Metadata-Version: 1.0
Name: py-gnuplot
Version: 0.5
Summary: py-gnuplot is a Python plot tools based on Gnuplot.
Home-page: http://www.gnuplot.info
Author: Yongping Guo
Author-email: guoyoooping@163.com
License: GPLv3
Description: py-gnuplot
        ***********
        
        py-gnuplot is a Python plot tools based on Gnuplot.
        
        To install easily::
        
            pip install -U py-gnuplot
        
        Or to install by source code::
        
            python install py-gnuplot
        
        1. Introduction
        ===============
        
        Gnuplot is a portable command-line driven graphing utility for Linux, OS/2, MS
        Windows, OSX, VMS, and many other platforms.
        
        But it seems gnuplot has its own grammer and couldn't be easily called in
        python. On the other hand we generage many data that need to be plotted in
        python, but we have barrier to pass the data to gnuplot. Matplotlib/mplfiance
        could plot the data generaged in python, but how about leveraging the power of
        gnuplot?
        
        For example, for finance data, we can plot it in Gnuplot, we can also plot it
        directly in python in the exactly same result: `6.2 multiplot`_:
        
        .. image:: http://gnuplot.sourceforge.net/demo/finance.13.png
        
        The py-gnuplot include two parts:
        
            * gnuplot: the exact python implementation of gnuplot, it has the same
              functions as gnuplot, we can plot the file and function as in gnuplot;
            * pyplot: We can plot the pandas dataframe generated in python, it process
              the python generated data;
        
        For functions and file data, it's fit to use gnuplot submodule to plot the
        data as in gnuplot. For python generated data, especially `pandas`_ dataframe,
        it's easy to use pyplot submodule to plot the data;
        
        .. _pandas: https://pandas.pydata.org/
        
        For each submodule, we both have two means to call the functions:
        
            * object-oriented interface: Via class Gnuplot and you will always have a
              Gnuplot object. 
            * Easy way: Via global class-less functions, almost one single function
              could plot what you want..
        
        object-oriented interface is straightworward and it is simple interepretation
        for Gnuplot script and easy to understand if you are familar with Gnuplot. But
        it's a little complex to plot data, especially the panda data in the memory.
        
        For easy use and for users who are not very familar with Gnuplot, we develop
        the sub module pyplot for easily plotting the data, it refer to the syntax of
        matplotlib and mplfinance, the syntax is easy to understand and you needn't
        know too much about gnuplot syntax;
        
        It's recommended to use **Easy way** since it's easy to understand based on
        the point of python's view.
        
        2. object-oriented interface and Easy way
        =========================================
        
        As describe above, object-oriented interface is simple and easy to understand
        as gnuplot's logic. Easy way plot the data in python way.
        
        2.1 object-oriented interface
        ------------------------------
        
        object-oriented interface is a simple wrapper for gnuplot, you can convert the
        gnuplot script to py-gnuplot script line by line in py-gnuplot.gnuplot module.
        For example we can convert gnuplot demo script: `simple.dem`_ to python script
        easily by wrapping them with cmd(''), you can see it's straightforward and is
        1:1 mapping:
        
        module gnuplot has an object-oriented interface (via class gnuplotlib) and you
        must allocate a gnuplot object before you use it.
        
        .. _simple.dem: http://gnuplot.sourceforge.net/demo/simple.1.gnu
        .. code-block:: python
        
            #!/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))')
        
        The generated output is as following:
        
        .. image:: http://gnuplot.sourceforge.net/demo/simple.1.png
        
        Meanwhile we provide more complex wrapper and you can think plot action as a
        plot() method and others are set() method, it also works as following:
        
        .. code-block:: python
        
            #!/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))')
        
        It's equivalent to method 1 but seems muck like a python script.
        
        2.2 easy way
        --------------
        
        The recommended way is simple and easy to understand in python way:
        
        .. code-block:: python
        
            #!/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. gnuplot and pyplot
        ======================
        
        In brief, gnuplot submodule is for plotting functions and data in file, while
        pyplot submodule is for plotting python itself generated data in pandas
        dataframe format.
        
        3.1 Sub module gnuplot: the original gnuplot
        --------------------------------------------
        
        gnuplot demo script: `surface2.dem`_ could be writen as python script as
        following:
        
        .. _surface2.dem: http://gnuplot.sourceforge.net/demo/simple.1.gnu
        
        .. code-block:: python
        
            #!/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:
        
        .. image:: http://gnuplot.sourceforge.net/demo/surface2.9.png
        
        3.2 Sub module pyplot: plot the python generated data
        -----------------------------------------------------
        
        Sub module gnuplot is straightworward and easy to understand but a little
        complex. It's simple intepretion for Gnuplot script so you must understand
        Gnuplot deeply at first.
        
        For easy use and for users who are not very familar with Gnuplot, we develop a
        new sub module for easily plotting the data, it refer to the syntax of matplotlib
        and mplfinance, the syntax is easy to understand and you needn't know too much
        about gnuplot syntax;
        
        Meanwhile, sub module gnuplot is object oriented and you must allocate a
        gnuplot object before you use it while submodule pyplot don't need that.
        Submodule pyplot need that the data should be panda dataframe format. Let's
        see the example `histograms.1.gnu`_ from gnuplot demo, the python sciprt is as
        following:
        
        .. _histograms.1.gnu: http://gnuplot.sourceforge.net/demo/histograms.1.gnu
        
        .. code-block:: python
        
            #!/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,
                    '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:
        
        .. image:: http://gnuplot.sourceforge.net/demo/histograms.1.png
        
        4. Mapping between the original Gnuplot and py-gnuplot
        ======================================================
        
        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.
        
        4.1 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:
        
        .. code-block:: python
        
            #!/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')
        
        .. important:: Submodule gnuplot and submodule pyplot have difference in plot(), gnuplot.plot() support functions and file data while pyplot.plot() support pandas dataframe data type. Further more pyplot.plot() pass the df as the first parameter. This is the only difference between gnuplot submodule and pyplot module.
        
        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:
        
        .. code-block:: python
        
            #!/usr/bin/env python3
            #coding=utf8
            from pygnuplot import gnuplot, pyplot
        
            # pyplot.plot() is definied as:
            # def plot(df, *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')
        
        
        4.2 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:
        
        .. code-block:: python
        
            #!/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>"')
        
        4.3 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:
        
        .. code-block:: python
        
            #!/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:
        
        .. code-block:: python
        
            #!/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')
        
        
        4.4 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_subplot(\*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.
        
        Before call multiplot() we must generate the subplot object by calling
        make_subplot(), It is much like mplfinance.add_plot(), it only add the subplot
        command for further call:
        
        .. code-block:: python
        
            #!/usr/bin/env python3
            #coding=utf8
            from pygnuplot import gnuplot, pyplot
        
            sub1 = gnuplot.make_subplot('sin(x)', ylabel = 'ylabel')
            sub2 = gnuplot.make_subplot('cos(x)', xlabel = 'xlabel')
            sub3 = gnuplot.make_subplot('sin(2*x)', noxlabel = '', ylabel = '')
            sub4 = gnuplot.make_subplot('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')
        
        5. Plot methods detail
        ======================
        
        5.1 methods in gnuplot
        -----------------------
        
        5.2 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 as data.
        
        plot(df, \*args, \*\*kwargs)
        +++++++++++++++++++++++++++++
        
        @ df: The data that need to plot. it should be pandas dataframe format.
        In gnuplot we pass the data as a function or data file. But normally in
        python script, we normally get the data in the memory, not in the file. So
        we develop the submodule to plot the data in memory, we should pass the df
        in pandas dataframe format, for example::
        
            df = pd.read_csv('immigration.dat', index_col = 0,
                            sep='\t', comment='#')
            pyplot.plot(df, ...)
        
