NOTE: I recommend using Plots.jl as the plotting interface for Qwt.
Plotting using PyQt/PyQwt and a clean interface for 2D scenes using QCanvas. Add to PyQt GUIs and compose many widgets together for complex visualizations.
Tip: On OS X,
brew install pyqt and
brew install pyqwt might be all you need.
using Qwt # simple 2D line plot plot(1:10) # these are equivalent x = randn(100) * 5 y = sin(x) plot(x, y, linetype=:dots) scatter(x, y) # create a heatmap (and optionally fine-tune coloring) # heatmap_n is the number of bins on each axis # heatmap_c is the cutoff points of the color range heatmap(randn(10000), randn(10000); heatmap_n = 20, heatmap_c = (0.05, 0.3)) # pass in vectors or matrices, and it should slice it up properly Y = rand(100,9) # matrix with series in columns subplot(Y) # creates a 3x3 grid of subplots, (one per column) subplot(rand(100), Y, linetype=:dots) # same, but has shared x-data plot(Y) # plots 5 lines on the same axis (one per column) # use both axes plot(Y[:,1:2], axiss=[:left, :right], colors=[:blue, :green]) # you could also add it after the fact y1, y2 = Y[:,1], Y[:,2] plt = plot(y1, color = :blue) oplot(plt, y2, axis = :right, color = :green) # there are lots of things to adjust plot(y1, axis = :right, color = :red, label = "my line", width = 5, linetype = :step, linestyle = :dashdot, marker = :ellipse, markercolor = :cyan, markersize = 20, title = "my title", xlabel = "my x label", ylabel = "my y label" yrightlabel = "my right axis y label", reg = true # adds a regression line for each series ) # and anything can be pluralized by adding an "s" to the end and passing a vector plot(Y[:,1:2], colors = [:red, :blue]) # add to a plot in real time plt = plot(,) for x in 0:0.1:100 push!(plt, 1, x, sin(x)) refresh(plt) sleep(0.01) end # save a png savepng(plt, "/tmp/png/plot0001.png") # save an animated gif (requires ffmpeg... saves to $dir/out.gif) empty!(plt) a = animation(plt, "/tmp/png") for x in 0:0.1:5 push!(plt, 1, x, sin(x)) refresh(plt) saveframe(a) end makegif(a)
about 1 month ago