This folder contains Jupyter notebooks to plot and manipulate data recorded at V20 in February 2018, when diffraction experiments were performed.

There are different ways to use ROOT. Installation instructions can be found here.
But here, in order to be able to use Python3 as well, we will create a conda environment using uproot.
These instructions can be used to install on MacOS, Linux and Windows. Windows' support is the reason for using uproot (version 4 at least) instead of the ROOT conda package, which is not available on Windows.

Here are the steps to follow:

• install anaconda or miniconda (see link)

• download or clone this repository from https://github.com/celinedurniak/V20Diffraction.

• unzip if you chose to download.

• in a terminal, move to where you put the folder

• create a conda environment using the environment.yml file, by typing the following line in a terminal:

  conda env create -f environment.yml
  

• activate your environment, called v20_root_env by typing in the terminal

  conda activate v20_root_env
  

The created environment also contains:

• ess, required for Wave-Frame Multiplication stitching

• Pillow required to convert datasets stored in ROOT files to tiff images,

• McStasScriptrequired to run McStas simulations from Python script or Jupyter notebook. Note that you have to configure your installation by following the instructions at link.

You can now use the Jupyter notebooks. The next step is to specify the path to the different datafiles by running make_config.py either in the notebooks folder. In the terminal where your conda environment is activated, type the following commands, if you want to use the notebooks:

cd notebooks
python makeconfig.py

You will be asked to enter different absolute paths respectively to:

• the ROOT files
• the folder containing results of the McStas simulation you want to compare the ROOT files to
• the folder to the He3 tubes ASCII files related to the same experiment as the one, when the ROOT files have been recorded.
• the fourth path has to be entered if you already have converted some of the ROOT files to .tif, .dat or .png (using plot_all_root_data.py or ROOTfile_metadata_and_plots.ipynb).

Simply press Enter if you do not have such types of files. The output is written in a dataconfig.py, which is imported in the notebooks to get access the datafiles' contents.

You can also directly create a dataconfig.py by hand. Its content is

data_root="Absolute path to ROOT data files to be specified"
data_mcstas="Absolute path to McStas output data files to be specified"
data_he3="Absolute path to He3 tubes ASCII data files to be specified"
data_extracted_from_root="Absolute path to output files from conversion of ROOT files to be specified"

If you do not have any particular type of datafiles, leave an empty quote "".

Comments:

• help about Jupyter notebook: jupyter.org

• to deactivate your environment:

  conda deactivate
  

• to remove the environmwent:

  conda remove --name v20_root_env --all
  

• another way to use ROOT and Python is to write Python scripts importing uproot and running these scripts in the activated environment created above.

  To run the Python script, in a terminal:

  * activate your conda environment (if it is not already the case)
  * move to where your Python script is located
    (*i.e.*, typing `cd path_to_where_your_python_script_is`)
  * type `python name_of_python_script.py`
  

  Note that you will have to edit the script to specify the path where the data (ROOT file, McStas output files, data from He3 tubes)

• on Linux, I had to install something else:

  conda install -c conda-forge mpich
  conda install -c anaconda gxx_linux-64
  

• to test your installation, in the activated conda environment you can run the Python script check_installation.py located in the tools folder. It will check if uproot, PIL, McStasScript and matplotlib are installed.

Content of the folder:

For different aspects of the data treatment, Jupyter notebooks are provided:

Actions performed	Jupyter notebook
run McStas simulation	Run_McStas_simulation.ipynb+
plot McStas output datafiles	Plot_McStas_output.ipynb+
plot ascii files from He3 tubes	Plot_He3_data.ipynb
plot all ROOT files	ROOTfile_metadata_and_plots.ipynb*
display metadata stored in a selected ROOT file	ROOTfile_metadata_and_plots.ipynb*
plot and compare data from He3, ROOT and McStas	Plot_ROOT_He3_McStas.ipynb+
calculate and display projection of 2D data	projection_2D.py
generate tif, dat, png from ROOT files	ROOTfile_metadata_and_plots.ipynb*
open and display tif, dat, png from ROOT files	Open_tiff_png_dat_from_ROOT.ipynb*
compare 2D data from ROOT and McStas	Compare_2D_datasets.ipynb
perform WFM stitching on ROOT and Mcstas data	WFM_Stitching_2D.ipynb
compare 1D projections from 2D ROOT and Mcstas data	Projection_2D.ipynb

Legend of the table: * : requires PIL and + : requires McstasScript

Here are short descriptions about these documents:

• Compare_2D_datasets.ipynb Notebook to compare 2D data from ROOT and McStas. The data are cropped, resampled to the coarser grid and rescaled in intensity.

• Open_tiff_png_dat_from_ROOT.ipynb Notebook to open and display .dat, .png, .tiff files of 1D and 2D datasets, originally stored in ROOT files

• Plot_He3_data.ipynb Notebook to plot ascii files from He3 tubes (after rebinning)

• Plot_McStas_output.ipynb Notebook to plot output data of McStas simulation. Two approaches are presented: - using McStasScript - using matplotlib

• Plot_ROOT_He3_McStas.ipynb Notebook to plot data from He3, ROOT files and McStas simulations. We focus on He3tube5 and Spectrum03

• Projection_2D.ipynb Notebook to compare 1D projections from 2D ROOT and McStas data. The range of y-values to generate the projections is set by the user

• ROOTfile_metadata_and_plots.ipynb Notebook to display metadata in a user-selected ROOT file and display and save all graphs stored in ROOT files

• Run_McStas_simulation.ipynb Notebook to run a McStas simulation using McStasScript functionalities

• WFM_Stitching_2D.ipynb Notebook to perform WFM stitching on ROOT and McStas data