Animated Scatter Plot Over Surface (plotly)

I'm trying to make an animated scatter plot over fixed surface using plotly. This is a code I use to draw the surface: import plotly.graph_objects as go def surface(x, y, z, opac

Solution 1:

I've posted this question as an issue at plotly's repository.

Here's an answer I've received.

@empet - thank you <3

When your fig.data contains only one trace then it is supposed that each frame updates that trace and the trace is no more displayed during the animation. That's why you must define:

fig = go.Figure(
    data=[
        go.Scatter(
            y=y,
            x=x,
            mode="lines",
            ine_shape='spline'

         )
    ]*2
)

i.e. include the same trace twice in fig.data. At the same time modify the frame definition as follows:

 frame = go.Frame(data=[scatter], traces=[1])

traces = [1] informs plotly.js that each frame updates the trace fig.data[1], while fig.data[0] is unchanged during the animation.

Here's a complete example of how to make an animation over some based plot.

import math
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
import plotly.offline as pyo


classPlot:

    def__init__(
        self,
        image_width: int = 1200,
        image_height: int = 900) -> None:

        self.IMAGE_WIDTH = image_width
        self.IMAGE_HEIGHT = image_height

        pyo.init_notebook_mode(connected=False)
        pio.renderers.default = 'notebook'defpopulation(self, filename: str = 'population'):

        x_spline = np.linspace(
            start=0,
            stop=20,
            num=100,
            endpoint=True
        )

        y_spline = np.array([math.sin(x_i) for x_i in x_spline])

        x_min = np.min(x_spline)
        x_max = np.max(x_spline)

        y_min = np.min(y_spline)
        y_max = np.max(y_spline)

        spline = go.Scatter(
            y=y_spline,
            x=x_spline,
            mode="lines",
            line_shape='spline'
        )

        fig = go.Figure(
            data=[spline] * 2
        )

        frames = []

        for i inrange(50):
            x = np.random.random_sample(size=5)
            x *= x_max

            y = np.array([math.sin(x_i) for x_i in x])

            scatter = go.Scatter(
                x=x,
                y=y,
                mode='markers',
                marker=dict(
                    color='Green',
                    size=12,
                    line=dict(
                        color='Red',
                        width=2
                    )
                ),
            )
            frame = go.Frame(data=[scatter], traces=[1])
            frames.append(frame)

        fig.frames = frames

        fig.layout = go.Layout(
            xaxis=dict(
                range=[x_min, x_max],
                autorange=False
            ),
            yaxis=dict(
                range=[y_min, y_max],
                autorange=False
            ),
            title="Start Title",
            updatemenus=[
                dict(
                    type="buttons",
                    buttons=[
                        dict(
                            label="Play",
                            method="animate",
                            args=[None]
                        )
                    ]
                )
            ]
        )

        fig.update_layout(
            xaxis_title='x',
            yaxis_title='y',
            title='Fitness landscape',
            # autosize=True
        )

        pyo.iplot(
            fig,
            filename=filename,
            image_width=self.IMAGE_WIDTH,
            image_height=self.IMAGE_HEIGHT
        )

plot = Plot()
plot.population()