gwaihir 0.0.5

Python package for BCDI data analysis

Welcome

Contact : [e-mail]

You can install gwaihir via the setup.py script (pip install .)

Gwaihir is also avaible on pypi.org, each new stable version from the master branch is uploaded: https://pypi.org/project/gwaihir/ On the contrary, if you follow the github changes on the you will have the latest updates.

Here is a link to a poster that tries to present Gwaihir: Poster_Gwaihir.pdf

And to the paper

To increase the width of the cells in Jupyter Notebook:

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:75% !important; }</style>"))

To avoid automatic cell scrolling:

%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
    return false;
}

GUI Preview:

Pre-processing data

https://user-images.githubusercontent.com/51970962/154160601-f3e7878a-d2c6-4560-95e5-adf7087f59ab.mp4

Phase retrieval

https://user-images.githubusercontent.com/51970962/154160830-f3c6460b-14e5-4bcc-99f5-e8691278a4e9.mp4

Data plotting

https://user-images.githubusercontent.com/51970962/154160549-c5caea1b-afa0-4a29-a5a8-aff8a1a5158b.mp4

Post-processing

https://user-images.githubusercontent.com/51970962/154236802-24643473-1ee9-4d01-823c-beca07ea1c58.mp4

Facet analysis

No video yet.

CXI file

An example file can be downloaded at: https://www.dsimonne.eu/Attachments/align_031968.cxi

Clusters at ESRF

Gwaihir only works on slurm, while using the p9 GPUs, for phase retrieval.

if you want to use it for data analysis, you can install gwaihir and bcdi on rnice.

SLURM

How to access: ssh -X <login>@slurm-nice-devel

Ask for a GPU: srun -N 1 --partition=p9gpu --gres=gpu:1 --time=06:00:00 --pty bash

Environments on slurm

• /usr/bin/python3: your personal environemnt
• p9.dev : optimised for BCDI, gwaihir and PyNX, development version, source /data/id01/inhouse/david/p9.dev/bin/activate
• p9.stable : optimised for BCDI, gwaihir and PyNX, stable version, source /data/id01/inhouse/david/p9.stable/bin/activate
• p9.pynx-devel : pynx only, frequently updated : source /sware/exp/pynx/devel.p9/bin/activate

You are not allowed to modify these environments but you can link a kernel if you wish to use them in jupyter.

To do so:

• Source the environment; e.g. source /data/id01/inhouse/david/p9.dev/bin/activate
• Make sure that:
  • you are on slurm
  • you requested a GPU
• Create the kernel:
  • python3 -m ipykernel install --user --name p9.stable
• Documentation

Once you feel confident, you should create your own environment, to avoid sudden updates that may impact your work!

To list the kernels you have installed: jupyter kernelspec list

And to remove them: jupyter kernelspec uninstall <kernelname>

Connect with ssh without using password (mandatory for batch jobs)

• Login into slurm (make sure that you asked for a GPU)
• Open a terminal (new -> terminal)

Enter the following commands (replace <username> with your username, for me it is simonne)

• cd
• ssh-keygen -t rsa (press enter when prompted, ~ 3 times)
• ssh <username>@slurm-nice-devel mkdir -p .ssh
• cat .ssh/id_rsa.pub | ssh <username>@slurm-nice-devel 'cat >> .ssh/authorized_keys'

You should not need a password anymore when login into slurm, make sure it is the case by typing

• ssh <username>@slurm-nice-devel

CLuster at SOLEIL

GRADES

To analyse data recorded at SOLEIL from your personal computer, you can use Jupyter Notebook via GRADES. The documentation if here (accessible from SOLEIL) : http://confluence.synchrotron-soleil.fr/display/EG/Service%3A+Jupyter+Notebook

Use this link to open Jupyter Notebook

http://grades-01.synchrotron-soleil.fr/notebook/

SixS

A GPU is installed on sixs3, a computer available on the beamline, for phase retrieval.

Please respect the following steps:

• Make sure that you are logged in as com-sixs
• Activate the environment source_py3.9 or source /home/experiences/sixs/simonne/Documents/py39-env/bin/activate, this environment is protected and you cannot modify it.
• Launch jupyter notebook
• Go to the test_data folder and then choose the beamline you want to test
• Follow the instructions in the notebook

Cristal

A GPU is installed on cristal4, a computer available on the beamline, for phase retrieval.

Please respect the following steps:

• Make sure that you are logged in as com-cristal
• Activate the environment source_gwaihir or source /home/experiences/crystal/com-cristal/PackagesGwaihir/py-gwaihir/bin/activate, this environment is protected and you cannot modify it.
• Launch jupyter notebook
• Go to the test_data folder and then choose the beamline you want to test
• Follow the instructions in the notebook

Installing different packages yourself

• First, I advise you to create a /Packages directory to keep these.
• Secondly, I advise you to create a virtual environment to help with debogging, and so that once everything works, you don't update a package by mistake. To do so please follow the following steps:

Create a virtual environment

• mkdir py38-env
• cd py38-env/
• python3.8 -m venv .
• source bin/activate # To activate the environment
• Make sure wheel is installed: pip install wheel

Then you should create an alias such as: alias source_p9="source /home/user/py38-env/bin/activate"

1) Install PyNX

• cd /Packages
• mkdir PyNX_install
• cd PyNX_install/
• curl -O http://ftp.esrf.fr/pub/scisoft/PyNX/pynx-devel-nightly.tar.bz2 # Installation details within install-pynx-venv.sh
• source_p9
• pip install pynx-devel-nightly.tar.bz2[cuda,gui,mpi] # Install with extras cuda, mpi, cdi
• cite PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators is out: J. Appl. Cryst. 53 (2020), 1404, also available as arXiv:2008.11511

2) Install gwaihir

• cd /Packages
• git clone https://github.com/DSimonne/gwaihir.git
• cd gwaihir
• source_p9
• pip install .
• cite <>

3) Install bcdi

• cd /Packages
• git clone https://github.com/carnisj/bcdi.git
• cd bcdi
• source_p9
• pip install .
• cite DOI: 10.5281/zenodo.3257616
• If vtk does not install (on slurm), you can type : pip install --trusted-host www.silx.org --find-links http://www.silx.org/pub/wheelhouse vtk, you may also need to remove the version requirements in bcdi/setup.py

4) Install facet-analyser (Debian 11 only)

• Send a thank you email to Fred Picca =D
• cd /Packages
• git clone https://salsa.debian.org/science-team/facet-analyser.git
• cd facet-analyser
• git checkout
• sudo mk-build-deps -i
• Make sure that you have qt installed, for me I had to install libqt5opengl5-dev (debian-testing)
• debuild -b
• if the package creation fail, try to ignore the test in /debian/rules (line 19)
• sudo debi
• The package is now installed. You can check the locations of its files with the command dpkg -L facet-analyser
• You should see a file named /usr/lib/x86_64-linux-gnu/paraview-5.9/plugins/FacetAnalyser/FacetAnalyser.so
• Now launch /usr/bin/paraview (if not installed yet, good luck, refer to https://www.paraview.org/Wiki/ParaView:Build_And_Install#Installing)
• In paraview, go to Tools > Manage Plugins > Load New
• Here type the path to the plugin that was printed with the dpkg -L facet-analyser command.
• Feel free to add it to /usr/bin/plugin so that it is loaded automatically.
• cite Grothausmann, R. (2015). Facet Analyser : ParaView plugin for automated facet detection and measurement of interplanar angles of tomographic objects. March.

To go further ...

Using Gwaihir only as a plotting tool in Jupyter Notebook

Quick navigation between vtk files in the GUI (outdated)

It is possible to automate the navigation in the GUI !

Here I have a pandas DataFrame that contains data about my scans, I use to automate the navigation:

import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob

df = pd.read_csv("reconstructions/scans_data.csv")

GUI.tab_facet.children[3].value = False
GUI.window.selected_index = 0
time.sleep(1)

GUI._list_widgets_init_dir.children[7].value = False
time.sleep(1)

scan = 3600
row = df[df.scan == scan]

particle = row.particle.values[0]
temp = row.temp_given.values[0]
condition = row.condition.values[0]

GUI._list_widgets_init_dir.children[2].value = scan
GUI._list_widgets_init_dir.children[
    3].value = f"/data/id01/inhouse/david/SIXS_June_2021/reconstructions/{temp}/{condition}/{particle}/S{scan}/data/"
GUI._list_widgets_init_dir.children[
    4].value = f"/data/id01/inhouse/david/SIXS_June_2021/reconstructions/{temp}/{condition}/{particle}/"
time.sleep(1)
GUI._list_widgets_init_dir.children[7].value = True

time.sleep(1)

GUI.window.selected_index = 9

GUI.tab_facet.children[3].value = "load_csv"