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MET_waves.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 8 08:58:23 2021
@author: KonstantinChri
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import xarray as xr
import ecco_v4_py as ecco
import pandas as pd
import numpy as np
import time
import os
from nco import Nco
from cdo import Cdo
def estimate_WEF(hs, tp):
WEF = 0.5*(hs**2)*(0.85*tp) # KW/m
return WEF
def estimate_WPD(wnd):
WPD = 0.5*1.225*(wnd**3)
return WPD
def distance_2points(lat1, lon1, lat2, lon2):
R = 6371.0
lat1 = np.radians(lat1)
lon1 = np.radians(lon1)
lat2 = np.radians(lat2)
lon2 = np.radians(lon2)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat / 2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2)**2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
distance = R * c # in km
return distance
def find_nearest(lon_model, lat_model, lat0, lon0):
#print('find nearest point...')
dx = distance_2points(lat0, lon0, lat_model, lon_model)
rlat0 = dx.where(dx == dx.min(), drop=True).rlat
rlon0 = dx.where(dx == dx.min(), drop=True).rlon
return rlon0, rlat0
def url_agg(product):
if product == 'NORA3':
url = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_agg/wam3kmhindcastaggregated.ncml'
elif product == 'WAM4':
url = 'https://thredds.met.no/thredds/dodsC/sea/mywavewam4/mywavewam4_be'
elif product == 'WAM4C47':
url = 'https://thredds.met.no/thredds/dodsC/sea/mywavewam4/mywavewam4_c47_be'
return url
def plot_grid_spec_points(url,s=0.5,color='red'):
"""
Plot all grid points where spectrum is available in NORA3/WAM4/WW3 datasets (https://thredds.met.no)
url : link or nc-file for NORA3/WAM4/WW3 dataset
e.g., url for NORA3: https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_spectra/2020/12/SPC2020123100.nc
url for WAM4: https://thredds.met.no/thredds/dodsC/fou-hi/mywavewam4archive/2022/05/24/MyWave_wam4_SPC_20220524T00Z.nc
url for WW3: https://thredds.met.no/thredds/dodsC/ww3_4km_latest_files/ww3_POI_SPC_20220524T12Z.nc
s : marker size
color: marker color
"""
data = xr.open_dataset(url)
fig, ax = plt.subplots()
ax = plt.axes(projection=ccrs.Orthographic(-10, 45))
ax.stock_img()
#ax.add_feature(cfeature.LAND,color='darkkhaki')
#ax.add_feature(cfeature.LAKES)
#ax.coastlines(resolution='50m', color='darkkhaki', linewidth=0.5)
ax.scatter(data.longitude,data.latitude,s=s, marker='.', color=color, transform=ccrs.PlateCarree())
plt.title('Grid Points (2D Spectrum)',fontsize=18)
plt.show()
plt.savefig('spec_points.png',dpi=300)
return fig, ax
def plot_NorthPolarStereo(product, var,lon, lat, min_value,max_value,cmap,ax=None):
if ax is None:
fig, ax = plt.subplots()
plt.axes(projection=ccrs.NorthPolarStereo(
true_scale_latitude=70))
ecco.plot_proj_to_latlon_grid(lon, lat,
var,
projection_type='stereo',
plot_type='contourf',
show_colorbar=True,
cmap=cmap,
dx=1, dy=1, cmin=min_value, cmax=max_value,
lat_lim=50)
if ax is None:
return fig, ax
else:
return ax
def plot_timeseries(start_time, end_time, lon, lat, product, variable, write_csv, ts_obs, **plotargs):
start = time.time()
date_list = pd.date_range(start=start_time, end=end_time, freq='H')
df = pd.DataFrame({'time': date_list, variable: np.zeros(len(date_list))})
df = df.set_index('time')
url = url_agg(product=product)
ds = xr.open_dataset(url)
units = ds[variable].units
print('Find nearest point to lon.='+str(lon)+','+'lat.='+str(lat))
rlon, rlat = find_nearest(ds.longitude, ds.latitude, lat, lon)
lon_near = ds.longitude.sel(rlat=rlat, rlon=rlon).values[0][0]
lat_near = ds.latitude.sel(rlat=rlat, rlon=rlon).values[0][0]
print('Found nearest: lon.='+str(lon_near)+',lat=' + str(lat_near))
print('Extract time series...:'+str(start_time)+'--->'+str(end_time))
for i in range(len(date_list)):
df[variable][i] = ds[variable].sel(
rlat=rlat, rlon=rlon).loc[date_list[i]].values[0][0]
if write_csv is True:
print('Write data to file...')
df.to_csv(variable+'_'+product+'_lon'
+ str(lon_near)+'_lat'+str(lat_near)+start_time + '-'
+ end_time+'.csv')
else:
pass
print('Plot time series...')
fig, ax = plt.subplots(**plotargs)
df.plot(ax=ax)
if ts_obs is not None:
ts_obs.plot(ax=ax)
ax.legend([product,'obs.'])
else:
ax.legend([product])
ax.grid()
ax.set_ylabel(variable+' ['+units+']', fontsize=14)
ax.set_title(product+',lon.='
+ str(lon_near)+',lat.='+str(lat_near), fontsize=16)
plt.savefig(variable+'_'+product+'_lon'
+ str(lon_near)+'_lat'+str(lat_near)+start_time + '-'
+ end_time+'.png', bbox_inches='tight')
end = time.time()
hours, rem = divmod(end-start, 3600)
minutes, seconds = divmod(rem, 60)
print("{:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds))
return
def plot_panarctic_map(start_time, end_time, product, variable, method, cmap='jet'):
"""
Plots in a panarctic map a given variable
start_time = start date for plotting e.g., '2005-01-07T18'
end_time = end date for plotting e.g., '2005-01-09T18'
Product: 'nora3' / 'wam4'
variable: e.g., 'hs', 'tp', 'ff' for wind
method: 'timestep' for plotting all timesteps for given period or 'mean'
Overview of the NORAE3 wave variables is given in:
https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_agg/wam3kmhindcastaggregated.ncml.html
"""
url = url_agg(product=product)
date_list = pd.date_range(start=start_time, end=end_time, freq='H')
#var = xr.open_dataset(url)[variable].sel(time=slice(start_time, end_time))
if variable == 'WEF':
var = estimate_WEF(xr.open_dataset(url)['hs'].sel(time=slice(start_time, end_time)), xr.open_dataset(url)['tp'].sel(time=slice(start_time, end_time)))
else:
var = xr.open_dataset(url)[variable]
var = var.sel(time=~var.get_index("time").duplicated())
var = var.sel(time=slice(start_time, end_time))
lon = xr.open_dataset(url).longitude
lat = xr.open_dataset(url).latitude
if method == 'timestep':
min_value = var.min()
max_value = var.max()
#print(max_value)
for i in range(len(date_list)):
print(date_list[i])
fig, ax = plt.subplots()
ax = plot_NorthPolarStereo(product=product,
var=var.loc[date_list[i]],lon=lon, lat=lat,
min_value=min_value,max_value=max_value,
cmap=cmap, ax=ax)
plt.title(variable+'['+var.units+']'+','+product+','+str(date_list[i])+'UTC')
plt.savefig(variable+str(date_list[i])+'.png', bbox_inches='tight')
plt.close()
elif method == 'mean':
ax = plot_NorthPolarStereo(product=product,
var=var.mean('time'),lon=lon, lat=lat,
min_value=var.mean('time').min(),
max_value=var.mean('time').max(),
cmap=cmap)
plt.title(product+',Mean:'+start_time+'--'+end_time)
plt.savefig(variable+'_Mean_'+start_time+'-'+end_time+'.png', bbox_inches='tight',dpi=300)
plt.close()
def get_url(product, day):
if product == 'SPEC_NORA3':
url = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_spectra/' + \
day.strftime('%Y') + '/'+day.strftime('%m') + \
'/SPC'+day.strftime('%Y%m%d')+'00.nc'
elif product == 'SPEC_WW3':
url = 'https://thredds.met.no/thredds/dodsC/ww3_4km_latest_files/' + \
'ww3_POI_SPC_'+day.strftime('%Y%m%d')+'T00Z.nc'
elif product == 'NORA3':
url = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_files/' + \
day.strftime('%Y') + '/'+day.strftime('%m') + '/' + \
day.strftime('%Y%m%d')+'_MyWam3km_hindcast.nc'
return url
def plot_2D_spectra(start_time, end_time, lon, lat, product):
"""
Plot 2D spectra for a given time period
start_time : start date for plotting e.g., '2005-01-07T18'
end_time : end date for plotting e.g., '2005-01-09T18'
lon : longtitude
lat latitude
product: 'SPEC_NORA3' / 'SPEC_WW3'
"""
data = []
date_list = pd.date_range(start=start_time, end=end_time, freq='D')
for k in range(len(date_list)): # loop over days
url = get_url(product, date_list[k])
ds = xr.open_dataset(url)
