.. _table-py:

########
table.py
########


def table2array(table):
=======================

Create a single Numpy array from XML cash_flow table::

    >>> from lxml import etree
    >>> from simo.builder.modelbase.table import table2array
    >>> tablexml = u"""<table>
    ...        <cash_flow>1 1 1 160 370 55</cash_flow>
    ...        <cash_flow>2 1 1 160   400 56 </cash_flow>
    ...    </table>"""
    >>> elem = etree.fromstring(tablexml)
    >>> arr = table2array(elem)
    >>> arr
    array([[   1.,    1.,    1.,  160.,  370.,   55.],
           [   2.,    1.,    1.,  160.,  400.,   56.]])


def table2dict(table):
======================

Create a dictionary with the classifying values joined together as the 
dictionary key and the cash flow as the value; i.e., a row 
'1 2 3 4 5 6 7 100' translates to cdict['1:2:3:4:5:6:7']=100::

    >>> from lxml import etree
    >>> from simo.builder.modelbase.table import table2dict
    >>> tablexml = u"""<table>
    ...        <cash_flow>1 1 1 160 370 55</cash_flow>
    ...        <cash_flow>2 1 1 160 400 56</cash_flow>
    ...    </table>"""
    >>> elem = etree.fromstring(tablexml)
    >>> dct = table2dict(elem)
    >>> dct
    {'1:1:1:160:370': 55.0, '2:1:1:160:400': 56.0}


def parse_classifiers(ns, elem):
================================

Parse cash flow table and/or trend classifiers::

    >>> class Lexicon(object):
    ...     def variable_ind(self, level, var, active=False):
    ...         return (1, 1)
    ...     def level_ind(self, level):
    ...         return 1
    >>> class Validator(object):
    ...     def __init__(self, lexicon):
    ...         self.errors = set([])
    ...         self.warnings = []
    ...         self.lexicon = lexicon
    ...     def variable_ind(self, level, variable):
    ...         return self.lexicon.variable_ind(level, variable)
    ...     def level_ind(self, level):
    ...         return self.lexicon.level_ind(level)
    ...     def add_error(self, msg):
    ...         self.errors.add(msg)
    ...     def add_warnings(self, msg):
    ...         self.warnings.append(msg)
    ...     model_type = 'cash_flow_table'
    >>> validator = Validator(Lexicon())
    >>> from lxml import etree
    >>> from simo.builder.modelbase.table import parse_classifiers
    >>> cfierxml = u"""<classifiers>
    ...     <classifier>
    ...         <variable>PRICE_REGION</variable>
    ...         <level>comp_unit</level>
    ...     </classifier>
    ...     <classifier to_db="true">
    ...         <variable>SP</variable>
    ...         <level>comp_unit</level>
    ...     </classifier>
    ...     <classifier>
    ...        <variable>assortment</variable>
    ...        <level>within-operation</level>
    ...        <labels>
    ...           <label>
    ...              <value>1</value>
    ...              <name>log</name>
    ...           </label>
    ...           <label>
    ...              <value>2</value>
    ...              <name>pulp</name>
    ...           </label>
    ...        </labels>
    ...     </classifier>
    ... </classifiers>"""
    >>> elem = etree.fromstring(cfierxml)
    >>> ns = ""
    >>> cfiers, db_cfiers, db_cfiers_ind, corder, keycount = \
    ...    parse_classifiers(ns, elem, validator)
    >>> 'PRICE_REGION' in cfiers
    True
    >>> c = cfiers['assortment']
    >>> c.name
    'assortment'
    >>> c.level
    'within-operation'
    >>> c.ind
    >>> c.order
    2
    >>> c.variable_type
    >>> len(c.labels)
    2
    >>> l = c.labels[1]
    >>> l.label
    'pulp'
    >>> l.value
    2
    >>> db_cfiers
    ['SP']
    >>> keycount
    2


def parse_tables(ns, elem):
===========================

Parse <tables> element contents into ctypes array, value
dictionary and activation time objects for each table::

    >>> from lxml import etree
    >>> from simo.builder.modelbase.table import parse_tables
    >>> tablesxml = u"""<tables>
    ...        <table>
    ...            <cash_flow>1 1 1 160 370 55</cash_flow>
    ...            <cash_flow>2 1 1 160 400 56</cash_flow>
    ...        </table>
    ...    </tables>"""
    >>> elem = etree.fromstring(tablesxml)
    >>> tables, n = parse_tables(elem)
    >>> tables[0].array
    array([[   1.,    1.,    1.,  160.,  370.,   55.],
           [   2.,    1.,    1.,  160.,  400.,   56.]])
    >>> tables[0].dict
    {'1:1:1:160:370': 55.0, '2:1:1:160:400': 56.0}
    >>> n
    1


********************
class Label(object):
********************

Class for cash flow table classifier labels

Attributes:

- label: label text
- value: label value


*************************
class Classifier(object):
*************************

Class for cash flow table classifiers

Attributes:

- level: classifier level as string
- name: classifier name as string
- ind: classifier level-variable indices a tuple: (level, variable,)
- labels: list of Label objects
- order: classifier order as list of integers


********************
class Array(object):
********************

Class for cash flow table arrays

Attributes:

- array: table values in a numpy array
- dict: table values in a dictionary
- start_date: date object
- end_date: date object