gnuplotlib: a gnuplot-based plotting backend for numpy
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)*5,
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',
_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=1, binwidth=10000)),
(x*x, dict(histogram='cumulative', y2=1)))
[ A density and cumulative histogram of x^2 are plotted together ]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(plot_options)
g.plot( curve, curve, .... )The global functions consolidate this into a single call:
import gnuplotlib as gp
gp.plot( curve, curve, ...., plot_options_and_default_curve_options )Each plot contains multiple curves (different datasets to be plotted). Plot generation is controlled by two sets of options:
- Plot options: parameters that affect the whole plot, like the title of the plot, the axis labels, the extents, 2D/3D selection, etc. These are passed into the gnuplotlib constructor or appear as keyword arguments in a global plot() call. All the plot options are described below in “Plot options”.
- Curve options: parameters that affect only a single curve. These are given as a python dict after all the numpy arrays in a curve. Each is described below in “Curve options”.
Each curve is passed in as a python tuple (data, data, data, …, curve_options) where each “data” is a numpy array, and “curve_options” is a dict. If we’re plotting a single curve, the tuple can be inlined. Thus 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
gp.plot( (x,y, dict(_with = 'lines')), title = 'parabola')
# Global function. Inlined curves (possible because we have only one curve)
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:
import gnuplotlib as gp
import numpy as np
x = np.arange(10)
y = x*x
z = x*x*x
# Global function
gp.plot( (x,y, dict(_with = 'lines', legend = 'parabola')),
(x,z, dict(_with = 'lines', legend = 'cubic')),
title = 'parabola and cubic')
# Object-oriented function
p = gp.gnuplotlib(title = 'parabola and cubic')
p.plot((x,y, dict(_with = 'lines', legend = 'parabola')),
(x,z, dict(_with = 'lines', legend = 'cubic')),)Another way to plot multiple curves is to use broadcasting; described below.
The plot_and_default_curve_options passed to the global plot(…) calls are keyword arguments. The curve options present here are used as defaults for each curve; these defaults can be overriden in each curve, as desired. For instance:
gp.plot( (x1,y1),
(x2,y2, dict(_with = 'points')),
_with = 'lines')would plot the first curve with lines, but the second with points.
plot3d(…) is equivalent to plot(…, _3d=True)
plotimage(…) is equivalent to plot(…, _with=’image’, tuplesize=3)
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. 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.
Broadcasting 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.
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.
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:
x,y = np.ogrid[-10:11,-10:11]
gp.plot( x**2 + y**2,
title = 'Heat map',
set = 'view map',
_with = 'image',
tuplesize = 3)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).
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 options. These plot options are 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.
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
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’:1 to the plot 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.
The plot options are a dictionary, passed as the keyword arguments to the global plot() function or as the only arguments to the gnuplotlib contructor. 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
These take either a string of a list. If given a string, a set or unset gnuplot command is executed with that argument. If given a list, elements of that list are set/unset separately. Example:
plot(..., set='grid', unset=['xtics', 'ytics])
[ turns on the grid, turns off the x and y axis tics ]- 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
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. Using either of these in 3D requires Gnuplot >= 4.4
- {x,y,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, ylabel, zlabel, y2label
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 plot options), then it is up to the user to flip the axis, if that’s what they want.
- equation, equation_above, equation_below
These options allows equations represented as formula strings to be plotted along with data passed in as numpy arrays. These can be a string (for a single equation) or an array/tuple of strings (for multiple equations). 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’
- hardcopy
Instead of drawing a plot on screen, plot into a file instead. The output filename is the value associated with this key. The output format is inferred from the filename. Currently only eps, ps, pdf, png, svg, gp are supported with some default sets of options. This option is simply a shorthand for the ‘terminal’ and ‘output’ options. If the defaults provided by the ‘hardcopy’ option are insufficient, use ‘terminal’ and ‘output’ manually. 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.
- output
Sets the plot output file. You generally only need to set this if you’re generating a hardcopy, such as a PDF.
There are several gnuplot terminals that are known (at this time) 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 here. 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
- cmds
Arbitrary extra commands to pass to gnuplot before the plots are created. These are passed directly to gnuplot, without any validation. The value is either a string of a list of strings, one per command
- 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). Usually there’s no reason to touch this option. Binary input works for most plots, but not for all of them. An example where binary plotting doesn’t work is ‘with labels’, and gnuplotlib will use ascii there regardless of this setting
- 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.
