Python Element.from_row_and_group方法代码示例

# 需要导入模块: from pymatgen.core.periodic_table import Element [as 别名]
# 或者: from pymatgen.core.periodic_table.Element import from_row_and_group [as 别名]
def periodic_table_heatmap(elemental_data, cbar_label="",
                           show_plot=False, cmap="YlOrRd", blank_color="grey",
                           value_format=None, max_row=9):
    """
    A static method that generates a heat map overlapped on a periodic table.

    Args:
         elemental_data (dict): A dictionary with the element as a key and a
            value assigned to it, e.g. surface energy and frequency, etc.
            Elements missing in the elemental_data will be grey by default
            in the final table elemental_data={"Fe": 4.2, "O": 5.0}.
         cbar_label (string): Label of the colorbar. Default is "".
         figure_name (string): Name of the plot (absolute path) being saved
            if not None.
         show_plot (bool): Whether to show the heatmap. Default is False.
         value_format (str): Formatting string to show values. If None, no value
            is shown. Example: "%.4f" shows float to four decimals.
         cmap (string): Color scheme of the heatmap. Default is 'coolwarm'.
         blank_color (string): Color assigned for the missing elements in
            elemental_data. Default is "grey".
         max_row (integer): Maximum number of rows of the periodic table to be
            shown. Default is 9, which means the periodic table heat map covers
            the first 9 rows of elements.
    """

    # Convert elemental data in the form of numpy array for plotting.
    max_val = max(elemental_data.values())
    min_val = min(elemental_data.values())
    max_row = min(max_row, 9)

    if max_row <= 0:
        raise ValueError("The input argument 'max_row' must be positive!")

    value_table = np.empty((max_row, 18)) * np.nan
    blank_value = min_val - 0.01

    for el in Element:
        if el.row > max_row: continue
        value = elemental_data.get(el.symbol, blank_value)
        value_table[el.row - 1, el.group - 1] = value

    # Initialize the plt object
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    plt.gcf().set_size_inches(12, 8)

    # We set nan type values to masked values (ie blank spaces)
    data_mask = np.ma.masked_invalid(value_table.tolist())
    heatmap = ax.pcolor(data_mask, cmap=cmap, edgecolors='w', linewidths=1,
                        vmin=min_val-0.001, vmax=max_val+0.001)
    cbar = fig.colorbar(heatmap)

    # Grey out missing elements in input data
    cbar.cmap.set_under(blank_color)
    cbar.set_label(cbar_label, rotation=270, labelpad=15)
    cbar.ax.tick_params(labelsize=14)

    # Refine and make the table look nice
    ax.axis('off')
    ax.invert_yaxis()

    # Label each block with corresponding element and value
    for i, row in enumerate(value_table):
        for j, el in enumerate(row):
            if not np.isnan(el):
                symbol = Element.from_row_and_group(i+1, j+1).symbol
                plt.text(j + 0.5, i + 0.25, symbol,
                         horizontalalignment='center',
                         verticalalignment='center', fontsize=14)
                if el != blank_value and value_format is not None:
                    plt.text(j + 0.5, i + 0.5, value_format % el,
                             horizontalalignment='center',
                             verticalalignment='center', fontsize=10)

    plt.tight_layout()

    if show_plot:
        plt.show()

    return plt