-
Notifications
You must be signed in to change notification settings - Fork 7
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Sven Eggimann
committed
May 14, 2019
1 parent
244b30a
commit ffa338b
Showing
2 changed files
with
210 additions
and
15 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,27 +1,222 @@ | ||
| import math | ||
|
|
||
| from smif.data_layer import Results | ||
| import matplotlib.pyplot as plt | ||
| import os | ||
| import seaborn as sbn | ||
| import geopandas as gpd | ||
| import pandas as pd | ||
|
|
||
| '''path = "C:/Users/cenv0553/nismod2/results/electricworld/energy_demand_constrained/decision_0" | ||
| a = os.listdir(path) | ||
| for i in a: | ||
| print(i)''' | ||
|
|
||
| path = 'C:/Users/cenv0553/nismod2' | ||
| ''' | ||
| results = Results({'interface': 'local_csv', 'dir': path}) | ||
| df = results.read(['energy_sd_optimised'], ['energy_demand_constrained'], ['service_gas']).drop(columns='decision') | ||
| agg = df.groupby(by=['model_run', 'timestep', 'lad_uk_2016'], squeeze=True, sort=False).sum().drop(columns='hourly') | ||
| agg = agg.reset_index() | ||
| plot_lads(agg, 'service_gas')''' | ||
|
|
||
| results = Results({'interface': 'local_csv','dir': path}) | ||
|
|
||
| print("--------") | ||
| model_runs = results.list_model_runs() | ||
|
|
||
| for i in model_runs: | ||
| print(i) | ||
|
|
||
| filenames = [ | ||
| 'industry_biomass_boiler_biomass', | ||
| 'industry_biomass_district_heating_biomass', | ||
| 'industry_biomass_non_heating', | ||
| 'industry_electricity_boiler_electricity', | ||
| 'industry_electricity_district_heating_electricity', | ||
| 'industry_electricity_heat_pumps_electricity', | ||
| 'industry_electricity_non_heating', | ||
| 'industry_gas_boiler_gas', | ||
| 'industry_gas_district_heating_CHP_gas', | ||
| 'industry_gas_non_heating', | ||
| 'industry_hydrogen_boiler_hydrogen', | ||
| 'industry_hydrogen_district_heating_fuel_cell', | ||
| 'industry_hydrogen_fuel_cell_hydrogen', | ||
| 'industry_hydrogen_heat_pumps_hydrogen', | ||
| 'industry_hydrogen_non_heating', | ||
| 'industry_oil_boiler_oil', | ||
| 'industry_oil_non_heating', | ||
| 'industry_solid_fuel_boiler_solid_fuel', | ||
| 'industry_solid_fuel_non_heating', | ||
| 'residential_biomass_boiler_biomass', | ||
| 'residential_biomass_district_heating_biomass', | ||
| 'residential_biomass_non_heating', | ||
| 'residential_electricity_boiler_electricity', | ||
| 'residential_electricity_district_heating_electricity', | ||
| 'residential_electricity_heat_pumps_electricity', | ||
| 'residential_electricity_non_heating', | ||
| 'residential_gas_boiler_gas', | ||
| 'residential_gas_district_heating_CHP_gas', | ||
| 'residential_gas_non_heating', | ||
| 'residential_hydrogen_boiler_hydrogen', | ||
| 'residential_hydrogen_district_heating_fuel_cell', | ||
| 'residential_hydrogen_fuel_cell_hydrogen', | ||
| 'residential_hydrogen_heat_pumps_hydrogen', | ||
| 'residential_hydrogen_non_heating', | ||
| 'residential_oil_boiler_oil', | ||
| 'residential_oil_non_heating', | ||
| 'residential_solid_fuel_boiler_solid_fuel', | ||
| 'residential_solid_fuel_non_heating', | ||
