v0.2: structure manipulation (#5)

* Initial commit for the baseline package

This package includes the code that was used in the publication of :
L. P. René de Cotret and B. J. Siwick, A general method for
baseline-removal in ultrafast electron powder diffraction data using the
dual-tree complex wavelet transform, Struct. Dyn. 4 (2016)

* Initial documentation for the baseline package

* Removed links to PyUp and saythanks

* Added installation of NumPy before setup.py can be run on Appveyor

* Revert "Added installation of NumPy before setup.py can be run on Appveyor"

This reverts commit dd4ab54.

* FIX: appveyor builds failing fue to numpy not being

* Removed TOX dependency in Appveyor CI

* FIX: Install numpy & friends from conda on appveyor CI

* FIX: remove optional installation of conda

* FIX: typo in appveyor.yml

* FIX: Escape characters

* FIX: Yet another typo

* f

* Installation of skimage through conda

* UTF-8 encoding

* appveyor.yml typo

* build_sphinx command

* Not installign the right python version

* f

* 23rd time's the charm

* Refactoring of the baseline package

Iterative baseline functions are pretty similar, and they now share a
common implementation for the 1D case.

* Documentation setup based on SHAMPOO

* DOC: updated documentation to reflect the pywavelets dependency

* Sphinx RTD theme added

* Appveyor testing via unittest

* FIX: appveyor.yml typo

* FIX: Python 3.5 install from Miniconda3s

* FIX: Appveyor.yml typo

* CI overhaul

- Removed travis CI
- switched Appveyor testing to a modified version of Astropy's
CI-Helpers

* FIX: appveyor os not found

* FIX: code climate and readme

* Angular average and tutorial (#3)

* Continuous integration and documentation (#2)

* Initial commit for the baseline package

This package includes the code that was used in the publication of :
L. P. René de Cotret and B. J. Siwick, A general method for
baseline-removal in ultrafast electron powder diffraction data using the
dual-tree complex wavelet transform, Struct. Dyn. 4 (2016)

* Initial documentation for the baseline package

* Removed links to PyUp and saythanks

* Added installation of NumPy before setup.py can be run on Appveyor

* Revert "Added installation of NumPy before setup.py can be run on Appveyor"

This reverts commit dd4ab54.

* FIX: appveyor builds failing fue to numpy not being

* Removed TOX dependency in Appveyor CI

* FIX: Install numpy & friends from conda on appveyor CI

* FIX: remove optional installation of conda

* FIX: typo in appveyor.yml

* FIX: Escape characters

* FIX: Yet another typo

* f

* Installation of skimage through conda

* UTF-8 encoding

* appveyor.yml typo

* build_sphinx command

* Not installign the right python version

* f

* 23rd time's the charm

* Refactoring of the baseline package

Iterative baseline functions are pretty similar, and they now share a
common implementation for the 1D case.

* Documentation setup based on SHAMPOO

* DOC: updated documentation to reflect the pywavelets dependency

* Sphinx RTD theme added

* Appveyor testing via unittest

* FIX: appveyor.yml typo

* FIX: Python 3.5 install from Miniconda3s

* FIX: Appveyor.yml typo

* CI overhaul

- Removed travis CI
- switched Appveyor testing to a modified version of Astropy's
CI-Helpers

* FIX: appveyor os not found

* FIX: code climate and readme

* Initial commit

* pseudo-voigt and friends

* DOC: angular_average tutorial

* DOC: References

* DOC: baseline tutorial

* API: angular_average returns intensity and radius

* DOC: angular_average tutorial

* Plot utils: spectrum (rainbow) colors

* Parallel utils: pmap

Parallel map that reduces to the use of map() for a single process.

* Structure package (#4)

* Affine transforms module

First commit for the affine transforms module, containing functions to
create affine transforms and transform points and other transforms.

* Initial commit for the core structure package

* Atom class tutorial

* structure tutorial enhancements

Added an example of crystal potential

.gitignore

@@ -9,6 +9,9 @@ __pycache__/
 # Visual studio cache
 *.vs/
 
+# Protein DataBank cache folder
+pdb_cache/
+
 # Distribution / packaging
 .Python
 env/

appveyor.yml

@@ -18,11 +18,11 @@ environment:
 
       - PYTHON_VERSION: "3.6"
         CONDA_DEPENDENCIES: "numpy scipy cython scikit-image"
-        PIP_DEPENDENCIES: "pywavelets"
+        PIP_DEPENDENCIES: "pywavelets spglib pycifrw"
 
       - PYTHON_VERSION: "3.5"
         CONDA_DEPENDENCIES: "numpy scipy cython scikit-image"
-        PIP_DEPENDENCIES: "pywavelets"
+        PIP_DEPENDENCIES: "pywavelets spglib pycifrw"
 
 matrix:
    fast_finish: true

dev-requirements.txt

@@ -3,4 +3,5 @@ nose==1.3.7
 coverage==4.3.1
 codecov==2.0.5
 Sphinx>=1.5.1
-cython>=0.25
+cython>=0.25
+matplotlib

docs/source/api.rst

@@ -4,6 +4,28 @@
 Reference/API
 *************
 
-.. automodapi:: scikit-ued
+Plot Utilities
+==============
+.. automodule:: skued.plot_utils
 
