neuralib.plot.plot

neuralib.plot.plot.dotplot(xlabel, ylabel, values, *, scale='area', max_marker_size=None, size_title=None, size_legend_num=None, size_legend_as_int=True, with_color=False, cmap='Reds', colorbar_title=None, norm=None, cbar_vmin=None, cbar_vmax=None, figure_title=None, figure_output=None, ax=None, **kwargs)[source]

Plot values as dots, with the option also in colormap

Dimension parameters:

X = number of x label

Y = number of y label

Parameters:

  • xlabel (ArrayLikeStr) – String arraylike. ArrayLike[str, X]

  • ylabel (ArrayLikeStr) – String arraylike. ArrayLike[str, X]

  • values (np.ndarray) – 2D value array. ArrayLike[str, [X, Y]]

  • scale (Literal['area', 'radius']) – Dot size representation. {‘area’, ‘radius’}

  • max_marker_size (float | None) – Marker size for the max value

  • size_title (str | None) – Size_title in the size legend

  • size_legend_num (int | None) – Number of legend to be shown

  • size_legend_as_int (bool) – Size legend show only Int

  • with_color (bool) – If dot with colormap

  • cmap (mcolors.Colormap) – Colormap

  • colorbar_title (str | None) – Title of the colorbar

  • norm (mcolors.Normalize | None) – Colorbar Normalize

  • cbar_vmin (float | None) – Value min for the colorbar

  • cbar_vmax (float | None) – Value nax for the colorbar

  • figure_title (str | None) – Figure title

  • figure_output (PathLike | None) – Figure save output path

  • ax (Axes | None) – If existing axes Axes

  • kwargs – additional arguments to ax.scatter()

neuralib.plot.plot.scatter_histogram(x, y, bins=15, *, linear_reg=True, output=None, **kwargs)[source]

plot the linear correlation scatter and histogram between two variables

Dimension parameters:

N = number of sample points

Parameters:

  • x (np.ndarray) – numerical array x. Array[float, N]

  • y (np.ndarray) – numerical array y. Array[float, N]

  • bins (int | Sequence[float] | str) – passed to numpy.histogram()

  • linear_reg (bool) – If show correlation coefficient

  • output (Path | None) – Figure save output

  • kwargs – additional args pass through ax.set()

Returns:

neuralib.plot.plot.scatter_binx_plot(ax, x, y, bins=10, *, order=1, linear_reg=True, bin_func='median', **kwargs)[source]

Regression to see the relationship between x and y

Dimension parameters:

N = number of sample points

Parameters:

  • ax (Axes) – Axes

  • x (np.ndarray) – Numerical array. Array[float, N]

  • y (np.ndarray) – Numerical array. Array[float, N]

  • bins (int | Sequence[float] | str) – passed to numpy.histogram()

  • order (int) – If order is greater than 1, use numpy.polyfit to estimate a polynomial regression

  • linear_reg (bool) – Show linear correlation coefficient

  • bin_func (Literal['median', 'mean']) – Literal[‘median’, ‘mean’]. default is median

  • kwargs – additional args passed to ax.set

Returns:

neuralib.plot.plot.hist_cutoff(ax, values, cutoff, bins=30, *, mask=None, **kwargs)[source]

Plot the histogram with a cutoff value

Parameters:

  • ax (Axes) – Axes

  • values (np.ndarray) – 1d array. Array[float, N]

  • cutoff (float) – cutoff (threshold) value for the certain value, >= represents pass

  • bins (int) – passed to numpy.histogram()

  • mask (np.ndarray | None) – mask for value. i.e., cell selection. Array[bool, N]

  • kwargs – passed to ax.set

Return type:

None

neuralib.plot.plot.violin_boxplot(ax, data, x=None, y=None, hue=None, scatter_alpha=0.7, scatter_size=3, output=None, **kwargs)[source]

Plot the data with half violin together with boxes and scatters

Parameters:

  • ax (Axes) – Axes

  • data (DataFrame | dict | list[np.ndarray]) – Dataset for plotting

  • x (str | None) – Names of variables in data or vector data: x

  • y (str | None) – Names of variables in data or vector data: y

  • hue (str | None) – Names of variables in data or vector data: hue

  • scatter_alpha (float) – Scatter alpha for the sns.stripplot()

  • scatter_size (float) – Scatter size for the sns.stripplot()

  • output (PathLike | None) – Fig save output path

  • kwargs – Common args pass through sns.violinplot(), sns.boxplot() and sns.stripplot()

Returns:

Return type:

None

neuralib.plot.plot.grid_subplots(data, images_per_row, plot_func, *, dtype, hide_axis=True, sharex=False, sharey=False, title=None, figsize=None, output=None, **kwargs)[source]

Plots a sequence of subplots in a grid format

Example for plot xy grid:

>>> data = np.random.sample((30, 10, 2))
>>> grid_subplots(data, 5, 'plot', dtype='xy')

Example for plot img array grid

>>> data = np.random.sample((30, 10, 10))
>>> grid_subplots(data, 5, 'imshow', dtype='img', cmap='gray')
Parameters:

  • data (np.ndarray | list[np.ndarray]) – 3D Array containing the data to be plotted. For ‘xy’ dtype, the shape must be (N, (*, 2)). For ‘img’ dtype, the shape must be (N, (*img)). Accepted also list of 2D array different size

  • images_per_row (int) – Number of images per row in the subplot grid

  • plot_func (Callable | str) – Function or method name to be used for plotting. If a string is provided, it should be a valid method name of a matplotlib Axes object

  • dtype (Literal['xy', 'img']) – {‘xy’, ‘img’}. Type of data. ‘xy’ for (x, y) coordinate data, ‘img’ for image data

  • hide_axis (bool) – If True, hides the axes of the subplots

  • sharex (bool) – sharex acrross grid plots

  • sharey (bool) – sharey acrross grid plots

  • title (list[str] | None) – List of title foreach show in the subplot

  • figsize (tuple[int, int] | None) – Figure_size pass to plt.subplots()

  • output (PathLike | None) – Path to save the plot image. If None, displays the plot.

  • kwargs – Additional keyword arguments passed to the plotting function plot_func

Returns:

Return type:

None