pax_global_header00006660000000000000000000000064141072631260014514gustar00rootroot0000000000000052 comment=d20942d72a5a364d2816ad53d943e6446c127bcc libpython-clj-2.000/000077500000000000000000000000001410726312600142735ustar00rootroot00000000000000libpython-clj-2.000/.github/000077500000000000000000000000001410726312600156335ustar00rootroot00000000000000libpython-clj-2.000/.github/FUNDING.yml000066400000000000000000000000221410726312600174420ustar00rootroot00000000000000github: cnuernber libpython-clj-2.000/.gitignore000066400000000000000000000002561410726312600162660ustar00rootroot00000000000000target /classes /checkouts profiles.clj pom.xml pom.xml.asc *.jar *.class /.lein-* .nrepl-port .hgignore .hg/ __pycache__ .cpcache/ cpython a.out *.pom *.pom.asc /.idea *.imllibpython-clj-2.000/.gitmodules000066400000000000000000000000001410726312600164360ustar00rootroot00000000000000libpython-clj-2.000/.travis.yml000066400000000000000000000007711410726312600164110ustar00rootroot00000000000000language: clojure dist: bionic lein: 2.9.1 sudo: required before_install: - sudo apt-get -y install python3 python3-pip - sudo python3 -mpip install setuptools - sudo python3 -mpip install numpy - curl -O https://download.clojure.org/install/linux-install-1.10.2.796.sh - chmod +x linux-install-1.10.2.796.sh - sudo ./linux-install-1.10.2.796.sh - clojure -Sdescribe addons: apt: update: true install: clojure -Sdescribe script: clojure -M:test cache: directories: - "$HOME/.m2" libpython-clj-2.000/CHANGELOG.md000066400000000000000000000413531410726312600161120ustar00rootroot00000000000000# Time for a ChangeLog! ## 2.000 * You can now create instance functions that accept both positional and keyword arguments - libpython-clj2.python/make-kw-instance-fn. See test at test/libpython-clj2.python.classes-test. * latest dtype-next - see documentation for tech.v3.datatype/set-value!. * Fix for using ffi on arm64 platforms. ## 2.00-beta-22 * Fix to PR-166 - strings in docs weren't properly escaped. ## 2.00-beta-20 * [Issue 167] - `py.` fails in a go-loop. * [PR 166](https://github.com/clj-python/libpython-clj/pull/166) - Fix codegen for special double values such as Nan and infinity. * [PR 165](https://github.com/clj-python/libpython-clj/pull/165) - remove codegen builtins and numpy due to codegen pathway not being stable and potential version conflicts between e.g. numpy versions. ## 2.00-beta-19 * [issue 126](https://github.com/clj-python/libpython-clj/issues/126) - Python maps sometimes fail to destructure. ## 2.00-beta-18 * Additional fix for 162 to allow booleans to be considered primitives and be output inline. * Experimental fix for [issue 164](https://github.com/clj-python/libpython-clj/issues/164) - Unsigned int64 fails with `OverflowError: int too big to convert`. ## 2.00-beta-17 * Almost fix fo [issue 163](https://github.com/clj-python/libpython-clj/issues/163) - codegen fails with JVM primitives. ## 2.00-beta-16 * Fix for [issue 162](https://github.com/clj-python/libpython-clj/issues/162) - many things failing when using JDK-16. Note there is now a :jdk-16 alias in deps.edn that shows how to use libpython-clj with JDK-16. ## 2.00-beta-15 * Fix for [issue 160](https://github.com/clj-python/libpython-clj/issues/160) - ->python has to realize lazy seqs. * Remove camel-snake-kebab as direct dependency; it comes in via dtype-next. ## 2.00-beta-13 * Update to dtype-next to support graal native static ffi and library generation. * Update to dtype-next to have correct definitions of ptr-t, offset-t, and size-t types. ## 2.00-beta-12 * Small refinements around embedding and documentation. ## 2.00-beta-11 * Major improvements to get libpython-clj v2 to work as robustly as v1 did across several operating systems. * First release that includes embedded functionality (!!). See the embedded topic for more details. ## 2.00-beta-8 * Add back in dynamically searching for libraries based on `java.library.path`. ## 2.00-beta-7 * Upgrade to dtype-next to ensure that JNA is the preferred FFI framework. * Re-introduce java.library.path shenanigans in order to support pyenv type systems. ## 2.00-beta-6 * Revert initialize! back to v1 optional argument style. ## 2.00-beta-5 * Fix codox document generation issue. * Added a document on environments. ## 2.00-beta-4 * [static namespace generation](https://clj-python.github.io/libpython-clj/libpython-clj2.codegen.html#var-write-namespace.21) - Generate AOT-safe clojure code that will dynamically find the symbol and cache it upon first call. ## 2.00-beta-3 * Fixes to allow Conda to work - [cnuernber/facial-rec](https://github.com/cnuernber/facial-rec) lives again!! ## 2.00-beta-2 * Fix for [numpy.all returns false](https://github.com/clj-python/libpython-clj/issues/148) ## 2.00-beta-1 * Major upgrade of tech system - moved to dtype-next. This release is only compatible with tech.ml.dataset versions 5.+. There are many changes but there should be very few user visible ones. Please see [cnuernber/dtype-next](https://cnuernber.github.io/dtype-next/) for more information. This brings a total refactor removing about 1/2 the code, 32bit support and and JDK-16 support. ## 1.45 * tech.datatype - 5.0 release ## 1.43 * `tech.datatype` - latest version to work with `tech.ml.dataset`. ## 1.42 * `tech.datatype` - latest version to work with `tech.ml.dataset`. ## 1.41 * `tech.datatype` - latest version to work with `tech.ml.dataset`. ## 1.40 * `tech.datatype` fixed bug in argsort. ## 1.39 * `tech.datatype` upgrade to version that supports datetime types. ## 1.38-SNAPSHOT * `tech.datatype` is upgrade to 5.0 (!!). ## 1.37 * Update to tech.datatype 4.88 - much faster group-by, lots of small improvements. ## 1.37 * Fix for metadata generation of sys module that was failing. This needs a deeper fix. * Race condition and stability fix. * `deps.edn` now supported in parallel with `project.clj` ## 1.36 * clojure.core.async upgrade ## 1.35 * [Examples are now done by gigasquid](https://github.com/gigasquid/libpython-clj-examples) * [datafy/nav](https://clojure.github.io/clojure/branch-master/clojure.datafy-api.html) are now extensible for custom Python objects. Extend `libpython-clj.require/pydafy` and `libpython-clj.require/pynav` respectively with the symbol of class you want to extend. See respective docstrings for details. * bugfix -- python.str now loaded by `import-python` ## 1.34 * Skipped due to build system issues. ## 1.33 * Better [windows anaconda support](https://github.com/cnuernber/libpython-clj/pull/67) thanks to [orolle](https://github.com/orolle). * Moved to PyGILState* functions for GIL management. This mainly due to [FongHou](https://github.com/cnuernber/libpython-clj/commits?author=FongHou) in PRs [here](https://github.com/cnuernber/libpython-clj/pull/64) and [here](https://github.com/cnuernber/libpython-clj/pull/65). * **BREAKING CHANGE** `require-python` now respects prefix lists -- unfortunately, the previous syntax was incorrect. ```clojure ;; WRONG (syntax version < 1.33) (require-python '(os math)) ``` would be equivalent to ```clojure ;; (do (require-python 'os) (require-python 'math)) ``` the correct syntax for this SHOULD have been ```clojure (require-python 'os 'math) ``` 1.33 fixes this mistake, and provides support for prefix lists, for example: ```clojure (require-python '[builtins :as python] '(builtins [list :as python.list] [dict :as python.dict] [tuple :as python.tuple] [set :as python.set] [frozenset :as python.frozenset])) ``` (**Note**: this is done for you by the function `libpython-clj.require/import-python`) This fix brought to you by [jjtolton](https://github.com/jjtolton). ## 1.32 * DecRef now happens cooperatively in python thread. We used to use separate threads in order to do decrement the refcount on objects that aren't reachable any more. Now it happens at the end of the `with-gil` macro and thus it is possible to have all python access confined to a single thread if this is desired for stability. It is also quite a bit faster as the GIL is captured once and all decrefs happen after that. * Major performance and stability enhancements. 