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Added a flag to specify vectorized theory function #21

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Added a flag to specify vectorized theory function #21

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28 changes: 10 additions & 18 deletions src/pspec_likelihood/likelihood.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@ def __init__(
bias_prior,
kbin_centers,
kbin_widths,
theoretical_model_vectorized=False,
little_h=True,
weight_by_cov=True,
history="",
Expand Down Expand Up @@ -70,6 +71,8 @@ def __init__(
The nuisance model is defined in data space
and can be treaded as the bias term in
\hat{p} = W p_true + b
theoretical_model_vectorized: bool
specifies whether the theoretical model accepts numpy-vectors for k and z.
little_h
specifies whether k units are in h/Mpc or 1/Mpc
bias_prior : func(params) -> prob
Expand Down Expand Up @@ -139,8 +142,7 @@ def discretized_ps(self, spw, theory_params, little_h=True, method=None):
redshifts are determined by the spherical windows (spw).

Possible methods: Just evaluate the power spectrum at the bin centers,
integrate over the power spectrum to take the bin average, or
evaluate at the bin edges (+ center?) and return the mean.
or integrate over the power spectrum to take the bin average.

Parameters
----------
Expand All @@ -156,29 +158,19 @@ def discretized_ps(self, spw, theory_params, little_h=True, method=None):
results
list of power spectrum values corresponding to the bins
errors
Estimation of the error through binning, if a suitable method has been
chosen, otherwise None.
Integration error if a suitable method has been chosen, otherwise None.
"""

#Todo: Mapping from spw to (one/many) redshift(s), see also issue #3
z = self.get_z_from_spw(spw)

# Q: Is little_h a keyword argument of theory_func?
# Q: Does the bin go from center-width/2 to center+width/2 ?
# The error is just an order of magnitude, not any precise confidence interval.
# If the power spectrum was monotonous, the error would be the maximal deviation.
#Todo: Implement an efficient wrapper in case
# theoretical_model_vectorized==False

if method == "bin_center":
results = self.theoretical_model(
self.kbin_centers, z, little_h, theory_params
)
elif method == "two_point":
lower = self.theoretical_model(
self.kbin_centers - self.kbin_widths / 2, z, little_h, theory_params
)
upper = self.theoretical_model(
self.kbin_centers + self.kbin_widths / 2, z, little_h, theory_params
)
results = (lower + upper) / 2
errors = (lower - upper) / 2
elif method == "integrate":

def pk_func(k):
Expand All @@ -192,7 +184,7 @@ def pk_func(k):
errors.append(error / width)
else:
raise ValueError(
f"method must be one of 'bin_center', 'two_point' or 'integrate'. Got '{method}'."
f"method must be one of 'bin_center' or 'integrate'. Got '{method}'."
)

return results, errors
Expand Down