From 4df652124b1724c5a6db5bd0f998f0a83da9ed93 Mon Sep 17 00:00:00 2001 From: Giorgio Calderone Date: Sat, 18 Apr 2020 16:21:31 +0200 Subject: [PATCH] Updated --- v1.2.0/advanced/index.html | 32 +++++++++++++-------- v1.2.0/api/index.html | 51 +++++++++++++++++++-------------- v1.2.0/assets/advanced011.png | Bin 23957 -> 23681 bytes v1.2.0/assets/advanced011b.png | Bin 11355 -> 20363 bytes v1.2.0/assets/advanced012.png | Bin 51143 -> 48505 bytes v1.2.0/assets/advanced013a.png | Bin 0 -> 9580 bytes v1.2.0/assets/advanced013b.png | Bin 7924 -> 10439 bytes v1.2.0/assets/advanced014a.png | Bin 0 -> 11653 bytes v1.2.0/assets/advanced014b.png | Bin 11379 -> 11363 bytes v1.2.0/assets/advanced014c.png | Bin 20222 -> 20197 bytes v1.2.0/assets/advanced014d.png | Bin 44163 -> 39781 bytes v1.2.0/assets/advanced014e.png | Bin 0 -> 35583 bytes v1.2.0/assets/basic007a.png | Bin 8608 -> 8742 bytes v1.2.0/assets/recipes002.png | Bin 12367 -> 11924 bytes v1.2.0/assets/recipes002a.png | Bin 11348 -> 11282 bytes v1.2.0/assets/recipes002b.png | Bin 0 -> 40701 bytes v1.2.0/basic/index.html | 14 ++++----- v1.2.0/examples/index.html | 2 +- v1.2.0/index.html | 2 +- v1.2.0/install/index.html | 2 +- v1.2.0/options/index.html | 2 +- v1.2.0/recipes/index.html | 10 +++++-- v1.2.0/search/index.html | 2 +- v1.2.0/search_index.js | 2 +- v1.2.0/style/index.html | 2 +- v1.2.0/terminals/index.html | 2 +- 26 files changed, 71 insertions(+), 52 deletions(-) create mode 100644 v1.2.0/assets/advanced013a.png create mode 100644 v1.2.0/assets/advanced014a.png create mode 100644 v1.2.0/assets/advanced014e.png create mode 100644 v1.2.0/assets/recipes002b.png diff --git a/v1.2.0/advanced/index.html b/v1.2.0/advanced/index.html index b9b74cf..e710922 100644 --- a/v1.2.0/advanced/index.html +++ b/v1.2.0/advanced/index.html @@ -17,9 +17,9 @@ name = "\$MyDataSet1" a = vars.a, b = vars.b, c = vars.c)
┌ Info: Best fit values:
-│   a = 1.49358345451425
-│   b = 0.297338855330415
-└   c = 0.704689661505663

Multiplot

Gnuplot.jl can draw multiple plots in the same figure by exploiting the multiplot command. Each plot is identified by a positive integer number, which can be used as argument to @gp to redirect commands to the appropriate plot.

Recycling data from the previous example we can plot both data and best fit model (in plot 1) and residuals (in plot 2):

@gp    "f(x) = a * sin(b + c*x)"
+│   a = 1.4697928313124
+│   b = 0.32380674639466
+└   c = 0.69649253012739

Multiplot

Gnuplot.jl can draw multiple plots in the same figure by exploiting the multiplot command. Each plot is identified by a positive integer number, which can be used as argument to @gp to redirect commands to the appropriate plot.

Recycling data from the previous example we can plot both data and best fit model (in plot 1) and residuals (in plot 2):

@gp    "f(x) = a * sin(b + c*x)"
 @gp :- "a=$(vars.a)" "b=$(vars.b)" "c=$(vars.c)"
 @gp :- name=>(x, y, err)
 @gp :- "set multiplot layout 2,1"
@@ -33,7 +33,7 @@ name = "\$MyDataSet1"
 @gp :- 2 "p $name u 1:((f(\$1)-\$2) / \$3):(1) w errorbars t 'Resid. [{/Symbol s}]'"
 @gp :-   [extrema(x)...] [0,0] "w l notit dt 2 lc rgb 'black'" # reference line
 @gp :- 1 "p $name w errorbars t 'Data'"
-@gp :-   "p $name u 1:(f(\$1)) w l t 'Best fit model'"

Customized layout

It is also possible to customize the plot layout using the margin keywords (see Histograms for further info):

# Generate random numbers
+@gp :-   "p $name u 1:(f(\$1)) w l t 'Best fit model'"

Customized layout

It is also possible to customize the plot layout using the margin keywords (see Histograms for further info on how to generate andi display histograms):

# Generate random numbers
 x = randn(1000);
 y = randn(1000);
 
@@ -79,18 +79,26 @@ fxy = [sinc2d(x,y) for x in x, y in y]
 @gp :GP2 "plot sin(x)"    # opens secondo window
 @gp :- :GP1 "plot cos(x)" # add a plot on first window

The session ID can appear in every position in the argument list, but only one ID can be present in each call. If the session ID is not specified the :default session is used.

The names of all current sessions can be retrieved with session_names():

julia> println(session_names())
 [:default, :GP1, :GP2]

To quit a specific session use Gnuplot.quit():

julia> Gnuplot.quit(:GP1)
-0

The output value is the exit status of the underlying gnuplot process.

You may also quit all active sessions at once with Gnuplot.quitall():

julia> Gnuplot.quitall()

Histograms

Gnuplot.jl provides a facility to compute (see hist() function) an histogram. It allows to set the range to consider (range= keyword) and either the bin size (bs=) or the total number of bins (nbins=) in the histogram (see hist() documentation for further information) and return a Gnuplot.Histogram1D structure, whose content can be visualized as follows:

x = randn(1000);
+0

The output value is the exit status of the underlying gnuplot process.

You may also quit all active sessions at once with Gnuplot.quitall():

julia> Gnuplot.quitall()

Histograms

Gnuplot.jl provides facilities to compute and display histograms, e.g.:

x = randn(1000);
+@gp hist(x)

The hist() function also accept keywords to set the range to consider (range= keyword) and either the bin size (bs=) or the total number of bins (nbins=) in the histogram. A finer control on the output is achieved by exploiting the fields of the returned (Gnuplot.Histogram1D) structure, e.g.:

x = randn(1000);
 h = hist(x, range=3 .* [-1,1], bs=0.5)
-@gp h.bins h.counts "w histep t 'Data' lc rgb 'red'"

Gnuplot.jl also allows to compute 2D histograms by passing two vectors (with the same lengths) to hist(). Again, a finer control can be achieved by specifying ranges, bin size or number of bins (along both dimensions) and by explicitly using the content of the returned Gnuplot.Histogram2D structure:

x = randn(10_000)
+@gp h.bins h.counts "w histep t 'Data' lc rgb 'red'"

The hist() function compute also 2D histograms by passing two vectors (with the same lengths), e.g.: 

x = randn(10_000)
+y = randn(10_000)
+h = hist(x, y)
+@gp h

Again, a finer control can be achieved by specifying ranges, bin size or number of bins (along both dimensions) and by explicitly using the content of the returned Gnuplot.Histogram2D structure:

x = randn(10_000)
 y = randn(10_000)
 h = hist(x, y, bs1=0.25, nbins2=20, range1=[-3,3], range2=[-3,3])
-@gp "set size ratio -1" h.bins1 h.bins2 h.counts "w image notit"

Alternatively, 2D histograms may be displayed using the boxxyerror plot style which allows more flexibility in, e.g., handling transparencies and drawing the histogram grid. In this case the data can be prepared using the boxxyerror() function, as follows:

box = boxxyerror(h.bins1, h.bins2, cartesian=true)
-@gp "set size ratio -1" "set style fill solid 0.5 border lc rgb 'gray'" :-
-@gp :- box... h.counts "w boxxyerror notit lc pal"

See also Histogram recipes for a quicker way to preview histogram plots.

Contour lines

Although gnuplot already handles contours by itself (with the set contour command), Gnuplot.jl provides a way to calculate contour lines paths before displaying them, using the contourlines() function. We may use it for, e.g., plot contour lines with customized widths and palette, according to their z level. Continuing with the previous example:

clines = contourlines(h.bins1, h.bins2, h.counts, cntrparam="levels discrete 10, 30, 60, 90");
+@gp "set size ratio -1" h.bins1 h.bins2 h.counts "w image notit"

Alternatively, 2D histograms may be displayed using the boxxyerror plot style which allows more flexibility in, e.g., handling transparencies and drawing the histogram grid. In this case the data can be prepared using the boxxy() function, as follows:

@gp "set size ratio -1" "set style fill solid 0.5 border lc rgb 'gray'" :-
+@gp :- boxxy(h) "w boxxy notit lc pal"

Contour lines

Although gnuplot already handles contours by itself (with the set contour command), Gnuplot.jl provides a way to calculate contour lines paths before displaying them, using the contourlines() function. We may preview such lines with:

x = randn(10_000)
+y = randn(10_000)
+h = hist(x, y)
+clines = contourlines(h, "levels discrete 10, 30, 60, 90");
+@gp clines

By exploiting the fields of the Gnuplot.IsoContourLines structure we may also customize line widths, colors and dashed pattern according to their z level, and plot them on top of the 2D histogram:

@gp "set size ratio -1" "set style fill solid 0.5 border lc rgb 'gray'" :-
+@gp :- boxxy(h) "w boxxy notit lc pal"
 for i in 1:length(clines)
-    @gp :- clines[i].data "w l t '$(clines[i].z)' lw $i lc pal" :-
+    @gp :- clines[i].data "w l t '$(clines[i].z)' lw $i dt $i lc pal" :-
 end
-@gp :- key="outside top center box horizontal"

Animations

The Multiplot capabilities can also be used to stack plots one above the other in order to create an animation, as in the following example:

x = y = -10:0.33:10
+@gp :- key="outside top center box horizontal"

Animations

The Multiplot capabilities can also be used to stack plots one above the other in order to create an animation, as in the following example:

x = y = -10:0.33:10
 fz(x,y) = sin.(sqrt.(x.^2 + y.^2))./sqrt.(x.^2+y.^2)
 fxy = [fz(x,y) for x in x, y in y]
 @gsp "set xyplane at 0" "unset colorbox" cbr=[-1,1] zr=[-1,1]
@@ -102,4 +110,4 @@ for direction in [-1,1]
     end
 end
 @gsp

Here the frame variable is used as multiplot index. The animation can be saved in a GIF file with:

save(term="gif animate size 480,360 delay 5", output="assets/animation.gif")

Direct command execution

When gnuplot commands are passed to @gp or @gsp they are stored in a session for future use, or to be saved in Gnuplot scripts. If you simply wish to execute a command without storing it in the session, and possibly retrieve a value, use gpexec. E.g., to retrieve the value of a gnuplot variable:

julia> gpexec("print GPVAL_TERM")
-"unknown"

You may also provide a session ID as first argument (see Multiple sessions) to redirect the command to a specific session.

Alternatively you may start the The gnuplot REPL to type commands directly from the Julia prompt.

The gnuplot REPL

The Gnuplot.jl package comes with a built-in REPL mode to directly send commands to the underlying gnuplot process. Since the REPL is a global resource, the gnuplot mode is not enabled by default. You can start it with:

Gnuplot.repl_init(start_key='>')

The customizable start_key character is the key which triggers activation of the REPL mode. To quit the gnuplot REPL mode hit the backspace key.

Dry sessions

A "dry session" is a session with no underlying gnuplot process. To enable dry sessions type:

Gnuplot.options.dry = true;

before starting a session (see also Options). Note that the dry option is a global one, i.e. it affects all sessions started after setting the option.

Clearly, no plot can be generated in dry sessions. Still, they are useful to run Gnuplot.jl code without raising errors (no attempt will be made to communicate with the underlying process). Moreover, Gnuplot scripts can also be generated in a dry session, without the additional overhead of sending data to the gnuplot process.

If a gnuplot process can not be started the package will print a warning, and automatically enable dry sessions.

+"unknown"

You may also provide a session ID as first argument (see Multiple sessions) to redirect the command to a specific session.

Alternatively you may start the The gnuplot REPL to type commands directly from the Julia prompt.

The gnuplot REPL

The Gnuplot.jl package comes with a built-in REPL mode to directly send commands to the underlying gnuplot process. Since the REPL is a global resource, the gnuplot mode is not enabled by default. You can start it with:

Gnuplot.repl_init(start_key='>')

The customizable start_key character is the key which triggers activation of the REPL mode. To quit the gnuplot REPL mode hit the backspace key.

Dry sessions

A "dry session" is a session with no underlying gnuplot process. To enable dry sessions type:

Gnuplot.options.dry = true;

before starting a session (see also Options). Note that the dry option is a global one, i.e. it affects all sessions started after setting the option.

Clearly, no plot can be generated in dry sessions. Still, they are useful to run Gnuplot.jl code without raising errors (no attempt will be made to communicate with the underlying process). Moreover, Gnuplot scripts can also be generated in a dry session, without the additional overhead of sending data to the gnuplot process.

If a gnuplot process can not be started the package will print a warning, and automatically enable dry sessions.

diff --git a/v1.2.0/api/index.html b/v1.2.0/api/index.html index 2e843b4..42f7f1e 100644 --- a/v1.2.0/api/index.html +++ b/v1.2.0/api/index.html @@ -1,43 +1,50 @@ -API · Gnuplot.jl
Edit on GitHub

API

Index

Exported symbols

The list of Gnuplot.jl exported symbols is as follows:

Gnuplot.@gp — Macro
@gp args...

The @gp macro, and its companion @gsp for 3D plots, allows to send data and commands to the gnuplot using an extremely concise syntax. The macros accepts any number of arguments, with the following meaning:

  • one, or a group of consecutive, array(s) of either Real or String build up a dataset. The different arrays are accessible as columns 1, 2, etc. from the gnuplot process. The number of required input arrays depends on the chosen plot style (see gnuplot documentation);

  • a string occurring before a dataset is interpreted as a gnuplot command (e.g. set grid);

  • a string occurring immediately after a dataset is interpreted as a plot element for the dataset, by which you can specify using clause, with clause, line styles, etc.. All keywords may be abbreviated following gnuplot conventions. Moreover, "plot" and "splot" can be abbreviated to "p" and "s" respectively;

  • the special symbol :- allows to split one long statement into multiple (shorter) ones. If given as first argument it avoids starting a new plot. If it given as last argument it avoids immediately running all commands to create the final plot;

  • any other symbol is interpreted as a session ID;

  • an Int (>= 1) is interpreted as the plot destination in a multi-plot session (this specification applies to subsequent arguments, not previous ones);

  • an input in the form "\$name"=>(array1, array2, etc...) is interpreted as a named dataset. Note that the dataset name must always start with a "$";

  • an input in the form keyword=value is interpreted as a keyword/value pair. The accepted keywords and their corresponding gnuplot commands are as follows:

    • xrange=[low, high] => "set xrange [low:high];
    • yrange=[low, high] => "set yrange [low:high];
    • zrange=[low, high] => "set zrange [low:high];
    • cbrange=[low, high]=> "set cbrange[low:high];
    • key="..." => "set key ...";
    • title="..." => "set title "..."";
    • xlabel="..." => "set xlabel "..."";
    • ylabel="..." => "set ylabel "..."";
    • zlabel="..." => "set zlabel "..."";
    • cblabel="..." => "set cblabel "..."";
    • xlog=true => set logscale x;
    • ylog=true => set logscale y;
    • zlog=true => set logscale z.
    • cblog=true => set logscale cb;
    • margins=... => set margins ...;
    • lmargin=... => set lmargin ...;
    • rmargin=... => set rmargin ...;
    • bmargin=... => set bmargin ...;
    • tmargin=... => set tmargin ...;

All Keyword names can be abbreviated as long as the resulting name is unambiguous. E.g. you can use xr=[1,10] in place of xrange=[1,10].

  • a PlotElement object is expanded in its fields and processed as one of the previous arguments;

  • any other data type is processed through an implicit recipe. If a suitable recipe do not exists an error is raised.

source
Gnuplot.@gsp — Macro
@gsp args...

This macro accepts the same syntax as @gp, but produces a 3D plot instead of a 2D one.

source
Gnuplot.contourlines — Function
contourlines(x::Vector{Float64}, y::Vector{Float64}, h::Matrix{Float64}; cntrparam="level auto 10")

Compute paths of contour lines for 2D data, and return a vector of IsoContourLines object.

Arguments:

  • x, y: Coordinates;
  • h: the levels on which iso contour lines are to be calculated
  • cntrparam: settings to compute contour line paths (see gnuplot documentation for cntrparam).

Example

x = randn(5000);
+API · Gnuplot.jl
Edit on GitHub
API
Index
Exported symbols
The list of Gnuplot.jl exported symbols is as follows:
Gnuplot.@gp — Macro
@gp args...
The @gp macro, and its companion @gsp for 3D plots, allows to send data and commands to the gnuplot using an extremely concise syntax.  The macros accepts any number of arguments, with the following meaning:
  • one, or a group of consecutive, array(s) of either Real or String build up a dataset.  The different arrays are accessible as columns 1, 2, etc. from the gnuplot process.  The number of required input arrays depends on the chosen plot style (see gnuplot documentation);
  • a string occurring before a dataset is interpreted as a gnuplot command (e.g. set grid);
  • a string occurring immediately after a dataset is interpreted as a plot element for the dataset, by which you can specify using clause, with clause, line styles, etc..  All keywords may be abbreviated following gnuplot conventions.  Moreover, "plot" and "splot" can be abbreviated to "p" and "s" respectively;
  • the special symbol :- allows to split one long statement into multiple (shorter) ones.  If given as first argument it avoids starting a new plot.  If it given as last argument it avoids immediately running all commands to create the final plot;
  • any other symbol is interpreted as a session ID;
  • an Int (>= 1) is interpreted as the plot destination in a multi-plot session (this specification applies to subsequent arguments, not previous ones);
  • an input in the form "\$name"=>(array1, array2, etc...) is interpreted as a named dataset.  Note that the dataset name must always start with a "$";
  • an input in the form keyword=value is interpreted as a keyword/value pair.  The accepted keywords and their corresponding gnuplot commands are as follows:
    • xrange=[low, high] => "set xrange [low:high];
    • yrange=[low, high] => "set yrange [low:high];
    • zrange=[low, high] => "set zrange [low:high];
    • cbrange=[low, high]=> "set cbrange[low:high];
    • key="..."  => "set key ...";
    • title="..."  => "set title "..."";
    • xlabel="..." => "set xlabel "..."";
    • ylabel="..." => "set ylabel "..."";
    • zlabel="..." => "set zlabel "..."";
    • cblabel="..." => "set cblabel "..."";
    • xlog=true   => set logscale x;
    • ylog=true   => set logscale y;
    • zlog=true   => set logscale z.
    • cblog=true  => set logscale cb;
    • margins=... => set margins ...;
    • lmargin=... => set lmargin ...;
    • rmargin=... => set rmargin ...;
    • bmargin=... => set bmargin ...;
    • tmargin=... => set tmargin ...;
All Keyword names can be abbreviated as long as the resulting name is unambiguous.  E.g. you can use xr=[1,10] in place of xrange=[1,10].
  • a PlotElement object is expanded in its fields and processed as one of the previous arguments;
  • any other data type is processed through an implicit recipe. If a suitable recipe do not exists an error is raised.
source
Gnuplot.@gsp — Macro
@gsp args...
This macro accepts the same syntax as @gp, but produces a 3D plot instead of a 2D one.
source
Gnuplot.boxxy — Function
boxxy(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)
+boxxy(h::Histogram2D)
source
Gnuplot.contourlines — Function
contourlines(x::Vector{Float64}, y::Vector{Float64}, z::Matrix{Float64}, cntrparam="level auto 10")
+contourlines(h::Histogram2D, cntrparam="level auto 10")
Compute paths of contour lines for 2D data, and return a vector of IsoContourLines object.
Note
This feature is not available in dry mode and will raise an error if used.
Arguments:
  • x, y: Coordinates;
  • z: the levels on which iso contour lines are to be calculated
  • cntrparam: settings to compute contour line paths (see gnuplot documentation for cntrparam).
Example
x = randn(5000);
 y = randn(5000);
 h = hist(x, y, nbins1=20, nbins2=20);
-clines = contourlines(h.bins1, h.bins2, h.counts, cntrparam="levels discrete 15, 30, 45");
+clines = contourlines(h, "levels discrete 15, 30, 45");
+
+# Use implicit recipe
+@gp clines
+
+# ...or use IsoContourLines fields:
 @gp "set size ratio -1"
 for i in 1:length(clines)
-    @gp :- clines[i].data "w l t '$(clines[i].z)' dt $i"
-end
source
Gnuplot.dataset_names — Function
dataset_names(sid::Symbol)
-dataset_names()
Return a vector with all dataset names for the sid session.  If sid is not provided the default session is considered.
source
Gnuplot.gpexec — Function
gpexec(sid::Symbol, command::String)
+    @gp :- clines[i].data "w l t '$(clines[i].z)' lw $i dt $i"
+end
source
Gnuplot.dataset_names — Function
dataset_names(sid::Symbol)
+dataset_names()
Return a vector with all dataset names for the sid session.  If sid is not provided the default session is considered.
source
Gnuplot.gpexec — Function
gpexec(sid::Symbol, command::String)
 gpexec(command::String)
Execute the gnuplot command command on the underlying gnuplot process of the sid session, and return the results as a Vector{String}.  If a gnuplot error arises it is propagated as an ErrorException.
If the sid argument is not provided, the default session is considered.
Examples:
gpexec("print GPVAL_TERM")
-gpexec("plot sin(x)")
source
Gnuplot.gpmargins — Function
gpmargins(sid::Symbol)
-gpmargins()
Return a NamedTuple with keys l, r, b and t containing respectively the left, rigth, bottom and top margins of the current plot (in screen coordinates).
source
Gnuplot.gpranges — Function
gpranges(sid::Symbol)
-gpranges()
Return a NamedTuple with keys x, y, z and cb containing respectively the current plot ranges for the X, Y, Z and color box axis.
source
Gnuplot.gpvars — Function
gpvars(sid::Symbol)
-gpvars()
Return a NamedTuple with all currently defined gnuplot variables.  If the sid argument is not provided, the default session is considered.
source
Gnuplot.hist — Function
hist(v::Vector{T}; range=extrema(v), bs=NaN, nbins=0, pad=true) where T <: Real
Calculates the histogram of the values in v and returns a Histogram1D structure.
Arguments
  • v: a vector of values to compute the histogra;
  • range: values of the left edge of the first bin and of the right edge of the last bin;
  • bs: size of histogram bins;
  • nbins: number of bins in the histogram;
  • pad: if true add one dummy bins with zero counts before the first bin and after the last.
If bs is given nbins is ignored.
Example
v = randn(1000)
+gpexec("plot sin(x)")
source
Gnuplot.gpmargins — Function
gpmargins(sid::Symbol)
+gpmargins()
Return a NamedTuple with keys l, r, b and t containing respectively the left, rigth, bottom and top margins of the current plot (in screen coordinates).
source
Gnuplot.gpranges — Function
gpranges(sid::Symbol)
+gpranges()
Return a NamedTuple with keys x, y, z and cb containing respectively the current plot ranges for the X, Y, Z and color box axis.
source
Gnuplot.gpvars — Function
gpvars(sid::Symbol)
+gpvars()
Return a NamedTuple with all currently defined gnuplot variables.  If the sid argument is not provided, the default session is considered.
source
Gnuplot.hist — Function
hist(v::Vector{T}; range=extrema(v), bs=NaN, nbins=0, pad=true) where T <: Real
Calculates the histogram of the values in v and returns a Histogram1D structure.
Arguments
  • v: a vector of values to compute the histogra;
  • range: values of the left edge of the first bin and of the right edge of the last bin;
  • bs: size of histogram bins;
  • nbins: number of bins in the histogram;
  • pad: if true add one dummy bins with zero counts before the first bin and after the last.
If bs is given nbins is ignored.
Example
v = randn(1000)
 h = hist(v, bs=0.5)
 @gp h  # preview
-@gp h.bins h.counts "w histep notit"
source
hist(v1::Vector{T1 <: Real}, v2::Vector{T2 <: Real}; range1=[NaN,NaN], bs1=NaN, nbins1=0, range2=[NaN,NaN], bs2=NaN, nbins2=0)
Calculates the 2D histogram of the values in v1 and v2 and returns a Histogram2D structure.
Arguments
  • v1: a vector of values along the first dimension;
  • v2: a vector of values along the second dimension;
  • range1: values of the left edge of the first bin and of the right edge of the last bin, along the first dimension;
  • range1: values of the left edge of the first bin and of the right edge of the last bin, along the second dimension;
  • bs1: size of histogram bins along the first dimension;
  • bs2: size of histogram bins along the second dimension;
  • nbins1: number of bins along the first dimension;
  • nbins2: number of bins along the second dimension;
If bs1 (bs2) is given nbins1 (nbins2) is ignored.
Example
v1 = randn(1000)
+@gp h.bins h.counts "w histep notit"
source
hist(v1::Vector{T1 <: Real}, v2::Vector{T2 <: Real}; range1=[NaN,NaN], bs1=NaN, nbins1=0, range2=[NaN,NaN], bs2=NaN, nbins2=0)
Calculates the 2D histogram of the values in v1 and v2 and returns a Histogram2D structure.
Arguments
  • v1: a vector of values along the first dimension;
  • v2: a vector of values along the second dimension;
  • range1: values of the left edge of the first bin and of the right edge of the last bin, along the first dimension;
  • range1: values of the left edge of the first bin and of the right edge of the last bin, along the second dimension;
  • bs1: size of histogram bins along the first dimension;
  • bs2: size of histogram bins along the second dimension;
  • nbins1: number of bins along the first dimension;
  • nbins2: number of bins along the second dimension;
If bs1 (bs2) is given nbins1 (nbins2) is ignored.
Example
v1 = randn(1000)
 v2 = randn(1000)
 h = hist(v1, v2, bs1=0.5, bs2=0.5)
 @gp h  # preview
-@gp "set size ratio -1" "set auto fix" h.bins1 h.bins2 h.counts "w image notit"
source
Gnuplot.linetypes — Function
linetypes(cmap::ColorScheme; lw=1, ps=1, dashed=false, rev=false)
-linetypes(s::Symbol; lw=1, ps=1, dashed=false, rev=false)
Convert a ColorScheme object into a string containing the gnuplot commands to set up linetype colors.
If the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes.
If rev=true the line colors are reversed.  If a numeric or string value is provided through the lw and ps keywords thay are used to set the line width and the point size respectively.  If dashed is true the linetypes with index greater than 1 will be displayed with dashed pattern.
source
Gnuplot.palette — Function
palette(cmap::ColorScheme; rev=false)
-palette(s::Symbol; rev=false)
Convert a ColorScheme object into a string containing the gnuplot commands to set up the corresponding palette.
If the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes. If rev=true the palette is reversed.
source
Gnuplot.recipe — Function
recipe(h::Histogram1D)
-recipe(h::Histogram2D)
Implicit recipes to visualize 1D and 2D histograms.
source
recipe(M::Matrix{ColorTypes.RGB{T}}, opt="flipy")
+@gp "set size ratio -1" "set auto fix" h.bins1 h.bins2 h.counts "w image notit"
source
Gnuplot.linetypes — Function
linetypes(cmap::ColorScheme; lw=1, ps=1, dashed=false, rev=false)
+linetypes(s::Symbol; lw=1, ps=1, dashed=false, rev=false)
Convert a ColorScheme object into a string containing the gnuplot commands to set up linetype colors.
If the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes.
If rev=true the line colors are reversed.  If a numeric or string value is provided through the lw and ps keywords thay are used to set the line width and the point size respectively.  If dashed is true the linetypes with index greater than 1 will be displayed with dashed pattern.
source
Gnuplot.palette — Function
palette(cmap::ColorScheme; rev=false)
+palette(s::Symbol; rev=false)
Convert a ColorScheme object into a string containing the gnuplot commands to set up the corresponding palette.
If the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes. If rev=true the palette is reversed.
source
Gnuplot.recipe — Function
recipe(h::Histogram1D)
+recipe(h::Histogram2D)
Implicit recipes to visualize 1D and 2D histograms.
source
recipe(M::Matrix{ColorTypes.RGB{T}}, opt="flipy")
 recipe(M::Matrix{ColorTypes.RGBA{T}}, opt="flipy")
 recipe(M::Matrix{ColorTypes.Gray{T}}, opt="flipy")
-recipe(M::Matrix{ColorTypes.GrayA{T}}, opt="flipy")
Implicit recipes to show images.
source
Gnuplot.save — Function
save(sid::Symbol; term="", output="")
+recipe(M::Matrix{ColorTypes.GrayA{T}}, opt="flipy")
Implicit recipes to show images.
source
Gnuplot.save — Function
save(sid::Symbol; term="", output="")
 save(sid::Symbol, script_filename::String, ;term="", output="")
 save(; term="", output="")
-save(script_filename::String ;term="", output="")
Export a (multi-)plot into the external file name provided in the output= keyword.  The gnuplot terminal to use is provided through the term= keyword.
If the script_filename argument is provided a gnuplot script will be written in place of the output image.  The latter can then be used in a pure gnuplot session (Julia is no longer needed) to generate exactly the same original plot.
If the sid argument is provided the operation applies to the corresponding session.
source
Gnuplot.stats — Function
stats(sid::Symbol,name::String)
+save(script_filename::String ;term="", output="")
Export a (multi-)plot into the external file name provided in the output= keyword.  The gnuplot terminal to use is provided through the term= keyword.
If the script_filename argument is provided a gnuplot script will be written in place of the output image.  The latter can then be used in a pure gnuplot session (Julia is no longer needed) to generate exactly the same original plot.
If the sid argument is provided the operation applies to the corresponding session.
source
Gnuplot.stats — Function
stats(sid::Symbol,name::String)
 stats(name::String)
 stats(sid::Symbol)
-stats()
Print a statistical summary for the name dataset, belonging to sid session.  If name is not provdied a summary is printed for each dataset in the session.  If sid is not provided the default session is considered.
This function is actually a wrapper for the gnuplot command stats.
source
Gnuplot.terminals — Function
terminals()
Return a Vector{String} with the names of all the available gnuplot terminals.
source
Gnuplot.terminal — Function
terminal(sid::Symbol)
-terminal()
Return a String with the current gnuplot terminal (and its options) of the process associated to session sid, or to the default session (if sid is not provided).
source
Gnuplot.test_terminal — Function
test_terminal(term=nothing; linetypes=nothing, palette=nothing)
Run the test and test palette commands on a gnuplot terminal.
If no term is given it will use the default terminal. If lt and pal are given they are used as input to the linetypes and palette function repsetcively to load the associated color scheme.
Examples
test_terminal()
-test_terminal("wxt", lt=:rust, pal=:viridis)
source
Non-exported symbols
The following functions are not exported by the Gnuplot.jl package since they are typically not used in every day work, or aimed to debugging purposes.  Still, they can be useful in some case, hence they are documented here.
In order to call these functions you should add the Gnuplot. prefix to the function name.
Gnuplot.DatasetText — Type
DatasetText
A dataset whose data are stored as a text buffer.
Transmission to gnuplot may be slow for large datasets, but no temporary file is involved, and the dataset can be saved directly into a gnuplot script.  Also, the constructor allows to build more flexible datasets (i.e. mixing arrays with different dimensions).
Constructors are defined as follows:
DatasetText(data::Vector{String})
-DatasetText(data::Vararg{AbstractArray, N}) where N =
In the second form the type of elements of each array must be one of Real, AbstractString and Missing.
source
Gnuplot.DatasetBin — Type
DatasetBin
A dataset whose data are stored as a binary file.
Ensure best performances for large datasets, but involve use of a temporary files.  When saving a script the file is stored in a directory with the same name as the main script file.
Constructors are defined as follows:
DatasetBin(cols::Vararg{AbstractMatrix, N}) where N
-DatasetBin(cols::Vararg{AbstractVector, N}) where N
In both cases the element of the arrays must be a numeric type.
source
Gnuplot.Histogram1D — Type
Histogram1D
A 1D histogram data.
Fields
  • bins::Vector{Float64}: bin center values;
  • counts::Vector{Float64}: counts in the bins;
  • binsize::Float64: size of each bin;
source
Gnuplot.Histogram2D — Type
Histogram2D
A 2D histogram data.
Fields
  • bins1::Vector{Float64}: bin center values along first dimension;
  • bins2::Vector{Float64}: bin center values along second dimension;
  • counts::Vector{Float64}: counts in the bins;
  • binsize1::Float64: size of each bin along first dimension;
  • binsize2::Float64: size of each bin along second dimension;
source
Gnuplot.IsoContourLines — Type
IsoContourLines
Coordinates of all contour lines of a given level.
Fields
  • paths::Vector{Path2d}: vector of Path2d objects, one for each continuous path;
  • data::Vector{String}: vector with string representation of all paths (ready to be sent to gnuplot);
  • z::Float64: level of the contour lines.
source
Gnuplot.Options — Type
Options
Structure containing the package global options, accessible through Gnuplot.options.
Fields
  • dry::Bool: whether to use dry sessions, i.e. without an underlying Gnuplot process (default: false)
  • cmd::String: command to start the Gnuplot process (default: "gnuplot")
  • default::Symbol: default session name (default: :default)
  • term::String: default terminal for interactive use (default: empty string, i.e. use gnuplot settings);
  • term_svg::String: terminal to save png files (default "svg background rgb 'white' dynamic");
  • term_png::String: terminal to save png files (default "pngcairo");
  • init::Vector{String}: commands to initialize the session when it is created or reset (e.g., to set default palette);
  • verbose::Bool: verbosity flag (default: false)
  • preferred_format::Symbol: preferred format to send data to gnuplot.  Value must be one of:
    • bin: fastest solution for large datasets, but uses temporary files;
    • text: may be slow for large datasets, but no temporary file is involved;
    • auto (default) automatically choose the best strategy.
source
Gnuplot.PlotElement — Type
PlotElement
Structure containing element(s) of a plot (commands, data, plot specifications) that can be used directly in @gp and @gsp calls.
Fields
  • mid::Int: multiplot ID (use 0 for single plots);
  • is3d::Bool: true if the data are supposed to be displayed in a 3D plot;
  • cmds::Vector{String}: commands to set plot properties;
  • name::String: name of the dataset (use "" to automatically generate a unique name);
  • data::Dataset: a dataset
  • plot::Vector{String}: plot specifications for the associated Dataset;
The constructor is defined as follows:
PlotElement(;mid::Int=0, is3d::Bool=false,
+stats()
Print a statistical summary for the name dataset, belonging to sid session.  If name is not provdied a summary is printed for each dataset in the session.  If sid is not provided the default session is considered.
This function is actually a wrapper for the gnuplot command stats.
source
Gnuplot.terminals — Function
terminals()
Return a Vector{String} with the names of all the available gnuplot terminals.
source
Gnuplot.terminal — Function
terminal(sid::Symbol)
+terminal()
Return a String with the current gnuplot terminal (and its options) of the process associated to session sid, or to the default session (if sid is not provided).
source
Gnuplot.test_terminal — Function
test_terminal(term=nothing; linetypes=nothing, palette=nothing)
Run the test and test palette commands on a gnuplot terminal.
If no term is given it will use the default terminal. If lt and pal are given they are used as input to the linetypes and palette function repsetcively to load the associated color scheme.
Examples
test_terminal()
+test_terminal("wxt", lt=:rust, pal=:viridis)
source
Non-exported symbols
The following functions are not exported by the Gnuplot.jl package since they are typically not used in every day work, or aimed to debugging purposes.  Still, they can be useful in some case, hence they are documented here.
In order to call these functions you should add the Gnuplot. prefix to the function name.
Gnuplot.DatasetText — Type
DatasetText
A dataset whose data are stored as a text buffer.
Transmission to gnuplot may be slow for large datasets, but no temporary file is involved, and the dataset can be saved directly into a gnuplot script.  Also, the constructor allows to build more flexible datasets (i.e. mixing arrays with different dimensions).
Constructors are defined as follows:
DatasetText(data::Vector{String})
+DatasetText(data::Vararg{AbstractArray, N}) where N =
In the second form the type of elements of each array must be one of Real, AbstractString and Missing.
source
Gnuplot.DatasetBin — Type
DatasetBin
A dataset whose data are stored as a binary file.
Ensure best performances for large datasets, but involve use of a temporary files.  When saving a script the file is stored in a directory with the same name as the main script file.
Constructors are defined as follows:
DatasetBin(cols::Vararg{AbstractMatrix, N}) where N
+DatasetBin(cols::Vararg{AbstractVector, N}) where N
In both cases the element of the arrays must be a numeric type.
source
Gnuplot.Histogram1D — Type
Histogram1D
A 1D histogram data.
Fields
  • bins::Vector{Float64}: bin center values;
  • counts::Vector{Float64}: counts in the bins;
  • binsize::Float64: size of each bin;
source
Gnuplot.Histogram2D — Type
Histogram2D
A 2D histogram data.
Fields
  • bins1::Vector{Float64}: bin center values along first dimension;
  • bins2::Vector{Float64}: bin center values along second dimension;
  • counts::Vector{Float64}: counts in the bins;
  • binsize1::Float64: size of each bin along first dimension;
  • binsize2::Float64: size of each bin along second dimension;
source
Gnuplot.IsoContourLines — Type
IsoContourLines
Coordinates of all contour lines of a given level.
Fields
  • paths::Vector{Path2d}: vector of Path2d objects, one for each continuous path;
  • data::Vector{String}: vector with string representation of all paths (ready to be sent to gnuplot);
  • z::Float64: level of the contour lines.
source
Gnuplot.Options — Type
Options
Structure containing the package global options, accessible through Gnuplot.options.
Fields
  • dry::Bool: whether to use dry sessions, i.e. without an underlying Gnuplot process (default: false)
  • cmd::String: command to start the Gnuplot process (default: "gnuplot")
  • default::Symbol: default session name (default: :default)
  • term::String: default terminal for interactive use (default: empty string, i.e. use gnuplot settings);
  • term_svg::String: terminal to save png files (default "svg background rgb 'white' dynamic");
  • term_png::String: terminal to save png files (default "pngcairo");
  • init::Vector{String}: commands to initialize the session when it is created or reset (e.g., to set default palette);
  • verbose::Bool: verbosity flag (default: false)
  • preferred_format::Symbol: preferred format to send data to gnuplot.  Value must be one of:
    • bin: fastest solution for large datasets, but uses temporary files;
    • text: may be slow for large datasets, but no temporary file is involved;
    • auto (default) automatically choose the best strategy.
source
Gnuplot.PlotElement — Type
PlotElement
Structure containing element(s) of a plot (commands, data, plot specifications) that can be used directly in @gp and @gsp calls.
Fields
  • mid::Int: multiplot ID (use 0 for single plots);
  • is3d::Bool: true if the data are supposed to be displayed in a 3D plot;
  • cmds::Vector{String}: commands to set plot properties;
  • name::String: name of the dataset (use "" to automatically generate a unique name);
  • data::Dataset: a dataset
  • plot::Vector{String}: plot specifications for the associated Dataset;
The constructor is defined as follows:
PlotElement(;mid::Int=0, is3d::Bool=false,
             cmds::Union{String, Vector{String}}=Vector{String}(),
             name::String="",
             data::Dataset=DatasetEmpty(),
             plot::Union{String, Vector{String}}=Vector{String}(),
-            kwargs...)
No field is mandatory, i.e. even Gnuplot.PlotElement() provides a valid structure. The constructor also accept all the keywords accepted by parseKeywords.
source
Gnuplot.gpversion — Function
Gnuplot.gpversion()
Return the gnuplot application version.
Raise an error if version is < 5.0 (required to use data blocks).
source
Gnuplot.quit — Function
Gnuplot.quit(sid::Symbol)
Quit the session identified by sid and the associated gnuplot process (if any).
source
Gnuplot.quitall — Function
Gnuplot.quitall()
Quit all the sessions and the associated gnuplot processes.
source
Gnuplot.repl_init — Function
Gnuplot.init_repl(; start_key='>')
Install a hook to replace the common Julia REPL with a gnuplot one.  The key to start the REPL is the one provided in start_key (default: >).
Note: the gnuplot REPL operates only on the default session.
source

+            kwargs...)

No field is mandatory, i.e. even Gnuplot.PlotElement() provides a valid structure. The constructor also accept all the keywords accepted by parseKeywords.

source
Gnuplot.gpversion — Function
Gnuplot.gpversion()

Return the gnuplot application version.

Raise an error if version is < 5.0 (required to use data blocks).

source
Gnuplot.quit — Function
Gnuplot.quit(sid::Symbol)

Quit the session identified by sid and the associated gnuplot process (if any).

source
Gnuplot.quitall — Function
Gnuplot.quitall()

Quit all the sessions and the associated gnuplot processes.

source
Gnuplot.repl_init — Function
Gnuplot.init_repl(; start_key='>')

Install a hook to replace the common Julia REPL with a gnuplot one. The key to start the REPL is the one provided in start_key (default: >).

Note: the gnuplot REPL operates only on the default session.

source
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"w l tit 'Pow 2' dt 3 lw 2 lc rgb 'purple'"

Note

The above example lacks the trailing :- symbol. This means the plot will be updated at each command, adding one curve at a time.

Keywords for common commands

In order to avoid typing long, and very frequently used gnuplot commands, Gnuplot.jl provides a few keywords which can be used in both @gp and @sgp calls:

  • xrange=[low, high] => "set xrange [low:high];
  • yrange=[low, high] => "set yrange [low:high];
  • zrange=[low, high] => "set zrange [low:high];
  • cbrange=[low, high]=> "set cbrange[low:high];
  • key="..." => "set key ...";
  • title="..." => "set title \"...\"";
  • xlabel="..." => "set xlabel \"...\"";
  • ylabel="..." => "set ylabel \"...\"";
  • zlabel="..." => "set zlabel \"...\"";
  • cblabel="..." => "set cblabel \"...\"";
  • xlog=true => set logscale x;
  • ylog=true => set logscale y;
  • zlog=true => set logscale z;
  • margins=... => set margins ...;
  • lmargin=... => set lmargin ...;
  • rmargin=... => set rmargin ...;
  • bmargin=... => set bmargin ...;
  • tmargin=... => set tmargin ...;

All such keywords can be abbreviated to unambiguous names.

By using the above keywords the first lines of the previous example:

@gp    "set grid" "set key left" "set logscale y"
 @gp :- "set title 'Plot title'" "set label 'X label'" "set xrange [0:*]"

can be replaced with a shorter version:

@gp    "set grid" k="left" ylog=true
-@gp :- tit="Plot title" xlab="X label" xr=[0,NaN]

where NaN in the xrange keyword means using axis autoscaling.

Plot matrix as images

Gnuplot.jl can display a 2D matrix as an image:

img = randn(Float64, 10, 5)
-img[10,:] .= -5
-@gp img "w image notit"

Note that the first index in the img matrix corresponds to the rows in the displayed image coordinate when the image is displayed.

A simple way to remember the convention is to compare how a matrix is displayed in the REPL:

img = reshape(1:15, 5, 3)
5×3 reshape(::UnitRange{Int64}, 5, 3) with eltype Int64:
+@gp :- tit="Plot title" xlab="X label" xr=[0,NaN]

where NaN in the xrange keyword means using axis autoscaling.

Plot matrix as images

Gnuplot.jl can display a 2D matrix as an image:

img = randn(Float64, 8, 5)
+img[2,:] .= -5
+@gp img "w image notit"

Note that the first index in the img matrix corresponds to the rows in the displayed image.

A simple way to remember the convention is to compare how a matrix is displayed in the REPL:

img = reshape(1:15, 5, 3)
5×3 reshape(::UnitRange{Int64}, 5, 3) with eltype Int64:
  1   6  11
  2   7  12
  3   8  13
  4   9  14
- 5  10  15

and its image representation, which is essentially upside down:

@gp img "w image notit"

Also note that the img[1,1] pixel is shown at coordinates x=0, y=0. See Image recipes for further info.

3D plots

3D plots follow the same rules as 2D ones, just replace the @gp macro with @gsp and add the required columns (according to the plotting style).

E.g., to plot a spiral increasing in size along the X direction:

x = 0:0.1:10pi
-@gsp cbr=[-1,1].*30  x  sin.(x) .* x  cos.(x) .* x  x./20  "w p pt 7 ps var lc pal"

Note that the fourth array in the dataset, x./20, is used as by gnuplot as point size (ps var). Also note that all the keywords discussed above can also be used in 3D plots.

Palettes and line types

The Gnuplot.jl package comes with all the ColorSchemes palettes readily available.

A gnuplot-compliant palette can be retrieved with palette(), and used as any other command. The previous example may use an alternative palette with:

x = 0:0.1:10pi
+ 5  10  15

and its image representation, which is essentially upside down (since the Y coordinates increase upwards):

@gp img "w image notit"

Also note that the img[1,1] pixel is shown at coordinates x=0, y=0. See Image recipes for further info.

3D plots

3D plots follow the same rules as 2D ones, just replace the @gp macro with @gsp and add the required columns (according to the plotting style).

E.g., to plot a spiral increasing in size along the X direction:

x = 0:0.1:10pi
+@gsp cbr=[-1,1].*30  x  x.*sin.(x)  x.*cos.(x)  x./20  "w p pt 7 ps var lc pal"

Note that the fourth array in the dataset, x./20, is used as by gnuplot as point size (ps var). Also note that all the keywords discussed above can also be used in 3D plots.

Palettes and line types

The Gnuplot.jl package comes with all the ColorSchemes palettes readily available.

A gnuplot-compliant palette can be retrieved with palette(), and used as any other command. The previous example may use an alternative palette with:

x = 0:0.1:10pi
 @gsp palette(:viridis) cbr=[-1,1].*30 :-
-@gsp :-  x  sin.(x) .* x  cos.(x) .* x  x./20  "w p pt 7 ps var lc pal"

The list of all available palette can be retrieved with palette_names():

julia> palette_names()
+@gsp :-  x  x.*sin.(x)  x.*cos.(x)  x./20  "w p pt 7 ps var lc pal"

The list of all available palette can be retrieved with palette_names():

julia> palette_names()
 478-element Array{Symbol,1}:
  :BuPu_6
  :Spectral_4
@@ -93,4 +93,4 @@ set autoscale fix
 plot  \
    './script2_data/jl_OQrt9A' binary array=(300, 100) flipy with image notit
 set output

The above scripts can be loaded into a pure gnuplot session (Julia is no longer needed) as follows:

gunplot> load 'script1.gp'
-gunplot> load 'script2.gp'

to generate a plot identical to the original one.

The purpose of gnuplot scripts is to allow sharing all data, alongside a plot, in order to foster collaboration among scientists and replicability of results. Moreover, a script can be used at any time to change the details of a plot, without the need to re-run the Julia code used to generate it the first time.

Finally, the scripts are the only possible output when Dry sessions are used (i.e. when gnuplot is not available in the user platform.

  • 1a previous knowledge of gnuplot usage is, nevertheless, required.
+gunplot> load 'script2.gp'

to generate a plot identical to the original one.

The purpose of gnuplot scripts is to allow sharing all data, alongside a plot, in order to foster collaboration among scientists and replicability of results. Moreover, a script can be used at any time to change the details of a plot, without the need to re-run the Julia code used to generate it the first time.

Finally, the scripts are the only possible output when Dry sessions are used (i.e. when gnuplot is not available in the user platform.

