semvec/pypi-packages huggingface dataset:
seismic-classifier 0.2.8
trace by trace seismic classification
Requires Python: >=3.9,<3.12

# swagger-client
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)

This Python package is automatically generated by the [Swagger Codegen](https://github.com/swagger-api/swagger-codegen) project:

- API version: 0.1.0
- Package version: 1.0.0
- Build package: io.swagger.codegen.v3.generators.python.PythonClientCodegen

## Requirements.

Python 2.7 and 3.4+

## Installation & Usage
### pip install

If the python package is hosted on Github, you can install directly from Github

```sh
pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git
```
(you may need to run `pip` with root permission: `sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git`)

Then import the package:
```python
import swagger_client 
```

### Setuptools

Install via [Setuptools](http://pypi.python.org/pypi/setuptools).

```sh
python setup.py install --user
```
(or `sudo python setup.py install` to install the package for all users)

Then import the package:
```python
import swagger_client
```

## Getting Started

Please follow the [installation procedure](#installation--usage) and then run the following:

```python
from __future__ import print_function
import time
import swagger_client
from swagger_client.rest import ApiException
from pprint import pprint

# create an instance of the API class
api_instance = swagger_client.DefaultApi(swagger_client.ApiClient(configuration))
stream = 'stream_example' # str | 

try:
    # Predict
    api_response = api_instance.predict_classifier_predict_stream_mseed_post(stream)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling DefaultApi->predict_classifier_predict_stream_mseed_post: %s\n" % e)
```

## Documentation for API Endpoints

All URIs are relative to */*

Class | Method | HTTP request | Description
------------ | ------------- | ------------- | -------------
*DefaultApi* | [**predict_classifier_predict_stream_mseed_post**](docs/DefaultApi.md#predict_classifier_predict_stream_mseed_post) | **POST** /classifier/predict/stream/mseed | Predict

## Documentation For Models

 - [BodyPredictClassifierPredictStreamMseedPost](docs/BodyPredictClassifierPredictStreamMseedPost.md)
 - [ClassifierResults](docs/ClassifierResults.md)
 - [HTTPValidationError](docs/HTTPValidationError.md)
 - [ValidationError](docs/ValidationError.md)

## Documentation For Authorization

 All endpoints do not require authorization.


## Author
interactive-multi-category-table 0.0.4
A python package to create interactive tables with alternative category views from formatted json input.
Requires Python: >=3.7

# interactive-multi-category-table

In many different scenarios, contents are single-categorized and multi-tagged, by which I mean there exist multiple tags but one single (though maybe hierarchical) category system, e.g., file systems, blogs, etc. However, sometimes contents can be categorized from different category views. For storage or lookup, one category system is OK; but for visualization and analysis, flexibly organizing the contents with different category views will be very handy.

This repo targets addressing such demand by creating interactive tables with alternative category views from formatted json input. It can both run as a service on the server side based on [dash](https://dash.plotly.com/), or generate a static webpage on the client side.

# usage

## run on the server side

```python
import interactive_multi_category_table as imct

imct.run_dash_app(json_file_path)
```
and the generated webpage can be accessed at http://127.0.0.1:8050/.

## generate a static webpage

```python
import interactive_multi_category_table as imct

imct.json_to_html(json_file_path)
```

## input json file format

Refer to the provided examples for the json file format.

Note that:
- There have to be at least two category trees.
- Leaf category items in the same category tree cannot be the same.
qaoa-quimb 0.0.1
A QAOA implementation in Quimb.
Requires Python: >=3.7

# QAOA-QUIMB

This librairy implements an optimized version of the Quantum Approximate Optimization Algorithm (QAOA) and various extensions with Quimb.

## Installation

To install with pip:

```
pip install qaoa_quimb
```

## References

Gray, J. (2018). quimb: a python library for quantum information and many-body calculations. Journal of Open Source Software, 3(29), 819. doi:10.21105/joss.00819.
bookocr 1.0.3
Optical character recognition (OCR) tool for printed book pages
Requires Python: >=3.9

# BookOcr

### Optical character recognition (OCR) tool for printed book pages.

### Usage examples:

```
from bookocr.ocr import Ocr

ocr = Ocr()
image_path = "my_image.png"
nested_list_structure = ocr.image_ocr(image_path)  # pages > text areas > lines > words
text = ocr.get_data_as_text()  # the same result, but joined

print(text)
```

```
from bookocr.config import OcrConfig
from bookocr.stats_config import OcrStatsConfig
from bookocr.ocr import Ocr

# optional
config = OcrConfig()
# ... set config values here

# optional as well
# provides intermediate results of image processing 
stats_config = OcrStatsConfig()
stats_config.set_enabled_true("stats_folder")
# ... set stats_config values here

ocr = Ocr(config, stats_config)
image_path = "my_image.png"
nested_list_structure = ocr.image_ocr(image_path)  # pages > text areas > lines > words
text = ocr.get_data_as_text()  # the same result, but joined

print(text)
```