BENCH: convert bencmarks to asv format

remove files that were part of the origal repo
rework randomgen docs to integrate with numpy and fix some links
remove convenience functions, require explicit call to gen.brng
move code out of numpy.random.randomgen into numpy.random

2 files changed, 137 insertions, 0 deletions

diff --git a/numpy/random/examples/numba/extending.py b/numpy/random/examples/numba/extending.py
new file mode 100644
index 000000000..72e903b1f
--- /dev/null
+++ b/numpy/random/examples/numba/extending.py
@@ -0,0 +1,77 @@
+import datetime as dt
+
+import numpy as np
+import numba as nb
+
+from randomgen import Xoroshiro128
+
+x = Xoroshiro128()
+f = x.ctypes.next_uint32
+s = x.ctypes.state
+
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = f(state) & mask
+    while val > delta:
+        val = f(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, s.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, s.value)
+
+g = x.cffi.next_double
+cffi_state = x.cffi.state
+state_addr = x.cffi.state_address
+
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * g(state) - 1.0
+        x2 = 2.0 * g(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * g(state) - 1.0
+            x2 = 2.0 * g(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+
+print(normals(10, cffi_state).var())
+# Warm up
+normalsj = nb.jit(normals, nopython=True)
+normalsj(1, state_addr)
+
+start = dt.datetime.now()
+normalsj(1000000, state_addr)
+ms = 1000 * (dt.datetime.now() - start).total_seconds()
+print('1,000,000 Polar-transform (numba/Xoroshiro128) randoms in '
+      '{ms:0.1f}ms'.format(ms=ms))
+
+start = dt.datetime.now()
+np.random.standard_normal(1000000)
+ms = 1000 * (dt.datetime.now() - start).total_seconds()
+print('1,000,000 Polar-transform (NumPy) randoms in {ms:0.1f}ms'.format(ms=ms))
diff --git a/numpy/random/examples/numba/extending_distributions.py b/numpy/random/examples/numba/extending_distributions.py
new file mode 100644
index 000000000..17ba2704c
--- /dev/null
+++ b/numpy/random/examples/numba/extending_distributions.py
@@ -0,0 +1,60 @@
+r"""
+On *nix, execute in randomgen/src/distributions
+
+export PYTHON_INCLUDE=#path to Python's include folder, usually \
+    ${PYTHON_HOME}/include/python${PYTHON_VERSION}m
+export NUMPY_INCLUDE=#path to numpy's include folder, usually \
+    ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/core/include
+gcc -shared -o libdistributions.so -fPIC distributions.c -I${NUMPY_INCLUDE} \
+    -I${PYTHON_INCLUDE}
+mv libdistributions.so ../../examples/numba/
+
+On Windows
+
+rem PYTHON_HOME is setup dependent, this is an example
+set PYTHON_HOME=c:\Anaconda
+cl.exe /LD .\distributions.c -DDLL_EXPORT \
+    -I%PYTHON_HOME%\lib\site-packages\numpy\core\include \
+    -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python36.lib
+move distributions.dll ../../examples/numba/
+"""
+import os
+
+import numba as nb
+import numpy as np
+from cffi import FFI
+
+from randomgen import Xoroshiro128
+
+ffi = FFI()
+if os.path.exists('./distributions.dll'):
+    lib = ffi.dlopen('./distributions.dll')
+elif os.path.exists('./libdistributions.so'):
+    lib = ffi.dlopen('./libdistributions.so')
+else:
+    raise RuntimeError('Required DLL/so file was not found.')
+
+ffi.cdef("""
+double random_gauss_zig(void *brng_state);
+""")
+x = Xoroshiro128()
+xffi = x.cffi
+brng = xffi.brng
+
+random_gauss_zig = lib.random_gauss_zig
+
+
+def normals(n, brng):
+    out = np.empty(n)
+    for i in range(n):
+        out[i] = random_gauss_zig(brng)
+    return out
+
+
+normalsj = nb.jit(normals, nopython=True)
+
+# Numba requires a memory address for void *
+# Can also get address from x.ctypes.brng.value
+brng_address = int(ffi.cast('uintptr_t', brng))
+
+norm = normalsj(1000, brng_address)