BENCH: Port ma/bench.py to asv

1 files changed, 146 insertions, 0 deletions

diff --git a/benchmarks/benchmarks/bench_ma.py b/benchmarks/benchmarks/bench_ma.py
index b214c0b86..cccb5a4cf 100644
--- a/benchmarks/benchmarks/bench_ma.py
+++ b/benchmarks/benchmarks/bench_ma.py
@@ -111,3 +111,149 @@ class Concatenate(Benchmark):
 
     def time_it(self, mode, n):
         np.ma.concatenate(self.args)
+
+
+class MAFunctions1v(Benchmark):
+    param_names = ['mtype', 'func', 'msize']
+    params = [['np', 'np.ma'],
+              ['sin', 'log', 'sqrt'],
+              ['small', 'big']]
+
+    def setup(self, mtype, func, msize):
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmxl = np.ma.array(xl, mask=maskx)
+
+    def time_functions_1v(self, mtype, func, msize):
+        # fun = {'np.ma.sin': np.ma.sin, 'np.sin': np.sin}[func]
+        fun = eval(f"{mtype}.{func}")
+        if msize == 'small':
+            fun(self.nmxs)
+        elif msize == 'big':
+            fun(self.nmxl)
+
+
+class MAMethod0v(Benchmark):
+    param_names = ['method', 'msize']
+    params = [['ravel', 'transpose', 'compressed', 'conjugate'],
+              ['small', 'big']]
+
+    def setup(self, method, msize):
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmxl = np.ma.array(xl, mask=maskx)
+
+    def time_methods_0v(self, method, msize):
+        if msize == 'small':
+            mdat = self.nmxs
+        elif msize == 'big':
+            mdat = self.nmxl
+        getattr(mdat, method)()
+
+
+class MAFunctions2v(Benchmark):
+    param_names = ['mtype', 'func', 'msize']
+    params = [['np', 'np.ma'],
+              ['multiply', 'divide', 'power'],
+              ['small', 'big']]
+
+    def setup(self, mtype, func, msize):
+        # Small arrays
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        ys = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        m2 = [[True, False, True], [False, False, True]]
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmys = np.ma.array(ys, mask=m2)
+        # Big arrays
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        yl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        masky = yl < -0.8
+        self.nmxl = np.ma.array(xl, mask=maskx)
+        self.nmyl = np.ma.array(yl, mask=masky)
+
+    def time_functions_2v(self, mtype, func, msize):
+        fun = eval(f"{mtype}.{func}")
+        if msize == 'small':
+            fun(self.nmxs, self.nmys)
+        elif msize == 'big':
+            fun(self.nmxl, self.nmyl)
+
+
+class MAMethodGetItem(Benchmark):
+    param_names = ['margs', 'msize']
+    params = [[0, (0, 0), [0, -1]],
+              ['small', 'big']]
+
+    def setup(self, margs, msize):
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmxl = np.ma.array(xl, mask=maskx)
+
+    def time_methods_getitem(self, margs, msize):
+        if msize == 'small':
+            mdat = self.nmxs
+        elif msize == 'big':
+            mdat = self.nmxl
+        getattr(mdat, '__getitem__')(margs)
+
+
+class MAMethodSetItem(Benchmark):
+    param_names = ['margs', 'mset', 'msize']
+    params = [[0, (0, 0), (-1, 0)],
+              [17, np.ma.masked],
+              ['small', 'big']]
+
+    def setup(self, margs, mset, msize):
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmxl = np.ma.array(xl, mask=maskx)
+
+    def time_methods_setitem(self, margs, mset, msize):
+        if msize == 'small':
+            mdat = self.nmxs
+        elif msize == 'big':
+            mdat = self.nmxl
+        getattr(mdat, '__setitem__')(margs, mset)
+
+
+class Where(Benchmark):
+    param_names = ['mtype', 'msize']
+    params = [['np', 'np.ma'],
+              ['small', 'big']]
+
+    def setup(self, mtype, msize):
+        # Small arrays
+        xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        ys = np.random.uniform(-1, 1, 6).reshape(2, 3)
+        m1 = [[True, False, False], [False, False, True]]
+        m2 = [[True, False, True], [False, False, True]]
+        self.nmxs = np.ma.array(xs, mask=m1)
+        self.nmys = np.ma.array(ys, mask=m2)
+        # Big arrays
+        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        yl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        maskx = xl > 0.8
+        masky = yl < -0.8
+        self.nmxl = np.ma.array(xl, mask=maskx)
+        self.nmyl = np.ma.array(yl, mask=masky)
+
+    def time_where(self, mtype, msize):
+        fun = eval(f"{mtype}.where")
+        if msize == 'small':
+            fun(self.nmxs > 2, self.nmxs, self.nmys)
+        elif msize == 'big':
+            fun(self.nmxl > 2, self.nmxl, self.nmyl)