#!/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( *nps.mv(image, -1,0), 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) 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 Let's plot 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() ) ** 3D plotting 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', _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') ** Hardcopies To send any plot to a file, instead of to the screen, one can simply do plot(x, y, hardcopy = 'output.pdf') For common output formats, the gnuplot terminal is inferred the filename. If this isn't possible or if we want to tightly control the output, the 'terminal' plot option can be given explicitly. 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', hardcopy = 'output.pdf') This command is equivalent to the 'hardcopy' shorthand used previously, but the fonts and sizes have been 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. ''' from __future__ import print_function import subprocess import time import sys import os import re import select import numbers import numpy as np import numpysane as nps gnuplot_executable='gnuplot' # setup.py assumes the version is a simple string in '' quotes __version__ = '0.46' # In a multiplot, the "process" options apply to the larger plot containing all # the subplots, and the "subplot" options apply to each invididual plot. # # In a "normal" plot (not multiplot), the plot options are a union of the # process and subplot options. There's exactly one subplot knownProcessOptions = frozenset(('cmds', # both process and subplot 'set', # both process and subplot 'unset', # both process and subplot 'dump', 'ascii', 'log', 'notest', 'wait', 'hardcopy', 'terminal', 'output', 'multiplot')) knownSubplotOptions = frozenset(('cmds', # both process and subplot 'set', # both process and subplot 'unset', # both process and subplot '3d', 'square', 'square_xy', 'square-xy', 'squarexy', 'title', 'with', # both a plot option and a curve option 'rgbimage', 'equation', 'equation_above', 'equation_below', 'xmax', 'xmin', 'xrange', 'xinv', 'xlabel', 'x2max', 'x2min', 'x2range', 'x2inv', 'x2label', 'ymax', 'ymin', 'yrange', 'yinv', 'ylabel', 'y2max', 'y2min', 'y2range', 'y2inv', 'y2label', 'zmax', 'zmin', 'zrange', 'zinv', 'zlabel', 'cbmax', 'cbmin', 'cbrange', 'cblabel')) knownCurveOptions = frozenset(( 'with', # both a plot option and a curve option 'legend', 'axes', 'y2', 'tuplesize', 'using', 'histogram', 'binwidth')) knownInteractiveTerminals = frozenset(('x11', 'wxt', 'qt', 'aquaterm')) knownAxes = frozenset(('x1y1','x1y2','x2y1','x2y2')) keysAcceptingIterable = frozenset(('cmds','set','unset','equation','equation_below','equation_above')) # when testing plots with ASCII i/o, this is the unit of test data testdataunit_ascii = 10 def _getGnuplotFeatures(): # Be careful talking to gnuplot here. If you use a tty then gnuplot messes # with the tty settings where it should NOT. For example it turns on the # local echo. So make sure to not use a tty. Furthermore, I turn off the # DISPLAY. I'm not actually plotting anything, so a DISPLAY can try to # X-forward and be really slow pointlessly # I pass in the current environment, but with DISPLAY turned off env = os.environ.copy() env['DISPLAY'] = '' # first, I run 'gnuplot --help' to extract all the cmdline options as features try: helpstring = subprocess.check_output([gnuplot_executable, '--help'], stderr=subprocess.STDOUT, env=env).decode() except FileNotFoundError: print("Couldn't run gnuplot. Is it installed? Is it findable in the PATH?", file=sys.stderr) raise features = set( re.findall(r'--([a-zA-Z0-9_]+)', helpstring) ) # then I try to set a square aspect ratio for 3D to see if it works equal_3d_works = True try: out = subprocess.check_output((gnuplot_executable, '-e', "set view equal"), stderr=subprocess.STDOUT, env=env).decode() if re.search(r"(undefined variable)|(unrecognized option)", out, re.I): equal_3d_works = False except: equal_3d_works = False if equal_3d_works: features.add('equal_3d') return frozenset(features) features = _getGnuplotFeatures() def _normalize_options_dict(d): r'''Normalizes a dict of options to handle human-targeted conveniences The options we accept allow some things that make life easier for humans, but complicate it for computers. This function takes care of these. It ingests a dict passed-in by the user, and outputs a massaged dict with these changes: - All keys that start with an '_' are renamed to omit the '_' - All keys that accept either an iterable or a value (those in keysAcceptingIterable) are converted to always contain an iterable - Any keys with a value of None or (None,) are removed: checking for a value of None ends up being identical to checking for the existence of a value - Similarly, any iterable-supporting keys with [] or () are removed ''' d2 = {} for key in d: add_plot_option(d2, key, d[key]) return d2 class GnuplotlibError(Exception): def __init__(self, err): self.err = err def __str__(self): return self.err def _data_dump_only(processOptions): '''Returns True if we're dumping a script, NOT actually running gnuplot''' def is_gp(): h = processOptions.get('hardcopy') return \ type(h) is str and \ re.match(r".*\.gp$", h) return \ processOptions.get('dump') or \ processOptions.get('terminal') == 'gp' or \ is_gp() def is_knownInteractiveTerminal(t): # I check the first word in the terminal string. This is the terminal type. # Everything else is options return t.split(maxsplit=1)[0] in knownInteractiveTerminals def _split_dict(d, *keysets): r'''Given a dict and some sets of keys, split into sub-dicts with keys Can be used to split a combined plot/curve options dict into separate dicts. If an option exists in multiple sets, the first matching one is used. If an option does not appear in ANY of the given sets, I barf ''' dicts = [{} for _ in keysets] for k in d: for i in range(len(keysets)): keyset,setname = keysets[i] if k in keyset: dicts[i][k] = d[k] break else: # k not found in any of the keysets raise GnuplotlibError("Option '{}' not not known in any '{}' options sets". \ format(k, [kn[1] for kn in keysets])) return dicts def _get_cmds__setunset(cmds,options): for setunset in ('set', 'unset'): if setunset in options: cmds += [ setunset + ' ' + setting for setting in options[setunset] ] def _massageProcessOptionsAndGetCmds(processOptions): r'''Compute commands to set the given process options, and massage the input, as needed ''' for option in processOptions: if not option in knownProcessOptions: raise GnuplotlibError(option + ' is not a valid process option') cmds = [] _get_cmds__setunset(cmds, processOptions) # "hardcopy" and "output" are synonyms. Use "output" from this point on if processOptions.get('hardcopy') is not None: if processOptions.get('output') is not None: raise GnuplotlibError("Pass in at most ONE of 'hardcopy' and 'output'") processOptions['output'] = processOptions['hardcopy'] del processOptions['hardcopy'] if processOptions.get('output') is not None and \ processOptions.get('terminal') is None: outputfile = processOptions['output'] m = re.search(r'\.