def __init__(self, band1, band2, expand = False):
"""
@param band1 First band (numpy masked array)
@param band2 Second band (numpy masked array)
@param expand If the param is True, use union of categories of the bands and compute NxN crosstable
"""
QObject.__init__(self)
if not sizes_equal(band1, band2):
raise CrossTabError('Sizes of rasters are not equal!')
band1, band2 = masks_identity(band1, band2, dtype=np.uint8)
self.X = np.ma.compressed(band1).flatten()
self.Y = np.ma.compressed(band2).flatten()
# Compute gradations of the bands
self.graduation_x = get_gradations(self.X)
self.graduation_y = get_gradations(self.Y)
if expand:
self.graduation_x = list(set(self.graduation_x + self.graduation_y))
self.graduation_y = self.graduation_x
rows, cols = len(self.graduation_x), len(self.graduation_y)
self.shape = (rows, cols)
self._T = None # Crosstable
self.n = None # Count of elements in the crosstable
def woe(factor, sites, unit_cell=1):
'''Weight of evidence method (multiclass form).
@param factor Multiclass pattern array used for prediction of point objects (sites).
@param sites Array layer consisting of the locations at which the point objects are known to occur.
@param unit_cell Method parameter, pixelsize of resampled rasters.
@return masked array Array of total weights of each factor.
'''
# Get list of categories from the factor raster
categories = get_gradations(factor.compressed())
# Try to binarize sites:
sCategories = get_gradations(sites.compressed())
if len(sCategories) != 2:
raise WoeError('Site raster must be binary!')
sites = binaryzation(sites, [sCategories[1]])
# List of the weights of evidence:
# weights[0] is (wPlus, wMinus) for the first category, weights[1] is (wPlus, wMinus) for the second category, ...
weights = []
if len(categories) >= 2:
for cat in categories:
fct = binaryzation(factor, [cat])
weights.append(_binary_woe(fct, sites, unit_cell))
else:
raise WoeError('Wrong count of categories in the factor raster!')
wTotalMin = sum([w[1] for w in weights])
# List of total weights of evidence of the categories:
# wMap[0] is the total weight of the first category, wMap[1] is the total weight of the second category, ...
wMap = [w[0] + wTotalMin - w[1] for w in weights]
# If len(categories) = 2, then [w[0] + wTotalMin - w[1] for w in weights] increases the answer.
# In this case:
if len(categories) == 2:
wMap = [w/2 for w in wMap]
resultMap =np.zeros(ma.shape(factor))
for i,cat in enumerate(categories):
resultMap[factor==cat] = wMap[i]
resultMap = ma.array(data=resultMap, mask=factor.mask)
result = {'map': resultMap, 'categories': categories, 'weights': wMap}
return result
def getBandGradation(self, bandNo):
'''
Return list of categories of raster's band
'''
try:
res = self.bandgradation[bandNo]
except KeyError:
band = self.getBand(bandNo)
res = get_gradations(band.compressed())
self.bandgradation[bandNo] = res
return res