Merge branch 'master' into ticket_1156_lattice

.github/workflows/test.yml

@@ -9,28 +9,34 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
+    - name: Free Disk Space (Ubuntu)
+      if: ${{ matrix.os == 'ubuntu-latest' }}
+      uses: jlumbroso/free-disk-space@main
+      with:
+        haskell: false
+        large-packages: false
+
     - name: setup apt dependencies for Linux
       if: ${{ matrix.os == 'ubuntu-latest' }}
       run: |
-        sudo apt-get update
-        sudo apt-get install opencl-headers ocl-icd-opencl-dev libpocl2
+        sudo apt update
 
     - name: Install Python dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install wheel setuptools
         python -m pip install mako
-        python -m pip install numpy scipy matplotlib docutils pytest sphinx bumps unittest-xml-reporting tinycc
+        python -m pip install numpy scipy matplotlib docutils pytest sphinx bumps==0.* unittest-xml-reporting tinycc siphash24
 
     - name: setup pyopencl on Linux + macOS
       if: ${{ matrix.os != 'windows-latest' }}
@@ -45,9 +51,11 @@ jobs:
         choco install opencl-intel-cpu-runtime
         python -m pip install --only-binary=pyopencl --find-links http://www.silx.org/pub/wheelhouse/ --trusted-host www.silx.org pyopencl
 
-    - name: Test with pytest
+    - name: Test with pytest (only on Windows for now since PoCL is failing on Ubuntu)
+
       env:
         PYOPENCL_COMPILER_OUTPUT: 1
+        SAS_OPENCL: none
       run: |
         # other CI uses the following, but `setup.py test` is a deprecated way
         # of running tests
@@ -57,5 +65,16 @@ jobs:
 
     - name: check that the docs build (linux only)
       if: ${{ matrix.os == 'ubuntu-latest' }}
+      env:
+        SAS_OPENCL: none
       run: |
         make -j 4 -C doc SPHINXOPTS="-W --keep-going -n" html
+
+    - name: Publish samodels docs
+      if: ${{ matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'}}
+      uses: actions/upload-artifact@v3
+      with:
+        name: sasmodels-docs-${{ matrix.os }}-${{ matrix.python-version }}
+        path: |
+          doc/_build/html
+        if-no-files-found: error

CHANGES.rst

@@ -1,7 +1,15 @@
 Release notes
 =============
 
-v1.0.7 2023-02-??
+v1.0.8 2024-09-26
+-----------------
+* New model: Bulk ferromagnets model from marketplace
+* Doc update: Archive built docs on Github
+* Doc update: Display math correctly
+* Fix error in FCC paracrystaline models
+* Fix parameter name checking in kernel call
+
+v1.0.7 2023-03-23
 ------------------
 * Doc upate: corefunc and optimizer documentation
 * Doc update: various models (cylinder, gel_fit, paracrystal, core_shell_ellipsoid)

LICENSE.txt

@@ -1,5 +1,4 @@
-Copyright (c) 2009-2022, SasView Developers
-
+Copyright (c) 2009-2024, SasView Developers
 
 All rights reserved.
 

doc/conf.py

@@ -27,6 +27,7 @@
 
 nitpick_ignore = [
     ('py:class', 'argparse.Namespace'),
+    ('py:class', 'bumps.parameter.Parameter'),
     ('py:class', 'collections.OrderedDict'),
     ('py:class', 'cuda.Context'),
     ('py:class', 'cuda.Function'),
@@ -40,6 +41,7 @@
     ('py:class', 'pyopencl._cl.Device'),
     ('py:class', 'pyopencl._cl.Kernel'),
     ('py:class', 'QWebView'),
+    ('py:class', 'types.ModuleType'),
     ('py:class', 'unittest.suite.TestSuite'),
     ('py:class', 'wx.Frame'),
     # autodoc and namedtuple is completely broken
@@ -55,6 +57,7 @@
     ('py:class', 'module'),
     ('py:class', 'SesansData'),
     ('py:class', 'SourceModule'),
+    ('py:class', 'TestCondition'),
     # KernelModel and Calculator breaking on git actions tests, even though
     # KernelModel is already listed. astropy example sometimes includes full
     # path to complaining symbol. Let's see if that helps here:
@@ -141,40 +144,6 @@
 # A list of ignored prefixes for module index sorting.
 #modindex_common_prefix = []
 
