Using the Histogram#
Lets start by creating a simple Histogram of a normal distribution.
values = np.random.normal(loc=2, scale=5, size=500)
histogram = gl.Histogram(values, number_of_bins=30, label="Distribution of values")
figure = gl.Figure(x_label="Values", y_label="Counts")
figure.add_elements(histogram)
figure.show()
In this example we can see that the legend includes the values of the distribution’s mean and standard deviation. These values are also available through the Histogram object using dot notation
mu = histogram.mean
sigma = histogram.standard_deviation
It is also possible to overlay a normal fit of the distribution simply by calling the add_pdf() (probability density function; currently, only normal is available):
values = np.random.normal(loc=2, scale=5, size=500)
histogram = gl.Histogram(
values, number_of_bins=30, label="Distribution of values"
)
histogram.add_pdf()
fig = gl.Figure()
fig.add_elements(histogram)
fig.show()
Plotting fit residuals#
You can create a Histogram from a previously created fit to display the residuals of said fit. Here is an example of how to create such a Histogram using the plot_residuals_from_fit() method:
x_data = np.linspace(0, 10, 100)
y_data = 3 * x_data**2 - 2 * x_data + np.random.normal(0, 10, 100)
scatter = gl.Scatter(x_data, y_data, label="Data")
fit = gl.FitFromPolynomial(scatter, degree=2, label="Fit", color="red")
residuals = gl.Histogram.from_fit_residuals(fit, 15, label="Residual")
residuals.add_pdf()
fig1 = gl.Figure()
fig1.add_elements(scatter, fit)
fig2 = gl.Figure(y_lim=(0, 0.06))
fig2.add_elements(residuals)
multifigure = gl.MultiFigure.from_row([fig1, fig2], size=(10, 5), reference_labels=False)
multifigure.show()
