plotvec 1.7.2

Description:

plotvec 1.7.2

Making Attractive 2-D Vector Diagrams in Python with PlotVec
How do I make attractive vector diagrams in Python? PlotVec makes plotting vectors easier.
(Note that the figures below use my matplotlibrc file, which is included in the GitHub repository for plotvec.)
Let's start by loading the plotvec() function and plotting a single vector:
a=[2,3]T
import numpy as np
from plotvec import plotvec

a = np.array([2, 3])

plotvec(a)


We can add multiple vectors to the diagram easily. Let's add these vectors:
b=[1,−2]T
c=[−1,−1]T
a = np.array([2, 3])
b = np.array([1, -2])
c = np.array([-2, -1])

plotvec(a, b, c)


Note that by default plotvec() uses an equal aspect ratio -- this is important in many vector diagrams, for instance to tell whether two vectors are orthogonal. For instance, in the diagram above, vectors b and c are at 90 degree angles because they are orthogonal.
If an equal aspect ratio is not needed, plotvecR() can be used to plot vectors but orthogonal vectors will not necessarily be at 90 degree angles:
from plotvec import plotvecR

plotvecR(a, b, c)


A legend can be added by specifying labels:
import numpy as np
from plotvec import plotvec, plotvecR

a = np.array([2, 3])
b = np.array([1, -2])

plotvecR(a, b,
labels = ['$\mathbf{a} = [ 2,3]^T$',
'$\mathbf{b} = [ 1, -2]^T$'],
legendloc='upper left')


By default, vectors will be plotted with their tails at the origin (0,0). We can specify a different tail using the tail keyword argument:
plotvec(c, tail=[2,1])


When plotting a sequence of vectors, we can have the tail of each vector be positioned at the head of the previous vector by settin chain=True:
plotvec(a, b, c, chain=True)


When plotting with chain=True, the head of the last vector is at the position of the sum of the vectors. We can ask plotvec() to show this sum as a vector using plotsum=True:
plotvec(a, b, c, chain=True, plotsum=True)


You can combine plot the result of multiple plotvec() commands on the same axes by specifying newfig=False. When using this option, it is good to either specify the colors of the vectors or else use the color_offset keyword parameter to tell later calls where to start in the color cycle. The example below also shows how to use plot.annotate() to label vectors:
import matplotlib.pyplot as plt

plotvec([3,0], [0,4], chain=True);
plotvec([3,4], newfig=False, color_offset=2)
plt.annotate('3', (1.6, 0.1) );
plt.annotate('4', (3.1, 1.8) );
plt.annotate('5', (1.1, 1.9) );