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aaf_plotInterface.py
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aaf_plotInterface.py
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from datetime import datetime,timezone
import numpy as np
from aaf_usersettings import *
from aaf_plot_mod import *
from aaf_func import *
import netCDF4
import fnmatch
import time as timer
# Plot prep functions
#####################################################################
def TZPrep(ncfileName,**kwargs):
Tpl = kwargs.get('Tpl', None)
Zpl = kwargs.get('Zpl', None)
Tml = kwargs.get('Tml', None)
Psl = kwargs.get('Psl', None)
lwe = kwargs.get('lwe', None)
prec= kwargs.get('prec', None)
# Fetch variables from user definitions for map plot
if (Tpl and Zpl):
var_list=VariableListTZMap()
if (Tml and Psl):
var_list=VariableListTPMap()
if (lwe and Psl):
var_list=VariableListPWMap()
if (prec and Psl):
var_list=VariableListPrecMap()
#print (var_list)
forecasts, forecast_length =ForecastSettings()
print(ncfileName)
ncfile = netCDF4.Dataset(ncfileName)
latitudes = ncfile.variables["latitude" ][:]
longitudes= ncfile.variables["longitude"][:]
timestep = ncfile.variables["time"][0:(forecasts+1)*forecast_length:forecast_length]
v_idx=[]
if Tml:
modellev = ncfile.variables["hybrid"][:]
#v_idx.append(np.where(modellev==Tml)[0])
v_idx.append(Tml-1)
if Tpl or Zpl:
pressure = ncfile.variables["pressure"][:]
if Tpl:
v_idx.append(np.where(pressure==Tpl)[0])
if Zpl:
v_idx.append(np.where(pressure==Zpl)[0])
var = np.empty((len(var_list),forecasts+1,len(latitudes),len(latitudes[0])))
unit={}
long_name={}
for i in range(len(var_list)):
long_name[i] = ncfile.variables[var_list[i]].long_name
#print(long_name[i])
if not fnmatch.fnmatch(long_name[i],'Sea_ice_fraction'):
if not fnmatch.fnmatch(long_name[i],'Mean Sea Level Pressure (MSLP)') and v_idx:
#print('nooo')
var[i,:,:,:] = np.squeeze(ncfile.variables[var_list[i]][0:(forecasts+1)*forecast_length:forecast_length,v_idx[i],:,:])
unit[i] = ncfile.variables[var_list[i]].units
else:
#print('yees')
var[i,:,:,:] = np.squeeze(ncfile.variables[var_list[i]][0:(forecasts+1)*forecast_length:forecast_length,:,:])
unit[i] = ncfile.variables[var_list[i]].units
else:
var[i,:,:,:] = np.squeeze(ncfile.variables[var_list[i]][0:(forecasts+1)*forecast_length:forecast_length,:,:])
unit[i]=""
ncfile.close()
# Plot maps
plot_TZ(longitudes,latitudes,var[:,:,:,:], timestep,forecasts+1,Tpl,Zpl,Tml,Psl,lwe,prec)
def MapPrep(ncfileName):
# Fetch variables from user definitiona for map plot
var_list=VariableListMap()
forecasts, forecast_length =ForecastSettings()
ncfile = netCDF4.Dataset(ncfileName)
latitudes = ncfile.variables["latitude" ][:]
longitudes= ncfile.variables["longitude"][:]
timestep = ncfile.variables["time"][0:(forecasts+1)*forecast_length:forecast_length]
