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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "* Summary\n\nThis is a collection of functions I find I've been writing over and over again.\n\nOften times I'm working with hierarchical data structures and attempting\nto apply transformations on them to yield a similar-but-different output\nhierarchical data structures.\n\nThere are lots of ways to do this... these are just utilities for reference to\ndo so.\n\nInput could be nested json, xml, etc and there are [[https://github.com/martinblech/xmltodict][various ways]] to get this into\na python dictionary.\n\nThis python dictionary however still retains the complexity of the original\ndocument.\n\nThis module provides some utility functions (in the spirit of =toolz= / =functoolz=)\nthat can be used to grok hierarchies.\n\n* Hierarchy Path Concepts\n\nMany of the functions receiver a /hierarchy path/ (=hp=).\n\nGiven the example data such as:\n\n#+BEGIN_SRC python\nteams = {\"compile-day\": \"monday\",\n         \"compile-secret\": \"SECRET!\",\n         \"nhl\": [{\"team\": \"stars\"  , \"players\": 10, \"pos\": [\"l\", \"r\", \"c\"]},\n                 {\"team\": \"bruins\" , \"players\": 30 },\n                 {\"team\": \"preds\"  , \"players\": 90 }],\n         \"nba\": [{\"team\": \"mavs\"   , \"players\": -9, \"details\": [{\"who\": \"bill\", \"pos\": [\"r\", \"c\", \"l\"]},\n                                                                {\"who\": \"ted\", \"pos\": [\"d\"]},\n                                                                {\"who\": \"fred\"}]},\n                 {\"team\": \"bucks\"  , \"players\": 3 , \"details\": [{\"who\": \"ken\" , \"pos\": [\"c\"]}]}],}\n#+END_SRC\n\nSome /hierarchy paths/ could look like this:\n\n#+BEGIN_SRC python\n/compile-day\n#+END_SRC\n\nOr any of these:\n#+BEGIN_SRC python\n/nhl/0/details/who\n/nba/*/details/*/\n/nba/*/details/*/who\n#+END_SRC\n\nNotice the use of numbers and/or =*= in the Hierarchy Path that acts as a\nindex-of-list identifier or a globing.\n\nThis idea of a hierarchy path wouldn't work with dictionaries that had numeric\nkeys like =0= and/or dictionaries that have keys such 's =*=.  This hasn't been\nan issue for me, as most of my dictionaries are generated from xml, and these\nkeys wouldn't be [[https://www.w3schools.com/xml/xml_elements.asp][valid tags in xml]].\n\n\n* Function listing and Samples\n** =get_in_hp=\n\nLike toolz's =get_in= but works with a Hierarchy Path:\n\n#+BEGIN_SRC python\nget_in_hp(\"/compile-day\", teams)\n# Output:\n'monday'\n\nget_in_hp(\"/nba/*/details/*/who\", teams)\n# Output:\n[['bill', 'ted', 'fred'], ['ken']]\n\nget_in_hp('/nhl/0/players'   , teams)\n# Output:\n10\n\nget_in_hp('/nba/*/details/*/'    , teams)\n# Output:\n[[{'pos': ['r', 'c', 'l'], 'who': 'bill'},\n  {'pos': ['d'], 'who': 'ted'},\n  {'who': 'fred'}],\n [{'pos': ['c'], 'who': 'ken'}]]\n\n\nget_in_hp('/nba/*/details/*/who' , teams)\n# Output:\n[['bill', 'ted', 'fred'], ['ken']]\n#+END_SRC\n\n** =flatten_recur=\n\nFlattens recursive lists.\n\n#+BEGIN_SRC python\n\nflatten_recur([['bill', 'ted', 'fred'], ['ken']])\n#=> ['bill', 'ted', 'fred', 'ken']\n\nflatten_recur(get_in_hp(\"/nba/*/details/*/who\", teams) )\n#=> =['bill', 'ted', 'fred', 'ken']\n\n#+END_SRC\n\n** =assoc_in_hp=\nLike toolz's =assoc_in= but works with a hierarchy path:\n\n#+BEGIN_SRC python\n# change team 0's name:\nassoc_in_hp({\"nhl\":[{\"team\": \"stars\"  , \"players\": 10},\n                    {\"team\": \"preds\"  , \"players\": 90}]},\n            \"/nhl/0/team/\",\n            \"STARS!