--- a
+++ b/tests/fig2_test.py
@@ -0,0 +1,122 @@
+import os
+import sys
+
+current_path = os.path.dirname(__file__)
+src_path = os.path.join(current_path, "..")
+sys.path.append(src_path)
+
+import pytest
+import numpy as np
+import sys
+import multivelo as mv
+import scanpy as sc
+import scvelo as scv
+
+scv.settings.verbosity = 3
+scv.settings.presenter_view = True
+scv.set_figure_params('scvelo')
+np.set_printoptions(suppress=True)
+
+
+@pytest.fixture(scope="session")
+def result_data_2():
+
+    # read in the data
+    adata_atac = sc.read("test_files/fig2_for_test.h5ad")
+
+    # aggregate peaks
+    adata_atac = mv.aggregate_peaks_10x(adata_atac,
+                                        'test_files/peak_annotation.tsv',
+                                        'test_files/feature_linkage.bedpe')
+
+    return adata_atac
+
+
+# test the aggregate_peaks_10x function
+def test_agg_peaks(result_data_2):
+
+    # the data indices we want to check
+    indices = [0, 100000, 200000, 300000, 400000]
+
+    # the results we should get
+    data = [8.0, 4.0, 2.0, 2.0, 2.0]
+    rows = [0, 1157, 2333, 3531, 4724]
+    cols = [9, 276, 291, 78, 201]
+
+    # convert the atac data into coo form
+    atac_coo = result_data_2.X.tocoo()
+
+    # check that there are the expected number of datapoints
+    assert len(atac_coo.data) == 412887
+
+    # make sure that the data, rows, and columns all match
+    for n, i in enumerate(indices):
+
+        assert atac_coo.data[i] == pytest.approx(data[n])
+        assert atac_coo.row[i] == rows[n]
+        assert atac_coo.col[i] == cols[n]
+
+
+def test_tfidf(result_data_2):
+
+    tfidf_result = result_data_2.copy()
+
+    # run tfidf
+    mv.tfidf_norm(tfidf_result)
+
+    # the data indices we want to check
+    indices = [0, 100000, 200000, 300000, 400000]
+
+    # the results we should get
+    data = [66.66449, 29.424345, 85.36392, 42.239613, 26.855387]
+    rows = np.array([0, 1157, 2333, 3531, 4724])
+    cols = np.array([9, 276, 291, 78, 201])
+
+    # convert the atac data into coo form
+    atac_coo = tfidf_result.X.tocoo()
+
+    # make sure that the length of the data array is correct
+    assert len(atac_coo.data) == 412887
+
+    # make sure that the data, rows, and columns all match
+    for n, i in enumerate(indices):
+
+        assert atac_coo.data[i] == pytest.approx(data[n])
+        assert atac_coo.row[i] == rows[n]
+        assert atac_coo.col[i] == cols[n]
+
+
+def test_smooth(result_data_2):
+
+    new_result = result_data_2.copy()
+
+    # load in the smoothing matrices
+    nn_idx = np.loadtxt("test_files/nn_idx.txt", delimiter=',')
+    nn_dist = np.loadtxt("test_files/nn_dist.txt", delimiter=',')
+
+    # subset the ATAC data to make sure we can use the matrices
+    atac_smooth = new_result[:nn_idx.shape[0], :]
+
+    # run knn_smooth_chrom
+    mv.knn_smooth_chrom(atac_smooth, nn_idx, nn_dist)
+
+    # the data indices we want to check
+    indices = [0, 70000, 140000, 210000, 280000]
+
+    # the results we should get
+    data = [8.0, 4.0, 2.0, 2.0, 2.0]
+    rows = [0, 809, 1615, 2453, 3295]
+    cols = [9, 327, 56, 25, 137]
+
+    # convert the atac data into coo form
+    atac_coo = atac_smooth.X.tocoo()
+
+    # make sure that the length of the data array is correct
+    assert len(atac_coo.data) == 285810
+
+    # make sure that the data, rows, and columns all match
+    for n, i in enumerate(indices):
+
+        assert atac_coo.data[i] == pytest.approx(data[n])
+        assert atac_coo.row[i] == rows[n]
+        assert atac_coo.col[i] == cols[n]