Changeset f64b154 in sasmodels for doc

Timestamp:

Apr 2, 2019 4:12:24 AM (6 years ago)

Author:

GitHub <noreply@…>

Branches:

master

Parents:

3448301 (diff), b955dd5 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

git-author:

Steve K <smk78@…> (04/02/19 04:12:24)

git-committer:

GitHub <noreply@…> (04/02/19 04:12:24)

Message:

Merge pull request #103 from SasView?/ticket-1263-source-link

Add local link to source files. Refs #1263.

Location:

doc

Files:

• conf.py (modified) (1 diff)
• genmodel.py (modified) (3 diffs)
• guide/fitting_sq.rst (modified) (1 diff)
• index.rst (modified) (3 diffs)
• guide/pd/polydispersity.rst (modified) (11 diffs)

doc/conf.py

@@ -34 +34 @@
               #'sphinx.ext.pngmath',
               'sphinx.ext.mathjax',
-              #'only_directives',
               #'matplotlib.sphinxext.mathmpl',
-              'matplotlib.sphinxext.only_directives',
               'matplotlib.sphinxext.plot_directive',
               'dollarmath',

doc/genmodel.py

@@ -7 +7 @@
 import matplotlib.pyplot as plt
 sys.path.insert(0, os.path.abspath('..'))
+import sasmodels
 from sasmodels import generate, core
 from sasmodels.direct_model import DirectModel, call_profile
@@ -127 +128 @@
     #print("figure saved in",path)
 
+def copy_if_newer(src, dst):
+    from os.path import dirname, exists, getmtime
+    import shutil
+    if not exists(dst):
+        path = dirname(dst)
+        if not exists(path):
+            os.makedirs(path)
+        shutil.copy2(src, dst)
+    elif getmtime(src) > getmtime(dst):
+        shutil.copy2(src, dst)
+
+def link_sources(model_info):
+    from os.path import basename, dirname, realpath, join as joinpath
+
+    # List source files in reverse order of dependency.
+    model_file = basename(model_info.filename)
+    sources = list(reversed(model_info.source + [model_file]))
+
+    # Copy files to src dir under models directory.  Need to do this
+    # because sphinx can't link to an absolute path.
+    root = dirname(dirname(realpath(__file__)))
+    src = joinpath(root, "sasmodels", "models")
+    dst = joinpath(root, "doc", "model", "src")
+    for path in sources:
+        copy_if_newer(joinpath(src, path), joinpath(dst, path))
+
+    # Link to local copy of the files
+    downloads = [":download:`%s <src/%s>`"%(path, path) for path in sources]
+
+    # Could do syntax highlighting on the model files by creating a rst file
+    # beside each source file named containing source file with
+    #
+    #    src/path.rst:
+    #
+    #    .. {{ path.replace('/','_') }}:
+    #
+    #    .. literalinclude:: {{ src/path }}
+    #        :language: {{ "python" if path.endswith('.py') else "c" }}
+    #        :linenos:
+    #
+    # and link to it using
+    #
+    #     colors = [":ref:`%s`"%(path.replace('/','_')) for path in sources]
+    #
+    # Probably need to dump all the rst files into an index.rst to build them.
+
+    # Link to github repo (either the tagged sasmodels version or master)
+    url = "https://github.com/SasView/sasmodels/blob/v%s"%sasmodels.__version__
+    #url = "https://github.com/SasView/sasmodels/blob/master"%sasmodels.__version__
+    links = ["`%s <%s/sasmodels/models/%s>`_"%(path, url, path) for path in sources]
+
+    sep = "\n$\\ \\star\\ $ "  # bullet
+    body = "\n**Source**\n"
+    #body += "\n" + sep.join(links) + "\n\n"
+    body += "\n" + sep.join(downloads) + "\n\n"
+    return body
+
 def gen_docs(model_info):
     # type: (ModelInfo) -> None
@@ -150 +208 @@
     match = re.search(pattern, docstr.upper())
 
