masstable/masstable.py
def chart_plot(self, ax=None, cmap='RdBu',
               xlabel='N', ylabel='Z', grid_on=True, colorbar=True):
        """Plot a nuclear chart with (N,Z) as axis and the values
        of the Table as a color scale

        Parameters
        ----------
        ax: optional matplotlib axes
                defaults to current axes
        cmap: a matplotlib colormap
                default: 'RdBu'
        xlabel: string representing the label of the x axis
            default: 'N'
        ylabel: string, default: 'Z'
            the label of the x axis
        grid_on: boolean, default: True,
            whether to draw the axes grid or not
        colorbar: boolean, default: True
            whether to draw a colorbar or not

        Returns
        -------
        ax: a matplotlib axes object

        Example
        -------
        Plot the theoretical deviation for the Möller's model::

        >>> Table('FRDM95').error().chart_plot()

        """
        from matplotlib.mlab import griddata
        from numpy import linspace, meshgrid
        import matplotlib.pyplot as plt

        # extract the 1D arrays to be plotted
        x = self.dropna().N
        y = self.dropna().Z
        z = self.dropna().values

        #convert to matplotlibs grid format
        xi = linspace(min(x), max(x), max(x) - min(x) + 1)
        yi = linspace(min(y), max(y), max(y) - min(y) + 1)
        Z = griddata(x, y, z, xi, yi)
        X, Y = meshgrid(xi, yi)

        # create and customize plot
        if ax is None:
            ax = plt.gca()
        chart = ax.pcolormesh(X, Y, Z, cmap=cmap)
        ax.set_xlabel(xlabel)
        ax.set_ylabel(ylabel)
        ax.grid(grid_on)
        ax.set_aspect('equal')
        if colorbar:
            plt.colorbar(chart)

        return ax
def chart_plot(self, ax=None, cmap='RdBu',
               xlabel='N', ylabel='Z', grid_on=True, colorbar=True):
        """Plot a nuclear chart with (N,Z) as axis and the values
        of the Table as a color scale

        Parameters
        ----------
        ax: optional matplotlib axes
                defaults to current axes
        cmap: a matplotlib colormap
                default: 'RdBu'
        xlabel: string representing the label of the x axis
            default: 'N'
        ylabel: string, default: 'Z'
            the label of the x axis
        grid_on: boolean, default: True,
            whether to draw the axes grid or not
        colorbar: boolean, default: True
            whether to draw a colorbar or not

        Returns
        -------
        ax: a matplotlib axes object

        Example
        -------
        Plot the theoretical deviation for the Möller's model::

        >>> Table('FRDM95').error().chart_plot()

        """
        from matplotlib.mlab import griddata
        from numpy import linspace, meshgrid
        import matplotlib.pyplot as plt

        # extract the 1D arrays to be plotted
        x = self.dropna().N
        y = self.dropna().Z
        z = self.dropna().values

        #convert to matplotlibs grid format
        xi = linspace(min(x), max(x), max(x) - min(x) + 1)
        yi = linspace(min(y), max(y), max(y) - min(y) + 1)
        Z = griddata(x, y, z, xi, yi)
        X, Y = meshgrid(xi, yi)

        # create and customize plot
        if ax is None:
            ax = plt.gca()
        chart = ax.pcolormesh(X, Y, Z, cmap=cmap)
        ax.set_xlabel(xlabel)
        ax.set_ylabel(ylabel)
        ax.grid(grid_on)
        ax.set_aspect('equal')
        if colorbar:
            plt.colorbar(chart)

        return ax
def chart_plot ( self , ax = None , cmap = 'RdBu' , xlabel = 'N' , ylabel = 'Z' , grid_on = True , colorbar = True ) : from matplotlib . mlab import griddata from numpy import linspace , meshgrid import matplotlib . pyplot as plt # extract the 1D arrays to be plotted x = self . dropna ( ) . N y = self . dropna ( ) . Z z = self . dropna ( ) . values #convert to matplotlibs grid format xi = linspace ( min ( x ) , max ( x ) , max ( x ) - min ( x ) + 1 ) yi = linspace ( min ( y ) , max ( y ) , max ( y ) - min ( y ) + 1 ) Z = griddata ( x , y , z , xi , yi ) X , Y = meshgrid ( xi , yi ) # create and customize plot if ax is None : ax = plt . gca ( ) chart = ax . pcolormesh ( X , Y , Z , cmap = cmap ) ax . set_xlabel ( xlabel ) ax . set_ylabel ( ylabel ) ax . grid ( grid_on ) ax . set_aspect ( 'equal' ) if colorbar : plt . colorbar ( chart ) return ax
Plot a nuclear chart with (N,Z) as axis and the values
        of the Table as a color scale

        Parameters
        ----------
        ax: optional matplotlib axes
                defaults to current axes
        cmap: a matplotlib colormap
                default: 'RdBu'
        xlabel: string representing the label of the x axis
            default: 'N'
        ylabel: string, default: 'Z'
            the label of the x axis
        grid_on: boolean, default: True,
            whether to draw the axes grid or not
        colorbar: boolean, default: True
            whether to draw a colorbar or not

        Returns
        -------
        ax: a matplotlib axes object

        Example
        -------
        Plot the theoretical deviation for the Möller's model::

        >>> Table('FRDM95').error().chart_plot()
Plot a nuclear chart with ( N Z ) as axis and the values of the Table as a color scale