def build_biomass_potentials():         snakemake.input['jrc_potentials'] = "data/biomass/JRC Biomass Potentials.xlsx"         snakemake.output = Dict()         snakemake.output['biomass_potentials'] = 'data/biomass_potentials.csv'+        snakemake.output['biomass_potentials_all']='resources/biomass_potentials_all.csv'         with open('config.yaml', encoding='utf8') as f:             snakemake.config = yaml.safe_load(f)-+    +    if 'Secondary Forestry residues sawdust' in snakemake.config['biomass']['classes']['solid biomass']:+        snakemake.config['biomass']['classes']['solid biomass'].remove('Secondary Forestry residues sawdust')+        snakemake.config['biomass']['classes']['solid biomass'].append('Secondary Forestry residues – sawdust')

 scenario:   # B for biomass supply, I for industry, shipping and aviation   # solarx or onwindx changes the available installable potential by factor x   # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv+  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value+  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative 

 override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"] override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lifetime","Input (optional)"] ++def co2_emissions_year(year):+    """+    calculate co2 emissions in one specific year (e.g. 1990 or 2018).+    """+    eea_co2 = build_eea_co2(year)+    +    #TODO: read Eurostat data from year>2014, this only affects the estimation of +    # CO2 emissions for "BA","RS","AL","ME","MK"+    if year > 2014:+        eurostat_co2 = build_eurostat_co2(year=2014)+    else:+        eurostat_co2 = build_eurostat_co2(year)+        +    co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)+    +    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)+    pop_layout["ct"] = pop_layout.index.str[:2]+    cts = pop_layout.ct.value_counts().index++    co2_emissions = co2_totals.loc[cts, "electricity"].sum()++    if "T" in opts:+        co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["rail","road"]]].sum().sum()+    if "H" in opts:+        co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["residential","services"]]].sum().sum()+    if "I" in opts:+        co2_emissions += co2_totals.loc[cts, ["industrial non-elec","industrial processes",+                                              "domestic aviation","international aviation",+                                              "domestic navigation","international navigation"]].sum().sum()+    co2_emissions *=0.001 #MtCO2 to GtCO2+    return co2_emissions++def historical_emissions():+    """+    read historical emissions to add them to the carbon budget plot+    """+    #https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16+    #downloaded 201228 (modified by EEA last on 201221)+    fn = "data/eea/UNFCCC_v23.csv"+    df = pd.read_csv(fn, encoding="latin-1")+    df.loc[df["Year"] == "1985-1987","Year"] = 1986+    df["Year"] = df["Year"].astype(int)+    df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()++    e = pd.Series()+    e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'+    e['residential non-elec'] = '1.A.4.b - Residential'+    e['services non-elec'] = '1.A.4.a - Commercial/Institutional'+    e['rail non-elec'] = "1.A.3.c - Railways"+    e["road non-elec"] = '1.A.3.b - Road Transportation'+    e["domestic navigation"] = "1.A.3.d - Domestic Navigation"+    e['international navigation'] = '1.D.1.b - International Navigation'+    e["domestic aviation"] = '1.A.3.a - Domestic Aviation'+    e["international aviation"] = '1.D.1.a - International Aviation'   +    e['total energy'] = '1 - Energy'+    e['industrial processes'] = '2 - Industrial Processes and Product Use'+    e['agriculture'] = '3 - Agriculture'+    e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'+    e['waste management'] = '5 - Waste management'+    e['other'] = '6 - Other Sector'+    e['indirect'] = 'ind_CO2 - Indirect CO2'+    e["total wL"] = "Total (with LULUCF)"+    e["total woL"] = "Total (without LULUCF)"+       +    pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"] ++    +    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)+    pop_layout["ct"] = pop_layout.index.str[:2]+    cts = pop_layout.ct.value_counts().index.to_list()+    if "GB" in cts:+        cts.remove("GB")+        cts.append("UK")+         +    year = np.arange(1990,2018).tolist()++    idx = pd.IndexSlice+    co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values)) +    +    co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2++    co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',+                                                                              'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',+                                                                              'international navigation']].sum()++    emissions = co2_totals.loc["electricity"]   +    if "T" in opts:+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()+    if "H" in opts:+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()+    if "I" in opts:+        emissions += co2_totals.loc[["industrial non-elec","industrial processes",+                                          "domestic aviation","international aviation",+                                          "domestic navigation","international navigation"]].sum()           +    return emissions+++def build_carbon_budget(o):+    #distribute carbon budget following beta or exponential transition path+    if "be" in o:    +        #beta decay+        carbon_budget = float(o[o.find("cb")+2:o.find("be")])+        be=float(o[o.find("be")+2:])        +    if "ex" in o:   +        #exponential decay+        carbon_budget = float(o[o.find("cb")+2:o.find("ex")])+        r=float(o[o.find("ex")+2:])+            +    e_1990 = co2_emissions_year(year=1990) +            +    #emissions at the beginning of the path (last year available 2018)+    e_0 = co2_emissions_year(year=2018) +    #emissions in 2019 and 2020 assumed equal to 2018 and substracted+    carbon_budget -= 2*e_0+    planning_horizons = snakemake.config['scenario']['planning_horizons']+    CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons, +                                             name='planning_horizon'),+                                             columns=pd.Series(data=[],+                                                              name='paths', +                                                              dtype='float'))+    t_0 = planning_horizons[0]+    if "be" in o: +        #beta decay                +        t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path +        #emissions (relative to 1990)+        CO2_CAP[o] = [(e_0/e_1990)*(1-beta.cdf((t-t_0)/(t_f-t_0), be, be)) for t in planning_horizons]+            +    if "ex" in o:   +        #exponential decay without delay+        T=carbon_budget/e_0+        m=(1+np.sqrt(1+r*T))/T+        CO2_CAP[o] = [(e_0/e_1990)*(1+(m+r)*(t-t_0))*np.exp(-m*(t-t_0)) for t in planning_horizons]+                +    CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',', +                   line_terminator='\n',  float_format='%.3f') + +    """+    Plot historical carbon emissions in the EU and decarbonization path+    """ +    import matplotlib.pyplot as plt

