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z$l%xGKfx+5h!?b+fYd^=9%ayR?AMcf+b`uuhQeWLCR}*JKb<04n)bz8w1bX(kccj?$>x zK309)NmMB-%kq4S)G0^)6Laggz++hiI$N&1jx2uYOyBo7%9#;sL;1)VeV&;*o3GEw zCn`bC|M^fm&I2oDziy;`+7D*Fe%Fp-@5$*teE!;6)rXGeSHU~ea98;w{J-ub_~$$j z4a_MY@bcqU=G}W7=#KyZpuUgx|GTk(yC#93 z2uE(!!%5!DA;19u1pk%3r_{mElR!oytgQxpPc#5vz!Ct!`~LuS$lz%R5F4!fejr+? zq_l8-4=c#H{s}W7`424jUoaV8s+oKo03gQepRkYA|ANsG`HUi*1Q@&CSU0{*ibmjA$m-#rNa zf13D@Q2u{uTwI0!S76UT{LgsqzZl$gcrf?Od-eSH;laqAKmu^W43GmR@ZjG$O7O`H zkPc?$@PA|+vp@pae>rO~)GQDO=I_ye&c8Pp4f0+c#dk5x<}p0jW)`RdvwZ>&?wSRv z!VsSQJ3#^Fo&z$&^qs?lt>%EDFsm2v;HrNmL@xhFjx+ymDevwb4f($nq`Ur)kbr~9 z)XH924hov_z1QFX?`8xb2mIUpgIV6o!C>CPgQe$zpNRjT=Xy8kcboiQTk1VLIBXus h4+DOJ2eV87DR7kKfbTX90Fb`#zur}5Kf}N0{ttI+vmgKf From 3433bbad9404153eb7fed1afcba01b4c2259d7be Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Mon, 29 Jul 2019 13:54:52 -0400 Subject: [PATCH 02/12] Some of the new waste treatment hybridization features were disabled as they were giving off the charts results. --- src/pylcaio.py | 28 +++++++++++++++++++--------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/src/pylcaio.py b/src/pylcaio.py index 5a379df..ec82df2 100644 --- a/src/pylcaio.py +++ b/src/pylcaio.py @@ -891,15 +891,18 @@ def hybridize(self, method_double_counting, capitals=False): self.extract_scaling_vector_biosphere('Wood', 'incineration', inflation, Geo) self.extract_scaling_vector_biosphere('Oil/hazardous', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Food', 'landfill', inflation, Geo) - self.extract_scaling_vector_biosphere('Paper', 'landfill', inflation, Geo) self.extract_scaling_vector_biosphere('Plastic', 'landfill', inflation, Geo) self.extract_scaling_vector_biosphere('Inert/metal/hazardous', 'landfill', inflation, Geo) - self.extract_scaling_vector_biosphere('Textiles', 'landfill', inflation, Geo) self.extract_scaling_vector_biosphere('Wood', 'landfill', inflation, Geo) - self.extract_scaling_vector_biosphere('Food', 'waste water treatment', inflation, Geo) - self.extract_scaling_vector_biosphere('Other', 'waste water treatment', inflation, Geo) + # self.extract_scaling_vector_biosphere('Food', 'waste water treatment', inflation, Geo) + # self.extract_scaling_vector_biosphere('Other', 'waste water treatment', inflation, Geo) + + #self.extract_scaling_vector_biosphere('Food', 'biogasification and land application', inflation, Geo) + #self.extract_scaling_vector_biosphere('Sewage sludge', 'biogasification and land application', inflation, + # Geo) + self.extract_scaling_vector_biosphere('Food', 'composting and land application', inflation, Geo) + self.extract_scaling_vector_biosphere('Paper and wood', 'composting and land application', inflation, Geo) self.A_io_f_uncorrected = self.A_io_f_uncorrected.astype(dtype='float32') @@ -1322,7 +1325,9 @@ def extract_scaling_vector_biosphere(self, what_is_treated, treatment, inflation add_on_H_for_hyb.loc[:, [i for i in add_on_H_for_hyb.columns if i not in list_add_on_to_hyb]] = 0 - if treatment == 'incineration': + if (treatment in ['incineration', 'biogasification and land application'] or + (treatment == 'composting and land application' + and what_is_treated == 'Paper and wood')): dff = self.extract_flow_amounts_biosphere(list_add_on_to_hyb, False) else: dff = self.extract_flow_amounts_biosphere(list_add_on_to_hyb, True) @@ -1330,9 +1335,14 @@ def extract_scaling_vector_biosphere(self, what_is_treated, treatment, inflation scaling_vector = self.PRO_f.price.copy() scaling_vector.loc[:] = 0 list_CO2_extensions = [i for i in self.F_io.index if 'CO2' in i] - scaling_vector.loc[dff.index] = (dff / (self.aggregated_F_io.loc[list_CO2_extensions, str(what_is_treated) + - ' waste for treatment: ' + str(treatment)]. - sum() /self.number_of_countries_IO)).iloc[:, 0] + if what_is_treated == 'Sewage sludge': + scaling_vector.loc[dff.index] = (dff / (self.aggregated_F_io.loc[list_CO2_extensions, str(what_is_treated) + + ' for treatment: ' + str(treatment)]. + sum() /self.number_of_countries_IO)).iloc[:, 0] + else: + scaling_vector.loc[dff.index] = (dff / (self.aggregated_F_io.loc[list_CO2_extensions, str(what_is_treated) + + ' waste for treatment: ' + str(treatment)]. + sum() /self.number_of_countries_IO)).iloc[:, 0] self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(add_on_H_for_hyb * inflation * Geo) * scaling_vector From 1a571b7078e3615ab28e24c6d5e44f4537d4540b Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Tue, 30 Jul 2019 14:05:21 -0400 Subject: [PATCH 03/12] new hybridization without prices has been optimized for a shorter overall runtime --- src/pylcaio.py | 233 ++++++++++++++++++++++--------------------------- 1 file changed, 102 insertions(+), 131 deletions(-) diff --git a/src/pylcaio.py b/src/pylcaio.py index ec82df2..01a8fc3 100644 --- a/src/pylcaio.py +++ b/src/pylcaio.py @@ -29,6 +29,7 @@ import pickle import ast import pkg_resources +import time pd.set_option('mode.chained_assignment', None) @@ -688,6 +689,7 @@ def __init__(self, **kwargs): self.A_io_f_uncorrected = pd.DataFrame() self.aggregated_A_io = pd.DataFrame() self.aggregated_F_io = pd.DataFrame() + self.add_on_H_scaled_vector = pd.DataFrame() self.list_of_capital_sectors = ast.literal_eval(pkg_resources.resource_string( __name__, '/Data/List_of_capitals_goods.txt').decode('utf-8')) @@ -822,88 +824,74 @@ def hybridize(self, method_double_counting, capitals=False): basic_A_io_f_uncorrected = (self.A_io + self.K_io).dot(basic_H_for_hyb * inflation * Geo) * self.PRO_f.price # The following lines of code are introducing add-ons to enable hybridization bypassing bad price quality + self.add_on_H_scaled_vector = pd.DataFrame(0, self.H.index, self.H.columns) self.aggregated_A_io = (self.A_io + self.K_io).groupby('sector', axis=1).sum() self.aggregated_A_io = self.aggregated_A_io.groupby('sector', axis=0).sum() - self.aggregated_F_io = self.F_io.groupby(level=1, axis=1).sum() - buildings_add_on = self.extract_scaling_vector_technosphere( - [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - 'Construction work' and i in self.listguillotine], 'concrete', - 'Cement, lime and plaster', 'Construction work') - self.A_io_f_uncorrected = basic_A_io_f_uncorrected + (self.A_io + self.K_io).dot(buildings_add_on[0] * - inflation * Geo) * \ - buildings_add_on[1] - del buildings_add_on - del basic_A_io_f_uncorrected - - transport_equipment_add_on = self.extract_scaling_vector_technosphere( - [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - == 'Other transport equipment' and i in self.listguillotine - and 'aircraft' not in self.PRO_f.productName[i]], 'steel', - 'Basic iron and steel and of ferro-alloys and first ' - 'products thereof', 'Other transport equipment') - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(transport_equipment_add_on[0] * inflation * Geo) * \ - transport_equipment_add_on[1] - del transport_equipment_add_on - - air_transport_equipment_add_on = self.extract_scaling_vector_technosphere( - [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - == 'Other transport equipment' and i in self.listguillotine - and 'aircraft' in self.PRO_f.productName[i]], 'aluminium', - 'Aluminium and aluminium products', - 'Other transport