1307 lines
48 KiB
Python
1307 lines
48 KiB
Python
# -*- coding: utf-8 -*-
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"""
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functions.py - Miscellaneous functions with no other home
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Copyright 2010 Luke Campagnola
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Distributed under MIT/X11 license. See license.txt for more infomation.
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"""
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from .python2_3 import asUnicode
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Colors = {
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'b': (0,0,255,255),
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'g': (0,255,0,255),
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'r': (255,0,0,255),
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'c': (0,255,255,255),
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'm': (255,0,255,255),
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'y': (255,255,0,255),
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'k': (0,0,0,255),
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'w': (255,255,255,255),
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}
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SI_PREFIXES = asUnicode('yzafpnµm kMGTPEZY')
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SI_PREFIXES_ASCII = 'yzafpnum kMGTPEZY'
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from .Qt import QtGui, QtCore
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import numpy as np
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import scipy.ndimage
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import decimal, re
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try:
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import scipy.weave
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USE_WEAVE = True
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except ImportError:
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USE_WEAVE = False
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from . import debug
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def siScale(x, minVal=1e-25, allowUnicode=True):
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"""
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Return the recommended scale factor and SI prefix string for x.
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Example::
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siScale(0.0001) # returns (1e6, 'μ')
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# This indicates that the number 0.0001 is best represented as 0.0001 * 1e6 = 100 μUnits
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"""
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if isinstance(x, decimal.Decimal):
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x = float(x)
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try:
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if np.isnan(x) or np.isinf(x):
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return(1, '')
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except:
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print(x, type(x))
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raise
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if abs(x) < minVal:
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m = 0
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x = 0
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else:
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m = int(np.clip(np.floor(np.log(abs(x))/np.log(1000)), -9.0, 9.0))
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if m == 0:
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pref = ''
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elif m < -8 or m > 8:
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pref = 'e%d' % (m*3)
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else:
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if allowUnicode:
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pref = SI_PREFIXES[m+8]
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else:
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pref = SI_PREFIXES_ASCII[m+8]
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p = .001**m
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return (p, pref)
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def siFormat(x, precision=3, suffix='', space=True, error=None, minVal=1e-25, allowUnicode=True):
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"""
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Return the number x formatted in engineering notation with SI prefix.
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Example::
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siFormat(0.0001, suffix='V') # returns "100 μV"
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"""
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if space is True:
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space = ' '
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if space is False:
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space = ''
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(p, pref) = siScale(x, minVal, allowUnicode)
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if not (len(pref) > 0 and pref[0] == 'e'):
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pref = space + pref
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if error is None:
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fmt = "%." + str(precision) + "g%s%s"
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return fmt % (x*p, pref, suffix)
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else:
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if allowUnicode:
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plusminus = space + asUnicode("±") + space
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else:
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plusminus = " +/- "
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fmt = "%." + str(precision) + "g%s%s%s%s"
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return fmt % (x*p, pref, suffix, plusminus, siFormat(error, precision=precision, suffix=suffix, space=space, minVal=minVal))
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def siEval(s):
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"""
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Convert a value written in SI notation to its equivalent prefixless value
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Example::
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siEval("100 μV") # returns 0.0001
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"""
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s = asUnicode(s)
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m = re.match(r'(-?((\d+(\.\d*)?)|(\.\d+))([eE]-?\d+)?)\s*([u' + SI_PREFIXES + r']?)$', s)
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if m is None:
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raise Exception("Can't convert string '%s' to number." % s)
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v = float(m.groups()[0])
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p = m.groups()[6]
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#if p not in SI_PREFIXES:
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#raise Exception("Can't convert string '%s' to number--unknown prefix." % s)
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if p == '':
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n = 0
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elif p == 'u':
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n = -2
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else:
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n = SI_PREFIXES.index(p) - 8
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return v * 1000**n
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class Color(QtGui.QColor):
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def __init__(self, *args):
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QtGui.QColor.__init__(self, mkColor(*args))
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def glColor(self):
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"""Return (r,g,b,a) normalized for use in opengl"""
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return (self.red()/255., self.green()/255., self.blue()/255., self.alpha()/255.)
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def __getitem__(self, ind):
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return (self.red, self.green, self.blue, self.alpha)[ind]()
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def mkColor(*args):
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"""
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Convenience function for constructing QColor from a variety of argument types. Accepted arguments are:
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================ ================================================
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'c' one of: r, g, b, c, m, y, k, w
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R, G, B, [A] integers 0-255
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(R, G, B, [A]) tuple of integers 0-255
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float greyscale, 0.0-1.0
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int see :func:`intColor() <pyqtgraph.intColor>`
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(int, hues) see :func:`intColor() <pyqtgraph.intColor>`
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"RGB" hexadecimal strings; may begin with '#'
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"RGBA"
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"RRGGBB"
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"RRGGBBAA"
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QColor QColor instance; makes a copy.
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================ ================================================
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"""
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err = 'Not sure how to make a color from "%s"' % str(args)
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if len(args) == 1:
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if isinstance(args[0], QtGui.QColor):
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return QtGui.QColor(args[0])
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elif isinstance(args[0], float):
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r = g = b = int(args[0] * 255)
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a = 255
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elif isinstance(args[0], basestring):
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c = args[0]
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if c[0] == '#':
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c = c[1:]
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if len(c) == 1:
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(r, g, b, a) = Colors[c]
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if len(c) == 3:
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r = int(c[0]*2, 16)
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g = int(c[1]*2, 16)
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b = int(c[2]*2, 16)
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a = 255
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elif len(c) == 4:
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r = int(c[0]*2, 16)
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g = int(c[1]*2, 16)
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b = int(c[2]*2, 16)
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a = int(c[3]*2, 16)
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elif len(c) == 6:
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r = int(c[0:2], 16)
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g = int(c[2:4], 16)
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b = int(c[4:6], 16)
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a = 255
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elif len(c) == 8:
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r = int(c[0:2], 16)
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g = int(c[2:4], 16)
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b = int(c[4:6], 16)
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a = int(c[6:8], 16)
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elif hasattr(args[0], '__len__'):
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if len(args[0]) == 3:
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(r, g, b) = args[0]
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a = 255
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elif len(args[0]) == 4:
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(r, g, b, a) = args[0]
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elif len(args[0]) == 2:
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return intColor(*args[0])
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else:
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raise Exception(err)
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elif type(args[0]) == int:
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return intColor(args[0])
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else:
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raise Exception(err)
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elif len(args) == 3:
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(r, g, b) = args
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a = 255
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elif len(args) == 4:
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(r, g, b, a) = args
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else:
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raise Exception(err)
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args = [r,g,b,a]
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args = [0 if np.isnan(a) or np.isinf(a) else a for a in args]
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args = list(map(int, args))
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return QtGui.QColor(*args)
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def mkBrush(*args):
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"""
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| Convenience function for constructing Brush.
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| This function always constructs a solid brush and accepts the same arguments as :func:`mkColor() <pyqtgraph.mkColor>`
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| Calling mkBrush(None) returns an invisible brush.
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"""
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if len(args) == 1:
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arg = args[0]
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if arg is None:
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return QtGui.QBrush(QtCore.Qt.NoBrush)
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elif isinstance(arg, QtGui.QBrush):
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return QtGui.QBrush(arg)
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else:
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color = arg
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if len(args) > 1:
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color = args
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return QtGui.QBrush(mkColor(color))
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def mkPen(*args, **kargs):
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"""
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Convenience function for constructing QPen.
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Examples::
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mkPen(color)
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mkPen(color, width=2)
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mkPen(cosmetic=False, width=4.5, color='r')
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mkPen({'color': "FF0", width: 2})
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mkPen(None) # (no pen)
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In these examples, *color* may be replaced with any arguments accepted by :func:`mkColor() <pyqtgraph.mkColor>` """
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color = kargs.get('color', None)
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width = kargs.get('width', 1)
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style = kargs.get('style', None)
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cosmetic = kargs.get('cosmetic', True)
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hsv = kargs.get('hsv', None)
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if len(args) == 1:
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arg = args[0]
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if isinstance(arg, dict):
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return mkPen(**arg)
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if isinstance(arg, QtGui.QPen):
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return arg
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elif arg is None:
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style = QtCore.Qt.NoPen
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else:
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color = arg
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if len(args) > 1:
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color = args
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if color is None:
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color = mkColor(200, 200, 200)
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if hsv is not None:
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color = hsvColor(*hsv)
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else:
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color = mkColor(color)
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pen = QtGui.QPen(QtGui.QBrush(color), width)
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pen.setCosmetic(cosmetic)
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if style is not None:
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pen.setStyle(style)
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return pen
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def hsvColor(hue, sat=1.0, val=1.0, alpha=1.0):
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"""Generate a QColor from HSVa values. (all arguments are float 0.0-1.0)"""
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c = QtGui.QColor()
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c.setHsvF(hue, sat, val, alpha)
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return c
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def colorTuple(c):
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"""Return a tuple (R,G,B,A) from a QColor"""
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return (c.red(), c.green(), c.blue(), c.alpha())
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def colorStr(c):
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"""Generate a hex string code from a QColor"""
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return ('%02x'*4) % colorTuple(c)
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def intColor(index, hues=9, values=1, maxValue=255, minValue=150, maxHue=360, minHue=0, sat=255, alpha=255, **kargs):
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"""
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Creates a QColor from a single index. Useful for stepping through a predefined list of colors.
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The argument *index* determines which color from the set will be returned. All other arguments determine what the set of predefined colors will be
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Colors are chosen by cycling across hues while varying the value (brightness).
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By default, this selects from a list of 9 hues."""
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hues = int(hues)
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values = int(values)
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ind = int(index) % (hues * values)
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indh = ind % hues
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indv = ind / hues
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if values > 1:
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v = minValue + indv * ((maxValue-minValue) / (values-1))
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else:
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v = maxValue
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h = minHue + (indh * (maxHue-minHue)) / hues
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c = QtGui.QColor()
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c.setHsv(h, sat, v)
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c.setAlpha(alpha)
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return c
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def glColor(*args, **kargs):
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"""
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Convert a color to OpenGL color format (r,g,b,a) floats 0.0-1.0
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Accepts same arguments as :func:`mkColor <pyqtgraph.mkColor>`.
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"""
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c = mkColor(*args, **kargs)
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return (c.red()/255., c.green()/255., c.blue()/255., c.alpha()/255.)
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def makeArrowPath(headLen=20, tipAngle=20, tailLen=20, tailWidth=3, baseAngle=0):
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"""
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Construct a path outlining an arrow with the given dimensions.
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The arrow points in the -x direction with tip positioned at 0,0.
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If *tipAngle* is supplied (in degrees), it overrides *headWidth*.
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If *tailLen* is None, no tail will be drawn.
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"""
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headWidth = headLen * np.tan(tipAngle * 0.5 * np.pi/180.)
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path = QtGui.QPainterPath()
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path.moveTo(0,0)
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path.lineTo(headLen, -headWidth)
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if tailLen is None:
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innerY = headLen - headWidth * np.tan(baseAngle*np.pi/180.)
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path.lineTo(innerY, 0)
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else:
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tailWidth *= 0.5
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innerY = headLen - (headWidth-tailWidth) * np.tan(baseAngle*np.pi/180.)
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path.lineTo(innerY, -tailWidth)
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path.lineTo(headLen + tailLen, -tailWidth)
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path.lineTo(headLen + tailLen, tailWidth)
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path.lineTo(innerY, tailWidth)
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path.lineTo(headLen, headWidth)
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path.lineTo(0,0)
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return path
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def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False, **kargs):
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"""
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Take a slice of any orientation through an array. This is useful for extracting sections of multi-dimensional arrays such as MRI images for viewing as 1D or 2D data.
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The slicing axes are aribtrary; they do not need to be orthogonal to the original data or even to each other. It is possible to use this function to extract arbitrary linear, rectangular, or parallelepiped shapes from within larger datasets. The original data is interpolated onto a new array of coordinates using scipy.ndimage.map_coordinates (see the scipy documentation for more information about this).
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For a graphical interface to this function, see :func:`ROI.getArrayRegion <pyqtgraph.ROI.getArrayRegion>`
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============== ====================================================================================================
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Arguments:
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*data* (ndarray) the original dataset
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*shape* the shape of the slice to take (Note the return value may have more dimensions than len(shape))
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*origin* the location in the original dataset that will become the origin of the sliced data.
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*vectors* list of unit vectors which point in the direction of the slice axes. Each vector must have the same
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length as *axes*. If the vectors are not unit length, the result will be scaled relative to the
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original data. If the vectors are not orthogonal, the result will be sheared relative to the
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original data.
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*axes* The axes in the original dataset which correspond to the slice *vectors*
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*order* The order of spline interpolation. Default is 1 (linear). See scipy.ndimage.map_coordinates
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for more information.
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*returnCoords* If True, return a tuple (result, coords) where coords is the array of coordinates used to select
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values from the original dataset.
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*All extra keyword arguments are passed to scipy.ndimage.map_coordinates.*
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--------------------------------------------------------------------------------------------------------------------
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============== ====================================================================================================
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Note the following must be true:
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| len(shape) == len(vectors)
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| len(origin) == len(axes) == len(vectors[i])
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Example: start with a 4D fMRI data set, take a diagonal-planar slice out of the last 3 axes
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* data = array with dims (time, x, y, z) = (100, 40, 40, 40)
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* The plane to pull out is perpendicular to the vector (x,y,z) = (1,1,1)
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* The origin of the slice will be at (x,y,z) = (40, 0, 0)
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* We will slice a 20x20 plane from each timepoint, giving a final shape (100, 20, 20)
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The call for this example would look like::
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affineSlice(data, shape=(20,20), origin=(40,0,0), vectors=((-1, 1, 0), (-1, 0, 1)), axes=(1,2,3))
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"""
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# sanity check
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if len(shape) != len(vectors):
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raise Exception("shape and vectors must have same length.")
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if len(origin) != len(axes):
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raise Exception("origin and axes must have same length.")
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for v in vectors:
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if len(v) != len(axes):
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raise Exception("each vector must be same length as axes.")
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shape = list(map(np.ceil, shape))
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## transpose data so slice axes come first
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trAx = list(range(data.ndim))
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for x in axes:
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trAx.remove(x)
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tr1 = tuple(axes) + tuple(trAx)
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data = data.transpose(tr1)
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#print "tr1:", tr1
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## dims are now [(slice axes), (other axes)]
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## make sure vectors are arrays
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vectors = np.array(vectors)
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origin = np.array(origin)
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origin.shape = (len(axes),) + (1,)*len(shape)
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## Build array of sample locations.
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grid = np.mgrid[tuple([slice(0,x) for x in shape])] ## mesh grid of indexes
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#print shape, grid.shape
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x = (grid[np.newaxis,...] * vectors.transpose()[(Ellipsis,) + (np.newaxis,)*len(shape)]).sum(axis=1) ## magic
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x += origin
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#print "X values:"
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#print x
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## iterate manually over unused axes since map_coordinates won't do it for us
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extraShape = data.shape[len(axes):]
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output = np.empty(tuple(shape) + extraShape, dtype=data.dtype)
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for inds in np.ndindex(*extraShape):
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ind = (Ellipsis,) + inds
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#print data[ind].shape, x.shape, output[ind].shape, output.shape
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output[ind] = scipy.ndimage.map_coordinates(data[ind], x, order=order, **kargs)
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tr = list(range(output.ndim))
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trb = []
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for i in range(min(axes)):
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ind = tr1.index(i) + (len(shape)-len(axes))
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tr.remove(ind)
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trb.append(ind)
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tr2 = tuple(trb+tr)
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## Untranspose array before returning
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output = output.transpose(tr2)
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if returnCoords:
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return (output, x)
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else:
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return output
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def transformToArray(tr):
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"""
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Given a QTransform, return a 3x3 numpy array.
