.. _cashflowtable-py:

################
cashflowtable.py
################

::

    >>> from simo.builder.modelbase.modelbase import ModelbaseDef
    >>> tdf = open('../../simulator/xml/schemas/Typedefs_SIMO.xsd')
    >>> typedef = tdf.read()
    >>> tdf.close()
    >>> sf = open('../../simulator/xml/schemas/cash_flow_table.xsd')
    >>> schema = sf.read()
    >>> sf.close()
    >>> xml = u'''<cash_flow_tables xmlns="http://www.simo-project.org/simo"
    ...      xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    ...    <cash_flow_table name="timber_prices" logged="true">
    ...       <base>
    ...          <classifiers>
    ...             <classifier>
    ...                <variable>PRICE_REGION</variable>
    ...                <level>comp_unit</level>
    ...             </classifier>
    ...             <classifier to_db="true">
    ...                <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>
    ...          <tables>
    ...              <table>
    ...                  <cash_flow>1 1 1 160 370 55</cash_flow>
    ...                  <cash_flow>2 1 1 160 400 56</cash_flow>
    ...                  <cash_flow>2 1 1 160 430 58</cash_flow>
    ...                  <cash_flow>2 1 1 160 460 60</cash_flow>
    ...                  <cash_flow>2 1 1 160 490 60</cash_flow>
    ...                  <cash_flow>1 1 2 70 300 20</cash_flow>
    ...                  <cash_flow>2 1 2 70 300 20</cash_flow>
    ...                  <cash_flow>1 2 2 70 300 20</cash_flow>
    ...                  <cash_flow>2 2 2 70 300 20</cash_flow>
    ...                  <cash_flow>1 3 2 70 300 20</cash_flow>
    ...                  <cash_flow>2 3 2 70 300 20</cash_flow>
    ...              </table>
    ...           </tables>
    ...       </base>
    ...    </cash_flow_table>
    ... </cash_flow_tables>'''
    >>> mb = ModelbaseDef(typedef)
    >>> mb.schema = ('cash_flow_table', schema)
    >>> try:
    ...     mb.xml = ('testxml', xml, None, '', 'cash_flow_table')
    ... except ValueError, e:
    ...     e.message
    'errors in xml to object conversion'
    >>> mb.errors # doctest: +NORMALIZE_WHITESPACE
    set(["No lexicon set when validating variable for cash flow table 
          collection 'testxml'", 
         "No lexicon set when adding model for cash flow table 
          collection 'testxml'"])
    >>> class Lexicon(object):
    ...     def __init__(self):
    ...         self.models = {}
    ...     def get_variable_ind(self, level, var, active=False):
    ...         return (1, 1)
    ...     def get_level_ind(self, level):
    ...         return 1
    ...     def add_model(self, mtype, mname):
    ...         if mtype not in self.models:
    ...             self.models[mtype] = set()
    ...         self.models[mtype].add(mname)
    >>> mb.xml = ('testxml', xml, Lexicon(), '', 'cash_flow_table')
    >>> mb.xml['cash_flow_table']['testxml'][:17]
    u'<cash_flow_tables'
    >>> mb.errors
    set([])
    >>> mb.all_ok
    True

****************************
class CashFlowTable(object):
****************************

Class for cash flow tables used in operations::
    
    >>> cf = mb.obj['cash_flow_table']['testxml']['timber_prices']
    >>> cf.classifiers # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    {'PRICE_REGION': <simo.builder.modelbase.table.TableClassifier...>, 
    'assortment': <simo.builder.modelbase.table.TableClassifier...>}
    >>> cf.classifier_order
    ['PRICE_REGION', 'assortment']
    >>> cf.keycount
    1
    >>> cf.persist
    True
    >>> cf.db_cfiers
    ['assortment']

def __init__(self, ns, elem, validator, dirs):
==============================================

Initialize cash flow table


def _parse_base(self, ns, elem, validator):
============================================

Parse the classifiers used in the cash flow table as well as the table data:


def _get_trend(self, date):
============================

Set currently active trend, None if no active trends


def _adjust_table(self, trend, date):
======================================

Adjust cash flow table values with the current trend


********************
class Trend(object):
********************

Class for cash flow table time trends

Attributes:

- start: date object
- end: date object
- classifiers: dictionary of classifier objects
- keycount: number of classifiers as integer
- classifier_order: classifier order as list of integers
- factor_array: trend factors in a numpy array
- factor_dict: trend factors in a dictionary


def _parse_trend(self, ns, elem, validator):
============================================

Parse trend element into Trend object::

    >>> from lxml import etree
    >>> from simo.builder.modelbase.cashflowtable import Trend
    >>> trendxml = u"""
    ...     <trend>
    ...         <time_period>
    ...             <start>2000-01-01</start>
    ...             <end>2001-01-01</end>
    ...         </time_period>
    ...         <classifiers>
    ...             <classifier>
    ...                 <variable>SP</variable>
    ...                 <level>stratum</level>
    ...             </classifier>
    ...         </classifiers>
    ...         <cumulative_factors>
    ...             <factor>1 1.1</factor>
    ...             <factor>2 1.2</factor>
    ...             <factor>3 1.3</factor>
    ...         </cumulative_factors>
    ...     </trend>"""
    >>> elem = etree.fromstring(trendxml)
    >>> ns = ""
    >>> tr = Trend(ns, elem, mb)
    >>> tr.start
    datetime.date(2000, 1, 1)
    >>> tr.end
    datetime.date(2001, 1, 1)
    >>> tr.factor_array
    array([[ 1. ,  1.1],
           [ 2. ,  1.2],
           [ 3. ,  1.3]])
    >>> sorted([(k, '%.1f' % v) for k, v in tr.factor_dict.items()])
    [('1', '1.1'), ('2', '1.2'), ('3', '1.3')]


def adjust(self, date, values, factor):
=======================================

Calculate interpolation multiplier based on given date::

    >>> import datetime, numpy
    >>> dt = datetime.date(2005,1,1)
    >>> vals = numpy.ones(1, dtype=float)
    >>> factor = 0.05
    >>> tr.adjust(dt, vals, factor)
    array([ 1.25])


****************************
class CashFlowTable(object):
****************************

Class for cash flow tables

Attributes:

- name: cash flow table name
- classifiers: dictionary:
    - keys: classifier indices
    - values: Classifier objects
- keycount: classifier key count
- arrays: list of Array objects
- Narrays: number of tables (arrays)
- active_array: currently active table
- trends: list of Trend objects


def __init__(self, ns, elem, validator, dirs):
==============================================

Parses the XML data into class attributes::

    >>> mb.obj['cash_flow_table']['testxml']['timber_prices'] 
    ...    # doctest: +ELLIPSIS
    <simo.builder.modelbase.cashflowtable.CashFlowTable object at 0x...>
    >>> ct = mb.obj['cash_flow_table']['testxml']['timber_prices']
    >>> for i, j in ct.classifiers.iteritems(): print i, j 
    ...     # doctest: +ELLIPSIS
    PRICE_REGION <simo.builder.modelbase.table.TableClassifier object at ...>
    assortment <simo.builder.modelbase.table.TableClassifier object at ...>
    >>> ct.arrays[0].array
    array([[   1.,    1.,    1.,  160.,  370.,   55.],
           [   1.,    1.,    2.,   70.,  300.,   20.],
           [   1.,    2.,    2.,   70.,  300.,   20.],
           [   1.,    3.,    2.,   70.,  300.,   20.],
           [   2.,    1.,    1.,  160.,  400.,   56.],
           [   2.,    1.,    1.,  160.,  430.,   58.],
           [   2.,    1.,    1.,  160.,  460.,   60.],
           [   2.,    1.,    1.,  160.,  490.,   60.],
           [   2.,    1.,    2.,   70.,  300.,   20.],
           [   2.,    2.,    2.,   70.,  300.,   20.],
           [   2.,    3.,    2.,   70.,  300.,   20.]])
    >>> keys = ct.arrays[0].dict.keys()
    >>> keys.sort()
    >>> for key in keys:
    ...     print key, ct.arrays[0].dict[key]
    1:1:1:160:370 55.0
    1:1:2:70:300 20.0
    1:2:2:70:300 20.0
    1:3:2:70:300 20.0
    2:1:1:160:400 56.0
    2:1:1:160:430 58.0
    2:1:1:160:460 60.0
    2:1:1:160:490 60.0
    2:1:2:70:300 20.0
    2:2:2:70:300 20.0
    2:3:2:70:300 20.0


def _parse_base(self, ns, elem):
================================

Parses the classifiers used in the cash flow table as well as the table data


def _get_trend(self, date):
===========================

Set currently active trend, None if no active trends


def _check_classifiers(self, cfiers):
=====================================

docstring for __check_classifiers


def _adjust_table(self, trend, date):
=====================================

Adjust cash flow table values with the current trend


def set_active_array(self, date):
=================================

Set active array and adjust table values with trend::

    >>> import datetime
    >>> dt = datetime.date(2005,1,1)
    >>> ct.set_active_array(dt)


def get_values(self, classifiers, labels):
==========================================

Get values from the active array::

    >>> ct.get_values([1], [1])
    array([ 55.,  20.,  56.,  58.,  60.,  60.,  20.])
    >>> ct.get_values([0], [1])
    array([ 55.,  20.,  20.,  20.])
    >>> ct.get_values([0], [1])
    array([ 55.,  20.,  20.,  20.])
    >>> ct.get_values([0], [2])
    array([ 56.,  58.,  60.,  60.,  20.,  20.,  20.])
    >>> ct.get_values([0, 1], [1, 1])
    array([ 55.,  20.])
    >>> ct.get_values([0, 1], [2, 2])
    array([ 20.])


def set_values(self, classifiers, labels, values):
==================================================

Set values of active array::

    >>> ct.set_values([0, 1], [1, 1], numpy.array([65, 66]))
    >>> ct.active_array.array
    array([[   1.,    1.,    1.,  160.,  370.,   65.],
           [   1.,    1.,    2.,   70.,  300.,   66.],
           [   1.,    2.,    2.,   70.,  300.,   20.],
           [   1.,    3.,    2.,   70.,  300.,   20.],
           [   2.,    1.,    1.,  160.,  400.,   56.],
           [   2.,    1.,    1.,  160.,  430.,   58.],
           [   2.,    1.,    1.,  160.,  460.,   60.],
           [   2.,    1.,    1.,  160.,  490.,   60.],
           [   2.,    1.,    2.,   70.,  300.,   20.],
           [   2.,    2.,    2.,   70.,  300.,   20.],
           [   2.,    3.,    2.,   70.,  300.,   20.]])