Changes in / [5442c77:402c351] in sasmodels

Location:

sasmodels

Files:

• compare.py (modified) (7 diffs)
• compare_many.py (modified) (5 diffs)
• generate.py (modified) (3 diffs)
• kernel_template.c (modified) (4 diffs)
• kernelcl.py (modified) (1 diff)
• kerneldll.py (modified) (8 diffs)
• kernelpy.py (modified) (3 diffs)
• resolution.py (modified) (1 diff)

sasmodels/compare.py

@@ -130 +130 @@
     if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']:
         pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius']
-    if name == 'barbell' and pars['bell_radius'] < pars['radius']:
-        pars['radius'],pars['bell_radius'] = pars['bell_radius'],pars['radius']
-
-    # Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff)
-    if name == 'guinier':
-        #q_max = 0.2  # mid q maximum
-        q_max = 1.0  # high q maximum
-        rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max
-        pars['rg'] = min(pars['rg'],rg_max)
 
     # These constraints are only needed for comparison to sasview
@@ -233 +224 @@
     model_definition = core.load_model_definition(name)
 
-    view = ('linear' if '-linear' in opts
-            else 'log' if '-log' in opts
-            else 'q4' if '-q4' in opts
-            else 'log')
+    view = 'linear' if '-linear' in opts else 'log' if '-log' in opts else 'q4' if '-q4' in opts else 'log'
 
     opt_values = dict(split
@@ -242 +230 @@
                       if len(split) == 2)
     # Sort out data
-    qmax = (10.0 if '-exq' in opts
-            else 1.0 if '-highq' in opts
-            else 0.2 if '-midq' in opts
-            else 0.05)
+    qmax = 10.0 if '-exq' in opts else 1.0 if '-highq' in opts else 0.2 if '-midq' in opts else 0.05
     Nq = int(opt_values.get('-Nq', '128'))
     res = float(opt_values.get('-res', '0'))
@@ -254 +239 @@
 
     # modelling accuracy is determined by dtype and cutoff
-    dtype = ('longdouble' if '-quad' in opts
-             else 'double' if '-double' in opts
-             else 'single')
+    dtype = 'double' if '-double' in opts else 'single'
     cutoff = float(opt_values.get('-cutoff','1e-5'))
 
@@ -264 +247 @@
         seed = int(opt_values['-random']) if '-random' in opt_values else None
         pars, seed = randomize_model(pars, seed=seed)
+        constrain_pars(model_definition, pars)
         print "Randomize using -random=%i"%seed
     pars.update(set_pars)  # set value after random to control value
-    constrain_pars(model_definition, pars)
 
     # parameter selection
@@ -379 +362 @@
 
     -plot*/-noplot plots or suppress the plot of the model
-    -single*/-double/-quad use single/double/quad precision for comparison
+    -single*/-double uses double precision for comparison
     -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0
     -Nq=128 sets the number of Q points in the data set
@@ -403 +386 @@
 NAME_OPTIONS = set([
     'plot','noplot',
-    'single','double','longdouble',
+    'single','double',
     'lowq','midq','highq','exq',
     '2d','1d',

sasmodels/compare_many.py

@@ -57 +57 @@
     num_good = 0
     first = True
-    max_diff = 0
     for k in range(N):
         print >>sys.stderr, name, k
@@ -74 +73 @@
             columns.extend(stats)
             labels.append('GPU single')
-            max_diff = max(max_diff, stats[0])
-            good = good and (stats[0] < 5e-5)
+            good = good and (stats[0] < 1e-14)
         if 0 and environment().has_double:
             gpu_double_value = trymodel(eval_opencl, dtype='double', cutoff=cutoff)
@@ -81 +79 @@
             columns.extend(stats)
             labels.append('GPU double')
-            max_diff = max(max_diff, stats[0])
-            good = good and (stats[0] < 1e-12)
+            good = good and (stats[0] < 1e-14)
         if 1:
             cpu_double_value = trymodel(eval_ctypes, dtype='double', cutoff=cutoff)
@@ -88 +85 @@
             columns.extend(stats)
             labels.append('CPU double')
-            max_diff = max(max_diff, stats[0])
-            good = good and (stats[0] < 1e-12)
+            good = good and (stats[0] < 1e-14)
         if 0:
             stats = get_stats(cpu_double_value, gpu_single_value, index)
             columns.extend(stats)
             labels.append('single/double')
-            max_diff = max(max_diff, stats[0])
-            good = good and (stats[0] < 5e-5)
+            good = good and (stats[0] < 1e-14)
 
         columns += [v for _,v in sorted(pars_i.items())]
@@ -105 +100 @@
         else:
             print(("%d,"%seed)+','.join("%g"%v for v in columns))
-    print '"good","%d/%d","max diff",%g'%(num_good, N, max_diff)
+    print '"%d/%d good"'%(num_good, N)
 
 

sasmodels/generate.py

@@ -197 +197 @@
 # TODO: identify model files which have changed since loading and reload them.
 