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"""
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return np.array([[tr.m11(), tr.m12(), tr.m13()],[tr.m21(), tr.m22(), tr.m23()],[tr.m31(), tr.m32(), tr.m33()]])
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def solve3DTransform(points1, points2):
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"""
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Find a 3D transformation matrix that maps points1 onto points2
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points must be specified as a list of 4 Vectors.
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"""
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A = np.array([[points1[i].x(), points1[i].y(), points1[i].z(), 1] for i in range(4)])
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B = np.array([[points2[i].x(), points2[i].y(), points2[i].z(), 1] for i in range(4)])
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## solve 3 sets of linear equations to determine transformation matrix elements
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matrix = np.zeros((4,4))
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for i in range(3):
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matrix[i] = scipy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
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return matrix
|
|
|
|
def solveBilinearTransform(points1, points2):
|
|
"""
|
|
Find a bilinear transformation matrix (2x4) that maps points1 onto points2
|
|
points must be specified as a list of 4 Vector, Point, QPointF, etc.
|
|
|
|
To use this matrix to map a point [x,y]::
|
|
|
|
mapped = np.dot(matrix, [x*y, x, y, 1])
|
|
"""
|
|
## A is 4 rows (points) x 4 columns (xy, x, y, 1)
|
|
## B is 4 rows (points) x 2 columns (x, y)
|
|
A = np.array([[points1[i].x()*points1[i].y(), points1[i].x(), points1[i].y(), 1] for i in range(4)])
|
|
B = np.array([[points2[i].x(), points2[i].y()] for i in range(4)])
|
|
|
|
## solve 2 sets of linear equations to determine transformation matrix elements
|
|
matrix = np.zeros((2,4))
|
|
for i in range(2):
|
|
matrix[i] = scipy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
|
|
|
|
return matrix
|
|
|
|
|
|
|
|
|
|
|
|
def makeARGB(data, lut=None, levels=None, useRGBA=False):
|
|
"""
|
|
Convert a 2D or 3D array into an ARGB array suitable for building QImages
|
|
Will optionally do scaling and/or table lookups to determine final colors.
|
|
|
|
Returns the ARGB array (values 0-255) and a boolean indicating whether there is alpha channel data.
|
|
|
|
Arguments:
|
|
data - 2D or 3D numpy array of int/float types
|
|
|
|
For 2D arrays (x, y):
|
|
* The color will be determined using a lookup table (see argument 'lut').
|
|
* If levels are given, the data is rescaled and converted to int
|
|
before using the lookup table.
|
|
|
|
For 3D arrays (x, y, rgba):
|
|
* The third axis must have length 3 or 4 and will be interpreted as RGBA.
|
|
* The 'lut' argument is not allowed.
|
|
|
|
lut - Lookup table for 2D data. May be 1D or 2D (N,rgba) and must have dtype=ubyte.
|
|
Values in data will be converted to color by indexing directly from lut.
|
|
Lookup tables can be built using GradientWidget.
|
|
levels - List [min, max]; optionally rescale data before converting through the
|
|
lookup table. rescaled = (data-min) * len(lut) / (max-min)
|
|
useRGBA - If True, the data is returned in RGBA order. The default is
|
|
False, which returns in BGRA order for use with QImage.
|
|
|
|
"""
|
|
prof = debug.Profiler('functions.makeARGB', disabled=True)
|
|
|
|
## sanity checks
|
|
if data.ndim == 3:
|
|
if data.shape[2] not in (3,4):
|
|
raise Exception("data.shape[2] must be 3 or 4")
|
|
#if lut is not None:
|
|
#raise Exception("can not use lookup table with 3D data")
|
|
elif data.ndim != 2:
|
|
raise Exception("data must be 2D or 3D")
|
|
|
|
if lut is not None:
|
|
if lut.ndim == 2:
|
|
if lut.shape[1] not in (3,4):
|
|
raise Exception("lut.shape[1] must be 3 or 4")
|
|
elif lut.ndim != 1:
|
|
raise Exception("lut must be 1D or 2D")
|
|
if lut.dtype != np.ubyte:
|
|
raise Exception('lookup table must have dtype=ubyte (got %s instead)' % str(lut.dtype))
|
|
|
|
if levels is not None:
|
|
levels = np.array(levels)
|
|
if levels.shape == (2,):
|
|
pass
|
|
elif levels.shape in [(3,2), (4,2)]:
|
|
if data.ndim == 3:
|
|
raise Exception("Can not use 2D levels with 3D data.")
|
|
if lut is not None:
|
|
raise Exception('Can not use 2D levels and lookup table together.')
|
|
else:
|
|
raise Exception("Levels must have shape (2,) or (3,2) or (4,2)")
|
|
|
|
prof.mark('1')
|
|
|
|
if lut is not None:
|
|
lutLength = lut.shape[0]
|
|
else:
|
|
lutLength = 256
|
|
|
|
## weave requires contiguous arrays
|
|
global USE_WEAVE
|
|
if (levels is not None or lut is not None) and USE_WEAVE:
|
|
data = np.ascontiguousarray(data)
|
|
|
|
## Apply levels if given
|
|
if levels is not None:
|
|
|
|
try: ## use weave to speed up scaling
|
|
if not USE_WEAVE:
|
|
raise Exception('Weave is disabled; falling back to slower version.')
|
|
if levels.ndim == 1:
|
|
scale = float(lutLength) / (levels[1]-levels[0])
|
|
offset = float(levels[0])
|
|
data = rescaleData(data, scale, offset)
|
|
else:
|
|
if data.ndim == 2:
|
|
newData = np.empty(data.shape+(levels.shape[0],), dtype=np.uint32)
|
|
for i in range(levels.shape[0]):
|
|
scale = float(lutLength / (levels[i,1]-levels[i,0]))
|
|
offset = float(levels[i,0])
|
|
newData[...,i] = rescaleData(data, scale, offset)
|
|
elif data.ndim == 3:
|
|
newData = np.empty(data.shape, dtype=np.uint32)
|
|
for i in range(data.shape[2]):
|
|
scale = float(lutLength / (levels[i,1]-levels[i,0]))
|
|
offset = float(levels[i,0])
|
|
#print scale, offset, data.shape, newData.shape, levels.shape
|
|
newData[...,i] = rescaleData(data[...,i], scale, offset)
|
|
data = newData
|
|
except:
|
|
if USE_WEAVE:
|
|
debug.printExc("Error; disabling weave.")
|
|
USE_WEAVE = False
|
|
|
|
if levels.ndim == 1:
|
|
if data.ndim == 2:
|
|
levels = levels[np.newaxis, np.newaxis, :]
|
|
else:
|
|
levels = levels[np.newaxis, np.newaxis, np.newaxis, :]
|
|
else:
|
|
levels = levels[np.newaxis, np.newaxis, ...]
|
|
if data.ndim == 2:
|
|
data = data[..., np.newaxis]
|
|
data = ((data-levels[...,0]) * lutLength) / (levels[...,1]-levels[...,0])
|
|
|
|
prof.mark('2')
|
|
|
|
|
|
## apply LUT if given
|
|
if lut is not None and data.ndim == 2:
|
|
|
|
if data.dtype.kind not in ('i', 'u'):
|
|
data = data.astype(int)
|
|
|
|
data = np.clip(data, 0, lutLength-1)
|
|
try:
|
|
if not USE_WEAVE:
|
|
raise Exception('Weave is disabled; falling back to slower version.')
