def test_get_fueltype_str():
"""Testing function
"""
fueltypes = {'gas': 1}
in_value = 1
expected = 'gas'
# call function
out_value = tech_related.get_fueltype_str(fueltypes, in_value)
assert out_value == expected
def read_in_results(
path_result,
seasons,
model_yeardays_daytype
):
"""Read and post calculate results from txt files
and store into container
Arguments
---------
path_result : str
Paths
seasons : dict
seasons
model_yeardays_daytype : dict
Daytype of modelled yeardays
"""
logging.info("... Reading in results")
lookups = lookup_tables.basic_lookups()
results_container = {}
# -----------------
# Read in demands
# -----------------
try:
results_container['results_enduse_every_year'] = read_enduse_specific_results(
path_result)
except:
pass
try:
print("path_result " + str(path_result))
results_container['ed_fueltype_regs_yh'] = read_results_yh(
path_result, 'ed_fueltype_regs_yh')
except:
pass
# Read in residential demands
try:
results_container['residential_results'] = read_results_yh(
path_result, 'residential_results')
except:
pass
# Calculate total demand per fueltype for every hour
try:
tot_fueltype_yh = {}
for year in results_container['ed_fueltype_regs_yh']:
nr_of_fueltypes = results_container['ed_fueltype_regs_yh'][year].shape[0]
tot_fueltype_yh[year] = np.zeros((nr_of_fueltypes, 8760))
for year, ed_regs_yh in results_container['ed_fueltype_regs_yh'].items():
fuel_yh = np.sum(ed_regs_yh, axis=1) #Sum across all regions
tot_fueltype_yh[year] += fuel_yh
results_container['tot_fueltype_yh'] = tot_fueltype_yh
except:
pass
# -----------------
# Peak calculations
# -----------------
try:
results_container['ed_peak_h'] = {}
results_container['ed_peak_regs_h'] = {}
for year, ed_fueltype_reg_yh in results_container['ed_fueltype_regs_yh'].items():
results_container['ed_peak_h'][year] = {}
results_container['ed_peak_regs_h'][year] = {}
for fueltype_int, ed_reg_yh in enumerate(ed_fueltype_reg_yh):
fueltype_str = tech_related.get_fueltype_str(lookups['fueltypes'], fueltype_int)
# Calculate peak per fueltype for all regions (ed_reg_yh= np.array(fueltype, reg, yh))
all_regs_yh = np.sum(ed_reg_yh, axis=0) # sum regs
peak_h = np.max(all_regs_yh) # select max of 8760 h
results_container['ed_peak_h'][year][fueltype_str] = peak_h
results_container['ed_peak_regs_h'][year][fueltype_str] = np.max(ed_reg_yh, axis=1)
# -------------
# Load factors
# -------------
results_container['reg_load_factor_y'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_y"))
results_container['reg_load_factor_yd'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_yd"))
# -------------
# Post-calculations
# -------------
# Calculate average per season and fueltype for every fueltype
results_container['av_season_daytype_cy'], results_container['season_daytype_cy'] = calc_av_per_season_fueltype(
results_container['ed_fueltype_regs_yh'],
seasons,
model_yeardays_daytype)
'''results_container['load_factor_seasons'] = {}
results_container['load_factor_seasons']['winter'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_winter"))
results_container['load_factor_seasons']['spring'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_spring"))
results_container['load_factor_seasons']['summer'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_summer"))
results_container['load_factor_seasons']['autumn'] = read_lf_y(
os.path.join(path_result, "result_reg_load_factor_autumn"))'''
except:
pass
logging.info("... Reading in results finished")
return results_container
def create_geopanda_files(data, results_container,
path_data_results_shapefiles, regions, fueltypes_nr,
fueltypes, path_shapefile_input):
"""Create map related files (png) from results.
