Example Workflow for Tension Test
=================================

Introduction
------------

This document outlines the workflow for the initial version of the DIC analysis which 
used three .py files to perform the analysis. 

- **main_imada.py:** This file is used to perform the DIC analysis and obtain the strain curve.
- **post_1_obtain_dic_strain.py:** This file is used to obtain the strain curve from the DIC analysis.
- **post_2_merge_dic_ut.py:** This file is used to merge the DIC and UT data.

This analysis was tested for a tensile test using the Imada testing system. 
The process involves image processing, data 
extraction, and integration with digital image correlation (DIC) 
techniques to provide comprehensive analysis results.

This should be considered obsolete for production purposes and is only included for reference 
and for developers that will use the scripts for further development of the method. 


Workflow
--------

1. **Image Preparation:** Crop and rotate images to standardize the field 
of view and ensure uniformity across all samples.
2. **Parameter Adjustment:** Modify the JSON file to update 
or adjust testing parameters as required.
3. **Meta-Information File:** Create a "meta-info.txt" file to log metadata associated with the testing process.
4. **DIC Process:** Implement the Digital Image Correlation process to analyze image data.
5. **Data Extraction:** Extract the relevant data from the "result.dic" file for further analysis.
6. **Data Integration:** Merge the data obtained from DIC with the data from the Imada testing machine for a comprehensive analysis.

Additional Information: Video Preparation Using Openshot
--------------------------------------------------------

For visual representation or detailed analysis, the image sequences can be compiled into a video. Openshot, a versatile video editing software, can be utilized for this purpose by following these steps:

- **Importing Images:** Import the file into the project as an image sequence.
- **Sequence Arrangement:** Add the image sequence onto a track in the Openshot timeline.
- **Video Export:** Export the compiled sequence as a video file in the desired format.

Workflow Diagram
----------------

The following Mermaid diagram illustrates the current DIC Analysis procedure:

.. mermaid::

    flowchart TD 
        camImages[("Labview Output<br> Camera Images")]
        resultDIC[("result.dic")]
        LABVIEW_OUTPUT[("Labview Output<br> _image_times.txt")]
        meta_file[("_meta-data.txt")]
        INFILE_DIC[("myexcel.xlsx")]
        UTFILE[("IMADA.csv")]
        RESULT_XLSX_FNAME[("total_data.xlsx")]

        DICAnalysis --> Part1
        Part1 -->Part2

        LABVIEW_OUTPUT --> convert_to_meta -->meta_file
        camImages --> pydic.init
        pydic.init --> resultDIC
        resultDIC --> pydic.read_dic_file 
        meta_file-->df_img_meta

        df_dic_tot-->INFILE_DIC
        INFILE_DIC-->df_dico
        UTFILE -->read_imada_csv

        df_fin --> RESULT_XLSX_FNAME
        subgraph DICAnalysis ["main_imada.py"]
            pydic.init 
            convert_to_meta["Convert to Meta"]
        end

        subgraph Part1 ["post_1_obtain_dic_strain.py"]
            pydic.read_dic_file -->gridlist
            gridlist[("grid_listres")] --> obtainStrainCurve-->df_dic[("df_dic")] -->df_dic_tot[("df_dic_tot")]
            df_img_meta[("df_img_meta")] -->df_dic_tot
        end

        subgraph Part2 ["post_2_merge_dic_ut.py"]
            read_imada_csv["read_imada_csv()"] -->df_decimated[("df_decimated")]
            df_dico[("df_dico")]
            df_dico --> plotsyncedgraph["plot_synced_graph()<br>sync time vectors"]
            df_decimated --> plotsyncedgraph
            plotsyncedgraph --> df_dicrs[("DIC df with common time")]
            plotsyncedgraph --> df_utrs[("Imada df with common time")]
            df_dicrs-->df_fin[("DIC and UT <br> merged df <br> with common time")]
            df_utrs-->df_fin
        end