var = np.empty((len(var_list),forecasts+1,len(latitudes),len(latitudes[0])))
unit={}
long_name={}
for i in range(len(var_list)):
print (var_list[i])
var[i,:,:,:] = ncfile.variables[var_list[i]][0:(forecasts+1)*forecast_length:forecast_length,0,:,:]
long_name[i] = ncfile.variables[var_list[i]].long_name
if not fnmatch.fnmatch(long_name[i],'Sea_ice_fraction'):
print (long_name[i])
unit[i] = ncfile.variables[var_list[i]].units
print (unit)
else:
unit[i]=""
ncfile.close()
print (var.shape)
# Plot maps
#plot_pw_rotated_grid_separate(longitudes,latitudes,var[0,:,:,:], timestep,forecasts+1,unit[0],long_name[0])
#plot_pw_rotated_grid_separate(longitudes,latitudes,var[1,:,:,:], timestep,forecasts+1,unit[1],long_name[1])
plot_pw_rotated_grid_separate(longitudes,latitudes,var[2,:,:,:], timestep,forecasts+1,unit[2],long_name[2])
def PointPrepZ(ncfileName):
# Fetch point forecast loaction from user definition
[lon,lat],posd = ForecastPointPosition()
# constants
maxIdx=35#60
e=0.622
R=287.052874
g=9.82
ncfile = netCDF4.Dataset(ncfileName)
time = ncfile.variables['time'][:]
latitudes = ncfile.variables["latitude" ][:]
longitudes= ncfile.variables["longitude"][:]
ap = ncfile.variables["ap"][maxIdx:]
b = ncfile.variables["b"][maxIdx:]
cap = ncfile.variables["ap"][:]
cb = ncfile.variables["b"][:]
#print (len(posd))
ytest = np.empty(len(posd),dtype=int)
xtest = np.empty(len(posd),dtype=int)
i=0
for key in posd.keys():
print('Location', key)
#print( posd[key]['lat'])
ytest[i],xtest[i] = PointIdx(posd[key]['lat'],posd[key]['lon'],latitudes,longitudes)
print (ytest[i],xtest[i] )
print('lat: ',latitudes[ytest[i],xtest[i]], ' lon: ',longitudes[ytest[i],xtest[i]])
i=i+1
#print (sorted(posd.keys()))
#print('latitudes')
#print(latitudes[ytest,xtest])
#print('longitudes')
#print(longitudes[ytest,xtest])
# vertical coordinate conversion
print ('Read variables for vertical interpolation')
print ('pressure')
ps = np.squeeze(ncfile.variables['surface_air_pressure'][:,:,ytest,xtest])
print ('temperature')
T = ncfile.variables['air_temperature_ml'][:,maxIdx:,ytest,xtest]
print ('humidity')
q = 1000.*ncfile.variables['specific_humidity_ml'][:,maxIdx:,ytest,xtest]
print ('Read variables for plotting')
# temperature
print ('surface temperature')
T0 = np.squeeze(ncfile.variables['air_temperature_0m'][:,:,ytest,xtest])-273.15
T2 = np.squeeze(ncfile.variables['air_temperature_2m'][:,:,ytest,xtest])-273.15
# wind
print ('wind')
U = ncfile.variables['x_wind_ml'][:,maxIdx:,ytest,xtest]
V = ncfile.variables['y_wind_ml'][:,maxIdx:,ytest,xtest]
print ('surface wind')
ws10 = np.squeeze(ncfile.variables['wind_speed'][:,:,ytest,xtest])
wd10 = np.squeeze(ncfile.variables['wind_direction'][:,:,ytest,xtest])
print ('precipitation')
prec = np.squeeze(ncfile.variables['precipitation_amount_acc'][:,:,ytest,xtest])
print ('cloud water and ice (full atmosphere)')
