Bokehを用いた2x2の行列演算の可視化

本日は

先日に続いてBokehを使った行列演算

\begin{bmatrix}
x'\\
y'
\end{bmatrix}
=
\begin{bmatrix}
a & b\\
c & d
\end{bmatrix}
\begin{bmatrix}
x\\
y
\end{bmatrix}

の可視化をしてみます．

#　実行例

#実装例

import numpy as np
from bokeh.palettes import Category20
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import Slider
from bokeh.layouts import column, widgetbox, gridplot
from bokeh.io import curdoc


def matrix(a, b, c, d):
    mat = np.array([[a, b],
                    [c, d]])
    return mat


class MatrixViewer(object):

    def __init__(self):
        N = 11
        self.N = N
        xs = np.linspace(-np.pi, np.pi, N)
        ys = xs
        Xs, Ys = np.meshgrid(xs, ys)
        self.Xs, self.Ys = Xs.flatten(), Ys.flatten()
        a, b = 1, 0
        c, d = 0, 1
        mat = matrix(a, b, c, d)
        transXs, transYs = mat @ np.array([self.Xs, self.Ys])

        TOOLS = "pan,save,reset"
        self.dic_xs = {"Xs{}".format(step): self.Xs[N*step:N*(step+1)]
                       for step in range(N)}
        self.dic_ys = {"Ys{}".format(step): self.Ys[N*step:N*(step+1)]
                       for step in range(N)}
        dic_trasn_xs = {"transXs{}".format(
            step): transXs[N*step:N*(step+1)] for step in range(N)}
        dic_trans_ys = {"transYs{}".format(
            step): transYs[N*step:N*(step+1)] for step in range(N)}
        data = {**self.dic_xs, **self.dic_ys, **dic_trasn_xs, **dic_trans_ys}
        colors = Category20[11]
        self.source = ColumnDataSource(data=data)
        self.fig = figure(tools=TOOLS, title="target",
                          x_range=(-np.pi*np.sqrt(2)-1, np.pi*np.sqrt(2)+1),
                          y_range=(-np.pi*np.sqrt(2)-1, np.pi*np.sqrt(2)+1))
        for s in range(N):
            eval("""self.fig.scatter('Xs{0}', 'Ys{0}', 
                                     source=self.source, 
                                     color=colors[{0}])""".format(s))

        self.transfig = figure(tools=TOOLS, title="transformed",
                               x_range=self.fig.x_range,
                               y_range=self.fig.y_range)
        for s in range(N):
            eval("""self.transfig.scatter('transXs{0}', 'transYs{0}', 
                                          source=self.source, 
                                          color=colors[{0}])""".format(s))

        self.a_slider = Slider(title="a", value=a,
                               start=-10, end=10, step=0.1)
        self.b_slider = Slider(title="b", value=b,
                               start=-10, end=10, step=0.1)
        self.c_slider = Slider(title="c", value=c,
                               start=-10, end=10, step=0.1)
        self.d_slider = Slider(title="d", value=d,
                               start=-10, end=10, step=0.1)

        for widget in [self.a_slider, self.b_slider,
                       self.c_slider, self.d_slider]:
            widget.on_change('value', self.update_data)
        box = widgetbox([self.a_slider, self.b_slider,
                         self.c_slider, self.d_slider])
        self.plot = column(gridplot([[self.a_slider, self.b_slider],
                                     [self.c_slider, self.d_slider]]),
                           gridplot([[self.fig, self.transfig]]))

    def update_data(self, attr, old, new):
        N = self.N
        mat = matrix(self.a_slider.value,
                     self.b_slider.value,
                     self.c_slider.value,
                     self.d_slider.value)
        transXs, transYs = mat @ np.array([self.Xs, self.Ys])
        dic_trasn_xs = {"transXs{}".format(step): transXs[N*step:N*(step+1)]
                        for step in range(N)}
        dic_trans_ys = {"transYs{}".format(step): transYs[N*step:N*(step+1)]
                        for step in range(N)}
        data = {**self.dic_xs, **self.dic_ys, **dic_trasn_xs, **dic_trans_ys}
        self.source.data = data


def main():
    viewer = MatrixViewer()
    document = curdoc()
    document.add_root(viewer.plot)

main()

パレット， eval 辞書の連結の練習になりました．
凝ったことをするととても面倒なことをせざるを得ないですね．