forked from PascalLesage/database_wide_monte_carlo
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathwater_balancing.py
299 lines (242 loc) · 13 KB
/
water_balancing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
import os
import pickle
import numpy as np
def balance_water_exchanges(lca, common_dir):
""" Change values in A and B matrices of LCA"""
strategy_lists, \
initial_ratios_default, rows_of_interest_default, \
initial_ratios_inverse, rows_of_interest_inverse, \
set_static_data, \
rows_of_interest_tap_water, \
unit_scaling_techno_product, unit_scaling_techno_waste \
= load_water_exchange_balancing_data(common_dir)
print("rebalancing - default strategy")
for act in strategy_lists['default']:
lca = scale_exc_default(
lca=lca,
act=act,
rows_of_interest_default=rows_of_interest_default,
initial_ratios_default=initial_ratios_default,
unit_scaling_techno_product=unit_scaling_techno_product,
unit_scaling_techno_waste=unit_scaling_techno_waste
)
print("rebalancing - inverse strategy")
for act in strategy_lists['inverse']:
lca = scale_exc_inverse(
lca=lca,
act=act,
rows_of_interest_inverse=rows_of_interest_inverse,
initial_ratios_inverse=initial_ratios_inverse,
unit_scaling_techno_waste=unit_scaling_techno_waste,
unit_scaling_techno_product=unit_scaling_techno_product)
print("rebalancing - set_static strategy")
for act in strategy_lists['set_static']:
lca = scale_exc_static(lca, act, set_static_data)
if strategy_lists['tap_water_market']:
print("rebalancing tap water markets")
for act in strategy_lists['tap_water_market']:
scale_exc_tap_water_market(lca, act, rows_of_interest_tap_water)
return lca
def load_water_exchange_balancing_data(common_dir):
""" Load various files required for balancing water exchanges"""
water_dir = os.path.join(common_dir, "water_info")
# Dict used to assign acts to different strategies
with open(os.path.join(water_dir, "strategy_lists.pickle"), 'rb') as f:
strategy_lists = pickle.load(f)
# Unit conversion factors
try:
with open(os.path.join(water_dir, "unit_scaling_techno_product.pickle"), 'rb') as f:
unit_scaling_techno_product = pickle.load(f)
except:
unit_scaling_techno_product = None
try:
with open(os.path.join(water_dir, "unit_scaling_techno_waste.pickle"), 'rb') as f:
unit_scaling_techno_waste = pickle.load(f)
except:
unit_scaling_techno_waste = None
# Default strategy data
try:
with open(os.path.join(water_dir, "initial_ratios_default.pickle"), 'rb') as f:
initial_ratios_default = pickle.load(f)
except:
initial_ratios_default=None
try:
with open(os.path.join(water_dir, "rows_of_interest_default.pickle"), 'rb') as f:
rows_of_interest_default = pickle.load(f)
except:
rows_of_interest_default=None
# Inverse strategy data
try:
with open(os.path.join(water_dir, "initial_ratios_inverse.pickle"), 'rb') as f:
initial_ratios_inverse = pickle.load(f)
except:
initial_ratios_inverse = None
try:
with open(os.path.join(water_dir, "rows_of_interest_inverse.pickle"), 'rb') as f:
rows_of_interest_inverse = pickle.load(f)
except:
rows_of_interest_inverse = None
# Set_static strategy data
try:
with open(os.path.join(water_dir, "set_static_data.pickle"), 'rb') as f:
set_static_data = pickle.load(f)
except:
set_static_data = None
# Set_static strategy data
try:
with open(os.path.join(water_dir, "tap_water_market_data.pickle"), 'rb') as f:
tap_water_market_data = pickle.load(f)
except:
tap_water_market_data = None
return strategy_lists, \
initial_ratios_default, rows_of_interest_default, \
initial_ratios_inverse, rows_of_interest_inverse, \
set_static_data, \
tap_water_market_data, \
unit_scaling_techno_product, unit_scaling_techno_waste
def scale_exc_default(
lca, act,
rows_of_interest_default, initial_ratios_default,
unit_scaling_techno_product, unit_scaling_techno_waste):
""" Return indices and new amounts for exchanges that need scaling for specific act with default strategy"""
col = lca.activity_dict[act]
