before fixing file to write out YAML

energy_demand/config_data/population_from_andrew.py

@@ -0,0 +1,161 @@
+"""Download population projections from https://github.com/nismod/population/blob/master/README.md
+
+
+Info
+-----
+
+https://github.com/virgesmith/UKCensusAPI
+https://www.nomisweb.co.uk/myaccount/webservice.asp
+https://github.com/nismod/population
+https://github.com/virgesmith/UKCensusAPI
+
+Steps
+------
+1. optain nomis key
+2. in cmd line: set NOMIS_API_KEY=XXX
+3. ./setup.py install
+4. run python script from command line
+
+https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2015-10-29
+
+Potential variants
+-------------------
+hhh: High population,
+hpp: High fertility,
+lll: Low population,
+lpp: Low fertility,
+php: High life expectancy,
+pjp: Moderately high life expectancy,
+pkp: Moderately low life expectancy,
+plp: Low life expectancy,
+pph: High migration,
+ppl: Low migration,
+ppp: Principal,
+ppq: 0% future EU migration (non-ONS),
+ppr: 50% future EU migration (non-ONS),
+pps: 150% future EU migration (non-ONS),
+ppz: Zero net migration
+
+Select for paper I
+-------------------
+Prinicpal projection    ppp
+Low mnigration          ppl
+High migration          pph
+"""
+import pandas as pd
+import population.nppdata as NPPData
+import population.snppdata as SNPPData
+import population.utils as utils
+
+reg_pop = True
+extrapolate_pop = False
+extrapolate_specific_scenario = True
+
+if extrapolate_specific_scenario:
+    '''
+    Get extrapolated data for full time range for different ONS scenarios from 2015 - 2050
+    # https://github.com/nismod/population/blob/master/doc/example_variant_ex.py
+    '''
+
+    # initialise the population modules
+    npp = NPPData.NPPData()
+    snpp = SNPPData.SNPPData()
+
+    # 50 years, roughly half is extrapolated
+    years = range(2015, 2051)
+
+    # start with an empty data frame
+    result_ppp = pd.DataFrame()
+    result_ppl = pd.DataFrame()
+    result_pph = pd.DataFrame()
+
+    # loop over all the UK LAD (or LAD-equivalents)
+    for lad in snpp.data.GEOGRAPHY_CODE.unique():
+        print("Collection population for region {}".format(lad))
+
+        # calculate the the variants
+        region_ppp = snpp.create_variant("ppp", npp, lad, years)
+        region_ppl = snpp.create_variant("ppl", npp, lad, years)
+        region_pph = snpp.create_variant("pph", npp, lad, years)
+
+        # aggregate the calculated variants by age and gender
+        region_ppp = utils.aggregate(region_ppp, ["GENDER", "C_AGE"])
+        region_ppl = utils.aggregate(region_ppl, ["GENDER", "C_AGE"])
+        region_pph = utils.aggregate(region_pph, ["GENDER", "C_AGE"])
+
+        result_ppp.append(region_ppp, ignore_index=True)
+        result_ppl.append(region_ppl, ignore_index=True)
+        result_pph.append(region_pph, ignore_index=True)
+
+        break
+    print("tt")
+
+    # write out results
+    result_ppp.to_csv("C:/Users/cenv0553/mistral_population/__RESULTS/prinicpal_extrap_2050.csv", index=False)
+    result_ppl.to_csv("C:/Users/cenv0553/mistral_population/__RESULTS/lowmigration_extrap_2050.csv", index=False)
+    result_pph.to_csv("C:/Users/cenv0553/mistral_population/__RESULTS/highmigration_extrap_2050.csv", index=False)
+    print("FINISHED")
+'''
+if extrapolate_pop:
+
+    # ===================
+    # Extrapolating SNPP for 2040 - 2050
+    #https://github.com/nismod/population
+    # ===================
+
+    # initialise the population modules
+    npp = NPPData.NPPData()
+    snpp = SNPPData.SNPPData()
+
+    # get the first year where extrapolation is necessary
+    ex_start = snpp.max_year() + 1
+
+    # we extrapolate to 2050
+    ex_end = 2050
+
+    # start with an empty data frame
+    result = pd.DataFrame()
+
+    # loop over all the UK LAD (or LAD-equivalents)
+    for lad in snpp.data.GEOGRAPHY_CODE.unique():
+
+        # extrapolate
+        lad_ex = snpp.extrapolagg("GEOGRAPHY_CODE", npp, lad, range(ex_start, ex_end + 1))
+
+        # append to data
+        result = result.append(lad_ex, ignore_index=True)
+
+    # write out results
+    result.to_csv("C:/Users/cenv0553/mistral_population/population/__RESULTS/snpp_extrap_2050.csv")
+
+if reg_pop:
+
+    # initialise the population modules
+    npp = NPPData.NPPData()
+    snpp = SNPPData.SNPPData()
+
+    # get the first year where extrapolation is necessary
+    ex_start = snpp.max_year() + 1
+
+    # we extrapolate to 2050
+    ex_end = 2050 
+
+    # start with an empty data frame
+    result = pd.DataFrame()
+
+    # loop over all the UK LAD (or LAD-equivalents)
+    for lad in snpp.data.GEOGRAPHY_CODE.unique():
+
+        # get the total projected population for newcastle up to the SNPP horizon (2039)
+        lad_non_ex = snpp.aggregate("GEOGRAPHY_CODE", lad, range(2015, ex_start))
+
+        # extrapolate for another 25 years
+        lad_ex = snpp.extrapolagg("GEOGRAPHY_CODE", npp, lad, range(ex_end))
+
+        # append to data
+        result = result.append(lad_non_ex, ignore_index=True)
+        result = result.append(lad_ex, ignore_index=True)
+
+    # write out results
+    result.to_csv("C:/Users/cenv0553/mistral_population/population/__RESULTS/snpp_extrap_2015_2050_ppp.csv")
+'''

