diff --git a/.gitignore b/.gitignore
index 2ff834f..53792b2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,5 @@
 *pycache*
 .DS_Store
 *.pyc
-venv/*
\ No newline at end of file
+venv/*
+.vscode
\ No newline at end of file
diff --git a/README.md b/README.md
index 4a7111e..16e303f 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ On a typical Windows, Mac, or Linux computer:
 * Create a conda environment: `conda create --name multisero python=3.7`
 * Activate conda environment: `conda activate multisero`
 * Pip install dependencies: `pip install -r requirements.txt`
-* Add the package to PYTHONPATH. Inside the package directory (...\serology-COVID19), do: `export PYTHONPATH=$PYTHONPATH:$(pwd)` 
+* If operating from CLI, add the package to PYTHONPATH by navigating to package directory (eg: ...\serology-COVID19), and type `export PYTHONPATH=$PYTHONPATH:$(pwd)`
 
 For installation notes for Jetson Nano, see [these notes](docs/installation.md).
 
@@ -47,10 +47,12 @@ optional arguments:
   -m METADATA, --metadata METADATA
                         specify the file name for the experiment metadata.
                         Assumed to be in the same directory as images.
-                        Default: 'multisero_output_data_metadata.xlsx'
+                        Default: 'multisero_output_data_metadata.xlsx', therefore
+                        metadata flag is NOT necessary when running in -e mode IF
+                        metadata file has the default filename.
 ```
 ### Extract OD from antigen array images
-`python multisero.py -e -i <input> -o <output> -m <METADATA>` will take metadata for antigen array and images as input, and output optical densities for each antigen.
+`python multisero.py -e -i <input> -o <output>` will take metadata for antigen array and images as input, and output optical densities for each antigen.
 The optical densities are stored in an excel file at the following path: `<output>/multisero_<input>_<year><month><day>_<hour><min>/median_ODs.xlsx`
 
 If rerunning some of the wells, the input metadata file needs to contain a sheet named 'rerun_wells'
diff --git a/interpretation/od_analyzer.py b/interpretation/od_analyzer.py
deleted file mode 100644
index 9e54b57..0000000
--- a/interpretation/od_analyzer.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-import re
-from matplotlib import pyplot as plt
-import seaborn as sns
-from interpretation.plotting import roc_plot_grid, total_plots
-from interpretation.report_reader import slice_df, normalize_od, read_output_batch
-import array_analyzer.extract.constants as constants
-
-
-def read_config(input_dir):
-    """
-    Load analysis config from the input directory.
-    :param str input_dir: input directory
-    :return dataframe ntl_dirs_df: 'multisero output dirs' tab in the config file.
-    :return dataframe scn_scn_df: 'scienion output dirs' tab in the config file.
-    :return dataframe plot_setting_df: 'general plotting settings' tab in the config file.
-    :return dataframe roc_param_df: 'ROC plot' tab in the config file.
-    :return dataframe cat_param_df: 'categorical plot' tab in the config file.
-    :return dataframe fit_param_df: 'standard curves' tab in the config file.
-    """
-    config = dict()
-    config_path = os.path.join(input_dir, constants.METADATA_FILE)
-    if constants.METADATA_FILE not in os.listdir(input_dir):
-        raise IOError("analysis config file not found, aborting")
-    # check that the xlsx file contains necessary worksheets
-    ntl_dirs_df = scn_scn_df = roc_param_df = cat_param_df = fit_param_df = pd.DataFrame()
-    with pd.ExcelFile(config_path) as config_file:
-        plot_setting_df = pd.read_excel(config_file, sheet_name='general plotting settings',
-                                        index_col=0, squeeze=True, usecols='A,B')
-        plot_setting_df.where(plot_setting_df.notnull(), None, inplace=True)
-        if 'ROC plot' in config_file.sheet_names:
-            roc_param_df = pd.read_excel(config_file, sheet_name='ROC plot',
-                                         index_col=0, squeeze=True, usecols='A,B')
-            # replace NaN with None
-            roc_param_df.where(roc_param_df.notnull(), None, inplace=True)
-            if roc_param_df['serum ID'] is not None:
-                roc_param_df['serum ID'] = re.split(r'\s*,\s*', roc_param_df['serum ID'])
-        if 'categorical plot' in config_file.sheet_names:
-            cat_param_df = pd.read_excel(config_file, sheet_name='categorical plot',
-                                         index_col=0, squeeze=True, usecols='A,B')
-            cat_param_df.where(cat_param_df.notnull(), None, inplace=True)
-            if cat_param_df['serum ID'] is not None:
-                cat_param_df['serum ID'] = re.split(r'\s*,\s*', cat_param_df['serum ID'])
-        if 'standard curves' in config_file.sheet_names:
-            fit_param_df = pd.read_excel(config_file, sheet_name='standard curves',
-                                         index_col=0, squeeze=True, usecols='A,B')
-            fit_param_df.where(fit_param_df.notnull(), None, inplace=True)
-            if fit_param_df['serum ID'] is not None:
-                fit_param_df['serum ID'] = re.split(r'\s*,\s*', fit_param_df['serum ID'])
-        if not constants.LOAD_REPORT:
-            assert ('multisero output dirs' in config_file.sheet_names) or \
-            ('scienion output dirs' in config_file.sheet_names), \
-            "sheet by name 'multisero output dirs' or 'scienion output dirs' are required " \
-            "in analysis config file when load_report is False, aborting"
-            if 'multisero output dirs' in config_file.sheet_names:
-                ntl_dirs_df = pd.read_excel(config_file, sheet_name='multisero output dirs', comment='#')
-                if not ntl_dirs_df.isna().loc[0, 'well ID']:
-                    ntl_dirs_df['well ID'] = ntl_dirs_df['well ID'].str.split(pat=r'\s*,\s*')
-            if 'scienion output dirs' in config_file.sheet_names:
-                scn_scn_df = pd.read_excel(config_file, sheet_name='scienion output dirs', comment='#')
-    return ntl_dirs_df, scn_scn_df, plot_setting_df, roc_param_df, cat_param_df, fit_param_df
-
-def analyze_od(input_dir, output_dir, load_report):
-    """
-    Perform analysis on multisero or scienion OD outputs specified in the config files.
-    Save the combined table as 'master report' in the output directory.
-    :param str input_dir: Input directory
-    :param str output_dir: Output directory
-    :param bool load_report: If True, load the saved 'master report' in the output directory
-    from the previous run. Load from the master report is much faster.
