yaml2plot: A Python Toolkit for SPICE Simulation Waveform Visualization

yaml2plot is a modern Python toolkit for turning raw SPICE .raw files into beautiful, interactive Plotly graphs. It provides two powerful workflows to get from simulation data to interactive plots:

  • CLI-First Workflow – The fastest way to get from a .raw file to an interactive plot using the y2p command-line tool

  • Python API Workflow – Full programmatic control for custom analysis, scripting, and Jupyter integration

Features

  • Interactive Plotly Visualization – Modern, web-based plots with zoom, pan, and hover

  • Command Line Interface – Quick plotting from terminal with y2p plot

  • Clean API – only four public symbols: load_spice_raw, PlotSpec, plot, and WaveDataset

  • YAML-first configuration – version-controlled plot specs that live next to your simulations

  • Processed-data integration – mix NumPy-derived signals with raw traces in a single call

  • Automatic renderer detection – works seamlessly in Jupyter, VS Code, or standalone scripts

Quick Start: CLI Workflow

Get from a raw file to a plot in three steps using the y2p command-line tool.

Step 1: Generate a Plot Specification

Use y2p init to create a template spec.yaml file from your simulation output:

y2p init your_simulation.raw > spec.yaml

Step 2: Discover Signals

Find the exact names of the signals you want to plot:

# List the first 10 signals
y2p signals your_simulation.raw

# List all signals
y2p signals your_simulation.raw --all

# Filter signals using a regular expression
y2p signals your_simulation.raw --grep "clk"

Step 3: Plot

Edit your spec.yaml to include the signals you discovered, then generate an interactive plot:

# This command will open a browser window with your plot
y2p plot spec.yaml

# You can also override the raw file specified in the YAML
y2p plot spec.yaml your_simulation.raw

# To save the plot to a file instead
y2p plot spec.yaml --output my_plot.html

This approach is fast, requires no Python code, and keeps your plot configuration version-controlled alongside your simulation files.

Quick Start: Python API

For advanced use cases, the Python API provides full control over data loading, processing, and plotting:

import yaml2plot as y2p

# 1. Load data from a .raw file
dataset = y2p.load_spice_raw("your_simulation.raw")
print(f"Signals available: {list(dataset.data_vars)[:5]}...")

# 2. Configure the plot using a YAML string
spec = y2p.PlotSpec.from_yaml("""
title: "My Simulation Results"
x:
  signal: "time"
  label: "Time (s)"
y:
  - label: "Voltage (V)"
    signals:
      Output: "v(out)"
      Input:  "v(in)"
""")

# 3. Create and display the plot
fig = y2p.plot(dataset, spec)
fig.show()

Advanced Example: Plotting Derived Signals

With xarray Dataset integration, you can easily perform calculations and plot derived signals:

import numpy as np
import yaml2plot as y2p

# Load the data as xarray Dataset
dataset = y2p.load_spice_raw("your_simulation.raw")

# Calculate derived signals directly on the Dataset
dataset["diff_voltage"] = dataset["v(out_p)"] - dataset["v(out_n)"]
dataset["power"] = dataset["v(out)"] ** 2

# Create a spec that plots both raw and derived signals
spec = y2p.PlotSpec.from_yaml("""
title: "Differential Output Analysis"
x:
  signal: "time"
  label: "Time (s)"
y:
  - label: "Voltage (V)"
    signals:
      VOUT_P: "v(out_p)"
      VOUT_N: "v(out_n)"
      VOUT_DIFF: "diff_voltage"
  - label: "Power (W)"
    signals:
      Power: "power"
""")

# Create and display the plot
fig = y2p.plot(dataset, spec)
fig.show()

Installation

pip install yaml2plot

For development:

pip install -e ".[dev]"

Contents

User Guide:

API Reference:

Development:

Indices and tables