semvec/pypi-packages huggingface dataset:
percentum 0.1
A CLI application for the percentage of the day.
License: MIT
Requires Python: >=3.2
Keywords: time,clock,hourglass,arena

![perclock](https://github.com/1darshanpatil/percentum/assets/72539638/6ee23d37-6f33-49ff-8182-9909325f7ecc)



# Percentum

---

**percentum** is a Python command-line interface (CLI) package designed to provide a clear perception of time in terms of hours, minutes, and working hours as a percentage of total time.

The primary intention behind creating and publishing this package is to assist individuals who tend to be workaholics and often lose track of time. They may find themselves immersed in work, losing sight of the passage of time. This phenomenon is well-explained in [Psychology Stack Exchange](https://psychology.stackexchange.com/questions/27694/the-perception-of-time-of-workaholics).

The secondary purpose is that we often say, "What's the big deal? It's only 15 minutes." Essentially, we don't value time, as 14.4 minutes represent 1% of your day, and we all know that 1% is a significant portion.

## Usage

To use **percentum**, follow these simple steps:

1. Install the package:

   ```bash
   $ pip install percentum
   ```
2. Usage
    ```bash
    $ sand
    Time Used: |████████████████████████--------------------------| 48.91%
    ```



## License

percentum is released under the MIT License. See the [LICENSE](LICENSE) file for more details.
datadog-logger 1.0.2
Python logging handler for DataDog events
License: MIT
Requires Python: >=3.9,<4.0

# DataDog Logger #

Note: This library does not send logs to Datadog's Log Management product. See
Datadog's documentation for how to configure log collection. See
https://docs.datadoghq.com/logs/log_collection/

A Python `logging.Handler` for sending log messages to DataDog as Events in
the Events Explorer.

## Installation ##

```
pip install datadog-logger
```

## Usage ##

The simplest way to enable logging to DataDog is to use the
`log_error_events` helper, which will cause all `logging.ERROR` and
higher messages to be sent to DataDog:

```python
import datadog
from datadog_logger import log_error_events
import logging

# Authenticate with DataDog
datadog.initialize(api_key="api-key", app_key="app-key")

# Note, a normal STDOUT handler will not be configured if this is not
# called first
logging.basicConfig()

log_error_events(tags=["tag1:value", "tag2:value"], mentions=["@devs", "@slack"])

logging.error("Oh no!")
```

This will enable the handler on the root logger; `tags` and `mentions`
may both be `None`, in which case none will be included. The handler
will have a log level of `logging.ERROR`, meaning only `ERROR` and
`CRITICAL` (or any higher custom levels) will be sent to DataDog.

A specific logger may also be specified by name:

```python
log_error_events("some.logger")
```

### Details ###

The `Handler` may also be created and added to a logger manually:

```python
import datadog
from datadog_logger import DatadogLogHandler
import logging

datadog.initialize(api_key="api-key", app_key="app-key")

datadog_handler = DatadogLogHandler(
    tags=["some:tag"], mentions=["@some-mention"], level=logging.WARNING)

# Enable STDOUT logging
logging.basicConfig()

# The root logger
logger = logging.getLogger()
logger.addHandler(datadog_handler)

# This will be logged, because the level is WARNING
logger.warning("Watch out!")

# This will not
logger.info("Lets not log everything to DataDog")
```

When the event is created, the `msg` of the log record will be passed
as the title of the event. Normally, this would be the string passed
into the logging call, with any `%` substitutions applied.

The text of the event will be the formatted record. If the log
record includes `exc_info` (i.e. because you called
`logging.exception`, or passed `exc_info` to the log function), then
the full stack trace will be included in the event text.

Any `@mentions` passed via the `mentions` constructor argument will be
appended to the end of the text, so this library can be used to alert,
via DataDog, on particular log messages.
pycounts-test-doriscai 1.4.0
Calculate word counts in a text file!
License: MIT
Requires Python: >=3.9

# pycounts_test_doriscai

Author: Doris Cai.  

Calculate word counts in a text file!

## Installation

```bash
$ pip install pycounts_test_doriscai
```

## Usage

`pycounts_test_doriscai` can be used to count words in a text file and plot results
as follows:

```python
from pycounts.pycounts import count_words
from pycounts.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_test_doriscai` was created by Doris Cai. It is licensed under the terms of the MIT license.

## Credits

`pycounts_test_doriscai` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter). Source is adpoted from https://py-pkgs.org/welcome by Tomas Beuzen & Tiffany Timbers.