Module `iaf.plot`

Plotting functions.

Sub-modules

iaf.plot.utils: Utility plotting functions.
iaf.plot.validation: Validation plotting functions.

Functions

def add_fit(fig: matplotlib.figure.Figure, ax: matplotlib.axes._axes.Axes, x: Union[numpy.ndarray, list], y_hat: Union[numpy.ndarray, list], a: Optional[None] = None, b: Optional[None] = None, sse: Optional[None] = None, model_for_legend: str = 'linear', legend_location: str = 'best', dpi: int = 150, out_file_name: Union[ForwardRef(None), pathlib.Path, str] = None) ‑> tuple

Add another fit to an existing plot_data() figure.

Parameters

fig : Figure: A matplotlib figure.
ax : Union[np.ndarray, list]: A matplotlib figure axis.
x : Union[np.ndarray, list]: List or array of independent values x.
y_hat : Union[np.ndarray, list] (Optional): List or array of predicted values y_hat.
a : Union[None, float] (Optional): Value of the slope of the predicted line (to be shown in the legend).
b : Union[None, float] (Optional): Value of the intercept of the predicted line (to be shown in the legend).
sse : Union[None, float] (Optional): Value of the sum of squared difference between y and y_hat (to be shown in the legend).
model_for_legend : str (Optional): Set the type of the model to be displayed (based on parameters a and b) in the legend. One of "linear", "exp", "log", "sqrt"; default is "linear".
legend_location : str (Optional): Location of the legend. By default, it is 'best'. See https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html
dpi : int (Optional): Resolution of the figure (please notice, this is set whether the figure is saved or not).
out_file_name : Union[None, Path, str] (Optional): Full file name to save the figure. The figure is only displayed if no file name is passed.

Returns

handles : tuple: Tuple with current figure and axes.

def imshow(img: numpy.ndarray, *, cmap: Union[ForwardRef(None), str, matplotlib.colors.ListedColormap] = None, auto_stretch: bool = False, clip_percentile: float = 0.0, ax: matplotlib.axes._axes.Axes = None, title: Optional[str] = None, title_font_size: Optional[int] = 10) ‑> None

Wrapper around matplotlib.pyplot.imshow that allows for toggling intensity stretching, hides axes, maximizes the canvas.

Parameters

img : np.ndarray: 2D image to be displayed. The image can be gray-value (2D array) or RGB. If RGB, it must be in YXC or YXCA format.
cmap : None|str|ListedColormap: Color map to be used (optional). Only applies to gray-value images and is ignored for RGB images.
auto_stretch : bool: Whether to auto-stretch intensities for visualisation (optional, default = False). Please notice that this only applies to images of type np.uint8 or np.uint16: float images will always be stretched.
clip_percentile : float (Optional, default = 0.0): Percentile to clip intensity in the low and high parts of the dynamic range. Ignored if auto_stretch is False. As for auto_stretch, this only applies to images of type np.uint8 and np.uint16.
ax : matplotlib.axes.Axes (Optional, default = None): Axis handle. Pass a valid axes handle to display the image there; if omitted, a new figure and a new set of axes will be created.
title : str: Title for current axes (optional).
title_font_size : int: Font size for the title of current axes (optional).

def plot_data(x: Union[numpy.ndarray, list], y: Union[numpy.ndarray, list], data_name: str = 'y', x2: Union[ForwardRef(None), numpy.ndarray, list] = None, y_hat: Union[ForwardRef(None), numpy.ndarray, list] = None, a: Optional[None] = None, b: Optional[None] = None, sse: Optional[None] = None, model_for_legend: str = 'linear', errors: Union[ForwardRef(None), numpy.ndarray, list] = None, errors_label: Optional[str] = None, legend_location: str = 'best', label_x: str = 'x', label_y: str = 'y', lim_x: Optional[None] = None, lim_y: Optional[None] = None, marker_size: int = 100, alpha: float = 0.75, split_series: bool = True, figure_size: tuple = (12, 8), dpi: int = 150, out_file_name: Union[ForwardRef(None), pathlib.Path, str] = None) ‑> tuple

Flexible plotting function for raw data (single or multiple series), fitted model (optional) and error bars (optional).

Parameters

x : Union[np.ndarray, list]: List or array of independent values x.
y : Union[np.ndarray, list]: List or array of dependent/target values y.
data_name : str (Optional, default = "Data"): Name of the data set to be displayed in the legend.
x2 : Union[None, np.ndarray, list] (Optional): List or array of independent values x; it is used to plot the predicted value y_hat. Omit if x2 is the same as x (in the case x is (m x n), x2 will be x[0, :]). If specified, it must be a (1 x n) array.
y_hat : Union[np.ndarray, list] (Optional): List or array of predicted values y_hat. If specified, it must be a (1 x n) array.
a : Union[None, float] (Optional): Value of the slope of the predicted line (to be shown in the legend).
b : Union[None, float] (Optional): Value of the intercept of the predicted line (to be shown in the legend).
sse : Union[None, float] (Optional): Value of the sum of squared difference between y and y_hat (to be shown in the legend).
alpha : float (Optional): Transparency (between 0.0 and 1.0) for the dots in the scatter plot.
split_series : bool (Optional): Set to True (default) to display different series as separate scatter plots with own color, or to False to have them all in one plot (and one color).
model_for_legend : str (Optional)

Set the type of the model to be displayed (based on parameters a and b) in the legend. One of "linear", "exp", "log", "sqrt"; default is "linear".

errors : Union[None, np.ndarray, list] (Optional): Errors to be plotted on the data. If specified, it must be a (1 x n) array.
errors_label : Union[None, str] = None: Name of the errors for the legend.
legend_location : str (Optional): Location of the legend. By default, it is 'best'. See https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html
label_x : str (Optional): Label of the x-axis (by default it is set to "x").
label_y : str (Optional): Label of the y-axis (by default it is set to "y").
lim_x : Union[None, tuple] (Optional): By default the extent of the data vector x defines the x-axis limit range. Explicitly set to a tuple (x_min, x_max) to override the x-axis limits. Either one of x_min or x_max can be None if current value should not be changed. In detail: If lim_x is None: The x-axis range will be defined by the limits of the data vector x. If lim_x is (None, None): The x-axis range will be defined by the limits of everything plotted. If lim_x is (0, None): The x-axis range will be 0 to the higher limit of everything plotted. If lim_x is (None, 100): The x-axis range will from the lower limit of everything plotted to 100. If lim_x is (0, 100): The x-axis range will go from 0 to 100.
lim_y : Union[None, tuple] (Optional): By default the extent of the data vector/array y defines the y-axis limit range. Explicitly set to a tuple (y_min, y_max) to override the y-axis limits. Either one of y_min or y_max can be None if current value should not be changed. In detail: If lim_y is None: The y-axis range will be defined by the limits of the data vector/array y. If lim_y is (None, None): The y-axis range will be defined by the limits of everything plotted. If lim_y is (0, None): The y-axis range will be 0 to the higher limit of everything plotted. If lim_y is (None, 100): The y-axis range will from the lower limit of everything plotted to 100. If lim_y is (0, 100): The y-axis range will go from 0 to 100.
marker_size : int (Optional): Size of the marker. By default, it is set to 100.
split_series : bool (Optional): Set to True (default) to display different series as separate scatter plots with own color, or to False to have them all in one plot (and one color).
figure_size : tuple (Optional): Size of the figure.
dpi : int (Optional): Resolution of the figure (please notice, this is set whether the figure is saved or not).
out_file_name : Union[None, Path, str] (Optional): Full file name to save the figure. The figure is only displayed if no file name is passed.

Returns

handles : tuple: Tuple with current figure and axes.

def show_labels(labels: numpy.ndarray, cmap=None, plot_centroids: bool = False, plot_labels: bool = False, ax: matplotlib.axes._axes.Axes = None, title: Optional[str] = None, title_font_size: Optional[None] = 10.0, label_font_size: Union[float, str, ForwardRef(None)] = 10.0) ‑> None

Plots a labels image with a suited color map by default.

Parameters

labels : np.ndarray: 2D label image to be displayed. The image must be a label image.
cmap : None|ListedColormap: Color map to be used (optional). If omitted, a suitable one will be used.
plot_centroids : bool: Set to True to plot the centroids of the labels. Only one of plot_centroids and plot_labels can be True.
plot_labels : bool: Set to True to plot the label number on the object. Only one of plot_centroids and plot_labels can be True.
ax : matplotlib.axes.Axes (Optional, default = None): Axis handle. Pass a valid axes handle to display the image there; if omitted, a new figure and a new set of axes will be created.
title : str: Title for current axes (optional).
title_font_size : float: Font size for the title of current axes (optional).
label_font_size : float|str: Font size for the labels. It can be either a number or one of {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}