Major: made scripts pass pep8 and pyflakes
This commit is contained in:
Stephen M. McQuay
@@ -1,40 +1 @@
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import os
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import logging
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import logging.handlers
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import json
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LEVELS = {'debug': logging.DEBUG,
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'info': logging.INFO,
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'warning': logging.WARNING,
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'error': logging.ERROR,
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'critical': logging.CRITICAL}
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default_config = {
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'filename': '/tmp/interp.log',
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'level': 'debug',
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'size' : 102400,
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'logbackup': 10,
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'pypath': None,
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}
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try:
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with open(os.path.expanduser('~/.config/interp.json')) as config_file:
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d = json.load(config_file)
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except IOError as e:
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d = {}
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config = dict(default_config.items() + d.items())
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logger = logging.getLogger('interp')
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logger.setLevel(LEVELS[config['level']])
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my_format = logging.Formatter('%(asctime)s %(levelname)s (%(process)d) %(filename)s %(funcName)s:%(lineno)d %(message)s')
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handler = logging.handlers.RotatingFileHandler(
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config['filename'], maxBytes = config['size'] * 1024, backupCount = config['logbackup'])
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handler.setFormatter(my_format)
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logger.addHandler(handler)
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__version__ = '0.2'
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+167
-183
@@ -1,220 +1,204 @@
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import sys
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import numpy as np
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from functools import wraps
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import itertools
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import interp
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import logging
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log = logging.getLogger('interp')
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AGGRESSIVE_ERROR_SOLVE = True
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RAISE_PATHOLOGICAL_EXCEPTION = False
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__version__ = interp.__version__
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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
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point on a triangle or tetrahedron. This is equation (*\ref{eq:qlinarea}*)
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"""
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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 (Equation (*\ref{eq:qlinarea}*))
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in 2D:
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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 2-D triangular simplex
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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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this will return [0.333, 0.333, 0.333]
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in 3D:
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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 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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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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this will return [0.25, 0.25, 0.25, 0.25]
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"""
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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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[ 1, 1, 1],
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[R[0][0], R[1][0], R[2][0]],
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[R[0][1], R[1][1], R[2][1]],
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])
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b = np.array([ 1,
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X[0],
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X[1]
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])
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elif len(X) == 3:
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log.debug("running 3D")
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A = np.array([
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[ 1, 1, 1, 1 ],
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[R[0][0], R[1][0], R[2][0], R[3][0]],
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[R[0][1], R[1][1], R[2][1], R[3][1]],
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[R[0][2], R[1][2], R[2][2], R[3][2]],
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])
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b = np.array([ 1,
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X[0],
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X[1],
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X[2]
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])
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else:
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raise Exception("inapropriate demension on X")
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try:
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phi = np.linalg.solve(A,b)
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except np.linalg.LinAlgError as e:
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msg = "calculation of phis yielded a linearly dependant system (%s)" % e
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log.error(msg)
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# raise Exception(msg)
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phi = np.dot(np.linalg.pinv(A), b)
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log.debug("phi: %s", phi)
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return phi
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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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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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"""
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phis = get_phis(X, R.verts)
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qlin = np.sum([q_i * phi_i for q_i, phi_i in zip(R.q, phis)])
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log.debug("phis: %s", phis)
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log.debug("qlin: %s", qlin)
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return phis, qlin
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def get_error(phi, R, S, order = 2):
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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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for (s,q) in zip(S.verts, S.q):
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cur_phi, cur_qlin = qlinear(s, R)
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l = []
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for i in cur_pattern:
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cur_sum = cur_phi[i[0]]
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for j in i[1:]:
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cur_sum *= cur_phi[j]
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l.append(cur_sum)
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B.append(l)
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w.append(q - cur_qlin)
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log.info("B: %s" % B)
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log.info("w: %s" % w)
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# equations (*\ref{eq:lin3d}*) and (*\ref{eq:lin2d}*)
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if len(X) == 2:
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A = np.array([
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[1, 1, 1],
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[R[0][0], R[1][0], R[2][0]],
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[R[0][1], R[1][1], R[2][1]],
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])
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b = np.array([1, X[0], X[1]])
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elif len(X) == 3:
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A = np.array([
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[1, 1, 1, 1],
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[R[0][0], R[1][0], R[2][0], R[3][0]],
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[R[0][1], R[1][1], R[2][1], R[3][1]],
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[R[0][2], R[1][2], R[2][2], R[3][2]],
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])
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b = np.array([1, X[0], X[1], X[2]])
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else:
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raise Exception("inapropriate demension on X")
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phi = np.linalg.solve(A, b)
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return phi
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B = np.array(B)
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w = np.array(w)
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def qlinear(X, R, q):
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"""
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this calculates the linear portion of q from R to X
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A = np.dot(B.T, B)
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b = np.dot(B.T, w)
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This is equation (*\ref{eq:qlinbasis}*)
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try:
