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docs(tutorial_5): correct stale parameter-filtering prose #25

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Jammy2211 merged 1 commit into from
Jul 22, 2026
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docs(tutorial_5): correct stale parameter-filtering prose #25

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12 changes: 8 additions & 4 deletions notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -912,7 +912,7 @@
"\n",
"samples = samples.with_paths([(\"gaussian\", \"centre\")])\n",
"\n",
"print(\"All parameters of the very first sample (containing only the Gaussian centre.\")\n",
"print(\"All parameters of the very first sample (containing only the Gaussian centre).\")\n",
"print(samples.parameter_lists[0])\n",
"\n",
"print(\"Maximum Log Likelihood Model Instances (containing only the Gaussian centre):\\n\")\n",
Expand All @@ -928,7 +928,7 @@
"Above, we specified each path as a list of tuples of strings. \n",
"\n",
"This is how the source code internally stores the path to different components of the model, but it is not \n",
"in-profile_1d with the PyAutoFIT API used to compose a model.\n",
"in-line with the PyAutoFit API used to compose a model.\n",
"\n",
"We can alternatively use the following API:"
]
Expand All @@ -954,7 +954,11 @@
"Above, we filtered the `Samples` but asking for all parameters which included the path (\"gaussian\", \"centre\").\n",
"\n",
"We can alternatively filter the `Samples` object by removing all parameters with a certain path. Below, we remove\n",
"the Gaussian's `centre` to be left with 2 parameters; the `normalization` and `sigma`."
"the Gaussian's `centre`.\n",
"\n",
"Recall that the model fitted in this tutorial has two components, a `Gaussian` and an `Exponential`, and therefore\n",
"6 parameters in total. Removing one of them therefore leaves us with 5 parameters: the Gaussian's `normalization`\n",
"and `sigma`, plus all three of the Exponential's parameters."
]
},
{
Expand All @@ -972,7 +976,7 @@
"samples = samples.without_paths([\"gaussian.centre\"])\n",
"\n",
"print(\n",
" \"All parameters of the very first sample (containing only the Gaussian normalization and sigma).\"\n",
" \"All parameters of the very first sample (with the Gaussian centre removed).\"\n",
")\n",
"print(samples.parameter_lists[0])"
],
Expand Down
12 changes: 8 additions & 4 deletions scripts/chapter_1_introduction/tutorial_5_results_and_samples.py
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Original file line number Diff line number Diff line change
Expand Up @@ -626,7 +626,7 @@ def model_data_from(self, xvalues: np.ndarray):

samples = samples.with_paths([("gaussian", "centre")])

print("All parameters of the very first sample (containing only the Gaussian centre.")
print("All parameters of the very first sample (containing only the Gaussian centre).")
print(samples.parameter_lists[0])

print("Maximum Log Likelihood Model Instances (containing only the Gaussian centre):\n")
Expand All @@ -636,7 +636,7 @@ def model_data_from(self, xvalues: np.ndarray):
Above, we specified each path as a list of tuples of strings.

This is how the source code internally stores the path to different components of the model, but it is not
in-profile_1d with the PyAutoFIT API used to compose a model.
in-line with the PyAutoFit API used to compose a model.

We can alternatively use the following API:
"""
Expand All @@ -651,7 +651,11 @@ def model_data_from(self, xvalues: np.ndarray):
Above, we filtered the `Samples` but asking for all parameters which included the path ("gaussian", "centre").

We can alternatively filter the `Samples` object by removing all parameters with a certain path. Below, we remove
the Gaussian's `centre` to be left with 2 parameters; the `normalization` and `sigma`.
the Gaussian's `centre`.

Recall that the model fitted in this tutorial has two components, a `Gaussian` and an `Exponential`, and therefore
6 parameters in total. Removing one of them therefore leaves us with 5 parameters: the Gaussian's `normalization`
and `sigma`, plus all three of the Exponential's parameters.
"""
samples = result.samples

Expand All @@ -664,7 +668,7 @@ def model_data_from(self, xvalues: np.ndarray):
samples = samples.without_paths(["gaussian.centre"])

print(
"All parameters of the very first sample (containing only the Gaussian normalization and sigma)."
"All parameters of the very first sample (with the Gaussian centre removed)."
)
print(samples.parameter_lists[0])

Expand Down
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