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#!/usr/bin/python
r'''a gnuplot-based plotting backend for numpy
* SYNOPSIS
import numpy as np
import gnuplotlib as gp
x = np.arange(101) - 50
gp.plot(x**2)
[ basic parabola plot pops up ]
g1 = gp.gnuplotlib(title = 'Parabola with error bars',
_with = 'xyerrorbars')
g1.plot( x**2 * 10, np.abs(x)/10, np.abs(x)*25,
legend = 'Parabola',
tuplesize = 4 )
[ parabola with x,y errobars pops up in a new window ]
x,y = np.ogrid[-10:11,-10:11]
gp.plot( x**2 + y**2,
title = 'Heat map',
unset = 'grid',
cmds = 'set view map',
square = True,
_with = 'image',
tuplesize = 3)
[ Heat map pops up where first parabola used to be ]
theta = np.linspace(0, 6*np.pi, 200)
z = np.linspace(0, 5, 200)
g2 = gp.gnuplotlib(_3d = True)
g2.plot( np.cos(theta),
np.vstack((np.sin(theta), -np.sin(theta))),
z )
[ Two 3D spirals together in a new window ]
x = np.arange(1000)
gp.plot( (x*x, dict(histogram= True,
binwidth = 20000,
legend = 'Frequency')),
(x*x, dict(histogram='cumulative',
legend = 'Cumulative',
y2 = True )),
ylabel = 'Histogram frequency',
y2label = 'Cumulative sum')
[ A density and cumulative histogram of x^2 are plotted on the same plot ]
gp.plot( (x*x, dict(histogram=True,
binwidth =20000,
legend = 'Frequency')),
(x*x, dict(histogram='cumulative',
legend = 'Cumulative')),
_xmin=0, _xmax=1e6,
multiplot='title "multiplot histograms" layout 2,1',
_set='lmargin at screen 0.05')
[ Same histograms, but plotted on two separate plots ]
* DESCRIPTION
For an introductory tutorial and some demos, please see the guide:
https://github.com/dkogan/gnuplotlib/blob/master/guide/guide.org
This module allows numpy data to be plotted using Gnuplot as a backend. As much
as was possible, this module acts as a passive pass-through to Gnuplot, thus
making available the full power and flexibility of the Gnuplot backend. Gnuplot
is described in great detail at its upstream website: http://www.gnuplot.info
gnuplotlib has an object-oriented interface (via class gnuplotlib) and a few
global class-less functions (plot(), plot3d(), plotimage()). Each instance of
class gnuplotlib has a separate gnuplot process and a plot window. If multiple
simultaneous plot windows are desired, create a separate class gnuplotlib object
for each.
The global functions reuse a single global gnuplotlib instance, so each such
invocation rewrites over the previous gnuplot window.
The object-oriented interface is used like this:
import gnuplotlib as gp
g = gp.gnuplotlib(options)
g.plot( curve, curve, .... )
The global functions consolidate this into a single call:
import gnuplotlib as gp
gp.plot( curve, curve, ...., options )
** Option arguments
Each gnuplotlib object controls ONE gnuplot process. And each gnuplot process
produces ONE plot window (or hardcopy) at a time. Each process usually produces
ONE subplot at a time (unless we asked for a multiplot). And each subplot
contains multiple datasets (referred to as "curves").
These 3 objects (process, subplot, curve) are controlled by their own set of
options, specified as a python dict. A FULL (much more verbose than you would
ever be) non-multiplot plot command looks like
import gnuplotlib as gp
g = gp.gnuplotlib( subplot_options, process_options )
curve_options0 = dict(...)
curve_options1 = dict(...)
curve0 = (x0, y0, curve_options0)
curve1 = (x1, y1, curve_options1)
g.plot( curve0, curve1 )
and a FULL multiplot command wraps this once more:
import gnuplotlib as gp
g = gp.gnuplotlib( process_options, multiplot=... )
curve_options0 = dict(...)
curve_options1 = dict(...)
curve0 = (x0, y0, curve_options0)
curve1 = (x1, y1, curve_options1)
subplot_options0 = dict(...)
subplot0 = (curve0, curve1, subplot_options0)
curve_options2 = dict(...)
curve_options3 = dict(...)
curve2 = (x2, y2, curve_options2)
curve3 = (x3, y3, curve_options3)
subplot_options1 = dict(...)
subplot1 = (curve2, curve3, subplot_options1)
g.plot( subplot0, subplot1 )
This is verbose, and rarely will you actually specify everything in this much
detail:
- Anywhere that expects process options, you can pass the DEFAULT subplot
options and the DEFAULT curve options for all the children. These defaults may
be overridden in the appropriate place
- Anywhere that expects plot options you can pass DEFAULT curve options for all
the child curves. And these can be overridden also
- Broadcasting (see below) reduces the number of curves you have to explicitly
specify
- Implicit domains (see below) reduce the number of numpy arrays you need to
pass when specifying each curve
- If only a single curve tuple is to be plotted, it can be inlined
The following are all equivalent ways of making the same plot:
import gnuplotlib as gp
import numpy as np
x = np.arange(10)
y = x*x
# Global function. Non-inlined curves. Separate curve and subplot options
gp.plot( (x,y, dict(_with = 'lines')), title = 'parabola')
# Global function. Inlined curves (possible because we have only one curve).
# The curve, subplot options given together
gp.plot( x,y, _with = 'lines', title = 'parabola' )
# Object-oriented function. Non-inlined curves.
p1 = gp.gnuplotlib(title = 'parabola')
p1.plot((x,y, dict(_with = 'lines')),)
# Object-oriented function. Inlined curves.
p2 = gp.gnuplotlib(title = 'parabola')
p2.plot(x,y, _with = 'lines')
If multiple curves are to be drawn on the same plot, then each 'curve' must live
in a separate tuple, or we can use broadcasting to stack the extra data in new
numpy array dimensions. Identical ways to make the same plot:
import gnuplotlib as gp
import numpy as np
import numpysane as nps
x = np.arange(10)
y = x*x
z = x*x*x
# Object-oriented function. Separate curve and subplot options
p = gp.gnuplotlib(title = 'parabola and cubic')
p.plot((x,y, dict(_with = 'lines', legend = 'parabola')),
(x,z, dict(_with = 'lines', legend = 'cubic')))
# Global function. Separate curve and subplot options
gp.plot( (x,y, dict(_with = 'lines', legend = 'parabola')),
(x,z, dict(_with = 'lines', legend = 'cubic')),
title = 'parabola and cubic')
# Global function. Using the default _with
gp.plot( (x,y, dict(legend = 'parabola')),
(x,z, dict(legend = 'cubic')),
_with = 'lines',
title = 'parabola and cubic')
# Global function. Using the default _with, inlining the curve options, omitting
# the 'x' array, and using the implicit domain instead
gp.plot( (y, dict(legend = 'parabola')),
(z, dict(legend = 'cubic')),
_with = 'lines',
title = 'parabola and cubic')
# Global function. Using the default _with, inlining the curve options, omitting
# the 'x' array, and using the implicit domain instead. Using broadcasting for
# the data and for the legend, inlining the one curve
gp.plot( nps.cat(y,z),
legend = np.array(('parabola','cubic')),
_with = 'lines',
title = 'parabola and cubic')
When making a multiplot (see below) we have multiple subplots in a plot. For
instance I can plot a sin() and a cos() on top of each other:
import gnuplotlib as gp
import numpy as np
th = np.linspace(0, np.pi*2, 30)
gp.plot( (th, np.cos(th), dict(title="cos")),
(th, np.sin(th), dict(title="sin")),
_xrange = [0,2.*np.pi],
_yrange = [-1,1],
multiplot='title "multiplot sin,cos" layout 2,1')
Process options are parameters that affect the whole plot window, like the
output filename, whether to test each gnuplot command, etc. We have ONE set of
process options for ALL the subplots. These are passed into the gnuplotlib
constructor or appear as keyword arguments in a global plot() call. All of these
are described below in "Process options".
