From b4d788cf603dff4511814699ccef6df6dd134731 Mon Sep 17 00:00:00 2001 From: Tal Yarkoni Date: Wed, 11 Jul 2018 21:44:50 -0500 Subject: [PATCH] update image names --- neurosynth/analysis/decode.py | 2 +- neurosynth/analysis/meta.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/neurosynth/analysis/decode.py b/neurosynth/analysis/decode.py index 3b0e886..a6e1481 100644 --- a/neurosynth/analysis/decode.py +++ b/neurosynth/analysis/decode.py @@ -13,7 +13,7 @@ class Decoder: def __init__(self, dataset=None, method='pearson', features=None, - mask=None, image_type='specificity_z', threshold=0.001): + mask=None, image_type='association-test_z', threshold=0.001): """ Initialize a new Decoder instance. Args: diff --git a/neurosynth/analysis/meta.py b/neurosynth/analysis/meta.py index e500915..2032868 100644 --- a/neurosynth/analysis/meta.py +++ b/neurosynth/analysis/meta.py @@ -11,7 +11,7 @@ logger = logging.getLogger('neurosynth.meta') -def analyze_features(dataset, features=None, image_type='specificity_z', +def analyze_features(dataset, features=None, image_type='association-test_z', threshold=0.001, q=0.01, output_dir=None, prefix=None): """ Generate meta-analysis images for a set of features. Args: @@ -19,10 +19,10 @@ def analyze_features(dataset, features=None, image_type='specificity_z', features: A list of named features to generate meta-analysis maps for. If None, analyzes all features in the current dataset. image_type: The type of image to return. Specify one of the extensions - generated by the MetaAnalysis procedure--e.g., specificity_z, - consistency_z, etc. By default, will use specificity_z (i.e., - z-scores reflecting the probability that a Mappable has a feature - given that activation is present. + generated by the MetaAnalysis procedure--e.g., association-test_z, + uniformity-test_z, etc. By default, will use + association-test_z (i.e., z-scores reflecting the association + between presence of activation and presence of feature). threshold: The threshold for determining whether or not a Mappable has a feature. By default, this is 0.001, which is only sensible in the case of term-based features (so be sure to specify it for other @@ -180,10 +180,10 @@ def p_to_z(p, sign): 'pFgA': pFgA, ('pAgF_given_pF=%0.2f' % prior): pAgF_prior, ('pFgA_given_pF=%0.2f' % prior): pFgA_prior, - 'consistency_z': pAgF_z, - 'specificity_z': pFgA_z, - ('pAgF_z_FDR_%s' % q): pAgF_z_FDR, - ('pFgA_z_FDR_%s' % q): pFgA_z_FDR + 'uniformity-test_z': pAgF_z, + 'association-test_z': pFgA_z, + ('uniformity-test_z_FDR_%s' % q): pAgF_z_FDR, + ('association-test_z_FDR_%s' % q): pFgA_z_FDR } # Mask out all voxels below num_studies threshold