pycroscopy is a `python <http://www.python.org/>`_ package for image processing and scientific analysis of imaging modalities such as multi-frequency scanning probe microscopy, scanning tunneling spectroscopy, x-ray diffraction microscopy, and transmission electron microscopy. pycroscopy uses a data-centric model wherein the raw data collected from the microscope, results fron analysis and processing routines are all written to standardized heirarchical data format (HDF5) files for traceability, reproducability, and prevenance.
pycroscopy is a `python <http://www.python.org/>`_ package for image processing and scientific analysis of imaging modalities such as multi-frequency scanning probe microscopy, scanning tunneling spectroscopy, x-ray diffraction microscopy, and transmission electron microscopy. pycroscopy uses a data-centric model wherein the raw data collected from the microscope, results from analysis and processing routines are all written to standardized hierarchical data format (HDF5) files for traceability, reproducibility, and provenance.
With `pycroscopy <https://pycroscopy.github.io/pycroscopy/>`_ we aim to:
1. Serve as a hub for collaboration across scientific domains (microscopists, material scientists, biologists...)
...
...
@@ -87,9 +87,13 @@ Journal Papers using pycroscopy
4. `Direct Imaging of the Relaxation of Individual Ferroelectric Interfaces in a Tensile-Strained Film <http://onlinelibrary.wiley.com/doi/10.1002/aelm.201600508/full>`_ by L. Li et al.; Advanced Electronic Materials (2017), jupyter notebook `here 4 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/BE_Processing.ipynb>`_
5. Feature extraction via similarity search: application to atom finding and denosing in electon and scanning probe microscopy imaging by S. Somnath et al.; under review at Advanced Structural and Chemical Imaging (2017), jupyter notebook `here 5 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb>`_
5. `Decoding apparent ferroelectricity in perovskite nanofibers <http://pubs.acs.org/doi/pdf/10.1021/acsami.7b14257>`_ by R. Ganeshkumar et al., ACS Applied Materials & Interfaces (2017).
6. Many more coming soon....
6. Ultrafast Current Imaging via Bayesian Inference by S. Somnath et al., accepted at Nature Communications (2017).
7. Feature extraction via similarity search: application to atom finding and denosing in electon and scanning probe microscopy imaging by S. Somnath et al.; under review at Advanced Structural and Chemical Imaging (2017), jupyter notebook `here 5 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb>`_
8. Many more coming soon....
International conferences and workshops using pycroscopy
* Generic interactive visualizer for 3 and 4D float numpy arrays.
* No need to handle h5py datasets, compound datasets, complex datasets etc.
* Add features time permitting.
* Clean up Cluser results plotting
* Consider implementing doSVD as a Process. Maybe the Decomposition and Cluster classes could extend Process?
* Simplify and demystify analyis / optimize. Use parallel_compute instead of optimize and gues_methods and fit_methods
* multi-node computing capability in parallel_compute
* Data Generators
* Consistency in the naming of and placement of attributes (chan or meas group) in all translators
GUI
~~~~~~~~~~~
* Need to be able to run a visualizer even on sliced data. What would this object be? (sliced Main data + vectors for each axis + axis labels ....). Perhaps the slice() function should return this object instead of a numpy array? As it stands, the additional information (for the visualizer) is not returned by the slice function.
* Generic visualizer in plot.lly / dash? that can use the PycroDataset class
