# Histogram Document Screenshot

Python code used to generate the PGF file histogram.pgf:

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

matplotlib.use("pgf")
matplotlib.rcParams.update({
"pgf.texsystem": "pdflatex",
'font.family': 'serif',
'font.size' : 11,
'text.usetex': True,
'pgf.rcfonts': False,
})

np.random.seed(19680801)

# example data
mu = 100  # mean of distribution
sigma = 15  # standard deviation of distribution
x = mu + sigma * np.random.randn(437)

num_bins = 50

fig, ax = plt.subplots()

# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, density=1)

# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')

# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
fig.set_size_inches(4.7747,3.5)
plt.savefig('histogram.pgf')


LaTeX code used to generate the PDF document shown in the screenshot:

\documentclass[a4paper]{article}
\usepackage[utf8]{inputenc}

\usepackage{tikz}
\usepackage{tikz-cd}
\usepackage{pgfplots}
\pgfplotsset{compat=1.14}

\begin{document}

\section{Histogram}

\begin{figure}[h]
\begin{center}
\input{histogram.pgf}
\end{center}
\caption{A PGF histogram from \texttt{matplotlib}.}
\end{figure}

\end{document}

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Zürich-based Software Engineer with Google; opinions are my own. I am interested in data science, software engineering, 3d-printing, arts, music, microcontrollers, and sports.
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