ASCEE
/
pyqtgraph
4
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
pyqtgraph/pyqtgraph/functions.py

2570 lines
95 KiB
Python
Raw Blame History

# -*- coding: utf-8 -*-
"""
functions.py - Miscellaneous functions with no other home
Copyright 2010 Luke Campagnola
Distributed under MIT/X11 license. See license.txt for more information.
"""
from __future__ import division
import ctypes
import decimal
import re
import struct
import sys
import warnings
import numpy as np
from pyqtgraph.util.cupy_helper import getCupy
from . import debug, reload
from .Qt import QtGui, QtCore, QT_LIB, QtVersion
from . import Qt
from .metaarray import MetaArray
from .pgcollections import OrderedDict
from .python2_3 import asUnicode, basestring
Colors = {
'b': QtGui.QColor(0,0,255,255),
'g': QtGui.QColor(0,255,0,255),
'r': QtGui.QColor(255,0,0,255),
'c': QtGui.QColor(0,255,255,255),
'm': QtGui.QColor(255,0,255,255),
'y': QtGui.QColor(255,255,0,255),
'k': QtGui.QColor(0,0,0,255),
'w': QtGui.QColor(255,255,255,255),
'd': QtGui.QColor(150,150,150,255),
'l': QtGui.QColor(200,200,200,255),
's': QtGui.QColor(100,100,150,255),
}
SI_PREFIXES = asUnicode('yzafpnµm kMGTPEZY')
SI_PREFIXES_ASCII = 'yzafpnum kMGTPEZY'
SI_PREFIX_EXPONENTS = dict([(SI_PREFIXES[i], (i-8)*3) for i in range(len(SI_PREFIXES))])
SI_PREFIX_EXPONENTS['u'] = -6
FLOAT_REGEX = re.compile(r'(?P<number>[+-]?((((\d+(\.\d*)?)|(\d*\.\d+))([eE][+-]?\d+)?)|((?i:nan)|(inf))))\s*((?P<siPrefix>[u' + SI_PREFIXES + r']?)(?P<suffix>\w.*))?$')
INT_REGEX = re.compile(r'(?P<number>[+-]?\d+)\s*(?P<siPrefix>[u' + SI_PREFIXES + r']?)(?P<suffix>.*)$')
def siScale(x, minVal=1e-25, allowUnicode=True):
"""
Return the recommended scale factor and SI prefix string for x.
Example::
siScale(0.0001) # returns (1e6, 'μ')
# This indicates that the number 0.0001 is best represented as 0.0001 * 1e6 = 100 μUnits
"""
if isinstance(x, decimal.Decimal):
x = float(x)
try:
if np.isnan(x) or np.isinf(x):
return(1, '')
except:
print(x, type(x))
raise
if abs(x) < minVal:
m = 0
x = 0
else:
m = int(np.clip(np.floor(np.log(abs(x))/np.log(1000)), -9.0, 9.0))
if m == 0:
pref = ''
elif m < -8 or m > 8:
pref = 'e%d' % (m*3)
else:
if allowUnicode:
pref = SI_PREFIXES[m+8]
else:
pref = SI_PREFIXES_ASCII[m+8]
m1 = -3*m
p = 10.**m1
return (p, pref)
def siFormat(x, precision=3, suffix='', space=True, error=None, minVal=1e-25, allowUnicode=True):
"""
Return the number x formatted in engineering notation with SI prefix.
Example::
siFormat(0.0001, suffix='V') # returns "100 μV"
"""
if space is True:
space = ' '
if space is False:
space = ''
(p, pref) = siScale(x, minVal, allowUnicode)
if not (len(pref) > 0 and pref[0] == 'e'):
pref = space + pref
if error is None:
fmt = "%." + str(precision) + "g%s%s"
return fmt % (x*p, pref, suffix)
else:
if allowUnicode:
plusminus = space + asUnicode("±") + space
else:
plusminus = " +/- "
fmt = "%." + str(precision) + "g%s%s%s%s"
return fmt % (x*p, pref, suffix, plusminus, siFormat(error, precision=precision, suffix=suffix, space=space, minVal=minVal))
def siParse(s, regex=FLOAT_REGEX, suffix=None):
"""Convert a value written in SI notation to a tuple (number, si_prefix, suffix).
Example::
siParse('100 μV") # returns ('100', 'μ', 'V')
"""
s = asUnicode(s)
s = s.strip()
if suffix is not None and len(suffix) > 0:
if s[-len(suffix):] != suffix:
raise ValueError("String '%s' does not have the expected suffix '%s'" % (s, suffix))
s = s[:-len(suffix)] + 'X' # add a fake suffix so the regex still picks up the si prefix
m = regex.match(s)
if m is None:
raise ValueError('Cannot parse number "%s"' % s)
try:
sip = m.group('siPrefix')
except IndexError:
sip = ''
if suffix is None:
try:
suf = m.group('suffix')
except IndexError:
suf = ''
else:
suf = suffix
return m.group('number'), '' if sip is None else sip, '' if suf is None else suf
def siEval(s, typ=float, regex=FLOAT_REGEX, suffix=None):
"""
Convert a value written in SI notation to its equivalent prefixless value.
Example::
siEval("100 μV") # returns 0.0001
"""
val, siprefix, suffix = siParse(s, regex, suffix=suffix)
v = typ(val)
return siApply(v, siprefix)
def siApply(val, siprefix):
"""
"""
n = SI_PREFIX_EXPONENTS[siprefix] if siprefix != '' else 0
if n > 0:
return val * 10**n
elif n < 0:
# this case makes it possible to use Decimal objects here
return val / 10**-n
else:
return val
class Color(QtGui.QColor):
def __init__(self, *args):
QtGui.QColor.__init__(self, mkColor(*args))
def glColor(self):
"""Return (r,g,b,a) normalized for use in opengl"""
return (self.red()/255., self.green()/255., self.blue()/255., self.alpha()/255.)
def __getitem__(self, ind):
return (self.red, self.green, self.blue, self.alpha)[ind]()
def mkColor(*args):
"""
Convenience function for constructing QColor from a variety of argument types. Accepted arguments are:
================ ================================================
'c' one of: r, g, b, c, m, y, k, w
R, G, B, [A] integers 0-255
(R, G, B, [A]) tuple of integers 0-255
float greyscale, 0.0-1.0
int see :func:`intColor() <pyqtgraph.intColor>`
(int, hues) see :func:`intColor() <pyqtgraph.intColor>`
"RGB" hexadecimal strings; may begin with '#'
"RGBA"
"RRGGBB"
"RRGGBBAA"
QColor QColor instance; makes a copy.
================ ================================================
"""
err = 'Not sure how to make a color from "%s"' % str(args)
if len(args) == 1:
if isinstance(args[0], basestring):
c = args[0]
if c[0] == '#':
c = c[1:]
if len(c) == 1:
try:
return Colors[c]
except KeyError:
raise ValueError('No color named "%s"' % c)
if len(c) == 3:
r = int(c[0]*2, 16)
g = int(c[1]*2, 16)
b = int(c[2]*2, 16)
a = 255
elif len(c) == 4:
r = int(c[0]*2, 16)
g = int(c[1]*2, 16)
b = int(c[2]*2, 16)
a = int(c[3]*2, 16)
elif len(c) == 6:
r = int(c[0:2], 16)
g = int(c[2:4], 16)
b = int(c[4:6], 16)
a = 255
elif len(c) == 8:
r = int(c[0:2], 16)
g = int(c[2:4], 16)
b = int(c[4:6], 16)
a = int(c[6:8], 16)
elif isinstance(args[0], QtGui.QColor):
return QtGui.QColor(args[0])
elif isinstance(args[0], float):
r = g = b = int(args[0] * 255)
a = 255
elif hasattr(args[0], '__len__'):
if len(args[0]) == 3:
(r, g, b) = args[0]
a = 255
elif len(args[0]) == 4:
(r, g, b, a) = args[0]
elif len(args[0]) == 2:
return intColor(*args[0])
else:
raise TypeError(err)
elif type(args[0]) == int:
return intColor(args[0])
else:
raise TypeError(err)
elif len(args) == 3:
(r, g, b) = args
a = 255
elif len(args) == 4:
(r, g, b, a) = args
else:
raise TypeError(err)
args = [r,g,b,a]
args = [0 if np.isnan(a) or np.isinf(a) else a for a in args]
args = list(map(int, args))
return QtGui.QColor(*args)
def mkBrush(*args, **kwds):
"""
| Convenience function for constructing Brush.
| This function always constructs a solid brush and accepts the same arguments as :func:`mkColor() <pyqtgraph.mkColor>`
| Calling mkBrush(None) returns an invisible brush.
"""
if 'color' in kwds:
color = kwds['color']
elif len(args) == 1:
arg = args[0]
if arg is None:
return QtGui.QBrush(QtCore.Qt.NoBrush)
elif isinstance(arg, QtGui.QBrush):
return QtGui.QBrush(arg)
else:
color = arg
elif len(args) > 1:
color = args
return QtGui.QBrush(mkColor(color))
def mkPen(*args, **kargs):
"""
Convenience function for constructing QPen.
Examples::
mkPen(color)
mkPen(color, width=2)
mkPen(cosmetic=False, width=4.5, color='r')
mkPen({'color': "FF0", width: 2})
mkPen(None) # (no pen)
In these examples, *color* may be replaced with any arguments accepted by :func:`mkColor() <pyqtgraph.mkColor>` """
color = kargs.get('color', None)
width = kargs.get('width', 1)
style = kargs.get('style', None)
dash = kargs.get('dash', None)
cosmetic = kargs.get('cosmetic', True)
hsv = kargs.get('hsv', None)
if len(args) == 1:
arg = args[0]
if isinstance(arg, dict):
return mkPen(**arg)
if isinstance(arg, QtGui.QPen):
return QtGui.QPen(arg) ## return a copy of this pen
elif arg is None:
style = QtCore.Qt.NoPen
else:
color = arg
if len(args) > 1:
color = args
if color is None:
color = mkColor('l')
if hsv is not None:
color = hsvColor(*hsv)
else:
color = mkColor(color)
pen = QtGui.QPen(QtGui.QBrush(color), width)
pen.setCosmetic(cosmetic)
if style is not None:
pen.setStyle(style)
if dash is not None:
pen.setDashPattern(dash)
return pen
def hsvColor(hue, sat=1.0, val=1.0, alpha=1.0):
"""Generate a QColor from HSVa values. (all arguments are float 0.0-1.0)"""
c = QtGui.QColor()
c.setHsvF(hue, sat, val, alpha)
return c
def colorTuple(c):
"""Return a tuple (R,G,B,A) from a QColor"""
return (c.red(), c.green(), c.blue(), c.alpha())
def colorStr(c):
"""Generate a hex string code from a QColor"""
return ('%02x'*4) % colorTuple(c)
def intColor(index, hues=9, values=1, maxValue=255, minValue=150, maxHue=360, minHue=0, sat=255, alpha=255):
"""
Creates a QColor from a single index. Useful for stepping through a predefined list of colors.
The argument *index* determines which color from the set will be returned. All other arguments determine what the set of predefined colors will be
Colors are chosen by cycling across hues while varying the value (brightness).
By default, this selects from a list of 9 hues."""
hues = int(hues)
values = int(values)
ind = int(index) % (hues * values)
indh = ind % hues
indv = ind // hues
if values > 1:
v = minValue + indv * ((maxValue-minValue) // (values-1))
else:
v = maxValue
h = minHue + (indh * (maxHue-minHue)) // hues
c = QtGui.QColor()
c.setHsv(h, sat, v)
c.setAlpha(alpha)
return c
def glColor(*args, **kargs):
"""
Convert a color to OpenGL color format (r,g,b,a) floats 0.0-1.0
Accepts same arguments as :func:`mkColor <pyqtgraph.mkColor>`.
"""
c = mkColor(*args, **kargs)
return (c.red()/255., c.green()/255., c.blue()/255., c.alpha()/255.)
def makeArrowPath(headLen=20, headWidth=None, tipAngle=20, tailLen=20, tailWidth=3, baseAngle=0):
"""
Construct a path outlining an arrow with the given dimensions.
The arrow points in the -x direction with tip positioned at 0,0.
If *headWidth* is supplied, it overrides *tipAngle* (in degrees).
If *tailLen* is None, no tail will be drawn.
"""
if headWidth is None:
headWidth = headLen * np.tan(tipAngle * 0.5 * np.pi/180.)
path = QtGui.QPainterPath()
path.moveTo(0,0)
path.lineTo(headLen, -headWidth)
if tailLen is None:
innerY = headLen - headWidth * np.tan(baseAngle*np.pi/180.)
path.lineTo(innerY, 0)
else:
tailWidth *= 0.5
innerY = headLen - (headWidth-tailWidth) * np.tan(baseAngle*np.pi/180.)
path.lineTo(innerY, -tailWidth)
path.lineTo(headLen + tailLen, -tailWidth)
path.lineTo(headLen + tailLen, tailWidth)
path.lineTo(innerY, tailWidth)
path.lineTo(headLen, headWidth)
path.lineTo(0,0)
return path
def eq(a, b):
"""The great missing equivalence function: Guaranteed evaluation to a single bool value.
This function has some important differences from the == operator:
1. Returns True if a IS b, even if a==b still evaluates to False, such as with nan values.
2. Tests for equivalence using ==, but silently ignores some common exceptions that can occur
(AtrtibuteError, ValueError).
3. When comparing arrays, returns False if the array shapes are not the same.
4. When comparing arrays of the same shape, returns True only if all elements are equal (whereas
the == operator would return a boolean array).
5. Collections (dict, list, etc.) must have the same type to be considered equal. One
consequence is that comparing a dict to an OrderedDict will always return False.
"""
if a is b:
return True
# Avoid comparing large arrays against scalars; this is expensive and we know it should return False.
aIsArr = isinstance(a, (np.ndarray, MetaArray))
bIsArr = isinstance(b, (np.ndarray, MetaArray))
if (aIsArr or bIsArr) and type(a) != type(b):
return False
# If both inputs are arrays, we can speeed up comparison if shapes / dtypes don't match
# NOTE: arrays of dissimilar type should be considered unequal even if they are numerically
# equal because they may behave differently when computed on.
if aIsArr and bIsArr and (a.shape != b.shape or a.dtype != b.dtype):
return False
# Recursively handle common containers
if isinstance(a, dict) and isinstance(b, dict):
if type(a) != type(b) or len(a) != len(b):
return False
if set(a.keys()) != set(b.keys()):
return False
for k, v in a.items():
if not eq(v, b[k]):
return False
if isinstance(a, OrderedDict) or sys.version_info >= (3, 7):
for a_item, b_item in zip(a.items(), b.items()):
if not eq(a_item, b_item):
return False
return True
if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)):
if type(a) != type(b) or len(a) != len(b):
return False
for v1,v2 in zip(a, b):
if not eq(v1, v2):
return False
return True
# Test for equivalence.
# If the test raises a recognized exception, then return Falase
try:
try:
# Sometimes running catch_warnings(module=np) generates AttributeError ???
catcher = warnings.catch_warnings(module=np) # ignore numpy futurewarning (numpy v. 1.10)
catcher.__enter__()
except Exception:
catcher = None
e = a==b
except (ValueError, AttributeError):
return False
except:
print('failed to evaluate equivalence for:')
print(" a:", str(type(a)), str(a))
print(" b:", str(type(b)), str(b))
raise
finally:
if catcher is not None:
catcher.__exit__(None, None, None)
t = type(e)
if t is bool:
return e
elif t is np.bool_:
return bool(e)
elif isinstance(e, np.ndarray) or (hasattr(e, 'implements') and e.implements('MetaArray')):
try: ## disaster: if a is an empty array and b is not, then e.all() is True
if a.shape != b.shape:
return False
except:
return False
if (hasattr(e, 'implements') and e.implements('MetaArray')):
return e.asarray().all()
else:
return e.all()
else:
raise Exception("== operator returned type %s" % str(type(e)))
def affineSliceCoords(shape, origin, vectors, axes):
"""Return the array of coordinates used to sample data arrays in affineSlice().
"""
# sanity check
if len(shape) != len(vectors):
raise Exception("shape and vectors must have same length.")
if len(origin) != len(axes):
raise Exception("origin and axes must have same length.")
for v in vectors:
if len(v) != len(axes):
raise Exception("each vector must be same length as axes.")
shape = list(map(np.ceil, shape))
## make sure vectors are arrays
if not isinstance(vectors, np.ndarray):
vectors = np.array(vectors)
if not isinstance(origin, np.ndarray):
origin = np.array(origin)
origin.shape = (len(axes),) + (1,)*len(shape)
## Build array of sample locations.
grid = np.mgrid[tuple([slice(0,x) for x in shape])] ## mesh grid of indexes
x = (grid[np.newaxis,...] * vectors.transpose()[(Ellipsis,) + (np.newaxis,)*len(shape)]).sum(axis=1) ## magic
x += origin
return x
def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False, **kargs):
"""
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.
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 either interpolateArray if order<2
or scipy.ndimage.map_coordinates otherwise.
For a graphical interface to this function, see :func:`ROI.getArrayRegion <pyqtgraph.ROI.getArrayRegion>`
============== ====================================================================================================
**Arguments:**
*data* (ndarray) the original dataset
*shape* the shape of the slice to take (Note the return value may have more dimensions than len(shape))
*origin* the location in the original dataset that will become the origin of the sliced data.
*vectors* list of unit vectors which point in the direction of the slice axes. Each vector must have the same
length as *axes*. If the vectors are not unit length, the result will be scaled relative to the
original data. If the vectors are not orthogonal, the result will be sheared relative to the
original data.
*axes* The axes in the original dataset which correspond to the slice *vectors*
*order* The order of spline interpolation. Default is 1 (linear). See scipy.ndimage.map_coordinates
for more information.
*returnCoords* If True, return a tuple (result, coords) where coords is the array of coordinates used to select
values from the original dataset.
*All extra keyword arguments are passed to scipy.ndimage.map_coordinates.*
--------------------------------------------------------------------------------------------------------------------
============== ====================================================================================================
Note the following must be true:
| len(shape) == len(vectors)
| len(origin) == len(axes) == len(vectors[i])
Example: start with a 4D fMRI data set, take a diagonal-planar slice out of the last 3 axes
* data = array with dims (time, x, y, z) = (100, 40, 40, 40)
* The plane to pull out is perpendicular to the vector (x,y,z) = (1,1,1)
* The origin of the slice will be at (x,y,z) = (40, 0, 0)
* We will slice a 20x20 plane from each timepoint, giving a final shape (100, 20, 20)
The call for this example would look like::
affineSlice(data, shape=(20,20), origin=(40,0,0), vectors=((-1, 1, 0), (-1, 0, 1)), axes=(1,2,3))
"""
x = affineSliceCoords(shape, origin, vectors, axes)
## transpose data so slice axes come first
trAx = list(range(data.ndim))
for ax in axes:
trAx.remove(ax)
tr1 = tuple(axes) + tuple(trAx)
data = data.transpose(tr1)
#print "tr1:", tr1
## dims are now [(slice axes), (other axes)]
if order > 1:
try:
import scipy.ndimage
except ImportError:
raise ImportError("Interpolating with order > 1 requires the scipy.ndimage module, but it could not be imported.")
# iterate manually over unused axes since map_coordinates won't do it for us
extraShape = data.shape[len(axes):]
output = np.empty(tuple(shape) + extraShape, dtype=data.dtype)
for inds in np.ndindex(*extraShape):
ind = (Ellipsis,) + inds
output[ind] = scipy.ndimage.map_coordinates(data[ind], x, order=order, **kargs)
else:
# map_coordinates expects the indexes as the first axis, whereas
# interpolateArray expects indexes at the last axis.
tr = tuple(range(1, x.ndim)) + (0,)
output = interpolateArray(data, x.transpose(tr), order=order)
tr = list(range(output.ndim))
trb = []
for i in range(min(axes)):
ind = tr1.index(i) + (len(shape)-len(axes))
tr.remove(ind)
trb.append(ind)
tr2 = tuple(trb+tr)
## Untranspose array before returning
output = output.transpose(tr2)
if returnCoords:
return (output, x)
else:
return output
def interpolateArray(data, x, default=0.0, order=1):
"""
N-dimensional interpolation similar to scipy.ndimage.map_coordinates.
This function returns linearly-interpolated values sampled from a regular
grid of data. It differs from `ndimage.map_coordinates` by allowing broadcasting
within the input array.
============== ===========================================================================================
**Arguments:**
*data* Array of any shape containing the values to be interpolated.
*x* Array with (shape[-1] <= data.ndim) containing the locations within *data* to interpolate.
(note: the axes for this argument are transposed relative to the same argument for
`ndimage.map_coordinates`).
*default* Value to return for locations in *x* that are outside the bounds of *data*.
*order* Order of interpolation: 0=nearest, 1=linear.
============== ===========================================================================================
Returns array of shape (x.shape[:-1] + data.shape[x.shape[-1]:])
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.
"""
if order not in (0, 1):
raise ValueError("interpolateArray requires order=0 or 1 (got %s)" % order)
prof = debug.Profiler()
nd = data.ndim
md = x.shape[-1]
if md > nd:
raise TypeError("x.shape[-1] must be less than or equal to data.ndim")
totalMask = np.ones(x.shape[:-1], dtype=bool) # keep track of out-of-bound indexes
if order == 0:
xinds = np.round(x).astype(int) # NOTE: for 0.5 this rounds to the nearest *even* number
for ax in range(md):
mask = (xinds[...,ax] >= 0) & (xinds[...,ax] <= data.shape[ax]-1)
xinds[...,ax][~mask] = 0
# keep track of points that need to be set to default
totalMask &= mask
result = data[tuple([xinds[...,i] for i in range(xinds.shape[-1])])]
elif order == 1:
# First we generate arrays of indexes that are needed to
# extract the data surrounding each point
fields = np.mgrid[(slice(0,order+1),) * md]
xmin = np.floor(x).astype(int)
xmax = xmin + 1
indexes = np.concatenate([xmin[np.newaxis, ...], xmax[np.newaxis, ...]])
fieldInds = []
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]]
axisIndex[axisIndex < 0] = 0
axisIndex[axisIndex >= data.shape[ax]] = 0
fieldInds.append(axisIndex)
prof()
# Get data values surrounding each requested point
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()
if totalMask.ndim > 0:
result[~totalMask] = default
else:
if totalMask is False:
result[:] = 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 = np.ascontiguousarray(data)[offset:]
shape = tuple(shape)
extraShape = data.shape[1:]
strides = list(data.strides[::-1])
itemsize = strides[-1]
for s in stride[1::-1]:
strides.append(itemsize * s)
strides = tuple(strides[::-1])
return np.ndarray(buffer=data, shape=shape+extraShape, strides=strides, dtype=data.dtype)
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, clip=None):
"""Return data rescaled and optionally cast to a new dtype.
The scaling operation is::
data => (data-offset) * scale
"""
if dtype is None:
dtype = data.dtype
else:
dtype = np.dtype(dtype)
d2 = data.astype(np.float) - float(offset)
d2 *= scale
# Clip before converting dtype to avoid overflow
if dtype.kind in 'ui':
lim = np.iinfo(dtype)
if clip is None:
# don't let rescale cause integer overflow
d2 = np.clip(d2, lim.min, lim.max)
else:
d2 = np.clip(d2, max(clip[0], lim.min), min(clip[1], lim.max))
else:
if clip is not None:
d2 = np.clip(d2, *clip)
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.
Parameters
----------
data : ndarray
lut : ndarray
Either cupy or numpy arrays are accepted, though this function has only
consistently behaved correctly on windows with cuda toolkit version >= 11.1.
"""
if data.dtype.kind not in ('i', 'u'):
data = data.astype(int)
cp = getCupy()
if cp and cp.get_array_module(data) == cp:
# cupy.take only supports "wrap" mode
return cp.take(lut, cp.clip(data, 0, lut.shape[0] - 1), axis=0)
else:
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, output=None):
"""
Convert an array of values into an ARGB array suitable for building QImages,
OpenGL textures, etc.
Returns the ARGB array (unsigned byte) 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 255 if no lookup table is provided.
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:].
Lookup tables can be built using ColorMap or 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).
============== ==================================================================================
"""
cp = getCupy()
xp = cp.get_array_module(data) if cp else np
profile = debug.Profiler()
if data.ndim not in (2, 3):
raise TypeError("data must be 2D or 3D")
if data.ndim == 3 and data.shape[2] > 4:
raise TypeError("data.shape[2] must be <= 4")
if lut is not None and not isinstance(lut, xp.ndarray):
lut = xp.array(lut)
if levels is None:
# automatically decide levels based on data dtype
if data.dtype.kind == 'u':
levels = xp.array([0, 2**(data.itemsize*8)-1])
elif data.dtype.kind == 'i':
s = 2**(data.itemsize*8 - 1)
levels = xp.array([-s, s-1])
elif data.dtype.kind == 'b':
levels = xp.array([0,1])
else:
raise Exception('levels argument is required for float input types')
if not isinstance(levels, xp.ndarray):
levels = xp.array(levels)
levels = levels.astype(xp.float)
if levels.ndim == 1:
if levels.shape[0] != 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 both levels and lut have ndim > 2')
if levels.shape != (data.shape[-1], 2):
raise Exception('levels must have shape (data.shape[-1], 2)')
else:
raise Exception("levels argument must be 1D or 2D (got shape=%s)." % repr(levels.shape))
profile('check inputs')
# Decide on maximum scaled value
if scale is None:
if lut is not None:
scale = lut.shape[0]
else:
scale = 255.
# Decide on the dtype we want after scaling
if lut is None:
dtype = xp.ubyte
else:
dtype = xp.min_scalar_type(lut.shape[0]-1)
# awkward, but fastest numpy native nan evaluation
nanMask = None
if data.dtype.kind == 'f' and xp.isnan(data.min()):
nanMask = xp.isnan(data)
if data.ndim > 2:
nanMask = xp.any(nanMask, axis=-1)
# Apply levels if given
if levels is not None:
if isinstance(levels, xp.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 = xp.empty(data.shape, dtype=int)
for i in range(data.shape[-1]):
minVal, maxVal = levels[i]
if minVal == maxVal:
maxVal = xp.nextafter(maxVal, 2*maxVal)
rng = maxVal-minVal
rng = 1 if rng == 0 else rng
newData[...,i] = rescaleData(data[...,i], scale / rng, minVal, dtype=dtype)
data = newData
else:
# Apply level scaling unless it would have no effect on the data
minVal, maxVal = levels
if minVal != 0 or maxVal != scale:
if minVal == maxVal:
maxVal = xp.nextafter(maxVal, 2*maxVal)
rng = maxVal-minVal
rng = 1 if rng == 0 else rng
data = rescaleData(data, scale/rng, minVal, dtype=dtype)
profile('apply levels')
# apply LUT if given
if lut is not None:
data = applyLookupTable(data, lut)
else:
if data.dtype != xp.ubyte:
data = xp.clip(data, 0, 255).astype(xp.ubyte)
profile('apply lut')
# this will be the final image array
if output is None:
imgData = xp.empty(data.shape[:2]+(4,), dtype=xp.ubyte)
else:
imgData = output
profile('allocate')
# decide channel order
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.
# copy data into image array
if data.ndim == 2:
# This is tempting:
# imgData[..., :3] = data[..., xp.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('reorder channels')
# add opaque alpha channel if needed
if data.ndim == 2 or data.shape[2] == 3:
alpha = False
imgData[..., 3] = 255
else:
alpha = True
# apply nan mask through alpha channel
if nanMask is not None:
alpha = True
imgData[nanMask, 3] = 0
profile('alpha channel')
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 (height, width),
(height, width, 3), or (height, width, 4). If transpose is
True, then the first two axes are swapped. The array dtype
must be ubyte. For 2D arrays, the value is interpreted as
greyscale. For 3D arrays, the order of values in the 3rd
axis must be (b, g, r, a).
