pep8

scikits/image/color/colorconv.py

@@ -87,17 +87,17 @@ def convert_colorspace(arr, fromspace, tospace):
     >>> lena = imread(os.path.join(data_dir, 'lena.png'))
     >>> lena_hsv = convert_colorspace(lena, 'RGB', 'HSV')
     """
-    fromdict = {'RGB':lambda im: im, 'HSV':hsv2rgb, 'RGB CIE':rgbcie2rgb,
-                'XYZ':xyz2rgb}
-    todict = {'RGB':lambda im:im, 'HSV':rgb2hsv, 'RGB CIE':rgb2rgbcie,
-              'XYZ':rgb2xyz}
+    fromdict = {'RGB': lambda im: im, 'HSV': hsv2rgb, 'RGB CIE': rgbcie2rgb,
+                'XYZ': xyz2rgb}
+    todict = {'RGB': lambda im: im, 'HSV': rgb2hsv, 'RGB CIE': rgb2rgbcie,
+              'XYZ': rgb2xyz}
 
     fromspace = fromspace.upper()
     tospace = tospace.upper()
     if not fromspace in fromdict.keys():
-        raise ValueError('fromspace needs to be one of %s'%fromdict.keys())
+        raise ValueError('fromspace needs to be one of %s' % fromdict.keys())
     if not tospace in todict.keys():
-        raise ValueError('tospace needs to be one of %s'%todict.keys())
+        raise ValueError('tospace needs to be one of %s' % todict.keys())
 
     return todict[tospace](fromdict[fromspace](arr))
 
@@ -133,7 +133,8 @@ def rgb2hsv(rgb):
 
     Notes
     -----
-    The conversion assumes an input data range of [0, 1] for all color components.
+    The conversion assumes an input data range of [0, 1] for all
+    color components.
 
     Conversion between RGB and HSV color spaces results in some loss of
     precision, due to integer arithmetic and rounding [1]_.
@@ -160,26 +161,26 @@ def rgb2hsv(rgb):
     # -- S channel
     delta = arr.ptp(-1)
     out_s = delta / out_v
-    out_s[delta==0] = 0
+    out_s[delta == 0] = 0
 
     # -- H channel
     # red is max
-    idx = (arr[:,:,0] == out_v)
+    idx = (arr[:, :, 0] == out_v)
     out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
 
     # green is max
-    idx = (arr[:,:,1] == out_v)
-    out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0] ) / delta[idx]
+    idx = (arr[:, :, 1] == out_v)
+    out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
 
     # blue is max
-    idx = (arr[:,:,2] == out_v)
-    out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1] ) / delta[idx]
-    out_h = (out[:,:,0] / 6.) % 1.
+    idx = (arr[:, :, 2] == out_v)
+    out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]
+    out_h = (out[:, :, 0] / 6.) % 1.
 
     # -- output
-    out[:,:,0] = out_h
-    out[:,:,1] = out_s
-    out[:,:,2] = out_v
+    out[:, :, 0] = out_h
+    out[:, :, 1] = out_s
+    out[:, :, 2] = out_v
 
     # remove NaN
     out[np.isnan(out)] = 0
@@ -207,7 +208,8 @@ def hsv2rgb(hsv):
 
     Notes
     -----
-    The conversion assumes an input data range of [0, 1] for all color components.
+    The conversion assumes an input data range of [0, 1] for all
+    color components.
 
     Conversion between RGB and HSV color spaces results in some loss of
     precision, due to integer arithmetic and rounding [1]_.
@@ -228,12 +230,12 @@ def hsv2rgb(hsv):
     """
     arr = _prepare_colorarray(hsv)
 
-    hi = np.floor(arr[:,:,0] * 6)
-    f = arr[:,:,0] * 6 - hi
-    p = arr[:,:,2] * (1 - arr[:,:,1])
-    q = arr[:,:,2] * (1 - f * arr[:,:,1])
-    t = arr[:,:,2] * (1 - (1 - f) * arr[:,:,1])
-    v = arr[:,:,2]
+    hi = np.floor(arr[:, :, 0] * 6)
+    f = arr[:, :, 0] * 6 - hi
+    p = arr[:, :, 2] * (1 - arr[:, :, 1])
+    q = arr[:, :, 2] * (1 - f * arr[:, :, 1])
+    t = arr[:, :, 2] * (1 - (1 - f) * arr[:, :, 1])
+    v = arr[:, :, 2]
 
     hi = np.dstack([hi, hi, hi]).astype(np.uint8) % 6
     out = np.choose(hi, [np.dstack((v, t, p)),
@@ -250,14 +252,14 @@ def hsv2rgb(hsv):
 # Primaries for the coordinate systems
 #---------------------------------------------------------------
 cie_primaries = np.array([700, 546.1, 435.8])
-sb_primaries = np.array([1./155, 1./190, 1./225]) * 1e5
+sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5
 
 #---------------------------------------------------------------
 # Matrices that define conversion between different color spaces
 #---------------------------------------------------------------
 
 # From sRGB specification
-xyz_from_rgb =  np.array([[0.412453, 0.357580, 0.180423],
+xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423],
                           [0.212671, 0.715160, 0.072169],
                           [0.019334, 0.119193, 0.950227]])
 
@@ -284,6 +286,7 @@ grey_from_rgb = np.array([[0.2125, 0.7154, 0.0721],
 # The conversion functions that make use of the matrices above
 #-------------------------------------------------------------
 
+
 def _convert(matrix, arr):
     """Do the color space conversion.
 
@@ -350,6 +353,7 @@ def xyz2rgb(xyz):
     """
     return _convert(rgb_from_xyz, xyz)
 
+
 def rgb2xyz(rgb):
     """RGB to XYZ color space conversion.
 
@@ -388,6 +392,7 @@ def rgb2xyz(rgb):
     """
     return _convert(xyz_from_rgb, rgb)
 
+
 def rgb2rgbcie(rgb):
     """RGB to RGB CIE color space conversion.
 
@@ -422,6 +427,7 @@ def rgb2rgbcie(rgb):
     """
     return _convert(rgbcie_from_rgb, rgb)
 
+
 def rgbcie2rgb(rgbcie):
     """RGB CIE to RGB color space conversion.
 
@@ -457,6 +463,7 @@ def rgbcie2rgb(rgbcie):
     """
     return _convert(rgb_from_rgbcie, rgbcie)
 
+
 def rgb2grey(rgb):
     """Compute luminance of an RGB image.
 
@@ -499,4 +506,3 @@ def rgb2grey(rgb):
     return _convert(grey_from_rgb, rgb)[..., 0]
 
 rgb2gray = rgb2grey
-