|
|
|
|
newData = np.empty((data.size,) + lut.shape[1:], dtype=np.uint8)
|
|
flat = data.reshape(data.size)
|
|
size = data.size
|
|
ncol = lut.shape[1]
|
|
newStride = newData.strides[0]
|
|
newColStride = newData.strides[1]
|
|
lutStride = lut.strides[0]
|
|
lutColStride = lut.strides[1]
|
|
flatStride = flat.strides[0] / flat.dtype.itemsize
|
|
|
|
#print "newData:", newData.shape, newData.dtype
|
|
#print "flat:", flat.shape, flat.dtype, flat.min(), flat.max()
|
|
#print "lut:", lut.shape, lut.dtype
|
|
#print "size:", size, "ncols:", ncol
|
|
#print "strides:", newStride, newColStride, lutStride, lutColStride, flatStride
|
|
|
|
code = """
|
|
|
|
for( int i=0; i<size; i++ ) {
|
|
for( int j=0; j<ncol; j++ ) {
|
|
newData[i*newStride + j*newColStride] = lut[flat[i*flatStride]*lutStride + j*lutColStride];
|
|
}
|
|
}
|
|
"""
|
|
scipy.weave.inline(code, ['flat', 'lut', 'newData', 'size', 'ncol', 'newStride', 'lutStride', 'flatStride', 'newColStride', 'lutColStride'])
|
|
data = newData.reshape(data.shape + lut.shape[1:])
|
|
except:
|
|
if USE_WEAVE:
|
|
debug.printExc("Error; disabling weave.")
|
|
USE_WEAVE = False
|
|
data = lut[data]
|
|
else:
|
|
if data.dtype is not np.ubyte:
|
|
data = np.clip(data, 0, 255).astype(np.ubyte)
|
|
|
|
prof.mark('3')
|
|
|
|
|
|
## copy data into ARGB ordered array
|
|
imgData = np.empty(data.shape[:2]+(4,), dtype=np.ubyte)
|
|
if data.ndim == 2:
|
|
data = data[..., np.newaxis]
|
|
|
|
prof.mark('4')
|
|
|
|
if useRGBA:
|
|
order = [0,1,2,3] ## array comes out RGBA
|
|
else:
|
|
order = [2,1,0,3] ## for some reason, the colors line up as BGR in the final image.
|
|
|
|
if data.shape[2] == 1:
|
|
for i in range(3):
|
|
imgData[..., order[i]] = data[..., 0]
|
|
else:
|
|
for i in range(0, data.shape[2]):
|
|
imgData[..., order[i]] = data[..., i]
|
|
|
|
prof.mark('5')
|
|
|
|
if data.shape[2] == 4:
|
|
alpha = True
|
|
else:
|
|
alpha = False
|
|
imgData[..., 3] = 255
|
|
|
|
prof.mark('6')
|
|
|
|
prof.finish()
|
|
return imgData, alpha
|
|
|
|
|
|
def makeQImage(imgData, alpha):
|
|
"""Turn an ARGB array into QImage"""
|
|
## create QImage from buffer
|
|
prof = debug.Profiler('functions.makeQImage', disabled=True)
|
|
|
|
if alpha:
|
|
imgFormat = QtGui.QImage.Format_ARGB32
|
|
else:
|
|
imgFormat = QtGui.QImage.Format_RGB32
|
|
|
|
imgData = imgData.transpose((1, 0, 2)) ## QImage expects the row/column order to be opposite
|
|
try:
|
|
buf = imgData.data
|
|
except AttributeError:
|
|
imgData = np.ascontiguousarray(imgData)
|
|
buf = imgData.data
|
|
|
|
prof.mark('1')
|
|
qimage = QtGui.QImage(buf, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
prof.mark('2')
|
|
qimage.data = imgData
|
|
prof.finish()
|
|
return qimage
|
|
|
|
|
|
def rescaleData(data, scale, offset):
|
|
newData = np.empty((data.size,), dtype=np.int)
|
|
flat = data.reshape(data.size)
|
|
size = data.size
|
|
|
|
code = """
|
|
double sc = (double)scale;
|
|
double off = (double)offset;
|
|
for( int i=0; i<size; i++ ) {
|
|
newData[i] = (int)(((double)flat[i] - off) * sc);
|
|
}
|
|
"""
|
|
scipy.weave.inline(code, ['flat', 'newData', 'size', 'offset', 'scale'], compiler='gcc')
|
|
data = newData.reshape(data.shape)
|
|
return data
|
|
|
|
|
|
#def isosurface(data, level):
|
|
#"""
|
|
#Generate isosurface from volumetric data using marching tetrahedra algorithm.
|
|
#See Paul Bourke, "Polygonising a Scalar Field Using Tetrahedrons" (http://local.wasp.uwa.edu.au/~pbourke/geometry/polygonise/)
|
|
|
|
#*data* 3D numpy array of scalar values
|
|
#*level* The level at which to generate an isosurface
|
|
#"""
|
|
|
|
#facets = []
|
|
|
|
### mark everything below the isosurface level
|
|
#mask = data < level
|
|
|
|
#### make eight sub-fields
|
|
#fields = np.empty((2,2,2), dtype=object)
|
|
#slices = [slice(0,-1), slice(1,None)]
|
|
#for i in [0,1]:
|
|
#for j in [0,1]:
|
|
#for k in [0,1]:
|
|
#fields[i,j,k] = mask[slices[i], slices[j], slices[k]]
|
|
|
|
|
|
|
|
### split each cell into 6 tetrahedra
|
|
### these all have the same 'orienation'; points 1,2,3 circle
|
|
### clockwise around point 0
|
|
#tetrahedra = [
|
|
#[(0,1,0), (1,1,1), (0,1,1), (1,0,1)],
|
|
#[(0,1,0), (0,1,1), (0,0,1), (1,0,1)],
|
|
#[(0,1,0), (0,0,1), (0,0,0), (1,0,1)],
|
|
#[(0,1,0), (0,0,0), (1,0,0), (1,0,1)],
|
|
#[(0,1,0), (1,0,0), (1,1,0), (1,0,1)],
|
|
#[(0,1,0), (1,1,0), (1,1,1), (1,0,1)]
|
|
#]
|
|
|
|
### each tetrahedron will be assigned an index
|
|
### which determines how to generate its facets.
|
|
### this structure is:
|
|
### facets[index][facet1, facet2, ...]
|
|
### where each facet is triangular and its points are each
|
|
### interpolated between two points on the tetrahedron
|
|
### facet = [(p1a, p1b), (p2a, p2b), (p3a, p3b)]
|
|
### facet points always circle clockwise if you are looking
|
|
### at them from below the isosurface.
|
|
#indexFacets = [
|
|
#[], ## all above
|
|
#[[(0,1), (0,2), (0,3)]], # 0 below
|
|
#[[(1,0), (1,3), (1,2)]], # 1 below
|
|
#[[(0,2), (1,3), (1,2)], [(0,2), (0,3), (1,3)]], # 0,1 below
|
|
#[[(2,0), (2,1), (2,3)]], # 2 below
|
|
#[[(0,3), (1,2), (2,3)], [(0,3), (0,1), (1,2)]], # 0,2 below
|
|
#[[(1,0), (2,3), (2,0)], [(1,0), (1,3), (2,3)]], # 1,2 below
|
|
#[[(3,0), (3,1), (3,2)]], # 3 above
|
|
#[[(3,0), (3,2), (3,1)]], # 3 below
|
|
#[[(1,0), (2,0), (2,3)], [(1,0), (2,3), (1,3)]], # 0,3 below
|
|
#[[(0,3), (2,3), (1,2)], [(0,3), (1,2), (0,1)]], # 1,3 below
|
|
#[[(2,0), (2,3), (2,1)]], # 0,1,3 below
|
|
#[[(0,2), (1,2), (1,3)], [(0,2), (1,3), (0,3)]], # 2,3 below
|
|
#[[(1,0), (1,2), (1,3)]], # 0,2,3 below
|
|
#[[(0,1), (0,3), (0,2)]], # 1,2,3 below
|
|
#[] ## all below
|
|
#]
|
|
|
|
#for tet in tetrahedra:
|
|
|
|
### get the 4 fields for this tetrahedron
|
|
#tetFields = [fields[c] for c in tet]
|
|
|
|
### generate an index for each grid cell
|
|
#index = tetFields[0] + tetFields[1]*2 + tetFields[2]*4 + tetFields[3]*8
|
|
|
|
### add facets
|
|
#for i in xrange(index.shape[0]): # data x-axis
|
|
#for j in xrange(index.shape[1]): # data y-axis
|
|
#for k in xrange(index.shape[2]): # data z-axis
|
|
#for f in indexFacets[index[i,j,k]]: # faces to generate for this tet
|
|
#pts = []
|
|
#for l in [0,1,2]: # points in this face
|
|
#p1 = tet[f[l][0]] # tet corner 1
|
|
#p2 = tet[f[l][1]] # tet corner 2
|
|
#pts.append([(p1[x]+p2[x])*0.5+[i,j,k][x]+0.5 for x in [0,1,2]]) ## interpolate between tet corners
|
|
#facets.append(pts)
|
|
|
|
#return facets
|
|
|
|
|
|
def isocurve(data, level):
|
|
"""
|
|
Generate isocurve from 2D data using marching squares algorithm.
