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Add ultranest sampler (Sourcery refactored) #248

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@sourcery-ai sourcery-ai bot commented Jul 28, 2022

Pull Request #246 refactored by Sourcery.

If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.

NOTE: As code is pushed to the original Pull Request, Sourcery will
re-run and update (force-push) this Pull Request with new refactorings as
necessary. If Sourcery finds no refactorings at any point, this Pull Request
will be closed automatically.

See our documentation here.

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Review changes via command line

To manually merge these changes, make sure you're on the ultranest branch, then run:

git fetch origin sourcery/ultranest
git merge --ff-only FETCH_HEAD
git reset HEAD^

Help us improve this pull request!

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sourcery-ai bot commented Jul 28, 2022

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 0.26%.

Quality metrics Before After Change
Complexity 1.96 ⭐ 1.90 ⭐ -0.06 👍
Method Length 43.13 ⭐ 43.11 ⭐ -0.02 👍
Working memory 6.72 🙂 6.87 🙂 0.15 👎
Quality 79.88% 79.62% -0.26% 👎
Other metrics Before After Change
Lines 434 427 -7
Changed files Quality Before Quality After Quality Change
dddm/plotting/confidence_figures.py 86.12% ⭐ 86.01% ⭐ -0.11% 👎
dddm/samplers/multi_detectors.py 64.00% 🙂 63.95% 🙂 -0.05% 👎

Here are some functions in these files that still need a tune-up:

File Function Complexity Length Working Memory Quality Recommendation
dddm/samplers/multi_detectors.py CombinedMultinest.__init__ 1 ⭐ 168 😞 19 ⛔ 48.50% 😞 Try splitting into smaller methods. Extract out complex expressions
dddm/samplers/multi_detectors.py CombinedNestle.__init__ 1 ⭐ 168 😞 19 ⛔ 48.50% 😞 Try splitting into smaller methods. Extract out complex expressions
dddm/samplers/multi_detectors.py CombinedUltraNest.__init__ 1 ⭐ 168 😞 19 ⛔ 48.50% 😞 Try splitting into smaller methods. Extract out complex expressions
dddm/samplers/multi_detectors.py _CombinedInference.save_sub_configs 4 ⭐ 144 😞 8 🙂 63.85% 🙂 Try splitting into smaller methods
dddm/plotting/confidence_figures.py set_xticks_top 11 🙂 162 😞 68.01% 🙂 Try splitting into smaller methods

Legend and Explanation

The emojis denote the absolute quality of the code:

  • ⭐ excellent
  • 🙂 good
  • 😞 poor
  • ⛔ very poor

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.


Please see our documentation here for details on how these metrics are calculated.

We are actively working on this report - lots more documentation and extra metrics to come!

Help us improve this quality report!

if log_mass is None:
return -1
return round(np.power(10, log_mass), 3)
return -1 if log_mass is None else round(np.power(10, log_mass), 3)
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Function DDDMResult.mass refactored with the following changes:

Comment on lines -110 to +108
if param is None:
return None

return len(param)
return None if param is None else len(param)
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Function DDDMResult.n_parameters refactored with the following changes:

Comment on lines -266 to +261
if x <= 1:
return f'${x:.1f}$'
return f'${int(x)}$'
return f'${x:.1f}$' if x <= 1 else f'${int(x)}$'
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Function set_xticks_top refactored with the following changes:

Comment on lines -51 to +53
with open(save_as + 'config.json', 'w') as file:
with open(f'{save_as}config.json', 'w') as file:
json.dump(convert_dic_to_savable(c.config), file, indent=4)
np.save(save_as + 'config.npy', convert_dic_to_savable(c.config))
np.save(f'{save_as}config.npy', convert_dic_to_savable(c.config))
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Function _CombinedInference.save_sub_configs refactored with the following changes:

warnings.warn(f'Ultranest sampler is not completely implemented yet')
warnings.warn('Ultranest sampler is not completely implemented yet')
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Function UltraNestSampler.run refactored with the following changes:

Comment on lines -23 to +34
config = {**base_config, **kwargs}
config = base_config | kwargs
sampler = self.ct.get_sampler_for_detector(**config)

results, _ = sampler.run()

fails = []
for i, (thing, expected, avg, std) in enumerate(
zip(
for thing, expected, avg, std in zip(
base_config.get('fit_parameters'),
[getattr(sampler, f) for f in base_config.get('fit_parameters')],
results['posterior']['mean'],
results['posterior']['stdev']
)):
):
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Function PymultinestTest.test refactored with the following changes:

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