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STDPipe - Simple Transient Detection Pipeline

AKA: random codes noone else will ever use

STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.

Design principles

  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)
    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for

Features

  • pre-processing - should be handled before in an instrument-specific way
    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry
    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration
    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration
    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking
    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction
    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification
    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions
    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)
    • spatial clustering, color regression, variability analysis, ...

Installation

STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe

The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository

git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command

cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.

Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:

Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:

sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:

conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.

Usage

Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.

About

Simple Transient Detection Pipeline

stdpipe.readthedocs.io

Resources

Readme
License
Activity

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0 stars

Watchers

0 watching

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var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
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STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
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Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
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STDPipe - Simple Transient Detection Pipeline
AKA: random codes noone else will ever use
STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis.
Design principles
  • implemented as a library of routines covering most common tasks
  • operates on standard Python objects: NumPy arrays for images, Astropy Tables for catalogs and object lists, etc
  • does not try to re-implement the things already implemented in other Python packages
  • conveniently wraps external codes that do not have their own Python interfaces (SExtractor, SCAMP, PSFEx, HOTPANTS, Astrometry.Net, ...)

    • wrapping is transparent: all data passed from Python, all options customizable from Python, all (or most of) outputs available back
    • everything operates on temporary files, nothing is kept after the run unless explicitly asked for
Features
  • pre-processing - should be handled before in an instrument-specific way

    • bias/dark subtraction, flatfielding, masking
  • object detection and photometry

    • SExtractor, SEP, or photutils (pure Python) for detection
    • aperture, optimal extraction (Naylor 1998), and PSF fitting photometry
    • grouped fitting for crowded fields
  • astrometric calibration

    • Astrometry.Net for blind WCS solving
    • quad-hash pattern matching for refinement (pure Python, no external deps, default method)
    • SCAMP or Astropy-based code for refinement (alternative)
  • photometric calibration

    • Vizier catalogues, passband conversion (PS1 to Johnson, Gaia to Johnson, ...)
    • spatial polynomial + color term + intrinsic scatter
  • image reprojection and stacking

    • Lanczos interpolation with automatic oversampling and flux conservation (pure Python, default)
    • SWarp wrapper (alternative)
  • image subtraction

    • HiPS templates
    • PanSTARRS DR1 or Legacy Survey templates
    • HOTPANTS + custom noise model
    • ZOGY
  • transient detection and classification

    • noise-weighted detection, cutout adjustment, ...
    • feature-based real/bogus classification (no TensorFlow required)
    • CNN-based real/bogus classification (optional, requires TensorFlow)
  • auxiliary functions

    • PSF estimation, simulated stars, FITS header utilities, plotting, ...
  • light curve creation (soon)

    • spatial clustering, color regression, variability analysis, ...
Installation
STDpipe is available at https://github.com/karpov-sv/stdpipe and is mirrored at https://gitlab.in2p3.fr/icare/stdpipe
The package is in constant development, so to keep track of the changes the suggested way of installing it is by cloning the repository
git clone https://github.com/karpov-sv/stdpipe.git

and then installing from it in development (or "editable") mode by running the command
cd stdpipe
python3 -m pip install -e .

This way you may update the repository or apply local patches, and it will immediately be reflected in the installed package.
Apart of Python requirements that will be installed automatically, STDPipe also (optionally) makes use of the following external software:
Most of them may be installed from your package manager. E.g. on Debian or Ubuntu systems it may look like that:
sudo apt install sextractor scamp psfex swarp

or, on Miniconda/Anaconda, like that:
conda install -c conda-forge astromatic-source-extractor astromatic-scamp astromatic-psfex astromatic-swarp

You may also check more detailed installation instructions here.
Usage
Please consult the documentation for STDPipe for the basic usage patterns and description of its API. You may check the examples inside notebooks/ folder, especially the tutorial that demonstrates basic steps of a typical image processing.
About
Simple Transient Detection Pipeline
stdpipe.readthedocs.io
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages