Added pg.gaussianFilter, removed all dependency on gaussian_filter

examples/DataSlicing.py

@@ -11,7 +11,6 @@ a 2D plane and interpolate data along that plane to generate a slice image
 import initExample
 
 import numpy as np
-import scipy
 from pyqtgraph.Qt import QtCore, QtGui
 import pyqtgraph as pg
 

examples/FlowchartCustomNode.py

@@ -12,7 +12,6 @@ from pyqtgraph.flowchart.library.common import CtrlNode
 from pyqtgraph.Qt import QtGui, QtCore
 import pyqtgraph as pg
 import numpy as np
-import scipy.ndimage
 
 app = QtGui.QApplication([])
 
@@ -44,7 +43,7 @@ win.show()
 
 ## generate random input data
 data = np.random.normal(size=(100,100))
-data = 25 * scipy.ndimage.gaussian_filter(data, (5,5))
+data = 25 * pg.gaussianFilter(data, (5,5))
 data += np.random.normal(size=(100,100))
 data[40:60, 40:60] += 15.0
 data[30:50, 30:50] += 15.0
@@ -90,7 +89,7 @@ class ImageViewNode(Node):
 ## CtrlNode is just a convenience class that automatically creates its
 ## control widget based on a simple data structure.
 class UnsharpMaskNode(CtrlNode):
-    """Return the input data passed through scipy.ndimage.gaussian_filter."""
+    """Return the input data passed through pg.gaussianFilter."""
     nodeName = "UnsharpMask"
     uiTemplate = [
         ('sigma',  'spin', {'value': 1.0, 'step': 1.0, 'range': [0.0, None]}),
@@ -110,7 +109,7 @@ class UnsharpMaskNode(CtrlNode):
         # CtrlNode has created self.ctrls, which is a dict containing {ctrlName: widget}
         sigma = self.ctrls['sigma'].value()
         strength = self.ctrls['strength'].value()
-        output = dataIn - (strength * scipy.ndimage.gaussian_filter(dataIn, (sigma,sigma)))
+        output = dataIn - (strength * pg.gaussianFilter(dataIn, (sigma,sigma)))
         return {'dataOut': output}
 
 

examples/GLImageItem.py

@@ -12,7 +12,6 @@ from pyqtgraph.Qt import QtCore, QtGui
 import pyqtgraph.opengl as gl
 import pyqtgraph as pg
 import numpy as np
-import scipy.ndimage as ndi
 
 app = QtGui.QApplication([])
 w = gl.GLViewWidget()
@@ -22,8 +21,8 @@ w.setWindowTitle('pyqtgraph example: GLImageItem')
 
 ## create volume data set to slice three images from
 shape = (100,100,70)
-data = ndi.gaussian_filter(np.random.normal(size=shape), (4,4,4))
-data += ndi.gaussian_filter(np.random.normal(size=shape), (15,15,15))*15
+data = pg.gaussianFilter(np.random.normal(size=shape), (4,4,4))
+data += pg.gaussianFilter(np.random.normal(size=shape), (15,15,15))*15
 
 ## slice out three planes, convert to RGBA for OpenGL texture
 levels = (-0.08, 0.08)

examples/GLSurfacePlot.py

@@ -10,7 +10,6 @@ import initExample
 from pyqtgraph.Qt import QtCore, QtGui
 import pyqtgraph as pg
 import pyqtgraph.opengl as gl
-import scipy.ndimage as ndi
 import numpy as np
 
 ## Create a GL View widget to display data
@@ -29,7 +28,7 @@ w.addItem(g)
 
 ## Simple surface plot example
 ## x, y values are not specified, so assumed to be 0:50
-z = ndi.gaussian_filter(np.random.normal(size=(50,50)), (1,1))
+z = pg.gaussianFilter(np.random.normal(size=(50,50)), (1,1))
 p1 = gl.GLSurfacePlotItem(z=z, shader='shaded', color=(0.5, 0.5, 1, 1))
 p1.scale(16./49., 16./49., 1.0)
 p1.translate(-18, 2, 0)
@@ -46,7 +45,7 @@ w.addItem(p2)
 
 
 ## Manually specified colors
-z = ndi.gaussian_filter(np.random.normal(size=(50,50)), (1,1))
+z = pg.gaussianFilter(np.random.normal(size=(50,50)), (1,1))
 x = np.linspace(-12, 12, 50)
 y = np.linspace(-12, 12, 50)
 colors = np.ones((50,50,4), dtype=float)

examples/HistogramLUT.py

@@ -7,7 +7,6 @@ Use a HistogramLUTWidget to control the contrast / coloration of an image.
 import initExample
 
 import numpy as np
-import scipy.ndimage as ndi
 from pyqtgraph.Qt import QtGui, QtCore
 import pyqtgraph as pg
 
@@ -34,7 +33,7 @@ l.addWidget(v, 0, 0)
 w = pg.HistogramLUTWidget()
 l.addWidget(w, 0, 1)
 
-data = ndi.gaussian_filter(np.random.normal(size=(256, 256)), (20, 20))
+data = pg.gaussianFilter(np.random.normal(size=(256, 256)), (20, 20))
 for i in range(32):
     for j in range(32):
         data[i*8, j*8] += .1

examples/ImageView.py

@@ -14,7 +14,6 @@ displaying and analyzing 2D and 3D data. ImageView provides:
 import initExample
 
 import numpy as np
-import scipy.ndimage
 from pyqtgraph.Qt import QtCore, QtGui
 import pyqtgraph as pg
 
@@ -29,7 +28,7 @@ win.show()
 win.setWindowTitle('pyqtgraph example: ImageView')
 
 ## Create random 3D data set with noisy signals
-img = scipy.ndimage.gaussian_filter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
+img = pg.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
 img = img[np.newaxis,:,:]
 decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
 data = np.random.normal(size=(100, 200, 200))

examples/VideoSpeedTest.py

@@ -13,7 +13,6 @@ import initExample ## Add path to library (just for examples; you do not need th
 from pyqtgraph.Qt import QtGui, QtCore, USE_PYSIDE
 import numpy as np
 import pyqtgraph as pg
-import scipy.ndimage as ndi
 import pyqtgraph.ptime as ptime
 
 if USE_PYSIDE:
@@ -95,10 +94,13 @@ def mkData():
             
             if ui.rgbCheck.isChecked():
                 data = np.random.normal(size=(frames,width,height,3), loc=loc, scale=scale)
-                data = ndi.gaussian_filter(data, (0, 6, 6, 0))
+                data = pg.gaussianFilter(data, (0, 6, 6, 0))
             else:
                 data = np.random.normal(size=(frames,width,height), loc=loc, scale=scale)
-                data = ndi.gaussian_filter(data, (0, 6, 6))
+                print frames, width, height, loc, scale
+                data = pg.gaussianFilter(data, (0, 6, 6))
+                print data[0]
+                pg.image(data)
             if dtype[0] != 'float':
                 data = np.clip(data, 0, mx)
             data = data.astype(dt)

examples/crosshair.py

@@ -7,7 +7,6 @@ the mouse.
 
 import initExample ## Add path to library (just for examples; you do not need this)
 import numpy as np
-import scipy.ndimage as ndi
 import pyqtgraph as pg
 from pyqtgraph.Qt import QtGui, QtCore
 from pyqtgraph.Point import Point
@@ -33,8 +32,8 @@ p1.setAutoVisible(y=True)
 
 #create numpy arrays
 #make the numbers large to show that the xrange shows data from 10000 to all the way 0
-data1 = 10000 + 15000 * ndi.gaussian_filter(np.random.random(size=10000), 10) + 3000 * np.random.random(size=10000)
-data2 = 15000 + 15000 * ndi.gaussian_filter(np.random.random(size=10000), 10) + 3000 * np.random.random(size=10000)
+data1 = 10000 + 15000 * pg.gaussianFilter(np.random.random(size=10000), 10) + 3000 * np.random.random(size=10000)
+data2 = 15000 + 15000 * pg.gaussianFilter(np.random.random(size=10000), 10) + 3000 * np.random.random(size=10000)
 
 p1.plot(data1, pen="r")
 p1.plot(data2, pen="g")

examples/isocurve.py

@@ -10,7 +10,6 @@ import initExample ## Add path to library (just for examples; you do not need th
 from pyqtgraph.Qt import QtGui, QtCore
 import numpy as np
 import pyqtgraph as pg
-import scipy.ndimage as ndi
 
 app = QtGui.QApplication([])
 
@@ -18,7 +17,7 @@ app = QtGui.QApplication([])
 frames = 200
 data = np.random.normal(size=(frames,30,30), loc=0, scale=100)
 data = np.concatenate([data, data], axis=0)
-data = ndi.gaussian_filter(data, (10, 10, 10))[frames/2:frames + frames/2]
+data = pg.gaussianFilter(data, (10, 10, 10))[frames/2:frames + frames/2]
 data[:, 15:16, 15:17] += 1
 
 win = pg.GraphicsWindow()

pyqtgraph/flowchart/library/Filters.py

@@ -2,6 +2,7 @@
 from ...Qt import QtCore, QtGui
 from ..Node import Node
 from . import functions
+from ... import functions as pgfn
 from .common import *
 import numpy as np
 
@@ -161,7 +162,7 @@ class Gaussian(CtrlNode):
             import scipy.ndimage
         except ImportError:
             raise Exception("GaussianFilter node requires the package scipy.ndimage.")
-        return scipy.ndimage.gaussian_filter(data, self.ctrls['sigma'].value())
+        return pgfn.gaussianFilter(data, self.ctrls['sigma'].value())
 
 
 class Derivative(CtrlNode):

pyqtgraph/functions.py

@@ -1120,6 +1120,45 @@ def colorToAlpha(data, color):
     
     #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.
@@ -1556,7 +1595,7 @@ def traceImage(image, values, smooth=0.5):
     paths = []
     for i in range(diff.shape[-1]):    
         d = (labels==i).astype(float)
-        d = ndi.gaussian_filter(d, (smooth, smooth))
+        d = gaussianFilter(d, (smooth, smooth))
         lines = isocurve(d, 0.5, connected=True, extendToEdge=True)
         path = QtGui.QPainterPath()
         for line in lines: