Plot metrics from a Topaz training run
Project description
topaztrainmetrics
Plot metrics from a Topaz training run.
Installation
$ pip install topaztrainmetrics
Usage
$ topaztrainmetrics --help
Usage: topaztrainmetrics [OPTIONS] <file>
Plot validation metrics from a Topaz training run.
<file> is the results.txt file from standalone Topaz or the
model_plot.star file from Topaz run within RELION.
Options:
-l, --loss Plot loss.
-g, --gepenalty Plot GE penalty.
-p, --precision Plot precision.
-t, --tpr Plot true/false positive rates.
-c, --auprc Plot area under precision/recall curve (default).
-x, --xaxis [iter|epoch] X axis (iter or epoch; default: iter).
-o, --output TEXT File name to save the plot (optional: with no file
name, simply display plot on screen without saving
it; recommended file formats: .png, .pdf, .svg or
any format supported by matplotlib).
-h, --help Show this message and exit.
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
topaztrainmetrics-1.2.tar.gz
(3.8 kB
view details)
Built Distribution
File details
Details for the file topaztrainmetrics-1.2.tar.gz
.
File metadata
- Download URL: topaztrainmetrics-1.2.tar.gz
- Upload date:
- Size: 3.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e0be0f154ba3db892061907ec0a394fe2e18f46fb0198849da56179e59cc17ec |
|
MD5 | 852db1ea74c55b0724739dffd1f6ff67 |
|
BLAKE2b-256 | 7ffd1d1f65c36cd17422158317d3819a26975aae40a60e4feaaacb228114aad9 |
File details
Details for the file topaztrainmetrics-1.2-py3-none-any.whl
.
File metadata
- Download URL: topaztrainmetrics-1.2-py3-none-any.whl
- Upload date:
- Size: 4.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 155de85d2b26802c71d25155e116319a423076664ed4a6e9a7168f5be99d957d |
|
MD5 | d70509f1e2943e74b7d4eb0f181f2d20 |
|
BLAKE2b-256 | db3f1b934462a1d0982f01c115c1d1f2fce5d9d872545bdf5988aceb22cf15ca |