I think MHKiT would be a good platform to host a function that creates Sankey average power flow diagrams for WECs like this:
from matplotlib.sankey import Sankey
from typing import Tuple
from matplotlib.figure import Figure
from matplotlib.axes import Axes
import matplotlib.pyplot as plt
def power_flow_colors():
"""
Define and return a dictionary of colors to represent different stages of the power flow through a WEC.
The function creates a dictionary where each key corresponds to a specific stage of power flow,
and each value is a tuple representing an RGBA color. The colors are derived from the 'viridis'
colormap, which is perceptually uniform.
Returns:
dict: A dictionary containing the following stages of power flow and their associated colors:
- 'exc': Color for excitation power (RGBA: (0.267004, 0.004874, 0.329415, 1.0))
- 'rad': Color for radiated power (RGBA: (0.229739, 0.322361, 0.545706, 1.0))
- 'abs': Color for absorbed power (RGBA: (0.127568, 0.566949, 0.550556, 1.0))
- 'use': Color for useful power (RGBA: (0.369214, 0.788888, 0.382914, 1.0))
- 'elec': Color for electrical power (RGBA: (0.974417, 0.90359, 0.130215, 0.5))
Example:
colors = power_flow_colors()
print(colors['exc']) # Output: (0.267004, 0.004874, 0.329415, 1.0)
"""
clrs = {'exc': (0.267004, 0.004874, 0.329415, 1.0), #viridis(0.0)
'rad': (0.229739, 0.322361, 0.545706, 1.0), #viridis(0.25)
'abs': (0.127568, 0.566949, 0.550556, 1.0), #viridis(0.5)
'use': (0.369214, 0.788888, 0.382914, 1.0), #viridis(0.75)
'elec': (0.974417, 0.90359, 0.130215, 0.5), #viridis(0.99)
}
return clrs
def plot_power_flow(power_flows: dict[str, float],
plot_reference: bool = True,
axes_title: str = '',
axes: Axes = None,
return_fig_and_axes: bool = False
)-> Tuple[Figure, Axes]:
"""Plot power flow through a Wave Energy Converter (WEC) as a Sankey diagram.
This function visualizes the power flow through a WEC by creating a Sankey diagram.
If the model does not include mechanical and electrical components, customization of this function will be necessary.
Parameters
----------
power_flows : dict[str, float]
A dictionary containing power flow values produced by, for example,
:py:func:`wecopttool.utilities.calculate_power_flows`.
Required keys include:
- 'Optimal Excitation'
- 'Deficit Excitation'
- 'Excitation'
- 'Deficit Radiated'
- 'Deficit Absrobed'
- 'Radiated'
- 'Absorbed'
- 'Electrical'
- 'Useful'
- 'PTO Loss Mechanical'
- 'PTO Loss Electrical'
plot_reference : bool, optional
If True, the optimal absorbed reference powers will be plotted. Default is True.
axes_title : str, optional
A string to display as the title over the Sankey diagram. Default is an empty string.
axes : Axes, optional
A Matplotlib Axes object where the Sankey diagram will be drawn. If None, a new figure and axes will be created. Default is None.
return_fig_and_axes : bool, optional
If True, the function will return the Figure and Axes objects. Default is False.
Returns
-------
tuple[Figure, Axes] or None
A tuple containing the Matplotlib Figure and Axes objects if `return_fig_and_axes` is True.
Otherwise, returns None.
Example
-------
power_flows = {
'Optimal Excitation': 100,
'Deficit Excitation': 30,
'Excitation': 70,
'Deficit Radiated': 20,
'Deficit Absorbed': 10,
'Radiated': 30,
'Absorbed': 40,
'Electrical': 30,
'Useful': 35,
'PTO Loss Mechanical': 5,
'PTO Loss Electrical': 5
}
plot_power_flow(power_flows, axes_title='Power Flow Diagram')
"""
if axes is None:
fig, axes = plt.subplots(nrows = 1, ncols= 1,
tight_layout=True,
figsize= [8, 4])
clrs = power_flow_colors()
len_trunk = 1.0
if plot_reference:
sankey = Sankey(ax=axes,
scale= 1/power_flows['Optimal Excitation'],
offset= 0,
format = '%.1f',
shoulder = 0.02,
tolerance=1e-03*power_flows['Optimal Excitation'],
unit = 'W')
sankey.add(flows=[power_flows['Optimal Excitation'],
-1*power_flows['Deficit Excitation'],
-1*power_flows['Excitation']],
labels = [' Optimal \n Excitation ',
'Deficit \n Excitation',
'Excitation'],
orientations=[0, 0, 0],#arrow directions,
pathlengths = [0.15,0.15,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['exc'],
alpha = 0.1,
label = 'Reference',
)
n_diagrams = 1
init_diag = 0
if power_flows['Deficit Excitation'] > 0.1:
sankey.add(flows=[power_flows['Deficit Excitation'],
-1*power_flows['Deficit Radiated'],
-1*power_flows['Deficit Absorbed'],],
labels = ['XX Deficit Exc',
'Deficit \n Radiated',
'Deficit \n Absorbed', ],
prior= (0),
connect=(1,0),
orientations=[0, 1, 0],#arrow directions,
pathlengths = [0.15,0.01,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['rad'],
alpha = 0.3, #viridis(0.2)
label = 'Reference',
)
n_diagrams = n_diagrams +1
else:
sankey = Sankey(ax=axes,
scale= 1/power_flows['Excitation'],
offset= 0,
format = '%.1f',
shoulder = 0.02,
tolerance=1e-03*power_flows['Excitation'],
unit = 'W')
n_diagrams = 0
init_diag = None
sankey.add(flows=[power_flows['Excitation'],
-1*(power_flows['Absorbed']
+ power_flows['Radiated'])],
labels = ['Excitation',
'Excitation'],
prior = init_diag,
connect=(2,0),
orientations=[0, -0],#arrow directions,
pathlengths = [.15,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['exc'] #viridis(0.9)
)
sankey.add(flows=[
(power_flows['Absorbed'] + power_flows['Radiated']),
-1*power_flows['Radiated'],
-1*power_flows['Absorbed']],
labels = ['Excitation',
'Radiated',
'Absorbed'],
# prior= (0),
prior= (n_diagrams),
connect=(1,0),
orientations=[0, -1, -0],#arrow directions,
pathlengths = [0.15,0.2,0.15],
trunklength = len_trunk-0.2,
edgecolor = 'None',
facecolor = clrs['rad'] #viridis(0.5)
)
sankey.add(flows=[power_flows['Absorbed'],
-1*power_flows['PTO Loss Mechanical'],
-1*power_flows['Useful']],
labels = ['Absorbed',
'PTO-Loss Mechanical' ,
'Useful'],
prior= (n_diagrams+1),
connect=(2,0),
orientations=[0, -1, -0],#arrow directions,
pathlengths = [.15,0.2,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['abs'] #viridis(0.9)
)
sankey.add(flows=[(power_flows['Useful']),
-1*power_flows['PTO Loss Electrical'],
-1*power_flows['Electrical']],
labels = ['Useful',
'PTO-Loss Electrical' ,
'Electrical'],
prior= (n_diagrams+2),
connect=(2,0),
orientations=[0, -1, -0],#arrow directions,
pathlengths = [.15,0.2,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['use'] #viridis(0.9)
)
sankey.add(flows=[(power_flows['Electrical']),
-1*power_flows['Electrical']],
labels = ['',
'Electrical'],
prior= (n_diagrams+3),
connect=(2,0),
orientations=[0, -0],#arrow directions,
pathlengths = [.15,0.15],
trunklength = len_trunk,
edgecolor = 'None',
facecolor = clrs['elec'] #viridis(0.9)
)
sankey.ax.axis([sankey.extent[0] - sankey.margin,
sankey.extent[1] + sankey.margin,
sankey.extent[2] - sankey.margin,
sankey.extent[3] + sankey.margin])
sankey.ax.set_aspect('equal', adjustable='box')
diagrams = sankey.diagrams
for diagram in diagrams:
for text in diagram.texts:
text.set_fontsize(8)
#Remvove labels that are double
len_diagrams = len(diagrams)
diagrams[len_diagrams-4].texts[0].set_text('') #remove exciation from hydro
diagrams[len_diagrams-5].texts[-1].set_text('') #remove excitation from excitation
diagrams[len_diagrams-3].texts[0].set_text('') #remove absorbed from absorbed
diagrams[len_diagrams-2].texts[0].set_text('') #remove use from use-elec
diagrams[len_diagrams-2].texts[-1].set_text('') #remove electrical from use-elec
diagrams[len_diagrams-1].texts[0].set_text('') #remove electrical in from elec
if len_diagrams > 5:
axes.legend() #add legend for the reference arrows
if len_diagrams >6:
diagrams[1].texts[0].set_text('')
axes.set_aspect('equal')
axes.set_title(axes_title)
axes.axis("off")
if return_fig_and_axes:
return fig, axes
power_flows = {
'Optimal Excitation': 100,
'Deficit Excitation': 30,
'Excitation': 70,
'Deficit Radiated': 20,
'Deficit Absorbed': 10,
'Radiated': 30,
'Absorbed': 40,
'Electrical': 30,
'Useful': 35,
'PTO Loss Mechanical': 5,
'PTO Loss Electrical': 5
}
plot_power_flow(power_flows, axes_title='Power Flow Diagram')
I think MHKiT would be a good platform to host a function that creates Sankey average power flow diagrams for WECs like this:
This is the code to generate the figure: