diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..11a8d1c --- /dev/null +++ b/.editorconfig @@ -0,0 +1,40 @@ +# EditorConfig is awesome: http://EditorConfig.org + +# top-most EditorConfig file +root = true + +# Unix-style newlines with a newline ending every file +[*] +end_of_line = lf +insert_final_newline = true +trim_trailing_whitespace = true +charset = utf-8 + +# Matches multiple files with brace expansion notation +# Set default charset +[*.{js,py,java,r,R}] +indent_style = space + +# 4 space indentation +[*.py] +indent_size = 4 + +# Tab indentation (no size specified) +[*.js] +indent_size = 2 + +# Matches the exact files either package.json or .travis.yml +[*.{json,yml}] +indent_size = 2 + +[*.{md,Rmd}] +trim_trailing_whitespace = true + +[*.{cpp,hpp,cc,hh,c,h,cuh,cu}] +indent_size = 4 +indent_style = space + +[*.{sls,scm,ss}] +indent_size = 2 +indent_style = space + diff --git a/.gitignore b/.gitignore index a6d1392..428967f 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,4 @@ cover .*.swp doc/build venv +cache* diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..e17e3ba --- /dev/null +++ b/.travis.yml @@ -0,0 +1,19 @@ +language: python +sudo: false +python: + - "3.5" +install: + - pip install -U codacy-coverage + - pip install -U .[prov,numpy,test] +script: + - pytest test --cov=./noodles +after-script: + - coverage report + - coverage xml + - python-codacy-coverage -r coverage.xml + +branches: + only: + - master + - devel + diff --git a/COPYING b/COPYING deleted file mode 100644 index 94a9ed0..0000000 --- a/COPYING +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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Again, this function returns the name of the target executable file. The caller of this function already knows the name of the target file, but we need it to track dependencies. +In this case the function takes a list of object file names and the same configuration object that we saw before. Again, this function returns the name of the target executable file. The caller of this function already knows the name of the target file, but we need it to track dependencies between function calls. Since both the :py:func:`link` and the :py:func:`compile_source` functions do actual work that we'd like to see being done in a concurrent environment, they need to be decorated with the :py:func:`schedule` decorator. diff --git a/doc/source/conf.py b/doc/source/conf.py index fa5c8b2..edcae8e 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -41,10 +41,13 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + 'nbsphinx', 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', + 'nbsphinx', + 'IPython.sphinxext.ipython_console_highlighting' ] # Add any paths that contain templates here, relative to this directory. diff --git a/doc/source/development.rst b/doc/source/development.rst index 702fd64..cace057 100644 --- a/doc/source/development.rst +++ b/doc/source/development.rst @@ -1,34 +1,35 @@ Development documentation ========================= .. automodule:: noodles - :members: schedule, run_single, run_parallel, run_process, gather + :members: Internal Specs -------------- .. automodule:: noodles.workflow - :members: from_call, NodeData, FunctionNode, Workflow + :members: Promised object --------------- .. automodule:: noodles.interface - :members: PromisedObject + :members: Runners ------- .. automodule:: noodles.run.scheduler - :members: Scheduler + :members: .. automodule:: noodles.run.hybrid - :members: hybrid_threaded_worker, run_hybrid + :members: Serialisation ------------- .. automodule:: noodles.serial - :members: base, pickle, numpy + :members: .. automodule:: noodles.serial.registry - :members: Registry, Serialiser, RefObject + :members: Worker executable ----------------- .. automodule:: noodles.worker + :members: diff --git a/doc/source/eating.rst b/doc/source/eating.rst index 16f01a6..afc0e16 100644 --- a/doc/source/eating.rst +++ b/doc/source/eating.rst @@ -1,21 +1,15 @@ -.. highlight:: python - :linenothreshold: 5 +Eating noodles (user docs) +========================== -Eating of Noodles (user docs) -============================= - -The purpose of Noodles is to make it easy to design *computational workflows* straight from Python. -Noodles is meant to be used by scientists that want to do heavy number crunching, and need a way to organise these computations in a readable and sustainable manner. -These workflows are usually associated with a *directed acyclic graph* (DAG, or just: graph). -Each computation in the workflow is a represented as a node in the graph and may have several dependencies. -These dependencies are the arrows; or if you think in reverse, the arrows show transport of data. +The primary goal of the *noodles* library is to ease the construction and execution of *computational workflows* using the Python language. This library is meant for scientists who want to perform complex compute-intensive tasks on parallel/distributed infrastructures in a readable, scalable and sustainable/reproducible? manner. +A workflow is commonly modelled as a *directed acyclic graph* (DAG or simply graph) in which the computations are represented as nodes whereas the dependencies between them are represented as directed edges (indicating data transport). A first example --------------- -Let's look at a small example creating a diamond workflow. All the examples in this documentation do some silly arithmetic. In practice these functions would do quite a bit heavier lifting. +Let's look at a small example of creating a diamond workflow, which consists of simple (arithmetic) functions: -:: +.. code:: python from noodles import run_single from noodles.tutorial import (add, sub, mul) @@ -29,8 +23,8 @@ Let's look at a small example creating a diamond workflow. All the examples in t print("The answer is {0}.".format(answer)) -That allmost looks like normal Python! The only difference is the ``run_single`` statement at the end of this program. -The catch is that none of the computation is actually done until the ``run_single`` statement has been given. +That allmost looks like normal Python! The only difference is the :py:func:`~noodles.run_single` statement at the end of this program. +The catch is that none of the computation is actually done until the :py:func:`~noodles.run_single` statement has been given. The variables ``u``, ``v``, ``w``, and ``x`` only represent the *promise* of a value. The functions that we imported are wrapped, such that they construct the directed acyclic graph of the computation in stead of just computing the result immediately. This DAG then looks like this: @@ -55,7 +49,7 @@ At this point it is good to know what the module ``noodles.tutorial`` looks like It looks very simple. However, you should be aware of what happens behind the curtains, to understand the limitations of this approach. -:: +.. code:: python from noodles import schedule @@ -76,10 +70,10 @@ However, you should be aware of what happens behind the curtains, to understand ... -The ``@schedule`` decorators takes care that the functions actually return *promises* instead of values. -Such a ``PromisedObject`` is a placeholder for the expected result. +The :py:func:`@schedule ` decorators takes care that the functions actually return *promises* instead of values. +Such a :py:class:`~noodles.interface.PromisedObject` is a placeholder for the expected result. It stores the workflow graph that is needed to compute the promise. -When another `schedule`-decorated function is called with a promise, the graphs of the dependencies are merged to create a new workflow graph. +When another :py:func:`schedule `-decorated function is called with a promise, the graphs of the dependencies are merged to create a new workflow graph. .. NOTE:: The promised object can be of any type and can be used as a normal object. You access attributes and functions of the object that is promised as you normally would. @@ -92,7 +86,7 @@ Doing things parallel Using the Noodles approach it becomes very easy to paralellise computations. Let's look at a second example. -:: +.. code:: python from noodles import (gather, run_parallel) from noodles.tutorial import (add, sub, mul, accumulate) @@ -108,9 +102,9 @@ Using the Noodles approach it becomes very easy to paralellise computations. Let w = [my_func(i, v, u) for i in range(6)] x = accumulate(gather(*w)) - answer = run_parallel(r5, n_threads=4) + answer = run_parallel(x, n_threads=4) - print("The answer is ${0}, again.".format(answer)) + print("The answer is {0}, again.".format(answer)) This time the workflow graph will look a bit more complicated. @@ -122,13 +116,13 @@ This time the workflow graph will look a bit more complicated. The workflow graph of the second example. Here we see how a user can define normal python functions and use them to build a larger workflow. -Furthermore, we introduce a new bit of magic: the ``gather`` function. +Furthermore, we introduce a new bit of magic: the :py:func:`gather ` function. When you build a list of computations using a list-comprehension like above, you essentially store a *list of promises* in variable ``w``. However, schedule-decorated functions cannot easily see which arguments contain promised values, such as ``w``, and which arguments are plain Python. -The ``gather`` function converts the list of promises into a promise of a list, making it clear to the scheduled function this argument is a promise. -The ``gather`` function is defined as follows: +The :py:func:`gather ` function converts the list of promises into a promise of a list, making it clear to the scheduled function this argument is a promise. +The :py:func:`gather ` function is defined as follows: -:: +.. code:: python @schedule def gather(*lst): @@ -136,7 +130,7 @@ The ``gather`` function is defined as follows: By unpacking the list (by doing ``gather(*w)``) in the call to gather, each item in ``w`` becomes a dependency of the ``gather`` node in this workflow, as we can see in the figure above. -To make use of the parallelism in this workflow, we run it with ``run_parallel``. +To make use of the parallelism in this workflow, we run it with :py:func:`~noodles.run_parallel`. This runner function creates a specified number of threads, each taking jobs from the Noodles scheduler and returning results. Running workflows @@ -144,21 +138,21 @@ Running workflows Noodles ships with a few ready-made functions that run the workflow for you, depending on the amount of work that needs to be done. -``run_single``, local single thread -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:py:func:`~noodles.run_single`, local single thread +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Runs your workflow in the same thread as the caller. This function is mainly for testing. When running workflows you almost always want to use one of the other functions. -``run_parallel``, local multi-thread -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:py:func:`~noodles.run_parallel`, local multi-thread +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Runs your workflow in parallel using any number of threads. Usually, specifying the number of cores in your CPU will give optimal performance for this runner. .. NOTE:: If you are very **very** certain that your workflow will never need to scale to cluster-computing, this runner is more lenient on the kinds of Python that is supported, because function arguments are not converted to and from JSON. Think of nested functions, lambda forms, generators, etc. -``run_process``, local multi-process -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:py:func:`~noodles.run_process`, local multi-process +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Starts a second process to run jobs. This is usefull for testing the JSON compatability of your workflow on your own machine. Xenon @@ -193,11 +187,14 @@ The Xenon runner needs a way to setup the virtualenv on the remote side, so a wo If you need to setup some more aspects of the environment, load modules, set variables etc., modify this script and put it in the directory where you want to run the jobs. Specify this directory in the Python script. -:: +.. code:: python - from noodles import schedule, Scheduler, gather - from noodles.datamodel import get_workflow - from noodles.run_xenon import xenon_interactive_worker, XenonConfig + from noodles import ( + serial, gather) + from noodles.run.xenon import ( + XenonConfig, RemoteJobConfig, XenonKeeper, run_xenon_prov) + from noodles.display import ( + NCDisplay) from noodles.tutorial import add, accumulate @@ -205,24 +202,40 @@ If you need to setup some more aspects of the environment, load modules, set var a = [add(i, j) for i in range(5) for j in range(5)] b = accumulate(gather(*a)) - config = XenonConfig() # use default settings - config.working_dir = sys.getcwd() # this actually is the default - config.prefix = sys.prefix # virtual-env prefix or just '/usr' - - # options given to Xenon.newScheduler() - config.schedule_args = ('ssh', 'localhost', None, None) - - result = Scheduler().run( - xenon_interactive_worker(config), - get_workflow(b)) + # XenonKeeper is the root Xenon object that gives access + # to the Xenon Java library + with XenonKeeper() as Xe: + # We recommend loging in on your compute resource + # through private/public key pairs. This prevents + # passwords ending up as ASCII in your source files. + certificate = Xe.credentials.newCertificateCredential( + 'ssh', os.environ['HOME'] + '/.ssh/id_rsa', '', '', None) + + # Configure Xenon to access your favourite super computer. + xenon_config = XenonConfig( + jobs_scheme='slurm', + location='login.super-duper-computer.darpa.net', + credential=certificate + ) + + # Specify how to submit jobs. + job_config = RemoteJobConfig( + registry=serial.base, + prefix='', + working_dir='', + time_out=5000 + ) + + + # Run jobs with NCurses based console feedback + with NCDisplay() as display: + result = run_xenon_prov( + b, Xe, "cache.json", 2, xenon_config, job_config, + display=display) print("This test is working {0}%!".format(result)) -Fireworks -~~~~~~~~~ -Fireworks_ is a workflow engine that runs workflows as stored in a MongoDB. This is the `Dicke Bertha`_ in our armoury. Fireworks support is still in an early stage of development. The advantage of Fireworks is that it is here, it works and it is robust. However, it may be a hassle with the system admins to setup a MongoDB and be allowed to communicate with it from within the cluster environment. - Hybrid mode ~~~~~~~~~~~ We may have a situation where a workflow consists of some very heavy *compute* jobs and a lot of smaller jobs that do some bookkeeping. If we were to schedule all the menial jobs to a SLURM queue we actually slow down the computation through the overhead of job submission. The Noodles cook may provide the schedule functions with hints on the type of job the function represents. Depending on these hints we may dispatch the job to a remote worker or keep it on the local machine. @@ -231,7 +244,5 @@ We provide an example on how to use the hybrid worker in the source. If you really need to, it is not too complicated to develop your own job runner based on some of these examples. Elsewhere in this documentation we elaborate on the architecture and interaction between runners and the scheduler, see: :ref:`noodles-scheduler`. -.. _Fireworks: https://pythonhosted.org/FireWorks/index.html -.. _Dicke Bertha: https://en.wikipedia.org/wiki/Big_Bertha_%28howitzer%29 .. _Xenon: http://nlesc.github.io/Xenon/ .. _pyxenon: http://github.com/NLeSC/pyxenon diff --git a/doc/source/first_steps.ipynb b/doc/source/first_steps.ipynb new file mode 120000 index 0000000..df50a8e --- /dev/null +++ b/doc/source/first_steps.ipynb @@ -0,0 +1 @@ +../../notebooks/first_steps.ipynb \ No newline at end of file diff --git a/doc/source/first_steps.rst b/doc/source/first_steps.rst index efae898..1448d8e 100644 --- a/doc/source/first_steps.rst +++ b/doc/source/first_steps.rst @@ -1,7 +1,7 @@ -First Steps +First Steps =========== -**This tutorial is also available in the form of a Jupyter Notebook. Try it out, and play!** +**This tutorial is also available in the form of a** `Jupyter Notebook `_. **Try it out, and play!** Noodles is there to make your life easier, *in parallel*! The reason why Noodles can be easy and do parallel Python at the same time is its *functional* approach. In one part you'll define a set of functions that you'd like to run with Noodles, in an other part you'll compose these functions into a *workflow graph*. To make this approach work a function should not have any *side effects*. Let's not linger and just start noodling! First we define some functions to use. @@ -9,11 +9,11 @@ Noodles is there to make your life easier, *in parallel*! The reason why Noodles .. code:: python from noodles import schedule - + @schedule def add(x, y): return x + y - + @schedule def mul(x,y): return x * y @@ -27,21 +27,23 @@ Now we can create a workflow composing several calls to this function. c = add(a, a) d = mul(b, c) -That looks easy enough; the funny thing is though, that nothing has been computed yet! Noodles just created the workflow graphs corresponding to the values that still need to be computed. Until such time, we work with the *promise* of a future value. Using the module ``pygraphviz`` (`pip install pygraphviz`) we can look at the call graphs. +That looks easy enough; the funny thing is though, that nothing has been computed yet! Noodles just created the workflow graphs corresponding to the values that still need to be computed. Until such time, we work with the *promise* of a future value. Using the module ``pygraphviz`` (`pip install pygraphviz`, check `this post `_ if you have problems installing pygraphviz) we can look at the call graphs. .. code:: python from draw_workflow import draw_workflow import sys import os - + draw_workflow("wf1a.png", a._workflow) draw_workflow("wf1b.png", b._workflow) draw_workflow("wf1c.png", c._workflow) draw_workflow("wf1d.png", d._workflow) - + err = os.system("montage wf1?.png -tile 4x1 -geometry +10+0 wf1-series.png") +``draw_workflow`` imports ``pygraphviz``. ``wf1-series.png`` is saved to the directory from which you launched this notebook. + .. figure:: _static/images/wf1-series.png :alt: building the workflow :align: center @@ -52,7 +54,7 @@ Now, to compute the result we have to tell Noodles to evaluate the program. .. code:: python from noodles import run_parallel, run_single - + run_parallel(d, n_threads=2) @@ -61,4 +63,3 @@ Now, to compute the result we have to tell Noodles to evaluate the program. .. parsed-literal:: 16 - diff --git a/doc/source/implementation.rst b/doc/source/implementation.rst new file mode 100644 index 0000000..99156e7 --- /dev/null +++ b/doc/source/implementation.rst @@ -0,0 +1,9 @@ +Implementation +============== + +.. toctree:: + :maxdepth: 2 + + development + scheduler + brokers diff --git a/doc/source/index.rst b/doc/source/index.rst index 7ca1ce9..060c72e 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -6,33 +6,22 @@ Welcome to Noodles's documentation! =================================== -Contents: - -.. toctree:: - :maxdepth: 2 - - Introduction - eating - cooking - first_steps - poetry_tutorial - boil_tutorial - development - scheduler - brokers - Introduction -============ +------------ +Noodles offers a model for parallel programming in Python. It can be used for a variety of tasks including data pipelines, and computational workflows. + The primary goal of Noodles is to make it easy to run jobs on cluster supercomputers, in parallel, straight from a Python shell. The user enters a Python script that looks and feels like a serial program. The Noodles engine then converts this script into a call graph. This graph can be executed on a variety of machines using the different back-end runners that Noodles provides. This is not so much a design driven by technology but by social considerations. The end user may expect an elegant, easy to understand, interface to a computational library. This user experience we refer to as *eating of noodles*. -The computational library that is exposed to the user by means of Noodles needs to adhere to some design principles that are more strict than plain Python gives us. The library should follow a functional style of programming and is limited by the fact that function arguments need to pass through a layer where data is converted to and from a JSON format. The design of such a library is the *cooking of noodles*. As it is with ramen noodles, ofttimes the cook is also an avid consumer of noodles. +The computational library that is exposed to the user by means of Noodles needs to adhere to some design principles that are more strict than plain Python gives us. The library should follow a functional style of programming and is limited by the fact that function arguments need to pass through a layer where data is converted to and from a JSON format. The design of such a library is the *cooking of noodles*. As it is with ramen noodles, often the cook is also an avid consumer of noodles. The complexity of running a workflow in parallel on a wide variety of architectures is taken care of by the Noodles engine. This is the *production of noodles* which is left as an exercise for the Noodles dev-team at the Netherlands eScience Center. +Assumed knowledge for this tutorial: Familiarity with basics of the Python language, `decorators`_, and the distinction between functional and object oriented programming: https://en.wikipedia.org/wiki/Comparison_of_programming_paradigms. + Copyright & Licence ------------------- -Noodles 0.1.0 is copyright by the *Netherlands eScience Center (NLeSC)* and released under the `LGPLv3`_. +Noodles 0.3.0 is copyright by the *Netherlands eScience Center (NLeSC)* and released under the Apache v2 License. See http://www.esciencecenter.nl for more information on the NLeSC. @@ -41,25 +30,41 @@ Installation .. WARNING:: We don't support Python versions lower than 3.5. -The core of Noodles runs on **Python 3.5**. To run Noodles on your own machine, no extra dependencies are required. It is advised to install Noodles in a virtualenv. If you want support for `Xenon`_, install `pyxenon`_ too. +The core of Noodles runs on **Python 3.5** and above. To run Noodles on your own machine, no extra dependencies are required. It is advised to install Noodles in a virtualenv. If you want support for `Xenon`_, install `pyxenon`_ too. .. code-block:: bash - # pyxenon needs Java, which may needs JAVA_HOME set, put it in .bashrc - export JAVA_HOME="/usr/lib/jvm/default-java" # or similar... - # create the virtualenv - pyvenv-3.5 + virtualenv -p python3 . /bin/activate # install noodles - cd noodles # git pull git@github.com:NLeSC/noodles.git - pip install -r requirements.txt - pip install . + pip install noodles + +Noodles has several optional dependencies. To be able to use the Xenon job scheduler, install Noodles with:: + + pip install noodles[xenon] + +The provenance/caching feature needs TinyDB installed:: + + pip install noodles[prov] + +To be able to run the unit tests:: + + pip install noodles[test] + +Documentation Contents +====================== + +.. toctree:: + :maxdepth: 2 -.. NOTE:: In the future this will reduce to `pip install noodles`. + Introduction + eating + cooking + tutorials + implementation -To run the nosetests, you will need passwordless access to localhost with ssh. This is achieved by appending ``.ssh/id_rsa.pub`` to ``.ssh/authorized_keys``. If ``id_rsa.pub`` does not yet exist, generate it (GitHub has a good article on `generating SSH keys`_). Indices and tables ================== @@ -70,5 +75,5 @@ Indices and tables .. _Xenon: http://nlesc.github.io/Xenon/ .. _pyxenon: http://github.com/NLeSC/pyxenon -.. _LGPLv3: http://www.gnu.org/licenses/lgpl-3.0.html .. _`generating SSH keys`: https://help.github.com/articles/generating-ssh-keys/ +.. _`decorators`: https://www.thecodeship.com/patterns/guide-to-python-function-decorators/ diff --git a/doc/source/poetry.png b/doc/source/poetry.png new file mode 120000 index 0000000..c545758 --- /dev/null +++ b/doc/source/poetry.png @@ -0,0 +1 @@ +../../notebooks/poetry.png \ No newline at end of file diff --git a/doc/source/poetry_tutorial.ipynb b/doc/source/poetry_tutorial.ipynb new file mode 120000 index 0000000..05023fd --- /dev/null +++ b/doc/source/poetry_tutorial.ipynb @@ -0,0 +1 @@ +../../notebooks/poetry_tutorial.ipynb \ No newline at end of file diff --git a/doc/source/poetry_tutorial.rst b/doc/source/poetry_tutorial.rst deleted file mode 100644 index 5cb4318..0000000 --- a/doc/source/poetry_tutorial.rst +++ /dev/null @@ -1,335 +0,0 @@ -.. highlight:: python - -Real World Tutorial 1: Translating Poetry -========================================= - -Making loops ------------- - -Thats all swell, but how do we make a parallel loop? Let's look at a -``map`` operation; in Python there are several ways to perform a -function on all elements in an array. For this example, we will -translate some words using the Glosbe service, which has a nice REST -interface. We first build some functionality to use this interface. - -.. code:: python - - import urllib.request - import json - import re - - - class Translate: - """Translate words and sentences in the worst possible way. The Glosbe dictionary - has a nice REST interface that we query for a phrase. We then take the first result. - To translate a sentence, we cut it in pieces, translate it and paste it back into - a Frankenstein monster.""" - def __init__(self, src_lang='en', tgt_lang='fy'): - self.src = src_lang - self.tgt = tgt_lang - self.url = 'https://glosbe.com/gapi/translate?' \ - 'from={src}&dest={tgt}&' \ - 'phrase={{phrase}}&format=json'.format( - src=src_lang, tgt=tgt_lang) - - def query_phrase(self, phrase): - with urllib.request.urlopen(self.url.format(phrase=phrase.lower())) as response: - translation = json.loads(response.read().decode()) - return translation - - def word(self, phrase): - #translation = self.query_phrase(phrase) - translation = {'tuc': [{'phrase': {'text': phrase.lower()[::-1]}}]} - if len(translation['tuc']) > 0 and 'phrase' in translation['tuc'][0]: - result = translation['tuc'][0]['phrase']['text'] - if phrase[0].isupper(): - return result.title() - else: - return result - else: - return "<" + phrase + ">" - - def sentence(self, phrase): - words = re.sub("[^\w]", " ", phrase).split() - space = re.sub("[\w]+", "{}", phrase) - return space.format(*map(self.word, words)) - -We start with a list of strings that desparately need translation. - -.. code:: python - - shakespeare = [ - "If music be the food of love, play on,", - "Give me excess of it; that surfeiting,", - "The appetite may sicken, and so die."] - - def print_poem(intro, poem): - print(intro) - for line in poem: - print(" ", line) - print() - - print_poem("Original:", shakespeare) - - -.. parsed-literal:: - - Original: - If music be the food of love, play on, - Give me excess of it; that surfeiting, - The appetite may sicken, and so die. - - - -Beginning Python programmers like to append things; this is not how you -are supposed to program in Python; if you do, please go and read Jeff -Knupp's *Writing Idiomatic Python*. - -.. code:: python - - shakespeare_auf_deutsch = [] - for line in shakespeare: - shakespeare_auf_deutsch.append( - Translate('en', 'de').sentence(line)) - print_poem("Auf Deutsch:", shakespeare_auf_deutsch) - - -.. parsed-literal:: - - Auf Deutsch: - Fi cisum eb eht doof fo evol, yalp no, - Evig em ssecxe fo ti; taht gnitiefrus, - Eht etiteppa yam nekcis, dna os eid. - - - -Rather use a comprehension like so: - -.. code:: python - - shakespeare_ynt_frysk = \ - (Translate('en', 'fy').sentence(line) for line in shakespeare) - print_poem("Yn it Frysk:", shakespeare_ynt_frysk) - - -.. parsed-literal:: - - Yn it Frysk: - Fi cisum eb eht doof fo evol, yalp no, - Evig em ssecxe fo ti; taht gnitiefrus, - Eht etiteppa yam nekcis, dna os eid. - - - -Or use ``map``: - -.. code:: python - - shakespeare_pa_dansk = \ - map(Translate('en', 'da').sentence, shakespeare) - print_poem("PĆ„ Dansk:", shakespeare_pa_dansk) - - -.. parsed-literal:: - - PĆ„ Dansk: - Fi cisum eb eht doof fo evol, yalp no, - Evig em ssecxe fo ti; taht gnitiefrus, - Eht etiteppa yam nekcis, dna os eid. - - - -Noodlify! ---------- - -If your connection is a bit slow, you may find that the translations -take a while to process. Wouldn't it be nice to do it in parallel? How -much code would we have to change to get there in Noodles? Let's take -the slow part of the program and add a ``@schedule`` decorator, and run! -Sadly, it is not that simple. We can add ``@schedule`` to the ``word`` -method. This means that it will return a promise. - -- Rule: *Functions that take promises need to be scheduled functions, - or refer to a scheduled function at some level.* - -We could write - -:: - - return schedule(space.format)(*(self.word(w) for w in words)) - -in the last line of the ``sentence`` method, but the string format -method doesn't support wrapping. We rely on getting the signature of a -function by calling ``inspect.signature``. In some cases of build-in -function this raises an exception. We may find a work around for these -cases in future versions of Noodles. For the moment we'll have to define -a little wrapper function. - -.. code:: python - - from noodles import schedule - - - @schedule - def format_string(s, *args, **kwargs): - return s.format(*args, **kwargs) - - - import urllib.request - import json - import re - - - class Translate: - """Translate words and sentences in the worst possible way. The Glosbe dictionary - has a nice REST interface that we query for a phrase. We then take the first result. - To translate a sentence, we cut it in pieces, translate it and paste it back into - a Frankenstein monster.""" - def __init__(self, src_lang='en', tgt_lang='fy'): - self.src = src_lang - self.tgt = tgt_lang - self.url = 'https://glosbe.com/gapi/translate?' \ - 'from={src}&dest={tgt}&' \ - 'phrase={{phrase}}&format=json'.format( - src=src_lang, tgt=tgt_lang) - - def query_phrase(self, phrase): - with urllib.request.urlopen(self.url.format(phrase=phrase.lower())) as response: - translation = json.loads(response.read().decode()) - return translation - - @schedule - def word(self, phrase): - translation = {'tuc': [{'phrase': {'text': phrase.lower()[::-1]}}]} - #translation = self.query_phrase(phrase) - - if len(translation['tuc']) > 0 and 'phrase' in translation['tuc'][0]: - result = translation['tuc'][0]['phrase']['text'] - if phrase[0].isupper(): - return result.title() - else: - return result - else: - return "<" + phrase + ">" - - def sentence(self, phrase): - words = re.sub("[^\w]", " ", phrase).split() - space = re.sub("[\w]+", "{}", phrase) - return format_string(space, *map(self.word, words)) - - def __str__(self): - return "[{} -> {}]".format(self.src, self.tgt) - - def __serialize__(self, pack): - return pack({'src_lang': self.src, - 'tgt_lang': self.tgt}) - - @classmethod - def __construct__(cls, msg): - return cls(**msg) - -Let's take stock of the mutations to the original. We've added a -``@schedule`` decorator to ``word``, and changed a function call in -``sentence``. Also we added the ``__str__`` method; this is only needed -to plot the workflow graph. Let's run the new script. - -.. code:: python - - from noodles import gather - - shakespeare_en_esperanto = \ - map(Translate('en', 'eo').sentence, shakespeare) - - wf = gather(*shakespeare_en_esperanto) - draw_workflow('poetry.png', wf._workflow) - result = run_parallel(wf, n_threads=8) - print_poem("Shakespeare en Esperanto:", result) - - -.. parsed-literal:: - - Shakespeare en Esperanto: - Fi cisum eb eht doof fo evol, yalp no, - Evig em ssecxe fo ti; taht gnitiefrus, - Eht etiteppa yam nekcis, dna os eid. - - - -The last peculiar thing that you may notice, is the ``gather`` function. -It collects the promises that ``map`` generates and creates a single new -promise. The definition of ``gather`` is very simple: - -:: - - @schedule - def gather(*lst): - return lst - -The workflow graph of the Esperanto translator script looks like this: - -.. figure:: _static/images/poetry.png - :alt: a poetic workflow - :align: center - :figwidth: 100% - -Dealing with repetition ------------------------ - -In the following example we have a line with some repetition. It would -be a shame to look up the repeated words twice, wouldn't it? Let's build -a little counter routine to check if everything is working. - -.. code:: python - - line = "Mein Gott, mein Gott, warum hast Du mich verlassen?" - run_parallel(Translate('de', 'fr').sentence(line), n_threads=4) - - - - -.. parsed-literal:: - - 'Niem Ttog, niem Ttog, muraw tsah Ud hcim nessalrev?' - - - -To see how this program is being run, we monitor the job submission, -retrieval and result storage in a ``JobKeeper`` instance. - -.. code:: python - - from noodles.run.job_keeper import JobKeeper - from noodles.run.run_with_prov import run_parallel - from noodles import serial - - J = JobKeeper(keep=True) - wf = Translate('de', 'fr').sentence(line) - run_parallel(wf, - n_threads=4, registry=serial.base, - jobdb_file='matthew.json', job_keeper=J) - - - - -.. parsed-literal:: - - 'Niem Ttog, niem Ttog, muraw tsah Ud hcim nessalrev?' - - - -.. code:: python - - from itertools import starmap - import time - - def format_log_entry(tm, what, data, msg): - return "{}: {:16} - {}".format( - time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime(tm)), - '[' + what + ']', - data) - - for k,j in J.items(): - print('\n'.join(starmap(format_log_entry, j.log))) - print("-------------------------------------") - - diff --git a/doc/source/prime_numbers.ipynb b/doc/source/prime_numbers.ipynb new file mode 120000 index 0000000..4f76e42 --- /dev/null +++ b/doc/source/prime_numbers.ipynb @@ -0,0 +1 @@ +../../notebooks/prime_numbers.ipynb \ No newline at end of file diff --git a/doc/source/pythagoras.png b/doc/source/pythagoras.png new file mode 120000 index 0000000..fda8ed6 --- /dev/null +++ b/doc/source/pythagoras.png @@ -0,0 +1 @@ +../../notebooks/pythagoras.png \ No newline at end of file diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst new file mode 100644 index 0000000..53c0059 --- /dev/null +++ b/doc/source/tutorials.rst @@ -0,0 +1,10 @@ +Tutorials +========= + +.. toctree:: + :maxdepth: 2 + + first_steps + poetry_tutorial + boil_tutorial + prime_numbers diff --git a/doc/source/wf1-series.png b/doc/source/wf1-series.png new file mode 120000 index 0000000..e8cf9f4 --- /dev/null +++ b/doc/source/wf1-series.png @@ -0,0 +1 @@ +../../notebooks/wf1-series.png \ No newline at end of file diff --git a/examples/Cosmology.ipynb b/examples/Cosmology.ipynb deleted file mode 100644 index 0753447..0000000 --- a/examples/Cosmology.ipynb +++ /dev/null @@ -1,176 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Noodles example: his noodly cosmic structure formation.