minor fixes and address deprecation warnings
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@ -1,4 +1,4 @@
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version: 0.6.0
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version: 0.7.0
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logging_level: INFO
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@ -510,6 +510,7 @@ plotting:
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natural gas: '#e05b09'
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CCGT: '#a85522'
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CCGT marginal: '#a85522'
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allam: '#B98F76'
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gas for industry co2 to atmosphere: '#692e0a'
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gas for industry co2 to stored: '#8a3400'
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gas for industry: '#853403'
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@ -71,9 +71,9 @@ author = u'2019-2021 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), L
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# built documents.
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#
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# The short X.Y version.
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version = u'0.6'
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version = u'0.7'
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# The full version, including alpha/beta/rc tags.
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release = u'0.6.0'
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release = u'0.7.0'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
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@ -8,6 +8,11 @@ Future release
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.. note::
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This unreleased version currently may require the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
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* new feature
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PyPSA-Eur-Sec 0.7.0 (16th February 2023)
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========================================
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This release includes the addition of the European gas transmission network and
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incorporates retrofitting options to hydrogen.
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@ -178,7 +178,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
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busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
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inv_busmap = {}
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for k, v in busmap.iteritems():
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for k, v in busmap.items():
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inv_busmap[v] = inv_busmap.get(v, []) + [k]
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clustermaps = busmap_s.map(busmap)
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@ -40,7 +40,7 @@ if __name__ == '__main__':
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reference = ["RS", "BA"]
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average = urban_fraction[reference].mean()
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fill_values = pd.Series({ct: average for ct in missing})
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urban_fraction = urban_fraction.append(fill_values)
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urban_fraction = pd.concat([urban_fraction, fill_values])
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# population in each grid cell
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pop_cells = pd.Series(I.dot(nuts3['pop']))
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@ -28,8 +28,8 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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## Get overall demand curve for all vehicles
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traffic = pd.read_csv(
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traffic_fn, skiprows=2, usecols=["count"], squeeze=True
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)
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traffic_fn, skiprows=2, usecols=["count"]
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).squeeze("columns")
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transport_shape = generate_periodic_profiles(
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dt_index=snapshots,
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@ -118,7 +118,7 @@ def bev_availability_profile(fn, snapshots, nodes, options):
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Derive plugged-in availability for passenger electric vehicles.
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"""
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traffic = pd.read_csv(fn, skiprows=2, usecols=["count"], squeeze=True)
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traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
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avail_max = options["bev_avail_max"]
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avail_mean = options["bev_avail_mean"]
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@ -527,6 +527,8 @@ def add_co2_tracking(n, options):
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e_nom_max = pd.read_csv(snakemake.input.sequestration_potential, index_col=0).squeeze()
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e_nom_max = e_nom_max.reindex(spatial.co2.locations).fillna(0.).clip(upper=upper_limit).mul(1e6) / annualiser # t
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e_nom_max = e_nom_max.rename(index=lambda x: x + " co2 stored")
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else:
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e_nom_max = np.inf
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n.madd("Store",
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spatial.co2.nodes,
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@ -759,8 +761,8 @@ def add_ammonia(n, costs):
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carrier="Haber-Bosch",
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efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]), # output: MW_NH3 per MW_elec
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efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"], # input: MW_H2 per MW_elec
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capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
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lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
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capital_cost=costs.at["Haber-Bosch", "fixed"],
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lifetime=costs.at["Haber-Bosch", 'lifetime']
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)
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n.madd("Link",
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@ -2905,6 +2907,9 @@ if __name__ == "__main__":
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if "B" in opts:
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add_biomass(n, costs)
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if options['ammonia']:
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add_ammonia(n, costs)
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if "I" in opts:
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add_industry(n, costs)
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@ -2917,9 +2922,6 @@ if __name__ == "__main__":
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if options['dac']:
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add_dac(n, costs)
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if options['ammonia']:
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add_ammonia(n, costs)
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if "decentral" in opts:
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decentral(n)
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