Click on Y axis value of interest to adjust color bars

Question

I am trying to adjust a program, in order to add interactivity to my bar chart, so when I click on Y axis and choose a new value of interest, the color of the bars is adjusted accordingly. I appreciate any help on this as I am new to python and I don't know why the function Clickchart() is not working when I click on my chart.

This is my code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib 
import ipywidgets as wdg
from scipy.stats import norm
import matplotlib.gridspec as gridspec
from IPython.display import display
from matplotlib.cm import ScalarMappable


np.random.seed(12345)

#Raw Data

data = pd.DataFrame( { '1992': np.random.normal(32000,200000,3650), 
                   '1993': np.random.normal(43000,100000,3650), 
                   '1994': np.random.normal(43500,140000,3650), 
                   '1995': np.random.normal(48000,70000,3650) } ) 

#Mean of data
mean=data.mean(axis=0)

#Margin error of the standard error of the mean
sem=data.sem(axis=0)*1.96

    
# Create lists for the plot
year = ['1992', '1993', '1994', '1995']
x_pos = np.arange(len(year))

#Assume the user provides the y axis value of interest as a parameter or variable


my_cmap = matplotlib.cm.get_cmap('seismic')

#Y = int(input("Enter y axis value of interest: "))

#Create and display textarea widget
txt = wdg.Textarea(
    value='',
    placeholder='',
    description='Y Value:',
    disabled=False)

Y=42000

fig = plt.figure()
ax = fig.add_subplot(111)

#fig, ax = plt.subplots()

i=0

def get_color(y,m,ci):
    low = m-ci
    high = m+ci
    if y<=low:
        out = 1-1e-10
    elif y>=high:
        out = 0
    else:
        out = 1-(y-low)/(high-low)
    return out

c_list=[my_cmap(get_color(Y,mean[i], sem[i])) for i in range(4)]

    
# Build the initial plot

i=0    
while i < 4:
    bars=ax.bar(x_pos[i], mean[i], yerr=sem[i], color=c_list[i], align='center', alpha=0.5, ecolor='black', capsize=10)
    i=i+1    

#Set the labels for the Visualization 
ax.set_ylabel('Mean of the Sample Data')
ax.set_xticks(x_pos)
ax.set_xticklabels(year)
ax.set_title('Custom Visualization of a Sample Data')
plt.axhline(y=Y, color = 'black')
#plt.text(3.7, Y, Y)
#plt.text(3.7, Y-2500, "Value of Interest")
ax.yaxis.grid(True)    

#Formats color bar
sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0,1))
sm.set_array([])
cbar = plt.colorbar(sm)
cbar.set_label('Probability', rotation=270,labelpad=25)

# Show the figure
plt.show()    
    
#Interactivity
class ClickChart(object):
    
    def __init__(self, ax):
        self.fig=ax.figure
        self.ax = ax
        self.horiz_line = ax.axhline(y=Y, color='black', linewidth=2)
        self.fig.canvas.mpl_connect('button_press_event', self.onclick)

### Event handlers
    def onclick(self, event):
        self.horiz_line.remove()
        self.ypress = event.ydata
        self.horiz_line = ax.axhline(y=self.ypress, color='red', linewidth=2)
        txt.value = str(event.ydata)
        self.color_bar(event)

    def color_bar(self, event):

        for index, bar in enumerate(bars):
            bar.set_color(c=cmap(self.calc_prob(index)))
            print(index)
    
    def calc_prob(self, index):
        global mean, sem
        mean2 = mean[index]
        err = sem[index]
        result = norm.cdf(self.ypress, loc=mean2, scale=err) 
        return result
click=ClickChart(ax)  ~~~

Answer 1

You basically have two issues:

1.You need to call figure.canvas.draw() inside onclick for the change to display.

2.The way you plot bars is not good, you can plot them collectively, but I dont change that part, I just made some minimum edit to you code to make it run.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
import matplotlib 
from scipy.stats import norm
import matplotlib.gridspec as gridspec
from matplotlib.cm import ScalarMappable


np.random.seed(12345)

#Raw Data

data = pd.DataFrame( { '1992': np.random.normal(32000,200000,3650), 
                   '1993': np.random.normal(43000,100000,3650), 
                   '1994': np.random.normal(43500,140000,3650), 
                   '1995': np.random.normal(48000,70000,3650) } ) 

#Mean of data
mean=data.mean(axis=0)

#Margin error of the standard error of the mean
sem=data.sem(axis=0)*1.96

    
# Create lists for the plot
year = ['1992', '1993', '1994', '1995']
x_pos = np.arange(len(year))

#Assume the user provides the y axis value of interest as a parameter or variable


my_cmap = matplotlib.cm.get_cmap('seismic')

Y=42000

fig = plt.figure()
ax = fig.add_subplot(111)
i=0
def get_color(y,m,ci):
    low = m-ci
    high = m+ci
    if y<=low:
        out = 1-1e-10
    elif y>=high:
        out = 0
    else:
        out = 1-(y-low)/(high-low)
    return out

c_list=[my_cmap(get_color(Y,mean[i], sem[i])) for i in range(4)]

i=0
# I think you need four bars, I dont think plotting individual bar is good
bars = []
while i < 4:
    bc=ax.bar(x_pos[i], mean[i], yerr=sem[i], color=c_list[i], align='center', alpha=0.5, ecolor='black', capsize=10)
    bars.append(bc[0])
    i=i+1    

#Set the labels for the Visualization 
ax.set_ylabel('Mean of the Sample Data')
ax.set_xticks(x_pos)
ax.set_xticklabels(year)
ax.set_title('Custom Visualization of a Sample Data')
plt.axhline(y=Y, color = 'black')
#plt.text(3.7, Y, Y)
#plt.text(3.7, Y-2500, "Value of Interest")
ax.yaxis.grid(True)    

#Formats color bar
sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0,1))
sm.set_array([])
cbar = plt.colorbar(sm)
cbar.set_label('Probability', rotation=270,labelpad=25)

# Show the figure
plt.show()    
    
#Interactivity
class ClickChart(object):
    
    def __init__(self, ax):
        self.fig=ax.figure
        self.ax = ax
        self.horiz_line = ax.axhline(y=Y, color='black', linewidth=2)
        self.fig.canvas.mpl_connect('button_press_event', self.onclick)

### Event handlers
    def onclick(self, event):
        self.horiz_line.remove()
        self.ypress = event.ydata
        self.horiz_line = ax.axhline(y=self.ypress, color='red', linewidth=2)
        self.color_bar(event)
        # pls add this line
        self.fig.canvas.draw()

    def color_bar(self, event):
        for index, bar in enumerate(bars):
            # should use my_cmap, not cmap
            bar.set_color(c=my_cmap(self.calc_prob(index)))
            print(index)
    
    def calc_prob(self, index):
        global mean, sem
        mean2 = mean[index]
        err = sem[index]
        result = norm.cdf(self.ypress, loc=mean2, scale=err) 
        return result
click=ClickChart(ax)