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        "author": "Pythonian",
        "author_email": "aurelien.campeas@pythonian.fr",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Database",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Version Control"
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        "description": "TSHISTORY FORMULA\n=================\n\n# Purpose\n\nThis [tshistory][tshistory] component provides a formula language to\nbuild computed series.\n\nFormulas are defined using a simple lisp-like syntax, using a\npre-defined function library.\n\nFormulas are read-only series (you can't `update` or `replace`\nvalues).\n\nThey also have an history, which is built, time stamps wise, using the\nunion of all constituent time stamps, and value wise, by applying the\nformula.\n\nBecause of this the `staircase` operator is available on formulae.\nSome `staircase` operations can have a very fast implementation if the\nformula obeys commutativity rules.\n\n[tshistory]: https://hg.sr.ht/~pythonian/tshistory\n\n\n# Formula\n\n## General Syntax\n\nFormulas are expressed in a lisp-like syntax using `operators`,\npositional (mandatory) parameters and keyword (optional) parameters.\n\nThe general form is:\n\n `(<operator> <param1> ... <paramN> #:<keyword1> <value1> ... #:<keywordN> <valueN>)`\n\nHere are a couple examples:\n\n* `(add (series \"wallonie\") (series \"bruxelles\") (series \"flandres\"))`\n\nHere we see the two fundamental `add` and `series` operators at work.\n\nThis would form a new synthetic series out of three base series (which\ncan be either raw series or formulas themselves).\n\nSome notes:\n\n* operator names can contain dashes or arbitrary caracters\n\n* literal values can be: `3` (integer), `5.2` (float), `\"hello\"`\n  (string) and `#t` or `#f` (true ot false)\n\n\n## Pre-defined operators\n\n### *\n\nPerforms a scalar product on a series.\n\nExample: `(* -1 (series \"positive-things\"))`\n\n### +\n\nAdd a constant quantity to a series.\n\nExample: `(+ 42 (series \"i-feel-undervalued\"))`\n\n### /\n\nPerform a scalar division between numbers or a series and a scalar.\n\nExample: `(/ (series \"div-me\") (/ 3 2))`\n\n### add\n\nLinear combination of two or more series. Takes a variable number\nof series as input.\n\nExample: `(add (series \"wallonie\") (series \"bruxelles\") (series \"flandres\"))`\n\nTo specify the behaviour of the `add` operation in the face of missing\ndata, the series can be built with the `fill` keyword. This option is\nonly really applied when several series are combined. By default, if\nan input series has missing values for a given time stamp, the\nresulting series has no value for this timestamp (unless a fill rule\nis provided).\n\n### clip\n\nSet an upper/lower threashold for a series. Takes a series as\npositional parameter and accepts two optional keywords `min` and `max`\nwhich must be numbers (integers or floats).\n\nExample: `(clip (series \"must-be-positive\") #:min 0)`\n\n### date\n\nProduces an utc timestamp from its input string date in iso format.\n\nThe `tz` keyword allows to specify an alternate time zone.\nThe `naive` keyword forces production of a naive timestamp.\nBoth `tz` and `naive` keywords are mutually exlcusive.\n\n### div\n\nElement wise division of two series.\n\nExample: `(div (series \"$-to-\u20ac\") (series \"\u20ac-to-\u00a3\"))`\n\n### min\n\nComputes the row-wise minimum of its input series.\n\nExample: `(min (series \"station0\") (series \"station1\") (series \"station2\"))`\n\n### max\n\nComputes the row-wise maximum of its input series.\n\nExample: `(max (series \"station0\") (series \"station1\") (series \"station2\"))`\n\n### mul\n\nElement wise multiplication of series. Takes a variable number of series\nas input.\n\nExample: `(mul (series \"banana-spot-price ($)\") (series \"$-to-\u20ac\" #:fill 'ffill'))`\n\nThis might convert a series priced in dollars to a series priced in\neuros, using a currency exchange rate series with a forward-fill\noption.\n\n### naive\n\nAllow demoting a series from a tz-aware index (strongly recommended)\nto a tz-naive index (unfortunately sometimes unavoidable for interop\nwith other tz-naive series).\n\nOne must provide a country code and a target timezone.\n\nExample: `(naive (series \"tz-aware-series-from-poland\") \"PL\" \"Europe/Warsaw\")`\n\n### priority\n\nThe priority operator combines its input series as layers. For each\ntimestamp in the union of all series time stamps, the value comes from\nthe first series that provides a value.\n\nExample: `(priority (series \"realized\") (series \"nominated\") (series \"forecasted\"))`\n\nHere `realized` values show up first, and any missing values come from\n`nominated` first and then only from `forecasted`.\n\n### resample\n\nResamples its input series using `freq` and the aggregation method\n`method` (as described in the pandas documentation).\n\nExample: `(resample (series \"hourly\") \"D\")`\n\n### row-mean\n\nThis operator computes the row-wise mean of its input series using the\nseries `weight` option if present. The missing points are handled as\nif the whole series were absent.\n\nExample: `(row-mean (series \"station0\") (series \"station1\" #:weight 2) (series \"station2\"))`\n\nWeights are provided as a keyword to `series`. No weight is\ninterpreted as 1.\n\n### series\n\nThe `series` operator accepts several keywords:\n\n* `fill` to specify a filling policy to avoid `nans` when the series\n  will be `add`ed with others; accepted values are `\"ffill\"`\n  (forward-fill), `\"bfill\"` (backward-fill) or any floating value.\n\n* `prune` to indicate how many points must be truncated from the tail\n  end (useful for priorities).\n\nFor instance in `(add (series \"a\" #:fill 0) (series \"b\")` will make\nsure that series `a`, if shorter than series `b` will get zeroes\ninstead of nans where `b` provides values.\n\nIn `(series \"realized\" #:prune 3)` we would drop the last three points.\n\n### slice\n\nThis allows cutting a series at date points. It takes one positional\nparameter (the series) and two optional keywords `fromdate` and\n`todate` which must be strings in the [iso8601][iso8601] format.\n\nExample: `(slice (series \"cut-me\") #:fromdate \"2018-01-01\")`\n\n[iso8601]: https://en.wikipedia.org/wiki/ISO_8601\n\n### std\n\nComputes the standard deviation over its input series.\n\nExample: `(std (series \"station0\") (series \"station1\") (series \"station2\"))`\n\n### timedelta\n\nTakes a timestamp and a number of years, months, weekds, days,\nhours, minutes (int) and computes a new date according to the asked\ndelta elements.\n\nExample: `(timedelta (date \"2020-1-1\") #:weeks 1 #:hours 2)`\n\n### today\n\nProduces a timezone-aware timestamp as of today\n\nThe `tz` keyword allows to specify an alternate time zone.\nThe `naive` keyword forces production of a naive timestamp.\nBoth `tz` and `naive` keywords are mutually exlcusive.\n\nExample: `(today)`\n\n\n# Registering new operators\n\nThis is a fundamental need. Operators are fixed python functions\nexposed through a lispy syntax. Applications need a variety of fancy\noperators.\n\n## declaring a new operator\n\nOne just needs to decorate a python with the `func` decorator:\n\n```python\n  from tshistory_formula.registry import func\n\n  @func('identity')\n  def identity(series):\n      return series\n```\n\nThe operator will be known to the outer world by the name given to\n`@func`, not the python function name (which can be arbitrary).\n\nThis is enough to get a working transformation operator.  However\noperators built to construct series rather than just transform\npre-existing series are more complicated.\n\n## custom series operator\n\nWe start with an example, a `shifted` operator that gets a series with shifted\nfrom_value_date/to_value_date boundaries by a constant `delta` amount.\n\nWe would use it like this: `(shifted \"shiftme\" #:days -1)`\n\nAs we can see the standard `series` operator won't work there, that is\napplying a shift operator (`(shift (series \"shiftme\"))`) *after* the\ncall to series is too late. The from/to implicit parameters have\nalready been handled by `series` itself and there is nothing left to\n*shift*.\n\nHence `shifted` must be understood as an alternative to `series` itself.\nHere is a possible implementation:\n\n```python\n  from tshistory_formula.registry import func, finder\n\n  @func('shifted')\n  def shifted(__interpreter__, name, days=0):\n      args = __interpreter__.getargs.copy()\n      fromdate = args.get('from_value_date')\n      todate = args.get('to_value_date')\n      if fromdate:\n          args['from_value_date'] = fromdate + timedelta(days=days)\n      if todate:\n          args['to_value_date'] = todate + timedelta(days=days)\n\n      return __interpreter__.get(name, args)\n\n  @finder('shifted')\n  def find_series(cn, tsh, tree):\n      return {\n          tree[1]: tsh.metadata(cn, tree[1])\n      }\n```\n\nAs we can see, we use a new `finder` protocol. But first let's examine\nhow the `shiftme` operator is implemented.\n\nFirst it takes a special `__interpreter__` parameter, which will\nreceive the formula interpreter object, providing access to an\nimportant internal API of the evaluation process.\n\nIndeed from the interpreter we can read the `getargs` attribute, which\ncontains a dictionary of the actual query mapping. We are specially\ninterested in the `from_value_date` and `to_value_date` items in our\nexample, but all the parameters of `tshistory.get` are available\nthere.\n\nOnce we have shifted the from/to value date parameter we again use the\ninterpreter to make a call to `get` which will in turn perform a call\nto the underlying `tshistory.get` (which, we don't know in advance,\nmay yield a primary series or another formula computed series).\n\nImplementing the operator this way, we actually miss two important\npieces of information:\n\n* the system cannot determine a series is _produced_ by the `shifted`\n  operator like it can with `series`\n\n* and because of this it cannot know the technical metadata of the\n  produced series (e.g. the `tzaware` attribute)\n\nThis is where the `finder` protocol and its decorator function comes\ninto play. For `shifted` we define a finder. It is a function that\ntakes the db connection (`cn`), time series protocol handler (`tsh`)\nand formula syntax tree (`tree`), and must return a mapping from\nseries name to its metadata.\n\nThe tree is an obvious Python data structure representing a use of the\noperator in a formula.\n\nFor instance because of the `shifted` python signature, any use will\nbe like that:\n\n* in lisp `... (shifted \"shift-me\" #:hours +1) ... ` (the dots\n  indicate that it can be part of a larger formula)\n\n* tree in python: `['shifted', \"shift-me\", 'hours', 1]`\n\nThe name is always in position 1 in the list. Hence the implementation\nof the shifted *finder*:\n\n```python\n      return {\n          tree[1]: tsh.metadata(cn, tree[1])\n      }\n```\n\nFor the metadata we delegate the computation to the underlying series metadata.\n\nWe might want to provide an ad-hoc metadata dictionary if we had a\nproxy operator that would forward the series from an external source:\n\n```python\n  @func('proxy')\n  def proxy(\n          __interpreter__,\n          series_uid: str,\n          default_start: date,\n          default_end : date) -> pd.Series:\n      i = __interpreter__\n      args = i.getargs.copy()\n      from_value_date = args.get('from_value_date') or default_start\n      to_value_date = args.get('to_value_date') or default_end\n\n      proxy = ProxyClient()\n      return proxy.get(\n          series_uid,\n          from_value_date,\n          to_value_date,\n      )\n\n  @finder('proxy')\n  def proxy(cn, tsh, tree):\n      return {\n          tree[1]: {\n              'index_type': 'datetime64[ns]',\n              'tzaware': False,\n              'value_type': 'float64'\n          }\n      }\n```\n\nHere, because we have no other means to know (and the proxy provides\nsome useful documentation), we write the metadata ourselves\nexplicitly.\n\nAlso note how accessing the `__interpreter__` again is used to forward\nthe query arguments.\n\n\n## Editor Infos\n\nThe `tshistory_formula` package provides a custom callback for the\n`editor` capabilities of [tshistory_editor][tshistory_editor].\n\nA dedicated protocol is available to inform the editor on the way\nto decompose/display a formula.\n\nExample of such a function:\n\n```python\n from tshistory_formula.registry import editor_info\n\n @editor_info\n def operator_with_series(builder, expr):\n     for subexpr in expr[1:]:\n         with builder.series_scope(subexpr):\n             builder.buildinfo_expr(subexpr)\n\n```\n\nThe exact ways to use the builder will be provided soon.\n\n[tshistory_editor]: https://hg.sr.ht/~pythonian/tshistory_editor\n\n\n# Series API\n\nA few api calls are added to the `tshistory` base:\n\n* `.register_formula` to define a formula\n\n* `.eval_formula` to evaluate on-the-fly a formula (useful to check\n  that it computes before registering it)\n\n## register_formula\n\nExemple:\n\n```python\n  tsh.register_formula(\n      cn,\n      'my-sweet-formula',\n      '(* 3.14 (series \"going-round\"))',\n      reject_unkown=True,\n      update=True\n  )\n```\n\nFirst comes the db connection object, second the formula name, last\nthe actual expression.\n\nThe `reject_unknown` parameter, which is True by default, makes the\nmethod fail if one constituent of the formula does not exist\n(e.g. \"going-round\" is neither a primary series or a formula).\n\nThe `update` parameter tells wether an existing formula can be\noverwritten (False by default).\n\n# eval_formula\n\nExample:\n\n```python\n >>> tsh.eval_formula(cn, '(* 3.14 (series \"going-round\"))')\n ...\n 2020-01-01    3.14\n 2020-01-02    6.28\n 2020-01-03    9.42\n dtype: float64\n```\n\n# Command line\n\nThe `tsh` command carries formula specific subcommands. The output\nbelow shows only the specific formula subcommands:\n\n```shell\n$ tsh\nUsage: tsh [OPTIONS] COMMAND [ARGS]...\n\nOptions:\n  --help  Show this message and exit.\n\nCommands:\n  ingest-formulas           ingest a csv file of formulas Must be a...\n```\n\n\n",
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