| Name | aopp-deconv-tool JSON |
| Version |
0.1.17
JSON |
| download |
| home_page | None |
| Summary | Tool for performing deconvolution (using LucyRichardson and ModifiedClean algorithms), PSF fitting and filtering, and data manipulation for 2d images and 3d datacubes. |
| upload_time | 2024-05-13 14:43:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | None |
| keywords |
deconvolution
modifiedclean
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# aopp_obs_toolchain <a id="aopp_obs_toolchain"></a> #
Eventually this will consist of multiple packages, for now it just consists of aopp_deconv_tool.
See the [github](https://github.com/jackdobinson/aopp_obs_toolchain) for more details about the internal workings.
## TODO <a id="todo"></a> ##
* Python virtual environment setup guide [DONE]
* Add instructions for non-sudo access installation [DONE]
* Add instrucitons for CONDA install + virtual environment [DONE]
* Add instruction for updating to latest versions [DONE]
* Add how to find package source files [DONE]
* Get some **small** example files and add them to the package to be used with example deconvolution script.
- Look up python package index's policy on hosting example data files
- Zenodo - Site for uploading and sharing data.
- NOTE: Are on shared storage at the moment
* Deconvolution example code + files [DONE]
* PSF fitting example + files [DONE]
* SSA filtering example
* Usage information updates:
- Recreate all heading links by hand as PYPI doesn't understand markdown section links, have to use embedded HTML as `<a id="section-link-text"></a>` [DONE]
- Add link to github [DONE]
- Add information about what python REPL is [DONE]
- Add information about python slice syntax where required [DONE]
## Python Installation and Virtual Environment Setup <a id="python-installation-and-virtual-environment-setup"></a> ##
As Python is used by many operating systems as part of its tool-chain it's a good idea to avoid
fiddling with the "system" installation (so you don't unintentionally overwrite packages or
python versions and make it incompatible with your OS). Therefore the recommended way to use Python
is via a *virtual environment*. A *virtual environment* is isolated from your OS's Python installation.
It has its own packages, its own `pip` (stands for "pip install packages", recursive acronyms...),
and its own versions of the other bits it needs.
This package was developed using Python 3.12.2. Therefore, you need a Python 3.12.2 (or later)
installation, and ideally a *virtual environment* to run it in.
### Installing Python <a id="installing-python"></a> ###
It is recommended that you **do not add the installed python to your path**. If you do, the operating system may/will find the installed version
before the operating system's expected version. And as our new installation almost certainly doesn't have the packages the operating system requires,
and may be an incompatible version, annoying things can happen. Instead, install Python in an obvious place that you can access easily.
Suggested installation locations:
* Windows : `C:\Python\Python3.12`
* Unix/Linux/Mac: `${HOME}/python/python3.12`
Installation instructions for [windows, mac](#windows/mac-installation-instructions), [unix and linux](#unix/linux-installation-instructions) are slightly
different, so please refer to the appropriate section below.
Once installed, if using the suggested installation location, the actual Python interpreter executable will be at one of the following
locations or an equivalent relative location if not using the suggested install location:
* Windows: `C:\Python\Python3.12\bin\python3.exe`
* Unix/Linux/Mac: `${HOME}/python/python3.12/bin/python3`
* NOTE: if using *Anaconda*, you will have a `conda` command that manages the installation location of Python for you.
NOTE: I will assume a linux installation in this guide, so the executable will be at `${HOME}/python/python3.12/bin/python3`
in all code snippets. Alter this appropriately if using windows or a non-suggested installation location.
#### Windows/Mac Installation Instructions <a id="windows/mac-installation-instructions"></a> ####
* Download and run an installer from [the official Python site](https://www.python.org/downloads/).
#### Unix/Linux Installation Instructions <a id="unix/linux-installation-instructions"></a> ####
* **IF** you have `sudo` access [see the appendix for a test for sudo access](#sudo-access-test), try one of the following:
- Install the desired version of Python via the Package Manager included in your operating system
- Build and [install python from source](https://docs.python.org/3/using/unix.html).
+ NOTE: Building from source can be a little fiddly, but there are [online tools to help with building from source](https://www.build-python-from-source.com/).
There is also a [python installation script in the appendix](#linux-installation-bash-script) that will fetch the python
source code, install it, and create a virtual environment.
* **OTHERWISE**, if you don't have `sudo` access, [anaconda python](https://docs.anaconda.com/free/miniconda/index.html#quick-command-line-install)
is probably the easiest way as it does not require `sudo`. I recommend the `miniconda` version (linked above), as the main version installs many
packages you may not need. You can always install other packages later.
- NOTE: The main problem is that installing dependencies requires `sudo` access and, while there
[are ways around `sudo`](https://askubuntu.com/questions/339/how-can-i-install-a-package-without-root-access),
they are fiddly and annoying to use as you can quickly end up in [dependency hell](https://en.wikipedia.org/wiki/Dependency_hell).
### Creating and Activating a Virtual Environment <a id="creating-and-activating-a-virtual-environment"></a> ###
A virtual environment isolates the packages you are using for a project from your normal environment and other virtual environments.
Generally they are created in a directory which we will call `<VENV_DIR>`, and then activated and deactivated as required. NOTE:
*anaconda python* has slightly different commands for managing virtual environments, and uses **names** of virtual environments instead
of **directories**, however the concept and the idea of activating and deactivating them remains the same dispite the slightly different
technical details.
NOTE: For the rest of this guide, *python* refers to a manual Python installation and *anaconda python* to Python provided by Anaconda.
NOTE: I will assume python version `3.12.2` for the rest of this guide but this will also work for different versions as long as the
version number is changed appropriately.
#### Check Python Installation <a id="check-python-installation"></a> ####
With [Python installed](#installing-python), make sure you have the correct version via `${HOME}/python/python3.12/bin/python3 --version`. The command should print `Python 3.12.2`, or whichever version you expect.
#### Check Anaconda Python Installation <a id="check-anaconda-python-installation"></a> ####
If using *anaconda python* check that everything is installed correctly by using the command `conda --version`. This should print
a string like `conda X.Y.Z`, where X,Y,Z are the version number of anaconda.
#### Creating a Python Virtual Environment <a id="creating-a-python-virtual-environment"></a> ####
To create a virtual environment use the command `${HOME}/python/python3.12/bin/python3 -m venv <VENV_DIR>`, where `<VENV_DIR>` is the directory
you want the virtual environment to be in. E.g. `${HOME}/python/python3.12/bin/python3 -m venv .venv_3.12.2` will create the virtual
environment in the directory `.venv_3.12.2` in the current folder (NOTE: the `.` infront of the directory
name will make it hidden by default).
#### Creating an Anaconda Python Virtual Environment <a id="creating-an-anaconca-python-virtual-environment"></a> ####
*Anaconda Python* manages many of the background details for you. Use the command `conda create -n <VENV_NAME> python=3.12.2`, where
`<VENV_NAME>` is the name of the virtual environment to create. E.g. `conda create -n venv_3.12.2 python=3.12.2`
#### Activating and Deactivating a Python Virtual Environment <a id="activating-and-deactivating-a-python-virtual-environment"></a> ####
The process of activating the virtual environment varies depending on the terminal shell you are using.
