0]['dir'] + '/maxele.63.nc'
nc0 = netCDF4.Dataset(fname)
ncv0 = nc0.variables
zeta0 = ncv0['zeta_max'][:]
dep0 = ncv0['depth'][:]
date = None
##
fname = cases[key1]['dir'] + '/maxele.63.nc'
nc1 = netCDF4.Dataset(fname)
ncv1 = nc1.variables
zeta1 = ncv1['zeta_max'][:]
mask = zeta1 < 0
#tri.set_mask = maskDryElementsTri(tri,mask)
val = zeta1 - zeta0
#val = np.ma.masked_where(val<0,val)
if False:
fig, ax = adcp.make_map()
fig.set_size_inches(7,7)
else:
fig = plt.figure()
fig.set_size_inches(7,7)
ax = plt.gca()
#lon0 = -101.25
#lon1 = -74.53125
#lat0 = 13.9234038977
#lat1 = 33.1375511923
#fpng = 'lon0_lat0_lon1_lat1_-101.25_-74.53125_13.9234038977_33.1375511923.png'