| Conditions | 2 |
| Total Lines | 79 |
| Code Lines | 37 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | #!/usr/bin/env python3 |
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| 107 | def plot_generation( |
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| 108 | carrier,scenario, osm = False |
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| 109 | ): |
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| 110 | """ |
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| 111 | Plots color maps according to the capacity of different generators |
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| 112 | of the two existing scenarios (eGon2035 and eGon100RE) |
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| 113 | |||
| 114 | |||
| 115 | Parameters |
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| 116 | ---------- |
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| 117 | carrier : generators |
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| 118 | The list of generators: biomass, central_biomass_CHP, central_biomass_CHP_heat, |
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| 119 | industrial_biomass_CHP, solar, solar_rooftop, wind_offshore, wind_onshore. |
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| 120 | |||
| 121 | scenario: eGon2035, eGon100RE |
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| 122 | |||
| 123 | |||
| 124 | """ |
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| 125 | |||
| 126 | |||
| 127 | con = db.engine() |
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| 128 | #imports buses of Germany |
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| 129 | SQLBus = "SELECT bus_id, country FROM grid.egon_etrago_bus WHERE country='DE'" |
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| 130 | busDE = pd.read_sql(SQLBus,con) |
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| 131 | busDE = busDE.rename({'bus_id': 'bus'},axis=1) |
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| 132 | #Imports grid districs |
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| 133 | sql = "SELECT bus_id, geom FROM grid.egon_mv_grid_district" |
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| 134 | distr = gpd.GeoDataFrame.from_postgis(sql, con) |
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| 135 | distr = distr.rename({'bus_id': 'bus'},axis=1) |
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| 136 | distr = distr.set_index("bus") |
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| 137 | #merges grid districts with buses |
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| 138 | distr = pd.merge(busDE, distr, on='bus') |
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| 139 | #Imports generator |
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| 140 | sqlCarrier = "SELECT carrier, p_nom, bus FROM grid.egon_etrago_generator" |
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| 141 | sqlCarrier = "SELECT * FROM grid.egon_etrago_generator" |
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| 142 | Carriers = pd.read_sql(sqlCarrier,con) |
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| 143 | Carriers = Carriers.loc[Carriers['scn_name'] == scenario] |
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| 144 | Carriers = Carriers.set_index("bus") |
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| 145 | |||
| 146 | |||
| 147 | CarrierGen = Carriers.loc[Carriers['carrier'] == carrier] |
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| 148 | #merges districts with generators |
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| 149 | Merge = pd.merge(CarrierGen, distr, on ='bus', how="outer") |
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| 150 | |||
| 151 | |||
| 152 | Merge.loc[Merge ['carrier'] != carrier, "p_nom" ] = 0 |
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| 153 | Merge.loc[Merge ['country'] != "DE", "p_nom" ] = 0 |
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| 154 | |||
| 155 | gdf = gpd.GeoDataFrame(Merge , geometry='geom') |
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| 156 | pnom=gdf['p_nom'] # |
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| 157 | #0.95 quantile is used to filter values that are too high and make noise in the plots. |
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| 158 | max_pnom=pnom.quantile(0.95) |
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| 159 | gdf = gdf.to_crs(epsg=3857) |
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| 160 | |||
| 161 | |||
| 162 | # Plot osm map in background |
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| 163 | if osm != False: |
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| 164 | #if network.srid == 4326: |
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| 165 | #set_epsg_network(network) |
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| 166 | fig, ax = plot_osm(osm['x'], osm['y'], osm['zoom']) |
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| 167 | |||
| 168 | else: |
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| 169 | fig, ax = plt.subplots(1, 1) |
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| 170 | |||
| 171 | |||
| 172 | |||
| 173 | ax.set_axis_off(); |
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| 174 | plt.title(f" {carrier} installed capacity in MW , {scenario}") |
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| 175 | cmap = mpl.cm.coolwarm |
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| 176 | |||
| 177 | |||
| 178 | norm = mpl.colors.Normalize(vmin=0, vmax=max_pnom) |
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| 179 | gdf.plot(column='p_nom', ax=ax, legend=False, legend_kwds={'label': "p_nom(MW)", |
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| 180 | |||
| 181 | 'orientation': "vertical"}, cmap=cmap, norm=norm, edgecolor='black', linewidth=0.1,zorder=2) |
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| 182 | scatter = ax.collections[0] |
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| 183 | cbar=plt.colorbar(scatter, ax=ax, extend='max') |
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| 184 | cbar.set_label('p_nom(MW)', rotation=90) |
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| 185 | return 0 |
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| 186 | |||
| 191 |