-__all__ = ["make", "doc", "sources", "use_single", "use_long_double"]
+__all__ = ["make", "doc", "sources", "use_single"]
 
 import sys
@@ -206 +206 @@
 C_KERNEL_TEMPLATE_PATH = joinpath(dirname(__file__), 'kernel_template.c')
 
+F64 = np.dtype('float64')
 F32 = np.dtype('float32')
-F64 = np.dtype('float64')
-try:  # CRUFT: older numpy does not support float128
-    F128 = np.dtype('float128')
-except TypeError:
-    F128 = None
-
 
 # Scale and background, which are parameters common to every form factor
@@ -327 +322 @@
     source = re.sub(r'[^a-zA-Z_](\d*[.]\d+|\d+[.]\d*)([eE][+-]?\d+)?',
                     r'\g<0>f', source)
-    return source
-
-def use_long_double(source):
-    """
-    Convert code from double precision to long double precision.
-    """
-    # Convert double keyword to float.  Accept an 'n' parameter for vector
-    # values, where n is 2, 4, 8 or 16. Assume complex numbers are represented
-    # as cdouble which is typedef'd to double2.
-    source = re.sub(r'(^|[^a-zA-Z0-9_]c?)double(([248]|16)?($|[^a-zA-Z0-9_]))',
-                    r'\1long double\2', source)
-    # Convert floating point constants to single by adding 'f' to the end.
-    # OS/X driver complains if you don't do this.
-    source = re.sub(r'[^a-zA-Z_](\d*[.]\d+|\d+[.]\d*)([eE][+-]?\d+)?',
-                    r'\g<0>L', source)
     return source
 

sasmodels/kernel_template.c

@@ -125 +125 @@
     if (weight > cutoff) {
       const double scattering = Iq(qi, IQ_PARAMETERS);
-      //if (scattering >= 0.0) { // scattering cannot be negative
+      if (scattering >= 0.0) { // scattering cannot be negative
         ret += weight*scattering;
         norm += weight;
@@ -133 +133 @@
         norm_vol += vol_weight;
       #endif
-      //}
+      }
     //else { printf("exclude qx,qy,I:%%g,%%g,%%g\n",qi,scattering); }
     }
@@ -198 +198 @@
 
       const double scattering = Iqxy(qxi, qyi, IQXY_PARAMETERS);
-      //if (scattering >= 0.0) { // scattering cannot be negative
+      if (scattering >= 0.0) { // scattering cannot be negative
         // TODO: use correct angle for spherical correction
         // Definition of theta and phi are probably reversed relative to the
@@ -219 +219 @@
       #endif
         norm_vol += vol_weight;
-      //}
+      }
       //else { printf("exclude qx,qy,I:%%g,%%g,%%g\n",qi,scattering); }
     }

sasmodels/kernelcl.py

@@ -22 +22 @@
 devices, where it can be combined with other structure factors and form
 factors and have instrumental resolution effects applied.
-
-In order to use OpenCL for your models, you will need OpenCL drivers for
-your machine.  These should be available from your graphics card vendor.
-Intel provides OpenCL drivers for CPUs as well as their integrated HD
-graphics chipsets.  AMD also provides drivers for Intel CPUs, but as of
-this writing the performance is lacking compared to the Intel drivers.
-NVidia combines drivers for CUDA and OpenCL in one package.  The result
-is a bit messy if you have multiple drivers installed.  You can see which
-drivers are available by starting python and running:
-
-    import pyopencl as cl
-    cl.create_some_context(interactive=True)
-
-Once you have done that, it will show the available drivers which you
-can select.  It will then tell you that you can use these drivers
-automatically by setting the PYOPENCL_CTX environment variable.
-
-Some graphics cards have multiple devices on the same card.  You cannot
-yet use both of them concurrently to evaluate models, but you can run
-the program twice using a different device for each session.
-
-OpenCL kernels are compiled when needed by the device driver.  Some
-drivers produce compiler output even when there is no error.  You
-can see the output by setting PYOPENCL_COMPILER_OUTPUT=1.  It should be
-harmless, albeit annoying.
 """
 import os

sasmodels/kerneldll.py

@@ -1 @@
-r"""
+"""
 C types wrapper for sasview models.
 
@@ -5 +5 @@
 you wish to allow single precision floating point evaluation for the compiled
 models, otherwise it defaults to *False*.
-
-The compiler command line is stored in the attribute *COMPILE*, with string
-substitutions for %(source)s and %(output)s indicating what to compile and
-where to store it.  The actual command is system dependent.
-
-On windows systems, you have a choice of compilers.  *MinGW* is the GNU
-compiler toolchain, available in packages such as anaconda and PythonXY,
-or available stand alone. This toolchain has had difficulties on some
-systems, and may or may not work for you.  In order to build DLLs, *gcc*
-must be on your path.  If the environment variable *SAS_OPENMP* is given
-then -fopenmp is added to the compiler flags.  This requires a version
-of MinGW compiled with OpenMP support.
-
-An alternative toolchain uses the Microsoft Visual C++ compiler, available
-free from microsoft:
-
-    `http://www.microsoft.com/en-us/download/details.aspx?id=44266`_
-
-Again, this requires that the compiler is available on your path.  This is
-done by running vcvarsall.bat in a windows terminal.  Install locations are
-system dependent, such as:
-
-    C:\Program Files (x86)\Common Files\Microsoft\Visual C++ for Python\9.0\vcvarsall.bat
-
-or maybe
-
-    C:\Users\yourname\AppData\Local\Programs\Common\Microsoft\Visual C++ for Python\9.0\vcvarsall.bat
-
-And again, the environment variable *SAS_OPENMP* controls whether OpenMP is
-used to compile the C code.  This requires the Microsoft vcomp90.dll library,
-which doesn't seem to be included with the compiler, nor does there appear
-to be a public download location.  There may be one on your machine already
-in a location such as:
-
-    C:\Windows\winsxs\x86_microsoft.vc90.openmp*\vcomp90.dll
-
-If you copy this onto your path, such as the python directory or the install
-directory for this application, then OpenMP should be supported.
 """
 
@@ -49 +11 @@
 import tempfile
 import ctypes as ct
-from ctypes import c_void_p, c_int, c_longdouble, c_double, c_float
+from ctypes import c_void_p, c_int, c_double, c_float
 
 import numpy as np
@@ -96 +58 @@
     if np.dtype(dtype) == generate.F32:
         basename += "32"
-    elif np.dtype(dtype) == generate.F64:
-        basename += "64"
-    else:
-        basename += "128"
     return joinpath(DLL_PATH, basename+'.so')
 
@@ -122 +80 @@
     models are allowed as DLLs.
     """
+    if not ALLOW_SINGLE_PRECISION_DLLS: dtype = "double"   # Force 64-bit dll
     dtype = np.dtype(dtype)
-    if dtype == generate.F32 and not ALLOW_SINGLE_PRECISION_DLLS:
-        dtype = generate.F64  # Force 64-bit dll
 
     if callable(info.get('Iq',None)):
@@ -132 +89 @@
         source = generate.use_single(source)
         tempfile_prefix = 'sas_'+info['name']+'32_'
-    elif dtype == generate.F64:
-        tempfile_prefix = 'sas_'+info['name']+'64_'
     else:
-        source = generate.use_long_double(source)
-        tempfile_prefix = 'sas_'+info['name']+'128_'
+        tempfile_prefix = 'sas_'+info['name']+'_'
 
     source_files = generate.sources(info) + [info['filename']]
@@ -204 +158 @@
             raise
 
-        fp = (c_float if self.dtype == generate.F32
-              else c_double if self.dtype == generate.F64
-              else c_longdouble)
+        fp = c_float if self.dtype == generate.F32 else c_double
         pd_args_1d = [c_void_p, fp] + [c_int]*Npd1d if Npd1d else []
         pd_args_2d= [c_void_p, fp] + [c_int]*Npd2d if Npd2d else []
@@ -275 +227 @@
 
     def __call__(self, fixed_pars, pd_pars, cutoff):
-        real = (np.float32 if self.q_input.dtype == generate.F32
-                else np.float64 if self.q_input.dtype == generate.F64
-                else np.float128)
+        real = np.float32 if self.q_input.dtype == generate.F32 else np.float64
 
         nq = c_int(self.q_input.nq)

sasmodels/kernelpy.py

@@ -211 +211 @@
         if w > cutoff:
             I = form(*args)
-            #positive = (I >= 0.0)
+            positive = (I >= 0.0)
 
             # Note: can precompute spherical correction if theta_index is not
@@ -219 +219 @@
                                     if theta_index >= 0 else 1.0)
             #spherical_correction = 1.0
-            ret += w * I * spherical_correction #* positive
-            norm += w #* positive
+            ret += w * I * spherical_correction * positive
+            norm += w * positive
 
             # Volume normalization.
@@ -230 +230 @@
                 vol_args = [args[index] for index in vol_index]
                 vol_weight = np.prod(weight[vol_weight_index])
-                vol += vol_weight * form_volume(*vol_args) #* positive
-                vol_norm += vol_weight #* positive
+                vol += vol_weight * form_volume(*vol_args) * positive
+                vol_norm += vol_weight * positive
 
     positive = (vol * vol_norm != 0.0)

sasmodels/resolution.py

@@ -1074 +1074 @@
 
 if __name__ == "__main__":
-    #demo()
-    main()
+    demo()
+    #main()