API References

Scatterplot.

scatterd.scatterd.check_logger(verbose: [<class 'str'>, <class 'int'>] = 'info')

Check the logger.

scatterd.scatterd.convert_verbose_to_new(verbose)

Convert old verbosity to the new.

scatterd.scatterd.disable_tqdm()

Set the logger for verbosity messages.

scatterd.scatterd.get_logger()
scatterd.scatterd.init_figure(ax, z, dpi, figsize, visible, fig, fontsize)

Initialize figure.

scatterd.scatterd.jitter_func(x, jitter=0.01)

Add jitter to data.

Noise is generated from random normal distribution and added to the data.

Parameters:

  • x (numpy array) – input data.

  • jitter (float, optional) – Strength of generated noise. The default is 0.01.

Returns:

x – Data including noise.

Return type:

array-like

scatterd.scatterd.normalize_between_0_and_1(X)
scatterd.scatterd.scatterd(x, y, z=None, s=150, c=[0, 0.1, 0.4], labels=None, marker='o', alpha=0.8, edgecolor='#000000', gradient='opaque', opaque_type='per_class', density=True, density_on_top=False, norm=False, cmap='tab20c', figsize=(25, 15), dpi=100, legend=None, jitter=None, xlabel='x-axis', ylabel='y-axis', title='', fontsize=24, fontcolor=None, grid=True, fontweight='normal', args_density={'alpha': 0.66, 'bw_adjust': 0.6, 'cbar': False, 'cmap': 'Reds', 'fill': True, 'legend': False, 'thresh': 0.05}, visible=True, fig=None, ax=None, verbose='info')

Make scaterplot.

Parameters:

  • x (numpy array) – 1D Coordinates x-axis.

  • y (numpy array) – 1D Coordinates y-axis.

  • z (numpy array) – 1D Coordinates z-axis.

  • s (Int or list/array of sizes with same size as X) – Size of the samples.

  • c (list/array of RGB colors with same size as X) – Color of samples in RGB colors.

  • labels (list of labels with same size as X) – labels of the samples.

  • marker (list/array of strings (default: 'o').) –

    Marker for the samples.

    • ’x’ : All data points get this marker

    • (‘.’, ‘o’, ‘v’, ‘^’, ‘<’, ‘>’, ‘8’, ‘s’, ‘p’, ‘*’, ‘h’, ‘H’, ‘D’, ‘d’, ‘P’, ‘X’) : Specify per sample the marker type.

    • [0,3,0,1,2,1,..] : It is also possible to provide a list of labels. The markers will be assigned accordingly.

  • alpha (float, default: 0.8) – The alpha blending value, between 0 (transparent) and 1 (opaque).

  • edgecolors ((default: 'face')) –

    The edge color of the marker. Possible values:

    • ’face’: The edge color will always be the same as the face color.

    • ’none’: No patch boundary will be drawn.

    • ’#FFFFFF’ : A color or sequence of colors.

  • gradient (String, (default: 'opaque')) –

    Hex color to make a lineair gradient using the density.

    • None: Do not use gradient.

    • opaque: Towards the edges the points become more opaque and thus not visible.

    • ’#FFFFFF’: Towards the edges it smooths into this color

  • opaque_type (String, optional) –

    • ‘per_class’: Transprancy is determined on the density within the class label (y)

    • ’all’: Transprancy is determined on all available data points

    • ’lineair’: Transprancy is lineair set within the class label (y)

  • density (Bool (default: False)) – Include the kernel density in the scatter plot.

  • density_on_top (bool, (default: False)) – True : The density is the highest layer. False : The density is the lowest layer.

  • xlabel (String, optional) – Xlabel. The default is None.

  • ylabel (String, optional) – Ylabel. The default is None.

  • title (String, optional) – Title of the figure. The default is None.

  • fontsize (String, optional) – The fontsize of the y labels that are plotted in the graph. The default is 16.

  • fontcolor (list/array of RGB colors with same size as X (default : None)) – None : Use same colorscheme as for c ‘#000000’ : If the input is a single color, all fonts will get this color.

