Helpers for plotting data¶
-
breze.learn.display.
scatterplot_matrix
(X, C=None, symb='o', alpha=1, fig=None)¶ Return a figure containig a scatter plot matrix.
This is a useful tool for inspecting multi dimensional data. Each dimension will be plotted against each dimension as a scatter plot, arranged into a matrix. The diagonal will contain histograms.
Parameters: X : array_like
2D array containing the points to plot.
C : array_like
Class labels (optional). Each row of
X
with the same value inC
will be given the same color in the plots.symb : string
Symbol to use for plotting. Will be forwarded to
pylab.plot
.alpha : float
Between 0 and 1. Transparency of the points, where 1 means fully opaque.
fig : matplotlib.pyplot.Figure or None
Figure to plot into. If None, will be created itself.
-
breze.learn.display.
time_series_filter_plot
(filters, n_rows=None, n_cols=None, fig=None)¶ Plot filters for time series data.
Each filter is plotted into its own axis.
Parameters: filters : array_like
The argument
filters
is expected to be an array of shape(n_filters, window_size, n_channels)
.n_filters
is the number of filter banks,window_size
is the length of a time window andn_channels
is the number of different sensors.n_rows : int, optional, default: None
Number of rows for the plot. If not given, inferred from
n_cols
to match dimensions. Ifn_cols
is not given as well, both are taken to be roughly the square root of the number of filters.n_cols : int, optional, default: None
Number of rows for the plot. If not given, inferred from
n_rows
to match dimensions. Ifn_rows
is not given as well, both are taken to be roughly the square root of the number of filters.fig : Figure, optional
Figure to plot the axes into. If not given, a new one is created.
Returns: figure : matplotlib figre
Figure object to save or plot.
The following function was adapted from the scipy cookbook.
-
breze.learn.display.
hinton
(ax, W, max_weight=None)¶ Draws a Hinton diagram for the matrix W to axis ax.