Andrew-S-Rosen / periodic_trends

Python script to plot periodic trends as a heat map over the periodic table of elements

Periodic Trend Plotter

Python script to plot periodic trends as a heat map over the periodic table of elements.

Usage

This Python script (periodic_trends.py) can be used to plot a heat map over an image of the periodic table of elements for easy and automated visualization of periodic trends.

A minimal example is as follows:

from periodic_trends import plotter
plotter("ionization_energies.csv")

The only required argument to plotter() is a single positional argument for the full filepath/name (with extension) of the data file containing your periodic trend data. The data file must be in a comma-separated value (.csv) format with the first entry in each row being the atom symbol and the second entry being the value you wish to plot. An example .csv file is included in this repository for testing purposes under the name ionization_energies.csv. After the periodic_trends.py script is run, it will show the plot in your web browser. To save the image, simply click the save icon that appears in the web browser figure.

There are numerous optional arguments, which can be used to modify the appearance of the figure. The full argument list is below:

periodic_trends/periodic_trends.py

Lines 28 to 101 in 1f374d8

	def plotter(
	filename: str,
	show: bool = True,
	output_filename: str = None,
	width: int = 1050,
	cmap: str = "plasma",
	alpha: float = 0.65,
	extended: bool = True,
	periods_remove: List[int] = None,
	groups_remove: List[int] = None,
	log_scale: bool = False,
	cbar_height: float = None,
	cbar_standoff: int = 12,
	cbar_fontsize: int = 14,
	blank_color: str = "#c4c4c4",
	under_value: float = None,
	under_color: str = "#140F0E",
	over_value: float = None,
	over_color: str = "#140F0E",
	special_elements: List[str] = None,
	special_color: str = "#6F3023",
	) -> figure:
	"""
	Plot a heatmap over the periodic table of elements.
	Parameters
	----------
	filename : str
	Path to the .csv file containing the data to be plotted.
	show : str
	If True, the plot will be shown.
	output_filename : str
	If not None, the plot will be saved to the specified (.html) file.
	width : float
	Width of the plot.
	cmap : str
	plasma, inferno, viridis, magma, cividis, turbo
	alpha : float
	Alpha value (transparency).
	extended : bool
	If True, the lanthanoids and actinoids will be shown.
	periods_remove : List[int]
	Period numbers to be removed from the plot.
	groups_remove : List[int]
	Group numbers to be removed from the plot.
	log_scale : bool
	If True, the colorbar will be logarithmic.
	cbar_height : int
	Height of the colorbar.
	cbar_standoff : int
	Distance between the colorbar and the plot.
	cbar_fontsize : int
	Fontsize of the colorbar label.
	blank_color : str
	Hexadecimal color of the elements without data.
	under_value : float
	Values <= under_value will be colored with under_color.
	under_color : str
	Hexadecimal color to be used for the lower bound color.
	over_value : float
	Values >= over_value will be colored with over_color.
	under_color : str
	Hexadecial color to be used for the upper bound color.
	special_elements: List[str]
	List of elements to be colored with special_color.
	special_color: str
	Hexadecimal color to be used for the special elements.
	Returns
	-------
	figure
	Bokeh figure object.
	"""

A couple of examples using various optional keyword arguments are as follows:

from periodic_trends import plotter
plotter("ionization_energies.csv", log_scale=True)

from periodic_trends import plotter
plotter("ionization_energies.csv", cmap="viridis", alpha=0.7, extended=False, periods_remove=[1])

Dependencies

The script requires the following dependencies:

• Bokeh
• pandas
• matplotlib

These packages can be installed using pip via pip install -r requirements.txt in the periodic_trends base directory.