2251 lines
84 KiB
Python
2251 lines
84 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 __future__ import division
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from .python2_3 import asUnicode
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from .Qt import QtGui, QtCore, USE_PYSIDE
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Colors = {
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'b': QtGui.QColor(0,0,255,255),
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'g': QtGui.QColor(0,255,0,255),
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'r': QtGui.QColor(255,0,0,255),
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'c': QtGui.QColor(0,255,255,255),
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'm': QtGui.QColor(255,0,255,255),
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'y': QtGui.QColor(255,255,0,255),
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'k': QtGui.QColor(0,0,0,255),
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'w': QtGui.QColor(255,255,255,255),
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'd': QtGui.QColor(150,150,150,255),
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'l': QtGui.QColor(200,200,200,255),
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's': QtGui.QColor(100,100,150,255),
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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, USE_PYSIDE
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from . import getConfigOption, setConfigOptions
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import numpy as np
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import decimal, re
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import ctypes
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import sys, struct
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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], 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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try:
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return Colors[c]
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except KeyError:
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raise Exception('No color named "%s"' % 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 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 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, **kwds):
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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 'color' in kwds:
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color = kwds['color']
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elif 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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elif 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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dash = kargs.get('dash', 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 QtGui.QPen(arg) ## return a copy of this pen
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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('l')
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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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if dash is not None:
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pen.setDashPattern(dash)
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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 if it is available (see the scipy documentation for more information about this). If scipy is not available, then a slower implementation of map_coordinates is used.
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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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try:
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import scipy.ndimage
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have_scipy = True
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except ImportError:
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have_scipy = False
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have_scipy = False
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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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if not isinstance(vectors, np.ndarray):
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vectors = np.array(vectors)
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if not isinstance(origin, np.ndarray):
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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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if have_scipy:
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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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output[ind] = scipy.ndimage.map_coordinates(data[ind], x, order=order, **kargs)
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else:
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# map_coordinates expects the indexes as the first axis, whereas
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# interpolateArray expects indexes at the last axis.
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tr = tuple(range(1,x.ndim)) + (0,)
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output = interpolateArray(data, x.transpose(tr))
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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)
|
|
if returnCoords:
|
|
return (output, x)
|
|
else:
|
|
return output
|
|
|
|
def interpolateArray(data, x, default=0.0):
|
|
"""
|
|
N-dimensional interpolation similar scipy.ndimage.map_coordinates.
|
|
|
|
This function returns linearly-interpolated values sampled from a regular
|
|
grid of data.
|
|
|
|
*data* is an array of any shape containing the values to be interpolated.
|
|
*x* is an array with (shape[-1] <= data.ndim) containing the locations
|
|
within *data* to interpolate.
|
|
|
|
Returns array of shape (x.shape[:-1] + data.shape)
|
|
|
|
For example, assume we have the following 2D image data::
|
|
|
|
>>> data = np.array([[1, 2, 4 ],
|
|
[10, 20, 40 ],
|
|
[100, 200, 400]])
|
|
|
|
To compute a single interpolated point from this data::
|
|
|
|
>>> x = np.array([(0.5, 0.5)])
|
|
>>> interpolateArray(data, x)
|
|
array([ 8.25])
|
|
|
|
To compute a 1D list of interpolated locations::
|
|
|
|
>>> x = np.array([(0.5, 0.5),
|
|
(1.0, 1.0),
|
|
(1.0, 2.0),
|
|
(1.5, 0.0)])
|
|
>>> interpolateArray(data, x)
|
|
array([ 8.25, 20. , 40. , 55. ])
|
|
|
|
To compute a 2D array of interpolated locations::
|
|
|
|
>>> x = np.array([[(0.5, 0.5), (1.0, 2.0)],
|
|
[(1.0, 1.0), (1.5, 0.0)]])
|
|
>>> interpolateArray(data, x)
|
|
array([[ 8.25, 40. ],
|
|
[ 20. , 55. ]])
|
|
|
|
..and so on. The *x* argument may have any shape as long as
|
|
```x.shape[-1] <= data.ndim```. In the case that
|
|
```x.shape[-1] < data.ndim```, then the remaining axes are simply
|
|
broadcasted as usual. For example, we can interpolate one location
|
|
from an entire row of the data::
|
|
|
|
>>> x = np.array([[0.5]])
|
|
>>> interpolateArray(data, x)
|
|
array([[ 5.5, 11. , 22. ]])
|
|
|
|
This is useful for interpolating from arrays of colors, vertexes, etc.
|
|
"""
|
|
|
|
prof = debug.Profiler()
|
|
|
|
nd = data.ndim
|
|
md = x.shape[-1]
|
|
|
|
# First we generate arrays of indexes that are needed to
|
|
# extract the data surrounding each point
|
|
fields = np.mgrid[(slice(0,2),) * md]
|
|
xmin = np.floor(x).astype(int)
|
|
xmax = xmin + 1
|
|
indexes = np.concatenate([xmin[np.newaxis, ...], xmax[np.newaxis, ...]])
|
|
fieldInds = []
|
|
totalMask = np.ones(x.shape[:-1], dtype=bool) # keep track of out-of-bound indexes
|
|
for ax in range(md):
|
|
mask = (xmin[...,ax] >= 0) & (x[...,ax] <= data.shape[ax]-1)
|
|
# keep track of points that need to be set to default
|
|
totalMask &= mask
|
|
|
|
# ..and keep track of indexes that are out of bounds
|
|
# (note that when x[...,ax] == data.shape[ax], then xmax[...,ax] will be out
|
|
# of bounds, but the interpolation will work anyway)
|
|
mask &= (xmax[...,ax] < data.shape[ax])
|
|
axisIndex = indexes[...,ax][fields[ax]]
|
|
#axisMask = mask.astype(np.ubyte).reshape((1,)*(fields.ndim-1) + mask.shape)
|
|
axisIndex[axisIndex < 0] = 0
|
|
axisIndex[axisIndex >= data.shape[ax]] = 0
|
|
fieldInds.append(axisIndex)
|
|
prof()
|
|
|
|
# Get data values surrounding each requested point
|
|
# fieldData[..., i] contains all 2**nd values needed to interpolate x[i]
|
|
fieldData = data[tuple(fieldInds)]
|
|
prof()
|
|
|
|
## Interpolate
|
|
s = np.empty((md,) + fieldData.shape, dtype=float)
|
|
dx = x - xmin
|
|
# reshape fields for arithmetic against dx
|
|
for ax in range(md):
|
|
f1 = fields[ax].reshape(fields[ax].shape + (1,)*(dx.ndim-1))
|
|
sax = f1 * dx[...,ax] + (1-f1) * (1-dx[...,ax])
|
|
sax = sax.reshape(sax.shape + (1,) * (s.ndim-1-sax.ndim))
|
|
s[ax] = sax
|
|
s = np.product(s, axis=0)
|
|
result = fieldData * s
|
|
for i in range(md):
|
|
result = result.sum(axis=0)
|
|
|
|
prof()
|
|
totalMask.shape = totalMask.shape + (1,) * (nd - md)
|
|
result[~totalMask] = default
|
|
prof()
|
|
return result
|
|
|
|
|
|
def subArray(data, offset, shape, stride):
|
|
"""
|
|
Unpack a sub-array from *data* using the specified offset, shape, and stride.
