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from .common import Benchmark, TYPES1
import numpy as np
class ScalarMath(Benchmark):
# Test scalar math, note that each of these is run repeatedly to offset
# the function call overhead to some degree.
params = [TYPES1]
param_names = ["type"]
def setup(self, typename):
self.num = np.dtype(typename).type(2)
self.int32 = np.int32(2)
self.int32arr = np.array(2, dtype=np.int32)
def time_addition(self, typename):
n = self.num
res = n + n + n + n + n + n + n + n + n + n
def time_addition_pyint(self, typename):
n = self.num
res = n + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1
def time_multiplication(self, typename):
n = self.num
res = n * n * n * n * n * n * n * n * n * n
def time_power_of_two(self, typename):
n = self.num
res = n**2, n**2, n**2, n**2, n**2, n**2, n**2, n**2, n**2, n**2
def time_abs(self, typename):
n = self.num
res = abs(abs(abs(abs(abs(abs(abs(abs(abs(abs(n))))))))))
def time_add_int32_other(self, typename):
# Some mixed cases are fast, some are slow, this documents these
# differences. (When writing, it was fast if the type of the result
# is one of the inputs.)
int32 = self.int32
other = self.num
int32 + other
int32 + other
int32 + other
int32 + other
int32 + other
def time_add_int32arr_and_other(self, typename):
# `arr + scalar` hits the normal ufunc (array) paths.
int32 = self.int32arr
other = self.num
int32 + other
int32 + other
int32 + other
int32 + other
int32 + other
def time_add_other_and_int32arr(self, typename):
# `scalar + arr` at some point hit scalar paths in some cases, and
# these paths could be optimized more easily
int32 = self.int32arr
other = self.num
other + int32
other + int32
other + int32
other + int32
other + int32
class ScalarStr(Benchmark):
# Test scalar to str conversion
params = [TYPES1]
param_names = ["type"]
def setup(self, typename):
self.a = np.array([100] * 100, dtype=typename)
def time_str_repr(self, typename):
res = [str(x) for x in self.a]
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