cutility 0.1.4

Common Utility functions for development

• cutility
• Installation
• Usage
  • utils
    • Measure Execution Time
    • Check Path Existence
  • logger
    • simple logger
  • io
    • read write files
  • data
    • dir handler
  • Cleaner
    • Generic cleaner
    • Simple Text cleaner
    • PII Text cleaner
• Appendix
  • Getting names_list
• References

cutility

Common utils for faster development

Installation

You can install cutility using pip:

# to update to latest version
pip install --upgrade cutility

Usage

utils

Measure Execution Time

import cutility as cu

@cu.get_exec_time
def foo():
    import time
    time.sleep(1)

foo()

Output:

Time taken to execute 'foo': 0:00:01.005044

Check Path Existence

import cutility as cu

b = cu.check_path_exist("./data/temp.txt")
print(b)

Output:

False

logger

simple logger

import cutility as cu

# Create a simple logger instance
log = cu.get_simple_logger()

# Log an information message
log.i("hello world of loggers")

# also supports warning critical debug error messages
# log.i, log.d, log.w, log.e, log.c

Output:

[2023-12-17 02:21:03,847] - [INFO] : hello world of loggers

io

read write files

Read write files. Currently supports only 3 formats:

• text
• json
• yaml

import cutility as cu

# Example 1: Reading and Writing a text file
file_content = cu.read_text("./data/example_r.txt")
cu.write_text(file_content, "./data/example_w.txt")

# Example 2: Reading and Writing a JSON file
json_data = cu.read_json("./data/example_r.json")
cu.write_json(json_data, "./data/example_w.json")


# Example 3: Reading and Writing a YAML file
yaml_data = cu.read_yaml("./data/example_r.yaml")
cu.write_yaml(yaml_data, "./data/example_w.yaml")

data

dir handler

Method to standardize access to folders and configs

What is project_root?

• Directory that holds your src folder is your project_root

What is data_root?

• Directory that holds all your data folder is your data_root

from cutility import get_dir_handler

dirh = get_dir_handler(project_root="./", data_root="./data", verbose=True)
print(dirh.get_data_root())
print(dirh.get_project_root())

Output:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Setting paths:
Project Root: ./
Config Path: /path/to/config
Config Files: [list of config files]
Data Root: ./data

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Cleaner

Generic cleaner

Use this snippet to collectively apply multiple cleaning functions

from cutility.cleaners import GenericSimpleTextCleaner
from typing import List, Dict, Tuple, Any, Callable

# Create an instance of GenericSimpleTextCleaner
gtc = GenericSimpleTextCleaner()

# Sample text
sample_text = """Check out this link: https://example.com. 😎 #Python @user1, [e-mail] 123-456-7908 #testing # python"""

# List of names for name replacement
names_list = ["John", "Doe", "Jane", "Smith"]

# Define cleaning steps
all_cleaning_steps = [
    (gtc.replace_contacts, {"repl": " {{PHONE}} "}),
    (gtc.replace_emails, {"repl": " {{EMAIL}} "}),
    (gtc.replace_names, {"names_list": names_list, "repl": " {{PERSON_NAME}} "}),
    (gtc.clean_emojis, {}),
    (gtc.clean_extra_newlines, {}),
    (gtc.clean_extra_spaces, {}),
    (gtc.clean_hashtags, {}),
    (gtc.clean_profile_handle, {}),
    (gtc.clean_punctuations_except, {"exceptions": [",", ".", "\n", "?", "}", "{"]}),
    (gtc.clean_unicode_characters, {}),
    (gtc.clean_web_links, {}),
]

# Apply text cleaning functions
output = gtc.apply_text_cleaning_functions(sample_text, all_cleaning_steps)

# Print the original and cleaned text
print(sample_text)
print()
print(output)

Simple Text cleaner

Use this snippet to individually apply simple cleaning functions

# simpler text cleaner
from cutility.cleaners import SimpleTextCleaner as stc

t = stc.clean_emojis("🌟 Sed euismod justo t semper justo. 😊")
print(t)

PII Text cleaner

Use this snippet to individually apply PII cleaning functions

from cutility.cleaners import PiiTextCleaner as ptc

t = ptc.replace_emails(
    ptc.replace_contacts(
        "My contact number is [phone] and my email is [e-mail]"
    )
)
print(t)

Appendix

Getting names_list

I have curated list of first names and last names

• public github databases and compiled it here in a github gist.
• references mentioned in the end

Use this command to get names data.

wget https://gist.githubusercontent.com/sagarsrc/e6c7361f9ba6a64b2c9ac5bb10f0285a/raw/fbcca7c6821e7aff285271a6ce42361bbe95cc0c/pii_names.json

References

[1] PII names datasets:

• ~160k first names ~100k last names
• Indian Names dataset