Liking cljdoc? Tell your friends :D

libpython-clj

JNA libpython bindings to the tech ecosystem.

The exact same binary can run top of on multiple version of python reducing version dependency chain management issues.
Development of new functionality is faster because it can be done from purely from the REPL.

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 '[]'.

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

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. If you desire a different shared library you can override 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__.


(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
(def ones-shape *1)
#'user/ones-shape
user> (type ones-shape)
:pyobject
user> ones-shape
(2, 3)
user> (println ones-shape)
(2, 3)
nil
user> ones-ary
[[1. 1. 1.]
 [1. 1. 1.]]

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

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.

cljdoc is a website building & hosting documentation for Clojure/Script libraries

Keyboard shortcuts Report a problem cljdoc on GitHub

× close