On the command line, use one of the following commands:
* cmd.exe (Windows): `<VENV_DIR>\Scripts\activate.bat`
* PowerShell (Windows, maybe Linux): `<VENV_DIR>/bin/Activate.ps1`
* bash|zsh (Linux, Mac): `source <VENV_DIR>/bin/activate`
* fish (Linux, Mac): `source <VENV_DIR>/bin/activate.fish`
* csh|tcsh (Linux, Mac): `source <VENV_DIR>/bin/activate.csh`
Once activated, your command line prompt should change to have something like `(.venv_3.12.2)` infront of it.
To check everything is working, enter the following commands (NOTE: the full path is not required as we are now using the virtual environment):
* `python --version`
- Should output the version you expect, e.g. `Python 3.12.2`
* `python -c 'import sys; print(sys.prefix != sys.base_prefix)'`
- Should output `True` if you are in a virtual environment or `False` if you are not.
To deactivate the environment, use the command `deactivate`. Your prompt should return to normal.
#### Activating and Deactivating an Anaconda Python Virtual Environment <a id="activating-and-deactivating-an-anaconda-python-virtual-environment"></a> ####
*Anaconda python* has a simpler way of activating a virtual environment. Use the command `conda activate <VENV_NAME>`, your prompt
should change to have something like `(<VENV_NAME>)` infront of it. Use `python --version` to check that the activated environment
contains the expected python version.
To deactivate the environment, use the command `conda deactivate`. Your prompt should return to normal.
## Installing the Package via Pip <a id="installing-the-package-via-pip"></a> ##
NOTE: If using *anaconda python* you **may** be able to use `conda install` instead of `pip` but I have not tested this. Conda [should behave well](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages) when using packages installed via `pip`.
Once you have [installed Python 3.12.2 or higher](#installing-python), [created a virtual environment](#creating-and-activating-a-virtual-environment), and [activated
the virtual environment](#activating-and-deactivating-a-python-virtual-environment). Use the following command to install the package:
* `python -m pip install --upgrade pip`
- This updates pip to its latest version
* `python -m pip install aopp_deconv_tool`
- This actually installs the package.
NOTE: We are using `python -m pip` instead of just `pip` incase the `pip` command does not point to the virtual environment's `pip` executable. You can run
`pip --version` to see which version of python it is for and where the pip executable is located if you want. As explanation, `python -m pip` means "use python
to run its pip module", whereas `pip` means "look on my path for the first executable called 'pip' and run it". Usually they are the same, but not always.
To update the package to it's newest version use:
* `python -m pip install --upgrade aopp_deconv_tool`
# aopp_deconv_tool <a id="aopp_deconv_tool"></a> #
This tool provides deconvolution, psf fitting, and ssa filtering routines.
NOTE: It can be useful to look through the source files, see the appendix for how to find [the package's source files location](#location-of-package-source-files)
## Examples <a id="examples"></a> ##
See the `examples` folder of the github.
## Commandline Scripts <a id="commandline-scripts"></a> ##
### Spectral Rebinning <a id="spectral-rebinning-script"></a> ##
Invoke via `python -m aopp_deconv_tool.spectral_rebin`. Use the `-h` option to see the help message.
This routine accepts a FITS file specifier, it will spectrally rebin the fits extension and output a new fits file.
### Interpolation <a id="interpolation-script"></a> ##
Invoke via `python -m aopp_deconv_tool.interpolate`. Use the `-h` option to see the help message.
Accepts a FTIS file specifier, will find bad pixels and interpolate over them. The strategies used are
dependent on the options given to the program.
bad pixel strategies:
ssa
Uses singular spectrum analysis to determine bad pixels. Useful for situations where artifacts are not
seperable from the science data via a simple brightness threshold. Also interpolates over INF and NAN pixels.
simple
Only interpolates over INF and NAN pixels
interpolation strategies:
scipy
Uses scipy routines to interpolate over the bad pixels. Uses a convolution technique to assist with edge effect problems.
ssa
[EXPERIMENTAL] Interpolates over SSA components only where extreme values are present. Testing has shown this to give
results more similar to the underlying test data than `scipy`, but is substantially slower and requires parameter
fiddling to give any substantial improvement.
### PSF Normalisation <a id="psf-normalisation-script"></a> ###
Invoke via `python -m aopp_deconv_tool.psf_normalise`. Use the `-h` option to see the help message.
Peforms the following operations:
* Ensures image shape is odd, so there is a definite central pixel
* Removes any outliers (based on the `sigma` option)
* Recenters the image around the center of mass (uses the `threshold` and `n_largest_regions` options)
* Optionally trims the image to a desired shape around the center of mass to reduce data volume and speed up subsequent steps
* Normalises the image to sum to 1
### PSF Model Fitting <a id="psf-model-fitting-script"></a> ###
Invoke via `python -m aopp_deconv_tool.psf_normalise`. Use the `-h` option to see the help message.
NOTE: The `--model` option sets the model to fit. To see which parameters a model accepts use the `--model_help` option [NOTE: CHECK THIS WORKS]
Specifying the `--method` option sets the routine used for fitting. Two are available `scipy.minimize` (default) and `ultranest`.
scipy.minimize
A simple gradient descent solver. Fast and useful when the optimal solution is close to the passed starting parameters.
ultranest
Nested sampling. Much slower (but can be sped up), but works when the optimal solution has local maxima/minima that
would trap `scipy.minimize`. Currently the `muse_ao` model only finds a good solution with this method.
### Deconvolution <a id="deconvolution-script"></a> ###
Invoke via `python -m aopp_deconv_tool.deconvolve`. Use the `-h` option to see the help message.
NOTE: the `--parameter_help` option will show the help message for the deconvolution parameters [NOTE: CHECK THIS WORKS]
Assumes the observation data has no NAN or INF pixels, assumes the PSF data is centered and sums to 1. Use the `--plot` option
to see an progress plot that updates every 10 iterations of the MODIFIED_CLEAN algorithm, useful for working out what different
parameters do.
## Using the Package in Code <a id="using-the-package-in-code"></a> ##
The commandline scripts provide a blueprint of how the various routines can be used. However sometimes more customisation is
needed. Below is a quick overview of the main routines and how they function.
NOTE: You can always [get help within python](#getting-documentation-from-within-python) as the classes and functions have docstrings.
### Deconvolution <a id="deconvolution-code"></a> ###
The main deconvolution routines are imported via
```
from aopp_deconv_tool.algorithm.deconv.clean_modified import CleanModified
from aopp_deconv_tool.algorithm.deconv.lucy_richardson import LucyRichardson
```
`CleanModified` is the class implementeing the MODIFIED_CLEAN algorithm, the `LucyRichardson` class implements the Lucy-Richardson algorithm.
### PSF Fitting <a id="psf-fitting-code"></a> ###
The main PSF fitting routines are in `aopp_deconv_tools.psf_model_dependency_injector`, and `aopp_deconv_tools.psf_data_ops`.
The examples on the github deal with this area. Specifically `<REPO_DIR>/examples/psf_model_example.py` for adaptive optics
instrument fitting.
### SSA Filtering <a id="ssa-filtering-code"></a> ###
Singular Spectrum Analysis is performed by the `SSA` class in the `aopp_deconv_tools.py_ssa` module. An interactive
viewer that can show SSA components can be run via `python -m aopp_deconv_tool.graphical_frontends.ssa_filtering`.
By default it will show some test data, if you pass an **image** file (i.e. not a FITS file, but a `.jpg` etc.) it
will use that image instead of the default one.
The `ssa2d_sub_prob_map` function in the `aopp_deconv_tool.algorithm.bad_pixels.ssa_sub_prob` module attempts to
make an informed choice of hot/cold pixels for masking purposes. See the docstring for more details.
The `ssa_interpolate_at_mask` function in the `aopp_deconv_tool.algorithm.interpolate.ssa_interp` module attempts
to interpolate data by interpolating between SSA components, only when the value of the component at the point
to be interpolated is not an extreme value. See the docstring for more details.
# APPENDICES <a id="appendices"></a> #
## APPENDIX: Snippets <a id="appendix:-snippets"></a> ##
### Sudo Access Test <a id="sudo-access-test"></a> ###
Enter the following commands at the command line:
* `ls`
* `sudo ls`
If, after entering your password, you see the same output for both commands, you have `sudo` access. Otherwise, you do not.
### Location of package source files <a id="location-of-package-source-files"></a> ###
To find the location of the package's files, run the following command:
* `python -c 'import site; print(site.getsitepackages())'`
This will output the *site packages* directory for the python executable. The package's
files will be in the `aopp_deconv_tool` subdirectory.
### Getting documentation from within python <a id="getting-documentation-from-within-python"></a> ###
Python's command line, often called the "Read-Evaluate-Print-Loop (REPL)", has a built-in help system.
To get the help information for a class, function, or object use the following code. Note, `>>>` denotes the
python REPL (i.e. the command line you get when you type the `python` command), and `$` denotes the shell
commandline.
This example is for the built-in `os` module, but should work with any python object.
```
$ python
... # prints information about the python version etc. here
>>> import os
>>> help(os)
... # prints out the docstring of the 'os' module
```
### Python slice syntax <a id="python-slice-syntax"></a> ###
When specifying subsets of datacubes, it is useful to be able to select a N-square (i.e., square, cube, tesseract)
region to operate upon to reduce data volume and therefore processing time. [Python and numpy's slicing syntax](https://www.w3schools.com/python/numpy/numpy_array_slicing.asp)
is a nice way to represent these operations. A quick explanation of the synatx follows.
Let `a` be a 1 dimensional array, such that `a = np.array([10,11,15,16,19,20])`, selecting an element of the array
is done via square brackets `a[1]` is the 1^th element, and as python is 0-indexed is equal to 11 for our example array.
Slicing is also done via square brackets, instead of a number (that would select and element), we pass a slice. Slices are
defined via the format `<start>:<stop>:<step>`. Where `<start>` is the first index to include in the slice, `<stop>` is the
first index to **not** include in the slice, and `<step>` is what to add to the previously included index to get the next
included index.
E.g.
* `2:5:1` includes the indices 2,3,4 in the slice. So `a[2:5:1]` would select 15,16,19.
* `0:4:2` includes the indices 0,2 in the slice. So `a[0:4:2]` would select 10,15.
Eveything in a slice is optional exept the first colon. The defaults of everything are as follows:
* `<start>` defaults to 0
* `<stop>` defaults to the last + 1 index in the array. As python supports negative indexing (-ve indices "wrap around", with
-1 being the index **after** the largest index) this is often called the -1^th index, or that `<stop>` defaults to -1.
* `<step>` defaults to 1
Therefore, the slice `:` selects all of the array, and the slice `::-1` selects all of the array, but reverses the ordering.
When dealing with N-dimensional arrays, indexing accepts a tuple.
E.g. for a 2-dimensional array `b=np.array([[10,11,15],[16,19,20],[33,35,36]])`,
* `b[1,2]` is equal to 20
* `b[2,1]` is equal to 35
Similarly, slicing an N-dimensional array uses tuples of slices. E.g.
```
>>> b
array([[10, 11, 15],
[16, 19, 20],
[33, 35, 36]])
>>> b[::-1,:]
array([[33, 35, 36],
[16, 19, 20],
[10, 11, 15]])
>>> b[1:2,::-1]
array([[20, 19, 16]])
```
Slices and indices can be mixed, so you can slice one dimension and select an index from another. E.g.
```
>>> b[:1, 2]
array([15])
>>> b[::-1, 0]
array([33, 16, 10])
>>> b[0,::-1]
array([15, 11, 10])
```
There is a `slice` object in Python that can be used to programatically create slices, it's prototype is `slice(start,stop,step)`,
but only `stop` is required, and if `stop=None` the slice will continue until the end of the array dimension. Slice objects
are almost interchangeable with the slice syntax. E.g.
```
>>> s = slice(2)
>>> b[s,0]
array([10, 16])
>>> b[:2,0]
array([10, 16])
```
## APPENDIX: Scripts <a id="appendix:-scripts"></a> ##
### Linux Installation Bash Script <a id="linux-installation-bash-script"></a> ###
Below is an example *bash* script for building python from source and configuring a virtual environment.
Use it via copying the code into a file (recommended name `install_python.sh`). If Python's dependencies
are not already installed, you will need `sudo` access so the script can install them.
* Make the script executable : `chmod u+x install_python.sh`
* Get help on the scripts options with: `./install_python.sh -h`
* Run the script with : `./install_python.sh`
```
#!/usr/bin/env bash
# Turn on "strict" mode
set -o errexit -o nounset -o pipefail
# Remember values of environment variables as we enter the script
OLD_IFS=$IFS
INITIAL_PWD=${PWD}
############################################################################################
############## PROCESS ARGUMENTS ################
############################################################################################
# Set default parameters
PYTHON_VERSION=(3 12 2)
PYTHON_INSTALL_DIRECTORY="${HOME:?}/python/python_versions"
VENV_PREFIX=".venv_"
VENV_DIR="${PWD}"
# Get the usage string with the default values of everything
usage(){
echo "install_python.sh [-v INT.INT.INT] [-i PATH] [-p STR] [-d PATH] [-l PATH] [-h]"
echo " -v : Python version to install. Default = ${PYTHON_VERSION[0]}.${PYTHON_VERSION[1]}.${PYTHON_VERSION[2]}"
echo " -i : Path to install python to. Default = '${PYTHON_INSTALL_DIRECTORY}'"
echo " -p : Prefix for virtual environment (will have python version added as a suffix). Default = ${VENV_PREFIX}"
echo " -d : Directory to create virtual envronment. Default = '${VENV_DIR}'"
echo " -h : display this help message"
}
USAGE=$(usage)
# Parse input arguments
while getopts "v:i:p:d:h" OPT; do
case $OPT in
v)
IFS="."
