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README.rst
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README.rst
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BI for Publik
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=============
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w.c.s. OLAP
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~~~~~~~~~~~
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@ -27,227 +24,3 @@ cube.
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--key KEY HMAC key for signatures
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--pg-dsn PG_DSN Psycopg2 DB DSN
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--schema SCHEMA schema name
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Bi-Joe
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~~~~~~
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BI Joe is a library and a Django application to simplify querying a postgresql
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database containing a star schema with BI annotations in order to
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easily produce BI dashboards
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It's inspired by the Cubes project.
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Feature
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~~~~~~~
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* use PostgreSQL database as datastore,
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* declare joins to define star schemas of your cubes
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* declare dimensions as SQL query or exepression defining, label, group by,
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ordering or member list.
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* declare measure as SQL expression using aggregate functions
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Missing features
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~~~~~~~~~~~~~~~~
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* hierarchical dimensions
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* measure necessiting a rollup (percentage based on count() of facts at an upper
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level)
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Model
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~~~~~
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You declare your model using JSON files, those JSON files are targeted by a list
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of glob patterns in the Djagno setting BIJOE_SCHEMAS.
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.. code:: python
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BIJOE_SCHEMAS = ['/var/lib/bijoe/cubes/*.model']
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The JSON model files must conform to this schema:
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* name: technical identifier of the mode, a short string without space if
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preferred
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* label: any string describing the model
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* pg_dsn: string describing the connection to PostgreSQL, as expected by
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psycopg2, ex.: `"dbname=olap_db user=admin password=xxx"`
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* search_path: search path to set set if relations are not all in the public
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schema,
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* cubes: the list of cube descriptors,
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* name: technical identifier of the cube, same remark thant for models,
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* label: as for model,
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* fact_table: name of the table storing facts
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* key: column of the table identifying individual facts
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* joins: list of equality joins, all joins are RIGHT OUTER JOINS, table are
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cross joined when drill involve dimensions using multiple joins.
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* name: SQL identifier for naming the join,
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* table: name of the relation being joined,
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* master: table and column indicating the left part of the equality
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condition for the join, you can use `mydim_id` to reference the fact
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table or `otherjoin.mydim_id` to reference another join.
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* detail: name of the column on the joined table for the equality condition,
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* kind: type of join, must be `inner`, `left` or `right`, default is right.
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* dimensions: list of dimension descriptors,
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* name: technical identifier of the dimension, it will be used to name the
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dimension in the API,
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* label: human description for the dimension, used in UIs,
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* join: list of join names, indicate that some joins must be used when using
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this dimension,
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* type: indicate the type of the dimension, numerical, time-like,
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geographical, duration, etc..
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* value: SQL expression giving the value for the dimension,
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it can be different than the value used for filtering or grouping,
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* sql_filter: SQL expression that will be used in the SQL template
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`<sql_filter> IN (...)` when filtering along a dimension,
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* value_label: SQL expression giving the shown value
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* group_by: SQL expression to group facts along a dimension, default is to
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use value
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* order_by: SQL expression to order dimension values, default is to use
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value
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* measures: list of measure descriptors
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* name: as for models,
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* label: as for models,
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* type: type of the measure: integer or duration,
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* expression: SQL expression indicating how to compute the aggregate,
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ex.: `avg(delay)`, `count(product_type)`.
