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 primitive_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
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
def periodic_table_heatmap(
elemental_data,
cbar_label="",
cbar_label_size=14,
show_plot=False,
cmap="YlOrRd",
cmap_range=None,
blank_color="grey",
edge_color="white",
value_format=None,
value_fontsize=10,
symbol_fontsize=14,
max_row=9,
readable_fontcolor=False,
):
"""
A static method that generates a heat map overlaid 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 "".
cbar_label_size (float): Font size for the colorbar label. Default is 14.
cmap_range (tuple): Minimum and maximum value of the colormap scale.
If None, the colormap will automatically scale to the range of the
data.
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.
value_fontsize (float): Font size for values. Default is 10.
symbol_fontsize (float): Font size for element symbols. Default is 14.
cmap (string): Color scheme of the heatmap. Default is 'YlOrRd'.
Refer to the matplotlib documentation for other options.
blank_color (string): Color assigned for the missing elements in
elemental_data. Default is "grey".
edge_color (string): Color assigned for the edge of elements in the
periodic table. Default is "white".
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.
readable_fontcolor (bool): Whether to use readable fontcolor depending
on background color. Default is False.
"""
# Convert primitive_elemental data in the form of numpy array for plotting.
if cmap_range is not None:
max_val = cmap_range[1]
min_val = cmap_range[0]
else:
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=edge_color,
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)
# Set the colorbar label and tick marks
cbar.set_label(cbar_label, rotation=270, labelpad=25, size=cbar_label_size)
cbar.ax.tick_params(labelsize=cbar_label_size)
# Refine and make the table look nice
ax.axis("off")
ax.invert_yaxis()
# Set the scalermap for fontcolor
norm = colors.Normalize(vmin=min_val, vmax=max_val)
scalar_cmap = cm.ScalarMappable(norm=norm, cmap=cmap)
# 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
rgba = scalar_cmap.to_rgba(el)
fontcolor = _decide_fontcolor(
rgba) if readable_fontcolor else "black"
plt.text(
j + 0.5,
i + 0.25,
symbol,
horizontalalignment="center",
verticalalignment="center",
fontsize=symbol_fontsize,
color=fontcolor,
)
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=value_fontsize,
color=fontcolor,
)
plt.tight_layout()
if show_plot:
plt.show()
return plt
