spateo.plotting.static.dotplot¶
Dotplot class adapted from https://github.com/scverse/scanpy with modifications for suitability to cell-cell communication and interaction analyses
Development notes: some of the methods mention dendrograms/other extra plots and there is currently no capability to generate those- coming in future update…additions that will have to be made: functions for plot_dendrogram, plot_totals, additional if condition in make_figure()…
Classes¶
Functions¶
|
For the purposes of dot plotting, converts the information given in AnnData object to a dataframe in which the |
|
Initialize grid layout to place subplots within a figure environment |
|
Makes a dot plot of the expression values of var_names. For each var_name and each groupby category a dot |
Module Contents¶
- spateo.plotting.static.dotplot.adata_to_frame(adata: anndata.AnnData, var_names: Sequence[str], cat_key: str | Sequence[str], num_categories: int = 7, layer: None | str = None, gene_symbols_key: None | str = None)[source]¶
For the purposes of dot plotting, converts the information given in AnnData object to a dataframe in which the row names are categories defined by groups and column names correspond to variable names.
- Parameters:
- adata
class anndata.AnnData
- var_names
Should be a subset of adata.var_names
- cat_key
The key(s) in .obs of the grouping to consider. Should be a categorical observation; if not, will be subdivided into ‘num_categories’.
- num_categories
Only used if groupby observation is not categorical. This value determines the number of groups into which the groupby observation should be subdivided.
- layer
Key in .layers specifying layer to use. If not given, will use .X.
- gene_symbols_key
Key in .var containing gene symbols
- spateo.plotting.static.dotplot.make_grid_spec(ax_or_figsize: Tuple[int, int] | matplotlib.axes.Axes, nrows: int, ncols: int, wspace: float | None = None, hspace: float | None = None, width_ratios: Sequence[float] | None = None, height_ratios: Sequence[float] | None = None) Tuple[matplotlib.figure.Figure, matplotlib.gridspec.GridSpecBase] [source]¶
Initialize grid layout to place subplots within a figure environment
- Parameters:
- ax_or_figsize
Either already-existing ax object or the width and height to create a figure window
- nrows
Number of rows in the grid
- ncols
Number of columns in the grid
- wspace
The amount of width reserved for space between subplots, expressed as a fraction of the average axis width
- hspace
The amount of height reserved for space between subplots, expressed as a fraction of the average axis height
- width_ratios
Defines the relative widths of the columns. Each column gets a relative width of width_ratios[i] / sum( width_ratios). If not given, all columns will have the same width.
- height_ratios
Defines the relative heights of the rows. Each row gets a relative width of height_ratios[i] / sum( height_ratios). If not given, all columns will have the same width.
- Returns:
Instantiated Figure object gs: Instantiated gridspec object
- Return type:
fig
- class spateo.plotting.static.dotplot.Dotplot(adata: anndata.AnnData, var_names: Sequence[str], cat_key: str | Sequence[str], num_categories: int = 7, prescale_adata: bool = False, categories_order: None | Sequence[str] = None, title: None | str = None, figsize: None | Tuple[float, float] = None, gene_symbols_key: None | str = None, var_group_positions: None | Sequence[Tuple[int, int]] = None, var_group_labels: None | Sequence[str] = None, var_group_rotation: None | float = None, layer: None | str = None, expression_cutoff: float = 0.0, mean_only_expressed: bool = False, standard_scale: Literal['var', 'group'] = None, dot_color_df: pandas.DataFrame | None = None, dot_size_df: pandas.DataFrame | None = None, ax: None | matplotlib.axes.Axes = None, vmin: None | float = None, vmax: None | float = None, vcenter: None | float = None, norm: matplotlib.colors.Normalize | None = None, **kwargs)[source]¶
Simultaneous visualization of two variates that are encoded by the dot size and the dot color. Size usually represents the fraction of samples that have non-zero values, and color usually represents the magnitude of the value.
- Parameters:
- adata
class anndata.AnnData
- var_names
Should be a subset of adata.var_names
- cat_key
The key(s) in .obs of the grouping to consider. Should be a categorical observation; if not, will be subdivided into ‘num_categories’.
- num_categories
Only used if groupby observation is not categorical. This value determines the number of groups into which the groupby observation should be subdivided.
