# RCAIDE/Library/Plots/Geometry/plot_Layout_of_Passenger_Accommodations.py
#
# Created: Mar 2025, M. Clarke
# ----------------------------------------------------------------------------------------------------------------------
# IMPORT
# ----------------------------------------------------------------------------------------------------------------------
# RCAIDE imports
from RCAIDE.Library.Methods.Geometry.LOPA.compute_layout_of_passenger_accommodations import compute_layout_of_passenger_accommodations
# python imports
import plotly.graph_objects as go
import numpy as np
import os
import sys
# ----------------------------------------------------------------------------------------------------------------------
# plot_Layout_of_Passenger_Accommodations
# ----------------------------------------------------------------------------------------------------------------------
[docs]
def plot_layout_of_passenger_accommodations(fuselage,
save_figure = False,
save_filename = "Vehicle_Geometry",
show_figure = True):
'''
Plot aircraft layout of passenger accommodations
'''
if type(fuselage.layout_of_passenger_accommodations) != np.ndarray:
compute_layout_of_passenger_accommodations(fuselage)
LOPA = fuselage.layout_of_passenger_accommodations
fig = go.Figure()
# Set axes properties
fig.update_xaxes(range=[min(LOPA[:,2]) - 1 , max(LOPA[:,2]) + 1], showgrid=False)
fig.update_yaxes(range=[ min(LOPA[:,3]) - 1, max(LOPA[:,3]) + 1], showgrid=False)
fig.update_yaxes(
scaleanchor = "x",
scaleratio = 1,)
# Step 1: plot cabin bounds
# get points at x min
x_min_locs = np.where( LOPA[:,2] == min(LOPA[:,2]))[0]
x_min = LOPA[x_min_locs[0],2] - LOPA[x_min_locs[0],5]/2
x_min_y_max = max(LOPA[x_min_locs,3] + LOPA[x_min_locs,6]/2 )
x_min_y_min = min(LOPA[x_min_locs,3] - LOPA[x_min_locs,6]/2 )
x_border_pts = [x_min, x_min]
y_border_pts = [x_min_y_min, x_min_y_max]
# get points at y max
y_max_locs = np.where( LOPA[:,3] == max(LOPA[:,3]))[0]
y_max = LOPA[y_max_locs[0],3] + LOPA[y_max_locs[0],6]/2
y_max_x_max = max(LOPA[y_max_locs,2] + LOPA[y_max_locs[0],5]/2)
y_max_x_min = min(LOPA[y_max_locs,2] - LOPA[y_max_locs[0],5]/2)
x_border_pts.append(y_max_x_min)
x_border_pts.append(y_max_x_max)
y_border_pts.append(y_max)
y_border_pts.append(y_max)
# get points at x max
x_max_locs = np.where( LOPA[:,2] == max(LOPA[:,2]))[0]
x_max = LOPA[x_max_locs[0],2] + LOPA[x_max_locs[0],5]/2
x_max_y_max = max(LOPA[x_max_locs,3] + LOPA[x_max_locs,6]/2)
x_max_y_min = min(LOPA[x_max_locs,3] - LOPA[x_max_locs,6]/2)
x_border_pts.append(x_max)
x_border_pts.append(x_max)
y_border_pts.append(x_max_y_max)
y_border_pts.append(x_max_y_min)
# get points at y min
y_min_locs = np.where( LOPA[:,3] == min(LOPA[:,3]))[0]
y_min = LOPA[y_min_locs[0],3] - LOPA[y_min_locs[0],6]/2
y_min_x_max = max(LOPA[y_min_locs,2] + LOPA[y_min_locs[0],5]/2)
y_min_x_min = min(LOPA[y_min_locs,2] - LOPA[y_min_locs[0],5]/2)
x_border_pts.append(y_min_x_max)
x_border_pts.append(y_min_x_min)
y_border_pts.append(y_min)
y_border_pts.append(y_min)
# loop through points and determine if there are duplicates
y_border_pts = np.array(y_border_pts)
x_border_pts = np.array(x_border_pts)
# cut where y is negative
port_idxs = np.where(y_border_pts<0)[0]
starboard_x_points = np.delete(x_border_pts, port_idxs)
starboard_y_points = np.delete(y_border_pts, port_idxs)
fig.add_trace(go.Scatter(
x=starboard_x_points,
y=starboard_y_points,
mode='lines',
name='Line 1',
line_color="darkblue",
fill=None
))
fig.add_trace(go.Scatter(
x=starboard_x_points,
y=-starboard_y_points,
mode='lines',
name='Line 2',
line_color="darkblue",
fill='tonexty'
))
# Step 2: plot seats
economoy_seat_colors = ["steelblue", "deepskyblue", "skyblue" ]
business_seat_colors = ["seagreen", "mediumseagreen", "lightseagreen" ]
first_seat_colors = ["indianred", "lightcoral", "lightpink" ]
lavatory_color = ["sandybrown"]
for i in range(len(LOPA)):
x_center = LOPA[i,2]
y_center = LOPA[i,3]
s_l = LOPA[i,5]
s_w = LOPA[i,6]
F_c = LOPA[i,7]
B_c = LOPA[i,8]
E_c = LOPA[i,9]
seat = LOPA[i,10]
Em_row = LOPA[i,11]
Gal_Lav = LOPA[i,12]
if E_c == 1.0:
if seat == 1.0:
x0_pt = x_center - s_l / 2
x1_pt = x_center + s_l / 2
y0_pt = y_center - s_w / 2
y1_pt = y_center + s_w / 2
if Em_row == 1.0:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=economoy_seat_colors[0],
width=2,
),
fillcolor= economoy_seat_colors[1],
)
else:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=economoy_seat_colors[0],
width=2,
),
fillcolor=economoy_seat_colors[2],
)
if B_c == 1:
if seat == 1:
x0_pt = x_center - s_l / 2
x1_pt = x_center + s_l / 2
y0_pt = y_center - s_w / 2
y1_pt = y_center + s_w / 2
if Em_row == 1:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=business_seat_colors[0],
width=2,
),
fillcolor=business_seat_colors[1],
)
else:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=business_seat_colors[0],
width=2,
),
fillcolor=business_seat_colors[2],
)
if F_c == 1:
if seat == 1:
x0_pt = x_center - s_l / 2
x1_pt = x_center + s_l / 2
y0_pt = y_center - s_w / 2
y1_pt = y_center + s_w / 2
if Em_row == 1:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=first_seat_colors[0],
width=2,
),
fillcolor=first_seat_colors[1],
)
else:
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=first_seat_colors[0],
width=2,
),
fillcolor=first_seat_colors[2],
)
if Gal_Lav == 1:
x0_pt = x_center - s_l / 2
x1_pt = x_center + s_l / 2
y0_pt = y_center - s_w / 2
y1_pt = y_center + s_w / 2
fig.add_shape(type="rect",
x0=x0_pt, y0=y0_pt, x1=x1_pt, y1=y1_pt,
line=dict(
color=lavatory_color[0],
width=2,
),
fillcolor=lavatory_color[0],
)
# Use the first path from sys.path
save_filename = os.path.join(sys.path[0], save_filename)
if save_figure:
fig.write_image(save_filename + ".png")
if show_figure:
fig.write_html( save_filename + '.html', auto_open=True)
return
