NOTE: These instructions are no longer effective for the most recent versions of Raw2Data (versions greater than 5.x). The archiving process described in this article was written for the original version of Raw2Data that was a completely Matlab-based application. For that version of the software, parsing the raw data (.DAT) files were the most time consuming part of the conversion process and these instructions reduced that time significantly. Versions of Raw2data 5.x and newer are .NET-based applications and reading the raw data files is a relatively fast operation. Following these instructions with the latest version of raw2data will not result in a significant improvement in conversion times.
Conversion of new data with SAACR_raw2data can slow down considerably over time as historic data accumulates. The processing time for new data can be significantly improved while retaining all historic data values by creating an archive.
NOTE: You should be familiar with processing data with SAACR_raw2data before trying to follow the instructions in this guide. Please refer to the SAACR_raw2data How-To Guide for more information.
In SAASuite 3.0 or higher, the Raw2Data application is launched by clicking the Data Conversion button.
Figure 1: Raw2Data is launched by clicking the Data Conversion button
Step 1 - Create an 'archive' Folder
Raw data processed with SAACR_raw2data will produce a multi_saa_allcart.mat file in the project folder containing the logger project files. The first step to creating an archive to improve processing speed of large data sets is to create a folder named 'archive' in the project folder.
NOTE: The 'archive' folder's name should use all lowercase letters.
Figure 2: Create an 'archive' folder in the project folder
Step 2 - Move the multi_saa_allcart.mat or the multi_saa_allcart_no_adjustments.mat File to the Archive Folder and Rename It
Once the archive folder has been created, you need to move the multi_saa_allcart.mat file from the project folder to the newly created archive folder. If your project has any software adjustment configured, you need to move the multi_saa_allcart_no_adjustments.mat file into the archive folder instead.
Once moved to the archive folder, the multi_saa_allcart.mat file must be renamed to multi_saa_allcart_archive.mat. Alternatively, if your project has software adjustments applied, the multi_saa_allcart_no_adjustments.mat must be renamed to multi_saa_allcart_no_adjustments_archive.mat.
WARNING: If your project has software adjustments applied, do not move both the multi_saa_allcart.mat file and the multi_saa_allcart_no_adjustments.mat file into the archive folder. When your project has software adjustments applied, ONLY the multi_saa_allcart_no_adjustment.mat file should be placed into the archive folder and renamed to multi_saa_allcart_no_adjustments_archive.mat.
Figure 3: Rename the multi_saa_allcart.mat file to multi_saa_allcart_archive.mat
Step 3 - Backup and Then Delete the Raw Data Files From the Project Folder
From the project folder, backup all of the SAA#_DATA.dat files to another location. You should then delete the SAA#_DATA.dat files in the project folder. A new copy of these files containing only the data that is currently on the logger will be generated when your next data collection occurs. Because the newly generated SAA#_DATA.dat files will contain much less data than the previous ones, processing with SAACR_raw2data will be much faster.
NOTE: If you copied either the multi_saa_allcart.mat file or the multi_saa_allcart_no_adjustments.mat file to the archive folder instead of moving it in the previous step, you should delete it from the project folder at this time.
Figure 4: After creating the multi_saa_allcart_archive.mat file, delete the SAA#_DATA.dat and multi_saa_allcart.mat files from the project folder
After the three steps described above have been completed, the next conversion of the project data will retrieve the historical data from the archive folder and append newly collected data to it to create a new multi_saa_allcart.mat file containing all of the data. This process should be considerably faster if there was a large amount of historical data.