this is a random set that mostly doesn't work

.gitignore

@@ -12,3 +12,6 @@
 *.pyc
 *.egg-info
 .pytest_cache
+
+venv
+.venv

examples.py

@@ -0,0 +1,60 @@
+import glob as glob
+import os as os
+
+
+class Rectangle:
+    def __init__(self, width, height):
+        self.width = width
+        self.height = height
+
+    def get_area(self):
+        return self.width * self.height
+
+class Circle:
+    def __init__(self, radius):
+        self.radius = radius
+
+    def get_area(self):
+        #typically top of the file, doing it this way can increase speed though
+        import math
+
+        return math.pi * self.radius * self.radius
+
+#-----------------------------------------
+#running code
+#-----------------------------------------
+my_circle = Circle(10)
+my_rectangle = Rectangle(5,3)
+
+my_shapes = [my_circle, my_rectangle]
+#get_area() exists in both classes and can both be called. Is polymorphism
+total_area = sum(shape.get_area() for shape in my_shapes)
+#-----------------------------------------
+class JSONDataSource:
+    def __init__(self, dir_path):
+        self.dir_path = dir_path
+
+    def load_inflammation_data(self):
+        from inflammation import models
+
+        data_file_paths = glob.glob(os.path.join(self.dir_path, 'inflammation*.csv'))
+
+        if len(data_file_paths) == 0:
+            raise ValueError(f"No inflammation data CSV files found in path {self.dir_path}")
+        data_loc = map(models.load_csv, data_file_paths)
+        return list(data_loc)
+
+#-----------------------------------------
+#running code
+#-----------------------------------------
+"""
+_, extension = os.path.splitext(infiles[0])
+if extension == '.json':
+  data_source = JSONDataSource(os.path.dirname(infiles[0]))
+elif extension == '.csv':
+  data_source = CSVDataSource(os.path.dirname(infiles[0]))
+else:
+  raise ValueError(f'Unsupported data file format: {extension}')
+analyse_data(data_source)
+"""
+#-----------------------------------------

inflammation-analysis.py

@@ -2,8 +2,10 @@
 """Software for managing and analysing patients' inflammation data in our imaginary hospital."""
 
 import argparse
+import os
 
 from inflammation import models, views
+from inflammation.compute_data import analyse_data
 
 
 def main(args):
@@ -13,18 +15,27 @@ def main(args):
     - selecting the necessary models and views for the current task
     - passing data between models and views
     """
-    InFiles = args.infiles
-    if not isinstance(InFiles, list):
-        InFiles = [args.infiles]
+    infiles = args.infiles
+    if not isinstance(infiles, list):
+        infiles = [args.infiles]
 
 
-    for filename in InFiles:
+    if args.full_data_analysis:
+        analyse_data(os.path.dirname(infiles[0]))
+        return
+
+    for filename in infiles:
         inflammation_data = models.load_csv(filename)
 
-        view_data = {'average': models.daily_mean(inflammation_data), 'max': models.daily_max(inflammation_data), 'min': models.daily_min(inflammation_data)}
+        view_data = {
+            'average': models.daily_mean(inflammation_data),
+            'max': models.daily_max(inflammation_data),
+            'min': models.daily_min(inflammation_data)
+        }
 
         views.visualize(view_data)
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description='A basic patient inflammation data management system')
@@ -34,6 +45,11 @@ def main(args):
         nargs='+',
         help='Input CSV(s) containing inflammation series for each patient')
 
+    parser.add_argument(
+        '--full-data-analysis',
+        action='store_true',
+        dest='full_data_analysis')
+
     args = parser.parse_args()
 
     main(args)

inflammation/compute_data.py

@@ -0,0 +1,44 @@
+"""Module containing mechanism for calculating standard deviation between datasets.
+"""
+
+import glob
+import os
+import numpy as np
+
+from inflammation import models, views
+"""Calculates the standard deviation by day between datasets."""
+
+"""Gets all the inflammation data from CSV files within a directory"""
+class CSVDataSource:
+
+    def __init__(self, dir_path):
+        self.dir_path = dir_path
+
+    def load_inflammation_data(self):
+        data_file_paths = glob.glob(os.path.join(self.dir_path, 'inflammation*.csv'))
+
+        if len(data_file_paths) == 0:
+            raise ValueError(f"No inflammation data CSV files found in path {self.dir_path}")
+        data_loc = map(models.load_csv, data_file_paths)
+        return list(data_loc)
+
+"""
+works out the mean inflammation value for each day across all datasets,
+then plots the graphs of standard deviation of these means.
+"""
+
+def analyse_data(data):
+    means_by_day = map(models.daily_mean, data)
+    means_by_day_matrix = np.stack(list(means_by_day))
+
+    daily_standard_deviation = np.std(means_by_day_matrix, axis=0)
+
+    graph_data = {
+        'standard deviation by day': daily_standard_deviation,
+    }
+    views.visualize(graph_data)
+
+data_source =  CSVDataSource(os.path.dirname(infiles[0]))
+analyse_data(data_source)
+
+

inflammation/models.py

@@ -32,3 +32,7 @@ def daily_min(data):
     """Calculate the daily min of a 2d inflammation data array."""
     return np.min(data, axis=0)
 
+def analyse_data(data_source):
+    from compute_data import CSVDataSource
+
+    data = data_source.load

tests/test_compute_data.py

@@ -0,0 +1,26 @@
+from unittest.mock import Mock
+import glob
+import os
+from inflammation import models
+
+def test_computer_data_mock_source():
+    from inflammation.compute_data import analyse_data
+    data_source = Mock()
+
+    #TODO - create mock data to pass---------------
+
+    data_dir = "data/" #could have a different folder of mock data
+    data_file_paths = glob.glob(os.path.join(data_dir, 'inflammation*.csv'))
+
+    if len(data_file_paths) == 0:
+        raise ValueError(f"No inflammation data CSV files found in path {self.dir_path}")
+
+    data_loc = map(models.load_csv, data_file_paths)
+    mock_data = list(data_loc)
+
+    data_source.method_to_mock.return_value = mock_data
+
+    #----------------------------------------------
+
+
+    #TODO