Notebook name: water_intake_profile_calculator.ipynb
This notebook will calculate the water intake profile vs time of a sample.
This application is still under heavy development. The look of the UI will differ from the screenshot you can see in this tutorial and features are added on a regular basis. The tutorial will be fully rewrite once the development is done.
Here are the steps (bold for user input/manipulation)
If you need help accessing this notebook, check the How To > Start the python notebooks tutorial.
Need help using the IPTS selector?
Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.
Need help using the File Selector?
It’s possible to resize or move any of the plots.
By default, all the images are sorted using their time stamps. But in some cases (old IPTS), the time stamp may be wrong. So It’s possible to:
You can select the profile algorithm to use (to integrate over the x-axis of the region selected) using the profile algorithms available
The profile is calculated using the following method:
By default the water intake profile display the position of the “wave” as a pixel number vs the time. But it’s also possible to display this one using a real dimension (mm). To do so, just click the water intake y_axis -> distance check box and define the dimension of the pixel.
In the new version of the application, it is now possible to specify the direction of integration of the profiles. Select either y_axis or x_axis to change this direction.
It’s possible to chose between 2 different algorithms to calculate the “wave” front position.
This method is fully demonstrated in this PDF document
The signal is fitted using a modified version of the error function as shown here
You can now select a 3rd algorithm based on the following python library (changepy)
For very poor statistics data, you can rebin the data by 2, 3 or more pixels. This will decrease the resolution of the water intake peak position, but will improve its calculation by the various algorithms (sliding average, error function, …)
You can export the following data
For Advanced Users. keep reading!
If you want to play yourself with the data loaded, you can easily access all the data and metadata loaded
list_of_data = o_gui.dict_data['list_data']
list_of_files = o_gui.dict_data['list_images']
list_of_time_stamp = o_gui.dict_data['list_time_stamp'] list_of_time_stamp_user_format = o_gui.dict_data['list_time_stamp_user_format']