-nitpick_ignore = [
-    ('py:class', 'argparse.Namespace'),
-    ('py:class', 'bumps.parameter.Parameter'),
-    ('py:class', 'collections.OrderedDict'),
-    ('py:class', 'cuda.Context'),
-    ('py:class', 'cuda.Function'),
-    ('py:class', 'np.dtype'),
-    ('py:class', 'numpy.dtype'),
-    ('py:class', 'np.ndarray'),
-    ('py:class', 'numpy.ndarray'),
-    ('py:class', 'pyopencl.Program'),
-    ('py:class', 'pyopencl._cl.Context'),
-    ('py:class', 'pyopencl._cl.CommandQueue'),
-    ('py:class', 'pyopencl._cl.Device'),
-    ('py:class', 'pyopencl._cl.Kernel'),
-    ('py:class', 'QWebView'),
-    ('py:class', 'unittest.suite.TestSuite'),
-    ('py:class', 'wx.Frame'),
-    # autodoc and namedtuple is completely broken
-    ('py:class', 'integer -- return number of occurrences of value'),
-    ('py:class', 'integer -- return first index of value.'),
-    # autodoc doesn't handle these type definitions
-    ('py:class', 'Data'),
-    ('py:class', 'Data1D'),
-    ('py:class', 'Data2D'),
-    ('py:class', 'Kernel'),
-    ('py:class', 'ModelInfo'),
-    ('py:class', 'module'),
-    ('py:class', 'SesansData'),
-    ('py:class', 'SourceModule'),
-    ('py:class', 'TestCondition'),
-]
-
-
 
 # -- Options for HTML output ---------------------------------------------------
 

doc/guide/plugin.rst

@@ -568,7 +568,7 @@ value at a time::
 
     Iq.vectorized = False
 
-Return np.NaN if the parameters are not valid (e.g., cap_radius < radius in
+Return np.nan if the parameters are not valid (e.g., cap_radius < radius in
 barbell).  If I(q; pars) is NaN for any $q$, then those parameters will be
 ignored, and not included in the calculation of the weighted polydispersity.
 
@@ -851,6 +851,8 @@ Some non-standard constants and functions are also provided:
         $x^2$
     cube(x):
         $x^3$
+    clip(a, a_min, a_max):
+        $\min(\max(a, a_\text{min}), a_\text{max})$, or NaN if $a$ is NaN.
     sas_sinx_x(x):
         $\sin(x)/x$, with limit $\sin(0)/0 = 1$.
     powr(x, y):

doc/guide/theory.rst

@@ -1,3 +1,4 @@
+.. currentmodule:: sasmodels
 .. theory.rst
 
 .. Much of the following text was scraped from fitting_sq.py

explore/precision.py

@@ -190,13 +190,13 @@ def plotdiff(x, target, actual, label, diff):
     if diff == "relative":
         err = np.array([(abs((t-a)/t) if t != 0 else a) for t, a in zip(target, actual)], 'd')
         #err = np.clip(err, 0, 1)
-        pylab.loglog(x, err, '-', label=label)
+        pylab.loglog(x, err, '-', label=label, alpha=0.7)
     elif diff == "absolute":
         err = np.array([abs((t-a)) for t, a in zip(target, actual)], 'd')
-        pylab.loglog(x, err, '-', label=label)
+        pylab.loglog(x, err, '-', label=label, alpha=0.7)
     else:
         limits = np.min(target), np.max(target)
-        pylab.semilogx(x, np.clip(actual, *limits), '-', label=label)
+        pylab.semilogx(x, np.clip(actual, *limits), '-', label=label, alpha=0.7)
 
