"""Benchmarks for `numpy.lib`."""


from __future__ import absolute_import, division, print_function

from .common import Benchmark

import numpy as np


class Pad(Benchmark):
    """Benchmarks for `numpy.pad`."""

    param_names = ["shape", "pad_width", "mode"]
    params = [
        [(1000,), (10, 100), (10, 10, 10)],
        [1, 3, (0, 5)],
        ["constant", "edge", "linear_ramp", "mean", "reflect", "wrap"],
    ]

    def setup(self, shape, pad_width, mode):
        # avoid np.zeros or np.empty's lazy allocation.
        # np.full causes pagefaults to occur during setup
        # instead of during the benchmark
        self.array = np.full(shape, 0)

    def time_pad(self, shape, pad_width, mode):
        np.pad(self.array, pad_width, mode)

"""Benchmarks for `numpy.lib`."""


from __future__ import absolute_import, division, print_function

from .common import Benchmark

import numpy as np


class Pad(Benchmark):
    """Benchmarks for `numpy.pad`."""

    param_names = ["shape", "pad_width", "mode"]
    params = [
        [(1000,),
         (10, 100),
         (10, 10, 10),
         # 50 * 512 * 512 = 13 million points = 46 MB. should be a good
         # out of cache describing a typical usecase
         (50, 512, 512)],
        [1,
         3,
         (0, 5)],
        ["constant",
         "edge", "linear_ramp",
         # mean/median/minimum/maximum all use the same code path
         "mean",
         # reflect/symmetric share alot of the code path
         "reflect",
         "wrap"],
    ]

    def setup(self, shape, pad_width, mode):
        # Make sure to fill the array to make the OS page fault
        # in the setup phase and not the timed phase
        self.array = np.full(shape, fill_value=1)

    def time_pad(self, shape, pad_width, mode):
        np.pad(self.array, pad_width, mode)

Make the pad benchmark pagefault in setup
BENCH: Make the pad benchmark pagefault in setup

  """Benchmarks for `numpy.lib`."""
  
  
  from __future__ import absolute_import, division, print_function
  
  from .common import Benchmark
  
  import numpy as np
  
  
  class Pad(Benchmark):
      """Benchmarks for `numpy.pad`."""
  
      param_names = ["shape", "pad_width", "mode"]
      params = [
-         [(1000,), (10, 100), (10, 10, 10)],
+         [(1000,),
+          (10, 100),
+          (10, 10, 10),
+          # 50 * 512 * 512 = 13 million points = 46 MB. should be a good
+          # out of cache describing a typical usecase
+          (50, 512, 512)],
+         [1,
+          3,
-         [1, 3, (0, 5)],
+          (0, 5)],
-         ["constant", "edge", "linear_ramp", "mean", "reflect", "wrap"],
+         ["constant",
+          "edge", "linear_ramp",
+          # mean/median/minimum/maximum all use the same code path
+          "mean",
+          # reflect/symmetric share alot of the code path
+          "reflect",
+          "wrap"],
      ]
  
      def setup(self, shape, pad_width, mode):
+         # Make sure to fill the array to make the OS page fault
+         # in the setup phase and not the timed phase
-         # avoid np.zeros or np.empty's lazy allocation.
-         # np.full causes pagefaults to occur during setup
-         # instead of during the benchmark
-         self.array = np.full(shape, 0)
+         self.array = np.full(shape, fill_value=1)
  
      def time_pad(self, shape, pad_width, mode):
          np.pad(self.array, pad_width, mode)
  
Make the pad benchmark pagefault in setup
## Code Before:
"""Benchmarks for `numpy.lib`."""


from __future__ import absolute_import, division, print_function

from .common import Benchmark

import numpy as np


class Pad(Benchmark):
    """Benchmarks for `numpy.pad`."""

    param_names = ["shape", "pad_width", "mode"]
    params = [
        [(1000,), (10, 100), (10, 10, 10)],
        [1, 3, (0, 5)],
        ["constant", "edge", "linear_ramp", "mean", "reflect", "wrap"],
    ]

    def setup(self, shape, pad_width, mode):
        # avoid np.zeros or np.empty's lazy allocation.
        # np.full causes pagefaults to occur during setup
        # instead of during the benchmark
        self.array = np.full(shape, 0)

    def time_pad(self, shape, pad_width, mode):
        np.pad(self.array, pad_width, mode)

## Instruction:
Make the pad benchmark pagefault in setup
## Code After:
"""Benchmarks for `numpy.lib`."""


from __future__ import absolute_import, division, print_function

from .common import Benchmark

import numpy as np


class Pad(Benchmark):
    """Benchmarks for `numpy.pad`."""

    param_names = ["shape", "pad_width", "mode"]
    params = [
        [(1000,),
         (10, 100),
         (10, 10, 10),
         # 50 * 512 * 512 = 13 million points = 46 MB. should be a good
         # out of cache describing a typical usecase
         (50, 512, 512)],
        [1,
         3,
         (0, 5)],
        ["constant",
         "edge", "linear_ramp",
         # mean/median/minimum/maximum all use the same code path
         "mean",
         # reflect/symmetric share alot of the code path
         "reflect",
         "wrap"],
    ]

    def setup(self, shape, pad_width, mode):
        # Make sure to fill the array to make the OS page fault
        # in the setup phase and not the timed phase
        self.array = np.full(shape, fill_value=1)

    def time_pad(self, shape, pad_width, mode):
        np.pad(self.array, pad_width, mode)
