prose 2.0.1

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 is written for python 3 and can be installed from pypi with:

pip install prose

To install it through conda, once, in your newly created environment, start with:

conda install numpy scipy tensorflow netcdf4 numba

# then 

pip install prose