# Find the nearest grid point.
abslat = np.abs(ds.latitude-lat)
abslon = np.abs(ds.longitude-lon)
c = np.maximum(abslon, abslat)
([xloc], [yloc]) = np.where(c == np.min(c))
print('Nearest point lon, lat:'
+ str(ds.longitude.values[xloc, yloc])+','+str(ds.latitude.values[xloc, yloc]))
data.append(ds.SPEC[:, xloc, yloc, :, :])
SPEC = xr.concat(data, dim='time')
for i in range(SPEC.time.shape[0]): # loop over SPEC time
z = SPEC[i, :, :].values
nrows, ncols = z.shape
y = ds.freq.values
x = ds.direction.values
([y_peak], [x_peak]) = np.where(z == np.max(z))
x, y = np.meshgrid(x, y)
fig = plt.figure(figsize=(8, 6))
ax = plt.subplot(111, projection='3d')
ax.xaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('white')
ax.yaxis.pane.fill = False
ax.yaxis.pane.set_edgecolor('white')
ax.zaxis.pane.fill = False
ax.zaxis.pane.set_edgecolor('white')
ax.grid(False)
ax.w_zaxis.line.set_lw(0.)
ax.set_zticks([])
plot = ax.plot_surface(x, y, z, cmap='viridis', edgecolor='white',
vmin=SPEC.min(), vmax=SPEC.max(), alpha=0.8)
ax.contourf(x, y, z, zdir='x', vmin=SPEC.min(),
vmax=SPEC.max(), cmap='viridis', offset=0)
ax.contourf(x, y, z, zdir='y', vmin=SPEC.min(),
vmax=SPEC.max(), cmap='viridis', offset=0.55)
ax.set_zlim(SPEC.min(), SPEC.max())
cbar = fig.colorbar(plot, ax=ax, shrink=0.6)
cbar.ax.set_ylabel('m**2 s')
ax.set_ylabel(r'$\mathregular{f[Hz]}$', labelpad=20)
ax.set_xlabel(r'$\mathregular{\theta}[deg]$', labelpad=20)
ax.set_title(product+"\n"+'lon.='+str(ds.longitude.values[xloc, yloc])+',lat='+str(ds.latitude.values[xloc, yloc])
+ "\n"+str(SPEC['time'].values[i]).split(':')[0]
#+ "\n"
# + r'$\theta_{p}$='
#+ str(ds.direction.values[x_peak].round(2))+'deg'
#+ "\n"+r'$T_{p}$='+str((1/ds.freq.values[y_peak]).round(2))+'s'
,fontsize=16)
plt.savefig('SPEC_lon'+str(ds.longitude.values[xloc, yloc])+'lat'+str(ds.latitude.values[xloc, yloc])
+ str(SPEC['time'].values[i]).split(':')[0]+'.png', bbox_inches='tight')
plt.close()
def extract_2D_spectra(start_time, end_time, lon, lat, product):
"""
Extract and Save (in netcdf format) 2D spectra for a given time period
start_time : start date for plotting e.g., '2005-01-07T18'
end_time : end date for plotting e.g., '2005-01-09T18'
lon : longtitude
lat latitude
product: 'SPEC_NORA3' / 'SPEC_WW3'
"""
data = []
date_list = pd.date_range(start=start_time, end=end_time, freq='D')
for k in range(len(date_list)): # loop over days
url = get_url(product, date_list[k])
ds = xr.open_dataset(url)