equipment') - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot( - air_transport_equipment_add_on[0] * inflation * Geo) * \ - air_transport_equipment_add_on[1] - del air_transport_equipment_add_on - - machinery_add_on = self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - == 'Machinery and equipment n.e.c.' and i in self.listguillotine], - 'steel', - 'Basic iron and steel and of ferro-alloys and first products' - ' thereof', 'Machinery and equipment n.e.c.') - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(machinery_add_on[0] * inflation * Geo) * \ - machinery_add_on[1] - del machinery_add_on - - air_transportation_add_on = self.extract_scaling_vector_technosphere( - [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - == 'Air transport services' and i in self.listguillotine], - 'kerosene', 'Kerosene Type Jet Fuel', 'Air transport services') - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(air_transportation_add_on[0] * inflation * Geo) * \ - air_transportation_add_on[1] - del air_transportation_add_on - - rail_transportation_add_on = self.extract_scaling_vector_technosphere( - [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - == 'Railway transportation services' and i in self.listguillotine], - 'energy', 'Energy', 'Railway transportation services') - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(rail_transportation_add_on[0] * inflation * Geo) * \ - rail_transportation_add_on[1] - del rail_transportation_add_on - - self.extract_scaling_vector_biosphere('Food', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Paper', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Plastic', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Intert/metal', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Textiles', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Wood', 'incineration', inflation, Geo) - self.extract_scaling_vector_biosphere('Oil/hazardous', 'incineration', inflation, Geo) - - self.extract_scaling_vector_biosphere('Plastic', 'landfill', inflation, Geo) - self.extract_scaling_vector_biosphere('Inert/metal/hazardous', 'landfill', inflation, Geo) - self.extract_scaling_vector_biosphere('Wood', 'landfill', inflation, Geo) - - # self.extract_scaling_vector_biosphere('Food', 'waste water treatment', inflation, Geo) - # self.extract_scaling_vector_biosphere('Other', 'waste water treatment', inflation, Geo) - - #self.extract_scaling_vector_biosphere('Food', 'biogasification and land application', inflation, Geo) - #self.extract_scaling_vector_biosphere('Sewage sludge', 'biogasification and land application', inflation, - # Geo) - self.extract_scaling_vector_biosphere('Food', 'composting and land application', inflation, Geo) - self.extract_scaling_vector_biosphere('Paper and wood', 'composting and land application', inflation, Geo) - + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Construction work' and i in self.listguillotine], + 'concrete', + 'Cement, lime and plaster', + 'Construction work') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i]== + 'Other transport equipment' and i in self.listguillotine + and 'aircraft' not in self.PRO_f.productName[i]], + 'steel', + 'Basic iron and steel and of ferro-alloys and first products thereof', + 'Other transport equipment') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Other transport equipment' and i in self.listguillotine and + 'aircraft' in self.PRO_f.productName[i]], + 'aluminium', + 'Aluminium and aluminium products', + 'Other transport equipment') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i]== + 'Machinery and equipment n.e.c.' and i in self.listguillotine], + 'steel', + 'Basic iron and steel and of ferro-alloys and first products thereof', + 'Machinery and equipment n.e.c.') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Air transport services' and i in self.listguillotine], + 'kerosene', + 'Kerosene Type Jet Fuel', + 'Air transport services') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Railway transportation services' and i in self.listguillotine], + 'energy', + 'Energy', + 'Railway transportation services') + + self.extract_scaling_vector_biosphere('Food', 'incineration') + self.extract_scaling_vector_biosphere('Paper', 'incineration') + self.extract_scaling_vector_biosphere('Plastic', 'incineration') + self.extract_scaling_vector_biosphere('Intert/metal', 'incineration') + self.extract_scaling_vector_biosphere('Textiles', 'incineration') + self.extract_scaling_vector_biosphere('Wood', 'incineration') + self.extract_scaling_vector_biosphere('Oil/hazardous', 'incineration') + + self.extract_scaling_vector_biosphere('Plastic', 'landfill') + self.extract_scaling_vector_biosphere('Inert/metal/hazardous', 'landfill') + self.extract_scaling_vector_biosphere('Wood', 'landfill') + + # self.extract_scaling_vector_biosphere('Food', 'waste water treatment') + # self.extract_scaling_vector_biosphere('Other', 'waste water treatment') + + # self.extract_scaling_vector_biosphere('Food', 'biogasification and land application') + # self.extract_scaling_vector_biosphere('Sewage sludge', 'biogasification and land application') + + self.extract_scaling_vector_biosphere('Food', 'composting and land application') + self.extract_scaling_vector_biosphere('Paper and wood', 'composting and land application') + + # all the previous calls to extract_scaling functions were updating the self.add_on_H_scaled_vector, where + # each column of this matrix contains the scaling factor to use and from which sector to extrapolate + self.A_io_f_uncorrected = basic_A_io_f_uncorrected + (self.A_io + self.K_io).dot( + self.add_on_H_scaled_vector * inflation * Geo) self.A_io_f_uncorrected = self.A_io_f_uncorrected.astype(dtype='float32') self.description.append('Capitals endogenized') @@ -1279,6 +1267,7 @@ def extract_scaling_vector_technosphere(self, list_add_on_to_hyb, scaling_flow, sector_of_add_ons: the economic sector of the processes that are part of the add_on """ + add_on_H_for_hyb = self.H.copy() add_on_H_for_hyb.loc[:, [i for i in add_on_H_for_hyb.columns if i not in list_add_on_to_hyb]] = 0 @@ -1294,18 +1283,16 @@ def extract_scaling_vector_technosphere(self, list_add_on_to_hyb, scaling_flow, 'Electricity nec', 'Gas/Diesel Oil'] scaling_vector.loc[dff.index] = (dff / (self.aggregated_A_io.loc[list_energies, sector_of_add_ons].sum() / self.number_of_countries_IO)).iloc[:, 0] - elif sector_of_scaling_flow == 'CO2': - list_CO2_extensions = [i for i in self.F_io.index if 'CO2' in i] - scaling_vector.loc[dff.index] = (dff / (self.aggregated_F_io.loc[list_CO2_extensions, - sector_of_add_ons].sum() / - self.number_of_countries_IO)).iloc[:, 0] else: scaling_vector.loc[dff.index] = (dff / (self.aggregated_A_io.loc[sector_of_scaling_flow, sector_of_add_ons] / self.number_of_countries_IO)).iloc[:, 0] - return add_on_H_for_hyb, scaling_vector - def extract_scaling_vector_biosphere(self, what_is_treated, treatment, inflation, Geo): + self.add_on_H_scaled_vector = pd.concat([self.add_on_H_scaled_vector.drop(list_add_on_to_hyb, axis=1), + (add_on_H_for_hyb.multiply(scaling_vector)).loc[:, + list_add_on_to_hyb]], axis=1).reindex(columns=self.H.columns) + + def extract_scaling_vector_biosphere(self, what_is_treated, treatment): """This