diff --git a/readme_vectorizer.jl b/readme_vectorizer.jl
new file mode 100644
index 0000000..8c85242
Binary files /dev/null and b/readme_vectorizer.jl differ
diff --git a/text_analysis/topicModel.py b/text_analysis/topicModel.py
index bb00598..abb682f 100644
--- a/text_analysis/topicModel.py
+++ b/text_analysis/topicModel.py
@@ -20,6 +20,8 @@ from sklearn.decomposition import LatentDirichletAllocation
 from sklearn.model_selection import GridSearchCV
 from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 
+from statistics import mode
+
 # spacy and nltk for lemmatization
 import nltk 
 #nltk.download('stopwords')
@@ -120,9 +122,40 @@ def get_most_prevalent(distributions, documents):
     print(most_prevalent)
     return most_prevalent
 
+def prevalent_topics(vect_documents, file_list):
+    lda = joblib.load('0509_lda.jl')
+    distributions = lda.transform(vect_documents)
+    #figuring out what the max distribution is and then figuring out the mode
+    top_topic = []
+    count_of_multiple = 0
+    topic_arrays = []
+    for i, topic_distribution in enumerate(distributions):
+        max_dist = max(topic_distribution)
+        indexes = np.where(topic_distribution == max_dist)[0]
+        if len(indexes) == 1:
+            top_topic.append(indexes[0])
+        else:
+            count_of_multiple += 1
+        topic_arrays.append(topic_distribution)
+    #most_frequent(top_topic)
+    print(count_of_multiple)
+    df = pd.DataFrame(topic_arrays)
+    averages = df.mean()
+    print(averages)
+    
+def most_frequent(topic_prevalence):
+    most_frequent_array = []
+    for j in range(8):
+        topic = mode(topic_prevalence)
+        most_frequent_array.append(topic)
+        topic_prevalence = [i for i in topic_prevalence if i != topic]
+    print(most_frequent_array)
+
+
+
 if __name__ == "__main__":
-    readme_directory = "/data/users/mgaughan/kkex/time_specific_files/readme2"
-    contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/contributing2"
+    readme_directory = "/data/users/mgaughan/kkex/time_specific_files/partitioned_readme/p1"
+    contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/partitioned_contributing/p2"
     listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(readme_directory)
     print("Mean wordcount: ", mean(wordcounts))
     print("Median wordcount: ", median(wordcounts))
@@ -131,19 +164,26 @@ if __name__ == "__main__":
     lemmatized_corpus = preprocess(listed_corpus)
     #print(lemmatized_corpus)
     #prepped_corpus, id2word = text_preparation(lemmatized_corpus)
+    '''
     vectorizer = CountVectorizer(analyzer='word',       
                              min_df=2,                        
                              stop_words='english',             
                              lowercase=True,                   
                              token_pattern='[a-zA-Z0-9]{2,}',  
                             )
-    data_vectorized = vectorizer.fit_transform(lemmatized_corpus)    
+    data_vectorized = vectorizer.fit_transform(lemmatized_corpus)
+    '''
+    vectorizer = joblib.load('readme_vectorizer.jl')
+    data_vectorized = vectorizer.transform(lemmatized_corpus)
+    #joblib.dump(vectorizer, 'readme_vectorizer.jl')    
     #print(data_vectorized)                    
     #lda_model_identification(data_vectorized)
     #freqs = zip(vectorizer.get_feature_names_out(), data_vectorized.sum(axis=0).tolist()[0])    
     # sort from largest to smallest
     #print(sorted(freqs, key=lambda x: -x[1])[:25])
-    topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())
-    get_most_prevalent(topic_distributions, file_list)
+    #topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())
+    #get_most_prevalent(topic_distributions, file_list)
+    prevalent_topics(data_vectorized, file_list)
+