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 to plot beautiful image in efficicent way in python, we port gnuplot to python. Let’s see an example at first:
#!/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')From the example we can see, we plot the function by one plot command, with all the options as the function parameters. It’s easy to understand and comply with both gnuplot and python’s grammer. The generated plot is as below:
figure 1. pygnuplot demo 1: simple function
Let’s see the detail in the below:
1. Introduction and examples list
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.
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.
We will introduce it in detail in the following chapter and here list the exaples used in this article as below:
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 |
|---|---|---|---|
2. Plot elements in py-gnuplot
As we know, gnuplot use commands to plot all kinds of image, we port almost all the commands as functions in py-gnuplot. Meanwhile, we can use the options as parameters in plot()/splot() command so that you needn’t set all the options before calling the plot()/splot().
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;
2.1 member functions for Gnuplot class
2.1.1 cmd()
def cmd(self, *args):
'''
*args: all the line that need to pass to gnuplot. It could be a
list of lines, or a paragraph; Lines starting with "#" would be
omitted. Every line should be a clause that could be executed in
gnuplot.
'''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 a little hard to pass the python generated data to gnuplot, so we will introduce other functions later:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
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)2.1.2 set()
def set(self, *args, **kwargs):
'''
*args: options without value
*kwargs: options with value. The set and unset commands may optionally
contain an iteration clause, so the arg could be list.
'''The set command can be used to set lots of options. The set and unset commands may optionally contain an iteration clause, so the arg could be list. For examples:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# Gnuplot lines:
#set terminal pngcairo font "arial,10" fontscale 1.0 size 600, 400
#set output "test.png"
#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
# py-python lines:
g = gnuplot.Gnuplot()
g.set(terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"test.png"',
boxwidth = '0.9 relative',
style = 'fill solid 1.0',
label = ['"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'])
g.plot('"file.dat" with boxes')set() is flexible but indeed set() functions is not necessary. We could pass the options as parameter in the constructor and plot(). For examples the following script act equally with the above:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# py-python lines:
g = gnuplot.Gnuplot(terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"test.png"',
boxwidth = '0.9 relative',
style = 'fill solid 1.0',
label = ['"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'])
g.plot('"file.dat" with boxes')We can also write it as the following:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# py-python lines:
g = gnuplot.Gnuplot()
g.plot('"file.dat" with boxes',
terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"test.png"',
boxwidth = '0.9 relative',
style = 'fill solid 1.0',
label = ['"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'])2.1.3 unset()
def unset(self, *items):
'''
*args: options that need to be unset
'''Options set using the set() function may be returned to their default state by the corresponding unset() function:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
# py-python lines:
g = gnuplot.Gnuplot()
g.unset('xlabel', 'ylabel', 'xrange', 'yrange')
g.plot('sin(x) with lp')2.1.4 plot()
class gnuplot.Gnuplot(object):
def plot(self, *items, **kwargs):
'''
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''
class pyplot.Gnuplot(object):
def plot(self, data, *items, **kwargs):
'''
data: The data that need to be plotted. It's either the string of list
or the Pnadas Dataframe, if it's Pnadas Dataframe it would be converted
to string by data.to_csv(sep = ' ')
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''plot is the primary command for drawing image in gnuplot, We port it as a function in py-python. As description, the plot-element is passed as variable parameters, and options are passed as dictionary parameter. please be noted that the plot-element should be in the single quotation marks:
Note that there are two submodule in py-gnuplot: pygnuplot.gnuplot and pygnuplot.pyplot, all the other functions are the same while the plot()/splot() have a little difference:
gnuplot.plot() support functions and file name as input data
while pyplot.plot() support string or pandas dataframe data type, Further more the string that need to be plot 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 plot the gnuplot function or datafile we use pygnuplot.gnuplot:
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
g = gnuplot.Gnuplot()
g.plot('sin(x)')
g.plot('"file.dat" with boxes',
terminal = 'pngcairo font "arial,10" fontscale 1.0 size 600, 400',
output = '"test.png"',
boxwidth = '0.9 relative',
style = 'fill solid 1.0')If we plot the python generated data we use pygnuplot.pyplot:
#!/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]})
g = pyplot.Gnuplot()
g.plot(df, 'using 1:2 with lines', 'using 1:3 with points')As we stated in 2.1.2 set() , we can use parameter to replace set()/unset() in plot() function, here is something we need to know when using parameter to replace set()/unset():
If it’s flag parameter, for example:
set grid set hidden3d
we can pass it as a empty value:
#!/usr/bin/env python3
# Examples of gnuplot.plot()
g = gnuplot.Gnuplot()
g.plot('sin(x)',
'cos(x)',
...,
grid = '',
hidden3d = '',
...)
# Examples of pyplot.plot()
df = pd.DataFrame(data = {'col1': [1, 2],
'col2': [3, 4],
'col3': [5, 6]})
g = pyplot.Gnuplot()
g.plot(df, 'using 1:2 with lines', 'using 1:3 with points',
grid = '')3) We have two means to pass “unset” command, one is the no-xxx option and the other is xxx = None, for examples we’d like to unset the grid and xrange:
unset grid unset xrange
We can do that in py-gnuplot by:
g = gnuplot.Gnuplot()
# Example of use no-xxx to unset the flag
g.plot(df, 'using 0:2:3:4:5 notitle with financebars lt 8',
...,
nogrid = '',
noxlabel = '',
...)
# Example of use None to unset the flag
g.plot(df, 'using 0:2:3:4:5 notitle with financebars lt 8',
...,
grid = None,
xlabel = None,
...)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:
g = gnuplot.Gnuplot()
g.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'],
...,
...)2.1.5 splot()
class gnuplot.Gnuplot(object):
def splot(self, *items, **kwargs):
'''
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''
class pyplot.Gnuplot(object):
def splot(self, data, *items, **kwargs):
'''
data: The data that need to be plotted. It's either the string of list
or the Pnadas Dataframe, if it's Pnadas Dataframe it would be converted
to string by data.to_csv(sep = ' ')
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''The usage of splot() is exactly the same as plot().
2.2 functions for global class-less function call
We can implement all what we want by object-oriented interface, but sometimes we want to quick plot an image, we can call the global class-less function call:
2.2.1 plot()
#submodule gnuplot
def plot(*args, **kwargs):
'''
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''
#submodule pyplot
def plot(self, data, *items, **kwargs):
'''
data: The data that need to be plotted. It's either the string of list
or the Pnadas Dataframe, if it's Pnadas Dataframe it would be converted
to string by data.to_csv(sep = ' ')
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''The usage is the same as in 2.1.4 plot() except that you needn’t allocate a Gnuplot() instance at first:.
pyplot.plot(df.to_csv(sep = ' '),
'using 0:2:3:4:5 notitle with financebars lt 8',
...,
xrange = '[-10:10]',
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'],
...,
...)2.2.2 splot()
#submodule gnuplot
def splot(*args, **kwargs):
'''
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''
#submodule pyplot
def splot(self, data, *items, **kwargs):
'''
data: The data that need to be plotted. It's either the string of list
or the Pnadas Dataframe, if it's Pnadas Dataframe it would be converted
to string by data.to_csv(sep = ' ')
*items: The list of plot command;
**kwargs: The options that would be set before the plot command.
'''The usage is the same as in 2.1.5 splot() except that you needn’t allocate a Gnuplot() instance at first.
2.2.3 multiplot()
Since we don’t allocate the Gnuplot instance, there is a little trick to plot the multiplot image. To solve the issue we create 3 brand new function to implement that:
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')3. Summary of the py-gnuplot
3.1 Four kinds of plot patterns
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: Let’s have a deep check how to plot it:
3.1.1 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.
3.1.2 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.
3.1.3 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.
3.1.4 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')3.2 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:
3.3. 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. It plot the data in sequence as in gnuplot: 1) we set the option at first; 2) plot the subplot; 3) multiplot the whole plot;
global class-less function call: It refer to the syntax of matplotlib and mplfinance, only a few single function could plot what you want. We add two extra function: make_plot()/make_splot() to prepare the subplot and then call the multiplot() at the end. It seems much alike the matplotlib does.
