semvec/pypi-packages huggingface dataset:
sentry-dingding-feely 0.0.4
A Sentry extension which send errors stats to DingDing
License: MIT
Keywords: sentry dingding feely

# Sentry-DingDing

`Sentry`的`钉钉`通知插件

## 安装

```bash
$ pip install sentry-dingding
```

## 使用

在`项目`的所有集成页面找到`DingDing`插件，启用，并设置`Access Token`

![plugin](https://raw.githubusercontent.com/anshengme/sentry-dingding/master/docs/images/options.png)

在插件上使用`Test Plugin`进行测试，当配置好`Access Token`后，在钉钉群内会得到以下警告

![plugin](https://raw.githubusercontent.com/anshengme/sentry-dingding/master/docs/images/dingding.png)

点击`href`按钮，打开异常详情页面。
kerastuner-tensorboard-logger 0.2.3
Simple integration of keras-tuner (hyperparameter tuning) and tensorboard dashboard (interactive visualization).
License: MIT
Requires Python: >=3.6,<4.0
Keywords: tensorflow2,keras-tuner,tensorboard

# Keras-tuner Tensorboard logger

![](https://github.com/tokusumi/kerastuner-tensorboard-logger/workflows/Tests/badge.svg)
[![PyPI version](https://badge.fury.io/py/kerastuner-tensorboard-logger.svg)](https://badge.fury.io/py/kerastuner-tensorboard-logger)

[keras-tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner) logger for streaming search report to [Tensorboard plugins Hparams](https://www.tensorflow.org/tensorboard/hyperparameter_tuning_with_hparams), beautiful interactive visualization tool.

## Requirements

* Python 3.6+
* keras-tuner 1.0.0+
* Tensorboard 2.1+

## Installation

```
$ pip install kerastuner-tensorboard-logger
```

## Example

here is simple (and incomplete) code.

See details about how to use keras-tuner [here](https://github.com/keras-team/keras-tuner).

Add only one argument in tuner class and search it, then you can go to see search report in Tensorboard.

Optionally, you can call `setup_tb` to be more accurate TensorBoard visualization. It convert keras-tuner hyperparameter information and do [Tensorboard experimental setup](https://www.tensorflow.org/tensorboard/hyperparameter_tuning_with_hparams#1_experiment_setup_and_the_hparams_experiment_summary).

```python
# import this
from kerastuner_tensorboard_logger import (
    TensorBoardLogger,
    setup_tb  # Optional
)

tuner = Hyperband(
    build_model,
    objective="val_acc",
    max_epochs=5,
    directory="logs/tuner",
    project_name="tf_test",
    logger=TensorBoardLogger(
        metrics=["val_acc"], logdir="logs/hparams"
    ),  # add only this argument
)

setup_tb(tuner)  # (Optional) For more accurate visualization.
tuner.search(x, y, epochs=5, validation_data=(val_x, val_y))
```

### Tensorboard

```bash
$ tensorboard --logdir ./logs/hparams
```

Go to http://127.0.0.1:6006.

You will see the interactive visualization (provided by Tensorboard).

![Table View](https://raw.githubusercontent.com/tokusumi/kerastuner-tensorboard-logger/main/docs/src/table_view.jpg)

![Parallel Coordinates View](https://raw.githubusercontent.com/tokusumi/kerastuner-tensorboard-logger/main/docs/src/parallel_coordinates_view.jpg)
PyVisualizer 1.0.9
A simple static visualizer(I can make it dynamic though :)).
License: MIT

# PyVisualizer_Task2
A simple static visualizer(I can make it dynamic though), Works as a wrapper around Gspread(version should be 3.0.1 or above) lib for reading the google sheets.
https://pypi.org/project/PyVisualizer

# Installation
<pre>pip install PyVisualizer</pre>

# Points to keep in mind
<b>You can access any Google sheet, if that google sheet is shared with this user <h4>"new-service-acc@task-2-greendeck.iam.gserviceaccount.com"</h6>. This is because test account has been registered to access a google service account and configured accordingly.</b>

# Overview
<b>This lib has 3 parts</b><br>
  1 - Connect to the Google sheet by passing the <b>service_account.json</b> file path as a parameter<br>
  2 - After successfully connection, Read the Google sheet with the connection object and get the specified sheetobject in return.<br>
  3 - Once the data has been read successfully, Pass the data to pre-defined methods of Visualizer class for plotting.<br>

# QuickStart
<h3><b>1 - Import 2 classes of PyVisualizer package</b><br></h3>
CommonUtilities class holds 2 methods.<br>
  1 - connectAndAutorizeToServiceAccount() : This method accepts a service_account.json file path as a parameter and return clientConnectionObject.<br>
  2 - read_google_sheet() : This method accepts 3 parameters. clientConnectionObject, Google Sheet file name, Worksheet name. It returns sheetObject for access the data.<br>
<pre>
>>> from PyVisualizer.CommonUtilities import CommonUtilities
>>> from PyVisualizer.Visualizer import Visualizer
</pre>

<h3><b>2 - Create a connection and read the data.</b></h3>
<pre>
>>> commonUtils = CommonUtilities()
>>> connectionClient = commonUtils.connectAndAutorizeToServiceAccount("pass_the_service_account.json file path, you can download it from this repo /PyVisualizer/service_account.json.")
Successfully Authorized and Connected to Google Service Account. :)
>>> sheetObj = commonUtils.read_google_sheet(connectionClient,"Copy_of_Greendeck_SE_Assignment_Task_2","Sheet1") # 2nd parameter is the file name, which is already shared with test account
>>> sheet1_data = sheetObj.get_all_records() # This return the json array
</pre>

<h3><b>3 - Converting the sheet data into pandas DataFrame and pass it to pre-defined methods</b></h3>
<pre>
>>> import pandas as pd
>>> df = pd.DataFrame(sheet1_data)
>>> Visualizer().barPlotSalesByYear(pandasDataframe=df,xColName="timestamp",yColName="average_sales",sSaveWithFileName="salesByYear.png")
>>> Visualizer().barPlotSalesByMonth(pandasDataframe=df,xColName="timestamp",yColName="average_sales",sSaveWithFileName="salesByMonth.png")
>>> Visualizer().getSalesByYearsAndMonths(df)
</pre>
names_translator 1.2.3
Automatic translation and transliteration of ukrainian names into Russian and English + some extra tools to deal with mixed languages, etc
License: MIT
Keywords: ukrainian names translation transliteration

names-translator
================

That's a small tool to generate as many possible transliteration options for ukrainian names
First it translates the name into russian using dictionary, then transliterates ukrainian and russian names
using translit-ua package and 19 different transliteration tables. Finally, some heuristic is applied to cover
usual usual trasnliterations for well known names, which aren't covered by the transliteration tables

Installation
==================================
Install from PyPI.

.. code-block:: bash

    $ pip install names_translator

Usage
==================================

.. code-block:: python

    >>> from names_translator import Transliterator
    >>> tr = Transliterator()
    >>> tr.transliterate("Чаплинський", "Дмитро", "Олександрович")
        {"Chaplins'kij Dmitro Oleksandrovich",
         'Chaplinski Dmitri Aleksandrovich',
         'Chaplinskii Dimitry Aleksandrovich',
         'Chaplinskii Dmitrii Aleksandrovich',
         'Chaplinskii Dmitrii Alexandrovich',
         'Chaplinskii Dmitry Aleksandrovich',
         'Chaplinskij Dmitrij Aleksandrovich',
         'Chaplinskiy Dmitriy Aleksandrovich',
         'Chaplinsky Dmitry Aleksandrovich',
         "Chaplyns'cyi Dmytro Olecsandrovych",
         "Chaplyns'kyi Dmytro Oleksandrovych",
         "Chaplyns'kyj Dmytro Oleksandrovych",
         "Chaplyns'kyy Dmytro Oleksandrovych",
         'Chaplynski Dmytro Oleksandrovych',
         'Chaplynsky Dmytro Oleksandrovych',
         'Chaplynskyi Dimitry Oleksandrovych',
         'Chaplynskyi Dmitry Oleksandrovych',
         'Chaplynskyi Dmytro Oleksandrovych',
         'Chaplynskyi Dmytro Olexandrovych',
         'Chaplȳnskȳĭ Dmȳtro Oleksandrovȳch',
         'Tchaplynskyy Dmytro Oleksandrovytch',
         'Tschaplynskyj Dmytro Oleksandrowytsch',
         "Čaplins'kij Dmitro Oleksandrovič",
         'Čaplins′kij Dmitro Oleksandrovič',
         'Čaplynsʹkyj Dmytro Oleksandrovyč',
         'Чаплинский Дмитрий Александрович',
         'Чаплинський Дмитро Олександрович'}