|
|
|
|
Note that *stride* is specified in array elements, not bytes.
|
|
For example, we have a 2x3 array packed in a 1D array as follows::
|
|
|
|
data = [_, _, 00, 01, 02, _, 10, 11, 12, _]
|
|
|
|
Then we can unpack the sub-array with this call::
|
|
|
|
subArray(data, offset=2, shape=(2, 3), stride=(4, 1))
|
|
|
|
..which returns::
|
|
|
|
[[00, 01, 02],
|
|
[10, 11, 12]]
|
|
|
|
This function operates only on the first axis of *data*. So changing
|
|
the input in the example above to have shape (10, 7) would cause the
|
|
output to have shape (2, 3, 7).
|
|
"""
|
|
#data = data.flatten()
|
|
data = data[offset:]
|
|
shape = tuple(shape)
|
|
stride = tuple(stride)
|
|
extraShape = data.shape[1:]
|
|
#print data.shape, offset, shape, stride
|
|
for i in range(len(shape)):
|
|
mask = (slice(None),) * i + (slice(None, shape[i] * stride[i]),)
|
|
newShape = shape[:i+1]
|
|
if i < len(shape)-1:
|
|
newShape += (stride[i],)
|
|
newShape += extraShape
|
|
#print i, mask, newShape
|
|
#print "start:\n", data.shape, data
|
|
data = data[mask]
|
|
#print "mask:\n", data.shape, data
|
|
data = data.reshape(newShape)
|
|
#print "reshape:\n", data.shape, data
|
|
|
|
return data
|
|
|
|
|
|
def transformToArray(tr):
|
|
"""
|
|
Given a QTransform, return a 3x3 numpy array.
|
|
Given a QMatrix4x4, return a 4x4 numpy array.
|
|
|
|
Example: map an array of x,y coordinates through a transform::
|
|
|
|
## coordinates to map are (1,5), (2,6), (3,7), and (4,8)
|
|
coords = np.array([[1,2,3,4], [5,6,7,8], [1,1,1,1]]) # the extra '1' coordinate is needed for translation to work
|
|
|
|
## Make an example transform
|
|
tr = QtGui.QTransform()
|
|
tr.translate(3,4)
|
|
tr.scale(2, 0.1)
|
|
|
|
## convert to array
|
|
m = pg.transformToArray()[:2] # ignore the perspective portion of the transformation
|
|
|
|
## map coordinates through transform
|
|
mapped = np.dot(m, coords)
|
|
"""
|
|
#return np.array([[tr.m11(), tr.m12(), tr.m13()],[tr.m21(), tr.m22(), tr.m23()],[tr.m31(), tr.m32(), tr.m33()]])
|
|
## The order of elements given by the method names m11..m33 is misleading--
|
|
## It is most common for x,y translation to occupy the positions 1,3 and 2,3 in
|
|
## a transformation matrix. However, with QTransform these values appear at m31 and m32.
|
|
## So the correct interpretation is transposed:
|
|
if isinstance(tr, QtGui.QTransform):
|
|
return np.array([[tr.m11(), tr.m21(), tr.m31()], [tr.m12(), tr.m22(), tr.m32()], [tr.m13(), tr.m23(), tr.m33()]])
|
|
elif isinstance(tr, QtGui.QMatrix4x4):
|
|
return np.array(tr.copyDataTo()).reshape(4,4)
|
|
else:
|
|
raise Exception("Transform argument must be either QTransform or QMatrix4x4.")
|
|
|
|
def transformCoordinates(tr, coords, transpose=False):
|
|
"""
|
|
Map a set of 2D or 3D coordinates through a QTransform or QMatrix4x4.
|
|
The shape of coords must be (2,...) or (3,...)
|
|
The mapping will _ignore_ any perspective transformations.
|
|
|
|
For coordinate arrays with ndim=2, this is basically equivalent to matrix multiplication.
|
|
Most arrays, however, prefer to put the coordinate axis at the end (eg. shape=(...,3)). To
|
|
allow this, use transpose=True.
|
|
|
|
"""
|
|
|
|
if transpose:
|
|
## move last axis to beginning. This transposition will be reversed before returning the mapped coordinates.
|
|
coords = coords.transpose((coords.ndim-1,) + tuple(range(0,coords.ndim-1)))
|
|
|
|
nd = coords.shape[0]
|
|
if isinstance(tr, np.ndarray):
|
|
m = tr
|
|
else:
|
|
m = transformToArray(tr)
|
|
m = m[:m.shape[0]-1] # remove perspective
|
|
|
|
## If coords are 3D and tr is 2D, assume no change for Z axis
|
|
if m.shape == (2,3) and nd == 3:
|
|
m2 = np.zeros((3,4))
|
|
m2[:2, :2] = m[:2,:2]
|
|
m2[:2, 3] = m[:2,2]
|
|
m2[2,2] = 1
|
|
m = m2
|
|
|
|
## if coords are 2D and tr is 3D, ignore Z axis
|
|
if m.shape == (3,4) and nd == 2:
|
|
m2 = np.empty((2,3))
|
|
m2[:,:2] = m[:2,:2]
|
|
m2[:,2] = m[:2,3]
|
|
m = m2
|
|
|
|
## reshape tr and coords to prepare for multiplication
|
|
m = m.reshape(m.shape + (1,)*(coords.ndim-1))
|
|
coords = coords[np.newaxis, ...]
|
|
|
|
# separate scale/rotate and translation
|
|
translate = m[:,-1]
|
|
m = m[:, :-1]
|
|
|
|
## map coordinates and return
|
|
mapped = (m*coords).sum(axis=1) ## apply scale/rotate
|
|
mapped += translate
|
|
|
|
if transpose:
|
|
## move first axis to end.
|
|
mapped = mapped.transpose(tuple(range(1,mapped.ndim)) + (0,))
|
|
return mapped
|
|
|
|
|
|
|
|
|
|
def solve3DTransform(points1, points2):
|
|
"""
|
|
Find a 3D transformation matrix that maps points1 onto points2.
|
|
Points must be specified as either lists of 4 Vectors or
|
|
(4, 3) arrays.
|
|
"""
|
|
import numpy.linalg
|
|
pts = []
|
|
for inp in (points1, points2):
|
|
if isinstance(inp, np.ndarray):
|
|
A = np.empty((4,4), dtype=float)
|
|
A[:,:3] = inp[:,:3]
|
|
A[:,3] = 1.0
|
|
else:
|
|
A = np.array([[inp[i].x(), inp[i].y(), inp[i].z(), 1] for i in range(4)])
|
|
pts.append(A)
|
|
|
|
## solve 3 sets of linear equations to determine transformation matrix elements
|
|
matrix = np.zeros((4,4))
|
|
for i in range(3):
|
|
## solve Ax = B; x is one row of the desired transformation matrix
|
|
matrix[i] = numpy.linalg.solve(pts[0], pts[1][:,i])
|
|
|
|
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])
|
|
"""
|
|
import numpy.linalg
|
|
## 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] = numpy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
|
|
|
|
return matrix
|
|
|
|
def rescaleData(data, scale, offset, dtype=None):
|
|
"""Return data rescaled and optionally cast to a new dtype::
|
|
|
|
data => (data-offset) * scale
|
|
|
|
Uses scipy.weave (if available) to improve performance.