The same functions could be 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.
See the difference with the following examples(Both produce the same output). From the following two example for object-oriented interface call and global class-less function call, we can see:
finance example with object-oriented interface call:
#!/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.
g = pyplot.Gnuplot(output = '"finance.13.png"',
term = 'pngcairo font "arial,10" fontscale 1.0 size 900, 600',
multiplot = "")
g.plot(df,
'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']
)
g.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')finance with global class-less function call:
#!/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('examples/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')Both script generated the same output:
Let’s see what’s the difference with more examples(Click the script name to see the whole script) in Table1 and Table2:
4. More examples
4.1 histogram
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
df = pd.read_csv('examples/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('examples/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:
4.2 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:
4.3 pm3d
iterate.dem
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
#http://ayapin-film.sakura.ne.jp/Gnuplot/Pm3d/Part1/whale.html
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',
# xtics = '',
# ytics = '',
# ztics = '',
# title = '"Iteration over all available data in a file" ',
# lmargin = 'at screen 0.09',
# rmargin = 'at screen 0.9')
#g.splot('for [i=1:*] "examples/whale.dat" index i title sprintf("scan %d",i) with lines')
# Black and white version
g.splot('"examples/whale.dat" w pm3d',
term = 'pngcairo size 480,480',
out = '"whale.png"',
style = 'line 100 lw 0.1 lc "black"',
pm3d = 'depth hidden3d ls 100',
cbrange = '[-0.5:0.5]',
palette = 'rgb -3,-3,-3',
colorbox = None,
border = None,
key = None,
zrange = '[-2:2]',
tics = None,
view = '60,185,1.5')#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import pandas as pd
#gnuplot.splot('for [i=1:*] "examples/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"',
# border = '',
# 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',
# xtics = '',
# ytics = '',
# ztics = '',
# 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('"examples/whale.dat" w pm3d',
term = 'pngcairo size 480,480',
out = '"whale.png"',
style = 'line 100 lw 0.1 lc "black"',
pm3d = 'depth hidden3d ls 100',
cbrange = '[-0.5:0.5]',
palette = 'rgb -3,-3,-3',
colorbox = None,
border = None,
key = None,
zrange = '[-2:2]',
tics = None,
view = '60,185,1.5')And the generated output is as following:
5 Examples in matplotlib
Just for fun, I translate some examples in matplotlib to py-gnuplot:
5.1 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')Refer to the original script: Stacked bar chart and the original image:
5.2 Grouped bar chart with labels
#!/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/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')Refer to the original script: Grouped bar chart with labels and the original image:
5.3 Multiplot Axes Demo
#!/usr/bin/env python3
#coding=utf8
from pygnuplot import gnuplot, pyplot
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
#https://matplotlib.org/gallery/subplots_axes_and_figures/axes_demo.html#sphx-glr-gallery-subplots-axes-and-figures-axes-demo-py
#http://gnuplot.sourceforge.net/demo_5.2/bins.html
# 1) create some data to use for the plot
np.random.seed(19680801) # Fixing random state for reproducibility
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t / 0.05) # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)] * dt # colored noise
df = pd.DataFrame({'r': r, 'x': x, 's': s}, index = t)