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
- y2
If true, requests that this curve be plotted on the y2 axis instead of the main y axis
- 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.
A gnuplotlib object abstracts a gnuplot process and a plot window. Invocation:
import gnuplotlib as gp
g = gp.gnuplotlib(plot_options)
g.plot( curve, curve, .... )The plot 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.
The convenience plotting routine in gnuplotlib. Invocation:
import gnuplotlib as gp
gp.plot( curve, curve, ...., plot_and_default_curve_options )Each ‘plot()’ call reuses the same window.
Generates 3D plots. Shorthand for ‘plot(…, _3d=True)’
Generates an image plot. Shorthand for ‘plot(…, _with=’image’, tuplesize=3)’
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 plot options dict
Some different plots appear here. A longer set of demos is given in demos.py.
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)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.
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 )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)
plot(x, y, sizes,
_with = 'points pointtype 7 pointsize variable', tuplesize = 3 )Color-coded points
plot(x, y, colors,
_with = 'points palette', tuplesize = 3 )Variable-size AND color-coded circles. A Gnuplot (4.4.0) quirk makes it necessary to specify the color range here
plot(x, y, radii, colors,
cbmin = mincolor, cbmax = maxcolor,
_with = 'circles palette', tuplesize = 4 )Broadcasting example: the Conchoids of de Sluze. The whole family of curves is generated all at once, and plotted all at once with broadcasting. Broadcasting is also used to generate the labels. Generally these would be strings, but here just printing the numerical value of the parameter is sufficient.
theta = np.linspace(0, 2*np.pi, 1000) # dim=( 1000,)
a = np.arange(-4,3)[:, np.newaxis] # dim=(7,1)
gp.plot( theta,
1./np.cos(theta) + a*np.cos(theta), # broadcasted. dim=(7,1000)
_with = 'lines',
set = 'polar',
square = True,
yrange = [-5,5],
legend = a.ravel() )General style control works identically for 3D plots as in 2D plots.
To plot a set of 3D points, with a square aspect ratio (squareness requires Gnuplot >= 4.4):
plot3d(x, y, z, square = 1)If xy is a 2D array, we can plot it as a height map on an implicit domain
plot3d(xy)Ellipse and sphere plotted together, using broadcasting:
th = np.linspace(0, np.pi*2, 30)
ph = np.linspace(-np.pi/2, np.pi*2, 30)[:,np.newaxis]
x_3d = (np.cos(ph) * np.cos(th)) .ravel()
y_3d = (np.cos(ph) * np.sin(th)) .ravel()
z_3d = (np.sin(ph) * np.ones( th.shape )) .ravel()
gp.plot3d( (x_3d * np.array([[1,2]]).T,
y_3d * np.array([[1,2]]).T,
z_3d,
{ 'legend': np.array(('sphere', 'ellipse'))}),
title = 'sphere, ellipse',
square = True,
_with = 'points')Image arrays plots can be plotted as a heat map:
x,y = np.ogrid[-10:11,-10:11]
gp.plot( x**2 + y**2,
title = 'Heat map',
set = 'view map',
_with = 'image',
tuplesize = 3)Data plotted on top of an existing image. Useful for image annotations.
gp.plot( x, y,
title = 'Points on top of an image',
_with = 'points',
square = 1,
rgbimage = 'image.png')To send any plot to a file, instead of to the screen, one can simply do
plot(x, y,
hardcopy = 'output.pdf')The ‘hardcopy’ option is a shorthand for the ‘terminal’ and ‘output’ options (in all cases except when writing a .gp file; see below). If more control is desired, the latter can be used. For example to generate a PDF of a particular size with a particular font size for the text, one can do
plot(x, y,
terminal = 'pdfcairo solid color font ",10" size 11in,8.5in',
output = 'output.pdf')This command is equivalent to the ‘hardcopy’ shorthand used previously, but the fonts and sizes can be changed.
If we write to a “.gp” file:
plot(x, y,
hardcopy = 'data.gp')then instead of running gnuplot, we create a self-plotting file. gnuplot is invoked when we execute that file.