-    """
-    os.makedirs(output_dir, exist_ok=True)
-    ntl_dirs_df, scn_scn_df, plot_setting_df, roc_param_df, cat_param_df, fit_param_df =\
-        read_config(input_dir)
-    stitched_multisero_df = read_output_batch(output_dir, ntl_dirs_df, scn_scn_df, load_report)
-    if plot_setting_df['antigens to plot'] == 'all':
-        plot_setting_df['antigens to plot'] = stitched_multisero_df['antigen'].unique()
-    split_plots_by = plot_setting_df['split plots by']
-    split_plots_vals = [None]
-    if split_plots_by is not None:
-        split_plots_vals = stitched_multisero_df[split_plots_by].unique()
-    norm_antigen = plot_setting_df['normalize OD by']
-    norm_group = 'plate'
-    aggregate = 'mean'
-    # aggregate = None
-    antigen_list = plot_setting_df['antigens to plot']
-
-    suffix = ''
-    df_norm = normalize_od(stitched_multisero_df.copy(), norm_antigen, group=norm_group)
-    if norm_antigen is not None:
-        suffix = '_'.join([suffix, norm_antigen, 'norm_per', norm_group])
-
-    if aggregate is not None:
-        df_norm = df_norm.groupby(['antigen', 'antigen type', 'serum ID', 'well_id', 'plate ID', 'sample type',
-                                 'serum type', 'serum dilution', 'serum cat', 'pipeline', 'secondary ID',
-                                 'secondary dilution','PRNT'])['OD'].mean().reset_index()
-        suffix = '_'.join([suffix, aggregate])
-
-    for split_val in split_plots_vals:
-        split_suffix = suffix
-        if split_val is not None:
-            split_suffix = '_'.join([split_suffix, split_val])
-        df_norm_sub = slice_df(df_norm, 'keep', split_plots_by, split_val)
-        slice_cols = [split_plots_by, 'antigen type', 'antigen']
-        slice_keys = [[split_val], ['Diagnostic','Positive'], antigen_list]
-        #slice_keys = [[split_val], ['Negative'], antigen_list]
-        slice_actions = ['keep', 'keep', 'keep']
-        # general slicing
-        for col, action, key in zip(slice_cols, slice_actions, slice_keys):
-            df_norm_sub = slice_df(df_norm_sub, action, col, key)
-        #%% compute ROC curves and AUC
-        if not roc_param_df.empty:
-            sera_roc_list = roc_param_df['serum ID']
-            slice_action = roc_param_df['serum ID action']
-            # plot specific slicing
-            roc_df = slice_df(df_norm_sub, slice_action, 'serum ID', sera_roc_list)
-            fpr = 1 - roc_param_df['specificity']
-            ci = roc_param_df['confidence interval']
-            hue = roc_param_df['hue']
-            # df_norm = offset_od(df_norm, offset_antigen, offset_group)
-            roc_suffix = split_suffix
-            if ci is not None:
-                roc_suffix = '_'.join([roc_suffix, 'ci'])
-            #%%
-            print('{} unique positive sera'.format(len(roc_df.loc[roc_df['serum type']=='positive', 'serum ID'].unique())))
-            print('{} unique negative sera'.format(len(roc_df.loc[roc_df['serum type'] == 'negative', 'serum ID'].unique())))
-            roc_plot_grid(roc_df, constants.RUN_PATH, '_'.join(['ROC', roc_suffix]), 'png', ci=ci, fpr=fpr, hue=hue)
-    #%% Plot categorical scatter plot for episurvey
-        if not cat_param_df.empty:
-            sera_cat_list = cat_param_df['serum ID']
-            slice_action = cat_param_df['serum ID action']
-            split_subplots_by = cat_param_df['split subplots by']
-            hue = cat_param_df['hue']
-            # plot specific slicing
-            cat_df = slice_df(df_norm_sub, slice_action, 'serum ID', sera_cat_list) #serum ID --> antigen
-            assert not cat_df.empty, 'Plotting dataframe is empty. Please check the plotting keys'
-            sns.set_context("talk")
-            g = sns.catplot(x="serum type", y="OD", hue=hue, col=split_subplots_by, kind="swarm",
-                            data=cat_df, col_wrap=3)
-            g.set_xticklabels(rotation=65, horizontalalignment='right')
-
-
-            plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_{}.png'.format(split_suffix)),
-                                          dpi=300, bbox_inches='tight')
-            if cat_param_df['zoom']:
-                g.set(ylim=(-0.05, 0.4))
-                plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_zoom_{}.png'.format(split_suffix)),
-                                              dpi=300, bbox_inches='tight')
-        #%% 4PL fit
-        if not fit_param_df.empty:
-            slice_action = fit_param_df['serum ID action']
-            hue = fit_param_df['hue']
-            dilution_df = slice_df(df_norm_sub, slice_action, 'serum ID', fit_param_df['serum ID'])
-            split_subplots_by = fit_param_df['split subplots by']
-            total_plots(dilution_df, constants.RUN_PATH, 'fit_{}'.format(split_suffix), 'png', hue=hue,
-                                zoom=fit_param_df['zoom'], split_subplots_by=split_subplots_by, col_wrap=2)
-        plt.close('all')
-
diff --git a/array_analyzer/__init__.py b/multiSero/__init__.py
similarity index 100%
rename from array_analyzer/__init__.py
rename to multiSero/__init__.py
diff --git a/multiSero/array_analyzer/__init__.py b/multiSero/array_analyzer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/array_analyzer/extract/__init__.py b/multiSero/array_analyzer/extract/__init__.py
similarity index 100%
rename from array_analyzer/extract/__init__.py
rename to multiSero/array_analyzer/extract/__init__.py
diff --git a/array_analyzer/extract/background_estimator.py b/multiSero/array_analyzer/extract/background_estimator.py
similarity index 100%
rename from array_analyzer/extract/background_estimator.py
rename to multiSero/array_analyzer/extract/background_estimator.py
diff --git a/array_analyzer/extract/constants.py b/multiSero/array_analyzer/extract/constants.py
similarity index 100%
rename from array_analyzer/extract/constants.py
rename to multiSero/array_analyzer/extract/constants.py
diff --git a/array_analyzer/extract/image_parser.py b/multiSero/array_analyzer/extract/image_parser.py
similarity index 99%
rename from array_analyzer/extract/image_parser.py
rename to multiSero/array_analyzer/extract/image_parser.py
index 1cb6bd2..927bbe6 100644
--- a/array_analyzer/extract/image_parser.py