(eps|ps|pdf|png|svg|gp)$', outputfile) if not m: raise GnuplotlibError("Only .eps, .ps, .pdf, .png, .svg and .gp output filenames are supported if no 'terminal' plot option is given") outputfileType = m.group(1) terminalOpts = { 'eps': 'postscript noenhanced solid color eps', 'ps': 'postscript noenhanced solid color landscape 12', 'pdf': 'pdfcairo noenhanced solid color font ",12" size 8in,6in', 'png': 'pngcairo noenhanced size 1024,768 transparent crop font ",12"', 'svg': 'svg noenhanced solid dynamic size 800,600 font ",14"', 'gp': 'gp'} processOptions['terminal'] = terminalOpts[outputfileType] if processOptions.get('terminal') is not None: if is_knownInteractiveTerminal(processOptions['terminal']): # known interactive terminal if processOptions.get('output', '') != '': sys.stderr.write("Warning: requested a known-interactive gnuplot terminal AND an output file. Is this REALLY what you want?\n") if processOptions['terminal'] == 'gp': processOptions['dump' ] = 1 processOptions['notest'] = 1 if 'cmds' in processOptions: cmds += processOptions['cmds'] return cmds def _massageSubplotOptionsAndGetCmds(subplotOptions): r'''Compute commands to set the given subplot options, and massage the input, as needed ''' for option in subplotOptions: if not option in knownSubplotOptions: raise GnuplotlibError('"{}" is not a valid subplot option'.format(option)) # set some defaults # plot with lines and points by default if not 'with' in subplotOptions: subplotOptions['with'] = 'linespoints' # make sure I'm not passed invalid combinations of options # At most 1 'square...' option may be given Nsquare = 0 for opt in ('square', 'square_xy', 'square-xy', 'squarexy'): if subplotOptions.get(opt): Nsquare += 1 if Nsquare > 1: raise GnuplotlibError("At most 1 'square...' option could be enabled. Instead I got {}".format(Nsquare)) # square_xy and square-xy and squarexy are synonyms. Map all these to # square_xy if subplotOptions.get('square-xy') or subplotOptions.get('squarexy'): subplotOptions['square_xy'] = True if subplotOptions.get('3d'): if 'y2min' in subplotOptions or 'y2max' in subplotOptions: raise GnuplotlibError("'3d' does not make sense with 'y2...'") if not 'equal_3d' in features and \ ( subplotOptions.get('square_xy') or subplotOptions.get('square') ): sys.stderr.write("Your gnuplot doesn't support square aspect ratios for 3D plots, so I'm ignoring that\n") if 'square_xy' in subplotOptions: del subplotOptions['square_xy'] if 'square' in subplotOptions: del subplotOptions['square' ] else: # In 2D square_xy is the same as square if subplotOptions.get('square_xy'): subplotOptions['square'] = True # grid on by default cmds = ['set grid'] # set the curve labels for axis in ('x', 'x2', 'y', 'y2', 'z', 'cb'): if axis + 'label' in subplotOptions: cmds.append('set {axis}label "{label}"'.format(axis = axis, label = subplotOptions[axis + 'label'])) have_set_range = set() for axis in ('x', 'x2', 'y', 'y2', 'z', 'cb'): if subplotOptions.get('set') and \ any ( re.match(rf" *{axis}range[\s=]", s) for s in subplotOptions['set'] ): have_set_range.add(axis) elif subplotOptions.get('cmds') and \ any ( re.search(rf"^ *set +{axis}range[\s=]", s, flags=re.M) for s in subplotOptions.get('cmds') ): have_set_range.add(axis) have_set_link = set() for axis in ('x','y'): if subplotOptions.get('set') and \ any ( re.match(rf" *link +{axis}", s) for s in subplotOptions['set'] ): have_set_link.add(axis) elif subplotOptions.get('cmds') and \ any ( re.search(rf"^ *set +link +{axis}", s, flags=re.M) for s in subplotOptions.get('cmds') ): have_set_link.add(axis) # set the plot bounds ranges = dict() ranges_default = dict() for axis in ('x', 'x2', 'y', 'y2', 'z', 'cb'): # I deal with range bounds here. These can be given for the various # axes by variables (W-axis here; replace W with x, y, z, etc): # # Wmin, Wmax, Winv, Wrange # # Wrange is mutually exclusive with Wmin and Wmax. Winv turns # reverses the direction of the axis. This can also be achieved by # passing in Wmin>Wmax or Wrange[0]>Wrange[1]. If this is done then # Winv has no effect, i.e. setting Wmin>Wmax AND Winv results in a # flipped axis. # This axis was set up with the 'set' plot option, so I don't touch # it if axis in have_set_range: ranges_default[axis] = False continue # images generally have the origin at the top-left instead of the # bottom-left, so given nothing else, I flip the y axis if 'rgbimage' in subplotOptions and \ axis == 'y' and \ not any ( ('y'+what) in subplotOptions \ for what in ('min','max','range','inv')): ranges[axis] = "set yrange [:] reverse" ranges_default[axis] = True continue opt_min = subplotOptions.get( axis + 'min' ) opt_max = subplotOptions.get( axis + 'max' ) opt_range = subplotOptions.get( axis + 'range' ) opt_inv = subplotOptions.get( axis + 'inv' ) if (opt_min is not None or opt_max is not None) and opt_range is not None: raise GnuplotlibError("{0}min/{0}max and {0}range are mutually exclusive".format(axis)) # if we have a range, copy it to min/max and just work with those if opt_range is not None: if not isinstance(opt_range, (list, tuple)): opt_range = [ None if x == '*' else float(x) for x in opt_range.split(':')] if len(opt_range) != 2: raise GnuplotlibError('{}range should have exactly 2 elements'.format(axis)) opt_min,opt_max = opt_range opt_range = None # apply the axis inversion. It's only needed if we're given both # bounds and they aren't flipped if opt_inv: if opt_min is not None and opt_max is not None and opt_min < opt_max: opt_min,opt_max = opt_max,opt_min ranges[axis] = \ "set {}range [{}:{}] {}reverse". \ format(axis, '*' if opt_min is None else opt_min, '*' if opt_max is None else opt_max, '' if opt_inv else 'no') ranges_default[axis] = \ opt_min is None and \ opt_max is None and \ opt_inv is None # If we have linked axes, do NOT set the default axis parameters, since the # other axis will control it. Otherwise we hit # https://sourceforge.net/p/gnuplot/bugs/2783/ and surprising behaviors can # be triggered for axis in ('x','y'): axis2 = f"{axis}2" if axis in have_set_link: if axis in ranges and not ranges_default[axis ] and \ axis2 in ranges and not ranges_default[axis2]: raise GnuplotlibError(f"We're linking {axis},{axis2} so the range of at most one of these may be given") if axis in ranges and ranges_default[axis ]: del ranges[axis] if axis2 in ranges and ranges_default[axis2]: del ranges[axis2] cmds.extend(ranges.values()) # set the title if 'title' in subplotOptions: cmds.append('set title "' + subplotOptions['title'] + '"') # handle a requested square aspect ratio # set a square aspect ratio. Gnuplot does this differently for 2D and 3D plots if subplotOptions.get('3d'): if subplotOptions.get('square'): cmds.append("set view equal xyz") elif subplotOptions.get('square_xy'): cmds.append("set view equal xy") else: if subplotOptions.get('square'): cmds.append("set size ratio -1") _get_cmds__setunset(cmds, subplotOptions) if 'cmds' in subplotOptions: cmds += subplotOptions['cmds'] return cmds class gnuplotlib: def __init__(self, **plotOptions): # some defaults self._dumpPipe = None self.t0 = time.time() self.checkpoint_stuck = False self.sync_count = 1 plotOptions = _normalize_options_dict(plotOptions) self.curveOptions_base,self.subplotOptions_base,self.processOptions = \ _split_dict(plotOptions, (knownCurveOptions, 'curve'), (knownSubplotOptions, 'subplot'), (knownProcessOptions, 'process')) self.processOptionsCmds = _massageProcessOptionsAndGetCmds(self.processOptions) if _data_dump_only(self.processOptions): self.gnuplotProcess = None self.terminal_default = 'x11' else: # if we already have a gnuplot process, reset it. Otherwise make a new # one if hasattr(self, 'gnuplotProcess') and self.gnuplotProcess: self._printGnuplotPipe( "unset multiplot\nreset\nset output\n" ) self._checkpoint() else: self.gnuplotProcess = None self._startgnuplot() self._logEvent("_startgnuplot() finished") def _startgnuplot(self): self._logEvent("_startgnuplot()") cmd = [gnuplot_executable] # I dup the handle to standard output. The main use for this is the dumb # terminal. I want it to write to the console. Normally "set dumb" # writes to gnuplot's stdout, which normally IS the console. But when # talking to gnuplotlib, gnuplot's stdout is my control pipe. So when # using the dumb terminal I tell gnuplot to write to python's stdout try: self.fdDupSTDOUT = os.dup(sys.stdout.fileno()) except: self.fdDupSTDOUT = None # I need this to make fdDupSTDOUT available to the child gnuplot. This # would happen by default, but in python3 I need to do this extra thing # for some reason. And it's a new thing that didn't exist in python2, so # I need to explicitly allow this to fail in python2 if self.fdDupSTDOUT is not None: try: os.set_inheritable(self.fdDupSTDOUT, True) except AttributeError: pass self.gnuplotProcess = \ subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, # required to "autoflush" writes bufsize=0, # I need this to make fdDupSTDOUT available to the # child gnuplot. close_fds=False was default in # python2, but was changed in python3 close_fds = False, # This was helpful in python3 to implicitly # encode() strings, but it broke the # select()/read() mechanism: select() would # look at the OS file descriptor, but read() # would look at some buffer, so you'd get into # a situation where # # - data was read from the OS into a buffer, and is available to be read() # - select() blocks waiting for MORE data # # I guess I leave this off and manully # encode/decode everything #encoding = 'utf-8', ) # What is the default terminal? self._printGnuplotPipe( "show terminal\n" ) errorMessage, warnings = self._checkpoint('printwarnings') m = re.match(r"terminal type is +(.+?) +", errorMessage, re.I) if m: self.terminal_default = m.group(1) else: self.terminal_default = None # save the default terminal self._safelyWriteToPipe("set terminal push", 'terminal') def __del__(self): if hasattr(self, 'gnuplotProcess') and self.gnuplotProcess: try: self.gnuplotProcess.terminate() except: pass try: # Sometimes I see the terminate() call do nothing, and # explicitly asking gnuplot to exit is needed self._printGnuplotPipe('exit\n') self.gnuplotProcess.wait() except: pass self.gnuplotProcess = None if self.fdDupSTDOUT is not None: # When running inside IPython I sometimes see "os" set to None # at exit for some reason, so I let that fail silently try: os.close(self.fdDupSTDOUT) except: pass self.fdDupSTDOUT = None def _safelyWriteToPipe(self, input, flags=''): def barfOnDisallowedCommands(line): # I use STDERR as the backchannel, so I don't allow any "set print" # commands, since those can disable that if re.match(r'''(?: .*;)? # optionally wait for a semicolon \s* set\s+print\b''', line, re.X): raise GnuplotlibError("Please don't 'set print' since I use gnuplot's STDERR for error detection") if re.match(r'''(?: .*;)? # optionally wait for a semicolon \s* print\b''', line, re.X): raise GnuplotlibError("Please don't ask gnuplot to 'print' anything since this can confuse my error detection") if re.match(r'''(?: .*;)? # optionally wait for a semicolon \s* set\s+terminal\b''', line, re.X) and flags != 'terminal': raise GnuplotlibError("Please do not 'set terminal' manually. Use the 'terminal' plot option instead") if re.match(r'''(?: .*;)? # optionally wait for a semicolon \s* set\s+output\b''', line, re.X) and not re.match('output', flags): raise GnuplotlibError("Please do not 'set output' manually. Use the 'output' plot option instead") if not isinstance(input, (list,tuple)): input = (input,) for cmd in input: barfOnDisallowedCommands(cmd) self._printGnuplotPipe( cmd + '\n' ) errorMessage, warnings = self._checkpoint('printwarnings') if errorMessage: barfmsg = "Gnuplot error: '\n{}\n' while sending cmd '{}'\n".format(errorMessage, cmd) if warnings: barfmsg += "Warnings:\n" + str(warnings) raise GnuplotlibError(barfmsg) def _gnuplotStdin(self): if self.gnuplotProcess: return self.gnuplotProcess.stdin # In python2 I just return stdout. But the python3 people have no idea # what they're doing. The normal pipe return by Popen is a FileIO, so I # can ONLY write bytes to it; if I write a string to it, it barfs. So I # normally need to do the encode/decode dance. But sys.stdout is a # TextIOWrapper, which means that I must write STRINGS and it'll barf if # I write bytes. I can apparently reach inside and grab the # corresponding FileIO object to make it work like the pipe, so I do # that # debug dump. I return stdout if self._dumpPipe: try: return self._dumpPipe.buffer.raw except: return self._dumpPipe try: return sys.stdout.buffer.raw except: return sys.stdout def _printGnuplotPipe(self, string): self._gnuplotStdin().write( string.encode() ) self._logEvent("Sent to child process {} bytes ==========\n{}=========================". format(len(string), string)) def _receive_until_checkpoint_or_timeout(self, checkpoint, waitforever): fromerr = '' while not fromerr.endswith(checkpoint): # if no data received in 5 seconds, the gnuplot process is stuck. This # usually happens if the gnuplot process is not in a command mode, but in # a data-receiving mode. I'm careful to avoid this situation, but bugs in # this module and/or in gnuplot itself can make this happen self._logEvent("Trying to read byte from gnuplot") rlist,wlist,xlist = select.select([self.gnuplotProcess.stderr],[], [], None if waitforever else 15) if not rlist: self._logEvent("Gnuplot read timed out") self.checkpoint_stuck = True raise GnuplotlibError( r'''Gnuplot process no longer responding. This shouldn't happen... Is your X connection working?''') # read a byte. I'd like to read "as many bytes as are # available", but I don't know how to this in a very portable # way (I just know there will be windows users complaining if I # simply do a non-blocking read). Very little data will be # coming in anyway, so doing this a byte at a time is an # irrelevant inefficiency byte = self.gnuplotProcess.stderr.read(1) if len(byte) == 0: # Did the child process die? returncode = self.gnuplotProcess.poll() if returncode is not None: # Yep. It died. raise Exception(f"gnuplot child died. returncode = {returncode}") self._logEvent("read() returned no data") continue byte = byte.decode() fromerr += byte self._logEvent("Read byte '{}' ({}) from gnuplot child process".format(byte, hex(ord(byte)))) self._logEvent(f"Read string from gnuplot: '{fromerr}'") return fromerr # syncronizes the child and parent processes. After _checkpoint() returns, I # know that I've read all the data from the child. Extra data that represents # errors is returned. Warnings are explicitly stripped out def _checkpoint(self, flags=''): if _data_dump_only(self.processOptions): # There is no child process. There's nothing to checkpoint return None, None # I have no way of knowing if the child process has sent its error data # yet. It may be that an error has already occurred, but the message hasn't # yet arrived. I thus print out a checkpoint message and keep reading the # child's STDERR pipe until I get that message back. Any errors would have # been printed before this waitforever = re.search(r'waitforever', flags) final = re.search(r'final', flags) printwarnings = re.search(r'printwarnings', flags) ignore_known_test_failures = re.search(r'ignore_known_test_failures', flags) # I always checkpoint() before exiting. Even if notest==1. Without this # 'set terminal dumb' plots don't end up rendering anything: we kill the # process before it has time to make the plot if self.processOptions.get('notest') and not waitforever and not final and not printwarnings: return None, None checkpoint = f"gpsync{self.sync_count}xxx" self.sync_count += 1 self._printGnuplotPipe( 'print "{}"\n'.format(checkpoint) ) # if no error pipe exists, we can't check for errors, so we're done. # Usually happens if(we're dumping) if not self.gnuplotProcess or not self.gnuplotProcess.stderr: return '',[] fromerr = self._receive_until_checkpoint_or_timeout(checkpoint, waitforever) m = re.search(rf'\s*(.*?)\s*{checkpoint}$', fromerr, re.M + re.S) if m is None: raise Exception(f"checkpoint '{checkpoint}' not found in received string '{fromerr}'") fromerr = m.group(1) warningre = re.compile(r'^\s*(.*?(?:warning|undefined).*?)\s*$', re.M + re.I) warnings = warningre.findall(fromerr) if printwarnings: for w in warnings: sys.stderr.write("Gnuplot warns: {}\n".format(w)) # if asked, ignore and get rid of all the errors known to happen during # plot-command testing. These include # # 1. "invalid command" errors caused by the test data being sent to gnuplot # as a command. The plot command itself will never be invalid, so this # doesn't actually mask out any errors # # 2. "invalid range" and "Terminal canvas area too small to hold plot" # errors caused by the data or labels being out of bounds. The point # of the plot-command testing is to make sure the command is valid, # so any out-of-boundedness of the test data is irrelevant # # 3. image grid complaints if ignore_known_test_failures: r = re.compile(r'''^gnuplot>\s*(?:{}|e\b).*$ # report of the actual invalid command \n^\s+\^\s*$ # ^ mark pointing to where the error happened \n^.*invalid\s+command.*$''' # actual 'invalid command' complaint .format(testdataunit_ascii), re.X + re.M) fromerr = r.sub('', fromerr) # ignore a simple 'invalid range' error observed when, say only the # xmin bound is set and all the data is below it r = re.compile(r'''^gnuplot>\s*plot.*$ # the test plot command \n^\s+\^\s*$ # ^ mark pointing to where the error happened \n^.*range\s*is\s*invalid.*$''', # actual 'invalid range' complaint re.X + re.M) fromerr = r.sub('', fromerr) # fancier plots show a different 'invalid range' error. Observed when xmin # > xmax (inverted x axis) and when there's out-of-bounds data r = re.compile(r'''^gnuplot>\s*plot.*$ # the test plot command \n^\s+\^\s*$ # ^ mark pointing to where the error happened \n^.*all\s*points.*undefined.*$''', # actual 'all points undefined' complaint re.X + re.M) fromerr = r.sub('', fromerr) # Newer gnuplot sometimes says 'x_min should not equal x_max!' when # complaining about ranges. Ignore those here r = re.compile(r'^.*_min should not equal .*_max!.*$', # actual 'min != max' complaint re.M) fromerr = r.sub('', fromerr) # Labels or titles that are too long can complain about stuff being # too small to hold plot r = re.compile(r'''^.*too small to hold plot.*$''', re.M) fromerr = r.sub('', fromerr) r = re.compile(r'''^.*Check plot boundary.*$''', re.M) fromerr = r.sub('', fromerr) # 'with image' plots can complain about an uninteresting domain. Exact error: # GNUPLOT (plot_image): Image grid must be at least 4 points (2 x 2). r = re.compile(r'^.*Image grid must be at least.*$', re.X + re.M) fromerr = r.sub('', fromerr) # I've now read all the data up-to the checkpoint. Strip out all the warnings fromerr = warningre.sub('',fromerr) fromerr = fromerr.strip() return fromerr, warnings def _logEvent(self, event): # only log when asked if not self.processOptions.get('log'): return t = time.time() - self.t0 print( "==== PID {} at t={:.4f}: {}".format(self.gnuplotProcess.pid if self.gnuplotProcess else '(none)', t, event), file=sys.stderr ) def _plotCurveInASCII(self, curve): '''Should this curve be plotted in ascii? Mostly this just looks at the plot-level setting. But 'with labels' is an exception: such curves are ascii-only ''' return \ self.processOptions.get('ascii') or \ ( curve.get('with') and re.match(" *labels\\b", curve['with'], re.I) ) def _sendCurve(self, curve): pipe = self._gnuplotStdin() if self._plotCurveInASCII(curve): if curve.get('matrix'): np.savetxt(pipe, nps.glue(*curve['_data'], axis=-2).astype(np.float64,copy=False), '%s') self._printGnuplotPipe( "\ne\n" ) else: # Previously I was doing this: # np.savetxt( pipe, # nps.glue(*curve['_data'], axis=-2).transpose().astype(np.float64,copy=False), # '%s' ) # # That works in most cases, but sometimes we have disparate data # types in each column, so glueing the components together into # a single array is impossible (most notably when plotting 'with # labels' at some particular locations). Thus I loop myself # here. This is slow, but if we're plotting in ascii, we # probably aren't looking for maximal performance here. And # 'with labels' isn't super common Ncurves = len(curve['_data']) def write_element(e): r'''Writes value to pipe. Encloses strings in "". This is required to support labels with spaces in them ''' # Numpy 2 broke this (no more np.string_), and this extra # code is needed to work with both numpy 2 and numpy 1 try: is_string = type(e) is np.string_ except: is_string = False try: is_bytes = type(e) is np.bytes_ except: is_bytes = False if is_string or is_bytes or type(e) is np.str_: pipe.write(b'"') pipe.write(str(e).encode()) pipe.write(b'"') else: pipe.write(str(e).encode()) for i in range(curve['_data'][0].shape[-1]): for j in range(Ncurves-1): write_element(curve['_data'][j][i]) pipe.write(b' ') write_element(curve['_data'][Ncurves-1][i]) pipe.write(b'\n') self._printGnuplotPipe( "e\n" ) else: nps.mv(nps.cat(*curve['_data']), 0, -1).astype(np.float64,copy=False).tofile(pipe) self._logEvent("Sent the data to child process (not logged)") def _getPlotCmd(self, curves, subplotOptions): def optioncmd(curve): cmd = '' if 'legend' in curve: cmd += 'title "{}" '.format(curve['legend']) else: cmd += 'notitle ' # use the given per-curve 'with' style if there is one. Otherwise fall # back on the global _with = curve['with'] if 'with' in curve else subplotOptions['with'] if _with: cmd += "with {} ".format(_with) axes = curve.get('axes') if axes is not None: cmd += f"axes {axes} " return cmd def binaryFormatcmd(curve): # I make 2 formats: one real, and another to test the plot cmd, in case it # fails tuplesize = curve['tuplesize'] fmt = '' if curve.get('matrix'): fmt += 'binary array=({},{})'.format(curve['_data'][0].shape[-1], curve['_data'][0].shape[-2]) fmt += ' format="' + ('%double' * (tuplesize-2)) + '"' else: fmt += 'binary record=' + str(curve['_data'][0].shape[-1]) fmt += ' format="' + ('%double' * tuplesize) + '"' # when doing fancy things, gnuplot can get confused if I don't # explicitly tell it the tuplesize. It has its own implicit-tuples # logic that I don't want kicking in. For instance, 3d matrix plots # with image do not work in binary without 'using': using_Ncolumns = tuplesize if curve.get('matrix'): using_Ncolumns -= 2 using = curve.get('using') if using is None: using = ':'.join(str(x+1) for x in range(using_Ncolumns)) fmt += ' using ' + using # to test the plot I plot a single record fmtTest = fmt fmtTest = re.sub(r'record=\d+', 'record=1', fmtTest) fmtTest = re.sub(r'array=\(\d+,\d+\)', 'array=(2, 2)', fmtTest) return fmt,fmtTest def getTestDataLen(curve): # assuming sizeof(double)==8 if curve.get('matrix'): return 8 * 2*2*(curve['tuplesize']-2) return 8 * curve['tuplesize'] basecmd = '' if any( curve.get('axes','xxx')[-2:] == 'y2' for curve in curves ): basecmd += "set ytics nomirror\n" basecmd += "set y2tics\n" if any( curve.get('axes','xxx')[:2] == 'x2' for curve in curves ): basecmd += "set xtics nomirror\n" basecmd += "set x2tics\n" binwidth = None for curve in curves: if curve.get('histogram'): binwidth = 1 # default. Used if nothing else is specified if curve.get('binwidth'): binwidth = curve['binwidth'] break if binwidth is not None: basecmd += \ "set boxwidth {w}\nhistbin(x) = {w} * floor(0.5 + x/{w})\n".format(w=binwidth) if subplotOptions.get('3d'): basecmd += 'splot ' else: basecmd += 'plot ' plotCurveCmdsNonDataBefore = [] plotCurveCmdsNonDataAfter = [] plotCurveCmds = [] plotCurveCmdsMinimal = [] # same as above, but with a single data point per plot only # send all pre-data equations def set_equation(equation, cmds): if equation in subplotOptions: cmds += subplotOptions[equation] set_equation('equation', plotCurveCmdsNonDataBefore) set_equation('equation_below', plotCurveCmdsNonDataBefore) if 'rgbimage' in subplotOptions: if not os.access (subplotOptions['rgbimage'], os.R_OK) or \ not os.path.isfile(subplotOptions['rgbimage']): raise GnuplotlibError("Requested image '{}' is not a readable file".format(subplotOptions['rgbimage'])) plotCurveCmdsNonDataBefore.append('"{0}" binary filetype=auto flipy with rgbimage title "{0}"'.format(subplotOptions['rgbimage'])) testData = '' # data to make a minimal plot for curve in curves: optioncmds = optioncmd(curve) plot_pipe_name = '-' if not self._plotCurveInASCII(curve): # I get 2 formats: one real, and another to test the plot cmd, in case it # fails. The test command is the same, but with a minimal point count. I # also get the number of bytes in a single data point here formatFull,formatMinimal = binaryFormatcmd(curve) Ntestbytes_here = getTestDataLen(curve) plotCurveCmds .append( f"'{plot_pipe_name}' {formatFull} {optioncmds}" ) plotCurveCmdsMinimal.append( f"'{plot_pipe_name}' {formatMinimal} {optioncmds}" ) # If there was an error, these whitespace commands will simply do # nothing. If there was no error, these are data that will be plotted in # some manner. I'm not actually looking at this plot so I don't care # what it is. Note that I'm not making assumptions about how long a # newline is (perl docs say it could be 0 bytes). I'm printing as many # spaces as the number of bytes that I need, so I'm potentially doubling # or even tripling the amount of needed data. This is OK, since gnuplot # will simply ignore the tail. testData += " \n" * Ntestbytes_here else: # for some things gnuplot has its own implicit-tuples logic; I want to # suppress this, so I explicitly tell gnuplot to use all the columns we # have using = curve.get('using') if using is None: using = ':'.join(str(x+1) for x in range(curve['tuplesize'])) using = ' using ' + using # I'm using ascii to talk to gnuplot, so the minimal and "normal" plot # commands are the same (point count is not in the plot command) matrix = '' if curve.get('matrix'): matrix = 'matrix' plotCurveCmds.append( f"'{plot_pipe_name}' {matrix} {using} {optioncmds}" ) plotCurveCmdsMinimal.append( plotCurveCmds[-1] ) # same testing command testData_curve = '' if curve.get('matrix'): testmatrix = "{0} {0}\n" + "{0} {0}\n" + "\ne\n" testData_curve = testmatrix.format(testdataunit_ascii) * (curve['tuplesize'] - 2) else: testData_curve = ' '.join( ['{}'.format(testdataunit_ascii)] * curve['tuplesize']) + \ "\n" + "e\n" testData += testData_curve set_equation('equation_above', plotCurveCmdsNonDataAfter) # the command to make the plot and to test the plot cmd = basecmd + ','.join(plotCurveCmdsNonDataBefore + plotCurveCmds + plotCurveCmdsNonDataAfter) cmdMinimal = basecmd + ','.join(plotCurveCmdsNonDataBefore + plotCurveCmdsMinimal + plotCurveCmdsNonDataAfter) return (cmd, cmdMinimal, testData) def _massageAndValidateArgs(self, curves, curveOptions_base, subplotOptions): # Collect all the passed data into a tuple of lists, one curve per list. # The input is either a bunch of numerical arrays, in which we have one # curve (ignoring broadcasting) or a bunch of tuples containing # numerical arrays, where each tuple represents a curve. # # These numerical arrays can be numpy arrays or scalars. If we see # scalars, we convert them to a numpy array so that everything # downstream can assume we have arrays # convert any scalars in the data list if len(curves): curves = [ np.array((c,)) if isinstance(c, numbers.Real) else c for c in curves ] if all( isinstance(curve,np.ndarray) for curve in curves): curves = (list(curves),) elif all(type(curve) is tuple for curve in curves): # we have a list of tuples. I convert this into a list of lists, and # each scalar in each list becomes a numpy array curves = [ [ np.array((c,)) if isinstance(c, numbers.Real) else c for c in curve ] for curve in curves ] else: raise GnuplotlibError("all data arguments should be of type ndarray (one curve) or tuples") # add an options dict if there isn't one, apply the base curve # options to each curve # # I convert the curve definition from a list of # (data, data, data, ..., {options}) # to a dict # {options, '_data': (data, data, data, ....)} # # The former is nicer as a user interface, but the latter is easier for # the programmer (me!) to deal with. # # Also handle tuplesize<0 by splitting the innermost dimension # # Any curves that have no data in any of their arrays are reported as None def reformat(curve): if type(curve[-1]) is dict: d = _normalize_options_dict(curve[-1]) curve = curve[:-1] else: d = {} for k in curveOptions_base: if k not in d: d[k] = curveOptions_base[k] if all( x.size <= 0 for x in curve ): # ALL the data arrays are empty. Throw away the entire curve return None for x in curve: if x.size <= 0: # SOME of the data ararys are empty. I complain raise GnuplotlibError("Received data where SOME (but not ALL) of the arrays had length-0. Giving up") if 'tuplesize' in d and d['tuplesize'] < 0: if len(curve) != 1: raise GnuplotlibError("tuplesize<0 means that only a single numpy array of data should be given: all data is in this array") d['tuplesize'] = -d['tuplesize'] d['_data'] = list(nps.mv(nps.atleast_dims(curve[0],-2), -1, 0)) else: d['_data'] = list(curve) return d curves = [ reformat(curve) for curve in curves ] # throw out any "None" curves curves = [ curve for curve in curves if curve is not None ] binwidth = None for curve in curves: # make sure all the curve options are valid for opt in curve: if opt == '_data': continue if not opt in knownCurveOptions: raise GnuplotlibError("'{}' not a known curve option".format(opt)) if curve.get('y2') is not None and \ curve.get('axes') is not None: raise GnuplotlibError('"y2" and "axes" are mutually exclusive') # tuplesize is either given explicitly, or taken from the '3d' plot # option. 