Subplot options are parameters that affect a subplot. Unless we're
multiplotting, there's only one subplot, so we have a single set of process
options and a single set of subplot options. Together these are sometimes
referred to as "plot options". Examples are the title of the plot, the axis
labels, the extents, 2D/3D selection, etc. If we aren't multiplotting, these are
passed into the gnuplotlib constructor or appear as keyword arguments in a
global plot() call. In a multiplot, these are passed as a python dict in the last
element of each subplot tuple. Or the default values can be given where process
options usually live. All of these are described below in "Subplot options".
Curve options: parameters that affect only a single curve. These are given as a
python dict in the last element of each curve tuple. Or the defaults can appear
where process or subplot options are expected. Each is described below in "Curve
options".
A few helper global functions are available:
plot3d(...)
is equivalent to
plot(..., _3d=True)
And
plotimage(...)
is equivalent to
plot(..., _with='image', tuplesize=3)
** Data arguments
The 'curve' arguments in the plot(...) argument list represent the actual data
being plotted. Each output data point is a tuple (set of values, not a python
"tuple") whose size varies depending on what is being plotted. For example if
we're making a simple 2D x-y plot, each tuple has 2 values. If we're making a 3D
plot with each point having variable size and color, each tuple has 5 values:
(x,y,z,size,color). When passing data to plot(), each tuple element is passed
separately by default (unless we have a negative tuplesize; see below). So if we
want to plot N 2D points we pass the two numpy arrays of shape (N,):
gp.plot( x,y )
By default, gnuplotlib assumes tuplesize==2 when plotting in 2D and tuplesize==3
when plotting in 3D. If we're doing anything else, then the 'tuplesize' curve
option MUST be passed in:
gp.plot( x,y,z,size,color,
tuplesize = 5,
_3d = True,
_with = 'points ps variable palette' )
This is required because you may be using implicit domains (see below) and/or
broadcasting, so gnuplotlib has no way to know the intended tuplesize.
*** Broadcasting
Broadcasting (https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) is
fully supported, so multiple curves can be plotted by stacking data inside the
passed-in arrays. Broadcasting works across curve options also, so things like
curve labels and styles can also be stacked inside arrays:
th = np.linspace(0, 6*np.pi, 200)
z = np.linspace(0, 5, 200)
size = 0.5 + np.abs(np.cos(th))
color = np.sin(2*th)
# without broadcasting:
gp.plot3d( ( np.cos(th), np.sin(th),
z, size, color,
dict(legend = 'spiral 1') ),
( -np.cos(th), -np.sin(th),
z, size, color,
dict(legend = 'spiral 2') ),
tuplesize = 5,
title = 'double helix',
_with = 'points pointsize variable pointtype 7 palette' )
# identical plot using broadcasting:
gp.plot3d( ( np.cos(th) * np.array([[1,-1]]).T,
np.sin(th) * np.array([[1,-1]]).T,
z, size, color,
dict( legend = np.array(('spiral 1', 'spiral 2')))),
tuplesize = 5,
title = 'double helix',
_with = 'points pointsize variable pointtype 7 palette' )
This is a 3D plot with variable size and color. There are 5 values in the tuple,
which we specify. The first 2 arrays have shape (2,N); all the other arrays have
shape (N,). Thus the broadcasting rules generate 2 distinct curves, with varying
values for x,y and identical values for z, size and color. We label the curves
differently by passing an array for the 'legend' curve option. This array
contains strings, and is broadcast like everything else.
*** Negative tuplesize
If we have all the data elements in a single array, plotting them is a bit
awkward. Here're two ways:
xy = .... # Array of shape (N,2). Each slice is (x,y)
gp.plot(xy[:,0], xy[:,1])
gp.plot(*xy.T)
The *xy.T version is concise, but is only possible if we're plotting one curve:
python syntax doesn't allow any arguments after and *-expanded tuple. With more
than one curve you're left with the first version, which is really verbose,
especially with a large tuplesize. gnuplotlib handles this case with a
shorthand: negative tuplesize. The above can be represented nicely like this:
gp.plot(xy, tuplesize = -2)
This means that each point has 2 values, but that instead of reading each one in
a separate array, we have ONE array, with the values in the last dimension.
