ScatterPPV#

class mufasa.visualization.scatter_3D.ScatterPPV(parafile, fittype, vrange=None, verr_thres=5, meta_model=None)[source]#

Bases: object

A class to plot the fitted parameters in 3D scatter plots. Most of the data is stored in a pandas DataFrame.

Parameters:

  • parafile (str) – Path to the .fits file containing the MUFASA generated parameter maps.

  • fittype (str) – The name of the fit model, e.g., “nh3_multi_v” or “n2hp_multi_v”.

  • vrange (tuple of float, optional) – Velocity range to clip the data (in km/s). Data outside this range is excluded. Default is None.

  • verr_thres (float, optional) – Velocity error threshold (in km/s) to filter out data with higher errors. Data with a velocity error greater than this threshold is excluded. Default is 5.

Examples

Initialize the ScatterPPV object and plot the position-position-velocity (PPV) scatter plot:

>>> sc = scatter_3D.ScatterPPV("path/to/fname.fits", fittype="nh3_multi_v")
>>> sc.plot_ppv(savename='monR2.html', vel_scale=0.5)
add_peakI()[source]#

Calculate and add a peak intensity value for each model point in the DataFrame.

Parameters:

nu (float, optional) – Reference frequency (in GHz) to estimate the peak intensity. If not provided, defaults to the rest_freq attribute.

Return type:

None

add_wcs_del(ra_ref=None, dec_ref=None, unit='arcmin')[source]#

Calculate relative RA & Dec coordinates and add them to the DataFrame as columns.

Parameters:

  • ra_ref (float, optional) – Reference RA value to calculate relative RA. If not provided, uses the minimum RA in the data.

  • dec_ref (float, optional) – Reference Dec value to calculate relative Dec. If not provided, uses the minimum Dec in the data.

  • unit ({'arcmin', 'arcsec'}, optional) – Units for delta RA & Dec. Use ‘arcmin’ to plot in arcminutes or ‘arcsec’ for arcseconds. Default is ‘arcmin’.

Return type:

None

plot_ppv(label_key='peakT', vel_scale=0.8, xyunit='arcmin', savename=None, **kwargs)[source]#

Plot the fitted model in position-position-velocity (PPV) space as a 3D scatter plot.

Points in the PPV plot are colored based on a specified key (e.g., intensity values) from the DataFrame. The velocity axis can be scaled relative to the spatial axes, and units for the x and y axes can be customized.

Parameters:

  • label_key (str, default='peakT') – Column name in the DataFrame used for coloring data points. For example, ‘peakT’ for peak intensity or clustering labels.

  • vel_scale (float, default=0.8) – Scaling factor for the velocity axis (z-axis) relative to the spatial axes. The x-axis is normalized to 1.

  • xyunit ({'arcmin', 'pix'}, default='arcmin') – Units for the x and y coordinates: - ‘arcmin’: Plot relative RA and Dec in arcminutes. - ‘pix’: Plot coordinates in pixels.

  • savename (str, default=None) – File path to save the generated plot as an HTML file. If None, the plot is not saved.

  • **kwargs (dict) –

    Additional keyword arguments passed to the scatter_3D_df function. Useful options include:

    auto_open_htmlbool, default=True

    If True, automatically open the saved HTML file in a browser.

    mask_dfpandas.Series or None, default=None

    Boolean mask to filter the DataFrame before plotting.

  • kwargs (dict) – Additional options for customizing the scatter plot, such as color mapping and opacity scaling.

Returns:

fig – A 3D scatter plot figure representing the PPV data.

Return type:

plotly.graph_objs.Figure

Notes

  • If label_key is “peakT”, default color mapping (“magma_r”) and opacity ranges are applied automatically.

  • The velocity axis range is derived from the 1st and 99th percentiles of the “vlsr” column, with additional padding.

  • Color scaling uses the 1st and 99th percentiles of the column specified by label_key.

Examples

>>> model.plot_ppv(label_key='peakT', vel_scale=0.9, xyunit='pix', savename='ppv_plot.html')
>>> model.plot_ppv(label_key='cluster_label', auto_open_html=False, mask_df=mask)

See also

scatter_3D_df

Function used internally for generating the scatter plot.

set_meta_model(meta_model)[source]#