readme: add exercise 4 and 5
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README.md
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README.md
@ -34,15 +34,15 @@ libraries to build:
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* [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/)
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(build-time only)
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Additionally Python (version 3) with `numpy` and `matplotlib` is required to
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Additionally, Python (version 3) with `numpy` and `matplotlib` is required to
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generate plots.
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For convenience a `shell.nix` file is provided to set up the build environment.
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For convenience, a `shell.nix` file is provided to set up the build environment.
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See this [guide](https://nixos.org/nix/manual/#chap-quick-start) if you have
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never used Nix before. Running `nix-shell` in the top-level will drop you into
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the development shell.
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Once ready, invoke `make` with the program you wishes to build. For example
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Once ready, invoke `make` with the program you wish to build. For example
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$ make ex-1/bin/main
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@ -115,6 +115,30 @@ Note: the histogram parameters affect the computation of the χ² and the
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relative parameter estimation.
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### Exercise 4
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`ex-4/bin/main` generates a sample of particles with random oriented momentum
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and creates an histogram with average vertical component, in modulus, versus
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horizontal component. It is possible to set the maximum momentum with the
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option `-p`. A χ² fit and a t-test compatibility are performed with respect
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to the expected distribution and results are printed.
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To plot a histogram of the generated sample do
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$ ex-4/bin/main -o | ex-4/plot.py
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It is possible to set the number of particles and bins with the options `-n`
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and `-b`.
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### Exercise 5
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`ex-5/main` compute the integral of exp(1) between 0 and 1 with the methods
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plain MC, MISER and VEGAS. Being reiterative routines, it takes the number of
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iterations as its only argument.
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It prints out the obatined value and its estimated error for each method.
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### Exercise 6
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`ex-6/bin/main` simulates a Fraunhöfer diffraction experiment. The program
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@ -122,13 +146,13 @@ prints to `stdout` the bin counts of the intensity as a function of the
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diffraction angle. To plot a histogram simply pipe the output to the
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program `ex-6/plot.py`.
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The program convolves the original signal with a gaussian kernel (`-s` to
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The program convolves the original signal with a Gaussian kernel (`-s` to
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change the σ), optionally adds a Poisson noise (`-m` to change the mean μ) and
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performs either a naive deconvolution by a FFT (`-m fft` mode) or applying the
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Richard-Lucy deconvolution algorithm (`-m rl` mode), which is expected to
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perform optimally in this case.
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The `-c` and `-d` options controls whether the convolved or deconvolved
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The `-c` and `-d` options control whether the convolved or deconvolved
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histogram counts should be printed to `stdout`. For more options
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run the program with `-h` to see the usage screen.
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