semvec/pypi-packages huggingface dataset:
itakello-logging 0.2.1.7
A custom logging library by Itakello
Requires Python: >=3.10

<h1 align="center">Itakello Logging</h1>

## 🪵 What is Itakello Logging

Itakello Logging is a custom logging library built on top of Python's standard `logging` module, designed to offer a more straightforward and flexible logging setup for your applications. It provides a convenient way to control log output and format, enhancing the debugging process and monitoring of Python applications.

## 🚀 Getting Started

### 🛠️ Installation

To install Itakello Logging, run the following command in your terminal:

```shell
pip install itakello_logging
```

### 🤔 Usage

Using Itakello Logging is simple. Here's a quick example to get you started:

```python
import logging
from itakello_logging import ItakelloLogging

# Initialize the logging system with optional filtering by filename
ItakelloLogging(debug=False, excluded_modules=["test.py", "another_module.py"])

logging.debug("This is a debug message")
logging.info("This is an info message")
logging.warning("This is a warning message")
logging.error("This is an error message")
logging.critical("This is a critical message")
```

This setup initializes the logging system, and based on the `debug` parameter, it adjusts the log level. Additionally, by specifying the `excluded_modules` parameter, messages originating from the listed filenames (e.g., "test.py" and "another_module.py") will be excluded from the logs. This feature makes it easier to focus on the relevant information by filtering out noise from specific modules or scripts.
bestapi 0.1.0
I'm just trying to make my life easier! 🤷‍♂️
Requires Python: >=3.9

# BestAPI

This is how Starlette would look like if I could add breaking changes freely.

BestAPI doesn't support:

- Python 3.8 or older.
- Trio.
- ASGI 2.0 interface.

## Checklist

[ ] Drop AnyIO and Starlette dependency.
[ ] Add `BaseResponse` as base class for response classes.
[ ] Background tasks: I need to work on a better API.
[ ] Use ClientDisconnect mechanism instead of `receive()` -> `http.disconnect` on `StreamingResponse`.
echovault 0.2.8
echovault is git data storage
Requires Python: <4.0,>=3.11

# echovault

`echovault` is data storage which store dictionary collection inexed by name.
The dictionary can be any pickleable key to any pickleable value.

Any operation done to the vault are translated to its git opertaion equivalent.

You can use all the git operation then to duplicate, branch, tag, prune, rollback ... be creative ...

```python
from echovault.vault import Vault
from dulwich.object_store import DiskObjectStore
from dulwich.refs import DiskRefsContainer
from sys import argv

object_store = DiskObjectStore(argv[-1] + '/objects/')
refs_containers = DiskRefsContainer(argv[-1] + '/')

vault = Vault(object_store, refs_containers)

vault.commit('main')

vault['test'] = ((('all', 'your'),
                  ('base', 4),
                  ('re', b'b'),
                  (3, 'longs')),
                 {'cui':1})

for entry in vault['test']:
    print(entry)
    if 3 in entry.keys():
        print(entry['all'])
        vault['test'].remove(entry)
vault['cuicui'] = ({'troll':None},)

vault.commit('main')

del vault['cuicui']

vault.commit('main')
```
iceberg-tools 0.0.8rc3
FHIR schemas tools for bioinformatics.
Requires Python: <4,>=3.9
Keywords: FHIR PFB gen3 bioinformatics graph

# iceberg-schema-tools
Create and maintain central iceberg schema.  Render and validate FHIR data.

## Overview

![image](https://github.com/bmeg/iceberg-schema-tools/assets/47808/cf5f544c-081f-470f-b1d8-27f16ad21b67)



Code that generates the base schema from FHIR goes here.  Additional tools are provided to lints, validates and visualize the schema.

Note: The actual schemas are stored in [iceberg](https://github.com/bmeg/iceberg)


## Setup

```
pip install iceberg-tools
```


## Use

```
$ iceberg schema
Usage: iceberg schema [OPTIONS] COMMAND [ARGS]...

  Manage bmeg or gen3 schemas from FHIR resources.

Options:
  --help  Show this message and exit.

Commands:
  generate  Generate from FHIR resources.
  compile   Create aggregated json file from individual yaml schemas

$ iceberg data

Usage: iceberg data [OPTIONS] COMMAND [ARGS]...

  Project data (ResearchStudy, ResearchSubjects, Patient, etc.).

Options:
  --help  Show this message and exit.

Commands:
  simplify             Renders PFB friendly flattened records.
  validate             Check FHIR data for validity and conventions.
  validate-simplified  Check simplified data for validity and conventions.
  pfb                  Write simplified FHIR files to a PFB.
  migrate              Migrate from FHIR R4B to R5.0.
  report               Aggregate avro pfb files into a cytoscape tsv.

```

> Note: `pfb_fhir` and `iceberg` are synonymous in this context.

## Examples

The commands:
```commandline
pfb_fhir schema generate simplified
pfb_fhir data simplify --schema_path  iceberg/schemas/simplified/simplified-fhir.json tests/fixtures/simplify/study/ tmp/simplified
pfb_fhir data pfb tmp/simplified/ tmp/study.pfb
tree tmp

```

Will generate the following output:
```commandline
INFO:'Records with relationships': 59413
INFO:'Records': 59460
tmp
├── simplified
│   ├── Condition.ndjson
│   ├── DocumentReference.ndjson
│   ├── Encounter.ndjson
│   ├── MedicationAdministration.ndjson
│   ├── Observation.ndjson
│   ├── Patient.ndjson
│   ├── ResearchStudy.ndjson
│   ├── ResearchSubject.ndjson
│   ├── Specimen.ndjson
│   └── Task.ndjson
└── study.pfb

```

## Contributing
See [CONTRIBUTING.md](docs/CONTRIBUTING.md) for developer notes.
Samoosa 2.0
Samoosa is an innovative Python-based tool designed to provide concise and comprehensive summaries of YouTube videos. By
Requires Python: >=3.6



### Samoosa: A Python Tool for Summarizing YouTube Videos

**Overview:**
Samoosa is an innovative Python-based tool designed to provide concise and comprehensive summaries of YouTube videos. By leveraging google's gemini AI model, Samoosa extracts the most relevant information from video transcripts, offering users a quick and easy way to understand video content without having to watch the entire video.

**Key Features:**

- **Automated Summarization:** Generates accurate summaries of YouTube videos by analyzing the video transcripts.

- **User-Friendly Interface:** Simple to use with minimal setup, making it accessible to both technical and non-technical users.
- **Time-Saving:** Ideal for users who want to quickly grasp the essence of a video, saving valuable time.


**Benefits:**

- **Efficiency:** Quickly get the gist of lengthy YouTube videos without having to watch them in full.
- **Productivity:** Enhance productivity by focusing on key points rather than sifting through hours of content.
- **Accessibility:** Makes video content more accessible to those who prefer reading over watching.
- **Research Aid:** A valuable tool for researchers and students needing to review multiple videos on a topic efficiently.

**Use Cases:**

- **Educational Purposes:** Summarize educational videos to facilitate learning and revision.
- **Content Creation:** Helps content creators and marketers quickly gather information from various sources.
- **Professional Development:** Assists professionals in staying updated with industry trends by summarizing webinars and talks.

**Getting Started:**

1. **Install Samoosa:** Easily install via pip with the command `pip install samoosa`.
2. **Input Video URL:** Provide the YouTube video URL to the tool.
3. **Generate Summary:** Run the tool to get an instant summary of the video.

Samoosa simplifies the process of extracting valuable insights from YouTube videos, making it an indispensable tool for anyone looking to efficiently manage their time and enhance their learning experience.
f-icon 0.0.4
Set image as an icon of the target folder
Requires Python: >=3.8

# f-icon

Set Folder Icons.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install f-icon.

```bash
pip install f-icon
```

## Usage

### Parameters

f-icon [OPTIONS] [--] IMAGE

## Options:
    -h, --help                       Print this help text and exit
    -f, --folder                     Path to the folder that will have its icon changed. 
                                     It is ignored if the input is a text file.
    -p, --placement                  Path to the folder where to keep generated icons. 
                                     By default, they are stored inside the folder with a changed icon.
                                     Used only on Windows.
    -r, --relative                   If the icon is located in another folder, 
                                     it forces the path to be relative, 
                                     and it gets ignored if the drive for both locations is different.
                                     USed only on windows
    -v, --verbose                    Print various debugging information

### Direct

```python
import f_icon

# generate icon

image = "Path to the image."
folder = "Path to the folder" # is optional if the image is already in the folder that will use it
placement = "Path to the ico file" # is optional if icon stays in the folder. Ignored on macOS
relative = True # is placement set as relative path to the folder or direct path. Ignored on macOS
debug = False # display print() messages during work
f_icon.create_icon(image, folder, placement, relative, debug)
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)