energy_demand/plotting/plotting_multiple_scenarios.py

@@ -637,7 +637,6 @@ def plot_tot_fueltype_y_over_time(
     # Set figure size
     fig = plt.figure(figsize=plotting_program.cm2inch(9, 8))
 
-
     ax = fig.add_subplot(1, 1, 1)
 
     y_scenario = {}
@@ -845,9 +844,8 @@ def plot_tot_y_over_time(
 
     if plotshow:
         plt.show()
-        plt.close()
-    else:
-        plt.close()
+
+    plt.close()
 
 def plot_radar_plots_average_peak_day(
         scenario_data,
@@ -864,6 +862,8 @@ def plot_radar_plots_average_peak_day(
     name_spider_plot = os.path.join(
         fig_name, "spider_scenarios_{}.pdf".format(fueltype_to_model))
 
+    fueltype_int = fueltypes[fueltype_to_model]
+
     # ----------------
     # Create base year peak load profile
     # Aggregate load profiles of all regions
@@ -877,11 +877,12 @@ def plot_radar_plots_average_peak_day(
         print("-------Scenario: {} {}".format(scenario, fueltype_to_model))
         base_yr = 2015
 
+        # ------------------------
         # Future year load profile
+        # ------------------------
         all_regs_fueltypes_yh_by = np.sum(scenario_data[scenario]['results_every_year'][base_yr], axis=1)
         all_regs_fueltypes_yh_cy = np.sum(scenario_data[scenario]['results_every_year'][year_to_plot], axis=1)
 
-        fueltype_int = fueltypes[fueltype_to_model]
 
         # ---------------------------
         # Calculate load factors
@@ -897,6 +898,30 @@ def plot_radar_plots_average_peak_day(
         scen_load_factor_fueltype_y_cy = load_factors.calc_lf_y(all_regs_fueltypes_yh_cy)
         load_factor_fueltype_y_cy.append(round(scen_load_factor_fueltype_y_cy[fueltype_int], fueltype_int))
 
