def linearscale(input, boundfrom, boundto, extrema=None):
"""
Rescale the data in the range 'boundfrom' to the range 'boundto'.
"""
minfrom,maxfrom = boundfrom
minto,maxto = boundto
### default from bounds are min,max of the input
if minfrom is None:
if extrema:
minfrom = extrema[0]
else:
minfrom = arraystats.min(input)
if maxfrom is None:
if extrema:
maxfrom = extrema[1]
else:
maxfrom = arraystats.max(input)
rangefrom = maxfrom - minfrom
if rangefrom == 0:
# if min==max, do simple thresholding
output = numpy.where(input>maxfrom, maxto, minto)
else:
rangeto = maxto - minto
scale = float(rangeto) / rangefrom
offset = minfrom * scale
output = input * scale - offset
return output
def linearscale(input, boundfrom, boundto, extrema=None):
"""
Rescale the data in the range 'boundfrom' to the range 'boundto'.
"""
minfrom,maxfrom = boundfrom
minto,maxto = boundto
if minfrom is not None and maxfrom is not None:
if minfrom == maxfrom:
raise RuntimeError('Invalid range: %s' % (boundfrom,))
### default from bounds are min,max of the input
if minfrom is None:
if extrema:
minfrom = extrema[0]
else:
minfrom = arraystats.min(input)
if maxfrom is None:
if extrema:
maxfrom = extrema[1]
else:
maxfrom = arraystats.max(input)
rangefrom = maxfrom - minfrom
rangeto = maxto - minto
scale = float(rangeto) / rangefrom
offset = minfrom * scale
output = input * scale - offset
return output
def linearscale(input, boundfrom, boundto, extrema=None):
"""
Rescale the data in the range 'boundfrom' to the range 'boundto'.
"""
minfrom, maxfrom = boundfrom
minto, maxto = boundto
if minfrom is not None and maxfrom is not None:
if minfrom == maxfrom:
raise RuntimeError('Invalid range: %s' % (boundfrom, ))
### default from bounds are min,max of the input
if minfrom is None:
if extrema:
minfrom = extrema[0]
else:
minfrom = arraystats.min(input)
if maxfrom is None:
if extrema:
maxfrom = extrema[1]
else:
maxfrom = arraystats.max(input)
rangefrom = maxfrom - minfrom
rangeto = maxto - minto
scale = float(rangeto) / rangefrom
offset = minfrom * scale
output = input * scale - offset
return output
def linearscale(input, boundfrom, boundto, extrema=None):
"""
Rescale the data in the range 'boundfrom' to the range 'boundto'.
"""
minfrom, maxfrom = boundfrom
minto, maxto = boundto
### default from bounds are min,max of the input
if minfrom is None:
if extrema:
minfrom = extrema[0]
else:
minfrom = arraystats.min(input)
if maxfrom is None:
if extrema:
maxfrom = extrema[1]
else:
maxfrom = arraystats.max(input)
rangefrom = maxfrom - minfrom
if rangefrom == 0:
# if min==max, do simple thresholding
output = numpy.where(input > maxfrom, maxto, minto)
else:
rangeto = maxto - minto
scale = float(rangeto) / rangefrom
offset = minfrom * scale
output = input * scale - offset
return output