*** Implicit domains
gnuplotlib looks for tuplesize different arrays for each curve. It is common for
the first few arrays to be predictable by gnuplotlib, and in those cases it's a
chore to require for the user to pass those in. Thus, if there are fewer than
tuplesize arrays available, gnuplotlib will try to use an implicit domain. This
happens if we are EXACTLY 1 or 2 arrays short (usually when making 2D and 3D
plots respectively).
If exactly 1 dimension is missing, gnuplotlib will use np.arange(N) as the
domain: we plot the given values in a row, one after another. Thus
gp.plot(np.array([1,5,3,4,4]))
is equivalent to
gp.plot(np.arange(5), np.array([1,5,3,4,4]) )
Only 1 array was given, but the default tuplesize is 2, so we are 1 array short.
If we are exactly 2 arrays short, gnuplotlib will use a 2D grid as a domain.
Example:
xy = np.arange(21*21).reshape(21*21)
gp.plot( xy, _with = 'points', _3d=True)
Here the only given array has dimensions (21,21). This is a 3D plot, so we are
exactly 2 arrays short. Thus, gnuplotlib generates an implicit domain,
corresponding to a 21-by-21 grid. Note that in all other cases, each curve takes
in tuplesize 1-dimensional arrays, while here it takes tuplesize-2 2-dimensional
arrays.
Also, note that while the DEFAULT tuplesize depends on whether we're making a 3D
plot, once a tuplesize is given, the logic doesn't care if a 3D plot is being
made. It can make sense to have a 2D implicit domain when making 2D plots. For
example, one can be plotting a color map from an array of shape (H,W):
x,y = np.ogrid[-10:11,-10:11]
gp.plot( x**2 + y**2,
title = 'Heat map',
_with = 'image',
tuplesize = 3)
Or a full-color image from an array of shape (H,W,3)
gp.plot( image,
title = 'Full-color image',
_with = 'rgbimage',
tuplesize = -5)
Also note that the 'tuplesize' curve option is independent of implicit domains.
This option specifies not how many data arrays we have, but how many values
represent each data point. For example, if we want a 2D line plot with varying
colors plotted with an implicit domain, set tuplesize=3 as before (x,y,color),
but pass in only 2 arrays (y, color).
** Multiplots
Usually each gnuplotlib object makes one plot at a time. And as a result, we
have one set of process options and subplot options at a time (known together as
"plot options"). Sometimes this isn't enough, and we really want to draw
multiple plots in a single window (or hardcopy) with a gnuplotlib.plot() call.
This situation is called a "multiplot". We enter this mode by passing a
"multiplot" process option, which is a string passed directly to gnuplot in its
"set multiplot ..." command. See the corresponding gnuplot documentation for
details:
gnuplot -e "help multiplot"
Normally we make plots like this:
gp.plot( (x0, y0, curve_options0),
(x1, y1, curve_options1),
...,
subplot_options, process_options)
In multiplot mode, the gnuplotlib.plot() command takes on one more level of
indirection:
gp.plot( ( (x0, y0, curve_options0),
(x1, y1, curve_options1),
...
subplot_options0 ),
( (x2, y2, curve_options2),
(x3, y3, curve_options3),
...
subplot_options1 ),
...,
process_options )
The process options can appear at the end of the gp.plot() global call, or in
the gnuplotlib() constructor. Subplot option and curve option defaults can
appear there too. Subplot options and curve option defaults appear at the end of
each subplot tuple.
A few options are valid as both process and subplot options: 'cmds', 'set',
'unset'. If one of these ('set' for instance) is given as BOTH a process and
subplot option, we execute BOTH of them. This is different from the normal
behavior, where the outer option is treated as a default to be overridden,
instead of contributed to.
