Commit 5c46536a authored by syz's avatar syz
Browse files

Merge branch 'cades_dev' of https://github.com/pycroscopy/pycroscopy into cades_dev_local

# Conflicts:
#	setup.py
parents bcd4b795 75b75a95
==========
pycroscopy |statusimage|
pycroscopy
==========
|statusimage|
.. |statusimage| image:: https://travis-ci.org/pycroscopy/pycroscopy.svg?branch=master
.. contents::
What is pycroscopy?
-------------------
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...)
2. provide a community-developed, open standard for data formatting
3. provide a framework for developing data analysis routines
4. significantly lower the barrier to advanced data analysis procedures by simplifying I/O, processing, visualization, etc.
To learn more about the motivation, general structure, and philosophy of pycroscopy, please read this `short introduction <https://github.com/pycroscopy/pycroscopy/blob/master/docs/pycroscopy_2017_07_11.pdf>`_.
Package Structure
-----------------
The package structure is simple, with 4 main modules:
1. **io**: Reading and writing to HDF5 files + translating data from custom & proprietary microscope formats to HDF5.
2. **processing**: multivariate statistics, machine Learning, and signal filtering.
3. **analysis**: model-dependent analysis of information.
4. **viz**: Plotting functions and interactive jupyter widgets to visualize multidimenional data
Once a user converts their microscope's data format into an HDF5 format, using some of the `Translator` classes in `io`, the user gains access to the rest of the utilities present in `pycroscopy.*`.
Please visit our `homepage <https://pycroscopy.github.io/pycroscopy/index.html>`_ for more information.
Installation
------------
Pycroscopy requires many commonly used python packages such as numpy, scipy etc. To simplify the installation process, we recommend the installation of Anaconda which contains most of the prerequisite packages as well as a development environment - Spyder.
0. Recommended - uninstall existing Python distribution(s) if installed. Restart computer afterwards.
1. Install Anaconda 4.2 (Python 3.5) 64-bit - `Mac <https://repo.continuum.io/archive/Anaconda3-4.2.0-MacOSX-x86_64.pkg>`_ / `Windows <https://repo.continuum.io/archive/Anaconda3-4.2.0-Windows-x86_64.exe>`_ / `Linux <https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh>`_
1. Recommended - uninstall existing Python distribution(s) if installed. Restart computer afterwards.
2. Install pycroscopy - Open a terminal (mac / linux) or command prompt (windows - if possible with administrator priveleges) and type:
.. code:: bash
  2. Install Anaconda 4.2 (Python 3.5) 64-bit - `Mac <https://repo.continuum.io/archive/Anaconda3-4.2.0-MacOSX-x86_64.pkg>`_ / `Windows <https://repo.continuum.io/archive/Anaconda3-4.2.0-Windows-x86_64.exe>`_ / `Linux <https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh>`_
3. Install pycroscopy - Open a terminal (mac / linux) or command prompt (windows - if possible with administrator priveleges) and type:
.. code:: bash
pip install pycroscopy
3. Enjoy pycroscopy!
4. Enjoy pycroscopy!
We recommend `HDF View <https://support.hdfgroup.org/products/java/hdfview/>`_ for exploring HDF5 files generated by and used in pycroscopy.
Updating
~~~~~~~~
If you already have pycroscopy installed and want to update to the latest version, use the following command:
......@@ -58,56 +44,7 @@ If it does not work try reinstalling the package:
pip uninstall pycroscopy
pip install pycroscopy
If you would like to quickly view HDF5 files generated by and used in pycroscopy, we recommend `HDF View <https://support.hdfgroup.org/products/java/hdfview/>`_
Compatibility
~~~~~~~~~~~~~
* Pycroscopy was initially developed in python 2 but all current / future development for pycroscopy will be on python 3.5+. Nonetheless, we will do our best to ensure continued compatibility with python 2.
* We currently do not support 32 bit architectures
Getting Started
---------------
* Follow the instructions above to install pycroscopy
* See how we use pycroscopy for our scientific research in these `jupyter notebooks <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/tree/master/jupyter_notebooks/>`_. Many of them are linked to journal publications listed below.
* Please see the official `jupyter <http://jupyter.org>`_ website for more information about notebooks and consider watching this `youtube video <https://www.youtube.com/watch?v=HW29067qVWk>`_.