Arguments
---------
results_container : dict
Data container
paths : dict
Paths
regions : list
Region in a list with order how they are stored in result array
fueltypes_nr : int
Number of fueltypes
"""
logging.info("... create spatial maps of results")
# --------
# Read LAD shapefile and create geopanda
# --------
# Single scenario run
lad_geopanda_shp = gpd.read_file(path_shapefile_input)
# Attribute merge unique Key
unique_merge_id = 'name' #'geo_code'
# ======================================
# Spatial maps of difference in load factors
# ======================================
simulated_yrs = list(results_container['load_factors_y'].keys())
final_yr = simulated_yrs[-1]
base_yr = simulated_yrs[0]
for fueltype in range(fueltypes_nr):
logging.info("progress.. {}".format(fueltype))
fueltype_str = tech_related.get_fueltype_str(fueltypes, fueltype)
field_name = 'lf_diff_{}-{}_{}_'.format(base_yr, final_yr,
fueltype_str)
lf_end_yr = basic_functions.array_to_dict(
results_container['load_factors_y'][final_yr][fueltype], regions)
lf_base_yr = basic_functions.array_to_dict(
results_container['load_factors_y'][base_yr][fueltype], regions)
# Calculate load factor difference base and final year (100 = 100%)
diff_lf = {}
for reg in regions:
diff_lf[reg] = lf_end_yr[reg] - lf_base_yr[reg]
# Both need to be lists
merge_data = {
str(field_name): list(diff_lf.values()),
str(unique_merge_id): list(regions)
}
# Merge to shapefile
lad_geopanda_shp = merge_data_to_shp(lad_geopanda_shp, merge_data,
unique_merge_id)
# If user classified, defined bins [x for x in range(0, 1000000, 200000)]
#bins = [-4, -2, 0, 2, 4] # must be of uneven length containing zero
bins = [-15, -10, -5, 0, 5, 10,
15] # must be of uneven length containing zero
color_list, color_prop, user_classification, color_zero = colors_plus_minus_map(
bins=bins,
color_prop='qualitative',
color_order=True,
color_zero='#ffffff') #8a2be2
plot_lad_national(lad_geopanda_shp=lad_geopanda_shp,
legend_unit="%",
field_to_plot=field_name,
fig_name_part="lf_max_y",
result_path=path_data_results_shapefiles,
color_palette='Purples_9',
color_prop=color_prop,
user_classification=user_classification,
color_list=color_list,
color_zero=color_zero,
bins=bins)
# ======================================
# Population
# ======================================
for year in results_container['results_every_year'].keys():
field_name = 'pop_{}'.format(year)
# Both need to be lists
merge_data = {
str(field_name):
data['scenario_data']['population'][year].flatten().tolist(),
str(unique_merge_id):
list(regions)
}
# Merge to shapefile
lad_geopanda_shp = merge_data_to_shp(lad_geopanda_shp, merge_data,
unique_merge_id)
# If user classified, defined bins
bins = [50000, 300000]
plot_lad_national(lad_geopanda_shp=lad_geopanda_shp,
legend_unit="people",
field_to_plot=field_name,
fig_name_part="pop_",
result_path=path_data_results_shapefiles,
color_palette='Dark2_7',
color_prop='qualitative',
user_classification=True,
bins=bins)
# ======================================
# Total fuel (y) all enduses
# ======================================
base_yr = list(results_container['results_every_year'].keys())[0]
for year in results_container['results_every_year'].keys():
for fueltype in range(fueltypes_nr):
logging.info(" progress.. {}".format(fueltype))
fueltype_str = tech_related.get_fueltype_str(fueltypes, fueltype)
# ---------
# Sum per enduse and year (y)
# ---------
field_name = 'y_{}_{}'.format(year, fueltype_str)
# Calculate yearly sum
yearly_sum_gwh = np.sum(
results_container['results_every_year'][year][fueltype],
