Test Automation

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 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: .3 f} V, Frequency: {freq: .2 f} Hz" )

scope.close() Automated Test Sequence 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 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' ]: .4 f} +/- {result[ 'expanded_uncertainty' ]: .4 f} 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 Always verify instrument connections before starting test sequences Use appropriate timeouts for slow measurements Implement proper error handling for instrument communication failures Document measurement conditions (temperature, humidity, setup) Include calibration verification in test procedures 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 SCPI Standard Reference GUM Uncertainty Guide