|
|
|
|
*data* 2D numpy array of scalar values
|
|
*level* The level at which to generate an isosurface
|
|
|
|
This function is SLOW; plenty of room for optimization here.
|
|
"""
|
|
|
|
sideTable = [
|
|
[],
|
|
[0,1],
|
|
[1,2],
|
|
[0,2],
|
|
[0,3],
|
|
[1,3],
|
|
[0,1,2,3],
|
|
[2,3],
|
|
[2,3],
|
|
[0,1,2,3],
|
|
[1,3],
|
|
[0,3],
|
|
[0,2],
|
|
[1,2],
|
|
[0,1],
|
|
[]
|
|
]
|
|
|
|
edgeKey=[
|
|
[(0,1),(0,0)],
|
|
[(0,0), (1,0)],
|
|
[(1,0), (1,1)],
|
|
[(1,1), (0,1)]
|
|
]
|
|
|
|
|
|
lines = []
|
|
|
|
## mark everything below the isosurface level
|
|
mask = data < level
|
|
|
|
### make four sub-fields and compute indexes for grid cells
|
|
index = np.zeros([x-1 for x in data.shape], dtype=np.ubyte)
|
|
fields = np.empty((2,2), dtype=object)
|
|
slices = [slice(0,-1), slice(1,None)]
|
|
for i in [0,1]:
|
|
for j in [0,1]:
|
|
fields[i,j] = mask[slices[i], slices[j]]
|
|
#vertIndex = i - 2*j*i + 3*j + 4*k ## this is just to match Bourk's vertex numbering scheme
|
|
vertIndex = i+2*j
|
|
#print i,j,k," : ", fields[i,j,k], 2**vertIndex
|
|
index += fields[i,j] * 2**vertIndex
|
|
#print index
|
|
#print index
|
|
|
|
## add lines
|
|
for i in range(index.shape[0]): # data x-axis
|
|
for j in range(index.shape[1]): # data y-axis
|
|
sides = sideTable[index[i,j]]
|
|
for l in range(0, len(sides), 2): ## faces for this grid cell
|
|
edges = sides[l:l+2]
|
|
pts = []
|
|
for m in [0,1]: # points in this face
|
|
p1 = edgeKey[edges[m]][0] # p1, p2 are points at either side of an edge
|
|
p2 = edgeKey[edges[m]][1]
|
|
v1 = data[i+p1[0], j+p1[1]] # v1 and v2 are the values at p1 and p2
|
|
v2 = data[i+p2[0], j+p2[1]]
|
|
f = (level-v1) / (v2-v1)
|
|
fi = 1.0 - f
|
|
p = ( ## interpolate between corners
|
|
p1[0]*fi + p2[0]*f + i + 0.5,
|
|
p1[1]*fi + p2[1]*f + j + 0.5
|
|
)
|
|
pts.append(p)
|
|
lines.append(pts)
|
|
|
|
return lines ## a list of pairs of points
|
|
|
|
|
|
def isosurface(data, level):
|
|
"""
|
|
Generate isosurface from volumetric data using marching cubes algorithm.
|
|
See Paul Bourke, "Polygonising a Scalar Field"
|
|
(http://local.wasp.uwa.edu.au/~pbourke/geometry/polygonise/)
|
|
|
|
*data* 3D numpy array of scalar values
|
|
*level* The level at which to generate an isosurface
|
|
|
|
Returns a list of faces; each face is a list of three vertexes and each vertex is a tuple of three floats.
|
|
|
|
This function is SLOW; plenty of room for optimization here.
|
|
"""
|
|
|
|
## map from grid cell index to edge index.
|
|
## grid cell index tells us which corners are below the isosurface,
|
|
## edge index tells us which edges are cut by the isosurface.
|
|
## (Data stolen from Bourk; see above.)
|
|
edgeTable = [
|
|
0x0 , 0x109, 0x203, 0x30a, 0x406, 0x50f, 0x605, 0x70c,
|
|
0x80c, 0x905, 0xa0f, 0xb06, 0xc0a, 0xd03, 0xe09, 0xf00,
|
|
0x190, 0x99 , 0x393, 0x29a, 0x596, 0x49f, 0x795, 0x69c,
|
|
0x99c, 0x895, 0xb9f, 0xa96, 0xd9a, 0xc93, 0xf99, 0xe90,
|
|
0x230, 0x339, 0x33 , 0x13a, 0x636, 0x73f, 0x435, 0x53c,
|
|
0xa3c, 0xb35, 0x83f, 0x936, 0xe3a, 0xf33, 0xc39, 0xd30,
|
|
0x3a0, 0x2a9, 0x1a3, 0xaa , 0x7a6, 0x6af, 0x5a5, 0x4ac,
|
|
0xbac, 0xaa5, 0x9af, 0x8a6, 0xfaa, 0xea3, 0xda9, 0xca0,
|
|
0x460, 0x569, 0x663, 0x76a, 0x66 , 0x16f, 0x265, 0x36c,
|
|
0xc6c, 0xd65, 0xe6f, 0xf66, 0x86a, 0x963, 0xa69, 0xb60,
|
|
0x5f0, 0x4f9, 0x7f3, 0x6fa, 0x1f6, 0xff , 0x3f5, 0x2fc,
|
|
0xdfc, 0xcf5, 0xfff, 0xef6, 0x9fa, 0x8f3, 0xbf9, 0xaf0,
|
|
0x650, 0x759, 0x453, 0x55a, 0x256, 0x35f, 0x55 , 0x15c,
|
|
0xe5c, 0xf55, 0xc5f, 0xd56, 0xa5a, 0xb53, 0x859, 0x950,
|
|
0x7c0, 0x6c9, 0x5c3, 0x4ca, 0x3c6, 0x2cf, 0x1c5, 0xcc ,
|
|
0xfcc, 0xec5, 0xdcf, 0xcc6, 0xbca, 0xac3, 0x9c9, 0x8c0,
|
|
0x8c0, 0x9c9, 0xac3, 0xbca, 0xcc6, 0xdcf, 0xec5, 0xfcc,
|
|
0xcc , 0x1c5, 0x2cf, 0x3c6, 0x4ca, 0x5c3, 0x6c9, 0x7c0,
|
|
0x950, 0x859, 0xb53, 0xa5a, 0xd56, 0xc5f, 0xf55, 0xe5c,
|
|
0x15c, 0x55 , 0x35f, 0x256, 0x55a, 0x453, 0x759, 0x650,
|
|
0xaf0, 0xbf9, 0x8f3, 0x9fa, 0xef6, 0xfff, 0xcf5, 0xdfc,
|
|
0x2fc, 0x3f5, 0xff , 0x1f6, 0x6fa, 0x7f3, 0x4f9, 0x5f0,
|
|
0xb60, 0xa69, 0x963, 0x86a, 0xf66, 0xe6f, 0xd65, 0xc6c,
|
|
0x36c, 0x265, 0x16f, 0x66 , 0x76a, 0x663, 0x569, 0x460,
|
|
0xca0, 0xda9, 0xea3, 0xfaa, 0x8a6, 0x9af, 0xaa5, 0xbac,
|
|
0x4ac, 0x5a5, 0x6af, 0x7a6, 0xaa , 0x1a3, 0x2a9, 0x3a0,
|
|
0xd30, 0xc39, 0xf33, 0xe3a, 0x936, 0x83f, 0xb35, 0xa3c,
|
|
0x53c, 0x435, 0x73f, 0x636, 0x13a, 0x33 , 0x339, 0x230,
|
|
0xe90, 0xf99, 0xc93, 0xd9a, 0xa96, 0xb9f, 0x895, 0x99c,
|
|
0x69c, 0x795, 0x49f, 0x596, 0x29a, 0x393, 0x99 , 0x190,
|
|
0xf00, 0xe09, 0xd03, 0xc0a, 0xb06, 0xa0f, 0x905, 0x80c,
|
|
0x70c, 0x605, 0x50f, 0x406, 0x30a, 0x203, 0x109, 0x0 ]
