87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
import numpy as np
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from astropy.modeling import Fittable1DModel, Fittable2DModel, Parameter
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class Linear1D(Fittable1DModel):
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"""
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One dimensional Line model.
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Parameters
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----------
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slope : float
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Slope of the straight line
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intercept : float
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Intercept of the straight line
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"""
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slope = Parameter(default=1, description="Slope of the straight line")
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intercept = Parameter(default=0, description="Intercept of the straight line")
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@staticmethod
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def evaluate(x, slope, intercept):
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return slope * x + intercept
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@staticmethod
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def fit_deriv(x, slope, intercept):
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d_slope = x
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d_intercept = np.ones_like(x)
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return [d_slope, d_intercept]
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class FixedSlopeLine(Fittable1DModel):
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"""
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Linear model, but fix the slope
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Parameters
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----------
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slope : float
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Slope of the line
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intercept : float
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Intercept of the line
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"""
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slope = Parameter()
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intercept = Parameter()
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@staticmethod
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def evaluate(x, slope, intercept):
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return slope * x + intercept
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@staticmethod
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def fit_deriv(x, slope, intercept):
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d_slope = np.zeros_like(x)
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d_intercept = np.ones_like(x)
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return [d_slope, d_intercept]
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class pXLine(Fittable2DModel):
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"""
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pX linear model.
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$Px = \\frac{k_2y - k_1x}{E}L+CL$.
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Parameters
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----------
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L : float
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Slope of the straight line
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C : float
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Intercept / Slope of the straight line
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"""
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linear = True
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L, C = Parameter(default=40), Parameter(default=0)
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k1, k2, b = Parameter(), Parameter(), Parameter()
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@staticmethod
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def evaluate(x, y, L, C, k1, k2, b):
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E = k1 * x + k2 * y + b
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return (k2 * y - k1 * x) / E * L + C * L
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@staticmethod
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def fit_deriv(x, y, L, C, k1, k2, b):
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E = k1 * x + k2 * y + b
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d_L = (k2 * y - k1 * x) / E + C
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d_C = np.full(x.shape, L)
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d_k1, d_k2, d_b = np.zeros_like(x), np.zeros_like(x), np.zeros_like(x)
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return [d_L, d_C, d_k1, d_k2, d_b]
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