+        # ------------------------
+        # Calculate share or space and water heating of total
+        # electrictiy demand in 2050
+        # ------------------------
+        enduses_to_agg = ['rs_space_heating', 'rs_water_heating', 'ss_space_heating', 'ss_water_heating', 'is_space_heating']
+
+        aggregated_enduse_fueltype = np.zeros((365, 24))
+        aggregated_enduse_fueltype_by = np.zeros((365, 24))
+        total_demand = np.zeros((365, 24))
+        for enduse in scenario_data[scenario]['results_enduse_every_year'][2050].keys():
+            if enduse in enduses_to_agg:
+                aggregated_enduse_fueltype += scenario_data[scenario]['results_enduse_every_year'][2050][enduse][fueltype_int]
+                aggregated_enduse_fueltype_by += scenario_data[scenario]['results_enduse_every_year'][2015][enduse][fueltype_int]
+                total_demand += scenario_data[scenario]['results_enduse_every_year'][2050][enduse][fueltype_int]
+            else:
+                total_demand += scenario_data[scenario]['results_enduse_every_year'][2050][enduse][fueltype_int]
+
+        # Total demand
+        selected_enduses_total_peak_day = round(np.max(aggregated_enduse_fueltype[peak_day_nr_cy]), 1)
+        all_enduses_total_peak_day = round(np.max(total_demand[peak_day_nr_cy]), 1)
+        selected_enduses_peak_by = round(np.max(aggregated_enduse_fueltype_by), 1)
+        print("p_all_enduses_total_peak_day " + str(all_enduses_total_peak_day))
+        print("p_selected_enduses_total_peak_day " + str(selected_enduses_total_peak_day))
+
         # ----------
         # Calculate change in peak
         # ----------
@@ -905,11 +930,14 @@ def plot_radar_plots_average_peak_day(
 
         diff_max_h = round(((100 / by_max_h) * cy_max_h) - 100, 1)
 
-        label_max_h = " by: {} cy: {} d: {}, scen: {}".format(
+        label_max_h = " by: {} cy: {} d: {}, scen: {} ed_peakh_heating: {}  ed_tot_peah_ {} by_heating: {}".format(
             round(by_max_h, 2),
             round(cy_max_h, 1),
             round(diff_max_h, 1),
-            scenario,)
+            scenario,
+            selected_enduses_total_peak_day,
+            all_enduses_total_peak_day,
+            selected_enduses_peak_by)
 
         list_diff_max_h.append(label_max_h)
 

energy_demand/plotting/plotting_results.py

@@ -2219,34 +2219,26 @@ def plot_radar_plot_multiple_lines(
             color_line,
             alpha=0.05)
 
-    font_additional_info = plotting_styles.font_info(size=5)
+    # ------------
+    # Write outs
+    # ------------
+    font_additional_info = plotting_styles.font_info(size=4)
 
     for cnt, entry in enumerate(list_diff_max_h):
         plt.text(
-            0.25,
+            0.15,
             0 + cnt/50,
             entry,
             fontdict=font_additional_info,
             transform=plt.gcf().transFigure)
 
-    # ------------
-    # Title
-    # ------------
-    font_additional_info = plotting_styles.font_info(size=4)
-    #plt.title(
-    #    title_info,
-    #    loc='left',
-    #    fontdict=font_additional_info)
-
     # ------------
     # Legend
     # ------------
     plt.legend(
         ncol=1,
-        bbox_to_anchor=(0.5, -0.1),
-        prop={
-            'family': 'arial',
-            'size': 4},
+        bbox_to_anchor=(0.2, -0.1),
+        prop={'size': 4},
         frameon=False)
 
     plt.savefig(fig_name)
@@ -2267,8 +2259,8 @@ def plt_one_fueltype_multiple_regions_peak_h(
     Arguments
     ---------
     """
-    # Set figure size
-    fig = plt.figure(figsize=plotting_program.cm2inch(14, 8))
+    fig = plt.figure(
+        figsize=plotting_program.cm2inch(14, 8))
 
     ax = fig.add_subplot(1, 1, 1)