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abc = np.linalg.solve(A,b)
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except np.linalg.LinAlgError as e:
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log.error("linear calculation went bad, resorting to np.linalg.pinv: %s" % e)
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abc = np.dot(np.linalg.pinv(A), b)
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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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"""
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error_term = 0.0
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for (a, i) in zip(abc, cur_pattern):
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cur_sum = a
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for j in i:
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cur_sum *= phi[j]
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error_term += cur_sum
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phis = get_phis(X, R)
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qlin = np.sum([q_i * phi_i for q_i, phi_i in zip(q, phis)])
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log.debug("error_term: %s" % error_term)
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return error_term, abc
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return phis, qlin
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def run_baker(X, R, S, order=2):
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"""
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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
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def get_error(phi, R, R_q, S, S_q, order=2):
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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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R = Simplex
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S = extra points
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"""
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log.debug("order = %d" % order)
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log.debug("extra points = %d" % len(S.verts))
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cur_pattern = pattern(len(phi), order)
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answer = {
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'qlin': None,
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'error': None,
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'final': None,
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}
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# calculate values only for the simplex triangle
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phi, qlin = qlinear(X, R)
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for (s, cur_q) in zip(S, S_q):
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cur_phi, cur_qlin = qlinear(s, R, R_q)
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l = []
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for i in cur_pattern:
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cur_sum = cur_phi[i[0]]
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for j in i[1:]:
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cur_sum *= cur_phi[j]
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l.append(cur_sum)
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B.append(l)
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w.append(cur_q - cur_qlin)
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B = np.array(B)
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w = np.array(w)
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A = np.dot(B.T, B)
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b = np.dot(B.T, w)
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try:
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abc = np.linalg.solve(A, b)
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except np.linalg.LinAlgError:
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if not AGGRESSIVE_ERROR_SOLVE:
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return None, None
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abc = np.dot(np.linalg.pinv(A), b)
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error_term = 0.0
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for (a, i) in zip(abc, cur_pattern):
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cur_sum = a
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for j in i:
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cur_sum *= phi[j]
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error_term += cur_sum
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return error_term, abc
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def run_baker(X, R, R_q, S, S_q, order=2):
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"""
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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
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R = Simplex
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S = extra points
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"""
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answer = {
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'qlin': None,
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'error': None,
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'final': None,
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}
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# calculate values only for the simplex triangle
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phi, qlin = qlinear(X, R, R_q)
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if order == 1:
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answer['qlin'] = qlin
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answer['final'] = qlin
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return answer
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elif order in xrange(2, 11):
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error_term, abc = get_error(phi, R, R_q, S, S_q, order)
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# if a pathological vertex configuration was encountered and
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# AGGRESSIVE_ERROR_SOLVE is False, get_error will return (None, None)
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# indicating that only linear interpolation should be performed
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if (error_term is None) and (abc is None):
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if RAISE_PATHOLOGICAL_EXCEPTION:
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raise np.linalg.LinAlgError("Pathological Vertex Config")
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answer['qlin'] = qlin
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answer['final'] = qlin
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return answer
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else:
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raise Exception('unsupported order "%d" for baker method' % order)
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q_final = qlin + error_term
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if order == 1:
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answer['qlin'] = qlin
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answer['final'] = qlin
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answer['error'] = error_term
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answer['final'] = q_final
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answer['abc'] = abc
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return answer
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elif order in xrange(2,11):
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error_term, abc = get_error(phi, R, S, order)
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else:
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raise Exception('unsupported order "%d" for baker method' % order)
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q_final = qlin + error_term
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answer['qlin' ] = qlin
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answer['error'] = error_term
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answer['final'] = q_final
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answer['abc' ] = abc
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log.debug(answer)
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return answer
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def memoize(f):
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"""
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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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@wraps(f)
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def memf(simplex_size, nu):
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x = (simplex_size, nu)
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if x not in cache:
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log.debug("adding to cache: %s", x)
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cache[x] = f(simplex_size, nu)
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return cache[x]
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return memf
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"""
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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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@wraps(f)
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def memf(simplex_size, nu):
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x = (simplex_size, nu)
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if x not in cache:
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cache[x] = f(simplex_size, nu)
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return cache[x]
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return memf
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@memoize
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def pattern(simplex_size, nu):
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"""
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This function returns the pattern requisite to compose the error
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approximation function, and the matrix B.
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"""
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log.debug("pattern: simplex: %d, order: %d" % (simplex_size, nu))
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"""
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This function returns the pattern requisite to compose the error
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approximation function, and the matrix B.
