| Name | satdatagen JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | A Python package to generate datasets for satellite tasking schedulers. |
| upload_time | 2024-08-09 16:54:16 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# satdatagen
a Python package for generating datasets to be used in satellite tasking schedulers
## Installation
`satdatagen` is in the Python Package Index, and can be installed using pip. The command:
`python3 -m pip install satdatagen`
or
`pip3 install satdatagen`
will install the `satdatagen` library in addition to all necessary dependencies.
*Note: in some cases the library `openmeteo-requests` does not get automatically installed. Users will need to run `pip3 install openmeteo-requests` only once to solve this*
## User Requirements
This package relies on data collected from space-track.org, one of the main resource for satellite ephemeral data. Users of satdatagen must have an existing space-track.org login. Add a file called `credentials.json` to your working directory, with login information formatted as a JSON object:
```
{
identity : 'username@email.com',
password : 'yourPassword12345'
}
```
The satdatagen code will query the space-track.org servers for satellite data using the user's login information. Note that each user is subject to space-track's query limit - up to 30 queries per minute and up to 300 queries per hour.
## Usage
The `satdatagen` library is easy to use. A dataset can be generated with minimal lines of code. Dataset generation revolves around the creation of 2 objects: a `TimeRange` object and a `GroundLocation` object.
### `TimeRange`
`satdatagen.TimeRange(start_date, periods, delta = 0, end_date = None)`
#### Parameters:
**start_date**: `datetime` object or `str` in ISO-T format (`YYYY-MM-DDTHH:mm:ss`). Represents the start date/time for the dataset scheduling
**periods**: `int`. Represents the number of time steps in the time range for the duration of scheduling
**[delta]**: optional parameter. `int`. Represents the time between each time step in the time range
**[end_date]**: optional parameter. `datetime` object or `str` in ISO-T format (`YYYY-MM-DDTHH:mm:ss`). Represents the final date/time in the time range.
Users must provide *either* a `delta` value or `end_date` value to control the length of the time range.
***
### `GroundLocation`
`satdatagen.GroundLocation(space_track_credentials, latitude, longitude, time_range)`
#### Parameters:
**space_track_credentials**: `str`. Path to the `.json` file with space-track.org login info as described above in User Requirements.
**latitude**: `float`. Latitude of the ground location where the observatory is located
**longitude**: `float`. Longitude of the ground location where the observatory is located
**time_range**: `TimeRange` object. Represents the time range for which the dataset will find satellites overhead of the observatory.
#### Methods:
**generate_dataset**
`generate_dataset(self, method = 'krag', limit = None, orbit = 'all', mixing_coeff = 0.8, output_file = None)`
**method**: `str`. The method to use to calculate apparent visual magnitude (AVM). Options are `'krag'`, `'hejduk'`, or `'molczan'`.
**limit**: `int`. The number of satellites to include in the dataset. Default is no limit.
**orbit**: `str`. A filter for satellites in a certain orbit. Options are `'LEO'`, `'MEO'`, `'GEO'`, or `'all'`. Default is `'all'`, no filter.
**mixing_coeff**: `float`. A value between 0 and 1 that defines the ratio of diffuse reflection off objects. Default is `0.8`. *only used when `method=='hejduk'`*
**output_file**: `str`. Path to a `.json` file to output the dataset to. File does not need to be created beforehand.
*Note: all parameters described above for the `generate_dataset` method are optional.*
## Example
```
import satdatagen as sdg
from datetime import datetime
start_date = datetime(2024, 6, 18, hour = 20, minute = 0)
periods = 24
delta = 30
haystack_lon = -71.44 #degrees west
haystack_lat = 42.58 #degrees north
credentials = '/path/to/space/track/credentials.json'
tr = sdg.TimeRange(start_date = start_date, periods = periods, delta = delta) #time range is 12 hours long
gl = sdg.GroundLocation(credentials, haystack_lat, haystack_lon, tr)
#use Molczan's method to calculate AVM, only include 500 satellites in the dataset, and save the dataset to dataset.json
ds = gl.generate_dataset(method = 'molczan', limit = 500, output_file = 'dataset.json')
```
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