axis=1)
data_to_plot = basic_functions.array_to_dict(
yearly_sum_gwh, regions)
# Both need to be lists
merge_data = {
str(field_name): list(data_to_plot.values()),
str(unique_merge_id): list(regions)
}
# Merge to shapefile
lad_geopanda_shp = merge_data_to_shp(lad_geopanda_shp, merge_data,
unique_merge_id)
# If user classified, defined bins
print("... plot {}".format(lad_geopanda_shp))
plot_lad_national(lad_geopanda_shp=lad_geopanda_shp,
legend_unit="GWh",
field_to_plot=field_name,
fig_name_part="tot_all_enduses_y_",
result_path=path_data_results_shapefiles,
color_palette='Dark2_7',
color_prop='qualitative',
user_classification=False)
# ===============================================
# Differences in percent per enduse and year (y)
# ===============================================
field_name = 'y_diff_p_{}-{}_{}'.format(base_yr, year,
fueltype_str)
# Calculate yearly sums
yearly_sum_gwh_by = np.sum(
results_container['results_every_year'][base_yr][fueltype],
axis=1)
yearly_sum_gwh_cy = np.sum(
results_container['results_every_year'][year][fueltype],
axis=1)
# Calculate percentual difference
p_diff = ((yearly_sum_gwh_cy / yearly_sum_gwh_by) * 100) - 100
data_to_plot = basic_functions.array_to_dict(p_diff, regions)
# Both need to be lists
merge_data = {
str(field_name): list(data_to_plot.values()),
str(unique_merge_id): list(regions)
}
# Merge to shapefile
lad_geopanda_shp = merge_data_to_shp(lad_geopanda_shp, merge_data,
unique_merge_id)
# If user classified, defined bins [x for x in range(0, 1000000, 200000)]
#bins = get_reasonable_bin_values(list(data_to_plot.values()))
#bins = [-4, -2, 0, 2, 4] # must be of uneven length containing zero
bins = [-30, -20, -10, 0, 10, 20,
30] # must be of uneven length containing zero
color_list, color_prop, user_classification, color_zero = colors_plus_minus_map(
bins=bins,
color_prop='qualitative',
color_order=True,
color_zero='#ffffff') #8a2be2
# Plot difference in % per fueltype of total fuel (y)
plot_lad_national(lad_geopanda_shp=lad_geopanda_shp,
legend_unit="GWh",
field_to_plot=field_name,
fig_name_part="tot_all_enduses_y_",
result_path=path_data_results_shapefiles,
color_palette='Purples_9',
color_prop=color_prop,
user_classification=user_classification,
color_list=color_list,
color_zero=color_zero,
bins=bins)
# ======================================
# Load factors
# ======================================
for year in results_container['load_factors_y'].keys():
for fueltype in range(fueltypes_nr):
fueltype_str = tech_related.get_fueltype_str(fueltypes, fueltype)
field_name = 'lf_{}_{}'.format(year, fueltype_str)
results = basic_functions.array_to_dict(
results_container['load_factors_y'][year][fueltype], regions)
# Both need to be lists
merge_data = {
str(field_name): list(results.values()),
str(unique_merge_id): list(regions)
}
# Merge to shapefile
lad_geopanda_shp = merge_data_to_shp(lad_geopanda_shp, merge_data,
unique_merge_id)
# If user classified, defined bins
plot_lad_national(lad_geopanda_shp=lad_geopanda_shp,
legend_unit="%",
field_to_plot=field_name,
fig_name_part="lf_max_y",
result_path=path_data_results_shapefiles,
color_palette='Dark2_7',
color_prop='qualitative',
user_classification=False)
def plot_radar_plots_average_peak_day(
scenario_data,
year_to_plot,
fig_name,
plotshow
):
"""Compare averaged dh profile overall regions for peak day
for future year and base year
MAYBE: SO FAR ONLY FOR ONE SCENARIO
"""
lookups = lookup_tables.basic_lookups()
# Scenarios
all_scenarios = list(scenario_data.keys())
first_scenario = str(all_scenarios[:1][0])
# ----------------
# Create base year peak load profile
# -----------------
all_regs_fueltypes_yh_by = {}
for fueltype, fuels_regs in enumerate(scenario_data[first_scenario]['results_every_year'][2015]):
for region_array_nr, fuel_reg in enumerate(fuels_regs):
try:
all_regs_fueltypes_yh_by[fueltype] += fuel_reg
except KeyError:
all_regs_fueltypes_yh_by[fueltype] = fuel_reg
try:
all_fuels_by += fuel_reg
except:
all_fuels_by = fuel_reg
# ------------------------
# Future year load profile
# ------------------------
all_regs_fueltypes_yh_ey = {}
for fueltype, fuels_regs in enumerate(scenario_data[first_scenario]['results_every_year'][year_to_plot]):
for region_array_nr, fuel_reg in enumerate(fuels_regs):
try:
all_regs_fueltypes_yh_ey[fueltype] += fuel_reg
except KeyError:
all_regs_fueltypes_yh_ey[fueltype] = fuel_reg
# -----------
# get peak day across all fueltypes
# -----------
for fueltype in all_regs_fueltypes_yh_by.keys():
all_regs_fueltypes_yh_by[fueltype] = all_regs_fueltypes_yh_by[fueltype].reshape((365, 24))
for fueltype in all_regs_fueltypes_yh_ey.keys():
all_regs_fueltypes_yh_ey[fueltype] = all_regs_fueltypes_yh_ey[fueltype].reshape((365, 24))
all_fuels_by_d = all_fuels_by.reshape((365, 24))
peak_day_nr = np.argmax(np.sum(all_fuels_by_d, axis=1))
for fueltype in all_regs_fueltypes_yh_by.keys():
fueltype_str = tech_related.get_fueltype_str(lookups['fueltypes'], fueltype)
name_spider_plot = os.path.join(fig_name, "spider_{}.pdf".format(fueltype_str))
# ---------------------------
# Calculate load factors
# ---------------------------
max_load_h_by = max(all_regs_fueltypes_yh_by[fueltype][peak_day_nr])
average_load_y_by = np.average(all_regs_fueltypes_yh_by[fueltype])
load_factor_fueltype_y_by = (average_load_y_by / max_load_h_by) * 100 #convert to percentage
max_load_h_cy = max(all_regs_fueltypes_yh_ey[fueltype][peak_day_nr])
average_load_y_cy = np.average(all_regs_fueltypes_yh_ey[fueltype])
load_factor_fueltype_y_cy = (average_load_y_cy / max_load_h_cy) * 100 #convert to percentage
# ----------------------------------
# Plot dh for peak day for base year
# ----------------------------------
individ_radars_to_plot_dh = [
list(all_regs_fueltypes_yh_by[fueltype][peak_day_nr]),
list(all_regs_fueltypes_yh_ey[fueltype][peak_day_nr])]
plotting_results.plot_radar_plot_multiple_lines(
individ_radars_to_plot_dh,
name_spider_plot,
plot_steps=50,
plotshow=False,
lf_y_by=load_factor_fueltype_y_by,
lf_y_cy=load_factor_fueltype_y_cy)
def run_all_plot_functions(results_container, reg_nrs, regions, lookups,
result_paths, assumptions, enduses, plot_crit,
base_yr, comparison_year):
"""Summary function to plot all results
comparison_year : int
Year to generate comparison plots
"""
if plot_crit['plot_lad_cross_graphs']:
try:
# Plot cross graph where very region is a dot
fig_cross_graphs.plot_cross_graphs(
base_yr=base_yr,
comparison_year=comparison_year,
regions=regions,
ed_year_fueltype_regs_yh=results_container[
'ed_fueltype_regs_yh'],
reg_load_factor_y=results_container['reg_load_factor_y'],
fueltype_int=lookups['fueltypes']['electricity'],
fueltype_str='electricity',
fig_name=os.path.join(
result_paths['data_results_PDF'],
"comparions_LAD_cross_graph_electricity_by_cy.pdf"),
label_points=False,
plotshow=False)
fig_cross_graphs.plot_cross_graphs(
base_yr=base_yr,
comparison_year=comparison_year,
regions=regions,
ed_year_fueltype_regs_yh=results_container[
'ed_fueltype_regs_yh'],
reg_load_factor_y=results_container['reg_load_factor_y'],
fueltype_int=lookups['fueltypes']['gas'],
fueltype_str='gas',
fig_name=os.path.join(