clm= ncfile.variables['mass_fraction_of_cloud_condensed_water_in_air_ml'][:,:,ytest,xtest]
cim= ncfile.variables['mass_fraction_of_cloud_ice_in_air_ml'][:,:,ytest,xtest]
# Close file
ncfile.close()
# 2D variables
print ('calculate wind speed and direction')
ws=np.zeros((len(time),len(ap),len(posd)))
wd=np.zeros((len(time),len(ap),len(posd)))
for loc in range(len(posd)):
ws[:,:,loc]= np.sqrt(U[:,:,loc,loc]*U[:,:,loc,loc] + V[:,:,loc,loc]*V[:,:,loc,loc] )
wd[:,:,loc]=270.-np.arctan2(V[:,:,loc,loc],U[:,:,loc,loc])*(180./np.pi)
if np.max(wd[:,:,loc])>360.:
wd[:,:,loc]=np.where(wd[:,:,loc]>360.,wd[:,:,loc]-360., wd[:,:,loc])
# tic = timer.perf_counter()
# toc = timer.perf_counter()
# tac = timer.perf_counter()
#
# print(f"First {toc - tic:0.4f} seconds")
# print(f"Second {tac - toc:0.4f} seconds")
# Vertical integration of cloud variables
print ('vertical integration of cloud water and ice')
cpl=np.zeros((len(cap),len(time),len(posd),len(posd)))
for k in range(len(cap)):
cpl[k,:,:,:]= cap[k] + cb[k]*ps[:,:,:]
clm_v=np.zeros((len(time),len(posd)))
cim_v=np.zeros((len(time),len(posd)))
for i in range(len(time)):
for loc in range(len(posd)):
clm_v[i,loc]= np.sum(clm[i,1:,loc,loc]*np.diff(cpl[:,i,loc,loc])/g)
cim_v[i,loc]= np.sum(cim[i,1:,loc,loc]*np.diff(cpl[:,i,loc,loc])/g)
cwm=clm_v+cim_v
print('virtual temperature')
Tv=T*(q+e)/(e*(1.+q))
print('model levels to pressure to height')
plevs=np.zeros((len(ap),len(time),len(posd),len(posd)))
z =np.zeros((len(ap),len(time),len(posd),len(posd)))
dz =np.zeros((len(ap),len(time),len(posd),len(posd)))
for k in range(len(ap)):
plevs[k,:,:,:]= ap[k] + b[k]*ps[:,:,:]
if k>0:
dz[k,:,:,:]=(R*Tv[:,k,:,:]/g)*np.log(plevs[k,:,:,:]/plevs[k-1,:,:,:])
#print (dz[k,0,1,1])
#z[:k,:,:,:] = z[:k,:,:,:]+dz
#print (z[k-1,0,1,1])
z[0:k,:,:,:] =z[0:k,:,:,:]+ dz[k,:,:,:]
zv=np.linspace(0, 3000, num=31, endpoint=True)
#zv=np.linspace(0, 200, num=6, endpoint=True)
# Vertical interpolation to geometric height
print ('vertical interpolation')
qv=np.zeros((len(time),len(zv),len(posd)))
tv=np.zeros((len(time),len(zv),len(posd)))
wsv=np.zeros((len(time),len(zv),len(posd)))
wdv=np.zeros((len(time),len(zv),len(posd)))
for i in range(len(time)):
for loc in range(len(posd)):
qv[i,:,loc] = np.interp(zv,np.flip(z[:,i,loc,loc]),np.flip(q[i,:,loc,loc]))
tv[i,:,loc] = np.interp(zv,np.flip(z[:,i,loc,loc]),np.flip(T[i,:,loc,loc]))
wsv[i,:,loc]= np.interp(zv,np.flip(z[:,i,loc,loc]),np.flip(ws[i,:,loc]))
wdv[i,:,loc]= np.interp(zv,np.flip(z[:,i,loc,loc]),np.flip(wd[i,:,loc]))
print ('plot')
# Call plot function
plot_pointZ(time,zv,qv[:,:,:], prec,cwm, latitudes[ytest,xtest],longitudes[ytest,xtest],len(posd),field='q' )
plot_pointZ(time,zv,tv[:,:,:]-273.15,T0, T2, latitudes[ytest,xtest],longitudes[ytest,xtest],len(posd),field='t' )
