layup.predict
Attributes
Functions
Decomposes n vectors into two unit vectors to facilitate computation of on-sky angles |
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Calculate rates and geometry for objects within the field of view, with vectorisation capabilities |
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Calculates the on-sky rates and geometry for a set of orbits at certain pointings. |
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Helper function to create the result dtype with the correct primary ID column name. |
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This function appends two columns of the RA and Dec in sexagesimal to the input array. |
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This function is called by the parallelization function to call the C++ code. |
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The function to all that predict functionality interactively, i.e from a notebook or a script. |
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The function for calling predict through the command line interface. |
Module Contents
- layup_get_residual_vectors(v1)[source]
Decomposes n vectors into two unit vectors to facilitate computation of on-sky angles The decomposition is such that A = (-sin (RA), cos(RA), 0) is in the direction of increasing RA, and D = (-sin(dec)cos (RA), -sin(dec) sin(RA), cos(dec)) is in the direction of increasing Dec The triplet (A,D,v1) forms an orthonormal basis of the 3D vector space. Has vectorisation capabilities.
- Parameters:
v1 (array, shape = (3, n))) – The vectors to be decomposed
- Returns:
A (array, shape = (3, n))) – A vectors
D (array, shape = (3, n))) – D vectors
- layup_calculate_rates_and_geometry(pointing: pandas.DataFrame, ephem_geom_params: sorcha.ephemeris.simulation_driver.EphemerisGeometryParameters)[source]
Calculate rates and geometry for objects within the field of view, with vectorisation capabilities
- Parameters:
pointing (pandas dataframe) – The dataframe containing the pointing database.
ephem_geom_params (EphemerisGeometryParameters) – Various parameters necessary to calculate the ephemeris
- Returns:
Tuple containing the ephemeris parameters needed for Sorcha post processing.
- Return type:
tuple
- _get_on_sky_data(predictions, orbits_df, obs_pos_vel, primary_id_column_name, args, configs)[source]
Calculates the on-sky rates and geometry for a set of orbits at certain pointings.
- Parameters:
predictions (numpy recarray) – Numpy recarray containing the pre-calculated predictions of the objects.
orbits_df (pandas dataframe) – The dataframe containing the orbits being predicted.
obs_pos_vel (numpy recarray) – Numpy recarray containing the position and velocity of the observatory at the times requested for predictions.
primary_id_column_name (str) – The name of the primary ID column.
args (argparse) – The argparse object that was created when running from the CLI.
aux (AuxiliaryConfigs object) – The LayUp auxiliary arguments.
- Returns:
rates (numpy recarray) – Numpy recarray containing the on-sky rates.
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- _get_result_dtypes(primary_id_column_name: str)[source]
Helper function to create the result dtype with the correct primary ID column name.
- _convert_to_sg(data)[source]
This function appends two columns of the RA and Dec in sexagesimal to the input array.
- Parameters:
data (numpy structured array) – The data to be processed.
- Return type:
input array with ra and dec in sexagesimal appended, called ra_str_hms and dec_str_dms respectively.
- _predict(data, obs_pos_vel, times, cache_dir, primary_id_column_name)[source]
This function is called by the parallelization function to call the C++ code.
- Parameters:
data (nump structured array) – The data to be processed.
obs_pos_vel (numpy structured array) – The observer position and velocity.
times (list) – The times for the predictions, in jd_tdb.
cache_dir (str) – The directory to the cached kernels.
primary_id_column_name (str) – The name of the primary ID column.
- Return type:
numpy structured array with the flattened results
- predict(data, obscode, times, primary_id_column_name='provID', num_workers=-1, cache_dir=None, args=None, configs=None)[source]
The function to all that predict functionality interactively, i.e from a notebook or a script.
- Parameters:
data (numpy structured array) – The data to be processed.
obscode (str) – The observer code.
times (list) – The times for the predictions, in jd_tdb.
primary_id_column_name (str) – The name of the primary ID column.
num_workers (int) – The number of workers to use for parallelization. If -1, use all available cores.
cache_dir (str or None) – The directory to the cached kernels. If None, use the default cache directory.
args (argparse) – The argparse object that was created when running from the CLI.
aux (AuxiliaryConfigs object) – The LayUp auxiliary arguments. Needed if on-sky rates are requested.
- Return type:
numpy structured array with the flattened results
- predict_cli(cli_args: argparse.Namespace, input_file: str, start_date: float, end_date: float, timestep_day: float, output_file: str, cache_dir: pathlib.Path, configs: None)[source]
The function for calling predict through the command line interface.
- Parameters:
cli_args (Namespace) – The command line arguments.
input_file (str) – The input file to read the data from.
start_date (float) – The start date for the predictions, in jd_tdb.
end_date (float) – The end date for the predictions, in jd_tdb.
timestep_day (float) – The time step for the predictions, in days.
output_file (str) – The output file to write the predictions to.
cache_dir (Path) – The directory to the cached kernels.
aux (AuxiliaryConfigs object) – The LayUp auxiliary arguments