How to Use PyMeasure for Automated Instrument Control and Lab Experiments
Modern labs demand automation, precision, and speed—whether you're running I-V sweeps, temperature profiles, or optical characterizations. PyMeasure is an open-source Python package designed to automate these tasks by controlling lab instruments with clean, readable code.
In this post, we’ll walk through the basics of installing and using PyMeasure to run your first automated experiment.
🧪 What is PyMeasure?
PyMeasure is a Python package that simplifies instrument control and experimental automation. It wraps SCPI/GPIB/USB/serial commands in intuitive Python classes and provides tools for:
- Creating repeatable measurement procedures
- Logging and saving results
- Live data plotting
- GUI support for interactive control panels
⚙️ Step 1: Installation
You can install PyMeasure using either pip or conda:
# Recommended via conda
conda install -c conda-forge pymeasure
# Or via pip
pip install pymeasure
Ensure you also have VISA installed if you're using USB/GPIB interfaces. We recommend using NI-VISA or pyvisa-py backend.
🔌 Step 2: Connect to Your Instrument
PyMeasure comes with drivers for many common lab instruments. Here's a simple example using a Keithley 2400 source meter:
from pymeasure.instruments.keithley import Keithley2400
smu = Keithley2400("GPIB::24") # or "USB0::0x05E6::0x2400::XYZ::INSTR"
smu.apply_current(0.001, compliance_voltage=10)
print(f"Measured voltage: {smu.voltage} V")
You can now control the device just like any Python object—setting values and reading measurements.
📈 Step 3: Run an Automated Measurement Script
Let’s run a simple I-V sweep using the same Keithley:
import numpy as np
currents = np.linspace(-1e-3, 1e-3, 50)
voltages = []
for i in currents:
smu.source_current = i
voltages.append(smu.voltage)
print(f"I: {i:.6f} A, V: {voltages[-1]:.6f} V")
You can save the data to CSV for further analysis.
🧪 Step 4: Use Procedure for Full Experiments
Create a repeatable, configurable experiment using the Procedure class:
from pymeasure.experiment import Procedure, IntegerParameter, FloatParameter
from pymeasure.experiment.results import Results
from pymeasure.experiment.workers import Worker
class IVSweepProcedure(Procedure):
start = FloatParameter("Start Current", units="A", default=-1e-3)
stop = FloatParameter("Stop Current", units="A", default=1e-3)
steps = IntegerParameter("Steps", default=50)
def startup(self):
self.instrument = Keithley2400("GPIB::24")
def execute(self):
for i in np.linspace(self.start, self.stop, self.steps):
self.instrument.source_current = i
voltage = self.instrument.voltage
self.emit('results', {'current': i, 'voltage': voltage})
This procedure can now be run in CLI or GUI mode.
🖥️ Step 5: Add a GUI (Optional)
PyMeasure includes a GUI framework:
from pymeasure.display.windows import ManagedWindow
class IVApp(ManagedWindow):
def __init__(self):
super().__init__(
procedure_class=IVSweepProcedure,
inputs=["start", "stop", "steps"],
displays=["current", "voltage"],
x_axis="current", y_axis="voltage"
)
self.setWindowTitle("I-V Measurement")
if __name__ == "__main__":
app = IVApp()
app.show()
🔌 Supported Instruments
PyMeasure supports instruments from:
- Keithley
- Tektronix
- Keysight
- Thorlabs
- NI and more…
You can also create your own instrument class by inheriting from Instrument.
🧰 Use Cases
- Semiconductor I-V curve tracing
- Thermoelectric cooling tests
- Fiber optic testing
- Photovoltaic cell characterization
- Automated reliability tests
✅ Conclusion
PyMeasure makes it easy to automate your lab, save time, and ensure reproducibility. Whether you're in research or production QA, PyMeasure can scale from simple tests to full GUIs.
Get in Touch with us
Related Posts
- 基于启发式与新闻情绪的短期价格方向评估(Python)
- Estimating Short-Term Price Direction with Heuristics and News Sentiment (Python)
- Rust vs Python:AI 与大型系统时代的编程语言选择
- Rust vs Python: Choosing the Right Tool in the AI & Systems Era
- How Software Technology Can Help Chanthaburi Farmers Regain Control of Fruit Prices
- AI 如何帮助发现金融机会
- How AI Helps Predict Financial Opportunities
- 在 React Native 与移动应用中使用 ONNX 模型的方法
- How to Use an ONNX Model in React Native (and Other Mobile App Frameworks)
- 叶片病害检测算法如何工作:从相机到决策
- How Leaf Disease Detection Algorithms Work: From Camera to Decision
- Smart Farming Lite:不依赖传感器的实用型数字农业
- Smart Farming Lite: Practical Digital Agriculture Without Sensors
- 为什么定制化MES更适合中国工厂
- Why Custom-Made MES Wins Where Ready-Made Systems Fail
- How to Build a Thailand-Specific Election Simulation
- When AI Replaces Search: How Content Creators Survive (and Win)
- 面向中国市场的再生资源金属价格预测(不投机、重决策)
- How to Predict Metal Prices for Recycling Businesses (Without Becoming a Trader)
- Smart Durian Farming with Minimum Cost (Thailand)