\n", - "\n", - "This example shows how to do simple astrophysics with Noodles. At the moment this shows how we can handle NumPy data without too much of a fuzz. This may be extended to attempt some parallelisation, optimising for data transport, and experimenting with hybrid iteration schemes.\n", - "\n", - "### TODO\n", - "- include Amuse example" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "sys.path = ['.'] + sys.path" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import cosmology as csf\n", - "import noodles\n", - "from noodles.run_process import process_worker\n", - "from noodles.datamodel import get_workflow\n", - "# from cosmology.loops import power_2, compose_2, scale_2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "box = csf.BoxConfig(2, 256, 100.0)\n", - "force_box = csf.BoxConfig(2, 512, 100.0)\n", - "cosmos = csf.EdS\n", - "\n", - "delta_wf = csf.gaussian_random_field(\n", - " box,\n", - " noodles.Lambda(\"lambda k: 1 if k == 0 else 20000*k**(-0.75)\"))\n", - " \n", - "phi_wf = csf.compute_potential(box, delta_wf)\n", - "ics_wf = csf.zeldovich_approximation(box, phi_wf)\n", - "\n", - "za = csf.drift(cosmos, ics_wf, 0.1, 0.1)\n", - "dens = csf.compute_density(force_box, za)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Running the workflow with the `process_worker` to test the saving and retrieval of NumPy data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "rho = noodles.Scheduler().run(process_worker(), get_workflow(dens))\n", - "rho = rho.make()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import matplotlib\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n", - "plt.rcParams['image.cmap'] = 'PaulT_plusmin'\n", - "\n", - "## Register Paul Tol's colormap; see https://personal.sron.nl/~pault/\n", - "def pault_map(x):\n", - " rcol = 0.237 - 2.13*x + 26.92*x**2 - 65.5*x**3 + 63.5*x**4 - 22.36*x**5\n", - " gcol = ((0.572 + 1.524*x - 1.811*x**2)/(1 - 0.291*x + 0.1574*x**2))**2\n", - " bcol = 1/(1.579 - 4.03*x + 12.92*x**2 - 31.4*x**3 + 48.6*x**4 - 23.36*x**5)\n", - " return rcol, gcol, bcol\n", - "\n", - "cols = [pault_map(x) for x in np.linspace(0, 1, 256)]\n", - "cm_plusmin = matplotlib.colors.LinearSegmentedColormap.from_list(\"PaulT_plusmin\", cols)\n", - "plt.cm.register_cmap(\"PaulT_plusmin\", cm_plusmin)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "output_type": "execute_result", - "metadata": {} - }, - { - "data": { - "image/png": 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VLULTQdJjz94b7UcrFfC8ALfWIq1bOLIEhk1atw6uIyBv1w+wPm+F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\nsFdRTg5GRkYAtc3agQW936IewA/pdV446+5kUuSvHMUJTei6c0Xvtdt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TtAKJ/IkpjLVy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9c1kaL2stbpHVkojYUJUWy1dKy1XRKOh7tZJTBttP3mQwmvwYVj9EIJRTfjtNL+SD9xmrYhmCRm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3znpduMLYxy/lg/X/mPb5HeK6NNDiHIEt2DOWqFUKRsfnGUh/d3CXwft2UyMj9CudDAaugs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\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+zs5WGTMZww4E/JfYODqC3RaB0ZIxX+SpFVYxo2H/OC0Zg2INpWkQKVZpLe2wHZBxLAcr\nHUce6SW0nsdUgzjHRrFbFlQ1oYNR130Vx1Z/N6HBLOnhLB+6b4Rnv/kasXaGYUmSb1cMtFHyYhJ7\nGwWCD5xAi0Ww2z1RW1G48N4T5DJRNlwJbW2P4GAWu6Zx8r2ncOIR/vuPznJiII4WUMhkYhTKTTTH\nIzXeSz1fJViu+5G257g4rf1MRu/PIvWkkEp1ipEw5WKDk2fG2DZsBi/OkK/q2P1Z3L4u5EKVxz91\niVUUjKZQSgWwpodwYhF/QncEk3x3TjVIoDvpL0CK7dB7/1G05V3MVJx//UKQf/uHS7xVblHeLmOV\nG8S3i6QeP8ve4jaRch1bkQlN9NMyLKRMgsb8pljIRnpxXI9QQ8fKJLn/0iSfu3+EbTlMLRHjyHQf\nR44PokRD6M/fBESFI9gGOFmSJBbHi0d9u3bl+Cituo4ZCWPGo8Tbaq5mPEq0v4toLk3wzjrSUA/e\nxh76SB9mNIxa19DjUdHnbhqH2DyNXAbXg1Z/N9OPnWKz2ARbaHms3NniS196mK/94XPESrVD0tzW\nsJBbVusa4ZPjQrynt4vTxwfZvrZ86H1x2nRKRW8Jmf5MArnapL5ZJNI0aEVCBPq7yZ2fEm2/2XGh\nDTHSiyXLSLEwcrFGq714A1gru/dk3R3NkuixEZRICC0awWnTnTsj+MAJai0bqWXxwY+c59feN8bX\nv32TQEhF2i1TvyOAjx0/EjcQwAOK2xW/fO5tFrDH+jh7eZqtuzvguMiGSahQJVCqAQEGHj9DLSDz\ny198kB//+DaBaIjf/3evkRnqoloz0EMqZz98Yd9eO5tGTsVgr0KwZXHkiTM031iklU7wwKlB5l+c\nv2cdOohJkFyPnstHsW2X4M0VOD6GJsuYsQjKAY2B0Y/fT3Fhe18Pp7eLwd4U88/dIqwJgb+DlQnJ\nsn13U0uSSA5lGR5MUyprhzAv+kZhH9zdnaL7zATXb25S363w09dW8Ep1zHiU3lPC4bjRlaJ+wMTs\nYJABUHEQLKj2Ob3RPmzXIzDUQ9+pMQq6BWoQWhbyZoFgrYk90ocXj9zjVfNuo9HXjTOQhetL0NBp\nOcKZWhrvF8qqnUrhhSPCLbojQDaUo//sBOVyk5HHTlP47msETQszGqZrZpDTR/pYvbMt1oEzU1gV\n0e5zchmCy9v8+UtrKJk4ZJKcuDTNTr5GS5KIFqpIrtAGMtuYtY4eiakGyY32UMlX/eA5WK5jqkEC\nnqhaOCcnaKXixNZ2aSoKbr+wP5Adl5KkYLdssWEHFXqODVEqNjh+cZJqy+F7zy3wrau7fPyhSRLx\nMAtvrhLSWz7rqzU+gJdJUFzdI3ZsBHctL0zMDJPr87vIqRgugiJcb7YINQ1aNR21qWMFJCTLRjFt\nP+nTJgZwWhbmcA4lEUUv1gkloki7ZSE/rhkYapDAGwv7FflK41BF7eC4/sL8oWQb8HVNDqnJjvWj\n1DSMWIT+95xEX9wWZAPTYefVdxfskv6rZtR/g+FJEtHFDeL5MkpfF3Y0jK3IRK6cRJsYgGSMxlYR\nc3acN//yTeI7Rbon+nCjYf8cdlvKG0AxTIyO9n4yhjTQ7R8XbugC43DgJpqVJpgWkY09bNPi4i89\nxulP3E+qK45ru0h2W5Z7Zgirr0tcs+3wDz9/gUZN51/+m2eFjPjlYzSGcniKfIhxEU7H/P+3wioD\nfUkcrYVdEIuLmY7z3F++xY+emaPVMJANE6umwcQAv3/kT3ng5CA/WSjx9/7p05TqLVRF4uGzIwQa\nOh9/ZIaJ40O0jo9x4nOPYKpB7GgYklFAGPBEsknCc2sY8Qgz4z04t1aZW9jhj/+Hh/ntj8wwfmEK\nSZEYncihZVM8869/QGOjQOBA9hGeWyO6tC+fDhwCWYU1457su/iDawCcOT1COpdCNS1iyQhuTaNn\nZoDMk+fYXtoF26HnybM4apCBgQyDp8ZwNQM3HgFAWtslEFYBCOyUeO671/jN33+e2dEM3dkEX3hg\nmN95MsyffLJG34cvChT3mSlBw9MMQRcGmteX/eqN9MYC0VoTT1Wgfe5GNk1yoo+eXJJ0T5oR6gAA\nIABJREFUWtw/9xWRaVFpINfEM3VVBal9bV2Xj6LHxbFSMoajBlHyZW6+MI9aaYj5l0vjqUE+Phsh\n0JXk7SO6uEF8qyDonq/MIV08SrFQ56cvCWZH9/vP+8dG5teJ58s4A1mcgSzhdAxbkRl49CQA6tFh\nIskoStvp0m0/Q8/1wPWQlrYPsXTebeQuzmCqQcqLW2iVJurKNt5GgVb7XgHor8yBGiQ6M8SHZruZ\nL9gEbIf3PjBJM51g/BMPos8MizI1YPV1Ea01iQxlGfroZVEJOTNFMKzSkwoT3yqgpMU9lNy2cdfE\nAKVSg5e/WqeqW1x48Agb89vECxVuvnIXRVWIre3y1u39uRmwHZxKw5+fy1//KQDK9bv8xb/4lgi+\nLh+75zu79v59KT5zHfu1eczZcf7GR04TzpdJjeVwpX0g/eo3XjyMcXntDq999+o9DKPOUGxn/51J\nxnjw9BC3r68zMtWHK4nltxVWcWaG/d+JFyrUKxpWTaNvqp+AZuCFVeSuhGB/DeUotkHL0Jb3nh0/\n/IcNE+nAemfVNCJ9GaxCVcwT0yaSTRLPCwsBxXaILm4cet+b6cT+6aJhIlfEfEu89wzNriTBSh1v\np4TkuqimRcC0BCvnxrK/LgPoc+uEOqZlkkRqqJvqD64SK9XY+dYrgKjAhKcGuDQ7wOv/7kf+/dTz\nFeT2uUK3VlBsh3ihwtpL8/zqR2b53aciKGu7nHvsJJaqtJ+pQ6AdCN7/qfvRkjEyUwMUv/e6zyyx\n2++KMjuGNpAV/79+11/PgjslAgfaSKF4GK89D6SjI4RUhWAuzd0/f4UfP3sHI1/BvL7E83O7qIok\nmIGK7N9Dt9LAaehEd0rU5jawcxlCIzmaI70MXZymvlNGvSESjFibTRRpg7bjhQpeOo50btq/Hq/T\nUs1XcNfyQs30YBAxMUB4bg1taujeufEOw5UC9zAGw31dOGrw8GfpmH9s8XuvYysylWtLPp7u3cZf\nHevkkV8Upc+LR9HbKoCqblLVTNBaSHUdpaljGxbBpiF0FNpMAbddjusY6kDbKVHb75sdzMK8s1O+\n1a0+M0zyxCgDoz2Ul/OE9BaGorDbNBkZSHP7pXmURBSvHZE7DR25riM7LrGT4/z43/4EebsotOQt\nG2erhGfZRKcGaUiybxF8qNxqO+SX8zzykftYXhX0I2+0D6/SINbfRTgW4sLDR7nvzAg3X7jDZ7/4\nSVrE+KPv3SS8sMHmcp71uS02X5hDNUxu/fQOtXiUaDzC8rM3efJLj7C4Xubf/dZj/Pl3bzH78DHW\n2mXV0OlJkCVac+towSDfuVlkqebw9z80zqPnRvjs97/K53/n1/mPf36bcFtsCtoo65P7qH8Aebzv\n0PdqdiVxhnN+ZtYBEDXmNlhbKyAtiGi+aljE9ypUHEh0J6jv1RieHaE/m2D79galltB7CKQT0PYl\n6VjBB4s1pOOjqMs79F+Y4u5mlfUbaxTkEP/0j27zcmOQ4yMZMkeGCEVVyrfW6b8wjd7u9zrTQ9iq\n6Ed3jNTihaqQr3Y9Al0JJmf6+PD5YW5vVNAXt9FnhmkFg8QLlf1soN3nBpE9dj4Plmq+3LLaEPNE\nbRqitN/QeV1KUtgW6qLm7Dgtw/L1FwafukjzltBfae1VMWwXSnVh9b5dOWRHrR4ZJnhzRahQtoWM\n9EXhVKpJMj1D3dTrhlCnHciiOR6jZ8ao1nSSR4dx1/cEuykSOoRJiT16ikYb51OqGzipOEq1iduy\nCGsG5kCWQDZFy/VgcgArGsazbKy9Kt//9nVeLbawyg0W7uYJ1jV21otMnZ+g3AY0d1qFdqlOLaji\nKTLOap5Ifxd35raRyw2C7SoEgDHYw//8lUcY7c+wYE7yF6+sMn97E7eu+f3wzpxsOh6Js1M4a3kk\n28FOxe9tn144grtTpuf0OMXX7x4yiJJc75DEeWtqCFdrEdos8ErJoKUo/OYXL/PjpfKh4zqjI1r1\na3/nvVw3A7jrezQGsjDa6/evOzbbow8do/bmEhsvzRMs1Shtlg4p3nbmqCtJGNNDYvOQAnBjRYCT\nG7r/nv3Nv/c+/vnHE/zRVY1WMk40E8d7m+25PDOEXd1Xh1VrTdxcRgRjuTSNrRKWtd9uli8fu0fY\n6iCe4KDUeEceXbEdWinha6PWNTE/dBMrFSd2ZMjH8ZjRME5YGBhqqTiGYSFP9PvtJMd20QMSv/2l\nS7yxUkbPpvYl3lsWXiBAKxbBDgaZ+vAFqrc36HnoBJoL/+H1BgXbQwoFqVrC+PLih8+zVmwSLNc5\n88hxlmomT1ye4M2NCt5wDqeuMfj4WbSFLWFtX2veYx5mTQ/5+CyA5LERnPlNku85jfbGIuWlHSxZ\nJlxpYDoesmHS//AJmprFL10Z4S+/cwMjESMyIdZMSw2KtmxDx5sQxqFWpcl733eKjZ0aSiR0jwt1\nR0dq6sMX0F6aO7QWqxWBLfNmhnDVILGNvf09z3Jw2rgUEwi1/Z64cAS9to8TauQyeI6LYjv80m99\njBeurZF75CTVzaLQc9otCyDxAVxcRzyzU/3Uk3Hfs+XnsnUyNfw+AIxijXBDp+viDNpmken3nqRr\nOEtxvUjXuSkCc0IUqtmVRE5ECUgB3PZN7CDqO2XNg4umHo/iIUpW6s2VfaqXbtLaKGDcWtt/CdsI\n4K231ogcH8WoNPFiEZSaRu49p7DmN8WiogZRO5LiR0ewsymCe20Hza0iVlcSOR7Bq+vYQRFdS64Q\nKbK7U1y5OMGOYWOt7PL3f+N9/KPPDvLl6atYuctEVIX/+J9f5eN/7RINO8qfvb5JQzfxNgqixH50\nWICGwiq2GiS4lqfmBUjPDLL8py9hEeB7CxXsZIyN5T28hi5YJ5Wm35NMnZ5Au3aX8lurfGO+gh4M\nceVLn+bPbtu4Az2Ep/opLW4Dgl/fqh6WsQ5slw715TzPw2m7O8JhANHB8rHf5zdMGptFSMaIJqPc\nffoqblAhNd6HvLhJ9OgwdltzoGMFD6BJEqFaEzObxrIcnNU81WtLBMt1SrfWuF7U2dgqE46FcRIx\nDN1CXhe9RjMRJdKVECDJVJyTDx+nsLDF2Q+cY2txGy8c4mtfneJX/80NCit7OK6H1zRQ2gC24AMn\nqOlCZKijqKnPDOM2DXGPRvt8wy8rkwDd9INfIxrGjMd84STdhWDTQO/PYssS9VvrPutEPjJCtDuB\nUdOJTg8ixSPYPWnsbJrwyg5m+14Y8Sje9JCQPz43jTLUw8B4jvXbG8jtjMjQTUjFhcX4nXX0vSqt\ngSw2AY49cZp6Ik78yBCllo1W1fAsh0e/9CgPXZ7ixtIerhpEzsSxDIupyzPU6y2/mhOQZULJKKHu\nJE6+IjRWFraQejOEd0qohsnOXp2hizMYd7f9BUl2XKxKg/OPn0LqSZG/u0vfeC+NraKv+unOThBO\nRnFVlW89v8izry6TzaVovjqP8ja6X2detdpMJEsNorapvgfVLZuKQrCm0VrcwhrqwQ2rvknayC9c\n8mneAK1IGLlD5xvqwdZN/sHHB/iFByf54+fWhFz0IycpbosANHzpGC3DouTCzk9vC1VcvYXbLvWb\ns+O4t9cI71X8llJndHAFIJw3Q23V1vCDJ8j2pqjsVARFtR3caskYrbZjcHhmkJc2JFa/94ZgSrTv\nwcGhGxahhnboc62twBkZ7mHk6AD6y3f2v/sBYavGUA7lgB8LHJYaB1HtsDvZbhsgL+crPqPi4KZp\ndiUZvzCFtrDF9AfP092fIRQOUtkocvqxU+TvbDJybpKVoo7jeOx+51WsbBqpoeNODwkGRSaOA9Tf\nuIsZDjF+aowXvneNys013HiU5s1Vnx1YTSWwbq0hOy7LAYVf+uBJZnqjpAa6ufXWOgHd5Oj5CXav\nr/D+v/tBFl9eODQ3QNA4O2tAc6QX5/oyralBahtFIm3mViep7KhYF3SLoYkc6xWTYjSKXqhhmSKY\nM+NRwv3dAihbruNYDl5Q4Q//9ijhdD/P/+krPl3+9FP3UbixSrO3i9hEP9UfXjv0bF1JotnbhaUo\nZCd64eoiejxKwHV9Z2efijvai9MGLzc1E1Uz/DnhjfbhtIUqn5/bIVaqUVsv+L48APKRYb/110wn\nCJ+awNssMPD4GbSFLVTD9J/1z2WgMXL8YwA+R7emCv+N4nKe5q11Qm9zjRx8zykqhTqTRwYwVBW2\nioeyAoBaoe73ObseOkFjp4KUjh02NbNsEg8cR0vECBRq97xM3mZBgFRNm5De8kFKznAvSijoZypS\nqY5cPPz7FgECsTBeOs77P36RsXPjVNJJartVlK4ES/k65esCJf5Sw+VaQeED3XPcsE/y3NwO1evL\nzL+xzHMvLJJf3oVYBM0TC6oZi+A2DexcBtIJkUnXmtibRQKeR+riEfSrdwntlnFsl8h4n4976ARZ\ntXKTkG6ij/QSXdzEG87xk0Wd566uYTou0XAQersoNwwSR4dRFg+jjhtDOaLHhn2OvGI7KO2M451Q\n9loy5qsEgghEFMvG1Vs0N4uE9RbZh2fZW97FkmVaO2Xk8X5y5yYpmC5mJCwWreEcyl6F7KlxLh3v\nZ9300Gs6su0w+NRFjBdvYygKZ86MkklHWb657i/inQoACADlxl6dcENnpdQkVqiC1uI7hRCV2+sE\nomFcRSbWMaQKqdgbBX8B7ihqmokYAa0lyrMdzE0gQMAwGbxygspGESOdoPf8FH09SSq314lcOYm+\nXSbS1DGjYaR2xcAfPWm0Qo34dpGeMxP05hIEQ0EUVaGhqrixCPJgD4Gdkq9oqwWDBKNhiq8v4oYE\nLsnqStI1M4h7a9WXsbfCIQKZBNHNPc4+eoKHZgd4Y36X8SMDGA60LIfV7Sp3NsrCYyQR5aEHpllZ\n3CGcTVEvN7B3ysT7u5CuLgrAn+kg2Q7RwSy1nQrB7pTPNjC7U9TbNGISUR+bYk8N8iu/cIL//O9/\nSqi/m+r8Jk4khBkJ8+TnHyaejBJQZF5/+k1Y30OqNKgs7wrswNv0HjqGb5251cnA4DC9U6020VJx\nHEUhulvaB2O73qEgA0TW7zMNtsWxf/CdJf7Dc2uoNQ1cj4cfPcbtdlWyLiuEk1GG+9Ns3d3Zt0Vo\nn8OIRghW96m8za6koFxrBrHZMd8bKeCBEQ4JOuObd9EXtzETUcLZlA92tUIqXkgFwyQvB7k9t0VL\nlt8VrKy2PUYOfaaJynB1r0Z+VRhLtlICd3TQg0WZGsAu1A7p4TS7klghVSQeF45g2Q6eIovWi2Yc\nMnEEGP/Eg5TuCAl1ta5RkhWUQpXiwjaV+S2KpSbhhs5aRSdarKItbBGZHqCutQTosu3UHCjV+fBn\n7ufRcyO88fwdfv93P8lCIMxvvG+cv/j2TcKagasJQH+4nfA0Y1FCOyUhSb64zbIFT53tY7duc+3V\nJdTRXs4f6+etssGtGxtCa8eDgZOjtJZ2hIjZ7DhOviL8YnpSeNslrGiYYLHqP9/EuSlaW0UsNUhr\nIMvDj81SrOps79XZm99Caln7Ng22g10TuL/W1BDRgW7RAo328sMbW6THe0kcGeJzHzrF2ZEUq/EE\nxXxNuOFWGvR88D5KmwIUawUVItODmKaNviLo2KEzk2h1Y98rKZ0QLrEtC0lricC20jjs/nygkthJ\nBt9uoijtlPxjJNvBKDcJmhbawuGWOvycBhonfvkfC1rSuWn0SBgIIFWb5B46QWJ6gL2KfqiMpy1s\nESzW2K63MJstQWHs70Y9gA4+GHR0ZM0P2YwjlONM20WWZQJt6lDnofgo3nScQCyMGQziKTJeOk5A\nln21OxBByds3V9UwCRZrBKpNbq2VKJgeXZkoX/nMfWR6UpydznFtMS/Em04M89bVVd4MnueHr62w\n+8IcsdMT6DWdaLWBHQySHu9laKqP/HaFmfsmKZourmFy/5WjbN9Yo9nbhdOTFqDQzSLO5ABOLkNq\nvJdGsUEgqOC2XUq1iQE8wxIS3VOD6DWd4mYJOROn+NYqTdslmoxSfvoN37X27ahitdb0g4zO0CcH\noV/I2eozw8JH5fIx6qZDZKwXQzeRLBs9Gfdfgk7FwpUk5MEsluuRHu5h6NgQAVkiElb5mx85TW60\nh+vzu0Ta1YlyUKVuu8RiIcqFOqGGTnFZOGpaapC13RqTEzkiXfF7NhH/O3Taa20Qney4ohTcpvd2\nPu+AaQM9aey6jhkJkTgzKQz/kjGiY33I63lBl602UY4MI6fiFFf3+PLffoxaKIRhWKwt51EKVWqy\nwuT5SQobRdyggmTa/ny9/NffSyIVJdufYXO7QrlYp3J9hXKxgRdSSWTiaJtFlPU8rizhZZLIY33k\nhrPUX7kjdBlqTWxJAttBqwt32s6CGL94hOZ2GSubZuXZm7w8v4upWxTnt1CWd0ifHqd1cxV5s0hI\nb+Hk0qy+skCs0qC1tCM0JVoWenuRAWFN7hRqfOpjF7hV1FHevOuDZB3XA8MkMdFPa6+K1TaLi8wM\n8fRrawS2SwS3hEBbdGqQX/xr93H/eIr/9JNFdhd3iO+K1lBno/N0k8jW/rzTR/qQ23OumU5gZtPC\nHCuXwepOodQ0nFiYnvuP0VraEQtux8jqbeXjzrAV2ZcCbwzl/EBg+iOXmTo2wGqxSXSvwvKri/uO\nuJUGmqLwj79wlu/cEqXryJWTmOvtVnBbLyT4wAnc9b0D3jLaIQPGgOfh6sIVurMGSS0Lu7IPqOww\nMLxAAF1RYLskKMy2gz4zLAKY6juz7g5+RwigtgQI0YxFBBOqk8WrQaFLtL7nbzjNbnFPzEwCZBkz\nGkZJRLDzFQLRsMjq22J+HRNHQATunuf7qITbAVPmvWeo5quMXJqhslli8Pwkxt1ttIkB+ga6mH/9\nLmpdw86mCQ50Y1WajJwY4uX5PK1ba3x/u0W9bvDt17a4+MgxPviR89i5LjaqBma7rRfaFvuBoQaR\n9Rb1qsa339ji+rO3iLW1Z+ZeX8LrTnLizDj5uzsQCNBc28OMhJBsh6FzE5SX88JW4eaKeJ4H3ikQ\nDCzJ9TByGYaPD3N3pUBhYZtkLk0kE0ffKTNyYQp9cRsvECB+bhp7s0jq+Aj2i7do7VV59fY25XqL\nbC7JnZ/eIT2c5T/+8A7dmTj5nQpjx4fIN03Ma0v+nJUdl6ZmcuaRE+QXtn2A9cHqsytJwjcnE6fv\nxAgEFax85WdaUhw0muyMxlAOp23EKLke8uwYTk9GWIOU66QeP0upoqHqrZ8ZaPyVSZA/9Ik/ECwP\nNUikodHo6ya+U8Q5M4Wxlkfp6yLY3tg7QDb71CS9A2k2lvKEFjfvAbh1BHDUNijJiobvkTN+u9x4\nRz66M1phlYDrvass77sNUw1idiXvAcUclPtVzkxx+cwwv+38Ll+p/S3u/umL7wjScyUJrStJvFAR\nyF9JelewTSOXIZ4v+9LIruui3FpF6+ui/+gQI/0pxnriBGWJl+7sMjmQ4scvLGJtFYmVamgTAwTy\nFR8I1MhlSI71UlvZfUdJXvvUJMr1u4c+0yYGkHZKhDUDLRn7mbLqjVyGQBvEFsimUFe2kU5NYGot\nvI0CkYYmbI7X8simRfriEfbmt5AbOvHZUeq31vjlX3k/J/vD/F/Pb/Dqy3eJLm6IfuZYH9GlLQZ+\n4RKLz9zAS8f9vxtb2/Wl4KPtTSlYaxIyTB792+/jez++TXhujZ4P3sfmD66h2A7v/fITfPtPXkTp\n6yLQru6opoU2NUQql6JequPkK3hqECkeIboogpvu958XQKlTk3zlE2f55qtrfOS+Ef7F736fWKVO\nsyvpZ4MAxz57hWqzRTyiMv8nz4l7mozhpeN87lMX+dr3b5BIx9AaBlcujnNrpcjmS3eI1ppoU0O4\nlQbBhobd181f//RF/tM/+6YAVEsS0lbhHhEpEPO199GTbC7l/etuhVUGHj1J8XuvAyIoDxwAWL79\n9zv346Bkf+LMJNYLNzn1hUepNltEQ0Fe+/41YqUaF3/pMR6eyfL4SI1rxQy/8/UbAtyYy/DLX34P\nLy8WePPr+2DLt78bP+t6Dg733DR6oUbXSM87ylR3xkGJ9Xc9ZmoI1zAJFirveJw2NYRba/LrX3mM\nobTKr//Db4oSfKXxjmuIK0mMf/QSq9948dBnAx+8wNrTV+m5MkvxmevvuiE0R3oJdyXukYP//zpa\nYRVPkt5xboBYJ4OaQfjMJO4rc2SePMfWyh6uYRJb2xUtxUINN185BIRtjvQSy6XhtTvveN7O99VH\neomtCMZco6+bQBvg23HJdhWZ7Kkx9Gffesdr8+IRAg2dQDbF//mrV/idP73N5q11IbWOeC4nTo+y\nul48JPn/btdjxCNItqjSWdEwsUr9HtuE5lg/oY3DyrvG0RHOnx8nX9bY+86r/vkSV2bJX1smUmsK\nJkx7X4ldmGF2Osdz377qW0p0rs1WZMyxfn89k1yXmU8/TK3ZYvEN0X4NpuNEkxFGBrvIF+s0n7kO\nHH4fO+cy0glGL0yy+4NrxC4f4/hkD89/8zUUo0XIMDFnx/EWN0WVvKH/V9t7+EDat83R57/+pZ8/\nCfLec5/GyiR9hcSO6qKhtQg1dNxMguDefsVBNUzscoPybpXBE8McuX+G7RtraKk43sQASlvS2IyE\nhS7AfUewdsrIM8Oib5tNY6bjBOsadlvyvJFNkz4v/m4HeCPZDrELM7jre4fU8LSpIbw22A8OU2tB\nlDbD4320UvFDVZTi2h5By8ZWFM4+epxfON3L1OwoL+3GubNeQtFN3NOTSDslv59thoIk2uItVkgF\nT9hiK8dGhKlWNi3cBNs6B7Yi0+pOEb67idWVxKs2CTZ0jNU8mwvblEJhJEVic7fG+k5NyEE3DGG1\nrci+ABHA/Z96gPlXFw8FGc2xfqSGLu5vJnFPlchMxZHrIqDre88pGvGYLzTz9tGxtHclCcfzkFoW\n/+TXHucnb+3gFWs+z74jf10rNyERFXbZc2sMP3GWdCLM731njqW5TaJ3RQDgBQLIIzmknRL1O5sE\nT07w5KNH2frhm37G1pGCb4VVvIDE/8vcmwZJkp73fb88Kuuuruquvo/p7unp6enuuWfv2ROLcwGQ\nWAmiRJqQSIoMiyZhKCzLdtiSFeFQ2P7gcFhBX6GQbEu0SFPmTYMkCIAAASywF3Z2Znbu6fuuuyqz\n8s70h7cqu2pmQULhD97n20x31/Hmezzv8/yPcKTAS198mjfe3+Yf/NRlbvgKR/t1tP0K7UKWmqTg\nbBxy9oVllp48ST2ZpG44yHVd6Dw0DNJ1odjXCxbs8u0pNfjHf3eRv3nO439/x6Ly5l0cLcbEs2cI\nrh9TWDeqBpWtMpVra7inpnjqs5f5e3/jMqeXJvj9N9ZobR7hrR8gHVS59/AItwdfZMsyatsiYdqi\ndVccYGezTOqwipNLR89WHxuKyuyeqjD2sfOkkhr1Hz5g6KVzVOttkq328WdHaL50y6aPguUyz62g\nV3VGX1hF3yxhFnIkWm2MuvibfTVGPKFx+xvXSdd19JECu3WTN37r+/yLd3W+dadEq6ITGBaphsH3\nru+Qnhik3gG4OrPjjwEwnWwauUMh766VoU9dpr4jcB7ZVy7QKDXxKy3UhkG7cmwrz5XTGKrad+vv\nlVjve5/VOUxNiL0NXpjHC2F0ZSYCyQWyjLsyi3JUx05onH3uDGenB3hy3OZP9kH6YLMPp+MrMtZI\nAW84j1rXad4SzAZ9bEhUyTYPqW4c4eTSnDwzRfnOTp8UOIhWRP32NrJhRS2x/y/RrSqCOJTdwVzf\nePuqCpKELctI0yM0r62BbkXzKdguYcc1pI7QW1f4K95jXPho2AkNd34Cmm2ufu4y93dqzLxyjupe\njdFT49SRSR9UxK1ZkrAtUZHu7v+OFiP3/Cq6bpMZK+CrCoFh8dZ+m1hMwXT8Yzfvus6+5RHc23lM\nNdhTFbzl2agN7sRj5C8u4G6XSF5awC43CecniO9X+qq63QpVX9R1CouTPLx/rIkihSE1y0PKJCle\nPEmt0UaeHsHPpAg/2ODovTVkxyUMYaCnhdYLTu6+55bhcnZ5kpeePsnN9TKf//gKo8UMb/7eW7R0\nK6rA2nMTBAOZY9fdTIrEiVEa79xHczz8rSP2bmwh+QGZy4vE58YwagaxapNER0Twr4rofFqcxsuJ\n9/JUBXNqBK1hfDRbJyOv/F1Bbes8nC4eI+pz9iyyrvxrV8HSerjPhuUj13WkIBDo6U5mPvbKOYy1\nQ3Q3IKab2CBKhJ2Fmeq0EkCU0bvv2x7I8Jmfucr4yjS3fyhsy7uOfInFSWHis1M+3kAScdRWG8UP\naOfSwkvkqB49gG4cq8vBhReWsLyQN/YyfOOtdcKNw+gmGKu1GHnpLO31Q2Id7Q0QbAet3ZFp7mh7\nuN3s1bAwBnNIfkCio16olht4p2fw0kninYO7cVBn86iFc28Haf1AoIlzaZAk8Qx6+nb719aFiNmT\nS2hzYzS8gFg2FTnRPppkQAcB3fme5oN9YRRVbYrNXRO3OyOfxZ8ZJVZpCn8W0yZVF6/51Tc3SWwe\n9H2ObsRsISbVPSSPAombN7axt47Qyg3MfBY6yGmn2YZQWNTrAQyMFxg+e4Ity+fCpy+SWZxCOTHC\nqcsnGZgeolo1cCSZVtPkzoHOF68ucO1hSQBHC1mstkOyVOfw7i4lLcHQYJoXry5y74fCJOpRJcdH\nw1dkfnPN5bv7SfZKLXGY6yaVmhEtbO/cSf67L7/I128ecvKZ08QSGqenCvza//kmN/7wHRq6jWy7\nxE5OYkkShYUJWm0Ht4Pi19rHSaKTz/KHX7zDq6//df7o0Ee9eSxL3U3kAexUktzMMOu3dpFbbbx7\nu2imLRLryWEheKXFsOcmornsTBQJOkqrAO39msAwHYn5kDo1ibRbRnF97GSc2NYR7ft72Nk0WtvC\nl2T8tk3CsHAkCVe3kKutSNmRIOTKC0vUkkms7TJhIdsne+5kkmR2S1H5ePDqCvoN4bcXAAAgAElE\nQVRuBX39MAKv6R3cTxd82vt8goOaOATp0EEHMsfYEVURHjBdZ9JmO5KYdzrrxXq436cU6eTF5wtd\nj5qkcPtAJ5Mf5u27R3jD+QgX5M6NE6SThEFIaLtMPHcmSuacfBbl5jpmJkX24gLENXbXj1CbBn4+\n26ezUb27i/rUEtJIAcsTonddrZ4fN7pCWq4Ww54aib5/qJvIDQM7nSRxcUFccDrgyKnFCeoH9Ugh\nuTtG1tKMUGw2LMzRQZq1NoHr9/nTgNBdMUsNrGKeUJYJVZV4rcXDmqB6Pv/Jc9y6sY1/dwe1M+Zm\nOkU8m+TU4hi1u3sUnzmDsy5YZPV6GyWf4bkn5lj/4RorV5fY3aqgN03yxSzOdhlzblyoud7dxpkc\nZvLZM9S2yz3JmXDG7Y6d6gmhKikE3faIGSae7RJIUgR67M6TrhsxgL08y8wTp7h9bQO/puNkkgTT\nI6jlBqHnE58ZwXzrLpIf4Nku6fFBrERcyNJfWsT2AtQPNn708yrmSY0V2Nqusl1t036wx4M372MP\n5shMFVFSCZodgG+s2uw7d7pqy13Z/e68VfyApu1h7lV5+VPn8UYHqcfjUdL1l0X+2TPoBzUSB9Xo\nvVwthjIu1Eo/konGwtQnHvt/T1X6evnd6JV/HfrUZSpHTYYXJ1AnihheQKbTkzMzKYwH+1hjQ0iq\nQrIq1AO9kQLpvfJjKoe9oVkOd27tsl42SG4fiZ6cYRGrNnEPanib/SWzLrVWHylQXD1B+6CGOzaE\nul/pE3MKTwj1t1CS+N9+Oc7XNjP85m+/jV038LUYTjZNuoNBsDra+x8WcnCM+tasHlloPyBuu/10\n2lK9bwNSXY/QFm2I7mEz9ORpuLkhSr8dJLIUhpGUsR6LIV9fww9hYG4Ub7sUAfDiV1cxD2pCU7/n\nmdkJDWdmlNTaHnoxj7xxgA+otgthiNcBtnrFAfwgxC0OENouSo8QzaORfeUCVcPGnxwmbJlc/sR5\nMsUspd0aCd0knBvH6/SGpZVZrIBIMlydHaVU1THv7ZI7MczZ2UGu3T5g63u3Md5fRy03MB/sY7gB\ng1ND/Nmf3SSeSWKoqlhMNR2pk7gE2yVK2xXKMQ291KTw9FIkkqRPjQgG0qOb/oUFnr10grd//20a\n9TaEwPw4y1cWIpVKbzjP4twI50+PMzeS5Qc3drh+bUtUsRSFTLmOsjKLcu1B1Nt3MkmkjABZmpkU\nY6+cw3ywj2w5yE98gT+6UeHhg0PkaiuaT9ZuxydmfoLs3ChH1zdI75Wxp0aEWmWpjhdTIylyX1H6\nbkhaXe+jjnbBgzHbjVhXIBL27MoJpIki/miBQFGEOqPjMvr8Cu31Q5SFSX71S8/w/TuHhNceRq+3\n+c5DGrJCotwgVmkKy4FWmzAQla9IKE5V8DoXj95586PmENC3rroy4s6JMWxZxsulI8YKdIClQSha\nKyuzgsKZz+JkBA7CzKSQBtLI1RYx18MpZPHevc+ZZxZ5++au8LfoXKD84Tx+yyRRHCAIQ4wbG9H+\nkOxULRXPp13ViW8fdQ5/KWK39X7+FjLyg120lmCExE5OROPeZUJ1X9uYGe3DrwEoJyfwqy1CWYbO\nszXyWZKGqNQqrh/hcGK1FlrDoFIziJUb+IqCc3IySn684gBKpYkzM8rI/Cj6QZ1408DsMZV0tBhG\n2ybRMvGH89C2SR9UIvyKFIZMXJxj/e2HJM6fjICGicVJjAf7TC1OsFU1cLrMw9lx0FTOXZ7nB38s\ndDhKSgxCePbJeW6/t8HiK2dJ5lIYuoVfapA5NUH9OzfRHK9vLD8sQevu+aonmHSq6/WBHmNPLqHb\nHpphCf2bhMb5lUmaboByZ4vQDyJGpOr5GJLMc194iu22h5SKY1V1Evm0APh2XFZ7o70whdRJtgBk\nx8VptEnslKi7AUpbtDzUmVHOLwzzDz8zz8Xzs3ztnS2cTIrVzz3B4d1d5CAk/dI5jL0qQy+dw147\nwJgZRT4xJi6ZhRyS56OrMVRV5pNPzzN2fpbNdx72iRH2+oeBoPU/WklTPT9KUj6SicbM8hcEHiOT\nwvV8vBNjeK5PYlbI2HZFbLoLtqvgZt3fw4vFGF+a5HC3JkzA4hqaYREsTOLKMnKrjdRRsLRSCZI9\nbQA7oUWy2vpIAW90kFitJfpvk0V+/gsX8SeHOeroGzircwSDORzvmMZppRL4p6aQK01yF05ifeem\noOgNF5A6joxAxGf2YjHk09MYudP8xm98n8xuCa3VxkkK8TF3aABlbpx2p6VjLk5jJxPRROwqPfZG\ntxRuZdMUnhZaJDOffwojn8UqN/s2HGVuHGWvQquqR5t1tSXM2dzBHNrEENqJEVqOz+jKDPZWCaXV\nJvbkEtLGIUalhTdSQJkYwlRVbNNB7ZQR7VSSwrk53J2yACp2AG2J1VnsSovCuTnapQbe0AB0Kk+x\nWosAkIsDhLk0UnGAk8+foXJ/HycRZ/jlc1FyWa+0UNsWgemQaFus7zco79WQHAEm9VWFWLUlGAgH\n1T4Uvn5vF7eY57lXVpgbyfIH/8Mf4w8N4EmS0NWYKCLPjZNY36fUtCjMjtLcrUAI2vy4KJ/XdeyE\nxvgnLmHd2cEpZHFNh/ZOJUqIA8+PqHXdUJ4+g1FqMj1TpPTWPVFB8gN8L6D2/powaivmSW4c8NZ3\n73HbDhkaTHPr5g74AaEhRNxijouZTOC5Pt7sGE4IJOJIuok7nCdVqmOsHWKMDxGbG8dVFO5ulFFU\nBa9zo2nn0pz85EXKa4dIIwVc2+PK80tsbFeJl+vH1SrXi265Ughhj0fFo/EoULgbmiUo2UYygWvY\nBKaNk00J5kGpGXlhvPXde8Ln4ZHX6D0A7GQcpWWSNMxjnY1UgvGXz1HSHXA8rGz6uI2U0LBGCiTO\nzBDultGL+ajyA8cbuWa7eDGVIB5DbRjCbv2wFlE2u+8VhmAFISgK8l4ZpeO0G8gSHhLxTtlaLQuF\n2bfe3cCxXP7+z1/l3W8Jcyp3MMeVZxepfuMa7tAAcg+zpVu1FOq0SdQzMxiSTKqTgDwayvw4fie5\nAfqSCCebRmm2o2RLbZmE+9W+8ZUPqsfU8WpTuI1ePq50PUphBUicnRMML9cj6HGE7X7nWK1FvWmS\nqHUYTsN51JaJc+YEsYkh/CBEnhoWJnmdC6ExmEM+NYV8WKOez9HePMI0HaGXdPEUqZSG3jQp1dv8\n9OtX+MqXLvEzf32Zn3h+nt/93esctR0CS1DOpXIT13LYPGwR3y0hjw+x98YdHD9EGi0wWMxRr+rC\nHPFDKqYfFlYqgdxJpLsAWdXz0duOAHHbLv6pKXzd5BNXT2F4IZUPtgQdvsfhVbIc1tdLBLrJ9Mo0\n/g8f9EnmD3/mCZrrh1Fi4RUHkBrH2lDdy6UxM4qWzwgvr7pOXVFZ36mxZsBv/N574LgkGjq1eBw7\nlSAwLPQAUvPjVN9fF4lPy8Tv7JNOKoHk+rRNh/aNDa7f2sNKJmjE40xdnI+SK1+R8eMariQL2fXO\n+HUVh7vryk5oyEHI9q3f/WgmGnKnBBNIEp4kkynXowfRK2IDELu8KNxTdZOxV86xv1sjV0iTyqeF\nKVu1iVqqizKS7QpqZMfzwVqa4ezHz1O+uYk7P0HQkdl1CjlC1xOHfirB+NwI7z8ocfDVt6MF6jXb\nLD6zyODMMM07Ozirc7iuT+B4xJtGnzZ8rNrsm8zd8q2dS/OLP/00pZbN3fuHwkMESLTaSLNj+A2D\nT3x8lWog4W8d4ZtOxOcHYKKIV20x+PJ5WjsVFD+ISuH+zChGw8SVZax3H9AK+k2/1JZJWKo/Vkru\nto9i1SZOs83Fq0vs7NZE367ZjsqIcd1EXj4hDHvubuMWssRzaT77xae49/ZDNNshHBtEdzxSDQNp\negSnkMVePyCpm4xfmqd5e1sIcLk+wewYdibFwKlJeH+N1Y+d4+XLJ/js+VGOBvLU33tIc/u4RdW9\nHUQ0rLYlqhWOh5vQGFmeJijk8I7qtCeGH7slmE2Tofkxvv5b348OuV6/Gb+zQWqGELvSmoag993c\nINl1c/R8muuC2uoPF0gUMmhre1HvWHW9xzZodWaUyRNF1rYqpBYmkOIabrkplHA7z7XrN6L4AS3H\nZ3J+lM23HpAqHct1gzh8A1nCU5QISKsWBzhxehI9myYcKeDVdMKjOnv1Nu1Ki5HpIZzOhhE/f5L9\nt+8LAS7bRc6lqTYttLW9iHcPAkRptcT79hpngaja9BoHTr/2BK27u1GyJj9i/BfrSK67ioLk+oKR\n1XmG9vIsp55bovzgAHNoQIy9LDP/+rPs+0TeL1rPpguizcRRHefuDvL8BH5dZ7rTijAzKYaeXCT8\nYDNKsJwOGLhb/fNH8lAXr2lPDiMpMrGxQdwOm8Ap5o/piJ312y1LezE1ok+qno+PhKfFhET96hzs\nVYQPiuXw3Xc38GIxwsUpErc3Kd/cPN4fem6L3cPsxOvPULu1jVczSFcahDtl9LEhEssnCHfLmIvT\nuAMZ3JYZtXQeDa3ThuzGh7HiHg3FD6Ikw8ykCBYmyZ6Zxts8IvbsCkbNwHJ9MkeievkoZqbryRI/\nNYnTaCMFAbETo4Slju/Q2j6JcoOg0oyqg+JBBDhtG19VmVmZoXVjg8TKLJam4W4dwd0dnLjG5NIk\nH6yVeWl1jDtln3/6O7doVXUwLNROxcEYH2JwaZrXP77CjZpFq2agVJqiumDYWA/2YKKI7wV9LMYu\n3gA6DrAXFmjrFrGz86Snh9Fr4lJmTY9CRziwa+Kpmo5Q+aw2ed/w2bq2LtrbTy6hd5IBY3yI+Pw4\nv/KlZ7FzWe5/4zoxR1x87XQSzXKobZf7Ki2xjtVFd411K8WuIuwyMIUOhud4wiYjm8L+YJNkBz/H\nXgV3IINkWASOR3FulNUr82xYPlq5Qf7Fc+i7FfyYipzPEOrCbkIzbayH+8w+t8TOZgWnJS7LqucL\nzZmJIn7POnLiWpRwAagXFjBsj4N3f/OjmWh0o6uq2BuKH/QBt8LdcvQ75bKOL8s4js/k1CB626Et\ny2TPz0cbY/eWLwchlz59kTsPjwRl07BIdqok4dQwYV1YuzvJOIWJQQ7WhOdJl5JnjQ1hhxIL04Ps\nX1vHDARILtnjW/Gjoquzr7UtfrDb5EufOM2bG3UKU0PoboA3kEGSJajpbLVcbNNBOarjJOOiH94t\nQeYz+Okkn35+kesPS6y+doWNv/hA4DUGMvzDn3sWO5MWiZSi9CHJu6V/6Kh9fgiuQHU99m9uEa/r\nrH7qEnteSKxUP27PHNVRO6VOrSH8VqRChr2yjpNNU5wuYt0XgkQnry6jxGO0N4XTa/POjjBX6oBA\nveIA6tYRVrlJ+vIpYprC2GCK52dV/uKBTvnmMZBOL+YJO7LU3umZyLbbTsY58alLlKsGSkLDbJkC\nG5NJ9FV+PFUhd2WRk1N5Kig4+1XR/rl4CvYqmFMjJCvNCMgmhaEQeyo1SBom9vIsdsfptT02hB/C\nxMo0pVvbuAmN4tnZPq2X3mj6IX5MpbFV4qkn5nnw7Q8I8xmcWOzYNXXjkHYhKw5VwyK/PEPJDYnN\njeHvVXFj6vEN1fPxx4eYXJkmuL6OXG3R2BBzNQxDEsUcbqPNv//zL/CwZnG0UYqqE60Qkp02Su6p\nJUzd4sWnT3J7v9m3Ztq2h2aYkYNybxKdfgS0FhkHmjZew8BXZOyJYrRmuwdXcvuorxWqF/M8cfU0\ndd2manlohYywmk4nkYZyNHcqfW0SI59FXT6BtF/FjGvEmkI9082mSB5UI7xDzHEj/5bIgr6jJhrN\n845vBQgDwFitxdXXLvL3v/QEX//qDQGubvXLMLfnJ/BHCwydnqR11Ih8M4afOYMRgnZYxa6Lkr+5\nOM3pF1e5/PRJHl7fwvME7VozbWFXPjUcYZzil09hdHQPGreFG29vsqq27Shh8ofzXLoyz/6tbRJt\ni/RL52j0VCd/nHjUkbS3sgvgqwq+JtatZjkYNYNAVZA7CaI+NkTo+aieTzuXRj5zAst2yaycgLfv\nEsgSk69exPzOTdojgwyfHCNc24/mizE6iI8U4Wc0y2H6ExfZ+sO3Iv0i0RIUl0DVcqi0HbLFHH/4\ng02+8fVb+LKCH4LSMPBHCrhIZEp13J0yjcEBYvEY7VsdNmEsRrouLmfGB5uR6y10GFfPLUcO33IQ\nYri+aP3oFvK9neOLTV0XjrW5NMlyg7huYmZT5M7NY9QMXEmOmC7hbllox9guybqOtF/lO9d32d+p\nEtNNQknCGchE2kZ9bMjBXB8g08xlouq+Ztp4koTsijkSc1wCwFRV4gfVSP1WCkPcQhalrpM0TNq5\nDBvvPODcM6c5eLDP0lOncAdzkIzzqZeWKPvAxBBmJoU7mKP6/gYTK9P8Rz//HDfCWHSpjdVafetI\n64h8RbFXQWtbH93WifL0GXTdxsmlGbh8StzQLyxgdfpcVipBIEn9pi75LInZUYLtEsOnJ9n59k0m\nz56gfXMT1nus4Td6SlITQ2QzCQ7LLaZ6wFhqqR49XM2wKLVd5LhGGBKp3TmpBF4I63f3SS4LM5wu\n+Cy4dCqyWwZRmrVTouVhpRJ8/hde5sadfWK2y9hTi3zn5gG/+vlV3t+skR3M4AYh8p0t/JiKByTv\n7yA/uYTl+EjxzuteOY1dN0hvHPD+kYHUMDjUbcJ0UuBHClnihSzf/foNVMNipGcBgTCBatwW9MWx\nl88dA9G0WHSbBDCG84R+QOn2DlJNJ5Qk2iODMDuGeljr19NoWzRu74gb9kCa9u3t6LZXv71N/ajJ\nzIurHyrqopYbmMMFzn/sHFpcZf3BIXc3Krxz6NLQLV78zEWul4R8sD81jO/6xCwHrwPeWv3pFygH\nEsa3xcEQ7pbx2jZKrYW2349291SVExfn+NIzkyQyaa7d2BabaMf8KXFaSOdWjprRYeh0RMbcuEYY\nU4ntVfAVhfTiFHbDYPnCLHtVg6H5Mcy/uIHy9BnhqDs5zORzy7Tv79HOpfnKr7zKD37ze8y/uMpY\nIcXdewfgio3FSSWOAck9m0vpxiaUGrQbbZxcmnRH2reb8LzyyjLX//SaUOkbHQQ/IHVYRZ4ZIbi+\nJjbfRJxao43rHCuraqencUvikG3WDHxJZv3dNSTHi2Tiu5/lw27LQJ/uQ2/IQSgs07VYJCQXXDrF\nJz5zgdeenotaCPbyrEgUAT2RYP/WNnLTINHBJ0XS1rrZt4nJnh+5GmsNA3NqhGBoAFmRBVOiq5ja\nUyHotUzvhrM6h5WIHzOQVuewAth+6z5/8qCOFdcYuDDflzhmX7lA66CGulsmeLgfVSu72iuxjpNm\nVHmqNNlpmKSLAwT5NEa5xeT5Oey1A0LPj/r3INomvQlYe36C0LCETkY+S7xHwdGSZfbv75PutJXM\nnTLxzrzJvnIBfbfSt0c6WgyzkOsbA2NoIJKKBnDnJ/A7bsJWRyJcbpmollC2dRMaMdMmGBOtZX+i\nSGCKpCMMwTEd0tUmRiJB8dJJvHu70XrXdPOxBNwt5gkdLxL8MjMpDMPBG8wRPzlOK4DMTknY2S/P\n4uUzpDcO8DaPhHiabtL2Q+SEhlwcQFJkhhfG0VNJ/s1/+yk+f26An7/s8Ov3FWzTJdZhM7Z2KsQt\nB09VyL5wFndDVCZ798guNgMkCMMPbRcmLi5Errwx26VpuoJiK8sMnJujbrriNU6MEnZccIOlGeR0\nkpVL8xxaHsn5cWL3dpCaBlYm1TfPB55YjHBU0BFcOzhWYnZGByMwtjEzSiDLpDYOhPqwokR+Tv5o\nAanTCjUtYU1f7aik7tzfp9Fo49R0jtyQ2naZ7FCW4L2HjF46ycCJYX7quXlea/wzFp78PH98v4Yl\ny32u4N35ZU0OH18qOpYNf5mp2v9/rJMrfwvTsEUJrG0Lqo5hYTqdcn0Q4p2cEGWjkAj9K3s+dtsh\n1TRw1g/wYionz88SDOf7Jk9vVGUVFAXT9vDeES6O7YUpbFmOFqO9PEummMPar3Liwix+h36o6SbK\niVHcRhu7LOTSu4euX2r09Zk9x0Pt6HaorseNcpsTl05y1LJpNU1On57g9//Hr1FrmugtC89y8UFQ\nJDsPrRWCnIyjJuNiY+5By8dqgq5nxzVoGKIcfXKCu9e30PIZ1MPaY2NQengYbUK91EUnmSA+PXKs\npjg9gm+5ETBs6YtX2d+tInVKwmOffZLmg/2ob2mOF0mX6rh+wKlXz7Nv+TiZFHLbwp8awX3zDvC4\nXfVrX3mNz720yNsPSqx//y6xvTLyYY3D3SpeMkHbDyk/OMDJJIkdVKOyYLd3v2m4hJuH0aa/8MWr\nNN9fE4lRR3CpC4RSV2eJaSq5bJo/v7GHe7MjvNP5LN1Nt6+l1Ckluok4oSQx8+wZKrqNHI8RX9/n\n8PoGsUoTayCLfFhj6flltneF3XJuboyjhsns06cJJIntd9eoyCoN2xfJpG4KhtBk8bFWgzEzSuHi\nSYLhPOraPqrpwI443C1JQtJibGyUSXRow70bt5lM4MY1hk6MkErFKJdaaL1o9j1xEOljQ8QbOqnF\nKewASMYZODeHtStofI+K9XR1aX5U8tEb3b4/CFXNm3sNHjSEuq+nKoyen8MZGuDcc6dZ++4t0pUG\n9nCBcGqY4YvzVHwYurQQub52Qw7Cvufj+yGSbpLcKzP3/DIlWcV2fbIrJ6IKwVCHydIrNuTXDWK9\nQLuDKrLloHoBbiYJrTbu2gFWNi3K455PrdFGG8mj7VewlmawO7bnxmBOOO6OD8FeRRhndVpQTjrJ\nJ188TTKlsX59i+CWaJv0tv96o2ubQCGLXNdxk3ESs2OirfzsCs1QYuHySfTb2329+240Sk0Spo13\n7mTUArLTyb61DeJy0CsLHqscuwlbAxmUoRxf+OJT3NgQPk7F51dpuAHp9X3hsNyjIukkE6imLfAU\nuRSlGxt/JT1Sq+sQhEghaCdGkVUF+b37qKU6/l4VyXIIJUkIYMU1Jk6O4Y0UIqyKpyoULi0gXXuI\nNFkkDEIurkyytV3BSeX44MjnTj2LLcns79cJO5TN3jGr148tFXodngufuER9v4adz5A5PY1uiPXv\nq2rEonnUldfJZ8D1yOyW8DaPjpU1e0SxHC3Gq68s4/oBjb+4Qcv1cdJJcXE5NdmHsfE2jx5bZ13N\nlcZtoV7abV26ihK1LtTzJ7EP69E51lu1i2QEFPlYsXZuHOmozvDiBNb7awTrwsKjUjOo79epxDT+\nmz+Q2CNOXbdIDmZxLbffabiQI1bIopbqGIM5Vp5apHZjnb0bv/3RSzQmz/415PEhJs/PosdiKGlR\n9g7nxqPbq+N4JGdG0DYPaSaTyIc1zJkxkkfHN2zV9djaKrN6ZZ7BlRnKNzfRi/los3G0GImjGkwM\n8dSVuciC24GozweiuhHultHalpCQHszhDGQIXY/p83O0r69HjqDRZhX090G7N5uJzzyBfm+XWK1F\nc/2Qf/pffp5v/+471K+v4y3PEoQQ2yvz6l97krXrW8RsoQvgTg0LYam2BR3sQFfe20olmPrkJfR7\nuzjZVNT3Zq8ietk9LQP5ySV0RUFrGFjZdB+Qp/ez9m5EarnRd8Pa3q4QHx9C6xx21Y1j1o0XU1En\nBKXJzop+v1RuIA1kUOo6jBQEiPfKaRq3t0nUWwI4ujzL9XfXUQo57q+ViK/tR2MYs13Yq3Dx5RUe\n7tSI5TM4foDcoTuDwAnI8RhS/lirpNoplTqJOKnO4m0l4qy8sIIaj6HFFP7sm7d44Yk5bmxWBADX\n8yMwsDs08KGKit3eqO6HDE0O4r95B0eLRW2XdiyGVxyg8Z2bzLxyjvadHcpHTRKTRSo7FWxFobVV\n4sLLq9RbJo17u2Rmx3AMm0RH+0MfGyKcGUUt1XHSSYyaQWYoS0tVUWdHMUIJt5hHK2SEUNLGsa+P\nVtej5xWrtQTd8ajOwX4Dref3rFQCJxknZrsoC5N4dQNl40D8fV2n0bJIGBbtieHomXbDWZwmGMx9\nKKW5N9q5tFCm7Dls/qt/8hP86ks5/tUfPSTz1BITIzn2vnWDvXt7xDu2664sE7RM7Ls7nHn5LJtr\nh6jlBvrYUNRyeDS6pWOAWiKBXWkSa7WRNw6Pq1KdZMUdzEGn9N8rV96N+NPLtNoO6d1SdCiF8xO4\nro+b0JAGc/gHNdFCRSLWwU2ptotVbeF2bLXD+QncDgZg4NICZ+eGWB7P8n7VjipBvdTIXjPC1lFD\nUHI7/fkuLRFEFclJJag3LeTRAnZGAK3NTAo7l+7D8bRjsQi/EbPdxxLZvyy0tjABfP/uAbLtCruD\nDzZhahgTiezZOfR4HNfzUVwf5cwM+VMT/NrPXeC3v7tJYqM/ObSWZmCiP5lu59IMPbmItV1CHi0Q\nvn0s6CVdXmTy/CytRIK2F5A5qGBuHKG7QXQpkIMwqpK4dR0/nWTj3j6SqrD1x+9y7/v3uP6dOxSW\nZ2hYLsW5UZz1A9rzE8cmnD37m6PFUCwxl/TNIzRbVCTC3TLOYA5iKvGG3seiASIPrWRd/0tZjHIQ\n4mTTZIsDDGbj3DloMnfpJPW9KpmOP82PCk9Von2xW43ue17mcWXMHykwvzrNQUkkVsbsOC5S9F3N\nuXGk8eN1baoqqm7S3C6j2S7pl85Razukqi1GnlmiXjcJ1vZpJRPoe1WmF8aolVrMvbAcqS0PP3sG\n6937eKpKbH6C5rfeR/X8j2brZH7x86jlBvWdCtJgjtOnJ9jdr+ObDolhofFvF3KsXJilemuL7Jlp\njL0q2vQwUqeUH1w6hV9qIPkBRjbNzm4NeWqYQFUIOhPZGsiIHqlu85/87GX+cK0hDtWeJKMb7fkJ\npFYbzfFw4xoD82N87iev8L3fe5u4aQtHv8yxTLCVSjD04tmoipB84SxWOsn8iaIogyOy0vcdGbvT\nyvHrBrGG0JzffnctSjKkIMDXYriyzKd/9gXurJVwhwvEOo6pquuh3xMHlBTojgcAACAASURBVGpY\nxE076rPmnl+NqJYA/n41qoJ4UyP4Hflla2kGS1WjxWvMjEaH7KPlZjeu4dcFWvsr/+R1HoQq1UDC\nzaVJVprRxNXaggKsuD5yx2yI8SHk6REKg2kaFZ2/+YuvIM2OcVhqkhkeYHuvTnB9TehdjA1BD711\n852HuH7A7PlZ2j5YiiJMiVbn+PTHV5mcLLC+Xn4MmNalWJqZFGEqQe0Ht2muH1JbO+DsS6t87wcP\nePqFM+iyglkVlSFPUQiDENVyMMaH0FptISqUiONODRMfzuMc1GhXRI9SConaLr1Mk64om2Y5qCdG\nef6ZBf75xW/gXXqdd+8dUv/OTfy4hlbIMjCWp70vwHU4HoEuDgutJbQb/K0jQsOCgypDl07yuVfO\n8MufPMWf/5vv016Yir53IMsRhqM9P4GtKMTnx/nlf+8Zruk+/kgB+aCKNTSA1OkJywfVx+Z81Dps\nGo8xm9RyI0oy9GIetyiSMnt5Fts93rjtQg4pGSdwfYZfPsdLr13kX3/jHu+XJbbrJo7j0fj2jYjZ\nEiVLltN5DYkjZGZPjopEOpNC8oPos+ljQ4K6+0iiIB9UmXx+hcZmCWsgg2o5/S2+uh6ZQfWySYx8\nlsLTS1jfuflYi0XtAHG9mErgB+JQAPweFU0nHiOIqUihSHy6fwPC5+XJpXFkCb715lo0fvKlRYHp\naLUjM0JvXBi/yUEoLgemg2ba+BcWaMsyniShDA8QHNWJ7ZVRGiKRsHNp5NzxPqRPjUAQkPgrJMj/\nsjAGc5x+cQVtOE/bFzpGylE9Akk7uTSSaRPMjiHLMovzI/wfX3+AsfO4bIBabjwODrYFhsacGmFk\nakh4SC1MsvTSKg3dotW0sHWLmZVpzE7ltI9B1uM0+tlf/iRl02VwLE/72hoLP/kM1bu7hJJEMD5I\nba/G8vIk227IyHSR9nYpMu5LPruCv3UkKoYdXYneqp0xmENp24SKTDg+RGqnJFr8bfFswsWpyGvI\nUxXMfBZtZVZcPuYnIspxIMv4Qzn+l19apWarvPvGfZrrhyQ7eKauVMCHxcRnnqC6cdT38/b8BJ57\njOPpgkq9IKRWNVBqLYEfSsSRHZfg5CT+cJ7kve2+da11tJ8iocmNw87eFlKxPNRbm4II0JGNKO3V\nWHhygVMTeW5uVXHjGq2WRaLWIpSkaH+GjzC9FURJ0XJ8nGScz792nqYao7J+KDZe3WQfWZRoMik8\n49h+3dVi2KGE1mzjqwpnnz3NwVGT9kGNoMf+uAvEknMpLp+dRsmk2PAkKDUwRgf7JnM4WUQqNwCJ\npU9f5vNPzzKcjfPGG/dFmarc6KPfyX5Ac78W+Xb4xTz5wQyVepurn7nIzYOWsBFeP+gzFnt0w5x4\n9QLOrS38ICS7PIPuCNv0VKkefd+uOI8xM0pyUVgwd/usxoYAcEWeCvGYuPGYNkpdxxsaECJMPY6E\nALMvrlLeFItw9OpyH9vDGR0k1GIk6jp//v4uv/bl52gn0+xW27RlmWCyiO0FuGNDwvNhegStkxTJ\nhzV0L0BKJfjia+f5xnvbPPjzGyR2ShiaRoiE1t2IZsfwW8feE10p3eTkEOlMAjcEjuoMn59jv2Iw\nkImzeWMLrW31Ibi7t0SpYRDEYyRqgsqlej7VW1uo5QYnrpzk3jdvkOoA/mK2oMi5MZV4R/DMGhsi\nPTuKu10iVszh+SGxSoNgaQb1sCb6z6qCm0yQahpCiCyVOMa6tEweblbYHXuJf3hxk4WFVf54o0Vs\nt4ypWxiGTbIzhxQ/iJDoTjKOOznM5c9e5jCUKSxNo//5+7x3e5/rRkhJd5hdnYncio3hfIThkKZH\nkNMJ7KMG79w/InZjLboVd1VLu/x4faSAenoap6bjpBKRwNZfRf3zVJGUaW0LK4RY+1izQdNNtIaB\nf2qKX/mJs/z6127TfLhP6b01kpUGvm7iJuKPMXO6IYUhaqkeJdKabvbdIpWFyT41zF50/9Slk1QC\niez4oDDl6wjo2UMDkRmfO1IglOVorWuWQ83xUTsb7IdFzHZxYzFURyRGxRdWMbZKorQ/KBSN6Wzu\nAPGrq6gzI1w8M8GDgxZb1TavPjnHze/fF++xJ1oSeke4Kl1r4Y8WIhaMWdOjtqxXbRHTTeIrs8zO\nj2BdE0l51L/vUMS74coyhHDqUxc/9Ab8YdFL27SXZ/Edj+q9PYLbW30VLGN2HG1hkuGJAs1Ki8xo\ngfDaQ7Zaoi2W/BCKsl7MIztu3z7nnTuJaXvERws0yi38EMZPjnF41ES/vY2rKFy4NMedN++LRL5z\niey+Rre16xTzzC+McvOP3qFW0SEE/f21iGVTrRqo9Ra7hsuVK/Os/ckPI0ydHIQ0Og6mvetn7ief\nPh63kxOQSZIZLZDMJPF2K9gl4Ww7+PGLtN5fZ+GFFVp3dzFzGXKnp9CPGmh1XWBs9gXOQrq8iGPY\n/PInBvnhgUJJVpEKWVpth2CkgNSt+sJjibt+b5fEU2do9Zik2ekkSo84nzU0gJzP8As/9RS/9KlT\n/MFb29GZufDaFepv3D7eY7vPZaJ47OXUI6GgjxTA9VHaNgs/+QyNW1vRWpGCgMNSi/s3t1m6eoZ/\n9h88xbfWdYxUgrCYj5SZ4SOeaNgJjeTSDN7bd3n/9j7N3SrENaHvUNcJRgsoR3VRph/IkF6axvBD\n4idGSdwTWheq53N4fYPF55fR/ZDRmSJ6p+dlZlJI+QxhtcV3v3mLtZLOi1cXubvf4H/+Lz7Jnx5Y\nvPr6k9wxPALXJ94R1/obf+MJnp+NszgUUs2PcKfpkDkzQzuTiiaIOTeONjGEfFAVLY7xIV66NMPa\nbp1QlokPpCgbTt+NvTe6JbIuiEpePsFAPo2iyLTVGNkz09QsT+BEOoe41jCOsQWdPmt3MjZDSQBR\n81nUYeF26SsysY40t68oqB3+OkA9mRQbuOvRiMfxEvGob+tKMomqKOfOvnqef/lbP2R2foR7bz1g\n7vJJfvrVMzxs+8QzScxSg2Sl2Sca1jVme2e7jvfBRuQZEKu1ovHTi3mCutHnXZF7fhW91KRRaqK3\nXb78U1d46zt3ePpjq9x5cMT5UyPcvrMvKjSFLMqQAASGYYjnh6QaAu/SzqVxRgf72iIH8QRm00Rb\nPtEvZOQHxw6vTQNnMIdnOkjbpch901ZVtKZB8oWztGoG6QVxc3FTCUjEj91ibVGdOEgk+f0HSb59\nu8S//ZUl/uUbR2gNg3jnOYLYYCxFIUjGkfJZUg93aeazmO89xM2lxfO2HMwH+7hajFq5hZfPoDUM\n3FgMafMQV4shDw0g3d7El2VSu8egzejGZdpMfeoyzQf7KJaDV2ni5rPIQzlcL2Dg7NyPBHt2o5uU\ngTjouod+bw945pnTBJLM+u+/GTmFWqkE3tAA8mA2UoXt2mv3roEumHP8tcdvc139h26Eu+Vonu5s\nlnnxk+e4/6fvRQeKnU0jJeMgSWKNNIzHWG3ps3PYBzWkEOZffzYSUHO0GNoTpzESCZ752Cpb9wUI\n1F47vixohoVaqkf2BAAtVWVmbpgvvzLDYDbFb/zmm/za3x7hjXCUQ0nB7UinO4O5CGje20/vrfS4\ncY1AUVA3D3+kOWBvaJYTAbRBaLiYVb0veYxfXaXZNKO9wpocRhrOo5Yb2H5ArNWGyWHRNpoeFXYL\nF09xammcVsvC+s5NnAFh1W7HVL78c8/zzvfu9SlndkNZnMLtMa4LZFkYwckyUjxGYWQA4jGqd3eF\nVoMk85W/8ywPDlo033sASMgTRdgT4l76RJEnnl3Ez6bQKy3uX9sQtMy2Rf7ZMxEbqDsWqueTODXJ\n1maZSx87y+WXV3jw5n0xzpaDr8gkWu1oH+2Om6cqKEd14nNjNLdKaNkkVrXFx372BTYcmJ0eYnen\nSsN0kWdGCOMxrpyf4ahhYlouX/iFl7mxK3B7Vi5N6AV8t6Txze/co75TwT6okak0+vZAIErc7Yki\nYYfOb5YaUYsROs7CXQVgLcbEU4v4b9/lrQOdP/rOGpntY/Bt7e4eXkzl7BefY1N3orWS7lnnnn2M\nJyw8sYihqASZJK0f3BHdAlmm+MlL2Pd2cUcKKANpSre2OXV2jnfulTA3j/A9v4+l9ZE1VYOOgVhx\ngES1iZMSt0Qzk4KJIfxqS0iHyxLxzkZuZ1Kk6y3MxWkUTeX82Wk2dmuU7u0xMDvCS5dP8IMP9mg1\nTeQf3u97X09VGH7lPDv39smODNDcKZM6qNIuDpCZGSEIAoLra3hajDf++QXKwQw/8Yu/Fx2S+kiB\n5MQQ0nVhcONqamRMtPIzL/LsqSJfv3HA8nSe/+v/fpvEQQV/cRq32Sazc4Q+UqCwMIH7xgeAkOet\nPdgjc1TDO3eS2flhcePu3JS/8LNXiccUvvbWBrXr69GB/FcZl/2osFIJvMEcmZ0jYeEehJGZGnRE\nwKpNNMdFefoMjQf7qG0LP6H1Ge54qnBxVByXz/zCKzzYb3D/3gHOQY1MWSyg9sIUibU9nv47L/PG\nv/rWhyK59akRsJzob3rDyGc5efUMWxsltJvr0f/Jno8UBKTPzeH/4Hbf3/SaZJmZFH4+Q2bnKHp2\nqXKD8Nw8YxMFSl99m3YuzeiVU7S+eQ1ndY5EKs7C3DA//MYNMuU6ZiaF4rjCSK3TE7cGcx9uNqcq\n+IvTxG9tRGN56cVlnl0c5vUln7/1z9cjA6Ru9L4+iPnwL37pLK//gz/50DGxUgnCqWGS97YFO2vj\nkLjexhopIOsmn/mZq/y1C0V+7h99FSmhIdV14vPjP7YBlz5RpDA7Gs3PHyeMmVGSxRy2bqFoKtzb\nQV2dJbi+xqu/+Cr/z2+80Wdo1TXVS9cF1mDi2SVqX/shsWdXaNzcQBobJHA8YntlkpcWHnvGj33m\nqREmlyZpNc0faQT46HfMTRUJ3rrzoT/vGhpGFdG/xFxRnyhG1EZ7eRZ/p4TiuPhTIwR1nf/0Kx/n\nL+6WaLVdYqrMtXfXUTSV+K0N9GKe8XOztL55Db2YZ3R1Jpofxuw4AMnBDP/0bz/BP/jPf49AVUCW\nfizjKyOfjcy8umEnNGKO96EmjnoxjxQEFFdPUFo7JDNW4BPPzON4Ad/4/kPstX1STUO0alW1b8/4\ncaJr1Dg9VWBhfIA//V//DKtjFiknNBaWJ2npNpqmUPrae2RfWMXzgsgQL351lepOBeWoJmzhHzHC\n7O7LHzbPzUyKUJZILU7BO3fRp0bIjBXgnbs4q3OotzZxNRUpCBl4eonKTgVlr0zcclj44lXuPTzE\neiBICIUrp6hcW0O1HLJPnuZnXjnNXt3kT/7ZV6Nx1JoGmiP8lIrPLjOYT6K3HQ736szOD3PnnTVx\nFvT8bvRZF6eRdkpCBkBVop8FskzhlfM0vv5e3/OMzqVH5k/yhbOU7uyg6iYDlxZovnMvYt509+H2\n/ATqTgmtIwgWO6g+NtfNTCoyPC3Mj9G4uYHiuISyzJlPX+bGm/ej/RU+oqZqM8tfEBN8bozk+j5W\nJhXRr2KOi60oEII8mGPs9CQNNyDwA5InRYlbahgUV09wWNb5wtUFttsevzP765x5/nP8q689ZOXM\nJLE5UZpLXzpFsF3CW54lX8jQ+mAT03JBUQjHB9F2y9h1g//6K6+wmUxz9skFauEo/+F//Pt97n2a\nYSEfVHHOnCAoDuAn45x9cYXtgzr/2ZeuoCkKmZTGv/7d96CuM/rsGbw374hbshYj2TRw9qvH5cDt\nkvArmR1Hjim0dFsAbKqCbnn9wRE37uwLfY3BHH5LtImKTyzibhwK4E8+izwzinxYi6hQdkLD02J9\nSm7d0jYdNT9nvEigxfoFrnraGEbTZOT8HIYfghbDyaSgQ33rAs5Uz+fGepnGjU3cmAp+ENGUpalh\npKM6ZjGPc39PsE+6FLDueDaPfWfMxWkcP4xuXJrlUNkq8T/9o0/xOzeOUObG8ZAIVIXhc7NU1g6R\nTRv50iLsVZCfXMJVVTxJlMhjjtv33ZJn5zBrBn61RfvuDmY+S2xqGPO9h0ghSONDqDGFzds7qANp\nUQ2aF0qbWqtN7Oy84NgXsriejz1ciGjMYpwFfiWS7NZNKh9s8d1b+6ROzPPmjV3kg6oAOqqqaHV1\nNAXa8xPYyTh22+G7OzamF+Dk0sRqLeHiGoJ7agp1OA9re0LIrqNPIQdhJFL3wVqJ3/v2Q2TLIQxC\nYYndo0T4V4XWauPtVvpK4fpEUYyB5Ry3eOLasc5Aw0Dar+INDXBmeYrDrRKOF6A129z9YCdqU/Wu\noQjE6riRAmywXRKKi6aDbFjEHA/dPK6iOKtzwuPDtKMqCMDo06cxDIfnL85QnCmyvlEikCS+8OVP\nc+1QFy2GK6ejCpZqWJE+xYeFFIpqg90xWFT84DFq7MCrF6lWDTKdW2k7l+bsM6c5fHjAz/3qp3j3\njXtkynW+9c4muw2LetNk+94eNA3BsupS5zugVa1tYW+V+oG+dR270eaP39slXa7j5LPQ4zVjLk4T\ndkB+ejEfUab1kQKZSuOxdobq+T+yTeTkM8JcMRHH8wIc3eLOOw+5d/9QqEk6nqjq5NKkGsIH54Vf\n+Bi3D/W+6hSIQ2/gyqkIuOlfWCBUFNTrazRu73D/vXUBzGy1cQdzZIs59m5uodfbNDeOkObHaR3U\n4YPNiKqcG8vTbpr4qliLj1a9hp5aollpCb2LHpyEPjWCPJjlzNOnOH1iiLslg8zOkQDTxjX+7T96\ngbNXV2FqhG0n5PlLJ7h3/5BLHz/PxlGT8gdb2AGkS3VB4d48ioDFRirBe/dLXP/B/eM9bX4c1xS4\nI2ukwNBEgQc3tqnvVPjEx1eoNC0aH2yJ/WJ+HM+w8BWFqU9eIj4/hv32PbH3Dg2QWTnRpxxbK7f6\nWC9dzImZz5KpNI7tECaKyDc30AwL/9QU+mEdtWMGOvXZJ6k9FJU5bziP1FGpDcaHKK6coF5p4U6P\nonQ0k5xUQpwpro9VapDq/L7qeuzU2khajOTSdPQ5P7KtkwCEIVpHAKRbyrJSCQJVJVOuC+XK9QO8\n4QKh7SKlEnjNNoGq4mfTBG/e5gcPSniSxPDLP8v8v/h5rvzSl/n1X/sz2nd2iJvHyG/lqI6eTeNV\nRSnps68/AQmNq6+eJT4xxK9cPOIP7qhcu7HDRt2kqjt9h5UxOy4MxEp11FIdpWEwtDyNHdd4/coY\nmirzj//7b5LcKQmwZgckaqUSjFxd6fxbipgkIG7C0tgg4foBsa2jPpCjZtriEKm18IZyhLpFom1F\nkypwPGTDEgdXXUd9aokWMmEmGWkaACTPCEnjXg+UR70ruuMTaQJ0wFuxWkv0H6eGocc8rQucTXQm\n8dDlUzj3djEPBGbFq+l4Woz22kGkQNibZDwKTLUVBSRJlC9dn+D8Sdxmm6/eKhHYHuraHomOd029\naZIqN8g8t4LRaGNJEo7lklrbe6xE3o1wp4w9UmDq0jzx6RFaTZORmWH+ya++RHt8mJ39BsZRg/he\nGa0DnlLLjePS4F4l8nPBCygsTODsVxl98WzkUfMoQNVKJZBdj5MXZrn25x8IB8qOTodm2ugTRQYu\nnmRwZIBmuUVm6xBjq4TfdiAtjPbCUwJ8lto8RD6sYU+NQDaFEwqHzRBwTk6i1HQmXljBsD3Clhlp\nLvy7xqOHkTOQQerQaAvPr6IVB7AlGTuukTgzg79fxZwbJ55JcvjmXZK6UK+UwhBfkY83xA/xMPqw\nUF1xqI2/9gS1ejuiIJoBSOmEcJR9comWJ8CCRjaNUWlx94Md9is6M+fnqB42OHF6grobYG+VBCOj\nrjP1k09TeXgg3JR76KCBLOOfnRfYorEhBi4t8IXPXeTaTh2truM128Qax9RYe+2g77vYhRzLq1Mc\nvHWfH779MMJaTHzsAn/vc2f53m+/SfH8HO3dCl4yjux6wj+nR8a9C0Z+dCy6yb3WGdduxCpNpHIz\nki9HktAMK0r0f9wwZkZJHFRhfAjXcknd38Er5uH/Ze5Ngyw707vO31nvfjPz5s19XyorsyqrVFUq\nlUoltdTdaql39QYGbIOxscEQwQDDMDExYZiAiSCCYDwwMQPBgGkYDJixARvTbXebbtnqVnertZZU\npVpz3zPvvp19mQ/vvSdvVpVkMzEToeeTVJlV955z3vO+z/JfWhboKkpvGt8LkB2X/naBI4Wwr+q4\njo8U16N13xzNg6ZGSUIrl0XaLQrWHyJZ9JoWM597nAMPUmt7Av8wP4pnOqBr+C0LvYsiqrf3OzsR\nQ47raJUGxZ0SmnM8amrsl/ETcYLhHEGpjlsRIoyp5WnMagtfURgdyPB//Lkl7qYHyOWzHN3e5t+9\nssWrKyXu39kjdnuTe1YAeyUWLkzhxGOY64eC0RSclCI35sfxq03ia3sn9jTlqBqti9D1MONx3HID\nyXa5f++A1nvrZJ44Tc0L4LCKarvkr51h7/vvU6kYkaS9blgPSdA/ihptJxPE50ZwSw3yz1+gqmn0\njOSiYkEu1YnVmmQ/tkzF8WlYHtpBGU9V0CYG0NrnoqVrZPJZjABGpgYw2rCDjiJomEqQOz1OLQB5\nehjT9khODuCW6uTG+iM7i49somH1pOmZGyHcKdL34iVKJYE3sFMJlHzPCe6urSpCLU9RGH18ntpR\njUBTCeoGvqYyd36K7bLJZ37uT/CTf/sVkuV6tBC7XQ5bboBq2ciux44nsf/2GiVJYe3uPv/qdZer\n50a58cYq6Xw2MnwC0b6KdZkYnf/TH2e9YvCF504znE/zy7/+Ht/41R+SqDUfribaM14A69Q4YRsI\nZMd1vPFBHrs4TRmZyasL0UxWubpEqy6EipqjecJKMxqXdPj18UoDb3r42Cp9pyi6BLXWiUOvs2jN\nhQmUysPf748SaqF6YvzRDWRr5ntp1gwBcgxDzPFBfF1j7IlTLD15itWygdqlPwLgWi6qYWH1Zkic\nn0G7u41+ZoqzV+b5yz93jdGRHHdrDtZemb7ZYfqXxonPjlA5FCwjb2IQSVGwK03kuE52uA+7N/Oh\nLoR6vYWxfojdm2Z8bpiJoSzffGObu994k9zpcUJVxTuqYvWkcVKJiAYLoj0bhDBwfprxxTF23t9G\nGhugeX31obFQIMuEbefH/PwItZaLqWsYVYNUqRb5LjiJGJMLI2y+8j6pYpXmYB+ZczNYhRpBGxPS\nQf5H11AVVbo73C+UHU2bIJtCLwmDuK/8mY9xY0VIqTfzvaiWg9GTjlgXVjJO6slF/K0jnOUZbEu4\nVD5I1e6+Z8riJJZh87GPL7F71EB6ZwX99ASNzSM0w0aZHES5viLWeVwXa603w7WvPsnBuxsnVA6B\nqIOjN4yoO9GRc+/cv8OaiZKMEbbphQEQGx9APijT0nXUTJJgqE+4ooZik4/vlzDu76GbNndu7fIz\nf/JJ/uyfvMiNikt5t4xxYyN6nka7U9W5bisjulGy5eBsF7j14/uR8VfHZA3EO6SV6liLk3j5Hpxs\nCjkRw5VlSlWDZP0YHFnZLrKpxmisHWBoGkqhxui1JaqHYg1MffbxSGG1W6G1OZrH6UnjAZ/5mee4\n997WIzFeUQekZUVrpFtSXFqcPNbJ6UpqHF3DOz1J/2Mz/O0/e4W7clzYONwQI2FbVVBNm2S5juOH\naIYlmHjtAkcKQ4YuzCLrKkFI9Fwnn1umvHFErG3L3m14CMBonr7ZIbKpOIUbG8KwTlU5uzxB6Z1V\nEqV6xIroROyZZXEwO56wi2hZpJ5cpNWmFoMQwJN7UsycGuF//oWr3DIljPt7tCot9HKdcOOQresb\nvB/LEdcU3v+3rxDMjaHsl9B3i7h+gD3Qh6xrxHcLrNkhkqLgZ1PYmiowF11S5LH5UexyE09VyD19\nNupMBbJMa6A3ktTPnR4X1vV9ac5cmmWvUMfar5AuVKNuKe+tCfXoDyiQHoxWLkt/281Wc4SZphRC\nyXAI/QDl5voJt1Zom6G1rC7HWglDPmaLqC0Lc+sIz/WpF+uEbTaVUmsRaye4NS9Aaln4tRZkkySy\nSbR7OzT2ywRx4TX2kUw0+p/5GdKlGs2mHVm/d7fNu5MMaAOwXA+91qJcqIsNbiiH2xTV29LTi9zf\nKvG7t5vUdkqC19/RZpekaLF31CZjLYuWK1p4zVBibG6Iyk6Juz++TxjT+Ds/dxV5boz4/CibTYdY\npRHRJ7Xzs8xP5sgO9PDdH61y99vvCPrZh7hHdkIr1SMgULA4ydLyJKu/8SryQZnyyv6xXHjVIN5G\nxXdXlcb8OOH7G8SrQr1THs3jNEycicFHOhJ65+egIFqp/kAvSuk4AWvlslF19V8b3UC2jm29ajrM\nf/kpWl6Afm+bYsvhoGry9DMLxGeHOdguE8yPiSSrjTNJnJ+heXOT3DNnKd/dpfHGPX73fpmegSzl\nhoV8e4umpvGP/8JlTo3l+M5r66iWzeIzixxsl6BYY+jMJOPDvVheQF1W0KvNh1q4IFQUf+5nnuaN\n2wfYtsfqj+4S3habeK1QR9s4wBzo43Nfu8Lo7BBf+uIFzJEB4vMjlGsWgeth2B7Vd1YJM0lmFsfQ\nhnNUaoZYb4aFHdeZ+PQlWnd3cAf6+IlnZnn5zU2eemyC+5slXE3l6peeYOveHk9/+Qqlmkl9S4wL\n9ZaFs1/G11TCrjb5o0KvNgVIuMuDIpBl9vQYs6dG2GnYZCcHcI6qBP1ZwnbbWfaDyMfBREJrmmSf\nOE2jdixmpF07i3VYjZ6vlYwTeAFrB3Wkd8Qc3KybxNqHglttYo/mCVwfeW4UW5ZJH1XYu7ElRhFt\nlcNOOCEopoMUBEx+7jKNu7v0XV2ktVdG8QPGvngFEjHyQz00bF+8M66H2T60AtNGHepj6fQo42N9\n7O6U8X2hY2EtTorKtmZwpMf4td+7Q3mnRP+ZSVqHVRJXFrEOhZS9r2m4yThOJnnsoDw9AkM5nvrs\nRQqxGOFO8YRwmTQmXF7lcoP43AhWqUFoudRLTXqmBzHLTTFaclxS4aSZtQAAIABJREFUV5c42i0T\nO6ygFms4iTiGohDfFpo0nSQDOCH37kkSuD6yH3D/oM78M0tU7+0RXpyPgMTdhpCPCikIsNomhgCD\nn71MebeMnUqQe3yev/q1C7xwboi/8X/+kNpb93GqreNxWMsSyXY2hZrP4rUtBFq9GZTFST75lSd4\n47ffhJ40zp3taER7UDWicRJAUGudADSOXp7nX//ZUf7Rt3bQ1vcJhnIgy9RfeS/qFHjtAtOO69jp\nJPK9HeFaPDlI4IfHYG1Via598LlzNG9uYr2/yW9d36e+so83Ncy5Z5aotn1mzJkRCpUW/+CnFvh3\nv3MPT9dQGuL9cWM6YUwnuborWFl7JaxiXbjptsdlHSnyVm8GKZVA2i+RaFknVFmdmEay7Zg7+MJF\n9n90l8R+CeWoSvX2Nk4qgTrQQ1A30Ao1tKZ5wmbgUfGgeaFu2g+J2rXGBpAbBlq1if7E6Uca8nU7\n1kpheGJv6cj265YjnF3b1OmRzz9BaavNSnxqETJJrLpB8qCMc1SNDNqUQQEq/kgmGiMX/wS65TD0\n7DLW6r7AKbSxBo+K7hsunZ7EBuIru7hjA7j9PVy7MMnbN3do1k2CEGhnv3BS5lkr1cXDimmEikLM\nsBm7eppEXKN8e0fMoVoW33hji3/480ssjeX47R9uMP+JZfZaLpLp4Koqq2+tUbm+hl9pMP78w8jr\njsV1p9odfenJiEpqn5nG7cvgmw6VhtlVhUuYGSFNqznuCcRxVO0N55BLdYLlWcwgxKsbKLZDkErg\nZlPET09gZpJYuobeMLBSicinpOO42Anl9AROU4ytPFU5YUrmX5jHbY9KOh2UDzr4Orb1UhhSvb1N\nS9dxsyn0vjRey2bz9g6VW9sMPnmaXD5DZbvI2RcuUE7EefriJBvvrOPf3z32APEDDl6/T71u4iZi\n9E4P8Y239vn2q6vkZ4Zo+jAzO0jdcpF7UlTWBPuivFMitBzRZjYsnO1idL2JZ8/xs586TcXweOPl\nmwzODtGwfaS2PbiTjIs5uKrw3JVZrq8VubVdIQB2dipoN9fRTZvk0iRWoU6yUOXQCQgVBadl84/+\n1meJLUwij+S59eYqf/6vfJr/5XMO9XCAtzar/JMvy7T651mtWrRsj0a5SRWZ8lGdeBdTpIN/cXvT\nJJYmaXihqLIXJgiGcyhHQo2PuVGUo2r0XrRyWbx8D61KiycuTrL38nuEu8XIM6jTlo/E0RAHiuIH\n+Fsn/UjqbTM9K5XAzvfw2JOnaL5570Tyr7Xpi83RPMpIP8PTg7QkmditzeizOmyUB8PTNYKYTrxp\ncuiGeK5PeG9HSEPHdRp3d2k4PvWKgbxfig7uTiUnByEM9WE6HsWKgfLuKj2XT2HsV4gdVpCKddSz\nUxQ3jvCbFsn9Eg1VQyvXufrCOeqpJH0TebJj/dQOqiTG8tBWXO0wAlbrNvZuSewVCxN42TS2ruG1\nfSfcwT6sQo102zFYr7fEeG6knzCVQK82qddNZi/MCEXdxxfQNg4Iyw0+/udfYP3t9RP3p7ujo9mu\nUCd1XGKlOvU7O0hhiJmIR4eFL8miwm/vbX0vXhIFWBeVsxs7UdkuEjdtwtlRLpwd4zdfWeG3//O7\nJDcOTqiWdnRkkCWQJJKbh9E++pVffIFnz4/zjR+t4eyXyUwNworY95r5XrIzwyfVR7uMMJWrS/z8\nC6dp+gl+91d/gNWT5ktfvMh+1YySrLBpIrfN4axcNmLOQXuM2dl/uryXAKwueXg/hGTDwOvvofDW\nSvTn8dPjGFsFLj6+wH+5WyB0PHxVFVRh28UDFNdn/Nll+hdG0QZ6xci+ixVlpxJI+R582438srqT\nBNXzo453WVZRukzSOu+bWqzBuVkufuocGy2X+NSQwJMM90deMN0RXpzHiB2zAbuj42jOWB6lrQzd\njWsC0R1TWxZWKvGB9PJO2HEdctmoQ968txudX8WayfOfPMP91QJuTBOU/rZdQOcZfSQTjfnZzwFE\nYDC1bhAWBW/aU5UT3GYrGT9Bo5IPK9Fm5mSSDE4NsDiZ49Z6kaXTozx5cZLdP7j5yM/2zs9huj5+\nTEfpz+KoKtPzw9y9s0cgy5GkuOy4TF4+w//49TcIgZ/+1BI/fGuT+Hie2K0N+p48TVPVSE4NcbRR\noO/CLLVyE0/TBMgtpkcgHIDG3eOHJpfqhE2T9PwoM9P56DrtROyEY6aVjONMDZ8Yg6iFqqBbtmy0\nlnDfUz0fL9+LJElYR1XChonWBu5olcYHjkrkg3K0wXiqKqSBO+1tWY5ayx1XUxAaBu5h5UO7IL4X\nILcs/IYwEvprf/ET/OiNDdz7u0JtdbdI+e4Odkxn/TXhKuqdn8NQFELXI//EAj2nx5heHOcLL5zl\n7nYFXVfx31mlXjUIPR8nprM4O0CxYpAe6OEvff4MP/zmOwTpJE46gd4wkMKQ1MfP424cEgzlyPdn\n+Lf/5Lska02aqSTJniTG+oHw+FiY4KXPnef2rV3ufPs65b0K1b0K9VvbeIUqscsLwpRvuxC9sHLT\nxC3W6Fue5q2tOl+8MMJa0eCXfvICczmV37oX49mpgJ+6pPLLP07wyvVtFEUhnY5j3toS82PHI3Nx\nHneneOI5+X5IuHWEOj2MV2sRb1dG4ocBjh8w+8IF5LE89pqQ4g+B9F6RQjp1QsDtD4tuiipA6AeE\nkoSXSvCFrz7Ba7/2agRa9QZ60Up14WLctAiDkKBhYq7tI7dtqAH6P/M4ZcvD90PcuE72ycWou2T3\npJHaZnUdfYDEE6ep2x761DCWJJHeExRWT1MjK/NKKIFhIy3PYB5VsW0P5V0x3qzVhJ28mU6SuDiH\nUWlBrYXkB2iWgzyWR90vsXZzG2O/gndnm1YmRagqxFNxWoaNarsYE4O4PWnmlic5d3Gala0SC4/P\n0mjaLF2YJjvYQ6nQQDLtSAbamB9HqTYJL87jVlskB3vInZvGf3+Tg7qFm4jhtc2pAlnmSI+TOTVK\nenY42v86BVcnpOUZYpODnPvEMhtrRyiej+8cuwRrjoubiEWJhrW6j6cJ/ZyOKrJ/YR6jja/4G//T\nV3n5+jaxfJa11+6hr+0JVdxT4wQHFdLPCR8Qa7if5efOUmzapLYOseM6Pc+cZf6p09RMl56Uzmu/\n9y7Jeot6pUX6idNCAMtySM2PRFRTO66LpMPz8TQV0w0w43HWiiara0d85aeeZjAb54e/+boQI8z3\nEg7liLcP8OhQbkd39/XDnGk754RWqiOdnWby6gLphTH++heWePn2Ed+/dYhvuyDL9M8OR3vt8CfO\nY93fo1BoUK60CK+v4qlK1HGz+jIsfmyJyvU1Bpenoq5C4tlzVFtOdLg3h/vJXJgj3ZOkbvv0XZqj\nikw4mseK6ciGhbpXYuveHomjSpSYdFsKaNfOYhZqAsC7X35kkgGgXDwlwPqlOr4kEas2HxKg6xSb\nvRdmaeg6tqrgKQrOSF68X6rCwIuXMFf2hT9LMo4TjxE/M4U8MRCN7y9/7SoAG+8K/A0hJynqlxbY\n+u6vfPTorV/6ZzcpfestgUV4b524YZ2gV1rJOKEs/5GoVONfvort+FSrBr/2F5d4eUPiH//N33jk\n77Z6M+2ZY1u6d36cwLBQ6gby5CCxWxsEskzi2hlKawfEjwSbw03G6T0ziet48OZdQfWaHCJxb5vW\n9AhnLk3TNBy2Vw5Jrvzh3PfmcD/zl+fY+dZbj6R+gsgwveF+Uhv7tHLZiNpmn5nGLdZIH1WwknHi\n7Za9l4wzeeUUzaZN4/W7OPke5Kb5/4oK+2HfO3lUeSRNDto6BI5LM99Lslz/wN/rXEe4cYA6P4bn\nuHhNi/TOEdbiJC+9cJZv/WAF7+YGgao8RLFt5bJkF8bo7U1Sr1v051Ls7VUw1w6QguD4mi+f5jPX\n5tgutXj9B/dQjiqojoudTvLsl59gZafC0V6F0cl+LMul8LY4uDp/v9M2d7KpiPIqjecJHI/Q88mO\n5qjvlZk8M87/+icWGIvt8251jL/zH2+hqjJXF4eJawrfeWeb2nfeEeJxyRjSUZWZjy9z8I3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JR7l//r9jB2RegOeZp6QkzLPTuN0z6/pDCktH5SPl8r1wl3ioQ7ReSDMobpkhrth2SMsSsLONdX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107lONZ0gmoximS2Uy/PEHz1O7uIc8kgfrXyFWK5E9+MnKTx/GWsqi2vZfPyZE3zx958jfHLS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BKm0xGt3eKBIvN3DzInGv+xCUm8JkbmZUVDjCf/dcH2OnJPRGCY2+qSEswyK+W4r2LflY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\n9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WCABbDYg3tpubtccckVKTGmRSqKrLJkO04s20mpbOVwlW1ZUdkVOeX4iGPHsh0rlmSa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QdkUDMZp766hxWPCjfcngw4HtEHW0ycm0FLRpHH+lDH+7n2u2+Ru7ZGrmXHXmviVRr0Xlyk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DRAd7Oojb7dfFpVtoU1mM3iSe6yGrChu5CrG58Q7iOoCkhLh3ZZk/XOzmi9tP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lTvbSGdm2bvg3X8j7dioF78PS3NCIhjx4SmoOBKRw3RGXjjPI6mIX/4ePBaOi5uW6w1iuuhVFu4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\nMk+Q2t2JE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OnlDuc/9BydTkACWcbcr/G1v/wJ3jjqEk6N4Dc/GhTanSoR+IF4CMryBxLu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\nros0M4rXOZF7SD+3BpuHQgU00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1DdriA9OeJu0+bc2jT2cIGOqiKlEgSnRKjcSD1SCsN4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Rtf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\nz5AZ6MFvuY52lgAAHv/ocTZfuU3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VUGotQj0/h/iRNUFfmDgm+Z944TyRnRJWLk1nJI9U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\ndnB6pGtHkelmBHo38/nLlFb28AmBaYPn4yyMEz1iitl34/73IRr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IZ2aw6h3MdEKkpGoG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\nSiXwYwbNpsP29TUpBb+wwdWXFsifvIfD0wO8/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- }, - "output_type": "display_data", - "metadata": {} - } - ], - "source": [ - "plt.imshow(rho.data, interpolation='nearest')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3.0 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.0+" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/examples/boil/boil b/examples/boil/boil index 95cb231..5e3c58a 100755 --- a/examples/boil/boil +++ b/examples/boil/boil @@ -86,7 +86,7 @@ def object_filename(srcdir, filename, config): return os.path.join(target_dir, basename + '.o') -@noodles.schedule_hint(display="ā”‚ Compiling {source_file} ... ", +@noodles.schedule_hint(display=" Compiling {source_file} ... ", confirm=True, annotated=True) def compile_source(source_file, object_file, config): @@ -118,7 +118,7 @@ def get_object_file(src_dir, src_file, config): return obj_path -@noodles.schedule_hint(display="ā”‚ Linking ... ", +@noodles.schedule_hint(display=" Linking ... ", confirm=True, annotated=True) def link(object_files, config): @@ -147,10 +147,10 @@ def get_target(obj_files, config): if is_out_of_date(config['target'], obj_files): return link(obj_files, config) else: - return message("ā”‚ target is up-to-date.") + return message("target is up-to-date.") -@noodles.schedule_hint(display="ā•­ā”€(Building target {config[target]})") +@noodles.schedule_hint(display="Building target {config[target]}") def make_target(config): """Make a target. First compiles the source files, then links the object files to create an executable. diff --git a/examples/class-decorator.py b/examples/class-decorator.py index f5152ba..e3a201f 100644 --- a/examples/class-decorator.py +++ b/examples/class-decorator.py @@ -1,5 +1,5 @@ -from noodles import schedule, run -from prototype import draw_workflow +from noodles import schedule, run_single +from noodles.draw_workflow import draw_workflow @schedule @@ -42,6 +42,7 @@ def attr(self, x): class B: pass + a = A(5).multiply(10) a.second = 7 a.attr = 1.0 @@ -51,7 +52,7 @@ class B: b.second = a.second b.attr = a.attr -draw_workflow("oop-wf.svg", b) -result = run(b) +draw_workflow("oop-wf.svg", b._workflow) +result = run_single(b) print(result.x, result.second, result.attr) diff --git a/examples/cosmology/__init__.py b/examples/cosmology/__init__.py deleted file mode 100644 index ec537ad..0000000 --- a/examples/cosmology/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .pm3 import ( - gaussian_random_field, zeldovich_approximation, Particles, BoxConfig, - compute_density, compute_potential, drift, kick) - -from .cosmology import ( - Cosmology, EdS, LCDM) - -__all__ = [ - gaussian_random_field, zeldovich_approximation, Particles, - BoxConfig, compute_density, compute_potential, drift, kick, - Cosmology, EdS, LCDM] diff --git a/examples/cosmology/amuse.py b/examples/cosmology/amuse.py deleted file mode 100644 index 9dab2d4..0000000 --- a/examples/cosmology/amuse.py +++ /dev/null @@ -1 +0,0 @@ -from amuse import diff --git a/examples/cosmology/cosmology.py b/examples/cosmology/cosmology.py deleted file mode 100644 index 7653b0f..0000000 --- a/examples/cosmology/cosmology.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np -from scipy.integrate import quad -from noodles import Storable - - -class Cosmology(Storable): - """This object stores all relevant information wrt the background - cosmology, parametrized by OmegaM, OmegaL and H0.""" - def __init__(self, H0, OmegaM, OmegaL): - super(Cosmology, self).__init__() - - self.H0 = H0 - self.OmegaM = OmegaM - self.OmegaL = OmegaL - self.OmegaK = 1 - OmegaM - OmegaL - self.grav_cst = 3./2 * OmegaM * H0**2 - self.factor = self._growing_mode_norm() - - def adot(self, a): - return self.H0 * a * np.sqrt( - self.OmegaL - + self.OmegaM * a**-3 - + self.OmegaK * a**-2) - - def _growing_mode_norm(self): - """result D(1) = 1. d/d0 + 0.001 = 1""" - d = self.adot(1) * quad(lambda b: self.adot(b)**(-3), 0.00001, 1)[0] - return 0.99999/d - - def growing_mode(self, a): - if isinstance(a, np.ndarray): - return np.array([self.growing_mode(b) for b in a]) - elif a <= 0.001: - return a - else: - return self.factor * self.adot(a)/a * quad( - lambda b: self.adot(b)**(-3), 0.00001, a)[0] \ - + 0.00001 - - -LCDM = Cosmology(68.0, 0.31, 0.69) -EdS = Cosmology(70.0, 1.0, 0.0) diff --git a/examples/cosmology/filter.py b/examples/cosmology/filter.py deleted file mode 100644 index 44b7d7a..0000000 --- a/examples/cosmology/filter.py +++ /dev/null @@ -1,108 +0,0 @@ -import numpy as np -from numbers import Number -from functools import reduce -import operator - - -class Filter: - def __init__(self, f): - self.f = f - - def __call__(self, K): - return self.f(K) - - def __mul__(self, other): - if isinstance(other, Filter): - return Filter(lambda K: self.f(K) * other.f(K)) - - elif isinstance(other, Number): - return Filter(lambda K: other * self.f(K)) - - def __pow__(self, n): - return Filter(lambda K: self.f(K)**n) - - def __truediv__(self, other): - if isinstance(other, Number): - return Filter(lambda K: self.f(K) / other) - - def __add__(self, other): - return Filter(lambda K: self.f(K) + other.f(K)) - - def __invert__(self): - return Filter(lambda K: self.f(K).conj()) - - def abs(self, B, P): - return np.sqrt(self.cc(B, P, self)) - - def cc(self, B, P, other): - return (~self * other * P)(B.K).sum().real / B.size * B.res**2 - - def cf(self, B, other): - return ((~self)(B.K) * other).sum().real / B.size * B.res**2 - - -class Identity(Filter): - def __init__(self): - Filter.__init__(self, lambda K: 1) - - -class Zero(Filter): - def __init__(self): - Filter.__init__(self, lambda K: 0) - - -def _scale_filter(B, t): - """returns discrete scale space filter, take care with units: - [res] = Mpc / pixel, [k] = 1 / Mpc, [t] = Mpc**2""" - def f(K): - return reduce( - lambda x, y: x*y, - (np.exp(t / B.res**2 * (np.cos(k * B.res) - 1)) for k in K)) - return f - - -class Scale(Filter): - def __init__(self, B, sigma): - Filter.__init__(self, _scale_filter(B, sigma**2)) - - -def _K_dot(X, K): - return sum(X[i]*K[i] for i in range(len(X))) - - -class Pos(Filter): - def __init__(self, x): - Filter.__init__(self, lambda K: np.exp(-1j * _K_dot(x, K))) - - -class D(Filter): - def __init__(self, n): - def d(i): - return Filter(lambda K: -1j * K[i]) - - A = [d(int(i)-1) for i in str(n)] - Filter.__init__(self, reduce(operator.mul, A)) - - -def _K_pow(k, n): - """raise |k| to the -th power safely""" - save = np.seterr(divide='ignore') - a = np.where(k == 0, 0, k**n) - np.seterr(**save) - return a - - -class Power_law(Filter): - def __init__(self, n): - Filter.__init__(self, lambda K: _K_pow((K**2).sum(axis=0), n/2)) - - -class Cutoff(Filter): - def __init__(self, B): - Filter.__init__(self, lambda K: np.where( - (K**2).sum(axis=0) <= B.k_max**2, 1, 0)) - - -class Potential(Filter): - def __init__(self): - Filter.__init__(self, lambda K: -_K_pow((K**2).sum(axis=0), -1.)) diff --git a/examples/cosmology/gnuplot.py b/examples/cosmology/gnuplot.py deleted file mode 100644 index 685046c..0000000 --- a/examples/cosmology/gnuplot.py +++ /dev/null @@ -1,186 +0,0 @@ -# -*- coding: utf-8 -*- -# module that plots gnuplot to IPython notebook - -import sys -from subprocess import Popen, PIPE -# from functools import reduce, partial -import numpy as np - - -class Gnuplot: - """Create an instance of the Gnuplot process. If dummy is given, the - process will just print to stdout in stead of running Gnuplot.""" - def __init__(self, persist=False, dummy=False, binary=True): - if dummy: - self.f = sys.stdout - elif persist: - self.p = Popen(["gnuplot", "-persist"], stdin=PIPE) - self.f = self.p.stdin - else: - self.p = Popen("gnuplot", stdin=PIPE) - self.f = self.p.stdin - - if binary: - self.put_array = self.put_array_binary - else: - self.put_array = self.put_array_text - - def __call__(self, *args): - """Send commands to Gnuplot. If an instance of a numpy array is given, - it is converted to inline data. You can use this if you give a - 'plot '-' ... ' command just before the data.""" - for a in args: - if isinstance(a, bytes): - self.f.write(a) - self.f.flush() - - if isinstance(a, str): - self.f.write(a.encode()) - self.f.write('\n'.encode()) - self.f.flush() - - if isinstance(a, np.ndarray): - self.put_array(a) - - if isinstance(a, list): - for i in a: - self(i) - - def flush(self): - self.f.flush() - - def terminate(self): - """Close the Gnuplot input pipe, and wait for the process to exit.""" - self.f.close() - self.p.wait() - - def put_array_binary(self, data): - self.f.write(data.tostring()) - - def put_array_text(self, data): - """Print array data such that Gnuplot understands it as inlined data entries. - Doesn't work with matrix data yet, since Gnuplots protocol for entering - matrix data by text is different from other data.""" - if len(data.shape) > 3: - raise - - if len(data.shape) == 1: - for x in data: - self(str(x)) - - if len(data.shape) == 2: - for x in data: - self(" ".join([str(l) for l in x])) - - if len(data.shape) == 3: - for x in data: - for y in x: - self(" ".join([str(l) for l in y])) - self("") - - self("EOF") - - -class Multiplot: - def __init__(self, w, h, n, m): - self.w = w - self.h = h - self.n = n - self.m = m - self.set_margin(0.1, 0.1, 0.8, 0.3) - - def set_margin(self, t, r, b, l): - "set margins (clockwise: t, r, b, l) and recalculate plot positions" - self.margin_left = l - self.margin_bottom = b - self.margin_top = t - self.margin_right = r - - self.fw = self.w*self.n + self.margin_left + self.margin_right - self.fh = self.h*self.m + self.margin_top + self.margin_bottom - - self.ml = self.margin_left / self.fw - self.mr = self.margin_right / self.fw - self.mt = self.margin_top / self.fh - self.mb = self.margin_bottom / self.fh - - self.ew = (1 - (self.ml + self.mr)) / self.n - self.eh = (1 - (self.mt + self.mb)) / self.m - - def set_term(self, term="pdf"): - return "set term {2} size {0:f},{1:f} " \ - "font 'Bitstream Charter, 10'".format( - self.fw, self.fh, term) - - def subplot(self, i, j): - left = self.ml + self.ew * i - right = self.ml + self.ew * (i+1) - bottom = self.mb + self.eh * (self.m - j - 1) - top = self.mb + self.eh * (self.m - j) - return "set l{0} {1:f}; set r{0} {2:f}; " \ - "set b{0} {3:f} ; set t{0} {4:f}".format( - "margin at screen", left, right, bottom, top) - - def left(self, i): - return self.ml + self.ew * i - - def bottom(self, j): - return self.mb + self.eh * (self.m - j - 1) - - -def matrix(data, using=None): - plot_using = "" - d = np.zeros([s+1 for s in data.shape], dtype='float32') - plot_cmd = "binary matrix" - d[1:, 1:] = data - d[1:, 0] = np.arange(data.shape[0]) - d[0, 1:] = np.arange(data.shape[1]) - d[0, 0] = data.shape[1] - - n = np.ones(1, dtype='int32') * data.shape[1] - if using is not None: - plot_using += " " - plot_using += using - - return (plot_cmd + plot_using, n.tostring() + d.flat[1:].tostring()) - - -def array(data, using=None): - plot_cmd = "binary array={0} format='%{1}'".format(data.shape, data.dtype) - plot_using = "" - if using is not None: - plot_using += " " - plot_using += using - plot_data = data.tostring() - return (plot_cmd + plot_using, plot_data) - - -def record(data, using=None): - plot_cmd = "binary record={0} format='{1}'".format( - data.shape[0], ("%" + str(data.dtype)) * data.shape[1]) - plot_using = "" - if using is not None: - plot_using += " " - plot_using += using - plot_data = data - return (plot_cmd + plot_using, plot_data) - - -def plot_data(*args): - plot_cmd = "plot '-' " + args[0][0] + ", '' ".join(a[0] for a in args[1:]) - plot_data = [a[1] for a in args] - return [plot_cmd] + plot_data - - -def splot_data(*args): - plot_cmd = "splot '-' " + ", '' ".join(a[0] for a in args) - plot_data = [a[1] for a in args] - return [plot_cmd] + plot_data - - -default_palette = """# default blue to red palette -rcol(x) = 0.237 - 2.13*x + 26.92*x**2 - 65.5*x**3 + 63.5*x**4 - 22.36*x**5 -gcol(x) = ((0.572 + 1.524*x - 1.811*x**2)/(1 - 0.291*x + 0.1574*x**2))**2 -bcol(x) = 1/(1.579 - 4.03*x + 12.92*x**2 - 31.4*x**3 + 48.6*x**4 - 23.36*x**5) -set palette model RGB functions rcol(gray), gcol(gray), bcol(gray) -""" diff --git a/examples/cosmology/loops.pyx b/examples/cosmology/loops.pyx deleted file mode 100644 index 39ee033..0000000 --- a/examples/cosmology/loops.pyx +++ /dev/null @@ -1,84 +0,0 @@ -cimport numpy as np -from libc.math cimport (exp, cos) - - -cdef double tau = 6.28318530717958647692 - - -cdef int _wave_number(int N, int i): - if i > N/2: - return i - N - else: - return i - - -cpdef double complex potential_2(double kx, double ky): - cdef double k2 = (kx*kx + ky*ky) - if k2 > 0: - return -1./k2 - else: - return 1.0 - - -cdef class power_2: - cdef double power - - def __init__(self, power): - self.power = power - - def __call__(self, double kx, double ky): - cdef double k2 = (kx*kx + ky*ky) - if k2 > 0: - return k2**(self.power/2) - else: - return 1.0 - - -cdef class compose_2: - cdef object f, g - - def __init__(self, f, g): - self.f = f - self.g = g - - def __call__(self, kx, ky): - return self.f(kx, ky) * self.g(kx, ky) - - -cdef class cutoff_2: - cdef double k_max - - def __init__(self, box): - self.k_max = box.k_max - - def __call__(self, double kx, double ky): - cdef double k2 = (kx*kx + ky*ky) - if k2 <= self.k_max**2: - return 1 - else: - return 0 - - -cdef class scale_2: - cdef double t - cdef double res - - def __init__(self, object box, double sigma): - self.t = (sigma / box.res)**2 - self.res = box.res - - def __call__(self, double kx, double ky): - cdef double Gx = exp(self.t * (cos(kx * self.res) - 1.0)) - cdef double Gy = exp(self.t * (cos(ky * self.res) - 1.0)) - - return Gx * Gy - - -def apply_filter_2(object box, object f, np.ndarray data): - for j in range(box.N): - ky = _wave_number(box.N, j) * tau/box.L - - for i in range(box.N): - kx = _wave_number(box.N, i) * tau/box.L - - data[i, j] *= f(ky, kx) diff --git a/examples/cosmology/maths.py b/examples/cosmology/maths.py deleted file mode 100644 index 9b37a53..0000000 --- a/examples/cosmology/maths.py +++ /dev/null @@ -1,71 +0,0 @@ -import numpy as np -from math import pi - -tau = 2*pi - - -def md_cic(B, X_): - X = X_ / B.res - f = X - np.floor(X) - - rho = np.zeros(B.shape, dtype='float64') - rho_, x_, y_ = np.histogram2d( - X[:, 0] % B.N, X[:, 1] % B.N, - bins=B.shape, - range=[[0, B.N], [0, B.N]], - weights=(1 - f[:, 0]) * (1 - f[:, 1])) - - rho += rho_ - rho_, x_, y_ = np.histogram2d( - (X[:, 0] + 1) % B.N, X[:, 1] % B.N, - bins=B.shape, - range=[[0, B.N], [0, B.N]], - weights=(f[:, 0]) * (1 - f[:, 1])) - - rho += rho_ - rho_, x_, y_ = np.histogram2d( - X[:, 0] % B.N, (X[:, 1] + 1) % B.N, - bins=B.shape, - range=[[0, B.N], [0, B.N]], - weights=(1 - f[:, 0]) * (f[:, 1])) - - rho += rho_ - rho_, x_, y_ = np.histogram2d( - (X[:, 0] + 1) % B.N, (X[:, 1] + 1) % B.N, - bins=B.shape, - range=[[0, B.N], [0, B.N]], - weights=(f[:, 0])*(f[:, 1])) - rho += rho_ - - return rho - - -class Interp2D: - "Reasonably fast bilinear interpolation routine" - def __init__(self, data): - self.data = data - self.shape = data.shape - - def __call__(self, x): - X1 = np.floor(x).astype(int) % self.shape - X2 = np.ceil(x).astype(int) % self.shape - xm = x % 1.0 - xn = 1.0 - xm - - f1 = self.data[X1[:, 0], X1[:, 1]] - f2 = self.data[X2[:, 0], X1[:, 1]] - f3 = self.data[X1[:, 0], X2[:, 1]] - f4 = self.data[X2[:, 0], X2[:, 1]] - - return \ - f1 * xn[:, 0] * xn[:, 1] + \ - f2 * xm[:, 0] * xn[:, 1] + \ - f3 * xn[:, 0] * xm[:, 1] + \ - f4 * xm[:, 0] * xm[:, 1] - - -def gradient_2nd_order(F, i): - return 1/12 * np.roll(F, 2, axis=i) \ - - 2/3 * np.roll(F, 1, axis=i) \ - + 2/3 * np.roll(F, -1, axis=i) \ - - 1/12 * np.roll(F, -2, axis=i) diff --git a/examples/cosmology/pm3.py b/examples/cosmology/pm3.py deleted file mode 100644 index 17f38b4..0000000 --- a/examples/cosmology/pm3.py +++ /dev/null @@ -1,112 +0,0 @@ -from noodles import schedule, Storable -from noodles.numpy_data import NumpyData - -import numpy as np -from math import sqrt -from numpy import random -from numpy import fft - -# from scipy.spatial import KDTree - -from .maths import tau, Interp2D, md_cic, gradient_2nd_order -from .loops import apply_filter_2, potential_2 - - -class BoxConfig(Storable): - def __init__(self, dim, N, L): - super(BoxConfig, self).__init__() - - self.dim = dim - self.N = N - self.L = L - - self.shape = (N,) * dim - self.res = L / N - self.k_max = (N * tau) / (2 * L) - self.k_min = tau / L - - def as_dict(self): - return {'dim': self.dim, - 'N': self.N, - 'L': self.L} - - @classmethod - def from_dict(cls, dim, N, L): - return cls(dim, N, L) - - -class Particles(Storable): - def __init__(self, p): - super(Particles, self).