def get_A_rows(product_keys, lca=lca):
return [lca.product_dict[k] for k in product_keys]
def get_B_rows(ef_keys, lca=lca):
return [lca.biosphere_dict[k] for k in ef_keys]
def get_values(value_type, matrix):
assert matrix in ['A', 'B']
if matrix == 'A':
matrix = lca.technosphere_matrix
get_rows = get_A_rows
elif matrix == 'B':
matrix = lca.biosphere_matrix
get_rows = get_B_rows
return np.asarray(np.squeeze((matrix[get_rows(rows_of_interest_default[act][value_type]), col]).todense()))
ef_in_static_sampled = get_values('ef_in_static', 'B')
ef_in_to_balance_sampled = get_values('ef_in_to_balance', 'B')
ef_out_sampled = get_values('ef_out', 'B')
techno_in_product_static_sampled = - get_values('techno_in_product_static', 'A')
techno_in_product_to_balance_sampled = - get_values('techno_in_product_to_balance', 'A')
techno_in_waste_static_sampled = - get_values('techno_in_waste_static', 'A')
techno_in_waste_to_balance_sampled = - get_values('techno_in_waste_to_balance', 'A')
techno_out_product_sampled = get_values('techno_out_product', 'A')
techno_out_waste_sampled = get_values('techno_out_waste', 'A')
techno_in_product_static_unit_conversion = np.array(
[unit_scaling_techno_product[r]
for r in rows_of_interest_default[act]['techno_in_product_static']
]
)
techno_in_product_to_balance_unit_conversion = np.array(
[unit_scaling_techno_product[r]
for r in rows_of_interest_default[act]['techno_in_product_to_balance']
]
)
techno_in_waste_static_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_default[act]['techno_in_waste_static']])
techno_in_waste_to_balance_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_default[act]['techno_in_waste_to_balance']])
techno_out_product_unit_conversion = np.array(
[unit_scaling_techno_product[r] for r in rows_of_interest_default[act]['techno_out_product']])
techno_out_waste_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_default[act]['techno_out_waste']])
total_out = np.sum(ef_out_sampled * 1000) \
+ np.sum(techno_out_product_sampled * techno_out_product_unit_conversion) \
+ np.sum(techno_out_waste_sampled * techno_out_waste_unit_conversion)
constant_in = np.sum(ef_in_static_sampled * 1000) \
+ np.sum(techno_in_product_static_sampled * techno_in_product_static_unit_conversion) \
+ np.sum(techno_in_waste_static_sampled * techno_in_waste_static_unit_conversion)
variable_in = np.sum(ef_in_to_balance_sampled * 1000) \
+ np.sum(techno_in_product_to_balance_sampled * techno_in_product_to_balance_unit_conversion) \
+ np.sum(techno_in_waste_to_balance_sampled * techno_in_waste_to_balance_unit_conversion)
scaling = (initial_ratios_default[act] * total_out - constant_in) / variable_in
tech_amounts_product = scaling * -techno_in_product_to_balance_sampled
tech_amounts_waste = scaling * -techno_in_waste_to_balance_sampled
ef_amounts = scaling * ef_in_to_balance_sampled
tech_rows_product = get_A_rows(rows_of_interest_default[act]['techno_in_product_to_balance'])
tech_rows_waste = get_A_rows(rows_of_interest_default[act]['techno_in_waste_to_balance'])
ef_rows = get_B_rows(rows_of_interest_default[act]['ef_in_to_balance'])
if tech_amounts_product.size > 0:
lca.technosphere_matrix[tech_rows_product, col] = tech_amounts_product.T
if tech_amounts_waste.size > 0:
lca.technosphere_matrix[tech_rows_waste, col] = tech_amounts_waste.T
if ef_amounts.size > 0:
lca.biosphere_matrix[ef_rows, col] = ef_amounts.T
return lca
def scale_exc_inverse(
lca, act,
rows_of_interest_inverse, initial_ratios_inverse,
unit_scaling_techno_product, unit_scaling_techno_waste):
col = lca.activity_dict[act]
rev_product_dict = {v:k for k, v in lca.product_dict.items()}
def get_A_rows(product_keys, lca=lca):
return [lca.product_dict[k] for k in product_keys]
def get_B_rows(ef_keys, lca=lca):
return [lca.biosphere_dict[k] for k in ef_keys]
def get_values(value_type, matrix):
assert matrix in ['A', 'B']
if matrix=='A':
