def make_simonrobertsdata(S, U, Y, stressor, allyears, meta):
data = np.zeros(shape=[meta['use_dd']['len_col'], len(allyears) + 1])
count = 0
for yr in allyears:
Z = io.make_Z_from_S_U(S[yr], U[yr])
bigY = np.zeros(shape=[np.size(Y[yr], 0) * 2, np.size(Y[yr], 1)])
bigY[np.size(Y[yr], 0):np.size(Y[yr], 0) * 2, :] = Y[yr]
x = io.make_x(Z, bigY)
L = io.make_L(Z, x)
bigstressor = np.zeros(shape=[np.size(Y[yr], 0) * 2, 1])
bigstressor[0:np.size(Y[yr], 0), :] = stressor[yr]
e = np.sum(bigstressor, 1) / x
temp = np.dot(np.dot(np.diag(e), L), np.diag(np.sum(bigY[:, 0:7], 1)))
for a in range(0, meta['use_dd']['len_col']):
temp2 = 0
for b in range(1, meta['reg']['len']):
temp2 = temp2 + np.sum(temp[b * meta['use_dd']['len_col'] + a,
424:848])
data[a, count] = temp2
count = count + 1
return data
def make_exp_mult_LCF(S_d, U_d, Y_d, nrg, years, yhh_prop, yhh_wide, lcf_136):
# e = {}
nrg_CC_mult = {}
file = '/Users/earao/Source Data/IEA/direct_from_ons.xlsx'
direct = pd.read_excel(file, sheet_name='direct')
lcf_exp = {}
foots = np.zeros(shape=[len(years), 1])
count = 0
eL = {}
# L = {}
nrg_prod_mult = np.zeros(shape=[136, 106, len(years)])
for yr in years:
Z = io.make_Z_from_S_U(S_d[yr], U_d[yr])
bigY = np.zeros(shape=[2120, 8])
bigY[1060:2120, 0:8] = Y_d[yr]
x = io.make_x(Z, bigY)
L = io.make_L(Z, x)
bignrg = np.zeros(shape=[2120, 1])
bignrg[0:1060, :] = nrg[yr]
e = np.sum(bignrg, 1) / x
eL[yr] = np.dot(e, L)
tempyprop = np.zeros(shape=(len(Z)))
tempyprop[len(S_d[yr]):] = yhh_prop[yr]
temp_nrg_prod_mult = np.zeros(shape=[106])
nrg_prod_big = np.multiply(eL[yr], tempyprop)
for a in range(0, 10):
temp_nrg_prod_mult = temp_nrg_prod_mult + nrg_prod_big[
(a * 106) + 1060:((a + 1) * 106) + 1060]
nrg_prod_mult[:, :, count] = np.tile(temp_nrg_prod_mult, [136, 1])
foot = np.multiply(np.transpose(nrg_prod_mult[:, :, count]),
yhh_wide[yr])
foot = np.nan_to_num(foot)
foot = np.sum(foot, 0) + (direct.loc[:, int(yr)] * 1000000)
foots[count, 0] = np.sum(foot, 0) / 1000000
nrg_CC_mult[yr] = np.divide(foot, np.sum(yhh_wide[yr], 0)) / 1000000
np.nan_to_num(nrg_CC_mult[yr], copy=False)
temp = np.divide(np.sum(yhh_wide[yr], 0), np.sum(lcf_136[yr], 1))
lcf_exp[yr] = df(np.dot(np.diag(temp), lcf_136[yr]),
index=lcf_136[yr].index,
columns=lcf_136[yr].columns)
np.nan_to_num(lcf_exp[yr], copy=False)
count = count + 1
return (lcf_exp, nrg_CC_mult)
def makefoot(S,U,Y,stressor,years):
footbyCOICOP = {}
for yr in years:
temp = np.zeros(shape = 307)
Z = io.make_Z_from_S_U(S[yr],U[yr])
bigY = np.zeros(shape = [np.size(Y[yr],0)*2,np.size(Y[yr],1)])
bigY[np.size(Y[yr],0):np.size(Y[yr],0)*2,0:] = Y[yr]
x = io.make_x(Z,bigY)
L = io.make_L(Z,x)
bigstressor = np.zeros(shape = [np.size(Y[yr],0)*2,1])
bigstressor[0:np.size(Y[yr],0),:] = stressor[yr]
e = np.sum(bigstressor,1)/x
eL = np.dot(e,L)
for a in range(0,307):
temp[a] = np.dot(eL,bigY[:,a])
footbyCOICOP[yr] = temp
return footbyCOICOP
def make_footprint(S, U, Y, nrg, allyears):
footprint = np.zeros(shape=[10, len(allyears)])
count = 0
for yr in allyears:
Z = io.make_Z_from_S_U(S[yr], U[yr])
bigY = np.zeros(
shape=[np.size(Y['1997'], 0) *
2, np.size(Y['1997'], 1)])
bigY[np.size(Y['1997'], 0):np.size(Y['1997'], 0) * 2, 0:8] = Y[yr]
x = io.make_x(Z, bigY)
L = io.make_L(Z, x)
bignrg = np.zeros(shape=[np.size(Y['1997'], 0) * 2, 1])
bignrg[0:np.size(Y['1997'], 0), :] = nrg[yr]
e = np.sum(bignrg, 1) / x
temp = np.dot(np.dot(np.diag(e), L), np.diag(np.sum(bigY[:, 0:7], 1)))
for a in range(0, 4):
footprint[a, count] = np.sum(temp[a * 106:(a + 1) * 106, :])
count = count + 1
return footprint
def make_hhfootprint(S, U, Y, stressor, allyears, meta):
footprint = np.zeros(shape=[meta['reg']['len'], len(allyears)])
count = 0
for yr in allyears:
Z = io.make_Z_from_S_U(S[yr], U[yr])
bigY = np.zeros(shape=[np.size(Y[yr], 0) * 2, np.size(Y[yr], 1)])
bigY[np.size(Y[yr], 0):np.size(Y[yr], 0) * 2, :] = Y[yr]
x = io.make_x(Z, bigY)
L = io.make_L(Z, x)
bigstressor = np.zeros(shape=[np.size(Y[yr], 0) * 2, 1])
bigstressor[0:np.size(Y[yr], 0), :] = stressor[yr]
e = np.sum(bigstressor, 1) / x
temp = np.dot(np.dot(np.diag(e), L), np.diag(np.sum(bigY[:, 0:36], 1)))
for a in range(0, meta['reg']['len']):
footprint[a, count] = np.sum(temp[a * 106:(a + 1) * 106, :])
count = count + 1
return footprint
def make_defra_foot(S, U, Y, stressor, years):
defra_foot = {}
for yr in years:
Z = io.make_Z_from_S_U(S[yr], U[yr])
bigY = np.zeros(
shape=[np.size(Y['1997'], 0) *
2, np.size(Y['1997'], 1)])
bigY[np.size(Y['1997'], 0):np.size(Y['1997'], 0) * 2, 0:8] = Y[yr]
x = io.make_x(Z, bigY)
L = io.make_L(Z, x)
bigstressor = np.zeros(shape=[np.size(Y['1997'], 0) * 2, 1])
bigstressor[0:np.size(Y['1997'], 0), :] = stressor[yr]
e = np.sum(bigstressor, 1) / x
hhold = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 0]))
npish = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 1]))
cgovr = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 2]))
lgovr = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 3]))
gfcap = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 4]))
valua = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 5]))
chinv = np.dot(np.dot(np.diag(e), L), np.diag(bigY[:, 6]))
temp = np.zeros(shape=[106, 49])
temp[:, 0] = np.sum(hhold[0:106, 424:530], 0) + np.sum(
hhold[0:106, 530:636], 0) + np.sum(
hhold[0:106, 636:742], 0) + np.sum(hhold[0:106, 742:848], 0)
temp[:, 1] = np.sum(npish[0:106, 424:530], 0) + np.sum(
npish[0:106, 530:636], 0) + np.sum(
npish[0:106, 636:742], 0) + np.sum(npish[0:106, 742:848], 0)
temp[:, 2] = np.sum(cgovr[0:106, 424:530], 0) + np.sum(
cgovr[0:106, 530:636], 0) + np.sum(
cgovr[0:106, 636:742], 0) + np.sum(cgovr[0:106, 742:848], 0)
temp[:, 3] = np.sum(lgovr[0:106, 424:530], 0) + np.sum(
lgovr[0:106, 530:636], 0) + np.sum(
