How to plot graphs of functions¶

Useful tutorial: https://matplotlib.org/api/colors_api.html

Here are some python scripts:

Sinus x durch x

#-*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)

# Changing colors and line widths
plt.figure(figsize=(10, 6), dpi=80)
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-")

# Setting limits Current limits of the figure are a bit too tight 
# and we want to make some space in order to clearly see all data points.
plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.ylim(C.min() * 1.1, C.max() * 1.1)

# Setting ticks
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
plt.yticks([-1, 0, +1])

# Moving spines
# Spines are the lines connecting the axis tick marks and noting the 
# boundaries of the data area. They can be placed at arbitrary positions 
# and until now, they were on the border of the axis. We’ll change that 
# since we want to have them in the middle. Since there are four of them 
# (top/bottom/left/right), we’ll discard the top and right by setting 
# their color to none and we’ll move the bottom and left ones to 
# coordinate 0 in data space coordinates.

ax = plt.gca()  # gca stands for 'get current axis'
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

# Setting tick labels: 
# Ticks are now properly placed but their label 
# is not very explicit. We could guess that 3.142 is π but it would be 
# better to make it explicit. When we set tick values, we can also 
# provide a corresponding label in the second argument list. 
# Note that we’ll use latex to allow for nice rendering of the label.
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
                   [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])
plt.yticks([-1, 0, +1],
                   [r'$-1$', r'', r'$+1$'])

plt.plot(X, C)
plt.plot(X, S)

plt.show()
                                                                                                                                                                                         1,1           Top

Log und exp

#-*- coding: utf-8 -*-
# Figure size before plot

import matplotlib.pyplot as plt
import numpy as np

# Definitionsbereich

t = np.arange(-4, 4, 0.001)

# Funktionendeklaration
n = np.log(10)
ex, ln  = np.exp(t), np.log(t)
pot, lg10 = np.exp(t*n), np.log10(t)

plt.figure(figsize=(10, 10), dpi=100)

# Set x limits
plt.xlim(-4.0, 4.0)

# Set y limits
plt.ylim(-4, 4)

# Ticks 
plt.xticks([-1,0,1])
plt.yticks([-1,0,1])

# Beschriftung der Achsen
plt.xlabel("x") 
plt.ylabel("y", rotation='horizontal') 

plt.axhline(0, color='black')
plt.axvline(0, color='black')

plt.plot(t, ln,  color="blue", linewidth=2.0, linestyle="-" , label="$Nat\ddot{u}rlicher \, Logarithmus$ ")
plt.plot(t, lg10,  color="red" , linewidth=2.0, linestyle="-" , label="$Dekadischer Logrithmus$ ")
plt.plot(t, ex,  color="blue", linewidth=2.0, linestyle="dashed" , label="$\mathrm{\,\mathbb{R}}$ ")

plt.legend(loc='lower right')

# Moving spines
ax = plt.gca()  # gca stands for 'get current axis'
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

# Setting tick labels: 
plt.xticks([-1, 0, 1],
                           [-1, r'' , +1])

plt.yticks([-1, 0, +1],
                           [-1, r'' , +1])

# Title 
plt.title(r' $Logarithmus \, und \,  Exponentialfunktion$')
#plt.title(r'$\ddot{o}\acute{e}\grave{e}\hat{O}\breve{i}\bar{A}\tilde{n}\vec{q}$', fontsize=20)
plt.grid(True)
plt.savefig("exp-log_de.png")
plt.show()

Was bei der Einbindung zu beachten ist:¶

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