py-gnuplot is a Python plot tools based on Gnuplot.
Project description
Gnuplot is a portable command-line driven graphing utility for many platforms. To leverage the powful gnuplot and plot beautiful image in python, we port gnuplot to python.
1. A glance view and examples tables
1.1 A glance view
py-python only support python3 since the function dictionary paramaters in python2 is not in order.
This package has an object-oriented design as well as direct function call to allows the user flexibility to set plot options and to run multiple gnuplot sessions simultaneously.
At first let’s have an intuitive overview by showing an simple example, you can run “python3 demo.py” to see the result:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
gnuplot.plot('[-10:10] sin(x)', 'atan(x)', 'cos(atan(x))',
terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"simple.1.png"',
key = 'fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid',
style = 'increment default',
samples = '50, 50',
title = '"Simple Plots" font ",20" norotate')The generated plot is as below, We will introduce the detail in the below:
figure 1. pygnuplot demo 1: simple function
1.2 examples used in this article
More examples used in this article are listed as below, we will introduce them case by case and please click the source code for details.
gnuplot demo script |
object-oriented interface script |
direct function call script |
All the script produce the same image |
|---|---|---|---|
gnuplot demo script |
object-oriented interface script |
direct function call script |
All the script produce the same image |
|---|---|---|---|
Matplotlib script |
py-gnuplot script |
image |
|---|---|---|
2. Introduction and basic concept
As we know Gnuplot is a portable and powerful command-line driven graphing utility for many platforms. To leverage the power of Gnuplot, many wrapper are developed but it’s hard to use. We develop the py-gnuplot in an easy way and it’s streightforward: If you are familar with Gnuplot, you could seamlessly turn to py-python. If you are not very familar with Gnuplot, you can also write the gnuplot script in pure python easily.
Gnuplot use ‘plot/splot’ commands to plot data, use all kinds of ‘set/unset’ commands to change the plotting style or options for subsequent plot/splot command. If we implement the plot()/splot() functions with all those kinds of options, is it possible for us to call Gnuplot in python script? The answer is yes. There are several ways to plot the data in py-python:
It’s easy to port the orginal gnuplot to python and we use the submodule: pygnuplot.gnuplot to implement it. Ploting the python self-generated data is a little tricky, we implement it in the submodule: pygnuplot.pyplot.
We take the Gnuplot demo simple.dem (click to see the original Gnuplot script) as a example and let’s see how to plot it in pyton way. We have 3 ways to plot the simple function in pyton, they are(plotting function/datafile and plotting pythong generated data should use different sub module, so there are 4 examples):
line by line: simple1.1.py, simple1.1.py
Wrapper as object-oriented interface: simple2.py
- global class-less function call
plot function/datafile: simple3.py
plot python generated data, it’s in pandas.DataFrame or string format: simple4.py
All the script including the original gnuplot script generate the same output: figure1, Let’s have a deep check how to plot it:
2.1 plot data line by line
We implemented the function cmd() and pass the command to call Gnuplot to plot the data, Thus we could do everything with the only one simple function. It’s the easiest way to call Gnuplot and it’s hard to pass the python generated data to gnuplot:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# Illustration of object-oriented interface, you can see we only wrap the
# gnuplot script by g.cmd('...') and it's simple and straitfoward if you
# are familar with Gnuplot.
g = gnuplot.Gnuplot()
g.cmd('set terminal pngcairo font "arial,10" fontscale 1.0 size 600, 400')
g.cmd('set output "simple.1.png"')
g.cmd('set key fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid')
g.cmd('set style increment default')
g.cmd('set samples 50, 50')
g.cmd('set title "Simple Plots" ')
g.cmd('set title font ",20" norotate')
g.cmd('plot [-10:10] sin(x),atan(x),cos(atan(x))')Or you can even pass the Gnuplot command as a string list or a text paragraph:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# Illustration of object-oriented interface, you can see we only wrap the
# gnuplot script by g.cmd('...') and it's simple and straitfoward if you
# are familar with Gnuplot.
g = gnuplot.Gnuplot()
# Take all the Gnuplot command as a list of command:
g.cmd('set terminal pngcairo font "arial,10" fontscale 1.0 size 600, 400',
'set output "simple.1.png"',
'set key fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid',
'set style increment default',
'set samples 50, 50',
'set title "Simple Plots" ',
'set title font ",20" norotate',
'plot [-10:10] sin(x),atan(x),cos(atan(x))')
# Take all the Gnuplot command as a script paragraph:
plot_cmd = '''
set terminal pngcairo font "arial,10" fontscale 1.0 size 600, 400
set output "simple.1.png"
set key fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid
set style increment default
set samples 50, 50
set title "Simple Plots
set title font ",20" norotate
plot [-10:10] sin(x),atan(x),cos(atan(x))'''
g.cmd(plot_cmd)By this way we can do everything that Gnuplot can do and cannot do what Gnuplot itself can’t do. It’s the exact way that the Gnuplot do it. and we don’t get any benifit besides we can call Gnuplot in python.
2.2 Wrapper as object-oriented interface
As we know Gnuplot use ‘plot/splot’ commands to plot data, use all kinds of ‘set/unset’ commands to change the plotting style or options for subsequent plot/splot command. So we implement the plot()/splot()/set() and so on functions to draw the data in python way:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
g = gnuplot.Gnuplot()
g.set(terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"simple.1.png"',
key = 'fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid',
style = 'increment default',
samples = '50, 50',
title = '"Simple Plots" font ",20" norotate')
g.plot('[-10:10] sin(x),atan(x),cos(atan(x))')We set the options before plot and then call plot to render the image. It’s equivalent to method 1 but seems muck like a python script.
2.3 global class-less function call
In above way we need to allocate a Gnuplot object and will use it whenever we call Gnuplot function. It’s convenient but sometimes we only need one the plot command and don’t want to hande the Gnuplot instance, this is a new way to draw the same image:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
gnuplot.plot('[-10:10] sin(x),atan(x),cos(atan(x))',
terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"simple.1.png"',
key = 'fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid',
style = 'increment default',
samples = '50, 50',
title = '"Simple Plots" font ",20" norotate')This generates exact the same output but is more simple and seems muck like a python script.
2.4 Plot python generated data
It’s powerful for the above plot function. But they only can plot the functions and data in file. How about plotting the python generated data? We’ve developed another submodule pyplot and you use this summodule with the same function, there only 2 differeces:
Use the different submodule name: pyplot.
plot()/splot() parameter has some differences, we always need pass the content of data as the first paramater of plot()/splot(): it could be either pandas.Dataframe format or pure string format, indeed if it’s pandas.Dataframe format, we convert it using df.to_csv(sep = ‘ ‘) to convert it to the string.
#!/usr/bin/env python3
#coding=utf8
import numpy as np
import pandas as pd
from pygnuplot import gnuplot, pyplot
# Illusration of submodule: pyplot, Note that we use the pyplot.plot()
# insteading of gnuplot.plot() in the following line and the parameters
# are a little difference. See detail in the following section.
df = pd.DataFrame(np.random.randn(8,3))
pyplot.plot(
df.to_csv(sep = ' '), # You can call df directly,
'using 1:2',
'using 1:3',
'using 1:4',
terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"simple.1.png"',
key = 'fixed left top vertical Right noreverse enhanced autotitle box lt black linewidth 1.000 dashtype solid',
style = 'increment default',
samples = '50, 50',
title = '"Simple Plots" font ",20" norotate')3 object-oriented interface and global class-less function call
As we see in section 1, we have several types of script to plot the data, but they could be classified as two types:
object-oriented interface: It’s simple wrapper for gnuplot, every gnuplot instance is a Gnuplot object and every Gnuplot command is a line of python directive.
global class-less function call: It refer to the syntax of matplotlib and mplfinance, only a few single function could plot what you want.
The same functions could bey achieved by both kinds of call way, object-oriented interface call is object-oriented and global class-less function call is simple, it’s your up to decide which way to use.
Let’s see what’s the difference with more examples(Click the script name to see the whole script) in Table1 and Table2:
As describe above, object-oriented interface is simple and easy to understand as gnuplot’s logic. Easy way plot the data in python way.
4. Sub moduels: gnuplot and pyplot
We develop two submodule for different use cases:
gnuplot: To plot the functions and file data as in gnuplot.
pyplot: To plot the data generated in python itself, normally it’s in pandas dataframe format.
For each submodule, we both have an object-oriented interface (via class Gnuplot) and a few global class-less functions (plot(), splotlot3d(), multiplot()).
