Move ploting of carbon budget distribution to plot_summary.py
This commit is contained in:
martavp
parent
faebbc493b
commit
e180931ce1
@ -1,6 +1,6 @@
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import numpy as np
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import pandas as pd
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#allow plotting without Xwindows
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@ -9,7 +9,7 @@ matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from prepare_sector_network import co2_emissions_year
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#consolidate and rename
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def rename_techs(label):
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@ -237,7 +237,137 @@ def plot_balances():
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fig.savefig(snakemake.output.balances[:-10] + k + ".pdf",transparent=True)
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def historical_emissions(cts):
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"""
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read historical emissions to add them to the carbon budget plot
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"""
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#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
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#downloaded 201228 (modified by EEA last on 201221)
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fn = "data/eea/UNFCCC_v23.csv"
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df = pd.read_csv(fn, encoding="latin-1")
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df.loc[df["Year"] == "1985-1987","Year"] = 1986
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df["Year"] = df["Year"].astype(int)
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df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
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e = pd.Series()
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e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
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e['residential non-elec'] = '1.A.4.b - Residential'
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e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
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e['rail non-elec'] = "1.A.3.c - Railways"
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e["road non-elec"] = '1.A.3.b - Road Transportation'
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e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
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e['international navigation'] = '1.D.1.b - International Navigation'
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e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
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e["international aviation"] = '1.D.1.a - International Aviation'
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e['total energy'] = '1 - Energy'
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e['industrial processes'] = '2 - Industrial Processes and Product Use'
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e['agriculture'] = '3 - Agriculture'
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e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
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e['waste management'] = '5 - Waste management'
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e['other'] = '6 - Other Sector'
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e['indirect'] = 'ind_CO2 - Indirect CO2'
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e["total wL"] = "Total (with LULUCF)"
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e["total woL"] = "Total (without LULUCF)"
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pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
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cts
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if "GB" in cts:
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cts.remove("GB")
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cts.append("UK")
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year = np.arange(1990,2018).tolist()
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idx = pd.IndexSlice
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co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values))
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co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
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co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
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'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
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'international navigation']].sum()
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emissions = co2_totals.loc["electricity"]
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if "T" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
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if "H" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
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if "I" in opts:
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emissions += co2_totals.loc[["industrial non-elec","industrial processes",
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"domestic aviation","international aviation",
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"domestic navigation","international navigation"]].sum()
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return emissions
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def plot_carbon_budget_distribution():
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"""
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Plot historical carbon emissions in the EU and decarbonization path
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"""
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import matplotlib.gridspec as gridspec
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import seaborn as sns; sns.set()
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sns.set_style('ticks')
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plt.style.use('seaborn-ticks')
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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plt.rcParams['xtick.labelsize'] = 20
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plt.rcParams['ytick.labelsize'] = 20
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plt.figure(figsize=(10, 7))
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gs1 = gridspec.GridSpec(1, 1)
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ax1 = plt.subplot(gs1[0,0])
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ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
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ax1.set_ylim([0,5])
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ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
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path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
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countries=pd.read_csv(path_cb + 'countries.csv', index_col=1)
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cts=countries.index.to_list()
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e_1990 = co2_emissions_year(cts, opts, year=1990)
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CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
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index_col=0)
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ax1.plot(e_1990*CO2_CAP[o],linewidth=3,
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color='dodgerblue', label=None)
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emissions = historical_emissions(cts)
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ax1.plot(emissions, color='black', linewidth=3, label=None)
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#plot commited and uder-discussion targets
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#(notice that historical emissions include all countries in the
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# network, but targets refer to EU)
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ax1.plot([2020],[0.8*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2030],[0.45*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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markeredgecolor='black')
