#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...core import ExecutableTuple
from ...serialization.serializables import Int64Field, BoolField, AnyField
from ...config import options
from ..utils import decide_chunk_sizes
from .core import TensorNoInput
from ..array_utils import create_array
class TensorLinspace(TensorNoInput):
_op_type_ = OperandDef.TENSOR_LINSPACE
_start = AnyField("start")
_stop = AnyField("stop")
_num = Int64Field("num")
_endpoint = BoolField("endpoint")
def __init__(
self, start=None, stop=None, num=None, endpoint=None, dtype=None, **kw
):
dtype = np.dtype(np.linspace(0, 1, 1).dtype if dtype is None else dtype)
super().__init__(
_start=start, _stop=stop, _num=num, _endpoint=endpoint, dtype=dtype, **kw
)
def to_chunk_op(self, *args):
start, stop, num, endpoint = args
op = self.copy().reset_key()
op._start = start
op._stop = stop
op._num = num
op._endpoint = endpoint
return op
@classmethod
def tile(cls, op):
tensor = op.outputs[0]
chunk_length = tensor.extra_params.raw_chunk_size or options.chunk_size
chunk_length = decide_chunk_sizes(
tensor.shape, chunk_length, tensor.dtype.itemsize
)
start, stop, num, endpoint = (
tensor.op.start,
tensor.op.stop,
tensor.op.num,
tensor.op.endpoint,
)
if num > 1:
step = float(stop - start) / (num if not endpoint else num - 1)
else:
step = 0.0
chunks = []
chunk_start = start
nsplit = []
for i, cs in enumerate(chunk_length[0]):
chunk_stop = chunk_start + (cs - 1) * step
chunk_op = op.to_chunk_op(chunk_start, chunk_stop, cs, True)
chunk_shape = (cs,)
chunk_idx = (i,)
chunk = chunk_op.new_chunk(None, shape=chunk_shape, index=chunk_idx)
chunks.append(chunk)
nsplit.append(cs)
chunk_start = chunk_start + cs * step
new_op = op.copy()
return new_op.new_tensors(
op.inputs, op.outputs[0].shape, chunks=chunks, nsplits=(tuple(nsplit),)
)
@property
def start(self):
return self._start
@property
def stop(self):
return self._stop
@property
def num(self):
return self._num
@property
def endpoint(self):
return self._endpoint
@classmethod
def execute(cls, ctx, op):
ctx[op.outputs[0].key] = create_array(op)(
"linspace",
op.start,
op.stop,
num=op.num,
endpoint=op.endpoint,
dtype=op.dtype,
)
[docs]def linspace(
start,
stop,
num=50,
endpoint=True,
retstep=False,
dtype=None,
gpu=None,
chunk_size=None,
):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output tensor. If `dtype` is not given, infer the data
type from the other input arguments.
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
Returns
-------
samples : Tensor
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
logspace : Samples uniformly distributed in log space.
Examples
--------
>>> import mars.tensor as mt
>>> mt.linspace(2.0, 3.0, num=5).execute()
array([ 2. , 2.25, 2.5 , 2.75, 3. ])
>>> mt.linspace(2.0, 3.0, num=5, endpoint=False).execute()
array([ 2. , 2.2, 2.4, 2.6, 2.8])
>>> mt.linspace(2.0, 3.0, num=5, retstep=True).execute()
(array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = mt.zeros(N)
>>> x1 = mt.linspace(0, 10, N, endpoint=True)
>>> x2 = mt.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1.execute(), y.execute(), 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2.execute(), y.execute() + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = int(num)
op = TensorLinspace(start, stop, num, endpoint, dtype=dtype, gpu=gpu)
shape = (num,)
ret = op(shape, chunk_size=chunk_size)
if not retstep:
return ret
if num > 1:
step = float(stop - start) / (num if not endpoint else num - 1)
else:
step = np.nan
return ExecutableTuple([ret, step])