Module conduction.interpolation.interpolator_legacy
Copyright 2017 Ben Mather
This file is part of Conduction https://git.dias.ie/itherc/conduction/
Conduction is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
Conduction is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Conduction. If not, see http://www.gnu.org/licenses/.
Expand source code
"""
Copyright 2017 Ben Mather
This file is part of Conduction <https://git.dias.ie/itherc/conduction/>
Conduction is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or any later version.
Conduction is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with Conduction. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
from scipy.interpolate import interp1d
class LinearInterpolation(interp1d):
"""
Extends basic 1D interpolation
"""
def __init__(self, x, y):
"""
Inherit the interp1D class from SciPy
This includes all methods, such as __call__()
"""
interp1d.__init__(self, x, y, kind='linear')
def gradient(self, xi):
"""
In the equation y = mx + c
Return m
"""
srt = np.absolute(xi - self.x).argsort()
x0, x1 = self.x[srt[0]], self.x[srt[1]]
y0, y1 = self.y[srt[0]], self.y[srt[1]]
return (y1-y0)/(x1-x0)
def tangent_linear(self, xi):
y_tl = np.zeros_like(xi)
for i in range(len(xi)):
srt = np.absolute(xi[i] - self.x).argsort()
x0, x1 = self.x[srt[0]], self.x[srt[1]]
dy0, dy1 = self.y[srt[0]], self.y[srt[1]]
dydy0 = 1.0 - (xi[i]-x0) / (x1-x0)
dydy1 = (xi[i]-x0) / (x1-x0)
y_tl[i] = dydy0 * dy0 + dydy1 * dy1
return y_tl
def adjoint_legacy(self, xi, yi):
"""
Evaluates the value at neighbouring grid points given pos and val
Arguments
xi : (1,n) array
yi : (1,n) array
Returns
y_adj
"""
y_adj = np.zeros_like(self.y)
for i in range(len(xi)):
srt = np.absolute(xi[i] - self.x).argsort()
x0, x1 = self.x[srt[0]], self.x[srt[1]]
y0, y1 = self.y[srt[0]], self.y[srt[1]]
m = (y1-y0)/(x1-x0)
c = -m*xi[i] + yi[i]
y_adj[srt[0]] += m*x0 + c
y_adj[srt[1]] += m*x1 + c
return y_adj
def adjoint(self, xi, dy):
"""
Evaluates the value at neighbouring grid points given pos and val
Arguments
xi : (1,n) array
dy : (1,n) array
Returns
y_adj
"""
y_adj = np.zeros_like(self.y)
for i in range(len(xi)):
srt = np.absolute(xi[i] - self.x).argsort()
x0, x1 = self.x[srt[0]], self.x[srt[1]]
dydy0 = 1.0 - (xi[i]-x0) / (x1-x0)
dydy1 = (xi[i]-x0) / (x1-x0)
y_adj[srt[0]] += dydy0 * dy[i]
y_adj[srt[1]] += dydy1 * dy[i]
return y_adjClasses
class LinearInterpolation (x, y)-
Extends basic 1D interpolation
Inherit the interp1D class from SciPy This includes all methods, such as call()
Expand source code
class LinearInterpolation(interp1d): """ Extends basic 1D interpolation """ def __init__(self, x, y): """ Inherit the interp1D class from SciPy This includes all methods, such as __call__() """ interp1d.__init__(self, x, y, kind='linear') def gradient(self, xi): """ In the equation y = mx + c Return m """ srt = np.absolute(xi - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] y0, y1 = self.y[srt[0]], self.y[srt[1]] return (y1-y0)/(x1-x0) def tangent_linear(self, xi): y_tl = np.zeros_like(xi) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] dy0, dy1 = self.y[srt[0]], self.y[srt[1]] dydy0 = 1.0 - (xi[i]-x0) / (x1-x0) dydy1 = (xi[i]-x0) / (x1-x0) y_tl[i] = dydy0 * dy0 + dydy1 * dy1 return y_tl def adjoint_legacy(self, xi, yi): """ Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array yi : (1,n) array Returns y_adj """ y_adj = np.zeros_like(self.y) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] y0, y1 = self.y[srt[0]], self.y[srt[1]] m = (y1-y0)/(x1-x0) c = -m*xi[i] + yi[i] y_adj[srt[0]] += m*x0 + c y_adj[srt[1]] += m*x1 + c return y_adj def adjoint(self, xi, dy): """ Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array dy : (1,n) array Returns y_adj """ y_adj = np.zeros_like(self.y) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] dydy0 = 1.0 - (xi[i]-x0) / (x1-x0) dydy1 = (xi[i]-x0) / (x1-x0) y_adj[srt[0]] += dydy0 * dy[i] y_adj[srt[1]] += dydy1 * dy[i] return y_adjAncestors
- scipy.interpolate.interpolate.interp1d
- scipy.interpolate.polyint._Interpolator1D
Methods
def adjoint(self, xi, dy)-
Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array dy : (1,n) array Returns y_adj
Expand source code
def adjoint(self, xi, dy): """ Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array dy : (1,n) array Returns y_adj """ y_adj = np.zeros_like(self.y) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] dydy0 = 1.0 - (xi[i]-x0) / (x1-x0) dydy1 = (xi[i]-x0) / (x1-x0) y_adj[srt[0]] += dydy0 * dy[i] y_adj[srt[1]] += dydy1 * dy[i] return y_adj def adjoint_legacy(self, xi, yi)-
Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array yi : (1,n) array Returns y_adj
Expand source code
def adjoint_legacy(self, xi, yi): """ Evaluates the value at neighbouring grid points given pos and val Arguments xi : (1,n) array yi : (1,n) array Returns y_adj """ y_adj = np.zeros_like(self.y) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] y0, y1 = self.y[srt[0]], self.y[srt[1]] m = (y1-y0)/(x1-x0) c = -m*xi[i] + yi[i] y_adj[srt[0]] += m*x0 + c y_adj[srt[1]] += m*x1 + c return y_adj def gradient(self, xi)-
In the equation y = mx + c Return m
Expand source code
def gradient(self, xi): """ In the equation y = mx + c Return m """ srt = np.absolute(xi - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] y0, y1 = self.y[srt[0]], self.y[srt[1]] return (y1-y0)/(x1-x0) def tangent_linear(self, xi)-
Expand source code
def tangent_linear(self, xi): y_tl = np.zeros_like(xi) for i in range(len(xi)): srt = np.absolute(xi[i] - self.x).argsort() x0, x1 = self.x[srt[0]], self.x[srt[1]] dy0, dy1 = self.y[srt[0]], self.y[srt[1]] dydy0 = 1.0 - (xi[i]-x0) / (x1-x0) dydy1 = (xi[i]-x0) / (x1-x0) y_tl[i] = dydy0 * dy0 + dydy1 * dy1 return y_tl