diff --git a/spatial_access/Models.py b/spatial_access/Models.py
index 09538bf..82b3199 100644
--- a/spatial_access/Models.py
+++ b/spatial_access/Models.py
@@ -542,14 +542,14 @@ def _log_category_weight_dict(self, category_weight_dict):
         self.logger.info("Using weights: {}".format(presented_weight_dict))
 
     def calculate(self, upper_threshold, category_weight_dict=None,
-                  normalize=False, normalize_type='z_score'):
+                  normalize=False, normalize_type='minmax'):
         """
         Args:
             category_weight_dict: category_weight_dict: dictionary of {category : [numeric weights]} or None
             upper_threshold: time in seconds.
             normalize: boolean. If true, results will be normalized
                 from 0 to 100.
-            normalize_type: 'z_score'.
+            normalize_type: 'z_score', 'minmax'
         Returns: DataFrame.
         Raises:
             UnexpectedNormalizeColumnsException
@@ -630,7 +630,7 @@ def _normalize(self, column, normalize_type):
         Normalize results.
         Args:
             column: which column to normalize.
-            normalize_type: 'z-score'
+            normalize_type: 'z-score', 'minmax'
 
         Raises:
             UnexpectedEmptyColumnException
@@ -642,6 +642,12 @@ def _normalize(self, column, normalize_type):
                                               - self.model_results[column].mean()) / self.model_results[column].std()
             except ZeroDivisionError:
                 raise UnexpectedEmptyColumnException(column)
+        elif normalize_type == 'minmax':
+            try:
+                normalize_factor = self.model_results[column].max() - self.model_results[column].min()
+            except ZeroDivisionError:
+                raise RuntimeError("column max == column min ({})".format(self.model_results[column].max()))
+            self.model_results[column] = (self.model_results[column] - self.model_results[column].min()) / normalize_factor
         else:
             raise UnexpectedNormalizeTypeException(normalize_type)
 
diff --git a/spatial_access/__init__.py b/spatial_access/__init__.py
index dc8cafb..8354de2 100644
--- a/spatial_access/__init__.py
+++ b/spatial_access/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '0.1.18'
+__version__ = '1.0.0'
 __author__ = 'Logan Noel (lmnoel)'
 __url__ = 'https://github.com/GeoDaCenter/spatial_access'
 __license__ = 'GPL'
\ No newline at end of file
diff --git a/spatial_access/src/CMakeLists.txt b/spatial_access/src/CMakeLists.txt
deleted file mode 100644
index eeb4e73..0000000
--- a/spatial_access/src/CMakeLists.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-cmake_minimum_required(VERSION 3.13)
-set(CMAKE_CXX_STANDARD 11)
-set (CMAKE_CXX_FLAGS "-O3")
-#set(CMAKE_CXX_FLAGS -march=native)
-include_directories(include)
-set(SOURCE_FILES
-        Serializer.cpp
-        Matrix.cpp
-        threadUtilities.cpp
-        csvParser.cpp
-        tmxParser.cpp)
-project(matrix)
-add_executable(matrix ${SOURCE_FILES})
\ No newline at end of file
diff --git a/spatial_access/src/Matrix.cpp b/spatial_access/src/Matrix.cpp
deleted file mode 100644
index a60d66a..0000000
--- a/spatial_access/src/Matrix.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-// This is a test file used for development of the package
-// it should be removed prior to distribution
-
-#include <iostream>
-#include "transitMatrix.h"
-#include <vector>
-#include <unordered_map>
-#include <cstdlib>
-#include <chrono>
-
-int main()
-{
-    transitMatrix<unsigned long, unsigned long, unsigned int> matrix2;
-    matrix2.readCSV("ct_health.csv");
-//    matrix2.printDataFrame();
-    matrix2.getValuesBySource(17031841100, true);
-
-    return 0;
-};
-
-  
diff --git a/tests/test_Models.py b/tests/test_Models.py
index 1d599fa..da287b5 100644
--- a/tests/test_Models.py
+++ b/tests/test_Models.py
@@ -400,7 +400,8 @@ def test_16(self):
                                 'D': [4, 3, 1],
                                 'C': [1]}
         coverage_model.transit_matrix = self.mock_transit_matrix_values(coverage_model.transit_matrix)
-        coverage_model.calculate(category_weight_dict=category_weight_dict, upper_threshold=700, normalize=True)
+        coverage_model.calculate(category_weight_dict=category_weight_dict, upper_threshold=700, normalize=True,
+                                 normalize_type='z_score')
 
         assert almost_equal(coverage_model.model_results['all_categories_score'].max(), 1.336)
         assert almost_equal(coverage_model.model_results['all_categories_score'].min(), -1.336)
@@ -441,11 +442,20 @@ def test_18(self):
         category_weight_dict = {'A': [5, 4, 3, 2, 1],
                                 'D': [4, 3, 1]}
         coverage_model.transit_matrix = self.mock_transit_matrix_values(coverage_model.transit_matrix)
-        coverage_model.calculate(category_weight_dict=category_weight_dict, upper_threshold=200, normalize=['A'])
+        coverage_model.calculate(category_weight_dict=category_weight_dict, upper_threshold=200, normalize=['A'],
+                                 normalize_type='z_score')
 
         assert almost_equal(coverage_model.model_results['A_score'].max(), 2.041)
         assert almost_equal(coverage_model.model_results['A_score'].min(), -0.408)
 
+        coverage_model.calculate(category_weight_dict=category_weight_dict, upper_threshold=200, normalize=True,
+                                 normalize_type='minmax')
+        assert almost_equal(coverage_model.model_results['A_score'].max(), 1.0)
+        assert almost_equal(coverage_model.model_results['A_score'].min(), 0.0)
+        assert almost_equal(coverage_model.model_results['D_score'].max(), 1.0)
+        assert almost_equal(coverage_model.model_results['D_score'].min(), 0.0)
+
+
     def test_19(self):
         """
         Test the DestFloatingCatchmentAreaModel aggregation.