text: #!/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,
          )
  
  
  def linspace(
      start,
      stop,
      num=50,
      endpoint=True,
      retstep=False,
      dtype=None,
      gpu=False,
      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])
  