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import plotly.graph_objects as go
import plotly.express as px
import numpy as np
def plot_3D_invfitness(trait, fitness, resident, range, color="RdBu"):
X, Y = np.meshgrid(trait, trait)
f_projection = (np.min(fitness) - np.mean(fitness)) * np.ones(fitness.shape)
axis = dict(
showbackground=True,
backgroundcolor="rgb(230, 230,230)",
showgrid=False,
zeroline=False,
showline=False,
)
layout = go.Layout(
autosize=False,
width=700,
height=600,
scene=dict(
xaxis=dict(axis),
yaxis=dict(axis),
zaxis=dict(axis, range=range),
aspectratio=dict(x=1, y=1, z=1),
xaxis_title="Resident trait (z_r)",
yaxis_title="Mutant trait (z_m)",
zaxis_title="Invasion fitness",
),
)
x_projection = resident * np.ones(fitness.shape)
fitness_surface = go.Surface(x=X, y=Y, z=fitness, colorscale=color)
PIP = go.Surface(
x=X,
y=Y,
z=f_projection,
surfacecolor=(fitness > 0),
colorscale="Greens",
showlegend=False,
showscale=False,
)
slice = go.Surface(
x=x_projection,
y=Y,
z=(fitness * 1e5),
surfacecolor=x_projection,
colorscale="Greys",
opacity=0.7,
showlegend=False,
showscale=False,
)
fig = go.Figure(
data=[
fitness_surface,
PIP,
slice,
],
layout=layout,
)
return fig
def plot_invasionfitness(zm, zlist, fitness_func, pars, range):
inv_fitness = fitness_func(zm, zlist, pars)
fig = px.line(
x=zlist, y=inv_fitness, labels={"x": "Mutant trait value (z_m)", "y": "Invasion fitness"}
)
fig.add_vline(x=zm, line_dash="dashdot")
fig.add_hline(y=0, line_dash="dash")
fig.update_layout(
title="Interactive invasion process",
xaxis=dict(range=[0, zlist[-1]], autorange=False),
yaxis=dict(range=range, autorange=False),
autosize=False,
width=450,
height=400,
)
return fig
def make_interactive_video(z_start, z_end, steps, zlist, fitness_func, pars, range):
inv_vid = []
for z_val in np.linspace(z_start, z_end, steps):
inv_vid.append(fitness_func(z_val, zlist, pars))
vid = go.Figure(
data=[
go.Line(x=zlist, y=fitness_func(z_start, zlist, pars), name="invasion fitness"),
go.Line(
x=zlist,
y=np.zeros(len(zlist)),
line=dict(color="black", width=1, dash="dash"),
name="Invasion threshold",
),
go.Scatter(
x=[z_start] * 10,
y=np.linspace(-2, 2, 10),
mode="lines",
line=dict(color="black", dash="dashdot"),
name="Resident trait value",
),
],
layout=go.Layout(
autosize=False,
width=650,
height=500,
xaxis=dict(range=[0, zlist[-1]], autorange=False),
yaxis=dict(range=range, autorange=False),
xaxis_title="Mutant trait value (z_m)",
updatemenus=[
dict(
type="buttons",
buttons=[
dict(
label="Play",
method="animate",
args=[
None,
{
"frame": {"duration": 500, "redraw": False},
"fromcurrent": True,
"transition": {"duration": 300, "easing": "quadratic-in-out"},
},
],
),
dict(
label="Pause",
method="animate",
args=[
[None],
{
"frame": {"duration": 0, "redraw": False},
"mode": "immediate",
"transition": {"duration": 0},
},
],
),
],
),
],
),
frames=[
go.Frame(
data=[
go.Line(x=zlist, y=i),
go.Line(
x=zlist,
y=np.zeros(len(zlist)),
line=dict(color="black", dash="dash"),
),
go.Scatter(
x=[z_val] * 10,
y=np.linspace(-2, 2, 10),
line=dict(color="black", dash="dashdot"),
mode="lines",
),
]
)
for i, z_val in zip(inv_vid, np.linspace(z_start, z_end, steps))
],
)
return vid
def plot_PIP(zlist, fitness_func, pars):
X, Y = np.meshgrid(zlist, zlist)
inv_fitness3D = fitness_func(X, Y, pars)
fig = go.Figure(
data=go.Contour(
x=zlist,
y=zlist,
z=inv_fitness3D,
colorscale="PRGn",
showscale=False,
contours=dict(
start=-20,
end=0,
size=10,
),
)
)
fig.update_layout(
autosize=False,
width=400,
height=500,
xaxis_title=r"Resident trait (z_r)",
yaxis_title=r"Mutant trait (z_m)",
title="Pairwise invasibility plot (PIP)",
)
return fig
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