matrix = lca.technosphere_matrix
get_rows = get_A_rows
elif matrix=='B':
matrix = lca.biosphere_matrix
get_rows = get_B_rows
return np.asarray(np.squeeze((matrix[get_rows(rows_of_interest_inverse[act][value_type]), col]).todense()))
ef_out_static_sampled = get_values('ef_out_static', 'B')
ef_out_to_balance_sampled = get_values('ef_out_to_balance', 'B')
ef_in_sampled = get_values('ef_in', 'B')
techno_out_product_static_sampled = get_values('techno_out_product_static', 'A')
techno_out_product_to_balance_sampled = get_values('techno_out_product_to_balance', 'A')
techno_out_waste_static_sampled = get_values('techno_out_waste_static', 'A')
techno_out_waste_to_balance_sampled = get_values('techno_out_waste_to_balance', 'A')
techno_in_product_sampled = get_values('techno_in_product', 'A')
techno_in_waste_sampled = - get_values('techno_in_waste', 'A')
techno_out_product_static_unit_conversion = np.array(
[unit_scaling_techno_product[rev_product_dict[r]] for r in rows_of_interest_inverse[act]['techno_out_product_static']])
techno_out_product_to_balance_unit_conversion = np.array([unit_scaling_techno_product[rev_product_dict[r]] for r in
rows_of_interest_inverse[act]['techno_out_product_to_balance']])
techno_out_waste_static_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_inverse[act]['techno_out_waste_static']])
techno_out_waste_to_balance_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_inverse[act]['techno_out_waste_to_balance']])
techno_in_product_unit_conversion = np.array(
[unit_scaling_techno_product[r] for r in rows_of_interest_inverse[act]['techno_in_product']])
techno_in_waste_unit_conversion = np.array(
[unit_scaling_techno_waste[r] for r in rows_of_interest_inverse[act]['techno_in_waste']])
total_in = np.sum(ef_in_sampled * 1000) \
+ np.sum(techno_in_product_sampled * techno_in_product_unit_conversion) \
+ np.sum(techno_in_waste_sampled * techno_in_waste_unit_conversion)
constant_out = np.sum(ef_out_static_sampled * 1000) \
+ np.sum(techno_out_product_static_sampled * techno_out_product_static_unit_conversion) \
+ np.sum(techno_out_waste_static_sampled * techno_out_waste_static_unit_conversion)
variable_out = np.sum(ef_out_to_balance_sampled * 1000) \
+ np.sum(techno_out_product_to_balance_sampled * techno_out_product_to_balance_unit_conversion) \
+ np.sum(techno_out_waste_to_balance_sampled * techno_out_waste_to_balance_unit_conversion)
scaling = (initial_ratios_inverse[act] * total_in - constant_out) / variable_out
tech_rows_product = get_A_rows(rows_of_interest_inverse[act]['techno_out_product_to_balance'])
tech_rows_waste = get_A_rows(rows_of_interest_inverse[act]['techno_out_waste_to_balance'])
ef_rows = get_B_rows(rows_of_interest_inverse[act]['ef_out_to_balance'])
tech_amounts_product = scaling * techno_out_product_to_balance_sampled
tech_amounts_waste = - scaling * techno_out_waste_to_balance_sampled
ef_amounts = scaling * ef_out_to_balance_sampled
if tech_amounts_product.size > 0:
lca.technosphere_matrix[tech_rows_product, col] = tech_amounts_product.T
if tech_amounts_waste.size > 0:
lca.technosphere_matrix[tech_rows_waste, col] = tech_amounts_waste.T
if ef_amounts.size > 0:
lca.biosphere_matrix[ef_rows, col] = ef_amounts.T
return lca
def scale_exc_static(lca, act, set_static_data):
data = set_static_data[act]
col = lca.activity_dict[act]
techno_rows = [lca.product_dict[k] for k in data['techno_rows']]
bio_rows = [lca.biosphere_dict[k] for k in data['bio_rows']]
lca.technosphere_matrix[techno_rows, col] = np.array(data['techno_values']).reshape(-1, 1)
lca.biosphere_matrix[bio_rows, col] = np.array(data['bio_values']).reshape(-1, 1)
return lca
def scale_exc_tap_water_market(lca, act, rows_of_interest_tap_water):
col = lca.activity_dict[act]
loss = 1-lca.technosphere_matrix[col, col]
bio_row = [lca.biosphere_dict[k] for k in rows_of_interest_tap_water[act]]
lca.biosphere_matrix[bio_row, col] = loss
return lca