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All scipy imports in the library are now optional (need to test each of these changes)

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Several examples still require scipy.
This commit is contained in:
Luke Campagnola 2013-11-23 14:35:09 -05:00
parent 00418e4921
commit 816069c020
8 changed files with 68 additions and 93 deletions
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1
examples/ViewBox.py
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@ -14,7 +14,6 @@ import initExample
## This example uses a ViewBox to create a PlotWidget-like interface
#from scipy import random
import numpy as np
from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph as pg

4
pyqtgraph/SRTTransform3D.py
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@ -4,7 +4,6 @@ from .Vector import Vector
from .Transform3D import Transform3D
from .Vector import Vector
import numpy as np
import scipy.linalg
class SRTTransform3D(Transform3D):
"""4x4 Transform matrix that can always be represented as a combination of 3 matrices: scale * rotate * translate
@ -118,6 +117,7 @@ class SRTTransform3D(Transform3D):
The input matrix must be affine AND have no shear,
otherwise the conversion will most likely fail.
"""
import numpy.linalg
for i in range(4):
self.setRow(i, m.row(i))
m = self.matrix().reshape(4,4)
@ -134,7 +134,7 @@ class SRTTransform3D(Transform3D):
## rotation axis is the eigenvector with eigenvalue=1
r = m[:3, :3] / scale[:, np.newaxis]
try:
evals, evecs = scipy.linalg.eig(r)
evals, evecs = numpy.linalg.eig(r)
except:
print("Rotation matrix: %s" % str(r))
print("Scale: %s" % str(scale))

6
pyqtgraph/colormap.py
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@ -1,5 +1,4 @@
import numpy as np
import scipy.interpolate
from .Qt import QtGui, QtCore
class ColorMap(object):
@ -84,6 +83,11 @@ class ColorMap(object):
qcolor Values are returned as an array of QColor objects.
=========== ===============================================================
"""
try:
import scipy.interpolate
except:
raise Exception("Colormap.map() requires the package scipy.interpolate, but it could not be imported.")
if isinstance(mode, basestring):
mode = self.enumMap[mode.lower()]

19
pyqtgraph/flowchart/library/Filters.py
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@ -1,9 +1,6 @@
# -*- coding: utf-8 -*-
from ...Qt import QtCore, QtGui
from ..Node import Node
from scipy.signal import detrend
from scipy.ndimage import median_filter, gaussian_filter
#from ...SignalProxy import SignalProxy
from . import functions
from .common import *
import numpy as np
@ -119,7 +116,11 @@ class Median(CtrlNode):
@metaArrayWrapper
def processData(self, data):
return median_filter(data, self.ctrls['n'].value())
try:
import scipy.ndimage
except ImportError:
raise Exception("MedianFilter node requires the package scipy.ndimage.")
return scipy.ndimage.median_filter(data, self.ctrls['n'].value())
class Mode(CtrlNode):
"""Filters data by taking the mode (histogram-based) of a sliding window"""
@ -156,7 +157,11 @@ class Gaussian(CtrlNode):
@metaArrayWrapper
def processData(self, data):
return gaussian_filter(data, self.ctrls['sigma'].value())
try:
import scipy.ndimage
except ImportError:
raise Exception("GaussianFilter node requires the package scipy.ndimage.")
return scipy.ndimage.gaussian_filter(data, self.ctrls['sigma'].value())
class Derivative(CtrlNode):
@ -189,6 +194,10 @@ class Detrend(CtrlNode):
@metaArrayWrapper
def processData(self, data):
try:
from scipy.signal import detrend
except ImportError:
raise Exception("DetrendFilter node requires the package scipy.signal.")
return detrend(data)

