contour¶

This section describes various options available for contour plots in fivecentplots

Setup¶

Import packages:

import fivecentplots as fcp
import pandas as pd
from pathlib import Path

Read some fake contour data:

df = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_contour.csv')
df.head()

	Experiment	Batch	X	Y	Value
0	Control	101	1	-4	3.5
1	Control	101	1	-2	2.1
2	Control	101	1	0	3.3
3	Control	101	1	2	3.2
4	Control	101	1	4	4.0

Optionally set the design theme (skipping here and using default):

#fcp.set_theme('gray')
#fcp.set_theme('white')

Basic contour¶

A basic contour plot without fill can be generated using keyword filled=False. We can disable a colorbar using keyword cbar=False:

fcp.contour(df, x='X', y='Y', z='Value', filled=False, cbar=False)

Contour lines¶

We can control the style and number of the contour lines as shown below to get a better result:

fcp.contour(df, x='X', y='Y', z='Value', filled=False, cbar=False, levels=40, contour_width=2,
            xmin=-4, xmax=5, ymin=-4, ymax=6)

Contour points¶

The x and y points used to make the contour can also be overlaid on the contour lines, using keyword show_points=True. Markers can be styled according to typical keywords for the markers Element class:

fcp.contour(df, x='X', y='Y', z='Value', filled=False, cbar=False, levels=40, contour_width=2,
            xmin=-4, xmax=5, ymin=-4, ymax=6, show_points=True, marker_size=26, marker_fill_color='#00FF00')

Grouping¶

We can also use other columns in the DataFrame to easily group the data into multiple contour subplots:

fcp.contour(df, x='X', y='Y', z='Value', row='Batch', col='Experiment', filled=False,
            cbar=False, xmin=-3, xmax=3, ymin=-3, ymax=3, ax_size=[250,250], contour_width=2,
            label_rc_font_size=12)

Filled contour¶

Next we can fill the contours using the keyword filled or contour_filled. We can also add a colorbar to show the range of z-values in the plot using the keyword cmap=True:

fcp.contour(df, x='X', y='Y', z='Value', row='Batch', col='Experiment', filled=True,
            cbar=True, xmin=-3, xmax=3, ymin=-3, ymax=3, ax_size=[250,250],
            label_rc_font_size=12, levels=40)

And with a fixed z-range:

fcp.contour(df, x='X', y='Y', z='Value', row='Batch', col='Experiment', filled=True,
            cbar=True, xmin=-3, xmax=3, ymin=-3, ymax=3, ax_size=[250,250],
            label_rc_font_size=12, zmin=1, zmax=3, levels=40)

Data interpolation¶

The x, y, and z data provided in the DataFrame needs to be transformed into a grid for the contour. This is done using numpy and scipy modules:

xi = np.linspace(min(xx), max(xx))
yi = np.linspace(min(yy), max(yy))
zi = scipy.interpolate.griddata((xx, yy), zz, (xi[None, :], yi[:, None]), method=method)

where the method is supplied using the keyword interp. Any standard scipy argument is allowed but a default of “cubic” is used. Observe the differences between “cubic” and “linear” below:

fcp.contour(df, x='X', y='Y', z='Value', cbar=False, levels=40, contour_width=2,
            xmin=-3, xmax=3, ymin=-3, ymax=3, interp='cubic')

fcp.contour(df, x='X', y='Y', z='Value', cbar=False, levels=40, contour_width=2,
            xmin=-3, xmax=3, ymin=-3, ymax=3, interp='linear')