|
|
"""
|
|
if dtype is None:
|
|
dtype = data.dtype
|
|
else:
|
|
dtype = np.dtype(dtype)
|
|
|
|
try:
|
|
if not getConfigOption('useWeave'):
|
|
raise Exception('Weave is disabled; falling back to slower version.')
|
|
try:
|
|
import scipy.weave
|
|
except ImportError:
|
|
raise Exception('scipy.weave is not importable; falling back to slower version.')
|
|
|
|
## require native dtype when using weave
|
|
if not data.dtype.isnative:
|
|
data = data.astype(data.dtype.newbyteorder('='))
|
|
if not dtype.isnative:
|
|
weaveDtype = dtype.newbyteorder('=')
|
|
else:
|
|
weaveDtype = dtype
|
|
|
|
newData = np.empty((data.size,), dtype=weaveDtype)
|
|
flat = np.ascontiguousarray(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] = ((double)flat[i] - off) * sc;
|
|
}
|
|
"""
|
|
scipy.weave.inline(code, ['flat', 'newData', 'size', 'offset', 'scale'], compiler='gcc')
|
|
if dtype != weaveDtype:
|
|
newData = newData.astype(dtype)
|
|
data = newData.reshape(data.shape)
|
|
except:
|
|
if getConfigOption('useWeave'):
|
|
if getConfigOption('weaveDebug'):
|
|
debug.printExc("Error; disabling weave.")
|
|
setConfigOptions(useWeave=False)
|
|
|
|
#p = np.poly1d([scale, -offset*scale])
|
|
#data = p(data).astype(dtype)
|
|
d2 = data-offset
|
|
d2 *= scale
|
|
data = d2.astype(dtype)
|
|
return data
|
|
|
|
def applyLookupTable(data, lut):
|
|
"""
|
|
Uses values in *data* as indexes to select values from *lut*.
|
|
The returned data has shape data.shape + lut.shape[1:]
|
|
|
|
Note: color gradient lookup tables can be generated using GradientWidget.
|
|
"""
|
|
if data.dtype.kind not in ('i', 'u'):
|
|
data = data.astype(int)
|
|
|
|
return np.take(lut, data, axis=0, mode='clip')
|
|
|
|
|
|
def makeRGBA(*args, **kwds):
|
|
"""Equivalent to makeARGB(..., useRGBA=True)"""
|
|
kwds['useRGBA'] = True
|
|
return makeARGB(*args, **kwds)
|
|
|
|
def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
|
|
"""
|
|
Convert an array of values into an ARGB array suitable for building QImages, OpenGL textures, etc.
|
|
|
|
Returns the ARGB array (values 0-255) and a boolean indicating whether there is alpha channel data.
|
|
This is a two stage process:
|
|
|
|
1) Rescale the data based on the values in the *levels* argument (min, max).
|
|
2) Determine the final output by passing the rescaled values through a lookup table.
|
|
|
|
Both stages are optional.
|
|
|
|
============== ==================================================================================
|
|
**Arguments:**
|
|
data numpy array of int/float types. If
|
|
levels List [min, max]; optionally rescale data before converting through the
|
|
lookup table. The data is rescaled such that min->0 and max->*scale*::
|
|
|
|
rescaled = (clip(data, min, max) - min) * (*scale* / (max - min))
|
|
|
|
It is also possible to use a 2D (N,2) array of values for levels. In this case,
|
|
it is assumed that each pair of min,max values in the levels array should be
|
|
applied to a different subset of the input data (for example, the input data may
|
|
already have RGB values and the levels are used to independently scale each
|
|
channel). The use of this feature requires that levels.shape[0] == data.shape[-1].
|
|
scale The maximum value to which data will be rescaled before being passed through the
|
|
lookup table (or returned if there is no lookup table). By default this will
|
|
be set to the length of the lookup table, or 256 is no lookup table is provided.
|
|
For OpenGL color specifications (as in GLColor4f) use scale=1.0
|
|
lut Optional lookup table (array with dtype=ubyte).
|
|
Values in data will be converted to color by indexing directly from lut.
|
|
The output data shape will be input.shape + lut.shape[1:].
|
|
|
|
Note: the output of makeARGB will have the same dtype as the lookup table, so
|
|
for conversion to QImage, the dtype must be ubyte.
|
|
|
|
Lookup tables can be built using GradientWidget.
|
|
useRGBA If True, the data is returned in RGBA order (useful for building OpenGL textures).
|
|
The default is False, which returns in ARGB order for use with QImage
|
|
(Note that 'ARGB' is a term used by the Qt documentation; the _actual_ order
|
|
is BGRA).
|
|
============== ==================================================================================
|
|
"""
|
|
profile = debug.Profiler()
|
|
|
|
if lut is not None and not isinstance(lut, np.ndarray):
|
|
lut = np.array(lut)
|
|
if levels is not None and not isinstance(levels, np.ndarray):
|
|
levels = np.array(levels)
|
|
|
|
if levels is not None:
|
|
if levels.ndim == 1:
|
|
if len(levels) != 2:
|
|
raise Exception('levels argument must have length 2')
|
|
elif levels.ndim == 2:
|
|
if lut is not None and lut.ndim > 1:
|
|
raise Exception('Cannot make ARGB data when bot levels and lut have ndim > 2')
|
|
if levels.shape != (data.shape[-1], 2):
|
|
raise Exception('levels must have shape (data.shape[-1], 2)')
|
|
else:
|
|
print(levels)
|
|
raise Exception("levels argument must be 1D or 2D.")
|
|
|
|
profile()
|
|
|
|
if scale is None:
|
|
if lut is not None:
|
|
scale = lut.shape[0]
|
|
else:
|
|
scale = 255.