+Voigt Profile
+=============
+.. automodule:: skued.voigt
 
+Affine Transforms
+=================
+.. automodule:: skued.transformations
+
+Structure
+=========
+.. autoclass:: skued.structure.Lattice
+
+Baseline-determination
+======================
+.. autofunction:: skued.baseline.baseline_dt
+
+.. autofunction:: skued.baseline.baseline_dwt
+
+Image Analysis
+==============
+.. automodule:: skued.image_analysis.symmetry

docs/source/conf.py

@@ -37,7 +37,10 @@
               'sphinx.ext.todo',
               'sphinx.ext.intersphinx',
               'sphinx.ext.autodoc',
-			  'sphinx.ext.napoleon']
+			  'sphinx.ext.napoleon',
+			  'matplotlib.sphinxext.plot_directive']
+
+intersphinx_mapping = {'numpy': ('http://docs.scipy.org/doc/numpy/', None)}
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']

docs/source/tutorials/baseline.rst

@@ -6,28 +6,45 @@
 Baseline Tutorials
 ************************
 
-Due to the high electron cross-section, background signals (or baseline) are
-much more of a problem for electron diffraction than equivalent X-ray experiments.
-
 Contents
 ========
 
 * :ref:`dual_tree_baseline`
 
-.. _dual_tree_baseline:
-
-Iterative Baseline Determination using the Dual-Tree Complex Wavelet Transform
-==============================================================================
+Due to the high electron cross-section, background signals (or baseline) are
+much more of a problem for electron diffraction than equivalent X-ray experiments.
 
 In the case of polycrystalline samples (i.e. 1D diffraction signals),
 the definite way of removing background signals is to use an iterative
 approach based on the dual-tree complex wavelet transform.
 
 First, we load an example dataset::
-    
-    import numpy as np
-    import matplotlib.pyplot as plt
 
-    # TODO: this
+	import matplotlib.pyplot as plt
+	import numpy as np
+
+	s, intensity = np.load('powder.py')
+
+.. plot :: tutorials/plots/powder_plot.py
+
+We can add a background typical of silicon nitride substrates, as well
+as inelastic scattering effects::
+
+	from skued import gaussian
+	
+	background = 75 * np.exp(-7 * s) + 55 * np.exp(-2 * s)
+	substrate1 = 0.8 * gaussian(s, center = s.mean(), fwhm = s.mean()/4)
+	substrate2 = 0.9 * gaussian(s, center = s.mean()/2.5, fwhm = s.mean()/4)
+
+.. plot:: tutorials/plots/powder_w_background.py
+
+Scikit-ued offers two ways of removing the background.
+
+.. _dual_tree_baseline:
+
+Iterative Baseline Determination using the Dual-Tree Complex Wavelet Transform
+==============================================================================
+
+.. plot:: tutorials/plots/powder_dt_baseline_progression.py
 