functions extracts the scaling vector used to bypass the poor price data quality of some processes of ecoinvent Args: @@ -1344,7 +1331,9 @@ def extract_scaling_vector_biosphere(self, what_is_treated, treatment, inflation ' waste for treatment: ' + str(treatment)]. sum() /self.number_of_countries_IO)).iloc[:, 0] - self.A_io_f_uncorrected += (self.A_io + self.K_io).dot(add_on_H_for_hyb * inflation * Geo) * scaling_vector + self.add_on_H_scaled_vector = pd.concat([self.add_on_H_scaled_vector.drop(list_add_on_to_hyb, axis=1), + (add_on_H_for_hyb.multiply(scaling_vector)).loc[:, + list_add_on_to_hyb]], axis=1).reindex(columns=self.H.columns) def extract_flow_amounts_technosphere(self, list_of_uuids, flow): """This function extracts the amount (in euros) necessary for the determination of a scaling vector @@ -1359,33 +1348,27 @@ def extract_flow_amounts_technosphere(self, list_of_uuids, flow): ------- a dataframe of the values of the desired flow (concrete, steel, etc.) per UUID """ - dictt = {} - for element in list_of_uuids: - dict_technosphere = {} - amount = 0 - technosphere = self.A_ff.loc[:, element] - for index in technosphere.index: - if technosphere[index] != 0: - dict_technosphere[index] = technosphere[index] - for key in dict_technosphere.keys(): - if flow == 'concrete': - if 'concrete' in self.PRO_f.productName[key] or 'cement, unspecified' in \ - self.PRO_f.productName[key]: - amount += dict_technosphere[key] * self.PRO_f.price[key] - elif 'building, hall' in self.PRO_f.productName[key]: - # 0.3m3 is the amount of concrete/m2 of building hall, 185 is the price of 1m3 of concrete - amount += 0.3 * dict_technosphere[key] * 185 - elif flow == 'steel': - if 'steel' in self.PRO_f.productName[key] or 'cast iron' in self.PRO_f.productName[key]: - amount += dict_technosphere[key] * self.PRO_f.price[key] - elif flow == 'energy': - if 'electricity' in self.PRO_f.productName[key] or 'diesel' in self.PRO_f.productName[key]: - amount += dict_technosphere[key] * self.PRO_f.price[key] - else: - if flow in self.PRO_f.productName[key]: - amount += dict_technosphere[key] * self.PRO_f.price[key] - dictt[element] = amount - return pd.DataFrame(list(dictt.values()), index=list(dictt.keys())) + if flow == 'concrete': + keyword = [i for i in self.A_ff.index if + ('concrete' in self.PRO_f.productName[i] or 'cement, unspecified' in self.PRO_f.productName[i])] + second_keyword = [i for i in self.A_ff.index if ('building, hall') in self.PRO_f.productName[i]] + return pd.DataFrame( + self.A_ff.loc[keyword, list_of_uuids].mul(self.PRO_f.price.loc[keyword], axis=0).sum(axis=0) + ( + self.A_ff.loc[second_keyword, list_of_uuids] * 0.3 * 185).sum(axis=0)) + elif flow == 'steel': + keyword = [i for i in self.A_ff.index if + ('steel' in self.PRO_f.productName[i] or 'cast iron' in self.PRO_f.productName[i])] + return pd.DataFrame( + self.A_ff.loc[keyword, list_of_uuids].mul(self.PRO_f.price.loc[keyword], axis=0).sum(axis=0)) + elif flow == 'energy': + keyword = [i for i in self.A_ff.index if + ('electricity' in self.PRO_f.productName[i] or 'diesel' in self.PRO_f.productName[i])] + return pd.DataFrame( + self.A_ff.loc[keyword, list_of_uuids].mul(self.PRO_f.price.loc[keyword], axis=0).sum(axis=0)) + else: + keyword = [i for i in self.A_ff.index if flow in self.PRO_f.productName[i]] + return pd.DataFrame( + self.A_ff.loc[keyword, list_of_uuids].mul(self.PRO_f.price.loc[keyword], axis=0).sum(axis=0)) def extract_flow_amounts_biosphere(self, list_of_uuids, biogenic=True): """This function extracts the amount (in euros) necessary for the determination of a scaling vector @@ -1401,24 +1384,12 @@ def extract_flow_amounts_biosphere(self, list_of_uuids, biogenic=True): ------- a dataframe of the values of the desired flow (concrete, steel, etc.) per UUID """ - dictt = {} - for element in list_of_uuids: - dict_biosphere = {} - amount = 0 - biosphere = self.F_f.loc[:, element] - for index in biosphere.index: - if biosphere[index] != 0: - dict_biosphere[index] = biosphere[index] - for key in dict_biosphere.keys(): - if biogenic: - if 'Carbon dioxide' in self.STR_f.loc[key, 'FULLNAME']: - amount += dict_biosphere[key] - elif not biogenic: - if ('Carbon dioxide' in self.STR_f.loc[key, 'FULLNAME'] - and 'non-fossil' not in self.STR_f.loc[key, 'FULLNAME']): - amount += dict_biosphere[key] - dictt[element] = amount - return pd.DataFrame(list(dictt.values()), index=list(dictt.keys())) + if biogenic: + keyword = [i for i in self.F_f.index if 'Carbon dioxide' in self.STR_f.FULLNAME[i]] + else: + keyword = [i for i in self.F_f.index if + ('Carbon dioxide' in self.STR_f.FULLNAME[i] and 'non-fossil' not in self.STR_f.FULLNAME[i])] + return pd.DataFrame(self.F_f.loc[keyword, list_of_uuids].sum()) # -------------------------- EXPORT RESULTS ----------------------------------- From 66a8ba0b3ce0bcff8091f39877aa9c96e4b9fd9c Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Thu, 1 Aug 2019 11:20:36 -0400 Subject: [PATCH 04/12] supports csv exporting --- src/pylcaio.py | 74 +++++++++++++++++++++++++++++++++----------------- 1 file changed, 49 insertions(+), 25 deletions(-) diff --git a/src/pylcaio.py b/src/pylcaio.py index 01a8fc3..19b34fe 100644 --- a/src/pylcaio.py +++ b/src/pylcaio.py @@ -1393,7 +1393,7 @@ def extract_flow_amounts_biosphere(self, list_of_uuids, biogenic=True): # -------------------------- EXPORT RESULTS ----------------------------------- - def save_system(self): + def save_system(self, format): """ Export the hybridized database to dataframe via pickle Returns: @@ -1422,12 +1422,18 @@ def save_system(self): os.mkdir(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + self.io_database_name_and_version + '_with_capitals_STAM/__init__.py')) - with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + - self.lca_database_name_and_version + '_' + - self.io_database_name_and_version + - '_with_capitals_STAM/hybrid_system.pickle')), - 'wb') as f: - pickle.dump(hybrid_system, f) + if format == 'pickle': + with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_with_capitals_STAM/hybrid_system.pickle')), + 'wb') as f: + pickle.dump(hybrid_system, f) + elif format == 'csv': + self.A_io_f.to_csv(pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_with_capitals_STAM/A_io_f.csv')) elif self.capitals and self.double_counting == 'binary': if not os.path.exists(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + @@ -1443,12 +1449,18 @@ def save_system(self): os.mkdir(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + self.io_database_name_and_version + '_with_capitals_binary/__init__.py')) - with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + - self.lca_database_name_and_version + '_' + - self.io_database_name_and_version + - '_with_capitals_binary/hybrid_system.pickle')), - 'wb') as f: - pickle.dump(hybrid_system, f) + if format == 'pickle': + with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_with_capitals_binary/hybrid_system.pickle')), + 'wb') as f: + pickle.dump(hybrid_system, f) + elif format == 'csv': + self.A_io_f.to_csv(pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_with_capitals_binary/A_io_f.csv')) elif not self.capitals and self.double_counting == 'STAM': if not os.path.exists(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + @@ -1464,12 +1476,18 @@ def save_system(self): os.mkdir(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + self.io_database_name_and_version + '_no_capitals_STAM/__init__.py')) - with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + - self.lca_database_name_and_version + '_' + - self.io_database_name_and_version + - '_no_capitals_STAM/hybrid_system.pickle')), - 'wb') as f: - pickle.dump(hybrid_system, f) + if format == 'pickle': + with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_no_capitals_STAM/hybrid_system.pickle')), + 'wb') as f: + pickle.dump(hybrid_system, f) + elif format == 'csv': + self.A_io_f.to_csv(pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_no_capitals_STAM/A_io_f.csv')) elif not self.capitals and self.double_counting == 'binary': if not os.path.exists(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + @@ -1485,12 +1503,18 @@ def save_system(self): os.mkdir(pkg_resources.resource_filename(__name__, '/Databases/' + self.lca_database_name_and_version + '_' + self.io_database_name_and_version + '_no_capitals_binary/__init__.py')) - with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + - self.lca_database_name_and_version + '_' + - self.io_database_name_and_version + - '_no_capitals_binary/hybrid_system.pickle')), - 'wb') as f: - pickle.dump(hybrid_system, f) + if format == 'pickle': + with gzip.open((pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_no_capitals_binary/hybrid_system.pickle')), + 'wb') as f: + pickle.dump(hybrid_system, f) + elif format == 'csv': + self.A_io_f.to_csv(pkg_resources.resource_filename(__name__, '/Databases/' + + self.lca_database_name_and_version + '_' + + self.io_database_name_and_version + + '_no_capitals_binary/A_io_f.csv')) class Analysis: From 21a89c485b957cbc1001ee16ea646596abbf1657 Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Mon, 16 Sep 2019 14:37:49 -0400 Subject: [PATCH 05/12] Separated capital matrix from vanilla matrix, so we can visualize the share of capitals. --- src/pylcaio.py | 332 ++++++++++++++++++++++++++----------------------- 1 file changed, 173 insertions(+), 159 deletions(-) diff --git a/src/pylcaio.py b/src/pylcaio.py index 19b34fe..95928d1 100644 --- a/src/pylcaio.py +++ b/src/pylcaio.py @@ -29,11 +29,10 @@ import pickle import ast import pkg_resources -import time +from time import time pd.set_option('mode.chained_assignment', None) - # pylint: disable-msg=C0103 @@ -167,13 +166,14 @@ def __init__(self, lca_database_processed, io_database_processed, if self.io_database_name_and_version not in versions_of_exiobase: print('The IO database version you entered is not supported currently') - def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals=''): + def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals='', complete_extensions=False): """ Loads every needed parameter to hybridize ecoinvent with exiobase as well as both databases Args --- * path_to_io_database : the path leading to the io database folder (only required if using exiobase2) * path_to_capitals : the path leading to the capitals new matrix (only required if wanting to endogenize capitals) + * complete_extensions : boolean (True to complete exiobase3 extensions based on USEEIO extension) """ version_ecoinvent = extract_version_from_name(self.lca_database_name_and_version) @@ -283,6 +283,26 @@ def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals='' self.C_io = self.C_io.astype(dtype='float32') self.reference_year_IO = int(self.IO_database.meta.description[-4:]) + if complete_extensions: + with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Completed_extensions_exio3/' + 'S_completed.pickle'), 'rb') as f: + F_extensions = pd.read_pickle(f) + with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Completed_extensions_exio3/' + 'C_completed_in_CML.pickle'), 'rb') as f: + C_extensions = pd.read_pickle(f) + + self.F_io = self.F_io.drop([i for i in self.IO_database.satellite.unit.index if + self.IO_database.satellite.unit.loc[i][0] in ['kg', 'kg CO2-eq']], axis=0) + self.F_io = pd.concat([self.F_io, F_extensions]) + self.F_io = self.F_io.reindex(self.A_io.index, axis=1) + + self.C_io = self.C_io.drop([i for i in self.IO_database.satellite.unit.index if + self.IO_database.satellite.unit.loc[i][0] in ['kg', 'kg CO2-eq']], axis=1) + self.C_io = self.C_io.drop([i for i in self.C_io.index if + self.C_io.loc[[i]].sum(axis=1)[0] == 0], axis=0) + self.C_io = pd.concat([self.C_io, C_extensions.transpose()], axis=1, sort=False).fillna(0) + self.C_io = self.C_io.reindex(self.F_io.index, axis=1) + self.F_io_f = pd.DataFrame(0, self.F_io.index, self.F_f.columns, dtype='float32') # CAPITAL GOODS @@ -650,6 +670,7 @@ def __init__(self, **kwargs): self.STR_io = pd.DataFrame() self.K_io = pd.DataFrame() + self.K_io_f = pd.DataFrame() self.io_categories = defaultdict(list) self.categories_same_functionality = [] @@ -687,6 +708,7 @@ def __init__(self, **kwargs): self.H = pd.DataFrame() self.G = pd.DataFrame() self.A_io_f_uncorrected = pd.DataFrame() + self.K_io_f_uncorrected = pd.DataFrame() self.aggregated_A_io = pd.DataFrame() self.aggregated_F_io = pd.DataFrame() self.add_on_H_scaled_vector = pd.DataFrame() @@ -696,6 +718,7 @@ def __init__(self, **kwargs): self.double_counting = '' self.capitals = '' self.description = [] + self.basic_A_io_f_uncorrected = pd.DataFrame() allowed_keys = list(self.__dict__.keys()) self.__dict__.update((key, value) for key, value in kwargs.items() if key in allowed_keys) @@ -729,10 +752,12 @@ def hybridize(self, method_double_counting, capitals=False): self.identify_rows() self.update_prices_electricity() self.calc_productions() + self.correct_inconsistencies() self.low_production_volume_processes() self.extend_inventory() self.A_io_f = pd.DataFrame(0.0, index=self.A_io.index, columns=self.A_ff.columns, dtype='float32') + self.K_io_f = pd.DataFrame(0.0, index=self.A_io.index, columns=self.A_ff.columns, dtype='float32') # ---- CONVERSION PART ------ @@ -815,154 +840,35 @@ def hybridize(self, method_double_counting, capitals=False): Geo = weighted_concordance_geography.dot(region_covered_per_process) # product concordance matrix filtered - basic_H_for_hyb = self.H.copy() - basic_H_for_hyb.loc[:, self.list_not_to_hyb] = 0 + H_for_hyb = self.H.copy() + H_for_hyb.loc[:, self.list_not_to_hyb] = 0 + + self.A_io_f_uncorrected = self.A_io.dot(H_for_hyb * inflation * Geo) * self.PRO_f.price + + # ---- HYBRIDIZATION WITHOUT PRICES ------ + + self.apply_scaling_without_prices() + + self.A_io_f_uncorrected = self.A_io_f_uncorrected + self.A_io.dot( + self.add_on_H_scaled_vector * inflation * Geo) + self.A_io_f_uncorrected = self.A_io_f_uncorrected.astype(dtype='float32') if capitals: + self.K_io_f_uncorrected = self.K_io.dot(H_for_hyb * inflation * Geo) * self.PRO_f.price - # the uncorrected A_io_f matrix is then defined by the following operation - basic_A_io_f_uncorrected = (self.A_io + self.K_io).dot(basic_H_for_hyb * inflation * Geo) * self.PRO_f.price - - # The following lines of code are introducing add-ons to enable hybridization bypassing bad price quality - self.add_on_H_scaled_vector = pd.DataFrame(0, self.H.index, self.H.columns) - self.aggregated_A_io = (self.A_io + self.K_io).groupby('sector', axis=1).sum() - self.aggregated_A_io = self.aggregated_A_io.groupby('sector', axis=0).sum() - self.aggregated_F_io = self.F_io.groupby(level=1, axis=1).sum() - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - 'Construction work' and i in self.listguillotine], - 'concrete', - 'Cement, lime and plaster', - 'Construction work') - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i]== - 'Other transport equipment' and i in self.listguillotine - and 'aircraft' not in self.PRO_f.productName[i]], - 'steel', - 'Basic iron and steel and of ferro-alloys and first products thereof', - 'Other transport equipment') - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - 'Other transport equipment' and i in self.listguillotine and - 'aircraft' in self.PRO_f.productName[i]], - 'aluminium', - 'Aluminium and aluminium products', - 'Other transport equipment') - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i]== - 'Machinery and equipment n.e.c.' and i in self.listguillotine], - 'steel', - 'Basic iron and steel and of ferro-alloys and first products thereof', - 'Machinery and equipment n.e.c.') - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - 'Air transport services' and i in self.listguillotine], - 'kerosene', - 'Kerosene Type Jet Fuel', - 'Air transport services') - - self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - 'Railway transportation services' and i in self.listguillotine], - 'energy', - 'Energy', - 'Railway transportation services') - - self.extract_scaling_vector_biosphere('Food', 'incineration') - self.extract_scaling_vector_biosphere('Paper', 'incineration') - self.extract_scaling_vector_biosphere('Plastic', 'incineration') - self.extract_scaling_vector_biosphere('Intert/metal', 'incineration') - self.extract_scaling_vector_biosphere('Textiles', 'incineration') - self.extract_scaling_vector_biosphere('Wood', 'incineration') - self.extract_scaling_vector_biosphere('Oil/hazardous', 'incineration') - - self.extract_scaling_vector_biosphere('Plastic', 'landfill') - self.extract_scaling_vector_biosphere('Inert/metal/hazardous', 'landfill') - self.extract_scaling_vector_biosphere('Wood', 'landfill') - - # self.extract_scaling_vector_biosphere('Food', 'waste water treatment') - # self.extract_scaling_vector_biosphere('Other', 'waste water treatment') - - # self.extract_scaling_vector_biosphere('Food', 'biogasification and land application') - # self.extract_scaling_vector_biosphere('Sewage sludge', 'biogasification and land application') - - self.extract_scaling_vector_biosphere('Food', 'composting and land application') - self.extract_scaling_vector_biosphere('Paper and wood', 'composting and land application') - - # all the previous calls to extract_scaling functions were updating the self.add_on_H_scaled_vector, where - # each column of this matrix contains the scaling factor to use and from which sector to extrapolate - self.A_io_f_uncorrected = basic_A_io_f_uncorrected + (self.A_io + self.K_io).dot( - self.add_on_H_scaled_vector * inflation * Geo) - self.A_io_f_uncorrected = self.A_io_f_uncorrected.astype(dtype='float32') + self.apply_scaling_without_prices() + self.K_io_f_uncorrected = self.K_io_f_uncorrected + self.K_io.dot( + self.add_on_H_scaled_vector * inflation * Geo) + self.K_io_f_uncorrected = self.K_io_f_uncorrected.astype(dtype='float32') self.description.append('Capitals endogenized') self.capitals = True - else: - # the uncorrected A_io_f matrix is then defined by the following operation - basic_A_io_f_uncorrected = self.A_io.dot(basic_H_for_hyb * inflation * Geo) * self.PRO_f.price - - # The following lines of code are introducing add-ons to enable hybridization bypassing bad price quality - # self.aggregated_A_io = self.A_io.groupby('sector', axis=1).sum() - # self.aggregated_A_io = self.aggregated_A_io.groupby('sector', axis=0).sum() - # - # buildings_add_on = self.extract_scaling_vector( - # [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == - # 'Construction work' and i in self.listguillotine], 'concrete', - # 'Cement, lime and plaster', 'Construction work') - # self.A_io_f_uncorrected = basic_A_io_f_uncorrected + self.A_io.dot(buildings_add_on[0] * - # inflation * Geo) *\ - # buildings_add_on[1] - # del buildings_add_on - # del basic_A_io_f_uncorrected - # - # transport_equipment_add_on = self.extract_scaling_vector( - # [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - # == 'Other transport equipment' and i in self.listguillotine - # and 'aircraft' not in self.PRO_f.productName[i]], 'steel', - # 'Basic iron and steel and of ferro-alloys and first ' - # 'products thereof', 'Other transport equipment') - # self.A_io_f_uncorrected += self.A_io.dot(transport_equipment_add_on[0] * inflation * Geo) * \ - # transport_equipment_add_on[1] - # del transport_equipment_add_on - # - # air_transport_equipment_add_on = self.extract_scaling_vector( - # [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - # == 'Other transport equipment' and i in self.listguillotine - # and 'aircraft' in self.PRO_f.productName[i]], 'aluminium', - # 'Aluminium and aluminium products', - # 'Other transport equipment') - # self.A_io_f_uncorrected += self.A_io.dot(air_transport_equipment_add_on[0] * inflation * Geo) * \ - # air_transport_equipment_add_on[1] - # del air_transport_equipment_add_on - # - # machinery_add_on = self.extract_scaling_vector([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - # == 'Machinery and equipment n.e.c.' and i in self.listguillotine], - # 'steel', - # 'Basic iron and steel and of ferro-alloys and first products' - # ' thereof', 'Machinery and equipment n.e.c.') - # self.A_io_f_uncorrected += self.A_io.dot(machinery_add_on[0] * inflation * Geo) * machinery_add_on[1] - # del machinery_add_on - # - # air_transportation_add_on = self.extract_scaling_vector( - # [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - # == 'Air transport services' and i in self.listguillotine], - # 'kerosene', 'Kerosene Type Jet Fuel', 'Air transport services') - # self.A_io_f_uncorrected += self.A_io.dot(air_transportation_add_on[0] * inflation * Geo) * \ - # air_transportation_add_on[1] - # del air_transportation_add_on - # - # rail_transportation_add_on = self.extract_scaling_vector( - # [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] - # == 'Railway transportation services' and i in self.listguillotine], - # 'energy', 'Energy', 'Railway transportation services') - # self.A_io_f_uncorrected += self.A_io.dot(rail_transportation_add_on[0] * inflation * Geo) * \ - # rail_transportation_add_on[1] - # del rail_transportation_add_on - - # REMOVE AFTER - self.A_io_f_uncorrected = self.A_io.dot(basic_H_for_hyb * inflation * Geo) * self.PRO_f.price - self.A_io_f_uncorrected = self.A_io_f_uncorrected.astype(dtype='float32') + # Since we endogenized capitals we need to remove them from both final demand and factors of production + self.y_io.loc[:, [i for i in self.y_io.columns if i[1] == 'Gross fixed capital formation']] = 0 + self.F_io.loc['Operating surplus: Consumption of fixed capital', :] = 0 + elif not capitals: self.capitals = False self.description.append('Capitals were not endogenized') @@ -980,6 +886,7 @@ def hybridize(self, method_double_counting, capitals=False): lambda_filter_matrix = lambda_filter_matrix.astype(dtype='float32') self.A_io_f = self.A_io_f_uncorrected.multiply(lambda_filter_matrix) + self.K_io_f = self.K_io_f_uncorrected.multiply(lambda_filter_matrix) self.double_counting = 'binary' self.description.append('binary') @@ -1017,13 +924,13 @@ def hybridize(self, method_double_counting, capitals=False): gamma_filter_matrix.multiply(lambda_filter_matrix.multiply(self.A_io_f_uncorrected))) self.A_io_f = self.A_io_f.astype(dtype='float32') + self.K_io_f = phi_filter_matrix.multiply( + gamma_filter_matrix.multiply(lambda_filter_matrix.multiply(self.K_io_f_uncorrected))) + self.K_io_f = self.K_io_f.astype(dtype='float32') + self.double_counting = 'STAM' self.description.append('STAM') - # Since we endogenized capitals we need to remove them from both final demand and factors of production - self.y_io.loc[:, [i for i in self.y_io.columns if i[1] == 'Gross fixed capital formation']] = 0 - self.F_io.loc['Operating surplus: Consumption of fixed capital', :] = 0 - # ---------------------PREPARATIONS FOR THE HYBRIDIZATION---------------------- def identify_rows(self): @@ -1218,6 +1125,27 @@ def calc_productions(self): listdffff[i] = pd.DataFrame(listlisteeee[i], listact, [list(self.dictRoW.keys())[i]]) self.total_prod_RoW = self.total_prod_RoW.join(listdffff[i], how='outer') + def correct_inconsistencies(self): + """ While introducing the completed extensions of EXIOBASE based on USEEIO, some missing emissions are added + to sectors which in reality produce no products. For these sectors, the added emissions are put back to zero.""" + + self.F_io = self.F_io.drop( + self.F_io.loc[:, [i for i in self.F_io.columns if self.total_prod_country.loc[i][0] == 0]].columns, + axis=1) + + self.F_io = pd.concat([ + self.F_io, + pd.DataFrame(0, + index=self.F_io.index, + columns=self.A_io.loc[:, [i for i in self.A_io.columns if + self.total_prod_country.loc[i][0] == 0]].columns)], + axis=1) + + self.F_io = self.F_io.reindex(self.A_io.columns, axis=1) + + self.F_io = self.F_io.groupby(self.F_io.index, sort=False).sum() + self.F_io_f = self.F_io_f.groupby(self.F_io_f.index, sort=False).sum() + def low_production_volume_processes(self): list_low_prod_sectors = self.total_prod_country[self.total_prod_country < 10].dropna().index.tolist() @@ -1308,20 +1236,30 @@ def extract_scaling_vector_biosphere(self, what_is_treated, treatment): list_add_on_to_hyb = [i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == str(what_is_treated) + ' waste for treatment: ' + str(treatment) and (i in self.listguillotine or self.null_price) - and 'market for' not in self.PRO_f.activityName[i] and i not in self.list_to_hyb] + and 'market for' not in self.PRO_f.activityName[i] and i not in self.list_to_hyb + and 'open' not in self.PRO_f.activityName[i]] add_on_H_for_hyb.loc[:, [i for i in add_on_H_for_hyb.columns if i not in list_add_on_to_hyb]] = 0 - if (treatment in ['incineration', 'biogasification and land application'] or - (treatment == 'composting and land application' - and what_is_treated == 'Paper and wood')): - dff = self.extract_flow_amounts_biosphere(list_add_on_to_hyb, False) - else: + biogenic_sectors = ['Other waste for treatment: waste water treatment', + 'Food waste for treatment: waste water treatment', + 'Paper waste for treatment: landfill', + 'Food waste for treatment: landfill', + 'Wood waste for treatment: landfill', + 'Food waste for treatment: composting and land application', + 'Wood waste for treatment: incineration', + 'Food waste for treatment: incineration', + 'Paper waste for treatment: incineration'] + + if str(what_is_treated) + ' for treatment: ' + str(treatment) in biogenic_sectors: dff = self.extract_flow_amounts_biosphere(list_add_on_to_hyb, True) + list_CO2_extensions = [i for i in self.F_io.index if 'CO2' in i and 'non-fossil' in i] + else: + dff = self.extract_flow_amounts_biosphere(list_add_on_to_hyb, False) + list_CO2_extensions = [i for i in self.F_io.index if 'CO2' in i and 'non-fossil' not in i] scaling_vector = self.PRO_f.price.copy() scaling_vector.loc[:] = 0 - list_CO2_extensions = [i for i in self.F_io.index if 'CO2' in i] if what_is_treated == 'Sewage sludge': scaling_vector.loc[dff.index] = (dff / (self.aggregated_F_io.loc[list_CO2_extensions, str(what_is_treated) + ' for treatment: ' + str(treatment)]. @@ -1385,17 +1323,89 @@ def extract_flow_amounts_biosphere(self, list_of_uuids, biogenic=True): a dataframe of the values of the desired flow (concrete, steel, etc.) per UUID """ if biogenic: - keyword = [i for i in self.F_f.index if 'Carbon dioxide' in self.STR_f.FULLNAME[i]] + keyword = [i for i in self.F_f.index if + ('Carbon dioxide' in self.STR_f.FULLNAME[i] and 'non-fossil' in self.STR_f.FULLNAME[i])] else: keyword = [i for i in self.F_f.index if ('Carbon dioxide' in self.STR_f.FULLNAME[i] and 'non-fossil' not in self.STR_f.FULLNAME[i])] return pd.DataFrame(self.F_f.loc[keyword, list_of_uuids].sum()) + def apply_scaling_without_prices(self): + # The following lines of code are introducing add-ons to enable hybridization bypassing bad price quality + self.add_on_H_scaled_vector = pd.DataFrame(0, self.H.index, self.H.columns) + + # keep A+K cause we are looking at all the concrete (so renovation + construction) + self.aggregated_A_io = (self.A_io + self.K_io).groupby('sector', axis=1).sum() + self.aggregated_A_io = self.aggregated_A_io.groupby('sector', axis=0).sum() + self.aggregated_F_io = self.F_io.groupby(level=1, axis=1).sum() + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Construction work' and i in self.listguillotine], + 'concrete', + 'Cement, lime and plaster', + 'Construction work') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Other transport equipment' and i in self.listguillotine + and 'aircraft' not in self.PRO_f.productName[i]], + 'steel', + 'Basic iron and steel and of ferro-alloys and first products thereof', + 'Other transport equipment') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Other transport equipment' and i in self.listguillotine and + 'aircraft' in self.PRO_f.productName[i]], + 'aluminium', + 'Aluminium and aluminium products', + 'Other transport equipment') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Machinery and equipment n.e.c.' and i in self.listguillotine], + 'steel', + 'Basic iron and steel and of ferro-alloys and first products thereof', + 'Machinery and equipment n.e.c.') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Air transport services' and i in self.listguillotine], + 'kerosene', + 'Kerosene Type Jet Fuel', + 'Air transport services') + + self.extract_scaling_vector_technosphere([i for i in self.H.columns if self.PRO_f.ProductTypeName[i] == + 'Railway transportation services' and i in self.listguillotine], + 'energy', + 'Energy', + 'Railway transportation services') + + self.extract_scaling_vector_biosphere('Food', 'incineration') + self.extract_scaling_vector_biosphere('Paper', 'incineration') + self.extract_scaling_vector_biosphere('Plastic', 'incineration') + self.extract_scaling_vector_biosphere('Intert/metal', 'incineration') + self.extract_scaling_vector_biosphere('Textiles', 'incineration') + self.extract_scaling_vector_biosphere('Wood', 'incineration') + self.extract_scaling_vector_biosphere('Oil/hazardous', 'incineration') + + self.extract_scaling_vector_biosphere('Plastic', 'landfill') + self.extract_scaling_vector_biosphere('Inert/metal/hazardous', 'landfill') + self.extract_scaling_vector_biosphere('Wood', 'landfill') + + self.extract_scaling_vector_biosphere('Food', 'waste water treatment') + self.extract_scaling_vector_biosphere('Other', 'waste water treatment') + + self.extract_scaling_vector_biosphere('Food', 'biogasification and land application') + self.extract_scaling_vector_biosphere('Sewage sludge', 'biogasification and land application') + + self.extract_scaling_vector_biosphere('Food', 'composting and land application') + self.extract_scaling_vector_biosphere('Paper and wood', 'composting and land application') + # -------------------------- EXPORT RESULTS ----------------------------------- def save_system(self, format): - """ Export the hybridized database to dataframe via pickle + """ Export the hybridized database to dataframe via pickle or csv + Args: + ----- + format: 'pickle' to return results as a pickle / 'csv' to return the A_io_f matrix as a csv file Returns: -------- A pickle of the hybrid system exported to the corresponding folder in the pylcaio package @@ -1404,7 +1414,7 @@ def save_system(self, format): hybrid_system = {'PRO_f': self.PRO_f, 'A_ff': self.A_ff, 'A_io': self.A_io, 'A_io_f': self.A_io_f, 'F_f': self.F_f, 'F_io': self.F_io, 'F_io_f': self.F_io_f, - 'C_f': self.C_f, 'C_io': self.C_io, 'K_io': self.K_io, + 'C_f': self.C_f, 'C_io': self.C_io, 'K_io': self.K_io, 'K_io_f' : self.K_io_f, 'list_to_hyb': self.list_to_hyb, 'description': self.description} if self.capitals and self.double_counting == 'STAM': @@ -1593,6 +1603,7 @@ def __init__(self, lca_database_name_and_version, io_database_name_and_version, self.C_f = self.hybrid_system['C_f'] self.C_io = self.hybrid_system['C_io'] self.K_io = self.hybrid_system['K_io'] + self.K_io_f = self.hybrid_system['K_io_f'] self.list_to_hyb = self.hybrid_system['list_to_hyb'] self.description = self.hybrid_system['description'] @@ -1614,6 +1625,7 @@ def __init__(self, lca_database_name_and_version, io_database_name_and_version, np.fill_diagonal(self.y, 1, wrap=False) self.d = pd.DataFrame() + self.d_k = pd.DataFrame() self.GWP100_CML2001 = ast.literal_eval( pkg_resources.resource_string(__name__, '/Data/Characterization_matching/GWP.txt').decode('utf-8')) @@ -1659,14 +1671,16 @@ def calc_lifecycle(self): ak = np.concatenate( [np.concatenate([self.A_ff.values, np.zeros((len(self.A_ff), len(self.A_io))).astype(dtype='float32')], axis=1), - np.concatenate([self.A_io_f.values, self.A_io.values + self.K_io.values], axis=1)], + np.concatenate([self.A_io_f.values+self.K_io_f.values, self.A_io.values+self.K_io.values], axis=1)], axis=0).astype(dtype='float32') x = np.linalg.solve(np.eye(len(self.A_ff) + len(self.A_io)) - ak, self.y) + # need to do total - only A to capture value chain interaction between A and K (cannot just take K) d = self.C.values.dot(self.F).dot(x) self.d = pd.DataFrame(d, self.C.index, self.A_ff.columns) + print('Calculations done! Results are contained in self.d') - if not self.capitals: + else: a = np.concatenate( [np.concatenate([self.A_ff.values, np.zeros((len(self.A_ff), len(self.A_io))).astype(dtype='float32')], axis=1), From 3a03eb8534749551a412524b97c1ca0370d56bab Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:04:41 -0500 Subject: [PATCH 06/12] Corrected typo --- src/Data/Characterization_matching/Acid.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/Data/Characterization_matching/Acid.txt b/src/Data/Characterization_matching/Acid.txt index 4bcc194..0cb5064 100644 --- a/src/Data/Characterization_matching/Acid.txt +++ b/src/Data/Characterization_matching/Acid.txt @@ -1 +1,3 @@ -['CML 2001; acidification potential; average European; kg SO2-Eq',[('Problem oriented approach: baseline (CML, 2001)','acidification (incl. fate, average Europe total, A&B)','AP ( Huijbregts, 1999; average Europe total, A&B)','kg SO2 eq.')]] \ No newline at end of file +['CML 2001; acidification potential; average European; kg SO2-Eq', +[('Problem oriented approach: baseline (CML, 2001)','acidification (incl. fate, average Europe total, A&B)','AP ( Huijbregts, 1999; average Europe total, A&B)','kg SO2 eq.')], +'Acid Rain'] \ No newline at end of file From cf59240c3904dd5b03c3fddd090df59a2f209ee2 Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:04:52 -0500 Subject: [PATCH 07/12] Corrected typo --- src/Data/Characterization_matching/Eutro.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/Data/Characterization_matching/Eutro.txt b/src/Data/Characterization_matching/Eutro.txt index a882a72..60d851d 100644 --- a/src/Data/Characterization_matching/Eutro.txt +++ b/src/Data/Characterization_matching/Eutro.txt @@ -1 +1,3 @@ -['CML 2001; eutrophication potential; average European; kg NOx-Eq',[('Problem oriented approach: non baseline (CML, 2001)', 'eutrophication (incl. fate, average Europe total, A&B)', 'EP ( Huijbregts, 1999; average Europe total, A&B)', 'kg NOx eq.')]] \ No newline at end of file +['CML 2001; eutrophication potential; average European; kg NOx-Eq', +[('Problem oriented approach: non baseline (CML, 2001)', 'eutrophication (incl. fate, average Europe total, A&B)', 'EP ( Huijbregts, 1999; average Europe total, A&B)', 'kg NOx eq.')], +'Eutrophication'] \ No newline at end of file From 9c197df828b5860dc4d21f3d020dbd3c1c7c2119 Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:05:00 -0500 Subject: [PATCH 08/12] Corrected typo --- src/Data/Characterization_matching/GWP.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/Data/Characterization_matching/GWP.txt b/src/Data/Characterization_matching/GWP.txt index 4830af2..3190c0a 100644 --- a/src/Data/Characterization_matching/GWP.txt +++ b/src/Data/Characterization_matching/GWP.txt @@ -1,2 +1,3 @@ ['CML 2001; climate change; GWP 100a; kg CO2-Eq', -[('Problem oriented approach: baseline (CML, 2001)','global warming (GWP100)','GWP100 (IPCC, 2007)','kg CO2 eq.')]] +[('Problem oriented approach: baseline (CML, 2001)','global warming (GWP100)','GWP100 (IPCC, 2007)','kg CO2 eq.')], +'Global Climate Change'] From addd864d33a2aec38dc353cd05fc2666f56cd59e Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:05:06 -0500 Subject: [PATCH 09/12] Corrected typo --- src/Data/Characterization_matching/HTox.