2d plots default to tuplesize=2 and 3d plots to # tuplesize=3. This means that the tuplesize can be omitted for # basic plots but MUST be given for anything fancy Ndata = len(curve['_data']) if curve.get('histogram'): if subplotOptions.get('3d'): raise GnuplotlibError("histograms don't make sense in 3d") if 'tuplesize' in curve and curve['tuplesize'] != 1: raise GnuplotlibError("histograms only make sense with tuplesize=1. I'll assume this if you don't specify a tuplesize") curve['tuplesize'] = 1 if 'using' in curve: raise GnuplotlibError("'using' cannot be given with 'histogram'. I'll make up my own 'using' in this case") if type(curve['histogram']) is not str: curve['histogram'] = 'freq' histogram_type = curve['histogram'] curve['using'] = '(histbin($1)):(1.0) smooth ' + histogram_type if 'with' not in curve: if re.match(r'freq|fnorm', histogram_type) and 'with' not in curve: curve['with'] = 'boxes fill solid border lt -1' else: curve['with'] = 'lines' if 'binwidth' in curve: if binwidth is not None and binwidth != curve['binwidth']: raise GnuplotlibError("Histogram binwidths must all match. This is a gnuplot limitation mostly. Got: {} and {}". \ format(binwidth,curve['binwidth'])) binwidth = curve['binwidth'] else: if 'binwidth' in curve: raise GnuplotlibError("'binwidth' only makes sense with 'histogram'") if not 'tuplesize' in curve: curve['tuplesize'] = 3 if subplotOptions.get('3d') else 2 if Ndata > curve['tuplesize']: raise GnuplotlibError("Got {} tuples, but the tuplesize is {}. Giving up". \ format(Ndata, curve['tuplesize'])) if Ndata < curve['tuplesize']: # I got fewer data elements than I expected. Set up the implicit # domain if that makes sense if Ndata+1 == curve['tuplesize']: # A plot is one data element short. Fill in a sequential # domain 0,1,2,... curve['_data'].insert(0, np.arange(curve['_data'][0].shape[-1])) elif Ndata+2 == curve['tuplesize']: # a plot is 2 elements short. Use a grid as a domain. I simply set the # 'matrix' flag and have gnuplot deal with it later if self.processOptions.get('ascii') and curve['tuplesize'] > 3: raise GnuplotlibError( \ "Can't make more than 3-dimensional plots on a implicit 2D domain\n" + \ "when sending ASCII data. I don't think gnuplot supports this. Use binary data\n" + \ "or explicitly specify the domain\n" ) curve['matrix'] = True else: raise GnuplotlibError( \ "plot() needed {} data arrays, but only got {}".format(curve['tuplesize'],Ndata)) # The curve is now set up. I look at the input matrices to make sure # the dimensions line up # Make sure the domain and ranges describe the same number of data points dim01 = [None, None] for datum in curve['_data']: if curve.get('matrix') and datum.ndim < 2: raise GnuplotlibError("Tried to plot against an implicit 2D domain, but was given less than 2D data") def checkdim(idim): dim_here = datum.shape[-1 - idim] if dim01[idim]: if dim_here != dim01[idim]: raise GnuplotlibError("plot() was given mismatched tuples to plot. {} vs {}". \ format(dim01[idim], dim_here)) else: dim01[idim] = dim_here checkdim(0) if curve.get('matrix'): checkdim(1) # broadcast through the arguments AND all the options that are arrays curves_flattened = [] for curve in curves: ndims_input = 2 if curve.get('matrix') else 1 prototype_onearg = tuple('n{}'.format(i) for i in range(ndims_input)) prototype = (prototype_onearg,) * len(curve['_data']) # grab all option keys that have numpy arrays as values. I broadcast # these as well np_options_keys = [ k for k in curve.keys() if isinstance(curve[k], np.ndarray) ] N_options_keys = len(np_options_keys) prototype_np_options = ((),) * N_options_keys for args in nps.broadcast_generate( prototype + prototype_np_options, curve['_data'] + list(curve[k] for k in np_options_keys)): # make a copy of the options curve_slice = dict(curve) # replace the data with the slice curve_slice['_data'] = args[:-N_options_keys] if N_options_keys else args for ikey in range(N_options_keys): curve_slice[np_options_keys[ikey]] = args[-N_options_keys + ikey] curves_flattened.append( curve_slice ) curves = curves_flattened # if anything is to be plotted on the y2 axis, set it up for curve in curves: axes = curve.get('axes') if axes is not None: if not axes in knownAxes: raise GnuplotlibError(f'"axes" must be one of {knownAxes}, but got {axes=}') if curve.get('y2'): del curve['y2'] curve['axes'] = 'x1y2' if subplotOptions.get('3d') and axes is not None: raise GnuplotlibError("3d plots cannot have 'axes' specified") return curves def wait(self): r'''Waits until the open interactive plot window is closed Note: 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 ''' self._printGnuplotPipe('pause mouse close\n') self._logEvent("Waiting for data from gnuplot") self._checkpoint('waitforever') def plot(self, *curves, **jointOptions): r'''Main gnuplotlib API entry point''' is_multiplot = self.processOptions.get('multiplot') def test_plot(testcmd, testdata): '''Test the plot command by making a dummy plot with the test command.''' # I send a test plot command. Gnuplot implicitly uses && if multiple # commands are present on the same line. Thus if I see the post-plot print # in the output, I know the plot command succeeded self._printGnuplotPipe( testcmd + "\n" ) self._printGnuplotPipe( testdata ) checkpointMessage,warnings = self._checkpoint('ignore_known_test_failures') if checkpointMessage: # There's a checkpoint message. I explicitly ignored and threw away all # errors that are allowed to occur during a test. Anything leftover # implies a plot failure. barfmsg = "Gnuplot error: '\n{}\n' while sending plotcmd '{}'\n".format(checkpointMessage, testcmd) if warnings: barfmsg += "Warnings:\n" + "\n".join(warnings) raise GnuplotlibError(barfmsg) def plot_process_header(): # I'm now ready to send the plot command. If the plot command fails, # I'll get an error message; if it succeeds, gnuplot will sit there # waiting for data. I don't want to have a timeout waiting for the error # message, so I try to run the plot command to see if it works. I make a # dummy plot into the 'dumb' terminal, and then _checkpoint() for # errors. To make this quick, the test plot command contains the minimum # number of data points if self.processOptions.get('terminal') == 'gp': self._dumpPipe = open(self.processOptions['output'],'w') os.chmod(self.processOptions['output'], 0o755) import distutils.spawn gnuplotpath = distutils.spawn.find_executable('gnuplot') self._safelyWriteToPipe('#!' + gnuplotpath) self._safelyWriteToPipe(self.processOptionsCmds) else: self._safelyWriteToPipe(self.processOptionsCmds) if 'terminal' in self.processOptions: self._safelyWriteToPipe("set terminal " + self.processOptions['terminal'], 'terminal') # I always set the output. If no plot option explicitly is given then I # either "set output" for a known interactive terminal, or redirect to # python's STDOUT otherwise if 'output' in self.processOptions: if self.processOptions['output'] != '': # user requested an explicit output self._safelyWriteToPipe('set output "' + self.processOptions['output'] + '"', 'output') else: # user requested null output self._safelyWriteToPipe('set output', 'output') else: # user requested nothing. Is this a known interactive terminal or an # unspecified terminal (unspecified terminal assumed to be # interactive)? Then set the null output if 'terminal' not in self.processOptions or \ is_knownInteractiveTerminal(self.processOptions['terminal']): self._safelyWriteToPipe('set output', 'output') else: if not _data_dump_only(self.processOptions): if self.fdDupSTDOUT is None: raise GnuplotlibError("I need to plot to STDOUT, but STDOUT wasn't available") self.processOptions['output'] = '/dev/fd/' + str(self.fdDupSTDOUT) else: self.processOptions['output'] = '/dev/fd/DUMPONLY' self._safelyWriteToPipe('set output "' + self.processOptions['output'] + '"', 'output') def plot_subplot(plotcmd, curves): # all done. make the plot self._printGnuplotPipe( plotcmd + "\n") for curve in curves: self._sendCurve(curve) # There's some bug in gnuplot right now, where it sometimes reads too # many bytes after receiving inline data, which swallows the initial # bytes in a subsequent command, breaking things. I workaround by # stuffing newlines into the pipe. These don't do anything, and gnuplot # is allowed to steal some number of them without breaking anything. I # running gnuplot=5.2.6+dfsg1-1 on Debian. I can tickle the bug by doing # this: # gp.plot(np.arange(5)) # Error: # ... # File "/home/dima/projects/gnuplotlib/gnuplotlib.py", line 1221, in _safelyWriteToPipe # raise GnuplotlibError(barfmsg) # gnuplotlib.GnuplotlibError: Gnuplot error: ' # " # ^ # line 0: invalid command # ' while sending cmd 'set output' self._printGnuplotPipe('\n\n\n\n') def plot_process_footer(): if self.processOptions.get('terminal') == 'gp': self._printGnuplotPipe('pause mouse close\n') self._dumpPipe.close() self._dumpPipe = None else: # read and report any warnings that happened during the plot self._checkpoint('printwarnings') # These are uncertain. These are True if I'm SURE that we are or # are not interactive. If I have some terminal not in # knownInteractiveTerminals, then I don't know, and these could # both be False. Note that a very common case is hardcopy=None # and terminal=None, which would mean the default which USUALLY # is interactive terminal = self.processOptions.get('terminal', self.terminal_default) is_non_interactive = self.processOptions.get('output') is_interactive = \ not self.processOptions.get('output') and \ is_knownInteractiveTerminal(terminal) # This is certain is_multiplot = self.processOptions.get('multiplot') if is_multiplot: self._safelyWriteToPipe('unset multiplot') # Some noninteractive terminals need to be told we're done # plotting (set output) to actually write the data to disk in # full. For instance "svg" needs this to write out some closing # stanza # If we're using an unknown interactive terminal, this will 'set # output', and make multiplots break. Unknown interactive # terminals aren't likely to happen if not is_interactive: self._safelyWriteToPipe('set output', 'output') # If I KNOW that I'm using a non-interactive terminal, I don't # bother to wait even if asked. If it's some unknown-to-me # terminal (is_non_interactive is False, incorrectly), then we # wait anyway. Changing "not is_non_interactive" to # "is_interactive" will make us not wait if we don't know if self.processOptions.get('wait') and \ not is_non_interactive: self.wait() # I force gnuplot to tell me it's done before exiting. Without this 'set # terminal dumb' plots don't end up rendering anything: we kill the # process before it has time to do anything self._checkpoint('final printwarnings') def ingest_joint_options(jointOptions, subplotOptions_base, curveOptions_base): '''Takes in a set of joint options, and overrides a given base I have a some default plot,curve options that came from above (global plot(), __init__(), etc). I combine those defaults with the joint options I have HERE, and return the updated sets ''' # process options are only allowed in self.__init__(), so I'm not # handling those here curveOptions_here, subplotOptions_here = \ _split_dict( jointOptions, (knownCurveOptions, 'curve'), (knownSubplotOptions, 'subplot'),) subplotOptions = dict(subplotOptions_base) subplotOptions.update(subplotOptions_here) curveOptions = dict(curveOptions_base) curveOptions.update(curveOptions_here) return subplotOptions,curveOptions def make_subplot_data(subplotOptions_base, curveOptions_base, *curves, **jointOptions): subplotOptions,curveOptions = \ ingest_joint_options( _normalize_options_dict(jointOptions), subplotOptions_base, curveOptions_base ) subplotOptionsCmds = _massageSubplotOptionsAndGetCmds(subplotOptions) curves = self._massageAndValidateArgs(curves, curveOptions, subplotOptions) plotcmd_testcmd_testdata = self._getPlotCmd( curves, subplotOptions ) return (curves, subplotOptionsCmds, plotcmd_testcmd_testdata[0], plotcmd_testcmd_testdata[1], plotcmd_testcmd_testdata[2],) if not is_multiplot: # basic case subplots = ( make_subplot_data( self.subplotOptions_base, self.curveOptions_base, *curves, **jointOptions), ) else: # OK, this actually isn't just a plot, so the arguments are misnamed subplots = curves subplotOptions_base,curveOptions_base = \ ingest_joint_options( _normalize_options_dict(jointOptions), self.subplotOptions_base, self.curveOptions_base ) def make_subplot_data_embedded_kwargs(subplot): if type(subplot[-1]) is dict: d = _normalize_options_dict(subplot[-1]) subplot = subplot[:-1] else: d = {} return make_subplot_data(subplotOptions_base, curveOptions_base, *subplot, **d) subplots = [make_subplot_data_embedded_kwargs(subplot) for subplot in subplots] # Test the plot if not self.processOptions.get('notest'): # I don't actually want to see the plot, I just want to make sure that # no errors are thrown. I thus send the output to /dev/null. Note that I # never actually read stdout, so if this test plot goes to the default # stdout output, then eventually the buffer fills up and gnuplot blocks. # So keep it going to /dev/null, or make sure to read the test plot from # stdout self._printGnuplotPipe( "set output '/dev/null'\n" ) self._printGnuplotPipe( "set terminal dumb\n" ) if self.processOptions.get('multiplot'): self._safelyWriteToPipe('set multiplot ' + \ (self.processOptions['multiplot'] if type(self.processOptions['multiplot']) is str else '')) for curves,subplotOptionsCmds,plotcmd,testcmd,testdata in subplots: if self.processOptions.get('multiplot'): # we're multiplotting, so I need to wipe the slate clean so # that other subplots don't affect this one self._safelyWriteToPipe('reset') self._safelyWriteToPipe(subplotOptionsCmds) test_plot(testcmd, testdata) if