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Example
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+++++++
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.. code:: json
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{
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"name" : "cam",
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"label" : "cam",
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"pg_dsn" : "dbname=wcs-olap",
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"search_path" : [
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"cam",
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"public"
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],
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"cubes" : [
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{
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"name" : "all_formdata",
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"label" : "Tous les formulaires",
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"fact_table" : "formdata",
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"key" : "id",
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"joins" : [
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{
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"name" : "formdef",
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"master" : "formdef_id",
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"detail" : "id",
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"table" : "formdef"
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},
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{
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"name" : "dates",
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"master" : "receipt_time",
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"detail" : "date",
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"table" : "dates"
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}
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],
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"dimensions" : [
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{
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"name" : "formdef",
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"label" : "formulaire",
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"type" : "integer",
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"join" : ["formdef"],
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"value" : "formdef.id",
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"value_label" : "formdef.label"
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},
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{
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"join": [
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"receipt_time"
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],
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"label": "date de la demande",
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"name": "receipt_time",
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"type": "date",
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"value": "receipt_time.date"
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},
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],
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"measures" : [
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{
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"name": "count",
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"label": "nombre de demandes",
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"type": "integer",
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"expression": "count({fact_table}.id)",
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},
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{
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"name" : "avg_endpoint_delay",
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"label" : "Délai de traitement",
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"type" : "duration",
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"expression" : "avg(endpoint_delay)"
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}
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]
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}
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]
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}
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API
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~~~
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Model description is handled by `bijoe.schema` and model querying by
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`bijoe.engine`.
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bijoe.schema.Warehouse
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++++++++++++++++++++++
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`Warehouse` is the main class to manipulate models, it has two class methods:
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* `from_json(d)` which transform a dictionnary obtained by eventually parsing
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some JSON file to a Warehouse object.
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* `to_json()` which transform a Warehouse object into a JSON compatible
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dictionnary.
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bijoe.engine.Engine
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+++++++++++++++++++
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`Engine(warehouse)` is the entry-point for querying a model, you get an
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`EngineCube` object by indexing the engine with the name of a cube.
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.. code:: python
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cube = Engine(warehouse)['mycube']
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You can query your cube using the `query` method.
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.. code:: python
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cube.query(filters=[('year', [2013, 2014, 2015])],
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drilldown=['year', 'product'],
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measures=['count', 'avg_sale'])
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It returns a sequence of rows whose elements are the values of the drilldown
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dimensions in the same order as in the query followed by the values of the
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measures also in the same ordre as the query.
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The `count` measure is a special measure which is always present whose
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expression is always `count({fact_table}.{key})` where `fact_table` and `key`
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are the corresponding attributes of the cube.
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@ -1,5 +1,5 @@
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publik-bi (0.0.0-1) jessie; urgency=low
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wcs-olap (0.0.0-1) jessie; urgency=low
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* Initial package, targetting jessie.
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-- Benjamin Dauvergne <bdauvergne@entrouvert.com> Mon, 18 May 2016 13:59:28 +0200
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-- Benjamin Dauvergne <bdauvergne@entrouvert.com> Mon, 4 Jun 2016 13:59:28 +0200
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@ -1,4 +1,4 @@
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Source: publik-bi
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Source: wcs-olap
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Section: python
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Priority: optional
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Maintainer: Benjamin Dauvergne <bdauvergne@entrouvert.com>
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@ -7,8 +7,8 @@ Standards-Version: 3.9.1
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X-Python-Version: >= 2.7
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Homepage: http://dev.entrouvert.org/projects/publik-bi/
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Package: publik-bi
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Package: wcs-olap
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Architecture: all
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Depends: ${python:Depends}
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XB-Python-Version: ${python:Versions}
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Description: Publik BI tools
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Description: Export w.c.s. datas into a snowflake schema built on PostgreSQL
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python-ldap python-ldap
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isodate python-isodate
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psycopg2 python-psycopg2
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6
setup.py
6
setup.py
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return '0.0.0'
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setup(name="publik-bi",
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setup(name="wcs-olap",
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version=get_version(),
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license="AGPLv3+",
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description="Export w.c.s. data to an OLAP cube",
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maintainer_email="bdauvergne@entrouvert.com",
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packages=find_packages(),
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include_package_data=True,
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install_requires=['requests', 'django', 'psycopg2', 'isodate', 'Django-Select2',
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'XStatic-ChartNew.js'],
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install_requires=['requests', 'psycopg2', 'isodate'],
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entry_points={
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'console_scripts': ['wcs-olap=wcs_olap.cmd:main'],
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},
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scripts=['bijoe-ctl'],
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cmdclass={'sdist': eo_sdist})
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