Astronet-Vetting | A Neural Network for TESS Light Curve Vetting

by yuliang419 Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | Astronet-Vetting Summary

Astronet-Vetting is a Python library typically used in Institutions, Learning, Education applications. Astronet-Vetting has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However Astronet-Vetting build file is not available. You can download it from GitHub.

A Neural Network for TESS Light Curve Vetting

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Quality

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Support

Astronet-Vetting has a low active ecosystem.

It has 8 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Astronet-Vetting is current.

Quality

Astronet-Vetting has no bugs reported.

Security

Astronet-Vetting has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Astronet-Vetting is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Astronet-Vetting releases are not available. You will need to build from source code and install.

Astronet-Vetting has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Astronet-Vetting and discovered the below as its top functions. This is intended to give you an instant insight into Astronet-Vetting implemented functionality, and help decide if they suit your requirements.

Builds an input pipeline from files
Pad tensors to a given batch size
Pad tensor to a given batch size
Helper function to pad tensors to a given batch size
Split time and flux into overlapping segments
Process a tce table
Preprocess a TimeSeries
Preprocess a tce
Set a bytes feature
Calculate a median filter of data
Fill empty bin edges with non - NaN values
Choose a k - means spline
Fit a Gaussian spline
Unflatten nested config values
Split time into overlapping segments
Generate a source for a local time series
R Calculates the incidence depth of a light curve
Wrapper for continuous evaluation
Generate a double - global view for two time series
Generate a global view of a time series
Builds the hidden layers for time series
Build hidden layers for time series
Generate a secondary view
Create an estimator
Predict the prediction
Create a training set
Train and evaluate an estimator
Remove events from a list of time series

Get all kandi verified functions for this library.

Astronet-Vetting Key Features

No Key Features are available at this moment for Astronet-Vetting.

Astronet-Vetting Examples and Code Snippets

No Code Snippets are available at this moment for Astronet-Vetting.

Community Discussions

No Community Discussions are available at this moment for Astronet-Vetting.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Astronet-Vetting

First, ensure that you have installed the following required packages:.
TensorFlow (instructions)
Pandas (instructions)
NumPy (instructions)
AstroPy (instructions)
PyDl (instructions)
A light curve is a plot of the brightness of a star over time. We will be focusing on light curves produced by the TESS space telescope. An example light curve (produced by Kepler) is shown below. To train a model to identify planets in TESS light curves, you will need a training set of labeled Threshold Crossing Events (TCEs). A TCE is a periodic signal that has been detected in a Kepler light curve, and is associated with a period (the number of days between each occurrence of the detected signal), a duration (the time taken by each occurrence of the signal), an epoch (the time of the first observed occurrence of the signal), and possibly additional metadata like the signal-to-noise ratio. An example TCE is shown below. The labels are ground truth classifications (decided by humans) that indicate which TCEs in the training set are actual planets signals and which are caused by other phenomena.
row_id: Integer ID of the row in the TCE table.
tic_id: TIC ID of the target star.
toi_id: TCE number within the target star. These are structured funny so we'll ignore them for now.
Disposition: Final disposition from group vetting (should be one of the following: PC (planet candidate), EB (eclipsing binary), IS (instrumental noise), V (variability), O (other), J (junk). The J class includes a mix of V and IS. I didn't distinguish all of them since these two are always lumped together anyway.
RA: RA in degrees.
DEC: Dec in degrees.
Tmag: TESS magnitude.
Epoc: The time corresponding to the center of the first detected event in Barycentric Julian Day (BJD) minus a constant offset.
Period: Period of the detected event, in days.
Duration: Duration of the detected event, in hours.
Transit Depth: Transit depth in ppm.
Sectors: Sector number.
camera: Camera number.
ccd: CCD number.
star_rad, star_mass, teff, logg: Stellar parameters from Gaia DR2 or the TIC. Since a lot of TCEs are missing these values, we're not using them right now.
SN: Signal-to-pink noise ratio from BLS.
q_ingress: Fractional ingress duration from VARTOOLS.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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