1. Doubled down on single-interpreter design. This simplified some important aspects and led to a bit of perf gain. 2. Implemented JNA [DirectMapping](https://github.com/java-native-access/jna/blob/master/www/DirectMapping.md) for quite a few hotspots found via profiling some simple examples. Lots of people helped out with this (John Collins, Tom Poole (joinr)). * Python executables can now be specified directly using the syntax ```clojure (py/initialize! :python-executable ) ``` where **executable** can be a system installation of Python such as `"python3"`, `"python3.7"`; it can also be a fully qualified path such as `"/usr/bin/python3.7"`; or any Python executable along your discoverable system path. * Python virtual environments can now be used instead of system installations! This has been tested on Linux/Ubuntu variants with virtual environments installed with ```bash virtualenv -p $(which ) env ``` and then invoked using ```clojure (py/initialize! :python-executable "/abs/path/to/env/bin/python") ``` Tested on Python 3.6.8 and Python 3.7. **WARNING**: This is suitable for casual hacking and exploratory development -- however, at this time, we still strongly recommend using Docker and a system installation of Python in production environments. * **breaking change** (and remediation): `require-python` no longer automatically binds the Python module to the Clojure the namespace symbol. If you wish to bind the module to the namespace symbol, you need to use the `:bind-ns` flag. Example: ```clojure (require-python 'requests) ;;=> nil requests ;;=> throws Exception (require-python '[requests :bind-ns]) ;;=> nil (py.. requests (get "https://www.google.com) -content (decode "latin-1)) ;; works ``` * Python method helper syntax for programmatic passing of maps to satisfy `*args`, `**kwargs` situations on the `py.` family of macros. Two new macros have been introduced to address this ```clojure (py* obj method args) (py* obj method args kwargs) (py** obj method kwargs) (py** obj method arg1 arg2 arg3 ... argN kwargs) ``` and the `py..` syntax has been extended to accomodate these conventions as well. ```clojure (py.. obj (*method args)) (py.. obj (*method args kwargs)) (py.. obj (**method kwargs)) (py.. obj (**method arg1 arg2 arg3 ... argN kwargs)) ``` ### Bugs Fixed: * [attribute calls with argument given in map](https://github.com/cnuernber/libpython-clj/issues/46) * [allow specification of python executable](https://github.com/cnuernber/libpython-clj/issues/52) * [difference in calling conventions leads to strange behavior in pandas](https://github.com/cnuernber/libpython-clj/issues/50) with [screencast of fix](https://drive.google.com/file/d/1PTXzWqNaRAiIDDZWqkeffIK2KESRWSRh/view?usp=sharing) * [Allow single threaded use of Python](https://github.com/cnuernber/libpython-clj/issues/48) * [Simplify interpreter design for only one interpreter](https://github.com/cnuernber/libpython-clj/issues/47) ## 1.31 * Python objects are now datafy-able and nav-igable. `require-python` is now rebuilt using datafy. * `py.`, `py.-`, and `py..` added to the `libpython-clj` APIs to allow method/attribute access more consistent with idiomatic Clojure forms. ## 1.30 This release is a big one. With finalizing `require-python` we have a clear way to use Python in daily use and make it look good in normal Clojure usage. There is a demo of [facial recognition](https://github.com/cnuernber/facial-rec) using some of the best open systems for doing this; this demo would absolutely not be possible without this library due to the extensive use of numpy and cython to implement the face detection. We can now interact with even very complex Python systems with roughly the same performance as a pure Python system. #### Finalized `require-python` Lots of work put in to make the `require-python` pathway work with classes and some serious refactoring overall. #### Better Numpy Support * Most of the datatype libraries math operators supported by numpy objects (+,-,etc). * Numpy objects can be used in datatype library functions (like `copy`, `make-container`) and work in optimized ways. ```clojure libpython-clj.python.numpy-test> (def test-ary (py/$a np-mod array (->> (range 9) (partition 3) (mapv vec)))) #'libpython-clj.python.numpy-test/test-ary libpython-clj.python.numpy-test> test-ary [[0 1 2] [3 4 5] [6 7 8]] libpython-clj.python.numpy-test> (dfn/+ test-ary 2) [[ 2 3 4] [ 5 6 7] [ 8 9 10]] libpython-clj.python.numpy-test> (dfn/> test-ary 4) [[False False False] [False False True] [ True True True]] ``` #### Bugs Fixed * Support for java character <-> py string * Fixed potential crash related to use of delay mechanism and stack based gc. * Added logging to complain loudly if refcounts appear to be bad. ## 1.29 * Found/fixed issue with `->jvm` and large Python dictionaries. ## 1.28 * `(range 5)` - Clojure ranges <-> Python ranges when possible. * bridged types derive from `collections.abc.*` so that they pass instance checks in libraries that are checking for generic types. * Really interesting unit test for [generators, ranges and sequences](test/libpython_clj/iter_gen_seq_test.clj). ## 1.27 * Fixed bug where `(as-python {:is_train false})` results in a dictionary with a none value instead of a false value. This was found through hours of debugging why mxnet's forward function call was returning different values in Clojure than in Python. ## 1.26 * [python startup work](https://github.com/cnuernber/libpython-clj/commit/16da3d885f29bde59ea219c9438b9d3654387971) * [python generators & clojure transducers](https://github.com/cnuernber/libpython-clj/pull/27) * [requre-python reload fix](https://github.com/cnuernber/libpython-clj/pull/24) * Bugfix with `require-python` :reload semantics. ## 1.25 Fixed (with tests) major issue with `require-python`. ## 1.24 Clojure's range is now respected in two different ways: * `(range)` - bridges to a Python iterable * `(range 5)` - copies to a Python list ## 1.23 Equals, hashcode, nice default `.toString` of Python types: ```clojure user> (require '[libpython-clj.python :as py]) nil user> (def test-tuple (py/->py-tuple [1 2])) #'user/test-tuple user> (require '[libpython-clj.require :refer [require-python]]) nil user> (require-python '[builtins :as bt]) nil user> (bt/type test-tuple) builtins.tuple user> test-tuple (1, 2) user> (def new-tuple (py/->py-tuple [3 4])) #'user/new-tuple user> (= test-tuple new-tuple) false user> (= test-tuple (py/->py-tuple [1 2])) true user> (.hashCode test-tuple) 2130570162 user> (.hashCode (py/->py-tuple [1 2])) 2130570162 user> (require-python '[numpy :as np]) nil user> (def np-ary (np/array [1 2 3])) #'user/np-ary user> np-ary [1 2 3] user> (bt/type np-ary) numpy.ndarray user> (py/python-type *1) :type ``` ## 1.22 Working to make more Python environments work out of the box. Currently have a testcase for conda working in a clean install of a docker container. There is now a new method: `libpython-clj.python.interpreter/detect-startup-info` that attempts call `python3-config --prefix` and `python3 --version` in order to automagically configure the Python library. ## 1.21 Bugfix release. Passing infinite sequences to Python functions was causing a hang as libpython-clj attempted to copy the sequence. The current calling convention does a shallow copy of things that are list-like or map-like, while bridging things that are iterable or don't fall into the above categories. This exposed a bug that caused reference counting to be subtly wrong when Python iterated through a bridged object. And that was my life for a day. ## 1.20 With too many huge things we had to skip a few versions! #### require-python `require-python` works like require but it works on Python modules. `require-python` dynamically loads the module and exports it's symbols into a Clojure namespace. There are many options available for this pathway. This implements a big step towards embedding Python in Clojure in a simple, clear, and easy to use way. One important thing to consider is the require has a `:reload:` option to allow you to actively develop a Python module and test it via Clojure. This excellent work was in large part done by [James Tolton](https://github.com/jjtolton). * [require-python-test](test/libpython_clj/require_python_test.clj) #### Clojure-defined Python classes You can now extend a tuple of Python classes (or implement a new one). This system allows, among many things, us to use frameworks that use derivation as part of their public API. Please see [classes-test](test/libpython_clj/classes_test.clj) for a documented example of a simple pathway through the new API. Note that if you use vanilla `->py-fn` functions as part of the class definition you won't get access to the `self` object. #### Bugfixes A general stability bugfix was made that was involved in the interoperation of Clojure functions within Python. Clojure functions weren't currently adding a refcount to their return values. ## 1.16 Fixed a bug where the system would load multiple Python libraries, not stopping after the first valid library loaded. There are two ways to control the system's Python library loading mechanism: 1. Pass in a library name in `initialize!` 2. `alter-var-root` the list of libraries in `libpython-clj.jna.base` before calling `initialize!`. ## 1.15 Moar syntax sugar -- ```clojure user> (py/$. numpy linspace) user> (py/$.. numpy random shuffle) ``` ## 1.14 libpython-clj now searches for several shared libraries instead of being hardcoded to just one of them. Because of this, there is now: ```clojure libpython-clj.jna.base/*python-library-names* ``` This is a sequence of library names that will be tried in order. You can also pass in the desired library name as part of the `initialize!` call and only this name will be tried. libpython-clj-2.000/CONTRIBUTING.md000066400000000000000000000020341410726312600165230ustar00rootroot00000000000000# Contributing Contributions are welcome and come in many forms. Let's be polite, professional, and keep this a place we want to be. ## Issues Issues are best if they come with a test. The exact version of python you are running and your operating system, clojure version, java version, etc. if relevant will save everyone time who reviews your issue. Consider some form of: 1. What I did 1. What I expected to happen 1. What actually happened ## Failing Tests A public branch with failing tests of things that you would like to work is also a great idea. Or just more tests. Things like: >> are java lists and maps actually substitutable for python lists and dicts in python code and vice versa are most likely ripe areas for tests and will greatly help with expectations and code portability. ## PR's Shorter the PR, the less back and forth there will be and the more likely to be accepted. ## Documentation Documentation is key but needs to be accurate and brief. 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Except as expressly stated in Sections 2(a) and 2(b) above, Recipient receives no rights or licenses to the intellectual property of any Contributor under this Agreement, whether expressly, by implication, estoppel or otherwise. All rights in the Program not expressly granted under this Agreement are reserved. Nothing in this Agreement is intended to be enforceable by any entity that is not a Contributor or Recipient. No third-party beneficiary rights are created under this Agreement. Exhibit A - Form of Secondary Licenses Notice "This Source Code may also be made available under the following Secondary Licenses when the conditions for such availability set forth in the Eclipse Public License, v. 2.0 are satisfied: {name license(s), version(s), and exceptions or additional permissions here}." Simply including a copy of this Agreement, including this Exhibit A is not sufficient to license the Source Code under Secondary Licenses. If it is not possible or desirable to put the notice in a particular file, then You may include the notice in a location (such as a LICENSE file in a relevant directory) where a recipient would be likely to look for such a notice. You may add additional accurate notices of copyright ownership. libpython-clj-2.000/README.md000066400000000000000000000206461410726312600155620ustar00rootroot00000000000000# Deep Clojure/Python Integration ### Version Info [![Clojars Project](https://img.shields.io/clojars/v/clj-python/libpython-clj.svg)](https://clojars.org/clj-python/libpython-clj) [![travis integration](https://travis-ci.com/clj-python/libpython-clj.svg?branch=master)](https://travis-ci.com/clj-python/libpython-clj) ## BETA Versions Are HOT! Huge New Features! You Can't Afford To Miss Out! * You can now [Embed Clojure in Python](https://clj-python.github.io/libpython-clj/embedded.html) - you can launch a Clojure REPL from a Python host process. * Main namespace is now `libpython-clj2` -- docs have not been updated. * **32 bit support**!! * Partial support for JDK-16 - The tests pass for core Python functionality but numpy failed to load - the JDK-16 dynamic library loading path is different than JNA and of course this causes issues. * 20-30% better performance. * - Note - Please avoid deprecated versions such as `[cnuernber/libpython-clj "1.36"]` (***note name change***). * [API Documentation](https://clj-python.github.io/libpython-clj/) * This library, which has received the efforts of many excellent people, is built mainly upon [cnuernber/dtype-next](https://github.com/cnuernber/dtype-next/) and the [JNA library](https://github.com/java-native-access/jna). * [Static code generation](https://clj-python.github.io/libpython-clj/libpython-clj2.codegen.html#var-write-namespace.21) - generate clojure namespaces wrapping python modules that are safe to use with AOT and load much faster than analogous `require-python` calls. These namespace will not automatically initialize the python subsystem -- initialize! must be called first (or a nice exception is throw). ## libpython-clj features * Bridge between JVM objects and Python objects easily; use Python in your Java and use some Java in your Python. * Python objects are linked to the JVM GC such that when they are no longer reachable from the JVM their references are released. Scope based resource contexts are [also available](topics/scopes-and-gc.md). * Finding the python libraries is done dynamically allowing one system to run on multiple versions of python. * REPL oriented design means fast, smooth, iterative development. * Carin Meier has written excellent posts on [plotting](http://gigasquidsoftware.com/blog/2020/01/18/parens-for-pyplot/) and [advanced text generation](http://gigasquidsoftware.com/blog/2020/01/10/hugging-face-gpt-with-clojure/). She also has some great [examples](https://github.com/gigasquid/libpython-clj-examples). ## Vision We aim to integrate Python into Clojure at a deep level. This means that we want to be able to load/use python modules almost as if they were Clojure namespaces. We also want to be able to use Clojure to extend Python objects. I gave a [talk at Clojure Conj 2019](https://www.youtube.com/watch?v=vQPW16_jixs) that outlines more of what is going on. This code is a concrete example that generates an [embedding for faces](https://github.com/cnuernber/facial-rec): ```clojure (ns facial-rec.face-feature (:require [libpython-clj2.require :refer [require-python]] [libpython-clj2.python :refer [py. py.. py.-] :as py] [tech.v3.datatype :as dtype])) (require-python 'mxnet '(mxnet ndarray module io model)) (require-python 'cv2) (require-python '[numpy :as np]) (defn load-model [& {:keys [model-path checkpoint] :or {model-path "models/recognition/model" checkpoint 0}}] (let [[sym arg-params aux-params] (mxnet.model/load_checkpoint model-path checkpoint) all-layers (py. sym get_internals) target-layer (py/get-item all-layers "fc1_output") model (mxnet.module/Module :symbol target-layer :context (mxnet/cpu) :label_names nil)] (py. model bind :data_shapes [["data" [1 3 112 112]]]) (py. model set_params arg-params aux-params) model)) (defonce model (load-model)) (defn face->feature [img-path] (py/with-gil-stack-rc-context (if-let [new-img (cv2/imread img-path)] (let [new-img (cv2/cvtColor new-img cv2/COLOR_BGR2RGB) new-img (np/transpose new-img [2 0 1]) input-blob (np/expand_dims new-img :axis 0) data (mxnet.ndarray/array input-blob) batch (mxnet.io/DataBatch :data [data])] (py. model forward batch :is_train false) (-> (py. model get_outputs) first (py. asnumpy) (#(dtype/make-container :java-array :float32 %)))) (throw (Exception. (format "Failed to load img: %s" img-path)))))) ``` ## Usage #### Config namespace ```clojure (ns my-py-clj.config (:require [libpython-clj2.python :as py])) ;; When you use conda, it should look like this. (py/initialize! :python-executable "/opt/anaconda3/envs/my_env/bin/python3.7" :library-path "/opt/anaconda3/envs/my_env/lib/libpython3.7m.dylib") ``` #### Update project.clj ```clojure {... ;; This namespace going to run when the REPL is up. :repl-options {:init-ns my-py-clj.config} ...} ``` ```clojure user> (require '[libpython-clj2.require :refer [require-python]]) ...logging info.... nil user> (require-python '[numpy :as np]) nil user> (def test-ary (np/array [[1 2][3 4]])) #'user/test-ary user> test-ary [[1 2] [3 4]] ``` We have a [document](topics/Usage.md) on all the features but beginning usage is pretty simple. Import your modules, use the things from Clojure. We have put effort into making sure things like sequences and ranges transfer between the two languages. #### Environments One very complimentary aspect of Python with respect to Clojure is it's integration with cutting edge native libraries. Our support isn't perfect so some understanding of the mechanism is important to diagnose errors and issues. Current, we launch the python3 executable and print out various different bits of configuration as json. We parse the json and use the output to attempt to find the `libpython3.Xm.so` shared library so for example if we are loading python 3.6 we look for `libpython3.6m.so` on Linux or `libpython3.6m.dylib` on the Mac. This pathway has allowed us support Conda albeit with some work. For examples using Conda, check out the facial rec repository a)bove or look into how we [build](scripts/build-conda-docker) our test [docker containers](dockerfiles/CondaDockerfile). ## Community We like to talk about libpython-clj on [Zulip](https://clojurians.zulipchat.com/#streams/215609/libpython-clj-dev) as the conversations are persistent and searchable. ## Further Information * Clojure Conj 2019 [video](https://www.youtube.com/watch?v=vQPW16_jixs) and [slides](https://docs.google.com/presentation/d/1uegYhpS6P2AtEfhpg6PlgBmTSIPqCXvFTWcGYG_Qk2o/edit?usp=sharing). * [development discussion forum](https://clojurians.zulipchat.com/#narrow/stream/215609-libpython-clj-dev) * [design documentation](topics/design.md) * [scope and garbage collection docs](topics/scopes-and-gc.md) * [examples](https://github.com/gigasquid/libpython-clj-examples) * [docker setup](https://github.com/scicloj/docker-hub) * [pandas bindings (!!)](https://github.com/alanmarazzi/panthera) * [nextjournal notebooks](https://nextjournal.com/kommen) * [scicloj video](https://www.youtube.com/watch?v=ajDiGS73i2o) * [Clojure/Python interop technical blog post](http://techascent.com/blog/functions-across-languages.html) * [persistent datastructures in python](https://github.com/tobgu/pyrsistent) ## New To Clojure New to Clojure or the JVM? Try remixing the nextjournal entry and playing around there. For more resources on learning and getting more comfortable with Clojure, we have an [introductory document](topics/new-to-clojure.md). ## Resources * [libpython C api](https://docs.python.org/3.7/c-api/index.html#c-api-index) * [create numpy from C ptr](https://stackoverflow.com/questions/23930671/how-to-create-n-dim-numpy-array-from-a-pointer) * [create C ptr from numpy](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.ctypes.html) To install jar to local .m2 : ```bash $ lein install ``` ### Deploy to clojars ```bash $ lein deploy clojars ``` This command will sign jar before deploy, using your gpg key. (see dev/src/build.clj for signing options) ## License Copyright © 2019 Chris Nuernberger This program and the accompanying materials are made available under the terms of the Eclipse Public License 2.0 which is available at http://www.eclipse.org/legal/epl-2.0. libpython-clj-2.000/cljbridge.py000066400000000000000000000175211410726312600166000ustar00rootroot00000000000000"""Python bindings to start a Clojure repl from a python process. Relies on libpython-clj being in a deps.edn pathway as clojure is called from the command line to build the classpath. Expects javabridge to be installed and functional. Javabridge will dynamically find the java library that corresponds with calling 'java' from the command line and load it. We then initialize Clojure and provide pathways to require namespaces, find symbols, and call functions. There are two import initialization methods - init_clojure and init_clojure_repl - these take care of starting up everything in the correct order. """ import subprocess import javabridge try: from collections.abc import Callable # noqa except ImportError: from collections import Callable def init_clojure_runtime(): """Initialize the clojure runtime. This needs to happen at least once before attempting to require a namespace or lookup a clojure var.""" javabridge.static_call("clojure/lang/RT", "init", "()V") def find_clj_var(fn_ns, fn_name): """Use the clojure runtime to find a var. Clojure vars are placeholders in namespaces that forward their operations to the data they point to. This allows someone to hold a var and but recompile a namespace to get different behavior. They implement both `clojure.lang.IFn` and `clojure.lang.IDeref` so they can act like a function and you can dereference them to get to their original value.""" return javabridge.static_call("clojure/lang/RT", "var", "(Ljava/lang/String;Ljava/lang/String;)Lclojure/lang/Var;", fn_ns, fn_name) class CLJFn(Callable): """Construct a python callable from a clojure object. This callable will forward function calls to it's Clojure object expecting a clojure.lang.IFn interface.""" applyTo = javabridge.make_method("applyTo", "(clojure/lang/ISeq;)Ljava/lang/Object;") def __init__(self, ifn_obj): self.o = ifn_obj def __call__(self, *args, **kw_args): if not kw_args: invoker = getattr(self, "invoke"+str(len(args))) return invoker(*args) else: raise Exception("Unable to handle kw_args for now") print(len(args), len(kw_args)) for i in range(20): opargs = "" for j in range(i): opargs += "Ljava/lang/Object;" setattr(CLJFn, "invoke" + str(i), javabridge.make_method("invoke", "(" + opargs + ")Ljava/lang/Object;" )) def resolve_fn(namespaced_name): """Resolve a clojure var given a fully qualified namespace name. The return value is callable. Note that the namespace itself needs to be required first.""" ns_name, sym_name = namespaced_name.split("/") return CLJFn(find_clj_var(ns_name, sym_name)) def resolve_call_fn(namespaced_fn_name, *args): """Resolve a function given a fully qualified namespace name and call it.""" return resolve_fn(namespaced_fn_name)(*args) def symbol(sym_name): """Create a clojure symbol from a string""" return javabridge.static_call("clojure/lang/Symbol", "intern", "(Ljava/lang/String;)Lclojure/lang/Symbol;", sym_name) __REQUIRE_FN = None def require(ns_name): """Require a clojure namespace. This needs to happen before you find symbols in that namespace else you will be uninitialized var errors.""" if not __REQUIRE_FN: _REQUIRE_FN = resolve_fn("clojure.core/require") return _REQUIRE_FN(symbol(ns_name)) def init_libpy_embedded(): """Initialize libpy on a mode where it looks for symbols in the local process and it itself doesn't attempt to run the python intialization procedures but expects the python system to be previously initialized.""" require("libpython-clj2.embedded") return resolve_call_fn("libpython-clj2.embedded/initialize!") def classpath(classpath_args=[]): """Call clojure at the command line and return the classpath in as a list of strings. Clojure will pick up a local deps.edn or deps can be specified inline.""" return subprocess.check_output(['clojure'] + list(classpath_args) + ['-Spath']).decode("utf-8").strip().split(':') DEFAULT_NREPL_VERSION = "0.8.3" DEFAULT_CIDER_NREPL_VERSION = "0.25.5" def repl_classpath(nrepl_version=DEFAULT_NREPL_VERSION, cider_nrepl_version=DEFAULT_CIDER_NREPL_VERSION, classpath_args=[], **kw_args): """Return the classpath with the correct deps to run nrepl and cider. Positional arguments are added after the -Sdeps argument to start the nrepl server.""" return classpath(classpath_args=["-Sdeps", '{:deps {nrepl/nrepl {:mvn/version "%s"} cider/cider-nrepl {:mvn/version "%s"}}}' % (nrepl_version, cider_nrepl_version)] + list(classpath_args)) def init_clojure(classpath_args=[]): """Initialize a vanilla clojure process using the clojure command line to output the classpath to use for the java vm. At the return of this function clojure is initialized and libpython-clj2.python's public functions will work. * classpath_args - List of arguments that will be passed to the clojure command line process when building the classpath. """ javabridge.start_vm(run_headless=True, class_path=classpath(classpath_args=classpath_args)) init_clojure_runtime() init_libpy_embedded() return True def py_dict_to_keyword_map(py_dict): hash_map = None keyword = resolve_fn("clojure.core/keyword") assoc = resolve_fn("clojure.core/assoc") for k in py_dict: hash_map = assoc(hash_map, keyword(k), py_dict[k]) return hash_map def init_clojure_repl(**kw_args): """Initialize clojure with extra arguments specifically for embedding a cider-nrepl server. Then start an nrepl server. The port will both be printed to stdout and output to a .nrepl_server file. This function does not return as it leaves the GIL released so that repl threads have access to Python. libpython-clj2.python is initialized 'require-python' pathways should work. Keyword arguments in python are mapped to a hash-map of keyword options and passed directly to clojure so any nrepl are valid arguments to this function. * `classpath_args` - List of additional arguments that be passed to the clojure process when building the classpath. * `port` - Integer port to open up repl. A random port will be found if not provided. * `bind` - Bind address. If you are having connectivity issues try bind=\"0.0.0.0\"""" javabridge.start_vm(run_headless=True, class_path=repl_classpath(**kw_args)) init_clojure_runtime() init_libpy_embedded() resolve_call_fn("libpython-clj2.embedded/start-repl!", py_dict_to_keyword_map(kw_args)) class GenericJavaObj: __str__ = javabridge.make_method("toString", "()Ljava/lang/String;") get_class = javabridge.make_method("getClass", "()Ljava/lang/Class;") __repl__ = javabridge.make_method("toString", "()Ljava/lang/String;") def __init__(self, jobj): self.o = jobj def longCast(jobj): "Cast a java object to a primitive long value." return javabridge.static_call("clojure/lang/RT", "longCast", "(Ljava/lang/Object;)J", jobj) def to_ptr(pyobj): """Create a tech.v3.datatype.ffi.Pointer java object from a python object. This allows you to pass python objects directly into libpython-clj2.python-derived pathways (such as ->jvm). If java is going to hold onto the python data for a long time and it will fall out of Python scope object should be 'incref-tracked' - 'libpython-clj2.python.ffi/incref-track-pyobject'.""" return javabridge.static_call("tech/v3/datatype/ffi/Pointer", "constructNonZero", "(J)Ltech/v3/datatype/ffi/Pointer;", id(pyobj)) libpython-clj-2.000/deps.edn000066400000000000000000000056701410726312600157260ustar00rootroot00000000000000{:paths ["src"] :deps {org.clojure/clojure {:mvn/version "1.10.2" :scope "provided"} cnuernber/dtype-next {:mvn/version "8.024"} net.java.dev.jna/jna {:mvn/version "5.7.0"} org.clojure/data.json {:mvn/version "1.0.0"}} :aliases {:dev {:extra-deps {criterium/criterium {:mvn/version"0.4.5"} ch.qos.logback/logback-classic {:mvn/version "1.1.3"}}} :jdk-16 {:jvm-opts ["--add-modules" "jdk.incubator.foreign" "-Dforeign.restricted=permit"]} :codox {:extra-deps {codox-theme-rdash/codox-theme-rdash {:mvn/version "0.1.2"} nrepl/nrepl {:mvn/version "0.8.3"} cider/cider-nrepl {:mvn/version "0.25.5"} codox/codox {:mvn/version "0.10.7" :exclusions [org.ow2.asm/asm-all]}} :ns-default tech.v3.libs.lein-codox :exec-fn tech.v3.libs.lein-codox/-main :exec-args {:arg-paths [[:aliases :depstar :exec-args]] :description "Python bindings for Clojure" :metadata {:doc/format :markdown} :themes [:rdash] :source-paths ["src"] :output-path "docs" :doc-paths ["topics"] :source-uri "https://github.com/clj-python/libpython-clj/blob/master/{filepath}#L{line}" :namespaces [libpython-clj2.python libpython-clj2.codegen libpython-clj2.python.np-array libpython-clj2.require libpython-clj2.embedded python.builtins python.numpy]}} :test {:extra-deps {com.cognitect/test-runner {:git/url "https://github.com/cognitect-labs/test-runner" :sha "209b64504cb3bd3b99ecfec7937b358a879f55c1"} ch.qos.logback/logback-classic {:mvn/version "1.1.3"}} :extra-paths ["test"] :main-opts ["-m" "cognitect.test-runner"]} :depstar {:replace-deps {com.github.seancorfield/depstar {:mvn/version "2.0.193"}} :ns-default hf.depstar :exec-fn hf.depstar/jar :exec-args {:group-id "clj-python" :artifact-id "libpython-clj" :version "2.000" :sync-pom true :jar "target/libpython-clj.jar"}} :deploy {:replace-deps {slipset/deps-deploy {:mvn/version "0.1.5"}} :exec-fn deps-deploy.deps-deploy/deploy :exec-args {:installer :remote :sign-releases? true :artifact "target/libpython-clj.jar"}}}} libpython-clj-2.000/dockerfiles/000077500000000000000000000000001410726312600165655ustar00rootroot00000000000000libpython-clj-2.000/dockerfiles/CondaDockerfile000066400000000000000000000027031410726312600215260ustar00rootroot00000000000000# We will use Ubuntu for our image FROM ubuntu:latest # Updating Ubuntu packages ARG CLOJURE_TOOLS_VERSION=1.10.1.507 RUN apt-get -qq update && apt-get -qq -y install curl wget bzip2 openjdk-8-jdk-headless \ && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ && bash /tmp/miniconda.sh -bfp /usr/local \ && rm -rf /tmp/miniconda.sh \ && conda install -y python=3 \ && conda update conda \ && curl -o install-clojure https://download.clojure.org/install/linux-install-${CLOJURE_TOOLS_VERSION}.sh \ && chmod +x install-clojure \ && ./install-clojure && rm install-clojure \ && wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein \ && chmod a+x lein \ && mv lein /usr/bin \ && apt-get -qq -y autoremove \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ && conda clean --all --yes ENV PATH /opt/conda/bin:$PATH ARG USERID ARG GROUPID ARG USERNAME RUN groupadd -g $GROUPID $USERNAME RUN useradd -u $USERID -g $GROUPID $USERNAME RUN mkdir /home/$USERNAME && chown $USERNAME:$USERNAME /home/$USERNAME USER $USERNAME RUN conda create -n pyclj python=3.6 && conda install -n pyclj numpy mxnet \ && echo "source activate pyclj" > /home/$USERNAME/.bashrc ## cause leiningen to install RUN lein -v ## To install pip packages into the pyclj environment do RUN conda run -n pyclj python3 -mpip install numpylibpython-clj-2.000/dockerfiles/Py38Dockerfile000066400000000000000000000017311410726312600212450ustar00rootroot00000000000000# We will use Ubuntu for our image FROM ubuntu:latest # Updating Ubuntu packages ARG CLOJURE_TOOLS_VERSION=1.10.1.507 RUN apt-get -qq update && apt-get -qq -y install curl wget bzip2 openjdk-8-jdk-headless python3.8 libpython3.8 python3-pip \ && curl -o install-clojure https://download.clojure.org/install/linux-install-${CLOJURE_TOOLS_VERSION}.sh \ && chmod +x install-clojure \ && ./install-clojure && rm install-clojure \ && wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein \ && chmod a+x lein \ && mv lein /usr/bin \ && apt-get -qq -y autoremove \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log ARG USERID ARG GROUPID ARG USERNAME RUN groupadd -g $GROUPID $USERNAME RUN useradd -u $USERID -g $GROUPID $USERNAME RUN mkdir /home/$USERNAME && chown $USERNAME:$USERNAME /home/$USERNAME USER $USERNAME # Install leiningen during build process RUN lein -v RUN python3.8 -mpip install numpylibpython-clj-2.000/dockerfiles/Py39Dockerfile000066400000000000000000000021501410726312600212420ustar00rootroot00000000000000# We will use Ubuntu for our image FROM ubuntu:latest # Updating Ubuntu packages ARG CLOJURE_TOOLS_VERSION=1.10.1.507 RUN apt-get -qq update \ && apt install -y software-properties-common \ && add-apt-repository -y ppa:deadsnakes/ppa RUN apt-get -qq update && apt-get -qq -y install curl wget bzip2 openjdk-8-jdk-headless python3.9 libpython3.9 python3-pip python3-distutils\ && curl -o install-clojure https://download.clojure.org/install/linux-install-${CLOJURE_TOOLS_VERSION}.sh \ && chmod +x install-clojure \ && ./install-clojure && rm install-clojure \ && wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein \ && chmod a+x lein \ && mv lein /usr/bin \ && apt-get -qq -y autoremove \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log ARG USERID ARG GROUPID ARG USERNAME RUN groupadd -g $GROUPID $USERNAME RUN useradd -u $USERID -g $GROUPID $USERNAME RUN mkdir /home/$USERNAME && chown $USERNAME:$USERNAME /home/$USERNAME USER $USERNAME # Install leiningen during build process RUN lein -v # RUN python3.9 -mpip install numpylibpython-clj-2.000/docs/000077500000000000000000000000001410726312600152235ustar00rootroot00000000000000libpython-clj-2.000/docs/Usage.html000066400000000000000000000450531410726312600171640ustar00rootroot00000000000000 Usage

