Usage

Python objects are essentially two dictionaries, one for 'attributes' and one for 'items'. When you use python and use the '.' operator, you are referencing attributes. If you use the '[]' operator, then you are referencing items. Attributes are built in, item access is optional and happens via the __getitem__ and __setitem__ attributes. This is important to realize in that the code below doesn't look like python because we are referencing the item and attribute systems by name and not via '.' or '[]'.

Installation

Ubuntu

sudo apt install libpython3.6
# numpy and pandas are required for unit tests.  Numpy is required for
# zero copy support.
python3.6 -m pip install numpy pandas --user

MacOSX

Python installation instructions here.

Initialize python

user>

user> (require '[libpython-clj.python
                 :refer [as-python as-jvm
                         ->python ->jvm
                         get-attr call-attr call-attr-kw
                         get-item att-type-map
                         call call-kw initialize!
                         as-numpy as-tensor ->numpy
                         run-simple-string
                         add-module module-dict
                         import-module
                         python-type]])
nil


user> (initialize!)
Jun 30, 2019 4:47:39 PM clojure.tools.logging$eval7369$fn__7372 invoke
INFO: executing python initialize!
Jun 30, 2019 4:47:39 PM clojure.tools.logging$eval7369$fn__7372 invoke
INFO: Library python3.6m found at [:system "python3.6m"]
Jun 30, 2019 4:47:39 PM clojure.tools.logging$eval7369$fn__7372 invoke
INFO: Reference thread starting
:ok

This dynamically finds the python shared library and loads it using output from the python3 executable on your system. For information about how that works, please checkout the code here.

Execute Some Python

*out* and *err* capture python stdout and stderr respectively.

user> (run-simple-string "print('hey')")
hey
{:globals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>},
 :locals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>}}

The results have been 'bridged' into java meaning they are still python objects but there are java wrappers over the top of them. For instance, Object.toString forwards its implementation to the python function __str__.

(def bridged (run-simple-string "print('hey')"))
(instance? java.util.Map (:globals bridged))
true
user> (:globals bridged)
{'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>}

We can get and set global variables here. If we run another string, these are in the environment. The globals map itself is the global dict of the main module:

(def main-globals (-> (add-module "__main__")
                            (module-dict)))
#'user/main-globals

user> main-globals
{'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>}
user> (keys main-globals)
("__name__"
 "__doc__"
 "__package__"
 "__loader__"
 "__spec__"
 "__annotations__"
 "__builtins__")
user> (get main-globals "__name__")
"__main__"
user> (.put main-globals "my_var" 200)
nil

user> (run-simple-string "print('your variable is:' + str(my_var))")
your variable is:200
{:globals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, 'my_var': 200},
 :locals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, 'my_var': 200}}

Running Python isn't ever really necessary, however, although it may at times be convenient. You can call attributes from clojure easily:

user> (def np (import-module "numpy"))
#'user/np
user> (def ones-ary (call-attr np "ones" [2 3]))
#'user/ones-ary
user> ones-ary
[[1. 1. 1.]
 [1. 1. 1.]]
user> (call-attr ones-ary "__len__")
2
user> (vec ones-ary)
[[1. 1. 1.] [1. 1. 1.]]
user> (type (first *1))
:pyobject
user> (get-attr ones-ary "shape")
(2, 3)
user> (vec (get-attr ones-ary "shape"))
[2 3]

user> (att-type-map ones-ary)
{"T" :ndarray,
 "__abs__" :method-wrapper,
 "__add__" :method-wrapper,
 "__and__" :method-wrapper,
 "__array__" :builtin-function-or-method,
 "__array_finalize__" :none-type,
 "__array_function__" :builtin-function-or-method,
 "__array_interface__" :dict,
 "__array_prepare__" :builtin-function-or-method,
 "__array_priority__" :float,
 "__array_struct__" :py-capsule,
 "__array_ufunc__" :builtin-function-or-method,
 "__array_wrap__" :builtin-function-or-method,
 "__bool__" :method-wrapper,
 "__class__" :type,
 "__complex__" :builtin-function-or-method,
 "__contains__" :method-wrapper,
 ...
 "std" :builtin-function-or-method,
 "strides" :tuple,
 "sum" :builtin-function-or-method,
 "swapaxes" :builtin-function-or-method,
 "take" :builtin-function-or-method,
 "tobytes" :builtin-function-or-method,
 "tofile" :builtin-function-or-method,
 "tolist" :builtin-function-or-method,
 "tostring" :builtin-function-or-method,
 "trace" :builtin-function-or-method,
 "transpose" :builtin-function-or-method,
 "var" :builtin-function-or-method,
 "view" :builtin-function-or-method}

att-type-map

It can be extremely helpful to print out the attribute name->attribute type map:

user> (att-type-map ones-ary)
{"T" :ndarray,
 "__abs__" :method-wrapper,
 "__add__" :method-wrapper,
 "__and__" :method-wrapper,
 "__array__" :builtin-function-or-method,
 "__array_finalize__" :none-type,
 "__array_function__" :builtin-function-or-method,
 "__array_interface__" :dict,
 ...
  "real" :ndarray,
 "repeat" :builtin-function-or-method,
 "reshape" :builtin-function-or-method,
 "resize" :builtin-function-or-method,
 "round" :builtin-function-or-method,
 "searchsorted" :builtin-function-or-method,
 "setfield" :builtin-function-or-method,
 "setflags" :builtin-function-or-method,
 "shape" :tuple,
 "size" :int,
 "sort" :builtin-function-or-method,
 ...
}

