Interface to the Predict satellite tracking and orbital prediction library
Project description
PyPredict
NOTE: To preserve compatibility with
predict
, pypredict uses north latitude and west longitude for terrestrial coordinates.
Do you want accurate and time-tested satellite tracking and pass prediction in a convenient python wrapper? You're in the right place.
PyPredict is a C Python extension directly adapted from the ubiquitous predict satellite tracking command line application. Originally written for the commodore 64, predict has a proven pedigree; We just aim to provide a convenient API. PyPredict is a port of the predict codebase and should yield identical results.
If you think you've found an error in pypredict
, please include output from predict
on same inputs to the bug report.
If you think you've found a bug in predict, please report and we'll coordinate with upstream.
Installation
sudo apt-get install python-dev
sudo python setup.py install
Usage
Observe a satellite (relative to a position on earth)
import predict
tle = """0 LEMUR 1
1 40044U 14033AL 15013.74135905 .00002013 00000-0 31503-3 0 6119
2 40044 097.9584 269.2923 0059425 258.2447 101.2095 14.72707190 30443"""
qth = (37.771034, 122.413815, 7) # lat (N), long (W), alt (meters)
predict.observe(tle, qth) # optional time argument defaults to time.time()
# => {'altitude': 676.8782276657903,
# 'azimuth': 96.04762045174824,
# 'beta_angle': -27.92735429908726,
# 'decayed': 0,
# 'doppler': 1259.6041017128405,
# 'eci_obs_x': -2438.227652191655,
# 'eci_obs_y': -4420.154476060397,
# 'eci_obs_z': 3885.390601342013,
# 'eci_sun_x': 148633398.020844,
# 'eci_sun_y': -7451536.44122029,
# 'eci_sun_z': -3229999.50056359,
# 'eci_vx': 0.20076213530665032,
# 'eci_vy': -1.3282146055077213,
# 'eci_vz': 7.377067234096598,
# 'eci_x': 6045.827328897242,
# 'eci_y': -3540.5885778261277,
# 'eci_z': -825.4065096776636,
# 'eclipse_depth': -87.61858291647795,
# 'elevation': -43.711904591801726,
# 'epoch': 1521290038.347793,
# 'footprint': 5633.548906707907,
# 'geostationary': 0,
# 'has_aos': 1,
# 'latitude': -6.759563817939698,
# 'longitude': 326.1137007912563,
# 'name': '0 LEMUR 1',
# 'norad_id': 40044,
# 'orbit': 20532,
# 'orbital_model': 'SGP4',
# 'orbital_phase': 145.3256815318047,
# 'orbital_velocity': 26994.138671706416,
# 'slant_range': 9743.943478523843,
# 'sunlit': 1,
# 'visibility': 'D'
# }
Show upcoming transits of satellite over ground station
# start and stop transit times as UNIX timestamp
transit_start = 1680775200
transit_stop = 1681034400
p = predict.transits(tle, qth, transit_start, transit_stop)
print("Start of Transit\tTransit Duration (s)\tPeak Elevation")
for transit in p:
print(f"{transit.start}\t{transit.duration()}\t{transit.peak()['elevation']}")
Modeling an entire constellation
Generating transits for a lot of satellites over a lot of ground stations can be slow. Luckily, generating transits for each satellite-groundstation pair can be parallelized for a big speed-up.
import itertools
from multiprocessing.pool import Pool
import time
import predict
import requests
# Define a function that returns arguments for all the transits() calls you want to make
def _transits_call_arguments():
now = time.time()
tle = requests.get('http://tle.spire.com/25544').text.rstrip()
for latitude in range(-90, 91, 15):
for longitude in range(-180, 181, 15):
qth = (latitude, longitude, 0)
yield {'tle': tle, 'qth': qth, 'ending_before': now+60*60*24*7}
# Define a function that calls the transit function on a set of arguments and does per-transit processing
def _transits_call_fx(kwargs):
try:
transits = list(predict.transits(**kwargs))
return [t.above(10) for t in transits]
except predict.PredictException:
pass
# Map the transit() caller across all the arguments you want, then flatten results into a single list
pool = Pool(processes=10)
array_of_results = pool.map(_transits_call_fx, _transits_call_arguments())
flattened_results = list(itertools.chain.from_iterable(filter(None, array_of_results)))
transits = flattened_results
NOTE: If precise accuracy isn't necessary (for modeling purposes, for example) setting the tolerance argument
to the above
call to a larger value, say 1 degree, can provide a significant performance boost.