+    sources = link_sources(model_info)
+
+    insertion = captionstr + sources
+
     if match:
         docstr1 = docstr[:match.start()]
         docstr2 = docstr[match.start():]
-        docstr = docstr1 + captionstr + docstr2
+        docstr = docstr1 + insertion + docstr2
     else:
         print('------------------------------------------------------------------')
         print('References NOT FOUND for model: ', model_info.id)
         print('------------------------------------------------------------------')
-        docstr += captionstr
-
-    open(sys.argv[2],'w').write(docstr)
+        docstr += insertion
+
+    open(sys.argv[2], 'w').write(docstr)
 
 def process_model(path):

doc/guide/fitting_sq.rst

@@ -90 +90 @@
    later.
 
-* *radius_effective_mode*:
+ *radius_effective_mode*:
 
     Defines how the effective radius (parameter **radius_effective**) should

doc/index.rst

@@ -16 +16 @@
 polydispersity and orientational dispersion.
 
-.. htmlonly::
+.. only:: html
+
    :Release: |version|
    :Date:    |today|
@@ -23 +24 @@
 
 .. toctree::
-   :numbered:
    :maxdepth: 4
 
@@ -37 +37 @@
 * :ref:`modindex`
 
-.. htmlonly::
+.. only:: html
+
   * :ref:`search`

doc/guide/pd/polydispersity.rst

@@ -11 +11 @@
 --------------------------------------------
 
-For some models we can calculate the average intensity for a population of 
-particles that possess size and/or orientational (ie, angular) distributions. 
-In SasView we call the former *polydispersity* but use the parameter *PD* to 
-parameterise both. In other words, the meaning of *PD* in a model depends on 
+For some models we can calculate the average intensity for a population of
+particles that possess size and/or orientational (ie, angular) distributions.
+In SasView we call the former *polydispersity* but use the parameter *PD* to
+parameterise both. In other words, the meaning of *PD* in a model depends on
 the actual parameter it is being applied too.
 
-The resultant intensity is then normalized by the average particle volume such 
+The resultant intensity is then normalized by the average particle volume such
 that
 
@@ -24 +24 @@
   P(q) = \text{scale} \langle F^* F \rangle / V + \text{background}
 
-where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an 
+where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an
 average over the distribution $f(x; \bar x, \sigma)$, giving
 
 .. math::
 
-  P(q) = \frac{\text{scale}}{V} \int_\mathbb{R} 
+  P(q) = \frac{\text{scale}}{V} \int_\mathbb{R}
   f(x; \bar x, \sigma) F^2(q, x)\, dx + \text{background}
 
 Each distribution is characterized by a center value $\bar x$ or
 $x_\text{med}$, a width parameter $\sigma$ (note this is *not necessarily*
-the standard deviation, so read the description of the distribution carefully), 
-the number of sigmas $N_\sigma$ to include from the tails of the distribution, 
-and the number of points used to compute the average. The center of the 
-distribution is set by the value of the model parameter.
-
-The distribution width applied to *volume* (ie, shape-describing) parameters 
-is relative to the center value such that $\sigma = \mathrm{PD} \cdot \bar x$. 
-However, the distribution width applied to *orientation* parameters is just 
-$\sigma = \mathrm{PD}$.
+the standard deviation, so read the description carefully), the number of
+sigmas $N_\sigma$ to include from the tails of the distribution, and the
+number of points used to compute the average. The center of the distribution
+is set by the value of the model parameter. The meaning of a polydispersity
+parameter *PD* (not to be confused with a molecular weight distributions
+in polymer science) in a model depends on the type of parameter it is being
+applied too.
+
+The distribution width applied to *volume* (ie, shape-describing) parameters
+is relative to the center value such that $\sigma = \mathrm{PD} \cdot \bar x$.
+However, the distribution width applied to *orientation* (ie, angle-describing)
+parameters is just $\sigma = \mathrm{PD}$.
 
 $N_\sigma$ determines how far into the tails to evaluate the distribution,
@@ -52 +55 @@
 
 Users should note that the averaging computation is very intensive. Applying
-polydispersion and/or orientational distributions to multiple parameters at 
-the same time, or increasing the number of points in the distribution, will 
-require patience! However, the calculations are generally more robust with 
+polydispersion and/or orientational distributions to multiple parameters at
+the same time, or increasing the number of points in the distribution, will
+require patience! However, the calculations are generally more robust with
 more data points or more angles.
 
@@ -66 +69 @@
 *  *Schulz Distribution*
 *  *Array Distribution*
+*  *User-defined Distributions*
 
 These are all implemented as *number-average* distributions.
 
-Additional distributions are under consideration.
 
 **Beware: when the Polydispersity & Orientational Distribution panel in SasView is**
@@ -75 +78 @@
 **This may not be suitable. See Suggested Applications below.**
 
-.. note:: In 2009 IUPAC decided to introduce the new term 'dispersity' to replace 
-           the term 'polydispersity' (see `Pure Appl. Chem., (2009), 81(2), 
-           351-353 <http://media.iupac.org/publications/pac/2009/pdf/8102x0351.pdf>`_ 
-           in order to make the terminology describing distributions of chemical 
-           properties unambiguous. However, these terms are unrelated to the 
-           proportional size distributions and orientational distributions used in 
+.. note:: In 2009 IUPAC decided to introduce the new term 'dispersity' to replace
+           the term 'polydispersity' (see `Pure Appl. Chem., (2009), 81(2),
+           351-353 <http://media.iupac.org/publications/pac/2009/pdf/8102x0351.pdf>`_
+           in order to make the terminology describing distributions of chemical
+           properties unambiguous. However, these terms are unrelated to the
+           proportional size distributions and orientational distributions used in
            SasView models.
 
@@ -92 +95 @@
 or angular orientations, consider using the Gaussian or Boltzmann distributions.
 
-If applying polydispersion to parameters describing angles, use the Uniform 
-distribution. Beware of using distributions that are always positive (eg, the 
+If applying polydispersion to parameters describing angles, use the Uniform
+distribution. Beware of using distributions that are always positive (eg, the
 Lognormal) because angles can be negative!
 
-The array distribution allows a user-defined distribution to be applied.
+The array distribution provides a very simple means of implementing a user-
+defined distribution, but without any fittable parameters. Greater flexibility
+is conferred by the user-defined distribution.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -334 +339 @@
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
 