result_paths['data_results_PDF'],
"comparions_LAD_cross_graph_gas_by_cy.pdf"),
label_points=False,
plotshow=False)
except KeyError:
logging.info(
"Check if correct comparison year is provided, i.e. really data exists for this year"
)
# ----------
# Plot LAD differences for first and last year
# ----------
try:
fig_lad_related.plot_lad_comparison(
base_yr=2015,
comparison_year=2050,
regions=regions,
ed_year_fueltype_regs_yh=results_container['ed_fueltype_regs_yh'],
fueltype_int=lookups['fueltypes']['electricity'],
fueltype_str='electricity',
fig_name=os.path.join(
result_paths['data_results_PDF'],
"comparions_LAD_modelled_electricity_by_cy.pdf"),
label_points=False,
plotshow=False)
print("... plotted by-cy LAD energy demand compariosn")
# Plot peak h for every hour
fig_lad_related.lad_comparison_peak(
base_yr=2015,
comparison_year=2050,
regions=regions,
ed_year_fueltype_regs_yh=results_container['ed_fueltype_regs_yh'],
fueltype_int=lookups['fueltypes']['electricity'],
fueltype_str='electricity',
fig_name=os.path.join(
result_paths['data_results_PDF'],
"comparions_LAD_modelled_electricity_peakh_by_cy.pdf"),
label_points=False,
plotshow=False)
print("... plotted by-cy LAD energy demand compariosn")
except:
pass
# ----------------
# Plot demand for every region over time
# -------------------
if plot_crit['plot_line_for_every_region_of_peak_demand']:
logging.info(
"... plot fuel per fueltype for every region over annual teimsteps"
)
fig_one_fueltype_multiple_regions_peak_h.plt_regions_peak_h(
results_container['ed_fueltype_regs_yh'],
lookups,
regions,
os.path.join(result_paths['data_results_PDF'],
'peak_h_total_electricity.pdf'),
fueltype_str_to_plot="electricity")
if plot_crit['plot_fuels_enduses_y']:
#... Plot total fuel (y) per fueltype as line chart"
fig_fuels_enduses_y.run(
results_container['ed_fueltype_regs_yh'], lookups,
os.path.join(result_paths['data_results_PDF'],
'y_fueltypes_all_enduses.pdf'))
# ------------
# Plot stacked annual enduses
# ------------
if plot_crit['plot_stacked_enduses']:
rs_enduses_sorted = [
'rs_space_heating', 'rs_water_heating', 'rs_lighting', 'rs_cold',
'rs_wet', 'rs_consumer_electronics', 'rs_home_computing',
'rs_cooking'
]
ss_enduses_sorted = [
'ss_space_heating', 'ss_water_heating', 'ss_lighting',
'ss_catering', 'ss_small_power', 'ss_fans',
'ss_cooling_humidification', 'ss_ICT_equipment', 'ss_other_gas',
'ss_other_electricity', 'ss_cooled_storage'
]
is_enduses_sorted = [
'is_space_heating', 'is_lighting', 'is_refrigeration', 'is_motors',
'is_compressed_air', 'is_high_temp_process', 'is_low_temp_process',
'is_other', 'is_drying_separation'
]
rs_color_list = plotting_styles.rs_color_list_selection()
ss_color_list = plotting_styles.ss_color_list_selection()
is_color_list = plotting_styles.is_color_list_selection()
# Residential
fig_stacked_enduse.run(assumptions['sim_yrs'],
results_container['results_enduse_every_year'],
rs_enduses_sorted,
rs_color_list,
os.path.join(result_paths['data_results_PDF'],
"stacked_rs_country.pdf"),
plot_legend=True)
# Service
fig_stacked_enduse.run(assumptions['sim_yrs'],
results_container['results_enduse_every_year'],
ss_enduses_sorted,
ss_color_list,
os.path.join(result_paths['data_results_PDF'],
"stacked_ss_country.pdf"),
plot_legend=True)
# Industry
fig_stacked_enduse.run(assumptions['sim_yrs'],
results_container['results_enduse_every_year'],
is_enduses_sorted,
is_color_list,
os.path.join(result_paths['data_results_PDF'],
"stacked_is_country_.pdf"),
plot_legend=True)
