https://github.com/anuja-rahul/python_datalogger

simplified datalogger using python for easier and faster data logging.
https://github.com/anuja-rahul/python_datalogger

easy-to-use logging-library python-package

Last synced: about 7 hours ago
JSON representation

simplified datalogger using python for easier and faster data logging.

• Host: GitHub
• URL: https://github.com/anuja-rahul/python_datalogger
• Owner: anuja-rahul
• License: mit
• Created: 2024-01-17T07:58:06.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-02-09T07:33:36.000Z (9 months ago)
• Last Synced: 2024-02-14T11:34:41.960Z (9 months ago)
• Topics: easy-to-use, logging-library, python-package
• Language: Python
• Homepage: https://test.pypi.org/project/python-datalogger/
• Size: 86.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
## python_datalogger (v0.0.4)

#### Simplified datalogger using python for easier and faster data logging.

    Datalogger will save all the recorded logs in a local directory (./logs)

## How to install datalogger from test-pypi

#### Visit :

[https://test.pypi.org/project/python-datalogger/](https://test.pypi.org/project/python-datalogger/)

    

#### Or run on your terminal :

    pip install -i https://test.pypi.org/simple/ python-datalogger

## How to install datalogger locally

    01. Download this project.

    02. Extract the master zip file

    03. Open command prompt in the master folder location

    04. Run (for windows) -> pip install . 

        Run (for mac)     -> pip3 install .

    Now you can use datalogger locally any time you want

### How to use Datalogger.logger decorator for basic exception handling. (Example):

```python

from python_datalogger import DataLogger

# using datalogger decorator to record basic exceptions

@DataLogger.logger

def test_method(num: int) -> float:

    return 1000/num

test_method(2)  # if no exceptions are encountered, logs the time taken for this method to run

# raises ZeroDivisionError for demonstration

test_method(0) # logs the error, in case of an exception

```

### How to use Datalogger.timeit decorator to time a function runtime. (Example):

```python

from python_datalogger import DataLogger

@DataLogger.timeit

def test_method(num: int) -> float:

    return 1000/num

test_method(10) # displays the runtime before returning the result for test_method

```

### How to use datalogger for specific info logging. (Example):

```python

from python_datalogger import DataLogger

    

logger = DataLogger(name="TestLogger", level="DEBUG", propagate=True)

# name can be any name you like for the current instance of the Datalogger

# level has 5 security options (DEBUG, INFO, WARNING, ERROR, CRITICAL)

# propagate has 2 options (True/False), if true, the current log is displayed on the terminal

def test_method():

    try:

        print("Starting to do something !")

        # logs a regular information log

        logger.log_info("test_method did something !")

    except Exception as exception:

        # in case of an exception, logs an error log containing the specified exception

        logger.log_error(str(exception))

test_method()

```

### There are 5 possible types of logging methods you can use for each security level.

```python

from python_datalogger import DataLogger

logger = DataLogger(name="TestLogger", level="DEBUG", propagate=True)

    

logger.log_debug(info="this is a debug log")

logger.log_info(info="this is an info log")

logger.log_warning(info="this is a warning log")

logger.log_error(info="this is an error log")

logger.log_critical(info="this is a critical log")

"""Each of these methods accept one (string) parameter containing the information you want to log."""

```