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Progress meter for long-running computations

License: MIT License

Julia 100.00%

progressmeter.jl's Introduction

ProgressMeter.jl

Build Status

Progress meter for long-running operations in Julia

Installation

Within julia, execute

Pkg.add("ProgressMeter")

Usage

Progress meters for tasks with a pre-determined number of steps

This works for functions that process things in loops. Here's a demonstration of how to use it:

using ProgressMeter

@showprogress 1 "Computing..." for i in 1:50
    sleep(0.1)
end

This will use a minimum update interval of 1 second, and show the ETA and final duration. If your computation runs so quickly that it never needs to show progress, no extraneous output will be displayed.

The @showprogress macro wraps a for loop or comprehension, as long as the object being iterated over implements the length method. This macro will correctly handle any continue statements in a for loop as well.

You can also control progress updates and reports manually:

function my_long_running_function(filenames::Array)
    n = length(filenames)
    p = Progress(n, 1)   # minimum update interval: 1 second
    for f in filenames
        # Here's where you do all the hard, slow work
        next!(p)
    end
end

For tasks such as reading file data where the progress increment varies between iterations, you can use update!:

using ProgressMeter

function readFileLines(fileName::String)
    file = open(fileName,"r")

    seekend(file)
    fileSize = position(file)

    seekstart(file)
    p = Progress(fileSize, 1)   # minimum update interval: 1 second
    while !eof(file)
        line = readline(file)
        # Here's where you do all the hard, slow work

        update!(p, position(file))
    end
end

Optionally, a description string can be specified which will be prepended to the output, and a progress meter M characters long can be shown. E.g.

p = Progress(n, 1, "Computing initial pass...", 50)

will yield

Computing initial pass...53%|███████████████████████████                       |  ETA: 0:09:02

in a manner similar to python-progressbar.

Also, the glyphs used in the bar may be specified by passing a BarGlyphs object as the keyword argument barglyphs. The BarGlyphs constructor can either take 5 characters as arguments or a single 5 character string. E.g.

p = Progress(n, 1, barglyphs=BarGlyphs("[=> ]"), 50)

will yield

Progress: 53%[==========================>                       ]  ETA: 0:09:02

Progress meters for tasks with an unknown number of steps

Some tasks only terminate when some criterion is satisfied, for example to achieve convergence within a specified tolerance. In such circumstances, you can use the ProgressThresh type:

prog = ProgressThresh(1e-5, "Minimizing:")
for val in logspace(2, -6, 20)
    ProgressMeter.update!(prog, val)
    sleep(0.1)
end

This will display progress until val drops below the threshold value (1e-5).

Printing additional information

You can also print and update information related to the computation by using the showvalues keyword. The following example displays the iteration counter and the value of a dummy variable x below the progress meter:

x,n = 1,10
p = Progress(n)
for iter = 1:10
    x *= 2
    sleep(0.5)
    ProgressMeter.next!(p; showvalues = [(:iter,iter), (:x,x)])
end

Credits

Thanks to Alan Bahm, Andrew Burroughs, and Jim Garrison for major enhancements to this package.

progressmeter.jl's People

Contributors

aaw avatar abahm avatar ahwillia avatar alexmorley avatar alyst avatar bicycle1885 avatar burrowsa avatar dennisprangle avatar garrison avatar hofmannmartin avatar jiahao avatar lendle avatar mweastwood avatar quinnj avatar robertfeldt avatar timholy avatar tkelman avatar yonghee-kim avatar zsunberg avatar

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