semvec/pypi-packages huggingface dataset:
drt-sdk-wallet 0.0.2
Wallet & crypto components of Dharitri Python SDK.
Requires Python: >=3.8

# drt-sdk-py-wallet

Wallet & crypto components of Dharitri Python SDK.

## Distribution

 - GitHub: `git+https://git@github.com/DharitriOne/drt-sdk-py-wallet.git@v{Version}#egg=drt_sdk_wallet`
 - [PyPi](https://pypi.org/user/xfactor.toml/)

## Documentation

[docs.dharitri.com](https://docs.dharitri.com/sdk-and-tools/moapy/moapy/)

## Development setup

### Virtual environment

Create a virtual environment and install the dependencies:

```
python3 -m venv ./venv
source ./venv/bin/activate
pip install -r ./requirements.txt --upgrade
```

Install development dependencies, as well:

```
pip install -r ./requirements-dev.txt --upgrade
```

Above, `requirements.txt` should mirror the **dependencies** section of `pyproject.toml`.

If using VSCode, restart it or follow these steps:
 - `Ctrl + Shift + P`
 - _Select Interpreter_
 - Choose `./venv/bin/python`.

### Tests

Run the tests as follows:

```
pytest .
```

### Linting

First, install [`pyright`](https://github.com/microsoft/pyright) as follows:

```
npm install --global pyright
```

Run `pyright`:

```
pyright
```

Run `flake8`:

```
flake8 drt_sdk_wallet
```
streamlit-doubutsushogi 0.0.21
A streamlit custom component for doubutsushogi (animal chess) game
Requires Python: >=3.6

streamlit-doubutsushogi
=============
[![](https://badge.fury.io/py/streamlit-doubutsushogi.svg)](https://badge.fury.io/py/streamlit-doubutsushogi)
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://kota7-streamlit-doubutsushogi-appstreamlit-app-weir3f.streamlit.app/)

A streamlit custom component for interactive board of [doubutsushogi (animal chess)](https://en.wikipedia.org/wiki/D%C5%8Dbutsu_sh%C5%8Dgi) game.


## Install

```shell
# from pypi
pip3 install streamlit-doubutsushogi

# or from github
# see also the streamlit component API manual (https://docs.streamlit.io/library/components/components-api)
git clone https://github.com/kota7/streamlit-doubutsushogi.git
cd streamlit-doubutsushogi/streamlit_doubutsushogi/frontend
npm run build
pip3 install -U ../../
```

## Usage

See the minimal example at [example.py](./example.py):

```python
import streamlit as st
from streamlit_doubutsushogi import st_doubutsushogi

c1, c2 = st.columns([6, 6])
with c1:
    state, status, action = st_doubutsushogi()

if status != 0:
    c2.write(f"Game over! Won by the player {status}")
```

Run the script by:

```shell
streamlit run example.py
```

![example.gif](example.gif)


## Doubutsu Shogi Master App

An interactive app is deployed on the [Streamlit Cloud](https://kota7-doubutsushogi-py-streamlitapp-fyc9on.streamlit.app/).
To run the app on the computer locally, run the following:

```shell
streamlit run app/streamlit_app.py
```


## Tips

### How to upload this app to PyPI

```shell
# 1. build javascript sources
cd streamlit_doubutsushogi/frontend && npm run build && cd ../../
# 2. make python package wheel
rm -r build dist *.egg-info   # only for the second time or later
python3 setup.py sdist bdist_wheel
# 3. upload to PyPI
twine upload --repository pypi dist/*
```

1. Build the javascript application
1. Make Python package wheel
1. Upload to PyPI
fhan 0.1.16
Add your description here
Requires Python: >=3.10

# Fhan

**At least Python 3.10 is required**

Fhan is a small [FHIR](https://www.hl7.org/fhir/overview.html) query client with a focus on performance and usability.

```python
from fhan.client import Client

client = Client(base_url="https://demo.kodjin.com/fhir", authenticate=False)
patients = client.get("Patient", count=1)
print(patients)
```

## Installation

To get started with Fhan, install it using pip:

```shell
pip install fhan
```

## Basic Usage

**Check out the [example notebooks](./examples).**

After installation, you can begin using Fhan to query FHIR resources:

```python
from fhan.client import Client

# The server is public and does not require authentication
client = Client("https://demo.kodjin.com/fhir/Condition", authenticate=False)
```

Get a Resource by id:

```python
client.get("Condition", "13b810dc-58d5-42e3-b34f-5e2454401561")
```

Search for resources:

```python
client.get("Observation", search_params={"code":"249227004"}, count=20)
```

## Authentication

For servers that require authentication:

Copy `.env.example` to `.env`.
Fill in your authentication details in the .env file.
Currently, Fhan supports a limited set of authentication methods. Contributions to expand authentication support are welcome!