|
|
|
|
## Apply levels if given
|
|
if levels is not None:
|
|
|
|
if isinstance(levels, np.ndarray) and levels.ndim == 2:
|
|
## we are going to rescale each channel independently
|
|
if levels.shape[0] != data.shape[-1]:
|
|
raise Exception("When rescaling multi-channel data, there must be the same number of levels as channels (data.shape[-1] == levels.shape[0])")
|
|
newData = np.empty(data.shape, dtype=int)
|
|
for i in range(data.shape[-1]):
|
|
minVal, maxVal = levels[i]
|
|
if minVal == maxVal:
|
|
maxVal += 1e-16
|
|
newData[...,i] = rescaleData(data[...,i], scale/(maxVal-minVal), minVal, dtype=int)
|
|
data = newData
|
|
else:
|
|
minVal, maxVal = levels
|
|
if minVal == maxVal:
|
|
maxVal += 1e-16
|
|
if maxVal == minVal:
|
|
data = rescaleData(data, 1, minVal, dtype=int)
|
|
else:
|
|
data = rescaleData(data, scale/(maxVal-minVal), minVal, dtype=int)
|
|
|
|
profile()
|
|
|
|
## apply LUT if given
|
|
if lut is not None:
|
|
data = applyLookupTable(data, lut)
|
|
else:
|
|
if data.dtype is not np.ubyte:
|
|
data = np.clip(data, 0, 255).astype(np.ubyte)
|
|
|
|
profile()
|
|
|
|
## copy data into ARGB ordered array
|
|
imgData = np.empty(data.shape[:2]+(4,), dtype=np.ubyte)
|
|
|
|
profile()
|
|
|
|
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.ndim == 2:
|
|
# This is tempting:
|
|
# imgData[..., :3] = data[..., np.newaxis]
|
|
# ..but it turns out this is faster:
|
|
for i in range(3):
|
|
imgData[..., i] = data
|
|
elif data.shape[2] == 1:
|
|
for i in range(3):
|
|
imgData[..., i] = data[..., 0]
|
|
else:
|
|
for i in range(0, data.shape[2]):
|
|
imgData[..., i] = data[..., order[i]]
|
|
|
|
profile()
|
|
|
|
if data.ndim == 2 or data.shape[2] == 3:
|
|
alpha = False
|
|
imgData[..., 3] = 255
|
|
else:
|
|
alpha = True
|
|
|
|
profile()
|
|
return imgData, alpha
|
|
|
|
|
|
def makeQImage(imgData, alpha=None, copy=True, transpose=True):
|
|
"""
|
|
Turn an ARGB array into QImage.
|
|
By default, the data is copied; changes to the array will not
|
|
be reflected in the image. The image will be given a 'data' attribute
|
|
pointing to the array which shares its data to prevent python
|
|
freeing that memory while the image is in use.
|
|
|
|
============== ===================================================================
|
|
**Arguments:**
|
|
imgData Array of data to convert. Must have shape (width, height, 3 or 4)
|
|
and dtype=ubyte. The order of values in the 3rd axis must be
|
|
(b, g, r, a).
|
|
alpha If True, the QImage returned will have format ARGB32. If False,
|
|
the format will be RGB32. By default, _alpha_ is True if
|
|
array.shape[2] == 4.
|
|
copy If True, the data is copied before converting to QImage.
|
|
If False, the new QImage points directly to the data in the array.
|
|
Note that the array must be contiguous for this to work
|
|
(see numpy.ascontiguousarray).
|
|
transpose If True (the default), the array x/y axes are transposed before
|
|
creating the image. Note that Qt expects the axes to be in
|
|
(height, width) order whereas pyqtgraph usually prefers the
|
|
opposite.
|
|
============== ===================================================================
|
|
"""
|
|
## create QImage from buffer
|
|
profile = debug.Profiler()
|
|
|
|
## If we didn't explicitly specify alpha, check the array shape.
|
|
if alpha is None:
|
|
alpha = (imgData.shape[2] == 4)
|
|
|
|
copied = False
|
|
if imgData.shape[2] == 3: ## need to make alpha channel (even if alpha==False; QImage requires 32 bpp)
|
|
if copy is True:
|
|
d2 = np.empty(imgData.shape[:2] + (4,), dtype=imgData.dtype)
|
|
d2[:,:,:3] = imgData
|
|
d2[:,:,3] = 255
|
|
imgData = d2
|
|
copied = True
|
|
else:
|
|
raise Exception('Array has only 3 channels; cannot make QImage without copying.')
|
|
|
|
if alpha:
|
|
imgFormat = QtGui.QImage.Format_ARGB32
|
|
else:
|
|
imgFormat = QtGui.QImage.Format_RGB32
|
|
|
|
if transpose:
|
|
imgData = imgData.transpose((1, 0, 2)) ## QImage expects the row/column order to be opposite
|
|
|
|
profile()
|
|
|
|
if not imgData.flags['C_CONTIGUOUS']:
|
|
if copy is False:
|
|
extra = ' (try setting transpose=False)' if transpose else ''
|
|
raise Exception('Array is not contiguous; cannot make QImage without copying.'+extra)
|
|
imgData = np.ascontiguousarray(imgData)
|
|
copied = True
|
|
|
|
if copy is True and copied is False:
|
|
imgData = imgData.copy()
|
|
|
|
if USE_PYSIDE:
|
|
ch = ctypes.c_char.from_buffer(imgData, 0)
|
|
img = QtGui.QImage(ch, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
else:
|
|
#addr = ctypes.addressof(ctypes.c_char.from_buffer(imgData, 0))
|
|
## PyQt API for QImage changed between 4.9.3 and 4.9.6 (I don't know exactly which version it was)
|
|
## So we first attempt the 4.9.6 API, then fall back to 4.9.3
|
|
#addr = ctypes.c_char.from_buffer(imgData, 0)
|
|
#try:
|
|
#img = QtGui.QImage(addr, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
#except TypeError:
|
|
#addr = ctypes.addressof(addr)
|
|
#img = QtGui.QImage(addr, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
try:
|
|
img = QtGui.QImage(imgData.ctypes.data, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
except:
|
|
if copy:
|
|
# does not leak memory, is not mutable
|
|
img = QtGui.QImage(buffer(imgData), imgData.shape[1], imgData.shape[0], imgFormat)
|
|
else:
|
|
# mutable, but leaks memory
|
|
img = QtGui.QImage(memoryview(imgData), imgData.shape[1], imgData.shape[0], imgFormat)
|
|
|
|
img.data = imgData
|
|
return img
|
|
#try:
|
|
#buf = imgData.data
|
|
#except AttributeError: ## happens when image data is non-contiguous
|
|
#buf = imgData.data
|
|
|
|
#profiler()
|
|
#qimage = QtGui.QImage(buf, imgData.shape[1], imgData.shape[0], imgFormat)
|
|
#profiler()
|
|
#qimage.data = imgData
|
|
#return qimage
|
|
|
|
def imageToArray(img, copy=False, transpose=True):
|
|
"""
|
|
Convert a QImage into numpy array. The image must have format RGB32, ARGB32, or ARGB32_Premultiplied.
|
|
By default, the image is not copied; changes made to the array will appear in the QImage as well (beware: if
|
|
the QImage is collected before the array, there may be trouble).
|
|
The array will have shape (width, height, (b,g,r,a)).
|
|
"""
|
|
fmt = img.format()
|
|
ptr = img.bits()
|
|
if USE_PYSIDE:
|
|
arr = np.frombuffer(ptr, dtype=np.ubyte)
|
|
else:
|
|
ptr.setsize(img.byteCount())
|
|
arr = np.asarray(ptr)
|
|
if img.byteCount() != arr.size * arr.itemsize:
|
|
# Required for Python 2.6, PyQt 4.10
|
|
# If this works on all platforms, then there is no need to use np.asarray..
|
|
arr = np.frombuffer(ptr, np.ubyte, img.byteCount())
|
|
|
|
if fmt == img.Format_RGB32:
|
|
arr = arr.reshape(img.height(), img.width(), 3)
|
|
elif fmt == img.Format_ARGB32 or fmt == img.Format_ARGB32_Premultiplied:
|
|
arr = arr.reshape(img.height(), img.width(), 4)
|
|
|
|
if copy:
|
|
arr = arr.copy()
|
|
|
|
if transpose:
|
|
return arr.transpose((1,0,2))
|
|
else:
|
|
return arr
|
|
|
|
def colorToAlpha(data, color):
|
|
"""
|
|
Given an RGBA image in *data*, convert *color* to be transparent.
