Fixed handling of too few measurements for remeasurement, and turned …

…off some debugging messages

FAVE-extract/bin/remeasure.py

@@ -98,7 +98,7 @@ def excludeOutliers(vowels, vowelMeans, vowelCovs):
     """
     Finds outliers and excludes them.
     """
-    sys.stderr.write("Excluding outlying vowels...")
+    # sys.stderr.write("Excluding outlying vowels...")
     outvowels = {}
     for vowel in vowels:
         if vowel in vowelCovs:
@@ -142,9 +142,9 @@ def pruneVowels(vowels, vowel, vowelMeans, vowelCovs, outlie):
 def calculateVowelMeans(vowels):
     """
     calculates [means] and [covariance matrices] for each vowel class.
-    It returns these as R objects in dictionaries indexed by the vowel class.
+    It returns these as numpy arrays in dictionaries indexed by the vowel class.
     """
-    sys.stderr.write("Calculating vowel means...")
+    #sys.stderr.write("Calculating vowel means...")
     vowelMeans = {}
     vowelCovs = {}
     for vowel in vowels:
@@ -156,9 +156,10 @@ def calculateVowelMeans(vowels):
 
 
         vowelMeans[vowel] = np.array([vF1.mean(), vF2.mean(), vB1.mean(), vB2.mean(), vDur.mean()])
-        vowel_cov = np.cov(np.vstack((vF1, vF2, vB1, vB2, vDur)))
-        if linalg.det(vowel_cov) != 0:
-            vowelCovs[vowel] = linalg.inv(vowel_cov)
+        if vF1.shape[0] >= 7:
+            vowel_cov = np.cov(np.vstack((vF1, vF2, vB1, vB2, vDur)))
+            if linalg.det(vowel_cov) != 0:
+                vowelCovs[vowel] = linalg.inv(vowel_cov)
     #sys.stderr.write("Vowel means calculated\n")
     return vowelMeans, vowelCovs
 
@@ -172,7 +173,7 @@ def repredictF1F2(measurements, vowelMeans, vowelCovs, vowels):
     """
     Predicts F1 and F2 from the speaker's own vowel distributions based on the mahalanobis distance.
     """
-    print "\nREMEASURING..."
+    #print "\nREMEASURING..."
     remeasurements = []
     for vm in measurements: