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        "description": "Arrayish\n========\n\nA library for downstream compatibility of Numpy-compatible arrays.\n\nUsage\n-----\n\nFor an alternative array implementation\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code-block:: python\n\n   import numpy as np\n   import arrayish as ai\n\n\n   class CustomArray(object):\n       \"\"\"Your custom ``ndarray`` compatible implementation.\"\"\"\n       pass\n\n\n   def dot(a, b, out=None)\n       \"\"\" Your custom implementation of ``np.dot``. \"\"\"\n       pass\n\n\n   # Between your own arrays\n   ai.dot.add((CustomArray, CustomArray), dot)\n\n   # With Numpy Arrays, if you'd like. Can also pass another function\n   ai.dot.add((np.ndarray, CustomArray), dot)\n   ai.dot.add((CustomArray, np.ndarray), dot)\n\nFor a downstream library willing to support alternative arrays\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code-block:: python\n\n   import arrayish as ai\n   import numpy as np\n\n   # Instead of this:\n   np.dot(a, b)\n\n   # Do this:\n   ai.dot(a, b)\n\n\n",
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