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Con_E_Recyc_Plots.py
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Con_E_Recyc_Plots.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 16 13:24:45 2016
@author: Ent00002
"""
#%% Import libraries
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
from getconstants import getconstants
from matplotlib.colors import LinearSegmentedColormap
import os
#%% BEGIN OF INPUT (FILL THIS IN)
years = np.arange(2002,2009) #fill in the years
# Manage the extent of your dataset (FILL THIS IN)
# Define the latitude and longitude cell numbers to consider and corresponding lakes that should be considered part of the land
latnrs = np.arange(7,114)
lonnrs = np.arange(0,240)
# the lake numbers below belong to the ERA-Interim data on 1.5 degree starting at Northern latitude 79.5 and longitude -180
lake_mask_1 = np.array([9,9,9,12,12,21,21,22,22,23,24,25,23,23,25,25,53,54,61,23,24,23,24,25,27,22,23,24,25,26,27,28,22,25,26,27,28,23,23,12,18])
lake_mask_2 = np.array([120+19,120+40,120+41,120+43,120+44,120+61,120+62,120+62,120+63,120+62,120+62,120+62,120+65,120+66,120+65,120+66,142-120,142-120,143-120,152-120,152-120,153-120,153-120,153-120,153-120,154-120,154-120,154-120,154-120,154-120,154-120,154-120,155-120,155-120,155-120,155-120,155-120,159-120,160-120,144-120,120+55])
lake_mask = np.transpose(np.vstack((lake_mask_1,lake_mask_2))) #recreate the arrays of the matlab model
# obtain the constants
invariant_data = 'Interim_data/full/invariants.nc'#invariants
latitude,longitude,lsm,g,density_water,timestep,A_gridcell,L_N_gridcell,L_S_gridcell,L_EW_gridcell,gridcell = getconstants(latnrs,lonnrs,lake_mask,invariant_data)
A_gridcell2D = np.tile(A_gridcell,len(longitude));
# BEGIN OF INPUT 2 (FILL THIS IN)
timetracking = 1 # 0 for not tracking time and 1 for tracking time
Region = lsm
output_folder = r'C:\Users\bec\Desktop\WAM2\output'
# END OF INPUT
#%% Datapaths (FILL THIS IN)
def data_path(years,timetracking):
load_data_pypm = os.path.join(output_folder, 'E_track_continental_full' + str(years[0]) + '-' + str(years[-1]) + '-timetracking' + str(timetracking) + '.mat')
load_data_hf = os.path.join(output_folder, 'Hor_Fluxes_full' + str(years[0]) + '-' + str(years[-1]) + '.mat')
return load_data_pypm, load_data_hf
#%% Calculations
# loading vertical fluxes and storages
datapath = data_path(years,timetracking)
loading_pypm = sio.loadmat(datapath[0])
E_per_year_per_month = loading_pypm['E_per_year_per_month']
E_track_per_year_per_month = loading_pypm['E_track_per_year_per_month']
P_per_year_per_month = loading_pypm['P_per_year_per_month']
Sa_track_down_per_year_per_month = loading_pypm['Sa_track_down_per_year_per_month']
Sa_track_top_per_year_per_month = loading_pypm['Sa_track_top_per_year_per_month']
Sa_time_down_per_year_per_month = loading_pypm['Sa_time_down_per_year_per_month']
Sa_time_top_per_year_per_month = loading_pypm['Sa_time_top_per_year_per_month']