plot_pointZ(time,zv,wsv[:,:,:],ws10,wd10,latitudes[ytest,xtest],longitudes[ytest,xtest],len(posd),field='wind',var_aux3=wdv)
# detele variables
del qv,tv,wsv,wdv
del q,T,ws,wd,Tv,clm,cim,U,V
#del q,T,ws,wd,Tv,U,V
def PointPrep(ncfileName):
# Fetch point forecast loaction from user definition
#lon,lat = ForecastPointPosition()
[lon,lat],posd = ForecastPointPosition()
print(ForecastPointPosition())
# Fetch variables from user definitiona for point forecast
var_list=VariableListPoint()
print ( var_list)
# extra variables needed
# p(n,k,j,i) = ap(k) + b(k)*ps(n,j,i)
#aux_var= 'surface_air_pressure'
#aux_var= 'hybrid'
# 'ap'
# 'b'
print(ncfileName)
ncfile = netCDF4.Dataset(ncfileName)
latitudes = ncfile.variables["latitude" ][:]
longitudes= ncfile.variables["longitude"][:]
#ps= ncfile.variables["surface_air_pressure"][:]
ap= ncfile.variables["ap"][60:]
b= ncfile.variables["b"][60:]
y_idx, x_idx= PointIdx(lat,lon,latitudes,longitudes)
print( posd['point1']['lat'])
print( posd['point1']['lon'])
print (y_idx,x_idx )
print (len(posd))
ytest = np.empty(len(posd),dtype=int)
xtest = np.empty(len(posd),dtype=int)
#ytest=[]
#xtest=[]
i=0
for key in posd.keys():
print('key')
print(key)
print( posd[key]['lat'])
ytest[i],xtest[i] = PointIdx(posd[key]['lat'],posd[key]['lon'],latitudes,longitudes)
print (ytest,xtest )
i=i+1
print (sorted(posd.keys()))
print('ytest')
print(ytest)
print('xtest')
print(xtest)
time = ncfile.variables['time'][:]
height = ncfile.variables['pressure'][:]
p_lat=latitudes[y_idx,x_idx]
p_lon=longitudes[y_idx,x_idx]
# vertical coordinate conversion
e=0.622
print ('hej1')
ps = np.squeeze(ncfile.variables['surface_air_pressure'][:,:,ytest,xtest])
prec = np.squeeze(ncfile.variables['precipitation_amount_acc'][:,:,ytest,xtest])
print ('hej2')
clm= ncfile.variables['mass_fraction_of_cloud_condensed_water_in_air_ml'][:,60:,ytest,xtest]
cim= ncfile.variables['mass_fraction_of_cloud_ice_in_air_ml'][:,60:,ytest,xtest]
T = ncfile.variables['air_temperature_ml'][:,60:,ytest,xtest]
print ('hej3')
q = 1000.*ncfile.variables['specific_humidity_ml'][:,60:,ytest,xtest]
print ('hej4')
Tv=T*(q+e)/(e*(1.+q))
#Tv2=np.multiply(T,np.divide((q+e),(e*(1.+q))))
print ('ps shape')
print (ps.shape)
print (ap.shape)
print (Tv.shape)
R=287.052874
g=9.82
#print(T[0,:,0,0])
#print(Tv[0,:,0,0])
plevs=np.zeros((len(ap),len(time),len(posd),len(posd)))
z=np.zeros((len(ap),len(time),len(posd),len(posd)))
dz=np.zeros((len(ap),len(time),len(posd),len(posd)))
#Tv=np.zeros((len(ap),len(time),len(posd),len(posd)))
for k in range(len(ap)):
#for k in range(len(posd)):
plevs[k,:,:,:]= ap[k] + b[k]*ps[:,:,:]
if k>0:
dz[k,:,:,:]=(R*Tv[:,k,:,:]/g)*np.log(plevs[k,:,:,:]/plevs[k-1,:,:,:])
#print (dz[k,0,1,1])
#z[:k,:,:,:] = z[:k,:,:,:]+dz
#print (z[k-1,0,1,1])
z[0:k,:,:,:] =z[0:k,:,:,:]+ dz[k,:,:,:]
#print (z[k-1,0,1,1])
#print (z[0:k-1,0,1,1])
#print (z[0:0,0,1,1])
#print (z[0:1,0,1,1])
# Vertical integration of cloud variables
#for k in range(0, len(ap)):
# clm_v[:,:,:] = clm_v[:,:,:] + clm[i,k,:,:]*np.diff(plevs[:,0,0])/9.81
# #cim_v[:,:,:] = cim_v[:,:,:] + cim[i,k,:,:]*dp[k,:,:]/9.81
#atm = atm + dp[k,:,:]/9.81
clm_v=np.zeros(len(time))
cim_v=np.zeros(len(time))
for i in range(len(time)):
clm_v[i]= np.sum(clm[i,1:,0,0]*np.diff(plevs[:,i,0,0])/g)
cim_v[i]= np.sum(cim[i,1:,0,0]*np.diff(plevs[:,i,0,0])/g)
cwm=clm_v+cim_v
print('clm')
print(clm[0,1:,0,0])
print(cim[0,1:,0,0])
print(clm_v)
print(cim_v)
dp = np.diff(plevs[:,0,0,0])
atm = np.sum(np.diff(plevs[:,0,0,0]))/g
print(dp)
print('atm')
print(atm)
#print (dz[:,0,1,1])
print ('z')
print (z[:,0,0,0])
#print (z[:,0,1,1])
print (plevs[:,0,1,1])
print (plevs.shape)
zv=np.linspace(0, 200, num=6, endpoint=True)
print ('zv')
print (zv)
#z = dz
# User variables
var = np.empty((len(var_list),len(time),len(height),len(posd),len(posd)))
unit={}
long_name={}
for i in range(len(var_list)):
print (var_list[i])
#p_var = np.squeeze(ncfile.variables['specific_humidity_pl'][:,:,y_idx,x_idx])
#var[i,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,y_idx,x_idx])
#var[i,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,ytest,xtest])
var[i,:,:,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,ytest,xtest])
unit[i] = ncfile.variables[var_list[i]].units
long_name[i] = ncfile.variables[var_list[i]].long_name
ncfile.close()
print (q.shape)
qv=np.zeros((len(time),len(zv),len(posd),len(posd)))
qv[0,:,0,0]= np.interp(zv,np.flip(z[:,0,0,0]),np.flip(q[0,:,0,0]))
print(qv[0,:,0,0])
print(q[0,:,0,0])
for i in range(len(time)):
qv[i,:,0,0]= np.interp(zv,np.flip(z[:,i,0,0]),np.flip(q[i,:,0,0]))
plot_pointZ(time,zv,qv[:,:,:,:],prec,cwm,p_lat,p_lon,len(posd),unit[0],long_name[0] )
#plot_point(time,height,var[0,:,:,:,:],p_lat,p_lon,len(posd),unit[0],long_name[0] )
#plot_point(time,height,var[1,:,:,:,:],p_lat,p_lon,len(posd),unit[1],long_name[1] )
def PointPrepPL(ncfileName):
# Fetch point forecast loaction from user definition
#lon,lat = ForecastPointPosition()
[lon,lat],posd = ForecastPointPosition()
print(ForecastPointPosition())
# Fetch variables from user definitiona for point forecast
var_list=VariableListPoint()
print ( var_list)
# extra variables needed
# p(n,k,j,i) = ap(k) + b(k)*ps(n,j,i)
#aux_var= 'surface_air_pressure'
#aux_var= 'hybrid'
# 'ap'
# 'b'
ncfile = netCDF4.Dataset(ncfileName)
latitudes = ncfile.variables["latitude" ][:]
longitudes= ncfile.variables["longitude"][:]
#ps= ncfile.variables["surface_air_pressure"][:]
ap= ncfile.variables["ap"][60:]
b= ncfile.variables["b"][60:]
y_idx, x_idx= PointIdx(lat,lon,latitudes,longitudes)
print( posd['point1']['lat'])
print( posd['point1']['lon'])
print (y_idx,x_idx )
print (len(posd))
ytest = np.empty(len(posd),dtype=int)
xtest = np.empty(len(posd),dtype=int)
#ytest=[]
#xtest=[]
i=0
for key in posd.keys():
print('key')
print(key)
print( posd[key]['lat'])
ytest[i],xtest[i] = PointIdx(posd[key]['lat'],posd[key]['lon'],latitudes,longitudes)
print (ytest,xtest )
i=i+1
print (sorted(posd.keys()))
print('ytest')
print(ytest)
print('xtest')
print(xtest)
time = ncfile.variables['time'][:]
height = ncfile.variables['pressure'][:]
p_lat=latitudes[y_idx,x_idx]
p_lon=longitudes[y_idx,x_idx]
# vertical coordinate conversion
e=0.622
print ('hej1')
ps = np.squeeze(ncfile.variables['surface_air_pressure'][:,:,ytest,xtest])
print ('hej2')
T = ncfile.variables['air_temperature_ml'][:,60:,ytest,xtest]
print ('hej3')
q = 1000.*ncfile.variables['specific_humidity_ml'][:,60:,ytest,xtest]
print ('hej4')
Tv=T*(q+e)/(e*(1.+q))
#Tv2=np.multiply(T,np.divide((q+e),(e*(1.+q))))
print ('ps shape')
print (ps.shape)
print (ap.shape)
print (Tv.shape)
R=287.052874
g=9.82
#print(T[0,:,0,0])
#print(Tv[0,:,0,0])
plevs=np.zeros((len(ap),len(time),len(posd),len(posd)))
z=np.zeros((len(ap),len(time),len(posd),len(posd)))
dz=np.zeros((len(ap),len(time),len(posd),len(posd)))
#Tv=np.zeros((len(ap),len(time),len(posd),len(posd)))
for k in range(len(ap)):
#for k in range(len(posd)):
plevs[k,:,:,:]= ap[k] + b[k]*ps[:,:,:]
if k>0:
dz[k,:,:,:]=(R*Tv[:,k,:,:]/g)*np.log(plevs[k,:,:,:]/plevs[k-1,:,:,:])
#print (dz[k,0,1,1])
#z[:k,:,:,:] = z[:k,:,:,:]+dz
#print (z[k-1,0,1,1])
z[0:k,:,:,:] =z[0:k,:,:,:]+ dz[k,:,:,:]
#print (z[k-1,0,1,1])
#print (z[0:k-1,0,1,1])
#print (z[0:0,0,1,1])
#print (z[0:1,0,1,1])
#print (dz[:,0,1,1])
print ('z')
print (z[:,0,0,0])
#print (z[:,0,1,1])
print (plevs[:,0,1,1])
print (plevs.shape)
zv=np.linspace(0, 200, num=6, endpoint=True)
print ('zv')
print (zv)
#z = dz
# User variables
var = np.empty((len(var_list),len(time),len(height),len(posd),len(posd)))
unit={}
long_name={}
for i in range(len(var_list)):
print (var_list[i])
#p_var = np.squeeze(ncfile.variables['specific_humidity_pl'][:,:,y_idx,x_idx])
#var[i,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,y_idx,x_idx])
#var[i,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,ytest,xtest])
var[i,:,:,:,:] = np.squeeze(ncfile.variables[var_list[i]][:,:,ytest,xtest])
unit[i] = ncfile.variables[var_list[i]].units
long_name[i] = ncfile.variables[var_list[i]].long_name
ncfile.close()
print (q.shape)
qv=np.zeros((len(time),len(zv),len(posd),len(posd)))
qv[0,:,0,0]= np.interp(zv,np.flip(z[:,0,0,0]),np.flip(q[0,:,0,0]))
print(qv[0,:,0,0])
print(q[0,:,0,0])
for i in range(len(time)):
qv[i,:,0,0]= np.interp(zv,np.flip(z[:,i,0,0]),np.flip(q[i,:,0,0]))
plot_pointZ(time,zv,qv[:,:,:,:],p_lat,p_lon,len(posd),unit[0],long_name[0] )
plot_point(time,height,var[0,:,:,:,:],p_lat,p_lon,len(posd),unit[0],long_name[0] )
plot_point(time,height,var[1,:,:,:,:],p_lat,p_lon,len(posd),unit[1],long_name[1] )