expression.py

>>> import datetime

class OutputExpr(Output):

Class for expression output, i.e. output variables defined with a syntax similar to optimisation task definitions

def __init__(self, datadb, data_type, id_list, main_level, result_type, output_formats, output_filenames, output_constraint=None, default_decimal_places=1, archiving=False, result_padding=True, aggregation_def=None, expression_def=None, opres_vars=None):

Creates the output class. fully inherited, executes self.run()

>>> from simo.output.test.init_objects import *
>>> from collections import defaultdict
>>> import datetime
>>> import numpy
>>> idata = InitData()
>>> idata.init()
>>> testdb = idata.testdb
>>> const_obj = idata.const_obj
>>> aggr_obj = idata.aggr_obj
>>> expr_obj = idata.expr_obj
>>> from simo.output.expression import OutputExpr
>>> out = OutputExpr(testdb, 'result', ['stand1'], 'stratum',
...                  'optimized',
...                  'output/test/expression.txt', TestLogger(),
...                  const_obj, 1, False, True, aggr_obj, expr_obj,
...                  ['cash_flow', 'Income', 'Scrapwood', 'Volume',
...                  'BIOMASS_branches', 'BIOMASS_stumps'])
>>> try: 
...     f = open('output/test/expression.txt', 'r')
...     for line in f:
...         print line.rstrip('\n')
... finally:
...     f.close()
level;unit;iteration;branch;1st expression;2nd expression
comp_unit;stand1;0;0;0.0;0.0
comp_unit;stand1;1;0;0.0;0.0
comp_unit;stand2;0;0;0.0;0.0
comp_unit;stand2;0;1;0.0;0.0
comp_unit;stand2;1;0;0.0;0.0
comp_unit;stand2;1;1;0.0;0.0
stratum;stratum1-1;0;0;0.0;0.0
stratum;stratum1-1;1;0;0.0;0.0
stratum;stratum1-2;0;0;0.0;0.0
stratum;stratum1-2;1;0;0.0;0.0
stratum;stratum2-1;0;0;31.0;0.0
stratum;stratum2-1;1;0;31.0;0.0
stratum;stratum2-2;0;0;0.0;0.0
stratum;stratum2-2;1;0;0.0;0.0
stratum;stratum2-3;0;1;0.0;0.0
stratum;stratum2-3;1;1;0.0;0.0

def run(self):

Uses data from init to run the class-specific output (self.expression)

def _run_expr(self, expr_dict):

Executes the given expression dictionary for all reporting periods and return the results in a list of lists of strings.

def _execute_var(self, discount_rate, time_unit, time_length, var):

Executes the given aggregation set with the given values and returns results as a list of strings.

def _analyse_data(self):

Go through expressions and input database and analyse some data properties.

def _process_exprs(self, exprs):

Go through the expressions and try to deduce data levels and earliest and latest dates.

def _create_tables(self):

Create expression data tables, using Numpy arrays.

def _fill_tables(self):

Fill expression data tables with values from input databases.

def _collect_values(self, expr, array, forced_level=None):

Get values for a single expression operand from either data or operation result database.

Parameters:

epxr -- subobjective or constraint expression in postfix form
array -- target Numpy array

def _get_values_from_db(self, operand, dates, norm, condition=None,

forced_level=None):

---

Get value(s) for a single operand from data or operation result database.

Parameters:

operand -- expression operand, i.e. variable, value or operator
dates -- date range
norm -- boolean indicating whether a normal or condition operand
condition -- operand condition
forced_level -- forced level name, i.e. use this level for getting the data

def _eval_constraint(self, dates, array, operand, norm):

Evaluate operand constraint and store only those objects for which the condition evaluated True.

Parameters:

dates -- date range
arr -- temporary data array
operand -- current operand
norm -- boolean indicating whether a normal or condition operand

def _set_value(self, array, it, uid, br, date, value):

Store a single value into a correct location in a matrix and update branch number container.

Parameters:

array -- Numpy array
it -- iteration, int
uid -- object id, str
br -- branch, int
i_e -- expression operand index, int
date -- datetime object
value -- value to store, float

def _filter_data_by_last_date(self, data):

Filter data so that only the values from the last recorded date of each iteration-branch-object combination are selected. This is needed when operand dates are defined as [-1:-1]

Parameters

data -- data generator object from datadb

>>> data = [
...     ('1', 0, datetime.date(2000,12,31), 1000.),
...     ('1', 0, datetime.date(2005,12,31), 1005.),
...     ('1', 0, datetime.date(2010,12,31), 1010.),
...     ('1', 1, datetime.date(2000,12,31), 1100.),
...     ('1', 1, datetime.date(2005,12,31), 1105.),
...     ('1', 1, datetime.date(2010,12,31), 1110.),
...     ('2', 0, datetime.date(2000,12,31), 2000.),
...     ('2', 0, datetime.date(2005,12,31), 2005.),
...     ('2', 0, datetime.date(2010,12,31), 2010.),
...     ('2', 1, datetime.date(2000,12,31), 2100.),
...     ('2', 1, datetime.date(2005,12,31), 2105.),
...     ('2', 1, datetime.date(2010,12,31), 2110.)]
>>> data = out._filter_data_by_last_date(data)
>>> for item in data: print item
('1', 1, datetime.date(2010, 12, 31), 1110.0)
('2', 0, datetime.date(2010, 12, 31), 2010.0)
('1', 0, datetime.date(2010, 12, 31), 1010.0)
('2', 1, datetime.date(2010, 12, 31), 2110.0)

def _construct_strings(self):

Construct strings from expression value table contents for output.