|
|
|
|
## Table of triangles to use for filling each grid cell.
|
|
## Each set of three integers tells us which three edges to
|
|
## draw a triangle between.
|
|
## (Data stolen from Bourk; see above.)
|
|
triTable = [
|
|
[],
|
|
[0, 8, 3],
|
|
[0, 1, 9],
|
|
[1, 8, 3, 9, 8, 1],
|
|
[1, 2, 10],
|
|
[0, 8, 3, 1, 2, 10],
|
|
[9, 2, 10, 0, 2, 9],
|
|
[2, 8, 3, 2, 10, 8, 10, 9, 8],
|
|
[3, 11, 2],
|
|
[0, 11, 2, 8, 11, 0],
|
|
[1, 9, 0, 2, 3, 11],
|
|
[1, 11, 2, 1, 9, 11, 9, 8, 11],
|
|
[3, 10, 1, 11, 10, 3],
|
|
[0, 10, 1, 0, 8, 10, 8, 11, 10],
|
|
[3, 9, 0, 3, 11, 9, 11, 10, 9],
|
|
[9, 8, 10, 10, 8, 11],
|
|
[4, 7, 8],
|
|
[4, 3, 0, 7, 3, 4],
|
|
[0, 1, 9, 8, 4, 7],
|
|
[4, 1, 9, 4, 7, 1, 7, 3, 1],
|
|
[1, 2, 10, 8, 4, 7],
|
|
[3, 4, 7, 3, 0, 4, 1, 2, 10],
|
|
[9, 2, 10, 9, 0, 2, 8, 4, 7],
|
|
[2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4],
|
|
[8, 4, 7, 3, 11, 2],
|
|
[11, 4, 7, 11, 2, 4, 2, 0, 4],
|
|
[9, 0, 1, 8, 4, 7, 2, 3, 11],
|
|
[4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1],
|
|
[3, 10, 1, 3, 11, 10, 7, 8, 4],
|
|
[1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4],
|
|
[4, 7, 8, 9, 0, 11, 9, 11, 10, 11, 0, 3],
|
|
[4, 7, 11, 4, 11, 9, 9, 11, 10],
|
|
[9, 5, 4],
|
|
[9, 5, 4, 0, 8, 3],
|
|
[0, 5, 4, 1, 5, 0],
|
|
[8, 5, 4, 8, 3, 5, 3, 1, 5],
|
|
[1, 2, 10, 9, 5, 4],
|
|
[3, 0, 8, 1, 2, 10, 4, 9, 5],
|
|
[5, 2, 10, 5, 4, 2, 4, 0, 2],
|
|
[2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8],
|
|
[9, 5, 4, 2, 3, 11],
|
|
[0, 11, 2, 0, 8, 11, 4, 9, 5],
|
|
[0, 5, 4, 0, 1, 5, 2, 3, 11],
|
|
[2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5],
|
|
[10, 3, 11, 10, 1, 3, 9, 5, 4],
|
|
[4, 9, 5, 0, 8, 1, 8, 10, 1, 8, 11, 10],
|
|
[5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3],
|
|
[5, 4, 8, 5, 8, 10, 10, 8, 11],
|
|
[9, 7, 8, 5, 7, 9],
|
|
[9, 3, 0, 9, 5, 3, 5, 7, 3],
|
|
[0, 7, 8, 0, 1, 7, 1, 5, 7],
|
|
[1, 5, 3, 3, 5, 7],
|
|
[9, 7, 8, 9, 5, 7, 10, 1, 2],
|
|
[10, 1, 2, 9, 5, 0, 5, 3, 0, 5, 7, 3],
|
|
[8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2],
|
|
[2, 10, 5, 2, 5, 3, 3, 5, 7],
|
|
[7, 9, 5, 7, 8, 9, 3, 11, 2],
|
|
[9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11],
|
|
[2, 3, 11, 0, 1, 8, 1, 7, 8, 1, 5, 7],
|
|
[11, 2, 1, 11, 1, 7, 7, 1, 5],
|
|
[9, 5, 8, 8, 5, 7, 10, 1, 3, 10, 3, 11],
|
|
[5, 7, 0, 5, 0, 9, 7, 11, 0, 1, 0, 10, 11, 10, 0],
|
|
[11, 10, 0, 11, 0, 3, 10, 5, 0, 8, 0, 7, 5, 7, 0],
|
|
[11, 10, 5, 7, 11, 5],
|
|
[10, 6, 5],
|
|
[0, 8, 3, 5, 10, 6],
|
|
[9, 0, 1, 5, 10, 6],
|
|
[1, 8, 3, 1, 9, 8, 5, 10, 6],
|
|
[1, 6, 5, 2, 6, 1],
|
|
[1, 6, 5, 1, 2, 6, 3, 0, 8],
|
|
[9, 6, 5, 9, 0, 6, 0, 2, 6],
|
|
[5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8],
|
|
[2, 3, 11, 10, 6, 5],
|
|
[11, 0, 8, 11, 2, 0, 10, 6, 5],
|
|
[0, 1, 9, 2, 3, 11, 5, 10, 6],
|
|
[5, 10, 6, 1, 9, 2, 9, 11, 2, 9, 8, 11],
|
|
[6, 3, 11, 6, 5, 3, 5, 1, 3],
|
|
[0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6],
|
|
[3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9],
|
|
[6, 5, 9, 6, 9, 11, 11, 9, 8],
|
|
[5, 10, 6, 4, 7, 8],
|
|
[4, 3, 0, 4, 7, 3, 6, 5, 10],
|
|
[1, 9, 0, 5, 10, 6, 8, 4, 7],
|
|
[10, 6, 5, 1, 9, 7, 1, 7, 3, 7, 9, 4],
|
|
[6, 1, 2, 6, 5, 1, 4, 7, 8],
|
|
[1, 2, 5, 5, 2, 6, 3, 0, 4, 3, 4, 7],
|
|
[8, 4, 7, 9, 0, 5, 0, 6, 5, 0, 2, 6],
|
|
[7, 3, 9, 7, 9, 4, 3, 2, 9, 5, 9, 6, 2, 6, 9],
|
|
[3, 11, 2, 7, 8, 4, 10, 6, 5],
|
|
[5, 10, 6, 4, 7, 2, 4, 2, 0, 2, 7, 11],
|
|
[0, 1, 9, 4, 7, 8, 2, 3, 11, 5, 10, 6],
|
|
[9, 2, 1, 9, 11, 2, 9, 4, 11, 7, 11, 4, 5, 10, 6],
|
|
[8, 4, 7, 3, 11, 5, 3, 5, 1, 5, 11, 6],
|
|
[5, 1, 11, 5, 11, 6, 1, 0, 11, 7, 11, 4, 0, 4, 11],
|
|
[0, 5, 9, 0, 6, 5, 0, 3, 6, 11, 6, 3, 8, 4, 7],
|
|
[6, 5, 9, 6, 9, 11, 4, 7, 9, 7, 11, 9],
|
|
[10, 4, 9, 6, 4, 10],
|
|
[4, 10, 6, 4, 9, 10, 0, 8, 3],
|
|
[10, 0, 1, 10, 6, 0, 6, 4, 0],
|
|
[8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10],
|
|
[1, 4, 9, 1, 2, 4, 2, 6, 4],
|
|
[3, 0, 8, 1, 2, 9, 2, 4, 9, 2, 6, 4],
|
|
[0, 2, 4, 4, 2, 6],
|
|
[8, 3, 2, 8, 2, 4, 4, 2, 6],
|
|
[10, 4, 9, 10, 6, 4, 11, 2, 3],
|
|
[0, 8, 2, 2, 8, 11, 4, 9, 10, 4, 10, 6],
|
|
[3, 11, 2, 0, 1, 6, 0, 6, 4, 6, 1, 10],
|
|
[6, 4, 1, 6, 1, 10, 4, 8, 1, 2, 1, 11, 8, 11, 1],
|
|
[9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3],
