def __overlay_curves(axis, curve_stack): plot_line_family(axis, x_vec, curve_stack, label_prefix='Cluster', cmap=cmap) if legend_mode == 0: axis.legend(loc='best', fontsize=14) elif legend_mode == 1: axis.legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=14) else: sm = make_scalar_mappable(0, num_clusters - 1, cmap=discrete_cmap(num_clusters, cmap)) plt.colorbar(sm)
def __overlay_curves(axis, curve_stack): plot_line_family(axis, x_vec, curve_stack, label_prefix='Cluster', cmap=cmap) if legend_mode == 0: axis.legend(loc='best', fontsize=14) elif legend_mode == 1: axis.legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=14) else: sm = make_scalar_mappable(0, num_clusters - 1, cmap=discrete_cmap(num_clusters, cmap)) plt.colorbar(sm)
def plot_cluster_labels(labels_mat, num_clusters=None, x_label=None, y_label=None, title=None, axis=None, **kwargs): """ Plots the cluster labels Parameters ---------- labels_mat : numpy.ndarray 1D or 2D unsigned integer array containing the labels of the clusters num_clusters : int, optional Number of clusters x_label : str, optional Label for x axis y_label : str, optional Label for y axis title : str, optional Title for the plot axis : matplotlib.axes.Axes object, optional. Axis to plot this image onto. Will create a new figure by default or will use this axis object to plot into kwargs : dict will be passed on to plot() or plot_map() Returns ------- fig : matplotlib.pyplot.Figure object figure object axis : matplotlib.Axes object axis object """ if not isinstance(labels_mat, np.ndarray): raise TypeError('labels_mat should be numpy array') if labels_mat.ndim > 2: raise ValueError('labels_mat should be a 1D or 2D array') if not isinstance(num_clusters, int): raise TypeError('num_clusters should be an integer') if axis is not None: if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if num_clusters is not None: if not isinstance(num_clusters, int) or num_clusters < 1: raise TypeError('num_clusters should be a positive integer') else: num_clusters = np.max(labels_mat) if axis is None: fig, axis = plt.subplots(figsize=kwargs.pop('figsize', (5.5, 5))) else: fig = None if labels_mat.ndim > 1: _, _ = plot_map(axis, labels_mat, clim=[0, num_clusters - 1], aspect='auto', show_xy_ticks=True, cmap=discrete_cmap(num_clusters, kwargs.pop('cmap', default_cmap)), **kwargs) else: x_vec = kwargs.pop('x_vec', np.arange(labels_mat.size)) axis.plot(x_vec, labels_mat, **kwargs) for var, var_name, func in zip([title, x_label, y_label], ['title', 'x_label', 'y_label'], [axis.set_title, axis.set_xlabel, axis.set_ylabel]): if var is not None: if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' should be a string') func(var) if fig is not None: fig.tight_layout() return fig, axis
def plot_cluster_labels(labels_mat, num_clusters=None, x_label=None, y_label=None, title=None, axis=None, **kwargs): """ Plots the cluster labels Parameters ---------- labels_mat : numpy.ndarray 1D or 2D unsigned integer array containing the labels of the clusters num_clusters : int, optional Number of clusters x_label : str, optional Label for x axis y_label : str, optional Label for y axis title : str, optional Title for the plot axis : matplotlib.axes.Axes object, optional. Axis to plot this image onto. Will create a new figure by default or will use this axis object to plot into kwargs : dict will be passed on to plot() or plot_map() Returns ------- fig : matplotlib.pyplot.Figure object figure object axis : matplotlib.Axes object axis object """ if not isinstance(labels_mat, np.ndarray): raise TypeError('labels_mat should be numpy array') if labels_mat.ndim > 2: raise ValueError('labels_mat should be a 1D or 2D array') if not isinstance(num_clusters, int): raise TypeError('num_clusters should be an integer') if axis is not None: if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if num_clusters is not None: if not isinstance(num_clusters, int) or num_clusters < 1: raise TypeError('num_clusters should be a positive integer') else: num_clusters = np.max(labels_mat) if axis is None: fig, axis = plt.subplots(figsize=kwargs.pop('figsize', (5.5, 5))) else: fig = None if labels_mat.ndim > 1: _, _ = plot_map(axis, labels_mat, clim=[0, num_clusters - 1], aspect='auto', show_xy_ticks=True, cmap=discrete_cmap(num_clusters, kwargs.pop('cmap', default_cmap)), **kwargs) else: x_vec = kwargs.pop('x_vec', np.arange(labels_mat.size)) axis.plot(x_vec, labels_mat, **kwargs) for var, var_name, func in zip( [title, x_label, y_label], ['title', 'x_label', 'y_label'], [axis.set_title, axis.set_xlabel, axis.set_ylabel]): if var is not None: if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' should be a string') func(var) if fig is not None: fig.tight_layout() return fig, axis
def test_cmap_not_str(self): with self.assertRaises(ValueError): plot_utils.discrete_cmap(num_bins=1, cmap='hello')
def test_numbins_is_not_uint(self): with self.assertRaises(TypeError): plot_utils.discrete_cmap(num_bins='hello')