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`DiffPy project <http://www.diffpy.org >`_ tool for unbiased peak extraction from
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atomic pair distribution functions.
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- SrMise is an implementation of the `ParSCAPE algorithm
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- <https://dx.doi.org/10.1107/S2053273315005276> `_ for peak extraction from
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- atomic pair distribution functions (PDFs). It is designed to function even
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- when *a priori * knowledge of the physical sample is limited, utilizing the
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- Akaike Information Criterion (AIC) to estimate whether peaks are
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- statistically justified relative to alternate models. Three basic use cases
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- are anticipated for SrMise. The first is peak fitting a user-supplied
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- collections of peaks. The second is peak extraction from a PDF with no (or
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- only partial) user-supplied peaks. The third is an AIC-driven multimodeling
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- analysis where the output of multiple SrMise trials are ranked.
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-
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- The framework for peak extraction defines peak-like clusters within the data,
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- extracts a single peak within each cluster, and iteratively combines nearby
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- clusters while performing a recursive search on the residual to identify
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- occluded peaks. Eventually this results in a single global cluster
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- containing many peaks fit over all the data. Over- and underfitting are
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+ SrMise is an implementation of the `ParSCAPE algorithm
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+ <https://dx.doi.org/10.1107/S2053273315005276> `_ for peak extraction from
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+ atomic pair distribution functions (PDFs). It is designed to function even
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+ when *a priori * knowledge of the physical sample is limited, utilizing the
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+ Akaike Information Criterion (AIC) to estimate whether peaks are
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+ statistically justified relative to alternate models. Three basic use cases
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+ are anticipated for SrMise. The first is peak fitting a user-supplied
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+ collections of peaks. The second is peak extraction from a PDF with no (or
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+ only partial) user-supplied peaks. The third is an AIC-driven multimodeling
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+ analysis where the output of multiple SrMise trials are ranked.
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+
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+ The framework for peak extraction defines peak-like clusters within the data,
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+ extracts a single peak within each cluster, and iteratively combines nearby
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+ clusters while performing a recursive search on the residual to identify
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+ occluded peaks. Eventually this results in a single global cluster
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+ containing many peaks fit over all the data. Over- and underfitting are
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discouraged by use of the AIC when adding or, during a pruning step, removing
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- peaks. Termination effects, which can lead to physically spurious peaks in
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- the PDF, are incorporated in the mathematical peak model and the pruning step
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- attempts to remove peaks which are fit better as termination ripples due to
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- another peak.
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-
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- Where possible, SrMise provides physically reasonable default values
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- for extraction parameters. However, the PDF baseline should be estimated by
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- the user before extraction, or by performing provisional peak extraction with
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- varying baseline parameters. The package defines a linear (crystalline)
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- baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline,
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- and an arbitrary baseline interpolated from a list of user-supplied values.
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- In addition, PDFs with accurate experimentally-determined uncertainties are
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- necessary to provide the most reliable results, but historically such PDFs
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- are rare. In the absence of accurate uncertainties an *ad hoc * uncertainty
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- must be specified.
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+ peaks. Termination effects, which can lead to physically spurious peaks in
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+ the PDF, are incorporated in the mathematical peak model and the pruning step
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+ attempts to remove peaks which are fit better as termination ripples due to
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+ another peak.
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+
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+ Where possible, SrMise provides physically reasonable default values
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+ for extraction parameters. However, the PDF baseline should be estimated by
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+ the user before extraction, or by performing provisional peak extraction with
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+ varying baseline parameters. The package defines a linear (crystalline)
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+ baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline,
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+ and an arbitrary baseline interpolated from a list of user-supplied values.
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+ In addition, PDFs with accurate experimentally-determined uncertainties are
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+ necessary to provide the most reliable results, but historically such PDFs
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+ are rare. In the absence of accurate uncertainties an *ad hoc * uncertainty
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+ must be specified.
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For more information about SrMise, see the users manual at
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http://diffpy.github.io/diffpy.srmise.
@@ -65,7 +65,7 @@ individual and/or academic use, but some also have commercial version. Links to
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executables, installation instructions, and licensing information
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for some popular options are listed below.
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- * `Anaconda <http ://www.continuum.io/downloads >`_
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+ * `Anaconda <https ://www.anaconda.com/download >`_
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* `Enthought Canopy <https://www.enthought.com/products/canopy/ >`_
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* `Python(x,y) <https://code.google.com/p/pythonxy/ >`_
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* `WinPython <http://winpython.github.io >`_
@@ -120,7 +120,7 @@ in MacPorts::
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The simplest way to obtain diffpy.srmise on Mac OS X systems
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is using ``pip `` to download and install the latest release from
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- `PyPI <https://pypi.python.org >`_. ::
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+ `PyPI <https://pypi.python.org >`_. ::
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sudo pip install diffpy.srmise
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@@ -131,9 +131,9 @@ Uncompress them to a directory, and from that directory run ::
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sudo python setup.py install
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- This installs diffpy.srmise for all users in the default system location. If
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- administrator (root) access is not available, see the usage info from
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- ``python setup.py install --help `` for options to install to user-writable
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+ This installs diffpy.srmise for all users in the default system location. If
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+ administrator (root) access is not available, see the usage info from
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+ ``python setup.py install --help `` for options to install to user-writable
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directories.
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@@ -154,7 +154,7 @@ For other Linux distributions consult the appropriate package manager.
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The simplest way to obtain diffpy.srmise on Linux systems
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is using ``pip `` to download and install the latest release from the
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- `PyPI <https://pypi.python.org >`_. ::
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+ `PyPI <https://pypi.python.org >`_. ::
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sudo pip install diffpy.srmise
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@@ -165,24 +165,24 @@ Uncompress them to a directory, and from that directory run ::
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sudo python setup.py install
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- This installs diffpy.srmise for all users in the default system location. If
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- administrator (root) access is not available, see the usage info from
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- ``python setup.py install --help `` for options to install to user-writable
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- directories.
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+ This installs diffpy.srmise for all users in the default system location. If
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+ administrator (root) access is not available, see the usage info from
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+ ``python setup.py install --help `` for options to install to user-writable
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+ directories.
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DEVELOPMENT
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===========
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- diffpy.srmise is open-source software developed with support of the Center of
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- Research Excellence in Complex Materials at Michigan State University, in
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- cooperation with the DiffPy-CMI complex modeling initiative at the Brookhaven
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- National Laboratory. The diffpy.srmise sources are hosted at
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- https://github.com/diffpy/diffpy.srmise.
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+ diffpy.srmise is open-source software developed with support of the Center of
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+ Research Excellence in Complex Materials at Michigan State University, in
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+ cooperation with the DiffPy-CMI complex modeling initiative at the Brookhaven
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+ National Laboratory. The diffpy.srmise sources are hosted at
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+ https://github.com/diffpy/diffpy.srmise.
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- Feel free to fork the project and contribute. To install diffpy.srmise in a
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- development mode, with its sources being directly used by Python rather than
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- copied to a package directory, use ::
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+ Feel free to fork the project and contribute. To install diffpy.srmise in a
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+ development mode, with its sources being directly used by Python rather than
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+ copied to a package directory, use ::
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python setup.py develop --user
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