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Behold! the power of python

I was writing code to do bootstrapping on a set of data. I wanted a test case where if I asked for one bootstrap I would be returned the original data. lambdas and function references saved me from inefficient code.

if bootstraps == 1:
srri = lambda low, high, size: range(size)
else:
srri = scipy.random.random_integers

for boot in range(bootstraps):
for r in range(n1):
for c in range(n0):
sample_size = trial_result[r][c].size
choices = srri(0, sample_size-1, sample_size)
meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean()

model_grid[:,:,:,boot], params[:,boot] = \
process_grid(s0, s1, meas_grid)


Instead of

srri = scipy.random.random_integers
if bootstraps == 1:
for r in range(n1):
for c in range(n0):
meas_grid[r,c] = pylab.array(trial_result[r][c],dtype=float).mean()

model_grid[:,:,:,boot], params[:,boot] = \
process_grid(s0, s1, meas_grid)
else:
for boot in range(bootstraps):
for r in range(n1):
for c in range(n0):
sample_size = trial_result[r][c].size
choices = srri(0, sample_size-1, sample_size)
meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean()

model_grid[:,:,:,boot], params[:,boot] = \
process_grid(s0, s1, meas_grid)


OR
srri = scipy.random.random_integers
for boot in range(bootstraps):
if bootstraps == 1:
for r in range(n1):
for c in range(n0):
meas_grid[r,c] = pylab.array(trial_result[r][c],dtype=float).mean()

model_grid[:,:,:,boot], params[:,boot] = \
process_grid(s0, s1, meas_grid)
else:
for r in range(n1):
for c in range(n0):
sample_size = trial_result[r][c].size
choices = srri(0, sample_size-1, sample_size)
meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean()

model_grid[:,:,:,boot], params[:,boot] = \
process_grid(s0, s1, meas_grid)

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