+++ b/multiSero/array_analyzer/extract/image_parser.py
@@ -12,7 +12,7 @@
 from skimage import measure
 
 from .img_processing import thresh_and_binarize
-from array_analyzer.transform.point_registration import icp
+from multiSero.array_analyzer.transform.point_registration import icp
 
 """
 method is
@@ -338,7 +338,7 @@ def find_fiducials_markers(props_,
     return cent_map
 
 
-def grid_from_centroids(props_, n_rows, n_cols, grid_spacing=82):
+def grid_from_centroids(props_, n_rows, n_cols, grid_spacing=90):
     """
     based on the region props, creates a dictionary of format:
         key = (centroid_x, centroid_y)
diff --git a/array_analyzer/extract/img_processing.py b/multiSero/array_analyzer/extract/img_processing.py
similarity index 100%
rename from array_analyzer/extract/img_processing.py
rename to multiSero/array_analyzer/extract/img_processing.py
diff --git a/array_analyzer/extract/metadata.py b/multiSero/array_analyzer/extract/metadata.py
similarity index 98%
rename from array_analyzer/extract/metadata.py
rename to multiSero/array_analyzer/extract/metadata.py
index c474389..55a6eaa 100644
--- a/array_analyzer/extract/metadata.py
+++ b/multiSero/array_analyzer/extract/metadata.py
@@ -3,8 +3,8 @@
 import pandas as pd
 import shutil
 
-import array_analyzer.extract.txt_parser as txt_parser
-import array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.txt_parser as txt_parser
+import multiSero.array_analyzer.extract.constants as constants
 
 
 class MetaData:
diff --git a/array_analyzer/extract/notebooks/Array_analyzer_bbox.ipynb b/multiSero/array_analyzer/extract/notebooks/Array_analyzer_bbox.ipynb
similarity index 100%
rename from array_analyzer/extract/notebooks/Array_analyzer_bbox.ipynb
rename to multiSero/array_analyzer/extract/notebooks/Array_analyzer_bbox.ipynb
diff --git a/array_analyzer/extract/notebooks/image_parser_nb.ipynb b/multiSero/array_analyzer/extract/notebooks/image_parser_nb.ipynb
similarity index 100%
rename from array_analyzer/extract/notebooks/image_parser_nb.ipynb
rename to multiSero/array_analyzer/extract/notebooks/image_parser_nb.ipynb
diff --git a/array_analyzer/extract/notebooks/image_parser_well_mask.ipynb b/multiSero/array_analyzer/extract/notebooks/image_parser_well_mask.ipynb
similarity index 100%
rename from array_analyzer/extract/notebooks/image_parser_well_mask.ipynb
rename to multiSero/array_analyzer/extract/notebooks/image_parser_well_mask.ipynb
diff --git a/array_analyzer/extract/notebooks/spot_detection_icp.ipynb b/multiSero/array_analyzer/extract/notebooks/spot_detection_icp.ipynb
similarity index 100%
rename from array_analyzer/extract/notebooks/spot_detection_icp.ipynb
rename to multiSero/array_analyzer/extract/notebooks/spot_detection_icp.ipynb
diff --git a/array_analyzer/extract/notebooks/txt_parser_nb.ipynb b/multiSero/array_analyzer/extract/notebooks/txt_parser_nb.ipynb
similarity index 100%
rename from array_analyzer/extract/notebooks/txt_parser_nb.ipynb
rename to multiSero/array_analyzer/extract/notebooks/txt_parser_nb.ipynb
diff --git a/array_analyzer/extract/txt_parser.py b/multiSero/array_analyzer/extract/txt_parser.py
similarity index 99%
rename from array_analyzer/extract/txt_parser.py
rename to multiSero/array_analyzer/extract/txt_parser.py
index 2466925..982d574 100644
--- a/array_analyzer/extract/txt_parser.py
+++ b/multiSero/array_analyzer/extract/txt_parser.py
@@ -9,8 +9,6 @@
 import pandas as pd
 import math
 
-import array_analyzer.extract.constants as constants
-
 """
 functions like "create_<extension>_dict" parse files of <extension> and return:
 fiduc: list of dict describing fiducial positions
diff --git a/array_analyzer/load/__init__.py b/multiSero/array_analyzer/load/__init__.py
similarity index 100%
rename from array_analyzer/load/__init__.py
rename to multiSero/array_analyzer/load/__init__.py
diff --git a/array_analyzer/load/debug_plots.py b/multiSero/array_analyzer/load/debug_plots.py
similarity index 100%
rename from array_analyzer/load/debug_plots.py
rename to multiSero/array_analyzer/load/debug_plots.py
diff --git a/array_analyzer/load/report.py b/multiSero/array_analyzer/load/report.py
similarity index 99%
rename from array_analyzer/load/report.py
rename to multiSero/array_analyzer/load/report.py
index 10318e7..ca9dcbe 100644
--- a/array_analyzer/load/report.py
+++ b/multiSero/array_analyzer/load/report.py
@@ -5,7 +5,7 @@
 import os
 import pandas as pd
 
-import array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.constants as constants
 
 
 class ReportWriter:
diff --git a/array_analyzer/transform/__init__.py b/multiSero/array_analyzer/transform/__init__.py
similarity index 100%
rename from array_analyzer/transform/__init__.py
rename to multiSero/array_analyzer/transform/__init__.py
diff --git a/array_analyzer/transform/array_generation.py b/multiSero/array_analyzer/transform/array_generation.py
similarity index 94%
rename from array_analyzer/transform/array_generation.py
rename to multiSero/array_analyzer/transform/array_generation.py
index 2ac4fab..819a0e8 100644
--- a/array_analyzer/transform/array_generation.py
+++ b/multiSero/array_analyzer/transform/array_generation.py
@@ -2,10 +2,10 @@
 import numpy as np
 import pandas as pd
 
-import array_analyzer.extract.constants as constants
-import array_analyzer.extract.img_processing as img_processing
-import array_analyzer.extract.txt_parser as txt_parser
-import array_analyzer.utils.spot_regionprop as regionprop
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.img_processing as img_processing
+import multiSero.array_analyzer.extract.txt_parser as txt_parser
+import multiSero.array_analyzer.utils.spot_regionprop as regionprop
 
 
 def build_centroid_binary_blocks(cent_list, image_, params_, return_type='region'):
@@ -59,7 +59,7 @@ def build_centroid_binary_blocks(cent_list, image_, params_, return_type='region
         return target
 
 
-def get_spot_intensity(coords, im, background, search_range=3):
+def get_spot_intensity(coords, im, background, search_range=2):
     """
     Extract signal and background intensity at each spot given the spot coordinate
     with the following steps:
diff --git a/array_analyzer/transform/point_registration.py b/multiSero/array_analyzer/transform/point_registration.py
