Utility functions are designed to streamline your workflow, so you can focus on what matters.
The package is set up so that it only imports needed libraries when an object is created. Simply pip install the needed package as you go.
git clone git@github.com:zzangupenn/utilitySuite.git
cd utilitySuite
pip install -e .
For example:
import utilsuite will only import a catalog; no functions/classes are imported.
config = utilsuite.ConfigYAML() First time using the functions/classes triggers the import.
See examples.ipynb for usages.
| Name | Description |
|---|---|
utilsuite |
Root package entry — supports lazy loading so only the tools you import get loaded. Clean, efficient, and namespace-friendly. Can also be imported as an Suite |
ConfigYAML |
Simplifies reading and writing YAML configuration files. Offers intuitive dot-access, saves and loads directly to YAML files, and supports nested structures, making experiment setup and configuration management clean and scriptable. |
ListDict |
A hybrid structure combining list ordering, dictionary style initialization, and dataclass access. Useful for recording data with many categories. Support simple save/load. |
QtMatplotlib |
A high-level interface mimicking matplotlib.pyplot, but with real-time, fast, and multiprocess-capable rendering using PyQtGraph. Currently implemented plot, scatter. Can be used with live=True to continuously stream plots in a separate process. |
keyMonitor |
Captures keypress events in real time without blocking the program flow. Ideal for interactive command-line tools or live-control systems. |
coloredText |
Adds styled ANSI colors to terminal output. Great for improving readability in logs, CLI feedback, or debugging messages. |
Logger |
A minimal, file-saving logger with optional color output and timestamping. Let you track events or experiment states without setting up complex logging frameworks. |
Timer |
Simple stopwatch and context-based timing. Perfect for profiling function runtimes, monitoring loop durations, or annotating performance bottlenecks. |
DataProcessor |
Contains helpers for filtering, structuring, and transforming datasets. Useful for preprocessing logs, lists, or config-driven data. |
colorPalette |
A curated set of visually balanced color themes for plots and visual output. Enables consistent aesthetics across matplotlib or CLI tools. |
pltUtils |
Quick-plot functions built on matplotlib — for fast rendering of line plots, histograms, and comparisons with minimal code. Great for debugging or visually summarizing data. |