Errors

Errors are caught and an exception is thrown. The error text is saved verbatim in the exception:

user> (run-simple-string "print('syntax errrr")
Execution error (ExceptionInfo) at libpython-clj.python.interpreter/check-error-throw (interpreter.clj:260).
  File "<string>", line 1
    print('syntax errrr
                      ^
SyntaxError: EOL while scanning string literal

Some Syntax Sugar

user> (py/from-import numpy linspace)
#'user/linspace
user> (linspace 2 3 :num 10)
[2.         2.11111111 2.22222222 2.33333333 2.44444444 2.55555556
 2.66666667 2.77777778 2.88888889 3.        ]
user> (doc linspace)
-------------------------
user/linspace

    Return evenly spaced numbers over a specified interval.

    Returns `num` evenly spaced samples, calculated over the
    interval [`start`, `stop`].

• from-import - sugar around python from a import b. Takes multiple b's.
• import-as - surgar around python import a as b.
• $a - call an attribute using symbol att name. Keywords map to kwargs
• $c - call an object mapping keywords to kwargs

Experimental Sugar

We are trying to find the best way to handle attributes in order to shorten generic python notebook-type usage. The currently implemented direction is:

• $. - get an attribute. Can pass in symbol, string, or keyword
• $.. - get an attribute. If more args are present, get the attribute on that result.

user> (py/$. numpy linspace)
<function linspace at 0x7fa6642766a8>
user> (py/$.. numpy random shuffle)
<built-in method shuffle of numpy.random.mtrand.RandomState object at 0x7fa66410cca8>

New sugar (fixme)

libpython-clj offers syntactic forms similar to those offered by Clojure for interacting with Python classes and objects.

Class/object methods Where in Clojure you would use (. obj method arg1 arg2 ... argN), you can use (py. pyobj method arg1 arg2 ... argN).

In Python, this is equivalent to pyobj.method(arg1, arg2, ..., argN). Concrete examples are shown below.

Class/object attributes Where in Clojure you would use (.- obj attr), you can use (py.- pyobj attr).

In Python, this is equivalent to pyobj.attr. Concrete examples shown below.

Nested attribute access To achieve a chain of method/attribute access, use the py.. for.

(py.. (requests/get "http://www.google.com") 
      -content
      (decode "latin-1"))

(Note: requires Python requests module installled)

Examples

user=> (require '[libpython-clj.python :as py :refer [py. py.. py.-]])
nil
user=> (require '[libpython-clj.require :refer [require-python]])

... debug info ...

user=> (require-python '[builtins :as python])
WARNING: AssertionError already refers to: class java.lang.AssertionError in namespace: builtins, being replaced by: #'builtins/AssertionError
WARNING: Exception already refers to: class java.lang.Exception in namespace: builtins, being replaced by: #'builtins/Exception
nil
user=> (def xs (python/list))
#'user/xs
user=> (py. xs append 1)
nil
user=> xs
[1]
user=> (py. xs extend [1 2 3])
nil
user=> xs
[1, 1, 2, 3]
user=> (py. xs __len__)
4
user=> ((py.- xs __len__)) ;; attribute syntax to get then call method
4
user=> (py. xs pop)
3
user=> (py. xs clear)
nil
;; requires Python requests module installed
user=> (require-python 'requests)
nil
user=> (def requests (py/import-module "requests"))
#'user/requests
user=> (py.. requests (get "http://www.google.com") -content (decode "latin-1"))
"<!doctype html><html itemscope=\"\" ... snip ... "

Numpy

Speaking of numpy, you can move data between numpy and java easily.

user> (def tens-data (as-tensor ones-ary))
#'user/tens-data
user> (println tens-data)
#tech.v2.tensor<float64>[2 3]
[[1.000 1.000 1.000]
 [1.000 1.000 1.000]]
nil


user> (require '[tech.v2.datatype :as dtype])
nil
user> (def ignored (dtype/copy! (repeat 6 5) tens-data))
#'user/ignored
user> (.put main-globals "ones_ary" ones_ary)
Syntax error compiling at (*cider-repl cnuernber/libpython-clj:localhost:39019(clj)*:191:7).
Unable to resolve symbol: ones_ary in this context
user> (.put main-globals "ones_ary" ones-ary)
nil

user> (run-simple-string "print(ones_ary)")
[[5. 5. 5.]
 [5. 5. 5.]]
{:globals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, 'my_var': 200, 'ones_ary': array([[5., 5., 5.],
       [5., 5., 5.]])},
 :locals
 {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, 'my_var': 200, 'ones_ary': array([[5., 5., 5.],
       [5., 5., 5.]])}}

So heavy data has a zero-copy route. Anything backed by a :native-buffer has a zero copy pathway to and from numpy. For more information on how this happens, please refer to the datatype library documentation.

Just keep in mind, careless usage of zero copy is going to cause spooky action at a distance.