|
|
[8, 11, 1, 8, 1, 0, 11, 6, 1, 9, 1, 4, 6, 4, 1],
|
|
[3, 11, 6, 3, 6, 0, 0, 6, 4],
|
|
[6, 4, 8, 11, 6, 8],
|
|
[7, 10, 6, 7, 8, 10, 8, 9, 10],
|
|
[0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10],
|
|
[10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0],
|
|
[10, 6, 7, 10, 7, 1, 1, 7, 3],
|
|
[1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7],
|
|
[2, 6, 9, 2, 9, 1, 6, 7, 9, 0, 9, 3, 7, 3, 9],
|
|
[7, 8, 0, 7, 0, 6, 6, 0, 2],
|
|
[7, 3, 2, 6, 7, 2],
|
|
[2, 3, 11, 10, 6, 8, 10, 8, 9, 8, 6, 7],
|
|
[2, 0, 7, 2, 7, 11, 0, 9, 7, 6, 7, 10, 9, 10, 7],
|
|
[1, 8, 0, 1, 7, 8, 1, 10, 7, 6, 7, 10, 2, 3, 11],
|
|
[11, 2, 1, 11, 1, 7, 10, 6, 1, 6, 7, 1],
|
|
[8, 9, 6, 8, 6, 7, 9, 1, 6, 11, 6, 3, 1, 3, 6],
|
|
[0, 9, 1, 11, 6, 7],
|
|
[7, 8, 0, 7, 0, 6, 3, 11, 0, 11, 6, 0],
|
|
[7, 11, 6],
|
|
[7, 6, 11],
|
|
[3, 0, 8, 11, 7, 6],
|
|
[0, 1, 9, 11, 7, 6],
|
|
[8, 1, 9, 8, 3, 1, 11, 7, 6],
|
|
[10, 1, 2, 6, 11, 7],
|
|
[1, 2, 10, 3, 0, 8, 6, 11, 7],
|
|
[2, 9, 0, 2, 10, 9, 6, 11, 7],
|
|
[6, 11, 7, 2, 10, 3, 10, 8, 3, 10, 9, 8],
|
|
[7, 2, 3, 6, 2, 7],
|
|
[7, 0, 8, 7, 6, 0, 6, 2, 0],
|
|
[2, 7, 6, 2, 3, 7, 0, 1, 9],
|
|
[1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6],
|
|
[10, 7, 6, 10, 1, 7, 1, 3, 7],
|
|
[10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8],
|
|
[0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7],
|
|
[7, 6, 10, 7, 10, 8, 8, 10, 9],
|
|
[6, 8, 4, 11, 8, 6],
|
|
[3, 6, 11, 3, 0, 6, 0, 4, 6],
|
|
[8, 6, 11, 8, 4, 6, 9, 0, 1],
|
|
[9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6],
|
|
[6, 8, 4, 6, 11, 8, 2, 10, 1],
|
|
[1, 2, 10, 3, 0, 11, 0, 6, 11, 0, 4, 6],
|
|
[4, 11, 8, 4, 6, 11, 0, 2, 9, 2, 10, 9],
|
|
[10, 9, 3, 10, 3, 2, 9, 4, 3, 11, 3, 6, 4, 6, 3],
|
|
[8, 2, 3, 8, 4, 2, 4, 6, 2],
|
|
[0, 4, 2, 4, 6, 2],
|
|
[1, 9, 0, 2, 3, 4, 2, 4, 6, 4, 3, 8],
|
|
[1, 9, 4, 1, 4, 2, 2, 4, 6],
|
|
[8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1],
|
|
[10, 1, 0, 10, 0, 6, 6, 0, 4],
|
|
[4, 6, 3, 4, 3, 8, 6, 10, 3, 0, 3, 9, 10, 9, 3],
|
|
[10, 9, 4, 6, 10, 4],
|
|
[4, 9, 5, 7, 6, 11],
|
|
[0, 8, 3, 4, 9, 5, 11, 7, 6],
|
|
[5, 0, 1, 5, 4, 0, 7, 6, 11],
|
|
[11, 7, 6, 8, 3, 4, 3, 5, 4, 3, 1, 5],
|
|
[9, 5, 4, 10, 1, 2, 7, 6, 11],
|
|
[6, 11, 7, 1, 2, 10, 0, 8, 3, 4, 9, 5],
|
|
[7, 6, 11, 5, 4, 10, 4, 2, 10, 4, 0, 2],
|
|
[3, 4, 8, 3, 5, 4, 3, 2, 5, 10, 5, 2, 11, 7, 6],
|
|
[7, 2, 3, 7, 6, 2, 5, 4, 9],
|
|
[9, 5, 4, 0, 8, 6, 0, 6, 2, 6, 8, 7],
|
|
[3, 6, 2, 3, 7, 6, 1, 5, 0, 5, 4, 0],
|
|
[6, 2, 8, 6, 8, 7, 2, 1, 8, 4, 8, 5, 1, 5, 8],
|
|
[9, 5, 4, 10, 1, 6, 1, 7, 6, 1, 3, 7],
|
|
[1, 6, 10, 1, 7, 6, 1, 0, 7, 8, 7, 0, 9, 5, 4],
|
|
[4, 0, 10, 4, 10, 5, 0, 3, 10, 6, 10, 7, 3, 7, 10],
|
|
[7, 6, 10, 7, 10, 8, 5, 4, 10, 4, 8, 10],
|
|
[6, 9, 5, 6, 11, 9, 11, 8, 9],
|
|
[3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5],
|
|
[0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11],
|
|
[6, 11, 3, 6, 3, 5, 5, 3, 1],
|
|
[1, 2, 10, 9, 5, 11, 9, 11, 8, 11, 5, 6],
|
|
[0, 11, 3, 0, 6, 11, 0, 9, 6, 5, 6, 9, 1, 2, 10],
|
|
[11, 8, 5, 11, 5, 6, 8, 0, 5, 10, 5, 2, 0, 2, 5],
|
|
[6, 11, 3, 6, 3, 5, 2, 10, 3, 10, 5, 3],
|
|
[5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2],
|
|
[9, 5, 6, 9, 6, 0, 0, 6, 2],
|
|
[1, 5, 8, 1, 8, 0, 5, 6, 8, 3, 8, 2, 6, 2, 8],
|
|
[1, 5, 6, 2, 1, 6],
|
|
[1, 3, 6, 1, 6, 10, 3, 8, 6, 5, 6, 9, 8, 9, 6],
|
|
[10, 1, 0, 10, 0, 6, 9, 5, 0, 5, 6, 0],
|
|
[0, 3, 8, 5, 6, 10],
|
|
[10, 5, 6],
|
|
[11, 5, 10, 7, 5, 11],
|
|
[11, 5, 10, 11, 7, 5, 8, 3, 0],
|
|
[5, 11, 7, 5, 10, 11, 1, 9, 0],
|
|
[10, 7, 5, 10, 11, 7, 9, 8, 1, 8, 3, 1],
|
|
[11, 1, 2, 11, 7, 1, 7, 5, 1],
|
|
[0, 8, 3, 1, 2, 7, 1, 7, 5, 7, 2, 11],
|
|
[9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7],
|
|
[7, 5, 2, 7, 2, 11, 5, 9, 2, 3, 2, 8, 9, 8, 2],
|
|
[2, 5, 10, 2, 3, 5, 3, 7, 5],
|
|
[8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5],
|
|
[9, 0, 1, 5, 10, 3, 5, 3, 7, 3, 10, 2],
|
|
[9, 8, 2, 9, 2, 1, 8, 7, 2, 10, 2, 5, 7, 5, 2],
|
|
[1, 3, 5, 3, 7, 5],
|
|
[0, 8, 7, 0, 7, 1, 1, 7, 5],
|
|
[9, 0, 3, 9, 3, 5, 5, 3, 7],
|
|
[9, 8, 7, 5, 9, 7],
|
|
[5, 8, 4, 5, 10, 8, 10, 11, 8],
|
|
[5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0],
|
|