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"""
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r = []
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for i in itertools.product(xrange(simplex_size), repeat = nu):
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if len(set(i)) !=1:
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r.append(tuple(sorted(i)))
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unique_r = list(set(r))
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return unique_r
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r = []
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for i in itertools.product(xrange(simplex_size), repeat=nu):
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if len(set(i)) != 1:
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r.append(tuple(sorted(i)))
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unique_r = list(set(r))
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return unique_r
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+4
-3
@@ -5,12 +5,13 @@ import rlcompleter
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historyPath = os.path.expanduser("~/.pyhistory")
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def save_history(historyPath=historyPath):
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import readline
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readline.write_history_file(historyPath)
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import readline
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readline.write_history_file(historyPath)
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if os.path.exists(historyPath):
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readline.read_history_file(historyPath)
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readline.read_history_file(historyPath)
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atexit.register(save_history)
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del os, atexit, readline, rlcompleter, save_history, historyPath
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+20
-18
@@ -1,28 +1,30 @@
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from multiprocessing.managers import BaseManager
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import Queue
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tasks_q = Queue.Queue()
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tasks_q = Queue.Queue()
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results_q = Queue.Queue()
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minions_q = Queue.Queue()
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master_q = Queue.Queue()
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master_q = Queue.Queue()
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class QueueManager(BaseManager):
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"""
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One QueueManager to rule all network Queues
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"""
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pass
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"""
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One QueueManager to rule all network Queues
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"""
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pass
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QueueManager.register('get_tasks_q', callable=lambda: tasks_q)
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QueueManager.register('get_results_q', callable=lambda: results_q)
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QueueManager.register('get_minions_q', callable=lambda: minions_q)
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QueueManager.register('get_master_q', callable=lambda: master_q)
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QueueManager.register('get_tasks_q' , callable=lambda:tasks_q )
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QueueManager.register('get_results_q', callable=lambda:results_q )
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QueueManager.register('get_minions_q', callable=lambda:minions_q )
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QueueManager.register('get_master_q' , callable=lambda:master_q )
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def get_qs(qm):
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"""
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pass in a QueueManager, and this function returns all relevant
|
||||
queues attached to that QueueManager.
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"""
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return (qm.get_tasks_q(),
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||||
qm.get_results_q(),
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||||
qm.get_master_q(),
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||||
qm.get_minions_q())
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||||
"""
|
||||
pass in a QueueManager, and this function returns all relevant
|
||||
queues attached to that QueueManager.
|
||||
"""
|
||||
return (qm.get_tasks_q(),
|
||||
qm.get_results_q(),
|
||||
qm.get_master_q(),
|
||||
qm.get_minions_q())
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
import os
|
||||
|
||||
import json
|
||||
|
||||
default_config = {
|
||||
'filename': '/tmp/interp.log',
|
||||
'level': 'debug',
|
||||
'size': 102400,
|
||||
'logbackup': 10,
|
||||
'pypath': None,
|
||||
}
|
||||
|
||||
try:
|
||||
with open(os.path.expanduser('~/.config/interp.json')) as config_file:
|
||||
d = json.load(config_file)
|
||||
except IOError as e:
|
||||
d = {}
|
||||
|
||||
config = dict(default_config.items() + d.items())
|
||||
+2
-3
@@ -1,10 +1,9 @@
|
||||
from interp.grid.delaunay import dgrid as basegrid
|
||||
from interp.tools import baker_exact_2D as exact_func
|
||||
|
||||
from itertools import product
|
||||
|
||||
import numpy as np
|
||||
|
||||
from interp.grid.delaunay import dgrid as basegrid
|
||||
|
||||
class rect_grid(basegrid):
|
||||
def __init__(self, xres = 5, yres = 5):
|
||||
xmin = 0.0
|
||||
|
||||
+2
-6
@@ -1,10 +1,9 @@
|
||||
from interp.grid.delaunay import dgrid as basegrid
|
||||
from interp.tools import baker_exact_3D, log
|
||||
|
||||
from itertools import product
|
||||
|
||||
import numpy as np
|
||||
|
||||
from interp.grid.delaunay import dgrid as basegrid
|
||||
|
||||
class rect_grid(basegrid):
|
||||
def __init__(self, xres = 5, yres = 5, zres = 5):
|
||||
xmin = 0.0
|
||||
@@ -22,7 +21,6 @@ class rect_grid(basegrid):
|
||||
zspan = zmaz - zmin
|
||||
zdel = zspan / float(zres - 1)
|
||||
|
||||
|
||||
verts = []
|
||||
q = np.zeros(xres * yres * zres)
|
||||
for x in xrange(xres):
|
||||
@@ -41,8 +39,6 @@ class random_grid(rect_grid):
|
||||
def __init__(self, num_verts = 100):
|
||||
verts = []
|
||||
|
||||
r = np.random
|
||||
|
||||
appx_side_res = int(np.power(num_verts, 1/3.0))
|
||||
delta = 1.0 / float(appx_side_res)
|
||||
|
||||
|
||||
+199
-191
@@ -1,5 +1,4 @@
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from collections import defaultdict
|
||||
import pickle
|
||||
|
||||
from xml.dom.minidom import Document
|
||||
@@ -9,256 +8,265 @@ from scipy.spatial import KDTree
|
||||
|
||||
from interp.baker import run_baker
|
||||
from interp.baker import get_phis
|
||||
import interp
|
||||
|
||||
import logging
|
||||
log = logging.getLogger("interp")
|
||||
|
||||
MAX_SEARCH_COUNT = 256
|
||||
TOL = 1e-8
|
||||
|
||||
__version__ = interp.__version__
|
||||
|
||||
|
||||
class grid(object):
|
||||
def __init__(self, verts = None, q = None):
|
||||
"""
|
||||
verts = array of arrays (if passed in, will convert to numpy.array)
|
||||
[
|
||||
[x0,y0 <, z0>],
|
||||
[x1,y1 <, z1>],
|
||||
...
