def env_demand():
home = '/home/nealbob'
folder = '/Dropbox/Model/results/chapter7/chapter7/'
out = '/Dropbox/Thesis/IMG/chapter7/'
img_ext = '.pdf'
table_out = '/Dropbox/Thesis/STATS/chapter7/'
rows = ['CS', 'CS-HL', 'SWA', 'OA']
data = {}
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
data[row] = pickle.load(f)[2]['F3'][:, 1]
duration_curve(data, OUTFILE=home + out + 'down_win_dec' + img_ext)
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
data[row] = pickle.load(f)[2]['F3'][:, 0]
f.close()
duration_curve(data, OUTFILE=home + out + 'down_sum_dec' + img_ext)
from econlearn import TilecodeRegressor as TR
tr = TR(1, [11], 20)
fig = pylab.figure()
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
Q = np.sum(pickle.load(f)[2]['Q_env'], axis = 1) /1000
f.close()
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
S = np.sum(pickle.load(f)[2]['S'], axis = 1) / 2000
f.close()
#with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
# I = np.sum(pickle.load(f)[2]['I'], axis = 1)
# f.close()
tr.fit(S, Q)
tr.tile.plot(['x'], showdata=False, label=row)
pylab.xlabel('Storage volume, $S_t$ (GL)')
pylab.ylabel('Mean environmental flow, $q_{0t}$ (GL)')
setFigLinesBW_list(fig)
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_demand_S.pdf', bbox_inches='tight')
pylab.show()
tr = TR(1, [11], 20)
fig = pylab.figure()
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
Q = np.sum(pickle.load(f)[2]['Q_env'], axis = 1) /1000
f.close()
#with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
# S = np.sum(pickle.load(f)[2]['S'], axis = 1) / 2.0
# f.close()
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
I = np.sum(pickle.load(f)[2]['I'], axis = 1) / 1000
f.close()
tr.fit(I, Q)
tr.tile.plot(['x'], showdata=False, label=row)
pylab.xlabel('Inflow, $I_t$ (GL)')
pylab.ylabel('Mean environmental flow, $q_{0t}$ (GL)')
pylab.xlim(0, 2000)
setFigLinesBW_list(fig)
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_demand_I.pdf', bbox_inches='tight')
pylab.show()
tr = TR(1, [11], 20)
fig = pylab.figure()
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
B = np.sum(pickle.load(f)[2]['Budget'], axis = 1) / 1000000
f.close()
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
S = np.sum(pickle.load(f)[2]['S'], axis = 1) / 2000
f.close()
#with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
# I = np.sum(pickle.load(f)[2]['I'], axis = 1)
# f.close()
tr.fit(S, B)
tr.tile.plot(['x'], showdata=False, label=row)
pylab.xlabel('Storage volume, $S_t$ (GL)')
pylab.ylabel('Mean environmental trade, $P_t(a_{0t} - q_{0t})$ (\$m)')
setFigLinesBW_list(fig)
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_trade_S.pdf', bbox_inches='tight')
pylab.show()
tr = TR(1, [11], 20)
fig = pylab.figure()
for row in rows:
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
B = np.sum(pickle.load(f)[2]['Budget'], axis = 1) / 1000000
f.close()
with open(home + folder + '0' + row + '_' + '0' + '_result.pkl', 'rb') as f:
I = np.sum(pickle.load(f)[2]['I'], axis = 1) /1000
f.close()
tr.fit(I, B)
tr.tile.plot(['x'], showdata=False, label=row)
pylab.xlabel('Inflow, $I_t$ (GL)')
pylab.ylabel('Mean environmental trade, $P_t(a_{0t} - q_{0t})$ (\$m)')
pylab.xlim(0, 2000)
setFigLinesBW_list(fig)
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_trade_I.pdf', bbox_inches='tight')
pylab.show()
def planner(results):
"""
Generate charts and tables for central case scenarios
"""
home = '/home/nealbob'
folder = '/Dropbox/Model/results/chapter7/'
out = '/Dropbox/Thesis/IMG/chapter7/'
img_ext = '.pdf'
table_out = '/Dropbox/Thesis/STATS/chapter7/'
#with open(home + folder + 'central_result.pkl', 'rb') as f:
# results = pickle.load(f)
# f.close()
stats_envoff, timeseries_envoff, stats, timeseries = results
###### Summary results #####
cols = ['Mean', 'SD', '2.5th', '25th', '75th', '97.5th']
rows = ['Consumptive', 'Optimal']
series = ['SW', 'Profit', 'B', 'S', 'W', 'E', 'P']
scale = {'SW' : 1000000, 'Profit' : 1000000, 'S' : 1000, 'W' : 1000, 'E' : 1000, 'B' : 1000000, 'P' : 1}
m = 2
for x in series:
data0 = []
record = {}
for col in cols:
record[col] = stats_envoff[x]['Annual'][col][m] / scale[x]
data0.append(record)
record = {}
for col in cols:
record[col] = stats[x]['Annual'][col][m] / scale[x]
data0.append(record)
data = pandas.DataFrame(data0)
data.index = rows
with open(home + table_out + ' ' + x + '.txt', 'w') as f:
f.write(data.to_latex(float_format='{:,.2f}'.format, columns=cols))
f.close()
###### Environmental flows #####
cols = ['Mean', 'SD', '2.5th', '25th', '75th', '97.5th']
rows = ['Summer', 'Winter', 'Annual']
series = ['Q_env', 'Q']
scale = {'Q_env' : 1000, 'Q' : 1000}
m = 2
for x in series:
data0 = []
for row in rows:
record = {}
for col in cols:
record[col] = stats[x][row][col][m] / scale[x]
data0.append(record)
data = pandas.DataFrame(data0)
data.index = rows
with open(home + table_out + ' ' + x + '.txt', 'w') as f:
f.write(data.to_latex(float_format='{:,.2f}'.format, columns=cols))
f.close()
###### River flows #########
cols = ['Mean', 'SD', '2.5th', '25th', '75th', '97.5th']
rows = ['Summer', 'Winter', 'Annual']
series = ['F1', 'F1_tilde', 'F3', 'F3_tilde']
scale = 1000
m = 2
for x in series:
data0 = []
for row in rows:
record = {}
for col in cols:
record[col] = stats[x][row][col][m] / scale
data0.append(record)
data = pandas.DataFrame(data0)
data.index = rows
with open(home + table_out + ' ' + x + '.txt', 'w') as f:
f.write(data.to_latex(float_format='{:,.2f}'.format, columns=cols))
f.close()
for x in series:
data0 = []
for row in rows:
record = {}
for col in cols:
record[col] = stats_envoff[x][row][col][m] / scale
data0.append(record)
data = pandas.DataFrame(data0)
data.index = rows
with open(home + table_out + ' ' + x + '_envoff.txt', 'w') as f:
f.write(data.to_latex(float_format='{:,.2f}'.format, columns=cols))
f.close()
# Flow duration curves
data = {'Natural' : timeseries['F1_tilde'][:, 0]/1000,
'Consumptive' : timeseries_envoff['F1'][:, 0]/1000,
'Optimal' : timeseries['F1'][:, 0]/1000 }
duration_curve(data, OUTFILE=home + out + 'up_sum' + img_ext)
data = {'Natural' : timeseries['F1_tilde'][:, 1]/1000,
'Consumptive' : timeseries_envoff['F1'][:, 1]/1000,
'Optimal' : timeseries['F1'][:, 1]/1000 }
duration_curve(data, OUTFILE=home + out + 'up_win' + img_ext)
data = {'Natural' : timeseries['F3_tilde'][:, 0]/1000,
'Consumptive' : timeseries_envoff['F3'][:, 0]/1000,
'Optimal' : timeseries['F3'][:, 0]/1000 }
duration_curve(data, OUTFILE=home + out + 'down_sum' + img_ext)
data = {'Natural' : timeseries['F3_tilde'][:, 1] /1000,
'Consumptive' : timeseries_envoff['F3'][:, 1] /1000,
'Optimal' : timeseries['F3'][:, 1]/1000 }
duration_curve(data, OUTFILE=home + out + 'down_win' + img_ext)
from econlearn import TilecodeRegressor as TR
tr = TR(1, [11], 20)
I = timeseries['I'][1:100000,1] / 1000
idx = I < 1400
I = I[idx]
Q = timeseries['W'][1:100000,1] / 1000
Q = Q[idx]
S = timeseries['S'][1:100000,1] / 1000
S = S[idx]
S2 = timeseries['S'][1:100000,0] / 1000
S2 = S2[idx]
Popt = timeseries['P'][:,0]
Pcons = timeseries_envoff['P'][:,0]
tr.fit(I[1:30000], Q[1:30000])
tr.tile.plot(['x'], showdata=True)
pylab.xlabel('Inflow, $I_t$ (GL)')
pylab.ylabel('Release, $W_t$ (GL)')
pylab.xlim(0, 1400)
#setFigLinesBW_list(fig)
#pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_dem_planner.pdf', bbox_inches='tight')
pylab.show()
tr.fit(S[1:30000], Q[1:30000])
tr.tile.plot(['x'], showdata=True)
pylab.xlabel('Storage, $S_t$ (GL)')
pylab.ylabel('Release, $W_t$ (GL)')
#setFigLinesBW_list(fig)
#pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.savefig(home + out + 'env_dem_plannerS.pdf', bbox_inches='tight')
pylab.show()
pylab.hexbin(S2, I, C=Q)
pylab.xlabel('Summer storage, $S_t$ (GL)')
pylab.ylabel('Winter inflow, $I_t$ (GL)')
cb = pylab.colorbar()
cb.set_label('Winter release, $W_t$ (GL)')
#pylab.ylim(0, 1000)
pylab.savefig(home + out + 'env_dem_plannerZ.pdf', bbox_inches='tight')
pylab.show()
xopt = pylab.hist(Popt, bins=120)
xcons = pylab.hist(Pcons, bins=120)
pylab.show()
fig = pylab.figure()
pylab.plot(xopt[1][1::], xopt[0]/500000, label = 'Optimal')
pylab.plot(xcons[1][1::], xcons[0]/500000, label = 'Consumptive')
setFigLinesBW_list(fig)
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
pylab.xlabel('Summer shadow price, $ per ML')
pylab.ylabel('Frequency')
pylab.xlim(0, 500)
pylab.savefig(home + out + 'plan_price.pdf', bbox_inches='tight')
pylab.show()