E_time_per_year_per_month = loading_pypm['E_time_per_year_per_month']
#########
test = E_time_per_year_per_month[0,0,:,:]
W_down_per_year_per_month = loading_pypm['W_down_per_year_per_month']
W_top_per_year_per_month = loading_pypm['W_top_per_year_per_month']
north_loss_per_year_per_month = loading_pypm['north_loss_per_year_per_month']
south_loss_per_year_per_month = loading_pypm['south_loss_per_year_per_month']
down_to_top_per_year_per_month = loading_pypm['down_to_top_per_year_per_month']
top_to_down_per_year_per_month = loading_pypm['top_to_down_per_year_per_month']
water_lost_per_year_per_month = loading_pypm['water_lost_per_year_per_month']
E_total = np.squeeze(np.sum(np.sum(E_per_year_per_month,axis = 0), axis = 0))
E_c_total = np.squeeze(np.sum(np.sum(E_track_per_year_per_month,axis = 0), axis = 0))
P_total = np.squeeze(np.sum(np.sum(P_per_year_per_month,axis = 0), axis = 0))
# loading horizontal fluxes
loading_HF2S = sio.loadmat(datapath[1])
Fa_E_down_per_year_per_month = loading_HF2S['Fa_E_down_per_year_per_month']
Fa_E_top_per_year_per_month = loading_HF2S['Fa_E_top_per_year_per_month']
Fa_N_down_per_year_per_month = loading_HF2S['Fa_N_down_per_year_per_month']
Fa_N_top_per_year_per_month = loading_HF2S['Fa_N_top_per_year_per_month']
Fa_Vert_per_year_per_month = loading_HF2S['Fa_Vert_per_year_per_month']
Fa_E_down_total = np.squeeze(np.sum(np.sum(Fa_E_down_per_year_per_month,axis = 0),axis = 0))
Fa_E_top_total = np.squeeze(np.sum(np.sum(Fa_E_top_per_year_per_month,axis = 0),axis = 0))
Fa_N_down_total = np.squeeze(np.sum(np.sum(Fa_N_down_per_year_per_month,axis = 0),axis = 0))
Fa_N_top_total = np.squeeze(np.sum(np.sum(Fa_N_top_per_year_per_month,axis = 0),axis = 0))
Fa_E_total = Fa_E_down_total + Fa_E_top_total
Fa_N_total = Fa_N_down_total + Fa_N_top_total
# annual average fluxes per unit width (m3/m/a)
Fa_N_total_m2a = np.zeros(np.shape(Fa_N_total))
Fa_N_down_total_m2a = np.zeros(np.shape(Fa_N_down_total))
Fa_N_top_total_m2a = np.zeros(np.shape(Fa_N_top_total))
for i in range(len(latitude)):
Fa_N_total_m2a[i,:] = (Fa_N_total[i,:] / (0.5*(L_N_gridcell[i] + L_S_gridcell[i])))/len(years)
Fa_N_down_total_m2a[i,:] = (Fa_N_down_total[i,:] / (0.5*(L_N_gridcell[i] + L_S_gridcell[i])))/len(years)
Fa_N_top_total_m2a[i,:] = (Fa_N_top_total[i,:] / (0.5*(L_N_gridcell[i] + L_S_gridcell[i])))/len(years)
Fa_E_total_m2a = (Fa_E_total / L_EW_gridcell) / len(years) # Annual average flux per unit width (m3/m/a)
Fa_E_down_total_m2a = (Fa_E_down_total / L_EW_gridcell) / len(years) # Annual average flux per unit width (m3/m/a)
Fa_E_top_total_m2a = (Fa_E_top_total / L_EW_gridcell) / len(years) # Annual average flux per unit width (m3/m/a)
# fluxes for plotting
FluxDispMatSmall = np.zeros([5,5])
FluxDispMatSmall[3,2] = 1
FluxDispMat = np.tile(FluxDispMatSmall,[np.floor(len(latitude)/5+1),np.floor(len(longitude)/5)+1])
FluxDispMat = FluxDispMat[:len(latitude),:len(longitude)]
Fa_E_total_m2a_part = Fa_E_total_m2a * FluxDispMat
