semvec/pypi-packages huggingface dataset:
trk-to-annotation 0.0.1
Utilities for converting .trk tractography files into Neuroglancer precomputed annotation format, and serving them locally with HTTP requests.
Requires Python: >=3.8

# trk_to_annotation
This repository provides utilities for converting .trk tractography files into Neuroglancer precomputed annotation format, and serving them locally with HTTP requests.

## Install
```
python -m build
pip install .
```

## Usage
```
python -m trk_to_annotation <trk_file> \
    --annotation_output_dir ./precomputed_annotations \
    --segmentation_output_dir ./precomputed_segmentations \
    --grid_densities 1 2 4 8 16
```

Arguments
| Argument | Required | Default | Description |
| :---:   | :---: | :---: | :---: |
| trk_file | ✅ | — | Path to the input .trk file |
| --annotation_output_dir | ❌ | ./precomputed_annotations | Output directory for precomputed annotations |
| --segmentation_output_dir | ❌ | ./precomputed_annotations/precomputed_segmentations | Output directory for precomputed segmentations |
| --grid_densities | ❌ | [1, 2, 4, 8, 16] |Grid densities (powers of two, ascending order) |

## Running server for neuroglancer

Run the following command in the directory with your outputted folders
```
python -m trk_to_annotation.http_server
```

You should now be able to access the annotation and segmentation layers on neuroglancer via http://localhost:8000/
Impecunious-sql-etl 0.1.4
Parallel, config-driven SQL Server ETL tool
Requires Python: >=3.9
Keywords: etl, sql-server, data-engineering

# Impecunious SQL ETL

Config-driven, parallel ETL tool for SQL Server with Azure AD and SQL authentication.

## Install
pip install Impecunious-sql-etl

## Run
impecunious-sql-etl --config configs/etl.yaml --workers 4

## Defaults
| Config Item    | If Missing | Default Used                                            |
| -------------- | ---------- | ------------------------------------------------------- |
| `target_table` | omitted    | **Inferred automatically** from file name or SQL source |
| `mapping`      | omitted    | **No renaming** (1:1 columns)                           |
| `dtypes`       | omitted    | **Pandas inferred types**                               |
| `select`       | omitted    | **All columns loaded**                                  |
| `mode`         | omitted    | `truncate_load`                                         |
| `schema`       | omitted    | `dbo`                                                   |

## Minimal config example
```yaml
target:
  connection:
    server: myserver.database.windows.net
    database: TargetDB
    auth_mode: azure_interactive

sources:
  - type: csv
    path: data/customers.csv
```
## Multiple files + SQL table config example
```yaml
target:
  connection:
    server: myserver.database.windows.net
    database: TargetDB
    auth_mode: azure_interactive

sources:
  - type: csv
    path: data/customers.csv

  - type: parquet
    path: data/products.parquet

  - type: sql
    path: SELECT * FROM sales.orders
    connection:
      server: onprem-sql
      database: SourceDB
      auth_mode: sql
      username: etl_user
      password: secret
    mode: append
    save_parquet: true
```