def generateSamples(self, low, high):
"""Used by C{prepare} to generate an array of samples.
@type low: number
@param low: Minimum value to sample.
@type high: number
@param high: Maximum value to sample.
@rtype: 1d Numpy array
@return: An array of uniform, random, or adaptive samples of an interval.
"""
numSamples = self.get("numSamples",
defaultFromXsd=True,
convertType=True)
samplingMethod = self.get("samplingMethod", defaultFromXsd=True)
if samplingMethod == "uniform":
samples = NP("linspace", low, high, numSamples, endpoint=True)
elif samplingMethod == "random":
samples = NP(
NP(NP(NP.random.rand(numSamples)) * (high - low)) + low)
samples.sort()
else:
raise NotImplementedError("TODO: add 'adaptive'")
return samples
def generateSamples(self, low, high):
"""Used by C{prepare} to generate an array of samples.
@type low: number
@param low: Minimum value to sample.
@type high: number
@param high: Maximum value to sample.
@rtype: 1d Numpy array
@return: An array of uniform, random, or adaptive samples of an interval.
"""
numSamples = self.get("numSamples", defaultFromXsd=True, convertType=True)
samplingMethod = self.get("samplingMethod", defaultFromXsd=True)
if samplingMethod == "uniform":
samples = NP("linspace", low, high, numSamples, endpoint=True)
elif samplingMethod == "random":
samples = NP(NP(NP(NP.random.rand(numSamples)) * (high - low)) + low)
samples.sort()
else:
raise NotImplementedError("TODO: add 'adaptive'")
return samples
