semvec/pypi-packages huggingface dataset:
pdfmatrix 1.1.2
Uplink a PDF for detailed information about each char, rectangle, and line.

# pdfmatrix

[![Version](https://img.shields.io/pypi/v/pdfplumber.svg)](https://pypi.python.org/pypi/pdfplumber) ![Tests](https://github.com/jsvine/pdfplumber/workflows/Tests/badge.svg) [![Code coverage](https://codecov.io/gh/jsvine/pdfplumber/branch/stable/graph/badge.svg)](https://codecov.io/gh/jsvine/pdfplumber/branch/stable) [![Support Python versions](https://img.shields.io/pypi/pyversions/pdfplumber.svg)](https://pypi.python.org/pypi/pdfplumber)

Uplink a PDF for detailed information about each text character, rectangle or truncated rectangle, and line. Plus: Table extraction and visual debugging.

Works best on machine-generated, rather than scanned, PDFs. Built on [`pdfminer.six`](https://github.com/goulu/pdfminer). 

Currently [tested](tests/) on [Python 3.6, 3.7, and 3.8](.github/workflows/tests.yml).

**Note:** `pdfmatrix v1.0.7 was the final version to support Python 3.5.

## Table of Contents

- [Installation](#installation)
- [Command line interface](#command-line-interface)
- [Python library](#python-library)
- [Visual debugging](#visual-debugging)
- [Extracting tables](#extracting-tables)
- [Extracting form values](#extracting-form-values)
- [Demonstrations](#demonstrations)
- [Comparison to other libraries](#comparison-to-other-libraries)
- [Acknowledgments / Contributors](#acknowledgments--contributors)
- [Contributing](#contributing)

## Installation

```sh
pip install pdfmatrix
```

## Command line interface

### Basic example


### Options

| Argument | Description |
|----------|-------------|
|`--format [format]`| `csv` or `json`. The `json` format returns more information; it includes PDF-level and page-level metadata, plus dictionary-nested attributes.|
|`--pages [list of pages]`| A space-delimited, `1`-indexed list of pages or hyphenated page ranges. E.g., `1, 11-15`, which would return data for pages 1, 11, 12, 13, 14, and 15.|
|`--types [list of object types to extract]`| Choices are `char`, `rect`, `line`, `curve`, `image`, `annot`. Defaults to all.|

## Python library

### Basic example

```python
import pdfmatrix

clean_list, text_data, nested_list = readPdf(filename)
```

### Loading a PDF

To start working with a PDF, call `readPdf(filename)`, where `filename` can be a:

- path to your PDF file
- file object, loaded as bytes
- file-like object, loaded as bytes

The `readPdf` method returns an instance of the `pdfmatrix` class.

To load a password-protected PDF, pass the `password` keyword argument, e.g., `readPdf("file.pdf", password = "test")`.

Invalid metadata values are treated as a warning by default. If that is not intended, pass `strict_metadata=True` to the `readPdf()` method and `readPdf()` will raise an exception if it is unable to parse the metadata.
CitadelML 1.0.5
Upload and train machine learning models on a remote server.
Requires Python: >=3.0

# CitadelML

Upload and train machine learning models on a remote server.


## Setup
```
pip install citadelml
```

## Quick use

Create a tensorflow model, and then send it to the server.

```python
from citadelml import upload_model
import tensorflow as tf

model = tf.keras.models.Sequential([
    tf.keras.layers.experimental.preprocessing.Rescaling(1./255),
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

upload_model(
    server='http://localhost:5000',
    dataset='example_dataset',
    username='usernameGoesHere',
    password='passwordGoesHere',
    model=model,
    loss_function='tf.keras.losses.SparseCategoricalCrossentropy',
    optimizer='adam',
    n_epochs=5,
    seed=12345,
    split=0.2,
    batch_size=32,
    save_training_results=False
)
```