 scenario:   # B for biomass supply, I for industry, shipping and aviation   # solarx or onwindx changes the available installable potential by factor x   # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv+  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value

 override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"] override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lifetime","Input (optional)"] ++def co2_emissions_year(year):+    """+    calculate co2 emissions in one specific year (e.g. 1990 or 2018).+    """+    eea_co2 = build_eea_co2(year)+    +    #TODO: read Eurostat data from year>2014, this only affects the estimation of +    # CO2 emissions for "BA","RS","AL","ME","MK"+    if year > 2014:+        eurostat_co2 = build_eurostat_co2(year=2014)+    else:+        eurostat_co2 = build_eurostat_co2(year)+        +    co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)+    +    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)+    pop_layout["ct"] = pop_layout.index.str[:2]+    cts = pop_layout.ct.value_counts().index++    co2_emissions = co2_totals.loc[cts, "electricity"].sum()++    if "T" in opts:+        co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["rail","road"]]].sum().sum()+    if "H" in opts:+        co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["residential","services"]]].sum().sum()+    if "I" in opts:+        co2_emissions += co2_totals.loc[cts, ["industrial non-elec","industrial processes",+                                              "domestic aviation","international aviation",+                                              "domestic navigation","international navigation"]].sum().sum()+    co2_emissions *=0.001 #MtCO2 to GtCO2+    return co2_emissions++def historical_emissions():+    """+    read historical emissions to add them to the carbon budget plot+    """+    #https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16+    #downloaded 201228 (modified by EEA last on 201221)+    fn = "data/eea/UNFCCC_v23.csv"+    df = pd.read_csv(fn, encoding="latin-1")+    df.loc[df["Year"] == "1985-1987","Year"] = 1986+    df["Year"] = df["Year"].astype(int)+    df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()++    e = pd.Series()+    e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'+    e['residential non-elec'] = '1.A.4.b - Residential'+    e['services non-elec'] = '1.A.4.a - Commercial/Institutional'+    e['rail non-elec'] = "1.A.3.c - Railways"+    e["road non-elec"] = '1.A.3.b - Road Transportation'+    e["domestic navigation"] = "1.A.3.d - Domestic Navigation"+    e['international navigation'] = '1.D.1.b - International Navigation'+    e["domestic aviation"] = '1.A.3.a - Domestic Aviation'+    e["international aviation"] = '1.D.1.a - International Aviation'   +    e['total energy'] = '1 - Energy'+    e['industrial processes'] = '2 - Industrial Processes and Product Use'+    e['agriculture'] = '3 - Agriculture'+    e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'+    e['waste management'] = '5 - Waste management'+    e['other'] = '6 - Other Sector'+    e['indirect'] = 'ind_CO2 - Indirect CO2'+    e["total wL"] = "Total (with LULUCF)"+    e["total woL"] = "Total (without LULUCF)"+       +    pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"] ++    +    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)+    pop_layout["ct"] = pop_layout.index.str[:2]+    cts = pop_layout.ct.value_counts().index.to_list()+    if "GB" in cts:+        cts.remove("GB")+        cts.append("UK")+         +    year = np.arange(1990,2018).tolist()++    idx = pd.IndexSlice+    co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values)) +    +    co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2++    co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',+                                                                              'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',+                                                                              'international navigation']].sum()++    emissions = co2_totals.loc["electricity"]   +    if "T" in opts:+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()+    if "H" in opts:+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()+    if "I" in opts:+        emissions += co2_totals.loc[["industrial non-elec","industrial processes",+                                          "domestic aviation","international aviation",+                                          "domestic navigation","international navigation"]].sum()           +    return emissions+++def build_carbon_budget(o):+    #distribute carbon budget following beta or