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Copy file name to clipboardExpand all lines: README.md
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## Installation
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### Getting R
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First things first, you will probably want to install a recent version of **R** that lets you build packages from source. The advantage of choosing this route is you can then update bleeding edge versions directly from GitHub. If you rely on the pre-built binaries on CRAN you might be waiting much longer.
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#### Mac
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For Mac just get the latest binaries from the **R** project pages:
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<https://cloud.r-project.org/bin/macosx/>
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#### Windows
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For Windows just get the latest binaries from the **R** project pages:
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<https://cloud.r-project.org/bin/windows/>
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#### Linux
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Debian: `sudo apt-get install r-base r-base-dev`
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Fedora: `sudo yum install R`
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Suse: More of a pain, see here <https://cloud.r-project.org/bin/linux/suse/README.html>
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Ubuntu: `sudo apt-get install r-base-dev`
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All the info on binaries is here: <https://cloud.r-project.org/bin/linux/>
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If you have a poorly supported version of Linux (e.g. CentOS) you will need to install **R** from source with the development flags (this bit is important). You can read more here: <https://cloud.r-project.org/sources.html>
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Now you have the development version of **R** installed (hopefully) I would also suggest you get yourself **R-Studio**. It is a very popular and well maintained **R** IDE that gives you a lot of helpful shortcuts to scripting and analysing with **R**. The latest version can be grabbed from <https://www.rstudio.com/products/rstudio/> where you almost certainly want the free Desktop version.
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If you wish to use the command line version of **R** on Mac (why?!) then you might need to separately install **XQuartz** and set the DISPLAY system variable via something like export DISPLAY=:0 (this is not an issue for most people however).
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### Build Tools
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Some of **ProFound** requires compiling, so here is what you might need depending on your platform.
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#### Linux Users
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You know what you are doing. You do you!
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#### Mac Users
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You should not need to install separate compilers with any **R** after v4.0.0, but in case you are stuck on a museum version you can follow the extra instructions here:
Windows users might need to go through a couple of additional steps depending on how their system is set up, but most likely you will need to at least install *Rtools* for later parts of this course, which are available at [https://cran.r-project.org/bin/windows/Rtools/](https://cran.r-project.org/bin/windows/Rtools/) and follow the instructions about how to link these into your system path. You will know it is working because the following will not be empty:
Copy file name to clipboardExpand all lines: vignettes/ProFound-Stack-Images.Rmd
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To convince yourself consider you have 8 exposures with sky RMS equal to 16 originally: 4 you combine, so the sky RMS becomes $\frac{1}{\sqrt{\frac{1}{16^2}+\frac{1}{16^2}+\frac{1}{16^2}+\frac{1}{16^2}}}=\frac{1}{\sqrt{\frac{4}{16^2}}}=8$. Now later on you want to combine the 5 images you have. Clearly the optimal weighting you can possibly get will be $\frac{1}{\sqrt{\frac{8}{16^2}}}=\frac{8}{\sqrt{2}}=5.65$ (the direct stack of the original 8 images). With our already stacked image added to our 4 others we can achieve the same S/N by weighting our stacks by the inverse variance: $\frac{1}{\sqrt{\frac{1}{16^2}+\frac{1}{16^2}+\frac{1}{16^2}+\frac{1}{16^2}+\frac{1}{8^2}}}=\frac{1}{\sqrt{\frac{4}{16^2}+\frac{1}{8^2}}}=\frac{1}{\sqrt{\frac{8}{16^2}}}=\frac{8}{\sqrt{2}}=5.65$.
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**ProFound** comes with a handy function that does all the weight-watching for us called **profoundMakeStack**. In this vignette we will make a simulated image (with a trivially predictable improvement in S/N) and check to see that our stacking behaves as expected.
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**ProPane** comes with a handy function that does all the weight-watching for us called **propaneStackFlatInVar**. In this vignette we will make a simulated image (with a trivially predictable improvement in S/N) and check to see that our stacking behaves as expected.
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First we generate a random image with 200 stars and 200 extended sources. The value used roughly correspoond to the source densities and magnitude distributions you might expect to find in a Z-band VIKING frame (this was used to derive the image statistics).
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First we generate a random image with 200 stars and 200 extended sources. The value used roughly correspond to the source densities and magnitude distributions you might expect to find in a Z-band VIKING frame (this was used to derive the image statistics).
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