From a5595a34d7ac4ebc2fc2913fd033858e15d00f5b Mon Sep 17 00:00:00 2001
From: "Juhil Dungrani (dungranij)" <dungranij@coventry.ac.uk>
Date: Mon, 5 Jul 2021 16:33:28 +0100
Subject: [PATCH] Add files via upload

---
 SequentialProcessing.m | 69 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 SequentialProcessing.m

diff --git a/SequentialProcessing.m b/SequentialProcessing.m
new file mode 100644
index 0000000..d5deca7
--- /dev/null
+++ b/SequentialProcessing.m
@@ -0,0 +1,69 @@
+%% This script allows you to open and explore the data in a *.nc file
+clear all
+close all
+
+FileName = '/Users/juhildungrani/Desktop/Sem 4/Big Data/work/5011CEM2021-dungranij/Model/o3_surface_20180701000000.nc';
+
+Contents = ncinfo(FileName);
+
+Lat = ncread(FileName, 'lat'); % load the latitude locations
+Lon = ncread(FileName, 'lon'); % loadthe longitude locations
+
+%% Processing parameters provided by customer
+RadLat = 30.2016; % cluster radius value for latitude
+RadLon = 24.8032; % cluster radius value for longitude
+RadO3 = 4.2653986e-08; % cluster radius value for the ozone data
+
+%% Cycle through the hours and load all the models for each hour and record memory use
+% We use an index named 'NumHour' in our loop
+% The section 'sequential processing' will process the data location one
+% after the other, reporting on the time involved.
+
+StartLat = 1; % latitude location to start laoding
+NumLat = 400; % number of latitude locations ot load
+StartLon = 1; % longitude location to start loading
+NumLon = 700; % number of longitude locations ot load
+tic
+for NumHour = 1:25 % loop through each hour
+    fprintf('Processing hour %i\n', NumHour)
+    DataLayer = 1; % which 'layer' of the array to load the model data into
+    for idx = [1, 2, 4, 5, 6, 7, 8] % model data to load
+        % load the model data
+        HourlyData(DataLayer,:,:) = ncread(FileName, Contents.Variables(idx).Name,...
+            [StartLon, StartLat, NumHour], [NumLon, NumLat, 1]);
+        DataLayer = DataLayer + 1; % step to the next 'layer'
+    end
+    
+    % We need to prepare our data for processing. This method is defined by
+    % our customer. You are not required to understand this method, but you
+    % can ask your module leader for more information if you wish.
+    [Data2Process, LatLon] = PrepareData(HourlyData, Lat, Lon);
+    
+    %% Sequential analysis    
+    t1 = toc;
+    t2 = t1;
+    for idx = 1: size(Data2Process,1) % step through each data location to process the data
+        
+        % The analysis of the data creates an 'ensemble value' for each
+        % location. This method is defined by
+        % our customer. You are not required to understand this method, but you
+        % can ask your module leader for more information if you wish.
+        [EnsembleVector(idx, NumHour)] = EnsembleValue(Data2Process(idx,:,:,:), LatLon, RadLat, RadLon, RadO3);
+        
+        % To monitor the progress we will print out the status after every
+        % 50 processes.
+        if idx/50 == ceil( idx/50)
+            tt = toc-t2;
+            fprintf('Total %i of %i, last 50 in %.2f s  predicted time for all data %.1f s\n',...
+                idx, size(Data2Process,1), tt, size(Data2Process,1)/50*25*tt)
+            t2 = toc;
+        end
+    end
+    T2(NumHour) = toc - t1; % record the total processing time for this hour
+    fprintf('Processing hour %i - %.2f s\n\n', NumHour, sum(T2));
+    
+        
+end
+tSeq = toc;
+
+fprintf('Total time for sequential processing = %.2f s\n\n', tSeq)
\ No newline at end of file