alpha If the input array is 3D and *alpha* is 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()
copied = False
if imgData.ndim == 2:
imgFormat = QtGui.QImage.Format_Grayscale8
elif imgData.ndim == 3:
# If we didn't explicitly specify alpha, check the array shape.
if alpha is None:
alpha = (imgData.shape[2] == 4)
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.')
profile("add alpha channel")
if alpha:
imgFormat = QtGui.QImage.Format_ARGB32
else:
imgFormat = QtGui.QImage.Format_RGB32
else:
raise TypeError("Image array must have ndim = 2 or 3.")
if transpose:
imgData = imgData.transpose((1, 0, 2)) # QImage expects row-major order
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
profile("ascontiguousarray")
if copy is True and copied is False:
imgData = imgData.copy()
profile("copy")
# C++ QImage has two kind of constructors
# - QImage(const uchar*, ...)
# - QImage(uchar*, ...)
# If the const constructor is used, subsequently calling any non-const method
# will trigger the COW mechanism, i.e. a copy is made under the hood.
if QT_LIB == 'PyQt5':
# PyQt5 -> non-const constructor
img_ptr = imgData.ctypes.data
elif QT_LIB == 'PyQt6':
# PyQt5 -> const constructor
# PyQt6 -> non-const constructor
img_ptr = Qt.sip.voidptr(imgData)
else:
# bindings that support ndarray
# PyQt5 -> const constructor
# PySide2 -> non-const constructor
# PySide6 -> non-const constructor
img_ptr = imgData
img = QtGui.QImage(img_ptr, imgData.shape[1], imgData.shape[0], imgFormat)
img.data = imgData
return img
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 QT_LIB in ['PySide', 'PySide2', 'PySide6']:
arr = np.frombuffer(ptr, dtype=np.ubyte)
else:
try:
# removed in Qt6
nbytes = img.byteCount()
except AttributeError:
# introduced in Qt 5.10
# however Python 3.7 + PyQt5-5.12 in the CI fails with
# "TypeError: QImage.sizeInBytes() is a private method"
nbytes = img.sizeInBytes()
ptr.setsize(nbytes)
arr = np.asarray(ptr)
arr = arr.reshape(img.height(), img.width(), 4)
if fmt == img.Format_RGB32:
arr[...,3] = 255
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)
"""
cp = getCupy()
xp = cp.get_array_module(data) if cp else np
if xp.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 = xp.arange(-ksize, ksize)
kernel = xp.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*xp.pi)**0.5)
filtered = scale * xp.fft.irfft(xp.fft.rfft(filtered, shape, axis=ax) *
xp.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[tuple(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
if n <= 1:
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 means this vertex does not connect
## 1(i4) x(f8) y(f8) <-- 1 means this vertex connects to the previous vertex
## ...
## cStart(i4) fillRule(i4)
##
## see: https://github.com/qt/qtbase/blob/dev/src/gui/painting/qpainterpath.cpp
## All values are big endian--pack using struct.pack('>d') or struct.pack('>i')
path = QtGui.QPainterPath()
n = x.shape[0]
# create empty array, pad with extra space on either end
arr = np.empty(n+2, dtype=[('c', '>i4'), ('x', '>f8'), ('y', '>f8')])
# write first two integers
byteview = arr.view(dtype=np.ubyte)
byteview[:16] = 0
byteview.data[16:20] = struct.pack('>i', n)
# Fill array with vertex values
arr[1:-1]['x'] = x
arr[1:-1]['y'] = y
# inf/nans completely prevent the plot from being displayed starting on
# Qt version 5.12.3; these must now be manually cleaned out.
isfinite = None
qtver = [int(x) for x in QtVersion.split('.')]
if qtver >= [5, 12, 3]:
isfinite = np.isfinite(x) & np.isfinite(y)
if not np.all(isfinite):
# credit: Divakar https://stackoverflow.com/a/41191127/643629
mask = ~isfinite
idx = np.arange(len(x))
idx[mask] = -1
np.maximum.accumulate(idx, out=idx)
first = np.searchsorted(idx, 0)
if first < len(x):
# Replace all non-finite entries from beginning of arr with the first finite one
idx[:first] = first
arr[1:-1] = arr[1:-1][idx]
# decide which points are connected by lines
if eq(connect, 'all'):
arr[1:-1]['c'] = 1
elif eq(connect, 'pairs'):
arr[1:-1]['c'][::2] = 0
arr[1:-1]['c'][1::2] = 1 # connect every 2nd point to every 1st one
elif eq(connect, 'finite'):