Let’s see what’s the difference with more examples(Click the script name to see the whole script) in section3:
5. Gnuplot command and py-gnuplot functions
The principle is if you can write Gnuplot script, you can write py-gnuplot. There is 1-1 mapping between almost all Gnuplot command and python function;
Gnuplot commands are mapped to py-python function. Gnuplot has many Commands but there is only a few ones which are related plot. We will portting more and more commands and now the following commands are available.
5.1 plot()
plot is the primary command for drawing plots with gnuplot:
plot {<ranges>} <plot-element> {, <plot-element>, <plot-element>}
# Examples:
plot sin(x)
plot sin(x), cos(x)
plot "datafile.1" with lines, "datafile.2" with points
We port it as a function in py-python and the plot-element is passed as variable parameters, please be noted that the plot-element should be in the single quotation marks:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# gnuplot.plot() is definied as:
# def plot(*args, **kwargs)
# usage examples, please note that we didn't give the
# output so # could only see the image flash on the
# screen. Will introduce how to output the image to
# files.
gnuplot.plot('sin(x)')
gnuplot.plot('sin(x)', 'cos(x)')
gnuplot.plot('"datafile.1" with lines',
'"datafile.2" with points')Note: Submodule gnuplot and submodule pyplot have only one difference in plot():
gnuplot.plot() support functions and file name as input data
while pyplot.plot() support string or pandas dataframe data type, Further more it must be the first parameter.
If we generate the data in the python insteading using the exist funtions and datafile, we should use pyplot to plot the data, for example:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# pyplot.plot() is definied as:
# def plot(df.to_csv(), *args, **kwargs)
# usage examples, please note that we didn't give the output so could only
# see the image flash on the screen. Will introduce how to output the
# image to files.
df = pd.DataFrame(data = {'col1': [1, 2],
'col2': [3, 4],
'col3': [5, 6]})
gnuplot.plot(df, 'using 1:2 with lines', 'using 1:3 with points')5.2 splot()
splot is the command for drawing 3D plots:
splot {<ranges>}
{<iteration>}
<function> | {{<file name> | <datablock name>} {datafile-modifiers}}
# Examples:
splot sin(sqrt(x**2+y**2))/sqrt(x**2+y**2)
splot ’<file_name>’
We port it as a function splot() in py-python and the plot-element is passed as variable parameters, please be noted that the plot-element should be in the single quotation marks:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# gnuplot.splot() is definied as:
# def splot(*args, **kwargs)
# usage examples, please note that we didn't give the output so
# could only see the image flash on the screen. Will introduce
# how to output the image to files.
gnuplot.splot('sin(sqrt(x**2+y**2))/sqrt(x**2+y**2)')
gnuplot.splot('"<file_name>"')5.3 set()
The set command can be used to set lots of options in gnuplot. for example:
set xtics offset 0,graph 0.05 set label "y=x" at 1,2 set label 2 "S" at graph 0.5,0.5 center font "Symbol,24" set label 3 "y=x^2" at 2,3,4 right
In py-gnuplot we use dictionary parameter to pass them to plot() function, We use each option name as the key, the option value as the dictionary value. If some option contain an iteration clause, we use list as the dictionary value, then the above set command could be writen as:
xtics = 'offset 0,graph 0.05'
labes = ['"y=x" at 1,2',
'2 "S" at graph 0.5,0.5 center font "Symbol,24"',
'3 "y=x^2" at 2,3,4 right']
For example the following Gnuplot script:
set boxwidth 0.9 relative set style fill solid 1.0 set label "y=x" at 1,2 set label 2 "S" at graph 0.5,0.5 center font "Symbol,24" set label 3 "y=x^2" at 2,3,4 right plot ’file.dat’ with boxes
could be implemented as the following:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# style is passed as function dictionary parameter
gnuplot.plot('"file.dat’with boxes',
boxwidth = '0.9 relative',
style = 'fill solid 1.0',
labes = ['"y=x" at 1,2',
'2 "S" at graph 0.5,0.5 center font "Symbol,24"',
'3 "y=x^2" at 2,3,4 right'])By default, Gnuplot display the output to the standard output. The set term and output command redirects the display to the specified file or device:
set terminal pngcairo font "arial,10" fontscale 1.0 size 600, 400 set output "test.png"
Then if we want to redirect the image to a file, we could do that by giving the term and output parameters:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# style is passed as function dictionary parameter
gnuplot.plot('"file.dat’with boxes',
boxwidth = '0.9 relative',
style = 'fill solid 1.0',
labes = ['"y=x" at 1,2',
'2 "S" at graph 0.5,0.5 center font "Symbol,24"',
'3 "y=x^2" at 2,3,4 right'],
output = '"finance.13.png"',
term = 'pngcairo font "arial,10" fontscale 1.0 size 900, 600')5.4 multiplot()
In Gnuplot, multiplot is not a command but a option to enable multiplot mode. But we use it as a seperate function multiplot() to plot several data next to each other on the same page or screen window:
def multiplot(\*args, \*\*kwargs):
@args: the subplot object list;
@kwargs: the setting options that need to be set before call plot;
def make_plot(\*args, \*\*kwargs)
The parameter definition is the same as plot(), but it doesn't plot
the data really, it only return the plot dictionary for later
multiplot() use.
