- Curve options: parameters that affect only a single curve. Each is described

below in "Curve options".

** Symbolic equations

Gnuplot can plot both data and equations. This module exists largely for the

data-plotting case, but sometimes it can be useful to plot equations together

with some data. This is supported by the 'equation' plot option. This plot

option is either a string (for a single equation) or a list/tuple containing

multiple strings for multiple equations. Note that plotting only equations

without data is not supported (and generally is better done with gnuplot

directly). An example:

#+BEGIN_SRC python

import numpy as np

import numpy.random as nr

import numpy.linalg

import gnuplotlib as gp

# generate data

x = np.arange(100)

c = np.array([1, 1800, -100, 0.8]) # coefficients

m = x[:, np.newaxis] ** np.arange(4) # 1, x, x**2, ...

noise = 1e4 * nr.random(x.shape)

y = np.dot( m, c) + noise # polynomial corrupted by noise

c_fit = np.dot(numpy.linalg.pinv(m), y) # coefficients obtained by a curve fit

# generate a string that describes the curve-fitted equation

fit_equation = '+'.join( '{} * {}'.format(c,m) for c,m in zip( c_fit.tolist(), ('x**0','x**1','x**2','x**3')))

# plot the data points and the fitted curve

gp.plot(x, y, _with='points', equation = fit_equation)

#+END_SRC

Here I generated some data, performed a curve fit to it, and plotted the data

points together with the best-fitting curve. Here the best-fitting curve was

plotted by gnuplot as an equation, so gnuplot was free to choose the proper

sampling frequency. And as we zoom around the plot, the sampling frequency is

adjusted to keep things looking nice.

Note that the various styles and options set by the other options do NOT apply

to these equation plots. Instead, the string is passed to gnuplot directly, and

any styling can be applied there. For instance, to plot a parabola with thick

lines, you can issue

#+BEGIN_SRC python

gp.plot( ....., equation = 'x**2 with lines linewidth 2')

#+END_SRC

As before, see the gnuplot documentation for details. You can also do fancy

things:

#+BEGIN_SRC python

x = np.arange(100, dtype=float) / 100 * np.pi * 2;

c,s = np.cos(x), np.sin(x)

gp.plot( c,s,

square=1, _with='points',

set = ('parametric', 'trange [0:2*3.14]'),

equation = "sin(t),cos(t)" )

#+END_SRC

Here the data are points evently spaced around a unit circle. Along with these

points we plot a unit circle as a parametric equation.

- equation

This option allows equations represented as formula strings to be plotted along

with data passed in as numpy arrays. This can be a string (for a single

equation) or an array/tuple of strings (for multiple equations). See the

"Symbolic equations" section above.