Skip to main content

Classify exoplanet candidates vs false positives from planetary features

Project description

grahana

grahana is a Python package for classifying exoplanet candidates using a pre-trained Keras model. It takes planetary features as input and predicts whether an object is a "candidate" exoplanet or a "false_positive".

Installation

Run the following command in the root of the project:

pip install grahana

Usage

You can use the predict function to get a classification label or raw probabilities.

Basic Usage (Get Label)

from grahana import predict

label = predict(
    orbital_period_days=3.52,
    transit_depth_ppm=1500,
    transit_duration_hours=2.5,
    snr=12.0,
    insolation_flux=1.3,
)
print(label)  # Output: "false_positive" or "candidate"

Advanced Usage (Get Probabilities)

from grahana import predict

probs = predict(
    3.52, 1500, 2.5, 12.0, 1.3,
    return_label=False,
)
print(probs)  # Output: e.g. [0.85, 0.15]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

grahana-0.2.0.tar.gz (132.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

grahana-0.2.0-py3-none-any.whl (132.1 kB view details)

Uploaded Python 3

File details

Details for the file grahana-0.2.0.tar.gz.

File metadata

  • Download URL: grahana-0.2.0.tar.gz
  • Upload date:
  • Size: 132.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.0

File hashes

Hashes for grahana-0.2.0.tar.gz
Algorithm Hash digest
SHA256 cc6c225d47e0f24f8b9849ce269ae3748243a6c8da4a9e4db4a379072124cb1e
MD5 23663be81ebac7cf4b44c39dae6fca43
BLAKE2b-256 2544c9efe664c8df08f22fbff8e9cc4476c55c97706bf4b2d88818f43c439bb2

See more details on using hashes here.

File details

Details for the file grahana-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: grahana-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 132.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.0

File hashes

Hashes for grahana-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 df6c1d83d163b1a8e47953c0bd33a4e301c7458307c0e248988593dfb5c86ced
MD5 dcce01b96e2f0e84ede9758bd99c41a2
BLAKE2b-256 a215890ce7653b585ff7149af53e21c18e5f25cb313a2e092fe9c176e6344cf8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page