## Tools

- [fhirmodels](https://github.com/trostalski/fhirmodels/tree/main) for python fhir objects.
- [Fhir-Views](https://fhir-views.vercel.app/) to inspect FHIR Bundles.
peakrdl-docx 0.4.7
Compile SystemRDL definition into a Docx (MsWord) document
Requires Python: >=3.10
Keywords: SystemRDL, PeakRDL, CSR, compiler, tool, documentation, generator, Verilog, SystemVerilog, register abstraction layer, FPGA, ASIC

# PeakRDL-docx
Compile SystemRDL into a Docx (MS-Word) document


# Command line
peakrdl docx  <input_file.rdl> -o <output_file.docx> --title "Registers of the PCIe BAR1"

where:
- <input_file.rdl> indicates the input path and filename
- -o <output_file.docx> indicates the path and filename of the output file
- --title indicates the (optional) document-title
flask-log-sku1 1.1.3
Flask log output
Requires Python: >=3.9

# flask_log_sku1

Simple print formatter

## Installation

```
pip install flask-log-sku1
```

example:

```
from flask_log_sku1 import log

log("Hello world").success()
log("INFO").info()
log("WARNING").warning()
log("ERROR").error()
```

options for log() are:
success()
info()
warning()
error()

## Settings

use the following environment variables to change the following settings:

```
FLASK_LOG_COLOR="True" # default
FLASK_LOG_DATE_FORMAT="%d/%b/%Y %H:%M:%S" # default
```
tg-bot-sender 0.7
This lib for sending message to tg bots

[Documentation link](https://magnificent-maamoul-2f7a55.netlify.app/)
# Python
How to install ?
```pip
pip install tg-bot-sender
```
## Imports 
```python
from tg_bot_sender import Data, TelegramSender
```
## Getting started
The logs parameter indicates that logs are saved in json format
```python
tg = TelegramSender(telegramToken, logs = False)
```
Response structure
```json
{ "amount": 0 } // number of messages sent
```
## Options for sending messages
#### sendFromIds - sending to users
```python
tg.sendFromIds([...telegramUserIds], Data(
    text = 'Hello',
    photo = 'Photo link',
    buttons = [{
        buttonTitle: 'Hello',
        buttonUrl: 'https://google.com'
    }]
))
```
#### sendFromId - sending to the user
```python
tg.sendFromIds(telegramUserId, Data(
    text = 'Hello',
    photo = 'Photo link',
    buttons = [{
        buttonTitle: 'Hello',
        buttonUrl: 'https://google.com'
    }]
))
```
drt-sdk 0.0.1
The Dharitri Python SDK.
Requires Python: >=3.8

# drt-sdk-py

The Python SDK for interacting with Dharitri. It's an all in one sdk that can be used to create transactions (including smart contract calls and deployments), sign and broadcast transactions, create wallets and many more.

## Documentation

- [Cookbook](./examples/Cookbook.ipynb)
- [Auto-generated documentation](https://dharitri.github.io/drt-sdk-py/)

## Development setup

### Virtual environment

Create a virtual environment and install the dependencies:

```
python3 -m venv ./venv
source ./venv/bin/activate
pip install -r ./requirements.txt --upgrade
```

Install development dependencies, as well:

```
pip install -r ./requirements-dev.txt --upgrade
```

Above, `requirements.txt` should mirror the **dependencies** section of `pyproject.toml`.

If using VSCode, restart it or follow these steps:
 - `Ctrl + Shift + P`
 - _Select Interpreter_
 - Choose `./venv/bin/python`.

### Tests

Run the tests as follows:

This command runs all tests:
```
pytest .
```

If you want to skip network interaction tests run:
```
pytest -m "not networkInteraction"
```

### Generate test coverage report

First, we run the tests using coverage:
```sh
coverage run -m pytest .
```

Then, we can generate a report in the terminal using:
```sh
coverage report
```

We can also generate a html report using:
```sh
coverage html
```

### Regenerating the docs

Each time a new module/submodule is added it needs to be added to the docs, as well. To do so `cd` in the root directory then run the following command:
```bash
sphinx-apidoc -f -o docs/ drt_sdk/ *_test.py *constants.py
```

This command will regenerate the `.rst` files for each module, excluding the tests and the `constants.py` files.

Also, each time a new version is released, the [**conf.py**](/docs/conf.py) file should be updated accordingly.
dharitri-sdk 0.11.1
The Dharitri Python SDK.
Requires Python: >=3.8

# drt-sdk-py

The Python SDK for interacting with Dharitri. It's an all in one sdk that can be used to create transactions (including smart contract calls and deployments), sign and broadcast transactions, create wallets and many more.

## Documentation

- [Cookbook](./examples/Cookbook.ipynb)
- [Auto-generated documentation](https://dharitri.github.io/drt-sdk-py/)

## Development setup

### Virtual environment

Create a virtual environment and install the dependencies:

```
python3 -m venv ./venv
source ./venv/bin/activate
pip install -r ./requirements.txt --upgrade
```

Install development dependencies, as well:

```
pip install -r ./requirements-dev.txt --upgrade
```

Above, `requirements.txt` should mirror the **dependencies** section of `pyproject.toml`.

If using VSCode, restart it or follow these steps:
 - `Ctrl + Shift + P`
 - _Select Interpreter_
 - Choose `./venv/bin/python`.

