diff --git a/freyja/_cli.py b/freyja/_cli.py
index 206676af..76ad38e6 100644
--- a/freyja/_cli.py
+++ b/freyja/_cli.py
@@ -274,8 +274,8 @@ def dash(agg_results, metadata, title, intro, thresh, headercolor, bodycolor,
     if 'coverage' in agg_df.columns:
         agg_df = agg_df[agg_df['coverage'] > mincov]
     else:
-        print('WARNING: Freyja should be updated \
-to include coverage estimates.')
+        print('WARNING: Freyja should be updated ' +
+              'to include coverage estimates.')
     agg_df = agg_df[agg_df['summarized'] != '[]']
 
     meta_df = pd.read_csv(metadata, index_col=0)
@@ -335,13 +335,20 @@ def relgrowthrate(agg_results, metadata, thresh, scale_by_viral_load, nboots,
     if 'coverage' in agg_df.columns:
         agg_df = agg_df[agg_df['coverage'] > mincov]
     else:
-        print('WARNING: Freyja should be updated \
-to include coverage estimates.')
+        print('WARNING: Freyja should be updated ' +
+              'to include coverage estimates.')
     agg_df = agg_df[agg_df['summarized'] != '[]']
 
     meta_df = pd.read_csv(metadata, index_col=0)
     meta_df['sample_collection_datetime'] = \
         pd.to_datetime(meta_df['sample_collection_datetime'])
+    if config is not None:
+        with open(config, "r") as f:
+            try:
+                config = yaml.safe_load(f)
+            except yaml.YAMLError as exc:
+                raise ValueError('Error in config file: ' + str(exc))
+
     with open(os.path.join(locDir, 'data/lineages.yml'), 'r') as f:
         try:
             lineages_yml = yaml.safe_load(f)
diff --git a/freyja/data/plot_config.yml b/freyja/data/plot_config.yml
index b3b94525..86af135b 100644
--- a/freyja/data/plot_config.yml
+++ b/freyja/data/plot_config.yml
@@ -26,6 +26,6 @@ Lineages:
     color: "green"
 # Currently users can only change the color and not the legend name.
 VOC:
-  Omicron:
+  Delta:
     name: "Delta"
     color: "green"
diff --git a/freyja/tests/test_utils.py b/freyja/tests/test_utils.py
index 6acd868d..3808d987 100644
--- a/freyja/tests/test_utils.py
+++ b/freyja/tests/test_utils.py
@@ -1,10 +1,158 @@
 import unittest
 import pandas as pd
-from freyja.utils import agg, checkConfig, prepLineageDict, prepSummaryDict
+from freyja.utils import agg, checkConfig, prepLineageDict, prepSummaryDict,\
+    get_color_scheme, get_abundance, calc_rel_growth_rates
 import os
+import plotly.express as px
+import yaml
 
 
 class UtilsTests(unittest.TestCase):
+    def setUp(self):
+        self.df_ab_lin = pd.DataFrame.from_dict({
+                "Q.3": {
+                    "2021-03-01": 99.86929930000001,
+                    "2021-03-03": 93.9596733,
+                    "2021-03-08": 89.77044079999999,
+                    "2021-03-10": 84.1962053,
+                    "2021-03-12": 64.68600500000001,
+                    "2021-03-14": 58.985296,
+                    "2021-03-17": 60.4915979,
+                    "2021-03-20": 54.0080433,
+                    "2021-03-25": 36.943905300000004,
+                    "2021-03-30": 20.987667199999997,
+                    "2021-03-31": 11.768960100000001,
+                    "2021-04-04": 0
+                },
+                "AY.48": {
+                    "2021-03-01": 0,
+                    "2021-03-03": 4.99648,
+                    "2021-03-08": 8.010876,
+                    "2021-03-10": 13.844148,
+                    "2021-03-12": 33.4055237,
+                    "2021-03-14": 36.6772838,
+                    "2021-03-17": 35.1807163,
+                    "2021-03-20": 41.6008049,
+                    "2021-03-25": 60.344586,
+                    "2021-03-30": 73.46381410000001,
+                    "2021-03-31": 87.07396659999999,
+                    "2021-04-04": 99.807238
+                },
+                "B.1.617.2": {
+                    "2021-03-01": 0,
+                    "2021-03-03": 0,
+                    "2021-03-08": 0,
+                    "2021-03-10": 0,
+                    "2021-03-12": 0,
+                    "2021-03-14": 3.37115719,
+                    "2021-03-17": 3.2468312,
+                    "2021-03-20": 2.9356701,
+                    "2021-03-25": 1.43804146,
+                    "2021-03-30": 2.22985247,
+                    "2021-03-31": 0,
+                    "2021-04-04": 0
+                },
+                "Other": {
+                    "2021-03-01": 0.13070067750000003,
+                    "2021-03-03": 1.0438466700499998,
+                    "2021-03-08": 2.2186831967230005,
+                    "2021-03-10": 1.9596465105119993,
+                    "2021-03-12": 1.9084712976769997,
+                    "2021-03-14": 0.966262983661,
+                    "2021-03-17": 1.080854620274,
+                    "2021-03-20": 1.4554817049518003,
+                    "2021-03-25": 1.27346720046,
+                    "2021-03-30": 3.3186662099999995,
+                    "2021-03-31": 1.1570733055280005,
+                    "2021-04-04": 0.19276375983000002
+                }
+            })
+        self.df_ab_sum = pd.DataFrame.from_dict({
+                "Alpha": {
+                    "2021-03-01": 99.87789999982671,
+                    "2021-03-03": 93.98129492955486,
+                    "2021-03-08": 89.9147441699543,
+                    "2021-03-10": 84.22558680720658,
+                    "2021-03-12": 64.70828169148619,
+                    "2021-03-14": 59.031199999535666,
+                    "2021-03-17": 60.52881597979205,
+                    "2021-03-20": 54.05737427761939,
+                    "2021-03-25": 36.95459482299681,
+                    "2021-03-30": 21.04474169994245,
+                    "2021-03-31": 11.78529999719067,
+                    "2021-04-04": 0
+                },
+                "Delta": {
+                    "2021-03-01": 0.014058802055376244,
+                    "2021-03-03": 5.177708342651904,
+                    "2021-03-08": 9.155290001286307,
+                    "2021-03-10": 14.964234802582208,
+                    "2021-03-12": 33.61162689800541,
+                    "2021-03-14": 40.27064950666095,
+                    "2021-03-17": 38.551629204685014,
+                    "2021-03-20": 44.72319999997791,
+                    "2021-03-25": 62.0014336019168,
+                    "2021-03-30": 75.85518917199204,
+                    "2021-03-31": 87.5540522317538,
+                    "2021-04-04": 99.85259954102992
+                },
+                "Other": {
+                    "2021-03-01": 0.10804120463362901,
+                    "2021-03-03": 0.8409967276198742,
+                    "2021-03-08": 0.9299658256744001,
+                    "2021-03-10": 0.8101782067119108,
+                    "2021-03-12": 1.680091400472183,
+                    "2021-03-14": 0.6981504842771986,
+                    "2021-03-17": 0.9195548174455725,
+                    "2021-03-20": 1.2194257038786276,
+                    "2021-03-25": 1.0439715589713492,
+                    "2021-03-30": 3.1000691040175408,
+                    "2021-03-31": 0.6606477742925074,
+                    "2021-04-04": 0.14740221471037052
+                }
+            })
+        self.dates_to_keep = ['2021-03-01 00:00:00',
+                              '2021-03-03 00:00:00',
+                              '2021-03-08 00:00:00',
+                              '2021-03-10 00:00:00',
+                              '2021-03-12 00:00:00',
+                              '2021-03-14 00:00:00',
+                              '2021-03-17 00:00:00',
+                              '2021-03-20 00:00:00',
+                              '2021-03-25 00:00:00',
+                              '2021-03-30 00:00:00',
+                              '2021-03-31 00:00:00',
+                              '2021-04-04 00:00:00']
+        agg_results = 'freyja/data/test_sweep.tsv'
+        metadata = 'freyja/data/sweep_metadata.csv'
+        config_file = 'freyja/data/plot_config.yml'
+        self.agg_df = pd.read_csv(agg_results, skipinitialspace=True, sep='\t',
+                                  index_col=0)
+        self.agg_df = self.agg_df[self.agg_df['summarized'] != '[]']
+        self.meta_df = pd.read_csv(metadata, index_col=0)
+        self.meta_df['sample_collection_datetime'] = \
+            pd.to_datetime(self.meta_df['sample_collection_datetime'])
+        with open('freyja/data/lineages.yml', 'r') as f:
+            try:
+                lineages_yml = yaml.safe_load(f)
+            except yaml.YAMLError as exc:
+                raise ValueError('lineages.yml error, run update: ' + str(exc))
+        with open(config_file, "r") as f:
+            try:
+                config = yaml.safe_load(f)
+            except yaml.YAMLError as exc:
+                raise ValueError('Error in config file: ' + str(exc))
+        if config is not None:
+            self.config = checkConfig(config)
+        # converts lineages_yml to a dictionary where the lineage names are the
+        # keys.
+        self.lineage_info = {}
+        for lineage in lineages_yml:
+            self.lineage_info[lineage['name']] = {
+                    'name': lineage['name'],
+                    'children': lineage['children']
+                }
+
     def test_agg(self):
         results = 'freyja/data/outputs/'
         agg_df = agg([results + fn
@@ -175,6 +323,81 @@ def test_checkConfig(self):
         for i, config in enumerate(invalid_configs):
             self.assertRaises(ValueError, checkConfig, config)
 