Call predict analogs directly
predict.quick_find(tle.split('\n'), time.time(), (37.7727, 122.407, 25))
predict.quick_predict(tle.split('\n'), time.time(), (37.7727, 122.407, 25))
API
observe(tle, qth[, at=None]) Return an observation of a satellite relative to a groundstation. qth groundstation coordinates as (lat(N),long(W),alt(m)) If at is not defined, defaults to current time (time.time()) Returns an "observation" or dictionary containing: altitude _ altitude of satellite in kilometers azimuth - azimuth of satellite in degrees from perspective of groundstation. beta_angle decayed - 1 if satellite has decayed out of orbit, 0 otherwise. doppler - doppler shift between groundstation and satellite. eci_obs_x eci_obs_y eci_obs_z eci_sun_x eci_sun_y eci_sun_z eci_vx eci_vy eci_vz eci_x eci_y eci_z eclipse_depth elevation - elevation of satellite in degrees from perspective of groundstation. epoch - time of observation in seconds (unix epoch) footprint geostationary - 1 if satellite is determined to be geostationary, 0 otherwise. has_aos - 1 if the satellite will eventually be visible from the groundstation latitude - north latitude of point on earth directly under satellite. longitude - west longitude of point on earth directly under satellite. name - name of satellite from first line of TLE. norad_id - NORAD id of satellite. orbit orbital_phase orbital_model orbital_velocity slant_range - distance to satellite from groundstation in meters. sunlit - 1 if satellite is in sunlight, 0 otherwise. visibility transits(tle, qth[, ending_after=None][, ending_before=None]) Returns iterator of Transit objects representing passes of tle over qth. If ending_after is not defined, defaults to current time If ending_before is not defined, the iterator will yield until calculation failure.
NOTE: We yield passes based on their end time. This means we'll yield currently active passes in the two-argument invocation form, but their start times will be in the past.
Transit(tle, qth, start, end) Utility class representing a pass of a satellite over a groundstation. Instantiation parameters are parsed and made available as fields. duration() Returns length of transit in seconds peak(epsilon=0.1) Returns epoch time where transit reaches maximum elevation (within ~epsilon) at(timestamp) Returns observation during transit via quick_find(tle, timestamp, qth) aboveb(elevation, tolerance) Returns portion of transit above elevation. If the entire transit is below the target elevation, both endpoints will be set to the peak and the duration will be zero. If a portion of the transit is above the elevation target, the endpoints will be between elevation and elevation + tolerance (unless endpoint is already above elevation, in which case it will be unchanged) quick_find(tle[, time[, (lat, long, alt)]]) time defaults to current time (lat, long, alt) defaults to values in ~/.predict/predict.qth Returns observation dictionary equivalent to observe(tle, time, (lat, long, alt)) quick_predict(tle[, time[, (lat, long, alt)]]) Returns an array of observations for the next pass as calculated by predict. Each observation is identical to that returned by quick_find.
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
Built Distributions
Hashes for pypredict-1.7.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a10b4ffdee63b8bf18a9917eb68f1a905647a9738803017a4f3f576c6b8cf51c |
|
MD5 | de54f1f8dde4896311344bb694828a6b |
|
BLAKE2b-256 | a474e36082e3b731e2effa938a10ae8be611db9d29499c78053ceddaa80623cb |
Hashes for pypredict-1.7.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cc06aaec93c5645fdfd5c556632adde4d5b741d8e32f46f03b6a439a2e9a5dce |
|
MD5 | a8ffc7bd98b2ffc62553e8432c4210ce |
|
BLAKE2b-256 | d536885f69ad5228606ad947e079e838e186ade0ec73a36227933644e400700c |
Hashes for pypredict-1.7.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 36046b4c9484627eb3d7591ea6c9eb6ac4a80a905123fea6d469f241b7e7496e |
|
MD5 | c22fc1e5c9dcd5ed142db4b80eb945cf |
|
BLAKE2b-256 | 45c9404a312e6afdf196d999baee1fca36ed668ef5a715d9205615124835018f |
Hashes for pypredict-1.7.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d6d00d263a5449c3a37325e64f264e864ffb6f6be8c15851ea3fb6746722e8c4 |
|
MD5 | b7626878236e1bbc6d1d365d0cc7871f |
|
BLAKE2b-256 | 49e3b73920ba9230d32de78f02592f0de880410624f64747b2275b6084fe9e75 |
Hashes for pypredict-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e58ed1a87f6067edcbe55ea9a4e2bb4d37baf4d6afa846629b4cfb5815c1c12d |
|
MD5 | fc585b47c336d737b2136c7cb94e7445 |
|
BLAKE2b-256 | 53c8af141af35f1663f41598a7fb8f986afba8ac0f6259b9086134349d981825 |
Hashes for pypredict-1.7.2-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7e4c556abab554b4334ee3b6bc7ab8515a1dfbc103f85ba0407b88ca773c723a |
|
MD5 | be65958b460798ccbc6bc0f5a4977777 |
|
BLAKE2b-256 | 1a0dd0f5fd120a93a3ade184f2b9307850840ace4c7511804f9481a261d6714a |
Hashes for pypredict-1.7.2-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 24aaaeb982fd2747e6944e6aa0d5fa267530d8fcea20355467ebae2a5e46f920 |
|
MD5 | c04b8a67544441c2caaab5d768679e79 |
|
BLAKE2b-256 | 50f84a3a6e89583f0b952dbed141018102670bfa1c374c017acc0d440a5db168 |