# Find the nearest grid point.
abslat = np.abs(ds.latitude-lat)
abslon = np.abs(ds.longitude-lon)
c = np.maximum(abslon, abslat)
([xloc], [yloc]) = np.where(c == np.min(c))
print('Nearest point lon, lat:'
+ str(ds.longitude.values[xloc, yloc])+','+str(ds.latitude.values[xloc, yloc]))
data.append(ds.SPEC[:, xloc, yloc, :, :])
SPEC = xr.concat(data, dim='time')
SPEC = SPEC.sel(time=slice(start_time, end_time))
SPEC.to_netcdf('SPEC_lon'+str(ds.longitude.values[xloc, yloc])+'_lat'+str(ds.latitude.values[xloc, yloc])
+'_'+ str(SPEC['time'].values[0]).split(':')[0]+'_'+ str(SPEC['time'].values[-1]).split(':')[0]+'.nc')
return SPEC
def plot_topaz(start_time, end_time, variable, method,save_data):
"""
info about the hindcast: https://thredds.met.no/thredds/myocean/ARC-MFC/arc-topaz-ran-arc.html
"""
url = 'https://thredds.met.no/thredds/dodsC/topaz/dataset-ran-arc-day-myoceanv2-be'
date_list = pd.date_range(start=start_time, end=end_time, freq='D')
product = 'Arctic Ocean Physics Reanalysis'
if variable == 'ice_speed':
var = xr.Dataset({"ice_speed": ((xr.open_dataset(url)[
'uice'].sel(time=slice(start_time, end_time)))**2 + (xr.open_dataset(url)['vice'].sel(time=slice(start_time, end_time)))**2)**0.5})
var = var[variable]
var = var.assign_attrs(units='m s-1')
else:
var = xr.open_dataset(url)[variable].sel(time=slice(start_time, end_time))
if method == 'timestep':
min_value = var.min()
max_value = var.max()
print(max_value)
for i in range(len(date_list)):
print(date_list[i])
plot_NorthPolarStereo(product=product,
var=var.loc[date_list[i]],lon=var.longitude,
lat=var.latitude,
min_value=min_value,max_value=max_value,cmap='ocean')
plt.title(product+'\n '+str(date_list[i]))
plt.savefig(variable + str(date_list[i]) +'.png', bbox_inches='tight')
plt.close
if save_data == True:
var.to_netcdf(variable+'_'+start_time+'-'+end_time+'.nc')
elif method == 'mean':
plot_NorthPolarStereo(product=product,
var=var.mean('time'),lon=var.longitude,
lat=var.latitude,
min_value=var.mean('time').min(),
max_value=var.mean('time').max(),cmap='ocean')
plt.title(product+'\n Mean:'+start_time+'--'+end_time)
plt.savefig(variable+'_Mean_'+start_time+'-'+end_time+'.png', bbox_inches='tight')
plt.close()
if save_data == True:
var.mean('time').to_netcdf(variable+'_Mean_'+start_time+'-'+end_time+'.nc')
def extract_ts_point(start_date,end_date,variable, lon, lat, product ='NORA3'):
"""
Extract times series of the nearest gird point (lon,lat) from
nora3 wave hindcast and save it as netcdf.
"""
nco = Nco()
date_list = pd.date_range(start=start_date , end=end_date, freq='D')
outfile = 'lon'+str(lon)+'_'+'lat'+str(lat)+'_'+date_list.strftime('%Y%m%d')[0]+'_'+date_list.strftime('%Y%m%d')[-1]+'.nc'
if os.path.exists(outfile):
os.remove(outfile)
print(outfile, 'already exists, so it will be deleted and create a new....')
else:
print("....")
