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make evidences an array

Merged Philipp Frank requested to merge iid2022 into main
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%% Cell type:markdown id: tags:
Nifty tutorial for Poisson count data
=====================================
%% Cell type:markdown id: tags:
Setup
-----
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``` python
import numpy as np
import matplotlib.pyplot as plt
import nifty8 as ift
from utils import plot_2D, load_psf, geovi_sampling, plot_posterior
ift.random.push_sseq_from_seed(42)
evidences = []
evidences = np.zeros(3)
```
%% Cell type:code id: tags:
``` python
# Load data and visualize
data = np.load('data/poisson.npz')
```
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``` python
position_space = ift.RGSpace([128, 128])
# Homogeneous poisson process
print(model1)
```
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``` python
# Set up likelihood & PSF
```
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``` python
# Inference model 1
print(evidence)
evidences += [evidence, ]
evidences[0] = evidence
```
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``` python
# Independent poisson process
# Inference model 2
evidences += [evidence, ]
evidences[1] = evidence
```
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``` python
# Compare evidence
print(evidences)
```
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``` python
# Diffuse poisson process
args = {
'offset_mean': .5,
'offset_std': (1., 1E-5),
# Amplitude of field fluctuations
'fluctuations': (1.5, 0.5), # 1.0, 1e-2
# Exponent of power law power spectrum component
'loglogavgslope': (-4., 1), # -6.0, 1
# Amplitude of integrated Wiener process power spectrum component
'flexibility': (1., 0.2), # 2.0, 1.0
# How ragged the integrated Wiener process component is
'asperity': (0.1, 0.01), # 0.1, 0.5
# Name of the input keys
'prefix' : 'diffuse'
}
correlated_field = ift.SimpleCorrelatedField(position_space, **args)
pspec = correlated_field.power_spectrum
```
%% Cell type:code id: tags:
``` python
# Prior samples
```
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``` python
# Inference model 3
evidences += [evidence, ]
evidences[2] = evidence
```
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``` python
# Compare evidence
print(evidences)
print(evidences - evidences[-1])
```
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Posterior visualization
-----------------------
%% Cell type:code id: tags:
``` python
plot_posterior(samples, data, model3, diffuse, model2, pspec)
```
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