How to speed up this code?
Dear All,
I have this code but when I run it it takes one day and half to run. How can I speed it up?
Best regards,
Ara
clear all;
clc;
% Set the folder path where the .nc files are located
folderPath = ‘E:datapodtc_apr’;
% Get a list of all NetCDF files in the folder
fileList = dir(fullfile(folderPath, ‘*_nc’));
numFiles = numel(fileList);
% Initialize the data structures
data = cell(numFiles, 1);
dateArray = [];
typeArray = cell(numFiles, 1);
% Loop through each file
for fileIndex = 1:numFiles
% Read the NetCDF file
filePath = fullfile(folderPath, fileList(fileIndex).name);
% Read the data from the NetCDF file
ncinfo_struct = ncinfo(filePath);
if isfield(ncinfo_struct, ‘Variables’)
variable_names = {ncinfo_struct.Variables.Name};
if all(ismember({‘time’, ‘TEC’, ‘S4’, ‘RFI’, ‘elevation’, ‘occheight’, ‘caL1_SNR’, ‘pL2_SNR’, ‘x_LEO’, ‘y_LEO’, ‘z_LEO’, ‘x_GPS’, ‘y_GPS’, ‘z_GPS’}, variable_names))
% Extract the date and type information from the file name
[~, filename, ~] = fileparts(fileList(fileIndex).name);
dateStr = regexp(filename, ‘d{4}.d{3}’, ‘match’, ‘once’);
typeStr = regexp(filename, ‘Gd{2}|Rd{2}’, ‘match’, ‘once’);
% Read the data from the NetCDF file
data{fileIndex}.time = ncread(filePath, ‘time’);
data{fileIndex}.TEC = ncread(filePath, ‘TEC’);
data{fileIndex}.S4 = ncread(filePath, ‘S4’);
data{fileIndex}.RFI = ncread(filePath, ‘RFI’);
data{fileIndex}.elevation = ncread(filePath, ‘elevation’);
data{fileIndex}.occheight = ncread(filePath, ‘occheight’);
data{fileIndex}.caL1_SNR = ncread(filePath, ‘caL1_SNR’);
data{fileIndex}.pL2_SNR = ncread(filePath, ‘pL2_SNR’);
data{fileIndex}.x_LEO = ncread(filePath, ‘x_LEO’);
data{fileIndex}.y_LEO = ncread(filePath, ‘y_LEO’);
data{fileIndex}.z_LEO = ncread(filePath, ‘z_LEO’);
data{fileIndex}.x_GPS = ncread(filePath, ‘x_GPS’);
data{fileIndex}.y_GPS = ncread(filePath, ‘y_GPS’);
data{fileIndex}.z_GPS = ncread(filePath, ‘z_GPS’);
% Store the date and type information
dateArray = [dateArray, str2double(dateStr)];
typeArray{fileIndex} = typeStr;
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain all the required variables. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain the "Variables" field. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
end
% Sort the data by date and type
[sortedDates, sortedIndices] = sort(dateArray);
sortedTypes = cellfun(@(x) x, typeArray(sortedIndices), ‘UniformOutput’, false);
% Create the sorted data structures
sortedData = cell(numFiles, 1);
for i = 1:numFiles
sortedData{i} = data{sortedIndices(i)};
end
% Save the data to a .mat file
save(‘podtc_apr_data.mat’, ‘sortedData’, ‘sortedDates’, ‘sortedTypes’);Dear All,
I have this code but when I run it it takes one day and half to run. How can I speed it up?
Best regards,
Ara
clear all;
clc;
% Set the folder path where the .nc files are located
folderPath = ‘E:datapodtc_apr’;
% Get a list of all NetCDF files in the folder
fileList = dir(fullfile(folderPath, ‘*_nc’));
numFiles = numel(fileList);
% Initialize the data structures
data = cell(numFiles, 1);
dateArray = [];
typeArray = cell(numFiles, 1);
% Loop through each file
for fileIndex = 1:numFiles
% Read the NetCDF file
filePath = fullfile(folderPath, fileList(fileIndex).name);
% Read the data from the NetCDF file
ncinfo_struct = ncinfo(filePath);
if isfield(ncinfo_struct, ‘Variables’)
variable_names = {ncinfo_struct.Variables.Name};
if all(ismember({‘time’, ‘TEC’, ‘S4’, ‘RFI’, ‘elevation’, ‘occheight’, ‘caL1_SNR’, ‘pL2_SNR’, ‘x_LEO’, ‘y_LEO’, ‘z_LEO’, ‘x_GPS’, ‘y_GPS’, ‘z_GPS’}, variable_names))
% Extract the date and type information from the file name
[~, filename, ~] = fileparts(fileList(fileIndex).name);
dateStr = regexp(filename, ‘d{4}.d{3}’, ‘match’, ‘once’);
typeStr = regexp(filename, ‘Gd{2}|Rd{2}’, ‘match’, ‘once’);
% Read the data from the NetCDF file
data{fileIndex}.time = ncread(filePath, ‘time’);
data{fileIndex}.TEC = ncread(filePath, ‘TEC’);
data{fileIndex}.S4 = ncread(filePath, ‘S4’);
data{fileIndex}.RFI = ncread(filePath, ‘RFI’);
data{fileIndex}.elevation = ncread(filePath, ‘elevation’);
data{fileIndex}.occheight = ncread(filePath, ‘occheight’);
data{fileIndex}.caL1_SNR = ncread(filePath, ‘caL1_SNR’);
data{fileIndex}.pL2_SNR = ncread(filePath, ‘pL2_SNR’);
data{fileIndex}.x_LEO = ncread(filePath, ‘x_LEO’);
data{fileIndex}.y_LEO = ncread(filePath, ‘y_LEO’);
data{fileIndex}.z_LEO = ncread(filePath, ‘z_LEO’);
data{fileIndex}.x_GPS = ncread(filePath, ‘x_GPS’);
data{fileIndex}.y_GPS = ncread(filePath, ‘y_GPS’);
data{fileIndex}.z_GPS = ncread(filePath, ‘z_GPS’);
% Store the date and type information
dateArray = [dateArray, str2double(dateStr)];
typeArray{fileIndex} = typeStr;
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain all the required variables. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain the "Variables" field. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
end
% Sort the data by date and type
[sortedDates, sortedIndices] = sort(dateArray);
sortedTypes = cellfun(@(x) x, typeArray(sortedIndices), ‘UniformOutput’, false);
% Create the sorted data structures
sortedData = cell(numFiles, 1);
for i = 1:numFiles
sortedData{i} = data{sortedIndices(i)};
end
% Save the data to a .mat file
save(‘podtc_apr_data.mat’, ‘sortedData’, ‘sortedDates’, ‘sortedTypes’); Dear All,
I have this code but when I run it it takes one day and half to run. How can I speed it up?
Best regards,
Ara
clear all;
clc;
% Set the folder path where the .nc files are located
folderPath = ‘E:datapodtc_apr’;
% Get a list of all NetCDF files in the folder
fileList = dir(fullfile(folderPath, ‘*_nc’));
numFiles = numel(fileList);
% Initialize the data structures
data = cell(numFiles, 1);
dateArray = [];
typeArray = cell(numFiles, 1);
% Loop through each file
for fileIndex = 1:numFiles
% Read the NetCDF file
filePath = fullfile(folderPath, fileList(fileIndex).name);
% Read the data from the NetCDF file
ncinfo_struct = ncinfo(filePath);
if isfield(ncinfo_struct, ‘Variables’)
variable_names = {ncinfo_struct.Variables.Name};
if all(ismember({‘time’, ‘TEC’, ‘S4’, ‘RFI’, ‘elevation’, ‘occheight’, ‘caL1_SNR’, ‘pL2_SNR’, ‘x_LEO’, ‘y_LEO’, ‘z_LEO’, ‘x_GPS’, ‘y_GPS’, ‘z_GPS’}, variable_names))
% Extract the date and type information from the file name
[~, filename, ~] = fileparts(fileList(fileIndex).name);
dateStr = regexp(filename, ‘d{4}.d{3}’, ‘match’, ‘once’);
typeStr = regexp(filename, ‘Gd{2}|Rd{2}’, ‘match’, ‘once’);
% Read the data from the NetCDF file
data{fileIndex}.time = ncread(filePath, ‘time’);
data{fileIndex}.TEC = ncread(filePath, ‘TEC’);
data{fileIndex}.S4 = ncread(filePath, ‘S4’);
data{fileIndex}.RFI = ncread(filePath, ‘RFI’);
data{fileIndex}.elevation = ncread(filePath, ‘elevation’);
data{fileIndex}.occheight = ncread(filePath, ‘occheight’);
data{fileIndex}.caL1_SNR = ncread(filePath, ‘caL1_SNR’);
data{fileIndex}.pL2_SNR = ncread(filePath, ‘pL2_SNR’);
data{fileIndex}.x_LEO = ncread(filePath, ‘x_LEO’);
data{fileIndex}.y_LEO = ncread(filePath, ‘y_LEO’);
data{fileIndex}.z_LEO = ncread(filePath, ‘z_LEO’);
data{fileIndex}.x_GPS = ncread(filePath, ‘x_GPS’);
data{fileIndex}.y_GPS = ncread(filePath, ‘y_GPS’);
data{fileIndex}.z_GPS = ncread(filePath, ‘z_GPS’);
% Store the date and type information
dateArray = [dateArray, str2double(dateStr)];
typeArray{fileIndex} = typeStr;
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain all the required variables. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
else
% Skip this file and move on to the next one
fprintf(‘File "%s" does not contain the "Variables" field. Skipping this file.n’, fileList(fileIndex).name);
continue;
end
end
% Sort the data by date and type
[sortedDates, sortedIndices] = sort(dateArray);
sortedTypes = cellfun(@(x) x, typeArray(sortedIndices), ‘UniformOutput’, false);
% Create the sorted data structures
sortedData = cell(numFiles, 1);
for i = 1:numFiles
sortedData{i} = data{sortedIndices(i)};
end
% Save the data to a .mat file
save(‘podtc_apr_data.mat’, ‘sortedData’, ‘sortedDates’, ‘sortedTypes’); speed up run MATLAB Answers — New Questions