* See our `examples <https://pycroscopy.github.io/pycroscopy/auto_examples/index.html>`_ to get started on using and writing your own pycroscopy functions
* Videos and other tutorials are available at the `Institute For Functional Imaging of Materials <http://ifim.ornl.gov/resources.html>`_
* For more information about our functions and classes, please see our `API <https://pycroscopy.github.io/pycroscopy/pycroscopy.html>`_
* We have many translators that transform data from popular microscope data formats to pycroscopy compatible .h5 files. We also have `tutorials to get you started on importing your data to pycroscopy <https://pycroscopy.github.io/pycroscopy/auto_examples/tutorial_01_translator.html>`_.
* Details regarding the defention, implementation, and guidelines for pycroscopy's `data format <https://pycroscopy.github.io/pycroscopy/Data_Format.html>`_ for `HDF5 <https://github.com/pycroscopy/pycroscopy/blob/master/docs/Pycroscopy_Data_Formatting.pdf>`_ are also available.
* If you are interested in contributing and are looking for topics we are / will work on, please look at our `To Do <https://github.com/pycroscopy/pycroscopy/blob/master/ToDo.rst>`_ page
Journal Papers using pycroscopy
-------------------------------
1. `Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography <https://www.nature.com/articles/srep26348>`_ by S. Jesse et al., Scientific Reports (2015); jupyter notebook `here 1 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Ptychography.ipynb>`_
 
2. `Rapid mapping of polarization switching through complete information acquisition <http://www.nature.com/articles/ncomms13290>`_ by S. Somnath et al., Nature Communications (2016); jupyter notebook `here 2 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/G_mode_filtering.ipynb>`_
 
3. `Improving superconductivity in BaFe2As2-based crystals by cobalt clustering and electronic uniformity <http://www.nature.com/articles/s41598-017-00984-1>`_ by L. Li et al., Scientific Reports (2017); jupyter notebook `here 3 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/STS_LDOS.ipynb>`_
 
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. `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. 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
--------------------------------------------------------
* Dec 2017 - Materials Research Society conference
* Oct 31 2017 @ 6:30 PM - American Vacuum Society conference; Session: SP-TuP1; poster 1641
* Aug 9 2017 @ 8:30 - 10:00 AM - Microscopy and Microanalysis conference; X40 - Tutorial session on `Large Scale Data Acquisition and Analysis for Materials Imaging and Spectroscopy <http://microscopy.org/MandM/2017/program/tutorials.cfm>`_ by S. Jesse and S. V. Kalinin
* Aug 8 2017 @ 10:45 AM - Microscopy and Microanalysis conference - poster session
* Apr 2017 - Lecture on `atom finding <https://physics.appstate.edu/events/aberration-corrected-stem-teaching-machines-and-atomic-forge>`_
* Dec 2016 - Poster + `abstract <https://mrsspring.zerista.com/poster/member/85350>`_ at the 2017 Spring Materials Research Society (MRS) conference
Contact us
----------
* We are interested in collaborating with industry members to integrate pycroscopy into instrumentation or analysis software and can help in exporting data to pycroscopy compatible .h5 files
* We can work with you to convert your file formats into pycroscopy compatible HDF5 files and help you get started with data analysis.
* Join our slack project at https://pycroscopy.slack.com to discuss about pycroscopy
* Feel free to get in touch with us at pycroscopy (at) gmail [dot] com
* If you find any bugs or if you want a feature added to pycroscopy, raise an `issue <https://github.com/pycroscopy/pycroscopy/issues>`_. You will need a free Github account to do this
......@@ -38,6 +38,10 @@ Core development
* No need to handle h5py datasets, compound datasets, complex datasets etc.
* Add features time permitting.
* EVERY process tool should implement two new features:
1. Check if the same process has been performed with the same paramters. When initializing the process, throw an exception. This is better than checking in the notebook stage.
2. (Gracefully) Abort and resume processing.
* 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
......
......@@ -406,7 +406,7 @@ from sphinx_gallery.sorting import ExampleTitleSortKey, ExplicitOrder #Can't use
# Sphinx-gallery configuration
sphinx_gallery_conf = dict(examples_dirs=['../examples',
'../examples/dev_tutorials',
'../examples/publications',
# '../examples/publications',
'../examples/user_tutorials'],
gallery_dirs=['auto_examples', 'auto_tutorials', 'auto_publications'],
within_subsection_order=ExampleTitleSortKey,
......
......@@ -3,28 +3,28 @@
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
=========================
Pycroscopy |statusimage|
=========================
==========
Pycroscopy
==========
.. |statusimage| image:: https://travis-ci.org/pycroscopy/pycroscopy.svg?branch=master
**Scientific analysis of nanoscale materials imaging data**
Scientific analysis of nanoscale materials imaging data
=======================================================
.. contents::
.. _what-label:
What?
--------------------
A suite of utilities 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.
Classes implemented here are ported to a high performance computing platform at Oak Ridge National
Laboratory (`ORNL <https://www.ornl.gov/>`_).
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.
More information on pycroscopy is available at our `project page <https://github.com/pycroscopy/pycroscopy>`_.
With pycroscopy we aim to:
1. Serve as a hub for collaboration across scientific domains (microscopists, material scientists, biologists...)
2. provide a community-developed, open standard for data formatting
3. provide a framework for developing data analysis routines
4. significantly lower the barrier to advanced data analysis procedures by simplifying I/O, processing, visualization, etc.
TL;DR? - see this `short introduction <https://github.com/pycroscopy/pycroscopy/blob/master/docs/pycroscopy_2017_07_11.pdf>`_.
To learn more about the motivation, general structure, and philosophy of pycroscopy, please read this `short introduction <https://github.com/pycroscopy/pycroscopy/blob/master/docs/pycroscopy_2017_07_11.pdf>`_.
.. _who-label:
......@@ -38,15 +38,14 @@ generate (thanks to the large CNMS users community!).
By sharing our methodology and code for analyzing materials imaging we hope that it will benefit the wider
community of materials science/physics. We also hope, quite ardently, that other materials scientists would
follow suit.
! |smilie|
.. |smilie| image:: https://raw.githubusercontent.com/pycroscopy/pycroscopy/gh-pages/images/smiley_wink.png
**The (core) pycroscopy team:**
`@nlaanait <https://github.com/nlaanait>`_ (Numan Laanait), `@ssomnath <https://github.com/ssomnath>`_
(Suhas Somnath), `@CompPhysChris <https://github.com/CompPhysChris>`_ (Chris R. Smith),
`@stephenjesse <https://github.com/stephenjesse>`_ (Stephen Jesse) and many more...
* `@ssomnath <https://github.com/ssomnath>`_ (Suhas Somnath),
* `@CompPhysChris <https://github.com/CompPhysChris>`_ (Chris R. Smith),
* `@nlaanait <https://github.com/nlaanait>`_ (Numan Laanait),
* `@stephenjesse <https://github.com/stephenjesse>`_ (Stephen Jesse)
* and many more...
.. _why-label:
......@@ -84,7 +83,8 @@ How?
-----------------
* pycroscopy uses an **instrument agnostic data structure** that facilitates the storage of data, regardless
of dimensionality (conventional 2D images to 9D multispectral SPM datasets) or instrument of origin (AFMs,
STMs, STEMs, TOF SIMS, and many more). This general defenition of data allows us to write a single and
STMs, STEMs, TOF SIMS, and many more).
* This general defenition of data allows us to write a single and
generalized version of analysis and processing functions that can be applied to any kind of data.
* The data is stored in `heirarchical
data format (HDF5) <http://extremecomputingtraining.anl.gov/files/2015/03/HDF5-Intro-aug7-130.pdf>`_
......@@ -101,8 +101,70 @@ How?
* Once a user converts their microscope's data format into a HDF5 format, by simply extending some of the
classes in \`io\`, the user gains access to the rest of the utilities present in `pycroscopy.\*`.
* (On a High Performance Computing Platform if she/he is a CNMS user! Sign up
`here <https://www.ornl.gov/facility/cnms/subpage/user-program-overview>`_!)
.. _pkgstr-label:
Package Structure
-----------------
The package structure is simple, with 4 main modules:
1. **io**: Reading and writing to HDF5 files + translating data from custom & proprietary microscope formats to HDF5.
2. **processing**: multivariate statistics, machine Learning, and signal filtering.
3. **analysis**: model-dependent analysis of information.
4. **viz**: Plotting functions and interactive jupyter widgets to visualize multidimenional data
.. _start-label:
Getting Started
---------------
* Follow the instructions on our `GitHub project page <https://github.com/pycroscopy/pycroscopy>`_ to install pycroscopy
* See how we use pycroscopy for our scientific research in these `jupyter notebooks <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/tree/master/jupyter_notebooks/>`_. Many of them are linked to journal publications listed below.
* Please see the official `jupyter <http://jupyter.org>`_ website for more information about notebooks and consider watching this `youtube video <https://www.youtube.com/watch?v=HW29067qVWk>`_.
* See our `examples <https://pycroscopy.github.io/pycroscopy/auto_examples/index.html>`_ to get started on using and writing your own pycroscopy functions
* Videos and other tutorials are available at the `Institute For Functional Imaging of Materials <http://ifim.ornl.gov/resources.html>`_
* For more information about our functions and classes, please see our `API <https://pycroscopy.github.io/pycroscopy/pycroscopy.html>`_
* We have many translators that transform data from popular microscope data formats to pycroscopy compatible .h5 files. We also have `tutorials to get you started on importing your data to pycroscopy <https://pycroscopy.github.io/pycroscopy/auto_examples/tutorial_01_translator.html>`_.
* Details regarding the defention, implementation, and guidelines for pycroscopy's `data format <https://pycroscopy.github.io/pycroscopy/Data_Format.html>`_ for `HDF5 <https://github.com/pycroscopy/pycroscopy/blob/master/docs/Pycroscopy_Data_Formatting.pdf>`_ are also available.
* If you are interested in contributing and are looking for topics we are / will work on, please look at our `To Do <https://github.com/pycroscopy/pycroscopy/blob/master/ToDo.rst>`_ page
.. _papers-label:
Journal Papers using pycroscopy
-------------------------------
1. `Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography <https://www.nature.com/articles/srep26348>`_ by S. Jesse et al., Scientific Reports (2015); jupyter notebook `here <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Ptychography.ipynb>`_
 
2. `Rapid mapping of polarization switching through complete information acquisition <http://www.nature.com/articles/ncomms13290>`_ by S. Somnath et al., Nature Communications (2016); jupyter notebook `here <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/G_mode_filtering.ipynb>`_
 
3. `Improving superconductivity in BaFe2As2-based crystals by cobalt clustering and electronic uniformity <http://www.nature.com/articles/s41598-017-00984-1>`_ by L. Li et al., Scientific Reports (2017); jupyter notebook `here <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/STS_LDOS.ipynb>`_
 
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 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/BE_Processing.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. 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....
.. _conferences-label:
International conferences and workshops using pycroscopy
--------------------------------------------------------
* Dec 2017 - Materials Research Society conference
* Oct 31 2017 @ 6:30 PM - American Vacuum Society conference; Session: SP-TuP1; poster 1641
* Aug 9 2017 @ 8:30 - 10:00 AM - Microscopy and Microanalysis conference; X40 - Tutorial session on `Large Scale Data Acquisition and Analysis for Materials Imaging and Spectroscopy <http://microscopy.org/MandM/2017/program/tutorials.cfm>`_ by S. Jesse and S. V. Kalinin
* Aug 8 2017 @ 10:45 AM - Microscopy and Microanalysis conference - poster session
* Apr 2017 - Lecture on `atom finding <https://physics.appstate.edu/events/aberration-corrected-stem-teaching-machines-and-atomic-forge>`_
* Dec 2016 - Poster + `abstract <https://mrsspring.zerista.com/poster/member/85350>`_ at the 2017 Spring Materials Research Society (MRS) conference
.. _contact-label:
Contact us
----------
* We are interested in collaborating with industry members to integrate pycroscopy into instrumentation or analysis software and can help in exporting data to pycroscopy compatible .h5 files
* We can work with you to convert your file formats into pycroscopy compatible HDF5 files and help you get started with data analysis.
* Join our slack project at https://pycroscopy.slack.com to discuss about pycroscopy
* Feel free to get in touch with us at pycroscopy (at) gmail [dot] com
* If you find any bugs or if you want a feature added to pycroscopy, raise an `issue <https://github.com/pycroscopy/pycroscopy/issues>`_. You will need a free Github account to do this
Acknowledgements
......@@ -116,7 +178,8 @@ packages:
+ `PyCharm <https://www.jetbrains.com/pycharm/>`_
+ `GitKraken <https://www.gitkraken.com/>`_
Documentation Index
===================
.. currentmodule:: index
.. autosummary::
......
==============================
Pycroscopy Developer Tutorials
==============================
\ No newline at end of file
===================
Developer Tutorials
===================
\ No newline at end of file
=========================
Pycroscopy User Tutorials
=========================
\ No newline at end of file
==============
User Tutorials
==============
\ No newline at end of file
version = '0.59.1'
version = '0.59.2'
date = '11/20/2017'
time = '11:30:27'
......@@ -4,8 +4,7 @@ import numpy as np
_end_tags = dict(grid=':HEADER_END:', scan='SCANIT_END', spec='[DATA]')
class NanonisFile:
class NanonisFile(object):
"""
Base class for Nanonis data files (grid, scan, point spectroscopy).