[0, 1, 9, 8, 4, 10, 8, 10, 11, 10, 4, 5],
|
|
[10, 11, 4, 10, 4, 5, 11, 3, 4, 9, 4, 1, 3, 1, 4],
|
|
[2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8],
|
|
[0, 4, 11, 0, 11, 3, 4, 5, 11, 2, 11, 1, 5, 1, 11],
|
|
[0, 2, 5, 0, 5, 9, 2, 11, 5, 4, 5, 8, 11, 8, 5],
|
|
[9, 4, 5, 2, 11, 3],
|
|
[2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4],
|
|
[5, 10, 2, 5, 2, 4, 4, 2, 0],
|
|
[3, 10, 2, 3, 5, 10, 3, 8, 5, 4, 5, 8, 0, 1, 9],
|
|
[5, 10, 2, 5, 2, 4, 1, 9, 2, 9, 4, 2],
|
|
[8, 4, 5, 8, 5, 3, 3, 5, 1],
|
|
[0, 4, 5, 1, 0, 5],
|
|
[8, 4, 5, 8, 5, 3, 9, 0, 5, 0, 3, 5],
|
|
[9, 4, 5],
|
|
[4, 11, 7, 4, 9, 11, 9, 10, 11],
|
|
[0, 8, 3, 4, 9, 7, 9, 11, 7, 9, 10, 11],
|
|
[1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11],
|
|
[3, 1, 4, 3, 4, 8, 1, 10, 4, 7, 4, 11, 10, 11, 4],
|
|
[4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2],
|
|
[9, 7, 4, 9, 11, 7, 9, 1, 11, 2, 11, 1, 0, 8, 3],
|
|
[11, 7, 4, 11, 4, 2, 2, 4, 0],
|
|
[11, 7, 4, 11, 4, 2, 8, 3, 4, 3, 2, 4],
|
|
[2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9],
|
|
[9, 10, 7, 9, 7, 4, 10, 2, 7, 8, 7, 0, 2, 0, 7],
|
|
[3, 7, 10, 3, 10, 2, 7, 4, 10, 1, 10, 0, 4, 0, 10],
|
|
[1, 10, 2, 8, 7, 4],
|
|
[4, 9, 1, 4, 1, 7, 7, 1, 3],
|
|
[4, 9, 1, 4, 1, 7, 0, 8, 1, 8, 7, 1],
|
|
[4, 0, 3, 7, 4, 3],
|
|
[4, 8, 7],
|
|
[9, 10, 8, 10, 11, 8],
|
|
[3, 0, 9, 3, 9, 11, 11, 9, 10],
|
|
[0, 1, 10, 0, 10, 8, 8, 10, 11],
|
|
[3, 1, 10, 11, 3, 10],
|
|
[1, 2, 11, 1, 11, 9, 9, 11, 8],
|
|
[3, 0, 9, 3, 9, 11, 1, 2, 9, 2, 11, 9],
|
|
[0, 2, 11, 8, 0, 11],
|
|
[3, 2, 11],
|
|
[2, 3, 8, 2, 8, 10, 10, 8, 9],
|
|
[9, 10, 2, 0, 9, 2],
|
|
[2, 3, 8, 2, 8, 10, 0, 1, 8, 1, 10, 8],
|
|
[1, 10, 2],
|
|
[1, 3, 8, 9, 1, 8],
|
|
[0, 9, 1],
|
|
[0, 3, 8],
|
|
[]
|
|
]
|
|
|
|
## translation between edge index and
|
|
## the vertex indexes that bound the edge
|
|
edgeKey = [
|
|
[(0,0,0), (1,0,0)],
|
|
[(1,0,0), (1,1,0)],
|
|
[(1,1,0), (0,1,0)],
|
|
[(0,1,0), (0,0,0)],
|
|
[(0,0,1), (1,0,1)],
|
|
[(1,0,1), (1,1,1)],
|
|
[(1,1,1), (0,1,1)],
|
|
[(0,1,1), (0,0,1)],
|
|
[(0,0,0), (0,0,1)],
|
|
[(1,0,0), (1,0,1)],
|
|
[(1,1,0), (1,1,1)],
|
|
[(0,1,0), (0,1,1)],
|
|
]
|
|
|
|
|
|
|
|
facets = []
|
|
|
|
## mark everything below the isosurface level
|
|
mask = data < level
|
|
|
|
### make eight sub-fields and compute indexes for grid cells
|
|
index = np.zeros([x-1 for x in data.shape], dtype=np.ubyte)
|
|
fields = np.empty((2,2,2), dtype=object)
|
|
slices = [slice(0,-1), slice(1,None)]
|
|
for i in [0,1]:
|
|
for j in [0,1]:
|
|
for k in [0,1]:
|
|
fields[i,j,k] = mask[slices[i], slices[j], slices[k]]
|
|
vertIndex = i - 2*j*i + 3*j + 4*k ## this is just to match Bourk's vertex numbering scheme
|
|
#print i,j,k," : ", fields[i,j,k], 2**vertIndex
|
|
index += fields[i,j,k] * 2**vertIndex
|
|
#print index
|
|
#print index
|
|
|
|
## add facets
|
|
for i in range(index.shape[0]): # data x-axis
|
|
for j in range(index.shape[1]): # data y-axis
|
|
for k in range(index.shape[2]): # data z-axis
|
|
tris = triTable[index[i,j,k]]
|
|
for l in range(0, len(tris), 3): ## faces for this grid cell
|
|
edges = tris[l:l+3]
|
|
pts = []
|
|
for m in [0,1,2]: # points in this face
|
|
p1 = edgeKey[edges[m]][0]
|
|
p2 = edgeKey[edges[m]][1]
|
|
v1 = data[i+p1[0], j+p1[1], k+p1[2]]
|
|
v2 = data[i+p2[0], j+p2[1], k+p2[2]]
|
|
f = (level-v1) / (v2-v1)
|
|
fi = 1.0 - f
|
|
p = ( ## interpolate between corners
|
|
p1[0]*fi + p2[0]*f + i + 0.5,
|
|
p1[1]*fi + p2[1]*f + j + 0.5,
|
|
p1[2]*fi + p2[2]*f + k + 0.5
|
|
)
|
|
pts.append(p)
|
|
facets.append(pts)
|
|
|
|
return facets
|
|
|
|
|
|
|
|
def invertQTransform(tr):
|
|
"""Return a QTransform that is the inverse of *tr*.
|
|
Rasises an exception if tr is not invertible.
|
|
|
|
Note that this function is preferred over QTransform.inverted() due to
|
|
bugs in that method. (specifically, Qt has floating-point precision issues
|
|
when determining whether a matrix is invertible)
|
|
"""
|
|
#return tr.inverted()[0]
|
|
arr = np.array([[tr.m11(), tr.m12(), tr.m13()], [tr.m21(), tr.m22(), tr.m23()], [tr.m31(), tr.m32(), tr.m33()]])
|
|
inv = scipy.linalg.inv(arr)
|
|
return QtGui.QTransform(inv[0,0], inv[0,1], inv[0,2], inv[1,0], inv[1,1], inv[1,2], inv[2,0], inv[2,1])
|
|
|
|
|