PYTHON_VERSION=(${OPTARG})
IFS=$OLD_IFS
;;
i)
PYTHON_INSTALL_DIRECTORY=${OPTARG}
;;
p)
VENV_PREFIX=${OPTARG}
;;
d)
VENV_DIR=${OPTARG}
;;
*)
echo "${USAGE}"
exit 0
;;
esac
done
# Perform argument processing
PYTHON_VERSION_STR="${PYTHON_VERSION[0]}.${PYTHON_VERSION[1]}.${PYTHON_VERSION[2]}"
# Print parameters to user so they know what's going on
echo "Parameters:"
echo " -v : PYTHON_VERSION=${PYTHON_VERSION_STR}"
echo " -i : PYTHON_INSTALL_DIRECTORY=${PYTHON_INSTALL_DIRECTORY}"
echo " -p : VENV_PREFIX=${VENV_PREFIX}"
echo " -d : VENV_DIR=${VENV_DIR}"
############################################################################################
############## DEFINE FUNCTIONS ################
############################################################################################
function install_pkg_if_not_present(){
# Turn on "strict" mode
set -o errexit -o nounset -o pipefail
REQUIRES_INSTALL=()
for PKG in ${@}; do
# We want the command to fail when a package is not installed, therefore unset errexit
set +o errexit
DPKG_RCRD=$(dpkg-query -l ${PKG} 2> /dev/null | grep "^.i.[[:space:]]${PKG}\(:\|[[:space:]]\)")
INSTALLED=$?
set -o errexit
if [ ${INSTALLED} -eq 0 ]; then
echo "${PKG} is installed"
else
echo "${PKG} is NOT installed"
REQUIRES_INSTALL[${#REQUIRES_INSTALL[@]}]=${PKG}
fi
done
if [ ${#REQUIRES_INSTALL[@]} -ne 0 ]; then
UNFOUND_PKGS=()
for PKG in ${REQUIRES_INSTALL[@]}; do
# We want the command to fail when a package is not installed, therefore unset errexit
set +o errexit
apt-cache showpkg ${PKG} | grep '^Package: ${PKG}$'
PKG_FOUND=$?
set -o errexit
if [ $PKG_FOUND -ne 0 ]; then
UNFOUND_PKGS[${#UNFOUND_PKGS[@]}]=${PKG}
fi
done
if [ ${#UNFOUND_PKGS[@]} -ne 0 ]; then
echo "ERROR: Cannot install. Could not find the following packages in apt: ${UNFOUND_PKGS[@]}"
return 1
fi
echo "Installing packages: ${REQUIRES_INSTALL[@]}"
sudo apt-get install -y ${REQUIRES_INSTALL[@]}
else
echo "All required packages are installed"
fi
}
############################################################################################
############## START SCRIPT ################
############################################################################################
# Define the dependencies that python requires for installation
PYTHON_DEPENDENCIES=( \
curl \
gcc \
libbz2-dev \
libev-dev \
libffi-dev \
libgdbm-dev \
liblzma-dev \
libncurses-dev \
libreadline-dev \
libsqlite3-dev \
libssl-dev \
make \
tk-dev \
wget \
zlib1g-dev \
)
# Get a temporary directory and make sure it's cleaned up when the script exits
TEMP_WORKSPACE=$(mktemp -d -t py_build_src.XXXXXXXX)
cleanup(){
echo "Cleaning up on exit..."
echo "Removing ${TEMP_WORKSPACE}"
rm -rf ${TEMP_WORKSPACE:?}
}
trap cleanup EXIT
# If there is an error, make sure we print the usage string with default parameter values
error_message(){
echo "${USAGE}"
}
trap error_message ERR
# Define variables
PYTHON_VERSION_INSTALL_DIR="${PYTHON_INSTALL_DIRECTORY}/${PYTHON_VERSION_STR}"
VENV_PATH="${VENV_DIR}/${VENV_PREFIX}${PYTHON_VERSION_STR}"
PYTHON_VERSION_SOURCE_URL="https://www.python.org/ftp/python/${PYTHON_VERSION_STR}/Python-${PYTHON_VERSION_STR}.tgz"
PY_SRC_DIR="${TEMP_WORKSPACE}/Python-${PYTHON_VERSION_STR}"
PY_SRC_FILE="${PY_SRC_DIR}.tgz"
# Perform actions
echo "Checking python dependencies and installing if required..."
install_pkg_if_not_present ${PYTHON_DEPENDENCIES}
echo "Downloading python source code to '${PY_SRC_FILE}'..."
curl ${PYTHON_VERSION_SOURCE_URL} --output ${PY_SRC_FILE}
echo "Extracting source file..."
mkdir ${PY_SRC_DIR}
tar -xvzf ${PY_SRC_FILE} -C ${TEMP_WORKSPACE}
cd ${PY_SRC_DIR}
echo "Configuring python installation..."
./configure \
--prefix=${PYTHON_VERSION_INSTALL_DIR:?} \
--enable-optimizations \
--with-lto \
--enable-ipv6
echo "Running makefile..."
make
echo "Created ${PYTHON_VERSION_INSTALL_DIR}"
mkdir -p ${PYTHON_VERSION_INSTALL_DIR}
echo "Performing installation"
make install
cd ${INITIAL_PWD}
echo "Creating virtual environment..."
${PYTHON_VERSION_INSTALL_DIR}/bin/python3 -m venv ${VENV_PATH}
echo "Virtual environment created at ${VENV_PATH}"
# Output information to user
echo ""
echo "Activate the virtual environment with the following command:"
echo " source ${VENV_PATH}/bin/activate"
```
Raw data
{
"_id": null,
"home_page": null,
"name": "aopp-deconv-tool",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.12",
"maintainer_email": null,
"keywords": "deconvolution, ModifiedClean",
"author": null,
"author_email": "Jack Dobinson <jack.dobinson@physics.ox.ac.uk>",
"download_url": "https://files.pythonhosted.org/packages/ab/ce/1c97481acc9d2d3948a8c6d5a23f8fecc9c3f138be680338b40d30a3a235/aopp_deconv_tool-0.1.17.tar.gz",
"platform": null,
"description": "# aopp_obs_toolchain <a id=\"aopp_obs_toolchain\"></a> #\n\nEventually this will consist of multiple packages, for now it just consists of aopp_deconv_tool.\n\nSee the [github](https://github.com/jackdobinson/aopp_obs_toolchain) for more details about the internal workings.\n\n## TODO <a id=\"todo\"></a> ##\n\n* Python virtual environment setup guide [DONE]\n\n* Add instructions for non-sudo access installation [DONE]\n\n* Add instrucitons for CONDA install + virtual environment [DONE]\n\n* Add instruction for updating to latest versions [DONE]\n\n* Add how to find package source files [DONE]\n\n* Get some **small** example files and add them to the package to be used with example deconvolution script.\n - Look up python package index's policy on hosting example data files\n - Zenodo - Site for uploading and sharing data.\n - NOTE: Are on shared storage at the moment\n\n* Deconvolution example code + files [DONE]\n\n* PSF fitting example + files [DONE]\n\n* SSA filtering example\n\n* Usage information updates:\n - Recreate all heading links by hand as PYPI doesn't understand markdown section links, have to use embedded HTML as `<a id=\"section-link-text\"></a>` [DONE]\n - Add link to github [DONE]\n - Add information about what python REPL is [DONE]\n - Add information about python slice syntax where required [DONE]\n\n\n## Python Installation and Virtual Environment Setup <a id=\"python-installation-and-virtual-environment-setup\"></a> ##\n\nAs Python is used by many operating systems as part of its tool-chain it's a good idea to avoid\nfiddling with the \"system\" installation (so you don't unintentionally overwrite packages or \npython versions and make it incompatible with your OS). Therefore the recommended way to use Python\nis via a *virtual environment*. A *virtual environment* is isolated from your OS's Python installation.\nIt has its own packages, its own `pip` (stands for \"pip install packages\", recursive acronyms...),\nand its own versions of the other bits it needs. \n\nThis package was developed using Python 3.12.2. Therefore, you need a Python 3.12.2 (or later)\ninstallation, and ideally a *virtual environment* to run it in.\n\n### Installing Python <a id=\"installing-python\"></a> ###\n\nIt is recommended that you **do not add the installed python to your path**. If you do, the operating system may/will find the installed version\nbefore the operating system's expected version. And as our new installation almost certainly doesn't have the packages the operating system requires,\nand may be an incompatible version, annoying things can happen. Instead, install Python in an obvious place that you can access easily. \n\nSuggested installation locations:\n\n* Windows : `C:\\Python\\Python3.12`\n\n* Unix/Linux/Mac: `${HOME}/python/python3.12`\n\nInstallation instructions for [windows, mac](#windows/mac-installation-instructions), [unix and linux](#unix/linux-installation-instructions) are slightly\ndifferent, so please refer to the appropriate section below.\n\nOnce installed, if using the suggested installation location, the actual Python interpreter executable will be at one of the following\nlocations or an equivalent relative location if not using the suggested install location:\n\n* Windows: `C:\\Python\\Python3.12\\bin\\python3.exe`\n\n* Unix/Linux/Mac: `${HOME}/python/python3.12/bin/python3`\n\n* NOTE: if using *Anaconda*, you will have a `conda` command that manages the installation location of Python for you.\n\nNOTE: I will assume a linux installation in this guide, so the executable will be at `${HOME}/python/python3.12/bin/python3`\nin all code snippets. Alter this appropriately if using windows or a non-suggested installation location.\n\n#### Windows/Mac Installation Instructions <a id=\"windows/mac-installation-instructions\"></a> ####\n\n* Download and run an installer from [the official Python site](https://www.python.org/downloads/).\n\n\n#### Unix/Linux Installation Instructions <a id=\"unix/linux-installation-instructions\"></a> ####\n\n* **IF** you have `sudo` access [see the appendix for a test for sudo access](#sudo-access-test), try one of the following:\n\n - Install the desired version of Python via the Package Manager included in your operating system\n\n - Build and [install python from source](https://docs.python.org/3/using/unix.html).\n\n + NOTE: Building from source can be a little fiddly, but there are [online tools to help with building from source](https://www.build-python-from-source.com/).\n There is also a [python installation script in the appendix](#linux-installation-bash-script) that will fetch the python \n source code, install it, and create a virtual environment.\n\n* **OTHERWISE**, if you don't have `sudo` access, [anaconda python](https://docs.anaconda.com/free/miniconda/index.html#quick-command-line-install)\n is probably the easiest way as it does not require `sudo`. I recommend the `miniconda` version (linked above), as the main version installs many\n packages you may not need. You can always install other packages later.\n\n - NOTE: The main problem is that installing dependencies requires `sudo` access and, while there \n [are ways around `sudo`](https://askubuntu.com/questions/339/how-can-i-install-a-package-without-root-access), \n they are fiddly and annoying to use as you can quickly end up in [dependency hell](https://en.wikipedia.org/wiki/Dependency_hell).\n\n\n\n\n\n### Creating and Activating a Virtual Environment <a id=\"creating-and-activating-a-virtual-environment\"></a> ###\n\nA virtual environment isolates the packages you are using for a project from your normal environment and other virtual environments.\nGenerally they are created in a directory which we will call `<VENV_DIR>`, and then activated and deactivated as required. NOTE:\n*anaconda python* has slightly different commands for managing virtual environments, and uses **names** of virtual environments instead\nof **directories**, however the concept and the idea of activating and deactivating them remains the same dispite the slightly different\ntechnical details.\n\nNOTE: For the rest of this guide, *python* refers to a manual Python installation and *anaconda python* to Python provided by Anaconda.\nNOTE: I will assume python version `3.12.2` for the rest of this guide but this will also work for different versions as long as the\n version number is changed appropriately.\n\n#### Check Python Installation <a id=\"check-python-installation\"></a> ####\n\nWith [Python installed](#installing-python), make sure you have the correct version via `${HOME}/python/python3.12/bin/python3 --version`. The command should print `Python 3.12.2`, or whichever version you expect.\n\n#### Check Anaconda Python Installation <a id=\"check-anaconda-python-installation\"></a> ####\n\nIf using *anaconda python* check that everything is installed correctly by using the command `conda --version`. This should print\na string like `conda X.Y.Z`, where X,Y,Z are the version number of anaconda.\n\n#### Creating a Python Virtual Environment <a id=\"creating-a-python-virtual-environment\"></a> ####\n\nTo create a virtual environment use the command `${HOME}/python/python3.12/bin/python3 -m venv <VENV_DIR>`, where `<VENV_DIR>` is the directory\nyou want the virtual environment to be in. E.g. `${HOME}/python/python3.12/bin/python3 -m venv .venv_3.12.2` will create the virtual\nenvironment in the directory `.venv_3.12.2` in the current folder (NOTE: the `.` infront of the directory\nname will make it hidden by default).\n\n#### Creating an Anaconda Python Virtual Environment <a id=\"creating-an-anaconca-python-virtual-environment\"></a> ####\n\n*Anaconda Python* manages many of the background details for you. Use the command `conda create -n <VENV_NAME> python=3.12.2`, where\n`<VENV_NAME>` is the name of the virtual environment to create. E.g. `conda create -n venv_3.12.2 python=3.12.2`\n\n\n#### Activating and Deactivating a Python Virtual Environment <a id=\"activating-and-deactivating-a-python-virtual-environment\"></a> ####\n\nThe process of activating the virtual environment varies depending on the terminal shell you are using.\nOn the command line, use one of the following commands:\n\n* cmd.exe (Windows): `<VENV_DIR>\\Scripts\\activate.bat` \n\n* PowerShell (Windows, maybe Linux): `<VENV_DIR>/bin/Activate.ps1`\n\n* bash|zsh (Linux, Mac): `source <VENV_DIR>/bin/activate`\n\n* fish (Linux, Mac): `source <VENV_DIR>/bin/activate.fish`\n\n* csh|tcsh (Linux, Mac): `source <VENV_DIR>/bin/activate.csh`\n\nOnce activated, your command line prompt should change to have something like `(.venv_3.12.2)` infront of it.