"""
NAME
Calibration module
DESCRIPTION
Module for the management of the calibration curve. Here are the functions
to be used for fitting. See Calibration class for details.
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
"""Functions used for film calibration."""
[docs]
def polynomial_g3(x, a, b, c, d):
"""
Polynomial function of degree 3.
"""
return a + b*x + c*x**2 + d*x**3
[docs]
def rational_func(x, a, b, c):
"""
Rational function.
"""
return -c + b/(x-a)
[docs]
class Calibration:
"""Class used to represent a calibration curve.
Attributes
----------
y : list
The doses values that were used to expose films for calibration.
x : list
Optical density if "P3" fit function is used, or normalized pixel value
for "RF" fit function.
func : str
The model function used for dose-film response relationship.
"P3": Polynomial function of degree 3.
"RF": Rational function.
channel : str
Color channel. "R": Red, "G": Green and "B": Blue.
popt : array
Parameters of the function.
pcov : 2-D array
The estimated approximate covariance of popt. The diagonals provide
the variance of the parameter estimate. To compute one standard
deviation errors on the parameters, use perr = np.sqrt(np.diag(pcov)).
"""
def __init__(self, y: list, x: list, func: str = "P3", channel: str = "R"):
self.doses = sorted(y)
if func in ["P3", "Polynomial"]:
self.x = sorted(x) # Film response.
elif func in ["RF", "Rational"]:
self.x = sorted(x, reverse=True)
self.func = func
if self.func in ["P3", "Polynomial"]:
self.popt, self.pcov = curve_fit(polynomial_g3, self.x, self.doses)
elif self.func in ["RF", "Rational"]:
self.popt, self.pcov = curve_fit(
rational_func,
self.x,
self.doses,
p0=[0.1, 200, 500]
)
else:
raise Exception("Invalid fit function.")
self.channel = channel
[docs]
def plot(self, ax: plt.Axes = None, show: bool = True, **kwargs) -> plt.Axes:
"""Plot the calibration curve.
Parameters
----------
ax : matplotlib.Axes instance
The axis to plot the image to. If None, creates a new figure.
show : bool
Whether to actually show the image. Set to false when plotting
multiple items.
kwargs
kwargs passed to plt.plot()
"""
if ax is None:
fig, ax = plt.subplots()
x = np.linspace(self.x[0], self.x[-1], 100)
if self.func in ["P3", "Polynomial"]:
y = polynomial_g3(x, *self.popt)
ax.set_xlabel("Optical density")
elif self.func in ["RF", "Rational"]:
y = rational_func(x, *self.popt)
ax.set_xlabel("Normalized pixel value")
if self.channel in ["R", "Red", "r", "red"]:
color = "red"
elif self.channel in ["G", "Green", "g", "green"]:
color = "green"
elif self.channel in ["B", "Blue", "b", "blue"]:
color = "blue"
elif self.channel in ["M", "Mean", "m", "mean"]:
color = "black"
ax.plot(
self.x,
self.doses,
color = color,
marker = '*',
linestyle = 'None',
**kwargs
)
ax.plot(
x,
y,
color=color,
)
ax.set_ylabel("Dose [Gy]")
if show:
plt.show()
return ax