21
pyqtgraph/flowchart/library/functions.py
View File

@ -1,4 +1,3 @@
import scipy
import numpy as np
from ...metaarray import MetaArray
@ -47,6 +46,11 @@ def downsample(data, n, axis=0, xvals='subsample'):
def applyFilter(data, b, a, padding=100, bidir=True):
"""Apply a linear filter with coefficients a, b. Optionally pad the data before filtering
and/or run the filter in both directions."""
try:
import scipy.signal
except ImportError:
raise Exception("applyFilter() requires the package scipy.signal.")
d1 = data.view(np.ndarray)
if padding > 0:
@ -67,6 +71,11 @@ def applyFilter(data, b, a, padding=100, bidir=True):
def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
"""return data passed through bessel filter"""
try:
import scipy.signal
except ImportError:
raise Exception("besselFilter() requires the package scipy.signal.")
if dt is None:
try:
tvals = data.xvals('Time')
@ -85,6 +94,11 @@ def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
def butterworthFilter(data, wPass, wStop=None, gPass=2.0, gStop=20.0, order=1, dt=None, btype='low', bidir=True):
"""return data passed through bessel filter"""
try:
import scipy.signal
except ImportError:
raise Exception("butterworthFilter() requires the package scipy.signal.")
if dt is None:
try:
tvals = data.xvals('Time')
@ -175,6 +189,11 @@ def denoise(data, radius=2, threshold=4):
def adaptiveDetrend(data, x=None, threshold=3.0):
"""Return the signal with baseline removed. Discards outliers from baseline measurement."""
try:
import scipy.signal
except ImportError:
raise Exception("adaptiveDetrend() requires the package scipy.signal.")
if x is None:
x = data.xvals(0)

