read_force_files_csv_mat.m

%% Import data from text file.
% Script for importing data from the following text file:
%
%    D:\computations_dir\cavitating_hydrofoil\cl_apha\aoa10\foam_dir\postProcessing\forces_object\0\force.dat
%
% To extend the code to different selected data or a different text file,
% generate a function instead of a script.

% Auto-generated by MATLAB on 2023/06/22 15:37:10

%% Initialize variables.
clear; clc; close all;
filename = 'D:\computations_dir\cavitating_hydrofoil\cl_apha\aoa10\foam_dir\postProcessing\forces_object\0\force.dat';
delimiter = '\t';
startRow = 5;

%% Read columns of data as strings:
% For more information, see the TEXTSCAN documentation.
formatSpec = '%s%s%s%s%[^\n\r]';

%% Open the text file.
fileID = fopen(filename,'r');

%% Read columns of data according to format string.
% This call is based on the structure of the file used to generate this
% code. If an error occurs for a different file, try regenerating the code
% from the Import Tool.
dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'HeaderLines' ,startRow-1, 'ReturnOnError', false);

%% Close the text file.
fclose(fileID);

%% Convert the contents of columns containing numeric strings to numbers.
% Replace non-numeric strings with NaN.
raw = repmat({''},length(dataArray{1}),length(dataArray)-1);
for col=1:length(dataArray)-1
    raw(1:length(dataArray{col}),col) = dataArray{col};
end
numericData = NaN(size(dataArray{1},1),size(dataArray,2));

% Converts strings in the input cell array to numbers. Replaced non-numeric
% strings with NaN.
rawData = dataArray{1};
for row=1:size(rawData, 1);
    % Create a regular expression to detect and remove non-numeric prefixes and
    % suffixes.
    regexstr = '(?<prefix>.*?)(?<numbers>([-]*(\d+[\,]*)+[\.]{0,1}\d*[eEdD]{0,1}[-+]*\d*[i]{0,1})|([-]*(\d+[\,]*)*[\.]{1,1}\d+[eEdD]{0,1}[-+]*\d*[i]{0,1}))(?<suffix>.*)';
    try
        result = regexp(rawData{row}, regexstr, 'names');
        numbers = result.numbers;
        
        % Detected commas in non-thousand locations.
        invalidThousandsSeparator = false;
        if any(numbers==',');
            thousandsRegExp = '^\d+?(\,\d{3})*\.{0,1}\d*$';
            if isempty(regexp(thousandsRegExp, ',', 'once'));
                numbers = NaN;
                invalidThousandsSeparator = true;
            end
        end
        % Convert numeric strings to numbers.
        if ~invalidThousandsSeparator;
            numbers = textscan(strrep(numbers, ',', ''), '%f');
            numericData(row, 1) = numbers{1};
            raw{row, 1} = numbers{1};
        end
    catch me
    end
end


%% Split data into numeric and cell columns.
rawNumericColumns = raw(:, 1);
rawCellColumns = raw(:, [2,3,4]);


%% Exclude rows with non-numeric cells
I = ~all(cellfun(@(x) (isnumeric(x) || islogical(x)) && ~isnan(x),rawNumericColumns),2); % Find rows with non-numeric cells
rawNumericColumns(I,:) = [];
rawCellColumns(I,:) = [];

%% Allocate imported array to column variable names
VarName1 = cell2mat(rawNumericColumns(:, 1));
VarName2 = rawCellColumns(:, 1);
VarName3 = rawCellColumns(:, 2);
VarName4 = rawCellColumns(:, 3);

for i = 1:length(VarName2)
force{i} = transpose(sscanf(VarName2{i,1},'(%f %f %f)'));
lift(i) = force{i}(1,1);
end

mean(lift(100:end))/(0.5*1000*9.144^2*0.0762)
%% Clear temporary variables
clearvars filename delimiter formatSpec fileID dataArray ans raw col numericData rawData row regexstr result numbers invalidThousandsSeparator thousandsRegExp me rawNumericColumns rawCellColumns I J K;