mirror of https://github.com/mpastell/Weave.jl
rm unused tests
Shuhei Kadowaki
parent
de2a6657ea
commit
423146f981
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#' % FIR filter design with Julia
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#' % Matti Pastell
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#' % 21th April 2016
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#' # Introduction
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#' This an example of a julia script that can be published using
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#' [Weave](http://mpastell.github.io/Weave.jl/latest/usage/).
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#' The script can be executed normally using Julia
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#' or published to HTML or pdf with Weave.
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#' Text is written in markdown in lines starting with "`#'` " and code
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#' is executed and results are included in the published document.
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#' Notice that you don't need to define chunk options, but you can using
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#' `#+`. just before code e.g. `#+ term=True, caption='Fancy plots.'`.
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#' If you're viewing the published version have a look at the
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#' [source](FIR_design.jl) to see the markup.
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#' # FIR Filter Design
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#' We'll implement lowpass, highpass and ' bandpass FIR filters. If
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#' you want to read more about DSP I highly recommend [The Scientist
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#' and Engineer's Guide to Digital Signal
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#' Processing](http://www.dspguide.com/) which is freely available
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#' online.
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#' ## Calculating frequency response
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#' DSP.jl package doesn't (yet) have a method to calculate the
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#' the frequency response of a FIR filter so we define it:
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using Gadfly, DSP
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function FIRfreqz(b::Array, w = range(0, stop=π, length=1024))
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n = length(w)
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h = Array{ComplexF32}(n)
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sw = 0
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for i = 1:n
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for j = 1:length(b)
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sw += b[j]*exp(-im*w[i])^-j
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end
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h[i] = sw
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sw = 0
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end
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return h
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end
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#' ## Design Lowpass FIR filter
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#' Designing a lowpass FIR filter is very simple to do with DSP.jl, all you
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#' need to do is to define the window length, cut off frequency and the
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#' window. We will define a lowpass filter with cut off frequency at 5Hz for a signal
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#' sampled at 20 Hz.
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#' We will use the Hamming window, which is defined as:
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#' $w(n) = \alpha - \beta\cos\frac{2\pi n}{N-1}$, where $\alpha=0.54$ and $\beta=0.46$
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fs = 20
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f = digitalfilter(Lowpass(5, fs = fs), FIRWindow(hamming(61)))
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w = range(0, stop=pi, length=1024)
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h = FIRfreqz(f, w)
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#' ## Plot the frequency and impulse response
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h_db = log10(abs(h))
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ws = w/pi*(fs/2)
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#' The next code chunk is executed in term mode, see the [script](FIR_design.jl) for syntax.
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#+ term=true
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plot(y = h_db, x = ws, Geom.line,
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Guide.xlabel("Frequency (Hz)"), Guide.ylabel("Magnitude (db)"))
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#' And again with default options
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h_phase = unwrap(-atan(imag(h),real(h)))
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plot(y = h_phase, x = ws, Geom.line,
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Guide.xlabel("Frequency (Hz)"), Guide.ylabel("Phase (radians)"))
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@ -1,112 +0,0 @@
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% FIR filter design with Julia
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% Matti Pastell
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% 21th April 2016
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# Introduction
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This an example of a julia script that can be published using
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[Weave](http://mpastell.github.io/Weave.jl/latest/usage/).
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The script can be executed normally using Julia
|
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or published to HTML or pdf with Weave.
|
||||
Text is written in markdown in lines starting with "`#'` " and code
|
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is executed and results are included in the published document.
|
||||
|
||||
Notice that you don't need to define chunk options, but you can using
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`#+`. just before code e.g. `#+ term=True, caption='Fancy plots.'`.
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If you're viewing the published version have a look at the
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[source](FIR_design.jl) to see the markup.
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# FIR Filter Design
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We'll implement lowpass, highpass and ' bandpass FIR filters. If
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you want to read more about DSP I highly recommend [The Scientist
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and Engineer's Guide to Digital Signal
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Processing](http://www.dspguide.com/) which is freely available
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online.
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## Calculating frequency response
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DSP.jl package doesn't (yet) have a method to calculate the
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the frequency response of a FIR filter so we define it:
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~~~~{.julia}
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using Gadfly, DSP
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function FIRfreqz(b::Array, w = linspace(0, π, 1024))
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n = length(w)
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h = Array{Complex64}(n)
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sw = 0
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for i = 1:n
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for j = 1:length(b)
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sw += b[j]*exp(-im*w[i])^-j
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end
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h[i] = sw
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sw = 0
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end
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return h
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end
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~~~~~~~~~~~~~
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## Design Lowpass FIR filter
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Designing a lowpass FIR filter is very simple to do with DSP.jl, all you
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need to do is to define the window length, cut off frequency and the
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window. We will define a lowpass filter with cut off frequency at 5Hz for a signal
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sampled at 20 Hz.
