update image names

neurosynth · Jul 12, 2018 · b4d788c · b4d788c
1 parent b79f6e6
commit b4d788c
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Showing 2 changed files with 10 additions and 10 deletions.
diff --git 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
@@ -11,18 +11,18 @@
 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:
         dataset: A Dataset instance containing feature and activation data.
         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