def make_splot(\*args, \*\*kwargs)
The parameter definition is the same as splot(), but it doesn't plot
the data really, it only return the plot dictionary for later
multiplot() use.
Before call multiplot() we must generate the subplot object by calling make_plot()/make_splot(), It is much like mplfinance.make_addplot(), it only add the subplot command for further call:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
sub1 = gnuplot.make_plot('sin(x)', ylabel = 'ylabel')
sub2 = gnuplot.make_plot('cos(x)', xlabel = 'xlabel')
sub3 = gnuplot.make_plot('sin(2*x)', noxlabel = '', ylabel = '')
sub4 = gnuplot.make_plot('cos(2*x)', xlabel = 'xlabel')
gnuplot.multiplot(sub1, sub2, sub3, sub4,
output = '"sample.multiplot.png"',
term = 'pngcairo size 900,600 font ",11"',
multiplot = 'layout 2,2 columnsfirst margins 0.1,0.9,0.1,0.9 spacing 0.1')6. Plot methods detail
6.1 methods in gnuplot
6.2 methods in pyplot
pyplot is easy to use and it only has a few functions, all the configuration are passed as function parameter.
pyplot take pandas dataframe or string as first parameter.
plot(data, *args, **kwargs)
@ data: The data that need to plot, it should be string or pandas.Dataframe format. The plot() in Another submodule doesn’t have the parameter, this is the only difference between the two sub modules. If it’s pandas.Dataframe format we convert it to string by calling df.to_csv(sep = ‘ ‘) in the module:
df = pd.read_csv('immigration.dat', index_col = 0,
sep='\t', comment='#')
pyplot.plot(df, ...)
pyplot.plot(df.to_csv(sep = ' '), ...)
@ args: The plot command we need to plot. Gnuplot plot data like that:
plot 'finance.dat' using 0:2:3:4:5 notitle with financebars lt 8, \
'finance.dat' using 0:9 notitle with lines lt 3, \
'finance.dat' using 0:10 notitle with lines lt 1, \
'finance.dat' using 0:11 notitle with lines lt 2
Now we omit the command “plot” and data “finance.dat” since we have already pass them in the function name and the first parameter “data”, we pass the command as a list of command as following:
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
'using 0:9 notitle with lines lt 3',
'using 0:10 notitle with lines lt 1',
'using 0:11 notitle with lines lt 2',
...)
# Or we can only pass the df as the first paramerter:
pyplot.plot(df,
'using 0:2:3:4:5 notitle with financebars lt 8',
'using 0:9 notitle with lines lt 3',
'using 0:10 notitle with lines lt 1',
'using 0:11 notitle with lines lt 2',
...)
@ kwargs: As we know The set command is used to set lots of options before plot, splot, or replot command is given. We skip the ‘set’ keyword and use the options name as the key, the following part is used the attribute value, for example we use the following line to set the xtics in gnuplot:
set xtics border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit
Then in the function, we will use:
xtics = 'border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit'
as a parameters. Some options order sensitive, so we need the python version > 3.7, which seems to pass the function parameter in order. Or there will some issue and cause exception:
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
...,
xtics = 'border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit',
...)
There are some cases we need pay attention:
We need always put the parameter in the single quotation marks(‘’) since we would pass the integrated string to gnuplot by PIPE:
pyplot.plot(df, 'using 0:2:3:4:5 notitle with financebars lt 8', ..., )If it’s flag parameter, for example:
set grid set hidden3d
we can pass it as a empty value:
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
...,
grid = '',
hidden3d = '',
...)
unset command use the no-xxx option, for example:
unset grid unset hidden3d
As we know they equal to:
set nogrid set nohidden3d
So the use them as:
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
...,
nogrid = '',
nohidden3d = '',
...)