tempfile = [None] *len(date_list)
# Create directory
dirName = 'temp'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists")
# extract point and create temp files
for i in range(len(date_list)):
tempfile[i] = 'temp/temp'+date_list.strftime('%Y%m%d')[i]+'.nc'
if product == 'NORA3':
infile = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_files/'+date_list.strftime('%Y')[i]+'/'+date_list.strftime('%m')[i]+'/'+date_list.strftime('%Y%m%d')[i]+'_MyWam3km_hindcast.nc'
#infile_http = 'https://thredds.met.no/thredds/fileServer/windsurfer/mywavewam3km_files/'+date_list.strftime('%Y')[i]+'/'+date_list.strftime('%m')[i]+'/'+date_list.strftime('%Y%m%d')[i]+'_MyWam3km_hindcast.nc'
print(infile)
if i==0:
ds = xr.open_dataset(infile)
print('Find nearest point to lon.='+str(lon)+','+'lat.='+str(lat))
rlon, rlat = find_nearest(ds.longitude, ds.latitude, lat, lon)
lon_near = ds.longitude.sel(rlat=rlat, rlon=rlon).values[0][0]
lat_near = ds.latitude.sel(rlat=rlat, rlon=rlon).values[0][0]
print('Found nearest: lon.='+str(lon_near)+',lat.=' + str(lat_near))
opt = ['-O -v '+",".join(variable)+' -d rlon,'+str(rlon.values[0])+' -d rlat,'+str(rlat.values[0])]
for x in range(0, 6): # try 6 times
try:
nco.ncks(input=infile , output=tempfile[i], options=opt)
except:
print('......Retry'+str(x)+'.....')
time.sleep(10) # wait for 10 seconds before re-trying
else:
break
#merge temp files
nco.ncrcat(input=tempfile, output=outfile)
# add lon, lat info in nc-file
ds = xr.open_dataset(outfile)
ds['lon'] = lon_near
ds['lat'] = lat_near
ds.to_netcdf(outfile)
#remove temp files
for i in range(len(date_list)):
os.remove(tempfile[i])
return
def extract_variable(start_date,end_date,variable,mean_method, product ='NORA3'):
"""
Extract a variable for the whole NORA3 domain and save it as netcdf.
"""
nco = Nco()
date_list = pd.date_range(start=start_date , end=end_date, freq='D')
outfile = "-".join(variable)+'_'+date_list.strftime('%Y%m%d')[0]+'_'+date_list.strftime('%Y%m%d')[-1]+'.nc'
if os.path.exists(outfile):
os.remove(outfile)
print(outfile, 'already exists, so it will be deleted and create a new....')
else:
print("....")
tempfile = [None] *len(date_list)
# Create directory
dirName = 'temp'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists")
# extract point and create temp files
for i in range(len(date_list)):
tempfile[i] = 'temp/temp'+date_list.strftime('%Y%m%d')[i]+'.nc'
if product == 'NORA3':
infile = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_files/'+date_list.strftime('%Y')[i]+'/'+date_list.strftime('%m')[i]+'/'+date_list.strftime('%Y%m%d')[i]+'_MyWam3km_hindcast.nc'
print(infile)
for x in range(0, 6): # try 6 times
try:
if mean_method == None:
#opt = ['-O -v '+",".join(variable)]
#nco.ncks(input=infile , output=tempfile[i], options=opt)
if variable[0] == 'WEF':
opt = ['-s '+"WEF=0.5*hs*hs*tmp"]
nco.ncap2(input=infile , output=tempfile[i], options=opt)
elif mean_method == 'daily':
opt = ['-O -v '+",".join(variable)+' -d time,,,24,24']
nco.ncra(input=infile , output=tempfile[i], options=opt)
except:
print('......Retry'+str(x)+'.....')
