polish for inclusion into thesis
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@ -2,9 +2,9 @@
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import sys
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import time
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import interp.bootstrap
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from interp.cluster import QueueManager, get_qs
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from optparse import OptionParser
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from interp.cluster import QueueManager, get_qs
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from optparse import OptionParser
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if __name__ == '__main__':
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parser = OptionParser(usage = "usage: %s [options] <status|watch|add|fresult #|slay|clear|clearall|clearresults>")
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@ -4,20 +4,18 @@ import sys
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import os
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import time
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import shelve
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from progressbar import *
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from collections import defaultdict
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from optparse import OptionParser
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import numpy as np
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import interp.bootstrap
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import logging
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log = logging.getLogger("interp")
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from interp.cluster import QueueManager, get_qs
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import numpy as np
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from interp.cluster import QueueManager, get_qs
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from progressbar import *
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if __name__ == '__main__':
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parser = OptionParser(usage = "usage: %s [options] <server> <interp count>")
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@ -11,7 +11,6 @@ import datetime
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import numpy as np
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import interp.bootstrap
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from interp.grid.gmsh import ggrid
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from interp.tools import baker_exact_3D as exact
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@ -6,8 +6,8 @@ import time
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from progressbar import *
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from interp.grid.DD import *
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from interp.tools import *
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from interp import bootstrap
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from interp.tools import *
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from interp import bootstrap
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EXTRA_POINTS = 64
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@ -10,8 +10,6 @@ import pickle
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import numpy as np
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import interp.bootstrap
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from interp.cluster import QueueManager, get_qs
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if __name__ == '__main__':
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@ -11,37 +11,35 @@ log = logging.getLogger('interp')
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def get_phis(X, R):
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"""
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The get_phis function is used to get barycentric coordonites for a point on
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a triangle or tetrahedron:
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The get_phis function is used to get barycentric coordonites for a
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point on a triangle or tetrahedron. This is equation (*\ref{eq:qlinarea}*)
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in 2D:
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X -- the destination point (2D)
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X = [0,0]
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r -- the three points that make up the containing triangular simplex (2D)
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r = [[-1, -1], [0, 2], [1, -1]]
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X - the destination point (2D)
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X = [0,0]
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R - the three points that make up the 2-D triangular simplex
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R = [[-1, -1], [0, 2], [1, -1]]
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this will return [0.333, 0.333, 0.333]
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in 3D:
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X -- the destination point (3D)
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X = [0,0,0]
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R -- the four points that make up the containing simplex, tetrahedron (3D)
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R = [
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[0.0, 0.0, 1.0],
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[0.94280904333606508, 0.0, -0.3333333283722672],
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[-0.47140452166803232, 0.81649658244673617, -0.3333333283722672],
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[-0.47140452166803298, -0.81649658244673584, -0.3333333283722672],
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]
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X - the destination point (3D)
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X = [0,0,0]
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R - the four points that make up the 3-D simplex (tetrahedron)
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R = [
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[ 0.0000, 0.0000, 1.0000],
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[ 0.9428, 0.0000, -0.3333],
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[-0.4714, 0.8165, -0.3333],
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[-0.4714, -0.8165, -0.3333],
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]
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this will return [0.25, 0.25, 0.25, 0.25]
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"""
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# baker: eq 7
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# TODO: perhaps also test len(R[0]) .. ?
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# equations (*\ref{eq:lin3d}*) and (*\ref{eq:lin2d}*)
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if len(X) == 2:
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log.debug("running 2D")
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A = np.array([
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@ -85,7 +83,7 @@ def qlinear(X, R):
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"""
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this calculates the linear portion of q from R to X
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also, this is baker eq 3
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This is equation (*\ref{eq:qlinbasis}*)
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X = destination point
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R = a inter.grid object; must have R.points and R.q
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@ -100,9 +98,12 @@ def qlinear(X, R):
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return phis, qlin
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def get_error(phi, R, S, order = 2):
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#TODO: change the equation names in the comments
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B = [] # baker eq 9
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w = [] # baker eq 11
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"""
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Calculate the error approximation terms, returning the unknowns
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a,b, and c in equation (*\ref{eq:quadratic2d}*).
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"""
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B = [] # equation ((*\ref{eq:B2d}*)
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w = [] # equation ((*\ref{eq:w}*)
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cur_pattern = pattern(len(phi), order)
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log.info("pattern: %s" % cur_pattern)
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@ -150,8 +151,7 @@ def run_baker(X, R, S, order=2):
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This is the main function to call to get an interpolation to X from the
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input meshes
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X -- the destination point (2D)
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X = [0,0]
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X -- the destination point
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R = Simplex
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S = extra points
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@ -190,9 +190,7 @@ def run_baker(X, R, S, order=2):
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def memoize(f):
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"""
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for more information on what I'm doing here,
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please read:
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for more information on what I'm doing here, please read:
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http://en.wikipedia.org/wiki/Memoize
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"""
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cache = {}
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@ -20,8 +20,9 @@ class grid(object):
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"""
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verts = array of arrays (if passed in, will convert to numpy.array)
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[
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[x0,y0],
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[x1,y1], ...
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[x0,y0 <, z0>],
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[x1,y1 <, z1>],
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...
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]
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q = array (1D) of physical values
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@ -97,10 +98,9 @@ class grid(object):
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"""
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this returns two grid objects: R and S.
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R is a grid object that is supposedly a containing simplex around point X
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R is a grid object that is a containing simplex around point X
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S is S_j from baker's paper : some verts from all point that are not the
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simplex
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S : some verts from all points that are not the simplex
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"""
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simplex_size = self.dim + 1
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log.debug("extra verts: %d" % extra_points)
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@ -228,8 +228,9 @@ class cell(object):
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X = point of interest
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G = corrensponding grid object (G.verts)
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because of the way i'm storing things, a cell simply stores indicies, and
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so one must pass in a reference to the grid object containing real verts.
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because of the way i'm storing things, a cell simply stores indicies,
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and so one must pass in a reference to the grid object containing real
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verts.
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this simply calls grid.simplex.contains
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"""
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@ -255,10 +256,6 @@ def contains(X, R):
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tests if X (point) is in R
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R is a simplex, represented by a list of n-degree coordinates
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it now correctly checks for 2/3-D verts
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TODO: write unit test ...
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"""
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phis = get_phis(X, R)
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@ -51,9 +51,6 @@ class dgrid(basegrid):
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def construct_connectivity(self):
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"""
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a call to this method prepares the internal connectivity structure.
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this is part of the __init__ for a interp.grid.delaunay.grid, but can be
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called from any grid object
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"""
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log.info('start')
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qdelaunay_string = get_qdelaunay_dump_str(self)
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@ -1,14 +0,0 @@
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def parse_qhull_file(filename, verbose=False):
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f = open(filename, 'r')
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if verbose:
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print 'filename: ', filename
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degree = int(f.readline().strip())
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print "degree:", degree
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print "number of points", f.readline().strip()
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verts = []
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for p in f:
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v = [float(i) for i in p.strip().split()]
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verts.append(v)
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return verts
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import os
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import inspect
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import numpy as np
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import logging
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@ -27,8 +26,6 @@ def baker_exact_2D(X):
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"""
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x ,y = X
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# TODO: this is not baker's function!! this is:
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# np.power(np.sin(x*np.pi/2.0) * np.sin(y*np.pi/2.0),2)
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answer = np.power((np.sin(x * np.pi) * np.cos(y * np.pi)), 2)
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log.debug(answer)
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return answer
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@ -83,6 +80,3 @@ def improved(qlin, err, final, exact):
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return True
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else:
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return False
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def percent_improvement(answer, exact):
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return np.abs(answer['error']) / exact
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