ENH: Add outputless mode for raw

Add outputles mode for raw to allow accurate performance testing of core
PRNG

Author: bashtage

randomstate/performance.py

@@ -41,7 +41,6 @@ def run_timer(dist, command, numpy_command=None, setup='', random_type=''):
 
     res = {}
     for rng in RNGS:
-        print(str(rng))
         mod = 'randomstate.prng' if rng != 'random' else 'numpy'
         key = '-'.join((mod, rng, dist)).replace('"', '')
         command = numpy_command if 'numpy' in mod else command
@@ -67,6 +66,15 @@ def run_timer(dist, command, numpy_command=None, setup='', random_type=''):
     print('-' * 80)
 
 
+def timer_raw():
+    dist = 'random_raw'
+    command = 'rs.random_raw(size=1000000, output=False)'
+    info = np.iinfo(np.int32)
+    command_numpy = 'rs.random_integers({max},size=1000000)'
+    command_numpy=command_numpy.format(max=info.max)
+    run_timer(dist, command, command_numpy, SETUP, 'Standard normals (Ziggurat)')
+
+
 def timer_uniform():
     dist = 'random_sample'
     command = 'rs.random_sample(1000000)'
@@ -108,6 +116,7 @@ def timer_normal_zig():
 
 
 if __name__ == '__main__':
+    timer_raw()
     timer_uniform()
     timer_32bit()
     timer_64bit()

randomstate/randomstate.pyx

@@ -723,7 +723,7 @@ cdef class RandomState:
 
         return array
 
-    def random_raw(self, size=None):
+    def random_raw(self, size=None, output=True):
         """
         random_raw(self, size=None)
 
@@ -735,6 +735,9 @@ cdef class RandomState:
             Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
             ``m * n * k`` samples are drawn.  Default is None, in which case a
             single value is returned.
+        output : bool, optional
+            Output values.  Used for performance testing since the generated 
+            values are not returned.
 
         Returns
         -------
@@ -755,6 +758,17 @@ cdef class RandomState:
         cdef aug_state* rng_state
         rng_state = &self.rng_state
 
+        if not output:
+            if size is None:
+                with self.lock:
+                    random_raw_values(rng_state)
+                return None
+            n = np.asarray(size).sum()
+            with self.lock, nogil:
+                for i in range(n):
+                    random_raw_values(rng_state)
+            return None
+        
         if size is None:
             with self.lock:
                 return random_raw_values(rng_state)