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Keywords: Python 

Full-field Tomography at PSI

This workflow has some details on the instrument the data is produced from (TOMCAT beamline) and the infrastructure PSI has concerning their data. If you are more interested in the science and want to reproduce the data and not bother with the surrounding details/context, please refer to the...

Keywords: synchrotron, imaging, Jupyter notebooks, Python, Pulmonary arterial hypertension

Resource type: workflow

Full-field Tomography at PSI https://pan-training.eu/materials/full-field-tomography-at-psi This workflow has some details on the instrument the data is produced from (TOMCAT beamline) and the infrastructure PSI has concerning their data. If you are more interested in the science and want to reproduce the data and not bother with the surrounding details/context, please refer to the Pulmonary arterial hypertension research workflow. Full-field Tomography at PSI Tomography datasets often present large volumes (100 GBs - few TBs) difficult to compress and transfer. The tomographic reconstruction is highly demanding on compute (GPU) and storage resources for the intermediate and/or final result. In addition, the optional image segmentation step may be demanding on computer memory. The offline analysis (after experiment) could be performed remotely by users at home making it attractive for deployment as a cloud-like use case. Finally, this technique is applied at many facilities and in different scientific domains - therefore a portable result is more useful. This entire process is illustrated with a typical experiment. synchrotron, imaging, Jupyter notebooks, Python, Pulmonary arterial hypertension research data scientist life scientists
Python Workshop (IKON21)

If you have self enrolled onto the course please wait 5 minutes before launching the JupyterHub Access the course by clicking on the JupyterHub link. Click Start My Server > Start and your container will launch. In the folder called Notebooks you will find the following: 1. Jupyter...

Keywords: data analysis library pandas, jupyter, Python, numpy, matplotlib, Ipywidgets, scipy, mcstas script

Resource type: Moodle course, e-learning

Python Workshop (IKON21) https://pan-training.eu/materials/python-workshop-ikon21 If you have self enrolled onto the course please wait 5 minutes before launching the JupyterHub Access the course by clicking on the JupyterHub link. Click Start My Server > Start and your container will launch. In the folder called Notebooks you will find the following: 1. Jupyter basics: Jupyter notebook introduction. 2. Python basics:  Basic language principles,  Sequence data types,  Control structures,  Working with functions,  Using modules,  Input and output,  Python 2 vs 3,  Python classes. 3. Using external Libraries:  Scientific libraries numpy,  Plotting with matplotlib,  Ipywidgets,  Fitting scipy,  Data analysis library pandas,  Testing. 4. Molecular visualization:  Visualization tutorial,  Atomistic simulation environment. 5. McStas script 6. SCIPP You can find a copy of the notebooks at https://github.com/ess-dmsc-dram/python-course-ikon/tree/master/notebooks This course was created by the Data Reduction, Analysis and Modelling group of the ESS. This version of the course was specifically created for IKON21 in September 2021. data analysis library pandas, jupyter, Python, numpy, matplotlib, Ipywidgets, scipy, mcstas script
Python Workshop (IKON20)

THIS IS THE PREVIOUS IKON PYTHON COURSE FROM MARCH 2021 - The new one can be found [here](https://pan-learning.org/moodle/course/view.php?id=89) AS OF 16/09/2021 THERE IS AN ISSUE WITH THE LINK - THIS WILL BE FIXED SOON This course has something for all levels of python and is split into 7...

Keywords: data analysis library pandas, jupyter, Python, numpy, matplotlib, Ipywidgets, scipy, mcstas script

Resource type: Moodle course, e-learning

Python Workshop (IKON20) https://pan-training.eu/materials/python-workshop-ikon20 THIS IS THE PREVIOUS IKON PYTHON COURSE FROM MARCH 2021 - The new one can be found [here](https://pan-learning.org/moodle/course/view.php?id=89) AS OF 16/09/2021 THERE IS AN ISSUE WITH THE LINK - THIS WILL BE FIXED SOON This course has something for all levels of python and is split into 7 sections. The first four contain a set of jupyter notebooks that are designed to be worked through individually. You are encouraged to pick and choose which sections you are interested in. The other three contain notebooks to help you learn how to use python based McStas script and SCIPP along with an extra module with an example of Covid data anlysis.  1. Jupyter basics: Jupyter notebook introduction. 2. Python basics:  Basic language principles,  Sequence data types,  Control structures,  Working with functions,  Using modules,  Input and output,  Python 2 vs 3,  Python classes. 3. Using external Libraries:  Scientific libraries numpy,  Plotting with matplotlib,  Ipywidgets,  Fitting scipy,  Data analysis library pandas,  Testing. 4. Molecular visualization:  Visualization tutorial,  Atomistic simulation environment. 5. McStas script 6. Extras 7. SCIPP The notebooks can accessed and run via the Jupyter hub link. A new window will open, which will ask you to Start My Server. You can then chose which environment you would like to use (Basic Jupyter with McStas or Jupyter with SCIPP). Note that both include all the notebooks. To access the notebooks please navigate to python-course-ikon > notebooks. This course was created by the Data Reduction, Analysis and Modelling Group of the ESS. This version of the course was specifically created for IKON20 in March 2021. data analysis library pandas, jupyter, Python, numpy, matplotlib, Ipywidgets, scipy, mcstas script
Python Laser Image Visualization

Tool showing pictures from different cameras (directories) in a grid and a stepwise counter-based scroll functionality. Most of the layout and (future) filter options are defined by command line to allow an easy integration into an workflow based on CWL, OWL (or Knime). In future developments...

Keywords: Python, Laser, Visualization, Cameras, Laser Ion Acceleration, Qt5

Resource type: software, git

Python Laser Image Visualization https://pan-training.eu/materials/python-laser-image-visualization Tool showing pictures from different cameras (directories) in a grid and a stepwise counter-based scroll functionality. Most of the layout and (future) filter options are defined by command line to allow an easy integration into an workflow based on CWL, OWL (or Knime). In future developments most of the parameters can be changed interactive and saved to a json file which can be used to describe the next workflow inputs, so that an interactive workflow development is possible. Python, Laser, Visualization, Cameras, Laser Ion Acceleration, Qt5 PaN Community Photon Community