  • 1a previous knowledge of gnuplot usage is, nevertheless, required.
diff --git a/v1.2.0/examples/index.html b/v1.2.0/examples/index.html index d993a83..c293bd1 100644 --- a/v1.2.0/examples/index.html +++ b/v1.2.0/examples/index.html @@ -1,2 +1,2 @@ -Examples · Gnuplot.jl
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Examples

The official gallery of high quality examples is maintained in a separate repository:

https://lazarusa.github.io/gnuplot-examples/

The examples in this documentation are intentionally very simple, in order to focus on the package functionalities. The only relatively complex, publication-quality plot, is discussed in The cairolatex terminal section.

Keep in mind that Gnuplot.jl is just an interface to gnuplot, so everything you can do with the latter is achievable from Julia. Further gnuplot examples can be found here:

+Examples · Gnuplot.jl
Edit on GitHub

Examples

The official gallery of high quality examples is maintained in a separate repository:

https://lazarusa.github.io/gnuplot-examples/

The examples in this documentation are intentionally very simple, in order to focus on the package functionalities. The only relatively complex, publication-quality plot, is discussed in The cairolatex terminal section.

Keep in mind that Gnuplot.jl is just an interface to gnuplot, so everything you can do with the latter is achievable from Julia. Further gnuplot examples can be found here:

diff --git a/v1.2.0/index.html b/v1.2.0/index.html index 96f0936..06f9d38 100644 --- a/v1.2.0/index.html +++ b/v1.2.0/index.html @@ -1,2 +1,2 @@ -Home · Gnuplot.jl
Edit on GitHub

Gnuplot.jl

A Julia interface to gnuplot.

Stars

The Gnuplot.jl package allows easy and fast use of gnuplot as a data visualization tool in Julia. Have a look at Basic usage and Examples for a quick overview. The package main features are:

  • fast time-to-first-plot;

  • extremely concise yet meaningful syntax, makes it ideal for interactive data exploration;

  • no need to learn new API functions or keywords: only two macros (@gp for 2D plots, @gsp for 3D plots) and a basic knowledge of gnuplot are enough to generate most plots;

  • transparent interface between Julia and gnuplot to exploit all functionalities of the latter, both present and future ones;

  • availability of all the palettes from ColorSchemes;

  • support for multiple plots in one window, multiple plotting windows, as well as ASCII and Sixel plots (to plot directly in a terminal);

  • support for histograms (both 1D and 2D);

  • enhanced support for contour plots;

  • export to a huge number of formats such as pdf, png, gif, $\LaTeX$, svg, etc. (actually all those supported by gnuplot);

  • compatibility with Jupyter and Juno;

  • save sessions into gnuplot scripts, to enable easy plot customization and reproducibility.

If you're unfamiliar with gnuplot have a look at:

Yet another plotting package?

A powerful plotting framework is among the most important tool in the toolbox of any modern scientist and engineer. As such, it is hard to find a single package to fit all needs, and many solutions are indeed available in the Julia ecosystem.

Gnuplot.jl package fills the niche of users who needs:

  1. publication-quality plots, by exploiting the capabilities of a widely used tool such as gnuplot, and its many output formats available;
  2. a well-documented framework, by taking advantage of all the gnuplot documentation, tutorials and examples available on the web;
  3. a fast response, by relying on an external program (rather than on a large Julia code base);
  4. an interactive data exploration framework, by exposing a carefully designed, extremely concise and easy to remember syntax (at least for users with minimal gnuplot knowledge);
  5. a procedure to decouple plot data and aesthetics from the Julia code used to generate them.

Unlike other packages Gnuplot.jl is not a pure Julia solution as it depends on an external package to actually generate plots. However, if gnuplot is not available on a given platform, the package could still be used in "dry" mode, and no error for a missing dependency will be raised (see Dry sessions).

The Gnuplot.jl package development follows a minimalistic approach: it is essentially a thin layer to send data and commands to gnuplot. This way all underlying capabilities, both present and future ones, are automatically exposed to the Julia user, with no need to implement dedicated wrappers.

The functionalities 1, 2 and 3 listed above are similar to those provided by the Gaston package. Gnuplot.jl also provides features 4 and 5, as well as the minimalistic approach.

Do Gnuplot.jl suits my needs?

Any modern plotting package is able to produce a simple scatter plot, with custom symbols, line styles, colors and axis labels. Indeed, this is exactly the example that is reported in every package documentation (also here: see 2D plots). Still, producing complex and publication-quality plots is not an easy task. As a consequence is also not easy to determine whether a package can cope with the most difficult cases (unless you actually try it out) and a reasonable choice is typically to rely on the size of the user base, the availability of documentation / tutorials, and the possibility to preview complex examples.

Gnuplot.jl aims to be ready for even the most challenging plots by relying on the widely used gnuplot application, and by allowing each native feature (both present and future ones) to be immediately available in the Julia language. Moreover, Gnuplot.jl provides a unique syntax specifically aimed to increase productivity while performing interactive data exploration.

Last but not least, have a look at the Gnuplot.jl Examples page.

Notation

In this documentation:

  • "Gnuplot.jl" refers to the Julia package;
  • "gnuplot" refers to the gnuplot application.

Table of Contents

+Home · Gnuplot.jl
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Gnuplot.jl

A Julia interface to gnuplot.

Stars

The Gnuplot.jl package allows easy and fast use of gnuplot as a data visualization tool in Julia. Have a look at Basic usage and Examples for a quick overview. The package main features are:

  • fast time-to-first-plot;

  • extremely concise yet meaningful syntax, makes it ideal for interactive data exploration;

  • no need to learn new API functions or keywords: only two macros (@gp for 2D plots, @gsp for 3D plots) and a basic knowledge of gnuplot are enough to generate most plots;

  • transparent interface between Julia and gnuplot to exploit all functionalities of the latter, both present and future ones;

  • availability of all the palettes from ColorSchemes;

  • support for multiple plots in one window, multiple plotting windows, as well as ASCII and Sixel plots (to plot directly in a terminal);

  • support for histograms (both 1D and 2D);

  • enhanced support for contour plots;

  • export to a huge number of formats such as pdf, png, gif, $\LaTeX$, svg, etc. (actually all those supported by gnuplot);

  • compatibility with Jupyter and Juno;

  • save sessions into gnuplot scripts, to enable easy plot customization and reproducibility.

If you're unfamiliar with gnuplot have a look at:

Yet another plotting package?

A powerful plotting framework is among the most important tool in the toolbox of any modern scientist and engineer. As such, it is hard to find a single package to fit all needs, and many solutions are indeed available in the Julia ecosystem.

Gnuplot.jl package fills the niche of users who needs:

  1. publication-quality plots, by exploiting the capabilities of a widely used tool such as gnuplot, and its many output formats available;
  2. a well-documented framework, by taking advantage of all the gnuplot documentation, tutorials and examples available on the web;
  3. a fast response, by relying on an external program (rather than on a large Julia code base);
  4. an interactive data exploration framework, by exposing a carefully designed, extremely concise and easy to remember syntax (at least for users with minimal gnuplot knowledge);
  5. a procedure to decouple plot data and aesthetics from the Julia code used to generate them.

Unlike other packages Gnuplot.jl is not a pure Julia solution as it depends on an external package to actually generate plots. However, if gnuplot is not available on a given platform, the package could still be used in "dry" mode, and no error for a missing dependency will be raised (see Dry sessions).

The Gnuplot.jl package development follows a minimalistic approach: it is essentially a thin layer to send data and commands to gnuplot. This way all underlying capabilities, both present and future ones, are automatically exposed to the Julia user, with no need to implement dedicated wrappers.

The functionalities 1, 2 and 3 listed above are similar to those provided by the Gaston package. Gnuplot.jl also provides features 4 and 5, as well as the minimalistic approach.

Do Gnuplot.jl suits my needs?

Any modern plotting package is able to produce a simple scatter plot, with custom symbols, line styles, colors and axis labels. Indeed, this is exactly the example that is reported in every package documentation (also here: see 2D plots). Still, producing complex and publication-quality plots is not an easy task. As a consequence is also not easy to determine whether a package can cope with the most difficult cases (unless you actually try it out) and a reasonable choice is typically to rely on the size of the user base, the availability of documentation / tutorials, and the possibility to preview complex examples.

Gnuplot.jl aims to be ready for even the most challenging plots by relying on the widely used gnuplot application, and by allowing each native feature (both present and future ones) to be immediately available in the Julia language. Moreover, Gnuplot.jl provides a unique syntax specifically aimed to increase productivity while performing interactive data exploration.

Last but not least, have a look at the Gnuplot.jl Examples page.

Notation

In this documentation:

  • "Gnuplot.jl" refers to the Julia package;
  • "gnuplot" refers to the gnuplot application.

Table of Contents

diff --git a/v1.2.0/install/index.html b/v1.2.0/install/index.html index cd1295c..0cca2aa 100644 --- a/v1.2.0/install/index.html +++ b/v1.2.0/install/index.html @@ -4,4 +4,4 @@ Status `~/.julia/environments/v1.4/Project.toml` [dc211083] Gnuplot v1.2.0

If the displayed version is not v1.2.0 you are probably having a dependency conflict. In this case try forcing installation of the latest version with:

julia> ]add Gnuplot@1.2.0

and check which package is causing the conflict.

Check execution and version of the underlying gnuplot process:

julia> using Gnuplot
 
 julia> Gnuplot.gpversion()
-v"5.2.0"

The minimum required version is v5.0.

Generate the first plot:

julia> @gp 1:9

Test default terminal capabilities:

test_terminal()
+v"5.2.0"

The minimum required version is v5.0.

Generate the first plot:

julia> @gp 1:9

Test default terminal capabilities:

test_terminal()
diff --git a/v1.2.0/options/index.html b/v1.2.0/options/index.html index 6666667..7b08bd6 100644 --- a/v1.2.0/options/index.html +++ b/v1.2.0/options/index.html @@ -63,4 +63,4 @@ julia> Gnuplot.options.term_svg = "svg dynamic";

At the Julia prompt you may load the package and the associated settings by typing:

julia> @gnuplotrc

and you're ready to go.

+end

At the Julia prompt you may load the package and the associated settings by typing:

julia> @gnuplotrc

and you're ready to go.

diff --git a/v1.2.0/recipes/index.html b/v1.2.0/recipes/index.html index 2f87eeb..a2e5e85 100644 --- a/v1.2.0/recipes/index.html +++ b/v1.2.0/recipes/index.html @@ -1,5 +1,5 @@ -Plot recipes · Gnuplot.jl
Edit on GitHub

Plot recipes

A plot recipe is a quicklook visualization procedure aimed at reducing the amount of repetitive code to generate a plot. More specifically, a recipe is a function that convert data from the "Julia world" into a form suitable to be ingested in Gnuplot.jl, namely a scalar (or a vector of) Gnuplot.PlotElement object(s). The latter contain informations on how to create a plot, or a part of it, and can be used directly as arguments in a @gp or @gsp call.

There are two kinds of recipes:

  • explicit recipe: a function which is explicitly invoked by the user. It can have any name and accept any number of arguments and keywords. It is typically used when the visualization of a data type requires some extra information, beside data itself (e.g. to plot data from a DataFrame object, see Explicit recipe (example));

  • implicit recipe: a function which is automatically called by Gnuplot.jl. It must extend the precipe()](@ref) function, and accept exactly one mandatory argument. It is typically used when the visualization is completely determined by the data type itself (e.g. the visualization of a Matrix{ColorTypes.RGB} object as an image, see Image recipes);

An implicit recipe is invoked whenever the data type of an argument to @gp or @gsp is not among the allowed ones (see @gp() documentation). If a suitable recipe do not exists an error is raised. On the other hand, an explicit recipe needs to be invoked by the user, and the output passed directly to @gp or @gsp.

Although recipes provides very efficient tools for data exploration, their use typically hide the details of plot generation. As a consequence they provide less flexibility than the approaches described in Basic usage and Advanced usage.

Currently, the Gnuplot.jl package provides no built-in explicit recipe. The implicit recipes are implemented in recipes.jl.