# Let's call a point with either x or y being nan is an invalid point.
# A point will anyway not connect to an invalid point regardless of the
# 'c' value of the invalid point. Therefore, we should set 'c' to 0 for
# the next point of an invalid point.
if isfinite is None:
isfinite = np.isfinite(x) & np.isfinite(y)
arr[2:]['c'] = isfinite
elif isinstance(connect, np.ndarray):
arr[2:-1]['c'] = connect[:-1]
else:
raise Exception('connect argument must be "all", "pairs", "finite", or array')
arr[1]['c'] = 0 # the first vertex has no previous vertex to connect
byteview.data[-20:-16] = struct.pack('>i', 0) # cStart
byteview.data[-16:-12] = struct.pack('>i', 0) # fillRule (Qt.OddEvenFill)
# 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[16:-12] # make sure data doesn't run away
try:
buf = QtCore.QByteArray.fromRawData(path.strn)
except TypeError:
buf = QtCore.QByteArray(bytes(path.strn))
except AttributeError:
# PyQt6 raises AttributeError
buf = QtCore.QByteArray(path.strn, path.strn.nbytes)
ds = QtCore.QDataStream(buf)
ds >> path
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
np.add(index, fields[i,j] * 2**vertIndex, out=index, casting='unsafe')
#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. Must be contiguous.
*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[0]] 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
# We use strides below, which means we need contiguous array input.
# Ideally we can fix this just by removing the dependency on strides.
if not data.flags['C_CONTIGUOUS']:
raise TypeError("isosurface input data must be c-contiguous.")
## 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
np.add(index, fields[i,j,k] * 2**vertIndex, out=index, casting='unsafe')
### 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()
np.add(verts[...,:3], cells[:,np.newaxis,np.newaxis,:], out=verts[...,:3], casting='unsafe') ## 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 _pinv_fallback(tr):
arr = np.array([tr.m11(), tr.m12(), tr.m13(),
tr.m21(), tr.m22(), tr.m23(),
tr.m31(), tr.m32(), tr.m33()])
arr.shape = (3, 3)
pinv = np.linalg.pinv(arr)
return QtGui.QTransform(*pinv.ravel().tolist())
def invertQTransform(tr):
"""Return a QTransform that is the inverse of *tr*.
A pseudo-inverse is returned 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:
det = tr.determinant()
detr = 1.0 / det # let singular matrices raise ZeroDivisionError
inv = tr.adjoint()
inv *= detr
return inv
except ZeroDivisionError:
return _pinv_fallback(tr)
def pseudoScatter(data, spacing=None, shuffle=True, bidir=False, method='exact'):
"""Return an array of position values needed to make beeswarm or column scatter plots.
Used for examining the distribution of values in an array.
Given an array of x-values, construct an array of y-values such that an x,y scatter-plot
will not have overlapping points (it will look similar to a histogram).
"""
if method == 'exact':
return _pseudoScatterExact(data, spacing=spacing, shuffle=shuffle, bidir=bidir)
elif method == 'histogram':
return _pseudoScatterHistogram(data, spacing=spacing, shuffle=shuffle, bidir=bidir)
def _pseudoScatterHistogram(data, spacing=None, shuffle=True, bidir=False):
"""Works by binning points into a histogram and spreading them out to fill the bin.
Faster method, but can produce blocky results.
"""
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
yvals = np.empty(len(data))
dmin = data.min()
dmax = data.max()
nbins = int((dmax-dmin) / spacing) + 1
bins = np.linspace(dmin, dmax, nbins)
dx = bins[1] - bins[0]
dbins = ((data - bins[0]) / dx).astype(int)
binCounts = {}
for i,j in enumerate(dbins):
c = binCounts.get(j, -1) + 1
binCounts[j] = c
yvals[i] = c
if bidir is True:
for i in range(nbins):
yvals[dbins==i] -= binCounts.get(i, 0) * 0.5
return yvals[np.argsort(inds)] ## un-shuffle values before returning
def _pseudoScatterExact(data, spacing=None, shuffle=True, bidir=False):
"""Works by stacking points up one at a time, searching for the lowest position available at each point.
This method produces nice, smooth results but can be prohibitively slow for large datasets.
"""
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
def disconnect(signal, slot):
"""Disconnect a Qt signal from a slot.
This method augments Qt's Signal.disconnect():
* Return bool indicating whether disconnection was successful, rather than
raising an exception
* Attempt to disconnect prior versions of the slot when using pg.reload
"""
while True:
try:
signal.disconnect(slot)
return True
except (TypeError, RuntimeError):
slot = reload.getPreviousVersion(slot)
if slot is None:
return False
class SignalBlock(object):
"""Class used to temporarily block a Qt signal connection::
with SignalBlock(signal, slot):
# do something that emits a signal; it will
# not be delivered to slot
"""
def __init__(self, signal, slot):
self.signal = signal
self.slot = slot
def __enter__(self):
self.reconnect = disconnect(self.signal, self.slot)
return self
def __exit__(self, *args):
if self.reconnect:
self.signal.connect(self.slot)
Reference in New Issue View Git Blame Copy Permalink