Multiplot mode is useful, but has a number of limitations and quirks. For
instance, interactive zooming, measuring isn't possible. And since each subplot
is independent, extra commands may be needed to align axes in different
subplots: "help margin" in gnuplot to see how to do this. Do read the gnuplot
docs in detail when touching any of this. Sample to plot two sinusoids above one another:
import gnuplotlib as gp
import numpy as np
th = np.linspace(0, np.pi*2, 30)
gp.plot( (th, np.cos(th), dict(title="cos")),
(th, np.sin(th), dict(title="sin")),
_xrange = [0,2.*np.pi],
_yrange = [-1,1],
multiplot='title "multiplot sin,cos" layout 2,1')
** 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...' subplot option. This is
either a string (for a single equation) or a list/tuple containing multiple
strings for multiple equations. An example:
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)
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
gp.plot( ....., equation = 'x**2 with lines linewidth 2')
As before, see the gnuplot documentation for details. You can do fancy things:
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)" )
Here the data are points evently spaced around a unit circle. Along with these
points we plot a unit circle as a parametric equation.
** Histograms
It is possible to use gnuplot's internal histogram support, which uses gnuplot
to handle all the binning. A simple example:
x = np.arange(1000)
gp.plot( (x*x, dict(histogram = 'freq', binwidth=10000)),
(x*x, dict(histogram = 'cumulative', y2=1))
To use this, pass 'histogram = HISTOGRAM_TYPE' as a curve option. If the type is
any non-string that evaluates to True, we use the 'freq' type: a basic frequency
histogram. Otherwise, the types are whatever gnuplot supports. See the output of
'help smooth' in gnuplot. The most common types are
- freq: frequency
- cumulative: integral of freq. Runs from 0 to N, where N is the number of samples
- cnormal: like 'cumulative', but rescaled to run from 0 to 1
The 'binwidth' curve option specifies the size of the bins. This must match for
ALL histogram curves in a plot. If omitted, this is assumed to be 1. As usual,
the user can specify whatever styles they want using the 'with' curve option. If
omitted, you get reasonable defaults: boxes for 'freq' histograms and lines for
cumulative ones.
This only makes sense with 2D plots with tuplesize=1
** Plot persistence and blocking
As currently written, gnuplotlib does NOT block and the plot windows do NOT
persist. I.e.
- the 'plot()' functions return immediately, and the user interacts with the
plot WHILE THE REST OF THE PYTHON PROGRAM IS RUNNING
- when the python program exits, the gnuplot process and any visible plots go
away
If you want to write a program that just shows a plot, and exits when the user
closes the plot window, you should do any of
- add wait=True to the process options dict
- call wait() on your gnuplotlib object
- call the global gnuplotlib.wait(), if you have a global plot
Please note that it's not at all trivial to detect if a current plot window
exists. If not, this function will end up waiting forever, and the user will
need to Ctrl-C.
* OPTIONS
** Process options
The process options are a dictionary, passed as the keyword arguments to the
global plot() function or to the gnuplotlib contructor. The supported keys of
this dict are as follows:
- hardcopy, output
These are synonymous. Instead of drawing a plot on screen, plot into a file
instead. The output filename is the value associated with this key. If the
"terminal" plot option is given, that sets the output format; otherwise the
output format is inferred from the filename. Currently only eps, ps, pdf, png,
svg, gp are supported with some default sets of options. For any other formats
you MUST provide the 'terminal' option as well. Example:
plot(..., hardcopy="plot.pdf")
[ Plots into that file ]
Note that the ".gp" format is special. Instead of asking gnuplot to make a plot
using a specific terminal, writing to "xxx.gp" will create a self-plotting data
file that is visualized with gnuplot.
- terminal
Selects the gnuplot terminal (backend). This determines how Gnuplot generates
its output. Common terminals are 'x11', 'qt', 'pdf', 'dumb' and so on. See the
Gnuplot docs for all the details.
There are several gnuplot terminals that are known to be interactive: "x11",
"qt" and so on. For these no "output" setting is desired. For noninteractive
terminals ("pdf", "dumb" and so on) the output will go to the file defined by
the output/hardcopy key. If this plot option isn't defined or set to the empty
string, the output will be redirected to the standard output of the python
process calling gnuplotlib.
