Merge pull request #19 from LaurentRDC/master

Release 0.4.7

Author: LaurentRDC

.appveyor/install-miniconda.ps1

@@ -5,10 +5,6 @@
 
 $MINICONDA_URL = "https://repo.continuum.io/miniconda/"
 
-$env:ASTROPY_LTS_VERSION = "1.0"
-$env:LATEST_ASTROPY_STABLE = "1.3"
-$env:LATEST_NUMPY_STABLE = "1.12"
-
 # We pin the version for conda as it's not the most stable package from
 # release to release. Add note here if version is pinned due to a bug upstream.
 if (! $env:CONDA_VERSION) {
@@ -110,7 +106,7 @@ if (! $env:CONDA_CHANNEL_PRIORITY) {
 # key may not yet exists
 conda config  --set channel_priority $CONDA_CHANNEL_PRIORITY
 
-# Create a conda environment using the astropy bonus packages
+# Create a conda environment 
 conda create -q -n test python=$env:PYTHON_VERSION
 activate test
 

.gitignore

@@ -9,7 +9,8 @@ __pycache__/
 # Visual studio cache
 *.vs/
 
-# Protein DataBank cache folder
+# Protein DataBank files and cache folder
+*.ent
 pdb_cache/
 
 # Jupyter notebooks

README.rst

@@ -4,7 +4,7 @@ scikit-ued
 .. image:: https://img.shields.io/appveyor/ci/LaurentRDC/scikit-ued/master.svg
     :target: https://ci.appveyor.com/project/LaurentRDC/scikit-ued
     :alt: Windows Build Status
-.. image:: https://readthedocs.org/projects/scikit-ued/badge/?version=latest
+.. image:: https://readthedocs.org/projects/scikit-ued/badge/?version=master
     :target: http://scikit-ued.readthedocs.io
     :alt: Documentation Build Status
 .. image:: https://img.shields.io/pypi/v/scikit-ued.svg

appveyor.yml

@@ -14,15 +14,13 @@ environment:
                         # to the matrix section.
       TEST_CMD: "python -m unittest discover --verbose"
 
+      CONDA_DEPENDENCIES: "numpy scipy cython scikit-image"
+      PIP_DEPENDENCIES: "pywavelets spglib pycifrw"
+
   matrix:
 
       - PYTHON_VERSION: "3.6"
-        CONDA_DEPENDENCIES: "numpy scipy cython scikit-image"
-        PIP_DEPENDENCIES: "pywavelets spglib pycifrw"
-
       - PYTHON_VERSION: "3.5"
-        CONDA_DEPENDENCIES: "numpy scipy cython scikit-image"
-        PIP_DEPENDENCIES: "pywavelets spglib pycifrw"
 
 matrix:
    fast_finish: true

docs/source/api.rst

@@ -6,18 +6,24 @@ Reference/API
 
 Parallel Utilities
 ==================
-.. automodule:: skued.parallel
+.. autofunction:: skued.pmap
+
+.. autofunction:: skued.preduce
 
 Plot Utilities
 ==============
-.. autofunction:: skued.plot_utils.spectrum_colors
+.. autofunction:: skued.spectrum_colors
 
-.. autofunction:: skued.plot_utils.rgb_sweep
+.. autofunction:: skued.rgb_sweep
 
 Array Utilities
 ===============
 .. automodule:: skued.array_utils
 
+Iteration/Generator Utilities
+=============================
+.. automodule:: skued.iter_utils
+
 Quantities
 ==========
 .. automodule:: skued.quantities
@@ -32,17 +38,14 @@ Affine Transforms
 
 Structure
 =========
-.. autoclass:: skued.structure.atom.Atom
+.. autoclass:: skued.structure.Atom
    :members:
-   :undoc-members:
 
-.. autoclass:: skued.structure.lattice.Lattice
+.. autoclass:: skued.structure.Lattice
    :members:
-   :undoc-members:
 
-.. autoclass:: skued.structure.crystal.Crystal
+.. autoclass:: skued.structure.Crystal
    :members:
-   :undoc-members:
 
 Simulation
 ==========

docs/source/tutorials/baseline.rst

@@ -78,7 +78,7 @@ Scikit-ued offers two ways of removing the background.
 Iterative Baseline Determination using the Discrete Wavelet Transform
 =====================================================================
 
-The procedure and rational for the :code:`baseline_dwt` routine is described in detail in:
+The procedure and rational for the :func:`baseline_dwt` routine is described in detail in:
 
 Galloway et al. 'An Iterative Algorithm for Background Removal in Spectroscopy by Wavelet 
 Transforms', Applied Spectroscopy pp. 1370 - 1376, September 2009.
@@ -140,7 +140,7 @@ Here is a usage example for the data presented above::
 Iterative Baseline Determination using the Dual-Tree Complex Wavelet Transform
 ==============================================================================
 
-In the case of 1D data (or along a 1D axis), there is a more performant alternative to :code:`baseline_dwt`. The 
+In the case of 1D data (or along a 1D axis), there is a more performant alternative to :func:`baseline_dwt`. The 
 **dual-tree complex wavelet transform** improves on the discrete wavelet transform in many ways.
 Therefore, the method presented in this section should be preferred.
 
@@ -201,7 +201,7 @@ Here is a usage example for the data presented above::
 		ax.legend()
 	plt.show()
 
-The :code:`baseline_dt` routine will usually be more accurate than its :code:`baseline_dwt` counterpart.
-However, :code:`baseline_dwt` can be applied to 1D and 2D data.
+The :func:`baseline_dt` routine will usually be more accurate than its :func:`baseline_dwt` counterpart.
+However, :func:`baseline_dwt` can be applied to 1D and 2D data.
 
 :ref:`Return to Top <baseline_tutorial>`