  • grid (str or bool (default: False)) – False or None : Do not plot grid True : Set axis color to: ‘#dddddd’ ‘#dddddd’ : Specify color with hex

  • norm (Bool, optional) – Normalize datapoints. The default is False.

  • legend (int, default: 0) – None: Set automatically based number of labels. False : Disable. True : Best position. 1 : ‘upper right’ 2 : ‘upper left’ 3 : ‘lower left’ 4 : ‘lower right’

  • jitter (float, default: None) –

    Add jitter to data points as random normal data. Values of 0.01 is usually good for one-hot data seperation.

    • None or False: Do not add jitter

    • True : adds 0.01

    • 0.05 : Specify the amount jitter to add.

  • cmap (String, optional) – ‘Set1’ (default) ‘Set2’ ‘rainbow’ ‘bwr’ Blue-white-red ‘binary’ or ‘binary_r’ ‘seismic’ Blue-white-red ‘Blues’ white-to-blue ‘Reds’ white-to-red ‘Pastel1’ Discrete colors ‘Paired’ Discrete colors ‘Set1’ Discrete colors

  • figsize (tuple, optional) – Figure size. The default is (15,10).

  • visible (Bool, default: True) – Visible status of the Figure. When False, figure is created on the background.

  • args_density (dict()) – Dictionary containing arguments for kernel density plotting.

  • verbose (int, (default: 'info')) – Print progress to screen. The default is ‘info’ 60: None, 40: error, 30: warning, 20: info, 10: debug

Return type:

Fig, ax

Examples

>>> # Import library
>>> from scatterd import scatterd
>>> import datazets as dz
>>>
>>> # Import example
>>> # pip install datazets
>>> df = dz.get(data='cancer')
>>>
>>> # Simple scatter
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], edgecolor='#FFFFFF')
>>>
>>> # Scatter with labels
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'])
>>>
>>> # Scatter with labels
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'])
>>>
>>> # Scatter with gradient
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], z=df['tsneY'], labels=df['labx'], gradient='#FFFFFF')
>>>
>>> # Change cmap
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], gradient='#FFFFFF', cmap='Set2')
>>>
>>> # Scatter with density
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=True)
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=False, gradient='#FFFFFF', edgecolor='#FFFFFF')
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=True, gradient='#FFFFFF', edgecolor='#FFFFFF')
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=True, gradient='#FFFFFF', c=None)
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=True, density_on_top=True, args_density={'alpha': 0.3}, gradient='#FFFFFF', edgecolor='#FFFFFF', grid=True, fontweight='normal', fontsize=26, legend=2)
>>>
>>> # Scatter with density and gradient
>>> fig, ax = scatterd(df['tsneX'], df['tsneY'], labels=df['labx'], density=True, gradient='#FFFFFF')
>>>

References

scatterd.scatterd.set_alpha(X, alpha, gradient, opaque, verbose=None)

Set alpha.

scatterd.scatterd.set_colors(X, labels, c, cmap='tab20c', gradient=None, opaque_type='per_class', verbose=None)

Set colors.

scatterd.scatterd.set_fontcolor(fontcolor, label, X, cmap, verbose=None)
scatterd.scatterd.set_logger(verbose: [<class 'str'>, <class 'int'>] = 'info')

Set the logger for verbosity messages.

Parameters:

verbose ([str, int], default is 'info' or 20) – Set the verbose messages using string or integer values. * [0, 60, None, ‘silent’, ‘off’, ‘no’]: No message. * [10, ‘debug’]: Messages from debug level and higher. * [20, ‘info’]: Messages from info level and higher. * [30, ‘warning’]: Messages from warning level and higher. * [50, ‘critical’]: Messages from critical level and higher.

Returns:

  • None.

  • > # Set the logger to warning

  • > set_logger(verbose=’warning’)

  • > # Test with different messages

  • > logger.debug(“Hello debug”)

  • > logger.info(“Hello info”)

  • > logger.warning(“Hello warning”)

  • > logger.critical(“Hello critical”)

scatterd.scatterd.set_marker(X, marker)

Set markers.

scatterd.scatterd.set_size(X, s)

Set size.