RigolWFM Quick Start

Scott Prahl

Apr 2026

This notebook shows the shortest useful path through RigolWFM: load a waveform, inspect what was parsed, work with the sample arrays, and plot the result.

Install

pip install RigolWFM

Most of the time you can let the library auto-detect the waveform family. Pass model= only when you want to override detection or when you already know the scope family.

%config InlineBackend.figure_format = 'retina'

import os
import tempfile

os.environ.setdefault("MPLCONFIGDIR", tempfile.mkdtemp())

import matplotlib.pyplot as plt

from RigolWFM import Wfm

repo = "https://raw.githubusercontent.com/scottprahl/RigolWFM/main/tests/files/"


def sample_url(relative_path: str) -> str:
    return repo + relative_path

1. Load a Sample Waveform

The docs notebooks always use real sample files from the GitHub repo. Here we load a two-channel DS1102E capture directly from a raw GitHub URL.

w = Wfm.from_url(sample_url("wfm/DS1102E-D.wfm"))
w

downloading 'https://raw.githubusercontent.com/scottprahl/RigolWFM/main/tests/files/wfm/DS1102E-D.wfm'

<RigolWFM.wfm.Wfm at 0x11488a3c0>

2. See What Was Parsed

A quick summary is usually enough to confirm that the right file, parser, and channels were found. Use w.describe() when you want the full scope settings and channel metadata dump.

print("Scope reported by file header :", w.header_name)
print("Parser used                 :", w.parser_name)
print("Enabled analog channels     :", [ch.name for ch in w.channels])
print("Points in first channel     :", len(w.channels[0].volts))

Scope reported by file header : DS1000E
Parser used                 : wfm1000e
Enabled analog channels     : ['CH 1', 'CH 2']
Points in first channel     : 8192

print(w.describe())

    General:
        File Model   = DS1000E
        User Model   = auto
        Parser Model = wfm1000e
        Firmware     = unknown
        Filename     = DS1102E-D.wfm
        Channels     = [1, 2]

    Trigger:
        Mode     = edge
        Source   = CH1
        Level    = 1.60  V
        Sweep    = AUTO
        Coupling = DC
        Derived Level     = -1.20  V

     Channel 1:
         Coupling =  unknown
            Scale =     2.00  V/div
           Offset =     2.40  V
            Probe =       1X
         Inverted =    False

        Time Base =  100.000 µs/div
           Offset =    0.000  s
            Delta =  400.000 ns/point
           Points =     8192

         Count    = [        1,        2,        3  ...      8191,     8192]
           Raw    = [       41,       41,       41  ...       110,      110]
           Times  = [-1.638 ms,-1.638 ms,-1.638 ms  ...  1.638 ms, 1.638 ms]
           Volts  = [  4.32  V,  4.32  V,  4.32  V  ...  -1.20  V, -1.20  V]

     Channel 2:
         Coupling =  unknown
            Scale =     5.00  V/div
           Offset =   -15.80  V
            Probe =       1X
         Inverted =    False

        Time Base =  100.000 µs/div
           Offset =    0.000  s
            Delta =  400.000 ns/point
           Points =     8192

         Count    = [        1,        2,        3  ...      8191,     8192]
           Raw    = [      204,      204,      204  ...       178,      178]
           Times  = [-1.638 ms,-1.638 ms,-1.638 ms  ...  1.638 ms, 1.638 ms]
           Volts  = [  0.00  V,  0.00  V,  0.00  V  ...   5.20  V,  5.20  V]

3. Work With the Sample Arrays

Each enabled analog channel is available as a Channel object in w.channels. The main arrays you will usually use are times, volts, and raw.

ch1 = w.channels[0]

print("Channel name:", ch1.name)
print("First five times (s):", ch1.times[:5])
print("First five volts (V):", ch1.volts[:5])
print("First five raw ADC counts:", ch1.raw[:5])

Channel name: CH 1
First five times (s): [-0.0016384 -0.001638  -0.0016376 -0.0016372 -0.0016368]
First five volts (V): [4.32 4.32 4.32 4.32 4.32]
First five raw ADC counts: [41 41 41 41 41]

fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(ch1.times * 1e3, ch1.volts, label=ch1.name)
ax.set_xlabel("Time (ms)")
ax.set_ylabel("Voltage (V)")
ax.set_title("First Analog Channel From DS1102E-D.wfm")
ax.grid(True, alpha=0.3)
ax.legend()
plt.show()

4. Quick Text Export Preview

Wfm.csv() returns a CSV-formatted string. The dedicated export notebooks go deeper, but a quick preview is handy when you just want to confirm the X axis and channel columns.

rows = w.csv().splitlines()
for row in rows[:4]:
    print(row)

X,CH 1,CH 2,Start,Increment
µs, V, V,-1638.3999999999999,0.39999999999999997
-1638.3999999999999,4.32,0.00
-1637.9999999999998,4.32,0.00

5. Digital Channels Are Exposed Separately

If a waveform file contains parsed logic data, those traces appear in logic_channels. Here is the simplest Z-series example in the repo, which exposes D6.

logic = Wfm.from_url(sample_url("wfm/DS1074Z-C.wfm"), model="Z")
sorted(logic.logic_channels)

downloading 'https://raw.githubusercontent.com/scottprahl/RigolWFM/main/tests/files/wfm/DS1074Z-C.wfm'

['D6']

d6 = logic.logic_channels["D6"]
t = logic.logic_times

fig, ax = plt.subplots(figsize=(10, 3))
ax.step(t[:1500] * 1e3, d6[:1500], where="post", color="tab:red")
ax.set_xlabel("Time (ms)")
ax.set_ylabel("State")
ax.set_yticks([0, 1])
ax.set_ylim(-0.2, 1.2)
ax.set_title("Digital Channel D6 From DS1074Z-C.wfm")
ax.grid(True, alpha=0.3)
plt.show()

6. Local Files and Channel Selection

When you already have a waveform on disk, use Wfm.from_file(...):

from RigolWFM import Wfm

w = Wfm.from_file('/path/to/capture.wfm')
ch1_only = Wfm.from_file('/path/to/capture.wfm', selected='1')

The selected string filters analog channels 1 through 4 before plotting or export.

Where To Go Next

• Exporting Waveforms as WAV Files

• Exporting Waveforms as NPZ Files

• Exporting Waveforms as MATLAB MAT Files

• Exporting Waveforms as Sigrok SR Files

• the family notebooks such as DS1000E Waveforms when you want model-specific examples