|
||||
]
|
||||
def __init__(self, verts=None, q=None):
|
||||
"""
|
||||
verts = array of arrays (if passed in, will convert to numpy.array)
|
||||
[
|
||||
[x0,y0 <, z0>],
|
||||
[x1,y1 <, z1>],
|
||||
...
|
||||
]
|
||||
|
||||
q = array (1D) of physical values
|
||||
"""
|
||||
q = array (1D) of physical values
|
||||
"""
|
||||
|
||||
if verts != None:
|
||||
self.verts = np.array(verts)
|
||||
self.tree = KDTree(self.verts)
|
||||
if verts != None:
|
||||
self.verts = np.array(verts)
|
||||
self.tree = KDTree(self.verts)
|
||||
|
||||
if q != None:
|
||||
self.q = np.array(q)
|
||||
if q != None:
|
||||
self.q = np.array(q)
|
||||
|
||||
self.cells = {}
|
||||
self.cells_for_vert = defaultdict(list)
|
||||
self.cells = {}
|
||||
self.cells_for_vert = defaultdict(list)
|
||||
|
||||
def get_containing_simplex(self, X):
|
||||
if not self.cells:
|
||||
raise Exception("cell connectivity is not set up")
|
||||
def get_containing_simplex(self, X):
|
||||
if not self.cells:
|
||||
raise Exception("cell connectivity is not set up")
|
||||
|
||||
# get closest point
|
||||
(dist, indicies) = self.tree.query(X, 2)
|
||||
closest_point = indicies[0]
|
||||
# get closest point
|
||||
(dist, indicies) = self.tree.query(X, 2)
|
||||
closest_point = indicies[0]
|
||||
|
||||
log.debug('X: %s' % X)
|
||||
log.debug('point index: %d' % closest_point)
|
||||
log.debug('actual point %s' % self.verts[closest_point])
|
||||
log.debug('distance = %0.4f' % dist[0])
|
||||
log.debug('X: %s' % X)
|
||||
log.debug('point index: %d' % closest_point)
|
||||
log.debug('actual point %s' % self.verts[closest_point])
|
||||
log.debug('distance = %0.4f' % dist[0])
|
||||
|
||||
simplex = None
|
||||
checked_cells = []
|
||||
cells_to_check = list(self.cells_for_vert[closest_point])
|
||||
simplex = None
|
||||
checked_cells = []
|
||||
cells_to_check = list(self.cells_for_vert[closest_point])
|
||||
|
||||
attempts = 0
|
||||
while not simplex and cells_to_check:
|
||||
attempts += 1
|
||||
attempts = 0
|
||||
while not simplex and cells_to_check:
|
||||
attempts += 1
|
||||
|
||||
if attempts > MAX_SEARCH_COUNT:
|
||||
raise Exception("Is the search becoming exhaustive? (%d attempts)" % attempts)
|
||||
if attempts > MAX_SEARCH_COUNT:
|
||||
raise Exception("Is the search becoming exhaustive?'\
|
||||
'(%d attempts)" % attempts)
|
||||
|
||||
cur_cell = cells_to_check.pop(0)
|
||||
checked_cells.append(cur_cell)
|
||||
cur_cell = cells_to_check.pop(0)
|
||||
checked_cells.append(cur_cell)
|
||||
|
||||
if cur_cell.contains(X, self):
|
||||
simplex = cur_cell
|
||||
continue
|
||||
if cur_cell.contains(X, self):
|
||||
simplex = cur_cell
|
||||
continue
|
||||
|
||||
for neighbor in cur_cell.neighbors:
|
||||
if (neighbor not in checked_cells) and (neighbor not in cells_to_check):
|
||||
cells_to_check.append(neighbor)
|
||||
for neighbor in cur_cell.neighbors:
|
||||
if (neighbor not in checked_cells) \
|
||||
and (neighbor not in cells_to_check):
|
||||
cells_to_check.append(neighbor)
|
||||
|
||||
if not simplex:
|
||||
raise Exception('no containing simplex found')
|
||||
if not simplex:
|
||||
raise Exception('no containing simplex found')
|
||||
|
||||
log.debug("simplex vert indicies: %s" % simplex.verts)
|
||||
R = self.create_mesh(simplex.verts)
|
||||
log.debug("R:\n%s", R)
|
||||
log.debug("simplex vert indicies: %s" % simplex.verts)
|
||||
R = self.create_mesh(simplex.verts)
|
||||
log.debug("R:\n%s", R)
|
||||
|
||||
log.debug('total attempts before finding simplex: %d' % attempts)
|
||||
return R
|
||||
log.debug('total attempts before finding simplex: %d' % attempts)
|
||||
return R
|
||||
|
||||
def create_mesh(self, indicies):
|
||||
"""
|
||||
this function takes a list of indicies, and then creates and returns a
|
||||
grid object (collection of verts and q).
|
||||
def create_mesh(self, indicies):
|
||||
"""
|
||||
this function takes a list of indicies, and then creates and
|
||||
returns a grid object (collection of verts and q).
|
||||
|
||||
note: the input is indicies, the grid contains verts
|
||||
"""
|
||||
note: the input is indicies, the grid contains verts
|
||||
"""
|
||||
|
||||
return grid(self.verts[indicies], self.q[indicies])
|
||||
return grid(self.verts[indicies], self.q[indicies])
|
||||
|
||||
def get_simplex_and_nearest_points(self, X, extra_points = 3):
|
||||
"""
|
||||
this returns two grid objects: R and S.
|
||||
def get_simplex_and_nearest_points(self, X, extra_points=3):
|
||||
"""
|
||||
this returns two grid objects: R and S.
|
||||
|
||||
R is a grid object that is a containing simplex around point X
|
||||
R is a grid object that is a containing simplex around point X
|
||||
|
||||
S : some verts from all points that are not the simplex
|
||||
"""
|
||||
simplex_size = self.dim + 1
|
||||
log.debug("extra verts: %d" % extra_points)
|
||||
log.debug("simplex size: %d" % simplex_size)
|
||||
S : some verts from all points that are not the simplex
|
||||
"""
|
||||
simplex_size = self.dim + 1
|
||||
log.debug("extra verts: %d" % extra_points)
|
||||
log.debug("simplex size: %d" % simplex_size)
|
||||
|
||||
r_mesh = self.get_containing_simplex(X)
|
||||
r_mesh = self.get_containing_simplex(X)
|
||||
|
||||
# and some UNIQUE extra verts
|
||||
(dist, indicies) = self.tree.query(X, simplex_size + extra_points)
|
||||
log.debug("extra indicies: %s" % indicies)
|
||||
# and some UNIQUE extra verts
|
||||
(dist, indicies) = self.tree.query(X, simplex_size + extra_points)
|
||||
log.debug("extra indicies: %s" % indicies)
|
||||
|
||||
unique_indicies = []
|
||||
for index in indicies:
|
||||
close_point_in_R = False
|
||||
for rvert in r_mesh.verts:
|
||||
if all(rvert == self.verts[index]):
|
||||
close_point_in_R = True
|
||||
break
|
||||
unique_indicies = []
|
||||
for index in indicies:
|
||||
close_point_in_R = False
|
||||
for rvert in r_mesh.verts:
|
||||
if all(rvert == self.verts[index]):
|
||||
close_point_in_R = True
|
||||
break
|
||||
|
||||
if not close_point_in_R:
|
||||
unique_indicies.append(index)
|
||||
else:
|
||||
log.debug('throwing out %s: %s' % (index, self.verts[index]))
|
||||
if not close_point_in_R:
|
||||
unique_indicies.append(index)
|
||||
else:
|
||||
log.debug('throwing out %s: %s' % (index, self.verts[index]))
|
||||
|
||||
log.debug("indicies: %s" % indicies)
|
||||
log.debug("unique indicies: %s" % unique_indicies)
|
||||
s_mesh = self.create_mesh(unique_indicies)
|
||||
log.debug("indicies: %s" % indicies)
|
||||
log.debug("unique indicies: %s" % unique_indicies)
|
||||
s_mesh = self.create_mesh(unique_indicies)
|
||||
|
||||
return (r_mesh, s_mesh)
|
||||
return (r_mesh, s_mesh)
|
||||
|
||||
def run_baker(self, X, order = 2, extra_points = 3):
|
||||
(R, S) = self.get_simplex_and_nearest_points(X, extra_points)
|
||||
answer = run_baker(X, R, S, order)
|
||||
return answer
|
||||
def run_baker(self, X, order=2, extra_points=3):
|
||||
(R, S) = self.get_simplex_and_nearest_points(X, extra_points)
|
||||
answer = run_baker(X, R, S, order)
|
||||
return answer
|
||||
|
||||
def for_qhull_generator(self):
|
||||
"""
|
||||
this returns a generator that should be fed into qdelaunay
|
||||
"""
|
||||
def for_qhull_generator(self):
|
||||
"""
|
||||
this returns a generator that should be fed into qdelaunay
|
||||
"""
|
||||
|
||||
yield str(len(self.verts[0]));
|
||||
yield '%d' % len(self.verts)
|
||||
yield str(len(self.verts[0]))
|
||||
yield '%d' % len(self.verts)
|
||||
|
||||
for p in self.verts:
|
||||
yield "%f %f %f" % tuple(p)
|
||||
for p in self.verts:
|
||||
yield "%f %f %f" % tuple(p)
|
||||
|
||||
def for_qhull(self):
|
||||
"""
|
||||
this returns a single string that should be fed into qdelaunay
|
||||
"""
|
||||
r = '%d\n' % len(self.verts[0])
|
||||
r += '%d\n' % len(self.verts)
|
||||
for p in self.verts:
|
||||
# r += "%f %f %f\n" % tuple(p)
|
||||
r += "%s\n" % " ".join("%f" % i for i in p)
|
||||
return r
|
||||
def for_qhull(self):
|
||||
"""
|
||||
this returns a single string that should be fed into qdelaunay
|
||||
"""
|
||||
r = '%d\n' % len(self.verts[0])
|
||||
r += '%d\n' % len(self.verts)
|
||||
for p in self.verts:
|
||||
# r += "%f %f %f\n" % tuple(p)
|
||||
r += "%s\n" % " ".join("%f" % i for i in p)
|
||||
return r
|
||||
|
||||
def __str__(self):
|
||||
r = ''
|
||||
assert( len(self.verts) == len(self.q) )
|
||||
for c, i in enumerate(zip(self.verts, self.q)):
|
||||
r += "%d vert(%s): q(%0.4f)" % (c,i[0], i[1])
|
||||
cell_str = ", ".join([str(f.name) for f in self.cells_for_vert[c]])
|
||||
r += " cells: [%s]" % cell_str
|
||||
r += "\n"
|
||||
if self.cells:
|
||||
for v in self.cells.itervalues():
|
||||
r += "%s\n" % v
|
||||
return r
|
||||
def __str__(self):
|
||||
r = ''
|
||||
assert(len(self.verts) == len(self.q))
|
||||
for c, i in enumerate(zip(self.verts, self.q)):
|
||||
r += "%d vert(%s): q(%0.4f)" % (c, i[0], i[1])
|
||||
cell_str = ", ".join([str(f.name) for f in self.cells_for_vert[c]])
|
||||
r += " cells: [%s]" % cell_str
|
||||
r += "\n"
|
||||
if self.cells:
|
||||
for v in self.cells.itervalues():
|
||||
r += "%s\n" % v
|
||||
return r
|
||||
|
||||
def normalize_q(self, new_max = 0.1):
|
||||
largest_number = np.max(np.abs(self.q))
|
||||
self.q *= new_max/largest_number
|
||||
def normalize_q(self, new_max=0.1):
|
||||
largest_number = np.max(np.abs(self.q))
|
||||
self.q *= new_max / largest_number
|
||||
|
||||
def dump_to_blender_files(self,
|
||||
pfile='/tmp/points.p', cfile='/tmp/cells.p'):
|
||||
if len(self.verts[0]) == 2:
|
||||
pickle.dump([(p[0], p[1], 0.0) for p in self.verts],
|
||||
open(pfile, 'w'))
|
||||
else:
|
||||
pickle.dump([(p[0], p[1], p[2]) for p in self.verts],
|
||||
open(pfile, 'w'))
|
||||
|
||||
def dump_to_blender_files(self, pfile = '/tmp/points.p', cfile = '/tmp/cells.p'):
|
||||
if len(self.verts[0]) == 2:
|
||||
pickle.dump([(p[0], p[1], 0.0) for p in self.verts], open(pfile, 'w'))
|
||||
else:
|
||||
pickle.dump([(p[0], p[1], p[2]) for p in self.verts], open(pfile, 'w'))
|
||||
pickle.dump([f.verts for f in self.cells.itervalues()],
|
||||
open(cfile, 'w'))
|
||||
|
||||
pickle.dump([f.verts for f in self.cells.itervalues()], open(cfile, 'w'))
|
||||
def get_xml(self):
|
||||
doc = Document()
|
||||
ps = doc.createElement("points")
|
||||
doc.appendChild(ps)
|
||||
for i in zip(self.verts, self.q):
|
||||
p = doc.createElement("point")
|
||||
|
||||
def get_xml(self):
|
||||
doc = Document()
|
||||
ps = doc.createElement("points")
|
||||
doc.appendChild(ps)
|
||||
for i in zip(self.verts, self.q):
|
||||
p = doc.createElement("point")
|
||||
p.setAttribute("x", str(i[0][0]))
|
||||
p.setAttribute('y', str(i[0][1]))
|
||||
p.setAttribute('z', str(i[0][2]))
|
||||
p.setAttribute('q', str(i[1]))
|
||||
ps.appendChild(p)
|
||||
|
||||
p.setAttribute("x", str(i[0][0]))
|
||||
p.setAttribute('y', str(i[0][1]))
|
||||
p.setAttribute('z', str(i[0][2]))
|
||||
p.setAttribute('q', str(i[1] ))
|
||||
ps.appendChild(p)
|
||||
return doc
|
||||
|
||||
return doc
|
||||
def toxml(self):
|
||||
return self.get_xml().toxml()
|
||||
|
||||
def toxml(self):
|
||||
return self.get_xml().toxml()
|
||||
def toprettyxml(self):
|
||||
return self.get_xml().toprettyxml()
|
||||
def toprettyxml(self):
|
||||
return self.get_xml().toprettyxml()
|
||||
|
||||
|
||||
class cell(object):
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.verts = []
|
||||
self.neighbors = []
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.verts = []
|
||||
self.neighbors = []
|
||||
|
||||
def add_vert(self, v):
|
||||
"""
|
||||
v should be an index into grid.verts
|
||||
"""
|
||||
self.verts.append(v)
|
||||
def add_vert(self, v):
|
||||
"""
|
||||
v should be an index into grid.verts
|
||||
"""
|
||||
self.verts.append(v)
|
||||
|
||||
def add_neighbor(self, n):
|
||||
"""
|
||||
reference to another cell object
|
||||
"""
|
||||
self.neighbors.append(n)
|
||||
def add_neighbor(self, n):
|
||||
"""
|
||||
reference to another cell object
|
||||
"""
|
||||
self.neighbors.append(n)
|
||||
|
||||
def contains(self, X, G):
|
||||
"""
|
||||
X = point of interest
|
||||
G = corrensponding grid object (G.verts)
|
||||
def contains(self, X, G):
|
||||
"""
|
||||
X = point of interest
|
||||
G = corrensponding grid object (G.verts)
|
||||
|
||||
because of the way i'm storing things, a cell simply stores indicies,
|
||||
and so one must pass in a reference to the grid object containing real
|
||||
verts.
|
||||
because of the way i'm storing things, a cell simply stores
|
||||
indicies, and so one must pass in a reference to the grid object
|
||||
containing real verts.
|
||||
|
||||
this simply calls grid.simplex.contains
|
||||
"""
|
||||
return contains(X, [G.verts[i] for i in self.verts])
|
||||
this simply calls grid.simplex.contains
|
||||
"""
|
||||
return contains(X, [G.verts[i] for i in self.verts])
|
||||
|
||||
def __str__(self):
|
||||
# neighbors = [str(i.name) for i in self.neighbors]
|
||||
return '<cell %s: verts: %s neighbor count: %s>' %\
|
||||
(
|
||||
self.name,
|
||||
self.verts,
|
||||
len(self.neighbors),
|
||||
# ", ".join(neighbors)
|
||||
)
|
||||
def __str__(self):
|
||||
# neighbors = [str(i.name) for i in self.neighbors]
|
||||
return '<cell %s: verts: %s neighbor count: %s>' %\
|
||||
(
|
||||
self.name,
|
||||
self.verts,
|
||||
len(self.neighbors),
|
||||
# ", ".join(neighbors)
|
||||
)
|
||||
|
||||
__repr__ = __str__
|
||||
__repr__ = __str__
|
||||
|
||||
|
||||
TOL = 1e-8
|
||||
|
||||
def contains(X, R):
|
||||
"""
|
||||
tests if X (point) is in R
|
||||
"""
|
||||
tests if X (point) is in R
|
||||
|
||||
R is a simplex, represented by a list of n-degree coordinates
|
||||
"""
|
||||
phis = get_phis(X, R)
|
||||
R is a simplex, represented by a list of n-degree coordinates
|
||||
"""
|
||||
phis = get_phis(X, R)
|
||||
|
||||
r = True
|
||||
if [i for i in phis if i < 0.0 - TOL]:
|
||||
r = False
|
||||
return r
|
||||
r = True
|
||||
if [i for i in phis if i < 0.0 - TOL]:
|
||||
r = False
|
||||
return r
|
||||
|
||||
+1
-4
@@ -1,7 +1,4 @@
|
||||
import pickle
|
||||
|
||||
from itertools import combinations
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
from scipy.spatial import KDTree
|
||||
@@ -36,7 +33,7 @@ class ggrid(grid):
|
||||
|
||||
|
||||
gmsh_file.readline() # $MeshFormat
|
||||
fmat = gmsh_file.readline()
|
||||
gmsh_file.readline()
|
||||
gmsh_file.readline() # $EndMeshFormat
|
||||
|
||||
gmsh_file.readline() # $Nodes
|
||||
|
||||
+52
-59
@@ -1,82 +1,75 @@
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
