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        "author": "Veit Heller",
        "author_email": "veit@veitheller.de",
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        "description": "hawkweed\n=============\n\nYet another implementation of missing functions.\n\nInstallation\n------------\n\n::\n\n  pip install hawkweed\n\nUsage\n-----\n\nhawkweed is roughly divided into three different parts: datatypes, monads and\nfunctions. All the functions are exhaustively documented with pydoc, all the\nparameters, the functions' time complexity (if applicable) and the return value\nare given.\n\nDatatypes\n---------\n\nMost of the datatypes implemented in hawkweed are mere wrappers around Python\nstandard datatypes. If the function does not return anything in the Python\ndatatype, this implementation will return ``self`` to allow for chaining.\n\nA notable exception is the largely unstable and undocumented ``Future`` class.\n\n.. code-block:: python\n\n    from hawkweed import List, Dict, Set\n\n    List([1]).append(2).extend([3, None, 4]).remove_empty() # => List([1, 2, 3, 4])\n    List(range(10)).take(5) # => generator from 0 to 4\n    List(range(10)).drop(5) # => generator from 5 to 9\n    List(range(10)).take_while(lambda x: x < 5) # => generator from 0 to 4\n    List(range(10)).drop_while(lambda x: x < 5) # => generator from 4 to 9\n    List(range(10)).nth(3) # => generator yielding 0, 3, 6 and 9 (lazily); works with any subclass of Iterable\n    List(range(10)).reset(range(5)) # => List([0, 1, 2, 3, 4])\n\n    Dict({1: 2, 3: 4}).reverse() # => Dict({2: 1, 4: 3})\n    Dict({1: 2, 3: 4, 2: None}).remove_empty() # => Dict({1: 2, 3: 4})\n    Dict({1: 2, 3: 4, None: \"go away\"}).remove_empty(filter_keys=True) # => Dict({1: 2, 3: 4})\n    Dict({1: 2, 3: 4, 2: 3}).remove_empty(fun=lambda x: x!=2) # => Dict({1: 2, 3: 4})\n    Dict({1: 2, 3: 4}).reduce(fun=lambda acc, k, v: acc + k + v, acc=0) # => 10\n    Dict({1: 2, 3: 4}).reduce(fun=lambda acc, k, v: acc + (k, v)) # => (1, 2, 3, 4)\n    Dict({1: 2, 3: 4, 5: 6}).pick(1, 5) # => Dict({1: 2, 5: 6})\n\n    Set({1, 2, 3, 4}).remove_empty(fun=lambda x: x!=3) # => Set({1, 2, 4})\n\n    # And now for something completely different\n    Dict({\n      \"foo\": List([1, 2, 3, Dict({\"bar\": \"baz\"})])\n    }).get_in(\"foo\", 3, \"bar\") # => \"baz\"\n    Dict({\n      \"foo\": List([1, 2, 3, Dict({\"bar\": \"baz\"})])\n    }).get_in(\"foo\", 100, \"bar\") # => None\n    Dict({\n      \"foo\": List([1, 2, 3, Dict({\"bar\": \"baz\"})])\n    }).get_in(\"foo\", 100, \"bar\", dflt=\"i am a default value\") # => \"i am a default value\"\n    Dict({\n      \"foo\": List([1, 2, 3, Dict({\"bar\": \"baz\"})])\n    }).update_in(\"foo\", 1, \"bar\", to=\"update\") # => Dict({\"foo\": List([1, 2, 3, Dict({\"bar\": \"update\"})])})\n    # if you want to insert your own datatype, just inherit from hawkweed.Collection\n    # and implement get(key, dflt=None) and __setitem__\n\nFunctions\n---------\n\nAll of the functions are standalone and curried whenever possible. They do not depend\non hawkweeds datatypes in any way.\n\n.. code-block:: python\n\n  from hawkweed import map, reduce, List, all, any, constantly, delay\n\n  map(inc, range(100)) # => range(1, 101)\n  incrementor = map(inc)\n  incrementor(List(range(100))) # => range(1, 101)\n  summator = reduce(add)\n  summator(range(5)) # => 10\n  all(lambda x: x > 100, [101, 102, 103]) # => True\n  any(lambda x: x > 10, [3, 5, 8]) # => False\n  constantly(10) # => an infinite generator of 10\n  delayed = delay(print, 'Hello, World!') # => this will return a variable that, when called, will compute the result of print with the argument 'Hello, World!'\n  # it will cache the result instead of recomputing it upon reevaluation, i.e. `delayed() or delayed()` will only print 'Hello, World!' once\n\nA few other functions that you might expect from a functional programming library (``compose``,\n``pipe``, ``identity``, ``apply``, ``flip``, ``curry`` and the like) are also implemented. They\nshould be intuitive and work as expected. If they do not or are not consider it a bug.\n\nMonads\n------\n\nThe implemented monads are: Identity, Maybe (Just/Nothing), Continuation, Either, IO, CachedIO,\nand List (called ListM). do notation is also supported.\n\n.. code-block:: python\n\n  from hawkweed import doM, wrapM, Just\n\n  def doMe():\n    res1 = yield Just(1)\n    res2 = yield Just(10)\n    yield Just(res1+ res2)\n\n  doM(doMe()) # => Just(11)\n  \n  wrapM(Just(10)).real\n  # => 10; the wrapper will try to call the wrapped values' function whenever it does not exist in the monad\n\nThere is a callcc function and all of the functions in Haskell's Data.Maybe_ and Data.Either_ are implemented.\n\n.. _Data.Maybe: https://hackage.haskell.org/package/base-4.9.0.0/docs/Data-Maybe.html\n.. _Data.Either: https://hackage.haskell.org/package/base-4.9.0.0/docs/Data-Either.html\n\n\nHave fun!",
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