before morging

energy_demand/basic/date_prop.py

@@ -32,6 +32,26 @@ def convert_h_to_day_year_and_h(hour):
 
     return day, day_h
 
+def get_8760_hrs_from_yeardays(yeardays):
+    """Get from yeadays all yearhours
+
+    Arguments
+    ---------
+    yeardays : list
+        Listh with year days (0 - 364)
+
+    Returns
+    ---------
+    year_hrs : list
+        All year hours (0 - 8759)
+    """
+    year_hrs = []
+    for day in yeardays:
+        hrs = list(range((day * 24), ((day + 1) * 24)))
+        year_hrs += hrs
+
+    return year_hrs
+
 def get_seasonal_weeks():
     """
     """

energy_demand/enduse_func.py

@@ -1096,13 +1096,13 @@ def apply_scenario_drivers(
                         by_driver_data = gva_per_head[base_yr][region]
                         cy_driver_data = gva_per_head[curr_yr][region]
 
-                    logging.info("gva  {}   {}".format(by_driver_data, cy_driver_data))
+                    #logging.info("gva  {}   {}".format(by_driver_data, cy_driver_data))
 
             elif scenario_driver == 'population':
                 by_driver_data = population[base_yr][region]
                 cy_driver_data = population[curr_yr][region]
 
-                logging.info("pop  {}   {}".format(population[base_yr][region], population[curr_yr][region]))
+                #logging.info("pop  {}   {}".format(population[base_yr][region], population[curr_yr][region]))
 
             # Multiply drivers
             by_driver *= by_driver_data
@@ -1118,15 +1118,9 @@ def apply_scenario_drivers(
         except ZeroDivisionError:
             factor_driver = 1
 
-        logging.info("  eda  {}   {}  {} {}".format(
-            scenario_drivers,
-            factor_driver,
-            by_driver_data,
-            cy_driver_data
+        #logging.info("  eda  {}   {}  {} {}".format(
+        #    scenario_drivers, factor_driver, by_driver_data, cy_driver_data))
 
-        ))
-
-
         fuel_y = fuel_y * factor_driver
     else:
         """Scenario driver calculation based on dwelling stock

energy_demand/plotting/fig_p2_spatial_val.py

@@ -1,5 +1,6 @@
 """
 """
+import os
 import operator
 import logging
 import numpy as np
@@ -71,19 +72,19 @@ def run(
         subnational_elec,
         regions,
         'electricity',
-        fig_path=fig_path,
+        fig_path=os.path.join(fig_path, "spatial_validation_electricity.pdf"),
         label_points=False,
         plotshow=plot_crit)
 
-    # Gas TODO
+    # Gas
     plot_spatial_validation(
         simulation_yr_to_plot,
         fuel_gas_regs_yh,
         demand_year_regional,
         subnational_gas,
         regions,
         'gas',
-        fig_path=fig_path,
+        fig_path=os.path.join(fig_path, "spatial_validation_gas.pdf"),
         label_points=False,
         plotshow=plot_crit)
 
@@ -244,7 +245,6 @@ def plot_spatial_validation(
     for region_vals in y_modelled_demands_non_regional:
         station_vals.append(region_vals[station_nr])
 
-
     plt.plot(
         x_values,
         station_vals,
@@ -377,7 +377,7 @@ def plot_spatial_validation(
     # Tight layout
     plt.margins(x=0)
     plt.tight_layout()
-    #plt.savefig(fig_name)
+    plt.savefig(fig_path)
 
     if plotshow:
         plt.show()

energy_demand/plotting/fig_p2_temporal_validation.py

@@ -2,14 +2,13 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
-#from scipy.stats import mstats
 import pandas as pd
 from scipy import stats
 
 from energy_demand.technologies import tech_related
 from energy_demand.plotting import basic_plot_functions
 
-def run(
+def run_fig_p2_temporal_validation(
         data_input,
         weather_yr,
         fueltype_str,
@@ -18,18 +17,20 @@ def run(
         validation_elec_2015,
         non_regional_elec_2015,
         fig_name,
+        titel="titel",
+        y_lim_val=100,
+        plot_validation=False,
         plot_show=False
     ):
     """
     """
-    # Select period and fueltype
     fueltype_int = tech_related.get_fueltype_int(fueltype_str)
 
     # -----------------------------------------------------------
     # Iterate overall weather_yrs and store data in dataframe
     # (columns = timestep, rows: value of year)
     # -----------------------------------------------------------
-    columns = period_h # hours in 8760 hours
+    selection_hours = period_h
 
     # List of selected data for every weather year (which is then converted to array)
     weather_yrs_data = []
@@ -40,15 +41,15 @@ def run(
         # Weather year specific data
         data_input_fueltype = data_weather_yr[simulation_yr_to_plot][fueltype_int]     # Select fueltype
         data_input_reshape = data_input_fueltype.reshape(8760)  # reshape
-        data_input_selection_hrs = data_input_reshape[period_h] # select period
+        data_input_selection_hrs = data_input_reshape[selection_hours] # select period
 
         weather_yrs_data.append(data_input_selection_hrs)
         weather_yrs_full_year.append(data_input_reshape)
 
     weather_yrs_data = np.array(weather_yrs_data)
 
     # Create dataframe
-    df = pd.DataFrame(weather_yrs_data, columns=columns)
+    df = pd.DataFrame(weather_yrs_data, columns=period_h)
 
     df_full_year = pd.DataFrame(weather_yrs_full_year, columns=range(8760))
 
@@ -64,13 +65,10 @@ def run(
     df_q_95 = df_q_95.T
     df_q_05 = df_q_05.T
 
-    fig = plt.figure() #(figsize = cm2inch(10,10))
+    fig = plt.figure()
 
     ax = fig.add_subplot(111)
 
-    # 2015 weather year
-    #data_2015 = data_weather_yr[2015][fueltype_int].reshape(8760)[period_h]
-
     # ---------------
     # Smoothing lines
     # ---------------
@@ -82,7 +80,6 @@ def run(
         period_h_smoothed = period_h
         df_q_95_smoothed = df_q_95
         df_q_05_smoothed = df_q_05
-        #data_2015_smoothed = data_2015
 
     #plt.plot(period_h_smoothed, data_2015_smoothed, color='tomato', linestyle='-', linewidth=2, label="2015 weather_yr")
     plt.plot(period_h_smoothed, df_q_05_smoothed, color='black', linestyle='--', linewidth=0.5, label="0.05")
@@ -95,30 +92,54 @@ def run(
         period_h_smoothed, #x
         df_q_95_smoothed,  #y1
         df_q_05_smoothed,  #y2
-        alpha=.25,
-        facecolor="grey",
+        alpha=1,
+        facecolor="lightgrey",
         label="uncertainty band")
 
     # -----------------
-    # Validation data
+    # All weather stations are used for this data
     # -----------------
-    validation_2015 = validation_elec_2015.reshape(8760)[period_h] 
-
-    try:
-        period_h_smoothed, validation_2015_smoothed = basic_plot_functions.smooth_data(period_h, validation_2015, num=40000)
-    except:
-        period_h_smoothed = period_h
-        validation_2015_smoothed = validation_2015
-
-    plt.plot(
-        period_h_smoothed,
-        validation_2015_smoothed,
-        color='green', linestyle='--', linewidth=3, label="validation 2015")
+    all_weather_stations_2015 = non_regional_elec_2015.reshape(8760)[period_h] 
 
     # -----------------
-    # All weather stations are used for this data
+    # Validation data
     # -----------------
-    all_weather_stations_2015 = non_regional_elec_2015.reshape(8760)[period_h] 
+    if plot_validation:
+        validation_2015 = validation_elec_2015.reshape(8760)[selection_hours] 
+
+        try:
+            period_h_smoothed, validation_2015_smoothed = basic_plot_functions.smooth_data(period_h, validation_2015, num=40000)
+        except:
+            period_h_smoothed = period_h
+            validation_2015_smoothed = validation_2015
+
+        plt.plot(
+            period_h_smoothed,
+            validation_2015_smoothed,
+            color='green',
+            linestyle='--',
+            linewidth=1.5, label="validation 2015")
+
+        # -----------
+        # statistics
+        # -----------
+        # Calculate mean of all all single station runs
+        mean_all_single_runs = df_full_year.mean(axis=0).tolist()
+        mean_all_single_runs = np.array(mean_all_single_runs)[period_h]
+
+        slope, intercept, r_value, p_value, std_err = stats.linregress(
+            validation_2015,
+            all_weather_stations_2015)
+        slope, intercept, r_value2, p_value, std_err = stats.linregress(
+            validation_2015,
+            mean_all_single_runs)
+
+        print("R_Value_all_stations: " + str(r_value))
+        print("R_Value_single_stations: " + str(r_value2))
+        plt.title(
+            "R_value: all stations: {} mean_single_weather_stations: {}".format(
+                round(r_value, 2),
+                round(r_value2, 2)))
 
     try:
         period_h_smoothed, all_weather_stations_2015_smoothed = basic_plot_functions.smooth_data(
@@ -127,36 +148,25 @@ def run(
         period_h_smoothed = period_h
         all_weather_stations_2015_smoothed = all_weather_stations_2015
 
-    # -----------
-    # statistics
-    # -----------
-    # Calculate mean of all all single station runs
-    mean_all_single_runs = df_full_year.mean(axis=0).tolist()
-    mean_all_single_runs = np.array(mean_all_single_runs)
-
-    slope, intercept, r_value, p_value, std_err = stats.linregress(
-        validation_2015,
-        all_weather_stations_2015)
-    slope, intercept, r_value2, p_value, std_err = stats.linregress(
-        validation_2015,
-        mean_all_single_runs)
-
-    print("R_Value_all_stations: " + str(r_value))
-    print("R_Value_single_stations: " + str(r_value2))
-
-    plt.title(
-        "R_value: all stations: {} mean_single_weather_stations: {}".format(
-            round(r_value, 2),
-            round(r_value2, 2)))
-
     plt.plot(
         period_h_smoothed,
         all_weather_stations_2015_smoothed,
         color='blue',
         linestyle='--',
-        linewidth=1,
-        label="all weather stations 2015")
-
+        linewidth=1.2,
+        label="all stations")
+
+    # Ticks for single day
+    #major_ticks_days, major_ticks_labels = get_date_strings(
+    #    days_to_plot,
+    #    daystep=1)
+    #plt.xticks(major_ticks_days, major_ticks_labels)
+    zero_entry = period_h[0]
+    plt.xticks([0 + zero_entry, 5 + zero_entry, 11 + zero_entry, 17 + zero_entry, 23 + zero_entry], ['1', '6', '12', '18', '24'])
+
+    plt.ylim(0, y_lim_val)
+    plt.xlim(period_h[0], period_h[-1])
+    plt.title("Peak day: " + str(titel))
     plt.legend(
         prop={
             'family':'arial',

energy_demand/plotting/fig_spatial_distribution_of_peak.py

@@ -15,7 +15,7 @@
 from energy_demand.plotting import result_mapping
 from energy_demand.plotting import fig_p2_weather_val
 
-def run(
+def run_fig_spatial_distribution_of_peak(
         scenarios,
         path_to_folder_with_scenarios,
         path_shapefile,
@@ -48,12 +48,9 @@ def run(
         # -----------
         # Used across different plots
         # -----------
-
-        #Fueltype to consider
         fueltype_str = 'electricity'
         fueltype_int = tech_related.get_fueltype_int(fueltype_str)
 
-        #results_container['ed_fueltype_regs_yh']
         container = {}
         container['abs_demand_in_peak_h'] = {}
         container['p_demand_in_peak_h'] = {}
@@ -69,8 +66,6 @@ def run(
             data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
             data['assumptions']['model_yeardays_daytype'], data['assumptions']['yeardays_month'], data['assumptions']['yeardays_month_days'] = date_prop.get_yeardays_daytype(year_to_model=2015)
 
-            path_out_plots = os.path.abspath(os.path.join(path_data_ed, '..', '_results_PDF_figs'))
-
             # Population 
             population_data = read_data.read_scenaric_population_data(os.path.join(path_data_ed, 'model_run_pop'))