# Tensi
**Interactive tensor shape visualization**
Tensi turns multi‑dimensional tensors into intuitive, interactive geometry so you can instantly see what your data looks like.
*I built Tensi while wrangling distributed sharding‑and‑concatenation of latent tensors. Interactive geometric visuals of tensor shapes finally made the process make sense.*
## Installation
```bash
pip install tensi
```
## Quick Start
```python
import tensi
import torch
# Visualize a 3D tensor
tensor = torch.randn(2, 4, 5) # [Batch, Rows, Cols]
fig = tensi.visualize(tensor)
fig.show()
```
## Usage Examples
### Basic Usage
```python
import tensi
import torch
import numpy as np
# Using the quick visualize function
tensor = torch.randn(3, 4, 5)
fig = tensi.visualize(tensor, title="My Tensor")
fig.show()
# Using different data types
data = [[1, 2, 3], [4, 5, 6]]
fig = tensi.visualize(data, dtype="int32")
fig.show()
# Using numpy arrays
np_array = np.random.rand(2, 3, 4)
fig = tensi.visualize(np_array, dtype="float64")
fig.show()
```
### Tensor Shapes
```python
# 2D Tensor
tensor_2d = torch.randn(5, 7) # [Rows, Cols]
fig = tensi.visualize(tensor_2d, title="2D Tensor")
fig.show()
# 3D Tensor
tensor_3d = torch.randn(3, 5, 7) # [Batch, Rows, Cols]
fig = tensi.visualize(tensor_3d, title="3D Tensor - 3 Batches")
fig.show()
# 4D Tensor
tensor_4d = torch.randn(2, 4, 5, 6) # [Batch, Width, Height, Depth]
fig = tensi.visualize(tensor_4d, title="4D Tensor - 2 Batches")
fig.show()
```
### Save Visualizations
```python
# Save as interactive HTML
fig = tensi.visualize(tensor)
fig.write_html("tensor_visualization.html")
# Save as static image (requires kaleido)
fig.write_image("tensor_visualization.png")
```
## Requirements
- Python >= 3.7
- PyTorch >= 1.8.0
- NumPy >= 1.19.0
- Plotly >= 5.0.0
## Contributing
Contributions are welcome! Please feel free to submit a PR.
## Links
- [Github](https://github.com/DorsaRoh/tensi)
- [PyPI](https://pypi.org/project/tensi/)
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