>>> gp.plot( np.linspace(-5,5,30)**2,
... unset='grid', terminal='dumb 80 40' )
25 A-+---------+-----------+-----------+----------+-----------+---------A-+
* + + + + + * +
|* * |
|* * |
| * * |
| A A |
| * * |
20 +-+ * * +-+
| * * |
| A A |
| * * |
| * * |
| * * |
| A A |
15 +-+ * * +-+
| * * |
| * * |
| A A |
| * * |
| * * |
| A A |
10 +-+ * * +-+
| * * |
| A A |
| * * |
| * * |
| A A |
| * * |
5 +-+ A A +-+
| * ** |
| A** A |
| * |
| A* *A |
| A* *A |
+ + + A** + *A* + + +
0 +-+---------+-----------+------A*A**A*A--------+-----------+---------+-+
0 5 10 15 20 25 30
- set/unset
Either a string or a list/tuple; if given a list/tuple, each element is used in
separate set/unset command. Example:
plot(..., set='grid', unset=['xtics', 'ytics])
[ turns on the grid, turns off the x and y axis tics ]
This is both a process and a subplot option. If both are given, BOTH are used,
instead of the normal behavior of a subplot option overriding the process option
- cmds
Either a string or a list/tuple; if given a list/tuple, each element is used in
separate command. Arbitrary extra commands to pass to gnuplot before the plots
are created. These are passed directly to gnuplot, without any validation.
This is both a process and a subplot option. If both are given, BOTH are used,
instead of the normal behavior of a subplot option overriding the process option
- dump
Used for debugging. If true, writes out the gnuplot commands to STDOUT instead
of writing to a gnuplot process. Useful to see what commands would be sent to
gnuplot. This is a dry run. Note that this dump will contain binary data unless
ascii-only plotting is enabled (see below). This is also useful to generate
gnuplot scripts since the dumped output can be sent to gnuplot later, manually
if desired. Look at the 'notest' option for a less verbose dump.
- log
Used for debugging. If true, writes out the gnuplot commands and various
progress logs to STDERR in addition to writing to a gnuplot process. This is NOT
a dry run: data is sent to gnuplot AND to the log. Useful for debugging I/O
issues. Note that this log will contain binary data unless ascii-only plotting
is enabled (see below)
- ascii
If set, ASCII data is passed to gnuplot instead of binary data. Binary is the
default because it is much more efficient (and thus faster). Any time you're
plotting something that isn't just numbers (labels, time/date strings, etc)
ascii communication is required instead. gnuplotlib tries to auto-detect when
this is needed, but sometimes you do have to specify this manually.
- notest
Don't check for failure after each gnuplot command. And don't test all the plot
options before creating the plot. This is generally only useful for debugging or
for more sparse 'dump' functionality.
- wait
When we're done asking gnuplot to make a plot, we ask gnuplot to tell us when
the user closes the interactive plot window that popped up. The python process
will block until the user is done looking at the data. This can also be achieved
by calling the wait() gnuplotlib method or the global gnuplotlib.wait()
function.
** Subplot options
The subplot options are a dictionary, passed as the keyword arguments to the
global plot() function or to the gnuplotlib contructor (when making single
plots) or as the last element in each subplot tuple (when making multiplots).
Default subplot options may be passed-in together with the process options. The
supported keys of this dict are as follows:
- title
Specifies the title of the plot
- 3d
If true, a 3D plot is constructed. This changes the default tuple size from 2 to
3
- _3d
Identical to '3d'. In python, keyword argument keys cannot start with a number,
so '_3d' is accepted for that purpose. Same issue exists with with/_with
- set/unset
Either a string or a list/tuple; if given a list/tuple, each element is used in
separate set/unset command. Example:
plot(..., set='grid', unset=['xtics', 'ytics])
[ turns on the grid, turns off the x and y axis tics ]
This is both a process and a subplot option. If both are given, BOTH are used,
instead of the normal behavior of a subplot option overriding the process option
- cmds
Either a string or a list/tuple; if given a list/tuple, each element is used in
separate command. Arbitrary extra commands to pass to gnuplot before the plots
are created. These are passed directly to gnuplot, without any validation.
