An Automated Pipeline for the Selection of Transmission Spectroscopy Candidates
Project description
PREFACE
Prioritization and Ranking of Exoplanets For Astronomical Characterization and Exploration (PREFACE) is a Python package for selection of promising exoplanet transmission spectroscopy observations based on their expected scientific return and observational feasibility.
To-do List
As this project is work in progress, here is the current to-do list:
- Make PREFACE pip-installable
- Fix a bug where the moonlight noise metric calculation sometimes return NaN
- Finish documentation
- (Maybe) Determine the default moonlight amplification factor that more properly and sensibly punishes full moon nights
- (Maybe) Reduce multiprocessing overhead
- (Maybe) Month- and location-dependent aerosol scattering parameters via end-to-end AERONET data retrieval
Installation
Install the latest stable release from PyPI:
pip install preface-spearnet
Usage
Using preface consists of four steps:
- Configure the observing instrument with
TelescopeConfigurations. - Define the observing window and output options with
OutputConfigurations. - Optionally configure moonlight modelling and multiprocessing with
MoonlightNoiseConfigurationsandMultiprocessingConfigurations. - Execute the complete pipeline with
run_preface().
Input validation is performed automatically before pipeline execution.
Full documentation (configuration reference, PREFACE workflow and output descriptions, and API) is available at preface-spearnet.readthedocs.io.
Example
import datetime as dt
from preface import run_preface
from preface.configs import (
TelescopeConfigurations,
OutputConfigurations,
MoonlightNoiseConfigurations,
MultiprocessingConfigurations,
)
ObsStart = dt.datetime(2025, 10, 1)
ObsEnd = dt.datetime(2026, 5, 31)
OutputFolder = r"C:\PREFACE_Output"
TelescopeConfigs = TelescopeConfigurations(
instrument="TNT ULTRASPEC",
filter_name="r",
run_mode="Half_Well",
toggle_sky_noise=True,
toggle_defocus=False
)
OutputConfigs = OutputConfigurations(
observation_start=ObsStart,
observation_end=ObsEnd,
output_folder=OutputFolder,
metric_mode="Rank",
viable_cumulative_cut=0.97
)
MoonlightConfigs = MoonlightNoiseConfigurations(
toggle_moonlight_noise=True,
scattering_aod=0.2,
absorption_aod=0.3,
asymmetry_factor=0.6,
moonlight_amplification_factor=5,
toggle_graph_outputs=True,
event_weight_graph_threshold=0.75
)
MultiprocessingConfigs = MultiprocessingConfigurations(
toggle_multiprocessing=True,
cores_to_leave_out=2
)
run_preface(
TelescopeConfigurations=TelescopeConfigs,
OutputConfigurations=OutputConfigs,
MoonlightNoiseConfigurations=MoonlightConfigs,
MultiprocessingConfigurations=MultiprocessingConfigs
)
Authors
Jake Staberg Morgan (Original author)
Chatdanai Sawangwong (Current maintainer)
email: chatdanai.saw@gmail.com
Supachai Awiphan
email: supachai@narit.or.th
Orarik Tasuya
email: orarik@narit.or.th
Napaporn A-thano
email: napaporn@narit.or.th
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file preface_spearnet-2.0.0b1-py3-none-any.whl.
File metadata
- Download URL: preface_spearnet-2.0.0b1-py3-none-any.whl
- Upload date:
- Size: 83.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.12.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
15e13683360e284975799c9c3740a0d34e44b16b32ae3f22d4460f437445e1fa
|
|
| MD5 |
09f75cd7c89c076359080c37d7d021a4
|
|
| BLAKE2b-256 |
a61f46d3837eb37d5bd65a7d0de7cba73532c36ab702d4661d273012893049e7
|