Usage

Regular Usage

TKEanalyst requires meta data (i.e. data about your data) defined in an input workbook. Therefore, download input.xlsx) and save it on your computer. Next, with Python installed and the code living on your computer:

  • Save your data in a folder and make sure the files are named with XX_YY_ZZ_something.FILEENDING where XX, YY, and ZZ are streamwise (x), perpendicular (y), and vertical (z) coordinates in CENTIMETERS, respectively. FILEENDING could be, for example, .vna.

  • Complete the required information on the experimental setup in input.xlsx (see figure below). IMPORTANT: Never modify column A or any list in the sourcetables sheet (unless you also modify load_input_defs in line 25ff of profile_analyst.py ). The code uses the text provided in these areas of input.xlsx to identify setups. If useful, consider substituting the Wood wording in your mind and with a note in column C with your characteristic turbulence objects, but do not modify column A. Ultimately, you can also save the input file under a different name and call the code with a different input file name.

input turbulent tke experiment setup parameters

Fig. 1 The interface of the input.xlsx workbook for entering experiment parameters and specifying a despiking method.

  • Implement the following code in a Python script and run that Python script:

import TKEanalyst
input_file = r"C:\\my\\project\\adv\\input.xlsx"
TKEanalyst.process_adv_files(input_file)
  • Alternatively:

    • run the code: python profile_analyst.py "C:/dir/to/input.xlsx)

  • Wait until the code finished with -- DONE -- ALL TASKS FINISHED --

  • After a successful run, the code will have produced the following files in ...\your-data\:

    • .xlsx files of full-time series data, with spikes and despiked.

    • .xlsx files of statistic summaries (i.e., average, standard deviation std, TKE) of velocity parameters with x, y, and z positions, with spikes and despiked (see workbook example in the figure below).

    • Two plots (norm-tke-x.png and norm-tke-x-despiked.png) showing normalized TKE plotted against normalized x, with spikes and despiked, respectively (see plot example in the figure below).

example output tke-calculator
example output normalized tke plot

Usage Example

For example, consider your data lives in a folder called C:\my-project\TKEanalysis\test01. To analyze *.vna files in test01 save the following code to a Python script named tke_analysis.py along with definitions in an input.xlsx workbook :

import TKEanalyst
input_file = r"C:\\my-project\\TKEanalysis\\test01\\input.xlsx"
TKEanalyst.process_adv_files(input_file)

The definitions in the above-shown input.xlsx define x-normalization as a function of a wood log length, for example, a wood log diameter of 0.114 m.

Cell B2 containing Input folder directory in input.xlsx defines that the input data for test01.

Important

The data directory of the subfolder definition in cell B2 may not end on any \ or / . Also, make sure to use the / sign for folder name separation (do not use \).

To run the code with the example data, open Anaconda Prompt (or any other Python-able Terminal) and:

  • cd into the code directory (e.g., cd "C:\my-project\TKEanalysis\test01"

  • run the code: python tke_analysis.py

  • wait until the code finished with -- DONE -- ALL TASKS FINISHED --

  • After a successful run, the code will have produced the following files in C:\my-project\TKEanalysis\test01:

    • .xlsx files of full-time series data, with spikes and despiked.

    • .xlsx files of statistic summaries (i.e., average, standard deviation std, TKE) of velocity parameters with x, y, and z positions, with spikes and despiked.

    • Two plots (norm-tke-x.png and norm-tke-x-despiked.png) showing normalized TKE plotted against normalized x, with spikes and despiked, respectively.