......@@ -33,6 +32,12 @@ class NanonisFile:
"""
def __init__(self, fname):
"""
Parameters
----------
fname
"""
self.datadir, self.basename = os.path.split(fname)
self.fname = fname
self.filetype = self._determine_filetype()
......@@ -170,7 +175,7 @@ class Grid(NanonisFile):
def __init__(self, fname):
_is_valid_file(fname, ext='3ds')
super(NanonisFile, self).__init__(fname)
super(Grid, self).__init__(fname)
self.header = _parse_3ds_header(self.header_raw)
self.signals = self._load_data()
self.signals['sweep_signal'] = self._derive_sweep_signal()
......@@ -296,7 +301,7 @@ class Scan(NanonisFile):
def __init__(self, fname):
_is_valid_file(fname, ext='sxm')
super(NanonisFile, self).__init__(fname)
super(Scan, self).__init__(fname)
self.header = _parse_sxm_header(self.header_raw)
# data begins with 4 byte code, add 4 bytes to offset instead
......@@ -368,7 +373,7 @@ class Spec(NanonisFile):
def __init__(self, fname):
_is_valid_file(fname, ext='dat')
super(NanonisFile, self).__init__(fname)
super(Spec, self).__init__(fname)
self.header = _parse_dat_header(self.header_raw)
self.signals = self._load_data()
......
......@@ -10,21 +10,31 @@ with open(os.path.join(here, 'README.rst')) as f:
long_description = f.read()
if on_rtd:
requirements = ['psutil', 'xlrd>=1.0.0']
requirements = ['psutil',
'xlrd>=1.0.0']
else:
requirements = ['numpy_groupies>=0.9.6', 'pyqtgraph>=0.10',
'h5py>=2.6.0', 'igor', 'matplotlib>=2.0.0',
'scikit-learn>=0.17.1', 'xlrd>=1.0.0','joblib>=0.11',
'psutil', 'scikit-image>=0.12.3', 'scipy>=0.17.1',
'numpy>=1.11.0', 'ipywidgets>=5.2.2', 'ipython>=5.1.0']
requirements = ['numpy_groupies>=0.9.6',
'pyqtgraph>=0.10',
'h5py>=2.6.0',
'igor',
'matplotlib>=2.0.0',
'scikit-learn>=0.17.1',
'xlrd>=1.0.0',
'joblib>=0.11',
'psutil',
'scikit-image>=0.12.3',
'scipy>=0.17.1',
'numpy>=1.11.0',
'ipywidgets>=5.2.2',
'ipython>=5.1.0']
setup(
name='pycroscopy',
version='0.59.2',
description='Python library for scientific analysis of microscopy data',
description='A suite of Python libraries for high performance scientific computing of microscopy data.',
long_description=long_description,
classifiers=[
'Development Status :: 3 - Alpha',
'Development Status :: 2 - Pre-Alpha',
'Environment :: Console',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
......@@ -37,23 +47,17 @@ setup(
'Programming Language :: Python :: Implementation :: CPython',
'Topic :: Scientific/Engineering :: Chemistry',
'Topic :: Scientific/Engineering :: Physics',
'Topic :: Scientific/Engineering :: Information Analysis'],
keywords=['EELS', 'STEM', 'TEM', 'XRD', 'AFM', 'SPM', 'STS', 'band excitation', 'BE', 'BEPS', 'Raman', 'NanoIR',
'ptychography', 'g-mode', 'general mode', 'electron microscopy', ' scanning probe', ' x-rays', 'probe',
'atomic force microscopy', 'SIMS', 'energy', 'spectroscopy', 'imaging', 'microscopy', 'spectra'
'characterization', 'spectrogram', 'hyperspectral', 'multidimensional', 'data format', 'universal',
'clustering', 'decomposition', 'curve fitting', 'data analysis PCA', ' SVD', ' NMF', ' DBSCAN', ' kMeans',
'machine learning', 'bayesian inference', 'fft filtering', 'signal processing', 'image cleaning',
'denoising', 'model', 'msa', 'quantification',
'png', 'tiff', 'hdf5', 'igor', 'ibw', 'dm3', 'oneview', 'KPFM', 'FORC', 'ndata',
'Asylum', 'MFP3D', 'Cypher', 'Omicron', 'Nion', 'Nanonis', 'FEI'],
'Topic :: Scientific/Engineering :: Information Analysis',
],
keywords='scientific microscopy data analysis',
packages=find_packages(exclude='tests'),
url='https://pycroscopy.github.io/pycroscopy/index.html',
url='http://github.com/pycroscopy/pycroscopy',
license='MIT',
author='S. Somnath, C. R. Smith, N. Laanait',
author_email='pycroscopy@gmail.com',
# I don't remember how to do this correctly!!!. NL
install_requires=requirements,
platforms=['Linux', 'Mac OSX', 'Windows 10/8.1/8/7'],
# package_data={'sample':['dataset_1.dat']}
test_suite='nose.collector',
tests_require='Nose',
......
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