|
|
*data* must be an array (w, h, 3 or 4) of ubyte values and *color* must be
|
|
an array (3) of ubyte values.
|
|
This is particularly useful for use with images that have a black or white background.
|
|
|
|
Algorithm is taken from Gimp's color-to-alpha function in plug-ins/common/colortoalpha.c
|
|
Credit:
|
|
/*
|
|
* Color To Alpha plug-in v1.0 by Seth Burgess, sjburges@gimp.org 1999/05/14
|
|
* with algorithm by clahey
|
|
*/
|
|
|
|
"""
|
|
data = data.astype(float)
|
|
if data.shape[-1] == 3: ## add alpha channel if needed
|
|
d2 = np.empty(data.shape[:2]+(4,), dtype=data.dtype)
|
|
d2[...,:3] = data
|
|
d2[...,3] = 255
|
|
data = d2
|
|
|
|
color = color.astype(float)
|
|
alpha = np.zeros(data.shape[:2]+(3,), dtype=float)
|
|
output = data.copy()
|
|
|
|
for i in [0,1,2]:
|
|
d = data[...,i]
|
|
c = color[i]
|
|
mask = d > c
|
|
alpha[...,i][mask] = (d[mask] - c) / (255. - c)
|
|
imask = d < c
|
|
alpha[...,i][imask] = (c - d[imask]) / c
|
|
|
|
output[...,3] = alpha.max(axis=2) * 255.
|
|
|
|
mask = output[...,3] >= 1.0 ## avoid zero division while processing alpha channel
|
|
correction = 255. / output[...,3][mask] ## increase value to compensate for decreased alpha
|
|
for i in [0,1,2]:
|
|
output[...,i][mask] = ((output[...,i][mask]-color[i]) * correction) + color[i]
|
|
output[...,3][mask] *= data[...,3][mask] / 255. ## combine computed and previous alpha values
|
|
|
|
#raise Exception()
|
|
return np.clip(output, 0, 255).astype(np.ubyte)
|
|
|
|
def gaussianFilter(data, sigma):
|
|
"""
|
|
Drop-in replacement for scipy.ndimage.gaussian_filter.
|
|
|
|
(note: results are only approximately equal to the output of
|
|
gaussian_filter)
|
|
"""
|
|
if np.isscalar(sigma):
|
|
sigma = (sigma,) * data.ndim
|
|
|
|
baseline = data.mean()
|
|
filtered = data - baseline
|
|
for ax in range(data.ndim):
|
|
s = sigma[ax]
|
|
if s == 0:
|
|
continue
|
|
|
|
# generate 1D gaussian kernel
|
|
ksize = int(s * 6)
|
|
x = np.arange(-ksize, ksize)
|
|
kernel = np.exp(-x**2 / (2*s**2))
|
|
kshape = [1,] * data.ndim
|
|
kshape[ax] = len(kernel)
|
|
kernel = kernel.reshape(kshape)
|
|
|
|
# convolve as product of FFTs
|
|
shape = data.shape[ax] + ksize
|
|
scale = 1.0 / (abs(s) * (2*np.pi)**0.5)
|
|
filtered = scale * np.fft.irfft(np.fft.rfft(filtered, shape, axis=ax) *
|
|
np.fft.rfft(kernel, shape, axis=ax),
|
|
axis=ax)
|
|
|
|
# clip off extra data
|
|
sl = [slice(None)] * data.ndim
|
|
sl[ax] = slice(filtered.shape[ax]-data.shape[ax],None,None)
|
|
filtered = filtered[sl]
|
|
return filtered + baseline
|
|
|
|
|
|
def downsample(data, n, axis=0, xvals='subsample'):
|
|
"""Downsample by averaging points together across axis.
|
|
If multiple axes are specified, runs once per axis.
|
|
If a metaArray is given, then the axis values can be either subsampled
|
|
or downsampled to match.
|
|
"""
|
|
ma = None
|
|
if (hasattr(data, 'implements') and data.implements('MetaArray')):
|
|
ma = data
|
|
data = data.view(np.ndarray)
|
|
|
|
|
|
if hasattr(axis, '__len__'):
|
|
if not hasattr(n, '__len__'):
|
|
n = [n]*len(axis)
|
|
for i in range(len(axis)):
|
|
data = downsample(data, n[i], axis[i])
|
|
return data
|
|
|
|
nPts = int(data.shape[axis] / n)
|
|
s = list(data.shape)
|
|
s[axis] = nPts
|
|
s.insert(axis+1, n)
|
|
sl = [slice(None)] * data.ndim
|
|
sl[axis] = slice(0, nPts*n)
|
|
d1 = data[tuple(sl)]
|
|
#print d1.shape, s
|
|
d1.shape = tuple(s)
|
|
d2 = d1.mean(axis+1)
|
|
|
|
if ma is None:
|
|
return d2
|
|
else:
|
|
info = ma.infoCopy()
|
|
if 'values' in info[axis]:
|
|
if xvals == 'subsample':
|
|
info[axis]['values'] = info[axis]['values'][::n][:nPts]
|
|
elif xvals == 'downsample':
|
|
info[axis]['values'] = downsample(info[axis]['values'], n)
|
|
return MetaArray(d2, info=info)
|
|
|
|
|
|
def arrayToQPath(x, y, connect='all'):
|
|
"""Convert an array of x,y coordinats to QPainterPath as efficiently as possible.
|
|
The *connect* argument may be 'all', indicating that each point should be
|
|
connected to the next; 'pairs', indicating that each pair of points
|
|
should be connected, or an array of int32 values (0 or 1) indicating
|
|
connections.
|
|
"""
|
|
|
|
## Create all vertices in path. The method used below creates a binary format so that all
|
|
## vertices can be read in at once. This binary format may change in future versions of Qt,
|
|
## so the original (slower) method is left here for emergencies:
|
|
#path.moveTo(x[0], y[0])
|
|
#if connect == 'all':
|
|
#for i in range(1, y.shape[0]):
|
|
#path.lineTo(x[i], y[i])
|
|
#elif connect == 'pairs':
|
|
#for i in range(1, y.shape[0]):
|
|
#if i%2 == 0:
|
|
#path.lineTo(x[i], y[i])
|
|
#else:
|
|
#path.moveTo(x[i], y[i])
|
|
#elif isinstance(connect, np.ndarray):
|
|
#for i in range(1, y.shape[0]):
|
|
#if connect[i] == 1:
|
|
#path.lineTo(x[i], y[i])
|
|
#else:
|
|
#path.moveTo(x[i], y[i])
|
|
#else:
|
|
#raise Exception('connect argument must be "all", "pairs", or array')