- categories_order
Sets order of categories given by ‘cat_key’ along the plotting axis
- title
Sets title for figure window
- figsize
The width and height of a figure
- gene_symbols_key
Key in .var containing gene symbols
- var_group_positions
Each item in the list should contain the start and end position that the bracket should cover. Eg. [(0, 4), (5, 8)] means that there are two brackets, one for the var_names in positions 0-4 and other for positions 5-8.
- var_group_labels
List of group labels for the variable names (e.g. can group var_names in positions 0-4 as being “group A”)
- var_group_rotation
Rotation in degrees of the variable name labels. If not given, small labels (<4 characters) are not rotated, but otherwise labels are rotated 90 degrees.
- layer
Key in .layers specifying layer to use. If not given, will use .X.
- expression_cutoff
Used for binarizing feature expression- feature is considered to be expressed only if the expression value is greater than this threshold
- mean_only_expressed
If True, gene expression is averaged only over the cells expressing the given features
- standard_scale
Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum. ‘val’ or ‘group’ is used to specify whether this should be done over variables or groups.
- dot_color_df
Pre-prepared dataframe with features as indices, categories as columns, and indices corresponding to color intensities
- dot_size_df
Pre-prepared dataframe with features as indices, categories as columns, and indices corresponding to dot sizes
- ax
Can be used to provide pre-existing plotting axis
- vmin
The data value that defines 0.0 in the normalization. Defaults to the min value of the dataset.
- vmax
The data value that defines 1.0 in the normalization. Defaults to the the max value of the dataset.
- vcenter
The data value that defines 0.5 in the normalization
- norm
Optional already-initialized normalizing object that scales data, typically into the interval [0, 1], for the purposes of mapping to color intensities for plotting. Do not pass both ‘norm’ and ‘vmin’/’vmax’, etc.
- **kwargs
Additional arguments passed to matplotlib.pyplot.scatter()
- default_size_legend_title = Multiline-String[source]¶
Show Value
"""Fraction of cells in group (%)"""
- swap_axes()[source]¶
Modifies variables to flip x- and y-axes of dotplot.
By default, the x axis contains ‘var_names’ (e.g. genes) and the y axis the groupby categories. By setting ‘swap_axes’ the x-axis becomes the categories and the y-axis becomes the variable names.
- add_dendrogram(show: bool = True, dendrogram_key: None | str = None, size: float = 0.8)[source]¶
Show dendrogram based on the hierarchical clustering between the cat_key categories. Categories are reordered to match the dendrogram order.
The dendrogram information is computed using
utils.dendrogram()
within Spateo. If utils.dendrogram has not been called previously the function is called with default parameters here.The dendrogram is by default shown on the right side of the plot or on top if the axes are swapped.
- Parameters:
- show
Boolean to turn on (True) or off (False) ‘add_dendrogram’
- dendrogram_key
Needed if :func utils.dendrogram saved the dendrogram using a key different than the default name.
- size
Size of the dendrogram. Corresponds to width when dendrogram shown on the right of the plot,
- matplotlib or height when shown on top. The unit is the same as in
- style(cmap: str = default_colormap, color_on: Literal['dot', 'square'] | None = default_color_on, dot_max: float | None = default_dot_max, dot_min: float | None = default_dot_min, smallest_dot: float | None = default_smallest_dot, largest_dot: float | None = default_largest_dot, dot_edge_color: float | None = default_dot_edgecolor, dot_edge_lw: float | None = default_dot_edgelw, size_exponent: float | None = default_size_exponent, grid: float | None = False, x_padding: float | None = default_plot_x_padding, y_padding: float | None = default_plot_y_padding)[source]¶
Modifies visual aspects of the dot plot
Args: cmap: Name of Matplotlib color map to use color_on: Options are ‘dot’ or ‘square’. By default the colormap is applied to the color of the dot,
but ‘square’ changes this to be applied to a square region behind the dot, in which case the dot becomes transparent with only the edge of the dot shown.
- dot_max: If none, the maximum dot size is set to the maximum fraction value found (e.g. 0.6). If given,
the value should be a number between 0 and 1. All fractions larger than dot_max are clipped to this value.
- dot_min: If none, the minimum dot size is set to 0. If given, the value should be a number between 0 and 1.
All fractions smaller than dot_min are clipped to this value.
smallest_dot: If none, the smallest dot has size 0. All expression fractions with dot_min are plotted with this size. largest_dot: If none, the largest dot has size 200. All expression fractions with dot_max are plotted with this size. dot_edge_color: Only used if ‘color_on’ is ‘square’. Sets dot edge color dot_edge_lw: Only used if ‘color_on’ is ‘square’. Sets dot edge line width size_exponent: Dot size is computed as:
fraction ** size exponent
and is afterwards scaled to match the ‘smallest_dot’ and ‘largest_dot’ size parameters. Using a different size exponent changes the relative sizes of the dots to each other.
- grid: Set to true to show grid lines. By default grid lines are not shown. Further configuration of the grid
lines can be achieved directly on the returned ax.
- x_padding: Space between the plot left/right borders and the dots center. A unit is the distance between the x
ticks. Only applied when ‘color_on’ = ‘dot’
- y_padding: Space between the plot top/bottom borders and the dots center. A unit is the distance between the x
ticks. Only applied when ‘color_on’ = ‘dot’
- Returns:
self (instance of class DotPlot)
Example
Creating a modified dot plot (w/ a loaded AnnData object given name ‘adata’): markers = [‘C1QA’, ‘PSAP’, ‘CD79A’, ‘CD79B’, ‘CST3’, ‘LYZ’] st.pl.DotPlot(adata, var_names=markers, cat_key=’Celltype’).style(cmap=’RdBu_r’, color_on=’square’).show()
- legend(show: bool = True, show_size_legend: bool = True, show_colorbar: bool = True, size_title: str | None = default_size_legend_title, colorbar_title: str | None = default_color_legend_title, base: int | None = default_base, num_colorbar_ticks: int | None = default_num_colorbar_ticks, num_size_legend_dots: int | None = default_num_size_legend_dots, width: float | None = default_legends_width)[source]¶
Configures colorbar and other legends for dotplot
- Parameters:
- show
Set to False to hide the default plot of the legends. This sets the legend width to zero, which will result in a wider main plot.
- show_size_legend
Set to False to hide the dot size legend
- show_colorbar
Set to False to hide the colorbar legend
- size_title
Title for the dot size legend. Use ‘n’ to add line breaks. Will be shown at the top of the dot size legend box
- colorbar_title
Title for the color bar. Use ‘n’ to add line breaks. Will be shown at the top of the color bar.
- base
To determine the size of each “benchmark” dot in the size legend, will use a logscale; this parameter sets the base of that scale.
- num_colorbar_ticks
Number of ticks for the colorbar
- num_size_legend_dots
Number of “benchmark” dots to include in the dot size legend
- width
Width of the legends area. The unit is the same as in matplotlib (inches)
- Returns:
self (instance of class DotPlot)
Example
Setting the colorbar title (w/ a loaded AnnData object given name ‘adata’): markers = {{‘T-cell’: ‘CD3D’, ‘B-cell’: ‘CD79A’, ‘myeloid’: ‘CST3’}} dp = st.pl.DotPlot(adata, markers, groupby=’Celltype’) dp.legend(colorbar_title=’log(UMI counts + 1)’).show()
- _plot_size_legend(size_legend_ax: matplotlib.axes.Axes)[source]¶
Given axis object, generates dot size legend and displays on plot
For the dot size “benchmarks” on the legend, adjust the difference in size between consecutive benchmarks based on how different ‘self.dot_max’ and ‘self.dot_min’ are.
- _plot_colorbar(color_legend_ax: matplotlib.axes.Axes, normalize: None | matplotlib.colors.Normalize = None)[source]¶
Given axis object, plots a horizontal colorbar
- Parameters:
- color_legend_ax
mpl.axes.Axes object Matplotlib axis object to plot onto
- normalize
mpl.colors.Normalize object The normalizing object that scales data, typically into the interval [0, 1], for the purposes of mapping to color intensities for plotting. If None, norm defaults to a colors.Normalize object and automatically scales based on min/max values in the data.
- _plot_legend(legend_ax: matplotlib.axes.Axes, return_ax_dict: dict, normalize: None | matplotlib.colors.Normalize = None)[source]¶
Organizes the size legend and color legend.
The structure for the legends is: First row: Empty space of variable size to control the size of the other rows Second row: Dot size legend Third row: Spacer to prevent titles/labels of the color and dot size legends overlapping Fourth row: Colorbar
- Parameters:
- legend_ax
mpl.axes.Axes Matplotlib axis object to plot onto
- return_ax_dict
- _mainplot(ax: matplotlib.axes.Axes)[source]¶
- static _dotplot(dot_size: pandas.DataFrame, dot_color: pandas.DataFrame, dot_ax: matplotlib.axes.Axes, cmap: str = 'Reds', color_on: str = 'dot', y_label: None | str = None, dot_max: None | float = None, dot_min: None | float = None, standard_scale: None | Literal['var', 'group'] = None, smallest_dot: float = 0.0, largest_dot: float = 200, size_exponent: float = 2, edge_color: None | str = None, edge_lw: None | float = None, grid: bool = False, x_padding: float = 0.8, y_padding: float = 1.0, vmin: None | float = None, vmax: None | float = None, vcenter: None | float = None, norm: None | matplotlib.colors.Normalize = None, **kwargs)[source]¶
Generate a dotplot given the axis object and two dataframes containing the dot size and dot color. The indices and columns of the dataframes are used to label the resultant image.
The dots are plotted using
matplotlib.pyplot.scatter()
. Thus, additional arguments can be passed.- Parameters:
- dot_size
pd.DataFrame Data frame containing the dot_size.
- dot_color
pd.DataFrame Data frame containing the dot_color, should have the same shape, columns and indices as dot_size.
- dot_ax
matplotlib Axes object Axis to plot figure onto
- cmap
str, default ‘Reds’ String denoting matplotlib color map
- color_on
str, default ‘dot’ Options: ‘dot’ or ‘square’. By default the colormap is applied to the color of the dot. Optionally, the colormap can be applied to an square behind the dot, in which case the dot is transparent and only the edge is shown.
- y_label
optional str Label for y-axis
- dot_max
optional float If none, the maximum dot size is set to the maximum fraction value found (e.g. 0.6). If given, the value should be a number between 0 and 1. All fractions larger than dot_max are clipped to this value.
- dot_min
optional float If none, the minimum dot size is set to 0. If given, the value should be a number between 0 and 1. All fractions smaller than dot_min are clipped to this value.
- standard_scale
‘None’, ‘val’, or ‘group’ Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum. ‘val’ or ‘group’ is used to specify whether this should be done over variables or groups.
- smallest_dot
optional float If none, the smallest dot has size 0. All expression fractions with dot_min are plotted with this size.
- largest_dot
optional float If none, the largest dot has size 200. All expression fractions with dot_max are plotted with this size.
- size_exponent
float, default 1.5 Dot size is computed as:
fraction ** size exponent
and is afterwards scaled to match the ‘smallest_dot’ and ‘largest_dot’ size parameters. Using a different size exponent changes the relative sizes of the dots to each other.
- edge_color
str, default ‘black’ Only used if ‘color_on’ is ‘square’. Sets dot edge color
- edge_lw
float, default 0.2 Only used if ‘color_on’ is ‘square’. Sets dot edge line width
- grid
bool, default False Set to true to show grid lines. By default grid lines are not shown. Further configuration of the grid lines can be achieved directly on the returned ax.
- x_padding
float, default 0.8 Space between the plot left/right borders and the dots center. A unit is the distance between the x ticks. Only applied when ‘color_on’ = ‘dot’
- y_padding
float, default 1.0 Space between the plot top/bottom borders and the dots center. A unit is the distance between the x ticks. Only applied when ‘color_on’ = ‘dot’
- vmin
optional float The data value that defines 0.0 in the normalization. Defaults to the min value of the dataset.
- vmax
optional float The data value that defines 1.0 in the normalization. Defaults to the the max value of the dataset.
- vcenter
optional float The data value that defines 0.5 in the normalization
- norm
optional matplotlib.colors.Normalize object Optional already-initialized normalizing object that scales data, typically into the interval [0, 1], for the purposes of mapping to color intensities for plotting. Do not pass both ‘norm’ and ‘vmin’/’vmax’, etc.
- **kwargs
Additional arguments passed to matplotlib.pyplot.scatter
- Returns:
- matplotlib.colors.Normalize object
The normalizing object that scales data, typically into the interval [0, 1], for the purposes of mapping to color intensities for plotting.
- dot_minfloat
The minimum dot size represented on the plot, given as a fration of the maximum value in the data
- dot_maxfloat
The maximum dot size represented on the plot, given as a fraction of the maximum value in the data
- Return type:
normalize
- reorder_categories_after_dendrogram(dendrogram_key)[source]¶
Reorders categorical observations along plot axis based on dendrogram results.
The function checks if a dendrogram has already been precomputed. If not, utils.dendrogram is run with default parameters.
The results found in .uns[dendrogram_key] are used to reorder var_group_labels and var_group_positions.
- static _plot_var_groups_brackets(gene_groups_ax: matplotlib.axes.Axes, group_positions: Iterable[Tuple[int, int]], group_labels: Sequence[str], left_adjustment: float = -0.3, right_adjustment: float = 0.3, rotation: float | None = None, orientation: Literal['top', 'right'] = 'top')[source]¶
Draws brackets that represent groups of features on the given axis.
The ‘gene_groups_ax’ Axes object should share the x-axis/y-axis (depending on the axis along which the features are plotted) with the main plot axis. For example, in instantiation: gene_groups_ax = fig.add_subplot(axs[0,0], sharex=dot_ax)
- Parameters:
- gene_groups_ax
matplotlib.axes.Axes object Axis to plot on, should correspond to the axis of the main plot on which the feature names/feature ticks are drawn
- group_positions
list of tuples of form (int, int) Each item in the list, should contain the start and end position that the bracket should cover. Eg. [(0, 4), (5, 8)] means that there are two brackets, one for the var_names (eg genes) in positions 0-4 and the second for positions 5-8.
- group_labels
list of str List of labels for the feature groups
- left_adjustment
float, default -0.3 Adjustment to plot the bracket start slightly before or after the first feature position. If the value is negative the start is moved before.
- right_adjustment
float, default 0.3 Adjustment to plot the bracket end slightly before or after the first feature position. If the value is negative the end is moved before, if positive the end is moved after.
- rotation
optional float In degrees, angle of rotation for the labels. If not given, small labels (<4 characters) are not rotated, otherwise, they are rotated 90 degrees
- orientation
str Options: ‘top’ or ‘right’ to set the location of the brackets
- _update_var_groups()[source]¶
Checks if var_names is a dict. Is this is the cases, then set the correct values for var_group_labels and var_group_positions
Updates var_names, var_group_labels, var_group_positions
- make_figure()[source]¶
Renders the image, but does not call
matplotlib.pyplot.show()
.
- class spateo.plotting.static.dotplot.CCDotplot(minn: float, delta: float, alpha: float, *args, **kwargs)[source]¶
Bases:
Dotplot
Because of the often much smaller values dealt with in cell-cell communication inference, this class creates a modified legend.
- Parameters:
- delta
optional float Distance between the largest value to consider and the smallest value to consider (see ‘minn’ parameter below)
- minn
optional float For the dot size legend, sets the value corresponding to the smallest dot on the legend
- alpha
optional float Significance threshold. If given, all elements w/ p-values <= ‘alpha’ will be marked by rings instead of dots.
- *args
Positional arguments to initialize :class Dotplot
- **kwargs
Keyword arguments to initialize :class Dotplot
- _plot_size_legend(size_legend_ax: matplotlib.axes.Axes)[source]¶
Given axis object, generates dot size legend and displays on plot
Overwrites the default :func plot_size_legend for :class Dotplot
- spateo.plotting.static.dotplot.dotplot(adata: anndata.AnnData, var_names: Sequence[str], cat_key: str | Sequence[str], num_categories: int = 7, cell_cell_dp: bool = False, delta: None | float = None, minn: None | float = None, alpha: None | float = None, prescale_adata: bool = False, expression_cutoff: float = 0.0, mean_only_expressed: bool = False, cmap: str = 'Reds', dot_max: float = Dotplot.default_dot_max, dot_min: float = Dotplot.default_dot_min, standard_scale: Literal['var', 'group'] = None, smallest_dot: float = Dotplot.default_smallest_dot, largest_dot: float = Dotplot.default_largest_dot, title: str = None, colorbar_title: str = Dotplot.default_color_legend_title, size_title: str = Dotplot.default_size_legend_title, figsize: None | Tuple[float, float] = None, dendrogram: bool | str = False, gene_symbols_key: None | str = None, var_group_positions: None | Sequence[Tuple[int, int]] = None, var_group_labels: None | Sequence[str] = None, var_group_rotation: None | float = None, layer: None | str = None, swap_axes: bool = False, dot_color_df: None | pandas.DataFrame = None, save_show_or_return: Literal['save', 'show', 'return', 'both', 'all'] = 'save', save_kwargs: dict = {}, ax: None | matplotlib.axes.Axes = None, vmin: None | float = None, vmax: None | float = None, vcenter: None | float = None, norm: None | matplotlib.colors.Normalize = None, **kwargs)[source]¶
Makes a dot plot of the expression values of var_names. For each var_name and each groupby category a dot is plotted. Each dot represents two values: mean expression within each category (visualized by color) and fraction of cells expressing the var_name in the category (visualized by the size of the dot). If groupby is not given, the dotplot assumes that all data belongs to a single category.
- Parameters:
- adata
object of class anndata.AnnData
- var_names
Should be a subset of adata.var_names
- cat_key
The key(s) in .obs of the grouping to consider. Should be a categorical observation; if not, will be subdivided into ‘num_categories’.
- num_categories
Only used if groupby observation is not categorical. This value determines the number of groups into which the groupby observation should be subdivided.
- cell_cell_dp
Set True to initialize specialized cell-cell dotplot instead of gene expression dotplot
- delta
Only used if ‘cell_cell_dp’ is True- distance between the largest value to consider and the smallest value to consider (see ‘minn’ parameter below)
- minn
Only used if ‘cell_cell_dp’ is True- for the dot size legend, sets the value corresponding to the smallest dot on the legend
- alpha
Only used if ‘cell_cell_dp’ is True- significance threshold. If given, all elements w/ p-values <= ‘alpha’ will be marked by rings instead of dots.
- prescale_adata
Set True to indicate that AnnData object should be scaled- if so, will use ‘delta’ and ‘minn’ to do so. If False, will proceed as though adata has already been processed as needed.
- expression_cutoff
Used for binarizing feature expression- feature is considered to be expressed only if the expression value is greater than this threshold
- mean_only_expressed
If True, gene expression is averaged only over the cells expressing the given features
- cmap
Name of Matplotlib color map to use
- dot_max
If none, the maximum dot size is set to the maximum fraction value found (e.g. 0.6). If given, the value should be a number between 0 and 1. All fractions larger than dot_max are clipped to this value.
- dot_min
If none, the minimum dot size is set to 0. If given, the value should be a number between 0 and 1. All fractions smaller than dot_min are clipped to this value.
- standard_scale
Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum. ‘val’ or ‘group’ is used to specify whether this should be done over variables or groups.
- smallest_dot
If None, the smallest dot has size 0. All expression fractions with dot_min are plotted with this size.
- largest_dot
If None, the largest dot has size 200. All expression fractions with dot_max are plotted with this size.
- title
Title for the entire plot
- colorbar_title
Title for the color legend. If None will use generic default title
- size_title
Title for the dot size legend. If None will use generic default title
- figsize
Sets width and height of figure window
- dendrogram
If True, adds dendrogram to plot. Will do the same thing if string is given here, but will recompute dendrogram and save using this argument to set key in .uns.
- gene_symbols_key
Key in .var containing gene symbols
- var_group_positions
Each item in the list should contain the start and end position that the bracket should cover. Eg. [(0, 4), (5, 8)] means that there are two brackets, one for the var_names in positions 0-4 and other for positions 5-8
- var_group_labels
List of group labels for the variable names (e.g. can group var_names in positions 0-4 as being “group A”)
- var_group_rotation
Rotation in degrees of the variable name labels. If not given, small labels (<4 characters) are not rotated, but otherwise labels are rotated 90 degrees.
- layer
Key in .layers specifying layer to use. If not given, will use .X.
- swap_axes
Set True to switch what is plotted on the x- and y-axes
- dot_color_df
Pre-prepared dataframe with features as indices, categories as columns, and indices corresponding to color intensities
- save_show_or_return
Options: “save”, “show”, “return”, “both”, “all” - “both” for save and show
- save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. But to change any of these parameters, this dictionary can be used to do so.
- ax
Pre-initialized axis object to plot on
- vmin
The data value that defines 0.0 in the normalization. Defaults to the min value of the dataset.
- vmax
The data value that defines 1.0 in the normalization. Defaults to the the max value of the dataset.
- vcenter
The data value that defines 0.5 in the normalization
- norm
Optional already-initialized normalizing object that scales data, typically into the interval [0, 1], for the purposes of mapping to color intensities for plotting. Do not pass both ‘norm’ and ‘vmin’/’vmax’, etc.
- kwargs
Additional keyword arguments passed to
matplotlib.pyplot.scatter()
- Returns:
Instantiated Figure object- only if ‘return’ is True axes: Instantiated Axes object- only if ‘return’ is True
- Return type:
fig