df.index.name = 't'
#print(df.tail().to_csv())
# 2) Plot the data
main = pyplot.make_plot(df.iloc[:1000],
'using 1:4 with line lw 2 lc "web-blue"',
title = '"Gaussian colored noise"',
xlabel = '"time (s)"',
ylabel = '"current (nA)"',
xrange = '[0:1]',
yrange = '[-0.015:0.03]',
size = ' 1, 1',
origin = '0, 0')
right = pyplot.make_plot(df,
'using 4 bins=400 with boxes title "20 bins" lw 2 lc "web-blue"',
title = '"Probability"',
xlabel = None,
ylabel = None,
xrange = None,
yrange = None,
origin = '0.65, 0.56',
size = '0.24, 0.32')
left = pyplot.make_plot(df,
'using 1:2 with line lw 2 lc "web-blue"',
title = '"Impulse response"',
xrange = '[0:0.2]',
origin = '0.15, 0.56',
size = '0.24, 0.32',
object = 'rectangle from graph 0,0 to graph 1,1 behind fc "black" fillstyle solid 1.0')
pyplot.multiplot(main, right, left,
output = '"sphx_glr_axes_demo_001.png"',
term = 'pngcairo font "arial,10" fontscale 1.0 size 640, 480',
key = '')Refer to the original script: Multiplot Axes Demo and the original image:
5.4 Basic pie chart
#!/usr/bin/env python3
#coding=utf8
import pandas as pd
import math
from pygnuplot import gnuplot, pyplot
#http://www.phyast.pitt.edu/~zov1/gnuplot/html/pie.html
#https://matplotlib.org/gallery/pie_and_polar_charts/pie_features.html#sphx-glr-gallery-pie-and-polar-charts-pie-features-py
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
startangle = math.pi/2
# Prepare the data: caculate the percentage
df = pd.DataFrame({'labels': labels, 'sizes': sizes, 'explode': explode})
df.index.name = 'index'
df['percentage'] = df['sizes'] / df['sizes'].sum()
df['end'] = df['percentage'].cumsum()*2*math.pi + startangle
#df['start'] = df['end'].shift(axis=0, fill_value = 0)
df['start'] = df['end'].shift(axis=0)
df = df.fillna(startangle)
#print(df)
#缩进部分的阴影
pie_shade = []
pie_graph = []
shade_offset = 0.03
for k, v in df.iterrows():
#print(k,v)
cos = math.cos((v['start']+v['end'])/2)
sin = math.sin((v['start']+v['end'])/2)
# If we'd like explode the piece, ad the dx/dy to move the origi point.
dx = v['explode'] * cos
dy = v['explode'] * sin
# make the shade for each piece
piece = gnuplot.make_plot('cos(t)+%f, sin(t)+%f with filledcurves xy=%f,%f lc "grey80"'
%(dx-shade_offset, dy-shade_offset, dx-shade_offset, dy-shade_offset),
trange = '[%f:%f]' %(v['start'], v['end']),
xrange = '[-1.5:1.5]',
yrange = '[-1.5:1.5]')
pie_shade.append(piece)
# make the pie and label
piece = gnuplot.make_plot('cos(t)+%f, sin(t)+%f with filledcurve xy=%f,%f lt %d'
%(dx, dy, dx, dy, k+3),
trange = '[%f:%f]' %(v['start'], v['end']),
xrange = '[-1.5:1.5]',
yrange = '[-1.5:1.5]',
label = ['1 "%s" at %f, %f center front' %(v['labels'], 1.2*cos+dx, 1.2*sin+dy),
'2 "%.1f%%" at %f, %f center front' %(v['percentage']*100, 0.6*cos, 0.6*sin)])
pie_graph.append(piece)
gnuplot.multiplot(*pie_shade, *pie_graph,
output = '"sphx_glr_pie_features_0011.png"',
terminal = 'pngcairo size 640, 480',
parametric = '',
border = '',
tics = '',
key = '',
multiplot = '')Refer to the original script: Basic pie chart and the original image:
6. Q/A
7. 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.
1.0.11 Fix the bug: gnuplot.multiplot() doesn’t work.
1.0.14 Enhancement: multiplot will automatically unset the following options for easy use in the following subplot, you needn’t unset them if not use:
label, title, xtics, x2tics, ytics, y2tics, ztics, cbtics, xlabel, x2label, ylabel, y2label, zlabel, cblabel, xrange, x2range, yrange, y2range, zrange, cbrange, rrange, trange, urange, vrange)
1.0.15 1) Add an example of comparing the object-oriented interface call and global class-less function call in multiplot() in multiplot() in multiplot() in multiplot(). 2) remove some duplicate setting line.
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