equser 0.0.6

User toolkit for power quality data from EQ Wave sensors

equser

User toolkit for power quality data from EQ Wave sensors.

Overview

equser is a Python library for loading, analyzing, and visualizing continuous waveform (CPOW) and power monitoring (PMon) data from EQ Wave hardware. It provides:

• Data loading (data): Load CPOW and PMon Parquet files with automatic scaling
• Waveform analysis (analysis): Zero-crossing detection, cycle extraction
• Visualization (plotting): Static plots for power quality data (requires [analysis])
• API client (api): REST and WebSocket clients for EQ gateways (requires [analysis])
• Live acquisition (pmon): Real-time sensor data acquisition (requires [daq])
• CLI tools: Command-line interface for monitoring and conversion

Installation

Base installation (data loading + analysis)

pip install equser

With plotting and API support

pip install equser[analysis]

With JupyterLab notebook environment

pip install equser[jupyter]

With live sensor acquisition

pip install equser[daq]

Full installation (all features)

pip install equser[full]

Quick Start

Load and explore CPOW data

from equser.data import load_cpow_scaled

result = load_cpow_scaled('20250623_075056.parquet')
print(f"Voltage A peak: {result['VA'].max():.1f} V")
print(f"Start time: {result['start_time']}")
print(f"Sample rate: {result['sample_rate']} Hz")

Load PMon summary data

from equser.data import load_pmon

table = load_pmon('20250623_0750.parquet')
print(table.column_names)

Analyze waveform zero crossings

import numpy as np
from equser.data import load_cpow_scaled, SAMPLE_RATE_HZ
from equser.analysis import find_zero_crossings

result = load_cpow_scaled('cpow_data.parquet')
time = np.arange(len(result['VA'])) / SAMPLE_RATE_HZ
crossings, indices = find_zero_crossings(result['VA'], time)
print(f"Found {len(crossings)} zero crossings")

Plot data (requires [analysis])

from equser.plotting import PowerMonitorPlotter, WaveformPlotter

# Plot power monitor data
plotter = PowerMonitorPlotter()
plotter.plot_file('pmon_data.parquet')

# Plot waveform data
wf_plotter = WaveformPlotter()
wf_plotter.plot_file('cpow_data.parquet')

Query a gateway (requires [analysis])

from equser.api import GatewayClient

client = GatewayClient('http://gateway')
devices = client.list_devices()
table = client.get_pmon_data(devices[0]['id'])

Command Line

# Start power monitoring (requires EQ Wave sensor + [daq])
equser pmon acquire -c config.yaml

# Convert Avro files to Parquet (requires [daq])
equser pmon convert data/*.avro --remove

# Plot data file (requires [analysis])
equser plot data.parquet

Reference notebooks

equser bundles a set of tutorial and analysis notebooks inside the installed package, so they travel with pip install (no separate download). Because they live under site-packages, use the CLI rather than hunting for the path:

# One step: copy the notebooks into ./equser-notebooks and open JupyterLab there
equser notebooks launch

# Or copy them into a directory of your choice (then open however you like)
equser notebooks copy --dest ~/eq-notebooks

# See what's bundled, or print where they live
equser notebooks list
equser notebooks path                                  # the bundled directory
equser notebooks path tutorials/03-backend-api.ipynb   # a specific notebook

launch requires the JupyterLab environment (pip install equser[jupyter]).

From inside an already-running JupyterLab, you can pull a writable copy into the current folder in one cell:

from equser.notebooks import copy_notebooks, get_notebooks_dir
copy_notebooks('.')          # writes tutorials/ and analysis/ here
print(get_notebooks_dir())   # or just locate the read-only originals

Bundled notebooks:

Notebook	What it covers
tutorials/01-parquet-files.ipynb	Load and plot CPOW/PMon data from parquet files
tutorials/02-local-duckdb.ipynb	Query parquet files with SQL via DuckDB
tutorials/03-backend-api.ipynb	Query a gateway over the REST API
tutorials/04-live-streaming.ipynb	Live CPOW and spectral WebSocket streaming
analysis/harmonic-analysis.ipynb	FFT-based harmonic analysis (IEEE 519)
analysis/power-trends.ipynb	Long-term voltage, frequency, and power trends
analysis/delta-analysis.ipynb	Delta-configuration analysis
analysis/ai-event-analysis.ipynb	AI event analysis via the RAG API (needs demo access)

Configuration

equser looks for configuration in the following locations (in order):

1. EQUSER_CONFIG environment variable
2. ./equser.yaml (current directory)
3. ~/.config/equser/config.yaml (XDG config)
4. /etc/equser/config.yaml (system-wide)

Example configuration:

sensor:
  address: "192.168.10.10"
  port: 1535

pmon:
  connection:
    retry_delay: 3
  parquet:
    interval: 86400
    compression:
      method: ZSTD
      level: 4

Dependency Tiers

Extra	Description	Key Packages
(base)	Data loading, analysis, CLI	numpy, pyarrow, pyyaml, argcomplete, colorlog
[daq]	Live sensor acquisition	avro, fastavro
[analysis]	Plotting + API client	matplotlib, requests, websocket-client
[jupyter]	Full notebook environment	[analysis] + jupyterlab, duckdb, ipywidgets
[dev]	Development tools	pytest, ruff, mypy
[full]	All of the above (except dev)	-

Requirements

• Python 3.10 or later
• Linux (for hardware integration features)

Documentation

• API Documentation
• Changelog

License

MIT License — © 2026 EQ Systems Inc.

About

equser is developed by Energy Quotient for continuous waveform intelligence in power systems. It provides Python access to data from EQ gateways running EQ Coherence™ software.