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Feature/test mode samples weight threshold #979

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merged 4 commits into from
Mar 22, 2024
Merged

Feature/test mode samples weight threshold #979

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b4f9efa
output
Jammy2211 Mar 19, 2024
11d328d
Merge branch 'main' of https://github.com/rhayes777/PyAutoFit into main
Jammy2211 Mar 19, 2024
d5ac42b
No threshold in test mode
Jammy2211 Mar 20, 2024
4a96585
weight
Jammy2211 Mar 20, 2024
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2 changes: 1 addition & 1 deletion autofit/config/output.yaml
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Expand Up @@ -36,7 +36,7 @@ samples: true

# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.

samples_weight_threshold: 1.0e-10
samples_weight_threshold:

### Search Internal ###

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3 changes: 3 additions & 0 deletions autofit/non_linear/search/abstract_search.py
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Expand Up @@ -926,6 +926,9 @@ def perform_update(

samples_weight_threshold = conf.instance["output"]["samples_weight_threshold"]

if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
samples_weight_threshold = None

if samples_weight_threshold is not None:
samples_for_csv = samples_for_csv.samples_above_weight_threshold_from(
weight_threshold=samples_weight_threshold
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2 changes: 1 addition & 1 deletion autofit/non_linear/search/optimize/pyswarms/search/abstract.py
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Expand Up @@ -159,7 +159,7 @@ def _fit(self, model: AbstractPriorModel, analysis):
"Resuming PySwarms non-linear search (previous samples found)."
)

except (FileNotFoundError, TypeError):
except (FileNotFoundError, TypeError, AttributeError):

unit_parameter_lists, parameter_lists, log_posterior_list = self.initializer.samples_from_model(
total_points=self.config_dict_search["n_particles"],
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8 changes: 3 additions & 5 deletions docs/api/plot.rst
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Expand Up @@ -22,8 +22,6 @@ Plotters
:template: custom-class-template.rst
:recursive:

DynestyPlotter
UltraNestPlotter
EmceePlotter
ZeusPlotter
PySwarmsPlotter
NestPlotter
MCMCPlotter
OptimizePlotter
2 changes: 1 addition & 1 deletion docs/overview/the_basics.rst
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Expand Up @@ -505,7 +505,7 @@ corner of the results.

.. code-block:: python

search_plotter = aplt.DynestyPlotter(samples=result.samples)
search_plotter = aplt.NestPlotter(samples=result.samples)
search_plotter.corner()

The plot appears as follows:
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