If there is multiple lines for one options, for exampe in gnuplot it is:
set arrow from 5,-5,-1.2 to 5,5,-1.2 lt -1 set arrow from 5,6,-1 to 5,5,-1 lt -1 set arrow from 5,6,sinc(5,5) to 5,5,sinc(5,5) lt -1
We pass them by a list of options:
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
...,
arrow = ['from 5,-5,-1.2 to 5,5,-1.2 lt -1',
'from 5,6,-1 to 5,5,-1 lt -1',
'from 5,6,sinc(5,5) to 5,5,sinc(5,5) lt -1'],
...,
...)
splot(data, *args, **kwargs)
The parameter are same as plot(), the only difference is it use “splot” to plot insteading of “plot”.
make_plot(data, *args, **kwargs)
The parameter definition is the same as plot()/splot, but it doesn’t plot the df really, it only return the plot dictionary for later multiplot() use.
It is much like mplfinance.add_plot(), it only add the subplot command for further call:
sub1 = pyplot.make_plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with candlesticks lt 8',
'using 0:9 notitle with lines lt 3',
logscale = 'y',
yrange = '[75:105]',
ytics = '(105, 100, 95, 90, 85, 80)',
xrange = '[50:253]',
grid = 'xtics ytics',
lmargin = '9',
rmargin = '2',
format = 'x ""',
xtics = '(66, 87, 109, 130, 151, 174, 193, 215, 235)',
title = '"Change to candlesticks"',
size = ' 1, 0.7',
origin = '0, 0.3',
bmargin = '0',
ylabel = '"price" offset 1',
label = ['1 "Acme Widgets" at graph 0.5, graph 0.9 center front',
'2 "Courtesy of Bollinger Capital" at graph 0.01, 0.07',
'3 " www.BollingerBands.com" at graph 0.01, 0.03']
)
multiplot(*args, **kwargs)
The multiplot set the setting in kwargs at first, and then call the subplot in args to multiplot.
@args: It is the list of subplot generated by make_plot() or make_splot(), it would be called one by one.
@kwargs: The global setting for multiplot;
For example:
pyplot.multiplot(sub1, sub2,
output = '"history.%s.png"' %(code),
term = 'pngcairo size 1920,1080 font ",11"')
multisplot(*args, **kwargs)
It’s the same as multiplot, the difference is it use splot() instead.
7. More examples
7.1 histogram
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
df = pd.read_csv('immigration.dat', index_col = 0, sep='\t', comment='#')
g = gnuplot.Gnuplot()
g.set(terminal = 'pngcairo transparent enhanced font "arial,10" fontscale 1.0 size 600, 400 ',
output = '"histograms.1.png"',
key = 'fixed right top vertical Right noreverse noenhanced autotitle nobox',
style = 'data linespoints',
datafile = ' missing "-"',
xtics = 'border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit',
title = '"US immigration from Europe by decade"')
pyplot.plot(df, 'using 2:xtic(1), for [i=3:22] "" using i ')#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
df = pd.read_csv('immigration.dat', index_col = 0, sep='\t', comment='#')
pyplot.plot(df.to_csv(sep = ' '),
'using 2:xtic(1), for [i=3:22] "" using i ',
terminal = 'pngcairo transparent enhanced font "arial,10" fontscale 1.0 size 600, 400 ',
output = '"histograms.1.png"',
key = 'fixed right top vertical Right noreverse noenhanced autotitle nobox',
style = 'data linespoints',
datafile = ' missing "-"',
xtics = 'border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit',
title = '"US immigration from Europe by decade"')And the generated output is as following:
7.2 splot
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
