1. 17 Nov, 2016 1 commit
    • Dean Moldovan's avatar
      Improve consistency of array and array_t with regard to other pytypes · 4de27102
      Dean Moldovan authored
      * `array_t(const object &)` now throws on error
      * `array_t::ensure()` is intended for casters —- old constructor is
        deprecated
      * `array` and `array_t` get default constructors (empty array)
      * `array` gets a converting constructor
      * `py::isinstance<array_T<T>>()` checks the type (but not flags)
      
      There is only one special thing which must remain: `array_t` gets
      its own `type_caster` specialization which uses `ensure` instead
      of a simple check.
      4de27102
  2. 16 Nov, 2016 1 commit
  3. 27 Oct, 2016 2 commits
  4. 13 Oct, 2016 1 commit
  5. 12 Oct, 2016 2 commits
    • Wenzel Jakob's avatar
      Permit creation of NumPy arrays with a "base" object that owns the data · 369e9b39
      Wenzel Jakob authored
      This patch adds an extra base handle parameter to most ``py::array`` and
      ``py::array_t<>`` constructors. If specified along with a pointer to
      data, the base object will be registered within NumPy, which increases
      the base's reference count. This feature is useful to create shallow
      copies of C++ or Python arrays while ensuring that the owners of the
      underlying can't be garbage collected while referenced by NumPy.
      
      The commit also adds a simple test function involving a ``wrap()``
      function that creates shallow copies of various N-D arrays.
      369e9b39
    • Wenzel Jakob's avatar
      added numpy test (minor): check that 'strides' is respected even when creating new arrays · 43f6aa68
      Wenzel Jakob authored
      - This actually works with no changes, I just wasn't 100% convinced and
        decided to write a test to see if it's true.
      43f6aa68
  6. 10 Sep, 2016 2 commits