GPU-accelerated Plavchan periodogram calculation
Project description
Plavchan-GPU
A GPU-accelerated implementation of the Plavchan periodogram for time series analysis in astronomy.
Installation
pip install plavchan_gpu
Requirements
- CUDA-capable GPU and CUDA toolkit > 11.0
- Python 3.6+
Usage
from plavchan_gpu import plavchan_periodogram
# Example data
mags = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] # List of magnitude lists for each object
times = [[1.0, 2.0, 3.0], [1.5, 2.5, 3.5]] # List of time lists for each object
trial_periods = [0.1, 0.2, 0.3, 0.4, 0.5] # List of trial periods
# Calculate periodogram
result = plavchan_periodogram(mags, times, trial_periods, width=0.1)
# result will be a list of periodogram values for each object and trial period
License
MIT
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
plavchan_gpu-1.0.9.tar.gz
(10.5 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file plavchan_gpu-1.0.9.tar.gz.
File metadata
- Download URL: plavchan_gpu-1.0.9.tar.gz
- Upload date:
- Size: 10.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
63b5f25a2e33eb8faf3b6e86c6c19fc07c4cf0913e4cd5e5bc10d82b4dbf02d1
|
|
| MD5 |
cc944514ea91784c64f4e8569462b9b2
|
|
| BLAKE2b-256 |
bf2d3cfd3faeac2e53053f7a9ed87c57481b090fb0421ab9d9328180f7e0c867
|
File details
Details for the file plavchan_gpu-1.0.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.
File metadata
- Download URL: plavchan_gpu-1.0.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
- Upload date:
- Size: 254.7 kB
- Tags: CPython 3.13, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
94012918a2b09cfee03495b471319e4df27883bdc40cad4d074445b8cdb0e6bb
|
|
| MD5 |
ae61386942dcd8e7c402f57071f47e6d
|
|
| BLAKE2b-256 |
efecf6a2d50340e9f28ac0951ca175ca55e379af457167d80e8020cad31cd221
|