How to insert a slicer in a scatter plot that can be adjusted to filter calculations in the scatter plot

Question

I have my data and codes for scatter plot as below:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# define X and y
np.random.seed(10)
x = np.arange(0,100)
y = np.random.randint(0,100,100)


df = pd.DataFrame(dict(x=x, y=y))


def dist(x,y):
    p1=np.array([0,0])
    p2=np.array([100,100])
    p3=np.array([x,y])
    return abs(np.cross(p2-p1,p3-p1)/np.linalg.norm(p2-p1))

max_dist = dist(0,10)

df["within_boundary"] = df.apply(lambda row: dist(row["x"], row["y"])<=max_dist, axis=1)

fig = plt.figure(dpi=100,figsize=(8,8))
ax = fig.subplots(1)

p0 = ax.scatter(x,y,c=df["within_boundary"]) # points
p1 = ax.plot([0,100],[0,100],'-',color="red")  # middle line
p2, = ax.plot([0,90],[10,100],'--',color="black")  # upper line
p3, = ax.plot([10,100],[0,90],'--',color="black")  # lower line
plt.xticks(np.arange(0,101,10))
plt.yticks(np.arange(0,101,10))
plt.grid(True)
plt.xlim([0,100])
plt.ylim([0,100])
percentage =df['within_boundary'].sum() / len(x)
plt.figtext(0.5,0.01, f"{percentage:.1%} in Spec", ha="center", va="center", fontsize=18, bbox={"facecolor":"grey", "alpha":0.5})
plt.show() # show the window"

This I use to produce the scatter plot shown as:

The Tolerance region are in the +-10 and the In Spec of 17% is the number of points within the 10 region divided by the number of total points in the plot.

However I want to have a slicer that I can use to just filter on the plot to change from the 10 region to either 15, 20, 25, etc and even back to 10 and it will still automatically calculate the In Spec percentage when the region is adjusted on the slicer to 10, 15, 20, or whatever I want and also make the region lines to run from 15, 20 and so on automatically if I am having the slicer to 15, or 20 and so on.

Answer 1

Matplotlib Slider Demo provides already a pretty good example.

Using your code as a basis, the following example updates the data dynamically, based on the selected tolerance, defined by the slider value.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button

def dist(x,y):
    p1=np.array([0,0])
    p2=np.array([100,100])
    p3=np.array([x,y])
    return abs(np.cross(p2-p1,p3-p1)/np.linalg.norm(p2-p1))

# define X and y
np.random.seed(10)
x = np.arange(0,100)
y = np.random.randint(0,100,100)
df = pd.DataFrame(dict(x=x, y=y))

tol0 = 10 #default tolerance
max_dist = dist(0,tol0)
df["within_boundary"] = df.apply(lambda row: dist(row["x"], row["y"])<=max_dist, axis=1)

# plot definition
fig = plt.figure(dpi=100,figsize=(8,8))
ax = fig.subplots(1)
plt.subplots_adjust(bottom=0.25)
plt.xticks(np.arange(0,101,10))
plt.yticks(np.arange(0,101,10))
plt.grid(True)
plt.xlim([0,100])
plt.ylim([0,100])

# initial plotting
p0 = ax.scatter(x,y,c=df["within_boundary"]) # points
p1 = ax.plot([0,100],[0,100],'-',color="red")  # middle line
p2, = ax.plot([0,90],[10,100],'--',color="black")  # upper line
p3, = ax.plot([10,100],[0,90],'--',color="black")  # lower line

percentage = df['within_boundary'].sum() / len(x)
figtext = plt.figtext(0.5,0.01, f"{percentage:.1%} in Spec", ha="center", va="center", fontsize=18, bbox={"facecolor":"grey", "alpha":0.5})

# tolerance slider
axtol = plt.axes([0.1, 0.1, 0.8, 0.05])
stol = Slider(ax=axtol, label='Tolerance', valmin=5, valmax=50, valstep=5,valinit=tol0)
def update(val):
    # current tolerance value
    tol = stol.val

    # update data according to tolerance
    max_dist = dist(0,tol)
    df["within_boundary"] = df.apply(lambda row: dist(row["x"], row["y"])<=max_dist, axis=1)
    percentage = df['within_boundary'].sum() / len(x)
    figtext.set_text(f"{percentage:.1%} in Spec")
    p0.set_array(df["within_boundary"])
    p2.set_data([0, 100-tol], [tol,100])
    p3.set_data([tol, 100], [0,100-tol])
    fig.canvas.draw_idle()
stol.on_changed(update)

# reset button
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', hovercolor='0.975')
def reset(event):
    stol.reset()
button.on_clicked(reset)


plt.show()