Merge pull request #353 from lmfit/prep094rc2

Prep094rc2

asv_benchmarking/asv.conf.json

@@ -15,7 +15,7 @@
 
     // List of branches to benchmark. If not provided, defaults to "master"
     // (for git) or "tip" (for mercurial).
-    "branches": ["master", "param_minmax_inf"], // for git
+    "branches": ["master", "prep094rc2"], // for git
     // "branches": ["tip"],    // for mercurial
 
     // The DVCS being used.  If not set, it will be automatically
@@ -36,15 +36,15 @@
 
     // The Pythons you'd like to test against.  If not provided, defaults
     // to the current version of Python used to run `asv`.
-    // "pythons": ["2.7", "3.3"],
+    "pythons": ["2.7", "3.5"],
 
     // The matrix of dependencies to test.  Each key is the name of a
     // package (in PyPI) and the values are version numbers.  An empty
     // list indicates to just test against the default (latest)
     // version.
-     "matrix": {
-          "pip+emcee": [],
-     },
+    // "matrix": {
+    //	"pip+emcee": [],
+    // },
 
     // The directory (relative to the current directory) that benchmarks are
     // stored in.  If not provided, defaults to "benchmarks"

asv_benchmarking/benchmarks/benchmarks.py

@@ -38,7 +38,36 @@ def time_minimize(self):
         params.add('shift', value= 0.0, min=-np.pi/2., max=np.pi/2)
         params.add('omega', value= 1.0, min=0, max=10)
 
-        out = minimize(obj_func, params, args=(x, data))
+        return minimize(obj_func, params, args=(x, data))
+
+    def time_minimize_withnan(self):
+        np.random.seed(201)
+        x = np.linspace(0, 15, 601)
+        x[53] = np.nan
+
+        data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +
+                np.random.normal(size=len(x), scale=0.3) )
+        params = Parameters()
+        params.add('amp',   value= 1,  min=0, max=100)
+        params.add('decay', value= 0.0, min=0, max=10)
+        params.add('shift', value= 0.0, min=-np.pi/2., max=np.pi/2)
+        params.add('omega', value= 1.0, min=0, max=10)
+
+        return minimize(obj_func, params, args=(x, data), nan_policy='omit')
+
+    def time_minimize_large(self):
+        np.random.seed(201)
+        x = np.linspace(0, 19, 70001)
+        data = (5. * np.sin(0.6* x - 0.1) * np.exp(-x*x*0.0165) +
+                np.random.normal(size=len(x), scale=0.3) )
+        params = Parameters()
+        params.add('amp',   value= 1,  min=0, max=100)
+        params.add('decay', value= 0.0, min=0, max=10)
+        params.add('shift', value= 0.0, min=-np.pi/2., max=np.pi/2)
+        params.add('omega', value= 0.40, min=0, max=10)
+
+        return minimize(obj_func, params, args=(x, data))
+
 
     def time_confinterval(self):
         np.random.seed(0)
@@ -56,7 +85,7 @@ def residual(p):
 
         minimizer = Minimizer(residual, p)
         out = minimizer.leastsq()
-        ci = conf_interval(minimizer, out)
+        return conf_interval(minimizer, out)
 
 
 class MinimizerClassSuite:
@@ -91,10 +120,10 @@ def setup(self):
                                     fcn_args=(self.x, self.y))
 
     def time_differential_evolution(self):
-        self.mini_de.minimize(method='differential_evolution')
+        return self.mini_de.minimize(method='differential_evolution')
 
     def time_emcee(self):
-        self.mini_emcee.emcee(self.p_emcee, steps=100, seed=1)
+        return self.mini_emcee.emcee(self.p_emcee, steps=100, seed=1)
 
 
 def Minimizer_Residual(p, x, y):

asv_benchmarking/run_benchmark_code.py

@@ -0,0 +1,14 @@
+from benchmarks.benchmarks import MinimizerClassSuite,  MinimizeSuite
+
+
+mtest = MinimizeSuite()
+mtest.setup()
+out = mtest.time_minimize()
+out = mtest.time_minimize_large()
+out = mtest.time_minimize_withnan()
+out = mtest.time_confinterval()
+
+mtest = MinimizerClassSuite()
+mtest.setup()
+out = mtest.time_differential_evolution()
+out = mtest.time_emcee()