        @ 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 "df", we
        pass the command as a list of command as following::
        
            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,
                        'using 0:2:3:4:5 notitle with financebars lt 8',
                        ...,
                        xtics = 'border in scale 1,0.5 nomirror rotate by -45 autojustify norangelimit',
                        ...)
        
        splot(df, \*args, \*\*kwargs)
        +++++++++++++++++++++++++++++
        
        The parameter are same as plot(), the only difference is it use "splot" to
        plot insteading of "plot".
        
        make_subplot(df, \*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_subplot(df,
                    '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_subplot(), 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)
        ++++++++++++++++++++++++++++++++++
        
        To be implemented.
        
        6. More examples
        ================
        
        6.1 splot
        ---------
        
        module gnuplot has an object-oriented interface (via class gnuplotlib) and you
        must allocate a gnuplot object before you use it.
        
        .. _simple.dem: http://gnuplot.sourceforge.net/demo/simple.1.gnu
        .. code-block:: python
        
            #!/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))')
        
        The generated output is as following:
        
        .. image:: http://gnuplot.sourceforge.net/demo/simple.1.png
        
        6.2 multiplot
        -------------
        
        we convert the gnuplot demo script: `finance.dem`_ to the final python script:
        
        .. _finance.dem: http://gnuplot.sourceforge.net/demo/finance.13.gnu
        
        .. code-block:: python
        
            #!/usr/bin/env python3
            #coding=utf8
            from pygnuplot import gnuplot, pyplot
            import pandas as pd
        
        
            # 10 May 2005
            # 
            # Though gnuplot is primarily a scientific plotting program, it can do a great
            # job of plotting finance charts as well. The primary challenge is the irregular
            # nature of financial time series. Stocks don't trade every day, so when you set
            # the x-axis to time gaps appear for non-trading days. Investors and traders
            # generally prefer that these gaps be omitted. Another challenge is that finance
            # charts are best presented in semi-log form (log y-axis, linear x-axis),
            # but gnuplot wants to span decades in its log scaling, something that stocks
            # rarely do. These and other challenges are met in finance.dem, a short 
            # demonstration script that proves that gnuplot can really shine in this area.
            # 
            # gnuplot plays a central role in our work. Almost all the graphs in "Bollinger
            # on Bollinger Bands" were plotted by gnuplot, many gnuplot visuals have
            # appeared on CNBC, our in-house analytics use gnuplot for visual display and
            # The Capital Growth Letter relies heavily on gnuplot for its charts.  
            # 
            # Finally, gnuplot is yet another successful demonstration of a powerful idea,
            # open source programming. Thanks to all who made gnuplot possible from the
            # earliest days to the present and to all those who will contribute in the
            # future. (Special thanks to Hans-Bernhard Broeker whose patience helped me to
            # climb the grade and to Ethan Merritt whose recent contributions have been
            # invaluable to our work.)
            # 
            # John Bollinger
            # www.BollingerBands.com 
        
            # a demonstration of gnuplot finance plot styles
            # by John Bollinger, CFA, CMT
            # www.BollingerBands.com
            # BBands@BollingerBands.com
        
            # data and indicators in finance.dat
            # data file layout: 
            # date, open, high, low, close, volume,
            # 50-day moving average volume, Intraday Intensity, 
            # 20-day moving average close, 
            # upper Bollinger Band, lower Bollinger Band 
        
        
            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 '])
            sub1 = pyplot.make_subplot(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']
                    )
        
            sub2 = pyplot.make_subplot(df,
                    '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')
        
            pyplot.multiplot(sub1, sub2,
                    output = '"finance.13.png"',
                    term = 'pngcairo font "arial,10" fontscale 1.0 size 900, 600')
        
        And this the generated output:
        
        .. image:: http://gnuplot.sourceforge.net/demo/finance.13.png
        
        7. Q/A
        ======
        
        8. TODO
        ============
        
        The 0.1 release only support plot/multiplot, will support splot/multisplot the
        next release
        
Platform: UNKNOWN