self.processOptions.get('multiplot'): self._safelyWriteToPipe('unset multiplot') # select the default terminal in case that's what we want self._safelyWriteToPipe("set terminal pop; set terminal push", 'terminal') # Testing done. Actually do the thing now plot_process_header() if self.processOptions.get('multiplot'): self._safelyWriteToPipe('set multiplot ' + \ (self.processOptions['multiplot'] if type(self.processOptions['multiplot']) is str else '')) for curves,subplotOptionsCmds,plotcmd,testcmd,testdata in subplots: if self.processOptions.get('multiplot'): # we're multiplotting, so I need to wipe the slate clean so that # other subplots don't affect this one self._safelyWriteToPipe('reset') self._safelyWriteToPipe(subplotOptionsCmds) plot_subplot(plotcmd,curves) # I don't "unset multiplot" here. That would make my plot go away plot_process_footer() globalplot = None def plot(*curves, **jointOptions): r'''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 +-+ |||||||| + +||||||||||+ +-+ | ||||||||+ | ++||||||||||| | | ||||||||| + ||||||||||||| | 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. ''' global globalplot # I make a brand new gnuplot process if necessary. If one already exists, I # re-initialize it. If we're doing a data dump then I also create a new # object. There's no gnuplot session to reuse in that case, and otherwise # the dumping won't get activated if not globalplot or _data_dump_only(globalplot.processOptions): globalplot = gnuplotlib(**jointOptions) else: globalplot.__init__(**jointOptions) globalplot.plot(*curves) def plot3d(*curves, **jointOptions): r'''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. ''' jointOptions['3d'] = True plot(*curves, **jointOptions) def plotimage(*curves, **jointOptions): r'''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. ''' jointOptions['_with'] = 'image' jointOptions['tuplesize'] = 3 plot(*curves, **jointOptions) def wait(*args): r'''Waits until the given interactive plot window(s) are closed SYNOPSIS import numpy as np import gnuplotlib as gp ### Waiting for the global plot window gp.plot(...) # interactive plot pops up gp.wait() # We get here when the user closes the plot window ### Waiting on some arbitrary plots plot0 = gp.gnuplotlib(...) plot1 = gp.gnuplotlib(...) plot0.plot(...) plot1.plot(...) gp.wait(plot0,plot1) # We get here when the user closes the plot windows DESCRIPTION Wait for the interactive plot window(s) to be closed by the user. Without any argument this applies to the global gnuplotlib object. Or the specific plots to wait for can be given in arguments (in-line or as a single iterable): - wait() waits on the global gnuplot object - wait(plot0,plot1) - wait((plot0,plot1),) both wait on the given gnuplotlib objects It's not at all trivial to detect if a plot object has an open plot window. If it does not, this function will end up waiting forever, and the user will need to Ctrl-C ''' global globalplot if len(args) == 0: if not globalplot: raise GnuplotlibError("There isn't a plot to wait on") plots = (globalplot,) elif all(isinstance(p,gnuplotlib) for p in args): plots = args elif len(args) == 1: plots = args[0] else: raise Exception("gnuplotlib.wait() takes an inline list of plots or a single list-of-plots argumnent. Got neither") if len(plots) == 1: # Special-case if we have exactly one plot to wait on. Can avoid forking # in this case, so I do that plots[0].wait() return # N plots pids = [0] * len(plots) for i,plot in enumerate(plots): pid = os.fork() if pid == 0: # child plot.wait() os._exit(0) pids[i] = pid for pid in pids: os.waitpid(pid,0) def add_plot_option(d, key = None, values = None, overwrite = None, **kwargs): r'''Ingests new key/value pairs into an option dict SYNOPSIS # A baseline plot_options dict was given to us. We want to make the # plot, but make sure to omit the legend key gp.add_plot_option(plot_options, 'unset', 'key') gp.plot(..., **plot_options) DESCRIPTION Given a plot_options dict we can easily add a new option with plot_options[key] = value This has several potential problems: - If an option for this key already exists, the above will overwrite the old value instead of adding a NEW option - All options may take a leading _ to avoid conflicting with Python reserved words (set, _set for instance). The above may unwittingly create a duplicate - Some plot options support multiple values, which the simple call ignores completely THIS function takes care of the _ in keys. And this function knows which keys support multiple values. If a duplicate is given, it will either raise an exception, or append to the existing list, as appropriate. If the given key supports multiple values, they can be given in a single call, as a list or a tuple. Multiple key/values can be given using keyword arguments. ARGUMENTS - d: the plot options dict we're updating - key: string. The key being set - values: string (if setting a single value) or iterable (if setting multiple values) - **kwargs: more key/value pairs to set. We set the key/value positional arguments first, and then move on to the kwargs - overwrite: optional boolean that controls how we handle overwriting keys that do not accept multiple values. By default (overwrite is None), trying to set a key that is already set results in an exception. elif overwrite: we overwrite the previous values. elif not overwrite: we leave the previous value ''' if kwargs: add_plot_option(d, key, values, overwrite) for key in kwargs: add_plot_option(d, key, kwargs[key], overwrite) return if key is None: if values is not None: raise Exception("key is None, but values is not. Giving up") return key_normalized = key if key[0] != '_' else key[1:] if not (key_normalized in keysAcceptingIterable and \ isinstance(values, (list,tuple))): values = (values,) values = [v for v in values if v is not None] if len(values) == 0: return if key_normalized not in keysAcceptingIterable: if len(values) > 1: raise GnuplotlibError("plot options given multiple values for key '{}'".format(key_normalized)) if key in d or key_normalized in d: # A value already exists. What do I do? if (overwrite is not None) and overwrite: pass elif (overwrite is not None) and not overwrite: return else: # overwrite is None (the default). Barf. raise GnuplotlibError("plot options already have a value for key '{}'. Pass 'overwrite=False' to use the existing one of 'overwrite=True' to use the new one".format(key_normalized)) d[key_normalized] = values[0] else: def listify(v): if isinstance(v, (list,tuple)): return v return [v] def accum(k,v): try: v += listify(d[k]) del d[k] except KeyError: pass v = [] accum(key,v) if key != key_normalized: accum(key_normalized,v) d[key_normalized] = v + values if __name__ == '__main__': import numpy as np import gnuplotlib as gp import time x = np.arange(101) - 50 gp.plot(x**2, dump=0, ascii=0) time.sleep(1) 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 ) time.sleep(5) x,y = np.ogrid[-10:11,-10:11] gp.plot( x**2 + y**2, title = 'Heat map', set = 'view map', _with = 'image', tuplesize = 3) time.sleep(5) 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.sin(theta), z), (np.cos(theta), -np.sin(theta), z)) time.sleep(60)