This is both a process and a subplot option. If both are given, BOTH are used,
instead of the normal behavior of a subplot option overriding the process option
- with
If no 'with' curve option is given, use this as a default. See the description
of the 'with' curve option for more detail
- _with
Identical to 'with'. In python 'with' is a reserved word so it is illegal to use
it as a keyword arg key, so '_with' exists as an alias. Same issue exists with
3d/_3d
- square, square_xy, square-xy, squarexy
If True, these request a square aspect ratio. For 3D plots, square_xy plots with
a square aspect ratio in x and y, but scales z. square_xy and square-xy and
squarexy are synonyms. In 2D, these are all synonyms. Using any of these in 3D
requires Gnuplot >= 4.4
- {x,x2y,y2,z,cb}{min,max,range,inv}
If given, these set the extents of the plot window for the requested axes.
Either min/max or range can be given but not both. min/max are numerical values.
'*range' is a string 'min:max' with either one allowed to be omitted; it can
also be a [min,max] tuple or list. '*inv' is a boolean that reverses this axis.
If the bounds are known, this can also be accomplished by setting max < min.
Passing in both max < min AND inv also results in a reversed axis.
If no information about a range is given, it is not touched: the previous zoom
settings are preserved.
The y2 axis is the secondary y-axis that is enabled by the 'y2' curve option.
The 'cb' axis represents the color axis, used when color-coded plots are being
generated
- xlabel, x2label, ylabel, zlabel, y2label, cblabel
These specify axis labels
- rgbimage
This should be set to a path containing an image file on disk. The data is then
plotted on top of this image, which is very useful for annotations, computer
vision, etc. Note that when plotting data, the y axis usually points up, but
when looking at images, the y axis of the pixel coordinates points down instead.
Thus, if the y axis extents aren't given and an rgbimage IS specified,
gnuplotlib will flip the y axis to make things look reasonable. If any y-axis
ranges are given, however (with any of the ymin,ymax,yrange,yinv subplot
options), then it is up to the user to flip the axis, if that's what they want.
- equation, equation_above, equation_below
Either a string or a list/tuple; if given a list/tuple, each element is used in
separate equation to plot. These options allows equations represented as formula
strings to be plotted along with data passed in as numpy arrays. See the
"Symbolic equations" section above.
By default, the equations are plotted BEFORE other data, so the data plotted
later may obscure some of the equation. Depending on what we're doing, this may
or may not be what we want. To plot the equations AFTER other data, use
'equation_above' instead of 'equation'. The 'equation_below' option is a synonym
for 'equation'
** Curve options
The curve options describe details of specific curves. They are in a dict, whose
keys are as follows:
- legend
Specifies the legend label for this curve
- with
Specifies the style for this curve. The value is passed to gnuplot using its
'with' keyword, so valid values are whatever gnuplot supports. Read the gnuplot
documentation for the 'with' keyword for more information
- _with
Identical to 'with'. In python 'with' is a reserved word so it is illegal to use
it as a keyword arg key, so '_with' exists as an alias
- axes
One of: ('x1y1','x1y2','x2y1','x2y2'). Specifies which xy axes in the plot
should define this curve. This is the left/right and up/down axes in the plot.
Only applies to 2D plots. Exclusive with the "y2" option
- y2
If true, requests that this curve be plotted on the x1y2 axes instead of the
default x1y1 axes. This is a special case of the "axes" option, and is exclusive
with it
- tuplesize
Described in the "Data arguments" section above. Specifies how many values
represent each data point. For 2D plots this defaults to 2; for 3D plots this
defaults to 3. These defaults are correct for simple plots. For each curve we
expect to get tuplesize separate arrays of data unless any of these are true
- If tuplesize < 0, we expect to get a single numpy array, with each data
tuple in the last dimension. See the "Negative tuplesize" section above for
detail.
- If we receive fewer than tuplesize arrays, we may be using "Implicit
domains". See the "Implicit domains" section above for detail.
