diff --git a/fununifrac/benchmarking_trees.py b/fununifrac/benchmarking_trees.py
deleted file mode 100644
index 1fcb8f1..0000000
--- a/fununifrac/benchmarking_trees.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import sys, os
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(ROOT_DIR)
-import glob
-import pandas as pd
-from src.objects.func_tree import FuncTreeEmduInput
-import src.factory.make_tree as make_tree
-import src.factory.make_emd_input as make_emd_input
-from src.algorithms.emd_unifrac import EarthMoverDistanceUniFracSolver
-import numpy as np
-from sklearn.metrics import silhouette_score
-
-
-RAND_TREE = 'reproducibility/data/kegg_trees/fununifrac_edge_lengths_kegg_ko00001_randomized_method.csv'
-UNIFORM_TREE = 'reproducibility/data/kegg_trees/kegg_ko00001_edge_length_1.txt'
-DETERMINISTIC_TREE = 'reproducibility/data/kegg_trees/kegg_ko00001_scaled_10_k_5_assigned_positivity_enforced.txt'
-BRITE = 'ko00001'
-
-metadata_file = 'reproducibility/data/simulated_data/simulated_metadata.csv'
-trees = {
-    RAND_TREE: 'randomized_tree',
-    UNIFORM_TREE: 'uniform_tree',
-    DETERMINISTIC_TREE: 'deterministic_tree',
-}
-input_dir = 'data/simulated_data'
-similarity_levels = ['high', 'medium', 'low']
-meta = pd.read_csv(metadata_file)
-meta_dict = dict(zip(meta['sample'], meta['env']))
-print(meta_dict)
-
-def make_fununifrac_inputs(raw_P, input, normalize=True):
-    #convert an array into one that's suitable for use
-    EMDU_index_2_node = input.idx_to_node
-    node_2_EMDU_index = {v: k for k, v in EMDU_index_2_node.items()}
-    if normalize:
-        raw_P = raw_P/raw_P.sum()
-    P = np.zeros(len(EMDU_index_2_node))
-    for ko in raw_P.index:
-        if ko not in node_2_EMDU_index:
-            print(f"Warning: {ko} not found in EMDU index, skipping.")
-        else:
-            P_index = node_2_EMDU_index[ko]
-            P[P_index] = raw_P[ko]
-    return P
-
-def compute_pw_fununifrac(tree_path, dataframe_file):
-    #compute pw_fununifrac of 1 file
-    solver = EarthMoverDistanceUniFracSolver()
-    tree = make_tree.import_graph(tree_path, directed=True)
-    input: FuncTreeEmduInput = make_emd_input.tree_to_EMDU_input(tree, BRITE)
-    sample_df = pd.read_csv(dataframe_file, index_col='name')
-    Ps_pushed = {}
-    for col in sample_df.columns:
-        P = make_fununifrac_inputs(sample_df[col], input)
-        P_pushed = solver.push_up_L1(P, input)
-        Ps_pushed[col] = P_pushed
-    dists, diffabs_sparse = solver.pairwise_computation(Ps_pushed, sample_df.columns, input, False, False)
-    return dists, sample_df.columns
-
-for sim in similarity_levels:
-    df_dict = {
-        'tree': [],
-        'score': [],
-    }
-    files = glob.glob(f"sim_*{sim}.csv")
-    for tree in trees:
-        for file in files:
-            dist_matrix, sample_ids = compute_pw_fununifrac(tree, file)
-            labels = [meta_dict[i] for i in sample_ids]
-            sil_score = silhouette_score(dist_matrix, labels, metric='precomputed')
-            df_dict['tree'].append(trees[tree])
-            df_dict['score'].append(sil_score)
-    df = pd.DataFrame.from_dict(df_dict)
-    print(df)
-    out_file_name = f"reproducibility/data/simulated_data/df_{sim}_{trees[tree]}"
-    df.to_csv(out_file_name, sep='\t')
-
-
-
-
diff --git a/fununifrac/reproducibility/benchmarking_trees.py b/fununifrac/reproducibility/benchmarking_trees.py
index c137afe..598b1d2 100644
--- a/fununifrac/reproducibility/benchmarking_trees.py
+++ b/fununifrac/reproducibility/benchmarking_trees.py
@@ -1,7 +1,8 @@
 import sys, os
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(ROOT_DIR)
-sys.path.append('..')
+sys.path.append('../../')
+sys.path.append('../../src')
 import glob
 import pandas as pd
 from src.objects.func_tree import FuncTreeEmduInput