Changes in sasmodels/compare.py [d0fdba2:bd7630d] in sasmodels

File:

• sasmodels/compare.py (modified) (5 diffs)

sasmodels/compare.py

@@ -41 +41 @@
 from . import kerneldll
 from . import kernelcl
-from . import weights
 from .data import plot_theory, empty_data1D, empty_data2D, load_data
 from .direct_model import DirectModel, get_mesh
 from .generate import FLOAT_RE, set_integration_size
+from .weights import plot_weights
 
 # pylint: disable=unused-import
@@ -114 +114 @@
 
     === environment variables ===
-    -DSAS_MODELPATH=~/.sasmodels/custom_models sets path to custom models
-    -DSAS_WEIGHTS_PATH=~/.sasview/weights sets path to custom distributions
+    -DSAS_MODELPATH=path sets directory containing custom models
     -DSAS_OPENCL=vendor:device|none sets the target OpenCL device
     -DXDG_CACHE_HOME=~/.cache sets the pyopencl cache root (linux only)
     -DSAS_COMPILER=tinycc|msvc|mingw|unix sets the DLL compiler
-    -DSAS_OPENMP=0 set to 1 to turn on OpenMP for the DLLs
-    -DSAS_DLL_PATH=~/.sasmodels/compiled_models sets the DLL cache
+    -DSAS_OPENMP=1 turns on OpenMP for the DLLs
+    -DSAS_DLL_PATH=path sets the path to the compiled modules
 
 The interpretation of quad precision depends on architecture, and may
@@ -784 +783 @@
             model_info = base._kernel.info
             dim = base._kernel.dim
-            weights.plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
+            plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
         if opts['show_profile']:
             import pylab
@@ -1289 +1288 @@
 
     if opts['datafile'] is not None:
-        data = load_data(os.path.expanduser(opts['datafile']))
+        data0 = load_data(os.path.expanduser(opts['datafile']))
+        data = data0, data0
     else:
         # Hack around the fact that make_data doesn't take a pair of resolutions
@@ -1440 +1440 @@
     #import pprint; pprint.pprint(model_info)
 
-    # Hack to load user-defined distributions; run through all parameters
-    # and make sure any pd_type parameter is a defined distribution.
-    if (any(p.endswith('pd_type') and v not in weights.MODELS
-            for p, v in pars.items()) or
-        any(p.endswith('pd_type') and v not in weights.MODELS
-            for p, v in pars2.items())):
-       weights.load_weights()
-
     if opts['show_pars']:
         if model_info.name != model_info2.name or pars != pars2: