Data visualization

In this section, we will show you how to create visualizations of your microfading data. A summary of this page has been compiled inside a Jupyter notebook and is available for download: Download notebook

The plot functions, accessible after creating an instance of the microfading class, start with the verb "plot", as illustrated below. The plot functions are briefly described in Table 1.

Table 1. Description of the plot functions.

Function name	Description
`plot_bars()`	plot the value of a single colorimetric coordinates at a given light dose
`plot_coordinates()`	plot the CIE colorimetric coordinates as a function of the light energy
`plot_CIELAB()`	plot the trajectory of the \(L^a^b^*\) coordinates in the CIELAB colour space
`plot_delta()`	plot the \(\Delta\) curves for one or several CIE colour coordinates
`plot_sp()`	plot the reflectance spectra
`plot_sp_delta()`	plot the difference between two reflectance spectra
`plot_swatches_circle()`	plot the colour changes as a series of coloured circles
`plot_swatches_rectangle()`	plot the colour changes as two coloured rectangles next to each other
`plots()`

Basic plotting¶

Each plot function has been written so that you can use them without passing any arguments, as illustrated below. Such a visualization will render the default behaviour of each function. The following section shows how to pass arguments to the function to adjust some aspects of the plots.

Advanced plotting¶

The advanced plotting functionalites will allow you to modify the aspects of the plots. To achieve that, you will need to enter specific values inside the brackets of the functions. The plotting functions contain several parameters which can be divided into three main categories:

Function-related parameters: a few useful parameters specific to each function
Settings dictionaries: a set of standard dictionaries used to adapt the visualization
Saving parameters: parameters to save the visualization as png files.

For example, the figure below shows the docstrings of a plotting function. The two first parameters coordinates and stdev allow the users to quickly change some important aspects of the visualizations. Then come the five settings dictionaries and finally two parameters save and path_fig to save the visualization.

There are five dictionaries that encompass all aspects of plotting:

data_settings : aspects directly related to the data
figure_settings : aspects about the figure
legend_settings : aspects about the legend
lines_settings : aspects about the data lines
text_settings : add a text box inside the figure

Inside each dictionary, you will need to enter the information, as key : value. For each dictionary, there are several valid keys that you will need to know to correctly use the settings dictionaries. As an illustration, the following lines of code show you how to modify the size of a figure:

m = mf.MFT(files=files)
m.plot_delta(figure_settings={'figsize':(15,10)})

Though the same dictionaries are used across all the plot functions, the valid keys for each dictionary can differ from one function to another. Table 2 lists all the valid keys according to each plot function. For a thorough description of each key and their corresponding values, see the paragraphs below.

Table 2. Valid keys for each function.

Plot functions	Parameters	Valid keys
plot_CIELAB	data_settings figure_settings legend_settings lines_settings	dose_unit, dose_values, data_type title, xlabel, ylabel, fontsize, fontsize_title fontsize,labels, ncols, obs_ill, position, title alpha, colors, markers, size
plot_coordinates	data_settings figure_settings legend_settings lines_settings	dose_unit, dose_values, derivation, smoothing
plot_delta	data_settings figure_settings legend_settings lines_settings text_settings	dose_unit, dose_values, derivation, smoothing figsize, title, xlabel, ylabel, xlim, ylim, fontsize, fontsize_title fontsize, labels, ncols, position, title colors, ls, lw text, xy, fontsize
plot_sp	data_settings figure_settings legend_settings lines_settings text_settings	mode, derivation, dose_unit, smoothing, wl_range figsize, title, xlabel, ylabel, xlim, ylim, fontsize, fontsize_title fontsize, labels, ncols, position, title colors, ls, lw text, xy, fontsize

data_settings:¶

The data_settings dictionary encompasses parameters related to the microfading data, for which the following keys can be used:

dose_unit [str], by default 'He'

This key defines the unit of the light dose energy. Any of the following units can be used as a value: 'He', 'Hv', 't'.

'He' corresponds to radiant energy (MJ/m2)

'Hv' corresponds to exposure dose (Mlxh)

't' corresponds to times (sec)

e.g. : m.plot_delta(data_settings={'dose_unit':'He'})
dose_values [int, float, list, tuple]

This key defines the dose values for which the colourimetric values will be returned. A single dose value (an integer or a float number) can be entered. A list of dose values, as integer or float, can also be entered. A tuple of three values (min, max, step) will be used in a numpy.arange() function to return an array of dose values.
derivation [bool], by default False

This key defines whether to compute the first derivative values. When True, it will plot the derivative values of the colourimetric delta values.
mode [str], by default 'R'

It defines the type or mode of the measurement (absorbance or refectance). When 'R', it returns reflectance spectra. When 'A', it returns absorption spectra using the following equation: A = -log(R).
smoothing [tuple of 2 integers], by default (1,0)

This key defines whether the data should be smoothed. It takes a tuple of two integers as value which is used as input for the Savitzky-Golay filter. The first value corresponds to the window_length and the second to the polyorder value. The first value should always be greater than the second value.
wl_range [tuple of 2 integers], by default None

It defines the wavelength range with a two-values integer tuple corresponding to the lowest and highest wavelength values. When None, it plot all the available wavelengths.

figure_settings:¶

The figure_settings concerns all aspects related to the figure, for which the following keys can be used:

figsize [tuple], by default (15,9)

It defines the size of the figure.
fontsize [int], by default 18

It defines the fontsize of the axes labels and ticks.
fontsize_title [int], by default 20

It defines the fontsize of the title if any.
title [str], by default None

Whether to display a title.
xlabel [str]

Label of the x-axis. The default value varies according to the plot function.
xlim [tuple], by default 0

It defines the limits of the x-axis as a tuple of two numerical values (start, end).
ylabel [str]

Label of the y-axis. The default value varies according to the plot function.
ylim [tuple], by default None

It defines the limits of the y-axis as a tuple of two numerical values (start, end).

legend_settings:¶

The legend_settings concerns all aspects related to the legend, for which the following keys can be used:

fontsize [int]

Fontsize of the legend labels
labels [list]
ncols [int], by default 1

Number of columns for the labels
position [str], by default 'in'

Whether to display the legend inside ('in') or outside ('out') the figure.
title [str], by default None

Whether to display a title above the legend.

lines_settings:¶

The lines_settings concerns all aspects related to the data lines, for which the following keys can be used:

alpha [float, list]

Modify the transparency of markers. You can enter the following types of input values:
- Float: it can be any float number between 0 and 1 included, where 0 corresponds to complete transparent markers and 1 to fully opaque markers.
  
  eg: 0.5 or 0.85
- List of float: If you want to attribute a specific alpha value to each group of markers. The amount of float elements in the list should therefore before equal to the amount of measurements.
  
  eg: [0.5, 0.85]
colors [str, list, float]

Modify the colour of the lines or points. You can enter the following types of input values:
- String: it can be any colour compatible with the matplotlib colour list. In that case, all the lines or points have the same colour.
  
  eg: "green", "midnightblue", etc.
- List of string: If you want to attribute a specific color to each curve or point. The amount of string elements in the list should therefore before equal to the amount of curves.
  
  eg: ["blue", "chartreuse"]
- 'sample': This will render each curve or point according to the colour of the corresponding sample. It takes the first reflectance spectrum of the microfading data and compute the sRGB values.
- Float: a float between 0 and 1 will give a grey line ranging from black (0) to white (1).
markers [str, list], by default 'o'

Modify the marker style of data points. You can enter the following types of input values:
- String: it can be any marker styles compatible with the matplotlib styles. In that case, all the measurements have the same style. The most common marker styles are: "o", "s", "D", or "X", but they are many more.
- List: If you want to attribute a specific marker style to each measurement, you can use a list of string elements, where each element follow the rule mentioned just above.
  
  eg: ["o", "X", "D", "^", "*"], etc.
ls [str, list], by default '-'

Modify the style of data lines. You can enter the following types of input values:
- String: it can be any line styles compatible with the matplotlib styles. In that case, all the lines have the same style. The most common styles are: "-", "--", ":", "-.".
  
  eg: "-" or "--", etc.
- List: If you want to attribute a line style to each measurement, you can use a list of string elements, where each element follow the rule mentioned just above.
  
  eg: ["-", "--", ":", "-"], etc.
lw [int, list], by default 2

text_settings:¶

The text_settings dictionary enables you to insert a text block inside the figure, for which the following keys can be used:

text [str]

The text you wish to display.
xy [tuple of two numerical values]

The position of the text inside figure.
fontsize [int]

Fontsize of the text.

Save plots¶

If you want to save the plot as an image file, then set the save parameter to True. I also recommend to enter a value for the path_fig parameter, otherwise it will save the figure in the current working directory with a very basic name.