#!/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)