lgovr[0:106, 636:742], 0) + np.sum(lgovr[0:106, 742:848], 0)
temp[:, 4] = np.sum(gfcap[0:106, 424:530], 0) + np.sum(
gfcap[0:106, 530:636], 0) + np.sum(
gfcap[0:106, 636:742], 0) + np.sum(gfcap[0:106, 742:848], 0)
temp[:, 5] = np.sum(valua[0:106, 424:530], 0) + np.sum(
valua[0:106, 530:636], 0) + np.sum(
valua[0:106, 636:742], 0) + np.sum(valua[0:106, 742:848], 0)
temp[:, 6] = np.sum(chinv[0:106, 424:530], 0) + np.sum(
chinv[0:106, 530:636], 0) + np.sum(
chinv[0:106, 636:742], 0) + np.sum(chinv[0:106, 742:848], 0)
temp[:, 7] = np.sum(hhold[106:212, 424:530], 0)
temp[:, 8] = np.sum(npish[106:212, 424:530], 0)
temp[:, 9] = np.sum(cgovr[106:212, 424:530], 0)
temp[:, 10] = np.sum(lgovr[106:212, 424:530], 0)
temp[:, 11] = np.sum(gfcap[106:212, 424:530], 0)
temp[:, 12] = np.sum(valua[106:212, 424:530], 0)
temp[:, 13] = np.sum(chinv[106:212, 424:530], 0)
temp[:, 14] = np.sum(hhold[212:318, 424:530], 0)
temp[:, 15] = np.sum(npish[212:318, 424:530], 0)
temp[:, 16] = np.sum(cgovr[212:318, 424:530], 0)
temp[:, 17] = np.sum(lgovr[212:318, 424:530], 0)
temp[:, 18] = np.sum(gfcap[212:318, 424:530], 0)
temp[:, 19] = np.sum(valua[212:318, 424:530], 0)
temp[:, 20] = np.sum(chinv[212:318, 424:530], 0)
temp[:, 21] = np.sum(hhold[318:424, 424:530], 0)
temp[:, 22] = np.sum(npish[318:424, 424:530], 0)
temp[:, 23] = np.sum(cgovr[318:424, 424:530], 0)
temp[:, 24] = np.sum(lgovr[318:424, 424:530], 0)
temp[:, 25] = np.sum(gfcap[318:424, 424:530], 0)
temp[:, 26] = np.sum(valua[318:424, 424:530], 0)
temp[:, 27] = np.sum(chinv[318:424, 424:530], 0)
temp[:, 28] = np.sum(hhold[106:212, 530:636], 0) + np.sum(
hhold[106:212, 636:742], 0) + np.sum(hhold[106:212, 742:848], 0)
temp[:, 29] = np.sum(npish[106:212, 530:636], 0) + np.sum(
npish[106:212, 636:742], 0) + np.sum(npish[106:212, 742:848], 0)
temp[:, 30] = np.sum(cgovr[106:212, 530:636], 0) + np.sum(
cgovr[106:212, 636:742], 0) + np.sum(cgovr[106:212, 742:848], 0)
temp[:, 31] = np.sum(lgovr[106:212, 530:636], 0) + np.sum(
lgovr[106:212, 636:742], 0) + np.sum(lgovr[106:212, 742:848], 0)
temp[:, 32] = np.sum(gfcap[106:212, 530:636], 0) + np.sum(
gfcap[106:212, 636:742], 0) + np.sum(gfcap[106:212, 742:848], 0)
temp[:, 33] = np.sum(valua[106:212, 530:636], 0) + np.sum(
valua[106:212, 636:742], 0) + np.sum(valua[106:212, 742:848], 0)
temp[:, 34] = np.sum(chinv[106:212, 530:636], 0) + np.sum(
chinv[106:212, 636:742], 0) + np.sum(chinv[106:212, 742:848], 0)
temp[:, 35] = np.sum(hhold[212:318, 530:636], 0) + np.sum(
hhold[212:318, 636:742], 0) + np.sum(hhold[212:318, 742:848], 0)
temp[:, 36] = np.sum(npish[212:318, 530:636], 0) + np.sum(
npish[212:318, 636:742], 0) + np.sum(npish[212:318, 742:848], 0)
temp[:, 37] = np.sum(cgovr[212:318, 530:636], 0) + np.sum(
cgovr[212:318, 636:742], 0) + np.sum(cgovr[212:318, 742:848], 0)
temp[:, 38] = np.sum(lgovr[212:318, 530:636], 0) + np.sum(