A simple wrapper around class gnuplotlib
SYNOPSIS
>>> import numpy as np
>>> import gnuplotlib as gp
>>> x = np.linspace(-5,5,100)
>>> gp.plot( x, np.sin(x) )
[ graphical plot pops up showing a simple sinusoid ]
>>> gp.plot( (x, np.sin(x), {'with': 'boxes'}),
... (x, np.cos(x), {'legend': 'cosine'}),
... _with = 'lines',
... terminal = 'dumb 80,40',
... unset = 'grid')
[ ascii plot printed on STDOUT]
1 +-+---------+----------+-----------+-----------+----------+---------+-+
+ +|||+ + + +++++ +++|||+ + +
| |||||+ + + +|||||| cosine +-----+ |
0.8 +-+ |||||| + + ++||||||+ +-+
| ||||||+ + ++||||||||+ |
| ||||||| + ++||||||||| |
| |||||||+ + ||||||||||| |
0.6 +-+ |||||||| + +||||||||||+ +-+
| ||||||||+ | ++||||||||||| |
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0.4 +-+ ||||||||| | ++||||||||||||+ +-+
| ||||||||| + +|||||||||||||| |
| |||||||||+ + ||||||||||||||| |
| ||||||||||+ | ++||||||||||||||+ + |
0.2 +-+ ||||||||||| + ||||||||||||||||| + +-+
| ||||||||||| | +||||||||||||||||+ | |
| ||||||||||| + |||||||||||||||||| + |
0 +-+ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +-+
| + ||||||||||||||||||+ | ++|||||||||| |
| | +||||||||||||||||| + ||||||||||| |
| + ++|||||||||||||||| | +|||||||||| |
-0.2 +-+ + ||||||||||||||||| + ||||||||||| +-+
| | ++||||||||||||||+ | ++||||||||| |
| + ||||||||||||||| + ++|||||||| |
| | +|||||||||||||| + ||||||||| |
-0.4 +-+ + ++||||||||||||+ | +|||||||| +-+
| + ||||||||||||| + ||||||||| |
| | +|||||||||||+ + ++||||||| |
-0.6 +-+ + ++|||||||||| | +||||||| +-+
| + ||||||||||| + ++|||||| |
| + +|||||||||+ + ||||||| |
| + ++|||||||| + +++||||| |
-0.8 +-+ + + ++||||||+ + + +||||| +-+
| + + +|||||| + + ++|||| |
+ + + ++ ++|||++ + + ++ + + ++||| +
-1 +-+---------+----------+-----------+-----------+----------+---------+-+
-6 -4 -2 0 2 4 6
DESCRIPTION
class gnuplotlib provides full power and flexibility, but for simple plots this wrapper is easier to use. plot() uses a global instance of class gnuplotlib, so only a single plot can be made by plot() at a time: the one plot window is reused.
Data is passed to plot() in exactly the same way as when using class gnuplotlib. The kwargs passed to this function are a combination of curve options and plot options. The curve options passed here are defaults for all the curves. Any specific options specified in each curve override the defaults given in the kwargs.
See the documentation for class gnuplotlib for full details.
A simple wrapper around class gnuplotlib to make 3d plots
SYNOPSIS
import numpy as np
import gnuplotlib as gp
th = np.linspace(0,10,1000)
x = np.cos(np.linspace(0,10,1000))
y = np.sin(np.linspace(0,10,1000))
gp.plot3d( x, y, th )
[ an interactive, graphical plot of a spiral pops up]DESCRIPTION
class gnuplotlib provides full power and flexibility, but for simple 3d plots this wrapper is easier to use. plot3d() simply calls plot(…, _3d=True). See the documentation for plot() and class gnuplotlib for full details.
A simple wrapper around class gnuplotlib to plot image maps
SYNOPSIS
import numpy as np
import gnuplotlib as gp
x,y = np.ogrid[-10:11,-10:11]
gp.plotimage( x**2 + y**2,
title = 'Heat map')DESCRIPTION
class gnuplotlib provides full power and flexibility, but for simple image-map plots this wrapper is easier to use. plotimage() simply calls plot(…, _with=’image’, tuplesize=3). See the documentation for plot() and class gnuplotlib for full details.
Waits until the open interactive plot window is closed
SYNOPSIS
import numpy as np
import gnuplotlib as gp
gp.plot(np.arange(5))
# interactive plot pops up
gp.wait()
# We get here when the user closes the plot windowDESCRIPTION
This applies to the global gnuplotlib object.
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
Python 2 and Python 3 should both be supported. Please report a bug if either one doesn’t work.
https://github.com/dkogan/gnuplotlib
Dima Kogan <dima@secretsauce.net>
Copyright 2015-2018 Dima Kogan.
This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License (version 3 or higher) as published by the Free Software Foundation