# RCAIDE/Library/Plots/Emissions/plot_CO2e_emissions
#
#
# Created: Jul 2024, M. Clarke
# ----------------------------------------------------------------------------------------------------------------------
# IMPORT
# ----------------------------------------------------------------------------------------------------------------------
from RCAIDE.Framework.Core import Units
from RCAIDE.Library.Plots.Common import set_axes, plot_style
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
# ----------------------------------------------------------------------------------------------------------------------
# PLOTS
# ----------------------------------------------------------------------------------------------------------------------
## @ingroup Library-Plots-Performance-Emissions
[docs]
def plot_CO2e_emissions(results,
save_figure = False,
show_legend = True,
save_filename = "CO2e_Emissions" ,
file_type = ".png",
width = 11, height = 7):
"""
Generate plots showing CO2-equivalent emissions over mission segments.
Parameters
----------
results : Data
Mission results data structure containing:
results.segments[i].conditions.emissions.total with fields:
- CO2 : array
Carbon dioxide emissions [kg]
- NOx : array
Nitrogen oxide emissions [kg]
- H2O : array
Water vapor emissions [kg]
- Contrails : array
Contrail formation impact [kg CO2e]
- Soot : array
Particulate emissions [kg]
- SO2 : array
Sulfur dioxide emissions [kg]
save_figure : bool, optional
Save figure to file if True, default False
show_legend : bool, optional
Display segment legend if True, default True
save_filename : str, optional
Name for saved figure file, default "CO2e_Emissions"
file_type : str, optional
File extension for saved figure, default ".png"
width : float, optional
Figure width in inches, default 11
height : float, optional
Figure height in inches, default 7
Returns
-------
fig : matplotlib.figure.Figure
Figure showing stacked emissions contributions
Notes
-----
Creates a stacked area plot showing:
- Individual contributions from each emission type
- Cumulative total CO2-equivalent impact
- Breakdown by mission segment
- Time history of emissions
Different emission types are distinguished by fill colors
and segments use different shades from the inferno colormap.
**Definitions**
'CO2-equivalent (CO2e)'
Combined climate impact normalized to CO2
'Global Warming Potential (GWP)'
Relative impact factor for different emissions
'Contrail Impact'
Climate forcing from aviation-induced cloudiness
"""
# get plotting style
ps = plot_style()
parameters = {'axes.labelsize': ps.axis_font_size,
'xtick.labelsize': ps.axis_font_size,
'ytick.labelsize': ps.axis_font_size,
'axes.titlesize': ps.title_font_size}
plt.rcParams.update(parameters)
fig = plt.figure(save_filename)
fig.set_size_inches(width,height)
axis_1 = plt.subplot(1,1,1)
line_colors = cm.inferno(np.linspace(0,0.9,len(results.segments)))
cum_y0 = 0
cum_y1 = 0
cum_y1_0 = 0
for i in range(len(results.segments)):
time = results.segments[i].conditions.frames.inertial.time[:, 0] / Units.min
emissions_CO2 = results.segments[i].conditions.emissions.total.CO2[:, 0]
emissions_NOx = results.segments[i].conditions.emissions.total.NOx[:, 0]
emissions_H2O = results.segments[i].conditions.emissions.total.H2O[:, 0]
emissions_Contrails = results.segments[i].conditions.emissions.total.Contrails[:, 0]
emissions_Soot = results.segments[i].conditions.emissions.total.Soot[:, 0]
emissions_SO2 = results.segments[i].conditions.emissions.total.SO2[:, 0]
cum_y0 = np.zeros_like(emissions_CO2)
cum_y1 = cum_y1_0 + emissions_CO2 + emissions_NOx + emissions_H2O + emissions_Contrails + emissions_Soot + emissions_SO2
segment_tag = results.segments[i].tag
segment_name = segment_tag.replace('_', ' ')
axis_1.fill_between(time, cum_y0, cum_y1, where=(cum_y0 < cum_y1), color= line_colors[i], interpolate=True, label = segment_name)
cum_y1_0 = cum_y1[-1]
axis_1.set_ylabel(r'CO2e Emissions (kg)')
set_axes(axis_1)
if show_legend:
leg = fig.legend(bbox_to_anchor=(0.5, 0.95), loc='upper center', ncol = 4)
leg.set_title('Flight Segment', prop={'size': ps.legend_font_size, 'weight': 'heavy'})
# Adjusting the sub-plots for legend
fig.subplots_adjust(top=0.8)
# set title of plot
title_text = 'CO2e Emissions'
fig.suptitle(title_text)
if save_figure:
plt.savefig(save_filename + file_type)
return fig