| 'service_biomass_boiler_biomass', | ||
| 'service_biomass_district_heating_biomass', | ||
| 'service_biomass_non_heating', | ||
| 'service_electricity_boiler_electricity', | ||
| 'service_electricity_district_heating_electricity', | ||
| 'service_electricity_heat_pumps_electricity', | ||
| 'service_electricity_non_heating', | ||
| 'service_gas_boiler_gas', | ||
| 'service_gas_district_heating_CHP_gas', | ||
| 'service_gas_non_heating', | ||
| 'service_hydrogen_boiler_hydrogen', | ||
| 'service_hydrogen_district_heating_fuel_cell', | ||
| 'service_hydrogen_fuel_cell_hydrogen', | ||
| 'service_hydrogen_heat_pumps_hydrogen', | ||
| 'service_hydrogen_non_heating', | ||
| 'service_oil_boiler_oil', | ||
| 'service_oil_non_heating', | ||
| 'service_solid_fuel_boiler_solid_fuel', | ||
| 'service_solid_fuel_non_heating', | ||
| 'service_solid_fuel_non_heating' | ||
| ] | ||
|
|
||
| filenames = [ | ||
| 'industry_electricity_boiler_electricity', | ||
| 'industry_electricity_district_heating_electricity', | ||
| 'industry_electricity_heat_pumps_electricity', | ||
| 'industry_electricity_non_heating', | ||
| 'residential_electricity_boiler_electricity', | ||
| 'residential_electricity_district_heating_electricity', | ||
| 'residential_electricity_heat_pumps_electricity', | ||
| 'residential_electricity_non_heating', | ||
| 'service_electricity_boiler_electricity', | ||
| 'service_electricity_district_heating_electricity', | ||
| 'service_electricity_heat_pumps_electricity', | ||
| 'service_electricity_non_heating', | ||
| ] | ||
|
|
||
| filenames = [ | ||
| 'industry_electricity_boiler_electricity', | ||
| 'industry_electricity_district_heating_electricity'] | ||
| # ['model_run', 'timestep', 'decision', 'lad_uk_2016', 'hourly', filenames...] | ||
|
|
||
| model_runs = ['electricworld', 'multivector'] | ||
| timesteps = [2015, 2020, 2030, 2050] | ||
| select_arc_regions = True | ||
|
|
||
| df = results.read( | ||
| model_run_names=model_runs, | ||
| model_names=['energy_demand_constrained'], | ||
| output_names=filenames, | ||
| timesteps=timesteps) | ||
|
|
||
|
|
||
| print(df.columns.tolist()) | ||
|
|
||
| # Selet model run | ||
| #df.loc[df['model_run'] == model_runs[0]] | ||
|
|
||
| # Select regions | ||
| if select_arc_regions: | ||
| arc_regions = [ | ||
| "E07000008", "E07000012", "E07000009", "E07000011", "E07000099", "E07000242", "E07000243", | ||
| "E07000178", "E07000180", "E07000179", "E07000181", "E07000177", "E06000030", "E06000042", | ||
| "E06000055", "E07000004", "E06000032", "E06000056", "E07000154", "E07000151", "E07000156", | ||
| "E07000155"] | ||
|
|
||
| df = df.loc[df['lad_uk_2016'].isin(arc_regions)] | ||
|
|
||
| # Drop columns | ||
| print(" ") | ||
| print("-----------ungrouped-------------") | ||
| print(df.sample(n=3)) | ||
| df = df.drop(columns=['decision']) | ||
|
|
||
| df_grouped = df.groupby( | ||
| by=['model_run', 'lad_uk_2016', 'timestep', 'hourly'], | ||
| as_index=False).sum() | ||
|
|
||
| print("-----------grouped-------------") | ||
| print(type(df_grouped)) | ||
| print(df.columns.tolist()) | ||
| print(df.sample(n=3).values) | ||
|
|
||
