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Vasudevan, Rama K.
pycroscopy
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eb4bf3c0
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eb4bf3c0
authored
7 years ago
by
Somnath, Suhas
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GitHub
7 years ago
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New features
------------
Core development
~~~~~~~~~~~~~~~~
* Data Generators
* Parallel framework for Processing - Should be similar to Optimize from Analysis.
External user contributions
~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Li Xin classification code
* Ondrej’s atom finding code – written but needs work before fully integrated
* Nina’s processing code (Tselev) – in progress
* Sabine's cKPFM code
* Josh Agar's convex hull
* Other workflows/functions that already exist as scripts or notebooks
Plotting updates
----------------
* Switch to using plot.ly and dash for interactive elements
* Possibly use MayaVi for 3d plotting
* Switch plot_map_stack order to (layer, x, y) from (x, y, layer)
Examples / Tutorials
--------------------
Short notebooks / tutorials on how to use pycroscopy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Access h5 files
* Find a specific dataset/group in the file
* Select data within a dataset in various ways
* Specific function examples as needed
* Writing data back to h5 - both creating a quick translator AND maing a quick analysis / processing routine on the fly
* Use the STS dataset as an example (simple and small 3D data)
* micro datasets / microdata groups
* position and spectroscopic datasets
* chunking the main dataset
* ioHDF5
* linking as main etc.
* Setting up interactive visualizers
Longer examples (probably scientific workflows / pipelines)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Ttranslators – probably only one needed
* Processing
* Clustering
* Decomposition
* Analysis
* How to use Optimize
* Vis
Documentation
-------------
* Switch to using Sphinx-gallery for documentation
* Done for existing documentation
* Work will be needed after examples are done
* Host docs somewhere other than readthedocs - On github?
* Include examples in documentation
Formatting changes
------------------
* Fix remaining PEP8 problems
* Ensure code and documentation is standardized
* Switch to standard version formatting
* Classes and major Functions should check to see if the results already exist
Notebooks
---------
* Add new workflows
* Direct downloading of notebooks (ipynb an html)
* nbviewer?
* Host somewhere other than github?
* Investigate using Jupyter hub and Jupyter lab
Testing
-------
* Write test code
* Unit tests for simple functions
* Longer tests using data (real or generated) for the workflow tests
* Use Travis-CI for automatic testing, document generation, versioning, uploading, etc.
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