From 4457a718c06d126cb6193280219ad866afce957d Mon Sep 17 00:00:00 2001
From: "Joachim Wuttke (o)" <j.wuttke@fz-juelich.de>
Date: Fri, 29 Sep 2023 11:45:45 +0200
Subject: [PATCH] bayesian example now reading data as 1D table
---
auto/Examples/bayesian/likelihood_sampling.py | 22 ++++++++-----------
.../bayesian/likelihood_sampling.py | 22 ++++++++-----------
rawEx/bayesian/likelihood_sampling.py | 22 ++++++++-----------
3 files changed, 27 insertions(+), 39 deletions(-)
diff --git a/auto/Examples/bayesian/likelihood_sampling.py b/auto/Examples/bayesian/likelihood_sampling.py
index 49ccddbce6b..1fa6401c3e3 100755
--- a/auto/Examples/bayesian/likelihood_sampling.py
+++ b/auto/Examples/bayesian/likelihood_sampling.py
@@ -57,9 +57,12 @@ def run_simulation(points, ni_thickness, ti_thickness):
if __name__ == '__main__':
filepath = os.path.join(datadir, "specular/genx_alternating_layers.dat.gz")
- data = ba.readData2D(filepath).npArray()
- data[:, 0] *= np.pi/360 # convert incident angles from deg to rad
- data[:, 2] = data[:, 1]*0.1 # arbitrary uncertainties of 10%
+ flags = ba.ImportSettings1D("2alpha (deg)", "#", "", 1, 2)
+ data = ba.readData1D(filepath, ba.csv1D, flags)
+
+ q = data.npXcenters()
+ y = data.npArray()
+ dy = y * 0.1 # arbitrary uncertainties
def log_likelihood(P):
"""
@@ -70,9 +73,6 @@ if __name__ == '__main__':
:array yerr: the ordinate uncertainty (dR-values)
:return: log-likelihood
"""
- q = data[:, 0]
- y = data[:, 1]
- dy = data[:, 2]
y_sim = run_simulation(q, *P)
sigma2 = dy**2 + y_sim**2
return -0.5*np.sum((y - y_sim)**2/sigma2 + np.log(sigma2))
@@ -104,14 +104,10 @@ if __name__ == '__main__':
plt.show()
# Plot and show MLE and data of reflectivity
- plt.errorbar(data[:, 0],
- data[:, 1],
- data[:, 2],
- marker='.',
- ls='')
+ plt.errorbar(q, y, dy, marker='.', ls='')
plt.plot(
- data[:, 0],
- run_simulation(data[:, 0], *flat_samples.mean(axis=0)),
+ q,
+ run_simulation(q, *flat_samples.mean(axis=0)),
'-')
plt.xlabel('$\\alpha$/rad')
plt.ylabel('$R$')
diff --git a/auto/MiniExamples/bayesian/likelihood_sampling.py b/auto/MiniExamples/bayesian/likelihood_sampling.py
index 91444312293..c5094bd6514 100755
--- a/auto/MiniExamples/bayesian/likelihood_sampling.py
+++ b/auto/MiniExamples/bayesian/likelihood_sampling.py
@@ -57,9 +57,12 @@ def run_simulation(points, ni_thickness, ti_thickness):
if __name__ == '__main__':
filepath = os.path.join(datadir, "specular/genx_alternating_layers.dat.gz")
- data = ba.readData2D(filepath).npArray()
- data[:, 0] *= np.pi/360 # convert incident angles from deg to rad
- data[:, 2] = data[:, 1]*0.1 # arbitrary uncertainties of 10%
+ flags = ba.ImportSettings1D("2alpha (deg)", "#", "", 1, 2)
+ data = ba.readData1D(filepath, ba.csv1D, flags)
+
+ q = data.npXcenters()
+ y = data.npArray()
+ dy = y * 0.1 # arbitrary uncertainties
def log_likelihood(P):
"""
@@ -70,9 +73,6 @@ if __name__ == '__main__':
:array yerr: the ordinate uncertainty (dR-values)
:return: log-likelihood
"""
- q = data[:, 0]
- y = data[:, 1]
- dy = data[:, 2]
y_sim = run_simulation(q, *P)
sigma2 = dy**2 + y_sim**2
return -0.5*np.sum((y - y_sim)**2/sigma2 + np.log(sigma2))
@@ -104,14 +104,10 @@ if __name__ == '__main__':
# plt.show()
# Plot and show MLE and data of reflectivity
- plt.errorbar(data[:, 0],
- data[:, 1],
- data[:, 2],
- marker='.',
- ls='')
+ plt.errorbar(q, y, dy, marker='.', ls='')
plt.plot(
- data[:, 0],
- run_simulation(data[:, 0], *flat_samples.mean(axis=0)),
+ q,
+ run_simulation(q, *flat_samples.mean(axis=0)),
'-')
plt.xlabel('$\\alpha$/rad')
plt.ylabel('$R$')
diff --git a/rawEx/bayesian/likelihood_sampling.py b/rawEx/bayesian/likelihood_sampling.py
index ce7f70578fd..be2f4332cfe 100755
--- a/rawEx/bayesian/likelihood_sampling.py
+++ b/rawEx/bayesian/likelihood_sampling.py
@@ -57,9 +57,12 @@ def run_simulation(points, ni_thickness, ti_thickness):
if __name__ == '__main__':
filepath = os.path.join(datadir, "specular/genx_alternating_layers.dat.gz")
- data = ba.readData2D(filepath).npArray()
- data[:, 0] *= np.pi/360 # convert incident angles from deg to rad
- data[:, 2] = data[:, 1]*0.1 # arbitrary uncertainties of 10%
+ flags = ba.ImportSettings1D("2alpha (deg)", "#", "", 1, 2)
+ data = ba.readData1D(filepath, ba.csv1D, flags)
+
+ q = data.npXcenters()
+ y = data.npArray()
+ dy = y * 0.1 # arbitrary uncertainties
def log_likelihood(P):
"""
@@ -70,9 +73,6 @@ if __name__ == '__main__':
:array yerr: the ordinate uncertainty (dR-values)
:return: log-likelihood
"""
- q = data[:, 0]
- y = data[:, 1]
- dy = data[:, 2]
y_sim = run_simulation(q, *P)
sigma2 = dy**2 + y_sim**2
return -0.5*np.sum((y - y_sim)**2/sigma2 + np.log(sigma2))
@@ -104,14 +104,10 @@ if __name__ == '__main__':
<%= sm ? "# " : "" %>plt.show()
# Plot and show MLE and data of reflectivity
- plt.errorbar(data[:, 0],
- data[:, 1],
- data[:, 2],
- marker='.',
- ls='')
+ plt.errorbar(q, y, dy, marker='.', ls='')
plt.plot(
- data[:, 0],
- run_simulation(data[:, 0], *flat_samples.mean(axis=0)),
+ q,
+ run_simulation(q, *flat_samples.mean(axis=0)),
'-')
plt.xlabel('$\\alpha$/rad')
plt.ylabel('$R$')
--
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