Python normalised histogram with just outline

histogram_with_outline.py

import numpy as np
import matplotlib.pyplot as plt

x_min = 0.51
x_max = 0.54
num_bins = 20
num_data=7000
x_mean = 0.53
plt.style.use("ggplot")

#generate data
data1 = np.random.normal(loc=x_mean, size=num_data)
data2 = np.random.normal(loc=x_mean, size=num_data)

#create histogram
d1_bin_height, d1_bin_boundary = np.histogram(data1,bins=np.linspace(x_min, x_max, num_bins))
d2_bin_height, d2_bin_boundary = np.histogram(data2, bins=np.linspace(x_min, x_max, num_bins))

#find the width of the bin
d1_width = d1_bin_boundary[1]-d1_bin_boundary[0]
d2_width = d2_bin_boundary[1]-d2_bin_boundary[0]

#standardize data in each column by dividing with the maximum height
d1_bin_height = d1_bin_height/float(max(d1_bin_height))
d2_bin_height = d2_bin_height/float(max(d2_bin_height))

#plot the data in two subplots
fig=plt.figure(figsize=(15,9))

ax0 = fig.add_subplot(131)
ax0.bar(d1_bin_boundary[:-1], d1_bin_height, width=d1_width, alpha=0.5, color=(0.6,0.7,0), label='data1')
ax0.step(d1_bin_boundary[:-1], d1_bin_height, where='mid')
ax0.legend(fontsize=20)
for tick in ax0.xaxis.get_major_ticks():
    tick.label.set_fontsize(14)
for tick in ax0.yaxis.get_major_ticks():
    tick.label.set_fontsize(14)
ax0.set_xticks(np.arange(x_min, x_max, 0.01), minor=False)
ticks = ax0.get_xticks()*10**2
ax0.set_xticklabels(ticks)

ax1 = fig.add_subplot(132)
ax1.bar(d2_bin_boundary[:-1], d2_bin_height, width=f_width, alpha=0.5, color=(0.,0.7,0.6), label='data2')
ax1.step(d2_bin_boundary[:-1], d2_bin_height, where='mid', color='k')
ax1.legend(fontsize=20)
ax1.set_xticks(np.arange(x_min, x_max, 0.01), minor=False)
#increase the font size
for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(14)
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(14)
#how to scale the ticks by a factor of 100 
ticks = ax1.get_xticks()*10**2
ax1.set_xticklabels(ticks)

#combine the data in a single subplot 
ax2 = fig.add_subplot(133)
plt.bar(d1_bin_boundary[:-1], d1_bin_height, width=r_width, alpha=0.5, color=(0.6,0.7,0), label='data1')
plt.step(d1_bin_boundary[:-1], d1_bin_height, where='mid')
plt.legend(fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.bar(d1_bin_boundary[:-1], d2_bin_height, width=f_width, alpha=0.5, color=(0.,0.7,0.6), label='data2')
plt.step(d2_bin_boundary[:-1], d2_bin_height, where='mid', color='k')
plt.legend(fontsize=20)
plt.yticks(fontsize=20)

#how to scale the tick labels
ticks = ax0.get_xticks()*10**2
ax0.set_xticklabels(ticks)

#plot the mean
plt.vlines(x_mean, 0, 1, color='b', linestyle='--', linewidth=6)
plt.show()