|
||||
import logging
|
||||
log = logging.getLogger("interp")
|
||||
|
||||
def rms(errors):
|
||||
"""
|
||||
root mean square calculation
|
||||
"""
|
||||
"""
|
||||
root mean square calculation
|
||||
"""
|
||||
|
||||
# slow pure python way for reference:
|
||||
# r = 0.0
|
||||
# for i in errors:
|
||||
# r += np.power(i, 2)
|
||||
# r = np.sqrt(r / len(errors))
|
||||
# return r
|
||||
# slow pure python way for reference:
|
||||
# r = 0.0
|
||||
# for i in errors:
|
||||
# r += np.power(i, 2)
|
||||
# r = np.sqrt(r / len(errors))
|
||||
# return r
|
||||
|
||||
return np.sqrt((errors ** 2).mean())
|
||||
|
||||
return np.sqrt((errors**2).mean())
|
||||
|
||||
def baker_exact_2D(X):
|
||||
"""
|
||||
the exact function (2D) used from baker's article (for testing, slightly
|
||||
modified)
|
||||
"""
|
||||
x ,y = X
|
||||
"""
|
||||
the exact function (2D) used from baker's article (for testing,
|
||||
slightly modified)
|
||||
"""
|
||||
x, y = X
|
||||
|
||||
answer = np.power((np.sin(x * np.pi) * np.cos(y * np.pi)), 2)
|
||||
return answer
|
||||
|
||||
answer = np.power((np.sin(x * np.pi) * np.cos(y * np.pi)), 2)
|
||||
log.debug(answer)
|
||||
return answer
|
||||
|
||||
def friendly_exact_2D(X):
|
||||
"""
|
||||
A friendlier 2D func
|
||||
"""
|
||||
x ,y = X
|
||||
answer = 1.0 + x*x + y*y
|
||||
log.debug(answer)
|
||||
return answer
|
||||
"""
|
||||
A friendlier 2D func
|
||||
"""
|
||||
x, y = X
|
||||
answer = 1.0 + x * x + y * y
|
||||
return answer
|
||||
|
||||
|
||||
def baker_exact_3D(X):
|
||||
"""
|
||||
the exact function (3D) used from baker's article (for testing)
|
||||
"""
|
||||
x = X[0]
|
||||
y = X[1]
|
||||
z = X[2]
|
||||
answer = np.power((np.sin(x * np.pi / 2.0) * np.sin(y * np.pi / 2.0) * np.sin(z * np.pi / 2.0)), 2)
|
||||
log.debug(answer)
|
||||
return answer
|
||||
"""
|
||||
the exact function (3D) used from baker's article (for testing)
|
||||
"""
|
||||
x, y, z = X
|
||||
answer = np.power((np.sin(x * np.pi / 2.0) * np.sin(y * np.pi / 2.0) *
|
||||
np.sin(z * np.pi / 2.0)), 2)
|
||||
return answer
|
||||
|
||||
|
||||
def friendly_exact_3D(X):
|
||||
x,y,z = X
|
||||
return 1 + x*x + y*y + z*z
|
||||
x, y, z = X
|
||||
return 1 + x * x + y * y + z * z
|
||||
|
||||
|
||||
def scipy_exact_2D(X):
|
||||
x,y = X
|
||||
return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2
|
||||
x, y = X
|
||||
return x * (1 - x) * np.cos(4 * np.pi * x) *\
|
||||
np.sin(4 * np.pi * y ** 2) ** 2
|
||||
|
||||
|
||||
def improved_answer(answer, exact):
|
||||
if not answer['error']:
|
||||
# was probably just a linear interpolation
|
||||
return False
|
||||
if not answer['error']:
|
||||
# was probably just a linear interpolation
|
||||
return False
|
||||
|
||||
log.debug('qlin: %s' % answer['qlin'])
|
||||
log.debug('error: %s' % answer['error'])
|
||||
log.debug('final: %s' % answer['final'])
|
||||
log.debug('exact: %s' % exact)
|
||||
if np.abs(answer['final'] - exact) <= np.abs(answer['qlin'] - exact):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
if np.abs(answer['final'] - exact) <= np.abs(answer['qlin'] - exact):
|
||||
log.debug(":) improved result")
|
||||
return True
|
||||
else:
|
||||
log.debug(":( damaged result")
|
||||
return False
|
||||
|
||||
def improved(qlin, err, final, exact):
|
||||
if np.abs(final - exact) <= np.abs(qlin - exact):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
if np.abs(final - exact) <= np.abs(qlin - exact):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
Reference in New Issue
Block a user