 def make_ocl(function, name, source=[]):
     class Kernel(object):
@@ -412,6 +412,54 @@ def add_function(name, mp_function, np_function, ocl_function,
     np_function=lambda x: np.fmod(x, 2*np.pi),
     ocl_function=make_ocl("return fmod(q, 2*M_PI);", "sas_fmod"),
 )
+
+def sas_langevin(x):
+    scalar = np.isscalar(x)
+    if scalar:
+        x = np.array([x]) # should inherit dtype for single if given single
+    f = np.empty_like(x)
+    cutoff = 0.1 if f.dtype == np.float64 else 1.0
+    #cutoff *= 10
+    index = x < cutoff
+    xp = x[index]
+    xpsq = xp*xp
+    f[index] = xp / (3. + xpsq / (5. + xpsq/(7. + xpsq/(9.))))
+    # 4 terms gets to 1e-7 single, 1e-14 double. Can get to 1e-15 double by adding
+    # another 4 terms and setting cutoff at 1.0. Not worthwhile. Instead we would
+    # need an expansion about x somewhere between 1 and 10 for the interval [0.1, 100.]
+    #f[index] = xp / (3. + xpsq / (5. + xpsq/(7. + xpsq/(9. + xpsq/(11.0 + xpsq/(13. + xpsq/(15. + xpsq/17.)))))))
+    xp = x[~index]
+    f[~index] = 1/np.tanh(xp) - 1/xp
+    return f[0] if scalar else f
+
+def sas_langevin_x(x):
+    scalar = np.isscalar(x)
+    if scalar:
+        x = np.array([x]) # should inherit dtype for single if given single
+    f = np.empty_like(x)
+    cutoff = 0.1 if f.dtype == np.float64 else 1.0
+    index = x < cutoff
+    xp = x[index]
+    xpsq = xp*xp
+    f[index] = 1. / (3. + xpsq / (5. + xpsq/(7. + xpsq/(9.))))
+    xp = x[~index]
+    f[~index] = (1/np.tanh(xp) - 1/xp)/xp
+    return f[0] if scalar else f
+
+add_function(
+    name="langevin(x)",
+    mp_function=lambda x: (1/mp.tanh(x) - 1/x),
+    np_function=sas_langevin,
+    #ocl_function=make_ocl("return q < 0.7 ? q*(1./3. + q*q*(-1./45. + q*q*(2./945. + q*q*(-1./4725.) + q*q*(2./93555.)))) : 1/tanh(q) - 1/q;", "sas_langevin"),
+    ocl_function=make_ocl("return q < 1e-5 ? q/3. : 1/tanh(q) - 1/q;", "sas_langevin"),
+)
+add_function(
+    name="langevin(x)/x",
+    mp_function=lambda x: (1/mp.tanh(x) - 1/x)/x,
+    #np_function=lambda x: sas_langevin(x)/x, # Note: need to test for x=0
+    np_function=sas_langevin_x,
+    ocl_function=make_ocl("return q < 1e-5 ? 1./3. : (1/tanh(q) - 1/q)/q;", "sas_langevin_x"),
+)
 add_function(
     name="gauss_coil",
     mp_function=lambda x: 2*(mp.exp(-x**2) + x**2 - 1)/x**4,

explore/realspace.py

@@ -625,7 +625,7 @@ def pad_vectors(boundary, *vectors):
             if new_size > old_size:
                 new = np.empty(new_size, dtype=old.dtype)
                 new[:old_size] = old
-                new[old_size:] = np.NaN
+                new[old_size:] = np.nan
                 yield new
             else:
                 yield old
@@ -1195,7 +1195,7 @@ def _sasmodels_Iqxy(kernel, qx, qy, pars, view):
     # calculator avoids masked values; instead set masked values to NaN
     result = np.empty_like(qx)
     result[calculator.index] = Iqxy
-    result[~calculator.index] = np.NaN
+    result[~calculator.index] = np.nan
     return result
 
 def wrap_sasmodel(name, **pars):

sasmodels/__init__.py

@@ -13,7 +13,7 @@
 OpenCL drivers are available.  See :mod:`.generate` for details on
 defining new models.
 """
-__version__ = "1.0.6"
+__version__ = "1.0.8"
 
 def data_files():
     """

sasmodels/compare_many.py

@@ -142,9 +142,9 @@ def try_model(fn, pars):
             traceback.print_exc()
             print("when comparing %s for %d"%(name, seed))
             if hasattr(data, 'qx_data'):
-                result = np.NaN*data.data
+                result = np.nan*data.data
             else:
-                result = np.NaN*data.x
+                result = np.nan*data.x
         return result
     def check_model(pars):
         """
@@ -165,7 +165,7 @@ def check_model(pars):
     except Exception as exc:
         #raise
         print('"Error: %s"'%str(exc).replace('"', "'"))
-        print('"good","%d of %d","max diff",%g' % (0, N, np.NaN))
+        print('"good","%d of %d","max diff",%g' % (0, N, np.nan))
         return
     expected = max(PRECISION[base], PRECISION[comp])
 

sasmodels/data.py

@@ -170,8 +170,8 @@ def __init__(self, x=None, y=None, dx=None, dy=None):
         self.y, self.dy = _as_numpy(y), _as_numpy(dy)
         self.dxl = None
         self.filename = None
-        self.qmin = self.x.min() if self.x is not None else np.NaN
-        self.qmax = self.x.max() if self.x is not None else np.NaN
+        self.qmin = self.x.min() if self.x is not None else np.nan
+        self.qmax = self.x.max() if self.x is not None else np.nan
         # TODO: why is 1D mask False and 2D mask True?
         self.mask = (np.isnan(y) if y is not None
                      else np.zeros_like(x, 'b') if x is not None
@@ -314,7 +314,7 @@ class Source(object):
     """
     def __init__(self):
         # type: () -> None
-        self.wavelength = np.NaN
+        self.wavelength = np.nan
         self.wavelength_unit = "A"
 
 class Sample(object):

sasmodels/direct_model.py

@@ -121,8 +121,10 @@ def get_mesh(model_info, values, dim='1d', mono=False):
     values = values.copy()
     mesh = [_pop_par_weights(p, values, active(p.name))
             for p in parameters.call_parameters]
+
     if values:
         raise TypeError(f"Unused parameters in call: {', '.join(values.keys())}")
+
     return mesh
 
 
@@ -561,16 +563,17 @@ def near(value, target):
         return np.allclose(value, target, rtol=1e-6, atol=0, equal_nan=True)
     # Note: target values taken from running main() on parameters.
     # Resolution was 5% dq/q.
-    pars = dict(radius=200)
+    pars = dict(radius=200, background=0)  # default background=1e-3, scale=1
     # simple sphere in 1D (perfect, pinhole, slit)
-    assert near(Iq('sphere', [0.1], **pars), [0.6200146273894904])
-    assert near(Iq('sphere', [0.1], dq=[0.005], **pars), [2.3019224683980215])
-    assert near(Iq('sphere', [0.1], qw=[0.005], ql=[1.0], **pars), [0.3673431784535172])
+    perfect_target = 0.6190146273894904
+    assert near(Iq('sphere', [0.1], **pars), [perfect_target])
+    assert near(Iq('sphere', [0.1], dq=[0.005], **pars), [2.3009224683980215])
+    assert near(Iq('sphere', [0.1], qw=[0.005], ql=[1.0], **pars), [0.3663431784535172])
     # simple sphere in 2D (perfect, pinhole)
-    assert near(Iqxy('sphere', [0.1], [0.1], **pars), [1.1781532874802199])
+    assert near(Iqxy('sphere', [0.1], [0.1], **pars), [1.1771532874802199])
     assert near(Iqxy('sphere', [0.1], [0.1], dqx=[0.005], dqy=[0.005], **pars), 
-        [0.8177780778578667])
-    # sesans
+        [0.8167780778578667])
+    # sesans (no background or scale)
     assert near(Gxi('sphere', [100], **pars), [-0.19146959126623486])
     # Check that single point sesans matches value in an array
     xi = np.logspace(1, 3, 100)
@@ -587,6 +590,11 @@ def near(value, target):
         radius=200, radius_pd=0.1, radius_pd_n=15, radius_pd_nsigma=2.5,
         radius_pd_type="uniform")
     assert near(Iq('sphere', [0.1], **pars), [2.703169824954617])
+    # background and scale
+    background, scale = 1e-4, 0.1
+    pars = dict(radius=200, background=background, scale=scale)
+    assert near(Iq('sphere', [0.1], **pars), [perfect_target*scale + background])
+
 
 if __name__ == "__main__":
     import logging