Skip to main content

Python API for miblab's trained deep-learning models

Project description

miblab-dl

Python API for miblab's trained deep-learning models

Installation in a working environment

If you are working in an existing environment with pytorch already installed, then all you need is this:

pip install miblab-dl

Installation from scratch

If you start from scratch, first create a virtual environment, activate it and make sure you have the latest pip version.

On Windows:

python -m venv myenv
myenv/Scripts/activate
python -m pip install --upgrade pip

On Mac or Linux:

python -m venv myenv
source myenv/bin/activate
python -m pip install --upgrade pip

Now install pytorch in your environment:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Then install the miblab-dl package:

pip install miblab-dl

Usage

You can import miblab-dl functionality in a python script like this:

import miblab_dl as dl

Then to compute fat and water maps from numpy arrays representing in_phase and opposed_phase Dixon images:

fat, water = dl.fatwater(opposed_phase, in_phase)

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

miblab_dl-0.0.3.tar.gz (9.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

miblab_dl-0.0.3-py3-none-any.whl (8.7 kB view details)

Uploaded Python 3

File details

Details for the file miblab_dl-0.0.3.tar.gz.

File metadata

  • Download URL: miblab_dl-0.0.3.tar.gz
  • Upload date:
  • Size: 9.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.9

File hashes

Hashes for miblab_dl-0.0.3.tar.gz
Algorithm Hash digest
SHA256 aee449e6017fb3bbf2324f94ead6005fe19331ee3dd0d97024c9bc11d35c1afd
MD5 5803a1b2a9aa5bc560d8127a1c1502c3
BLAKE2b-256 c6db1a35b5f5ec4c1ea9af1e722f6225a5a29460726a2917b0db377b8b55e40a

See more details on using hashes here.

File details

Details for the file miblab_dl-0.0.3-py3-none-any.whl.

File metadata

  • Download URL: miblab_dl-0.0.3-py3-none-any.whl
  • Upload date:
  • Size: 8.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.9

File hashes

Hashes for miblab_dl-0.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 b9ca0c87158b0dab59e218e0c720932fba86432dce4d27a75b3a2cbe651297a1
MD5 c81865ed4ec7ae892ad2ba182380c848
BLAKE2b-256 8accbdace883c296a8fa30e69a8489c8d004e733da4bf0689ea532742e0a9d41

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page