aggr.py

class OutputAggr(Output):

Module for data output in hierarchical “data in columns” format. (writing tables to files)

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.aggr import OutputAggr
>>> out = OutputAggr(testdb, 'result', ['stand1'], 'stratum',
...               'optimized',
...               'output/test/aggregation.txt', TestLogger(),
...               const_obj, 1, False, True, aggr_obj, expr_obj,
...               ['cash_flow', 'Income', 'Scrapwood', 'Volume',
...               'BIOMASS_branches', 'BIOMASS_stumps'])
>>> try: 
...     file = open('output/test/aggregation.txt', 'r')
...     for line in file:
...         print line.rstrip('\n')
... finally:
...     file.close() 
Results for expression (5 keys): sum[-1:-1](stratum:HgM[stratum:SP eq 2 and stratum:DgM gt 19])
 stratum:SP; stratum:DgM; 08/02/09-08/02/09
        1.0;        20.0;               0.0
        2.0;         0.0;               0.0
        2.0;        18.0;               0.0
        2.0;        30.0;              62.0
        3.0;        27.0;               0.0

Proportions for expression (5 keys): sum[0:99](stratum:SP*stratum:HgM)
 stratum:HgM; stratum:DgM; 01/01/09-08/02/09
         0.0;         0.0;               0.0
        18.5;        18.0;               0.2
        19.0;        20.0;               0.1
        26.0;        27.0;               0.4
        31.0;        30.0;               0.3

Results for expression (1 keys): sum[0:99](operation:Volume)
 01/01/09-08/02/09
             123.2

Results for expression (1 keys): sum[0:99](operation:cash_flow)
 01/01/09-08/02/09
            3000.0

Results for expression (2 keys): sum[0:99](operation:cash_flow)
 comp_unit:AREA; 01/01/09-08/02/09
            1.0;            3000.0
            2.0;               0.0

Results for expression (2 keys): sum[0:99](operation:cash_flow)
 operation:op_name; comp_unit:SC; comp_unit:DEV_CLASS; 01/01/09-08/02/09
          clearcut;          3.0;                 4.0;            3456...
          planting;          3.0;                 4.0;            -456...

def run(self):

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

def _run_aggr(self, aggr_dict):

Executes the given aggregation 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 _parse_experssion(self, expression, header, init_date):

Will parse a string expression into a list of tuples. Returns None if there are errors.

>>> out._parse_expression('sum[3:5](comp_unit:AREA)',
...                       'sum[3:5](comp_unit:AREA)',
...                       datetime.date(2009, 1, 1))
... 
[('data', ('comp_unit', u'AREA'),
 (datetime.date(2011, 1, 1), datetime.date(2013, 12, 31)), None, False),
 ('aggr', <function sum at ...>)]

def _get_classifiers(self, keys):

Build and return classifiers for the given grouping

>>> out._get_classifiers(['comp_unit:AREA', 'operation:Volume'])
[(1.0, 123.2)]

def _get_values(self, expr, expression, date_ranges, grouping, errors, date_dict):

Get the values from the database into the date_dict

>>> expr = [('op', u'Volume', (datetime.date(2009, 2, 7),
...          datetime.date(2108, 2, 6)), None, False), ('aggr', numpy.sum)]
>>> expression = 'Test expression string'
>>> date_ranges = [(datetime.date(2009, 2, 7), datetime.date(2009, 2, 8),
...                 'year', True)]
>>> grouping = None
>>> errors = defaultdict(set)
>>> date_dict = {}
>>> date_dict, classifiers, errors = out._get_values(expr, expression,
...                                                  date_ranges,
...                                                  grouping, errors,
...                                                  date_dict,
...                                                datetime.date(2009,2,7),
...                                                3.0)
>>> date_dict, classifiers, errors 
({(datetime.date(2009, 2, 7), datetime.date(2009, 2, 8), 'year', True):
    defaultdict(<type 'list'>,
 {'': [array([ 123.2])]})}, [''], defaultdict(<type 'set'>, {}))

def _get_results(self, date_dict, grouping, classifiers, expr, proportion):

Evaluate the expression and return a list of results and the header for the result grouping

>>> proportion = False
>>> grouping = {}
>>> out._get_results(date_dict, grouping, classifiers, expr, proportion)
([['07/02/09-08/02/09'], ['123.2']], [])

def _check_date_range(self, range, val):

Check if the val is inside the given range

>>> out._check_date_range(date_ranges[0], datetime.date(2009, 2, 6))
False
>>> out._check_date_range(date_ranges[0], datetime.date(2009, 2, 7))
True
>>> out._check_date_range(date_ranges[0], datetime.date(2009, 2, 8))
True
>>> out._check_date_range((datetime.date(2009, 2, 7),
...                        datetime.date(2009, 2, 8), 'year', False),
...                        datetime.date(2009, 2, 8))
False

def _get_date_ranges(self, init_date, all_dates, expr, time_unit, time_length):

