Create tools.py

tools.py

@@ -0,0 +1,168 @@
+import plotly.graph_objects as go
+import plotly.express as px
+import numpy as np
+
+ef plot_3D_invfitness(trait, fitness, resident, range, color="RdBu"):
+    X, Y = np.meshgrid(trait, trait)
+    f_projection = (np.min(fitness) - 0.5) * 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=600,
+        height=500,
+        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",
+            yaxis_title="Mutant trait",
+            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 * 5),
+        surfacecolor=x_projection,
+        colorscale="Greys",
+        opacity=0.5,
+        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)", "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, step, zlist, fitness_func, pars, range):
+    inv_vid = []
+    for z_val in np.arange(z_start, z_end, step):
+        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(
+            title="Invasion process video", autosize=False,
+            width=550,
+            height=500,
+            xaxis=dict(range=[0, zlist[-1]], autorange=False),
+            yaxis=dict(range=range, autorange=False),
+            xaxis_title="Mutant trait value",
+            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.arange(z_start, z_end, step))
+        ],
+    )
+    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="Resident trait",
+        yaxis_title="Mutant trait",
+    )
+    return fig