Fix anaconda url

README.rst

@@ -5,38 +5,38 @@ SrMise
 `DiffPy project <http://www.diffpy.org>`_ tool for unbiased peak extraction from
 atomic pair distribution functions.
 
-SrMise is an implementation of the `ParSCAPE algorithm  
-<https://dx.doi.org/10.1107/S2053273315005276>`_ for peak extraction from 
-atomic pair distribution functions (PDFs).  It is designed to function even 
-when *a priori* knowledge of the physical sample is limited, utilizing the 
-Akaike Information Criterion (AIC) to estimate whether peaks are 
-statistically justified relative to alternate models.  Three basic use cases 
-are anticipated for SrMise.  The first is peak fitting a user-supplied 
-collections of peaks.  The second is peak extraction from a PDF with no (or 
-only partial) user-supplied peaks.  The third is an AIC-driven multimodeling 
-analysis where the output of multiple SrMise trials are ranked. 
-
-The framework for peak extraction defines peak-like clusters within the data, 
-extracts a single peak within each cluster, and iteratively combines nearby 
-clusters while performing a recursive search on the residual to identify 
-occluded peaks.  Eventually this results in a single global cluster 
-containing many peaks fit over all the data.  Over- and underfitting are 
+SrMise is an implementation of the `ParSCAPE algorithm
+<https://dx.doi.org/10.1107/S2053273315005276>`_ for peak extraction from
+atomic pair distribution functions (PDFs).  It is designed to function even
+when *a priori* knowledge of the physical sample is limited, utilizing the
+Akaike Information Criterion (AIC) to estimate whether peaks are
+statistically justified relative to alternate models.  Three basic use cases
+are anticipated for SrMise.  The first is peak fitting a user-supplied
+collections of peaks.  The second is peak extraction from a PDF with no (or
+only partial) user-supplied peaks.  The third is an AIC-driven multimodeling
+analysis where the output of multiple SrMise trials are ranked.
+
+The framework for peak extraction defines peak-like clusters within the data,
+extracts a single peak within each cluster, and iteratively combines nearby
+clusters while performing a recursive search on the residual to identify
+occluded peaks.  Eventually this results in a single global cluster
+containing many peaks fit over all the data.  Over- and underfitting are
 discouraged by use of the AIC when adding or, during a pruning step, removing
-peaks.  Termination effects, which can lead to physically spurious peaks in 
-the PDF, are incorporated in the mathematical peak model and the pruning step 
-attempts to remove peaks which are fit better as termination ripples due to 
-another peak. 
-
-Where possible, SrMise provides physically reasonable default values 
-for extraction parameters.  However, the PDF baseline should be estimated by 
-the user before extraction, or by performing provisional peak extraction with 
-varying baseline parameters.  The package defines a linear (crystalline) 
-baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline, 
-and an arbitrary baseline interpolated from a list of user-supplied values.  
-In addition, PDFs with accurate experimentally-determined uncertainties are 
-necessary to provide the most reliable results, but historically such PDFs 
-are rare.  In the absence of accurate uncertainties an *ad hoc* uncertainty 
-must be specified. 
+peaks.  Termination effects, which can lead to physically spurious peaks in
+the PDF, are incorporated in the mathematical peak model and the pruning step
+attempts to remove peaks which are fit better as termination ripples due to
+another peak.
+
+Where possible, SrMise provides physically reasonable default values
+for extraction parameters.  However, the PDF baseline should be estimated by
+the user before extraction, or by performing provisional peak extraction with
+varying baseline parameters.  The package defines a linear (crystalline)
+baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline,
+and an arbitrary baseline interpolated from a list of user-supplied values.
+In addition, PDFs with accurate experimentally-determined uncertainties are
+necessary to provide the most reliable results, but historically such PDFs
+are rare.  In the absence of accurate uncertainties an *ad hoc* uncertainty
+must be specified.
 
 For more information about SrMise, see the users manual at
 http://diffpy.github.io/diffpy.srmise.
@@ -65,7 +65,7 @@ individual and/or academic use, but some also have commercial version.  Links to
 executables, installation instructions, and licensing information
 for some popular options are listed below.
 
-* `Anaconda <http://www.continuum.io/downloads>`_
+* `Anaconda <https://www.anaconda.com/download>`_
 * `Enthought Canopy <https://www.enthought.com/products/canopy/>`_
 * `Python(x,y) <https://code.google.com/p/pythonxy/>`_
 * `WinPython <http://winpython.github.io>`_
@@ -120,7 +120,7 @@ in MacPorts::
 
 The simplest way to obtain diffpy.srmise on Mac OS X systems
 is using ``pip`` to download and install the latest release from
-`PyPI <https://pypi.python.org>`_. :: 
+`PyPI <https://pypi.python.org>`_. ::
 
    sudo pip install diffpy.srmise
 
@@ -131,9 +131,9 @@ Uncompress them to a directory, and from that directory run ::
 
    sudo python setup.py install
 
-This installs diffpy.srmise for all users in the default system location. If 
-administrator (root) access is not available, see the usage info from 
-``python setup.py install --help`` for options to install to user-writable 
+This installs diffpy.srmise for all users in the default system location. If
+administrator (root) access is not available, see the usage info from
+``python setup.py install --help`` for options to install to user-writable
 directories.
 
 
@@ -154,7 +154,7 @@ For other Linux distributions consult the appropriate package manager.
 
 The simplest way to obtain diffpy.srmise on Linux systems
 is using ``pip`` to download and install the latest release from the
-`PyPI <https://pypi.python.org>`_. :: 
+`PyPI <https://pypi.python.org>`_. ::
 
    sudo pip install diffpy.srmise
 
@@ -165,24 +165,24 @@ Uncompress them to a directory, and from that directory run ::
 
    sudo python setup.py install
 
-This installs diffpy.srmise for all users in the default system location. If 
-administrator (root) access is not available, see the usage info from 
-``python setup.py install --help`` for options to install to user-writable 
-directories. 
+This installs diffpy.srmise for all users in the default system location. If
+administrator (root) access is not available, see the usage info from
+``python setup.py install --help`` for options to install to user-writable
+directories.
 
 
 DEVELOPMENT
 ===========
 
-diffpy.srmise is open-source software developed with support of the Center of 
-Research Excellence in Complex Materials at Michigan State University, in 
-cooperation with the DiffPy-CMI complex modeling initiative at the Brookhaven 
-National Laboratory.  The diffpy.srmise sources are hosted at 
-https://github.com/diffpy/diffpy.srmise. 
+diffpy.srmise is open-source software developed with support of the Center of
+Research Excellence in Complex Materials at Michigan State University, in
+cooperation with the DiffPy-CMI complex modeling initiative at the Brookhaven
+National Laboratory.  The diffpy.srmise sources are hosted at
+https://github.com/diffpy/diffpy.srmise.
 
-Feel free to fork the project and contribute.  To install diffpy.srmise in a 
-development mode, with its sources being directly used by Python rather than 
-copied to a package directory, use :: 
+Feel free to fork the project and contribute.  To install diffpy.srmise in a
+development mode, with its sources being directly used by Python rather than
+copied to a package directory, use ::
 
    python setup.py develop --user