Linea Solar System Portal (lineaSSP)

https://solarsystem.linea.org.br

lineaSSP is a Python package designed to simplify interactions with the Solar System Portal API. The package allows users to retrieve and analyze data related to asteroids, their predictions, occultation events, and more.

Features

• Prediction Queries: Access detailed occultation prediction events.
• Asteroid Queries: Retrieve detailed information about asteroids based on various parameters such as name, number, and dynamical class.
• Map Generation: Create visual maps for occultation events to assist with observation planning.
• Geographical Filtering: Filter predictions based on visibility from a specific geographic location.

Installation

To install the lineaSSP package, use pip:

pip install lineassp

Usage

Below are detailed examples demonstrating how to use the lineaSSP package.

Initializing the API

Before making any requests, you need to initialize the API with the desired endpoint:

from lineaSSP import Prediction, Asteroid

# Initialize the Prediction API
prediction_api = Prediction()

# Initialize the Asteroid API
asteroid_api = Asteroid()

Fetching Asteroids by Name

To retrieve information about asteroids by their name:

from lineaSSP import Asteroid

# Initialize the Prediction API
asteroid_api = Asteroid()

# Fetch asteroids by name
asteroids = asteroid_api.by_name(name='Ceres')
print(asteroids)

Fetching Asteroids with Predictions

To get a list of asteroids that have prediction data available:

from lineaSSP import Prediction

# Initialize the Prediction API
prediction_api = Prediction()

# Fetch asteroids with predictions
asteroids_with_predictions = prediction_api.asteroids_with_prediction()

# Print the asteroid list
print(asteroids_with_predictions['results'])

Fetching Occultation Predictions by Asteroid Name

To retrieve occultation predictions for a specific asteroid by its name:

from lineaSSP import Prediction
import pandas as pd

# Initialize the Prediction API
prediction_api = Prediction()

# Fetch occultation predictions for a specific asteroid by its name
predictions = prediction_api.by_name('Chariklo', limit='all')

# Print the predictions
df = pd.DataFrame(predictions)
df

IMPORTANT - Queries are paginated by default

Use the parameter limit = 'all' to retrive all predictions returned by the query. Due to pagination, the query is limited to 1000 entries by default.

Fetching Occultations Predictions by Date Range

Retrieve occultation events occurring within a specific date range:

from lineaSSP import Prediction
import pandas as pd

# Initialize the Prediction API
prediction_api = Prediction()
params = {
    'date_time_after': '2024-06-28T00:00:00Z',
    'date_time_before': '2024-06-29T23:59:59Z',
    'magnitude_max': 12,
    'nightside': True,
    'local_solar_time_after': '21:00',
    'local_solar_time_before': '03:00',
}

predictions = prediction_api.get_data(params=params)
df = pd.DataFrame(predictions)
df

Generating a Map for an Occultation Event

Generate a map directly from the results of an occultation prediction query.

Disclaimer: When using maps generated by lineaSSP, please cite the SORA package. To customize maps further, please refer to the detailed documentation available at SORA maps generation documentation.

This allows for easy visualization of where an occultation event will be observable:

from lineaSSP import generate_map, Prediction

# Initialize the Prediction API
prediction_api = Prediction()

# Fetch occultations within a certain date range
params = {
    'date_time_after': '2024-01-01T00:00:00Z',
    'date_time_before': '2024-12-31T23:59:59Z',
    'magnitude_max': 10,
}
occultations = prediction_api.get_data(params=params, limit=10)

# Generate the map using the fetched data
# the fucntion will iterate generation all maps in the list
generate_map(data=occultations, dpi=300, path='./')

You can use the genearte_map function as a wrapper of the original SORA function passing directly *argsand **kwargs. Please follow the SORA maps documentantion, specially docstrings, to check it out.

Filtering Predictions by Geographical Location with geofilter

The geofilter method allows you to filter occultation predictions to see which ones are visible from a specific geographical location. This is particularly useful for planning observations:

from lineaSSP import geofilter, Prediction
import pandas as pd

latitude = -23.55  # Latitude of the location (e.g., São Paulo, Brazil)
longitude = -46.63  # Longitude of the location
radius = 100  # Search radius in kilometers

# Initialize the Prediction API
prediction_api = Prediction()

# Fetch occultations within a certain date range
params = {
    'date_time_after': '2024-01-01T00:00:00Z',
    'date_time_before': '2024-12-31T23:59:59Z'
}
predictions = prediction_api.get_data(params=params)

# Filter these occultations by geographical visibility
filtered_predictions = geofilter(predictions, latitude=latitude, longitude=longitude, radius=radius)

df = pd.DataFrame(filtered_predictions)
df

API Query Parameters

The lineaSSP package allows querying the API with various parameters to retrieve specific data when passing the params dictionary. Below is a detailed table of all the parameters that can be used with the Prediction class.

Occultation Query Parameters

Parameter	Description	Type
date_time_after	Fetch occultations occurring after this date	String
date_time_before	Fetch occultations occurring before this date	String
diameter_max	Maximum diameter (km)	Double
diameter_min	Minimum diameter (km)	Double
base_dynclass	Object's base dynamical classification (Skybot)	String
dynclass	Object's dynamical subclass (Skybot)	String
event_duration_max	Maximum event duration (seconds)	Double
event_duration_min	Minimum event duration (seconds)	Double
jobid	Job ID of the occultation event	Integer
latitude	Latitude for geographical filtering	Double
local_solar_time_after	Local Solar Time After	String
local_solar_time_before	Local Solar Time Before	String
location_radius	Radius around the location to check for visibility	Double
longitude	Longitude for geographical filtering	Double
magnitude_drop_max	Maximum expected star's magnitude drop	Double
magnitude_drop_min	Minimum expected star's magnitude drop	Double
magnitude_max	Maximum magnitude (Gaia G magnitude)	Double
magnitude_min	Minimum magnitude (Gaia G magnitude)	Double
nightside	Filter for occultations on the nightside	Boolean
name	Object name (multiple values may be separated by commas)	String
number	Object number (multiple values may be separated by commas)	Integer
closest_approach_uncertainty_km_max	Closest approach uncertainty in geocentric distance (max, km)	Double
closest_approach_uncertainty_km_min	Closest approach uncertainty in geocentric distance (min, km)	Double
hash_id	Hash ID	String

Contributing

We welcome contributions to enhance the functionality of lineaSSP. Please submit issues and pull requests on the GitHub repository.

License

This project is licensed under the MIT License. See the LICENSE file for more details.