Module type2fuzzy.display.generaltype2fuzzysetplot

Classes

class GeneralType2FuzzySetPlot (gt2fs)

Plotter class for a general type-2 fuzzy set.

init

Arguments:

gt2fs – a general type-2 fuzzy set

Expand source code

class GeneralType2FuzzySetPlot():
        '''
        Plotter class for a general type-2 fuzzy set.
        '''

        def __init__(self, gt2fs):
                '''
                init

                Arguments:
                ----------
                gt2fs         -- a general type-2 fuzzy set
                '''
                self._generaltype2set = gt2fs
                self.divs = 5

        def plot(self, curr_ax):
                '''
                Plots a general type-2 fuzzy set as an image

                Arguments:
                ----------
                ax -- the axis for this plot
                row -- the row where this plot is placed
                col -- the column where this row is placed
                '''
                # and set them as current axis
                # curr_ax = ax[row,col]

                primary_domain, secondary_domain, set_array = self._generaltype2set.to_array_explicit()
                y_array_res, x_array_res = np.shape(set_array)

                curr_ax.imshow(set_array, cmap='Reds')

                # X axis ticks
                x_ticks = np.linspace(0, self.divs-1 ,self.divs)
                x_diff = (primary_domain[len(primary_domain)-1] - primary_domain[0]) / (self.divs-1)
                x_label = ((x_ticks * x_diff)+primary_domain[0])
                x_formatted_label =  ['%.2f' % elem for elem in x_label]
                curr_ax.set_xticks(x_ticks * (x_array_res / (self.divs-1)))
                curr_ax.set_xticklabels(x_formatted_label)

                # Y axis ticks
                y_ticks = np.linspace(0, self.divs-1 ,self.divs)
                y_diff = (secondary_domain[len(secondary_domain)-1] - secondary_domain[0]) / (self.divs-1)
                y_label = ((y_ticks * y_diff) + secondary_domain[0])
                y_formatted_label =  ['%.2f' % elem for elem in y_label]
                curr_ax.set_yticks(y_ticks * (y_array_res / (self.divs-1)))
                curr_ax.set_yticklabels(y_formatted_label)

                curr_ax.set_xlabel('x')
                curr_ax.set_ylabel('u')

                # invert axis as set arrays start from 0 value so sets appear inverted
                curr_ax.invert_yaxis()

Methods

def plot(self, curr_ax)

Plots a general type-2 fuzzy set as an image

Arguments:

ax – the axis for this plot row – the row where this plot is placed col – the column where this row is placed

Expand source code

def plot(self, curr_ax):
        '''
        Plots a general type-2 fuzzy set as an image

        Arguments:
        ----------
        ax -- the axis for this plot
        row -- the row where this plot is placed
        col -- the column where this row is placed
        '''
        # and set them as current axis
        # curr_ax = ax[row,col]

        primary_domain, secondary_domain, set_array = self._generaltype2set.to_array_explicit()
        y_array_res, x_array_res = np.shape(set_array)

        curr_ax.imshow(set_array, cmap='Reds')

        # X axis ticks
        x_ticks = np.linspace(0, self.divs-1 ,self.divs)
        x_diff = (primary_domain[len(primary_domain)-1] - primary_domain[0]) / (self.divs-1)
        x_label = ((x_ticks * x_diff)+primary_domain[0])
        x_formatted_label =  ['%.2f' % elem for elem in x_label]
        curr_ax.set_xticks(x_ticks * (x_array_res / (self.divs-1)))
        curr_ax.set_xticklabels(x_formatted_label)

        # Y axis ticks
        y_ticks = np.linspace(0, self.divs-1 ,self.divs)
        y_diff = (secondary_domain[len(secondary_domain)-1] - secondary_domain[0]) / (self.divs-1)
        y_label = ((y_ticks * y_diff) + secondary_domain[0])
        y_formatted_label =  ['%.2f' % elem for elem in y_label]
        curr_ax.set_yticks(y_ticks * (y_array_res / (self.divs-1)))
        curr_ax.set_yticklabels(y_formatted_label)

        curr_ax.set_xlabel('x')
        curr_ax.set_ylabel('u')

        # invert axis as set arrays start from 0 value so sets appear inverted
        curr_ax.invert_yaxis()