semvec/pypi-packages huggingface dataset:
identicons 0.1.0
A lightweight, easy-to-use Python package for generating unique identicons - graphical representations of hashed user information. It's perfect for adding a touch of personalization to user profiles in web applications, forums, or any platform requiring distinct visual user identification.

## Identicons

`identicons` is a Python package that generates unique 5x5 identicons based on MD5 hashes of input strings. These identicons are symmetrical images commonly used for representing user avatars in applications.

## Installation
```bash
pip install identicons
```

If you're downloading from a source, navigate to the root directory where `setup.py` is located and run:

```bash
git clone https://github.com/LVivona/identicons.git
python setup.py install
```

## Usage

To generate an identicons, you can use the `generate` function:

```python
from identicons import generate

# Generate an identicons with default colors
icon = generate('hello world')

# Generate an identicons with custom primary and secondary colors
icon_custom = generate('hello world', primary=0xFFD700, secondary=0x8B0000)
```

```bash
identicons --text "hello world" --file "output.png" -s 0xa0e7e5 -p 0xffffff
```

The `generate` function returns a NumPy array representing the identicons image. You can save this image using the `save` function:

```python
from identicons import save

# Save the generated identicons that is 500x500
save(icon, 'identicons.png', 500, 500)
```

## Features

- Generate unique and symmetrical 5x5 identicons.
- Customize identicons colors.
- Save identicons in different sizes.

## Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

### Why build this?
I was curious on how github generates there icons, and thought I should build a simple cli/package to do it for me lol. :)

## License

Distributed under the MIT License. See `LICENSE` for more information.

## Contact

Your Name - [your_email@example.com](mailto:lucavivona01@gmail.com)


Project Link: [https://github.com/LVivona/identicons](https://github.com/LVivona/identicons)
convertnotes 0.6
Converts notes from one application to another
Requires Python: >=3.11

# ConvertNotes

This tool converts your notes from one format/application to another. Currently, the only supported conversion is from *Logseq* to *Roam*.

## Features

The primary reason to use this suite of tools is that it does the following:
- Preserves block references
- Fixes date formatting (Daily Notes will work)
- Converts markdown tables to Roam tables
- Converts Logseq properties to Roam attributes
- Enables uploading assets (images, pdfs, etc) and preserving the links to those assets in your knowledge graph
- Removes unecessary Logseq metadata (image sizes, logbook, etc)

This makes the transition from Logseq to Roam as seamless as possible.

### Important Note

While I have tested this rather extensively with my own notes, it is entirely possible that I have forgotten something or made a mistake. Use these tools at your own risk and make sure you have everything backed up in case something goes wrong.

## Installation

```sh
$ pip install convertnotes
```

## Upload Assets to Roam Research

Roam does not provide a convenient way to upload images, however it does allow you to run somewhat arbitrary Javascript which can do the trick.

To do this, first you need to enable "user code" in your User settings:

![](./assets/user_code_option.png)

Then copy [this snippet](./assets/upload.txt) and paste it anywhere in your Roam notes.

When you do this, it will give you a warning like this:

![](./assets/warning.png)


Click the button that says you know what you are doing and then restart your Roam graph (restart the app, refresh the page, etc) to make sure that the javascript loads.

Once it restarts, run the keyboard shortcut `cmd+p` to open the command palette. Now just search for "ConvertNotes" and you should see an option like the following:

![](./assets/convertnotes.png)

Hit the `Enter` key. You will now be prompted to select the files you want to upload. If you are coming from logseq, the files you're looking for are in the `assets` directory within your graph folder.

Select *all* of the files that you want which are referenced in your notes and wait for them to upload. *Do not navigate or do anything.* It might take a while because the files are uploaded in batches. Once they are done you will be prompted to download a **metadata JSON file**. Save this. You will need it when running `convertnotes` so that the links in your notes can be updated to reference the new files managed by Roam.

## Usage

To export your notes from Logseq and import them into Roam, first export your notes as JSON from within Logseq (see [docs](https://docs.logseq.com/#/page/export)). Once you have the JSON file, navigate to the file in your terminal and execute a command like the following to convert them to Roam JSON:

```sh
$ convertnotes \
    --inputfile ./logseq.json \
    --outputfile ./roam.json \
    --metadatafile ./metadata.json \
    --profile logseqtoroam
```

Then open up Roam, and import the resulting JSON file.
financial-statement 0.1.2
Read the financial statement of listed company
Keywords: python,finance,financial statement,stock market

# financial-statement

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)\

# Overview

financial-statement is a Python package that allows you to read the financial statements of listed companies.\

## Installation

```bash\
pip install financial_statement
```

## Usage
```python
import financial_statement as fs
ticket = "AAPL"
year = "2023" # it is string

# get annual income statement
income_statement = fs.income_statement(ticket,year)

# get balance sheet
balance_sheet = fs.balance_sheet(ticket,year)

# get cash flow statement
cash_flow_statement = fs.cash_flow_statement(ticket,year)

# get dividend from company
dividend = fs.get_dividend(ticket)
# if you want get annual received dividend
annual_dividend = fs.get_dividend(ticket,YTM=True)

# get shareholder list
shareholder = fs.shareholder(ticket)

# get current price of share
current_price = fs.current_price(ticket)
```
#


## Source

The library extracts data from discounting cash flows and Yahoo Finance. Developers are not tasked with accumulating the data, and the library automatically goes offline in the case of an interest conflict.
carefree-learn 0.5.0
Deep Learning with PyTorch made easy
Keywords: python machine-learning deep-learning solution PyTorch

![carefree-learn][socialify-image]

Deep Learning with [PyTorch](https://pytorch.org/) made easy 🚀 !

## v0.5.x WIP!

Here are the main design principles:
- The codes should be '`module` first', which means all previous `model`s should be a simple `module` now.
  - And `model` should only be related to the training stuffs. If we only want to use the fancy AI models at inference stage, `module` should be all we need.
- The `module`s should be as 'native' as possible: no inheritance from base classes except `nn.Module` should be the best, and previous inheritance-based features should be achieved by **dependency injection**.
  - This helps the `module`s to be more `torch.compile` friendly.
- Training stuffs are not considered at the first place, but they will definitely be added later on, based on the modern AI developments.
- APIs will be as BC as possible.

## License

`carefree-learn` is MIT licensed, as found in the [`LICENSE`](https://carefree0910.me/carefree-learn-doc/docs/about/license) file.


[socialify-image]: https://socialify.git.ci/carefree0910/carefree-learn/image?description=1&descriptionEditable=Deep%20Learning%20%E2%9D%A4%EF%B8%8F%20PyTorch&forks=1&issues=1&logo=https%3A%2F%2Fraw.githubusercontent.com%2Fcarefree0910%2Fcarefree-learn-doc%2Fmaster%2Fstatic%2Fimg%2Flogo.min.svg&pattern=Floating%20Cogs&stargazers=1
crsysapi 6.22.1.post1
Python interface to CryptoSys API
Requires Python: >=3.6

Python for CryptoSys API
===================================

This is a Python interface to the **CryptoSys API** library <https://www.cryptosys.net/api.html>. 

CryptoSys API is a library to carry out "symmetrical" encryption using block ciphers like AES and Triple DES; 
stream ciphers ArcFour, Salsa and ChaCha20; Authenticated Encryption with Additional Data (AEAD);
message authentication algorithms HMAC, CMAC and KMAC; 
hash functions SHA-1, SHA-2 and SHA-3; the PBKDF2 and SCRYPT key derivation functions; and more. 

Requires: Python 3.
CryptoSys API v6.22.1 or above must be installed on your system.
This is available from

    https://www.cryptosys.net/api.html.


To use in Python's REPL
-----------------------

Using wild import for simplicity.

.. code-block:: python

    >>> from crsysapi import *  # @UnusedWildImport
    >>> Gen.version() # "hello world!" for CryptoSys API
    62201
    >>> Hash.hex_from_data(b'abc') # compute SHA-1 hash in hex of 'abc' as bytes
    'a9993e364706816aba3e25717850c26c9cd0d89d'
    >>> Hash.hex_from_string('abc', Hash.Alg.SHA256)   # same but over a string and using SHA-256
    'ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad'
    >>> h = Hash.data(b'abc')   # h is a byte array (bytes->bytes)
    >>> print(Cnv.tohex(h))     # display the byte array in hex
    A9993E364706816ABA3E25717850C26C9CD0D89D

The stricter way using the ``crsysapi`` prefix.

.. code-block:: python

    >>> import crsysapi
    >>> crsysapi.Gen.version() # Underlying core CryptoSys API dll
    62201
    >>> crsysapi.__version__  # crsysapi.py module version
    6.22.1.0000
    >>> crsysapi.Hash.hex_from_data(b'abc') # compute SHA-1 hash in hex of 'abc' as bytes
    'a9993e364706816aba3e25717850c26c9cd0d89d'
    >>> crsysapi.Hash.hex_from_string('abc', crsysapi.Hash.Alg.SHA256)   # same but over a string and using SHA-256
    'ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad'
    >>> h = crsysapi.Hash.data(b'abc')   # h is a byte array (bytes->bytes)
    >>> print(crsysapi.Cnv.tohex(h))     # display the byte array in hex
    A9993E364706816ABA3E25717850C26C9CD0D89D

Note that ``crsysapi.Gen.version()`` gives the version number of the underlying core (native) CryptoSys API DLL, 
and ``crsysapi.__version__`` gives the version of the Python crsysapi module. 

Examples
--------

There is a series of tests in ``test_crsysapi.py`` (`source <https://www.cryptosys.net/test_crsysapi.py.html>`_). 
This creates any required test files automatically.
You should find an example there of what you want to do.


post1 Update
------------

2023-01-08: Updated distribution to use pyproject.toml and build instead of setup.py, and removed troublesome and unnecessary requirements file
(which messed up pytest).


Contact
-------

For more information or to make suggestions, please contact us at
https://www.cryptosys.net/contact/

| David Ireland
| DI Management Services Pty Ltd
| Australia
| <https://www.di-mgt.com.au> <https://www.cryptosys.net>
| 8 January 2024
efvaccination 0.0.1
A program to show the effectiveness of vaccination with symptoms by age groups
Requires Python: >=3.8

vaccination
====

A program to show the effectiveness of vaccination with symptoms by age groups

##Description
This program reads the data from CSV file (https://data.cdc.gov/api/views/3rge-nu2a/rows.csv) and then, calculates the rate of the outcome with vaccinated and unvaccinated for each age group

##Demo
The following grapgh is an example of all ages.
![effectiveness_all_ages_adj_case](https://github.com/Ryumi-Ryumi/vaccination/assets/153894701/07ba7dae-22ff-4bac-bf19-d5400d3b1c9a)
![effectiveness_all_ages_adj_death](https://github.com/Ryumi-Ryumi/vaccination/assets/153894701/ca0b841b-99f5-4f39-bef7-d62613747efa)

##Usage
Create a file 'vaccination.py' in your directory and paste the code on your file 'vaccination.py'
Then, run the file

##Author
Ryumi Ryumi