Docs updated
This commit is contained in:
Giorgio Calderone
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@ -2,7 +2,7 @@ using Documenter, Gnuplot
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makedocs(sitename="Gnuplot.jl",
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authors = "Giorgio Calderone",
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#format = Documenter.HTML(prettyurls = false), # uncomment for local use, comment for deployment
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format = Documenter.HTML(prettyurls = false), # uncomment for local use, comment for deployment
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modules=[Gnuplot],
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pages = [
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"Home" => "index.md",
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@ -89,7 +89,7 @@ Note that the order of the plots is not relevant, i.e. we would get the same res
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## Customized layout
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It is also possible to customize the plot layout using the margin keywords (see [Histograms](@ref) for further info):
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It is also possible to customize the plot layout using the margin keywords (see [Histograms](@ref) for further info on how to generate andi display histograms):
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```@example abc
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# Generate random numbers
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x = randn(1000);
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@ -181,7 +181,15 @@ Gnuplot.quitall()
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```
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## Histograms
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**Gnuplot.jl** provides a facility to compute (see [`hist()`](@ref) 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()`](@ref) documentation for further information) and return a [`Gnuplot.Histogram1D`](@ref) structure, whose content can be visualized as follows:
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**Gnuplot.jl** provides facilities to compute and display histograms, e.g.:
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```@example abc
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x = randn(1000);
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@gp hist(x)
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saveas("advanced013a") # hide
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```
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The [`hist()`](@ref) 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`](@ref)) structure, e.g.:
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```@example abc
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x = randn(1000);
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h = hist(x, range=3 .* [-1,1], bs=0.5)
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@ -190,7 +198,17 @@ saveas("advanced013b") # hide
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```
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**Gnuplot.jl** also allows to compute 2D histograms by passing two vectors (with the same lengths) to [`hist()`](@ref). 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`](@ref) structure:
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The [`hist()`](@ref) function compute also 2D histograms by passing two vectors (with the same lengths), e.g.:
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```@example abc
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x = randn(10_000)
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y = randn(10_000)
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h = hist(x, y)
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@gp h
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saveas("advanced014a") # hide
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```
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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`](@ref) structure:
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```@example abc
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x = randn(10_000)
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y = randn(10_000)
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@ -201,30 +219,41 @@ saveas("advanced014b") # hide
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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()`](@ref) function, as follows:
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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()`](@ref) function, as follows:
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```@example abc
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box = boxxyerror(h.bins1, h.bins2, cartesian=true)
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@gp "set size ratio -1" "set style fill solid 0.5 border lc rgb 'gray'" :-
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@gp :- box... h.counts "w boxxyerror notit lc pal"
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@gp :- boxxy(h) "w boxxy notit lc pal"
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saveas("advanced014c") # hide
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```
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See also [Histogram recipes](@ref) for a quicker way to preview histogram plots.
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## Contour lines
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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()`](@ref) 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:
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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()`](@ref) function. We may preview such lines with:
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```@example abc
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clines = contourlines(h.bins1, h.bins2, h.counts, cntrparam="levels discrete 10, 30, 60, 90");
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for i in 1:length(clines)
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@gp :- clines[i].data "w l t '$(clines[i].z)' lw $i lc pal" :-
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end
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@gp :- key="outside top center box horizontal"
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x = randn(10_000)
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y = randn(10_000)
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h = hist(x, y)
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clines = contourlines(h, "levels discrete 10, 30, 60, 90");
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@gp clines
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saveas("advanced014d") # hide
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```
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By exploiting the fields of the [`Gnuplot.IsoContourLines`](@ref) structure we may also customize line widths, colors and dashed pattern according to their z level, and plot them on top of the 2D histogram:
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```@example abc
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@gp "set size ratio -1" "set style fill solid 0.5 border lc rgb 'gray'" :-
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@gp :- boxxy(h) "w boxxy notit lc pal"
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for i in 1:length(clines)
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@gp :- clines[i].data "w l t '$(clines[i].z)' lw $i dt $i lc pal" :-
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end
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@gp :- key="outside top center box horizontal"
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saveas("advanced014e") # hide
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```
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## Animations
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@ -10,7 +10,7 @@ The list of **Gnuplot.jl** exported symbols is as follows:
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```@docs
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@gp
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@gsp
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boxxyerror
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boxxy
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contourlines
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dataset_names
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gpexec
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@ -161,20 +161,20 @@ where `NaN` in the `xrange` keyword means using axis autoscaling.
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**Gnuplot.jl** can display a 2D matrix as an image:
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```@example abc
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img = randn(Float64, 10, 5)
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img[10,:] .= -5
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img = randn(Float64, 8, 5)
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img[2,:] .= -5
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@gp img "w image notit"
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saveas("basic007a") # hide
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```
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Note that the first index in the `img` matrix corresponds to the rows in the displayed image coordinate when the image is displayed.
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Note that the first index in the `img` matrix corresponds to the rows in the displayed image.
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A simple way to remember the convention is to compare how a matrix is displayed in the REPL:
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```@example abc
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img = reshape(1:15, 5, 3)
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```
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and its image representation, which is essentially upside down:
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and its image representation, which is essentially upside down (since the Y coordinates increase upwards):
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```@example abc
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@gp img "w image notit"
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saveas("basic007b") # hide
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@ -190,7 +190,7 @@ Also note that the `img[1,1]` pixel is shown at coordinates x=0, y=0. See [Imag
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E.g., to plot a spiral increasing in size along the `X` direction:
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```@example abc
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x = 0:0.1:10pi
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@gsp cbr=[-1,1].*30 x sin.(x) .* x cos.(x) .* x x./20 "w p pt 7 ps var lc pal"
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@gsp cbr=[-1,1].*30 x x.*sin.(x) x.*cos.(x) x./20 "w p pt 7 ps var lc pal"
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saveas("basic008") # hide
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```
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@ -205,7 +205,7 @@ A gnuplot-compliant palette can be retrieved with [`palette()`](@ref), and used
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```@example abc
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x = 0:0.1:10pi
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@gsp palette(:viridis) cbr=[-1,1].*30 :-
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@gsp :- x sin.(x) .* x cos.(x) .* x x./20 "w p pt 7 ps var lc pal"
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@gsp :- x x.*sin.(x) x.*cos.(x) x./20 "w p pt 7 ps var lc pal"
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saveas("basic008a") # hide
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```
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@ -83,6 +83,19 @@ saveas("recipes002a") # hide
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## Contour lines recipes
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The object returned by the [`contourlines()`](@ref) function can be readily visualized by means of implicit recipes defined on the `Gnuplot.IsoContourLines` types:
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```@example abc
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x = randn(10_000);
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y = randn(10_000);
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h = hist(x, y)
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clines = contourlines(h, "levels discrete 10, 30, 60, 90");
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@gp clines
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saveas("recipes002b") # hide
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```
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## Image recipes
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@ -10,7 +10,7 @@ import Base.show
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export session_names, dataset_names, palette_names, linetypes, palette,
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terminal, terminals, test_terminal,
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stats, @gp, @gsp, save, gpexec,
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boxxyerror, contourlines, hist, recipe, gpvars, gpmargins, gpranges
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boxxy, contourlines, hist, recipe, gpvars, gpmargins, gpranges
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# ╭───────────────────────────────────────────────────────────────────╮
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@ -1865,9 +1865,12 @@ end
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# --------------------------------------------------------------------
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"""
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boxxyerror(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)
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boxxy(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)
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boxxy(h::Histogram2D)
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"""
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function boxxyerror(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)
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boxxy(h::Histogram2D) = boxxy(h.bins1, h.bins2, h.counts, cartesian=true)
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function boxxy(x, y, aux...; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=false)
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function box(v; vmin=NaN, vmax=NaN)
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vlow = Vector{Float64}(undef, length(v))
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vhigh = Vector{Float64}(undef, length(v))
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@ -1889,11 +1892,11 @@ function boxxyerror(x, y; xmin=NaN, ymin=NaN, xmax=NaN, ymax=NaN, cartesian=fals
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xlow, xhigh = box(x, vmin=xmin, vmax=xmax)
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ylow, yhigh = box(y, vmin=ymin, vmax=ymax)
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if !cartesian
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return (x, y, xlow, xhigh, ylow, yhigh)
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return Dataset(x, y, xlow, xhigh, ylow, yhigh, aux...)
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end
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i = repeat(1:length(x), outer=length(y))
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j = repeat(1:length(y), inner=length(x))
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return (x[i], y[j], xlow[i], xhigh[i], ylow[j], yhigh[j])
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return Dataset([x[i], y[j], xlow[i], xhigh[i], ylow[j], yhigh[j], aux...])
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end
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@ -16,13 +16,19 @@ recipe(h::Histogram1D) =
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plot="w histep notit lw 2 lc rgb 'black'")
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recipe(h::Histogram2D) =
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PlotElement(cmds=["set autoscale fix", "set size ratio -1"],
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PlotElement(cmds=["set autoscale fix"],
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data=DatasetText(h.bins1, h.bins2, h.counts),
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plot="w image notit")
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# --------------------------------------------------------------------
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# Contour lines
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"""
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recipe(c::IsoContourLines)
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recipe(v::Vector{IsoContourLines})
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Implicit recipes to visualize iso-contour lines.
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"""
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recipe(c::IsoContourLines) = PlotElement(data=c.data, plot="w l t '$(c.z)'")
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recipe(v::Vector{IsoContourLines}) = recipe.(v)
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