- using
Overrides the 'using' directive we pass to gnuplot. No error checking is
performed, and the string is passed to gnuplot verbatim. This option is very
rarely needed. The most common usage is to apply a function to an implicit
domain. For instance, this basic command plots a line (linearly increasing
values) against a linearly-increasing line number::
gp.plot(np.arange(100))
We can plot the same values against the square-root of the line number to get a
parabola:
gp.plot(np.arange(100), using='(sqrt($1)):2')
- histogram
If given and if it evaluates to True, gnuplot will plot the histogram of this
data instead of the data itself. See the "Histograms" section above for more
details. If this curve option is a string, it's expected to be one of the
smoothing style gnuplot understands (see 'help smooth'). Otherwise we assume the
most common style: a frequency histogram. This only makes sense with 2D plots
and tuplesize=1
- binwidth
Used for the histogram support. See the "Histograms" section above for more
details. This sets the width of the histogram bins. If omitted, the width is set
to 1.
* INTERFACE
** class gnuplotlib
A gnuplotlib object abstracts a gnuplot process and a plot window. A basic
non-multiplot invocation:
import gnuplotlib as gp
g = gp.gnuplotlib(subplot_options, process_options)
g.plot( curve, curve, .... )
The subplot options are passed into the constructor; the curve options and the data
are passed into the plot() method. One advantage of making plots this way is
that there's a gnuplot process associated with each gnuplotlib instance, so as
long as the object exists, the plot will be interactive. Calling 'g.plot()'
multiple times reuses the plot window instead of creating a new one.
** global plot(...)
The convenience plotting routine in gnuplotlib. Invocation:
import gnuplotlib as gp
gp.plot( curve, curve, ...., subplot_and_default_curve_options )
Each 'plot()' call reuses the same window.
** global plot3d(...)
Generates 3D plots. Shorthand for 'plot(..., _3d=True)'
** global plotimage(...)
Generates an image plot. Shorthand for 'plot(..., _with='image', tuplesize=3)'
** global wait(...)
Blocks until the user closes the interactive plot window. Useful for python
applications that want blocking plotting behavior. This can also be achieved by
calling the wait() gnuplotlib method or by adding wait=1 to the process options
dict
* RECIPES
Some very brief usage notes appear here. For a tutorial and more in-depth
recipes, please see the guide:
https://github.com/dkogan/gnuplotlib/blob/master/guide/guide.org
** 2D plotting
If we're plotting y-values sequentially (implicit domain), all you need is
plot(y)
If we also have a corresponding x domain, we can plot y vs. x with
plot(x, y)
*** Simple style control
To change line thickness:
plot(x,y, _with='lines linewidth 3')
To change point size and point type:
gp.plot(x,y, _with='points pointtype 4 pointsize 8')
Everything (like _with) feeds directly into Gnuplot, so look at the Gnuplot docs
to know how to change thicknesses, styles and such.
*** Errorbars
To plot errorbars that show y +- 1, plotted with an implicit domain
plot( y, np.ones(y.shape), _with = 'yerrorbars', tuplesize = 3 )
Same with an explicit x domain:
plot( x, y, np.ones(y.shape), _with = 'yerrorbars', tuplesize = 3 )
Symmetric errorbars on both x and y. x +- 1, y +- 2:
plot( x, y, np.ones(x.shape), 2*np.ones(y.shape), _with = 'xyerrorbars', tuplesize = 4 )
To plot asymmetric errorbars that show the range y-1 to y+2 (note that here you
must specify the actual errorbar-end positions, NOT just their deviations from
the center; this is how Gnuplot does it)
plot( y, y - np.ones(y.shape), y + 2*np.ones(y.shape),
_with = 'yerrorbars', tuplesize = 4 )
*** More multi-value styles
Plotting with variable-size circles (size given in plot units, requires Gnuplot >= 4.4)
plot(x, y, radii,
_with = 'circles', tuplesize = 3)
Plotting with an variably-sized arbitrary point type (size given in multiples of
the "default" point size)
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