--- name: test-automation-b6c0c1f64735 description: Automated test equipment control and data acquisition skill for hardware validation, with VISA/SCPI instrument communication, test sequence scripting, and measurement uncertainty analysis. allowed-tools: Read, Grep, Write, Bash, Edit, Glob category: Testing backlog-id: SK-018 metadata: author: babysitter-sdk version: "1.0.0" graph: domains: [domain:electrical-engineering] skillAreas: [skill-area:hardware-abstraction-layer, skill-area:device-drivers, skill-area:firmware-development] roles: [role:embedded-engineer, role:systems-integration-engineer] --- # Test Equipment Automation Skill Automated test equipment control and data acquisition for hardware validation and characterization. ## Purpose This skill provides comprehensive capabilities for automating electronic test equipment, enabling consistent and repeatable hardware validation workflows. It supports instrument communication, automated test sequences, data logging, and analysis. ## Capabilities ### Instrument Communication - VISA/SCPI instrument communication protocols - Support for GPIB, USB-TMC, LAN/VXI-11, and serial interfaces - Instrument discovery and resource management - Error handling and timeout management - Connection pooling for multi-instrument setups ### Oscilloscope Automation - Keysight, Tektronix, Rohde & Schwarz oscilloscope control - Channel configuration and coupling - Trigger setup (edge, pulse, pattern, serial) - Waveform acquisition and transfer - Measurement extraction (rise time, frequency, duty cycle) - Screenshot capture and documentation ### Spectrum Analyzer Control - Frequency span and center frequency configuration - Resolution and video bandwidth settings - Marker operations and peak search - Trace capture and data export - Limit line testing and pass/fail determination - EMC pre-compliance measurements ### Power Supply and Electronic Load Control - Output voltage and current programming - Protection limit configuration - Sequencing and timing control - Load transient generation - Efficiency measurement automation ### DMM and SMU Automation - DC/AC voltage and current measurements - Resistance and continuity testing - Source-measure unit (SMU) operations - IV curve characterization - Temperature coefficient measurements ### Data Logging and Analysis - Real-time data acquisition - Statistical analysis (mean, std, min, max) - Data logging to CSV, JSON, HDF5 - Time-series trending - Alarm and limit monitoring ### Test Sequence Scripting - Sequential and parallel test execution - Conditional branching based on results - Loop and iteration support - Test fixture abstraction - Parameterized test cases ### Measurement Uncertainty Analysis - Type A (statistical) uncertainty evaluation - Type B (systematic) uncertainty estimation - Combined uncertainty calculation - Coverage factor and confidence intervals - GUM-compliant uncertainty budgets ### Report Generation - Automated test report creation - Pass/fail summary tables - Measurement data visualization - Traceability documentation - Export to PDF, HTML, Excel formats ## Prerequisites ### Installation ```bash pip install pyvisa pyvisa-py numpy pandas matplotlib ``` ### Optional Dependencies ```bash # For HDF5 data storage pip install h5py tables # For advanced analysis pip install scipy uncertainties # For report generation pip install jinja2 weasyprint ``` ## Usage Patterns ### Basic Instrument Communication ```python import pyvisa # Initialize resource manager rm = pyvisa.ResourceManager() # List available instruments print(rm.list_resources()) # Connect to oscilloscope scope = rm.open_resource('TCPIP::192.168.1.100::INSTR') scope.timeout = 5000 # 5 second timeout # Query identification idn = scope.query('*IDN?') print(f"Connected to: {idn}") # Configure and measure scope.write(':CHANnel1:DISPlay ON') scope.write(':CHANnel1:SCALe 1.0') # 1V/div scope.write(':TIMebase:SCALe 0.001') # 1ms/div scope.write(':TRIGger:EDGE:SOURce CHANnel1') scope.write(':TRIGger:EDGE:LEVel 0.5') # Read measurement vpp = float(scope.query(':MEASure:VPP? CHANnel1')) freq = float(scope.query(':MEASure:FREQuency? CHANnel1')) print(f"Vpp: {vpp:.3f} V, Frequency: {freq:.2f} Hz") scope.close() ``` ### Automated Test Sequence ```python from dataclasses import dataclass from typing import List, Dict, Any import time @dataclass class TestResult: name: str passed: bool value: float unit: str limit_low: float limit_high: float class TestSequence: def __init__(self, instruments: Dict[str, Any]): self.instruments = instruments self.results: List[TestResult] = [] def run_test(self, name: str, measure_func, limit_low: float, limit_high: float, unit: str) -> TestResult: value = measure_func() passed = limit_low <= value <= limit_high result = TestResult(name, passed, value, unit, limit_low, limit_high) self.results.append(result) return result def generate_report(self) -> Dict: return { 'total_tests': len(self.results), 'passed': sum(1 for r in self.results if r.passed), 'failed': sum(1 for r in self.results if not r.passed), 'results': [vars(r) for r in self.results] } # Example usage def measure_output_voltage(): return float(dmm.query(':MEASure:VOLTage:DC?')) sequence = TestSequence({'dmm': dmm, 'psu': psu}) sequence.run_test('Output Voltage', measure_output_voltage, 4.9, 5.1, 'V') ``` ### Measurement Uncertainty Analysis ```python from uncertainties import ufloat import numpy as np class UncertaintyAnalysis: def __init__(self): self.measurements = [] self.instrument_uncertainty = 0.0 def add_measurement(self, value: float): self.measurements.append(value) def set_instrument_uncertainty(self, uncertainty: float): """Set Type B uncertainty from instrument specifications""" self.instrument_uncertainty = uncertainty def calculate_combined_uncertainty(self, coverage_factor: float = 2.0): # Type A uncertainty (statistical) n = len(self.measurements) mean = np.mean(self.measurements) std = np.std(self.measurements, ddof=1) type_a = std / np.sqrt(n) # Type B uncertainty type_b = self.instrument_uncertainty / np.sqrt(3) # Rectangular distribution # Combined standard uncertainty combined = np.sqrt(type_a**2 + type_b**2) # Expanded uncertainty expanded = coverage_factor * combined return { 'mean': mean, 'type_a_uncertainty': type_a, 'type_b_uncertainty': type_b, 'combined_uncertainty': combined, 'expanded_uncertainty': expanded, 'coverage_factor': coverage_factor } # Usage analysis = UncertaintyAnalysis() for _ in range(10): analysis.add_measurement(float(dmm.query(':MEASure:VOLTage:DC?'))) analysis.set_instrument_uncertainty(0.001) # 1mV from spec sheet result = analysis.calculate_combined_uncertainty() print(f"Voltage: {result['mean']:.4f} +/- {result['expanded_uncertainty']:.4f} V (k=2)") ``` ## Usage Guidelines ### When to Use This Skill - Automated hardware validation and characterization - Production test development - Design verification testing - Environmental testing with data acquisition - EMC pre-compliance measurements ### Best Practices 1. **Always verify instrument connections** before starting test sequences 2. **Use appropriate timeouts** for slow measurements 3. **Implement proper error handling** for instrument communication failures 4. **Document measurement conditions** (temperature, humidity, setup) 5. **Include calibration verification** in test procedures 6. **Use statistical analysis** for production testing decisions ### Process Integration - ee-hardware-validation (all phases) - ee-environmental-testing (data acquisition) - ee-emc-design-testing (pre-compliance measurements) ## Dependencies - PyVISA and PyVISA-py for instrument communication - NI VISA or Keysight IO Libraries for driver support - Instrument-specific drivers as needed ## References - [PyVISA Documentation](https://pyvisa.readthedocs.io/) - [SCPI Standard Reference](https://www.ivifoundation.org/scpi/) - [GUM Uncertainty Guide](https://www.bipm.org/en/publications/guides/gum.html) --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Analyst, QA / Test Engineer Specialties: Testing Tools: H5Py, Jinja2, Matplotlib, Numpy, Pandas, Pyvisa, Scipy, Weasyprint Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 3ce8370f2a34b008e6b6570bceb56f1cb1aaf62118bba5cfe9c1b7e05378e609