Project

Topics

Namespaces

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 ‘[]’.

This would result in the following analogous code (full example further on):

table.loc[row_date]

(get-item (get-attr table :loc) row-date)

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

; Mac and Linux
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

; Windows with Anaconda
(initialize! ; Python executable
             :python-executable "C:\\Users\\USER\\AppData\\Local\\Continuum\\anaconda3\\python.exe"
             ; Python Library
             :library-path "C:\\Users\\USER\\AppData\\Local\\Continuum\\anaconda3\\python37.dll"
             ; Anacondas PATH environment to load native dlls of modules (numpy, etc.)
             :windows-anaconda-activate-bat "C:\\Users\\USER\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\activate.bat"
             )
...
: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,
 ...
}

DataFrame access full example

Here’s how to create Pandas DataFrame and accessing its rows via loc in both Python and Clojure:

# Python
import numpy as np
import pandas as pan

dates = pan.date_range("1/1/2000", periods=8)
table = pan.DataFrame(np.random.randn(8, 4), index=dates, columns=["A", "B", "C", "D"])
row_date = pan.date_range(start="2000-01-01", end="2000-01-01")
table.loc[row_date]

; Clojure
(require-python '[numpy :as np])
(require-python '[pandas :as pan])

(def dates (pan/date_range "1/1/2000" :periods 8))
(def table (pan/DataFrame (call-attr np/random :randn 8 4) :index dates :columns ["A" "B" "C" "D"]))
(def row-date (pan/date_range :start "2000-01-01" :end "2000-01-01"))
(get-item (get-attr table :loc) row-date)

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 - sugar 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.

