AlloyLens
Introduction
AlloyLens is an interactive visualization tool for visualizing and exploring high dimensional, high throughput alloy data. AlloyLens uses a novel framework to allow for real-time steering and exploration of data for inverse material design.
Python
Installation with pip
pip install alloylens
Usage:
import alloylens as al
# Load alloy dataset
df = pd.read_csv("./Rapid_Alloy_development.txt")
# Define feature groups
all_cols = df.columns.tolist()
element_cols = all_cols[6:18]
microstructure_cols = all_cols[18:56]
property_cols = all_cols[56:70]
groups = {
"elements": element_cols,
"microstructures": microstructure_cols,
"properties": property_cols,
}
# Run alloylens
app = al.AlloyLensApp(df, groups, x="elements", y="properties")
app
1 maintainer
Metadata
1 maintainer
Release files for alloylens 0.0.2
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| alloylens-0.0.2.tar.gz | 137.5 kB | Without provenance | Details for alloylens-0.0.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| alloylens-0.0.2-py3-none-any.whl | Python 3 | none | any | Details for alloylens-0.0.2-py3-none-any.whl |
Total release size: 277.1 kB
Release files / alloylens-0.0.2.tar.gz
| Download URL | alloylens-0.0.2.tar.gz |
|---|---|
| Size | 137.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.12
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Release files / alloylens-0.0.2-py3-none-any.whl
| Download URL | alloylens-0.0.2-py3-none-any.whl |
|---|---|
| Size | 139.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.12
|
Security for alloylens 0.0.2
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