Fa_N_total_m2a_part = Fa_N_total_m2a * FluxDispMat
Fa_E_down_total_m2a_part = Fa_E_down_total_m2a * FluxDispMat
Fa_N_down_total_m2a_part = Fa_N_down_total_m2a * FluxDispMat
Fa_E_top_total_m2a_part = Fa_E_top_total_m2a * FluxDispMat
Fa_N_top_total_m2a_part = Fa_N_top_total_m2a * FluxDispMat
# recycling metrics
eps_c_total = E_c_total / E_total
#only on land
E_landtotal = np.array(E_total)
E_landtotal[Region == 0] = 0
P_landtotal = np.array(P_total)
P_landtotal[Region == 0] = 0
E_c_landtotal = np.array(E_c_total)
E_c_landtotal[Region == 0] = 0
eps_c_landtotal = np.array(eps_c_total)
eps_c_landtotal[Region == 0] = -0.1
eps_c_abslandtotal = np.sum(np.sum(E_c_landtotal, axis = 0))/np.sum(np.sum(E_landtotal, axis = 0))
#what happens
Test_E_c = np.sum(E_c_total)
Test_P_land = np.sum(P_landtotal)
Mean_down_to_top = np.squeeze(np.sum(np.sum(down_to_top_per_year_per_month, axis = 0), axis = 0))
Test_down_to_top = np.sum(Mean_down_to_top)
Mean_top_to_down = np.squeeze(np.sum(np.sum(top_to_down_per_year_per_month, axis = 0), axis = 0))
Test_top_to_down = np.sum(Mean_top_to_down)
Mean_water_lost = np.squeeze(np.sum(np.sum(water_lost_per_year_per_month, axis = 0), axis = 0))
Test_water_lost = np.sum(Mean_water_lost)
Test_north_loss = np.sum(north_loss_per_year_per_month)
Test_south_loss = np.sum(south_loss_per_year_per_month)
Isthis100procent = (Test_E_c + Test_water_lost + Test_north_loss + Test_south_loss) / Test_P_land
print Isthis100procent*100
#just to know
water_lost_vertically = (Test_water_lost)/np.sum(E_landtotal)
water_lost_north = (Test_north_loss)/np.sum(E_landtotal)
water_lost_south = (Test_south_loss)/np.sum(E_landtotal)
print(water_lost_vertically*100, water_lost_north*100, water_lost_south*100)
#%% Plotting constants
get_ipython().magic(u'matplotlib qt')
from mpl_toolkits.basemap import Basemap
cdict = {'red': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 0, 0),
(0.66, 1, 1),
(0.89, 1, 1),
(1, 0.5, 0.5),
(1, 0, 0)),
'green': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 0, 0),
(0.375, 1, 1),
(0.64, 1, 1),
(0.91, 0, 0),
(1, 0, 0)),
'blue': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 1, 1),
(0.34, 1, 1),
(0.65, 0, 0),
(1, 0, 0))}
my_cmap = LinearSegmentedColormap('my_colormap',cdict,256)
vmax = 1.0
cmap = LinearSegmentedColormap.from_list('mycmap', [(0 / vmax, 'white'),
(0.01 / vmax, 'blue'),
(0.3 / vmax, 'lightgreen'),
(0.5 / vmax, 'orange'),
(0.6 / vmax, 'red'),
(1.0 / vmax, 'black')])
#%% Plots
#plt.close('all')
################### CONTOUR PLOT
plt.figure() # nice projections are 'cea', 'cyl', 'eck4' and 'robin', however only 'cea' and 'cyl' allows for not showing the entire world
map = Basemap(projection='robin',lat_0=0,lon_0=0,
llcrnrlon=-180, llcrnrlat=-78,
urcrnrlon=180, urcrnrlat=78)
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
map.drawmeridians(np.arange(0,360,30),linewidth=0.1)
map.drawparallels(np.arange(-90,90,30),linewidth=0.1)
x_shift,y_shift = np.meshgrid(longitude,latitude)
x = x_shift
y = y_shift
# contour data over the map.
clevs = np.linspace(0,1.00,11)
clevs2 = np.linspace(0,1,1100)
cs = map.contourf(x,y,eps_c_landtotal,clevs2,linewidths=1.5,latlon=True, cmap=cmap)
map.colorbar(cs,location='right',pad="5%", ticks = clevs, label = '(-)')
plt.title('Continental evaporation recycling ratio ' r'$\epsilon_c$', fontsize = 14)
plt.show()
#%% ###################### GRIDCELL PLOT
plt.figure() # nice projections are 'cea', 'cyl', 'eck4' and 'robin', however only 'cea' and 'cyl' allows for not showing the entire world
map = Basemap(projection='cyl',lat_0=0,lon_0=0,
llcrnrlon=-180, llcrnrlat=-78,
urcrnrlon=180, urcrnrlat=78)
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
x_shift,y_shift = np.meshgrid(longitude,latitude)
x = x_shift - 0.75
y = y_shift + 0.75
lol = map.pcolormesh(x,y,eps_c_landtotal, latlon=True, cmap=cmap,vmin=0,vmax=1)
map.colorbar(lol,location='right',pad="5%", label = '(-)')
#windplot = map.quiver(x,y,Fa_E_total_m2a_part,Fa_N_total_m2a_part,color = 'black',scale = 199999999,latlon=True,width = 0.002, headaxislength = 5, headwidth = 2)
#adjust scale till arrows are visible
plt.title('Continental evaporation recycling ratio ' r'$\epsilon_c$', fontsize = 14)
plt.show()
#%% simplest imshow plot
plt.figure()
plt.imshow(eps_c_landtotal)
plt.colorbar()
#%% Timetracking results
# these results are very crude and just meant for a quick check. For publications these have been calculated more detailed
#######################################
# possible check to do the same with daily output or timestep output
########################################
# for some reasons the sizes of the variables in the "variable explorer" of Spyder get wrongly displayed after running code below, but calling them from the memory works fine
if timetracking == 1:
Sa_track_down_average = np.squeeze(np.mean(np.mean(Sa_track_down_per_year_per_month, axis=0), axis = 0)) #m3
Sa_track_top_average = np.squeeze(np.mean(np.mean(Sa_track_top_per_year_per_month, axis=0), axis = 0)) #m3
Sa_track_average = Sa_track_down_average + Sa_track_top_average #m3
Sa_track_down_average_mm = Sa_track_down_average / A_gridcell2D *1000 #mm
Sa_track_top_average_mm = Sa_track_top_average / A_gridcell2D *1000 #mm
Sa_track_average_mm = Sa_track_average / A_gridcell2D *1000 #mm
Sa_time_down_average = np.squeeze(np.mean(np.mean(Sa_time_down_per_year_per_month, axis=0), axis = 0)) / 86400 #d quick assesment
Sa_time_top_average = np.squeeze(np.mean(np.mean(Sa_time_top_per_year_per_month, axis=0), axis = 0)) / 86400 #d quick assesment
Sa_time_average = (( np.squeeze(np.mean(np.mean(Sa_time_down_per_year_per_month * Sa_track_down_per_year_per_month, axis=0), axis = 0))
+ np.squeeze(np.mean(np.mean(Sa_time_top_per_year_per_month * Sa_track_top_per_year_per_month,axis=0), axis = 0))
) / Sa_track_average / 86400) #d
Sa_track_down_average = np.squeeze(np.mean(np.mean(Sa_track_down_per_year_per_month, axis=0), axis = 0)) #m3
Sa_track_top_average = np.squeeze(np.mean(np.mean(Sa_track_top_per_year_per_month, axis=0), axis = 0)) #m3
age_down_track_water_global_mean = (np.sum(np.sum(Sa_time_down_average[1:-1,:] * Sa_track_down_average[1:-1,:], axis = 0 ), axis = 0)
/ np.sum(np.sum(Sa_track_down_average[1:-1,:], axis = 0), axis = 0)) #d
age_top_track_water_global_mean = (np.sum(np.sum(Sa_time_top_average[1:-1,:] * Sa_track_top_average[1:-1,:], axis = 0 ), axis = 0)
/ np.sum(np.sum(Sa_track_top_average[1:-1,:], axis = 0), axis = 0)) #d
age_track_water_global_mean = (( np.sum(np.sum(Sa_time_down_average[1:-1,:] * Sa_track_down_average[1:-1,:], axis = 0), axis = 0)
+ np.sum(np.sum(Sa_time_top_average[1:-1,:] * Sa_track_top_average[1:-1,:], axis = 0 ), axis = 0) )
/ ( np.sum(np.sum(Sa_track_down_average[1:-1,:], axis = 0), axis = 0) + np.sum(np.sum(Sa_track_top_average[1:-1,:], axis = 0), axis = 0) )) #d
E_c_average = np.squeeze(np.sum(np.sum(E_track_per_year_per_month, axis = 0), axis = 0) ) / len(years) #m3 per year
E_c_time_average = ( ( np.squeeze(np.sum(np.sum(E_time_per_year_per_month * E_track_per_year_per_month, axis = 0), axis = 0))
/ len(years) ) / E_c_average / 86400 ) #d
# replace possible nans for a smooth plot
E_c_time_average[np.isnan(E_c_time_average)] = Sa_time_average[np.isnan(E_c_time_average)]