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idx, args_ = zip(*sorted(args_, key=lambda m: m[0]))
# remove args_ and their indices from kwargs_
args_dict_ = {n: kwargs_.pop(n) for n in idx}
return args_, args_dict_
if kwargs:
args, args_dict = args_from_kwargs(kwargs)
def f_(x_, *args_, **kwargs_):
""""""call original function with independent variables grouped""""""
args_dict_ = args_dict
if cov_keys:
kwargs_.update(zip(cov_keys, x_), **args_dict_)
if kwargs_:
args_, _ = args_from_kwargs(kwargs_)
return np.array(f(*args_, **kwargs_))
# assumes independent variables already grouped
return f(x_, *args_, **kwargs_)
# evaluate function and Jacobian
avg = f_(x, *args, **kwargs)
# number of returns and observations
if avg.ndim > 1:
nf, nobs = avg.shape
else:
nf, nobs = avg.size, 1
jac = jacobian(f_, x, nf, nobs, *args, **kwargs)
# calculate covariance
if cov is not None:
# covariance must account for all observations
# scale covariances by x squared in each direction
if cov.ndim == 3:
x = np.array([np.repeat(y, nobs) if len(y)==1
else y for y in x])
LOGGER.debug('x:\n%r', x)
cov = np.array([c * y * np.row_stack(y)
for c, y in zip(cov, x.T)])
else: # x are all only one dimension
x = np.asarray(x)
cov = cov * x * x.T
assert jac.size / nf / nobs == cov.size / len(x)
cov = np.tile(cov, (nobs, 1, 1))
# propagate uncertainty using different methods
if method.lower() == 'dense':
j, c = jflatten(jac), jflatten(cov)
cov = prop_unc((j, c))
# sparse
elif method.lower() == 'sparse':
j, c = jtosparse(jac), jtosparse(cov)
cov = j.dot(c).dot(j.transpose())
cov = cov.todense()
# pool
elif method.lower() == 'pool':
try:
p = Pool()
cov = np.array(p.map(prop_unc, zip(jac, cov)))
finally:
p.terminate()
# loop is the default
else:
cov = np.array([prop_unc((jac[o], cov[o]))
for o in xrange(nobs)])
# dense and spares are flattened, unravel them into 3-D list of
# observations
if method.lower() in ['dense', 'sparse']:
cov = np.array([
cov[(nf * o):(nf * (o + 1)), (nf * o):(nf * (o + 1))]
for o in xrange(nobs)
])
# unpack returns for original function with ungrouped arguments
if None in cov_keys or len(cov_keys) > 0:
return tuple(avg.tolist() + [cov, jac])
# independent variables were already grouped
return avg, cov, jac
return wrapped_function
return wrapper"
1677,"def assign_handler(query, category):
""""""assign_handler(query, category) -- assign the user's query to a
particular category, and call the appropriate handler.
""""""
if(category == 'count lines'):
handler.lines(query)
elif(category == 'count words'):
handler.words(query)
elif(category == 'weather'):
web.weather(query)
elif(category == 'no match'):
web.generic(query)
elif(category == 'file info'):
handler.file_info(query)
elif(category == 'executable'):
handler.make_executable(query)
elif(category == 'search'):
handler.search(query)
elif(category == 'path'):
handler.add_to_path(query)
elif(category == 'uname'):
handler.system_info(query)
else:
print 'I\'m not able to understand your query'"