 :ref:`Return to Top <baseline_tutorial>`

docs/source/tutorials/image_analysis.rst

@@ -0,0 +1,111 @@
+.. include:: ../references.txt
+
+.. _image_analysis_tutorial:
+
+***********************
+Image Analysis Tutorial
+***********************
+
+Due to the high electron cross-section, background signals (or baseline) are
+much more of a problem for electron diffraction than equivalent X-ray experiments.
+
+Contents
+========
+
+* :ref:`symmetry`
+
+.. _symmetry:
+
+Angular average of polycrystalline diffraction patterns
+=======================================================
+
+First, we create a test image::
+
+	import numpy as np
+	import matplotlib.pyplot as plt
+	from skued import gaussian
+
+	image = np.zeros( (256, 256) )
+	xc, yc = image.shape[0]/2, image.shape[1]/2	# center
+
+	extent = np.arange(0, image.shape[0])
+	xx, yy = np.meshgrid(extent, extent)
+	image += gaussian([xx, yy], center = [xc, yc], fwhm = 200)
+	image /= image.max()	# Normalize max to 1
+	image += np.random.random(size = image.shape)
+
+	plt.imshow(image)
+	plt.show()
+
+.. plot::
+
+	import numpy as np
+	import matplotlib.pyplot as plt
+	from skued import gaussian
+	image = np.zeros( (256, 256) )
+	xc, yc = image.shape[0]/2, image.shape[1]/2	# center
+	extent = np.arange(0, image.shape[0])
+	xx, yy = np.meshgrid(extent, extent)
+	image += gaussian([xx, yy], center = [xc, yc], fwhm = 100)
+	image /= image.max()
+	image += np.random.random(size = image.shape)
+	plt.imshow(image)
+	plt.show()
+
+
+... and we can easily compute an angular average::
+	
+	from skued.image_analysis import angular_average
+
+	radius, intensity = angular_average(image, (xc, yc))
+
+	plt.plot(radius, intensity)
+
+.. plot::
+
+	from skued.image_analysis import angular_average
+	from skued import gaussian
+	import numpy as np
+	import matplotlib.pyplot as plt
+	from skued import gaussian
+	image = np.zeros( (256, 256) )
+	xc, yc = image.shape[0]/2, image.shape[1]/2	# center
+	extent = np.arange(0, image.shape[0])
+	xx, yy = np.meshgrid(extent, extent)
+	image += gaussian([xx, yy], center = [xc, yc], fwhm = 200)
+	image /= image.max()
+	image += np.random.random(size = image.shape)
+	radius, intensity = angular_average(image, (xc, yc))
+	plt.plot(radius, intensity)
+	plt.show()
+
+We can also get the standard error in the mean of this average::
+
+	extras = dict()
+	radius, intensity = angular_average(image, (xc, yc), extras = extras)
+
+	plt.errorbar(radius, intensity, yerr - extras['error'])
+
+.. plot::
+
+	from skued.image_analysis import angular_average
+	from skued import gaussian
+	import numpy as np
+	import matplotlib.pyplot as plt
+	from skued import gaussian
+	image = np.zeros( (256, 256) )
+	xc, yc = image.shape[0]/2, image.shape[1]/2	# center
+	extent = np.arange(0, image.shape[0])
+	xx, yy = np.meshgrid(extent, extent)
+	image += gaussian([xx, yy], center = [xc, yc], fwhm = 200)
+	image /= image.max()
+	image += np.random.random(size = image.shape)
+	extras = dict()
+	radius, intensity = angular_average(image, (xc, yc), extras = extras)
+	plt.errorbar(radius, intensity, yerr = extras['error'])
+	plt.show()
+
+As expected, the points close to the center and very far from it display as higher
+amount of noise due to the number of points used in the averaging.
+
+:ref:`Return to Top <image_analysis_tutorial>`

docs/source/tutorials/index.rst

@@ -20,4 +20,6 @@ We currently have the following tutorials:
 .. toctree::
    :maxdepth: 1
 
+   structure
    baseline
+   image_analysis

docs/source/tutorials/plots/powder_dt_baseline_progression.py

@@ -0,0 +1,26 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from skued import gaussian
+from skued.baseline import baseline_dt
+
+s, intensity = np.load('powder.npy')
+
+# Double exponential inelastic background and substrate effects
+background = 75 * np.exp(-7 * s) + 55 * np.exp(-2 * s)
+substrate1 = 0.8 * gaussian(s, center = s.mean(), fwhm = s.mean()/4)
+substrate2 = 0.9 * gaussian(s, center = s.mean()/2.5, fwhm = s.mean()/4)
+
+signal = intensity + background + substrate1 + substrate2
+
+for l in range(5):
+	baseline = baseline_dt(signal, level = l, max_iter = 100)
+	plt.plot(s, baseline, color = 'r', label = 'Baseline level {}'.format(l))
+
+plt.plot(s, signal, 'k-', label = 'Diffraction')
+
+plt.title('Diffraction pattern of rutile VO$_2$')
+plt.xlabel('Scattering angle (1/$\AA$)')
+plt.ylabel('Diffracted intensity (counts)')
+plt.xlim([s.min(), s.max()])
+plt.ylim([0, 100])
+plt.show()

docs/source/tutorials/plots/powder_plot.py

@@ -0,0 +1,11 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+s, intensity = np.load('powder.npy')
+plt.plot(s, intensity, 'k-')
+plt.title('Background-subtracted diffraction pattern of rutile VO$_2$')
+plt.xlabel('Scattering angle (1/$\AA$)')
+plt.ylabel('Diffracted intensity (counts)')
+plt.xlim([s.min(), s.max()])
+plt.ylim([0, 20])
+plt.show()

docs/source/tutorials/plots/powder_w_background.py

@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from skued import gaussian
+
+s, intensity = np.load('powder.npy')
+
+# Double exponential inelastic background and substrate effects
+background = 75 * np.exp(-7 * s) + 55 * np.exp(-2 * s)
+substrate1 = 0.8 * gaussian(s, center = s.mean(), fwhm = s.mean()/4)
+substrate2 = 0.9 * gaussian(s, center = s.mean()/2.5, fwhm = s.mean()/4)
+
+plt.plot(s, intensity + background + substrate1 + substrate2, 'k-',
+		 label = 'Diffraction')
+plt.plot(s, background + substrate1 + substrate2, 'r-', 
+		 label = 'Baseline')
+
+plt.legend()
+
+plt.title('Diffraction pattern of rutile VO$_2$')
+plt.xlabel('Scattering angle (1/$\AA$)')
+plt.ylabel('Diffracted intensity (counts)')
+plt.xlim([s.min(), s.max()])
+plt.ylim([0, 100])
+plt.show()