similarity index 98%
rename from array_analyzer/transform/point_registration.py
rename to multiSero/array_analyzer/transform/point_registration.py
index dd933bf..e95186e 100644
--- a/array_analyzer/transform/point_registration.py
+++ b/multiSero/array_analyzer/transform/point_registration.py
@@ -2,7 +2,7 @@
 import logging
 import numpy as np
 
-import array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.constants as constants
 
 
 def icp(source, target, max_iterate=50, matrix_diff=1.):
@@ -166,10 +166,12 @@ def get_translation_matrix(particle):
         b = particle[3] * np.sin(particle[2] * np.pi / 180)
         t_matrix = np.array([[a, b, particle[0]],
                             [-b, a, particle[1]]])
+        #t_matrix = np.array([[0, 0, particle[0]],
+                           #[0, 0, particle[1]]])
         return t_matrix
 
     def particle_filter(self,
-                        max_iter=100,
+                        max_iter=1000,
                         stop_criteria=.1,
                         iter_decrease=.8,
                         nbr_outliers=0):
@@ -256,6 +258,7 @@ def particle_filter(self,
         self.registered_dist = min_dist / (self.fiducial_coords.shape[0] - nbr_outliers)
         self.logger.info("Particle filter min dist: {}".format(self.registered_dist))
         if self.registered_dist > constants.REG_DIST_THRESH:
+        #if self.registered_dist > 300:
             self.registration_ok = False
         else:
             self.registration_ok = True
diff --git a/array_analyzer/utils/__init__.py b/multiSero/array_analyzer/utils/__init__.py
similarity index 100%
rename from array_analyzer/utils/__init__.py
rename to multiSero/array_analyzer/utils/__init__.py
diff --git a/array_analyzer/utils/example_id2spots_AEPFeb3.csv b/multiSero/array_analyzer/utils/example_id2spots_AEPFeb3.csv
similarity index 100%
rename from array_analyzer/utils/example_id2spots_AEPFeb3.csv
rename to multiSero/array_analyzer/utils/example_id2spots_AEPFeb3.csv
diff --git a/array_analyzer/utils/io_utils.py b/multiSero/array_analyzer/utils/io_utils.py
similarity index 100%
rename from array_analyzer/utils/io_utils.py
rename to multiSero/array_analyzer/utils/io_utils.py
diff --git a/array_analyzer/utils/spot_regionprop.py b/multiSero/array_analyzer/utils/spot_regionprop.py
similarity index 98%
rename from array_analyzer/utils/spot_regionprop.py
rename to multiSero/array_analyzer/utils/spot_regionprop.py
index 5fed3fa..f466962 100644
--- a/array_analyzer/utils/spot_regionprop.py
+++ b/multiSero/array_analyzer/utils/spot_regionprop.py
@@ -3,7 +3,7 @@
 from skimage import measure
 from skimage.morphology import disk
 
-import array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.constants as constants
 
 
 class SpotRegionprop:
diff --git a/array_analyzer/utils/visualize_elisa_spots.py b/multiSero/array_analyzer/utils/visualize_elisa_spots.py
similarity index 100%
rename from array_analyzer/utils/visualize_elisa_spots.py
rename to multiSero/array_analyzer/utils/visualize_elisa_spots.py
diff --git a/multiSero/array_analyzer/workflows/__init__.py b/multiSero/array_analyzer/workflows/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/array_analyzer/workflows/interpolation_wf.py b/multiSero/array_analyzer/workflows/interpolation_wf.py
similarity index 87%
rename from array_analyzer/workflows/interpolation_wf.py
rename to multiSero/array_analyzer/workflows/interpolation_wf.py
index e3153f7..19ba32a 100644
--- a/array_analyzer/workflows/interpolation_wf.py
+++ b/multiSero/array_analyzer/workflows/interpolation_wf.py
@@ -4,16 +4,16 @@
 import pandas as pd
 import skimage.io as io
 
-import array_analyzer.extract.image_parser as image_parser
-import array_analyzer.extract.txt_parser as txt_parser
-import array_analyzer.extract.img_processing as img_processing
-import array_analyzer.load.debug_plots as debug_plots
-import array_analyzer.load.report as report
-import array_analyzer.extract.constants as constants
-import array_analyzer.transform.array_generation as array_gen
-import array_analyzer.extract.background_estimator as background_estimator
-import array_analyzer.utils.io_utils as io_utils
-from array_analyzer.extract.metadata import MetaData
+import multiSero.array_analyzer.extract.image_parser as image_parser
+import multiSero.array_analyzer.extract.txt_parser as txt_parser
+import multiSero.array_analyzer.extract.img_processing as img_processing
+import multiSero.array_analyzer.load.debug_plots as debug_plots
+import multiSero.array_analyzer.load.report as report
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.transform.array_generation as array_gen
+import multiSero.array_analyzer.extract.background_estimator as background_estimator
+import multiSero.array_analyzer.utils.io_utils as io_utils
+from multiSero.array_analyzer.extract.metadata import MetaData
 
 
 def interp(input_dir, output_dir):
diff --git a/array_analyzer/workflows/registration_workflow.py b/multiSero/array_analyzer/workflows/registration_workflow.py
similarity index 91%
rename from array_analyzer/workflows/registration_workflow.py
rename to multiSero/array_analyzer/workflows/registration_workflow.py
index 708894b..6825cd5 100644
--- a/array_analyzer/workflows/registration_workflow.py
+++ b/multiSero/array_analyzer/workflows/registration_workflow.py
@@ -1,21 +1,18 @@
-import cv2 as cv
 import logging
-import numpy as np
 import os
 import pandas as pd
 import time
 
-import array_analyzer.extract.background_estimator as background_estimator
-import array_analyzer.extract.image_parser as image_parser
-import array_analyzer.extract.img_processing as img_processing
-import array_analyzer.extract.metadata as metadata
-import array_analyzer.extract.txt_parser as txt_parser
-import array_analyzer.extract.constants as constants
-import array_analyzer.load.debug_plots as debug_plots
-import array_analyzer.load.report as report
-import array_analyzer.transform.point_registration as registration
-import array_analyzer.transform.array_generation as array_gen
-import array_analyzer.utils.io_utils as io_utils
+import multiSero.array_analyzer.extract.background_estimator as background_estimator
+import multiSero.array_analyzer.extract.img_processing as img_processing