\n\nTo check everything is working, enter the following commands (NOTE: the full path is not required as we are now using the virtual environment):\n\n* `python --version`\n - Should output the version you expect, e.g. `Python 3.12.2`\n\n* `python -c 'import sys; print(sys.prefix != sys.base_prefix)'`\n - Should output `True` if you are in a virtual environment or `False` if you are not.\n\nTo deactivate the environment, use the command `deactivate`. Your prompt should return to normal.\n\n#### Activating and Deactivating an Anaconda Python Virtual Environment <a id=\"activating-and-deactivating-an-anaconda-python-virtual-environment\"></a> ####\n\n*Anaconda python* has a simpler way of activating a virtual environment. Use the command `conda activate <VENV_NAME>`, your prompt\nshould change to have something like `(<VENV_NAME>)` infront of it. Use `python --version` to check that the activated environment\ncontains the expected python version.\n\nTo deactivate the environment, use the command `conda deactivate`. Your prompt should return to normal.\n\n\n\n## Installing the Package via Pip <a id=\"installing-the-package-via-pip\"></a> ##\n\nNOTE: If using *anaconda python* you **may** be able to use `conda install` instead of `pip` but I have not tested this. Conda [should behave well](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages) when using packages installed via `pip`.\n\nOnce you have [installed Python 3.12.2 or higher](#installing-python), [created a virtual environment](#creating-and-activating-a-virtual-environment), and [activated\nthe virtual environment](#activating-and-deactivating-a-python-virtual-environment). Use the following command to install the package:\n\n* `python -m pip install --upgrade pip`\n - This updates pip to its latest version\n\n* `python -m pip install aopp_deconv_tool`\n - This actually installs the package.\n\nNOTE: We are using `python -m pip` instead of just `pip` incase the `pip` command does not point to the virtual environment's `pip` executable. You can run\n`pip --version` to see which version of python it is for and where the pip executable is located if you want. As explanation, `python -m pip` means \"use python\nto run its pip module\", whereas `pip` means \"look on my path for the first executable called 'pip' and run it\". Usually they are the same, but not always.\n\n\nTo update the package to it's newest version use:\n\n* `python -m pip install --upgrade aopp_deconv_tool`\n\n\n# aopp_deconv_tool <a id=\"aopp_deconv_tool\"></a> #\n\nThis tool provides deconvolution, psf fitting, and ssa filtering routines.\n\nNOTE: It can be useful to look through the source files, see the appendix for how to find [the package's source files location](#location-of-package-source-files)\n\n## Examples <a id=\"examples\"></a> ##\n\nSee the `examples` folder of the github. \n\n## Commandline Scripts <a id=\"commandline-scripts\"></a> ##\n\n### Spectral Rebinning <a id=\"spectral-rebinning-script\"></a> ##\n\nInvoke via `python -m aopp_deconv_tool.spectral_rebin`. Use the `-h` option to see the help message.\n\nThis routine accepts a FITS file specifier, it will spectrally rebin the fits extension and output a new fits file.\n\n### Interpolation <a id=\"interpolation-script\"></a> ##\n\nInvoke via `python -m aopp_deconv_tool.interpolate`. Use the `-h` option to see the help message.\n\nAccepts a FTIS file specifier, will find bad pixels and interpolate over them. The strategies used are\ndependent on the options given to the program.\n\nbad pixel strategies:\n\tssa\n\t\tUses singular spectrum analysis to determine bad pixels. Useful for situations where artifacts are not\n\t\tseperable from the science data via a simple brightness threshold. Also interpolates over INF and NAN pixels.\n\tsimple\n\t\tOnly interpolates over INF and NAN pixels\n\ninterpolation strategies:\n\tscipy\n\t\tUses scipy routines to interpolate over the bad pixels. Uses a convolution technique to assist with edge effect problems.\n\tssa\n\t\t[EXPERIMENTAL] Interpolates over SSA components only where extreme values are present. Testing has shown this to give\n\t\tresults more similar to the underlying test data than `scipy`, but is substantially slower and requires parameter\n\t\tfiddling to give any substantial improvement.\n\n### PSF Normalisation <a id=\"psf-normalisation-script\"></a> ###\n\nInvoke via `python -m aopp_deconv_tool.psf_normalise`. Use the `-h` option to see the help message.\n\nPeforms the following operations:\n* Ensures image shape is odd, so there is a definite central pixel\n* Removes any outliers (based on the `sigma` option)\n* Recenters the image around the center of mass (uses the `threshold` and `n_largest_regions` options)\n* Optionally trims the image to a desired shape around the center of mass to reduce data volume and speed up subsequent steps\n* Normalises the image to sum to 1\n\n\n### PSF Model Fitting <a id=\"psf-model-fitting-script\"></a> ###\n\nInvoke via `python -m aopp_deconv_tool.psf_normalise`. Use the `-h` option to see the help message.\nNOTE: The `--model` option sets the model to fit. To see which parameters a model accepts use the `--model_help` option [NOTE: CHECK THIS WORKS]\n\nSpecifying the `--method` option sets the routine used for fitting. Two are available `scipy.minimize` (default) and `ultranest`.\n\nscipy.minimize\n\tA simple gradient descent solver. Fast and useful when the optimal solution is close to the passed starting parameters.\n\nultranest\n\tNested sampling. Much slower (but can be sped up), but works when the optimal solution has local maxima/minima that\n\twould trap `scipy.minimize`. Currently the `muse_ao` model only finds a good solution with this method.\n\n### Deconvolution <a id=\"deconvolution-script\"></a> ###\n\nInvoke via `python -m aopp_deconv_tool.deconvolve`. Use the `-h` option to see the help message.\nNOTE: the `--parameter_help` option will show the help message for the deconvolution parameters [NOTE: CHECK THIS WORKS]\n\nAssumes the observation data has no NAN or INF pixels, assumes the PSF data is centered and sums to 1. Use the `--plot` option\nto see an progress plot that updates every 10 iterations of the MODIFIED_CLEAN algorithm, useful for working out what different\nparameters do.