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 
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 
{'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']
...    
<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
...     
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.]])