exponential transition path+    if "be" in o:    +        #beta decay+        carbon_budget = float(o[o.find("cb")+2:o.find("be")])+        be=float(o[o.find("be")+2:])        +    if "ex" in o:   +        #exponential decay+        carbon_budget = float(o[o.find("cb")+2:o.find("ex")])+        r=float(o[o.find("ex")+2:])+            +    e_1990 = co2_emissions_year(year=1990) +            +    #emissions at the beginning of the path (last year available 2018)+    e_0 = co2_emissions_year(year=2018) +    #emissions in 2019 and 2020 assumed equal to 2018 and substracted+    carbon_budget -= 2*e_0+    planning_horizons = snakemake.config['scenario']['planning_horizons']+    CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons, +                                             name='planning_horizon'),+                                             columns=pd.Series(data=[],+                                                              name='paths', +                                                              dtype='float'))+    t_0 = planning_horizons[0]+    if "be" in o: +        #beta decay                +        t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path +        #emissions (relative to 1990)+        CO2_CAP[o] = [(e_0/e_1990)*(1-beta.cdf((t-t_0)/(t_f-t_0), be, be)) for t in planning_horizons]+            +    if "ex" in o:   +        #exponential decay without delay+        T=carbon_budget/e_0+        m=(1+np.sqrt(1+r*T))/T+        CO2_CAP[o] = [(e_0/e_1990)*(1+(m+r)*(t-t_0))*np.exp(-m*(t-t_0)) for t in planning_horizons]+                +    CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',', +                   line_terminator='\n',  float_format='%.3f') + +    """+    Plot historical carbon emissions in the EU and decarbonization path+    """ +    import matplotlib.pyplot as plt+    import matplotlib.gridspec as gridspec+    import seaborn as sns; sns.set()+    sns.set_style('ticks')+    plt.style.use('seaborn-ticks')+    plt.rcParams['xtick.direction'] = 'in'+    plt.rcParams['ytick.direction'] = 'in'+    plt.rcParams['xtick.labelsize'] = 20+    plt.rcParams['ytick.labelsize'] = 20   ++    plt.figure(figsize=(10, 7))+    gs1 = gridspec.GridSpec(1, 1)+    ax1 = plt.subplot(gs1[0,0])+    ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)+    ax1.set_ylim([0,5])+    ax1.set_xlim([1990,planning_horizons[-1]+1])+    ax1.plot(e_1990*CO2_CAP[o],linewidth=3, +                         color='dodgerblue', label=None)+            +    emissions = historical_emissions()++    ax1.plot(emissions, color='black', linewidth=3, label=None) +    +    #plot commited and uder-discussion targets  +    #(notice that historical emissions include all countries in the+    # network, but targets refer to EU)+    ax1.plot([2020],[0.8*emissions[1990]],+                     marker='*', markersize=12, markerfacecolor='black',+                     markeredgecolor='black')    +            +    ax1.plot([2030],[0.45*emissions[1990]],+                     marker='*', markersize=12, markerfacecolor='white',+                     markeredgecolor='black')    +            +    ax1.plot([2030],[0.6*emissions[1990]],+                     marker='*', markersize=12, markerfacecolor='black',+                     markeredgecolor='black')+            +    ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],+                  color='gray', linewidth=2, marker='_', alpha=0.5) +            +    ax1.plot([2050],[0.01*emissions[1990]],+                     marker='*', markersize=12, markerfacecolor='white', +                     linewidth=0, markeredgecolor='black', +                     label='EU under-discussion target', zorder=10, +                     clip_on=False) +            +    ax1.plot([2050],[0.125*emissions[1990]],'ro',+                     marker='*', markersize=12, markerfacecolor='black',+                     markeredgecolor='black', label='EU commited target')+            +    ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01), +                       facecolor='white', frameon=True) +            +    path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'             +    if not os.path.exists(path_cb_plot):+        os.makedirs(path_cb_plot)+    print('carbon budget distribution saved to ' + path_cb_plot + 'carbon_budget_plot.pdf')+    plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300) 

 scenario:   # B for biomass supply, I for industry, shipping and aviation   # solarx or onwindx changes the available installable potential by factor x   # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv+  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value