__init__() - - self._particles = NumpyData(p) - - @property - def q(self): - return self._particles.data[:, 0, :] - - @property - def p(self): - return self._particles.data[:, 1, :] - - @q.setter - def q(self, n): - self._particles.data[:, 0, :] = n - - @p.setter - def p(self, n): - self._particles.data[:, 1, :] = n - - -@schedule -def gaussian_random_field(box, P, seed=None): - if seed is not None: - random.seed(seed) - - white_noise = random.normal(0.0, 1.0, box.shape) - F = fft.fftn(white_noise) - apply_filter_2(box, lambda kx, ky: sqrt(P(sqrt(kx**2 + ky**2))), F) - return NumpyData(fft.ifftn(F).real) - - -@schedule -def zeldovich_approximation(box, potential_): - potential = potential_.data - vx = gradient_2nd_order(potential, 0) - vy = gradient_2nd_order(potential, 1) - p = Particles(np.zeros(shape=[box.N**2, 2, 2], dtype=np.float64)) - p.q = np.indices(box.shape).transpose([1, 2, 0]).reshape([-1, 2]) * box.res - p.p = np.array([vx, vy]).transpose([1, 2, 0]).reshape([-1, 2]) - return p - - -@schedule -def compute_density(box, particles): - return NumpyData(md_cic(box, particles.q)) - - -@schedule -def compute_potential(box, f_): - f = f_.data - F = fft.fftn(f) - apply_filter_2(box, potential_2, F) - return NumpyData(fft.ifftn(F).real) - - -@schedule -def drift(universe, particles, a, da): - adot = universe.adot(a) - particles.q += da * particles.p / (a**2 * adot) - return particles - - -@schedule -def kick(box, universe, potential, particles, a, da): - adot = universe.adot(a) - - for i in range(2): - g = gradient_2nd_order(potential, i) / box.res * universe.grav_cst / a - particles.p[:, i] -= da * Interp2D(g)( - (particles.q / box.res) % box.N) / adot - - return particles diff --git a/examples/cosmology/setup.py b/examples/cosmology/setup.py deleted file mode 100644 index d7f7f11..0000000 --- a/examples/cosmology/setup.py +++ /dev/null @@ -1,6 +0,0 @@ -from distutils.core import setup -from Cython.Build import cythonize - -setup( - ext_modules=cythonize("loops.pyx") -) diff --git a/examples/ea.py b/examples/ea.py index f24c02e..02c682d 100644 --- a/examples/ea.py +++ b/examples/ea.py @@ -1,9 +1,9 @@ -from noodles import gather, run_logging, schedule, has_scheduled_methods, run_process, serial, run_parallel, run_single +from noodles import (run_logging) import subprocess import numpy as np -from ea import (EA, Chromosome, Generation, Rastrigin, registry) -from noodles.display import SimpleDisplay, DumbDisplay +from ea import (EA, Rastrigin) +from noodles.display import SimpleDisplay def error_filter(xcptn): @@ -32,7 +32,7 @@ def error_filter(xcptn): print("ā•­ā”€(Running evolution...)") with SimpleDisplay(error_filter) as display: answer = run_logging(g, 1, display) - #answer = run_single(g) + # answer = run_single(g) for i in answer.individuals: print(i.fitness) diff --git a/examples/ea/ea.py b/examples/ea/ea.py index 1ccf1de..32b7bda 100644 --- a/examples/ea/ea.py +++ b/examples/ea/ea.py @@ -1,10 +1,9 @@ """ - This module implements a parallelized Evolutionary Strategy with Single Self-Adaptive Sigma. - - + This module implements a parallelized Evolutionary Strategy with Single + Self-Adaptive Sigma. """ -from noodles import (has_scheduled_methods, schedule, schedule_hint) +from noodles import (has_scheduled_methods, schedule_hint) from .chromosome import (Chromosome) from .generation import (Generation) @@ -25,8 +24,10 @@ def make_child(self, g: Generation) -> Chromosome: option2 = random.sample(g.individuals, 2) # Binary Tournament - parent = [option1[0] if option1[0].fitness < option1[1].fitness else option1[1], - option2[0] if option2[0].fitness < option2[1].fitness else option2[1]] + parent = [option1[0] if option1[0].fitness < option1[1].fitness + else option1[1], + option2[0] if option2[0].fitness < option2[1].fitness + else option2[1]] # Crossover child = self.crossover(parent) @@ -37,7 +38,8 @@ def make_child(self, g: Generation) -> Chromosome: return child def generate_offspring(self, g: Generation) -> Generation: - individuals = [self.make_child(g) for _ in range(self.config['offspring'])] + individuals = [self.make_child(g) + for _ in range(self.config['offspring'])] generation = Generation(individuals) generation.number = g.number + 1 @@ -45,14 +47,16 @@ def generate_offspring(self, g: Generation) -> Generation: def crossover(self, parents) -> Chromosome: values = np.where(np.random.random(parents[0].values.shape) > 0.5, - parents[0].values, parents[1].values) - sigma = parents[0].sigma if np.random.random() > 0.5 else parents[1].sigma + parents[0].values, parents[1].values) + sigma = parents[0].sigma if np.random.random() > 0.5 \ + else parents[1].sigma child = Chromosome(values=values, sigma=sigma) return child def mutate(self, child) -> Chromosome: # mutate the sigma - child.sigma = child.sigma + self.config['learning_rate'] * np.random.normal(0, child.sigma) + child.sigma = child.sigma + self.config['learning_rate'] \ + * np.random.normal(0, child.sigma) # mutate the child using the mutated sigma mut = np.random.normal(0, child.sigma, (len(child.values))) @@ -63,9 +67,12 @@ def mutate(self, child) -> Chromosome: # Generate random individuals on the range [range_lower, range_upper] def initialize(self) -> Generation: - individuals = [Chromosome(values=(np.random.rand(self.fitness_evaluator.dimensions) / 2 - 1) * - self.fitness_evaluator.range_upper, sigma=self.config['initial_sigma']) - for _ in range(self.config['population_size'])] + individuals = [ + Chromosome(values=(np.random.rand( + self.fitness_evaluator.dimensions) / 2 - 1) * + self.fitness_evaluator.range_upper, + sigma=self.config['initial_sigma']) + for _ in range(self.config['population_size'])] generation = Generation(individuals) generation.number = 1 @@ -80,7 +87,7 @@ def next_generation(self, g: Generation) -> Generation: return g else: # Generate Children - new_generation = self.generate_offspring(g).evaluate(fitness_evaluator=self.fitness_evaluator) + new_generation = self.generate_offspring(g).evaluate( + fitness_evaluator=self.fitness_evaluator) return self.next_generation(new_generation) - diff --git a/examples/ea/generation.py b/examples/ea/generation.py index af98569..e51fcce 100644 --- a/examples/ea/generation.py +++ b/examples/ea/generation.py @@ -4,13 +4,14 @@ class Generation(Storable): def __init__(self, individuals): - super(Storable, self).__init__() + super(Generation, self).__init__() self.individuals = individuals self.number = -1 # Evaluate individuals def evaluate(self, fitness_evaluator): - evaluate = [Chromosome(fitness=fitness_evaluator.evaluate(c.values), values=c.values, sigma=c.sigma) + evaluate = [Chromosome(fitness=fitness_evaluator.evaluate(c.values), + values=c.values, sigma=c.sigma) for c in self.individuals] self.individuals = gather(*evaluate) diff --git a/examples/example2.py b/examples/example2.py index ee3a0ae..8bb1a09 100644 --- a/examples/example2.py +++ b/examples/example2.py @@ -1,6 +1,6 @@ -from noodles import schedule, gather, run_parallel -from prototype import draw_workflow - +from noodles import schedule, gather, serial, run_logging +from noodles.run.run_with_prov import run_parallel_opt +from noodles.display import (DumbDisplay) @schedule def add(a, b): @@ -18,7 +18,7 @@ def mul(a, b): @schedule -def sum(a, buildin_sum=sum): +def my_sum(a, buildin_sum=sum): return buildin_sum(a) @@ -34,9 +34,14 @@ def foo(a, b, c): multiples = [foo(i, r2, r1) for i in range(6)] -r5 = sum(gather(*multiples)) +r5 = my_sum(gather(*multiples)) + +# draw_workflow("graph-example2.svg", r5) -draw_workflow("graph-example2.svg", r5) -answer = run_parallel(r5, 4) +with DumbDisplay() as display: + # answer = run_logging(r5, 4, display) + answer = run_parallel_opt( + r5, 4, serial.base, "cache.json", + display=display, cache_all=True) print("The answer is: {0}".format(answer)) diff --git a/examples/graph-example1.svg b/examples/graph-example1.svg deleted file mode 100644 index 6d9bfc9..0000000 --- a/examples/graph-example1.svg +++ /dev/null @@ -1,57 +0,0 @@ - - - - - - -%3 - - -140238561536256 - -add - (ā€”, ā€”) - - -140238561535824 - -add - (41, ā€”) - - -140238561535824->140238561536256 - - - - -140238561536040 - -sub - (ā€”, 44) - - -140238561536040->140238561536256 - - - - -140238561535608 - -add - (42, 43) - - -140238561535608->140238561535824 - - - - -140238561535608->140238561536040 - - - - - diff --git a/examples/graph-example2.svg b/examples/graph-example2.svg deleted file mode 100644 index 63d077e..0000000 --- a/examples/graph-example2.svg +++ /dev/null @@ -1,239 +0,0 @@ - - - - - - -%3 - - -140041583242912 - -add - (3, ā€”) - - -140041583243128 - -mul - (ā€”, ā€”) - - -140041583242912->140041583243128 - - - - -140041583281064 - -binder - (ā€”, ā€”, ā€”, ā€”, ā€”, ā€”) - - -140041583243128->140041583281064 - - - - -140041583281208 - -sum - (ā€”, <built-in function sum>) - - -140041583281064->140041583281208 - - - - -140041583280704 - -add - (5, ā€”) - - -140041583280920 - -mul - (ā€”, ā€”) - - -140041583280704->140041583280920 - - - - -140041583280920->140041583281064 - - - - -140041583241184 - -add - (1, 1) - - -140041583241184->140041583243128 - - - - -140041583241184->140041583280920 - - - - -140041583242696 - -mul - (ā€”, ā€”) - - -140041583241184->140041583242696 - - - - -140041583241832 - -mul - (ā€”, ā€”) - - -140041583241184->140041583241832 - - - - -140041583280488 - -mul - (ā€”, ā€”) - - -140041583241184->140041583280488 - - - - -140041583242264 - -mul - (ā€”, ā€”) - - -140041583241184->140041583242264 - - - - -140041583241400 - -sub - (3, ā€”) - - -140041583241184->140041583241400 - - - - -140041583242696->140041583281064 - - - - -140041583241832->140041583281064 - - - - -140041583280488->140041583281064 - - - - -140041583242264->140041583281064 - - - - -140041583241400->140041583242912 - - - - -140041583241400->140041583280704 - - - - -140041583280272 - -add - (4, ā€”) - - -140041583241400->140041583280272 - - - - -140041583242480 - -add - (2, ā€”) - - -140041583241400->140041583242480 - - - - -140041583241616 - -add - (0, ā€”) - - -140041583241400->140041583241616 - - - - -140041583242048 - -add - (1, ā€”) - - -140041583241400->140041583242048 - - - - -140041583280272->140041583280488 - - - - -140041583242480->140041583242696 - - - - -140041583241616->140041583241832 - - - - -140041583242048->140041583242264 - - - - - diff --git a/examples/maybe.py b/examples/maybe.py new file mode 100644 index 0000000..dd7561f --- /dev/null +++ b/examples/maybe.py @@ -0,0 +1,19 @@ +from noodles import (schedule, maybe, run_single) + + +@schedule +@maybe +def inv(x): + return 1/x + + +@schedule +@maybe +def add(**args): + return sum(args.values()) + + +if __name__ == "__main__": + wf = add(a=inv(0), b=inv(0)) + result = run_single(wf) + print(result) diff --git a/examples/noodles.ini b/examples/noodles.ini new file mode 100644 index 0000000..9513446 --- /dev/null +++ b/examples/noodles.ini @@ -0,0 +1,35 @@ +[default] +machine=local + +[Machines] + [local] + runner=parallel + features=prov, display + n_threads=4 + + [debug] + runner=process + features=msgpack + verbose=True + + [cartesius] + runner=xenon + features=prov, display + host=cartesius.surfsara.nl + scheme=slurm + user=jhidding + n_jobs=1 + n_threads_per_job=16 + + [das5] + runner=xenon + featuers=prov, display + host=fs0.das5.cs.vu.nl + scheme=slurm + user=jhidding + +[Users] + [] + username= + protocol=ssh + certificate=~/.ssh/id_rsa diff --git a/examples/oop-wf.svg b/examples/oop-wf.svg deleted file mode 100644 index 5736bd6..0000000 --- a/examples/oop-wf.svg +++ /dev/null @@ -1,150 +0,0 @@ - - - - - - -%3 - - -140295268461312 - -_setattr - (ā€”, attr, ā€”) - - -140295268874784 - -_setattr - (ā€”, x, ā€”) - - -140295268874840 - -_setattr - (ā€”, second, ā€”) - - -140295268874784->140295268874840 - - - - -140295268874840->140295268461312 - - - - -140295268874952 - -_getattr - (ā€”, second) - - -140295268874952->140295268874840 - - - - -140295274632304 - -A - (5) - - -140295274632528 - -_getattr - (ā€”, multiply) - - -140295274632304->140295274632528 - - - - -140295274632696 - -_do_call - (ā€”, 10) - - -140295274632528->140295274632696 - - - - -140295268972080 - -_setattr - (ā€”, second, 7) - - -140295274632696->140295268972080 - - - - -140295268972080->140295268874952 - - - - -140295268972304 - -_setattr - (ā€”, attr, 1.0) - - -140295268972080->140295268972304 - - - - -140295268461256 - -_getattr - (ā€”, attr) - - -140295268461256->140295268461312 - - - - -140295268952888 - -B - () - - -140295268952888->140295268874784 - - - - -140295268972304->140295268461256 - - - - -140295268874392 - -_getattr - (ā€”, x) - - -140295268972304->140295268874392 - - - - -140295268874392->140295268874784 - - - - - diff --git a/examples/run-on-das.py b/examples/run-on-das.py deleted file mode 100644 index 97421a8..0000000 --- a/examples/run-on-das.py +++ /dev/null @@ -1,38 +0,0 @@ -from noodles import ( - serial, gather) - -from noodles.run.xenon import ( - XenonConfig, RemoteJobConfig, XenonKeeper, run_xenon) - -from noodles.tutorial import ( - add, mul, sub, accumulate) - -import os - -if __name__ == '__main__': - A = add(1, 1) - B = sub(3, A) - - multiples = [mul(add(i, B), A) for i in range(6)] - C = accumulate(gather(*multiples)) - - with XenonKeeper() as Xe: - certificate = Xe.credentials.newCertificateCredential( - 'ssh', os.environ["HOME"] + '/.ssh/id_rsa', '', '', None) - - xenon_config = XenonConfig( - jobs_scheme='slurm', - location=None, - # credential=certificate - ) - - job_config = RemoteJobConfig( - registry=serial.base, - prefix='/home/jhidding/venv', - working_dir='/home/jhidding/noodles', - time_out=1 - ) - - result = run_xenon(Xe, 2, xenon_config, job_config, C) - print("The answer is", result) - diff --git a/examples/static_sum.py b/examples/static_sum.py index 74a6ed7..159966c 100644 --- a/examples/static_sum.py +++ b/examples/static_sum.py @@ -1,7 +1,7 @@ from noodles import (run_parallel, schedule) from noodles.tutorial import (add) -import numpy as np +# import numpy as np def static_sum(values, limit_n=1000): @@ -21,16 +21,16 @@ def dynamic_sum(values, limit_n=1000, acc=0, depth=4): """Example of dynamic sum.""" if len(values) < limit_n: return acc + sum(values) - + if depth > 0: - half = len(values) // 2 + half = len(values) // 2 return add( dynamic_sum(values[:half], limit_n, acc, depth=depth-1), dynamic_sum(values[half:], limit_n, 0, depth=depth-1)) - return dynamic_sum(values[limit_n:], limit_n, acc + sum(values[:limit_n]), depth) - + return dynamic_sum(values[limit_n:], limit_n, + acc + sum(values[:limit_n]), depth) + result = run_parallel(dynamic_sum(range(1000000000), 1000000), 4) print(result) - diff --git a/examples/worker.sh b/examples/worker.sh deleted file mode 100644 index abaa7c8..0000000 --- a/examples/worker.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -cd "$(dirname "${BASH_SOURCE[0]}")" - -if [ -e $1/bin/activate ]; then - source $1/bin/activate; -fi - -python -m noodles.worker ${@:2} - -if [ -z ${VIRTUAL_ENV+x} ]; then - deactivate; -fi diff --git a/integration/remote-docker/Dockerfile b/integration/remote-docker/Dockerfile new file mode 100644 index 0000000..27f6d6d --- /dev/null +++ b/integration/remote-docker/Dockerfile @@ -0,0 +1,45 @@ +FROM phusion/baseimage:0.9.19 +MAINTAINER Johan Hidding + +RUN rm -f /etc/service/sshd/down +RUN /etc/my_init.d/00_regen_ssh_host_keys.sh + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git python3.5 python3-numpy \ + python3-scipy ca-certificates python3-setuptools \ + slurmd slurm-client slurm-wlm +# && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* +RUN apt-get install -y python3-pip +RUN pip3 install noodles + +EXPOSE 22 + +RUN useradd -ms /bin/bash -p $(openssl passwd sixpack) joe + +USER joe +RUN mkdir /home/joe/.ssh +ADD id_rsa.pub /home/joe/.ssh/authorized_keys + +USER root + +ADD munge.key /etc/munge/munge.key +ADD service /etc/service +# Key was generated with openssl genrsa -out slurm.key 1024 && openssl rsa -in slurm.key -pubout -out slurm.cert +ADD etc /etc/slurm-llnl + +RUN mkdir /var/run/munge \ + && mkdir -p /var/spool/slurmctld/state \ + && mkdir -p /var/spool/slurmd.node-0 \ + && mkdir -p /var/spool/slurmd.node-1 \ + && mkdir -p /var/spool/slurmd.node-2 \ + && mkdir -p /var/spool/slurmd.node-3 \ + && mkdir -p /var/spool/slurmd.node-4 \ + && chmod 600 /etc/munge/munge.key \ + && chown root.root /var/lib/munge /etc/munge \ + && touch /var/spool/slurmctld/accounting.txt \ + && chown slurm /var/spool/slurmctld/accounting.txt \ + && chown slurm /var/spool/slurmctld/state \ + && chown slurm /etc/slurm-llnl/slurm.key + +ADD welcome.txt /etc/motd +CMD ["/sbin/my_init"] diff --git a/remote-docker/README.rst b/integration/remote-docker/README.rst similarity index 100% rename from remote-docker/README.rst rename to integration/remote-docker/README.rst diff --git a/integration/remote-docker/docker-compose.yml b/integration/remote-docker/docker-compose.yml new file mode 100644 index 0000000..52d5a1a --- /dev/null +++ b/integration/remote-docker/docker-compose.yml @@ -0,0 +1,9 @@ +mysql: + image: mysql:5.5 + environment: + - MYSQL_ROOT_PASSWORD=xenon-slurm-pw +noodles-remote: + image: nlesc/xenon-slurm + links: + - mysql + diff --git a/integration/remote-docker/etc/slurm.cert b/integration/remote-docker/etc/slurm.cert new file mode 100644 index 0000000..b051645 --- /dev/null +++ b/integration/remote-docker/etc/slurm.cert @@ -0,0 +1,6 @@ +-----BEGIN PUBLIC KEY----- +MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDCVGH1xuDtfulsZzGnpM++31vZ +xgnbkn8QwJfTeBvYWpud9yCz8YoRcXADlzFLo8Dqujq8Rpnwp5Sz2YGweIQakEqb +2M2oaCYbjHm/QdQftHthIjffW2y+FjJd4hHmkKr73t/tiHbrODsWQAOnxwWnqJuA +6z8xbz+zo40MMXhPgQIDAQAB +-----END PUBLIC KEY----- diff --git a/integration/remote-docker/etc/slurm.conf b/integration/remote-docker/etc/slurm.conf new file mode 100644 index 0000000..db5ab40 --- /dev/null +++ b/integration/remote-docker/etc/slurm.conf @@ -0,0 +1,165 @@ +# slurm.conf file generated by configurator.html. +# Put this file on all nodes of your cluster. +# See the slurm.conf man page for more information. +# +ControlMachine=localhost +#ControlAddr= +#BackupController= +#BackupAddr= +# +AuthType=auth/munge +CacheGroups=0 +#CheckpointType=checkpoint/none +CryptoType=crypto/openssl +JobCredentialPrivateKey=/etc/slurm-llnl/slurm.key +JobCredentialPublicCertificate=/etc/slurm-llnl/slurm.cert +#DisableRootJobs=NO +#EnforcePartLimits=NO +#Epilog= +#EpilogSlurmctld= +#FirstJobId=1 +#MaxJobId=999999 +#GresTypes= +#GroupUpdateForce=0 +GroupUpdateTime=2 +#JobCheckpointDir=/var/slurm/checkpoint +#JobCredentialPrivateKey= +#JobCredentialPublicCertificate= +#JobFileAppend=0 +#JobRequeue=1 +#JobSubmitPlugins=1 +#KillOnBadExit=0 +#Licenses=foo*4,bar +#MailProg=/bin/mail +#MaxJobCount=5000 +#MaxStepCount=40000 +#MaxTasksPerNode=128 +MpiDefault=none +#MpiParams=ports=#-# +#PluginDir= +#PlugStackConfig= +#PrivateData=jobs +ProctrackType=proctrack/pgid +#Prolog= +#PrologSlurmctld= +#PropagatePrioProcess=0 +#PropagateResourceLimits= +#PropagateResourceLimitsExcept= +ReturnToService=1 +#SallocDefaultCommand= +#SlurmctldPidFile=/var/run/slurmctld.pid +SlurmctldPort=6817 +SlurmdPidFile=/var/run/slurmd.%n.pid +SlurmdPort=6818 +SlurmdSpoolDir=/var/spool/slurmd.%n +SlurmUser=slurm +#SlurmdUser=root +#SrunEpilog= +#SrunProlog= +StateSaveLocation=/var/spool/slurmctld/state +SwitchType=switch/none +#TaskEpilog= +TaskPlugin=task/none +#TaskPluginParam= +#TaskProlog= +#TopologyPlugin=topology/tree +#TmpFs=/tmp +#TrackWCKey=no +#TreeWidth= +#UnkillableStepProgram= +#UsePAM=0 +# +# +# TIMERS +BatchStartTimeout=2 +#CompleteWait=0 +EpilogMsgTime=1 +#GetEnvTimeout=2 +#HealthCheckInterval=0 +#HealthCheckProgram= +InactiveLimit=0 +KillWait=2 +MessageTimeout=2 +#ResvOverRun=0 +MinJobAge=2 +#OverTimeLimit=0 +SlurmctldTimeout=2 +SlurmdTimeout=2 +#UnkillableStepTimeout=60 +#VSizeFactor=0 +Waittime=0 +# +# +# SCHEDULING +#DefMemPerCPU=0 +FastSchedule=1 +#MaxMemPerCPU=0 +#SchedulerRootFilter=1 +SchedulerTimeSlice=5 +SchedulerType=sched/backfill +SchedulerPort=7321 +SelectType=select/linear +#SelectTypeParameters= +# +# +# JOB PRIORITY +#PriorityType=priority/basic +#PriorityDecayHalfLife= +#PriorityCalcPeriod= +#PriorityFavorSmall= +#PriorityMaxAge= +#PriorityUsageResetPeriod= +#PriorityWeightAge= +#PriorityWeightFairshare= +#PriorityWeightJobSize= +#PriorityWeightPartition= +#PriorityWeightQOS= +# +# +# LOGGING AND ACCOUNTING +#AccountingStorageEnforce=0 +AccountingStorageHost=mysql +AccountingStorageLoc=slurm_acct_db +AccountingStoragePass=xenon-slurm-pw +#AccountingStoragePort= +AccountingStorageType=accounting_storage/mysql +AccountingStorageUser=root +AccountingStoreJobComment=YES +ClusterName=cluster +#DebugFlags= +JobCompHost=mysql +JobCompLoc=slurm_acct_db +JobCompPass=xenon-slurm-pw +#JobCompPort= +JobCompType=jobcomp/mysql +JobCompUser=root +JobAcctGatherFrequency=2 +JobAcctGatherType=jobacct_gather/linux +SlurmctldDebug=3 +#SlurmctldLogFile= +SlurmdDebug=3 +SlurmdLogFile=/var/log/slurm-llnl/slurmd.%n.log +#SlurmSchedLogFile= +#SlurmSchedLogLevel= +# +# +# POWER SAVE SUPPORT FOR IDLE NODES (optional) +#SuspendProgram= +#ResumeProgram= +#SuspendTimeout= +#ResumeTimeout= +#ResumeRate= +#SuspendExcNodes= +#SuspendExcParts= +#SuspendRate= +#SuspendTime= +# +# +# COMPUTE NODES +NodeName=node-0 Procs=2 NodeAddr=127.0.0.1 Port=17001 State=UNKNOWN +NodeName=node-1 Procs=2 NodeAddr=127.0.0.1 Port=17002 State=UNKNOWN +NodeName=node-2 Procs=2 NodeAddr=127.0.0.1 Port=17003 State=UNKNOWN +NodeName=node-3 Procs=2 NodeAddr=127.0.0.1 Port=17004 State=UNKNOWN +NodeName=node-4 Procs=2 NodeAddr=127.0.0.1 Port=17005 State=UNKNOWN +PartitionName=mypartition Nodes=node-[0-4] Default=YES MaxTime=INFINITE State=UP +PartitionName=otherpartition Nodes=node-[0-2] Default=NO MaxTime=INFINITE State=UP diff --git a/integration/remote-docker/etc/slurm.key b/integration/remote-docker/etc/slurm.key new file mode 100644 index 0000000..e7960cb --- /dev/null +++ b/integration/remote-docker/etc/slurm.key @@ -0,0 +1,15 @@ +-----BEGIN RSA PRIVATE KEY----- +MIICWwIBAAKBgQDCVGH1xuDtfulsZzGnpM++31vZxgnbkn8QwJfTeBvYWpud9yCz +8YoRcXADlzFLo8Dqujq8Rpnwp5Sz2YGweIQakEqb2M2oaCYbjHm/QdQftHthIjff +W2y+FjJd4hHmkKr73t/tiHbrODsWQAOnxwWnqJuA6z8xbz+zo40MMXhPgQIDAQAB +AoGAC0wgjctMFEKGi3jMWDSqpywRCO4McKlpna20peSbqc5KsbpGOm/WWQFBQ9Qs +V3tjocHF3GA2Xtk/Od1xPrMi6tY/q2OMkn9Den51kJpnBRRVIfipBj+IMCt9yhYP +KX2U/kqktwIH5TNduFd6bvYidz9nwBNqEEq3UGDfSPZr+4kCQQD/qNzRuhKX0Vf7 +Ww/sPkgBGwwvzF6gCf6y9MVqnRfq9Vd49kDZf6X1elACbzOO/MunmJI/m7qmYQs1 +Tfd3SbRPAkEAwpad4zLktS/1j8Wm3tDfIqGfI/C//TRFY6WAjnx/T6v5nxeoMt6Y +flt4FVjkhmsikFTOeYEU5rq0Pm25GP47LwJAFe5wcvVZXviwaXmYVhXJq36cfwUz +INt6o/NndAvh32AIjvHhAkd2/yXs8yiC7cM13TyzXw2G53rvsTTtA6io9QJAWUv7 +OrcxQ4C1xPukP15E98SJnlmuEUl6imb53ul9FxfJntuhYW7LNjlNmyZHAPSLUQbK +FfJJoDSP4JJDvhd1GwJAFgYozOFcKZGB5KnnL5B0JLVgCFYNzXuIHPNqwN6OsmKM ++7DGM1GAAfYLwTkU0F3fgNZx2ZCTRmmbMBRRMPC9qg== +-----END RSA PRIVATE KEY----- diff --git a/remote-docker/id_rsa b/integration/remote-docker/id_rsa similarity index 100% rename from remote-docker/id_rsa rename to integration/remote-docker/id_rsa diff --git a/remote-docker/id_rsa.pub b/integration/remote-docker/id_rsa.pub similarity index 100% rename from remote-docker/id_rsa.pub rename to integration/remote-docker/id_rsa.pub diff --git a/integration/remote-docker/munge.key b/integration/remote-docker/munge.key new file mode 100644 index 0000000..4c22ae5 Binary files /dev/null and b/integration/remote-docker/munge.key differ diff --git a/integration/remote-docker/service/munged/run b/integration/remote-docker/service/munged/run new file mode 100755 index 0000000..f403f9c --- /dev/null +++ b/integration/remote-docker/service/munged/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/munged -F \ No newline at end of file diff --git a/integration/remote-docker/service/slurmctld/run b/integration/remote-docker/service/slurmctld/run new file mode 100755 index 0000000..d5db3a3 --- /dev/null +++ b/integration/remote-docker/service/slurmctld/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmctld -D -c \ No newline at end of file diff --git a/integration/remote-docker/service/slurmd-node-0/run b/integration/remote-docker/service/slurmd-node-0/run new file mode 100755 index 0000000..2005681 --- /dev/null +++ b/integration/remote-docker/service/slurmd-node-0/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmd -D -N node-0 diff --git a/integration/remote-docker/service/slurmd-node-1/run b/integration/remote-docker/service/slurmd-node-1/run new file mode 100755 index 0000000..5c79809 --- /dev/null +++ b/integration/remote-docker/service/slurmd-node-1/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmd -D -N node-1 diff --git a/integration/remote-docker/service/slurmd-node-2/run b/integration/remote-docker/service/slurmd-node-2/run new file mode 100755 index 0000000..1c895f8 --- /dev/null +++ b/integration/remote-docker/service/slurmd-node-2/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmd -D -N node-2 diff --git a/integration/remote-docker/service/slurmd-node-3/run b/integration/remote-docker/service/slurmd-node-3/run new file mode 100755 index 0000000..ce1da83 --- /dev/null +++ b/integration/remote-docker/service/slurmd-node-3/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmd -D -N node-3 diff --git a/integration/remote-docker/service/slurmd-node-4/run b/integration/remote-docker/service/slurmd-node-4/run new file mode 100755 index 0000000..f20fd24 --- /dev/null +++ b/integration/remote-docker/service/slurmd-node-4/run @@ -0,0 +1,2 @@ +#!/bin/sh +exec /usr/sbin/slurmd -D -N node-4 diff --git a/remote-docker/slurm-wlm-configurator.easy.html b/integration/remote-docker/slurm-wlm-configurator.easy.html similarity index 100% rename from remote-docker/slurm-wlm-configurator.easy.html rename to integration/remote-docker/slurm-wlm-configurator.easy.html diff --git a/remote-docker/slurm.conf b/integration/remote-docker/slurm.conf similarity index 100% rename from remote-docker/slurm.conf rename to integration/remote-docker/slurm.conf diff --git a/remote-docker/start.sh b/integration/remote-docker/start.sh similarity index 100% rename from remote-docker/start.sh rename to integration/remote-docker/start.sh diff --git a/remote-docker/welcome.txt b/integration/remote-docker/welcome.txt similarity index 100% rename from remote-docker/welcome.txt rename to integration/remote-docker/welcome.txt diff --git a/integration/test_xenon/test_job_submission.py b/integration/test_xenon/test_job_submission.py index 1fd572a..de4e441 100644 --- a/integration/test_xenon/test_job_submission.py +++ b/integration/test_xenon/test_job_submission.py @@ -1,4 +1,5 @@ -from noodles.run.xenon import (XenonJob, XenonScheduler, XenonKeeper, XenonConfig) +from noodles.run.xenon import ( + XenonScheduler, XenonKeeper, XenonConfig) import docker import uuid diff --git a/lib/__init__.py b/lib/__init__.py deleted file mode 100644 index 10247a0..0000000 --- a/lib/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .exceptions import * - diff --git a/lib/exceptions.py b/lib/exceptions.py deleted file mode 100644 index e245935..0000000 --- a/lib/exceptions.py +++ /dev/null @@ -1,48 +0,0 @@ - -def _ordinal(n): - """ - Ugly hack to get ordinal number. Is there an internationalized solution - to this? - """ - return "%d%s" % (n,"tsnrhtdd"[(n/10%10!=1)*(n%10<4)*n%10::4]) - -class MissingArgument(Exception): - def __init__(self, func_name, pos, arg_name, doc): - self.msg = "Missing {rank} argument '{arg}'" \ - " in scheduling function call to '{func}'." \ - .format(rank = _ordinal(pos), arg = arg_name, func = func_name) - self.doc = doc if doc else "This function is not documented" - - def __str__(self): - return self.msg + "\n" + self.doc - -class DuplicateArgument(Exception): - def __init__(self, func_name, pos, arg_name, doc): - self.msg = "Duplicate argument '{arg}'; already given as {rank}" \ - " argument in call to '{func}'." \ - .format(arg = arg_name, rank = _ordinal(pos), func = func_name) - self.doc = doc if doc else "This function is not documented" - - def __str__(self): - return self.msg + "\n" + self.doc - -class SpuriousArgument(Exception): - def __init__(self, func_name, nargs, ngiven, doc): - self.msg = "Too many arguments; '{func}' takes {nargs} regular" \ - " arguments and does not take variadic arguments;" \ - " {ngiven} arguments given." \ - .format(func = func_name, nargs = nargs, ngiven = ngiven) - self.doc = doc if doc else "This function is not documented" - - def __str__(self): - return self.msg + "\n" + self.doc - -class SpuriousKeywordArgument(Exception): - def __init__(self, func_name, doc): - self.msg = "Function '{func}' does not take keyword arguments." \ - .format(func = func_name) - self.doc = doc if doc else "This function is not documented" - - def __str__(self): - return self.msg + "\n" + self.doc - diff --git a/noodles/__init__.py b/noodles/__init__.py index 1de1a3e..adefc45 100644 --- a/noodles/__init__.py +++ b/noodles/__init__.py @@ -1,17 +1,18 @@ from noodles.interface import ( delay, gather, lift, schedule, schedule_hint, unwrap, - has_scheduled_methods, update_hints, unpack) -from .eval_data import Lambda + has_scheduled_methods, update_hints, unpack, quote, unquote, + find_first, gather_dict, result, gather_all, maybe) +from noodles.workflow import (get_workflow) from .run.runners import run_parallel_with_display as run_logging from .run.runners import (run_single, run_parallel) from .run.process import run_process from .run.scheduler import Scheduler from .storable import Storable -__version__ = "0.2.0" +__version__ = "0.2.3" __all__ = ['schedule', 'schedule_hint', 'run_single', 'run_process', - 'run_logging', 'run_parallel', 'run_hybrid', 'unwrap', - 'Scheduler', 'Storable', 'has_scheduled_methods', 'Lambda', - 'gather', 'lift', 'unpack', 'delay', 'update_hints'] - + 'run_logging', 'run_parallel', 'unwrap', 'get_workflow', + 'Scheduler', 'Storable', 'has_scheduled_methods', + 'gather', 'gather_all', 'gather_dict', 'lift', 'unpack', 'maybe', + 'delay', 'update_hints', 'quote', 'unquote', 'find_first', 'result'] diff --git a/noodles/config.py b/noodles/config.py index f961071..db7a14c 100644 --- a/noodles/config.py +++ b/noodles/config.py @@ -1,17 +1,14 @@ +import configparser config = { # call_by_value: keep this True unless you know what you're doing. # The functioning of the DelayedObject and Storable classes depend # on deep-copying arguments to scheduled functions. 'call_by_value': True, +} - # use_prov: compute a MD5 hash of job descriptions and keep a database - # with the results. This is made optional, since it creates a computational - # overhead. - 'use_prov': True, - # prov_serialiser: use this serialiser to compute the MD5 hash that - # generates the key used to identify jobs and workflows in the provenance - # database. Only has an effect if use_prov is True. - 'prov_serialiser': 'noodles.serial.base' -} +def read_config(filename: str): + config = configparser.ConfigParser() + config.read(filename) + return config diff --git a/noodles/delay.py b/noodles/delay.py deleted file mode 100644 index 307be7e..0000000 --- a/noodles/delay.py +++ /dev/null @@ -1,23 +0,0 @@ -from copy import deepcopy -from .workflow import get_workflow - - -class DelayedWorkflow(object): - def __init__(self, obj): - self.data = get_workflow(obj) - - def __deepcopy__(self, memo): - # return DelayedWorkflow(self.data) - return DelayedWorkflow(deepcopy(self.data, memo)) - - -def delay(obj): - return DelayedWorkflow(obj) - - -def force(dw): - if isinstance(dw, DelayedWorkflow): - return dw.data - else: - return dw - diff --git a/noodles/display/dumb_term.py b/noodles/display/dumb_term.py index 6d8f100..c82fd40 100644 --- a/noodles/display/dumb_term.py +++ b/noodles/display/dumb_term.py @@ -1,7 +1,25 @@ from .pretty_term import OutStream +from inspect import Parameter import sys +def _format_arg_list(a, v): + if len(a) == 0: + if v: + return "(\u2026)" + else: + return "()" + + s = "({0}{1})" + for i in a[:-1]: + s = s.format(str(i) if i != Parameter.empty else "\u2014", ", {0}{1}") + + if v: + return s.format("\u2026", "") + + return s.format(str(a[-1]) if a[-1] != Parameter.empty else "\u2014", "") + + class DumbDisplay: """Monochrome, dumb term display""" def __init__(self, error_filter=None): @@ -9,34 +27,26 @@ def __init__(self, error_filter=None): self.out = OutStream(sys.stdout) self.errors = [] self.error_filter = error_filter + self.messages = [] - def start(self, key, job, _): - if job.hints and 'display' in job.hints: - self.add_job(key, job.hints) - self.out << job.hints['display'].format( - **job.bound_args.arguments) << "\n" - - def done(self, key, data, msg): - pass + def print_message(self, key, msg): + if key in self.jobs: + print("{1:12} | {2}".format( + key, '['+msg.upper()+']', self.jobs[key]['name']), + file=sys.stderr) - def error(self, key, _, msg): - pass - - def add_job(self, key, hints): - for k in self.jobs: - self.jobs[k]['line'] += 1 - self.jobs[key] = {'line': 1} - self.jobs[key].update(hints) + def add_job(self, key, name): + self.jobs[key] = {'name': name} def error_handler(self, job, xcptn): self.errors.append((job, xcptn)) def report(self): if len(self.errors) == 0: - self.out << "+---(success)\n" + self.out << "[success]\n" else: - self.out << "+---(ERROR!)\n\n" + self.out << "[ERROR!]\n\n" for job, e in self.errors: msg = 'ERROR ' @@ -56,9 +66,21 @@ def report(self): print(e) def __call__(self, key, status, data, err): - # self.q = q - # for status, key, data in q.source(): - getattr(self, status)(key, data, err) + if hasattr(data, 'hints'): + job = data + if job.hints and 'display' in job.hints: + msg = job.hints['display'].format(**job.bound_args.arguments) + else: + msg = "{0} {1}".format( + job.foo.__name__, + _format_arg_list(job.bound_args.args, None)) + + self.add_job(key, msg) + + if hasattr(self, status): + getattr(self, status)(key, data, err) + else: + self.print_message(key, status) def __enter__(self): return self diff --git a/noodles/display/pretty_term.py b/noodles/display/pretty_term.py index 6a8ce32..2b7076d 100644 --- a/noodles/display/pretty_term.py +++ b/noodles/display/pretty_term.py @@ -1,7 +1,7 @@ term_codes = { - 'fg': (38, lambda r, g, b: ';2;{0};{1};{2}'.format(r, g, b), 'm'), - 'bg': (48, lambda r, g, b: ';2;{0};{1};{2}'.format(r, g, b), 'm'), + 'fg': (38, ';2;{0};{1};{2}'.format, 'm'), + 'bg': (48, ';2;{0};{1};{2}'.format, 'm'), 'bold': (1, 'm'), 'underline': (4, 'm'), 'regular': (23, 'm'), diff --git a/noodles/display/simple_nc.py b/noodles/display/simple_nc.py index 1bde5f5..44f66f1 100644 --- a/noodles/display/simple_nc.py +++ b/noodles/display/simple_nc.py @@ -12,20 +12,45 @@ class Display: 'check' Makes sure that the user gets feedback on the success or failure of the job, meaning a green V or red X will appear behind the .""" - def __init__(self, error_filter=None): + def __init__(self, error_filter=None, title='running jobs'): self.jobs = {} self.out = OutStream(sys.stdout) self.errors = [] self.error_filter = error_filter or (lambda e: None) self.messages = [] + ascii_chars = { + 'check': 'V', + 'fail': 'X', + 'line-hor': '-', + 'line-ver': '|', + 'line-tl': '+', + 'line-bl': '+' + } + + utf8_chars = { + 'check': 'āœ”', + 'fail': 'āœ˜', + 'line-hor': 'ā”€', + 'line-ver': 'ā”‚', + 'line-tl': 'ā•­', + 'line-bl': 'ā•°' + } + + self.chars = utf8_chars \ + if sys.stdout.encoding == 'UTF-8' \ + else ascii_chars + + self.out << self.chars['line-tl'] << self.chars['line-hor'] \ + << '(' << title << ')' << "\n" + def start(self, key, job, _): if job.hints and 'display' in job.hints: if key not in self.jobs: msg = job.hints['display'].format( **job.bound_args.arguments)[:70] self.add_job(key, job, msg) - self.out << msg << "\n" + self.out << self.chars['line-ver'] << " " << msg << "\n" else: self.out << ['save'] << ['up', self.jobs[key]['line']] \ << ['forward', max(50, self.jobs[key]['length'] + 2)] @@ -35,8 +60,8 @@ def done(self, key, data, msg): if key in self.jobs and 'confirm' in self.jobs[key]: self.out << ['save'] << ['up', self.jobs[key]['line']] \ << ['forward', max(50, self.jobs[key]['length'] + 2)] - self.out << "(" << ['fg', 60, 180, 100] << "āœ”" << ['reset'] \ - << ")" << ['restore'] + self.out << "(" << ['fg', 60, 180, 100] << self.chars['check'] \ + << ['reset'] << ")" << ['restore'] if key in self.jobs and msg: self.message_handler(self.jobs[key], msg) @@ -45,7 +70,7 @@ def schedule(self, key, job, _): if job.hints and 'display' in job.hints: msg = job.hints['display'].format(**job.bound_args.arguments)[:70] self.add_job(key, job, msg) - self.out << "ā”‚ " << msg << "\n" + self.out << self.chars['line-ver'] << " " << msg << "\n" def retrieved(self, key, data, msg): if key in self.jobs and 'confirm' in self.jobs[key]: @@ -61,8 +86,8 @@ def error(self, key, _, data): if key in self.jobs and 'confirm' in self.jobs[key]: self.out << ['save'] << ['up', self.jobs[key]['line']] \ << ['forward', max(50, self.jobs[key]['length'] + 2)] - self.out << "(" << ['fg', 240, 100, 60] << "āœ˜" << ['reset'] \ - << ")" << ['restore'] + self.out << "(" << ['fg', 240, 100, 60] << self.chars['fail'] \ + << ['reset'] << ")" << ['restore'] def add_job(self, key, job, msg): for k in self.jobs: @@ -83,7 +108,8 @@ def message_handler(self, job, warning): def report(self): if len(self.errors) == 0: - self.out << "ā•°ā”€(success)\n" + self.out << self.chars['line-bl'] << self.chars['line-hor'] \ + << "(success)\n" if len(self.messages) != 0: self.out << "There were warnings: \n\n" @@ -101,7 +127,8 @@ def report(self): print(w) else: - self.out << "ā•°ā”€(" << ['fg', 240, 100, 60] << "ERROR!" \ + self.out << self.chars['line-bl'] << self.chars['line-hor'] \ + << "(" << ['fg', 240, 100, 60] << "ERROR!" \ << ['reset'] << ")\n\n" for job, e in self.errors: diff --git a/noodles/draw_workflow/__init__.py b/noodles/draw_workflow/__init__.py new file mode 100644 index 0000000..513b847 --- /dev/null +++ b/noodles/draw_workflow/__init__.py @@ -0,0 +1,4 @@ +from .draw_workflow import draw_workflow + +__all__ = ['draw_workflow'] + diff --git a/noodles/draw_workflow/draw_workflow.py b/noodles/draw_workflow/draw_workflow.py new file mode 100644 index 0000000..e68c1c5 --- /dev/null +++ b/noodles/draw_workflow/draw_workflow.py @@ -0,0 +1,33 @@ +from pygraphviz import AGraph +from inspect import Parameter + + +def _format_arg_list(a, v): + if len(a) == 0: + if v: + return "(\u2026)" + else: + return "()" + + s = "({0}{1})" + for i in a[:-1]: + s = s.format(str(i) if i != Parameter.empty else "\u2014", ", {0}{1}") + + if v: + return s.format("\u2026", "") + + return s.format(str(a[-1]) if a[-1] != Parameter.empty else "\u2014", "") + + +def draw_workflow(filename, workflow): + dot = AGraph(directed=True) # (comment="Computing scheme") + for i, n in workflow.nodes.items(): + dot.add_node(i, label="{0} \n {1}".format( + n.foo.__name__, _format_arg_list(n.bound_args.args, None))) + + for i in workflow.links: + for j in workflow.links[i]: + dot.add_edge(i, j[0]) + dot.layout(prog='dot') + + dot.draw(filename) diff --git a/noodles/eval_data.py b/noodles/eval_data.py deleted file mode 100644 index dc4dacc..0000000 --- a/noodles/eval_data.py +++ /dev/null @@ -1,37 +0,0 @@ -from .storable import Storable - - -class Lambda(Storable): - """Serializable storage for a lambda-expression. - - The user may want to sprinkle her/his code with little lambda-functions. - Python lambda functions are not easily storable. One may be tempted to - use pickle to store the function object, but pickle doesn't load the - namespace needed to run the function. For instance if the lambda-function - contains a reference to `math.sin`, `scipy.special.erf` or - what-have-you-not, pickling will fail to reproduce a working function - object. - - This class takes a Python expression as a string and uses `eval()` - to create a working function object. Dependencies can be given in the - `globals` dict. Those will be saved and imported back when the object is - loaded.""" - def __init__(self, expression, globals=None, locals=None): - super(Lambda, self).__init__() - - self.expression = expression - self.globals = globals - self.locals = locals - self.obj = eval(expression, globals, locals) - - def as_dict(self): - return {'expression': self.expression, - 'globals': self.globals, - 'locals': self.locals} - - @classmethod - def from_dict(cls, **kwargs): - return Lambda(**kwargs) - - def __call__(self, *args, **kwargs): - return self.obj(*args, **kwargs) diff --git a/noodles/interface/__init__.py b/noodles/interface/__init__.py index 6712d3d..4120578 100644 --- a/noodles/interface/__init__.py +++ b/noodles/interface/__init__.py @@ -1,9 +1,15 @@ -from .decorator import (PromisedObject, schedule, schedule_hint, - has_scheduled_methods, unwrap, update_hints) -from .functions import (delay, gather, lift, unpack) +from .decorator import ( + PromisedObject, schedule, schedule_hint, has_scheduled_methods, + unwrap, update_hints, result) +from .functions import ( + delay, gather, gather_all, gather_dict, lift, unpack, quote, + unquote, find_first) from .annotated_value import (AnnotatedValue) from .exceptions import (JobException) +from .maybe import (maybe) -__all__ = ['delay', 'gather', 'schedule_hint', 'schedule', 'unpack', - 'has_scheduled_methods', 'unwrap', 'update_hints', 'AnnotatedValue', - 'JobException', 'lift', 'PromisedObject'] +__all__ = ['delay', 'gather', 'gather_all', 'gather_dict', 'schedule_hint', + 'schedule', 'unpack', 'has_scheduled_methods', 'unwrap', + 'update_hints', 'AnnotatedValue', 'JobException', 'lift', + 'PromisedObject', 'quote', 'unquote', 'find_first', 'result', + 'maybe'] diff --git a/noodles/interface/decorator.py b/noodles/interface/decorator.py index 36c4143..175e3b3 100644 --- a/noodles/interface/decorator.py +++ b/noodles/interface/decorator.py @@ -1,3 +1,7 @@ +""" +.. default-domain:: py +""" + from functools import wraps from copy import deepcopy import hashlib @@ -11,14 +15,12 @@ def scheduled_function(f, hints=None): - """ - The Noodles schedule function decorator. + """The Noodles schedule function decorator. The decorated function will return a workflow in stead of being applied immediately. This workflow can then be passed to a job scheduler in order to be run on any architecture supporting the current - python environment. - """ + python environment.""" if hints is None: hints = {} @@ -38,15 +40,27 @@ def wrapped(*args, **kwargs): f, args, kwargs, deepcopy(hints), call_by_value=config['call_by_value'])) + # add *(scheduled)* to the beginning of the docstring. + if hasattr(wrapped, '__doc__') and wrapped.__doc__ is not None: + wrapped.__doc__ = "*(scheduled)* " + wrapped.__doc__ + return wrapped def schedule(f): + """Decorator; schedule calls to function `f` into a workflow, in stead of + running them at once. The decorated function returns a + :class:`PromisedObject`.""" return scheduled_function(f) def has_scheduled_methods(cls): - for name, member in cls.__dict__.items(): + """Decorator; use this on a class for which some methods have been + decorated with :func:`schedule` or :func:`schedule_hint`. Those methods + are then tagged with the attribute `__member_of__`, so that we may + serialise and retrieve the correct method. This should be considered + a patch to a flaw in the Python object model.""" + for member in cls.__dict__.values(): if hasattr(member, '__wrapped__'): member.__wrapped__.__member_of__ = cls @@ -54,10 +68,17 @@ def has_scheduled_methods(cls): def schedule_hint(**hints): + """Decorator; same as :func:`schedule`, with added hints. These + hints can be anything.""" return lambda f: scheduled_function(f, hints) def unwrap(f): + """Unwrap a wrapped function; the function needs to have been wrapped + using :func:`functools.wraps`, as is done in :func:`schedule`. + + If function `f` doesn't have the `__wrapped` attribute, the same + function `f` is returned.""" try: return f.__wrapped__ except AttributeError: @@ -102,13 +123,37 @@ def _do_call(obj, *args, **kwargs): def update_hints(obj, data): + """Update the hints on the root-node of a workflow. Usually, schedule + hints are fixed per function. Sometimes a user may want to set hints + manually on a specific promised object. :func:`update_hints` uses the + `update` method on the hints dictionary with `data` as its argument. + + :param obj: a :class:`PromisedObject`. + :param data: a :class:`dict` containing additional hints. + + The hints are modified, in place, on the node. All workflows that contain + the node are affected.""" root = obj._workflow.root obj._workflow.nodes[root].hints.update(data) -def get_result(obj): - root = obj._workflow.root - return obj._workflow.nodes[root].result +def result(obj): + """Results are stored on the nodes in the workflow at run time. This + function can be used to get at a result of a node in a workflow after + run time. This is not a recommended way of getting at results, but can + help with debugging.""" + return obj.__result__() + + +class ConstRef: + def __init__(self, this): + self.this = this + + def __getattr__(self, attr): + return getattr(self.this, attr) + + def __deepcopy__(self, _): + return self class PromisedObject: @@ -135,6 +180,9 @@ def __setattr__(self, attr, value): self._workflow = get_workflow( _setattr(self, attr, value)) + def __result__(self): + return self._workflow.root_node.result + # predicates # def __lt__(self, other): # return schedule(operator.lt)(self, other) diff --git a/noodles/interface/exceptions.py b/noodles/interface/exceptions.py index fcd1fa0..eebef7a 100644 --- a/noodles/interface/exceptions.py +++ b/noodles/interface/exceptions.py @@ -1,12 +1,18 @@ -class JobException(Exception): +class JobException: def __init__(self, exc_type, exc_value, exc_tb): self.exc_type = exc_type self.exc_value = exc_value - self.exc_tb = exc_tb + # self.exc_tb = exc_tb def reraise(self): - raise self.exc_value.with_traceback(self.exc_tb) + raise self.exc_value def __iter__(self): - return iter((self.exc_type, self.exc_value, self.exc_tb)) + return iter((self.exc_type, self.exc_value, None)) + + # def __getstate__(self): + # return self.__dict__ + # + # def __setstate__(self, state): + # self.__dict__.update(state) diff --git a/noodles/interface/functions.py b/noodles/interface/functions.py index 11bbf60..affb139 100644 --- a/noodles/interface/functions.py +++ b/noodles/interface/functions.py @@ -1,41 +1,63 @@ +""" +.. default-domain:: py +""" + from .decorator import (schedule, PromisedObject) # from copy import deepcopy +from ..serial.reasonable import Reasonable @schedule def delay(value): + """Creates a promise of a given value. TODO: this function should have a + different name.""" return value @schedule def gather(*a): - """ - Converts a list of workflows (i.e. :py:class:`PromisedObject`) to - a workflow representing the promised list of values. - - Currently the :py:func:`from_call` function detects workflows - only by their top type (using `isinstance`). If we have some - deeper structure containing :py:class:`PromisedObject`, these are not - recognised as workflow input and taken as literal values. - - This behaviour may change in the future, making this function - `gather` obsolete. - """ + """Converts a list of promises (i.e. :class:`PromisedObject`) to a promised + list of values.""" return list(a) +def gather_all(a): + """Converts an iterator of promises into a promise of a list.""" + return gather(*a) + + +@schedule +def gather_dict(**kwargs): + """Creates a promise of a dictionary.""" + return dict(**kwargs) + + def unpack(t, n): + """Iterates over a promised sequence, the sequence should support random + access by :meth:`object.__getitem__`. Also the length of the sequence + should be known beforehand. + + :param t: a sequence. + :param n: the length of the sequence. + :return: an unpackable generator for the elements in the sequence.""" return (t[i] for i in range(n)) @schedule def set_dict(obj, d): - obj.__dict__ = d + """Set the :attr:`object.__dict__` of `obj`. + + :param obj: input object. + :param d: dictionary. + :return: the modified object.""" + if d: + obj.__dict__ = d return obj @schedule def create_object(cls, members): + """Promise an object of class `cls` with content `members`.""" obj = cls.__new__(cls) obj.__dict__ = members return obj @@ -43,25 +65,135 @@ def create_object(cls, members): @schedule def make_tuple(*args): + """Promise a tuple.""" return args @schedule -def make_dict(*args): - return dict(args) +def make_dict(*kwargs): + """Promise a dictionary, from a list of 2-tuples.""" + return dict(kwargs) @schedule def make_list(*args): + """Promise a list explicitely; function is identical to :func:`gather`.""" return list(args) @schedule def make_set(*args): + """Promise a set.""" return set(args) +class Quote(Reasonable): + """Quote objects store the contents of a workflow, allowing the workflow to + be passed as an argument to a higher order function without its contents + being evaluated. Don't use this object, rather use the functions + :func:`quote` and :func:`unquote`.""" + def __init__(self, promise): + self.workflow = promise._workflow + + @property + def promise(self): + return PromisedObject(self.workflow) + + +def quote(promise): + """Quote a promise.""" + if isinstance(promise, PromisedObject): + return Quote(promise) + return promise + + +def unquote(quoted): + """Unquote a quoted promise.""" + if isinstance(quoted, Quote): + return quoted.promise + return quoted + + +def find_first(pred, lst): + """Find the first result of a list of promises `lst` that satisfies a + predicate `pred`. + + :param pred: a function of one argument returning `True` or `False`. + :param lst: a list of promises or values. + :return: a promise of a value or `None`. + + This is a wrapper around :func:`s_find_first`. The first item on the list + is passed *as is*, forcing evalutation. The tail of the list is quoted, and + only unquoted if the predicate fails on the result of the first promise. + + If the input list is empty, `None` is returned.""" + if lst: + return s_find_first(pred, lst[0], [quote(l) for l in lst[1:]]) + else: + return None + + +@schedule +def s_find_first(pred, first, lst): + """Evaluate `first`; if predicate `pred` succeeds on the result of `first`, + return the result; otherwise recur on the first element of `lst`. + + :param pred: a predicate. + :param first: a promise. + :param lst: a list of quoted promises. + :return: the first element for which predicate is true.""" + if pred(first): + return first + elif lst: + return s_find_first(pred, unquote(lst[0]), lst[1:]) + else: + return None + + +@schedule +def construct_object(cls, args): + return cls(args) + + def lift(obj, memo=None): + """Make a promise out of object `obj`, where `obj` may contain promises + internally. + + :param obj: Any object. + :param memo: used for internal caching (similar to :func:`deepcopy`). + + If the object is a :class:`PromisedObject`, or *pass-by-value* + (:class:`str`, :class:`int`, :class:`float`, :class:`complex`) it is + returned as is. + + If the object's `id` has an entry in `memo`, the value from `memo` is + returned. + + If the object has a method `__lift__`, it is used to get the promise. + `__lift__` should take one additional argument for the `memo` dictionary, + entirely analogous to :func:`deepcopy`. + + If the object is an instance of one of the basic container types (list, + dictionary, tuple and set), we use the analogous function + (:func:`make_list`, :func:`make_dict`, :func:`make_tuple`, and + :func:`make_set`) to promise their counterparts should these objects + contain any promises. First, we map all items in the container through + :func:`lift`, then check the result for any promises. Note that in the + case of dictionaries, we lift all the items (i.e. the list of key/value + tuples) and then construct a new dictionary. + + If the object is an instance of a subclass of any of the basic container + types, the `__dict__` of the object is lifted as well as the object cast + to its base type. We then use :func:`set_dict` to set the `__dict__` of + the new promise. Again, if the object did not contain any promises, + we return it without change. + + Otherwise, we lift the `__dict__` and create a promise of a new object of + the same class as the input, using :func:`create_object`. This works fine + for what we call *reasonable* objects. Since calling :func:`lift` is an + explicit action, we do not require reasonable objects to be derived from + :class:`Reasonable` as we do with serialisation, where such a default + behaviour could lead to unexplicable bugs.""" if memo is None: memo = {} @@ -103,7 +235,7 @@ def lift(obj, memo=None): internal = lift(subclass(obj), memo) if isinstance(internal, PromisedObject): - internal = schedule(obj.__class__)(internal) + internal = construct_object(obj.__class__, internal) rv = set_dict(internal, members) elif isinstance(members, PromisedObject): rv = set_dict(obj.__class__(internal), members) diff --git a/noodles/interface/maybe.py b/noodles/interface/maybe.py new file mode 100644 index 0000000..8fba49e --- /dev/null +++ b/noodles/interface/maybe.py @@ -0,0 +1,74 @@ +from functools import (wraps) +from itertools import (chain) +import inspect +from ..utility import (object_name) + + +class Fail: + def __init__(self, func, fails=None, exception=None): + self.name = "{} ({}:{})".format( + object_name(func), + inspect.getsourcefile(func), + inspect.getsourcelines(func)[1]) + self.fails = fails or [] + self.trace = [] + self.exception = exception + + def add_call(self, func): + self.trace.append("{} ({}:{})".format( + object_name(func), + inspect.getsourcefile(func), + inspect.getsourcelines(func)[1])) + + return self + + @property + def is_root_cause(self): + return self.exception is not None + + def __bool__(self): + return False + + def __str__(self): + msg = "Fail: " + " -> ".join(self.trace + [self.name]) + if self.exception is not None: + msg += "\n* exception: " + msg += "\n ".join(l for l in str(self.exception).split('\n')) + elif self.fails: + msg += "\n* failed arguments:\n " + msg += "\n ".join( + "{} `{}` ".format(foo, bar) + "\n ".join( + l for l in str(fail).split('\n')) + for foo, bar, fail in self.fails) + return msg + + +def maybe(f): + """Calls `f` in a try/except block, returning a `Fail` object if + the call fails in any way. If any of the arguments to the call are Fail + objects, the call is not attempted.""" + + name = object_name(f) + + @wraps(f) + def maybe_wrapped(*args, **kwargs): + fails = [(name, k, v) + for k, v in chain(enumerate(args), kwargs.items()) + if isinstance(v, Fail)] + + if fails: + return Fail(f, fails=fails) + + try: + result = f(*args, **kwargs) + + except Exception as e: + return Fail(f, exception=e) + + else: + if isinstance(result, Fail): + result.add_call(f) + + return result + + return maybe_wrapped diff --git a/noodles/logger.py b/noodles/logger.py deleted file mode 100644 index b838fea..0000000 --- a/noodles/logger.py +++ /dev/null @@ -1,11 +0,0 @@ -import sys - - -class Log: - def worker_stderr(self, wid, msg): - print( - "worker {0}: {1}".format(wid, msg.strip()), - file=sys.stderr, flush=True) - - -log = Log() diff --git a/noodles/prov/prov.py b/noodles/prov/prov.py index 4fdf101..4c392cf 100644 --- a/noodles/prov/prov.py +++ b/noodles/prov/prov.py @@ -1,12 +1,15 @@ from tinydb import (TinyDB, Query) import hashlib -import json from threading import Lock # from ..utility import on import time -import uuid import sys +try: + import ujson as json +except ImportError: + import json + def update_object_hash(m, obj): r = json.dumps(obj, sort_keys=True) @@ -14,7 +17,7 @@ def update_object_hash(m, obj): return m -def prov_key(job_msg): +def prov_key(job_msg, extra=None): """Retrieves a MD5 sum from a function call. This takes into account the name of the function, the arguments and possibly a version number of the function, if that is given in the hints. @@ -29,6 +32,9 @@ def prov_key(job_msg): if 'version' in job_msg['data']['hints']: update_object_hash(m, job_msg['data']['hints']['version']) + if extra is not None: + update_object_hash(m, extra) + return m.hexdigest() @@ -64,14 +70,14 @@ def get_result_or_attach(self, key, prov, running): rec = self.db.get(job.prov == prov) if 'result' in rec: - return 'retrieved', uuid.UUID(rec['key']), rec['result'] + return 'retrieved', rec['key'], rec['result'] - job_running = uuid.UUID(rec['key']) in running + job_running = rec['key'] in running wf_running = rec['link'] in running.workflows if job_running or wf_running: - self.db.update(attach_job(key.hex), job.prov == prov) - return 'attached', uuid.UUID(rec['key']), None + self.db.update(attach_job(key), job.prov == prov) + return 'attached', rec['key'], None print("WARNING: unfinished job in database. Removing it and " " rerunning.", file=sys.stderr) @@ -86,21 +92,21 @@ def job_exists(self, prov): def store_result(self, key, result): job = Query() with self.lock: - if not self.db.contains(job.key == key.hex): + if not self.db.contains(job.key == key): return self.add_time_stamp(key, 'done') with self.lock: self.db.update( {'result': result, 'link': None}, - job.key == key.hex) - rec = self.db.get(job.key == key.hex) - return map(uuid.UUID, rec['attached']) + job.key == key) + rec = self.db.get(job.key == key) + return rec['attached'] def new_job(self, key, prov, job_msg): with self.lock: self.db.insert({ - 'key': key.hex, + 'key': key, 'attached': [], 'prov': prov, 'link': None, @@ -115,13 +121,13 @@ def new_job(self, key, prov, job_msg): def add_link(self, key, ppn): job = Query() with self.lock: - self.db.update({'link': ppn}, job.key == key.hex) + self.db.update({'link': ppn}, job.key == key) def get_linked_jobs(self, ppn): job = Query() with self.lock: rec = self.db.search(job.link == ppn) - return [uuid.UUID(r['key']) for r in rec] + return [r['key'] for r in rec] def add_time_stamp(self, key, name): def update(r): @@ -131,4 +137,4 @@ def update(r): with self.lock: self.db.update( update, - job.key == key.hex) + job.key == key) diff --git a/noodles/prov/workflow.py b/noodles/prov/workflow.py new file mode 100644 index 0000000..1c6eabf --- /dev/null +++ b/noodles/prov/workflow.py @@ -0,0 +1,68 @@ +from ..workflow import (Workflow, is_node_ready, Empty) +from ..workflow.arguments import (serialize_arguments, ref_argument) +from ..serial import (Registry) +from .prov import (prov_key) + + +def links(wf, i, deps): + for d in deps: + for l in wf.links[d]: + if l[0] == i: + yield (l[1], wf.nodes[d].prov) + + +def empty_args(n): + for arg in serialize_arguments(n.bound_args): + if ref_argument(n.bound_args, arg) == Empty: + yield arg + + +def set_global_provenance(wf: Workflow, registry: Registry): + """Compute a global provenance key for the entire workflow + before evaluation. This key can be used to store and retrieve + results in a database. The key computed in this stage is different + from the (local) provenance key that can be computed for a node + if all its arguments are known. + + In cases where a result derives from other results that were + computed in child workflows, we can prevent the workflow system + from reevaluating the results at each step to find that we already + had the end-result somewhere. This is where the global prov-key + comes in. + + Each node is assigned a `prov` attribute. If all arguments for this + node are known, this key will be the same as the local prov-key. + If some of the arguments are still empty, we add the global prov-keys + of the dependent nodes to the hash. + + In this algorithm we traverse from the bottom of the DAG to the top + and back using a stack. This allows us to compute the keys for each + node without modifying the node other than setting the `prov` attribute + with the resulting key.""" + stack = [wf.root] + + while stack: + i = stack.pop() + n = wf.nodes[i] + + if n.prov: + continue + + if is_node_ready(n): + job_msg = registry.deep_encode(n) + n.prov = prov_key(job_msg) + continue + + deps = wf.inverse_links[i] + todo = [j for j in deps if not wf.nodes[j].prov] + + if not todo: + link_dict = dict(links(wf, i, deps)) + link_prov = registry.deep_encode( + [link_dict[arg] for arg in empty_args(n)]) + job_msg = registry.deep_encode(n) + n.prov = prov_key(job_msg, link_prov) + continue + + stack.append(i) + stack.extend(deps) diff --git a/noodles/run/config.py b/noodles/run/config.py new file mode 100644 index 0000000..f93f3ac --- /dev/null +++ b/noodles/run/config.py @@ -0,0 +1,167 @@ +import configparser +from typing import (List) +from ..utility import (look_up) + + +runners = [ + # ======================================================================= # + # Single threaded runners + # ======================================================================= # + { + 'name': 'single', + 'features': [], + 'description': """ + Run a workflow in a single thread. This is the absolute minimal + runner, consisting of a single queue for jobs and a worker running + jobs every time a result is pulled.""", + 'command': 'noodles.run.single', + 'arguments': {} + }, + + { + 'name': 'single', + 'features': ['display'], + 'description': """ + Adds a display to the single runner. Everything still runs in a + single thread. Every time a job is pulled by the worker, a message + goes to the display routine; when the job is finished the result is + sent to the display routine.""", + 'command': 'noodles.run.single_with_display', + 'arguments': { + 'display': { + 'default': 'noodles.display.NCDisplay', + 'reader': 'look-up', + 'help': 'the display routine' + } + } + }, + + # ======================================================================= # + # Multi-threaded runners + # ======================================================================= # + { + 'name': 'parallel', + 'features': [], + 'command': 'noodles.run.parallel', + 'arguments': { + 'n_threads': { + 'default': '1', + 'reader': 'integer' + } + } + }, + + { + 'name': 'parallel', + 'features': ['display'], + 'command': 'noodles.run.parallel_with_display', + 'arguments': { + 'n_threads': { + 'default': '1', + 'reader': 'integer' + }, + 'display': { + 'default': 'noodles.display.NCDisplay', + 'reader': 'look-up' + } + } + }, + + { + 'name': 'parallel', + 'features': ['prov', 'display'], + 'description': """ + Run a workflow in `n_threads` parallel threads. Now we replaced the + single worker with a thread-pool of workers. + + This version works with the JobDB to cache results; however we only + store the jobs that are hinted with the 'store' keyword, unless + `cache_all` is set to `True`.""", + 'command': 'noodles.run.run_with_prov.run_parallel_opt', + 'arguments': { + 'n_threads': { + 'default': '1', + 'reader': 'integer', + 'help': 'the number of threads to run' + }, + 'registry': { + 'default': 'noodles.serial.base', + 'reader': 'look-up', + 'help': 'the serialisation registry to use' + }, + 'display': { + 'default': 'noodles.display.NCDisplay', + 'reader': 'look-up', + 'help': 'the display to use' + }, + 'database': { + 'default': 'TinyDB', + 'help': 'the database backend for the job cache' + }, + 'cache_file': { + 'default': 'cache.json', + 'help': 'the file used to store the job cache' + }, + 'cache_all': { + 'default': 'False', + 'reader': 'boolean', + 'help': 'set this if you want to store all jobs in cache' + } + } + }, + + { + 'name': 'xenon', + 'features': ['prov', 'display'], + 'command': 'noodles.run.xenon.run_xenon_prov', + 'arguments': { + } + }, + + { + 'name': 'process', + 'features': ['msgpack'], + 'command': 'noodles.run.process.run_process', + 'arguments': { + } + } +] + + +def find_runner(name: str, features: List[str]) -> str: + name_candidates = filter( + lambda r: r['name'] == name, + runners) + feature_candidates = filter( + lambda r: all(f in r['features'] for f in features), + name_candidates) + return min(feature_candidates, lambda r: len(r['features'])) + + +def run_with_config(config_file, workflow, machine=None): + config = configparser.ConfigParser( + interpolation=configparser.ExtendedInterpolation()) + config.read(config_file) + + machine = config.get('default', 'machine', fallback=machine) + if machine is None: + print("No machine given, running local in single thread.") + runner = find_runner(name='single', features=[]) + settings = {} + + else: + M = config['Machines'] + runner_name = M.get(machine, 'runner') + features = map(str.strip, M.get(machine, 'features').split(',')) + runner = find_runner(name=runner_name, features=features) + settings = dict(M[machine]) + + del settings['runner'] + del settings['features'] + + if 'user' in settings: + settings['user'] = dict(config['Users'][settings['user']]) + + run = look_up(runner['command']) + + return run(workflow, **settings) diff --git a/noodles/run/hybrid.py b/noodles/run/hybrid.py index a19577b..de56e27 100644 --- a/noodles/run/hybrid.py +++ b/noodles/run/hybrid.py @@ -30,13 +30,14 @@ def hybrid_coroutine_worker(selector, workers): def get_result(): source = jobs.source() - for key, job in source: + for msg in source: + key, job = msg worker = selector(job) if worker is None: yield run_job(key, job) else: # send the worker a job and wait for it to return - worker_sink[worker].send((key, job)) + worker_sink[worker].send(msg) result = next(worker_source[worker]) yield result @@ -89,12 +90,12 @@ def dispatch_job(): default_sink = results.sink() while True: - key, job = yield - worker = selector(job) + msg = yield + worker = selector(msg.node) if worker: - job_sink[worker].send((key, job)) + job_sink[worker].send(msg) else: - default_sink.send(run_job(key, job)) + default_sink.send(run_job(*msg)) for key, worker in workers.items(): t = threading.Thread( diff --git a/noodles/run/job_keeper.py b/noodles/run/job_keeper.py index ce61f89..fc82059 100644 --- a/noodles/run/job_keeper.py +++ b/noodles/run/job_keeper.py @@ -5,6 +5,7 @@ from threading import Lock from .haploid import (coroutine) +from .messages import (JobMessage) class JobKeeper(dict): @@ -16,12 +17,12 @@ def __init__(self, keep=False): def register(self, job): with self.lock: - key = uuid.uuid1() + key = str(uuid.uuid4()) job.log = [] job.log.append((time.time(), 'register', None, None)) self[key] = job - return key, job.node + return JobMessage(key, job.node) def __delitem__(self, key): if not self.keep: @@ -66,10 +67,10 @@ def __init__(self, timing_file, registry=None): self.owner = False def register(self, job): - key = uuid.uuid1() + key = str(uuid.uuid4()) job.sched_time = time.time() self[key] = job - return key, job.node + return JobMessage(key, job.node) def __delitem__(self, key): pass diff --git a/noodles/run/messages.py b/noodles/run/messages.py new file mode 100644 index 0000000..84df25e --- /dev/null +++ b/noodles/run/messages.py @@ -0,0 +1,38 @@ +""" +Messages to facilitate communication between scheduler and remote workers. +There are currently three types of messages: + + * ``JobMessage``, sending a job to a worker. + * ``ResultMessage``, a worker returning a result. + * ``PilotMessage``, extra communication back and forth, status updates, + performance information, but also stopping a worker in a nice way. +""" + +from ..serial import Reasonable + + +class JobMessage(Reasonable): + def __init__(self, key, node): + self.key = key + self.node = node + + def __iter__(self): + return iter((self.key, self.node)) + + +class ResultMessage(Reasonable): + def __init__(self, key, status, value, msg): + self.key = key + self.status = status + self.value = value + self.msg = msg + + def __iter__(self): + return iter((self.key, self.status, self.value, self.msg)) + + +class PilotMessage(Reasonable): + def __init__(self, msg, **kwargs): + self.msg = msg + self.