gnuplot.splot('cos(u)+.5*cos(u)*cos(v),sin(u)+.5*sin(u)*cos(v),.5*sin(v) with lines',
'1+cos(u)+.5*cos(u)*cos(v),.5*sin(v),sin(u)+.5*sin(u)*cos(v) with lines',
terminal = 'pngcairo enhanced font "arial,10" fontscale 1.0 size 600, 400 ',
output = '"surface2.9.png"',
dummy = 'u, v',
key = 'bmargin center horizontal Right noreverse enhanced autotitle nobox',
style = ['increment default','data lines'],
parametric = '',
view = '50, 30, 1, 1',
isosamples = '50, 20',
hidden3d = 'back offset 1 trianglepattern 3 undefined 1 altdiagonal bentover',
xyplane = 'relative 0',
title = '"Interlocking Tori" ',
urange = '[ -3.14159 : 3.14159 ] noreverse nowriteback',
vrange = '[ -3.14159 : 3.14159 ] noreverse nowriteback')And the generated output is as following:
7.3 pm3d
iterate.dem
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
g = gnuplot.Gnuplot()
#g.set(terminal = 'pngcairo transparent enhanced font "arial,10" fontscale 1.0 size 600, 400 ',
# output = '"iterate.2.png"',
# noborder = '',
# key = ['title "splot for [scan=1:*] \'whale.dat\' index scan" center',
# 'bmargin center horizontal Right noreverse enhanced autotitle nobox',
# 'noinvert samplen 0.6 spacing 1 width 0 height 0 ',
# 'maxcolumns 0 maxrows 6'],
# style = 'increment default',
# view = '38, 341, 1, 1',
# noxtics = '',
# noytics = '',
# noztics = '',
# title = '"Iteration over all available data in a file" ',
# lmargin = 'at screen 0.09',
# rmargin = 'at screen 0.9')
#g.splot('for [i=1:*] "whale.dat" index i title sprintf("scan %d",i) with lines')
# Black and white version
g.set(style = 'line 100 lw 0.1 lc "black"',
term = 'pngcairo size 480,480',
out = '"whale.png"',
pm3d = 'depth hidden3d ls 100',
cbrange = '[-0.5:0.5]',
palette = 'rgb -3,-3,-3',
nocolorbox = '',
noborder = '',
nokey = '',
zrange = '[-2:2]',
notics = '',
view = '60,185,1.5')
g.splot('"whale.dat" w pm3d')#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
#gnuplot.splot('for [i=1:*] "whale.dat" index i title sprintf("scan %d",i) with lines',
# terminal = 'pngcairo transparent enhanced font "arial,10" fontscale 1.0 size 600, 400 ',
# output = '"iterate.2.png"',
# noborder = '',
# key = ['title "splot for [scan=1:*] \'whale.dat\' index scan" center',
# 'bmargin center horizontal Right noreverse enhanced autotitle nobox',
# 'noinvert samplen 0.6 spacing 1 width 0 height 0 ',
# 'maxcolumns 0 maxrows 6'],
# style = 'increment default',
# view = '38, 341, 1, 1',
# noxtics = '',
# noytics = '',
# noztics = '',
# title = '"Iteration over all available data in a file" ',
# lmargin = 'at screen 0.09',
# rmargin = 'at screen 0.9')
# Black and white version
gnuplot.splot('"whale.dat" w pm3d',
style = 'line 100 lw 0.1 lc "black"',
term = 'pngcairo size 480,480',
out = '"whale.png"',
pm3d = 'depth hidden3d ls 100',
cbrange = '[-0.5:0.5]',
palette = 'rgb -3,-3,-3',
nocolorbox = '',
noborder = '',
nokey = '',
zrange = '[-2:2]',
notics = '',
view = '60,185,1.5')And the generated output is as following:
7.4 multiplot
we convert the gnuplot demo script: finance.dem to the final python script:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
# A demostration to generate pandas data frame data in python.
df = pd.read_csv('finance.dat', sep='\t', index_col = 0, parse_dates = True,
names = ['date', 'open','high','low','close', 'volume','volume_m50',
'intensity','close_ma20','upper','lower '])
# make subplot at first, now there is still no real plot.
sub1 = pyplot.make_plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with candlesticks lt 8',
'using 0:9 notitle with lines lt 3',
'using 0:10 notitle with lines lt 1',
'using 0:11 notitle with lines lt 2',
'using 0:8 axes x1y2 notitle with lines lt 4',
logscale = 'y',
yrange = '[75:105]',
ytics = '(105, 100, 95, 90, 85, 80)',
xrange = '[50:253]',
grid = 'xtics ytics',
lmargin = '9',
rmargin = '2',
format = 'x ""',
xtics = '(66, 87, 109, 130, 151, 174, 193, 215, 235)',
title = '"Change to candlesticks"',
size = ' 1, 0.7',
origin = '0, 0.3',
bmargin = '0',
ylabel = '"price" offset 1',
label = ['1 "Acme Widgets" at graph 0.5, graph 0.9 center front',
'2 "Courtesy of Bollinger Capital" at graph 0.01, 0.07',
'3 " www.BollingerBands.com" at graph 0.01, 0.03']
)
sub2 = pyplot.make_plot(df.to_csv(sep = ' '),
'using 0:($6/10000) notitle with impulses lt 3',
'using 0:($7/10000) notitle with lines lt 1',
bmargin = '',
size = '1.0, 0.3',
origin = '0.0, 0.0',
tmargin = '0',
nologscale = 'y',
autoscale = 'y',
format = ['x', 'y "%1.0f"'],
ytics = '500',
xtics = '("6/03" 66, "7/03" 87, "8/03" 109, "9/03" 130, "10/03" 151, "11/03" 174, "12/03" 193, "1/04" 215, "2/04" 235)',
ylabel = '"volume (0000)" offset 1')
# plot at one time.
pyplot.multiplot(sub1, sub2,
output = '"finance.13.png"',
term = 'pngcairo font "arial,10" fontscale 1.0 size 900, 600')And this the generated output:
8 Examples in matplotlib
Just for fun, I translate some examples in matplotlib to py-gnuplot:
8.1 Stacked bar chart
#!/usr/bin/env python3
#coding=utf8
import pandas as pd
from pygnuplot import gnuplot, pyplot
# data is from https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html#sphx-glr-gallery-lines-bars-and-markers-bar-stacked-py
#https://matplotlib.org/_downloads/2ac62a2edbb00a99e8a853b17387ef14/bar_stacked.py
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
men_means = [20, 35, 30, 35, 27]
women_means = [25, 32, 34, 20, 25]
men_std = [2, 3, 4, 1, 2]
women_std = [3, 5, 2, 3, 3]
width = 0.35 # the width of the bars: can also be len(x) sequence
# Plot programme:
df = pd.DataFrame({'men_means': men_means,
'women_means': women_means,
'men_std': men_std,
'women_std': women_std}, index = labels)
#print(df)
pyplot.plot(df,
'using :($2 + $3):5:xtic(1) with boxerror title "women" lc "dark-orange"',
'using :2:4 with boxerror title "men" lc "royalblue"',
style = ['data boxplot', 'fill solid 0.5 border -1'],
boxwidth = '%s' %(width),
xrange = '[0.5:5.5]',
ylabel = '"Scores"',
title = '"Scores by group and gender"',
output = '"sphx_glr_bar_stacked_001.png"',
terminal = 'pngcairo size 640, 480')Compare to the original image:
8.2 Grouped bar chart with labels
#!/usr/bin/env python3
#coding=utf8
import pandas as pd
from pygnuplot import gnuplot, pyplot
from io import StringIO
# data is from https://matplotlib.org/gallery/lines_bars_and_markers/barchart.html#sphx-glr-gallery-lines-bars-and-markers-barchart-py
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
men_means = [20, 34, 30, 35, 27]
women_means = [25, 32, 34, 20, 25]
width = 0.35 # the width of the bars
# Plot programme:
df = pd.DataFrame({'men': men_means, 'women': women_means},
index = labels)
df.index.name = 'label'
#print(df)
pyplot.plot(df,
'using 2:xticlabels(1) title columnheader(2) lc "web-blue"',
'using 3:xticlabels(1) title columnheader(3) lc "orange"',
'using ($0-0.2):($2+1):2 with labels notitle column',
'using ($0+0.2):($3+1):3 with labels notitle column',
title = '"Scores by group and gender"',
xrange = '[-0.5:4.5]',
yrange = '[0:38]',
ylabel = '"Scores"',
style = ['data histogram',
'histogram cluster gap 1',
'fill solid border -1',
'textbox transparent'],
output = '"sphx_glr_barchart_001.png"',
terminal = 'pngcairo size 640, 480')Compare to the original image:
9. Q/A
10. CHANGLOG
1.0 Initial upload;
1.0.3 Now Gnuplot().plot()/splot() supplot set options as parameters.
1.0.7 The pyplot.plot() now can accept both string and pandas.Dataframe as the first parameter, Further more we need pandas installed at first.
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