Exemplos de array em Python

Exemplo n.º 1

def convert(x, coerce):
    if isinstance(x, xnd.array):
        return x

    try:
        return xnd.array.from_buffer(memoryview(x))
    except TypeError:
        pass

    if coerce:
        return xnd.array(x)

    return NotImplemented

Exemplo n.º 2

    def convert(x, coerce):
        if isinstance(x, xnd.array):
            return x

        try:
            return xnd.array.from_buffer(memoryview(x))
        except TypeError:
            pass

        if coerce:
            return xnd.array(x)

        if isinstance(x, (int, float, bool)):
            return x

        raise ua.BackendNotImplementedError("Unsupported output received.")

Exemplo n.º 3

Arquivo: data_conversion_tuple.py Projeto: xnd-project/xnd-benchmarks

#                          Python data conversion
# ========================================================================

#
# Timeit is too slow for large arrays, so this benchmark just uses time.time(),
# which is accurate enough for this purpose.
#

### List of structured dtypes, from the numpy documentation ###
lst = [('Rex', 9, 81.0)] * 10000000

# Infer the type. This is only possible in xnd, NumPy infers a Python object type.
print("\nType inference")
print("---------------\n")
start = time()
x = xnd.array(lst)
stop = time()
print("xnd:  ", stop - start)

# Dtype is provided.
print("\nDtype provided")
print("--------------\n")
start = time()
x = xnd.array(lst, dtype="(fixed_string(10), int32, float32)")
stop = time()
print("xnd:  ", stop - start)
del x

dt = np.dtype([('name', 'U10'), ('age', 'i4'), ('weight', 'f4')])
start = time()
x = np.array(lst, dtype=dt)

Exemplo n.º 4

import xnd
import numpy as np
from time import time


repeat = 1000000

### Small array view ###
lst = [[1,2,3], [3,4,5]]

print("\nSmall subarray view")
print("-------------------\n")
x = xnd.array(lst)
start = time()
for i in range(repeat):
    y = x[1]
stop = time()
print("   xnd:  ", stop-start)
del x, y

start = time()
x = np.array(lst)
for i in range(repeat):
    y = x[1]
stop = time()
print("   numpy:", stop-start)
del x, y


### Medium sized array view ###
print("\nMedium sized subarray view")

Exemplo n.º 5

Arquivo: data_conversion_int64.py Projeto: xnd-project/xnd-benchmarks

#                          Python data conversion
# ========================================================================

#
# Timeit is too slow for large arrays, so this benchmark just uses time.time(),
# which is accurate enough for this purpose.
#

### List of int64_t values ###
lst = [1] * 10000000

# Type inference for reading a medium sized Python list.
print("\nType inference")
print("--------------\n")
start = time()
x = xnd.array(lst)
stop = time()
print("   xnd:  ", stop - start)
del x

start = time()
x = np.array(lst)
stop = time()
print("   numpy:", stop - start)
del x

# The same, but with an explicit dtype.
print("\nDtype provided")
print("--------------\n")
start = time()
x = xnd.array(lst, dtype="int64")