# ------------------------------
# Plot annual demand for enduses for all submodels
# ------------------------------
if plot_crit['plot_y_all_enduses']:
fig_stacked_enduse_sectors.run(
lookups, assumptions['sim_yrs'],
results_container['results_enduse_every_year'],
enduses['residential'], enduses['service'], enduses['industry'],
os.path.join(result_paths['data_results_PDF'],
"stacked_all_enduses_country.pdf"))
# --------------
# Fuel per fueltype for whole country over annual timesteps
# ----------------
if plot_crit['plot_fuels_enduses_y']:
logging.info(
"... plot fuel per fueltype for whole country over annual timesteps"
)
#... Plot total fuel (y) per fueltype as line chart"
fig_fuels_enduses_y.run(
results_container['ed_fueltype_regs_yh'], lookups,
os.path.join(result_paths['data_results_PDF'],
'y_fueltypes_all_enduses.pdf'))
# ----------
# Plot seasonal typical load profiles
# Averaged load profile per daytpe for a region
# ----------
# ------------------------------------
# Load factors per fueltype and region
# ------------------------------------
if plot_crit['plot_lf']:
for fueltype_str, fueltype_int in lookups['fueltypes'].items():
'''fig_lf.plot_seasonal_lf(
fueltype_int,
fueltype_str,
results_container['load_factor_seasons'],
reg_nrs,
os.path.join(
result_paths['data_results_PDF'],
'lf_seasonal_{}.pdf'.format(fueltype_str)))'''
'''fig_lf.plot_lf_y(
fueltype_int,
fueltype_str,
results_container['reg_load_factor_yd'],
reg_nrs,
os.path.join(
result_paths['data_results_PDF'], 'lf_yd_{}.pdf'.format(fueltype_str)))'''
# reg_load_factor_yd = max daily value / average annual daily value
fig_lf.plot_lf_y(
fueltype_int, fueltype_str,
results_container['reg_load_factor_y'], reg_nrs,
os.path.join(result_paths['data_results_PDF'],
'lf_y_{}.pdf'.format(fueltype_str)))
# --------------
# Fuel week of base year
# ----------------
if plot_crit['plot_week_h']:
fig_fuels_enduses_week.run(
results_resid=results_container['ed_fueltype_regs_yh'],
lookups=lookups,
hours_to_plot=range(7 * 24),
year_to_plot=2015,
fig_name=os.path.join(result_paths['data_results_PDF'],
"tot_all_enduse03.pdf"))
# ------------------------------------
# Plot averaged per season and fueltype
# ------------------------------------
if plot_crit['plot_averaged_season_fueltype']:
for year in results_container['av_season_daytype_cy'].keys():
for fueltype_int in results_container['av_season_daytype_cy'][
year].keys():
fueltype_str = tech_related.get_fueltype_str(
lookups['fueltypes'], fueltype_int)
fig_load_profile_dh_multiple.run(
path_fig_folder=result_paths['data_results_PDF'],
path_plot_fig=os.path.join(
result_paths['data_results_PDF'],
'season_daytypes_by_cy_comparison__{}__{}.pdf'.format(
year, fueltype_str)),
calc_av_lp_modelled=results_container[
'av_season_daytype_cy'][year]
[fueltype_int], # current year
calc_av_lp_real=results_container['av_season_daytype_cy']
[base_yr][fueltype_int], # base year
calc_lp_modelled=results_container['season_daytype_cy']
[year][fueltype_int], # current year
calc_lp_real=results_container['season_daytype_cy']
[base_yr][fueltype_int], # base year
plot_peak=True,
plot_all_entries=False,
plot_max_min_polygon=True,
plotshow=False,
plot_radar=plot_crit['plot_radar_seasonal'],
max_y_to_plot=120,
fueltype_str=fueltype_str,
year=year)
# ---------------------------------
# Plot hourly peak loads over time for different fueltypes
# --------------------------------
if plot_crit['plot_h_peak_fueltypes']:
fig_fuels_peak_h.run(
results_container['ed_fueltype_regs_yh'], lookups,
os.path.join(result_paths['data_results_PDF'],
'fuel_fueltypes_peak_h.pdf'))
print("finisthed plotting")
return