+User-defined Distributions
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can also define your own distribution by creating a python file defining a
+*Distribution* object with a *_weights* method.  The *_weights* method takes
+*center*, *sigma*, *lb* and *ub* as arguments, and can access *self.npts*
+and *self.nsigmas* from the distribution.  They are interpreted as follows:
+
+* *center* the value of the shape parameter (for size dispersity) or zero
+  if it is an angular dispersity.  This parameter may be fitted.
+
+* *sigma* the width of the distribution, which is the polydispersity parameter
+  times the center for size dispersity, or the polydispersity parameter alone
+  for angular dispersity.  This parameter may be fitted.
+
+* *lb*, *ub* are the parameter limits (lower & upper bounds) given in the model
+  definition file.  For example, a radius parameter has *lb* equal to zero.  A
+  volume fraction parameter would have *lb* equal to zero and *ub* equal to one.
+
+* *self.nsigmas* the distance to go into the tails when evaluating the
+  distribution.  For a two parameter distribution, this value could be
+  co-opted to use for the second parameter, though it will not be available
+  for fitting.
+
+* *self.npts* the number of points to use when evaluating the distribution.
+  The user will adjust this to trade calculation time for accuracy, but the
+  distribution code is free to return more or fewer, or use it for the third
+  parameter in a three parameter distribution.
+
+As an example, the code following wraps the Laplace distribution from scipy stats::
+
+    import numpy as np
+    from scipy.stats import laplace
+
+    from sasmodels import weights
+
+    class Dispersion(weights.Dispersion):
+        r"""
+        Laplace distribution
+
+        .. math::
+
+            w(x) = e^{-\sigma |x - \mu|}
+        """
+        type = "laplace"
+        default = dict(npts=35, width=0, nsigmas=3)  # default values
+        def _weights(self, center, sigma, lb, ub):
+            x = self._linspace(center, sigma, lb, ub)
+            wx = laplace.pdf(x, center, sigma)
+            return x, wx
+
+You can plot the weights for a given value and width using the following::
+
+    from numpy import inf
+    from matplotlib import pyplot as plt
+    from sasmodels import weights
+
+    # reload the user-defined weights
+    weights.load_weights()
+    x, wx = weights.get_weights('laplace', n=35, width=0.1, nsigmas=3, value=50,
+                                limits=[0, inf], relative=True)
+
+    # plot the weights
+    plt.interactive(True)
+    plt.plot(x, wx, 'x')
+
+The *self.nsigmas* and *self.npts* parameters are normally used to control
+the accuracy of the distribution integral. The *self._linspace* function
+uses them to define the *x* values (along with the *center*, *sigma*,
+*lb*, and *ub* which are passed as parameters).  If you repurpose npts or
+nsigmas you will need to generate your own *x*.  Be sure to honour the
+limits *lb* and *ub*, for example to disallow a negative radius or constrain
+the volume fraction to lie between zero and one.
+
+To activate a user-defined distribution, put it in a file such as *distname.py*
+in the *SAS_WEIGHTS_PATH* folder.  This is defined with an environment
+variable, defaulting to::
+
+    SAS_WEIGHTS_PATH=~/.sasview/weights
+
+The weights path is loaded on startup.  To update the distribution definition
+in a running application you will need to enter the following python commands::
+
+    import sasmodels.weights
+    sasmodels.weights.load_weights('path/to/distname.py')
+
+.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+
 Note about DLS polydispersity
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Several measures of polydispersity abound in Dynamic Light Scattering (DLS) and 
-it should not be assumed that any of the following can be simply equated with 
+Several measures of polydispersity abound in Dynamic Light Scattering (DLS) and
+it should not be assumed that any of the following can be simply equated with
 the polydispersity *PD* parameter used in SasView.
 
-The dimensionless **Polydispersity Index (PI)** is a measure of the width of the 
-distribution of autocorrelation function decay rates (*not* the distribution of 
-particle sizes itself, though the two are inversely related) and is defined by 
+The dimensionless **Polydispersity Index (PI)** is a measure of the width of the
+distribution of autocorrelation function decay rates (*not* the distribution of
+particle sizes itself, though the two are inversely related) and is defined by
 ISO 22412:2017 as
 
@@ -350 +443 @@
     PI = \mu_{2} / \bar \Gamma^2
 
-where $\mu_\text{2}$ is the second cumulant, and $\bar \Gamma^2$ is the 
+where $\mu_\text{2}$ is the second cumulant, and $\bar \Gamma^2$ is the
 intensity-weighted average value, of the distribution of decay rates.
 
@@ -359 +452 @@
     PI = \sigma^2 / 2\bar \Gamma^2
 
-where $\sigma$ is the standard deviation, allowing a **Relative Polydispersity (RP)** 
+where $\sigma$ is the standard deviation, allowing a **Relative Polydispersity (RP)**
 to be defined as
 
@@ -366 +459 @@
     RP = \sigma / \bar \Gamma = \sqrt{2 \cdot PI}
 
-PI values smaller than 0.05 indicate a highly monodisperse system. Values 
+PI values smaller than 0.05 indicate a highly monodisperse system. Values
 greater than 0.7 indicate significant polydispersity.
 
-The **size polydispersity P-parameter** is defined as the relative standard 
-deviation coefficient of variation  
+The **size polydispersity P-parameter** is defined as the relative standard
+deviation coefficient of variation
 
 .. math::
@@ -377 +470 @@
 
 where $\nu$ is the variance of the distribution and $\bar R$ is the mean
-value of $R$. Here, the product $P \bar R$ is *equal* to the standard 
+value of $R$. Here, the product $P \bar R$ is *equal* to the standard
 deviation of the Lognormal distribution.