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} .doc dl { margin: 0 10px 20px 10px; } .doc dl dt { font-weight: bold; margin: 0; padding: 3px 0; border-bottom: 1px solid #ddd; } .doc dl dd { padding: 5px 0; margin: 0 0 5px 10px; } .doc abbr { border-bottom: 1px dotted #333; font-variant: none; cursor: help; } .src-link { margin-bottom: 15px; } .src-link a { font-size: 70%; padding: 1px 4px; text-decoration: none; color: #5555bb; background-color: #f6f6f6; } blockquote { opacity: 0.6; border-left: solid 2px #ddd; margin-left: 0; padding-left: 1em; } /* Responsiveness Theme */ @media (max-device-width: 480px) { .sidebar { display:none; } #content { position: relative; left: initial !important; top: 110px; padding: 0 1em; } #header { display: flex; flex-direction: column-reverse; height: 100px; } #header > h1 { font-size: 52px; } #header h2 { float: none; font-size: 20px; } .namespace-index > h1 { display: none; } .public, .doc, .namespace > .index, .namespace > .doc, .namespace > h3 { max-width: initial; } .doc { text-align: justify; } .public { padding-top: 2em; padding-bottom: 2em; } .public > h3 { font-size: 300%; } .public > h4.type, .public > h4.added, .public > h4.deprecated { font-size: 150%; margin-top: 1em; } pre > code { font-size: 200%; } } libpython-clj-2.000/docs/design.html000066400000000000000000000247561410726312600174000ustar00rootroot00000000000000 LibPython-CLJ Design Notes

Project

Topics

Namespaces

LibPython-CLJ Design Notes

Key Design Points

Code Organization

There are 3 rough sections of code: 1. A JNA layer which is a 1-1 mapping most of the C API with no changes and full documentation. The docstrings on the functions match the documentation if you lookup the 3.7 API documentation. Users must manually manage the GIL when using this API layer.

  1. An API implementation layer which knows about things like interpreters and type symbol tables. Users must know how to manipulate the GIL and how the two garbage collectors work with each other to add to this layer.

  2. A public API layer. Details like managing the GIL or messing with garbage collection in general do not leak out to this layer.

Interpreters

Interpreters are global objects. After initialize!, there is a main interpreter which is used automatically from any thread. Access to the interpreters relies on thread local variables and interpreter locking so you can do things like:

(with-interpreter interpreter
  some code)

You don’t have to do this, however. If you don’t care what context you run in you can just use the public API which under the covers simple does:

(with-gil
  some code)

This type of thing grabs the GIL if it hasn’t already been claimed by the current thread and off you go. When the code is finished, it saves the interpreter thread state back into a global atom and thus releases the GIL. Interpreters have both shared and per-interpreter state named :shared-state and :interpreter-state respectively. Shared state would be the global type symbol table. Interpreter state contains things like a map of objects to their per-interpreter python bridging class.

If the interpreter isn’t specified it uses the main interpreter. The simplest way to ensure you have the gil is with-gil. You may also use (ensure-bound-interpreter) if you wish to penalize users for using a function incorrectly. This returns the interpreter currently bound to this thread or throws.

Garbage Collection

The system uses the tech.resource library to attach a GC hook to appropriate java object that releases the associated python object if the java object goes out of scope. Bridges use a similar technique to unregister the bridge on destruction of their python counterpart. There should be no need for manual addref/release calls in any user code aside from potentially (and damn rarely) a (System/gc) call.

Copying Vs. Bridging

Objects either in python or in java may be either copied or bridged into the other ecosystem. Bridging allows sharing complex and potentially changing datastructures while copying allows a cleaner partitioning of concerns and frees both garbage collection systems to act more independently. Numeric buffers that have a direct representation as a C-ptr (the datatype native-buffer type) have a zero-copy pathway via numpy. If you want access to object functionality that object needs to be bridged; so for example if you want to call numpy functions then you need to bridge that object. Tensors are always represented in python as numpy objects using zero-copy where possible in all cases.

Bridging

Python Callables implement clojure.lang.IFn along with a python specific interface so in general you can call them like any other function but you also can use keyword arguments if you know you are dealing with a python function. Python dicts implement java.util.Map and clojure.lang.IFn and lists are java.util.List, java.util.RandomAccess, and clojure.lang.IFn. This allows fluid manipulation of the datastructures (even mutation) from both languages.

You can create a python function from a clojure function with create-function. You can create a new bridge type by implementing the libpython_clj.jna.JVMBridge interface:

public interface JVMBridge extends AutoCloseable
{
  public Pointer getAttr(String name);
  public void setAttr(String name, Pointer val);
  public String[] dir();
  public Object interpreter();
  public Object wrappedObject();
  // Called from python when the python mirror is deleted.
  // This had better not throw anything.
  public default void close() {}
}

If all you want to do is override the attribute map that is simple. Here is the bridge for java.util.Map:

(defn jvm-map->python
  ^Pointer [^Map jvm-data]
  (with-gil
    (let [att-map
          {"__contains__" (jvm-fn->python #(.containsKey jvm-data %))
           "__eq__" (jvm-fn->python #(.equals jvm-data %))
           "__getitem__" (jvm-fn->python #(.get jvm-data %))
           "__setitem__" (jvm-fn->python #(.put jvm-data %1 %2))
           "__hash__" (jvm-fn->python #(.hashCode jvm-data))
           "__iter__" (jvm-fn->python #(.iterator ^Iterable jvm-data))
           "__len__" (jvm-fn->python #(.size jvm-data))
           "__str__" (jvm-fn->python #(.toString jvm-data))
           "clear" (jvm-fn->python #(.clear jvm-data))
           "keys" (jvm-fn->python #(seq (.keySet jvm-data)))
           "values" (jvm-fn->python #(seq (.values jvm-data)))
           "pop" (jvm-fn->python #(.remove jvm-data %))}]
      (create-bridge-from-att-map jvm-data att-map))))

IO

sys.stdout and sys.stderr are redirected to *out* and *err* respectively. This rerouting is done using a bridge that is then set as sys.stdout or sys.stderr:

(defn create-var-writer
  "Returns an unregistered bridge"
  ^Pointer [writer-var]
  (with-gil
    (create-bridge-from-att-map
     writer-var
     {"write" (->python (fn [& args]
                          (.write ^Writer @writer-var (str (first args)))))
      "flush" (->python (fn [& args]))
      "isatty" (->python (fn [& args]
                           (libpy/Py_False)))
      })))

(defn setup-std-writer
  [writer-var sys-mod-attname]
  (with-gil
    (let [sys-module (import-module "sys")
          std-out-writer (get-or-create-var-writer writer-var)]
      (py-proto/set-attr! sys-module sys-mod-attname std-out-writer)
      :ok)))


(defn initialize!
  [& {:keys [program-name no-io-redirect?]}]
  (when-not @pyinterp/*main-interpreter*
    (pyinterp/initialize! program-name)
    ;;setup bridge mechansim and io redirection
    (pyinterop/register-bridge-type!)
    (when-not no-io-redirect?
      (pyinterop/setup-std-writer #'*err* "stderr")
      (pyinterop/setup-std-writer #'*out* "stdout")))
  :ok)
libpython-clj-2.000/docs/embedded.html000066400000000000000000000264731410726312600176560ustar00rootroot00000000000000 Embedding Clojure In Python

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Embedding Clojure In Python

The initial development push for libpython-clj was simply to embed Python in Clojure allowing Clojure developers to use Python modules simply transparently. This approach relied on libpython-clj being able to find the Python shared library and having some capability to setup various Python system variables before loading any modules. While this works great most of the time there are several reasons for which this approach is less than ideal:

  1. In some cases a mainly Python team wants to use some Clojure for a small part of their work. Telling them to host Python from Clojure is for them a potentially very disruptive change.
  2. The python ecosystem is moving away from the shared library and towards compiling a statically linked executable. This can never work with libpython-clj’s default pathway.
  3. Embedded Python cannot use all of Python functionality available due to the fact that the host process isn’t python. Specifically the multithreading module relies on forking the host process and thus produces a hang if the JVM is the main underlying process.
  4. Replicating the exact python environment is error prone especially when Python environment managers such as pyenv and Conda are taken into account.

