semvec/pypi-packages huggingface dataset:
pygantry 1.0.0
The ultra-lightweight Docker alternative for Python developers.
Requires Python: >=3.10
Keywords: container, deployment, venv, lightweight, docker-alternative

# 🏗️ Pygantry
**The ultra-lightweight Python container engine.**

Why ship a whole Linux OS with Docker when you only need a Python environment? 
**Pygantry** packages your app and its dependencies into a portable, relocatable "Gantry" that works anywhere.

## 🚀 Quick Start
1. **Init:** `python pyg.py init`
2. **Build:** `python pyg.py build`
3. **Run:** `python pyg.py run`
4. **Ship:** `python pyg.py ship` -> Get a lightweight ZIP and send it!

## 🌟 Why Pygantry?
- **Small:** 15MB vs 500MB+ for Docker.
- **Simple:** No daemon, no sudo, just Python.
- **Portable:** Move your folder anywhere, it just works.
postgres-fts-backend 0.0.4
A PostgreSQL Full Text Seach Backend for Haystack
Requires Python: >=3.12

# postgres-fts-backend

A [Django Haystack](https://django-haystack.readthedocs.io/) backend that uses
PostgreSQL's built-in full-text search. No external search service required.

## Requirements

- Python >= 3.12
- Django >= 5.0
- django-haystack >= 2.8.0
- PostgreSQL

## Installation

```bash
pip install postgres-fts-backend
```

Add to `INSTALLED_APPS`:

```python
INSTALLED_APPS = [
    "django.contrib.postgres",
    "haystack",
    "postgres_fts_backend",
    # ...
]
```

Set a migration module so the generated search index migrations live in
your project rather than inside the installed package:

```python
MIGRATION_MODULES = {
    "postgres_fts_backend": "myapp.search_migrations",
}
```

Configure Haystack:

```python
HAYSTACK_CONNECTIONS = {
    "default": {
        "ENGINE": "postgres_fts_backend.PostgresFTSEngine",
    },
}
```

To use a search configuration other than `"english"`:

```python
HAYSTACK_CONNECTIONS = {
    "default": {
        "ENGINE": "postgres_fts_backend.PostgresFTSEngine",
        "SEARCH_CONFIG": "spanish",
    },
}
```

## Other Peculiarities of this backend

### Build indexes through models and migrations

```bash
python manage.py build_postgres_schema
python manage.py migrate postgres_fts_backend
```

Run these two commands again whenever you change a `SearchIndex` definition.

### Fuzzy search

Fuzzy queries use PostgreSQL's trigram similarity matching (`pg_trgm`):

```python
results = SearchQuerySet().filter(author__fuzzy="Janee")
```

The similarity threshold is controlled by PostgreSQL's
`pg_trgm.similarity_threshold` setting (default 0.3). To adjust it:

```sql
ALTER DATABASE mydb SET pg_trgm.similarity_threshold = 0.5;
```

### `more_like_this` not implemented
PostgreSQL FTS doesn't provide any facilities for this. It could be done, but I just need to think more about it.

### `spelling_suggestions` are not supported
easyspec 1.0.4.0
The easiest way to do long-slit spectroscopy
Keywords: python, optical spectra, long-slit, spectroscopy, infrared, infrared spectroscopy, ultraviolet


# easyspec
The easiest way to do long-slit spectroscopy

# Install
Please folow the GitHub tutorial for installation:
https://github.com/ranieremenezes/easyspec/tree/main
gseda 1.19.3
gsetl
Requires Python: >=3.8

gseda
how to pack: https://cloud.tencent.com/developer/article/2401343

## bam_filter

* drop_multiple_mapping_reads: drop multiple mapping reads from aligned bam

## bam_surgery

* insert_cs_to_sbr_bam: insert consensus reads to subreads.bam
* insert_ref_to_sbr_bam: insert aligned ref seqeunce region to sbreads.bam

## fact_table_nan

* used to analysis the fact tables that gsetl generated

## msa_view

* show pileup msa alignment result


# ChangeLog

## 0.0.26

* + phreq-ana-cli

## 0.0.25

* + longIndel analysis

## 0.0.23

* + mm2 version check

## 0.0.21

* + bam-basic-stat-cli
* + msa-view-cli
CGMissingData 0.1.5
MICE + Random Forest + KNN to handle missing values of CGM device
Requires Python: >=3.9

# CGMissingData

CGMissingData is a simple missing-data benchmarking package that runs:

MICE imputation (IterativeImputer)

Random Forest regression

KNN regression

It helps you test model performance under different missing-value rates.


Your CSV must include at least these columns:

LBORRES — glucose value (target)

TimeSeries — time series data

TimeDifferenceMinutes — time difference in minutes

USUBJID — subject ID

How to Run?
1. Envirionment Setup- 
cd "C:\Path\To\Your\Project"

Create a virtual environment
python -m venv .venv

Activate the environment
.\.venv\Scripts\activate

2. Install python
python -m pip install --upgrade pip
pip install -e .

3. .\.venv\Scripts\python.exe -c "from CGMmissingData import run_missingness_benchmark; r=run_missingness_benchmark('MyData.csv', mask_rates=[0.05, 0.10, 0.20, 0.30, 0.40]); print(r); r.to_csv('results.csv', index=False)"
#Ensure your dataset (e.g., MyData.csv) is located in the project file. Execute the benchmark directly from the CLI to generate a results.csv file:


Using Google Colab?
1. !pip -q install CGMissingData==0.1.2 (change the version number depending our new release. You can also try with !pip -q install CGMissingData)
2. from CGMissingData import run_missingness_benchmark
3. df = "/content/drive/MyDrive/CGMExampleData.csv"  # your dataset path
4. results = run_missingness_benchmark(
    "CGMExampleData.csv",  # or df
    mask_rates=[0.05, 0.10, 0.20, 0.30, 0.40]
)

5. print(results)
6. results.to_csv("results.csv", index=False)