{
    "info": {
        "author": "Joao Pimentel, Leonardo Murta, Vanessa Braganholo, Fernando Chirigati, David Koop, and Juliana Freire",
        "author_email": "leomurta@ic.uff.br",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Build Tools"
        ],
        "description": "noWorkflow\n==========\n\nCopyright (c) 2016 Universidade Federal Fluminense (UFF). Copyright (c)\n2016 Polytechnic Institute of New York University. All rights reserved.\n\nThe noWorkflow project aims at allowing scientists to benefit from\nprovenance data analysis even when they don\u2019t use a workflow system.\nAlso, the goal is to allow them to avoid using naming conventions to\nstore files originated in previous executions. Currently, when this is\nnot done, the result and intermediate files are overwritten by every new\nexecution of the pipeline.\n\nnoWorkflow was developed in Python and it currently is able to capture\nprovenance of Python scripts using Software Engineering techniques such\nas abstract syntax tree (AST) analysis, reflection, and profiling, to\ncollect provenance without the need of a version control system or any\nother environment.\n\nInstalling and using noWorkflow is simple and easy. Please check our\ninstallation and basic usage guidelines below, and the `tutorial videos\nat our Wiki\npage <https://github.com/gems-uff/noworkflow/wiki/Videos>`__.\n\nTeam\n----\n\nThe main noWorkflow team is composed by researchers from Universidade\nFederal Fluminense (UFF) in Brazil and New York University (NYU), in the\nUSA.\n\n-  Jo\u00e3o Felipe Pimentel (UFF) (main developer)\n-  Juliana Freire (NYU)\n-  Leonardo Murta (UFF)\n-  Vanessa Braganholo (UFF)\n\nCollaborators\n\n-  David Koop (University of Massachusetts Dartmouth)\n-  Fernando Chirigati (NYU)\n-  Paolo Missier (Newcastle University)\n\nPublications\n------------\n\n-  `MURTA, L. G. P.; BRAGANHOLO, V.; CHIRIGATI, F. S.; KOOP, D.; FREIRE,\n   J.; noWorkflow: Capturing and Analyzing Provenance of Scripts. In:\n   International Provenance and Annotation Workshop (IPAW), 2014,\n   Cologne,\n   Germany. <https://github.com/gems-uff/noworkflow/raw/master/docs/ipaw2014.pdf>`__\n-  `PIMENTEL, J. F. N.; FREIRE, J.; MURTA, L. G. P.; BRAGANHOLO, V.;\n   Collecting and Analyzing Provenance on Interactive Notebooks: when\n   IPython meets noWorkflow. In: Theory and Practice of Provenance\n   (TaPP), 2015, Edinburgh,\n   Scotland. <https://github.com/gems-uff/noworkflow/raw/master/docs/tapp2015.pdf>`__\n-  `PIMENTEL, J. F.; FREIRE, J.; BRAGANHOLO, V.; MURTA, L. G. P.;\n   Tracking and Analyzing the Evolution of Provenance from Scripts. In:\n   International Provenance and Annotation Workshop (IPAW), 2016,\n   McLean,\n   Virginia. <https://github.com/gems-uff/noworkflow/raw/master/docs/ipaw2016a.pdf>`__\n-  `PIMENTEL, J. F.; FREIRE, J.; MURTA, L. G. P.; BRAGANHOLO, V.;\n   Fine-grained Provenance Collection over Scripts Through Program\n   Slicing. In: International Provenance and Annotation Workshop (IPAW),\n   2016, McLean,\n   Virginia. <https://github.com/gems-uff/noworkflow/raw/master/docs/ipaw2016b.pdf>`__\n-  `PIMENTEL, J. F.; DEY, S.; MCPHILLIPS, T.; BELHAJJAME, K.; KOOP, D.;\n   MURTA, L. G. P.; BRAGANHOLO, V.; LUD\u00c4SCHER B.; Yin & Yang:\n   Demonstrating Complementary Provenance from noWorkflow & YesWorkflow.\n   In: International Provenance and Annotation Workshop (IPAW), 2016,\n   McLean,\n   Virginia. <https://github.com/gems-uff/noworkflow/raw/master/docs/ipaw2016c.pdf>`__\n-  `PIMENTEL, J. F.; MURTA, L. G. P.; BRAGANHOLO, V.; FREIRE, J.;\n   noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance\n   from Python Scripts. In: International Conference on Very Large Data\n   Bases (VLDB), 2017, Munich,\n   Germany. <https://github.com/gems-uff/noworkflow/raw/master/docs/vldb2017.pdf>`__\n\nHistory\n-------\n\nThe project started in 2013, when Leonardo Murta and Vanessa Braganholo\nwere visiting professors at New York University (NYU) with Juliana\nFreire. At that moment, David Koop and Fernando Chirigati also joined\nthe project. They published the initial paper about noWorkflow in IPAW\n2014. After going back to their home university, Universidade Federal\nFluminense (UFF), Leonardo and Vanessa invited Jo\u00e3o Felipe Pimentel to\njoin the project in 2014 for his PhD. Jo\u00e3o, Juliana, Leonardo and\nVanessa integrated noWorkflow and IPython and published a paper about it\nin TaPP 2015. They also worked on provenance versioning and fine-grained\nprovenance collection and published papers in IPAW 2016. During the same\ntime, David, Jo\u00e3o, Leonardo and Vanessa worked with the YesWorkflow team\non an integration between noWorkflow & YesWorkflow and published a demo\nin IPAW 2016. The research and development on noWorkflow continues and\nis currently under the responsibility of Jo\u00e3o Felipe, in the context of\nhis PhD thesis.\n\n|Contribution Timeline|\n\nQuick Installation\n------------------\n\nThis version of noWorkflow only supports **Python 2.7** and **Python\n3.5**. If you want to use Python 3.6, please try the\n`2.0-alpha <https://github.com/gems-uff/noworkflow/tree/2.0-alpha>`__\nversion.\n\nTo install noWorkflow, you should follow these basic instructions:\n\nIf you have pip, just run:\n\n.. code:: bash\n\n    $ pip install noworkflow[all]\n\nThis installs noWorkflow, PyPosAST, SQLAlchemy, python-future, flask,\nIPython, Jupyter and PySWIP. The only requirements for running\nnoWorkflow are PyPosAST, SQLAlchemy and python-future. The other\nlibraries are only used for provenance analysis.\n\nIf you only want to install noWorkflow, PyPosAST, SQLAlchemy and\npython-future please do:\n\n.. code:: bash\n\n    $ pip install noworkflow\n\nIf you do not have pip, but already have Git (to clone our repository)\nand Python:\n\n.. code:: bash\n\n    $ git clone git@github.com:gems-uff/noworkflow.git\n    $ cd noworkflow/capture\n    $ python setup.py install\n\nThis installs noWorkflow on your system. It will download the\ndependencies from PyPI\n\nUpgrade\n-------\n\nTo upgrade the version of a previously installed noWorkflow using pip,\nyou should run the following command:\n\n.. code:: bash\n\n    $ pip install --upgrade noworkflow[all]\n\nBasic Usage\n-----------\n\nnoWorkflow is transparent in the sense that it requires neither changes\nto the script, nor any laborious configuration. Run\n\n.. code:: bash\n\n    $ now --help\n\nto learn the usage options.\n\nnoWorkflow comes with a demonstration project. To extract it, you should\nrun\n\n.. code:: bash\n\n    $ now demo 1\n    $ cd demo1\n\nTo run noWorkflow with the demo script called *simulation.py* with input\ndata *data1.dat* and *data2.dat*, you should run\n\n.. code:: bash\n\n    $ now run -v simulation.py data1.dat data2.dat\n\nThe *-v* option turns the verbose mode on, so that noWorkflow gives you\nfeedback on the steps taken by the tool. The output, in this case, is\nsimilar to what follows.\n\n.. code:: bash\n\n    $ now run -v simulation.py data1.dat data2.dat\n    [now] removing noWorkflow boilerplate\n    [now] setting up local provenance store\n    [now] collecting definition provenance\n    [now]   registering user-defined functions\n    [now] collecting deployment provenance\n    [now]   registering environment attributes\n    [now]   searching for module dependencies\n    [now]   registering provenance from 1369 modules\n    [now] collecting execution provenance\n    [now]   executing the script\n    [now] the execution of trial 10 finished successfully\n\nEach new run produces a different trial that will be stored with a\nsequential identification number in the relational database.\n\nVerifying the module dependencies is a time consuming step, and\nscientists can bypass this step by using the *-b* flag if they know that\nno library or source code has changed. The current trial then inherits\nthe module dependencies of the previous one.