prose 2.0.0

Reduction and analysis of FITS telescope observations

prose

A python framework to build FITS images pipelines.

prose is a Python tool to build pipelines dedicated to astronomical images processing (all based on pip packages 📦). Beyond providing all the blocks to do so, it features default pipelines to perform common tasks such as automated calibration, reduction and photometry.

Example

Here is a quick example pipeline to characterize the point-spread-function (PSF) of an example image

from prose import Sequence, blocks
from prose.tutorials import example_image
import matplotlib.pyplot as plt

# getting the example image
image = example_image()

sequence = Sequence([
    blocks.SegmentedPeaks(),  # stars detection
    blocks.Cutouts(size=21),  # cutouts extraction
    blocks.MedianPSF(),       # PSF building
    blocks.Moffat2D(),        # PSF modeling
])

sequence.run([image])

For more details check Quickstart.

Default pipelines

from prose.pipeline import Calibration, AperturePhotometry

destination = "reduced_folder"

reduction = Calibration(images=[...], flats=[...])
reduction.run(destination)

photometry = AperturePhotometry(destination)
photometry.run()

Installation

prose runs more safely in its own virtual environment and is tested on Python 3.6.

example on OSX

create your virtualenv and activate it

python3.6 -m venv prose_env
source prose_env/bin/activate.bin

Then to locally install prose

git clone https://github.com/lgrcia/prose.git
python3.6 -m pip install -e prose

Applicable to Linux-based and Windows OS