\n\n\n## Using the Package in Code <a id=\"using-the-package-in-code\"></a> ##\n\nThe commandline scripts provide a blueprint of how the various routines can be used. However sometimes more customisation is\nneeded. Below is a quick overview of the main routines and how they function. \n\nNOTE: You can always [get help within python](#getting-documentation-from-within-python) as the classes and functions have docstrings.\n\n\n### Deconvolution <a id=\"deconvolution-code\"></a> ###\n\nThe main deconvolution routines are imported via\n\n```\nfrom aopp_deconv_tool.algorithm.deconv.clean_modified import CleanModified\nfrom aopp_deconv_tool.algorithm.deconv.lucy_richardson import LucyRichardson\n```\n\n`CleanModified` is the class implementeing the MODIFIED_CLEAN algorithm, the `LucyRichardson` class implements the Lucy-Richardson algorithm.\n\n### PSF Fitting <a id=\"psf-fitting-code\"></a> ###\n\nThe main PSF fitting routines are in `aopp_deconv_tools.psf_model_dependency_injector`, and `aopp_deconv_tools.psf_data_ops`. \nThe examples on the github deal with this area. Specifically `<REPO_DIR>/examples/psf_model_example.py` for adaptive optics\ninstrument fitting.\n\n### SSA Filtering <a id=\"ssa-filtering-code\"></a> ###\n\nSingular Spectrum Analysis is performed by the `SSA` class in the `aopp_deconv_tools.py_ssa` module. An interactive \nviewer that can show SSA components can be run via `python -m aopp_deconv_tool.graphical_frontends.ssa_filtering`.\nBy default it will show some test data, if you pass an **image** file (i.e. not a FITS file, but a `.jpg` etc.) it\nwill use that image instead of the default one.\n\nThe `ssa2d_sub_prob_map` function in the `aopp_deconv_tool.algorithm.bad_pixels.ssa_sub_prob` module attempts to \nmake an informed choice of hot/cold pixels for masking purposes. See the docstring for more details.\n\nThe `ssa_interpolate_at_mask` function in the `aopp_deconv_tool.algorithm.interpolate.ssa_interp` module attempts\nto interpolate data by interpolating between SSA components, only when the value of the component at the point\nto be interpolated is not an extreme value. See the docstring for more details.\n\n\n# APPENDICES <a id=\"appendices\"></a> #\n\n## APPENDIX: Snippets <a id=\"appendix:-snippets\"></a> ##\n\n### Sudo Access Test <a id=\"sudo-access-test\"></a> ###\n\nEnter the following commands at the command line:\n\n* `ls`\n* `sudo ls` \n\nIf, after entering your password, you see the same output for both commands, you have `sudo` access. Otherwise, you do not.\n\n### Location of package source files <a id=\"location-of-package-source-files\"></a> ### \n\nTo find the location of the package's files, run the following command:\n\n* `python -c 'import site; print(site.getsitepackages())'`\n\nThis will output the *site packages* directory for the python executable. The package's\nfiles will be in the `aopp_deconv_tool` subdirectory.\n\n### Getting documentation from within python <a id=\"getting-documentation-from-within-python\"></a> ###\n\nPython's command line, often called the \"Read-Evaluate-Print-Loop (REPL)\", has a built-in help system. \n\nTo get the help information for a class, function, or object use the following code. Note, `>>>` denotes the\npython REPL (i.e. the command line you get when you type the `python` command), and `$` denotes the shell \ncommandline. \n\nThis example is for the built-in `os` module, but should work with any python object.\n\n```\n$ python\n... # prints information about the python version etc. here\n>>> import os\n>>> help(os)\n... # prints out the docstring of the 'os' module\n```\n\n### Python slice syntax <a id=\"python-slice-syntax\"></a> ###\n\nWhen specifying subsets of datacubes, it is useful to be able to select a N-square (i.e., square, cube, tesseract) \nregion to operate upon to reduce data volume and therefore processing time. [Python and numpy's slicing syntax](https://www.w3schools.com/python/numpy/numpy_array_slicing.asp)\nis a nice way to represent these operations. A quick explanation of the synatx follows.\n\nLet `a` be a 1 dimensional array, such that `a = np.array([10,11,15,16,19,20])`, selecting an element of the array\nis done via square brackets `a[1]` is the 1^th element, and as python is 0-indexed is equal to 11 for our example array.\n\nSlicing is also done via square brackets, instead of a number (that would select and element), we pass a slice. Slices are\ndefined via the format `<start>:<stop>:<step>`. Where `<start>` is the first index to include in the slice, `<stop>` is the\nfirst index to **not** include in the slice, and `<step>` is what to add to the previously included index to get the next\nincluded index.\n\nE.g. \n* `2:5:1` includes the indices 2,3,4 in the slice. So `a[2:5:1]` would select 15,16,19.\n* `0:4:2` includes the indices 0,2 in the slice. So `a[0:4:2]` would select 10,15.\n\nEveything in a slice is optional exept the first colon. The defaults of everything are as follows:\n* `<start>` defaults to 0\n* `<stop>` defaults to the last + 1 index in the array. As python supports negative indexing (-ve indices \"wrap around\", with \n -1 being the index **after** the largest index) this is often called the -1^th index, or that `<stop>` defaults to -1.\n* `<step>` defaults to 1\n\nTherefore, the slice `:` selects all of the array, and the slice `::-1` selects all of the array, but reverses the ordering.\n\nWhen dealing with N-dimensional arrays, indexing accepts a tuple. \nE.g. for a 2-dimensional array `b=np.array([[10,11,15],[16,19,20],[33,35,36]])`, \n* `b[1,2]` is equal to 20\n* `b[2,1]` is equal to 35\n\nSimilarly, slicing an N-dimensional array uses tuples of slices. E.g.\n```\n>>> b\narray([[10, 11, 15],\n [16, 19, 20],\n [33, 35, 36]])\n>>> b[::-1,:]\narray([[33, 35, 36],\n [16, 19, 20],\n [10, 11, 15]])\n>>> b[1:2,::-1]\narray([[20, 19, 16]])\n```\n\nSlices and indices can be mixed, so you can slice one dimension and select an index from another. E.g.\n```\n>>> b[:1, 2]\narray([15])\n>>> b[::-1, 0]\narray([33, 16, 10])\n>>> b[0,::-1]\narray([15, 11, 10])\n```\n\nThere is a `slice` object in Python that can be used to programatically create slices, it's prototype is `slice(start,stop,step)`,\nbut only `stop` is required, and if `stop=None` the slice will continue until the end of the array dimension. Slice objects\nare almost interchangeable with the slice syntax. E.g.\n```\n>>> s = slice(2)\n>>> b[s,0]\narray([10, 16])\n>>> b[:2,0]\narray([10, 16])\n```\n\n\n## APPENDIX: Scripts <a id=\"appendix:-scripts\"></a> ##\n\n\n### Linux Installation Bash Script <a id=\"linux-installation-bash-script\"></a> ###\n\nBelow is an example *bash* script for building python from source and configuring a virtual environment.