Due to the above reasons there is a solid argument for, if possible, embedding Clojure into Python allowing the Python executable to be the host process.

Enter: javabridge

Python already had a nascent system for embedding Java in Python - the javabridge module. You will need to have javabridge installed in your Python environment. In order to compile javabridge requires the JDK installed and not just the JRE. tristanstraub had found a way to use this in order to work with Blender. We took a bit more time and worked out ways to smooth out these interactions and make sure they were supported throughout the system.

python3 -m pip install javabridge

If the installation cannot find ‘jni.h’ then most likely you have the Java runtime (JRE) installed as opposed to the Java development kit (JDK).

From the Python REPL

The next step involves starting a python repl from our libpython-clj base directory. This is only required because we have a special python script that has code to start a Java VM with a correct classpath. So we start by importing that script:

Python 3.8.5 (default, Jan 27 2021, 15:41:15)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cljbridge
>>> test_var=10
>>> cljbridge.init_clojure_repl()
Mar 11, 2021 9:08:47 AM clojure.tools.logging$eval3186$fn__3189 invoke
INFO: nREPL server started on port 40241 on host localhost - nrepl://localhost:40241

At this point we do not get control back; we have released the GIL and java is blocking this thread to allow the Clojure REPL systems access to the GIL. We have two important libraries for clojure loaded, nrepl and cider which allow a rich, interactive development experience so let’s now connect to that port with our favorite Clojure editor - emacs of course ;-).

From the Clojure REPL

From emacs, I run the command ‘cider-connect’ which allows me to specify a host and port to connect to. Once connected, I get a minimal repl environment:

;; M-x cider-connect ...localhost...40241
;; I am not sure why but to initialize the user namespace I have to eval ns user

user> (eval '(ns user))
nil
user> (require '[libpython-clj2.python :as py])
nil
;; Python has been initialized and libpython-clj can detect this
user> (py/initialize!)
:already-initialized
user> ;;We can share data via the main module
user> (def main-mod (py/add-module "__main__"))
#'user/main-mod
user> (def mod-dict (py/module-dict main-mod))
#'user/mod-dict
user> (keys mod-dict)
("__name__"
 "__doc__"
 "__package__"
 "__loader__"
 "__spec__"
 "__annotations__"
 "__builtins__"
 "cljbridge"
 "test_var")
user> (get mod-dict "test_var")
10
user> (.put mod-dict "clj_fn" (fn [& args] (println "Printing from Clojure: " (vec args))))
nil
user> ;;Now if we stop the repl server we can access our python environment again
user> (require '[libpython-clj2.embedded :as embedded])
nil
user> (embedded/stop-repl!)

And Back to Python!!

Shutting down the repl always gives us an exception; something perhaps to work on. But the important thing is that we can access variables and data that we set in the main module -

>>> Exception in thread "nREPL-session-d684061e-f21c-4265-a9a2-828b99dcaf42" java.net.SocketException: Socket closed
	at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:118)
	at java.net.SocketOutputStream.write(SocketOutputStream.java:155)
	at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
	at java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)
	at nrepl.transport$bencode$fn__7714.invoke(transport.clj:121)
	at nrepl.transport.FnTransport.send(transport.clj:28)
	at nrepl.middleware.print$send_streamed.invokeStatic(print.clj:136)
	at nrepl.middleware.print$send_streamed.invoke(print.clj:122)
	at nrepl.middleware.print$printing_transport$reify__8149.send(print.clj:173)
	at cider.nrepl.middleware.track_state$make_transport$reify__17923.send(track_state.clj:228)
	at nrepl.middleware.caught$caught_transport$reify__8184.send(caught.clj:58)
	at nrepl.middleware.interruptible_eval$evaluate$fn__8250.invoke(interruptible_eval.clj:132)
	at clojure.main$repl$fn__9121.invoke(main.clj:460)
	at clojure.main$repl.invokeStatic(main.clj:458)
	at clojure.main$repl.doInvoke(main.clj:368)
	at clojure.lang.RestFn.invoke(RestFn.java:1523)
	at nrepl.middleware.interruptible_eval$evaluate.invokeStatic(interruptible_eval.clj:84)
	at nrepl.middleware.interruptible_eval$evaluate.invoke(interruptible_eval.clj:56)
	at nrepl.middleware.interruptible_eval$interruptible_eval$fn__8258$fn__8262.invoke(interruptible_eval.clj:152)
	at clojure.lang.AFn.run(AFn.java:22)
	at nrepl.middleware.session$session_exec$main_loop__8326$fn__8330.invoke(session.clj:202)
	at nrepl.middleware.session$session_exec$main_loop__8326.invoke(session.clj:201)
	at clojure.lang.AFn.run(AFn.java:22)
	at java.lang.Thread.run(Thread.java:748)

>>> # So let's call our new clojure fn
>>> clj_fn(1, 2, 3, 4, "Embedded Clojure FTW!!")
Printing from Clojure:  [1 2 3 4 Embedded Clojure FTW!!]
>>>

Are You Not Entertained???

So there you have it, embedding a Clojure repl in a Python process and passing data in between these two systems. This sidesteps a ton of issues with embedding Python and provides another interesting set of possibilities, essentially extending existing Python systems with some of the greatest tech the JVM has to offer :-).

libpython-clj-2.000/docs/environments.html000066400000000000000000000100651410726312600206420ustar00rootroot00000000000000 Python Environments

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Python Environments

pyenv

pyenv requires that you build the shared library. This is a separate configuration option than a lot of pyenv users have used before.

Conda

Conda requires that we set the LD_LIBRARY_PATH to the conda install.

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Project

Topics

Namespaces

2.000

Python bindings for Clojure.

Topics

Namespaces

libpython-clj2.codegen

Generate a namespace on disk for a python module or instances

Public variables and functions:

libpython-clj2.embedded

Tools for embedding clojure into a python host process. See jbridge.py for python details. This namespace relies on the classpath having nrepl and cider-nrepl on it. For example:

Public variables and functions:

libpython-clj2.python

Python bindings for Clojure. This library dynamically finds the installed python, loads the shared library and allows Clojure users to use Python modules as if they were Clojure namespaces.

Public variables and functions:

libpython-clj2.python.np-array

Bindings for deeper intergration of numpy into the tech.v3.datatype system. This allows seamless usage of numpy arrays in datatype and tensor functionality such as enabling the tech.v3.tensor/ensure-tensor call to work with numpy arrays – using zero copying when possible.

Public variables and functions:

libpython-clj2.require

Namespace implementing requiring python modules as Clojure namespaces. This works via scanning the module for metadata and dynamically building the Clojure namespace.

Public variables and functions:

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