|
|
|
|
## Speed this up using >> operator
|
|
## Format is:
|
|
## numVerts(i4) 0(i4)
|
|
## x(f8) y(f8) 0(i4) <-- 0 means this vertex does not connect
|
|
## x(f8) y(f8) 1(i4) <-- 1 means this vertex connects to the previous vertex
|
|
## ...
|
|
## 0(i4)
|
|
##
|
|
## All values are big endian--pack using struct.pack('>d') or struct.pack('>i')
|
|
|
|
path = QtGui.QPainterPath()
|
|
|
|
#profiler = debug.Profiler()
|
|
n = x.shape[0]
|
|
# create empty array, pad with extra space on either end
|
|
arr = np.empty(n+2, dtype=[('x', '>f8'), ('y', '>f8'), ('c', '>i4')])
|
|
# write first two integers
|
|
#profiler('allocate empty')
|
|
byteview = arr.view(dtype=np.ubyte)
|
|
byteview[:12] = 0
|
|
byteview.data[12:20] = struct.pack('>ii', n, 0)
|
|
#profiler('pack header')
|
|
# Fill array with vertex values
|
|
arr[1:-1]['x'] = x
|
|
arr[1:-1]['y'] = y
|
|
|
|
# decide which points are connected by lines
|
|
if connect == 'pairs':
|
|
connect = np.empty((n/2,2), dtype=np.int32)
|
|
if connect.size != n:
|
|
raise Exception("x,y array lengths must be multiple of 2 to use connect='pairs'")
|
|
connect[:,0] = 1
|
|
connect[:,1] = 0
|
|
connect = connect.flatten()
|
|
if connect == 'finite':
|
|
connect = np.isfinite(x) & np.isfinite(y)
|
|
arr[1:-1]['c'] = connect
|
|
if connect == 'all':
|
|
arr[1:-1]['c'] = 1
|
|
elif isinstance(connect, np.ndarray):
|
|
arr[1:-1]['c'] = connect
|
|
else:
|
|
raise Exception('connect argument must be "all", "pairs", or array')
|
|
|
|
#profiler('fill array')
|
|
# write last 0
|
|
lastInd = 20*(n+1)
|
|
byteview.data[lastInd:lastInd+4] = struct.pack('>i', 0)
|
|
#profiler('footer')
|
|
# create datastream object and stream into path
|
|
|
|
## Avoiding this method because QByteArray(str) leaks memory in PySide
|
|
#buf = QtCore.QByteArray(arr.data[12:lastInd+4]) # I think one unnecessary copy happens here
|
|
|
|
path.strn = byteview.data[12:lastInd+4] # make sure data doesn't run away
|
|
try:
|
|
buf = QtCore.QByteArray.fromRawData(path.strn)
|
|
except TypeError:
|
|
buf = QtCore.QByteArray(bytes(path.strn))
|
|
#profiler('create buffer')
|
|
ds = QtCore.QDataStream(buf)
|
|
|
|
ds >> path
|
|
#profiler('load')
|
|
|
|
return path
|
|
|
|
#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, connected=False, extendToEdge=False, path=False):
|
|
"""
|
|
Generate isocurve from 2D data using marching squares algorithm.
|
|
|
|
============== =========================================================
|
|
**Arguments:**
|
|
data 2D numpy array of scalar values
|
|
level The level at which to generate an isosurface
|
|
connected If False, return a single long list of point pairs
|
|
If True, return multiple long lists of connected point
|
|
locations. (This is slower but better for drawing
|
|
continuous lines)
|
|
extendToEdge If True, extend the curves to reach the exact edges of
|
|
the data.
|
|
path if True, return a QPainterPath rather than a list of
|
|
vertex coordinates. This forces connected=True.
|
|
============== =========================================================
|
|
|
|
This function is SLOW; plenty of room for optimization here.
|
|
"""
|
|
|
|
if path is True:
|
|
connected = True
|
|
|
|
if extendToEdge:
|
|
d2 = np.empty((data.shape[0]+2, data.shape[1]+2), dtype=data.dtype)
|
|
d2[1:-1, 1:-1] = data
|
|
d2[0, 1:-1] = data[0]
|
|
d2[-1, 1:-1] = data[-1]
|
|
d2[1:-1, 0] = data[:, 0]
|
|
d2[1:-1, -1] = data[:, -1]
|
|
d2[0,0] = d2[0,1]
|
|
d2[0,-1] = d2[1,-1]
|
|
d2[-1,0] = d2[-1,1]
|
|
d2[-1,-1] = d2[-1,-2]
|
|
data = d2
|
|
|
|
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
|
|
)
|
|
if extendToEdge:
|
|
## check bounds
|
|
p = (
|
|
min(data.shape[0]-2, max(0, p[0]-1)),
|
|
min(data.shape[1]-2, max(0, p[1]-1)),
|
|
)
|
|
if connected:
|
|
gridKey = i + (1 if edges[m]==2 else 0), j + (1 if edges[m]==3 else 0), edges[m]%2
|
|
pts.append((p, gridKey)) ## give the actual position and a key identifying the grid location (for connecting segments)
|
|
else:
|
|
pts.append(p)
|
|
|
|
lines.append(pts)
|
|
|
|
if not connected:
|
|
return lines
|
|
|
|
## turn disjoint list of segments into continuous lines
|
|
|
|
#lines = [[2,5], [5,4], [3,4], [1,3], [6,7], [7,8], [8,6], [11,12], [12,15], [11,13], [13,14]]
|
|
#lines = [[(float(a), a), (float(b), b)] for a,b in lines]
|
|
points = {} ## maps each point to its connections
|
|
for a,b in lines:
|
|
if a[1] not in points:
|
|
points[a[1]] = []
|
|
points[a[1]].append([a,b])
|
|
if b[1] not in points:
|
|
points[b[1]] = []
|
|
points[b[1]].append([b,a])
|
|
|
|
## rearrange into chains
|
|
for k in list(points.keys()):
|
|
try:
|
|
chains = points[k]
|
|
except KeyError: ## already used this point elsewhere
|
|
continue
|
|
#print "===========", k
|
|
for chain in chains:
|
|
#print " chain:", chain
|
|
x = None
|
|
while True:
|
|
if x == chain[-1][1]:
|
|
break ## nothing left to do on this chain
|
|
|
|
x = chain[-1][1]
|
|
if x == k:
|
|
break ## chain has looped; we're done and can ignore the opposite chain
|
|
y = chain[-2][1]
|
|
connects = points[x]
|
|
for conn in connects[:]:
|
|
if conn[1][1] != y:
|
|
#print " ext:", conn
|
|
chain.extend(conn[1:])
|
|
#print " del:", x
|
|
del points[x]
|
|
if chain[0][1] == chain[-1][1]: # looped chain; no need to continue the other direction
|
|
chains.pop()
|
|
break
|
|
|
|
|
|
## extract point locations
|
|
lines = []
|
|
for chain in points.values():
|
|
if len(chain) == 2:
|
|
chain = chain[1][1:][::-1] + chain[0] # join together ends of chain
|
|
else:
|
|
chain = chain[0]
|
|
lines.append([p[0] for p in chain])
|
|
|
|
if not path:
|
|
return lines ## a list of pairs of points
|
|
|
|
path = QtGui.QPainterPath()
|
|
for line in lines:
|
|
path.moveTo(*line[0])
|
|
for p in line[1:]:
|
|
path.lineTo(*p)
|
|
|
|
return path
|
|
|
|
|
|
def traceImage(image, values, smooth=0.5):
|
|
"""
|
|
Convert an image to a set of QPainterPath curves.