100
pyqtgraph/functions.py
View File

@ -34,17 +34,6 @@ import decimal, re
import ctypes
import sys, struct
try:
import scipy.ndimage
HAVE_SCIPY = True
if getConfigOption('useWeave'):
try:
import scipy.weave
except ImportError:
setConfigOptions(useWeave=False)
except ImportError:
HAVE_SCIPY = False
from . import debug
def siScale(x, minVal=1e-25, allowUnicode=True):
@ -422,7 +411,9 @@ def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False,
affineSlice(data, shape=(20,20), origin=(40,0,0), vectors=((-1, 1, 0), (-1, 0, 1)), axes=(1,2,3))
"""
if not HAVE_SCIPY:
try:
import scipy.ndimage
except ImportError:
raise Exception("This function requires the scipy library, but it does not appear to be importable.")
# sanity check
@ -579,15 +570,14 @@ def solve3DTransform(points1, points2):
Find a 3D transformation matrix that maps points1 onto points2.
Points must be specified as a list of 4 Vectors.
"""
if not HAVE_SCIPY:
raise Exception("This function depends on the scipy library, but it does not appear to be importable.")
import numpy.linalg
A = np.array([[points1[i].x(), points1[i].y(), points1[i].z(), 1] for i in range(4)])
B = np.array([[points2[i].x(), points2[i].y(), points2[i].z(), 1] for i in range(4)])
## solve 3 sets of linear equations to determine transformation matrix elements
matrix = np.zeros((4,4))
for i in range(3):
matrix[i] = scipy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
matrix[i] = numpy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
return matrix
@ -600,8 +590,7 @@ def solveBilinearTransform(points1, points2):
mapped = np.dot(matrix, [x*y, x, y, 1])
"""
if not HAVE_SCIPY:
raise Exception("This function depends on the scipy library, but it does not appear to be importable.")
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)])
@ -610,7 +599,7 @@ def solveBilinearTransform(points1, points2):
## solve 2 sets of linear equations to determine transformation matrix elements
matrix = np.zeros((2,4))
for i in range(2):
matrix[i] = scipy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
matrix[i] = numpy.linalg.solve(A, B[:,i]) ## solve Ax = B; x is one row of the desired transformation matrix
return matrix
@ -629,6 +618,10 @@ def rescaleData(data, scale, offset, dtype=None):
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:
@ -671,68 +664,13 @@ 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:]
Uses scipy.weave to improve performance if it is available.
Note: color gradient lookup tables can be generated using GradientWidget.
"""
if data.dtype.kind not in ('i', 'u'):
data = data.astype(int)
## using np.take appears to be faster than even the scipy.weave method and takes care of clipping as well.
return np.take(lut, data, axis=0, mode='clip')
### old methods:
#data = np.clip(data, 0, lut.shape[0]-1)
#try:
#if not USE_WEAVE:
#raise Exception('Weave is disabled; falling back to slower version.')
### number of values to copy for each LUT lookup
#if lut.ndim == 1:
#ncol = 1
#else:
#ncol = sum(lut.shape[1:])
### output array
#newData = np.empty((data.size, ncol), dtype=lut.dtype)
### flattened input arrays
#flatData = data.flatten()
#flatLut = lut.reshape((lut.shape[0], ncol))
#dataSize = data.size
### strides for accessing each item
#newStride = newData.strides[0] / newData.dtype.itemsize
#lutStride = flatLut.strides[0] / flatLut.dtype.itemsize
#dataStride = flatData.strides[0] / flatData.dtype.itemsize
### strides for accessing individual values within a single LUT lookup
#newColStride = newData.strides[1] / newData.dtype.itemsize
#lutColStride = flatLut.strides[1] / flatLut.dtype.itemsize
#code = """
#for( int i=0; i<dataSize; i++ ) {
#for( int j=0; j<ncol; j++ ) {
#newData[i*newStride + j*newColStride] = flatLut[flatData[i*dataStride]*lutStride + j*lutColStride];
#}
#}
#"""
#scipy.weave.inline(code, ['flatData', 'flatLut', 'newData', 'dataSize', 'ncol', 'newStride', 'lutStride', 'dataStride', 'newColStride', 'lutColStride'])
#newData = newData.reshape(data.shape + lut.shape[1:])
##if np.any(newData != lut[data]):
##print "mismatch!"
#data = newData
#except:
#if USE_WEAVE:
#debug.printExc("Error; disabling weave.")
#USE_WEAVE = False
#data = lut[data]
#return data
def makeRGBA(*args, **kwds):
"""Equivalent to makeARGB(..., useRGBA=True)"""
@ -1473,7 +1411,11 @@ def traceImage(image, values, smooth=0.5):
If image is RGB or RGBA, then the shape of values should be (nvals, 3/4)
The parameter *smooth* is expressed in pixels.
"""
import scipy.ndimage as ndi
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)
@ -1967,14 +1909,16 @@ def invertQTransform(tr):
bugs in that method. (specifically, Qt has floating-point precision issues
when determining whether a matrix is invertible)
"""
if not HAVE_SCIPY:
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]
arr = np.array([[tr.m11(), tr.m12(), tr.m13()], [tr.m21(), tr.m22(), tr.m23()], [tr.m31(), tr.m32(), tr.m33()]])
inv = scipy.linalg.inv(arr)
return QtGui.QTransform(inv[0,0], inv[0,1], inv[0,2], inv[1,0], inv[1,1], inv[1,2], inv[2,0], inv[2,1])
def pseudoScatter(data, spacing=None, shuffle=True, bidir=False):

5
pyqtgraph/graphicsItems/PlotDataItem.py
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@ -655,7 +655,10 @@ class PlotDataItem(GraphicsObject):
dx = np.diff(x)
uniform = not np.any(np.abs(dx-dx[0]) > (abs(dx[0]) / 1000.))
if not uniform:
import scipy.interpolate as interp
try:
import scipy.interpolate as interp
except:
raise Exception('Fourier transform of irregularly-sampled data requires the package scipy.interpolate.')
x2 = np.linspace(x[0], x[-1], len(x))
y = interp.griddata(x, y, x2, method='linear')
x = x2

5
pyqtgraph/graphicsItems/ROI.py
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@ -13,11 +13,8 @@ of how to build an ROI at the bottom of the file.
"""
from ..Qt import QtCore, QtGui
#if not hasattr(QtCore, 'Signal'):
#QtCore.Signal = QtCore.pyqtSignal
import numpy as np
from numpy.linalg import norm
import scipy.ndimage as ndimage
#from numpy.linalg import norm
from ..Point import *
from ..SRTTransform import SRTTransform
from math import cos, sin