time.sleep(10) # wait for 10 seconds before re-trying
else:
break
#merge temp files
nco.ncrcat(input=tempfile, output=outfile)
#remove temp files
for i in range(len(date_list)):
os.remove(tempfile[i])
return
def plot_swan_spec2D(start_time, end_time,infile, site=0):
from wavespectra import read_ncswan
ds = read_ncswan(infile).sel(time=slice(start_time, end_time))
hs_spec = ds.isel(site=site).efth.spec.hs()
# tp_spec = ds.isel(site=0).efth.spec.tp()
# dp_spec = ds.isel(site=0).efth.spec.dp()
vmax=ds.efth.max()
vmin=ds.efth.min()
for i in range(ds.time.shape[0]):
ax = plt.subplot(111, polar=True)
ds.isel(site=site,time=i).efth.spec.split(fmin=0.04).spec.plot(kind="contourf", cmap="ocean_r", as_period=True,normalised=True)
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
ax.set_ylabel('')
ax.set_xlabel('')
plt.title('$'+'lon.:'+str(ds.lon.values[site].round(2))+','+'lat.:'+str(ds.lat.values[site].round(2))+','+str(ds.time.values[i]).split(':')[0]+', H_{m0}:'+str(hs_spec.values[i].round(1))+
'm'+'$')
plt.savefig(str(ds.time.values[i]).split(':')[0]+'_site'+str(site)+'.png')
plt.close()
def download_WEF(start_date,end_date, product ='NORA3'):
"""
Create WEF from NORA3 data and save it as netcdf (using cdo).
"""
cdo = Cdo()
date_list = pd.date_range(start=start_date , end=end_date, freq='D')
outfile = "WEF"+'_'+date_list.strftime('%Y%m%d')[0]+'_'+date_list.strftime('%Y%m%d')[-1]+'.nc'
if os.path.exists(outfile):
os.remove(outfile)
print(outfile, 'already exists, so it will be deleted and create a new....')
else:
print("....")
outfile = [None] *len(date_list)
# Create directory
dirName = 'download'
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists")
# extract point and create temp files
for i in range(len(date_list)):
outfile[i] = 'download/WEF'+date_list.strftime('%Y%m%d')[i]+'.nc'
if product == 'NORA3':
infile = 'https://thredds.met.no/thredds/dodsC/windsurfer/mywavewam3km_files/'+date_list.strftime('%Y')[i]+'/'+date_list.strftime('%m')[i]+'/'+date_list.strftime('%Y%m%d')[i]+'_MyWam3km_hindcast.nc'
print(infile)
for x in range(0, 6): # try 6 times
try:
#cdo_command = ['cdo', 'expr,WEF=0.5*hs*hs*tmp', infile, ]
#call(cdo_command)
cdo.expr("'WEF=0.5*hs*hs*tmp'",input=infile, output=outfile[i], options="-f nc")
except:
print('......Retry'+str(x)+'.....')
time.sleep(10) # wait for 10 seconds before re-trying
else:
break
return
def plot_MET_forecast(url):
"url: opendap link of WW3_4km or WAM800 models from thredds.met.no (MET Norway)"
ds = xr.open_dataset(url)
print(url)
cut=8
print('Hs',ds.hs.max())
cmap_waves = create_cmap(type='wave')
for i in range(ds.time.shape[0]):
fig,ax = plt.subplots(figsize=(8,6), subplot_kw={"projection": ccrs.Orthographic(-15,60)})
levels = np.round(np.linspace(0,int(ds.hs.max()+1),int(ds.hs.max())*10),1)
im = ax.contourf(ds.longitude, ds.latitude,ds.hs.loc[ds.time[i]],levels = levels, transform = ccrs.PlateCarree(),cmap=cmap_waves) #ocean_r, coolwarm
plt.title(ds.title+' '+str(ds.time.values[i])[:13] +'UTC')
#ax.coastlines('50m')
#ax.stock_img()
ax.set_facecolor('lightgrey')
cbar_ax = fig.add_axes([0.8, 0.12, 0.05, 0.7])
cbar_ax.set_title('$'+'H_s[m]'+'$')