|
|
One curve will be generated for each item in *values*; each curve outlines the area
|
|
of the image that is closer to its value than to any others.
|
|
|
|
If image is RGB or RGBA, then the shape of values should be (nvals, 3/4)
|
|
The parameter *smooth* is expressed in pixels.
|
|
"""
|
|
try:
|
|
import scipy.ndimage as ndi
|
|
except ImportError:
|
|
raise Exception("traceImage() requires the package scipy.ndimage, but it is not importable.")
|
|
|
|
if values.ndim == 2:
|
|
values = values.T
|
|
values = values[np.newaxis, np.newaxis, ...].astype(float)
|
|
image = image[..., np.newaxis].astype(float)
|
|
diff = np.abs(image-values)
|
|
if values.ndim == 4:
|
|
diff = diff.sum(axis=2)
|
|
|
|
labels = np.argmin(diff, axis=2)
|
|
|
|
paths = []
|
|
for i in range(diff.shape[-1]):
|
|
d = (labels==i).astype(float)
|
|
d = gaussianFilter(d, (smooth, smooth))
|
|
lines = isocurve(d, 0.5, connected=True, extendToEdge=True)
|
|
path = QtGui.QPainterPath()
|
|
for line in lines:
|
|
path.moveTo(*line[0])
|
|
for p in line[1:]:
|
|
path.lineTo(*p)
|
|
|
|
paths.append(path)
|
|
return paths
|
|
|
|
|
|
|
|
IsosurfaceDataCache = None
|
|
def isosurface(data, level):
|
|
"""
|
|
Generate isosurface from volumetric data using marching cubes algorithm.
|
|
See Paul Bourke, "Polygonising a Scalar Field"
|
|
(http://paulbourke.net/geometry/polygonise/)
|
|
|
|
*data* 3D numpy array of scalar values
|
|
*level* The level at which to generate an isosurface
|
|
|
|
Returns an array of vertex coordinates (Nv, 3) and an array of
|
|
per-face vertex indexes (Nf, 3)
|
|
"""
|
|
## For improvement, see:
|
|
##
|
|
## Efficient implementation of Marching Cubes' cases with topological guarantees.
|
|
## Thomas Lewiner, Helio Lopes, Antonio Wilson Vieira and Geovan Tavares.
|
|
## Journal of Graphics Tools 8(2): pp. 1-15 (december 2003)
|
|
|
|
## Precompute lookup tables on the first run
|
|
global IsosurfaceDataCache
|
|
if IsosurfaceDataCache is None:
|
|
## 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 = np.array([
|
|
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
|
|
], dtype=np.uint16)
|
|
|
|
## 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],
|
|
[]
|
|
]
|
|
edgeShifts = np.array([ ## maps edge ID (0-11) to (x,y,z) cell offset and edge ID (0-2)
|
|
[0, 0, 0, 0],
|
|
[1, 0, 0, 1],
|
|
[0, 1, 0, 0],
|
|
[0, 0, 0, 1],
|
|
[0, 0, 1, 0],
|
|
[1, 0, 1, 1],
|
|
[0, 1, 1, 0],
|
|
[0, 0, 1, 1],
|
|
[0, 0, 0, 2],
|
|
[1, 0, 0, 2],
|
|
[1, 1, 0, 2],
|
|
[0, 1, 0, 2],
|
|
#[9, 9, 9, 9] ## fake
|
|
], dtype=np.uint16) # don't use ubyte here! This value gets added to cell index later; will need the extra precision.
|
|
nTableFaces = np.array([len(f)/3 for f in triTable], dtype=np.ubyte)
|
|
faceShiftTables = [None]
|
|
for i in range(1,6):
|
|
## compute lookup table of index: vertexes mapping
|
|
faceTableI = np.zeros((len(triTable), i*3), dtype=np.ubyte)
|
|
faceTableInds = np.argwhere(nTableFaces == i)
|
|
faceTableI[faceTableInds[:,0]] = np.array([triTable[j] for j in faceTableInds])
|
|
faceTableI = faceTableI.reshape((len(triTable), i, 3))
|
|
faceShiftTables.append(edgeShifts[faceTableI])
|
|
|
|
## Let's try something different:
|
|
#faceTable = np.empty((256, 5, 3, 4), dtype=np.ubyte) # (grid cell index, faces, vertexes, edge lookup)
|
|
#for i,f in enumerate(triTable):
|
|
#f = np.array(f + [12] * (15-len(f))).reshape(5,3)
|
|
#faceTable[i] = edgeShifts[f]
|
|
|
|
|
|
IsosurfaceDataCache = (faceShiftTables, edgeShifts, edgeTable, nTableFaces)
|
|
else:
|
|
faceShiftTables, edgeShifts, edgeTable, nTableFaces = IsosurfaceDataCache
|
|
|
|
|
|
|
|
## 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
|
|
index += fields[i,j,k] * 2**vertIndex
|
|
|
|
### Generate table of edges that have been cut
|
|
cutEdges = np.zeros([x+1 for x in index.shape]+[3], dtype=np.uint32)
|
|
edges = edgeTable[index]
|
|
for i, shift in enumerate(edgeShifts[:12]):
|
|
slices = [slice(shift[j],cutEdges.shape[j]+(shift[j]-1)) for j in range(3)]
|
|
cutEdges[slices[0], slices[1], slices[2], shift[3]] += edges & 2**i
|
|
|
|
## for each cut edge, interpolate to see where exactly the edge is cut and generate vertex positions
|
|
m = cutEdges > 0
|
|
vertexInds = np.argwhere(m) ## argwhere is slow!
|
|
vertexes = vertexInds[:,:3].astype(np.float32)
|
|
dataFlat = data.reshape(data.shape[0]*data.shape[1]*data.shape[2])
|
|
|
|
## re-use the cutEdges array as a lookup table for vertex IDs
|
|
cutEdges[vertexInds[:,0], vertexInds[:,1], vertexInds[:,2], vertexInds[:,3]] = np.arange(vertexInds.shape[0])
|
|
|
|
for i in [0,1,2]:
|
|
vim = vertexInds[:,3] == i
|
|
vi = vertexInds[vim, :3]
|
|
viFlat = (vi * (np.array(data.strides[:3]) // data.itemsize)[np.newaxis,:]).sum(axis=1)
|
|
v1 = dataFlat[viFlat]
|
|
v2 = dataFlat[viFlat + data.strides[i]//data.itemsize]
|
|
vertexes[vim,i] += (level-v1) / (v2-v1)
|
|
|
|
### compute the set of vertex indexes for each face.
|
|
|
|
## This works, but runs a bit slower.
|
|
#cells = np.argwhere((index != 0) & (index != 255)) ## all cells with at least one face
|
|
#cellInds = index[cells[:,0], cells[:,1], cells[:,2]]
|
|
#verts = faceTable[cellInds]
|
|
#mask = verts[...,0,0] != 9
|
|
#verts[...,:3] += cells[:,np.newaxis,np.newaxis,:] ## we now have indexes into cutEdges
|
|
#verts = verts[mask]
|
|
#faces = cutEdges[verts[...,0], verts[...,1], verts[...,2], verts[...,3]] ## and these are the vertex indexes we want.