+import multiSero.array_analyzer.extract.metadata as metadata
+import multiSero.array_analyzer.extract.txt_parser as txt_parser
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.load.debug_plots as debug_plots
+import multiSero.array_analyzer.load.report as report
+import multiSero.array_analyzer.transform.point_registration as registration
+import multiSero.array_analyzer.transform.array_generation as array_gen
+import multiSero.array_analyzer.utils.io_utils as io_utils
 
 
 def point_registration(input_dir, output_dir):
diff --git a/array_analyzer/workflows/well_wf.py b/multiSero/array_analyzer/workflows/well_wf.py
similarity index 92%
rename from array_analyzer/workflows/well_wf.py
rename to multiSero/array_analyzer/workflows/well_wf.py
index 075f9ae..47577b1 100644
--- a/array_analyzer/workflows/well_wf.py
+++ b/multiSero/array_analyzer/workflows/well_wf.py
@@ -1,15 +1,14 @@
-import array_analyzer.extract.image_parser as image_parser
-import array_analyzer.extract.img_processing as processing
-import array_analyzer.extract.constants as constants
-from array_analyzer.extract.metadata import MetaData
-import array_analyzer.utils.io_utils as io_utils
+import multiSero.array_analyzer.extract.image_parser as image_parser
+import multiSero.array_analyzer.extract.img_processing as processing
+import multiSero.array_analyzer.extract.constants as constants
+from multiSero.array_analyzer.extract.metadata import MetaData
+import multiSero.array_analyzer.utils.io_utils as io_utils
 
 import time
 import skimage.io as io
 import pandas as pd
 import string
 import os
-import re
 import numpy as np
 
 
diff --git a/multiSero/interpretation/od_analyzer.py b/multiSero/interpretation/od_analyzer.py
new file mode 100644
index 0000000..d82b966
--- /dev/null
+++ b/multiSero/interpretation/od_analyzer.py
@@ -0,0 +1,276 @@
+import pandas as pd
+import os
+import re
+from matplotlib import pyplot as plt
+import seaborn as sns
+
+from multiSero.plotting.plotting import roc_plot_grid
+from multiSero.interpretation.report_reader import slice_df, normalize_od, read_output_batch
+import multiSero.array_analyzer.extract.constants as constants
+
+
+def read_config(input_dir):
+    """
+    Load analysis config from the input directory.
+    :param str input_dir: input directory
+    :return dataframe ntl_dirs_df: 'multisero output dirs' tab in the config file.
+    :return dataframe scn_scn_df: 'scienion output dirs' tab in the config file.
+    :return dataframe plot_setting_df: 'general plotting settings' tab in the config file.
+    :return dataframe roc_param_df: 'ROC plot' tab in the config file.
+    :return dataframe cat_param_df: 'categorical plot' tab in the config file.
+    :return dataframe fit_param_df: 'standard curves' tab in the config file.
+    """
+    config = dict()
+    config_path = os.path.join(input_dir, constants.METADATA_FILE)
+    if constants.METADATA_FILE not in os.listdir(input_dir):
+        raise IOError("analysis config file not found, aborting")
+    # check that the xlsx file contains necessary worksheets
+    ntl_dirs_df = scn_scn_df = roc_param_df = cat_param_df = fit_param_df = pd.DataFrame()
+    with pd.ExcelFile(config_path) as config_file:
+        plot_setting_df = pd.read_excel(config_file, sheet_name='general plotting settings',
+                                        index_col=0, squeeze=True, usecols='A,B')
+        plot_setting_df.where(plot_setting_df.notnull(), None, inplace=True)
+        if 'ROC plot' in config_file.sheet_names:
+            roc_param_df = pd.read_excel(config_file, sheet_name='ROC plot',
+                                         index_col=0, squeeze=True, usecols='A,B')
+            # replace NaN with None
+            roc_param_df.where(roc_param_df.notnull(), None, inplace=True)
+            if roc_param_df['serum ID'] is not None:
+                roc_param_df['serum ID'] = re.split(r'\s*,\s*', roc_param_df['serum ID'])
+        if 'categorical plot' in config_file.sheet_names:
+            cat_param_df = pd.read_excel(config_file, sheet_name='categorical plot',
+                                         index_col=0, squeeze=True, usecols='A,B')
+            cat_param_df.where(cat_param_df.notnull(), None, inplace=True)
+            if cat_param_df['serum ID'] is not None:
+                cat_param_df['serum ID'] = re.split(r'\s*,\s*', cat_param_df['serum ID'])
+        if 'standard curves' in config_file.sheet_names:
+            fit_param_df = pd.read_excel(config_file, sheet_name='standard curves',
+                                         index_col=0, squeeze=True, usecols='A,B')
+            fit_param_df.where(fit_param_df.notnull(), None, inplace=True)
+            if fit_param_df['serum ID'] is not None:
+                fit_param_df['serum ID'] = re.split(r'\s*,\s*', fit_param_df['serum ID'])
+        if not constants.LOAD_REPORT:
+            assert ('multisero output dirs' in config_file.sheet_names) or \
+            ('scienion output dirs' in config_file.sheet_names), \
+            "sheet by name 'multisero output dirs' or 'scienion output dirs' are required " \
+            "in analysis config file when load_report is False, aborting"
+            if 'multisero output dirs' in config_file.sheet_names:
+                ntl_dirs_df = pd.read_excel(config_file, sheet_name='multisero output dirs', comment='#')
+                if not ntl_dirs_df.isna().loc[0, 'well ID']:
+                    ntl_dirs_df['well ID'] = ntl_dirs_df['well ID'].str.split(pat=r'\s*,\s*')
+            if 'scienion output dirs' in config_file.sheet_names:
+                scn_scn_df = pd.read_excel(config_file, sheet_name='scienion output dirs', comment='#')
+    return ntl_dirs_df, scn_scn_df, plot_setting_df, roc_param_df, cat_param_df, fit_param_df
+
+def analyze_od(input_dir, output_dir, load_report):
+    """
+    Perform analysis on multisero or scienion OD outputs specified in the config files.
+    Save the combined table as 'master report' in the output directory.
+    :param str input_dir: Input directory
+    :param str output_dir: Output directory
+    :param bool load_report: If True, load the saved 'master report' in the output directory
+    from the previous run. Load from the master report is much faster.
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    ntl_dirs_df, scn_scn_df, plot_setting_df, roc_param_df, cat_param_df, fit_param_df =\
+        read_config(input_dir)
+    stitched_multisero_df = read_output_batch(output_dir, ntl_dirs_df, scn_scn_df, load_report)
+    if plot_setting_df['antigens to plot'] == 'all':
+        plot_setting_df['antigens to plot'] = stitched_multisero_df['antigen'].unique()
+    split_plots_by = plot_setting_df['split plots by']
+    split_plots_vals = [None]
+    if split_plots_by is not None:
+        split_plots_vals = stitched_multisero_df[split_plots_by].unique()
+    norm_antigen = plot_setting_df['normalize OD by']
+    norm_group = 'plate'
+    aggregate = 'mean'
+    # aggregate = None
+    antigen_list = [plot_setting_df['antigens to plot'], 'SARS CoV2 RBD 250', 'SARS CoV2 spike 62.5']
+
+    suffix = ''
+    df_norm = normalize_od(stitched_multisero_df.copy(), norm_antigen, group=norm_group)
+    if norm_antigen is not None:
+        suffix = '_'.join([suffix, norm_antigen, 'norm_per', norm_group])
+
+    if aggregate is not None:
+        df_norm = df_norm.groupby(['antigen', 'antigen type', 'serum ID', 'well_id', 'plate ID', 'sample type',
+                                 'serum type', 'serum dilution', 'serum cat', 'pipeline', 'secondary ID',
+                                 'secondary dilution', 'visit value'])['OD'].mean().reset_index()
+        suffix = '_'.join([suffix, aggregate])
+
+    for split_val in split_plots_vals:
+        split_suffix = suffix
+        if split_val is not None:
+            split_suffix = '_'.join([split_suffix, split_val])
+        df_norm_sub = slice_df(df_norm, 'keep', split_plots_by, split_val)
+        slice_cols = [split_plots_by, 'antigen type', 'antigen']
+        slice_keys = [[split_val], ['Diagnostic'], antigen_list] # might be useful to eventually select for in UI
+        #slice_keys = [[split_val], ['Negative'], antigen_list]
+        slice_actions = ['keep', 'keep', 'keep']
+        # general slicing
+        for col, action, key in zip(slice_cols, slice_actions, slice_keys):
+            df_norm_sub = slice_df(df_norm_sub, action, col, key)
+
+    # %% compute ROC curves and AUC
+        if not roc_param_df.empty:
+            sera_roc_list = roc_param_df['serum ID']
+            slice_action = roc_param_df['serum ID action']
+            # plot specific slicing
+            roc_df = slice_df(df_norm_sub, slice_action, 'serum ID', sera_roc_list)
+            fpr = 1 - roc_param_df['specificity']
+            ci = roc_param_df['confidence interval']
+            hue = roc_param_df['hue']
+            # df_norm = offset_od(df_norm, offset_antigen, offset_group)
+            roc_suffix = split_suffix
+            if ci is not None:
+                roc_suffix = '_'.join([roc_suffix, 'ci'])
+            #%%
+            print('{} unique positive sera'.format(len(roc_df.loc[roc_df['serum type']=='positive', 'serum ID'].unique())))
+            print('{} unique negative sera'.format(len(roc_df.loc[roc_df['serum type'] == 'negative', 'serum ID'].unique())))
+            roc_plot_grid(roc_df, constants.RUN_PATH, '_'.join(['ROC', roc_suffix]), 'png', ci=ci, fpr=fpr, hue=hue)
+    #%% Plot categorical scatter plot for episurvey -- FIGURE 5
+        if not cat_param_df.empty:
+            # ms_tidy_df is created to create a tidy, concise, and contained dataframe for cat plotting (Fig 5a)
+            ms_tidy_df = df_norm_sub[
+                ['antigen', 'OD', 'visit value', 'serum cat', 'serum type', 'serum ID', 'serum dilution']]
+            ms_tidy_df.drop(ms_tidy_df.loc[ms_tidy_df['serum dilution'] > 0.00005].index, inplace=True)
+            ms_tidy_df.drop(ms_tidy_df.loc[(ms_tidy_df['serum cat'] != '[\'COVID-Vax+\']')
+                                           & (ms_tidy_df['serum cat'] != '[\'COVID+Vax+\']')
+                                           & (ms_tidy_df['serum cat'] != '[\'COVID+Vax-\']')].index, inplace=True)
+
+            group = ms_tidy_df.groupby(
+                'serum type')  # group data by patient ID and transfrom 'visit value' numbers into categorical values
+            df2 = group.apply(lambda x: x['visit value'].unique())
+            for element in df2.index:
+                if (len(df2[element]) > 1):
+                    if (df2[element][0][2:-2] > df2[element][1][2:-2]):
+                        ms_tidy_df.loc[
+                            ms_tidy_df['visit value'] == df2[element][0], 'vaccine availability'] = 'post-vax'
+                        ms_tidy_df.loc[ms_tidy_df['visit value'] == df2[element][1], 'vaccine availability'] = 'pre-vax'
+                    if (df2[element][0][2:-2] < df2[element][1][2:-2]):
+                        ms_tidy_df.loc[ms_tidy_df['visit value'] == df2[element][0], 'vaccine availability'] = 'pre-vax'
+                        ms_tidy_df.loc[
+                            ms_tidy_df['visit value'] == df2[element][1], 'vaccine availability'] = 'post-vax'
+                # else:
+                # print(element)
+
+            # delta_df serves a similar purpose to ms_tidy_df but contains the data for Fig 5b
+            prevaxdf = ms_tidy_df.loc[ms_tidy_df['vaccine availability'] == 'pre-vax']
+            prevaxdf['old OD'] = prevaxdf['OD']
+            # prevaxdf.set_index(['serum type', 'antigen'], inplace=True)
+            postvaxdf = ms_tidy_df.loc[ms_tidy_df['vaccine availability'] == 'post-vax']
+            postvaxdf['new OD'] = postvaxdf['OD']
+            # prevaxdf.set_index(['serum type', 'antigen'], inplace=True)
+            delta_df = pd.merge(prevaxdf, postvaxdf, how='inner', on=['antigen', 'serum type'])
+            delta_df['delta OD'] = delta_df['new OD'] - delta_df['old OD']
+            # extract params from cat_param_df
+            sera_cat_list = cat_param_df['serum ID']
+            slice_action = cat_param_df['serum ID action']
+            split_subplots_by = cat_param_df['split subplots by']
+            hue = cat_param_df['hue']
+            # plot specific slicing
+            # cat_df = slice_df(df_norm_sub, slice_action, 'serum ID', sera_cat_list) #serum ID --> antigen
+            ms_tidy_df.loc[ms_tidy_df['serum cat'] == '[\'COVID-Vax+\']', 'vaccine availability'] = 'post-vax'
+            cat_df = slice_df(ms_tidy_df, slice_action, 'serum ID', sera_cat_list)
+            assert not cat_df.empty, 'Plotting dataframe is empty. Please check the plotting keys'
+            sns.set_context("talk")
+
+            # FIGURE 5A
+            fig_palette = ["#ee2cef", "#21e020", "#248ff9", "#e7e5e6"]
+            g = sns.catplot(x="vaccine availability", y="OD", hue=hue, col=split_subplots_by, kind="swarm",
+                            legend=False,
+                            palette=fig_palette, order=["pre-vax", "post-vax"], dodge=True, data=cat_df, col_wrap=3)
+
+            od_medians = cat_df.groupby(['antigen', 'serum cat', 'vaccine availability'])['OD'].median()
+            median_palette = ["#a318a4", "#18a418", "#185ea4"]
+            # adding median lines using boxplot functionality:
+            g.map_dataframe(sns.boxplot, x="vaccine availability", y="OD", hue=hue,
+                            # medianprops={'color': 'k', 'ls': '-', 'lw': 3},
+                            whiskerprops={'visible': False},
+                            showfliers=False, showbox=False, showcaps=False, zorder=10, order=["pre-vax", "post-vax"],
+                            palette=median_palette,
+                            dodge=0.55)  # change hue
+            # specifying colors of median lines
+            for ax in g.axes.flat:
+                for line in ax.get_lines()[2:10:2]:
+                    line.set_color(median_palette[0])
+                for line in ax.get_lines()[10::2]:
+                    line.set_color(median_palette[2])
+                for line in ax.get_lines()[1::2]:
+                    line.set_color(median_palette[1])
+            # augmenting legend to make text more readable
+            leg = plt.legend(bbox_to_anchor=(1.02, 0.5), loc='center left', borderaxespad=0, handleheight=0.05,
+                             edgecolor="#000000")
+            leg.get_texts()[0].set_text(
+                f'COVID+/Vax+ median (pre-vax -- N: {od_medians[1]:.2f}, RBD: {od_medians[6]:.2f}, Spike: {od_medians[11]:.2f};'
+                f' post-vax -- N: {od_medians[0]:.2f}, RBD: {od_medians[5]:.2f}, Spike: {od_medians[10]:.2f})')
+            leg.get_texts()[1].set_text(
+                f'COVID+/Vax- median (pre-vax -- N: {od_medians[3]:.2f}, RBD: {od_medians[8]:.2f}, Spike: {od_medians[13]:.2f};'
+                f' post-vax -- N: {od_medians[2]:.2f}, RBD: {od_medians[7]:.2f}, Spike: {od_medians[12]:.2f})')
+            leg.get_texts()[2].set_text(
+                f'COVID-/Vax+ median (pre-vax not available; post-vax -- N: {od_medians[4]:.2f}, RBD: {od_medians[9]:.2f},'
+                f' Spike: {od_medians[14]:.2f})')
+            leg.get_texts()[3].set_text('COVID+/Vax+')
+            leg.get_texts()[4].set_text('COVID+/Vax-')
+            leg.get_texts()[5].set_text('COVID-/Vax+')
+            # augment axis & tick labels
+            g.set_axis_labels("vaccine availability", "OD")
+            g.set_xticklabels(rotation=0, horizontalalignment='center')
+            # save
+            plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_{}.svg'.format(split_suffix)),
+                        dpi=300, bbox_inches='tight')
+            if cat_param_df['zoom']:
+                g.set(ylim=(-0.05, 0.4))
+                plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_zoom_{}.svg'.format(split_suffix)),
+                            dpi=300, bbox_inches='tight')
+
+            # FIGURE 5B -- PLOTTING DELTA OD
+            # future fixes can involve making the plot fxns for 5a and 5b less redundant
+            hue = 'serum cat_x'
+            g = sns.catplot(x="serum cat_x", y="delta OD", hue=hue, col=split_subplots_by, kind="swarm",
+                            palette=fig_palette,
+                            data=delta_df, col_wrap=3, legend=False)
+            g.map_dataframe(sns.boxplot, x="serum cat_x", y="delta OD",
+                            # medianprops={'color': 'k', 'ls': '-', 'lw': 2},
+                            whiskerprops={'visible': False}, showfliers=False, hue=hue,
+                            showbox=False, palette=median_palette,
+                            showcaps=False, zorder=10,
+                            dodge=False)
+
+            dod_medians = delta_df.groupby(['antigen', 'serum cat_x'])['delta OD'].median()
+
+            for ax in g.axes.flat:
+                for line in ax.get_lines()[::2]:
+                    line.set_color(median_palette[0])
+                for line in ax.get_lines()[1::2]:
+                    line.set_color(median_palette[1])
+
+            leg = plt.legend(bbox_to_anchor=(1.02, 0.5), loc='center left', borderaxespad=0, handleheight=0.05,
+                             edgecolor="#000000")  # changing the edge color to #000000 did not work but the goal is transparency
+            leg.get_texts()[0].set_text(
+                f'COVID+/Vax+ median (N: {dod_medians[0]:.2f}, RBD: {dod_medians[2]:.2f}, Spike: {dod_medians[4]:.2f})')
+            leg.get_texts()[1].set_text(
+                f'COVID+/Vax- median (N: {dod_medians[1]:.2f}, RBD: {dod_medians[3]:.2f}, Spike: {dod_medians[5]:.2f})')
+            leg.get_texts()[2].set_text('COVID+/Vax+')
+            leg.get_texts()[3].set_text('COVID+/Vax-')
+
+            g.set_axis_labels("cohort", "ΔOD")
+            ls = ['COVID+/Vax+', 'COVID+/Vax-']  # the same as hue_labels, a bit redundant
+            g.set_xticklabels(ls, rotation=0)
+
+            plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_deltaod_{}.svg'.format(split_suffix)),
+                        dpi=300, bbox_inches='tight')
+            if cat_param_df['zoom']:
+                g.set(ylim=(-0.05, 0.4))
+                plt.savefig(os.path.join(constants.RUN_PATH, 'catplot_od_zoom_{}.svg'.format(split_suffix)),
+                            dpi=300, bbox_inches='tight')
+    #%% 4PL fit
+        if not fit_param_df.empty:
+            slice_action = fit_param_df['serum ID action']
+            hue = fit_param_df['hue']
+            dilution_df = slice_df(df_norm_sub, slice_action, 'serum ID', fit_param_df['serum ID'])
+            split_subplots_by = fit_param_df['split subplots by']
+            #total_plots(dilution_df, constants.RUN_PATH, 'fit_{}'.format(split_suffix), 'png', hue=hue,
+                        #zoom=fit_param_df['zoom'], split_subplots_by=split_subplots_by, col_wrap=2)
+        plt.close('all')
+
diff --git a/interpretation/report_reader.py b/multiSero/interpretation/report_reader.py
similarity index 99%
rename from interpretation/report_reader.py
rename to multiSero/interpretation/report_reader.py
index 7aa8e4c..2ba5563 100644
--- a/interpretation/report_reader.py
+++ b/multiSero/interpretation/report_reader.py
@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 import logging
-import array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.extract.constants as constants
 
 def antigen2D_to_df1D(xlsx_path, sheet, data_col):
     """
@@ -47,7 +47,7 @@ def read_plate_info(metadata_xlsx):
                    'serum cat',
                    'secondary ID',
                    'secondary dilution',
-                   'PRNT',
+                   'visit value',
                    'sample type']
     plate_info_df = pd.DataFrame()
     # get sheet names that are available in metadata
diff --git a/interpretation/train_classifier.py b/multiSero/interpretation/train_classifier.py
similarity index 98%
rename from interpretation/train_classifier.py
rename to multiSero/interpretation/train_classifier.py
index 39d6afd..4fe34f8 100644
--- a/interpretation/train_classifier.py
+++ b/multiSero/interpretation/train_classifier.py
@@ -22,9 +22,9 @@
 import pandas as pd
 import unicodedata
 import xgboost as xgb
-import array_analyzer.utils.io_utils as io_utils
-from interpretation.plotting import get_roc_df, roc_plot_grid, thr_plot_grid
-from interpretation.report_reader import slice_df, normalize_od, offset_od
+import multiSero.array_analyzer.utils.io_utils as io_utils
+from multiSero.plotting.plotting import get_roc_df, roc_plot_grid, thr_plot_grid
+from multiSero.interpretation.report_reader import slice_df, normalize_od, offset_od
 from sklearn import metrics
 from sklearn.model_selection import GridSearchCV, GroupKFold, GroupShuffleSplit
 from sklearn.linear_model import LogisticRegressionCV
diff --git a/multisero.py b/multiSero/multisero.py
similarity index 93%
rename from multisero.py
rename to multiSero/multisero.py
index 12787a4..a35f8cd 100644
--- a/multisero.py
+++ b/multiSero/multisero.py
@@ -2,12 +2,12 @@
 import logging
 import os
 
-import array_analyzer.extract.constants as constants
-import array_analyzer.utils.io_utils as io_utils
-import array_analyzer.workflows.registration_workflow as registration_wf
-import array_analyzer.workflows.interpolation_wf as interpolation_wf
-import array_analyzer.workflows.well_wf as well_wf
-import interpretation.od_analyzer as od_analyzer
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.utils.io_utils as io_utils
+import multiSero.array_analyzer.workflows.registration_workflow as registration_wf
+from multiSero import array_analyzer as interpolation_wf
+import multiSero.array_analyzer.workflows.well_wf as well_wf
+import multiSero.interpretation.od_analyzer as od_analyzer
 import matplotlib
 matplotlib.use('Agg')
 
diff --git a/multiSero/plotting/__init__.py b/multiSero/plotting/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/interpretation/plotting.py b/multiSero/plotting/plotting.py
similarity index 100%
rename from interpretation/plotting.py
rename to multiSero/plotting/plotting.py
diff --git a/tests/extract/test_metadata.py b/tests/extract/test_metadata.py
index 96dd95d..9559ff2 100644
--- a/tests/extract/test_metadata.py
+++ b/tests/extract/test_metadata.py
@@ -1,7 +1,7 @@
 import pytest
 
-from array_analyzer.extract.metadata import MetaData
-import array_analyzer.extract.constants as constants
+from multiSero.array_analyzer.extract.metadata import MetaData
+import multiSero.array_analyzer.extract.constants as constants
 
 """
 Tests to add:
diff --git a/tests/load/report_test.py b/tests/load/report_test.py
index c47bf79..d6d50a6 100644
--- a/tests/load/report_test.py
+++ b/tests/load/report_test.py
@@ -4,8 +4,8 @@
 import pandas as pd
 import pytest
 
-import array_analyzer.extract.constants as constants
-import array_analyzer.load.report as report
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.load.report as report
 
 
 @pytest.fixture
diff --git a/tests/pysero_test.py b/tests/pysero_test.py
index f223525..a720524 100644
--- a/tests/pysero_test.py
+++ b/tests/pysero_test.py
@@ -3,7 +3,7 @@
 import pytest
 from unittest.mock import patch
 
-import multisero as multisero
+from multiSero import multisero as multisero
 
 
 def test_parse_args():
diff --git a/tests/test_debug_plots.py b/tests/test_debug_plots.py
index c4d6417..fedafa1 100644
--- a/tests/test_debug_plots.py
+++ b/tests/test_debug_plots.py
@@ -1,9 +1,3 @@
-import pytest
-
-import array_analyzer.load as load
-import os
-
-
 def test_nonetype_integration():
     pass
 
diff --git a/tests/transform/point_registration_test.py b/tests/transform/point_registration_test.py
index 0dd98b1..7d1426a 100644
--- a/tests/transform/point_registration_test.py
+++ b/tests/transform/point_registration_test.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
-import array_analyzer.extract.constants as constants
-import array_analyzer.transform.point_registration as registration
+import multiSero.array_analyzer.extract.constants as constants
+import multiSero.array_analyzer.transform.point_registration as registration
 
 
 @pytest.fixture
diff --git a/tests/utils/io_utils_test.py b/tests/utils/io_utils_test.py
index 15a40ce..9e42b5e 100644
--- a/tests/utils/io_utils_test.py
+++ b/tests/utils/io_utils_test.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 import pytest
-import array_analyzer.utils.io_utils as io_utils
+import multiSero.array_analyzer.utils.io_utils as io_utils
 
 
 def test_read_gray_im(image_dir):
diff --git a/tests/utils/spot_regionprop_test.py b/tests/utils/spot_regionprop_test.py
index 667f401..5e4abe1 100644
--- a/tests/utils/spot_regionprop_test.py
+++ b/tests/utils/spot_regionprop_test.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-import array_analyzer.utils.spot_regionprop as regionprop
+import multiSero.array_analyzer.utils.spot_regionprop as regionprop
 
 
 @pytest.fixture