Explicit recipe (example)

To generate a plot using the data contained in a DataFrame object we need, beside the data itself, the name of the columns to use for the X and Y coordinates. The following example shows how to implement an explicit recipe to plot a DataFrame object:

using RDatasets, DataFrames, Gnuplot
+Plot recipes · Gnuplot.jl
Edit on GitHub
Plot recipes
A plot recipe is a quicklook visualization procedure aimed at reducing the amount of repetitive code to generate a plot.  More specifically, a recipe is a function that convert data from the "Julia world" into a form suitable to be ingested in Gnuplot.jl, namely a scalar (or a vector of) Gnuplot.PlotElement object(s).  The latter contain informations on how to create a plot, or a part of it, and can be used directly as arguments in a @gp or @gsp call.
There are two kinds of recipes:
  • explicit recipe: a function which is explicitly invoked by the user.  It can have any name and accept any number of arguments and keywords.  It is typically used when the visualization of a data type requires some extra information, beside data itself (e.g. to plot data from a DataFrame object, see Explicit recipe (example));
  • implicit recipe: a function which is automatically called by Gnuplot.jl.  It must extend the precipe()](@ref) function, and accept exactly one mandatory argument.  It is typically used when the visualization is completely determined by the data type itself (e.g. the visualization of a Matrix{ColorTypes.RGB} object as an image, see Image recipes);
An implicit recipe is invoked whenever the data type of an argument to @gp or @gsp is not among the allowed ones (see @gp() documentation).  If a suitable recipe do not exists an error is raised.  On the other hand, an explicit recipe needs to be invoked by the user, and the output passed directly to @gp or @gsp.
Although recipes provides very efficient tools for data exploration, their use typically hide the details of plot generation.  As a consequence they provide less flexibility than the approaches described in Basic usage and Advanced usage.
Currently, the Gnuplot.jl package provides no built-in explicit recipe.  The implicit recipes are implemented in recipes.jl.
Explicit recipe (example)
To generate a plot using the data contained in a DataFrame object we need, beside the data itself, the name of the columns to use for the X and Y coordinates.  The following example shows how to implement an explicit recipe to plot a DataFrame object:
using RDatasets, DataFrames, Gnuplot
 import Gnuplot: PlotElement, DatasetText
 
 function plotdf(df::DataFrame, colx::Symbol, coly::Symbol; group=nothing)
@@ -26,9 +26,13 @@ iris = dataset("datasets", "iris")
 @gp plotdf(iris, :SepalLength, :SepalWidth, group=:Species)
Histogram recipes
The object returned by the hist() function can be readily visualized by means of implicit recipes defined on the Gnuplot.Histogram1D and Gnuplot.Histogram2D types:
x = randn(1000);
 @gp hist(x)
x = randn(10_000);
 y = randn(10_000);
-@gp hist(x, y)
Image recipes
The Gnuplot.jl package provides implicit recipes to display images in the following formats:
  • Matrix{ColorTypes.RGB{T}};
  • Matrix{ColorTypes.RGBA{T}}
  • Matrix{ColorTypes.Gray{T}};
  • Matrix{ColorTypes.GrayA{T}};
To use these recipes simply pass an image to @gp, e.g.:
using TestImages
+@gp hist(x, y)
Contour lines recipes
The object returned by the contourlines() function can be readily visualized by means of implicit recipes defined on the Gnuplot.IsoContourLines types:
x = randn(10_000);
+y = randn(10_000);
+h = hist(x, y)
+clines = contourlines(h, "levels discrete 10, 30, 60, 90");
+@gp clines
Image recipes
The Gnuplot.jl package provides implicit recipes to display images in the following formats:
  • Matrix{ColorTypes.RGB{T}};
  • Matrix{ColorTypes.RGBA{T}}
  • Matrix{ColorTypes.Gray{T}};
  • Matrix{ColorTypes.GrayA{T}};
To use these recipes simply pass an image to @gp, e.g.:
using TestImages
 img = testimage("lighthouse");
 @gp img
All such recipes are defined as:
function recipe(M::Matrix{ColorTypes.RGB{T}}, opt="flipy")
   ...
 end
with only one mandatory argument.  In order to exploit the optional keyword we can explicitly invoke the recipe as follows:
img = testimage("walkbridge");
-@gp palette(:gray) recipe(img, "flipy rot=15deg")
Note that we used both a palette (:gray, see Palettes and line types) and a custom rotation angle.
The flipy option is necessary for proper visualization (see discussion in Plot matrix as images).

+@gp palette(:gray) recipe(img, "flipy rot=15deg")

Note that we used both a palette (:gray, see Palettes and line types) and a custom rotation angle.

The flipy option is necessary for proper visualization (see discussion in Plot matrix as images).

diff --git a/v1.2.0/search/index.html b/v1.2.0/search/index.html index 677ba1b..2af5e66 100644 --- a/v1.2.0/search/index.html +++ b/v1.2.0/search/index.html @@ -1,2 +1,2 @@ -Search · Gnuplot.jl

Loading search...

    +Search · Gnuplot.jl

    Loading search...

      diff --git a/v1.2.0/search_index.js b/v1.2.0/search_index.js index 7f55bb6..0f87681 100644 --- a/v1.2.0/search_index.js +++ b/v1.2.0/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"style/#Style-Guide-1","page":"Style guide","title":"Style Guide","text":"","category":"section"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"The Gnuplot.jl loose syntax allows to create a plot using very different approaches. While this was one of the initial purposes for the package, it may lead to decreased code readability if not used judiciously.","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"Here I will summarize a few, non-mandatory, guidelines which allows to maintain a neat syntax and a high readability:","category":"page"},{"location":"style/#Use-macros-without-parentheses-and-commas:-1","page":"Style guide","title":"1 - Use macros without parentheses and commas:","text":"","category":"section"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"The two most important symbols exported by the package (@gp and @gsp) are macros. As such they are supposed to be invoked without parentheses and commas. E.g. use:","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"@gp x y \"with lines\"","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"in place of","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"@gp(x, y, \"with lines\")","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"If you have very long lines you may split them in multiple statements using the :- symbol, which resembles both hyphenation in natural language and indentation for the plot-producing code:","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"@gp \"set grid\" :-\n@gp :- x y \"with lines\"","category":"page"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"Note that the trailing :- symbol is not mandatory. If omitted, the plot will be updated at each statement (rather than at the last one).","category":"page"},{"location":"style/#Use-keywords-in-place-of-gnuplot-commands:-1","page":"Style guide","title":"2 - Use keywords in place of gnuplot commands:","text":"","category":"section"},{"location":"style/#","page":"Style guide","title":"Style guide","text":"As discussed in Keywords for common commands several commonly used gnuplot commands can be replaced with a keyword. 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The macros accepts any number of arguments, with the following meaning:\n\none, or a group of consecutive, array(s) of either Real or String build up a dataset. The different arrays are accessible as columns 1, 2, etc. from the gnuplot process. The number of required input arrays depends on the chosen plot style (see gnuplot documentation);\na string occurring before a dataset is interpreted as a gnuplot command (e.g. set grid);\na string occurring immediately after a dataset is interpreted as a plot element for the dataset, by which you can specify using clause, with clause, line styles, etc.. All keywords may be abbreviated following gnuplot conventions. Moreover, \"plot\" and \"splot\" can be abbreviated to \"p\" and \"s\" respectively;\nthe special symbol :- allows to split one long statement into multiple (shorter) ones. If given as first argument it avoids starting a new plot. If it given as last argument it avoids immediately running all commands to create the final plot;\nany other symbol is interpreted as a session ID;\nan Int (>= 1) is interpreted as the plot destination in a multi-plot session (this specification applies to subsequent arguments, not previous ones);\nan input in the form \"\\$name\"=>(array1, array2, etc...) is interpreted as a named dataset. Note that the dataset name must always start with a \"$\";\nan input in the form keyword=value is interpreted as a keyword/value pair. The accepted keywords and their corresponding gnuplot commands are as follows:\nxrange=[low, high] => \"set xrange [low:high];\nyrange=[low, high] => \"set yrange [low:high];\nzrange=[low, high] => \"set zrange [low:high];\ncbrange=[low, high]=> \"set cbrange[low:high];\nkey=\"...\" => \"set key ...\";\ntitle=\"...\" => \"set title \"...\"\";\nxlabel=\"...\" => \"set xlabel \"...\"\";\nylabel=\"...\" => \"set ylabel \"...\"\";\nzlabel=\"...\" => \"set zlabel \"...\"\";\ncblabel=\"...\" => \"set cblabel \"...\"\";\nxlog=true => set logscale x;\nylog=true => set logscale y;\nzlog=true => set logscale z.\ncblog=true => set logscale cb;\nmargins=... => set margins ...;\nlmargin=... => set lmargin ...;\nrmargin=... => set rmargin ...;\nbmargin=... => set bmargin ...;\ntmargin=... => set tmargin ...;\n\nAll Keyword names can be abbreviated as long as the resulting name is unambiguous. E.g. you can use xr=[1,10] in place of xrange=[1,10].\n\na PlotElement object is expanded in its fields and processed as one of the previous arguments;\nany other data type is processed through an implicit recipe. If a suitable recipe do not exists an error is raised.\n\n\n\n\n\n","category":"macro"},{"location":"api/#Gnuplot.@gsp","page":"API","title":"Gnuplot.@gsp","text":"@gsp args...\n\nThis macro accepts the same syntax as @gp, but produces a 3D plot instead of a 2D one.\n\n\n\n\n\n","category":"macro"},{"location":"api/#Gnuplot.boxxyerror","page":"API","title":"Gnuplot.boxxyerror","text":"boxxyerror(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.contourlines","page":"API","title":"Gnuplot.contourlines","text":"contourlines(x::Vector{Float64}, y::Vector{Float64}, h::Matrix{Float64}; cntrparam=\"level auto 10\")\n\nCompute paths of contour lines for 2D data, and return a vector of IsoContourLines object.\n\nArguments:\n\nx, y: Coordinates;\nh: the levels on which iso contour lines are to be calculated\ncntrparam: settings to compute contour line paths (see gnuplot documentation for cntrparam).\n\nExample\n\nx = randn(5000);\ny = randn(5000);\nh = hist(x, y, nbins1=20, nbins2=20);\nclines = contourlines(h.bins1, h.bins2, h.counts, cntrparam=\"levels discrete 15, 30, 45\");\n@gp \"set size ratio -1\"\nfor i in 1:length(clines)\n @gp :- clines[i].data \"w l t '$(clines[i].z)' dt $i\"\nend\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.dataset_names","page":"API","title":"Gnuplot.dataset_names","text":"dataset_names(sid::Symbol)\ndataset_names()\n\nReturn a vector with all dataset names for the sid session. If sid is not provided the default session is considered.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.gpexec","page":"API","title":"Gnuplot.gpexec","text":"gpexec(sid::Symbol, command::String)\ngpexec(command::String)\n\nExecute the gnuplot command command on the underlying gnuplot process of the sid session, and return the results as a Vector{String}. If a gnuplot error arises it is propagated as an ErrorException.\n\nIf the sid argument is not provided, the default session is considered.\n\nExamples:\n\ngpexec(\"print GPVAL_TERM\")\ngpexec(\"plot sin(x)\")\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.gpmargins","page":"API","title":"Gnuplot.gpmargins","text":"gpmargins(sid::Symbol)\ngpmargins()\n\nReturn a NamedTuple with keys l, r, b and t containing respectively the left, rigth, bottom and top margins of the current plot (in screen coordinates).\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.gpranges","page":"API","title":"Gnuplot.gpranges","text":"gpranges(sid::Symbol)\ngpranges()\n\nReturn a NamedTuple with keys x, y, z and cb containing respectively the current plot ranges for the X, Y, Z and color box axis.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.gpvars","page":"API","title":"Gnuplot.gpvars","text":"gpvars(sid::Symbol)\ngpvars()\n\nReturn a NamedTuple with all currently defined gnuplot variables. If the sid argument is not provided, the default session is considered.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.hist","page":"API","title":"Gnuplot.hist","text":"hist(v::Vector{T}; range=extrema(v), bs=NaN, nbins=0, pad=true) where T <: Real\n\nCalculates the histogram of the values in v and returns a Histogram1D structure.\n\nArguments\n\nv: a vector of values to compute the histogra;\nrange: values of the left edge of the first bin and of the right edge of the last bin;\nbs: size of histogram bins;\nnbins: number of bins in the histogram;\npad: if true add one dummy bins with zero counts before the first bin and after the last.\n\nIf bs is given nbins is ignored.\n\nExample\n\nv = randn(1000)\nh = hist(v, bs=0.5)\n@gp h # preview\n@gp h.bins h.counts \"w histep notit\"\n\n\n\n\n\nhist(v1::Vector{T1 <: Real}, v2::Vector{T2 <: Real}; range1=[NaN,NaN], bs1=NaN, nbins1=0, range2=[NaN,NaN], bs2=NaN, nbins2=0)\n\nCalculates the 2D histogram of the values in v1 and v2 and returns a Histogram2D structure.\n\nArguments\n\nv1: a vector of values along the first dimension;\nv2: a vector of values along the second dimension;\nrange1: values of the left edge of the first bin and of the right edge of the last bin, along the first dimension;\nrange1: values of the left edge of the first bin and of the right edge of the last bin, along the second dimension;\nbs1: size of histogram bins along the first dimension;\nbs2: size of histogram bins along the second dimension;\nnbins1: number of bins along the first dimension;\nnbins2: number of bins along the second dimension;\n\nIf bs1 (bs2) is given nbins1 (nbins2) is ignored.\n\nExample\n\nv1 = randn(1000)\nv2 = randn(1000)\nh = hist(v1, v2, bs1=0.5, bs2=0.5)\n@gp h # preview\n@gp \"set size ratio -1\" \"set auto fix\" h.bins1 h.bins2 h.counts \"w image notit\"\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.linetypes","page":"API","title":"Gnuplot.linetypes","text":"linetypes(cmap::ColorScheme; lw=1, ps=1, dashed=false, rev=false)\nlinetypes(s::Symbol; lw=1, ps=1, dashed=false, rev=false)\n\nConvert a ColorScheme object into a string containing the gnuplot commands to set up linetype colors.\n\nIf the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes.\n\nIf rev=true the line colors are reversed. If a numeric or string value is provided through the lw and ps keywords thay are used to set the line width and the point size respectively. If dashed is true the linetypes with index greater than 1 will be displayed with dashed pattern.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.palette","page":"API","title":"Gnuplot.palette","text":"palette(cmap::ColorScheme; rev=false)\npalette(s::Symbol; rev=false)\n\nConvert a ColorScheme object into a string containing the gnuplot commands to set up the corresponding palette.\n\nIf the argument is a Symbol it is interpreted as the name of one of the predefined schemes in ColorSchemes. If rev=true the palette is reversed.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.palette_names","page":"API","title":"Gnuplot.palette_names","text":"palette_names()\n\nReturn a vector with all available color schemes for the palette and linetypes function.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.recipe","page":"API","title":"Gnuplot.recipe","text":"recipe(h::Histogram1D)\nrecipe(h::Histogram2D)\n\nImplicit recipes to visualize 1D and 2D histograms.\n\n\n\n\n\nrecipe(M::Matrix{ColorTypes.RGB{T}}, opt=\"flipy\")\nrecipe(M::Matrix{ColorTypes.RGBA{T}}, opt=\"flipy\")\nrecipe(M::Matrix{ColorTypes.Gray{T}}, opt=\"flipy\")\nrecipe(M::Matrix{ColorTypes.GrayA{T}}, opt=\"flipy\")\n\nImplicit recipes to show images.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.save","page":"API","title":"Gnuplot.save","text":"save(sid::Symbol; term=\"\", output=\"\")\nsave(sid::Symbol, script_filename::String, ;term=\"\", output=\"\")\nsave(; term=\"\", output=\"\")\nsave(script_filename::String ;term=\"\", output=\"\")\n\nExport a (multi-)plot into the external file name provided in the output= keyword. The gnuplot terminal to use is provided through the term= keyword.\n\nIf the script_filename argument is provided a gnuplot script will be written in place of the output image. The latter can then be used in a pure gnuplot session (Julia is no longer needed) to generate exactly the same original plot.\n\nIf the sid argument is provided the operation applies to the corresponding session.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.session_names","page":"API","title":"Gnuplot.session_names","text":"session_names()\n\nReturn a vector with all currently active sessions.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.stats","page":"API","title":"Gnuplot.stats","text":"stats(sid::Symbol,name::String)\nstats(name::String)\nstats(sid::Symbol)\nstats()\n\nPrint a statistical summary for the name dataset, belonging to sid session. If name is not provdied a summary is printed for each dataset in the session. If sid is not provided the default session is considered.\n\nThis function is actually a wrapper for the gnuplot command stats.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.terminals","page":"API","title":"Gnuplot.terminals","text":"terminals()\n\nReturn a Vector{String} with the names of all the available gnuplot terminals.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.terminal","page":"API","title":"Gnuplot.terminal","text":"terminal(sid::Symbol)\nterminal()\n\nReturn a String with the current gnuplot terminal (and its options) of the process associated to session sid, or to the default session (if sid is not provided).\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.test_terminal","page":"API","title":"Gnuplot.test_terminal","text":"test_terminal(term=nothing; linetypes=nothing, palette=nothing)\n\nRun the test and test palette commands on a gnuplot terminal.\n\nIf no term is given it will use the default terminal. If lt and pal are given they are used as input to the linetypes and palette function repsetcively to load the associated color scheme.\n\nExamples\n\ntest_terminal()\ntest_terminal(\"wxt\", lt=:rust, pal=:viridis)\n\n\n\n\n\n","category":"function"},{"location":"api/#Non-exported-symbols-1","page":"API","title":"Non-exported symbols","text":"","category":"section"},{"location":"api/#","page":"API","title":"API","text":"The following functions are not exported by the Gnuplot.jl package since they are typically not used in every day work, or aimed to debugging purposes. Still, they can be useful in some case, hence they are documented here.","category":"page"},{"location":"api/#","page":"API","title":"API","text":"In order to call these functions you should add the Gnuplot. prefix to the function name.","category":"page"},{"location":"api/#","page":"API","title":"API","text":"Gnuplot.Dataset\nGnuplot.DatasetEmpty\nGnuplot.DatasetText\nGnuplot.DatasetBin\nGnuplot.Histogram1D\nGnuplot.Histogram2D\nGnuplot.IsoContourLines\nGnuplot.Options\nGnuplot.Path2d\nGnuplot.PlotElement\nGnuplot.gpversion\nGnuplot.quit\nGnuplot.quitall\nGnuplot.repl_init\nGnuplot.version","category":"page"},{"location":"api/#Gnuplot.Dataset","page":"API","title":"Gnuplot.Dataset","text":"Dataset\n\nAbstract type for all dataset structures.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.DatasetEmpty","page":"API","title":"Gnuplot.DatasetEmpty","text":"DatasetEmpty\n\nAn empty dataset.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.DatasetText","page":"API","title":"Gnuplot.DatasetText","text":"DatasetText\n\nA dataset whose data are stored as a text buffer.\n\nTransmission to gnuplot may be slow for large datasets, but no temporary file is involved, and the dataset can be saved directly into a gnuplot script. Also, the constructor allows to build more flexible datasets (i.e. mixing arrays with different dimensions).\n\nConstructors are defined as follows:\n\nDatasetText(data::Vector{String})\nDatasetText(data::Vararg{AbstractArray, N}) where N =\n\nIn the second form the type of elements of each array must be one of Real, AbstractString and Missing.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.DatasetBin","page":"API","title":"Gnuplot.DatasetBin","text":"DatasetBin\n\nA dataset whose data are stored as a binary file.\n\nEnsure best performances for large datasets, but involve use of a temporary files. When saving a script the file is stored in a directory with the same name as the main script file.\n\nConstructors are defined as follows:\n\nDatasetBin(cols::Vararg{AbstractMatrix, N}) where N\nDatasetBin(cols::Vararg{AbstractVector, N}) where N\n\nIn both cases the element of the arrays must be a numeric type.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.Histogram1D","page":"API","title":"Gnuplot.Histogram1D","text":"Histogram1D\n\nA 1D histogram data.\n\nFields\n\nbins::Vector{Float64}: bin center values;\ncounts::Vector{Float64}: counts in the bins;\nbinsize::Float64: size of each bin;\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.Histogram2D","page":"API","title":"Gnuplot.Histogram2D","text":"Histogram2D\n\nA 2D histogram data.\n\nFields\n\nbins1::Vector{Float64}: bin center values along first dimension;\nbins2::Vector{Float64}: bin center values along second dimension;\ncounts::Vector{Float64}: counts in the bins;\nbinsize1::Float64: size of each bin along first dimension;\nbinsize2::Float64: size of each bin along second dimension;\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.IsoContourLines","page":"API","title":"Gnuplot.IsoContourLines","text":"IsoContourLines\n\nCoordinates of all contour lines of a given level.\n\nFields\n\npaths::Vector{Path2d}: vector of Path2d objects, one for each continuous path;\ndata::Vector{String}: vector with string representation of all paths (ready to be sent to gnuplot);\nz::Float64: level of the contour lines.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.Options","page":"API","title":"Gnuplot.Options","text":"Options\n\nStructure containing the package global options, accessible through Gnuplot.options.\n\nFields\n\ndry::Bool: whether to use dry sessions, i.e. without an underlying Gnuplot process (default: false)\ncmd::String: command to start the Gnuplot process (default: \"gnuplot\")\ndefault::Symbol: default session name (default: :default)\nterm::String: default terminal for interactive use (default: empty string, i.e. use gnuplot settings);\nterm_svg::String: terminal to save png files (default \"svg background rgb 'white' dynamic\");\nterm_png::String: terminal to save png files (default \"pngcairo\");\ninit::Vector{String}: commands to initialize the session when it is created or reset (e.g., to set default palette);\nverbose::Bool: verbosity flag (default: false)\npreferred_format::Symbol: preferred format to send data to gnuplot. Value must be one of:\nbin: fastest solution for large datasets, but uses temporary files;\ntext: may be slow for large datasets, but no temporary file is involved;\nauto (default) automatically choose the best strategy.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.Path2d","page":"API","title":"Gnuplot.Path2d","text":"Path2d\n\nA path in 2D.\n\nFields\n\nx::Vector{Float64}\ny::Vector{Float64}\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.PlotElement","page":"API","title":"Gnuplot.PlotElement","text":"PlotElement\n\nStructure containing element(s) of a plot (commands, data, plot specifications) that can be used directly in @gp and @gsp calls.\n\nFields\n\nmid::Int: multiplot ID (use 0 for single plots);\nis3d::Bool: true if the data are supposed to be displayed in a 3D plot;\ncmds::Vector{String}: commands to set plot properties;\nname::String: name of the dataset (use \"\" to automatically generate a unique name);\ndata::Dataset: a dataset\nplot::Vector{String}: plot specifications for the associated Dataset;\n\nThe constructor is defined as follows:\n\nPlotElement(;mid::Int=0, is3d::Bool=false,\n cmds::Union{String, Vector{String}}=Vector{String}(),\n name::String=\"\",\n data::Dataset=DatasetEmpty(),\n plot::Union{String, Vector{String}}=Vector{String}(),\n kwargs...)\n\nNo field is mandatory, i.e. even Gnuplot.PlotElement() provides a valid structure. The constructor also accept all the keywords accepted by parseKeywords.\n\n\n\n\n\n","category":"type"},{"location":"api/#Gnuplot.gpversion","page":"API","title":"Gnuplot.gpversion","text":"Gnuplot.gpversion()\n\nReturn the gnuplot application version.\n\nRaise an error if version is < 5.0 (required to use data blocks).\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.quit","page":"API","title":"Gnuplot.quit","text":"Gnuplot.quit(sid::Symbol)\n\nQuit the session identified by sid and the associated gnuplot process (if any).\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.quitall","page":"API","title":"Gnuplot.quitall","text":"Gnuplot.quitall()\n\nQuit all the sessions and the associated gnuplot processes.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.repl_init","page":"API","title":"Gnuplot.repl_init","text":"Gnuplot.init_repl(; start_key='>')\n\nInstall a hook to replace the common Julia REPL with a gnuplot one. The key to start the REPL is the one provided in start_key (default: >).\n\nNote: the gnuplot REPL operates only on the default session.\n\n\n\n\n\n","category":"function"},{"location":"api/#Gnuplot.version","page":"API","title":"Gnuplot.version","text":"Gnuplot.version()\n\nReturn the Gnuplot.jl package version.\n\n\n\n\n\n","category":"function"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"using Gnuplot\nGnuplot.quitall()\nmkpath(\"assets\")\nGnuplot.splash(\"assets/logo.png\")\nGnuplot.options.term = \"unknown\"\nempty!(Gnuplot.options.init)\npush!( Gnuplot.options.init, linetypes(:Set1_5, lw=1.5, ps=1.5))\nsaveas(file) = save(term=\"pngcairo size 550,350 fontscale 0.8\", output=\"assets/$(file).png\")","category":"page"},{"location":"basic/#Basic-usage-1","page":"Basic usage","title":"Basic usage","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The main purpose of the Gnuplot.jl package is to send data and commands to the underlying gnuplot process, in order to generate plots. Unlike other packages, however, the actual commands to plot, or the plot attributes, are not specified through function calls. This is what makes Gnuplot.jl easy to learn and use: there are no functions or keywords names to memorize[1].","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The most important symbols exported by the package are the @gp (for 2D plots) and @gsp (for 3D plots) macros. The simplemost example is as follows:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"using Gnuplot\n@gp 1:20\nsaveas(\"basic000\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The plots are displayed either in an interactive window (if running in the Julia REPL), as an inline image (if running in Jupyter) or in the plot pane (if running in Juno). See Options and Jupyter and Juno for further informations.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Both the @gp and @gsp macros accept any number of arguments, whose meaning is interpreted as follows:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"one, or a group of consecutive, array(s) build up a dataset. The different arrays are accessible as columns 1, 2, etc. from the gnuplot process. The number of required input arrays depends on the chosen plot style (see gnuplot documentation);\na string occurring before a dataset is interpreted as a gnuplot command (e.g. set grid);\na string occurring immediately after a dataset is interpreted as a plot element for the dataset, by which you can specify using clause, with clause, line styles, etc.;\nthe special symbol :-, whose meaning is to avoid starting a new plot (if given as first argument), or to avoid immediately running all commands to create the final plot (if given as last argument). Its purpose is to allow splitting one long statement into multiple (shorter) ones.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The above list shows all the fundamental concepts to follow the examples presented below. The @gp and @gsp macros also accepts further arguments, but their use will be discussed in Advanced usage.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"[1]: a previous knowledge of gnuplot usage is, nevertheless, required.","category":"page"},{"location":"basic/#plots2d-1","page":"Basic usage","title":"2D plots","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Here we will show a few examples to generate 2D plots. The examples are intentionally very simple to highlight the behavior of Gnuplot.jl. See Examples for more complex ones.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Remember to run:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"using Gnuplot","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"before running the examples.","category":"page"},{"location":"basic/#Simple-examples-involving-just-gnuplot-commands:-1","page":"Basic usage","title":"Simple examples involving just gnuplot commands:","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Plot-a-sinusoid:-1","page":"Basic usage","title":"Plot a sinusoid:","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp \"plot sin(x)\"\nsaveas(\"basic001\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Plot-two-curves:-1","page":"Basic usage","title":"Plot two curves:","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp \"set key left\" \"plot sin(x)\" \"pl cos(x)\"\nsaveas(\"basic002\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"note: Note\nNote that all gnuplot commands can be abbreviated as long as the resulting string is not ambiguous. In the example above we used pl in place of plot.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Split-a-@gp-call-in-three-statements:-1","page":"Basic usage","title":"Split a @gp call in three statements:","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp \"set grid\" :-\n@gp :- \"p sin(x)\" :-\n@gp :- \"plo cos(x)\"\nsaveas(\"basic003\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"note: Note\nThe trailing :- symbol means the plot will not be updated until the last statement.","category":"page"},{"location":"basic/#Send-data-from-Julia-to-gnuplot:-1","page":"Basic usage","title":"Send data from Julia to gnuplot:","text":"","category":"section"},{"location":"basic/#Plot-a-parabola-1","page":"Basic usage","title":"Plot a parabola","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp (1:20).^2\nsaveas(\"basic004\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Plot-a-parabola-with-scaled-x-axis,-lines-and-legend-1","page":"Basic usage","title":"Plot a parabola with scaled x axis, lines and legend","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"x = 1:20\n@gp \"set key left\" x ./ 20 x.^2 \"with lines tit 'Parabola'\"\nsaveas(\"basic005\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Multiple-datasets,-logarithmic-axis,-labels-and-colors,-etc.-1","page":"Basic usage","title":"Multiple datasets, logarithmic axis, labels and colors, etc.","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"x = 1:0.1:10\n@gp \"set grid\" \"set key left\" \"set logscale y\"\n@gp :- \"set title 'Plot title'\" \"set label 'X label'\" \"set xrange [0:*]\"\n@gp :- x x.^0.5 \"w l tit 'Pow 0.5' dt 2 lw 2 lc rgb 'red'\"\n@gp :- x x \"w l tit 'Pow 1' dt 1 lw 3 lc rgb 'blue'\"\n@gp :- x x.^2 \"w l tit 'Pow 2' dt 3 lw 2 lc rgb 'purple'\"\nsaveas(\"basic006\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"note: Note\nThe above example lacks the trailing :- symbol. This means the plot will be updated at each command, adding one curve at a time.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"","category":"page"},{"location":"basic/#Keywords-for-common-commands-1","page":"Basic usage","title":"Keywords for common commands","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"In order to avoid typing long, and very frequently used gnuplot commands, Gnuplot.jl provides a few keywords which can be used in both @gp and @sgp calls:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"xrange=[low, high] => \"set xrange [low:high];\nyrange=[low, high] => \"set yrange [low:high];\nzrange=[low, high] => \"set zrange [low:high];\ncbrange=[low, high]=> \"set cbrange[low:high];\nkey=\"...\" => \"set key ...\";\ntitle=\"...\" => \"set title \\\"...\\\"\";\nxlabel=\"...\" => \"set xlabel \\\"...\\\"\";\nylabel=\"...\" => \"set ylabel \\\"...\\\"\";\nzlabel=\"...\" => \"set zlabel \\\"...\\\"\";\ncblabel=\"...\" => \"set cblabel \\\"...\\\"\";\nxlog=true => set logscale x;\nylog=true => set logscale y;\nzlog=true => set logscale z;\nmargins=... => set margins ...;\nlmargin=... => set lmargin ...;\nrmargin=... => set rmargin ...;\nbmargin=... => set bmargin ...;\ntmargin=... => set tmargin ...;","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"All such keywords can be abbreviated to unambiguous names.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"By using the above keywords the first lines of the previous example:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp \"set grid\" \"set key left\" \"set logscale y\"\n@gp :- \"set title 'Plot title'\" \"set label 'X label'\" \"set xrange [0:*]\"","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"can be replaced with a shorter version:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp \"set grid\" k=\"left\" ylog=true\n@gp :- tit=\"Plot title\" xlab=\"X label\" xr=[0,NaN]","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"where NaN in the xrange keyword means using axis autoscaling.","category":"page"},{"location":"basic/#Plot-matrix-as-images-1","page":"Basic usage","title":"Plot matrix as images","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Gnuplot.jl can display a 2D matrix as an image:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"img = randn(Float64, 10, 5)\nimg[10,:] .= -5\n@gp img \"w image notit\"\nsaveas(\"basic007a\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Note that the first index in the img matrix corresponds to the rows in the displayed image coordinate when the image is displayed.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"A simple way to remember the convention is to compare how a matrix is displayed in the REPL:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"img = reshape(1:15, 5, 3)","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"and its image representation, which is essentially upside down:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp img \"w image notit\"\nsaveas(\"basic007b\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Also note that the img[1,1] pixel is shown at coordinates x=0, y=0. See Image recipes for further info.","category":"page"},{"location":"basic/#plots3d-1","page":"Basic usage","title":"3D plots","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"3D plots follow the same rules as 2D ones, just replace the @gp macro with @gsp and add the required columns (according to the plotting style).","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"E.g., to plot a spiral increasing in size along the X direction:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"x = 0:0.1:10pi\n@gsp cbr=[-1,1].*30 x sin.(x) .* x cos.(x) .* x x./20 \"w p pt 7 ps var lc pal\"\nsaveas(\"basic008\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Note that the fourth array in the dataset, x./20, is used as by gnuplot as point size (ps var). Also note that all the keywords discussed above can also be used in 3D plots.","category":"page"},{"location":"basic/#Palettes-and-line-types-1","page":"Basic usage","title":"Palettes and line types","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The Gnuplot.jl package comes with all the ColorSchemes palettes readily available.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"A gnuplot-compliant palette can be retrieved with palette(), and used as any other command. The previous example may use an alternative palette with:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"x = 0:0.1:10pi\n@gsp palette(:viridis) cbr=[-1,1].*30 :-\n@gsp :- x sin.(x) .* x cos.(x) .* x x./20 \"w p pt 7 ps var lc pal\"\nsaveas(\"basic008a\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The list of all available palette can be retrieved with palette_names():","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"palette_names()","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The ColorSchemes palettes can also be used to generate line type colors, and optionally the line width, point size and dashed pattern, by means of the linetypes() function, e.g.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp key=\"left\" linetypes(:Set1_5, lw=2)\nfor i in 1:10\n @gp :- i .* (0:10) \"w lp t '$i'\"\nend\nsaveas(\"basic009a\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"@gp key=\"left\" linetypes(:Set1_5, dashed=true, ps=2)\nfor i in 1:10\n @gp :- i .* (0:10) \"w lp t '$i'\"\nend\nsaveas(\"basic009b\") # hide","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(Image: )","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The first plot features the :Set1_5 palette, with solid lines whose width is 2 times the default. The second plot shows the same palette but default line widths are 1, default point size is 2 (for the first N line types, where N is the number of discrete colors in the palette), and the dashed pattern is automatically changed.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"As discussed in Options, you may set a default line types for all plots with:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"push!(Gnuplot.options.init, linetypes(:Set1_5, lw=1.5, ps=1.5))","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"All plot in this documentation were generated with these settings.","category":"page"},{"location":"basic/#Exporting-plots-to-files-1","page":"Basic usage","title":"Exporting plots to files","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Gnuplot.jl can export all plots (as well as multiplots, see Multiplot) to an external file using one of the many available gnuplot terminals. To check which terminals are available in your platform type:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"terminals()","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"(see also terminal() to check your current terminal).","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Once you choose the proper terminal (i.e. format of the exported file), use the save() function to export. As an example, all the plots in this page have been saved with:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"save(term=\"pngcairo size 550,350 fontscale 0.8\", output=\"assets/output.png\")","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Note that you can pass both the terminal name and its options via the term= keyword. See Gnuplot terminals for further info on the terminals.","category":"page"},{"location":"basic/#Gnuplot-scripts-1","page":"Basic usage","title":"Gnuplot scripts","text":"","category":"section"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"Besides exporting plots in image files, Gnuplot.jl can also save a script, i.e. a file containing the minimum set of data and commands required to re-create a figure using just gnuplot.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"The script allows a complete decoupling of plot data and aethetics, from the Julia code used to generate them. With scripts you can:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"modify all aesthetic details of a plot without re-running the (possibly complex and time-consuming) code used to generate it;\nshare both data and plots with colleagues without the need to share the Julia code.","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"To generate a script for one of the examples above use:","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"save(\"script.gp\")","category":"page"},{"location":"basic/#","page":"Basic usage","title":"Basic usage","text":"after the plot has been displayed. Note that when images or large datasets are involved, save() may store the data in binary files under a directory named

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