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working on plots
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Sven Eggimann committed Apr 23, 2019
1 parent 83ea2a5 commit 79e873a
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2 changes: 1 addition & 1 deletion energy_demand/plotting/chaudry_et_al.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@
# Create figures
# ------------------------
# Figure with vertical hourly values per scenario
chaudry_et_al_functions.fig_3(
chaudry_et_al_functions.fig_3_hourly_comparison(
path_out,
data_container,
scenarios=scenarios,
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244 changes: 183 additions & 61 deletions energy_demand/plotting/chaudry_et_al_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,12 @@
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
from matplotlib import rcParams

# Set default font
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Arial']


def export_legend(legend, filename="legend.png"):
"""Export legend as seperate file
Expand Down Expand Up @@ -38,13 +44,13 @@ def load_data(in_path, scenarios, simulation_name):
Returns
data_container : [scenario][mode][weather_scenario
"""

data_container = {}
modes = ['CENTRAL', 'DECENTRAL']

for scenario in scenarios:
data_container[scenario] = {}

for mode in ['CENTRAL', 'DECENTRAL']:
for mode in modes:
data_container[scenario][mode] = {}

# Iterate over weather scenarios
Expand Down Expand Up @@ -88,54 +94,62 @@ def load_data(in_path, scenarios, simulation_name):
return data_container


def fig_3(path_out, data_container, scenarios, weather_scearnio, fueltype, years=[2015, 2030, 2050]):
def fig_3_hourly_comparison(
path_out,
data_container,
scenarios,
weather_scearnio,
fueltype,
years=[2015, 2030, 2050]
):
"""Create x-y chart of a time-span (x-axis: demand, y-axis: time)
"""

'''
technologies_to_plot = {
electricity': [
'output_industry_electricity_boiler_electricity_timestep',
'output_industry_electricity_district_heating_electricity_timestep',
'output_industry_electricity_heat_pumps_electricity_timestep',
'output_industry_electricity_non_heating_timestep_2015',
'output_service_electricity_boiler_electricity_timestep',
'output_service_electricity_district_heating_electricity_timestep',
'output_service_electricity_heat_pumps_electricity_timestep',
'output_service_electricity_non_heating_timestep',
'output_residential_electricity_boiler_electricity_timestep',
'output_residential_electricity_district_heating_electricity_timestep',
'output_residential_electricity_heat_pumps_electricity_timestep',
'output_residential_electricity_non_heating_timestep'
]
}
'''
file_names_to_plot = {
'electricity': [
'output_eh_electricboiler_b_timestep',
'output_eh_heatpump_b_timestep',
'output_eh_pv_power_timestep',
'output_eh_tran_e_timestep'
'electricity': {
'output_eh_electricboiler_b_timestep': 'olive',
'output_eh_heatpump_b_timestep': 'green',
'output_eh_pv_power_timestep': 'red',
'output_eh_tran_e_timestep': 'orange',
#'output_elec_load_shed_eh_timestep',
#'output_tran_wind_power_timestep'
}
}
seperate_legend = True

]}
# Select hours to plots
hours_selected = range(1, 25) #TODO

modes = ['DECENTRAL', 'CENTRAL']
left = 'CENTRAL'
right = 'DECENTRAL'

fontsize_small = 8
fontsize_large = 10

# Font info axis labels
font_additional_info = {
'color': 'black',
'weight': 'bold',
'size': fontsize_large}

fig_dict = {}
fig_dict_piecharts = {}
path_out_folder = os.path.join(path_out, 'fig3')

for year in years:
fig_dict[year] = {}
for mode in ['DECENTRAL', 'CENTRAL']:
fig_dict_piecharts[year] = {}

for mode in modes:
fig_dict[year][mode] = {}
fig_dict_piecharts[year][mode] = {}

for scenario in scenarios:
fig_dict[year][mode][scenario] = {}

colors = []
data_files = data_container[scenario][mode][weather_scearnio]['energy_supply_constrained']
files_to_plot = file_names_to_plot[fueltype]
files_to_plot = file_names_to_plot[fueltype].keys()

# Get all correct data to plot
df_to_plot = pd.DataFrame()
Expand All @@ -146,77 +160,176 @@ def fig_3(path_out, data_container, scenarios, weather_scearnio, fueltype, years
file_name_split = file_name.split("_")
year_simulation = int(file_name_split[-1][:4])

if (year == year_simulation) and (file_name_split_without_timpestep in files_to_plot):
print("columns: " + str(file_data.columns))
value_column = list(file_data.columns)[1]
print("File_name: {} Value column: {}".format(file_name_split_without_timpestep, value_column))
df_to_plot[str(value_column)] = file_data[value_column]
if year == year_simulation:
if file_name_split_without_timpestep in files_to_plot:
value_column = list(file_data.columns)[1]
#print("columns: " + str(file_data.columns))
#print("File_name: {} Value column: {}".format(file_name_split_without_timpestep, value_column))
df_to_plot[str(value_column)] = file_data[value_column]

#random_color = np.random.rand(3,1)
color = file_names_to_plot[fueltype][file_name_split_without_timpestep]
colors.append(color)

# Aggregate across every energy hub region and group by "seasonal week"
#df_to_plot_national = df_to_plot.groupby(df_to_plot['seasonal_week']).sum()

# Select hours to plots
hours_selected = range(1, 25) #TODO SELECT TIME INTERVALE TP LOT
df_to_plot_hrs_to_plot = df_to_plot.loc[hours_selected]
fig_dict[year][mode][scenario] = df_to_plot.loc[hours_selected]

fig_dict[year][mode][scenario] = df_to_plot_hrs_to_plot
# Aggregate annual demand for pie-charts
fig_dict_piecharts[year][mode][scenario] = df_to_plot.sum()

print("--ploting --") #MOVE DOWN ELEMENTS

# ----------
# Plot graph
# PLot pie-charts
# ----------
fig_dict_piecharts
for scenario in scenarios:
for mode in [right, left]:
data_pie_chart = fig_dict_piecharts[year][mode][scenario]

df = pd.DataFrame(
{'annual_sum': data_pie_chart.values},
index=data_pie_chart.index)

# Calculate fig size
# 100% corresponds to 5 value.
'''abs_size = 100 #%
total_sum = data_pie_chart.sum() / 100000.0
print("A " + str(total_sum))
p_size = (5 / abs_size) * total_sum'''
p_size = 5

# Explode distance
explode_distance = [0.1 for i in range(len(data_pie_chart.index))]

# Plot
fig, ax = plt.subplots()

# Plots just for getting legend
#df.plot(kind='pie', y='annual_sum', explode=explode_distance)
#autopct='%1.1f%%'
#patches, texts = plt.pie(sizes, colors=colors, startangle=90)
#plt.legend(patches, labels, loc="best")

# Plotting
df.plot(
kind='pie',
y='annual_sum',
#labels=True,
shadow=False,
explode=explode_distance,
figsize=(p_size, p_size))



# Labels
plt.xlabel('')
plt.ylabel('')

left = 'CENTRAL'
right = 'DECENTRAL'
# Legend
# ------------
handles, labels = plt.gca().get_legend_handles_labels()

by_label = OrderedDict(zip(labels, handles)) # remove duplicates

legend = plt.legend(
by_label.values(),
by_label.keys(),
ncol=2,
prop={'size': 10},
loc='upper center',
bbox_to_anchor=(0.5, -0.1),
frameon=False)


# Save pdf of figure and legend
# ------------
fig_name = "pie_{}_{}.pdf".format(scenario, year)
path_out_file = os.path.join(path_out_folder, fig_name)

if seperate_legend:
export_legend(
legend,
os.path.join("{}__legend.pdf".format(path_out_file[:-4])))
legend.remove()

# Remove frame
# ------------
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

#plt.show()
plt.savefig(path_out_file)


# ----------
# Plot x-y graph
# ----------
for scenario in scenarios:

# Data and plot
# ------------
df_right = fig_dict[year][right][scenario]
df_left = fig_dict[year][left][scenario] * -1 # Convert to minus values
#df_to_plot.plot.area()
#df_to_plot.plot.bar(stacked=True)#, orientation='vertical')

fig, ax = plt.subplots(ncols=1, sharey=True)

df_right.plot(kind='barh', ax=ax, width=1.0, stacked=True)
df_left.plot(kind='barh', ax=ax, width=1.0, legend=False, stacked=True)
df_right.plot(kind='barh', ax=ax, width=1.0, stacked=True, color=colors)
df_left.plot(kind='barh', ax=ax, width=1.0, legend=False, stacked=True, color=colors)

# Add vertical line
# ------------
ax.axvline(linewidth=1, color='black')

# Limits
# ------------
plt.autoscale(enable=True, axis='x', tight=True)
plt.autoscale(enable=True, axis='y', tight=True)

#ax = plt.gca()
#ax.invert_xaxis()
#plt.gca().invert_yaxis()
#df = pd.DataFrame(df_right) #TODO: WHY NO GREEN AREA?
#df.plot.area()

# Labels
plt.xlabel("Unit")
plt.ylabel("time")
plt.title(left, fontdict=None, loc='left')
plt.title(right, fontdict=None, loc='right')
# ------------
plt.xlabel("Unit", fontdict=font_additional_info)
plt.ylabel("Time", fontdict=font_additional_info)
plt.title(left, fontdict=None, loc='left', fontsize=fontsize_small)
plt.title(right, fontdict=None, loc='right', fontsize=fontsize_small)

# Customize axis
# Customize x-axis
nr_of_bins = 4
bin_value = int(np.max(df_right.values) / 4)
right_ticks = np.array([bin_value * i for i in range(nr_of_bins + 1)])
left_ticks = right_ticks * -1
left_ticks = left_ticks[::-1]
right_labels = [str(bin_value * i) for i in range(nr_of_bins + 1)]
left_labels = right_labels[::-1]

ticks = list(left_ticks) + list(right_ticks)
labels = list(left_labels) + list(right_labels)

plt.xticks(
ticks=ticks,
labels=labels)
labels=labels,
fontsize=fontsize_small)

# Customize y-axis
nr_of_bins = 4
bin_value = int(np.max(hours_selected) / 4)

ticks = np.array([(bin_value * i) - 0.5 for i in range(nr_of_bins + 1)])
labels = np.array([str(bin_value * i) for i in range(nr_of_bins + 1)])

plt.yticks(
ticks=ticks,
labels=labels,
fontsize=fontsize_small)

# Legend
# ------------
handles, labels = plt.gca().get_legend_handles_labels()

by_label = OrderedDict(zip(labels, handles)) # remove duplicates
Expand All @@ -229,11 +342,22 @@ def fig_3(path_out, data_container, scenarios, weather_scearnio, fueltype, years
bbox_to_anchor=(0.5, -0.1),
frameon=False)

# Remove frame
# ------------
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

# Add grid lines
# ------------
ax.grid(which='major', color='black', axis='y', linestyle='--')

# Save pdf of figure and legend
# ------------
fig_name = "{}_{}.pdf".format(scenario, year)
path_out_file = os.path.join(path_out_folder, fig_name)

seperate_legend = True
if seperate_legend:
export_legend(
legend,
Expand All @@ -243,8 +367,6 @@ def fig_3(path_out, data_container, scenarios, weather_scearnio, fueltype, years
#plt.show()
plt.savefig(path_out_file)

return


def fig_4(data_container):
"""
Expand Down

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