+    def test_get_abundance(self):
+        self.df_ab_lin.index = pd.to_datetime(self.df_ab_lin.index)
+        self.df_ab_sum.index = pd.to_datetime(self.df_ab_sum.index)
+        lin, sum, dates = get_abundance(
+                self.agg_df,
+                self.meta_df,
+                0.01,
+                False,
+                {},
+                self.lineage_info)
+        pd.testing.assert_frame_equal(lin, self.df_ab_lin)
+        pd.testing.assert_frame_equal(sum, self.df_ab_sum)
+        assert type(dates) is pd.DatetimeIndex
+        dates = [str(i) for i in list(dates)]
+        self.assertListEqual(dates, self.dates_to_keep)
+
+    def test_get_color_scheme(self):
+        self.df_ab_lin.index = pd.to_datetime(self.df_ab_lin.index)
+        default_color_scheme = {
+            11: px.colors.qualitative.Vivid,
+            24: px.colors.qualitative.Dark24
+        }
+        color_scheme_without_config = {
+            'Q.3': 'rgb(229, 134, 6)',
+            'AY.48': 'rgb(93, 105, 177)',
+            'B.1.617.2': 'rgb(82, 188, 163)',
+            'Other': 'rgb(153, 201, 69)'
+        }
+
+        self.assertDictEqual(get_color_scheme(
+                self.df_ab_lin,
+                default_color_scheme,
+                {}
+            ), color_scheme_without_config)
+
+        self.df_ab_lin.rename(columns={'Q.3': 'grp_1', 'AY.48': 'grp_2'},
+                              inplace=True)
+        self.df_ab_lin.drop(columns=['B.1.617.2'], inplace=True)
+        color_scheme_with_config = {
+            'grp_1': 'orange',
+            'grp_2': 'green',
+            'Other': 'rgb(82, 188, 163)'
+        }
+
+        self.assertDictEqual(get_color_scheme(
+                self.df_ab_lin,
+                default_color_scheme,
+                self.config.get('Lineages')
+            ), color_scheme_with_config)
+
+    def test_calc_rel_growth_rates(self):
+        df_rel_growth_rates_expected = pd.read_csv(
+            'freyja/data/rel_growth_rates.csv')
+        self.df_ab_lin.index = pd.to_datetime(self.df_ab_lin.index)
+        nboots = 1000
+        serial_interval = 5.5
+        daysIncluded = 56
+
+        calc_rel_growth_rates(
+            self.df_ab_lin,
+            nboots,
+            serial_interval,
+            'rel_growth_rates.csv',
+            daysIncluded
+        )
+        df_rel_growth_rates = pd.read_csv('rel_growth_rates.csv')
+
+        pd.testing.assert_frame_equal(
+            df_rel_growth_rates[['Lineage', 'Estimated Advantage']],
+            df_rel_growth_rates_expected[['Lineage', 'Estimated Advantage']]
+        )
+
+        # Clean up
+        os.remove('rel_growth_rates.csv')
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/freyja/utils.py b/freyja/utils.py
index b70cfa07..e4018c3c 100644
--- a/freyja/utils.py
+++ b/freyja/utils.py
@@ -48,8 +48,8 @@ def logistic_growth(ndays, b, r):
 
 
 # Calcualate the relative growth rates of the lineages and return a dataFrame.
-def calc_rel_growth_rates(df, nboots, serial_interval, outputFn,
-                          daysIncluded=56):
+def calc_rel_growth_rates(df, nboots=1000, serial_interval=5.5,
+                          outputFn='rel_growth_rates.csv', daysIncluded=56):
     df.index.name = 'Date'
     df.reset_index(inplace=True)
     df['Date'] = pd.to_datetime(df['Date'])
@@ -69,9 +69,11 @@ def calc_rel_growth_rates(df, nboots, serial_interval, outputFn,
     }
     # get all lineages present at >0.1% average over last 8 weeks
     lineages = df.columns[df.iloc[-nBack:].mean(axis=0) > 0.001]
-    print(f"Starting rate calculations for {len(lineages)} lineages/groups")
-    for k, lineage in tqdm.tqdm(enumerate(lineages)):
-        print(f"\nCalculating relative rate for {lineage}")
+    rate_cal = tqdm.tqdm(enumerate(lineages), total=len(lineages),
+                         desc='Rate calculations for lineages/groups')
+    for k, lineage in rate_cal:
+        # print(f"\nCalculating relative rate for {lineage}")
+        rate_cal.set_postfix({"Calculating relative rate for": lineage})
         days = np.array([(dfi - df.index[-nBack]).days
                          for j, dfi in enumerate(df.index[-nBack:])])
         data = df[lineage][-len(days):]