### Tests

Run the tests as follows:

This command runs all tests:
```
pytest .
```

If you want to skip network interaction tests run:
```
pytest -m "not networkInteraction"
```

### Generate test coverage report

First, we run the tests using coverage:
```sh
coverage run -m pytest .
```

Then, we can generate a report in the terminal using:
```sh
coverage report
```

We can also generate a html report using:
```sh
coverage html
```

### Regenerating the docs

Each time a new module/submodule is added it needs to be added to the docs, as well. To do so `cd` in the root directory then run the following command:
```bash
sphinx-apidoc -f -o docs/ dharitri_sdk/ *_test.py *constants.py
```

This command will regenerate the `.rst` files for each module, excluding the tests and the `constants.py` files.

Also, each time a new version is released, the [**conf.py**](/docs/conf.py) file should be updated accordingly.
planet-download 0.2
A package for acquiring and processing Planet images
Requires Python: >=3.10

# Planet Image Download Using Planet API
This Github repository helps to dowload the planet scope image using the api provided by planet scope.

### How to use this package
Clone this repository and use it like example below:
We need three things to use the package:
1. Spatial extent or bounding box
2. Dates (dates is in list, we provide base year and target year )
3. Planet Api Key

```python
from planet_image_acquisition.planet_image_acquisition import get_planet_image
spatial_extent = (120.527,16.342,120.652,16.464) # xmin,ymin,xmax,ymax
dates = [2018,2019] # Should be provided in list format
api = '' # Provide the planet api key for your account

base,target=get_planet_image(spatial_extent,dates,api)
```

### Temporal Resolution for Planet Scope Image
We can download planet scope image from the year 2018. For the year 2018 and 2019, we can only download 6 months composite.
For 2020 we can download planet image of 6 monthe composite (2019-12_2020-05), 2 months composite (2020-06_2020-08),and all
other from this point is a one month composite

Years and Image composite for the respective year is given as below. One should provide dates like below inorder to download
planet image. I this package we only provide the year other logic is handled in date_logic python file.


| **Year**   | **Image Composite**      |
| --- | --- |
| 2018   | 2017-12_2018-05, 2018-06_2018-11 |
| 2019   | 2018-12_2019-05, 2019-06_2019-11 |
| 2020   | 2019-12_2020-05, 2020-06_2020-08, 2020-09, 2020-10, 2020-11, 2020-12 |
| 2021   | 2021-01, 2021-02, 2021-03, .... , 2021-12 |
| 2022   | 2022-01, 2022-02, 2022-03, .... , 2022-12 |
| 2023   | 2023-01, 2023-02, 2023-03, .... , 2023-12 |
| 2024   | 2024-01, 2024-02, 2024-03, .... , 2024-12 |
pyflaarum 1.3.0
A library to interact with flaarum database
Requires Python: >=3.8

# pyflaarum

a python client for flaarum

## Installation

1. Go to [sae.ng](https://sae.ng/flaarumtuts/install) to get installation instructions for flaarum

1. Make sure you have pip installed.

1. `pip install --upgrade pyflaarum`

## Tutorials

Go to [sae.ng](https://sae.ng/flaarumtuts/intro) for tutorials
calisum 1.1.4
A scraper and summarizer for caliap
Requires Python: >=3.8

# calisum - A caliap activity scraper and summarizer

This is a simple Python script that scraps Caliap and allow to you tu summarize your activity using the LLM of your choice.

## Installation

```bash
pip install calisum
```

## Usage

```
usage: calisum [-h] [--version] [-v] [-O OUTPUT] [-o] [-j] [-e EMAIL] [-p PASSWORD] [-c COOKIE] [-l LLM_URL] [-t LLM_TOKEN] [-n] [-m MODEL_ID] [-g] [url_to_scrape]

Summarize Caliap activity data.

options:
  -h, --help            show this help message and exit
  --version             Display the version of the package.
  -v, --verbose         Increase the verbosity of the output.
  -O OUTPUT, --output OUTPUT
                        Output file to save the summary to. default to stdout
  -o, --original-output
                        Output the original data instead of the summary.
  -j, --json            Output the result as json

Scraper options:
  url_to_scrape         URL to scrape the data from.
  -e EMAIL, --email EMAIL
                        Email address to use for authentication. Must be used together with --password.
  -p PASSWORD, --password PASSWORD
                        Password to use for authentication. Must be used together with --email.
  -c COOKIE, --cookie COOKIE
                        Cookie to use for authentication (PHPSESSID). Mutually exclusive with --email and --password.

LLM options:
  -l LLM_URL, --llm-url LLM_URL
                        URL to the LLM page open api endpoint (compatible with openai api standard).
  -t LLM_TOKEN, --llm-token LLM_TOKEN
                        LLM token (usefull with openai)
  -n, --jan-ai          Use localhost default jan-ai config.
  -m MODEL_ID, --model-id MODEL_ID
                        OpenAI form model id (eg: mistral-ins-7b-q4)
  -g, --global-summary  Do a global summary of individual activity (works only if original output is not selected)
```

## License
[GPL-3.0](LICENSE.md)
```