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Resolve maintenance issues: Remove google-cloud, fix typos, update docs/logic for fingerprint
1 parent 765a5b6 commit ca4bbe3

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Lines changed: 19 additions & 7 deletions

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docs/doc_requirements.txt

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@@ -13,7 +13,6 @@ sqlalchemy
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dill
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requests
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pydub
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google-cloud>=0.32.0,<0.34.0
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multiprocessing
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pathos
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jupyter_client

quail/analysis/analysis.py

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@@ -104,7 +104,7 @@ def analyze(egg, subjgroup=None, listgroup=None, subjname='Subject',
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if analysis not in analyses.keys():
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raise ValueError('Analysis not recognized. Choose one of the following: '
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'accuracy, spc, pfr, lag-crp, fingerprint, temporal')
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'accuracy, spc, pfr, lagcrp, fingerprint, temporal')
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from ..egg import FriedEgg
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quail/analysis/clustering.py

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@@ -17,14 +17,27 @@ def fingerprint_helper(egg, permute=False, n_perms=1000,
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egg : quail.Egg
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Data to analyze
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dist_funcs : dict
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Dictionary of distance functions for feature clustering analyses
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permute : bool
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Determines whether to correct clustering scores by shuffling recall order
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to create a distribution of clustering scores. Default is False.
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n_perms : int
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Number of permutations to run for "corrected" clustering scores.
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Default is 1000.
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match : str (exact, best)
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Matching approach to compute recall matrix. Default is 'exact'.
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distance : str
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The distance function used to compare items. Default is 'euclidean'.
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features : list
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List of features to analyze. If None, uses all available features.
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Returns
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----------
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probabilities : Numpy array
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Each number represents clustering along a different feature dimension
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"""
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if features is None:
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if len(rec) <= 2:
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warnings.warn('Not enough recalls to compute fingerprint, returning default'
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'fingerprint.. (everything is .5)')
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return np.nan
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return 0.5
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distmat = get_distmat(egg, feature, distdict)
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setup.py

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2020

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EXTRAS_REQUIRE={
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'speech-decoding': ["pydub", "openai-whisper", "google-cloud-storage>=2.0.0"],
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'speech-decoding': ["pydub", "openai-whisper"],
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'efficient-learning': ["sqlalchemy"],
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}
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