wf-smc-kalman

# smc_kalman

Sequential Monte Carlo modeling for linear Gaussian systems

## Installation

Clone this repo to any convenient location on your local drive
From within your Python development environment (base environment or a virtual environment), run pip install -e LOCAL_PATH_TO_REPO
pip will install the smc_kalman package in your Python development environment (along with all dependencies)
You can then import the modules like any other Python package (e.g., import smc_kalman)

Metadata

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for wf-smc-kalman 0.1.0
File	Size	Uploaded
wf-smc-kalman-0.1.0.tar.gz	3.8 kB	Dec 5, 2019	Details

Table of built distributions (wheels) for wf-smc-kalman 0.1.0
File	Interpreter	ABI	Platform	Reset
wf_smc_kalman-0.1.0-py3-none-any.whl 4.7 kB Dec 5, 2019	Python 3	none	any	Details

Total release size: 8.5 kB

Download URL	wf-smc-kalman-0.1.0.tar.gz
Size	3.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`f6f35c0c4f729a177e5971eaf971d742ff7997251c239edcfefb1e52ec6db5b8`
BLAKE2b-256 checksum How to use checksums	`e2296ff892d89f05ba6f7396f9cbd340d6abb7b47b16b3a02f454fb041afdf24`
Upload date	Dec 5, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/39.0.1 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.5`

Download URL	wf_smc_kalman-0.1.0-py3-none-any.whl
Size	4.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`4018749e20da14fdebe1c8f2d80162d8f00064f7860f7553ec3d8e7de7f749ad`
BLAKE2b-256 checksum How to use checksums	`b367c9f409948c2700a86e76890b1b6f88880315977208ef0b4a096dc3c0e62a`
Upload date	Dec 5, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/39.0.1 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.5`

This release

0.1.0 This release