semvec/pypi-packages huggingface dataset:
aimv 1.0.0
Python library for Analysis and Imputation of Missing Values
Requires Python: >=3.6

# aimv
The missed values in data may be an obstacle to the using of certain statistics methods for analysis that are not able to process them.
A large number of missed values ignored during statistical analysis, or incorrect methods of filling them can lead to erroneous conclusions and thereby negate the whole study.
The module aimv (analysis and imputation of missing values) is intended for solve the problems of analyzing missed values and their imputation in the language of programming Python.
It contains 20 functions:
aggr – calculate or plot the amount of missing values in each variable and the amount of missing values in certain combinations of variables;
bar_miss – barplot with highlighting of missing values in other variable for the studied variable;
chols – return submatrix of theta corresponding to the columns of mc;
em_norm – performs maximum-likelihood estimation on the matrix of incomplete data using the EM algorithm;
gauss – generates random numbers from a Gaussian distribution;
getparam_norm – takes a parameter vector, such as one produced by em_norm and returns a list of parameters on the original scale;
gtmc – finds the column numbers of the missing variables, and stores them in the first nmc elements of mc. Does not go beyond last column;
gtoc – finds the column numbers of the observed variables, and stores them in the first noc elements of oc. Does not go beyond last column;
hist_miss – histogram with highlighting of missing values in variables by splitting each bin into two parts;
imp_norm – draws missing elements of a data matrix under the multivariate normal model and a user-supplied parameter;
initn – initializes theta;
is1n – performs I-step of data augmentation. Randomly draws missing data Xmis given theta, and stores the sufficient statistics in t. Theta must be in sweep(0) condition. Answer is returned in unswept condition;
mcar_test – use Little’s (1988) test statistic to assess if data is missing completely at random (MCAR);
object_miss – for obtain the number of the rows in a data frame that have a "large" number of missing values. "Large" can be defined either as a proportion of the number of columns or as the number in itself;
prelim_norm – perform preliminary manipulations on matrix of continuous data. Rows are sorted by missing data pattern;
rangen – generates random numbers for a Gaussian distribution;
sigex – extracts submatrix of theta corresponding to the columns of mc;
swp – performs sweep on a symmetric matrix in packed storage. Sweeps on pivot position. Sweeps only the (0:submat,0:submat) submatrix. If dir=1, performs ordinary sweep. If dir=-1, performs reverse sweep;
swpobs – sweeps theta to condition on the observed variables;
tobsn – tabulates the known part of the sscp matrix for all missingness patterns.
streamdoctopdf 0.1.0
Download PDFs from ePapyrus StreamDocs viewers
Requires Python: >=3.10

# streamdoctopdf

> Slop coded. PRs welcome.

Download PDFs from ePapyrus StreamDocs viewers.

StreamDocs is an HTML5 document viewer by ePapyrus used on Korean government sites
(standard.go.kr, e-book.scourt.go.kr, etc.) that renders PDFs as page images
in-browser with download disabled. This tool reconstructs the PDF by downloading
page images and text data, producing a searchable PDF with selectable text.

## Install

```
pip install streamdoctopdf
```

## Usage

```
streamdoctopdf 'https://www.standard.go.kr/streamdocs/view/sd;streamdocsId=72059364864267375'
```

Output filename is auto-detected from the server metadata.

### Options

```
streamdoctopdf URL [-o OUTPUT] [-z ZOOM] [-j JOBS] [--font FONT] [--compress [LEVEL]]
```

| Option | Default | Description |
|--------|---------|-------------|
| `-o` | auto | Output PDF path |
| `-z` | 300 | Zoom level (100=native, 200=2x, 300=3x) |
| `-j` | 4 | Concurrent download threads |
| `--font` | auto | Path to TTF font for text layer |
| `--compress` | off | Compress with Ghostscript (screen/ebook/printer/prepress) |

### Compression

Use `--compress` to reduce output size via Ghostscript (`gs` must be installed):

```
streamdoctopdf URL --compress          # default: ebook quality
streamdoctopdf URL --compress printer  # higher quality
```

### Font auto-detection

The invisible text layer needs a Korean-capable font for search/copy. The tool tries:

1. NanumGothic from common system paths
2. `fc-match :lang=ko` (Linux with fontconfig)
3. Fallback to reportlab built-in CID font (HYSMyeongJo-Medium)

To use a specific font: `--font /path/to/font.ttf`

## How it works

1. Fetches document metadata from the StreamDocs v4 API
2. If the server allows direct download, fetches the original PDF
3. Otherwise, downloads page images and text data concurrently
4. Fixes the corrupted first byte on each image (server-side anti-scrape)
5. Builds a PDF with image backgrounds and invisible text overlay

## License

MIT
unused 0.0.0
Unused objects detection.
Requires Python: >=3.10

# unused

[![Github Actions](https://github.com/lycantropos/unused/workflows/CI/badge.svg)](https://github.com/lycantropos/unused/actions/workflows/ci.yml "Github Actions")
[![Codecov](https://codecov.io/gh/lycantropos/unused/branch/master/graph/badge.svg)](https://codecov.io/gh/lycantropos/unused "Codecov")
[![License](https://img.shields.io/github/license/lycantropos/unused.svg)](https://github.com/lycantropos/unused/blob/master/LICENSE "License")
[![PyPI](https://badge.fury.io/py/unused.svg)](https://badge.fury.io/py/unused "PyPI")

In what follows `python` is an alias for `python3.10` or `pypy3.10`
or any later version (`python3.11`, `pypy3.11` and so on).

## Installation

### Prerequisites

Install the latest `pip` & `setuptools` packages versions

```bash
python -m pip install --upgrade pip setuptools
```

### User

Download and install the latest stable version from `PyPI` repository

```bash
python -m pip install --upgrade unused
```

### Developer

Download the latest version from `GitHub` repository

```bash
git clone https://github.com/lycantropos/unused.git
cd unused
```

Install

```bash
python -m pip install -e '.'
```

## Development

### Bumping version

#### Prerequisites

Install [bump-my-version](https://github.com/callowayproject/bump-my-version#installation).

#### Release

Choose which version number category to bump following [semver
specification](http://semver.org/).

Test bumping version

```bash
bump-my-version bump --dry-run --verbose $CATEGORY
```

where `$CATEGORY` is the target version number category name, possible
values are `patch`/`minor`/`major`.

Bump version

```bash
bump-my-version bump --verbose $CATEGORY
```

This will set version to `major.minor.patch`.

### Running tests

#### Plain

Install with dependencies

```bash
python -m pip install -e '.[tests]'
```

Run

```bash
pytest
```

#### `Docker` container

Run

- with `CPython`

  ```bash
  docker-compose --file docker-compose.cpython.yml up
  ```

- with `PyPy`

  ```bash
  docker-compose --file docker-compose.pypy.yml up
  ```

#### `Bash` script

Run

- with `CPython`

  ```bash
  ./run-tests.sh
  ```

  or

  ```bash
  ./run-tests.sh cpython
  ```

- with `PyPy`

  ```bash
  ./run-tests.sh pypy
  ```

#### `PowerShell` script

Run

- with `CPython`

  ```powershell
  .\run-tests.ps1
  ```

  or

  ```powershell
  .\run-tests.ps1 cpython
  ```

- with `PyPy`

  ```powershell
  .\run-tests.ps1 pypy
  ```