|
|
|
|
|
|
## To allow this to be vectorized efficiently, we count the number of faces in each
|
|
## grid cell and handle each group of cells with the same number together.
|
|
## determine how many faces to assign to each grid cell
|
|
nFaces = nTableFaces[index]
|
|
totFaces = nFaces.sum()
|
|
faces = np.empty((totFaces, 3), dtype=np.uint32)
|
|
ptr = 0
|
|
#import debug
|
|
#p = debug.Profiler()
|
|
|
|
## this helps speed up an indexing operation later on
|
|
cs = np.array(cutEdges.strides)//cutEdges.itemsize
|
|
cutEdges = cutEdges.flatten()
|
|
|
|
## this, strangely, does not seem to help.
|
|
#ins = np.array(index.strides)/index.itemsize
|
|
#index = index.flatten()
|
|
|
|
for i in range(1,6):
|
|
### expensive:
|
|
#profiler()
|
|
cells = np.argwhere(nFaces == i) ## all cells which require i faces (argwhere is expensive)
|
|
#profiler()
|
|
if cells.shape[0] == 0:
|
|
continue
|
|
cellInds = index[cells[:,0], cells[:,1], cells[:,2]] ## index values of cells to process for this round
|
|
#profiler()
|
|
|
|
### expensive:
|
|
verts = faceShiftTables[i][cellInds]
|
|
#profiler()
|
|
verts[...,:3] += cells[:,np.newaxis,np.newaxis,:] ## we now have indexes into cutEdges
|
|
verts = verts.reshape((verts.shape[0]*i,)+verts.shape[2:])
|
|
#profiler()
|
|
|
|
### expensive:
|
|
verts = (verts * cs[np.newaxis, np.newaxis, :]).sum(axis=2)
|
|
vertInds = cutEdges[verts]
|
|
#profiler()
|
|
nv = vertInds.shape[0]
|
|
#profiler()
|
|
faces[ptr:ptr+nv] = vertInds #.reshape((nv, 3))
|
|
#profiler()
|
|
ptr += nv
|
|
|
|
return vertexes, faces
|
|
|
|
|
|
|
|
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)
|
|
"""
|
|
try:
|
|
import numpy.linalg
|
|
arr = np.array([[tr.m11(), tr.m12(), tr.m13()], [tr.m21(), tr.m22(), tr.m23()], [tr.m31(), tr.m32(), tr.m33()]])
|
|
inv = numpy.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])
|
|
except ImportError:
|
|
inv = tr.inverted()
|
|
if inv[1] is False:
|
|
raise Exception("Transform is not invertible.")
|
|
return inv[0]
|
|
|
|
|
|
def pseudoScatter(data, spacing=None, shuffle=True, bidir=False):
|
|
"""
|
|
Used for examining the distribution of values in a set. Produces scattering as in beeswarm or column scatter plots.
|
|
|
|
Given a list of x-values, construct a set of y-values such that an x,y scatter-plot
|
|
will not have overlapping points (it will look similar to a histogram).
|
|
"""
|
|
inds = np.arange(len(data))
|
|
if shuffle:
|
|
np.random.shuffle(inds)
|
|
|
|
data = data[inds]
|
|
|
|
if spacing is None:
|
|
spacing = 2.*np.std(data)/len(data)**0.5
|
|
s2 = spacing**2
|
|
|
|
yvals = np.empty(len(data))
|
|
if len(data) == 0:
|
|
return yvals
|
|
yvals[0] = 0
|
|
for i in range(1,len(data)):
|
|
x = data[i] # current x value to be placed
|
|
x0 = data[:i] # all x values already placed
|
|
y0 = yvals[:i] # all y values already placed
|
|
y = 0
|
|
|
|
dx = (x0-x)**2 # x-distance to each previous point
|
|
xmask = dx < s2 # exclude anything too far away
|
|
|
|
if xmask.sum() > 0:
|
|
if bidir:
|
|
dirs = [-1, 1]
|
|
else:
|
|
dirs = [1]
|
|
yopts = []
|
|
for direction in dirs:
|
|
y = 0
|
|
dx2 = dx[xmask]
|
|
dy = (s2 - dx2)**0.5
|
|
limits = np.empty((2,len(dy))) # ranges of y-values to exclude
|
|
limits[0] = y0[xmask] - dy
|
|
limits[1] = y0[xmask] + dy
|
|
while True:
|
|
# ignore anything below this y-value
|
|
if direction > 0:
|
|
mask = limits[1] >= y
|
|
else:
|
|
mask = limits[0] <= y
|
|
|
|
limits2 = limits[:,mask]
|
|
|
|
# are we inside an excluded region?
|
|
mask = (limits2[0] < y) & (limits2[1] > y)
|
|
if mask.sum() == 0:
|
|
break
|
|
|
|
if direction > 0:
|
|
y = limits2[:,mask].max()
|
|
else:
|
|
y = limits2[:,mask].min()
|
|
yopts.append(y)
|
|
if bidir:
|
|
y = yopts[0] if -yopts[0] < yopts[1] else yopts[1]
|
|
else:
|
|
y = yopts[0]
|
|
yvals[i] = y
|
|
|
|
return yvals[np.argsort(inds)] ## un-shuffle values before returning
|
|
|
|
|
|
|
|
def toposort(deps, nodes=None, seen=None, stack=None, depth=0):
|
|
"""Topological sort. Arguments are:
|
|
deps dictionary describing dependencies where a:[b,c] means "a depends on b and c"
|
|
nodes optional, specifies list of starting nodes (these should be the nodes
|
|
which are not depended on by any other nodes). Other candidate starting
|
|
nodes will be ignored.
|
|
|
|
Example::
|
|
|
|
# Sort the following graph:
|
|
#
|
|
# B ──┬─────> C <── D
|
|
# │ │
|
|
# E <─┴─> A <─┘
|
|
#
|
|
deps = {'a': ['b', 'c'], 'c': ['b', 'd'], 'e': ['b']}
|
|
toposort(deps)
|
|
=> ['b', 'd', 'c', 'a', 'e']
|
|
"""
|
|
# fill in empty dep lists
|
|
deps = deps.copy()
|
|
for k,v in list(deps.items()):
|
|
for k in v:
|
|
if k not in deps:
|
|
deps[k] = []
|
|
|
|
if nodes is None:
|
|
## run through deps to find nodes that are not depended upon
|
|
rem = set()
|
|
for dep in deps.values():
|
|
rem |= set(dep)
|
|
nodes = set(deps.keys()) - rem
|
|
if seen is None:
|
|
seen = set()
|
|
stack = []
|
|
sorted = []
|
|
for n in nodes:
|
|
if n in stack:
|
|
raise Exception("Cyclic dependency detected", stack + [n])
|
|
if n in seen:
|
|
continue
|
|
seen.add(n)
|
|
sorted.extend( toposort(deps, deps[n], seen, stack+[n], depth=depth+1))
|
|
sorted.append(n)
|
|
return sorted
|