fig.colorbar(im, cax=cbar_ax)
ax.gridlines(xlocs=range(-180,180,10),ylocs=range(-90, 90, 10),color='black',linestyle='dotted',draw_labels=True)
plt.savefig(ds.title.split(' ')[0] +'_' + str(ds.time.values[i])[:13]+'_Hs.png',bbox_inches = 'tight')
plt.close()
def plot_SWAN_map(url='Ianos500m_st6_20200914.nc',var='hs', start_time='2020-09-14T12', end_time='2020-09-20T16',max_value=None, point=None, cmap='jet'):
ds = xr.open_dataset(url).sel(time=slice(start_time, end_time))
if var=='wind':
ds['wind'] = (ds['xwnd']**2 + ds['ywnd']**2)**(1/2)
ds['wind'].attrs['units'] = 'm/s'
print('max '+var,ds[var].max())
for i in range(ds.time.shape[0]):
fig, ax = plt.subplots()
if max_value==None:
levels = np.round(np.linspace(0,int(ds[var].max()+1),int(ds[var].max()+1)*10),1)
else:
levels = np.round(np.linspace(0,int(max_value),int(max_value)*10),1)
im = ax.contourf(ds.longitude, ds.latitude,ds[var].loc[ds.time[i]],levels = levels,cmap=cmap) #, transform = ccrs.PlateCarree(),cmap='coolwarm') # coolwarm
if var=='wind':
ax.quiver(ds.longitude[::50], ds.latitude[::50],ds['xwnd'].loc[ds.time[i]][::50,::50],ds['ywnd'].loc[ds.time[i]][::50,::50],linewidths=0.01)
if point is not None:
for j in range(len(point)):
p = point[j]
plt.plot(p[0],p[1],marker ='^',color='magenta', markersize=8)
plt.title('DNORA/SWAN,'+str(ds.time.values[i])[:13] +'UTC')
cbar_ax = plt.colorbar(im)
cbar_ax.ax.set_title('$['+ds[var].units+']$')
ax.set_facecolor('beige')
plt.savefig(str(ds.time.values[i])[:13]+'_'+var+'.png',bbox_inches = 'tight')
plt.close()
def create_cmap(type = 'wave'):
from matplotlib.colors import ListedColormap
from skimage.transform import resize
if type == 'wind':
colors0 = np.array([
[0.62352941, 0.7254902 , 0.74901961, 1. ],
[0.18823529, 0.61568627, 0.7254902 , 1. ],
[0.21960784, 0.40784314, 0.74901961, 1. ],
[0.3372549 , 0.35294118, 0.77254902, 1. ],
[0.18823529, 0.38431373, 0.55294118, 1. ],
[0.18823529, 0.38431373, 0.55294118, 1. ],
[0. , 0.46666667, 0.43921569, 1. ],
[0. , 0.46666667, 0.43921569, 1. ],
[0. , 0.6 , 0.47843137, 1. ],
[0. , 0.6 , 0.47843137, 1. ],
[0. , 0.68235294, 0.40784314, 1. ],
[0. , 0.68235294, 0.40784314, 1. ],
[0.65882353, 0.78823529, 0.26666667, 1. ],
[0.65882353, 0.78823529, 0.26666667, 1. ],
[0.78431373, 0.25882353, 0.05098039, 1. ],
[0.78431373, 0.25882353, 0.05098039, 1. ],
[0.84313725, 0. , 0.19607843, 1. ],
[0.84313725, 0. , 0.19607843, 1. ],
[0.68627451, 0.31372549, 0.53333333, 1. ],
[0.68627451, 0.31372549, 0.53333333, 1. ],
[0.60392157, 0.18823529, 0.59215686, 1. ]])
elif type == 'wave':
colors0 = np.array([
[0.62109375, 0.72265625, 0.74609375, 1. ],
[0.1875 , 0.61328125, 0.72265625, 1. ],
[0.1875 , 0.3828125 , 0.55078125, 1. ],
[0.21875 , 0.40625 , 0.74609375, 1. ],
[0.22265625, 0.234375 , 0.5546875 , 1. ],
[0.73046875, 0.3515625 , 0.74609375, 1. ],
[0.6015625 , 0.1875 , 0.58984375, 1. ],
[0.51953125, 0.1875 , 0.1875 , 1. ],
[0.74609375, 0.19921875, 0.37109375, 1. ],
[0.74609375, 0.40234375, 0.33984375, 1. ],
[0.74609375, 0.74609375, 0.74609375, 1. ],
[0.6015625 , 0.49609375, 0.60546875, 1. ]])
colors0_resized = resize(colors0,(256,4),order=1)
cmap_new = ListedColormap(colors0_resized)
return cmap_new