__dict__.update(kwargs) + diff --git a/noodles/run/process.py b/noodles/run/process.py index 647bd24..58fb15f 100644 --- a/noodles/run/process.py +++ b/noodles/run/process.py @@ -1,11 +1,12 @@ import sys import uuid from subprocess import Popen, PIPE +import threading import os import random from ..workflow import get_workflow -from ..logger import log +# from ..logger import log from .connection import Connection from ..utility import object_name from .scheduler import Scheduler @@ -13,58 +14,25 @@ from .hybrid import hybrid_threaded_worker from .haploid import (pull, push) +from .remote.io import ( + MsgPackObjectReader, MsgPackObjectWriter, + JSONObjectReader, JSONObjectWriter) + try: - import msgpack + import msgpack # noqa has_msgpack = True except ImportError: has_msgpack = False -def get_result_tuple(msg): - key = msg['key'] - status = msg['status'] - - try: - key = uuid.UUID(key) - except ValueError: - pass - - return key, status, msg['result'], msg['err_msg'] - - -def read_result_json(registry, s): - obj = registry.from_json(s) - key = obj['key'] - status = obj['status'] - - try: - key = uuid.UUID(key) - except ValueError: - pass - - return key, status, obj['result'], obj['err_msg'] - - -def job_msg(registry, host, key, job): - obj = {'key': key if isinstance(key, str) else key.hex, - 'node': job} - return registry.to_msgpack(obj, host=host) - - -def put_job(registry, host, key, job): - obj = {'key': key if isinstance(key, str) else key.hex, - 'node': job} - return registry.to_json(obj, host=host) - - -def process_worker(registry, - verbose=False, jobdirs=False, +def process_worker(registry, verbose=False, jobdirs=False, init=None, finish=None, status=True, use_msgpack=False): name = "process-" + str(uuid.uuid4()) cmd = ["/bin/bash", os.getcwd() + "/worker.sh", sys.prefix, "online", "-name", name, "-registry", object_name(registry)] if use_msgpack: + assert has_msgpack cmd.append("-msgpack") if verbose: cmd.append("-verbose") @@ -73,9 +41,9 @@ def process_worker(registry, if not status: cmd.append("-nostatus") if init: - cmd.append("-init") + cmd.extend(["-init", object_name(init)]) if finish: - cmd.append("-finish") + cmd.extend(["-finish", object_name(finish)]) p = Popen( cmd, @@ -83,52 +51,35 @@ def process_worker(registry, def read_stderr(): for line in p.stderr: - log.worker_stderr(name, line) + print(name + ": " + line.strip(), file=sys.stderr, flush=True) - # t = threading.Thread(target=read_stderr) - # t.daemon = True - # t.start() + t = threading.Thread(target=read_stderr) + t.daemon = True + t.start() @push def send_job(): reg = registry() - while True: - key, job = yield - if use_msgpack: - p.stdin.buffer.write(job_msg(reg, name, key, job)) - p.stdin.flush() - else: - print(put_job(reg, name, key, job), file=p.stdin, flush=True) + if use_msgpack: + yield from MsgPackObjectWriter(reg, p.stdin.buffer) + else: + yield from JSONObjectWriter(reg, p.stdin) @pull def get_result(): reg = registry() if use_msgpack: - messages = msgpack.Unpacker( - p.stdout.buffer, object_hook=reg.decode) + newin = os.fdopen(p.stdout.fileno(), 'rb', buffering=0) + yield from MsgPackObjectReader(reg, newin) else: - messages = (reg.from_json(line) for line in p.stdout) - - for msg in messages: - result = get_result_tuple(msg) - yield result - - if init is not None: - send_job().send(("init", init()._workflow.root_node)) - key, status, result, err_msg = next(get_result()) - if key != "init" or not result: - raise RuntimeError( - "The initializer function did not succeed on worker.") - - if finish is not None: - send_job().send(("finish", finish()._workflow.root_node)) + yield from JSONObjectReader(reg, p.stdout) return Connection(get_result, send_job, aux=read_stderr) def run_process(wf, n_processes, registry, verbose=False, jobdirs=False, - init=None, finish=None, deref=False): + init=None, finish=None, deref=False, use_msgpack=False): """Run the workflow using a number of new python processes. Use this runner to test the workflow in a situation where data serial is needed. @@ -167,7 +118,8 @@ def run_process(wf, n_processes, registry, """ workers = {} for i in range(n_processes): - new_worker = process_worker(registry, verbose, jobdirs, init, finish) + new_worker = process_worker(registry, verbose, jobdirs, init, finish, + use_msgpack=use_msgpack) workers['worker {0:2}'.format(i)] = new_worker worker_names = list(workers.keys()) diff --git a/noodles/run/protect.py b/noodles/run/protect.py index 27a5fda..987ad8e 100644 --- a/noodles/run/protect.py +++ b/noodles/run/protect.py @@ -1,4 +1,5 @@ from functools import wraps +from .messages import (ResultMessage) from .haploid import (push_map, pull_map) import threading @@ -59,8 +60,8 @@ def protected_fn(*args, **kwargs): except Exception as exc: with self.lock: for key in self.jobs: - sink.send((key, 'aborted', None, exc)) + sink.send(ResultMessage(key, 'aborted', None, exc)) raise exc - return fn + return protected_fn diff --git a/noodles/run/remote/io.py b/noodles/run/remote/io.py new file mode 100644 index 0000000..65568fa --- /dev/null +++ b/noodles/run/remote/io.py @@ -0,0 +1,40 @@ +""" +Manage IO between remote worker/pilot job, and the scheduler. Here there are +two options: use json, or msgpack. +""" + +from ..coroutine import coroutine + +try: + import msgpack +except ImportError: + pass + + +def MsgPackObjectReader(registry, fi, deref=False): + yield from msgpack.Unpacker( + fi, object_hook=lambda o: registry.decode(o, deref), + encoding='utf-8') + + +@coroutine +def MsgPackObjectWriter(registry, fo, host=None): + while True: + obj = yield + fo.write(registry.to_msgpack(obj, host=host)) + fo.flush() + + +def JSONObjectReader(registry, fi, deref=False): + for line in fi: + yield registry.from_json(line, deref=deref) + + +@coroutine +def JSONObjectWriter(registry, fo, host=None): + # import sys + while True: + obj = yield + # obj_msg = registry.to_json(obj, host=host) + # print(obj_msg, file=sys.stderr) + print(registry.to_json(obj, host=host), file=fo, flush=True) diff --git a/noodles/run/run.py b/noodles/run/run.py new file mode 100644 index 0000000..b9437a0 --- /dev/null +++ b/noodles/run/run.py @@ -0,0 +1,83 @@ +""" +Next generation class of Workflow Runners. These are accessed by configuring +Noodles in a `noodles.ini` file. For this reason they have to obey similar +interfaces. +""" + +from .queue import (Queue) +from .scheduler import (Scheduler) +from .haploid import (push_map, sink_map, branch, patch) +from .thread_pool import (thread_pool) +from .worker import (worker) +from ..workflow import (get_workflow) + +from itertools import (repeat) +import threading + + +# =========================================================================== # +def run_single(wf): + """Run a workflow in a single thread. This is the absolute minimal + runner, consisting of a single queue for jobs and a worker running + jobs every time a result is pulled.""" + S = Scheduler() + W = Queue() >> worker + + return S.run(W, get_workflow(wf)) + + +def run_single_with_display(wf, display): + """Adds a display to the single runner. Everything still runs in a single + thread. Every time a job is pulled by the worker, a message goes to the + display routine; when the job is finished the result is sent to the display + routine.""" + + @push_map + def log_job_start(key, job): + return (key, 'start', job, None) + + S = Scheduler(error_handler=display.error_handler) + W = Queue() \ + >> branch(log_job_start.to(sink_map(display))) \ + >> worker \ + >> branch(sink_map(display)) + + return S.run(W, get_workflow(wf)) + + +def run_parallel(wf, n_threads): + """Run a workflow in `n_threads` parallel threads. Now we replaced the + single worker with a thread-pool of workers.""" + S = Scheduler() + W = Queue() >> thread_pool(*repeat(worker, n_threads)) + + return S.run(W, get_workflow(wf)) + + +def run_parallel_with_display(wf, n_threads, display): + """Adds a display to the parallel runner. Because messages come in + asynchronously now, we start an extra thread just for the display + routine.""" + + @push_map + def log_job_start(key, job): + return (key, 'start', job, None) + + LogQ = Queue() + + S = Scheduler(error_handler=display.error_handler) + + threading.Thread( + target=patch, + args=(LogQ.source, sink_map(display)), + daemon=True).start() + + W = Queue() \ + >> branch(log_job_start >> LogQ.sink) \ + >> thread_pool(*repeat(worker, n_threads)) \ + >> branch(LogQ.sink) + + result = S.run(W, get_workflow(wf)) + LogQ.wait() + + return result diff --git a/noodles/run/run_with_prov.py b/noodles/run/run_with_prov.py index 96035bd..89ddc4d 100644 --- a/noodles/run/run_with_prov.py +++ b/noodles/run/run_with_prov.py @@ -1,6 +1,7 @@ from .connection import (Connection) from .queue import (Queue) -from .scheduler import (Scheduler, Result) +from .scheduler import (Scheduler) +from .messages import (ResultMessage) from .haploid import ( pull, push, push_map, sink_map, branch, patch, broadcast) @@ -26,7 +27,7 @@ def run_single(wf, registry, jobdb_file, display=None): db = JobDB(jobdb_file) def decode_result(key, obj): - return Result(key, 'retrieved', registry.deep_decode(obj), None) + return ResultMessage(key, 'retrieved', registry.deep_decode(obj), None) @pull def pass_job(source): @@ -35,7 +36,7 @@ def pass_job(source): prov = prov_key(job_msg) if db.job_exists(prov): - status, other_key, result = db.get_result_or_attach( + status, _, result = db.get_result_or_attach( key, prov, {}) if status == 'retrieved': yield decode_result(key, result) @@ -103,7 +104,7 @@ def schedule_f(job_sink_): if status == 'retrieved': result_sink.send( (key, 'retrieved', - registry.deep_decode(result), None)) + registry.deep_decode(result, deref=True), None)) continue elif status == 'attached': continue @@ -132,7 +133,7 @@ def store_result(key, result, msg): attached = db.store_result(key, result_msg) if attached: for akey in attached: - yield (akey, 'attached', result, msg) + yield ResultMessage(akey, 'attached', result, msg) @pull def f(source): @@ -157,7 +158,7 @@ def f(source): for k in linked_jobs: yield from store_result(k, result, msg) - yield (key, status, result, msg) + yield ResultMessage(key, status, result, msg) return f @@ -209,10 +210,6 @@ def create_prov_worker( jobs = Queue() - @push_map - def log_job_start(key, job): - return (key, 'start', job, None) - r_src = jobs.source \ >> start_job(db) \ >> worker \ @@ -235,10 +232,6 @@ def prov_wrap_connection( registry = registry() db = JobDB(jobdb_file) - @push_map - def log_job_start(key, job): - return (key, 'start', job, None) - r_src = worker.source \ >> store_result_deep(registry, db, job_keeper, pred) @@ -254,7 +247,7 @@ def log_job_sched(key, job): def run_parallel_opt(wf, n_threads, registry, jobdb_file, - job_keeper=None, display=None): + job_keeper=None, display=None, cache_all=False): """Run a workflow in `n_threads` parallel threads. Now we replaced the single worker with a thread-pool of workers. @@ -288,8 +281,12 @@ def run_parallel_opt(wf, n_threads, registry, jobdb_file, results = Queue() - def pred(job): - return job.hints and 'store' in job.hints + if cache_all: + def pred(job): + return True + else: + def pred(job): + return job.hints and 'store' in job.hints LogQ = Queue() if display: diff --git a/noodles/run/scheduler.py b/noodles/run/scheduler.py index 23f5b4b..0e52325 100644 --- a/noodles/run/scheduler.py +++ b/noodles/run/scheduler.py @@ -1,5 +1,6 @@ from .connection import (Connection) from .job_keeper import (JobKeeper) + from ..workflow import ( is_workflow, get_workflow, insert_result, is_node_ready, Workflow) @@ -20,17 +21,6 @@ def node(self): return self.workflow.nodes[self.node_id] -class Result: - def __init__(self, key, status, value, msg): - self.key = key - self.status = status - self.value = value - self.msg = msg - - def __iter__(self): - return iter((self.key, self.status, self.value, self.msg)) - - class DynamicLink: def __init__(self, source, target, node): self.source = source @@ -136,6 +126,7 @@ def run(self, connection: Connection, master: Workflow): continue # insert the result in the nodes that need it + wf.nodes[n].result = result for (tgt, address) in wf.links[n]: insert_result(wf.nodes[tgt], address, result) if is_node_ready(wf.nodes[tgt]) and not graceful_exit: diff --git a/noodles/run/worker.py b/noodles/run/worker.py index 14ef4fe..2a849db 100644 --- a/noodles/run/worker.py +++ b/noodles/run/worker.py @@ -1,5 +1,5 @@ from .haploid import (pull_map) -from .scheduler import (Result) +from .messages import (ResultMessage) from ..interface import (AnnotatedValue, JobException) from ..utility import (object_name) import sys @@ -22,7 +22,7 @@ def run_job(key, job): result = job.apply() if isinstance(result, tuple) and len(result) == 2: value, msg = result - return Result(key, 'done', value, msg) + return ResultMessage(key, 'done', value, msg) else: raise TypeError("You promised annotation in call " "to function {} but return value " @@ -33,10 +33,10 @@ def run_job(key, job): result = job.apply() if isinstance(result, AnnotatedValue): value, message = result - return Result(key, 'done', value, message) + return ResultMessage(key, 'done', value, message) - return Result(key, 'done', result, None) + return ResultMessage(key, 'done', result, None) - except Exception as error: + except Exception: exc_info = sys.exc_info() - return Result(key, 'error', None, JobException(*exc_info)) + return ResultMessage(key, 'error', None, JobException(*exc_info)) diff --git a/noodles/run/xenon/__init__.py b/noodles/run/xenon/__init__.py index e0730c8..4998e7d 100644 --- a/noodles/run/xenon/__init__.py +++ b/noodles/run/xenon/__init__.py @@ -1,5 +1,13 @@ -from .runner import (run_xenon, run_xenon_prov) -from .xenon import (XenonConfig, RemoteJobConfig, XenonKeeper) +from .runner import (run_xenon) +from .xenon import (XenonJob, XenonScheduler, XenonConfig, RemoteJobConfig, + XenonKeeper) -__all__ = ['XenonConfig', 'RemoteJobConfig', 'XenonKeeper', - 'run_xenon', 'run_xenon_prov'] +# Only export run_xenon_prov if provenance is installed +try: + from .runner import run_xenon_prov + __all__ = ['run_xenon_prov'] +except ImportError: + __all__ = [] + +__all__ += ['XenonConfig', 'XenonJob', 'XenonScheduler', 'RemoteJobConfig', + 'XenonKeeper', 'run_xenon'] diff --git a/noodles/run/xenon/dynamic_pool.py b/noodles/run/xenon/dynamic_pool.py index 72494f0..7384c12 100644 --- a/noodles/run/xenon/dynamic_pool.py +++ b/noodles/run/xenon/dynamic_pool.py @@ -1,38 +1,23 @@ from ..queue import Queue from ..connection import Connection -from ..coroutine import coroutine from ..haploid import (push) -from ..scheduler import Result + +from ..messages import ( + ResultMessage) + +from ..remote.io import ( + JSONObjectWriter, JSONObjectReader) import xenon import jpype import threading import sys -import uuid from collections import namedtuple from .xenon import (XenonKeeper, XenonConfig, XenonScheduler) -def read_result(registry, s): - obj = registry.from_json(s) - status = obj['status'] - key = obj['key'] - try: - key = uuid.UUID(key) - except ValueError: - pass - - return Result(key, status, obj['result'], obj['err_msg']) - - -def put_job(registry, host, key, job): - obj = {'key': key if isinstance(key, str) else key.hex, - 'node': job} - return registry.to_json(obj, host=host) - - def xenon_interactive_worker(XeS: XenonScheduler, job_config): """Uses Xenon to run a single remote interactive worker. @@ -47,7 +32,8 @@ def xenon_interactive_worker(XeS: XenonScheduler, job_config): Job configuration. Specifies the command to be run remotely. """ cmd = job_config.command_line() - J = XeS.submit(cmd, interactive=True) + queue = job_config.queue + J = XeS.submit(cmd, interactive=True, queue=queue) def read_stderr(): jpype.attachThreadToJVM() @@ -60,19 +46,15 @@ def read_stderr(): registry = job_config.registry() - @coroutine + @push def send_job(): - out = xenon.conversions.OutputStream(J.streams.getStdin()) - - while True: - key, ujob = yield - print(put_job(registry, 'scheduler', key, ujob), - file=out, flush=True) + output_stream = xenon.conversions.OutputStream(J.streams.getStdin()) + yield from JSONObjectWriter(registry, output_stream, host="scheduler") + # @pull def get_result(): - """ Returns a result tuple: key, status, result, err_msg """ - for line in xenon.conversions.read_lines(J.streams.getStdout()): - yield read_result(registry, line) + input_stream = xenon.conversions.InputStream(J.streams.getStdout()) + yield from JSONObjectReader(registry, input_stream) return Connection(get_result, send_job, aux=J) @@ -93,17 +75,21 @@ def __init__(self, sched, job_config): def init_worker(self): """Sends the `init` and `finish` jobs to the worker if this is needed. This part will be called first, after a remote worker goes online.""" - if self.job_config.init is not None: - self.sink().send( - ("init", self.job_config.init()._workflow.root_node)) - key, status, result, err_msg = next(self.source()) - if key != "init" or not result: - raise RuntimeError( - "The initializer function did not succeed on worker.") - - if self.job_config.finish is not None: - self.sink().send( - ("finish", self.job_config.finish()._workflow.root_node)) + pass + # print("Initializing worker: ", end='', flush=True) + # if self.job_config.init is not None: + # print("[init] ", end='', flush=True) + # self.sink().send( + # ("init", self.job_config.init()._workflow.root_node)) + # key, status, result, err_msg = next(self.source()) + # if key != "init" or not result: + # raise RuntimeError( + # "The initializer function did not succeed on worker.") + # + # if self.job_config.finish is not None: + # self.sink().send( + # ("finish", self.job_config.finish()._workflow.root_node)) + # print("[done]", flush=True) def wait_until_running(self, callback=None): """Waits until the remote worker is running, then calls the callback. @@ -200,8 +186,8 @@ def activate(_): # lock end ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ for key in w.jobs: - sink.send(Result(key, 'aborted', None, - 'connection to remote worker lost.')) + sink.send(ResultMessage( + key, 'aborted', None, 'connection to remote worker lost.')) # Start the `activate` function when the worker goes online. threading.Thread( diff --git a/noodles/run/xenon/runner.py b/noodles/run/xenon/runner.py index e177a75..cd1c750 100644 --- a/noodles/run/xenon/runner.py +++ b/noodles/run/xenon/runner.py @@ -2,7 +2,6 @@ from ..scheduler import Scheduler from ..job_keeper import JobKeeper from ..haploid import (broadcast, sink_map, patch, branch) -from ..run_with_prov import (prov_wrap_connection) from ..queue import Queue from ...workflow import (get_workflow) @@ -10,9 +9,81 @@ import threading from copy import copy +# Only define run_xenon_prov if provenance is installed +try: + from ..run_with_prov import prov_wrap_connection +except ImportError: + pass +else: + def run_xenon_prov(wf, Xe, jobdb_file, n_processes, xenon_config, + job_config, *, deref=False, job_keeper=None, + display=None): + """Run the workflow using a number of online Xenon workers. + + :param Xe: + The XenonKeeper instance. + + :param wf: + The workflow. + :type wf: `Workflow` or `PromisedObject` + + :param n_processes: + Number of processes to start. + + :param xenon_config: + The :py:class:`XenonConfig` object that gives tells us how to use + Xenon. + + :param job_config: + The :py:class:`RemoteJobConfig` object that specifies the command + to run remotely for each worker. + + :param deref: + Set this to True to pass the result through one more encoding and + decoding step with object derefencing turned on. + :type deref: bool + + :returns: the result of evaluating the workflow + :rtype: any + """ + DP = DynamicPool(Xe, xenon_config) + + for i in range(n_processes): + cfg = copy(job_config) + cfg.name = 'xenon-{0:02}'.format(i) + DP.add_xenon_worker(cfg) + + if job_keeper is None: + job_keeper = JobKeeper() + S = Scheduler(job_keeper=job_keeper) + + LogQ = Queue() + if display: + tgt = broadcast(job_keeper.message, sink_map(display)) + else: + tgt = job_keeper.message + + threading.Thread( + target=patch, + args=(LogQ.source, tgt), + daemon=True).start() + + result = S.run( + prov_wrap_connection( + DP >> branch(LogQ.sink), + DP.result_queue, job_config.registry, + jobdb_file, job_keeper, + log_q=LogQ), + get_workflow(wf)) + + if deref: + return job_config.registry().dereference(result, host='localhost') + else: + return result + def run_xenon(wf, Xe, jobdb_file, n_processes, xenon_config, - job_config, *, deref=False, job_keeper=None, display=None): + job_config, *, deref=False, job_keeper=None): """Run the workflow using a number of online Xenon workers. :param Xe: @@ -81,70 +152,3 @@ def run_xenon(wf, Xe, jobdb_file, n_processes, xenon_config, return job_config.registry().dereference(result, host='localhost') else: return result - - -def run_xenon_prov(wf, Xe, jobdb_file, n_processes, xenon_config, - job_config, *, deref=False, job_keeper=None, display=None): - """Run the workflow using a number of online Xenon workers. - - :param Xe: - The XenonKeeper instance. - - :param wf: - The workflow. - :type wf: `Workflow` or `PromisedObject` - - :param n_processes: - Number of processes to start. - - :param xenon_config: - The :py:class:`XenonConfig` object that gives tells us how to use - Xenon. - - :param job_config: - The :py:class:`RemoteJobConfig` object that specifies the command to - run remotely for each worker. - - :param deref: - Set this to True to pass the result through one more encoding and - decoding step with object derefencing turned on. - :type deref: bool - - :returns: the result of evaluating the workflow - :rtype: any - """ - - DP = DynamicPool(Xe, xenon_config) - - for i in range(n_processes): - cfg = copy(job_config) - cfg.name = 'xenon-{0:02}'.format(i) - DP.add_xenon_worker(cfg) - - if job_keeper is None: - job_keeper = JobKeeper() - S = Scheduler(job_keeper=job_keeper) - - LogQ = Queue() - if display: - tgt = broadcast(job_keeper.message, sink_map(display)) - else: - tgt = job_keeper.message - - threading.Thread( - target=patch, - args=(LogQ.source, tgt), - daemon=True).start() - - result = S.run( - prov_wrap_connection( - DP >> branch(LogQ.sink), - DP.result_queue, job_config.registry, - jobdb_file, job_keeper, - log_q=LogQ), - get_workflow(wf)) - - if deref: - return job_config.registry().dereference(result, host='localhost') - else: - return result diff --git a/noodles/run/xenon/xenon.py b/noodles/run/xenon/xenon.py index dfc2b71..2c5da77 100644 --- a/noodles/run/xenon/xenon.py +++ b/noodles/run/xenon/xenon.py @@ -10,12 +10,6 @@ import sys -# from contextlib import redirect_stderr -# xenon_log = open('xenon_log.txt', 'w') -# with redirect_stderr(xenon_log): -xenon.init(log_level='ERROR') # noqa - - class XenonConfig: """Configuration to the Xenon library. @@ -23,7 +17,7 @@ class XenonConfig: These jobs may be run locally, over ssh ar against a queue manager like SLURM. - [Documentation to Xenon can be found online](http://nlesc.github.io/xenon) + [Documentation to Xenon can be found online](http://nlesc.github.io/Xenon) :param name: The quasi human readable name to give to this Xenon instance. @@ -136,10 +130,12 @@ def command_line(self): '-registry', object_name(self.registry)] if self.init: - cmd.append("-init") + cmd.extend(["-init", object_name(self.init)]) + # cmd.append("-init") if self.finish: - cmd.append("-finish") + cmd.extend(["-finish", object_name(self.finish)]) + # cmd.append("-finish") if self.verbose: cmd.append("-verbose") @@ -186,7 +182,13 @@ def interactive(self): class XenonKeeper: - def __init__(self): + is_initialized = False + + def __init__(self, log_level='ERROR'): + if not XenonKeeper.is_initialized: + xenon.init(log_level=log_level) # noqa + XenonKeeper.is_initialized = True + self._x = xenon.Xenon() self.jobs = self._x.jobs() self.credentials = self._x.credentials() diff --git a/noodles/serial/__init__.py b/noodles/serial/__init__.py index 67fb63a..ee42277 100644 --- a/noodles/serial/__init__.py +++ b/noodles/serial/__init__.py @@ -2,7 +2,9 @@ from .pickle import registry as pickle from .base import registry as base from .as_dict import AsDict +from .namedtuple import registry as namedtuple from .registry import (Registry, Serialiser, RefObject) +from .reasonable import (Reasonable) __all__ = ['pickle', 'base', 'Registry', 'Serialiser', - 'RefObject', 'AsDict'] + 'RefObject', 'AsDict', 'namedtuple', 'Reasonable'] diff --git a/noodles/serial/base.py b/noodles/serial/base.py index 7610ef8..f25635a 100644 --- a/noodles/serial/base.py +++ b/noodles/serial/base.py @@ -1,4 +1,5 @@ from .registry import (Registry, Serialiser) +from .reasonable import (Reasonable, SerReasonableObject) from ..interface import (PromisedObject) from ..utility import (object_name, look_up, importable) from ..workflow import (Workflow, NodeData, FunctionNode, ArgumentAddress, @@ -6,7 +7,7 @@ from ..storable import (Storable) # from .as_dict import (AsDict) # from enum import Enum -from inspect import isfunction +from inspect import isfunction, ismethod # from collections import namedtuple from itertools import count # import json @@ -24,6 +25,19 @@ def decode(self, cls, data): return cls(data) +class SerTuple(Serialiser): + """Tuples get converted to lists during serialisation. + We want to get tuples back, so make this explicit.""" + def __init__(self): + super(SerTuple, self).__init__(tuple) + + def encode(self, obj, make_rec): + return make_rec(list(obj)) + + def decode(self, cls, data): + return cls(data) + + class SerEnum(Serialiser): def __init__(self, cls): super(SerEnum, self).__init__(cls) @@ -100,6 +114,19 @@ def decode(self, cls, data): return getattr(cls, data['method']) +class SerBoundMethod(Serialiser): + def __init__(self): + super(SerBoundMethod, self).__init__('') + + def encode(self, obj, make_rec): + return make_rec({ + 'self': obj.__self__, + 'name': obj.__name__}) + + def decode(self, _, data): + return getattr(data['self'], data['name']) + + class SerImportable(Serialiser): def __init__(self): super(SerImportable, self).__init__('') @@ -112,8 +139,8 @@ def decode(self, cls, data): class SerStorable(Serialiser): - def __init__(self): - super(SerStorable, self).__init__(Storable) + def __init__(self, cls): + super(SerStorable, self).__init__(cls) def encode(self, obj, make_reca): return make_reca( @@ -125,19 +152,6 @@ def decode(self, _, data): return cls.from_dict(**data['dict']) -class SerAutoStorable(Serialiser): - def __init__(self, cls): - super(SerAutoStorable, self).__init__(cls) - - def encode(self, obj, make_rec): - return make_rec({'type': object_name(type(obj)), - 'dict': obj.as_dict()}) - - def decode(self, _, data): - cls = look_up(data['type']) - return cls.from_dict(**data['dict']) - - class SerNode(Serialiser): def __init__(self): super(SerNode, self).__init__(FunctionNode) @@ -153,12 +167,12 @@ def _noodles_hook(obj): if '__member_of__' in dir(obj) and obj.__member_of__: return '' - # if hasattr(obj, 'as_dict') and hasattr(type(obj), 'from_dict'): - # return '' - if importable(obj): return '' + if ismethod(obj): + return '' + if isfunction(obj): return '' @@ -170,17 +184,19 @@ def registry(): return Registry( types={ dict: SerDict(), + tuple: SerTuple(), + Reasonable: SerReasonableObject(Reasonable), ArgumentKind: SerEnum(ArgumentKind), FunctionNode: SerNode(), ArgumentAddress: SerNamedTuple(ArgumentAddress), Workflow: SerWorkflow(), - Storable: SerStorable(), + Storable: SerStorable(Storable), PromisedObject: SerPromisedObject() }, hooks={ '': SerMethod(), - '': SerImportable() - # '': SerAutoStorable() + '': SerBoundMethod(), + '': SerImportable(), }, hook_fn=_noodles_hook, default=Serialiser(object), diff --git a/noodles/serial/namedtuple.py b/noodles/serial/namedtuple.py new file mode 100644 index 0000000..8300af8 --- /dev/null +++ b/noodles/serial/namedtuple.py @@ -0,0 +1,45 @@ +from .registry import (Registry, Serialiser) +from ..utility import (object_name, look_up) + + +class SerNamedTuple(Serialiser): + def __init__(self, cls): + super(SerNamedTuple, self).__init__(cls) + + def encode(self, obj, make_rec): + return make_rec(tuple(obj)) + + def decode(self, cls, data): + return cls(*data) + + +class SerAutoNamedTuple(Serialiser): + def __init__(self): + super(SerAutoNamedTuple, self).__init__('') + + def encode(self, obj, make_rec): + return make_rec({ + 'name': object_name(type(obj)), + 'data': tuple(obj)}) + + def decode(self, cls, data): + return look_up(data['name'])(*data['data']) + + +def is_namedtuple(obj): + return isinstance(obj, tuple) and hasattr(obj, '_fields') + + +def namedtuple_hook(obj): + if is_namedtuple(obj): + return '' + else: + return None + + +def registry(): + return Registry( + hooks={ + '': SerAutoNamedTuple() + }, + hook_fn=namedtuple_hook) diff --git a/noodles/serial/numpy.py b/noodles/serial/numpy.py index 5581221..1b6471c 100644 --- a/noodles/serial/numpy.py +++ b/noodles/serial/numpy.py @@ -2,11 +2,31 @@ from ..utility import look_up import numpy import uuid +import io +import base64 +import filelock +import h5py +import msgpack +import hashlib class SerNumpyArray(Serialiser): - def __init__(self, file_prefix=None): + def __init__(self): super(SerNumpyArray, self).__init__(numpy.ndarray) + + def encode(self, obj, make_rec): + fo = io.BytesIO() + numpy.save(fo, obj, allow_pickle=False) + return make_rec(base64.b64encode(fo.getvalue()).decode()) + + def decode(self, cls, data): + fi = io.BytesIO(base64.b64decode(data.encode())) + return numpy.load(fi) + + +class SerNumpyArrayToFile(Serialiser): + def __init__(self, file_prefix=None): + super(SerNumpyArrayToFile, self).__init__(numpy.ndarray) self.file_prefix = file_prefix if file_prefix else '' def encode(self, obj, make_rec): @@ -18,6 +38,52 @@ def decode(self, cls, filename): return numpy.load(filename) +def array_sha256(a): + dtype = msgpack.dumps(str(a.dtype)) + shape = msgpack.dumps(a.shape) + bdata = a.flatten().view(numpy.uint8) + sha = hashlib.sha256() + sha.update(dtype) + sha.update(shape) + sha.update(bdata) + return sha.hexdigest() + + +class SerNumpyArrayToHDF5(Serialiser): + def __init__(self, filename, lockfile): + super(SerNumpyArrayToHDF5, self).__init__(numpy.ndarray) + self.filename = filename + self.lock = filelock.FileLock(lockfile) + + def encode(self, obj, make_rec): + key = array_sha256(obj) + with self.lock: + f = h5py.File(self.filename) + path = next( + (ds for ds in f if f[ds].attrs.get('hash') == key), + False) + if not path: + path = base64.b64encode(uuid.uuid4().bytes).decode() + dataset = f.create_dataset( + path, shape=obj.shape, dtype=obj.dtype) + dataset[...] = obj + dataset.attrs['hash'] = key + f.close() + + return make_rec({ + "filename": self.filename, + "path": path + }, files=[self.filename], ref=True) + + def decode(self, cls, data): + with self.lock: + f = h5py.File(self.filename) + obj = f[data["path"]].value + f.close() + + return obj + + class SerUFunc(Serialiser): def __init__(self): super(SerUFunc, self).__init__(None) @@ -36,16 +102,42 @@ def _numpy_hook(obj): return None -def registry(file_prefix=None): +def arrays_to_file(file_prefix=None): """Returns a serialisation registry for serialising NumPy data and as well as any UFuncs that have no normal way of retrieving qualified names.""" return Registry( types={ - numpy.ndarray: SerNumpyArray(file_prefix) + numpy.ndarray: SerNumpyArrayToFile(file_prefix) + }, + hooks={ + '': SerUFunc() + }, + hook_fn=_numpy_hook + ) + + +def arrays_to_string(file_prefix=None): + return Registry( + types={ + numpy.ndarray: SerNumpyArray() + }, + hooks={ + '': SerUFunc() + }, + hook_fn=_numpy_hook + ) + + +def arrays_to_hdf5(filename="cache.hdf5"): + return Registry( + types={ + numpy.ndarray: SerNumpyArrayToHDF5(filename, "cache.lock") }, hooks={ '': SerUFunc() }, hook_fn=_numpy_hook ) + +registry = arrays_to_string diff --git a/noodles/serial/reasonable.py b/noodles/serial/reasonable.py new file mode 100644 index 0000000..f7d4253 --- /dev/null +++ b/noodles/serial/reasonable.py @@ -0,0 +1,24 @@ +from .registry import Serialiser + + +class Reasonable(object): + """A Reasonable object is an object which is most reasonably serialised + using its `__dict__` property. To deserialise the object, we first create + an instance using the `__new__` method, then setting the `__dict__` + property manualy. This class is empty, it is used as a tag to designate + other objects as reasonable.""" + pass + + +class SerReasonableObject(Serialiser): + def __init__(self, cls): + super(SerReasonableObject, self).__init__(cls) + + def encode(self, obj, make_rec): + return make_rec(obj.__dict__) + + def decode(self, cls, data): + obj = cls.__new__(cls) + obj.__dict__ = data + return obj + diff --git a/noodles/serial/registry.py b/noodles/serial/registry.py index b1e57fa..dc312e3 100644 --- a/noodles/serial/registry.py +++ b/noodles/serial/registry.py @@ -3,8 +3,11 @@ object_name, look_up, deep_map, inverse_deep_map) import noodles + +# try: +# import ujson as json +# except ImportError: import json -# import sys try: import msgpack @@ -157,7 +160,7 @@ def encode(self, obj, host=None): if obj is None: return None - if type(obj) in [dict, list, str, int, float, bool, tuple]: + if type(obj) in [dict, list, str, int, float, bool]: return obj if isinstance(obj, RefObject): @@ -208,6 +211,7 @@ def decode(self, rec, deref=False): if '_noodles' not in rec: return rec + # if not deref: if rec.get('ref', False) and not deref: return RefObject(rec) @@ -233,8 +237,11 @@ def to_json(self, obj, host=None, indent=None): :param host: hostname where this object is being encoded. :type host: str""" - return json.dumps(deep_map(lambda o: self.encode(o, host), obj), - indent=indent) + if indent: + return json.dumps(deep_map(lambda o: self.encode(o, host), obj), + indent=indent) + else: + return json.dumps(deep_map(lambda o: self.encode(o, host), obj)) def to_msgpack(self, obj, host=None): return msgpack.packb(deep_map(lambda o: self.encode(o, host), obj)) @@ -251,6 +258,7 @@ def from_json(self, data, deref=False): :param deref: Whether to decode records that gave `ref=True` at encoding. :type deref: bool""" + # return self.deep_decode(json.loads(data), deref) return json.loads(data, object_hook=lambda o: self.decode(o, deref)) def from_msgpack(self, data, deref=False): @@ -323,7 +331,8 @@ def make_rec(rec, ref=False, files=None): raise NotImplementedError(msg) - def decode(self, cls, data): + @staticmethod + def decode(cls, data): """Should decode the data to an object of type 'cls'. :param cls: diff --git a/noodles/tutorial.py b/noodles/tutorial.py index 59cb0d8..e877445 100644 --- a/noodles/tutorial.py +++ b/noodles/tutorial.py @@ -1,11 +1,21 @@ -from . import schedule +from inspect import Parameter +from graphviz import Digraph +from . import schedule, schedule_hint, get_workflow + + +# scheduled functions @schedule def add(x, y): return x + y +@schedule_hint(display="{a} + {b}", confirm=True) +def log_add(a, b): + return a + b + + @schedule def sub(x, y): return x - y @@ -19,3 +29,42 @@ def mul(x, y): @schedule def accumulate(lst, start=0): return sum(lst, start) + + +# functions for printing workflow graphs in notebooks + +def _format_arg_list(a, v): + if len(a) == 0: + if v: + return "(\u2026)" + else: + return "()" + + s = "(" + for i in a[:-1]: + s += str(i) if i != Parameter.empty else "\u2014" + s += ", " + + if v: + s += "\u2014" + else: + s += str(a[-1]) if a[-1] != Parameter.empty else "\u2014" + + s += ")" + return s + + +def get_workflow_graph(promise): + workflow = get_workflow(promise) + + dot = Digraph() + for i,n in workflow.nodes.items(): + dot.node(str(i), label="{0} \n {1}".format( + n.foo.__name__, + _format_arg_list(n.bound_args.args, None))) + + for i in workflow.links: + for j in workflow.links[i]: + dot.edge(str(i), str(j[0]), label=str(j[1].name)) + + return dot diff --git a/noodles/worker.py b/noodles/worker.py index 3ac0503..3552b62 100644 --- a/noodles/worker.py +++ b/noodles/worker.py @@ -30,7 +30,6 @@ from contextlib import redirect_stdout import os -from noodles.run.worker import run_job from .utility import (look_up) try: @@ -39,32 +38,15 @@ except ImportError: has_msgpack = False +from .run.worker import ( + run_job) -def get_job(msg): - return msg['key'], msg['node'] +from .run.messages import ( + JobMessage) - -def get_job_json(registry, s): - obj = registry.from_json(s, deref=True) - return obj['key'], obj['node'] - - -def put_result_msgpack(registry, host, key, status, result, err_msg): - return registry.to_msgpack({ - 'key': key, - 'status': status, - 'result': result, - 'err_msg': err_msg - }, host=host) - - -def put_result_json(registry, host, key, status, result, err_msg): - return registry.to_json({ - 'key': key, - 'status': status, - 'result': result, - 'err_msg': err_msg - }, host=host) +from .run.remote.io import ( + MsgPackObjectReader, MsgPackObjectWriter, + JSONObjectReader, JSONObjectWriter) def run_batch_mode(args): @@ -77,41 +59,32 @@ def run_online_mode(args): finish = None if args.msgpack: - messages = msgpack.Unpacker(sys.stdin.buffer) + newin = os.fdopen(sys.stdin.fileno(), 'rb', buffering=0) + input_stream = MsgPackObjectReader( + registry, newin, deref=True) + output_stream = MsgPackObjectWriter( + registry, sys.stdout.buffer, host=args.name) else: - def msg_stream(): - for line in sys.stdin: - yield registry.from_json(line, deref=True) - - messages = msg_stream() + input_stream = JSONObjectReader( + registry, sys.stdin, deref=True) + output_stream = JSONObjectWriter( + registry, sys.stdout, host=args.name) # run the init function if it is given if args.init: - # line = sys.stdin.readline() - key, job = get_job(next(messages)) - if key != 'init': - raise RuntimeError("Expected init function.") - with redirect_stdout(sys.stderr): - result = run_job(key, job) - - if args.msgpack: - sys.stdout.buffer.write(put_result_msgpack( - registry, args.name, *result)) - sys.stdout.flush() - else: - print(put_result_json(registry, args.name, *result), - flush=True) + look_up(args.init)() if args.finish: - # line = sys.stdin.readline() - key, job = get_job(next(messages)) - if key != 'finish': - raise RuntimeError("Expected finish function.") - finish = job + finish = look_up(args.finish) - for msg in messages: - key, job = get_job(msg) + for msg in input_stream: + if isinstance(msg, JobMessage): + key, job = msg + elif isinstance(msg, tuple): + key, job = msg + else: + continue if args.jobdirs: # make a directory @@ -131,24 +104,15 @@ def msg_stream(): if args.verbose: print("result: ", result, file=sys.stderr, flush=True) - print("json: ", put_result_json( - registry, args.name, key, - 'success', result, None), file=sys.stderr, flush=True) if args.jobdirs: # parent directory os.chdir("..") - if args.msgpack: - sys.stdout.buffer.write(put_result_msgpack( - registry, args.name, *result)) - sys.stdout.flush() - else: - print(put_result_json(registry, args.name, *result), - flush=True) + output_stream.send(result) if finish: - run_job(0, finish) + finish() if __name__ == "__main__": @@ -201,11 +165,13 @@ def msg_stream(): help="worker identity", default="worker-" + str(uuid.uuid4())) online_parser.add_argument( - "-init", help="an init function will be send before other jobs", - default=False, action='store_true') + "-init", type=str, + help="an init function will be send before other jobs", + default=None) online_parser.add_argument( - "-finish", help="a finish function will be send before other jobs", - default=False, action='store_true') + "-finish", type=str, + help="a finish function will be send before other jobs", + default=None) online_parser.set_defaults(func=run_online_mode) diff --git a/noodles/workflow/arguments.py b/noodles/workflow/arguments.py index 5f93db2..8fb2a90 100644 --- a/noodles/workflow/arguments.py +++ b/noodles/workflow/arguments.py @@ -44,12 +44,12 @@ def serialize_arguments(bound_args): """ for p in bound_args.signature.parameters.values(): if p.kind == Parameter.VAR_POSITIONAL: - for i, v in enumerate(bound_args.arguments[p.name]): + for i, _ in enumerate(bound_args.arguments[p.name]): yield ArgumentAddress(ArgumentKind.variadic, p.name, i) continue if p.kind == Parameter.VAR_KEYWORD: - for k, v in bound_args.arguments[p.name].items(): + for k in bound_args.arguments[p.name].keys(): yield ArgumentAddress(ArgumentKind.keyword, p.name, k) continue diff --git a/noodles/workflow/model.py b/noodles/workflow/model.py index d480471..3824129 100644 --- a/noodles/workflow/model.py +++ b/noodles/workflow/model.py @@ -33,6 +33,7 @@ def __init__(self, foo, bound_args, hints, result=Empty): self.bound_args = bound_args self.hints = hints self.result = result + self.prov = None def apply(self): return self.foo(*self.bound_args.args, **self.bound_args.kwargs) @@ -41,8 +42,7 @@ def apply(self): def data(self): """Convert to a :py:class:`NodeData` for subsequent serial.""" return NodeData( - self.foo, get_arguments(self.bound_args), - self.hints) + self.foo, get_arguments(self.bound_args), self.hints) def __str__(self): s = self.foo.__name__ + '(' + \ @@ -81,13 +81,25 @@ def __iter__(self): def root_node(self): return self.nodes[self.root] + @property + def prov(self): + return self.root_node.prov + + @property + def inverse_links(self): + try: + return self._inverse_links + except AttributeError: + self.create_inverse_links() + return self._inverse_links + def create_inverse_links(self): - self.inverse_links = {} + self._inverse_links = {} for k, v in self.links.items(): for target, _ in v: if target not in self.inverse_links: - self.inverse_links[target] = set() - self.inverse_links[target].add(k) + self._inverse_links[target] = set() + self._inverse_links[target].add(k) def is_workflow(x): diff --git a/noodles/workflow/mutations.py b/noodles/workflow/mutations.py index f6e5146..eb1752d 100644 --- a/noodles/workflow/mutations.py +++ b/noodles/workflow/mutations.py @@ -1,8 +1,8 @@ -from .arguments import set_argument, ref_argument, format_address, Empty +from .arguments import set_argument, Empty def reset_workflow(workflow): - for src, tgt in workflow.links.items(): + for tgt in workflow.links.values(): for m, a in tgt: set_argument(workflow.nodes[m].bound_args, a, Empty) @@ -15,8 +15,8 @@ def insert_result(node, address, value): is redundant, but if we don't give an error here the program would continue with unexpected results. """ - #a = ref_argument(node.bound_args, address) - #if a != Empty: + # a = ref_argument(node.bound_args, address) + # if a != Empty: # raise RuntimeError( # "Noodle panic. Argument {arg} in {name} already given." \ # .format(arg=format_address(address), diff --git a/notebooks/An interactive introduction.ipynb b/notebooks/An interactive introduction.ipynb index 88c951c..81c94c4 100644 --- a/notebooks/An interactive introduction.ipynb +++ b/notebooks/An interactive introduction.ipynb @@ -13,9 +13,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from noodles import schedule\n", @@ -39,9 +37,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "a = add(1, 1)\n", @@ -60,9 +56,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from draw_workflow import draw_workflow\n", @@ -88,9 +82,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -179,9 +171,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -221,9 +211,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -255,9 +243,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -287,9 +273,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -328,9 +312,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from noodles import schedule\n", @@ -405,9 +387,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -464,9 +444,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -496,9 +474,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -526,9 +502,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -650,9 +624,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from noodles import run_single\n", @@ -697,9 +669,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from noodles import (schedule_hint, gather)\n", @@ -743,9 +713,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -811,9 +779,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2rc1" + "version": "3.6.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/first_steps.ipynb b/notebooks/first_steps.ipynb new file mode 100644 index 0000000..e687b05 --- /dev/null +++ b/notebooks/first_steps.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First Steps\n", + "\n", + "**This tutorial is also available in the form of a Jupyter Notebook. Try it out, and play!**\n", + "\n", + "Noodles is there to make your life easier, *in parallel*! The reason why Noodles can be easy and do parallel Python at the same time is its *functional* approach. In one part you'll define a set of functions that you'd like to run with Noodles, in an other part you'll compose these functions into a *workflow graph*. To make this approach work a function should not have any *side effects*. Let's not linger and just start noodling! First we define some functions to use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from noodles import schedule\n", + "\n", + "@schedule\n", + "def add(x, y):\n", + " return x + y\n", + "\n", + "@schedule\n", + "def mul(x,y):\n", + " return x * y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a workflow composing several calls to this function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = add(1, 1)\n", + "b = mul(a, 2)\n", + "c = add(a, a)\n", + "d = mul(b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks easy enough; the funny thing is though, that nothing has been computed yet! Noodles just created the workflow graphs corresponding to the values that still need to be computed. Until such time, we work with the *promise* of a future value. Using some function in `pygraphviz` we can look at the call graphs." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from noodles.tutorial import get_workflow_graph\n", + "\n", + "for i, wf in enumerate([a, b, c, d]):\n", + " get_workflow_graph(wf).render(\"wf{}.svg\".format(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from draw_workflow import draw_workflow\n", + "import sys\n", + "import os\n", + "\n", + "draw_workflow(\"wf1a.png\", a._workflow)\n", + "draw_workflow(\"wf1b.png\", b._workflow)\n", + "draw_workflow(\"wf1c.png\", c._workflow)\n", + "draw_workflow(\"wf1d.png\", d._workflow)\n", + "\n", + "err = os.system(\"montage wf1?.png -tile 4x1 -geometry +10+0 wf1-series.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![callgraph](wf1-series.png)\n", + "Now, to compute the result we have to tell Noodles to evaluate the program." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from noodles import run_parallel, run_single\n", + "\n", + "run_parallel(d, n_threads=2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/poetry_tutorial.ipynb b/notebooks/poetry_tutorial.ipynb new file mode 100644 index 0000000..1e00152 --- /dev/null +++ b/notebooks/poetry_tutorial.ipynb @@ -0,0 +1,902 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Real World Tutorial 1: Translating Poetry\n", + "=========================================\n", + "\n", + "First example\n", + "-------------\n", + "\n", + "We build workflows by calling functions. The simplest example of this\n", + "is the \"diamond workflow\":" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The answer is 42.\n" + ] + } + ], + "source": [ + "from noodles import run_single\n", + "from noodles.tutorial import (add, sub, mul)\n", + "\n", + "u = add(5, 4)\n", + "v = sub(u, 3)\n", + "w = sub(u, 2)\n", + "x = mul(v, w)\n", + "\n", + "answer = run_single(x)\n", + "\n", + "print(\"The answer is {0}.\".format(answer))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks like any other Python code! But this example is a bit silly.\n", + "How do we leverage Noodles to earn an honest living? Here's a slightly less\n", + "silly example (but only just!). We will build a small translation engine\n", + "that translates sentences by submitting each word to an online dictionary\n", + "over a Rest API. To do this we make loops (\"For thou shalt make loops of \n", + "blue\"). First we build the program as you would do in Python, then we\n", + "sprinkle some Noodles magic and make it work parallel! Furthermore, we'll\n", + "see how to:\n", + "\n", + " * make more loops\n", + " * cache results for reuse" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Making loops\n", + "\n", + "Thats all swell, but how do we make a parallel loop? Let's look at a `map` operation; in Python there are several ways to perform a function on all elements in an array. For this example, we will translate some words using the Glosbe service, which has a nice REST interface. We first build some functionality to use this interface." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import urllib.request\n", + "import json\n", + "import re\n", + "\n", + "\n", + "class Translate:\n", + " \"\"\"Translate words and sentences in the worst possible way. The Glosbe dictionary\n", + " has a nice REST interface that we query for a phrase. We then take the first result.\n", + " To translate a sentence, we cut it in pieces, translate it and paste it back into\n", + " a Frankenstein monster.\"\"\"\n", + " def __init__(self, src_lang='en', tgt_lang='fy'):\n", + " self.src = src_lang\n", + " self.tgt = tgt_lang\n", + " self.url = 'https://glosbe.com/gapi/translate?' \\\n", + " 'from={src}&dest={tgt}&' \\\n", + " 'phrase={{phrase}}&format=json'.format(\n", + " src=src_lang, tgt=tgt_lang)\n", + " \n", + " def query_phrase(self, phrase):\n", + " with urllib.request.urlopen(self.url.format(phrase=phrase.lower())) as response:\n", + " translation = json.loads(response.read().decode())\n", + " return translation\n", + "\n", + " def word(self, phrase):\n", + " translation = self.query_phrase(phrase)\n", + " #translation = {'tuc': [{'phrase': {'text': phrase.lower()[::-1]}}]}\n", + " if len(translation['tuc']) > 0 and 'phrase' in translation['tuc'][0]:\n", + " result = translation['tuc'][0]['phrase']['text']\n", + " if phrase[0].isupper():\n", + " return result.title()\n", + " else:\n", + " return result \n", + " else:\n", + " return \"<\" + phrase + \">\"\n", + " \n", + " def sentence(self, phrase):\n", + " words = re.sub(\"[^\\w]\", \" \", phrase).split()\n", + " space = re.sub(\"[\\w]+\", \"{}\", phrase)\n", + " return space.format(*map(self.word, words))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start with a list of strings that desparately need translation. And add a little\n", + "routine to print it in a gracious manner." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original:\n", + " If music be the food of love, play on,\n", + " Give me excess of it; that surfeiting,\n", + " The appetite may sicken, and so die.\n", + "\n" + ] + } + ], + "source": [ + "shakespeare = [\n", + " \"If music be the food of love, play on,\",\n", + " \"Give me excess of it; that surfeiting,\",\n", + " \"The appetite may sicken, and so die.\"]\n", + "\n", + "def print_poem(intro, poem):\n", + " print(intro)\n", + " for line in poem:\n", + " print(\" \", line)\n", + " print()\n", + "\n", + "print_poem(\"Original:\", shakespeare)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beginning Python programmers like to append things; this is not how you are\n", + "supposed to program in Python; if you do, please go and read Jeff Knupp's *Writing Idiomatic Python*." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Auf Deutsch:\n", + " Wenn Musik sein der Essen von Minne, spielen an,\n", + " Geben ich ƜbermaƟ von es; das Ć¼bersƤttigend,\n", + " Der Appetit dĆ¼rfen erkranken, und so sterben.\n", + "\n" + ] + } + ], + "source": [ + "shakespeare_auf_deutsch = []\n", + "for line in shakespeare:\n", + " shakespeare_auf_deutsch.append(\n", + " Translate('en', 'de').sentence(line))\n", + "print_poem(\"Auf Deutsch:\", shakespeare_auf_deutsch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather use a comprehension like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yn it Frysk:\n", + " At muzyk wĆŖze de fiedsel fan leafde, boartsje oan,\n", + " Jaan by fersin fan it; dat ,\n", + " De maaie , en dus deagean.\n", + "\n" + ] + } + ], + "source": [ + "shakespeare_ynt_frysk = \\\n", + " (Translate('en', 'fy').sentence(line) for line in shakespeare)\n", + "print_poem(\"Yn it Frysk:\", shakespeare_ynt_frysk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or use `map`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PĆ„ Dansk:\n", + " Hvis musik vƦre de mad af kƦrlighed, spil pĆ„,\n", + " Give mig udskejelser af det; som ,\n", + " De appetit mĆ„ , og sĆ„ dĆø.\n", + "\n" + ] + } + ], + "source": [ + "shakespeare_pa_dansk = \\\n", + " map(Translate('en', 'da').sentence, shakespeare)\n", + "print_poem(\"PĆ„ Dansk:\", shakespeare_pa_dansk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Noodlify!\n", + "If your connection is a bit slow, you may find that the translations take a while to process. Wouldn't it be nice to do it in parallel? How much code would we have to change to get there in Noodles? Let's take the slow part of the program and add a `@schedule` decorator, and run! Sadly, it is not that simple. We can add `@schedule` to the `word` method. This means that it will return a promise. \n", + "\n", + "* Rule: *Functions that take promises need to be scheduled functions, or refer to a scheduled function at some level.* \n", + "\n", + "We could write\n", + "\n", + " return schedule(space.format)(*(self.word(w) for w in words))\n", + " \n", + "in the last line of the `sentence` method, but the string format method doesn't support wrapping. We rely on getting the signature of a function by calling `inspect.signature`. In some cases of build-in function this raises an exception. We may find a work around for these cases in future versions of Noodles. For the moment we'll have to define a little wrapper function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from noodles import schedule\n", + "\n", + "\n", + "@schedule\n", + "def format_string(s, *args, **kwargs):\n", + " return s.format(*args, **kwargs)\n", + "\n", + "\n", + "import urllib.request\n", + "import json\n", + "import re\n", + "\n", + "\n", + "class Translate:\n", + " \"\"\"Translate words and sentences in the worst possible way. The Glosbe dictionary\n", + " has a nice REST interface that we query for a phrase. We then take the first result.\n", + " To translate a sentence, we cut it in pieces, translate it and paste it back into\n", + " a Frankenstein monster.\"\"\"\n", + " def __init__(self, src_lang='en', tgt_lang='fy'):\n", + " self.src = src_lang\n", + " self.tgt = tgt_lang\n", + " self.url = 'https://glosbe.com/gapi/translate?' \\\n", + " 'from={src}&dest={tgt}&' \\\n", + " 'phrase={{phrase}}&format=json'.format(\n", + " src=src_lang, tgt=tgt_lang)\n", + " \n", + " def query_phrase(self, phrase):\n", + " with urllib.request.urlopen(self.url.format(phrase=phrase.lower())) as response:\n", + " translation = json.loads(response.read().decode())\n", + " return translation\n", + " \n", + " @schedule\n", + " def word(self, phrase):\n", + " #translation = {'tuc': [{'phrase': {'text': phrase.lower()[::-1]}}]}\n", + " translation = self.query_phrase(phrase)\n", + " \n", + " if len(translation['tuc']) > 0 and 'phrase' in translation['tuc'][0]:\n", + " result = translation['tuc'][0]['phrase']['text']\n", + " if phrase[0].isupper():\n", + " return result.title()\n", + " else:\n", + " return result \n", + " else:\n", + " return \"<\" + phrase + \">\"\n", + " \n", + " def sentence(self, phrase):\n", + " words = re.sub(\"[^\\w]\", \" \", phrase).split()\n", + " space = re.sub(\"[\\w]+\", \"{}\", phrase)\n", + " return format_string(space, *map(self.word, words))\n", + " \n", + " def __str__(self):\n", + " return \"[{} -> {}]\".format(self.src, self.tgt)\n", + " \n", + " def __serialize__(self, pack):\n", + " return pack({'src_lang': self.src,\n", + " 'tgt_lang': self.tgt})\n", + "\n", + " @classmethod\n", + " def __construct__(cls, msg):\n", + " return cls(**msg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take stock of the mutations to the original. We've added a `@schedule` decorator to `word`, and changed a function call in `sentence`. Also we added the `__str__` method; this is only needed to plot the workflow graph. Let's run the new script." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shakespeare en Esperanto:\n", + " Se muziko esti la manĝaĵo de ami, ludi sur,\n", + " Doni mi eksceso de ĝi; ke ,\n", + " La apetito povi naÅ­zi, kaj tiel morti.\n", + "\n" + ] + } + ], + "source": [ + "from noodles import gather, run_parallel\n", + "from noodles.tutorial import get_workflow_graph\n", + "\n", + "shakespeare_en_esperanto = \\\n", + " map(Translate('en', 'eo').sentence, shakespeare)\n", + "\n", + "wf = gather(*shakespeare_en_esperanto)\n", + "workflow_graph = get_workflow_graph(wf._workflow)\n", + "result = run_parallel(wf, n_threads=8)\n", + "print_poem(\"Shakespeare en Esperanto:\", result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last peculiar thing that you may notice, is the `gather` function. It collects the promises that `map` generates and creates a single new promise. The definition of `gather` is very simple:\n", + " \n", + " @schedule\n", + " def gather(*lst):\n", + " return lst\n", + "\n", + "The workflow graph of the Esperanto translator script looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "140504116175424\n", + "\n", + "word \n", + " ([en -> eo], excess)\n", + "\n", + "\n", + "140504104094632\n", + "\n", + "format_string \n", + " ({} {} {} {} {}; {} {},, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”)\n", + "\n", + "\n", + "140504116175424->140504104094632\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504104093344\n", + "\n", + "word \n", + " ([en -> eo], music)\n", + "\n", + "\n", + "140504104092784\n", + "\n", + "format_string \n", + " ({} {} {} {} {} {} {}, {} {},, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”, ā€”)\n", + "\n", + "\n", + "140504104093344->140504104092784\n", + 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die)\n", + "\n", + "\n", + "140504116079640->140504116177440\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504116176376\n", + "\n", + "word \n", + " ([en -> eo], it)\n", + "\n", + "\n", + "140504116176376->140504104094632\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504116175704\n", + "\n", + "word \n", + " ([en -> eo], appetite)\n", + "\n", + "\n", + "140504116175704->140504116177440\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504116080256\n", + "\n", + "word \n", + " ([en -> eo], and)\n", + "\n", + "\n", + "140504116080256->140504116177440\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504125355568\n", + "\n", + "word \n", + " ([en -> eo], of)\n", + "\n", + "\n", + "140504125355568->140504104092784\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504125357304\n", + "\n", + "word \n", + " ([en -> eo], on)\n", + "\n", + "\n", + "140504125357304->140504104092784\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "140504104092952\n", + "\n", + "word \n", + " ([en -> eo], If)\n", + "\n", + "\n", + "140504104092952->140504104092784\n", + "\n", + "\n", + "args\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "workflow_graph.attr(size='10')\n", + "workflow_graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dealing with repetition\n", + "In the following example we have a line with some repetition. It would be a shame to look up the repeated words twice, wouldn't it? Let's build a little counter routine to check if everything is working." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Mon , mon , pourquoi as Tu me quitter?'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line = \"Mein Gott, mein Gott, warum hast Du mich verlassen?\"\n", + "run_parallel(Translate('de', 'fr').sentence(line), n_threads=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "To see how this program is being run, we monitor the job submission, retrieval and result storage in a `JobKeeper` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Mon , mon , pourquoi as Tu me quitter?'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from noodles.run.job_keeper import JobKeeper\n", + "from noodles.run.run_with_prov import run_parallel\n", + "from noodles import serial\n", + "\n", + "J = JobKeeper(keep=True)\n", + "wf = Translate('de', 'fr').sentence(line)\n", + "run_parallel(wf,\n", + " n_threads=4, registry=serial.base,\n", + " jobdb_file='matthew.json', job_keeper=J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can see how the results were obtained by inspecting the\n", + "``JobKeeper`` object. Running the first time, you may see that\n", + "some jobs *attached* themselves to other jobs as they are identical.\n", + "\n", + "Then try running above cell again. All the results should be cached\n", + "in the `matthew.json` file, and no queries are send to Glosbe, saving\n", + "us from certain doom of IP black listing." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - mon\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - pourquoi\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - me\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - Mon , mon , pourquoi as Tu me quitter?\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - Tu\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - Mon\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - \n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - quitter\n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - \n", + "-------------------------------------\n", + "2017-09-19T15:01:10Z: [register] - None\n", + "2017-09-19T15:01:10Z: [retrieved] - as\n", + "-------------------------------------\n" + ] + } + ], + "source": [ + "from itertools import starmap\n", + "import time\n", + "\n", + "def format_log_entry(tm, what, data, msg):\n", + " return \"{}: {:16} - {}\".format(\n", + " time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime(tm)),\n", + " '[' + what + ']',\n", + " data)\n", + "\n", + "for k,j in J.items():\n", + " print('\\n'.join(starmap(format_log_entry, j.log)))\n", + " print(\"-------------------------------------\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/prime_numbers.ipynb b/notebooks/prime_numbers.ipynb new file mode 100644 index 0000000..a6dcd1d --- /dev/null +++ b/notebooks/prime_numbers.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Real World Tutorial 3: Parallel Number crunching using Cython\n", + "\n", + "Python was not designed to be very good at parallel processing. There are two major problems at the core of the language that make it hard to implement parallel algorithms.\n", + "\n", + "* The Global Interpreter Lock\n", + "* Flexible object model\n", + "\n", + "The first of these issues is the most famous obstacle towards a convincing multi-threading approach, where a single instance of the Python interpreter runs in several threads. The second point is more subtle, but makes it harder to do multi-processing, where several independent instances of the Python interpreter work together to achieve parallelism. We will first explain an elegant way to work around the Global Interpreter Lock, or GIL: use Cython.\n", + "\n", + "## Using Cython to lift the GIL\n", + "The GIL means that the Python interpreter will only operate on one thread at a time. Even when we think we run in a gazillion threads, Python itself uses only one. Multi-threading in Python is only usefull to wait for I/O and to perform system calls. To do useful CPU intensive work in multi-threaded mode, we need to develop functions that are implemented in C, and tell Python when we call these functions not to worry about the GIL. The easiest way to achieve this, is to use Cython. We develop a number-crunching prime adder, and have it run in parallel threads. \n", + "\n", + "We'll load the ``multiprocessing``, ``threading`` and ``queue`` modules to do our plumbing, and the ``cython`` extension so we can do the number crunching, as is shown in [this blog post](https://lbolla.info/blog/2013/12/23/python-threads-cython-gil)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext cython\n", + "import multiprocessing\n", + "import threading\n", + "import queue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define a function that computes the sum of all primes below a certain integer `n`, and don't try to be smart about it; the point is that it needs a lot of computation. These functions are designated ``nogil``, so that we can be certain no Python objects are accessed. Finally we create a single Python exposed function that uses the:\n", + "\n", + "```python\n", + " with nogil:\n", + " ...\n", + "```\n", + "\n", + "statement. This is a context-manager that lifts the GIL for the duration of its contents." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%%cython\n", + "\n", + "from libc.math cimport ceil, sqrt\n", + "\n", + "\n", + "cdef inline int _is_prime(int n) nogil:\n", + " \"\"\"return a boolean, is the input integer a prime?\"\"\"\n", + " if n == 2:\n", + " return True\n", + " cdef int max_i = ceil(sqrt(n))\n", + " cdef int i = 2\n", + " while i <= max_i:\n", + " if n % i == 0:\n", + " return False\n", + " i += 1\n", + " return True\n", + "\n", + "\n", + "cdef unsigned long _sum_primes(int n) nogil:\n", + " \"\"\"return sum of all primes less than n \"\"\"\n", + " cdef unsigned long i = 0\n", + " cdef int x\n", + " for x in range(2, n):\n", + " if _is_prime(x):\n", + " i += x\n", + " return i\n", + "\n", + "\n", + "def sum_primes(int n):\n", + " with nogil:\n", + " result = _sum_primes(n)\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In fact, we only loaded the ``multiprocessing`` module to get the number of CPUs on this machine. We also get a decent amount of work to do in the ``input_range``." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have 4 cores to work on!\n" + ] + } + ], + "source": [ + "input_range = range(int(1e6), int(2e6), int(5e4))\n", + "ncpus = multiprocessing.cpu_count()\n", + "print(\"We have {} cores to work on!\".format(ncpus))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single thread\n", + "\n", + "Let's first run our tests in a single thread:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37550402023 41276629127 45125753695 49161463647 53433406131 57759511224 62287995772 66955471633 71881256647 76875349479 82074443256 87423357964 92878592188 98576757977 104450958704 110431974857 116581137847 122913801665 129451433482 136136977177 \n", + "CPU times: user 8.62 s, sys: 5.05 ms, total: 8.62 s\n", + "Wall time: 8.63 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "for i in input_range:\n", + " print(sum_primes(i), end=' ', flush=True)\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-threading: Worker pool\n", + "\n", + "We can do better than that! We now create a queue containing the work to be done, and a pool of threads eating from this queue. The workers will keep on working as long as the queue has work for them." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37550402023 41276629127 45125753695 49161463647 53433406131 57759511224 62287995772 66955471633 71881256647 76875349479 82074443256 87423357964 92878592188 98576757977 104450958704 110431974857 116581137847 122913801665 129451433482 136136977177 \n", + "CPU times: user 14.7 s, sys: 9.1 ms, total: 14.7 s\n", + "Wall time: 3.98 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "### We need to define a worker function that fetches jobs from the queue.\n", + "def worker(q):\n", + " while True:\n", + " try:\n", + " x = q.get(block=False)\n", + " print(sum_primes(x), end=' ', flush=True)\n", + " except queue.Empty:\n", + " break\n", + "\n", + "### Create the queue, and fill it with input values\n", + "work_queue = queue.Queue()\n", + "for i in input_range:\n", + " work_queue.put(i)\n", + "\n", + "### Start a number of threads\n", + "threads = [\n", + " threading.Thread(target=worker, args=(work_queue,))\n", + " for i in range(ncpus)]\n", + "\n", + "for t in threads:\n", + " t.start()\n", + "\n", + "### Wait until all of them are done\n", + "for t in threads:\n", + " t.join()\n", + "\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Noodles\n", + "\n", + "On my laptop, a dual-core hyper-threaded `Intel(R) Core(TM) i5-5300U CPU`, this runs just over two times faster than the single threaded code. However, setting up a queue and a pool of workers is quite cumbersome. Also, this approach doesn't scale up if the dependencies between our computations get more complex. Next we'll use Noodles to provide the multi-threaded environment to execute our work. We'll need three functions:\n", + "\n", + "* ``schedule`` to decorate our work function\n", + "* ``run_parallel`` to run the work in parallel\n", + "* ``gather`` to collect our work into a workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from noodles import (schedule, run_parallel, gather)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37550402023 41276629127 45125753695 49161463647 53433406131 57759511224 62287995772 66955471633 71881256647 76875349479 82074443256 87423357964 92878592188 98576757977 104450958704 110431974857 116581137847 122913801665 129451433482 136136977177 \n", + "CPU times: user 14.7 s, sys: 11.8 ms, total: 14.7 s\n", + "Wall time: 4.08 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "@schedule\n", + "def s_sum_primes(n):\n", + " result = sum_primes(n)\n", + " print(result, end=' ', flush=True)\n", + " return result\n", + "\n", + "p_prime_sums = gather(*(s_sum_primes(i) for i in input_range))\n", + "prime_sums = run_parallel(p_prime_sums, n_threads=ncpus)\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "That does look much nicer! We have much less code, the code we do have is clearly separating function and form, and this approach is easily expandable to more complex situations." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/wf1-series.png b/notebooks/wf1-series.png index 0a01463..f84466d 100644 Binary files a/notebooks/wf1-series.png and b/notebooks/wf1-series.png differ diff --git a/patches/jnius-patch.diff b/patches/jnius-patch.diff deleted file mode 100644 index e6ea0e5..0000000 --- a/patches/jnius-patch.diff +++ /dev/null @@ -1,26 +0,0 @@ -diff --git a/setup.py b/setup.py -index b3c422d..8df0dca 100644 ---- a/setup.py -+++ b/setup.py -@@ -12,13 +12,14 @@ PY3 = sys.version_info >= (3,0,0) - - def getenv(key): - val = environ.get(key) -- if val is not None: -- if PY3: -- return val.decode() -- else: -- return val -- else: -- return val -+ return val -+# if val is not None: -+# if PY3: -+# return val.decode() -+# else: -+# return val -+# else: -+# return val - - files = [ - 'jni.pxi', diff --git a/readthedocs.yml b/readthedocs.yml new file mode 100644 index 0000000..f8b3b41 --- /dev/null +++ b/readthedocs.yml @@ -0,0 +1,3 @@ +python: + version: 3.5 + pip_install: true diff --git a/remote-docker/Dockerfile b/remote-docker/Dockerfile deleted file mode 100644 index cf6fd01..0000000 --- a/remote-docker/Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -FROM debian:testing -MAINTAINER Johan Hidding - -RUN apt-get update && \ - apt-get install -y --no-install-recommends git python3.5 python3-numpy \ - python3-scipy ca-certificates python3-setuptools openssh-server \ - openssh-sftp-server slurmd slurm-client slurm-wlm - -RUN cd /tmp && \ - git clone https://github.com/NLeSC/noodles.git && \ - cd noodles && \ - git checkout devel && \ - python3.5 setup.py install - -EXPOSE 22 - -RUN mkdir /var/run/sshd - -RUN useradd -ms /bin/bash -p $(openssl passwd sixpack) joe - -USER joe -RUN mkdir /home/joe/.ssh -ADD id_rsa.pub /home/joe/.ssh/authorized_keys - -USER root - -ADD slurm.conf /etc/slurm-llnl -RUN mkdir /var/run/slurm-llnl - -ADD welcome.txt /etc/motd -ADD start.sh /usr/bin -RUN chmod +x /usr/bin/start.sh - -CMD ["/usr/bin/start.sh"] diff --git a/requirements.txt b/requirements.txt index e69de29..8e30686 100644 --- a/requirements.txt +++ b/requirements.txt @@ -0,0 +1,2 @@ +nbsphinx +ipykernel diff --git a/setup.py b/setup.py index 32787d3..adbf88f 100755 --- a/setup.py +++ b/setup.py @@ -12,19 +12,19 @@ setup( name='Noodles', - version='0.1.9300', + version='0.2.3', description='Workflow Engine', author='Johan Hidding', url='https://github.com/NLeSC/noodles', packages=[ 'noodles', 'noodles.serial', 'noodles.run', 'noodles.run.xenon', + 'noodles.run.remote', 'noodles.display', 'noodles.interface', 'noodles.workflow', 'noodles.files', - 'noodles.prov'], + 'noodles.prov', 'noodles.draw_workflow'], classifiers=[ - 'License :: OSI Approved :: ' - 'GNU Lesser General Public License v3 or later (LGPLv3+)', + 'License :: OSI Approved :: Apache Software License', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Environment :: Console', @@ -32,10 +32,13 @@ 'Programming Language :: Python :: 3.5', 'Topic :: System :: Distributed Computing'], - install_requires=[], + install_requires=['graphviz'], extras_require={ - 'prov': ['tinydb'], + 'doc': ['sphinx', 'shpinx_rtd_theme', 'nbsphinx'], + 'prov': ['tinydb', 'ujson'], 'xenon': ['pyxenon'], - 'test': ['nose', 'coverage', 'pyflakes', 'pep8'] + 'numpy': ['numpy', 'h5py', 'msgpack-python', 'filelock'], + 'test': ['pytest', 'pytest-cov', 'codacy-coverage', 'pyflakes', 'pep8', + 'docker-py'] }, ) diff --git a/test/test_autostorable.py b/test/test_autostorable.py deleted file mode 100644 index b4b7558..0000000 --- a/test/test_autostorable.py +++ /dev/null @@ -1,54 +0,0 @@ -from noodles import ( - schedule, run_process, gather, serial -) - - -def registry(): - return serial.pickle() + serial.base() - - -class A(object): - def __init__(self, a): - self.a = a - - @classmethod - def from_dict(cls, a): - return cls(a) - - def as_dict(self): - return {'a': self.a} - - -class B(object): - def __init__(self): - super(B, self).__init__() - - -@schedule -def g(a): - return A(a) - - -@schedule -def f(a): - b = B() - b.b = A(a.a * 5) - return b - - -@schedule -def h(b): - return b.b.a + 7 - - -def test_autostorable(): - a = g(7) - b = f(a) - c = h(b) - d = gather(c, a) - - result = run_process(d, 1, registry) - assert result[0] == 42 - assert result[1].a == 7 - - diff --git a/test/test_broker.py b/test/test_broker.py index c9ba763..0e0342f 100644 --- a/test/test_broker.py +++ b/test/test_broker.py @@ -1,8 +1,8 @@ import noodles from noodles.tutorial import (add, sub, mul, accumulate) from noodles.display import (NCDisplay) -from noodles.run.runners import (run_single, run_parallel, run_parallel_with_display) -import time +from noodles.run.runners import ( + run_single, run_parallel, run_parallel_with_display) def test_broker_01(): @@ -25,7 +25,7 @@ def test_broker_02(): assert run_single(C) == 42 -@noodles.schedule_hint(display="ā”‚ {a} + {b}", confirm=True) +@noodles.schedule_hint(display="{a} + {b}", confirm=True) def log_add(a, b): return a + b @@ -41,8 +41,6 @@ def test_broker_logging(): multiples = [mul(log_add(i, B), A) for i in range(6)] C = accumulate(noodles.gather(*multiples)) - wf = message("\nā•­ā”€(Running the test)", lambda: C) - - with NCDisplay() as display: - assert run_parallel_with_display(wf, 4, display) == 42 + with NCDisplay(title="Running the test") as display: + assert run_parallel_with_display(C, 4, display) == 42 diff --git a/test/test_capture_output.py b/test/test_capture_output.py index 609faf3..b5b380b 100644 --- a/test/test_capture_output.py +++ b/test/test_capture_output.py @@ -1,13 +1,25 @@ +import pytest + from noodles import run_process, schedule, serial +try: + import msgpack # noqa + has_msgpack = True +except ImportError: + has_msgpack = False @schedule def writes_to_stdout(): print("Hello Noodles!") return 42 - +@pytest.mark.skipif(not has_msgpack, reason="msgpack needed.") def test_capture_output(): a = writes_to_stdout() - result = run_process(a, n_processes=1, registry=serial.base) + result = run_process(a, n_processes=1, registry=serial.base, use_msgpack=True) + assert result == 42 + +def test_capture_output_nomsgpack(): + a = writes_to_stdout() + result = run_process(a, n_processes=1, registry=serial.base, use_msgpack=False) assert result == 42 diff --git a/test/test_class_decorator.py b/test/test_class_decorator.py index 61ac656..ddbc6db 100644 --- a/test/test_class_decorator.py +++ b/test/test_class_decorator.py @@ -1,5 +1,6 @@ -from noodles import schedule, run_single, unwrap -from nose.tools import raises +from noodles import schedule, run_single, unwrap, run_process, Storable +from noodles import serial +from pytest import raises @schedule @@ -18,8 +19,13 @@ def mul(x, y): @schedule -class A: +def make_object(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class A(Storable): def __init__(self, value): + # super(A, self).__init__() self.value = value self.m_attr = 0 @@ -39,24 +45,36 @@ def multiply(self, factor): return self -@schedule class B: pass def test_class_decorator(): - a = A(5).multiply(10) + a = make_object(A, 5).multiply(10) a.second = 7 result = run_single(a) assert result.value == 50 assert result.second == 7 +def registry(): + return serial.base() + + +def test_class_decorator_process(): + a = make_object(A, 5).multiply(10) + a.second = 7 + result = run_process( + a, n_processes=1, registry=registry) + assert result.value == 50 + assert result.second == 7 + + def test_class_property(): - a = A(10) + a = make_object(A, 10) a.attr = 1.0 - b = B() + b = make_object(B) b.first = a.attr b.second = a.mul_attr(3) @@ -101,9 +119,9 @@ def test_unwrap(): assert f == unwrap(f) -@raises(AttributeError) def test_class_decorator2(): - a = A(6).multiply(7) - b = a.divide(0) - result = run_single(b) - print(dir(result)) + with raises(AttributeError): + a = make_object(A, 6).multiply(7) + b = a.divide(0) + result = run_single(b) + print(dir(result)) diff --git a/test/test_conditionals.py b/test/test_conditionals.py new file mode 100644 index 0000000..423d50d --- /dev/null +++ b/test/test_conditionals.py @@ -0,0 +1,71 @@ +from noodles import ( + schedule, run_single, quote, unquote, run_process, + schedule_hint, run_logging, find_first) +from noodles.tutorial import (add, sub, mul) +from noodles.serial import base +from noodles.display import NCDisplay + + +def cond(t, wt, wf): + return s_cond(t, quote(wt), quote(wf)) + + +@schedule +def s_cond(truth, when_true, when_false): + if truth: + return unquote(when_true) + else: + return unquote(when_false) + + +@schedule +def should_not_run(): + raise RuntimeError("This function should never have been called.") + + +def test_truthfulness(): + w = sub(4, add(3, 2)) + a = cond(True, w, should_not_run()) + b = cond(False, should_not_run(), w) + result = run_single(mul(a, b)) + assert result == 1 + + +counter = 0 + + +def is_sixteen(n): + return n == 16 + + +@schedule +def counted_sqr(x): + global counter + counter += 1 + return x*x + + +@schedule_hint(display="squaring {x}", confirm=True) +def display_sqr(x): + return x*x + + +def test_find_first(): + global counter + + wfs = [counted_sqr(x) for x in range(10)] + w = find_first(is_sixteen, wfs) + result = run_single(w) + assert result == 16 + assert counter == 5 + + wfs = [counted_sqr(x) for x in range(10)] + w = find_first(is_sixteen, wfs) + result = run_process(w, n_processes=1, registry=base) + assert result == 16 + + wfs = [display_sqr(x) for x in range(10)] + w = find_first(is_sixteen, wfs) + with NCDisplay() as display: + result = run_logging(w, n_threads=2, display=display) + assert result == 16 diff --git a/test/test_delay.py b/test/test_delay.py deleted file mode 100644 index e0127fb..0000000 --- a/test/test_delay.py +++ /dev/null @@ -1,26 +0,0 @@ -import noodles -from noodles import (schedule, run_single) -from noodles.delay import (delay, force) -from noodles.tutorial import (add, sub, mul, accumulate) - - -@schedule -def cond(truth, when_true, when_false): - if truth: - return force(when_true) - else: - return force(when_false) - - -@schedule -def should_not_run(): - raise RuntimeError("This function should never have been called.") - - -def test_truthfulness(): - w = sub(4, add(3, 2)) - a = cond(True, delay(w), delay(should_not_run())) - b = cond(False, delay(should_not_run()), delay(w)) - result = run_single(mul(a, b)) - assert result == 1 - diff --git a/test/test_exceptions.py b/test/test_exceptions.py index 1b8d233..4d588bc 100644 --- a/test/test_exceptions.py +++ b/test/test_exceptions.py @@ -1,4 +1,4 @@ -from nose.tools import raises +from pytest import raises from noodles import (schedule, schedule_hint, run_single) @@ -12,10 +12,10 @@ def raises_my_exception(): raise MyException("Error!") -@raises(MyException) def test_exception_00(): - wf = raises_my_exception() - run_single(wf) + with raises(MyException): + wf = raises_my_exception() + run_single(wf) @schedule_hint(annotated=True) @@ -23,7 +23,7 @@ def not_really_annotated(): return 0 -@raises(TypeError) def test_exception_01(): - wf = not_really_annotated() - run_single(wf) + with raises(TypeError): + wf = not_really_annotated() + run_single(wf) diff --git a/test/test_gather.py b/test/test_gather.py index 78b5af2..977e1e7 100644 --- a/test/test_gather.py +++ b/test/test_gather.py @@ -1,8 +1,27 @@ import noodles from noodles.run.runners import run_single +from noodles.tutorial import add def test_empty_gather(): d = noodles.gather() result = run_single(d) assert result == [] + + +def test_gather(): + d = noodles.gather(*[add(x, x) for x in range(10)]) + result = run_single(d) + assert result == list(range(0, 20, 2)) + + +def test_gather_dict(): + d = noodles.gather_dict(a=1, b=add(1, 1), c=add(2, 3)) + result = run_single(d) + assert result == {'a': 1, 'b': 2, 'c': 5} + + +def test_gather_all(): + d = noodles.gather_all(add(x, 2*x) for x in range(10)) + result = run_single(d) + assert result == list(range(0, 30, 3)) diff --git a/test/test_global_prov.py b/test/test_global_prov.py new file mode 100644 index 0000000..5478100 --- /dev/null +++ b/test/test_global_prov.py @@ -0,0 +1,26 @@ +from noodles.prov.workflow import (set_global_provenance) +from noodles.tutorial import (add, sub, mul) +from noodles.serial import base + + +def test_global_prov(): + a = add(4, 5) + b = sub(a, 3) + c = mul(b, 7) + d = mul(b, 7) + e = sub(c, 3) + f = sub(c, 5) + + set_global_provenance(c._workflow, base()) + + assert c._workflow.prov is not None + assert b._workflow.prov is not None + assert c._workflow.prov != b._workflow.prov + + set_global_provenance(d._workflow, base()) + set_global_provenance(e._workflow, base()) + set_global_provenance(f._workflow, base()) + + assert c._workflow.prov == d._workflow.prov + assert b._workflow.prov != e._workflow.prov + assert f._workflow.prov != e._workflow.prov diff --git a/test/test_globals.py b/test/test_globals.py index a94d0db..b52a880 100644 --- a/test/test_globals.py +++ b/test/test_globals.py @@ -1,14 +1,14 @@ from noodles import schedule, run_process, serial -@schedule +# @schedule def init(): global s s = "This global variable needs to be here!" return True -@schedule +# @schedule def finish(): return "Finish functino was run!" @@ -23,4 +23,3 @@ def test_globals(): result = run_process(a, n_processes=1, registry=serial.base, init=init, finish=finish) assert result - diff --git a/test/test_iterator.py b/test/test_iterator.py index c77141d..d2ec259 100644 --- a/test/test_iterator.py +++ b/test/test_iterator.py @@ -1,12 +1,12 @@ import noodles -import nose +from pytest import raises -@nose.tools.raises(TypeError) def test_iterate(): - a = noodles.delay((1,2,3)) - b, c, d = a - e = noodles.gather(d, c, b) + with raises(TypeError): + a = noodles.delay((1, 2, 3)) + b, c, d = a + e = noodles.gather(d, c, b) - result = noodles.run_single(e) - assert result == (3, 2, 1) + result = noodles.run_single(e) + assert result == (3, 2, 1) diff --git a/test/test_lambda.py b/test/test_lambda.py deleted file mode 100644 index 3222151..0000000 --- a/test/test_lambda.py +++ /dev/null @@ -1,22 +0,0 @@ -from noodles import schedule, Lambda, run_process, serial -from noodles.tutorial import accumulate - - -@schedule -def nmap(f, lst): - return list(map(f, lst)) - - -@schedule -def value(x): - return x - - -def test_lambda(): - a = Lambda("lambda x: x**2") - b = nmap(a, [0.1, 0.2, 0.3, 0.4, 0.5]) - c = accumulate(b) - - # result = run(c) - result = run_process(c, 1, serial.base) - assert result == 0.55 diff --git a/test/test_lift.py b/test/test_lift.py index cdb06bc..702e77f 100644 --- a/test/test_lift.py +++ b/test/test_lift.py @@ -1,5 +1,19 @@ from noodles import (schedule, run_single, gather, lift) from noodles.tutorial import (add, sub, mul) +from collections import OrderedDict + + +@schedule +def g(x): + return x['a'] + x['b'] + +def test_lift_ordered_dict(): + x = OrderedDict() + x['a'] = 1 + x['b'] = add(1, 2) + y = g(lift(x)) + result = run_single(y) + assert result == 4 class A: diff --git a/test/test_merge_workflow.py b/test/test_merge_workflow.py index c52f493..352fc25 100644 --- a/test/test_merge_workflow.py +++ b/test/test_merge_workflow.py @@ -1,4 +1,4 @@ -from nose.tools import raises +from pytest import raises from noodles.workflow import ( Empty, ArgumentAddress, ArgumentKind, is_workflow, get_workflow, Workflow) @@ -74,7 +74,7 @@ def test_binder(): @schedule def takes_keywords(s, **kwargs): - pass + return s def test_with_keywords(): @@ -113,14 +113,14 @@ def test_arg_by_ref(): assert result.y.x == 5 -@raises(TypeError) def test_hidden_promise(): - a = Normal() - b = Scheduled() - c = Scheduled() + with raises(TypeError): + a = Normal() + b = Scheduled() + c = Scheduled() - a.x = b - c.x = a + a.x = b + c.x = a def test_tuple_unpack(): diff --git a/test/test_namedtuple.py b/test/test_namedtuple.py new file mode 100644 index 0000000..09921de --- /dev/null +++ b/test/test_namedtuple.py @@ -0,0 +1,30 @@ +from collections import namedtuple +from noodles import serial +from noodles.serial.namedtuple import (SerNamedTuple) +from noodles.serial import (Registry) + +A = namedtuple('A', ['x', 'y']) +B = namedtuple('B', ['u', 'v']) + + +def registry1(): + return Registry( + parent=serial.base(), + types={ + A: SerNamedTuple(A) + }) + + +def registry2(): + return serial.base() + serial.namedtuple() + + +def test_namedtuple_00(): + a = A(73, 38) + r = registry1() + assert r.deep_decode(r.deep_encode(a)) == a + + b = B(783, 837) + r = registry2() + print(r.deep_encode(b)) + assert r.deep_decode(r.deep_encode(b)) == b diff --git a/test/test_numpy.py b/test/test_numpy.py index d9113e0..a2d766f 100644 --- a/test/test_numpy.py +++ b/test/test_numpy.py @@ -1,26 +1,28 @@ -from nose.plugins.skip import SkipTest +import pytest try: - import numpy import numpy as np from numpy import (random, fft, exp) + from noodles.serial.numpy import arrays_to_hdf5 - from noodles.serial.numpy import registry as numpy_registry + from noodles.run.run_with_prov import ( + run_parallel_opt) except ImportError: - raise SkipTest("No NumPy installed.") + has_numpy = False +else: + has_numpy = True -from noodles import schedule, run_process, serial -import os -from shutil import (copyfile, rmtree) +from noodles.display import (NCDisplay) +from noodles import schedule, serial, schedule_hint def registry(): - return serial.base() + numpy_registry(file_prefix='test-numpy/') + return serial.base() + arrays_to_hdf5() -@schedule +@schedule_hint(display="fft", confirm=True, store=True) def do_fft(a): return fft.fft(a) @@ -30,7 +32,7 @@ def make_kernel(n, sigma): return exp(-fft.fftfreq(n)**2 * sigma**2) -@schedule +@schedule_hint(display="ifft", confirm=True, store=True) def do_ifft(a): return fft.ifft(a).real @@ -40,31 +42,26 @@ def apply_filter(a, b): return a * b -@schedule -def make_noise(n): +@schedule_hint(display="make noise {seed}", confirm=True, store=True) +def make_noise(n, seed=0): + random.seed(seed) return random.normal(0, 1, n) -def test_pickle(): - x = make_noise(256) - k = make_kernel(256, 10) - x_smooth = do_ifft(apply_filter(do_fft(x), k)) - if os.path.exists("./test-numpy"): - rmtree("./test-numpy") +def run(wf): + with NCDisplay() as display: + result = run_parallel_opt( + wf, 2, registry, "cache.json", display=display) - os.mkdir("./test-numpy") + return result - result = run_process(x_smooth, 1, registry, deref=True) + +@pytest.mark.skipif(not has_numpy, reason="NumPy needed.") +def test_hdf5(): + x = make_noise(256) + k = make_kernel(256, 10) + x_smooth = do_ifft(apply_filter(do_fft(x), k)) + result = run(x_smooth) assert isinstance(result, np.ndarray) assert result.size == 256 - - # above workflow has five steps - lst = os.listdir("./test-numpy") - assert len(lst) == 5 - - # remove the .npy files - for f in lst: - os.remove("./test-numpy/" + f) - - os.rmdir("./test-numpy") diff --git a/test/test_prov.py b/test/test_prov.py index 1050095..cef1111 100644 --- a/test/test_prov.py +++ b/test/test_prov.py @@ -1,12 +1,13 @@ -from nose.plugins.skip import SkipTest +import pytest try: from noodles.prov import prov_key from noodles.run.run_with_prov import ( run_single, run_parallel, run_parallel_opt) - -except ImportError as e: - raise SkipTest(str(e)) +except ImportError: + has_prov = False +else: + has_prov = True from noodles import serial, gather, schedule_hint from noodles.tutorial import (add, mul, sub, accumulate) @@ -17,96 +18,93 @@ import os -def test_prov_00(): - reg = serial.base() - a = add(3, 4) - b = sub(3, 4) - c = add(3, 4) - d = add(4, 3) - - enc = [reg.deep_encode(x._workflow.root_node) for x in [a, b, c, d]] - key = [prov_key(o) for o in enc] - assert key[0] == key[2] - assert key[1] != key[0] - assert key[3] != key[0] - - -def test_prov_01(): - reg = serial.base() - a = add(3, 4) - - enc = reg.deep_encode(a._workflow.root_node) - dec = reg.deep_decode(enc) - - result = run_job(0, dec) - assert result.value == 7 +@schedule_hint(store=True) +def add2(x, y): + return x + y -def test_prov_02(): - db_file = "prov1.json" +@schedule_hint(store=True) +def fib(n): + if n < 2: + return 1 + else: + return add(fib(n - 1), fib(n - 2)) - A = add(1, 1) - B = sub(3, A) - multiples = [mul(add(i, B), A) for i in range(6)] - C = accumulate(gather(*multiples)) +@pytest.mark.skipif(not has_prov, reason="TinyDB needed") +class TestProv: + def test_prov_00(self): + reg = serial.base() + a = add(3, 4) + b = sub(3, 4) + c = add(3, 4) + d = add(4, 3) - result = run_single(C, serial.base, db_file) - assert result == 42 - os.unlink(db_file) + enc = [reg.deep_encode(x._workflow.root_node) for x in [a, b, c, d]] + key = [prov_key(o) for o in enc] + assert key[0] == key[2] + assert key[1] != key[0] + assert key[3] != key[0] + def test_prov_01(self): + reg = serial.base() + a = add(3, 4) -def test_prov_03(): - db_file = "prov2.json" + enc = reg.deep_encode(a._workflow.root_node) + dec = reg.deep_decode(enc) - A = add(1, 1) - B = sub(3, A) + result = run_job(0, dec) + assert result.value == 7 - multiples = [mul(add(i, B), A) for i in range(6)] - C = accumulate(gather(*multiples)) + def test_prov_02(self): + db_file = "prov1.json" - result = run_parallel(C, 4, serial.base, db_file, JobKeeper(keep=True)) - assert result == 42 - os.unlink(db_file) + A = add(1, 1) + B = sub(3, A) + multiples = [mul(add(i, B), A) for i in range(6)] + C = accumulate(gather(*multiples)) -@schedule_hint(store=True) -def add2(x, y): - return x + y + result = run_single(C, serial.base, db_file) + assert result == 42 + os.unlink(db_file) + def test_prov_03(self): + db_file = "prov2.json" -def test_prov_04(): - db_file = "prov3.json" + A = add(1, 1) + B = sub(3, A) - A = add2(1, 1) - B = sub(3, A) + multiples = [mul(add(i, B), A) for i in range(6)] + C = accumulate(gather(*multiples)) - multiples = [mul(add2(i, B), A) for i in range(6)] - C = accumulate(gather(*multiples)) + result = run_parallel(C, 4, serial.base, db_file, JobKeeper(keep=True)) + assert result == 42 + os.unlink(db_file) - result = run_parallel_opt(C, 4, serial.base, db_file) - assert result == 42 - os.unlink(db_file) + def test_prov_04(self): + db_file = "prov3.json" + A = add2(1, 1) + B = sub(3, A) -@schedule_hint(store=True) -def fib(n): - if n < 2: - return 1 - else: - return add(fib(n - 1), fib(n - 2)) + multiples = [mul(add2(i, B), A) for i in range(6)] + C = accumulate(gather(*multiples)) + result = run_parallel_opt(C, 4, serial.base, db_file) + assert result == 42 + os.unlink(db_file) -def test_prov_05(): - import time - db_file = "testjobs.json" + def test_prov_05(self): + import time + db_file = "testjobs.json" - wf = fib(20) - start = time.time() - result = run_parallel(wf, 4, serial.base, db_file) - end = time.time() + wf = fib(20) + start = time.time() + result = run_parallel(wf, 4, serial.base, db_file) + end = time.time() - assert (end - start) < 1.0 - assert result == 10946 + assert (end - start) < 5.0 # weak test + assert result == 10946 - os.unlink(db_file) + os.unlink(db_file) diff --git a/test/test_recursion.py b/test/test_recursion.py index 60316ae..d4d69a2 100644 --- a/test/test_recursion.py +++ b/test/test_recursion.py @@ -16,10 +16,10 @@ def factorial(n): def test_recursion_parallel(): - f100 = factorial(100) + f100 = factorial(50.0) result = run_parallel(f100, n_threads=1) print(result) - assert floor(log(result)) == 363 + assert floor(log(result)) == 148 def registry(): @@ -27,7 +27,7 @@ def registry(): def test_recursion_process(): - f100 = factorial(100) + f100 = factorial(50.0) result = run_process(f100, n_processes=1, registry=registry, verbose=False) print(result) - assert floor(log(result)) == 363 + assert floor(log(result)) == 148 diff --git a/test/test_haploid.py b/test/test_run/test_haploid.py similarity index 100% rename from test/test_haploid.py rename to test/test_run/test_haploid.py diff --git a/test/test_run/test_remote_io.py b/test/test_run/test_remote_io.py new file mode 100644 index 0000000..f437562 --- /dev/null +++ b/test/test_run/test_remote_io.py @@ -0,0 +1,48 @@ +import pytest +import io + +from noodles.run.remote.io import ( + MsgPackObjectReader, MsgPackObjectWriter, + JSONObjectReader, JSONObjectWriter) +from noodles.serial import base as registry +import math + +try: + import msgpack # noqa + has_msgpack = True +except ImportError: + has_msgpack = False + + +objects = ["Hello", 42, [3, 4], (5, 6), {"hello": "world"}, + math.tan, object] + + +@pytest.mark.skipif(not has_msgpack, reason="No MsgPack installed.") +def test_msgpack(): + f = io.BytesIO() + output_stream = MsgPackObjectWriter(registry(), f) + + for obj in objects: + output_stream.send(obj) + + f.seek(0) + input_stream = MsgPackObjectReader(registry(), f) + + new_objects = list(input_stream) + assert new_objects == objects + + +def test_json(): + f = io.StringIO() + output_stream = JSONObjectWriter(registry(), f) + + for obj in objects: + output_stream.send(obj) + + f.seek(0) + input_stream = JSONObjectReader(registry(), f) + + new_objects = list(input_stream) + + assert new_objects == objects diff --git a/test/test_xenon_local.py b/test/test_run/test_xenon_local.py similarity index 50% rename from test/test_xenon_local.py rename to test/test_run/test_xenon_local.py index 3f4f560..f47836a 100644 --- a/test/test_xenon_local.py +++ b/test/test_run/test_xenon_local.py @@ -1,24 +1,25 @@ -from nose.plugins.skip import SkipTest +import pytest try: - from noodles.run.xenon import ( - run_xenon_prov, XenonConfig, - RemoteJobConfig, XenonKeeper) - + # Only test xenon if it is installed + from noodles.run.xenon import (XenonConfig, RemoteJobConfig, XenonKeeper) except ImportError as e: - raise SkipTest(str(e)) + has_xenon = False +else: + has_xenon = True + # Only test run_xenon_prov if provenance is installed, otherwise run_xenon + try: + from noodles.run.xenon import run_xenon_prov as run_xenon + except ImportError: + from noodles.run.xenon import run_xenon import noodles from noodles.display import (NCDisplay) from noodles import serial -from noodles.tutorial import (mul, sub, accumulate) - - -@noodles.schedule_hint(display="{a} + {b}", confirm=True) -def log_add(a, b): - return a + b +from noodles.tutorial import (log_add, mul, sub, accumulate) +@pytest.mark.skipif(not has_xenon, reason="No (py)xenon installed.") def test_xenon_42(): A = log_add(1, 1) B = sub(3, A) @@ -32,11 +33,11 @@ def test_xenon_42(): job_config = RemoteJobConfig( registry=serial.base, - time_out=1 + time_out=1000 ) with XenonKeeper() as Xe, NCDisplay() as display: - result = run_xenon_prov( + result = run_xenon( C, Xe, "cache.json", 2, xenon_config, job_config, display=display) diff --git a/test/test_runner.py b/test/test_runner.py index 9c7babd..da28075 100644 --- a/test/test_runner.py +++ b/test/test_runner.py @@ -1,4 +1,4 @@ -from noodles import schedule, run_single, run_parallel, gather +from noodles import schedule, run_single, run_parallel, gather, result import time @@ -33,6 +33,8 @@ def test_runner_01(): C = add(A, B) assert run_single(C) == 2 + assert result(A) == 1 + assert result(B) == 1 def test_runner_02(): @@ -43,6 +45,8 @@ def test_runner_02(): C = sum(gather(*multiples)) assert run_single(C) == 42 + assert result(C) == 42 + assert result(A) == 2 def test_parallel_runner_01(): diff --git a/test/test_storable.py b/test/test_storable.py index ccf0b06..c47930e 100644 --- a/test/test_storable.py +++ b/test/test_storable.py @@ -1,6 +1,6 @@ from noodles import schedule, run_single, run_process, serial, lift from noodles.storable import Storable -from nose.tools import raises +from pytest import raises class A(Storable): @@ -44,14 +44,14 @@ def test_storable(): assert result.x == 7 -@raises(TypeError) def test_nonstorable(): - a = A() - b = M() + with raises(TypeError): + a = A() + b = M() - a.x = 1 - b.x = f(3, 4) + a.x = 1 + b.x = f(3, 4) - zzc = g(a, b) - result = run_single(c) - assert result == 8 + c = g(a, b) + result = run_single(c) + assert result == 8 diff --git a/test/test_worker.py b/test/test_worker.py deleted file mode 100644 index f0762f3..0000000 --- a/test/test_worker.py +++ /dev/null @@ -1,95 +0,0 @@ -from noodles import ( - schedule, Scheduler, gather, - serial, Storable) -from noodles.serial import (Registry, AsDict) -from noodles.workflow import get_workflow -from noodles.run.process import process_worker - - -def registry(): - return Registry(parent=serial.base(), default=AsDict) - - -@schedule -def f(x, y): - return x + y - - -@schedule -def ssum(lst, acc=sum): - return acc(lst) - - -def test_worker(): - a = [f(i, j) for i in range(5) for j in range(5)] - b = ssum(gather(*a)) - - result = Scheduler().run( - process_worker(registry), - get_workflow(b)) - assert result == 100 - - -class A(Storable): - def __init__(self, **kwargs): - super(A, self).__init__() - self.__dict__.update(kwargs) - - -@schedule -def g(a): - return a.x + a.y - - -@schedule -def cons(i, lst): - return [i] + lst - - -def stupid_range(n, m): - if n == m: - return [] - else: - return cons(n, stupid_range(n+1, m)) - - -@schedule -def map(f, lst): - return gather(*[f(i) for i in lst]) - - -@schedule -def sqr(x): - return x*x - - -@schedule -def concatenate(lst): - return sum(lst, []) - - -# def test_stupid_range(): -# a = stupid_range(0, 5) -# result1 = Scheduler().run(process_worker(registry), get_workflow(a)) -# assert result1 == list(range(5)) -# -# b = map(sqr, stupid_range(0, 5)) -# result2 = Scheduler().run(process_worker(registry), get_workflow(b)) -# assert result2 == [i*i for i in range(5)] - - -def dmap(f, lst): - return gather(*[gather(*[ - f(x) for x in row]) for row in lst]) - - -# def test_worker_with_storable(): -# a = [[A(y=j) for j in range(5)] for i in range(5)] -# for i, lst in enumerate(a): -# for q in lst: -# q.x = f(i, q.y) -# -# c = dmap(g, a) -# b = ssum(concatenate(c)) -# result = Scheduler().run(process_worker(registry), get_workflow(b)) -# assert result == 150