| df_empty = pd.DataFrame(columns=model_runs, index=timesteps) | ||
|
|
||
| for model_run in model_runs: | ||
| for timestep in timesteps: | ||
| print("aaaaaa") | ||
| print(df_grouped.columns.tolist()) | ||
| annual_all_reg_all_hourly = df_grouped[( | ||
| df_grouped['model_run'] == model_run) & ( | ||
| df_grouped['timestep'] == timestep)] | ||
|
|
||
| # Some data | ||
| fracs = [15, 30, 45, 10] | ||
| print("ddd " + str(annual_all_reg_all_hourly.shape)) | ||
| annual_all_reg_all_hourly = annual_all_reg_all_hourly.drop(columns=['model_run', 'timestep', 'lad_uk_2016', 'hourly']) | ||
| annual_all_reg_all_hourly = annual_all_reg_all_hourly.sum().sum() | ||
| print("annual_all_reg_all_hourly") | ||
| print(annual_all_reg_all_hourly) | ||
| df_empty[model_run][timestep] = annual_all_reg_all_hourly | ||
|
|
||
| # Make figure and axes | ||
| fig, axs = plt.subplots(2,2) | ||
| print("===========") | ||
| print(df_empty) | ||
| df_empty.plot() | ||
| plt.show() | ||
| raise Exception("tt") | ||
|
|
||
| for model_run in model_runs: | ||
| df_modelrun = df[df['model_run'] == model_run] | ||
| df_modelrun = df_modelrun.drop(columns='model_run') | ||
|
|
||
| for timestep in timesteps: | ||
| df_modelrun_year = df_modelrun[df_modelrun['timestep'] == timestep] | ||
| df_modelrun = df_modelrun.drop(columns='timestep') | ||
|
|
||
| standard_area_to_define_radius = 4 | ||
| df_modelrun_year = df_modelrun_year.groupby(by=['lad_uk_2016', 'hourly'], as_index=False).sum() | ||
| df_modelrun_year = df_modelrun_year.drop(columns=['lad_uk_2016', 'hourly']) | ||
|
|
||
| old_area = 1 | ||
| new_area = 2 | ||
| # Sum demand across timesteps and regions | ||
| df_modelrun_year = df_modelrun_year.sum() | ||
| print("...") | ||
| print(df_modelrun_year) | ||
| df_empty[model_run][timestep] = df_modelrun_year | ||
|
|
||
| area_change = new_area / old_area | ||
|
|
||
| old_radius = (1 / standard_area_to_define_radius) * old_area | ||
|
|
||
| #new_radius = math.sqrt(area_change * new_area / math.pi) | ||
| new_radius = math.sqrt(area_change) * old_radius | ||
| #'model_run', 'timestep', 'seasonal_week | ||
| df = df.groupby(by=['model_run', 'lad_uk_2016', 'timestep', 'hourly']).sum() | ||
| print(" ") | ||
| print("A ==================") | ||
| print(df.columns.tolist()) | ||
| print(df.shape) | ||
| print(df.sample(n=3)) | ||
|
|
||
| axs[1,0].pie(fracs, radius=old_radius) | ||
| axs[1,1].pie(fracs, radius=new_radius) | ||
| print("Sum annual across all filenames") | ||
| df = df.sum(axis=1).to_frame() | ||
| print(" ") | ||
| print("B ==================") | ||
| print(df.shape) | ||
| print(df.sample(n=3)) | ||
|
|
||
| # Sum across all regions | ||
| df = df.sum().to_frame() | ||
| print("___3_______") | ||
| print(df.shape) | ||
| print(df) | ||
| # Sum all filenames | ||
| #df = df.sum(axis=1) | ||
| df.plot() | ||
| #print(df.columns.tolist()) | ||
| #print(df['residential_electricity_non_heating'].shape) | ||
| #df = df.reset_index() | ||
| #df['residential_electricity_non_heating'].plot() | ||
| #df.plot_lads('residential_electricity_non_heating') | ||
| plt.show() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters