ZC Plot is an extensible set of scripts for plotting waveforms (or groups of waveforms, or even multiple series of groups), Maestro summary data, or even raw CSV files. The included scripts use Seaborn, but users can write their own plot functions using any Python data visualization package!

Plots generated from one simple command!

These utilities have been tested with Linux 4.18.0 and Python 3.9. Run pip install --requirement requirements.txt to install additional dependencies.

This project is theoretically OS-insensitive, but unforeseen issues might arise. Please report (or fix!) any problems with non-Linux operating systems.

If plotting waveforms, select all the waves in the waveform viewer and export your data as a .csv file. Make sure to enable the "Interpolate" option for transient simulations so each waveform has the same time axis. Then, invoke the main script as follows:

./zc_plot.py [options] PLOT INPUT [kwargs]
    -h  --help      [PLOT]  Display this message or usage for PLOT
    -k  --kwargs     FILE   Load additional external kwargs from FILE
    -x  --export     FILE   Exports the current kwargs to FILE
    -t  --type       TYPE   Input file type ('wave', 'summary', 'mc', or 'raw', default: 'wave')
    -l  --log        FILE   Logfile name (or 'none' to disable)
    -i  --interact          View data ingest before setting kwargs
    -q  --quiet             Surpress verbose output
    -v  --version           Print version string

List of available PLOTs:
    inputrefnoise
    replot
    gmid
    etc. etc. etc.

INPUT must be a valid CSV, e.g. `data/my_data.csv`.

Kwargs can also be loaded from an external file by using the -k or --kwargs switch. This file must be line-delimited and lines starting with # will be treated as comments. When using an external file, any CLI kwargs will always be appended after the external kwargs.

Alternatively, using interactive mode (-i or --interact) previews the parsed data and opens a kwarg editor. This is especially powerful when combined with the -x or --export switch, which saves the current kwargs to a properly-formatted text file.

Pro tip: The csv_dump function turns this tool into a Cadence-to-actually-readable-csv parser.

The most versatile of the plotting functions is replot, which accepts either a waveform or summary data or a raw CSV and plots arbitrary axes. replot supports several Seaborn plot styles, including lineplots, scatterplots, histograms, kde plots, and even some combinations (like histograms with kde, invoked with any string that contains both hist and kde). Jointplots are also supported; you can include the type of the jointplot in ptype (e.g. jointkde, hexjoint). Heatmaps are in beta support. Most of the Seaborn settings are exposed with the kwargs listed below:

./cadence_plot.py replot [options] INPUT [kwargs]

Data
    x=str                       Change x (default: first column)
    y=list                      Change y (default: second column)
    hue=str         h=str       Specify hue (WARNING default: None)
    style=str       s=str       Specify style (default: None)
    size=str                    Specify size (default: None)
    xscale=float    xs=float    Rescale x axis (default: 1)
    yscale=float    ys=float    Rescale y axis (default: 1)
    hscale=float    hs=float    Rescale hue (default: 1)
    sscale=float    ss=float    Rescale style (default: 1)
Figure
    figsize=tuple   fs=tuple    Change figsize (default: '6,3')
    xlabel=str      xl=str      Change x axis label (default: x)
    ylabel=str      yl=str      Change y axis label (default: y)
    ltitle=str      lt=str      Change legend title (default: automatic)
    axes=str        ax=str      Change axes style (default: 'whitegrid')
    context=str     cx=str      Scale plot elements (default: 'notebook')
    logx=bool       lx=bool     Enable/disable log for x-axis (default: False)
    logy=bool       ly=bool     Enable/disable log for y-axis (default: False)
    bbox=bool       bb=bool     Enable/disable bbox for legend (default: True)
    xlim=tuple                  Change xlim (default: full range)
    ylim=tuple                  Change ylim (default: full range)
Drawing
    width=float     w=float     Change marker or line width (default: Depends)
    alpha=float     a=float     Change alpha (default: 0.5)
    palette=list    c=list      Palette, accepts cubehelix (default: 'crest')
    ptype=str       pt=str      Change the plot type (default: 'line')
    ci=float                    Change confidence interval size (default: 95)
    stat=str                    Change stat/estimator (default: Depends)
    bins=int                    Change number of bins/levels (default: 10)
    fill=bool                   Enable/disable fill (default: Depends)
    multiple=str                Change multiple behavior (default: 'layer')
File
    filetype=str    ft=str      Change filetype (default: 'svg')
    filename=str    fn=str      Custom filename (default: automatic)

Yeah, well, it's extremely jenky software, so do temper your expectations. I recommend checking the logfile to make sure you have set the args and kwargs correctly and chosen the right data ingest type. Remember that failing to specify additional plot dimensions (i.e. hue, size, and style) will cause your data to be flattened to two dimensions. The logfile also includes a representation of the internal DataFrame, which you can check for oddities.

You can also post an issue on the GitHub page. If you do so, please include the input file, log file, and any error messages (printed to stderr).

Each new plot function should be defined in a different file in the plot_functions directory. This file must have a function called plot with two mandatory arguments: df, the input Pandas DataFrame, and kwargs, which is a list of additional arguments passed to the function. This file must also have a function called usage with no arguments.

# Other function defs here

def usage():
    print('Interesting things about this program')

def plot(df, kwargs):
    sns.lineplot(...)

It is also possible to call other plot functions. For example, using replot is a convenient way to draw plots without re-parsing the kwargs. When there are duplicate kwarg definitions, the last one is used.

from plot_functions import replot

df_new = # Some DataFrame transformations here
kwargs += ['hue=my_new_metric', 'ptype=scatter']

replot.plot(df_new, kwargs)

Each plot function will also be given the kwarg time, which is an execution timestamp in ISO format generated by the main cadence_plot.py script.

It is recommended, though not required, to expect each additional argument to be of the form key=value, which makes parsing the kwargs trivial. For example:

./cadence_plot.py replot path/to/data.csv filetype=png logx=true hue=Vov

kwargs = ['filetype=png', 'logx=true', 'hue=Vov']

param = dict(
    # Default values
    )

for arg in kwargs:
    key, value = arg.split('=')
    param[key] = value