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added numerical integrator and libray call to __innit
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import numpy as np | ||
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class NumericalIntegral(object): | ||
""" | ||
A class for numerically evaluating and tabulating some 1D integral. | ||
:arg lower_bound: lower bound of integral | ||
:arg upper_bound: upper_bound of integral | ||
:arg num_points: number of points to tabulate integral at | ||
""" | ||
def __init__(self, lower_bound, upper_bound, num_points=500): | ||
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# if upper_bound <= lower_bound: | ||
# raise ValueError('lower_bound must be lower than upper_bound') | ||
self.x = np.linspace(lower_bound, upper_bound, num_points) | ||
self.x_double = np.linspace(lower_bound, upper_bound, 2*num_points-1) | ||
self.lower_bound = lower_bound | ||
self.upper_bound = upper_bound | ||
self.num_points = num_points | ||
self.tabulated = False | ||
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def tabulate(self, expression): | ||
""" | ||
Tabulate some integral expression using Simpson's rule. | ||
:arg expression: a function representing the integrand to be evaluated. | ||
Should take a numpy array as an argument. | ||
""" | ||
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self.cumulative = np.zeros_like(self.x) | ||
self.interval_areas = np.zeros(len(self.x)-1) | ||
# Evaluate expression in advance to make use of numpy optimisation | ||
# We evaluate at the tabulation points and the midpoints of the intervals | ||
f = expression(self.x_double) | ||
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# Just do Simpson's rule for evaluating area of each interval | ||
self.interval_areas = ((self.x[1:] - self.x[:-1]) / 6.0 | ||
* (f[2::2] + 4.0 * f[1::2] + f[:-1:2])) | ||
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# Add the interval areas together to create cumulative integral | ||
for i in range(self.num_points - 1): | ||
self.cumulative[i+1] = self.cumulative[i] + self.interval_areas[i] | ||
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self.tabulated = True | ||
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def evaluate_at(self, points): | ||
""" | ||
Evaluates the integral at some point using linear interpolation. | ||
:arg points: the point value, or array of point values to evaluate | ||
the integral at. | ||
""" | ||
# Do linear interpolation from tabulated values | ||
if not self.tabulated: | ||
raise RuntimeError( | ||
'Integral must be tabulated before we can evaluate it at a point') | ||
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return np.interp(points, self.x, self.cumulative) |