semvec/pypi-packages huggingface dataset:
mindfultensors 0.0.12
Dataloader that serves MRI images from a mogodb
License: MIT

# mindfultensors
Dataloader that serves MRI images from a mogodb.

The main idea is to keep MRI images and corresponding training labels
for segmentation tasks in a `mongo` database. However, each <img src="https://render.githubusercontent.com/render/math?math=256^3"> 3D MRI
tensor even in 8 bit precision is 16Mb. `mongo`'s records cannot be
larger than this limit and we need to also store the labels of the
same dimensions. `mindfultensors` fetches and aggregates each <img src="https://render.githubusercontent.com/render/math?math=256^3">
tensor stored across multiple records, together with corresponding labels
either for gray and white matter, 104 regions atlas, volume of each of 104 ROIs, or a 50 region
atlas.

# installation

The package is on `pypy` and the simplest way to install it is
```
pip install mindfultensors
```
However, to tinker with it you can also clone the repo:
```
git clone git@github.com:neuroneural/mindfultensors.git
```
Then change directory to the newly cloned repository:
```
cd mindfultensors
```
And install locally by
```
pip intall -e .
```
# usage
A detailed example of how to create a dataloader using provided
dataset class and the corresponding tools is in
`scripts/usage_example.py`

Do not forget to move the batches to the GPU once obtained.
hubview 0.8
GitHub-like local file viewer built with Flask
License: MIT

# HubView — Scripting file viewer

A clean, fast, local file viewer with a GitHub‑style UI. Browse folders, preview code with syntax highlighting, render Markdown (tables, task lists, admonitions), show images/video/audio/PDF, and even draw Mermaid diagrams via fenced blocks.

## Example Run Code

1. Create python Script to run hubview

```python

# Example Hubview Run

from hubview import app


scripts = {
    "Python": {
        "python": ".venv/bin/python3",
        "path": "library/pylibs/hubview/example_script.py",
        "args": ["--foo", "bar"],
        "log": "hubview_demo.log",
    },
    "Shell": {

    }
    "Streamlit": {
        "cmd": ["bash", "run_gpc.sh", "--dry-run"],
        "path": "run_gpc.sh",
        "log": "cleanup.log",
    },
}

app.create_hub(root="./", host="0.0.0.0", port=3000, scripts=scripts)

```


2. Create shell script to run python

```shell

# navigate to control directory
cd '/home/pi/Desktop/Code'
py_exec="venv/bin/python3"
py_script="can_hub.py"
echo "============= RUNNING CAN Hub =================="
$py_exec $py_script
/bin/bash
$SHELL

```

Or

1. Run hubview from shell script

```shell

#!/bin/sh
# ------------------------------------
cd '/home/pi/Desktop/Code'
py_exec=".venv/bin/activate"
. $py_exec
echo "============= RUNNING Hubview =================="
hubview --host=0.0.0.0
/bin/bash
$SHELL

```

## Quick start

1. pip install hubview
2. hubview --root 'your directory'
3. open **http://127.0.0.1:5000** in your browser.