\")\n\n# Output:\n{'nhl': [{'team': 'STARS!', 'players': 10},\n         {'team': 'preds', 'players': 90}]}\n\n\n# Change team 1's players count:\nassoc_in_hp({\"nhl\":[{\"team\": \"stars\"  , \"players\": 10},\n                    {\"team\": \"preds\"  , \"players\": 90}]},\n            \"/nhl/1/players\",\n            40)\n\n# Output:\n{'nhl': [{'players': 10, 'team': 'stars'},\n         {'players': 40, 'team': 'preds'}]}\n\n# Should work with deeply nested lists too;\n# Change the second position on the  first team to \"coach!\"\nassoc_in_hp({\"nhl\":[{\"team\": \"stars\"  , \"players\": 10, \"pos\": [\"l\", \"r\", \"c\"]},\n                    {\"team\": \"preds\"  , \"players\": 90}]},\n            \"/nhl/0/pos/2\",\n            \"coach!\")\n\n# Output:\n{'nhl': [{'players': 10, 'pos': ['l', 'r', 'coach!'], 'team': 'stars'},\n         {'players': 90, 'team': 'preds'}]}\n#+END_SRC\n\n** =update_in_hp=\nSimilar to toolz's =update_in=.\n\nUpdates an input dictionaries value with the result of calling a function on\nthat value.\n\n#+BEGIN_SRC python\n\nmy_fn = lambda x: x + 1\n\nupdate_in_hp({\"nhl\":[{\"team\": \"stars\"  , \"players\": 10},\n                     {\"team\": \"preds\"  , \"players\": 90}]},\n             \"/nhl/0/players/\",\n             my_fn, default=0)\n\n{\"nhl\":[{\"team\": \"stars\"  , \"players\": 11},\n        {\"team\": \"preds\"  , \"players\": 90}]},\n#+END_SRC\n\nIt can gets interesting (hopefully useful?) with wildcards.\n\nLets pretend that we want to apply a 10% raise to all players across the board,\non all teams.\n\n\nThis would be our function:\n\n#+BEGIN_SRC python\ndef ten_percent_raise(salary):\n    if salary:\n        return salary * 1.10\n    else:\n        # If they don't have a salary then lets pretend they own us money.\n        return -40\n#+END_SRC\n\nThen just apply that function to the correct path for the input dictionary:\n#+BEGIN_SRC python\nupdate_in_hp({\"payroll\": [{\"team\": \"stars\", \"players\": [{\"player\": \"bill\" , \"salary\": 10, },\n                                                        {\"player\": \"ted\"  , \"salary\": 30, },\n                                                        {\"player\": \"ned\"  , \"salary\": 20, },\n                                                        {\"player\": \"fred\" ,               },]},\n                          {\"team\": \"preds\", \"players\": [{\"player\": \"ken\"  , \"salary\":  5, },\n                                                        {\"player\": \"jen\"  , \"salary\":  8, },\n                                                        {\"player\": \"ben\"  , \"salary\":  9, },\n                                                        {\"player\": \"len\"  ,               },]}]},\n             \"/payroll/*/players/*/salary\", # path is payroll for all teams, all players, salary node.\n             ten_percent_raise)\n\n\n\n{'payroll': [{'players': [{'player': 'bill', 'salary': 11.0},\n                          {'player': 'ted', 'salary': 33.0},\n                          {'player': 'ned', 'salary': 22.0},\n                          {'player': 'fred', 'salary': -40}],\n              'team': 'stars'},\n             {'players': [{'player': 'ken', 'salary': 5.5},\n                          {'player': 'jen', 'salary': 8.8},\n                          {'player': 'ben', 'salary': 9.9},\n                          {'player': 'len', 'salary': -40}],\n              'team': 'preds'}]}\n\n#+END_SRC\n\nHere's another (similar) example; Lets b64 encode all the players ssn's:\n\n#+BEGIN_SRC python\nimport base64\n\ndef make_data_not_super_secure(ssn):\n    return base64.b64encode(ssn.encode()).decode(\"utf-8\")\n\n\nupdate_in_hp({\"payroll\": [{\"team\": \"stars\", \"players\": [{\"player\": \"bill\" , \"salary\": 10, \"ssn\": \"001-01-0001\"},\n                                                        {\"player\": \"ted\"  , \"salary\": 30, \"ssn\": \"001-01-0002\"},\n                                                        {\"player\": \"ned\"  , \"salary\": 20, \"ssn\": \"001-01-0003\"},\n                                                        {\"player\": \"fred\"               , \"ssn\": \"001-01-0004\"},]},\n                          {\"team\": \"preds\", \"players\": [{\"player\": \"ken\"  , \"salary\":  5, \"ssn\": \"001-01-0005\"},\n                                                        {\"player\": \"jen\"  , \"salary\":  8, \"ssn\": \"001-01-0006\"},\n                                                        {\"player\": \"ben\"  , \"salary\":  9, \"ssn\": \"001-01-0007\"},\n                                                        {\"player\": \"len\"                , \"ssn\": \"001-01-0008\"},]}]},\n             \"/payroll/*/players/*/ssn\", # path is payroll for all teams, all players, salary node.\n             make_data_not_super_secure)\n\n{'payroll': [{'players': [{'player': 'bill', 'salary': 10, 'ssn': 'MDAxLTAxLTAwMDE='},\n                          {'player': 'ted', 'salary': 30, 'ssn': 'MDAxLTAxLTAwMDI='},\n                          {'player': 'ned', 'salary': 20, 'ssn': 'MDAxLTAxLTAwMDM='},\n                          {'player': 'fred', 'ssn': 'MDAxLTAxLTAwMDQ='}],\n              'team': 'stars'},\n             {'players': [{'player': 'ken', 'salary': 5, 'ssn': 'MDAxLTAxLTAwMDU='},\n                          {'player': 'jen', 'salary': 8, 'ssn': 'MDAxLTAxLTAwMDY='},\n                          {'player': 'ben', 'salary': 9, 'ssn': 'MDAxLTAxLTAwMDc='},\n                          {'player': 'len', 'ssn': 'MDAxLTAxLTAwMDg='}],\n              'team': 'preds'}]}\n\n\n#+END_SRC\n\n* TODO test results\n- [ ] add this to jenkins/ci\n\n#+BEGIN_SRC bash\ncwd: /Users/me/git-repos/hierarchy_utils/\ncmd: pytest --color=no\n\n==================================================================================================================================================== test session starts =====================================================================================================================================================\nplatform darwin -- Python 3.7.3, pytest-4.4.1, py-1.8.0, pluggy-0.9.0 -- /usr/local/opt/python/bin/python3.7\ncachedir: .pytest_cache\nrootdir: /Users/me/git-repos/hierarchy_utils, inifile: pytest.ini\ncollected 10 items\n\ntests/test_hierarchy_utils.py::test_version PASSED\ntests/test_hierarchy_utils.py::test_path_switchers PASSED\ntests/test_hierarchy_utils.py::test_get_in_usage PASSED\ntests/test_hierarchy_utils.py::test_get_in_hp_atomic PASSED\ntests/test_hierarchy_utils.py::test_get_in_hp_integers PASSED\ntests/test_hierarchy_utils.py::test_get_in_hp_stars_single PASSED\ntests/test_hierarchy_utils.py::test_get_in_hp_stars_multiple PASSED\ntests/test_hierarchy_utils.py::test_my_assoc_in_coll PASSED\ntests/test_hierarchy_utils.py::test_my_assoc_in_hp PASSED\ntests/test_hierarchy_utils.py::test_update_in_hp PASSED\n\n================================================================================================================================================= 10 passed in 0.09 seconds ==================================================================================================================================================\n#+END_SRC\n\n* Ideas/References\n\nhttps://stackoverflow.com/questions/7320319/xpath-like-query-for-nested-python-dictionaries\n\nhttps://jmespath.readthedocs.io/en/latest/\n\nProbably could do a lot of this with xlst... or a billion other ways.\n",
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