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ax1.plot([2030],[0.6*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
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color='gray', linewidth=2, marker='_', alpha=0.5)
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ax1.plot([2050],[0.01*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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linewidth=0, markeredgecolor='black',
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label='EU under-discussion target', zorder=10,
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clip_on=False)
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ax1.plot([2050],[0.125*emissions[1990]],'ro',
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black', label='EU commited target')
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ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
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facecolor='white', frameon=True)
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path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'
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plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300)
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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@ -249,13 +379,16 @@ if __name__ == "__main__":
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snakemake.config = yaml.safe_load(f)
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snakemake.input = Dict()
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snakemake.output = Dict()
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snakemake.wildcards = Dict()
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#snakemake.wildcards['sector_opts']='3H-T-H-B-I-solar3-dist1-cb48be3'
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for item in ["costs", "energy"]:
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snakemake.input[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
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snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/graphs/{item}.pdf'.format(name=snakemake.config['run'],item=item)
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snakemake.input["balances"] = snakemake.config['summary_dir'] + '/test/csvs/supply_energy.csv'
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snakemake.output["balances"] = snakemake.config['summary_dir'] + '/test/graphs/balances-energy.csv'
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snakemake.input["balances"] = snakemake.config['summary_dir'] + '/{name}/csvs/supply_energy.csv'.format(name=snakemake.config['run'],item=item)
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snakemake.output["balances"] = snakemake.config['summary_dir'] + '/{name}/graphs/balances-energy.csv'.format(name=snakemake.config['run'],item=item)
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n_header = 4
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plot_costs()
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@ -263,3 +396,9 @@ if __name__ == "__main__":
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plot_energy()
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plot_balances()
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for sector_opts in snakemake.config['scenario']['sector_opts']:
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opts=sector_opts.split('-')
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for o in opts:
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if "cb" in o:
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plot_carbon_budget_distribution()
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@ -48,7 +48,7 @@ override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"
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override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lifetime","Input (optional)"]
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def co2_emissions_year(year):
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def co2_emissions_year(cts, opts, year):
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"""
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calculate co2 emissions in one specific year (e.g. 1990 or 2018).
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"""
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@ -62,10 +62,6 @@ def co2_emissions_year(year):
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eurostat_co2 = build_eurostat_co2(year)
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co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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cts = pop_layout.ct.value_counts().index
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co2_emissions = co2_totals.loc[cts, "electricity"].sum()
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@ -80,70 +76,6 @@ def co2_emissions_year(year):
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co2_emissions *=0.001 #MtCO2 to GtCO2
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return co2_emissions
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def historical_emissions():
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"""
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read historical emissions to add them to the carbon budget plot
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"""
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#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
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#downloaded 201228 (modified by EEA last on 201221)
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fn = "data/eea/UNFCCC_v23.csv"
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df = pd.read_csv(fn, encoding="latin-1")
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df.loc[df["Year"] == "1985-1987","Year"] = 1986
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df["Year"] = df["Year"].astype(int)
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df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
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e = pd.Series()
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e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
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e['residential non-elec'] = '1.A.4.b - Residential'
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e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
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e['rail non-elec'] = "1.A.3.c - Railways"
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e["road non-elec"] = '1.A.3.b - Road Transportation'
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e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
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e['international navigation'] = '1.D.1.b - International Navigation'
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e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
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e["international aviation"] = '1.D.1.a - International Aviation'
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e['total energy'] = '1 - Energy'
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e['industrial processes'] = '2 - Industrial Processes and Product Use'
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e['agriculture'] = '3 - Agriculture'
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e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
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e['waste management'] = '5 - Waste management'
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e['other'] = '6 - Other Sector'
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e['indirect'] = 'ind_CO2 - Indirect CO2'
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e["total wL"] = "Total (with LULUCF)"
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e["total woL"] = "Total (without LULUCF)"
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pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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cts = pop_layout.ct.value_counts().index.to_list()
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if "GB" in cts:
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cts.remove("GB")
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cts.append("UK")
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year = np.arange(1990,2018).tolist()
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idx = pd.IndexSlice
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co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values))
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co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
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co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
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'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
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'international navigation']].sum()
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emissions = co2_totals.loc["electricity"]
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if "T" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
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if "H" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
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if "I" in opts:
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emissions += co2_totals.loc[["industrial non-elec","industrial processes",
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"domestic aviation","international aviation",
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"domestic navigation","international navigation"]].sum()
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return emissions
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def build_carbon_budget(o):
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#distribute carbon budget following beta or exponential transition path
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@ -155,11 +87,16 @@ def build_carbon_budget(o):
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#exponential decay
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carbon_budget = float(o[o.find("cb")+2:o.find("ex")])
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r=float(o[o.find("ex")+2:])
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e_1990 = co2_emissions_year(year=1990)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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cts = pop_layout.ct.value_counts().index
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e_1990 = co2_emissions_year(cts, opts, year=1990)
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#emissions at the beginning of the path (last year available 2018)
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e_0 = co2_emissions_year(year=2018)
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e_0 = co2_emissions_year(cts, opts, year=2018)
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#emissions in 2019 and 2020 assumed equal to 2018 and substracted
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carbon_budget -= 2*e_0
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planning_horizons = snakemake.config['scenario']['planning_horizons']
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@ -183,69 +120,9 @@ def build_carbon_budget(o):
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CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',',
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line_terminator='\n', float_format='%.3f')
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"""
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Plot historical carbon emissions in the EU and decarbonization path
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"""
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import seaborn as sns; sns.set()
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sns.set_style('ticks')
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plt.style.use('seaborn-ticks')
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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plt.rcParams['xtick.labelsize'] = 20
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plt.rcParams['ytick.labelsize'] = 20
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plt.figure(figsize=(10, 7))
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gs1 = gridspec.GridSpec(1, 1)
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ax1 = plt.subplot(gs1[0,0])
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ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
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ax1.set_ylim([0,5])
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ax1.set_xlim([1990,planning_horizons[-1]+1])
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ax1.plot(e_1990*CO2_CAP[o],linewidth=3,
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color='dodgerblue', label=None)
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emissions = historical_emissions()
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ax1.plot(emissions, color='black', linewidth=3, label=None)
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#plot commited and uder-discussion targets
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#(notice that historical emissions include all countries in the
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# network, but targets refer to EU)
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ax1.plot([2020],[0.8*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2030],[0.45*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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markeredgecolor='black')
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ax1.plot([2030],[0.6*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
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color='gray', linewidth=2, marker='_', alpha=0.5)
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ax1.plot([2050],[0.01*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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linewidth=0, markeredgecolor='black',
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label='EU under-discussion target', zorder=10,
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clip_on=False)
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ax1.plot([2050],[0.125*emissions[1990]],'ro',
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black', label='EU commited target')
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ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
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facecolor='white', frameon=True)
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path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'
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if not os.path.exists(path_cb_plot):
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os.makedirs(path_cb_plot)
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print('carbon budget distribution saved to ' + path_cb_plot + 'carbon_budget_plot.pdf')
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plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300)
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countries=pd.Series(data=cts)
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countries.to_csv(path_cb + 'countries.csv', sep=',',
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line_terminator='\n', float_format='%.3f')
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def add_lifetime_wind_solar(n):
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"""
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@ -1985,7 +1862,7 @@ if __name__ == "__main__":
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
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opts='', planning_horizons='2020',
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sector_opts='120H-T-H-B-I-solar3-dist1-cb40ex0'),
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sector_opts='120H-T-H-B-I-solar3-dist1-cb48be3'),
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input=dict( network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
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||||
energy_totals_name='resources/energy_totals.csv',
|
||||
co2_totals_name='resources/co2_totals.csv',
|
||||
@ -2024,7 +1901,7 @@ if __name__ == "__main__":
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
|
||||
),
|
||||
output=['results/version-8/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
output=['results/version-cb48be3/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user