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We will use the Hamming window, which is defined as:
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$w(n) = \alpha - \beta\cos\frac{2\pi n}{N-1}$, where $\alpha=0.54$ and $\beta=0.46$
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~~~~{.julia}
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fs = 20
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f = digitalfilter(Lowpass(5, fs = fs), FIRWindow(hamming(61)))
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w = linspace(0, pi, 1024)
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h = FIRfreqz(f, w)
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~~~~~~~~~~~~~
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## Plot the frequency and impulse response
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~~~~{.julia}
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h_db = log10(abs(h))
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ws = w/pi*(fs/2)
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~~~~~~~~~~~~~
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The next code chunk is executed in term mode, see the [script](FIR_design.jl) for syntax.
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~~~~{.julia}
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julia> plot(y = h_db, x = ws, Geom.line,
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Guide.xlabel("Frequency (Hz)"), Guide.ylabel("Magnitude (db)"))
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Plot(...)
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~~~~~~~~~~~~~
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\
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And again with default options
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~~~~{.julia}
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h_phase = unwrap(-atan2(imag(h),real(h)))
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plot(y = h_phase, x = ws, Geom.line,
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Guide.xlabel("Frequency (Hz)"), Guide.ylabel("Phase (radians)"))
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~~~~~~~~~~~~~
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\
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@ -1,56 +0,0 @@
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[source,julia]
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--------------------------------------
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using Gadfly
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x = linspace(0, 2π, 200)
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plot(x=x, y = sin(x), Geom.line)
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--------------------------------------
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image::figures/gadfly_formats_test_sin_fun_1.png[width=600,title="sin(x) function."]
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image::figures/gadfly_formats_test_2_1.png[width=600,title="cos(x) function."]
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image::figures/gadfly_formats_test_cos2_fun_1.png[width=600]
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[source,julia]
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--------------------------------------
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julia> x = linspace(0, 2π, 200)
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200-element LinSpace{Float64}:
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0.0,0.0315738,0.0631476,0.0947214,0.126295,…,6.18846,6.22004,6.25161,6.28319
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julia> plot(x=x, y = sin(x), Geom.line)
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Plot(...)
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--------------------------------------
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image::figures/gadfly_formats_test_4_1.png[width=600]
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[source,julia]
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--------------------------------------
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julia> y = 20
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20
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julia> plot(x=x, y = cos(x), Geom.line)
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Plot(...)
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--------------------------------------
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image::figures/gadfly_formats_test_4_2.png[width=600]
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[source,julia]
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--------------------------------------
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x = linspace(0, 2π, 200)
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plot(x=x, y = sin(x), Geom.line)
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--------------------------------------
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image::figures/gadfly_formats_test_5_1.png[width=15cm]
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[source,julia]
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--------------------------------------
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y = 20
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plot(x=x, y = cos(x), Geom.line)
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--------------------------------------
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image::figures/gadfly_formats_test_5_2.png[width=15cm]
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File diff suppressed because one or more lines are too long
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\section{Header}
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\begin{minted}[mathescape, fontsize=\small, xleftmargin=0.5em]{julia}
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using Plots
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gr()
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scatter(1:10)
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\end{minted}
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\includegraphics[width=\linewidth]{figures/publish_test_1_1.pdf}
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\begin{minted}[mathescape, fontsize=\small, xleftmargin=0.5em]{julia}
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plot(1:10)
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\end{minted}
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\begin{figure}[!h]
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\center
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\includegraphics[width=\linewidth]{figures/publish_test_somefig_1.pdf}
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\caption{Hello}
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\label{fig:somefig}
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\end{figure}
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---
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title : Test
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author: Matti Pastell
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date : today
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---
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# Header
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```julia
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using Plots
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gr()
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scatter(1:10)
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```
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```julia; fig_cap = "Hello"; label = "somefig"
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plot(1:10)
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```
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<<fig_cap="sin(x) function."; label="sin_fun"; fig_pos="ht">>=
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using PyPlot
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x = linspace(0, 2π, 200)
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plot(x, sin(x))
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@
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<<echo=false; fig_cap="cos(x) function.">>=
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plot(x, cos(x))
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@
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<<echo=false; label="cos2_fun">>=
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plot(x, cos(2x))
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@
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<<term=true;fig_width=5>>=
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x = linspace(0, 2π, 200)
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plot(x, sin(x))
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y = 20
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plot(x, cos(x))
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@
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<<out_width="15cm">>=
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x = randn(100, 100)
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contourf(x)
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@
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````julia
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using PyPlot
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x = linspace(0, 2π, 200)
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plot(x, sin(x))
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7f5326a4a290>
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7f5326a56c10>
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7f5326d76350>
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````
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````julia
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julia> x = linspace(0, 2π, 200)
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linspace(0.0,6.283185307179586,200)
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julia> plot(x, sin(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7f5326de8c10>
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julia> y = 20
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20
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julia> plot(x, cos(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7f5326de8e50>
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````
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````julia
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x = randn(100, 100)
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contourf(x)
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````
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````
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PyObject <matplotlib.contour.QuadContourSet object at 0x7f53346d2910>
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````
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````julia
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using PyPlot
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x = range(0, stop=2π, length=200)
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plot(x, sin(x))
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd590d7860>
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd60094160>
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````
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````
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd5905c7b8>
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````
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````julia
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julia> x = range(0, stop=2π, length=200)
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200-element LinSpace{Float64}:
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0.0,0.0315738,0.0631476,0.0947214,0.126295,…,6.18846,6.22004,6.25161,6.28319
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julia> plot(x, sin(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd60047fd0>
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julia> y = 20
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20
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julia> plot(x, cos(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd6004eeb8>
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````
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````julia
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x = randn(100, 100)
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contourf(x)
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````
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````
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PyObject <matplotlib.contour.QuadContourSet object at 0x7fbd5a506128>
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````
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@ -1,85 +0,0 @@
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.. code-block:: julia
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using PyPlot
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x = linspace(0, 2π, 200)
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plot(x, sin(x))
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::
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd50edb0b8>
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.. figure:: figures/pyplot_formats_sin_fun_1.svg
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:width: 15 cm
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sin(x) function.
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::
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd514a7780>
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.. figure:: figures/pyplot_formats_2_1.svg
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:width: 15 cm
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cos(x) function.
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::
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd580c6080>
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.. image:: figures/pyplot_formats_cos2_fun_1.svg
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:width: 15 cm
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.. code-block:: julia
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julia> x = linspace(0, 2π, 200)
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200-element LinSpace{Float64}:
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0.0,0.0315738,0.0631476,0.0947214,0.126295,…,6.18846,6.22004,6.25161,6.28319
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julia> plot(x, sin(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd585a7320>
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julia> y = 20
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20
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julia> plot(x, cos(x))
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1-element Array{Any,1}:
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PyObject <matplotlib.lines.Line2D object at 0x7fbd58612128>
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.. image:: figures/pyplot_formats_4_1.svg
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:width: 15 cm
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.. code-block:: julia
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x = randn(100, 100)
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contourf(x)
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::
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PyObject <matplotlib.contour.QuadContourSet object at 0x7fbd5a2d8e48>
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.. image:: figures/pyplot_formats_5_1.svg
|
||||
:width: 15cm
|
||||
|
||||
|
|
@ -1,59 +0,0 @@
|
|||
\begin{juliacode}
|
||||
using PyPlot
|
||||
x = linspace(0, 2π, 200)
|
||||
plot(x, sin(x))
|
||||
\end{juliacode}
|
||||
\begin{juliaout}
|
||||
1-element Array{Any,1}:
|
||||
PyObject <matplotlib.lines.Line2D object at 0x7fbd66371c50>
|
||||
\end{juliaout}
|
||||
\begin{figure}[ht]
|
||||
\center
|
||||
\includegraphics[width=\linewidth]{figures/pyplot_formats_sin_fun_1.pdf}
|
||||
\caption{sin(x) function.}
|
||||
\label{fig:sin_fun}
|
||||
\end{figure}
|
||||
|
||||
\begin{juliaout}
|
||||
1-element Array{Any,1}:
|
||||
PyObject <matplotlib.lines.Line2D object at 0x7fbd66325a20>
|
||||
\end{juliaout}
|
||||
\begin{figure}[htpb]
|
||||
\center
|
||||
\includegraphics[width=\linewidth]{figures/pyplot_formats_2_1.pdf}
|
||||
\caption{cos(x) function.}
|
||||
\end{figure}
|
||||
|
||||
\begin{juliaout}
|
||||
1-element Array{Any,1}:
|
||||
PyObject <matplotlib.lines.Line2D object at 0x7fbd6629cf28>
|
||||
\end{juliaout}
|
||||
\includegraphics[width=\linewidth]{figures/pyplot_formats_cos2_fun_1.pdf}
|
||||
|
||||
\begin{juliaterm}
|
||||
julia> x = linspace(0, 2π, 200)
|
||||
200-element LinSpace{Float64}:
|
||||
0.0,0.0315738,0.0631476,0.0947214,0.126295,…,6.18846,6.22004,6.25161,6.28319
|
||||
|
||||
julia> plot(x, sin(x))
|
||||
1-element Array{Any,1}:
|
||||
PyObject <matplotlib.lines.Line2D object at 0x7fbd600ca588>
|
||||
|
||||
julia> y = 20
|
||||
20
|
||||
|
||||
julia> plot(x, cos(x))
|
||||
1-element Array{Any,1}:
|
||||
PyObject <matplotlib.lines.Line2D object at 0x7fbd600f4c18>
|
||||
|
||||
\end{juliaterm}
|
||||
\includegraphics[width=\linewidth]{figures/pyplot_formats_4_1.pdf}
|
||||
|
||||
\begin{juliacode}
|
||||
x = randn(100, 100)
|
||||
contourf(x)
|
||||
\end{juliacode}
|
||||
\begin{juliaout}
|
||||
PyObject <matplotlib.contour.QuadContourSet object at 0x7fbd600ba9b0>
|
||||
\end{juliaout}
|
||||
\includegraphics[width=15cm]{figures/pyplot_formats_5_1.pdf}
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
# Test for Gadfly with different chunk options and figure formatsusing Weave
|
||||
|
||||
using Gadfly
|
||||
using Gadfly, Cairo
|
||||
|
||||
|
||||
function test_gadfly(doctype, fig_ext)
|
||||
|
|
|
|||
|
|
@ -1,17 +0,0 @@
|
|||
using Weave
|
||||
using Test
|
||||
import Plots
|
||||
|
||||
function publish_test(outfile, format)
|
||||
outfile = joinpath("documents/publish", outfile)
|
||||
infile = "documents/publish_test.jmd"
|
||||
weave(infile, doctype = format, out_path = outfile, template = "templates/mini.tpl")
|
||||
result = read(outfile, String)
|
||||
ref = read(outfile * ".ref", String)
|
||||
@test result == ref
|
||||
rm(outfile)
|
||||
end
|
||||
|
||||
#Test formatters
|
||||
publish_test("publish_tex.tex", "md2tex")
|
||||
#!is_windows() && publish_test("publish_test.html", "md2html")
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
using Weave
|
||||
using Test
|
||||
|
||||
cleanup = true
|
||||
|
||||
weave("documents/pyplot_formats.txt", plotlib="pyplot", doctype="tex")
|
||||
result = read("documents/pyplot_formats.tex", String)
|
||||
ref = read("documents/pyplot_formats_ref.tex", String)
|
||||
result = replace(result, r"\s*PyObject.*\n", "\n") #Remove PyObjects, because they change
|
||||
ref = replace(ref, r"\s*PyObject.*\n", "\n")
|
||||
@test result == ref
|
||||
|
||||
weave("documents/pyplot_formats.txt", plotlib="pyplot", doctype="github")
|
||||
result = read("documents/pyplot_formats.md", String)
|
||||
ref = read("documents/pyplot_formats_ref.md", String)
|
||||
result = replace(result, r"\s*PyObject.*\n", "")
|
||||
ref = replace(ref, r"\s*PyObject.*\n", "")
|
||||
@test result == ref
|
||||
|
||||
|
||||
weave("documents/pyplot_formats.txt", plotlib="pyplot", doctype="rst", fig_ext=".svg")
|
||||
result = read("documents/pyplot_formats.rst", String)
|
||||
ref = read("documents/pyplot_formats_ref.rst", String)
|
||||
result = replace(result, r"\s*PyObject.*\n", "")
|
||||
ref = replace(ref, r"\s*PyObject.*\n", "")
|
||||
@test result == ref
|
||||
|
||||
if cleanup
|
||||
rm("documents/pyplot_formats.tex")
|
||||
rm("documents/pyplot_formats.rst")
|
||||
rm("documents/pyplot_formats.md")
|
||||
rm("documents/figures", recursive = true)
|
||||
end
|
||||
Loading…
Reference in New Issue