lgovr[212:318, 636:742], 0) + np.sum(lgovr[212:318, 742:848], 0)
temp[:, 39] = np.sum(gfcap[212:318, 530:636], 0) + np.sum(
gfcap[212:318, 636:742], 0) + np.sum(gfcap[212:318, 742:848], 0)
temp[:, 40] = np.sum(valua[212:318, 530:636], 0) + np.sum(
valua[212:318, 636:742], 0) + np.sum(valua[212:318, 742:848], 0)
temp[:, 41] = np.sum(chinv[212:318, 530:636], 0) + np.sum(
chinv[212:318, 636:742], 0) + np.sum(chinv[212:318, 742:848], 0)
temp[:, 42] = np.sum(hhold[318:424, 530:636], 0) + np.sum(
hhold[318:424, 636:742], 0) + np.sum(hhold[318:424, 742:848], 0)
temp[:, 43] = np.sum(npish[318:424, 530:636], 0) + np.sum(
npish[318:424, 636:742], 0) + np.sum(npish[318:424, 742:848], 0)
temp[:, 44] = np.sum(cgovr[318:424, 530:636], 0) + np.sum(
cgovr[318:424, 636:742], 0) + np.sum(cgovr[318:424, 742:848], 0)
temp[:, 45] = np.sum(lgovr[318:424, 530:636], 0) + np.sum(
lgovr[318:424, 636:742], 0) + np.sum(lgovr[318:424, 742:848], 0)
temp[:, 46] = np.sum(gfcap[318:424, 530:636], 0) + np.sum(
gfcap[318:424, 636:742], 0) + np.sum(gfcap[318:424, 742:848], 0)
temp[:, 47] = np.sum(valua[318:424, 530:636], 0) + np.sum(
valua[318:424, 636:742], 0) + np.sum(valua[318:424, 742:848], 0)
temp[:, 48] = np.sum(chinv[318:424, 530:636], 0) + np.sum(
chinv[318:424, 636:742], 0) + np.sum(chinv[318:424, 742:848], 0)
defra_foot[yr] = temp
return defra_foot
def makeSDA(S, U, Y, nrg, allyears):
yr0 = '2005'
Z0 = io.make_Z_from_S_U(S[yr0], U[yr0])
bigY0 = np.zeros(shape=[2120, 8])
bigY0[1060:2120, 0:8] = Y[yr0]
bigYhh0 = bigY0[:, 0]
totalY0 = np.sum(np.sum(bigY0[:, 0]))
propY0 = bigY0[:, 0] / totalY0
x0 = io.make_x(Z0, bigY0)
L0 = io.make_L(Z0, x0)
bignrg0 = np.zeros(shape=[2120, 1])
bignrg0[0:1060, :] = nrg[yr0]
e0 = np.sum(bignrg0, 1) / x0
count = 0
temp = np.zeros(shape=[12, 3])
temp2 = np.zeros(shape=[12, 3])
for yr in allyears:
yr1 = yr
Z1 = io.make_Z_from_S_U(S[yr1], U[yr1])
bigY1 = np.zeros(shape=[2120, 8])
bigY1[1060:2120, 0:8] = Y[yr1]
bigYhh1 = bigY1[:, 0]
totalY1 = np.sum(np.sum(bigY1[:, 0]))
propY1 = bigY1[:, 0] / totalY1
x1 = io.make_x(Z1, bigY1)
L1 = io.make_L(Z1, x1)
bignrg1 = np.zeros(shape=[2120, 1])
bignrg1[0:1060, :] = nrg[yr1]
e1 = np.sum(bignrg1, 1) / x1
foot_0 = np.dot(e0, L0)
foot_0 = np.dot(foot_0, bigYhh0)
foot_1 = np.dot(e1, L1)
foot_1 = np.dot(foot_1, bigYhh1)
foot_2 = np.dot(e0, L0)
foot_2 = np.dot(foot_2, propY0)
foot_2 = np.dot(foot_2, totalY0)
foot_3 = np.dot(e1, L1)
foot_3 = np.dot(foot_3, propY1)
foot_3 = np.dot(foot_3, totalY1)
sda_0 = {}
sda_0[0] = e0
sda_0[1] = L0
sda_0[2] = bigYhh0
sda_1 = {}
sda_1[0] = e1
sda_1[1] = L1
sda_1[2] = bigYhh1
sda_2 = {}
sda_2[0] = np.dot(e0, L0)
sda_2[1] = propY0
sda_2[2] = totalY0
sda_3 = {}
sda_3[0] = np.dot(e1, L1)
sda_3[1] = propY1
sda_3[2] = totalY1
result = io.sda(sda_1, sda_0)
temp[count, :] = result[6, 1:4]
result2 = io.sda(sda_3, sda_2)
temp2[count, :] = result2[6, 1:4]
count = count + 1
return (temp, temp2)