\n\nIt is possible to collect more information than what is collected by\ndefault, such as variable usages and dependencias. To perform a dynamic\nprogram slicing and capture those information, just run\n\n.. code:: bash\n\n    $ now run -e Tracer simulation.py data1.dat data2.dat\n\nTo list all trials, just run\n\n.. code:: bash\n\n    $ now list\n\nAssuming we run the experiment again and then run ``now list``, the\noutput would be as follows. Note that 9 trials were extracted from the\ndemonstration.\n\n.. code:: bash\n\n    $ now list\n    [now] trials available in the provenance store:\n      Trial 1: simulation.py data1.dat data2.dat\n               with code hash 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n               ran from 2016-01-13 19:06:53.740877 to 2016-01-13 19:07:13.250622\n      Trial 2: simulation_complete.py \n               with code hash 705471548f6253da20302333f0a3f79059d79e40\n               ran from 2016-01-13 19:07:13.583000 to 2016-01-13 19:07:39.225553\n      Trial 3: simulation.py data1.dat data2.dat\n               with code hash ba58136d9eac420930d352c127a78988c226dff8\n               ran from 2016-01-13 19:07:39.530637 to 2016-01-13 19:07:58.513666\n      Trial 4: simulation.py data2.dat data1.dat\n               with code hash 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n               ran from 2016-01-13 19:07:58.953236 to 2016-01-13 19:08:20.822072\n      Trial 5: simulation.py <restore 3>\n               with code hash 16d9ba96a1dfa97d26fd5009b19f872a4fa5cb57\n               ran from 2016-01-13 19:08:21.146970 to None\n      Trial 6: simulation.py data1.dat data2.dat\n               with code hash ba58136d9eac420930d352c127a78988c226dff8\n               ran from 2016-01-13 19:08:42.827121 to 2016-01-13 19:09:02.137061\n      Trial 7: simulation.py data1.dat data2.dat\n               with code hash 16d9ba96a1dfa97d26fd5009b19f872a4fa5cb57\n               ran from 2016-01-13 19:09:02.430346 to None\n      Trial 8: simulation_complete.py \n               with code hash 705471548f6253da20302333f0a3f79059d79e40\n               ran from 2016-01-13 19:09:22.637177 to 2016-01-13 19:09:46.327150\n      Trial 9: simulation.py data1.dat data2.dat\n               with code hash 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n               ran from 2016-01-13 19:09:46.711818 to 2016-01-13 19:10:10.998172\n      Trial 10: simulation.py data1.dat data2.dat\n                with code hash 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n                ran from 2016-01-13 19:10:21.587332 to 2016-01-13 19:10:41.900566\n      Trial 11: simulation.py data1.dat data2.dat\n                with code hash 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n                ran from 2016-01-13 19:11:00.033094 to 2016-01-13 19:11:25.632197\n\nTo look at details of an specific trial, use\n\n.. code:: bash\n\n    $ now show [trial]\n\nThis command has several options, such as *-m* to show module\ndependencies; *-d* to show function definitions; *-e* to show the\nenvironment context; *-a* to show function activations; and *-f* to show\nfile accesses.\n\nRunning\n\n.. code:: bash\n\n    $ now show -a 1\n\nwould show details of trial 1. Notice that the function name is preceded\nby the line number where the call was activated.\n\n.. code:: bash\n\n    $ now show -a 1\n    [now] trial information:\n      Id: 1\n      Inherited Id: None\n      Script: simulation.py\n      Code hash: 9f13b9b35f5215a82f9b12f9f32238dddf02646a\n      Start: 2016-01-13 19:06:53.740877\n      Finish: 2016-01-13 19:07:13.250622\n    [now] this trial has the following function activation graph:\n      54: /home/joao/demotest/demo1/simulation.py (2016-01-13 19:07:12.135981 - 2016-01-13 19:07:13.250515)\n          Return value: None\n        38: run_simulation (2016-01-13 19:07:12.136067 - 2016-01-13 19:07:12.201430)\n            Arguments: data_a = 'data1.dat', data_b = 'data2.dat'\n            Return value: [['0.0', '0.6'], ['1.0', '0.0'], ['1.0', '0.0'],\n            ...\n\nTo restore files used by trial 1, run\n\n.. code:: bash\n\n    $ now restore 1\n\nBy default, the restore command will restore the trial script, imported\nlocal modules and the first access to files. Use the option *-s* to\nleave out the script; the option *-l* to leave out modules; and the\noption *-a* to leave out file accesses. The restore command track the\nevolution history. By default, subsequent trials are based on the\nprevious Trial (e.g.\u00a0Trial 2 is based on Trial 1). When you restore a\nTrial, the next Trial will be based on the restored Trial (e.g.\u00a0Trial 3\nbased on Trial 1).\n\nThe restore command also provides a *-f path* option. This option can be\nused to restore a single file. With this command there are extra\noptions: *-t path2* specifies the target of restored file; *-i id*\nidentifies the file. There are 3 possibilities to identify files: by\naccess time, by code hash, or by number of access.\n\n.. code:: bash\n\n    $ now restore 1 -f data1.dat -i \"A|2016-01-13 19:06:59\"\n    $ now restore 1 -f output.png -i 90451b101 -t output_trial1.png\n    $ now restore 1 -f simulation.py -i 1\n\nThe first command queries data1.dat of Trial 1 accessed at \u201c2016-01-13\n19:06:59\u201d, and restores the resulting content after the access. The\nsecond command restores output.png with subhash 90451b101, and save it\nto output_trial1.png. The third command restores the first access to\nsimulation.py, which represents the trial script.\n\nThe option *-f* does not affect evolution history.\n\nThe remaining options of noWorkflow are *diff*, *export*, *history*,\n*dataflow*, and *vis*.\n\nThe *diff* option compares two trials. It has options to compare modules\n(*-m*), environment (*-e*), file accesses (*-f*). It has also an option\nto present a brief diff, instead of a full diff (*\u2013brief*)\n\nThe *export* option exports provenance data of a given trial to Prolog\nfacts, so inference queries can be run over the database.\n\nThe *history* option presents a textual history evolution graph of\ntrials.\n\nThe *dataflow* option exports fine-grained provenance data (captured\nthrough *-e Tracer*) to a graphviz dot representing the dataflow. This\ncommand has many options to change the resulting graph. Please, run \u201cnow\ndataflow -h\u201d to get their descriptions.\n\n.. code:: bash\n\n    $ now dataflow 6 -l -m prospective | dot -Tpng -o prospective.png\n\nThe *vis* option starts a visualization tool that allows interactive\nanalysis:\n\n.. code:: bash\n\n    $ now vis -b\n\nThe visualization tool shows the evolution history, the trial\ninformation, an activation graph. It is also possible to compare\ndifferent trials in the visualization tool.\n\nThe visualization tool requires Flask to be installed. To install Flask,\nyou can run\n\n.. code:: bash\n\n    $ pip install flask\n\nIPython Interface\n-----------------\n\nAnother way to run, visualize, and query trials is to use Jupyter\nnotebook with IPython kernel. To install Jupyter notebook and IPython\nkernel, you can run\n\n.. code:: bash\n\n    $ pip install jupyter\n    $ pip install ipython\n    $ jupyter nbextension install --py --sys-prefix noworkflow\n    $ jupyter nbextension enable noworkflow --py --sys-prefix\n\nThen, to run Jupyter notebook, go to the project directory and execute:\n\n.. code:: bash\n\n    $ jupyter notebook\n\nIt will start a local webserver where you can create notebooks and run\npython code.\n\nBefore loading anything related to noworkflow on a notebook, you must\ninitialize it:\n\n.. code:: python\n\n    In  [1]: %load_ext noworkflow\n        ...: import noworkflow.now.ipython as nip\n\nIt is equivalent to:\n\n.. code:: python\n\n    In  [1]: %load_ext noworkflow\n        ...: nip = %now_ip\n\nAfter that, you can either run a new trial or load an existing object\n(*History*, *Trial*, *Diff*).\n\nThere are two ways to run a new trial:\n\n1- Load an external file\n\n.. code:: python\n\n    In  [1]: arg1 = \"data1.dat\"\n             arg2 = \"data2.dat\"\n\n    In  [2]: trial = %now_run simulation.py {arg1} {arg2}\n        ...: trial\n    Out [2]: <Trial 12> # Loads the trial object represented as a graph\n\n2- Load the code inside a cell\n\n.. code:: python\n\n    In  [3]: arg = 4\n\n    In  [4]: %%now_run --name new_simularion --interactive\n        ...: l = range(arg)\n        ...: c = sum(l)\n        ...: print(c)\n             6\n    Out [4]: <Trial 13> # Loads the trial object represented as a graph\n\n    In  [5]: c\n    Out [5]: 6\n\nBoth modes supports all the ``now run`` parameters.\n\nThe *\u2013interactive* mode allows the cell to share variables with the\nnotebook.\n\nLoading existing trials, histories and diffs:\n\n.. code:: python\n\n    In  [6]: trial = nip.Trial(1) # Loads trial with Id = 1\n        ...: trial # Shows trial graph\n    Out [6]: <Trial 1>\n\n    In  [7]: history = nip.History() # Loads history\n        ...: history # Shows history graph\n    Out [7]: <History>\n\n    In  [8]: diff = nip.Diff(1, 3) # Loads diff between trial 1 and 3\n        ...: diff # Shows diff graph\n    Out [8]: <Diff 1 3>\n\nTo visualize the dataflow of a trial, it is possible to use the dot\nattribute of trial objects: \\```python In [9]: trial.dot Out [9]:\n\nThis command requires an installation of graphviz.\n\nThere are attributes on those objects to change the graph visualization,\nwidth, height and filter values. Please, check the documentation by\nrunning the following code on jupyter notebook:\n\n.. code:: python\n\n    In  [10]: trial?\n\n    In  [11]: history?\n\nIt is also possible to run prolog queries on IPython notebook. To do so,\nyou will need to install SWI-Prolog with shared libraries and the pyswip\nmodule.\n\nYou can install pyswip module with the command:\n\n.. code:: bash\n\n    $ pip install pyswip-alt\n\nCheck how to install SWI-Prolog with shared libraries at\nhttps://github.com/yuce/pyswip/blob/master/INSTALL\n\nTo query a specific trial, you can do:\n\n.. code:: python\n\n    In  [12]: result = trial.query(\"activation(_, 550, X, _, _, _)\")\n        ...: next(result) # The result is a generator\n    Out [12]: {'X': 'range'}\n\nTo check the existing rules, please do:\n\n.. code:: python\n\n    In  [13]: %now_schema prolog -t\n    Out [13]: [...]\n\nFinally, it is possible to run the CLI commands inside ipython notebook:\n\n.. code:: python\n\n    In  [14]: !now export {trial.id}\n    Out [14]: %\n         ...: % FACT: activation(trial_id, id, name, start, finish, caller_activation_id).\n         ...: %\n         ...: ...\n\nIncluded Software\n-----------------\n\nParts of the following software were used by noWorkflow directly or in\nan adapted form:\n\n| The Python Debugger\n| Copyright (c) 2001-2016 Python Software Foundation.\n| All Rights Reserved.\n\nAcknowledgements\n----------------\n\nWe would like to thank CNPq, FAPERJ, and the National Science Foundation\n(CNS-1229185, CNS-1153503, IIS-1142013) for partially supporting this\nwork.\n\nLicense Terms\n-------------\n\nThe MIT License (MIT)\n\nCopyright (c) 2013 Universidade Federal Fluminense (UFF), Polytechnic\nInstitute of New York University.\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the\n\u201cSoftware\u201d), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be included\nin all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \u201cAS IS\u201d, WITHOUT WARRANTY OF ANY KIND, EXPRESS\nOR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n.. |Contribution Timeline| image:: history/history.png\n   :target: history/history.svg\n\n\n",
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