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plot_series_hindcast.py
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plot_series_hindcast.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 7 16:41:59 2018
@author: siirias
"""
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 3 14:11:45 2018
@author: siirias
"""
import datetime
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import netcdf
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import Basemap, shiftgrid, cm
from netCDF4 import Dataset
from smartseahelper import smh
import os
import cmocean
import re
startdate=datetime.datetime(1985,1,1)
enddate=datetime.datetime(1986,12,31)
ss=smh()
ss.grid_type='T'
ss.interwall='d'
ss.save_interval='year'
ss.main_data_folder= ss.root_data_in+"/tmp/ice_test/low_pstar/"
#ss.main_data_folder= ss.root_data_in+"/REANALYSIS/"
ss.file_name_format="SS-GOB_1{}_{}_{}_grid_{}.nc"
#ss.file_name_format="NORDIC-GoB_1{}_{}_{}_grid_{}.nc"
datadir = ss.root_data_out+"/tmp/ice_test/figures2/" #where everyt output is stored
image_per_month={'h':24*30.5,'d':30.5,'m':1}
name_marker="testing"
compare_two=False
other_name_marker='x'
var1 = 'ICE' #'VEL', 'SST', 'SSS'
combined_name_markers=name_marker
if(compare_two):
combined_name_markers="{}-{}".format(name_marker,other_name_marker)
if '1' in name_marker: #the 001 series are hindcasts, all other scenarios
series_name='Hindcast_{}_{}'.format(combined_name_markers,var1)
else:
series_name='Scenario_{}_{}'.format(combined_name_markers,var1)
#Let's try to plot soemthing to start with:
lon_min=16;lat_min=60;lon_max=26.01;lat_max=66.01;
running_number=0
show_contours=True
plotted_depth=0. #meters
var2 = None #None, 'icecon'
var2_min=0.0
var2_max=1.0
var2_cm='gray'
if var1 in ['VEL']:
var_min=0.0
var_max=0.15
var1_cm=cmocean.cm.speed
var1_cm=cmocean.cm.speed
ss.grid_type='U' #U,V,T
ss.interval='m'
plotted_depth=-10000. #meters
var2=None
show_contours=False
if var1 in ['SST']:
var_min=-0.5
var_max=20.0
var1_cm=cmocean.cm.thermal
ss.grid_type='T' #U,V,T
ss.interval='d'
if(compare_two):
var_min=-5.0
var_max=5.0
if var1 in ['SSH']:
var_min=1.0
var_max=6.0
var1_cm=cmocean.cm.haline
ss.grid_type='T' #U,V,T
ss.interval='d'
if var1 in ['ICE']:
var_min=0.0
var_max=2.0
var1_cm=cmocean.cm.haline
ss.grid_type='T' #U,V,T
ss.interval='d'
font_size=10.0
resolution='h'
projection='laea'
just_one=False
filenames=ss.filenames_between(startdate,enddate)
ok_files=0
files_working=[]
for f in filenames:
if(os.path.isfile(ss.main_data_folder+f)):
ok_files+=1
files_working.append(f)
else:
print(f)
print()
print("ok {} out of {}".format(ok_files,len(filenames)))
if compare_two:
real_main_data_folder=ss.main_data_folder
ss.main_data_folder= ss.root_data_in+"/OUTPUT{}/".format(other_name_marker)
filenames=ss.filenames_between(startdate,enddate)
ok_files=0
other_files_working=[]
for f in filenames:
if(os.path.isfile(ss.main_data_folder+f)):
ok_files+=1
other_files_working.append(f)
else:
print(f)
other_main_data_folder=ss.main_data_folder
ss.main_data_folder=real_main_data_folder
print()
print("ok {} out of {}".format(ok_files,len(filenames)))
def give_bottom_values(array4d):
bottom_index=(array4d[0,:,:,:]!=0.0).sum(axis=0)-1 #first axis time, then depth, x,y
bottom_index[bottom_index<0]=0
values=array4d[:,0,:,:].copy()
for i in range(values.shape[1]):
for j in range(values.shape[2]):
if ~bottom_index.mask[i,j]:
values[:,i,j]=array4d[:,bottom_index[i,j],i,j]
return values
if(just_one):
files_working=[files_working[0]]
#first setup teh main map:
fig=plt.figure(figsize=(6,6))
if projection in ['laea']:
lat_0=0.5*(lat_min+lat_max)
lon_0=0.5*(lon_min+lon_max)
bmap = Basemap(llcrnrlon=lon_min,llcrnrlat=lat_min,urcrnrlon=lon_max,urcrnrlat=lat_max, \
lat_0=lat_0, lon_0=lon_0,resolution = resolution,
projection=projection,fix_aspect=False) #resolution c,l,i,h,f
elif projection in ['merc','cyl']:
bmap = Basemap(llcrnrlon=lon_min,llcrnrlat=lat_min,urcrnrlon=lon_max,urcrnrlat=lat_max, \
resolution = resolution,
projection=projection,fix_aspect=False) #resolution c,l,i,h,f
bmap.drawcoastlines(zorder=21,linewidth=0.5,color='gray')
#bmap.fillcontinents([0.9,0.9,0.9],lake_color=[0.85,0.85,0.85],zorder=20)
bmap.drawparallels(np.arange(lat_min,lat_max,1.),linewidth=1,zorder=50,labels=[True,False,False,False],dashes=[1,0],color="#00000020",fontsize=10)
bmap.drawmeridians(np.arange(lon_min,lon_max,2.),linewidth=1,zorder=50,labels=[False,False,False,True],dashes=[1,0],color="#00000020",fontsize=10)
is_first=True
if not compare_two:
other_files_working=files_working
for f, other_f in zip(files_working,other_files_working):
data=Dataset(ss.main_data_folder+f)
if(compare_two):
other_data=Dataset(other_main_data_folder+other_f)
if(var1 in ['SST','SSH']):
d=data.variables[var1][:]
d=np.ma.masked_where(d==0.0,d)
if(var1 in ['ICE']):
if('icevolume' in data.variables.keys()):
d=data.variables['icevolume'][:]
else:
d=data.variables['icethic'][:]*data.variables['icecon']
d=np.ma.masked_where(d==0.0,d)
if(var1 in ['VEL']):
data2=Dataset(ss.main_data_folder+re.sub("_grid_.","_grid_V",f))
if(plotted_depth>=0):
depth_index=np.abs(data.variables['depthu'][:]-plotted_depth).argmin()
d=data.variables['uos'][:,depth_index,:,:]
d2=data2.variables['vos'][:,depth_index,:,:]
else: #negative plotted depth measn bottom.
d=give_bottom_values(data.variables['uos'][:,:,:,:])
d2=data2.variables['vos'][:,:,:]
d=np.ma.masked_where(d==0.0,d)
#this one requires that we take another file too:
d2=np.ma.masked_where(d==0.0,d)
d=np.sqrt(d*d+d2*d2)
data2.close()
if(var2 is not None):
ice_d=data.variables[var2][:]
ice_d=np.ma.masked_where(ice_d<0.2,ice_d)
lons = data.variables['nav_lon'][:]
lats = data.variables['nav_lat'][:]
lats,lons=ss.fix_latslons(lats,lons)
times=data.variables['time_counter'][:].data
if compare_two:
d=d-other_data.variables[var1][:]
other_data.close()
data.close()
if(just_one):
times=[times[0]]
for time_frame in range(len(times)):
time=ss.nemo_time_to_datetime(times[time_frame])
tmp_lon,tmp_lat=bmap(lons,lats)
#cb=plt.colorbar(fraction=0.027, pad=0.01)
colors_fig=bmap.pcolormesh(tmp_lon,tmp_lat,d[time_frame,:,:],vmin=var_min,vmax=var_max,zorder=-3,cmap=var1_cm)
if is_first:
cb=plt.colorbar()
cb.set_clim(vmin=var_min,vmax=var_max)
cb.ax.tick_params(labelsize=font_size)
is_first=False
if(var2 is not None):
ice_fig=bmap.pcolormesh(tmp_lon,tmp_lat,ice_d[time_frame,:,:],vmin=var2_min,vmax=var2_max,zorder=16,cmap=var2_cm)
if show_contours:
cont_fig=bmap.contour(tmp_lon,tmp_lat,d[time_frame,:,:],vmin=var_min,vmax=var_max,zorder=15,colors='black',linewidths=0.5,alpha=0.5)
cont_labels=plt.clabel(cont_fig,inline=1,fontsize=5,fmt="%1.1f")
annotation=plt.annotate(time.strftime("%Y-%m-%d"),xy=(0.25, 0.95), xycoords='axes fraction',zorder=100)
plt.savefig("{}{}{:05d}.png".format(datadir,series_name,running_number),facecolor='w',dpi=300)
if(not just_one):
#clean up the changing things, so we don't have to do everything again, just these:
colors_fig.remove()
if(var2 is not None):
ice_fig.remove()
annotation.remove()
if show_contours:
for i in cont_labels:
i.remove()
for i in cont_fig.collections:
i.remove()
# plt.close(fig)
running_number+=1
print("Image {} of (approx){} done.".format(running_number,ss.give_file_interval('moths')*len(files_working)*image_per_month[ss.interval]))