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/Data/Characterization_matching/HTox.txt b/src/Data/Characterization_matching/HTox.txt index 0f5f123..317d6cf 100644 --- a/src/Data/Characterization_matching/HTox.txt +++ b/src/Data/Characterization_matching/HTox.txt @@ -1 +1,3 @@ -['CML 2001; human toxicity; HTP 100a; kg 1,4-DC.',[('Problem oriented approach: non baseline (CML, 2001)','human toxicity (HTP100)','HTP 100 (Huijbregts, 1999 & 2000)','kg 1,4-dichlorobenzene eq.')]] \ No newline at end of file +['CML 2001; human toxicity; HTP 100a; kg 1,4-DC.', +[('Problem oriented approach: non baseline (CML, 2001)','human toxicity (HTP100)','HTP 100 (Huijbregts, 1999 & 2000)','kg 1,4-dichlorobenzene eq.')], +['Human Health - Respiratory Effects','Human Health Cancer','Human Health Noncancer']] \ No newline at end of file From 7224a6c617d05bf4887730e2c97fc1e2979765fd Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:05:21 -0500 Subject: [PATCH 10/12] Updated --- ...isationEXIOBASE3_adaptedFromEXIOBASE2.xlsx | Bin 384277 -> 384285 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/src/Data/characterisationEXIOBASE3_adaptedFromEXIOBASE2.xlsx b/src/Data/characterisationEXIOBASE3_adaptedFromEXIOBASE2.xlsx index ecb0a9b33c9eb6382fb215b175910037412a65c8..c2a2861c743437bbc3ce26c8887b2ba3686a2f07 100644 GIT binary patch delta 12589 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z)`L(0MG{vFh|~}CmA#*vhLy;ZHAzqCld2Yeq7R0~Ed6ocszkv(7t{eL&d z|Eia-N7VL!W%2e=U_?v}m=-~T1`Ff;YWXX%BAU@)2*Mu?rp41}`yZhiOTg7`M{JlnoruIg*?HRPd3nBg#b^EE1)`=KOo3qS$Nm6|d>A3u4;H>f KJA!or=>GuXi8!PH From 7d5e5d435bf427d6f06ffe9e25eb0e289da1cec8 Mon Sep 17 00:00:00 2001 From: MaximeAgez <11.maxime.agez@gmail.com> Date: Wed, 22 Jan 2020 14:06:06 -0500 Subject: [PATCH 12/12] Introduce IW+, but requires too much RAM --- src/pylcaio.py | 62 ++++++++++++++++++++++++++++++++++---------------- 1 file changed, 43 insertions(+), 19 deletions(-) diff --git a/src/pylcaio.py b/src/pylcaio.py index 95928d1..bc7c174 100644 --- a/src/pylcaio.py +++ b/src/pylcaio.py @@ -166,7 +166,8 @@ def __init__(self, lca_database_processed, io_database_processed, if self.io_database_name_and_version not in versions_of_exiobase: print('The IO database version you entered is not supported currently') - def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals='', complete_extensions=False): + def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals='', complete_extensions=False, + impact_world=False): """ Loads every needed parameter to hybridize ecoinvent with exiobase as well as both databases Args --- @@ -287,21 +288,33 @@ def combine_ecoinvent_exiobase(self, path_to_io_database='', path_to_capitals='' with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Completed_extensions_exio3/' 'S_completed.pickle'), 'rb') as f: F_extensions = pd.read_pickle(f) - with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Completed_extensions_exio3/' - 'C_completed_in_CML.pickle'), 'rb') as f: - C_extensions = pd.read_pickle(f) self.F_io = self.F_io.drop([i for i in self.IO_database.satellite.unit.index if self.IO_database.satellite.unit.loc[i][0] in ['kg', 'kg CO2-eq']], axis=0) self.F_io = pd.concat([self.F_io, F_extensions]) self.F_io = self.F_io.reindex(self.A_io.index, axis=1) - self.C_io = self.C_io.drop([i for i in self.IO_database.satellite.unit.index if - self.IO_database.satellite.unit.loc[i][0] in ['kg', 'kg CO2-eq']], axis=1) - self.C_io = self.C_io.drop([i for i in self.C_io.index if - self.C_io.loc[[i]].sum(axis=1)[0] == 0], axis=0) - self.C_io = pd.concat([self.C_io, C_extensions.transpose()], axis=1, sort=False).fillna(0) - self.C_io = self.C_io.reindex(self.F_io.index, axis=1) + if not impact_world: + with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Completed_extensions_exio3/' + 'C_completed_in_CML.pickle'), 'rb') as f: + C_extensions = pd.read_pickle(f) + + self.C_io = self.C_io.drop([i for i in self.IO_database.satellite.unit.index if + self.IO_database.satellite.unit.loc[i][0] in ['kg', 'kg CO2-eq']], axis=1) + self.C_io = self.C_io.drop([i for i in self.C_io.index if + self.C_io.loc[[i]].sum(axis=1)[0] == 0], axis=0) + self.C_io = pd.concat([self.C_io, C_extensions.transpose()], axis=1, sort=False).fillna(0) + self.C_io = self.C_io.reindex(self.F_io.index, axis=1) + else: + with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Characterization_matrix_IW+/' + 'EXIOBASE_not_regionalized.pickle'), + 'rb') as f: + self.C_io = pd.read_pickle(f) + + with gzip.open(pkg_resources.resource_filename(__name__, '/Data/Characterization_matrix_IW+/' + 'Ecoinvent_not_regionalized.pickle'), + 'rb') as f: + self.C_f = pd.read_pickle(f) self.F_io_f = pd.DataFrame(0, self.F_io.index, self.F_f.columns, dtype='float32') @@ -924,9 +937,10 @@ def hybridize(self, method_double_counting, capitals=False): gamma_filter_matrix.multiply(lambda_filter_matrix.multiply(self.A_io_f_uncorrected))) self.A_io_f = self.A_io_f.astype(dtype='float32') - self.K_io_f = phi_filter_matrix.multiply( - gamma_filter_matrix.multiply(lambda_filter_matrix.multiply(self.K_io_f_uncorrected))) - self.K_io_f = self.K_io_f.astype(dtype='float32') + if capitals: + self.K_io_f = phi_filter_matrix.multiply( + gamma_filter_matrix.multiply(lambda_filter_matrix.multiply(self.K_io_f_uncorrected))) + self.K_io_f = self.K_io_f.astype(dtype='float32') self.double_counting = 'STAM' self.description.append('STAM') @@ -1156,7 +1170,8 @@ def low_production_volume_processes(self): pkg_resources.resource_string(__name__, '/Data/Classing_countries.txt').decode('utf-8')) for process in self.PRO_f.index: if process in df.index: - self.PRO_f.io_geography[process] = dict_[self.PRO_f.io_geography[process]] + if dict_[self.PRO_f.io_geography[process]] == 'RER': + self.PRO_f.io_geography[process] = dict_[self.PRO_f.io_geography[process]] def extend_inventory(self): """ Method creating a new technology matrix for the LCA database in which the inputs of processes not to @@ -1414,7 +1429,7 @@ def save_system(self, format): hybrid_system = {'PRO_f': self.PRO_f, 'A_ff': self.A_ff, 'A_io': self.A_io, 'A_io_f': self.A_io_f, 'F_f': self.F_f, 'F_io': self.F_io, 'F_io_f': self.F_io_f, - 'C_f': self.C_f, 'C_io': self.C_io, 'K_io': self.K_io, 'K_io_f' : self.K_io_f, + 'C_f': self.C_f, 'C_io': self.C_io, 'K_io': self.K_io, 'K_io_f': self.K_io_f, 'list_to_hyb': self.list_to_hyb, 'description': self.description} if self.capitals and self.double_counting == 'STAM': @@ -1625,7 +1640,8 @@ def __init__(self, lca_database_name_and_version, io_database_name_and_version, np.fill_diagonal(self.y, 1, wrap=False) self.d = pd.DataFrame() - self.d_k = pd.DataFrame() + self.d_vanilla = pd.DataFrame() + self.d_extensions = pd.DataFrame() self.GWP100_CML2001 = ast.literal_eval( pkg_resources.resource_string(__name__, '/Data/Characterization_matching/GWP.txt').decode('utf-8')) @@ -1674,9 +1690,17 @@ def calc_lifecycle(self): np.concatenate([self.A_io_f.values+self.K_io_f.values, self.A_io.values+self.K_io.values], axis=1)], axis=0).astype(dtype='float32') x = np.linalg.solve(np.eye(len(self.A_ff) + len(self.A_io)) - ak, self.y) - # need to do total - only A to capture value chain interaction between A and K (cannot just take K) - d = self.C.values.dot(self.F).dot(x) - self.d = pd.DataFrame(d, self.C.index, self.A_ff.columns) + + F_vanilla = pd.DataFrame(self.F, index=self.F_f.index.tolist()+self.F_io.index.tolist(), + columns=self.A_ff.index.tolist()+self.A_io.index.tolist()) + F_extensions = F_vanilla.copy() + F_vanilla.loc[[i for i in F_vanilla.index if '*' in i]] = 0 + F_extensions.loc[[i for i in F_vanilla.index if '*' not in i]] = 0 + + d_vanilla = self.C.values.dot(F_vanilla).dot(x) + d_extensions = self.C.values.dot(F_extensions).dot(x) + self.d_vanilla = pd.DataFrame(d_vanilla, self.C.index, self.A_ff.columns) + self.d_extensions = pd.DataFrame(d_extensions, self.C.index, self.A_ff.columns) print('Calculations done! Results are contained in self.d')