\nUse it via copying the code into a file (recommended name `install_python.sh`). If Python's dependencies\nare not already installed, you will need `sudo` access so the script can install them.\n\n* Make the script executable : `chmod u+x install_python.sh`\n\n* Get help on the scripts options with: `./install_python.sh -h`\n\n* Run the script with : `./install_python.sh`\n\n\n```\n#!/usr/bin/env bash\n\n# Turn on \"strict\" mode\nset -o errexit -o nounset -o pipefail\n\n# Remember values of environment variables as we enter the script\nOLD_IFS=$IFS \nINITIAL_PWD=${PWD}\n\n\n\n############################################################################################\n############## PROCESS ARGUMENTS ################\n############################################################################################\n\n# Set default parameters\nPYTHON_VERSION=(3 12 2)\nPYTHON_INSTALL_DIRECTORY=\"${HOME:?}/python/python_versions\"\nVENV_PREFIX=\".venv_\"\nVENV_DIR=\"${PWD}\"\n\n# Get the usage string with the default values of everything\nusage(){\n\techo \"install_python.sh [-v INT.INT.INT] [-i PATH] [-p STR] [-d PATH] [-l PATH] [-h]\"\n\techo \" -v : Python version to install. Default = ${PYTHON_VERSION[0]}.${PYTHON_VERSION[1]}.${PYTHON_VERSION[2]}\"\n\techo \" -i : Path to install python to. Default = '${PYTHON_INSTALL_DIRECTORY}'\"\n\techo \" -p : Prefix for virtual environment (will have python version added as a suffix). Default = ${VENV_PREFIX}\"\n\techo \" -d : Directory to create virtual envronment. Default = '${VENV_DIR}'\"\n\techo \" -h : display this help message\"\n\n}\nUSAGE=$(usage)\n\n# Parse input arguments\nwhile getopts \"v:i:p:d:h\" OPT; do\n\tcase $OPT in\n\t\tv)\n\t\t\tIFS=\".\"\n\t\t\tPYTHON_VERSION=(${OPTARG})\n\t\t\tIFS=$OLD_IFS\n\t\t\t;;\n\t\ti)\n\t\t\tPYTHON_INSTALL_DIRECTORY=${OPTARG}\n\t\t\t;;\n\t\tp)\n\t\t\tVENV_PREFIX=${OPTARG}\n\t\t\t;;\n\t\td)\n\t\t\tVENV_DIR=${OPTARG}\n\t\t\t;;\n\t\t*)\n\t\t\techo \"${USAGE}\"\n\t\t\texit 0\n\t\t\t;;\n\tesac\ndone\n\n# Perform argument processing\nPYTHON_VERSION_STR=\"${PYTHON_VERSION[0]}.${PYTHON_VERSION[1]}.${PYTHON_VERSION[2]}\"\n\n# Print parameters to user so they know what's going on\necho \"Parameters:\"\necho \" -v : PYTHON_VERSION=${PYTHON_VERSION_STR}\"\necho \" -i : PYTHON_INSTALL_DIRECTORY=${PYTHON_INSTALL_DIRECTORY}\"\necho \" -p : VENV_PREFIX=${VENV_PREFIX}\"\necho \" -d : VENV_DIR=${VENV_DIR}\"\n\n\n############################################################################################\n############## DEFINE FUNCTIONS ################\n############################################################################################\n\n\nfunction install_pkg_if_not_present(){\n\n\t# Turn on \"strict\" mode\n\tset -o errexit -o nounset -o pipefail\n\tREQUIRES_INSTALL=()\n\n\tfor PKG in ${@}; do\n\t\t# We want the command to fail when a package is not installed, therefore unset errexit\n\t\tset +o errexit \n\t\t\tDPKG_RCRD=$(dpkg-query -l ${PKG} 2> /dev/null | grep \"^.i.[[:space:]]${PKG}\\(:\\|[[:space:]]\\)\")\n\t\t\tINSTALLED=$?\n\t\tset -o errexit\n\t\t\n\t\tif [ ${INSTALLED} -eq 0 ]; then\n\t\t\techo \"${PKG} is installed\"\t\n\t\telse\n\t\t\techo \"${PKG} is NOT installed\"\n\t\t\tREQUIRES_INSTALL[${#REQUIRES_INSTALL[@]}]=${PKG}\n\t\tfi\n\n\tdone\n\n\n\tif [ ${#REQUIRES_INSTALL[@]} -ne 0 ]; then\n\n\n\t\tUNFOUND_PKGS=()\n\t\tfor PKG in ${REQUIRES_INSTALL[@]}; do\n\t\t\t# We want the command to fail when a package is not installed, therefore unset errexit\n\t\t\tset +o errexit \n\t\t\t\tapt-cache showpkg ${PKG} | grep '^Package: ${PKG}$'\n\t\t\t\tPKG_FOUND=$?\n\t\t\tset -o errexit\n\n\t\t\tif [ $PKG_FOUND -ne 0 ]; then\n\t\t\t\tUNFOUND_PKGS[${#UNFOUND_PKGS[@]}]=${PKG}\n\t\t\tfi\n\t\tdone\n\n\t\tif [ ${#UNFOUND_PKGS[@]} -ne 0 ]; then \n\t\t\techo \"ERROR: Cannot install. Could not find the following packages in apt: ${UNFOUND_PKGS[@]}\"\n\t\t\treturn 1\n\t\tfi\n\n\t\techo \"Installing packages: ${REQUIRES_INSTALL[@]}\"\n\t\tsudo apt-get install -y ${REQUIRES_INSTALL[@]}\n\telse\n\t\techo \"All required packages are installed\"\n\tfi\n}\n\n\n############################################################################################\n############## START SCRIPT ################\n############################################################################################\n\n# Define the dependencies that python requires for installation\nPYTHON_DEPENDENCIES=( \\\n\tcurl \\\n\tgcc \\\n\tlibbz2-dev \\\n\tlibev-dev \\\n\tlibffi-dev \\\n\tlibgdbm-dev \\\n\tliblzma-dev \\\n\tlibncurses-dev \\\n\tlibreadline-dev \\\n\tlibsqlite3-dev \\\n\tlibssl-dev \\\n\tmake \\\n\ttk-dev \\\n\twget \\\n\tzlib1g-dev \\\n)\n\n# Get a temporary directory and make sure it's cleaned up when the script exits\nTEMP_WORKSPACE=$(mktemp -d -t py_build_src.XXXXXXXX)\ncleanup(){\n\techo \"Cleaning up on exit...\"\n\techo \"Removing ${TEMP_WORKSPACE}\"\n\trm -rf ${TEMP_WORKSPACE:?}\n}\ntrap cleanup EXIT\n\n# If there is an error, make sure we print the usage string with default parameter values\nerror_message(){\n\techo \"${USAGE}\"\n}\ntrap error_message ERR\n\n\n# Define variables\n\nPYTHON_VERSION_INSTALL_DIR=\"${PYTHON_INSTALL_DIRECTORY}/${PYTHON_VERSION_STR}\"\nVENV_PATH=\"${VENV_DIR}/${VENV_PREFIX}${PYTHON_VERSION_STR}\"\nPYTHON_VERSION_SOURCE_URL=\"https://www.python.org/ftp/python/${PYTHON_VERSION_STR}/Python-${PYTHON_VERSION_STR}.tgz\"\n\nPY_SRC_DIR=\"${TEMP_WORKSPACE}/Python-${PYTHON_VERSION_STR}\"\nPY_SRC_FILE=\"${PY_SRC_DIR}.tgz\"\n\n\n# Perform actions\n\necho \"Checking python dependencies and installing if required...\"\ninstall_pkg_if_not_present ${PYTHON_DEPENDENCIES}\n\necho \"Downloading python source code to '${PY_SRC_FILE}'...\"\ncurl ${PYTHON_VERSION_SOURCE_URL} --output ${PY_SRC_FILE}\n\necho \"Extracting source file...\"\nmkdir ${PY_SRC_DIR}\ntar -xvzf ${PY_SRC_FILE} -C ${TEMP_WORKSPACE}\n\n\ncd ${PY_SRC_DIR}\necho \"Configuring python installation...\"\n./configure \\\n\t--prefix=${PYTHON_VERSION_INSTALL_DIR:?} \\\n\t--enable-optimizations \\\n\t--with-lto \\\n\t--enable-ipv6\n\necho \"Running makefile...\"\nmake\n\necho \"Created ${PYTHON_VERSION_INSTALL_DIR}\"\nmkdir -p ${PYTHON_VERSION_INSTALL_DIR}\n\necho \"Performing installation\"\nmake install\n\ncd ${INITIAL_PWD}\n\necho \"Creating virtual environment...\"\n${PYTHON_VERSION_INSTALL_DIR}/bin/python3 -m venv ${VENV_PATH}\n\necho \"Virtual environment created at ${VENV_PATH}\"\n\n\n# Output information to user\necho \"\"\necho \"Activate the virtual environment with the following command:\"\necho \" source ${VENV_PATH}/bin/activate\"\n```\n",
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