cytonormpy.pl.histogram

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cytonormpy.pl.histogram#

cytonormpy.pl.histogram(cnp, file_name, x_channel=None, x_scale='linear', y_scale='linear', xlim=None, ylim=None, linthresh=500, subsample=None, display_reference=True, grid=None, grid_n_cols=None, channels=None, vary_textures=False, figsize=None, ax=None, return_fig=False, show=True, save=None, **kwargs)#

Histogram visualization.

Parameters:

  • file_name (str) – The file name of the file that is supposed to be plotted.

  • x_channel (Optional[str]) – The channel plotted on the x-axis.

  • x_scale (Literal['biex', 'log', 'linear']) – The scale type of the x-axis. Can be one of biex, linear or log. Defaults to biex.

  • y_scale (Literal['biex', 'log', 'linear']) – The scale type of the y-axis. Can be one of biex, linear or log. Defaults to biex.

  • legend_labels – The labels displayed in the legend.

  • linthresh (float) – The value to switch from a linear to a log axis. Ignored if neither x- nor y-scale are biex.

  • subsample (Optional[int]) – A number of events to subsample to. Can prevent overcrowding of the plot.

  • display_reference (bool) – Whether to display the reference data from that batch as well. Defaults to True.

  • grid (Optional[Literal['channels']]) – Can be’channels’. Will plot a grid where each channel gets its own plot. A file_name has to be provided.

  • channels (Optional[Union`[:py:class:`list[str], str]]) – Optional. Can be used to select one or more channels that will be plotted in the grid.

  • vary_textures (bool) – If True, apply different line styles per origin category.

  • ax (Optional[ndarray[Sequence[Sequence[Axes]], dtype[object_]], Axes]]) – A Matplotlib Axes to plot into.

  • return_fig (bool) – Returns the figure. Defaults to False.

  • show (bool) – Whether to show the figure.

  • save (Optional[str]) – A string specifying a file path. Defaults to None, where no image is saved.

  • kwargs – keyword arguments ultimately passed to sns.scatterplot.

Return type:

If show==False, a Axes.

Examples

import cytonormpy as cnp

cn = cnp.example_cytonorm()
cnp.pl.histogram(cn,
                 cn._datahandler.metadata.validation_file_names[0],
                 x_channel = "Ho165Di",
                 x_scale = "linear",
                 y_scale = "linear",
                 figsize = (4,4))
../_images/cytonormpy-pl-histogram-1.png

Note

If you want additional separation of the individual point classes, you can pass ‘vary_textures=True’.

import cytonormpy as cnp

cn = cnp.example_cytonorm()
cnp.pl.histogram(cn,
                 cn._datahandler.metadata.validation_file_names[0],
                 x_channel = "Ho165Di",
                 x_scale = "linear",
                 y_scale = "linear",
                 figsize = (4,4),
                 vary_textures = True)
../_images/cytonormpy-pl-histogram-2.png
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