This gets the date ranges to be used for the data

>>> init_date = datetime.date(2009, 2, 7)
>>> all_dates = [datetime.date(2009, 2, 7), datetime.date(2009, 3, 1)]
>>> out._get_date_ranges(init_date, all_dates, expr, 'day', 5)
... 
[(datetime.date(2009, 2, 7), datetime.date(2009, 2, 12), 'day', False),
 (datetime.date(2009, 2, 12), datetime.date(2009, 2, 17), 'day', False),
 (datetime.date(2009, 2, 17), datetime.date(2009, 2, 22), 'day', False),
 (datetime.date(2009, 2, 22), datetime.date(2009, 2, 27), 'day', False),
 (datetime.date(2009, 2, 27), datetime.date(2009, 3, 1), 'day', True)]

def _build_ranges(self, dates, time_unit, time_length, first=None, last=None):

Returns a list of tuples of (start_date, end_date, time_unit, is_last_range)

>>> out._build_ranges(all_dates, 'day', 5, first=datetime.date(2009,2,20),
...                   last=datetime.date(2009,2,28))
... 
[(datetime.date(2009, 2, 20), datetime.date(2009, 2, 25), 'day', False),
 (datetime.date(2009, 2, 25), datetime.date(2009, 2, 28), 'day', True)]

def _get_string_range(self, rng):

Turns the given range tuple into a string representation

>>> out._get_string_range((datetime.date(2009, 2, 20),
...                        datetime.date(2009, 3, 25), 'day', False))
'20/02/09-24/03/09'
>>> out._get_string_range((datetime.date(2009, 2, 20),
...                        datetime.date(2009, 3, 25), 'month', True))
'20/02/09-25/03/09'
>>> out._get_string_range((datetime.date(2009, 2, 20),
...                        datetime.date(2009, 3, 25), 'year', True))
'20/02/09-25/03/09'

def _unpack_weighted_average(self, data):

Unpacks a weighted average into a formula the expression parser can handle.

>>> out._unpack_weighted_average('sum[1:5](one:thing)')
'sum[1:5](one:thing)'
>>> out._unpack_weighted_average('wavg[1:5](one:thing, another:thing)')
'sum[1:5](one:thing*another:thing)/sum[1:5](another:thing)'

def _get_values_from_db(self, operand, dates, date_ranges, norm,

opresconst=None, grouping=None):

---

Get value(s) for a single operand from data or operation result database. Valid grouping pairs (in either way): level-level, operation-“comp_unit”, level-child, level-parent

>>> r = out._get_values_from_db(('data', ('stratum', u'SP'),
...                         (datetime.date(2009, 2, 7),
...                          datetime.date(2108, 2, 6)), None, False),
...                         (datetime.date(2009, 2, 7),
...                          datetime.date(2108, 2, 6)),
...                          [(datetime.date(2009, 1, 1),
...                            datetime.date(2009, 2, 25), 'day', False),
...                           (datetime.date(2009, 2, 25),
...                            datetime.date(2009, 2, 28), 'day', True)],
...                          True, None,
...                          {'stratum': ['HgM', 'DgM']})
>>> r[0].sort()
>>> r 
([[1.0, datetime.date(2009, 2, 7), 19.0, 20.0],
  [1.0, datetime.date(2009, 2, 7), 19.0, 20.0],
  [2.0, datetime.date(2009, 2, 7), 18.5, 18.0],
  [2.0, datetime.date(2009, 2, 7), 18.5, 18.0],
  [2.0, datetime.date(2009, 2, 8), 0.0, 0.0],
  [2.0, datetime.date(2009, 2, 8), 0.0, 0.0],
  [2.0, datetime.date(2009, 2, 8), 31.0, 30.0],
  [2.0, datetime.date(2009, 2, 8), 31.0, 30.0],
  [3.0, datetime.date(2009, 2, 8), 26.0, 27.0],
  [3.0, datetime.date(2009, 2, 8), 26.0, 27.0]],
  [datetime.date(2009, 2, 8), datetime.date(2009, 2, 7)],
  ['stratum:HgM', 'stratum:DgM'])
>>> out._get_values_from_db(('op', u'cash_flow',
...                         (datetime.date(2009, 2, 7),
...                          datetime.date(2108, 2, 6)), None, False),
...                         (datetime.date(2009, 2, 7),
...                          datetime.date(2108, 2, 6)),
...                         [(datetime.date(2009, 1, 1),
...                           datetime.date(2009, 2, 25), 'day', False),
...                          (datetime.date(2009, 2, 25),
...                           datetime.date(2009, 2, 28), 'day', True)],
...                          True, None,
...                          {'comp_unit': ['AREA']})
... 
([[3000.0, datetime.date(2009, 2, 8), 1.0]],
  [datetime.date(2009, 2, 8)], ['comp_unit:AREA'])

def _calculate_NPV(self, value, discountrate, presentdate, eventdate):

Calculate the discounted NPV (Net Present Value) for a single value

>>> out._calculate_NPV(10.5, 5.4, datetime.date(2009,1,1),
...                    datetime.date(2011,5,4))
... 
9.45165971144533...

def aggregation(self, file):

This method will get the data requested from the databases and writes them into the given file.

>>> out.aggregation('output/test/aggregation.txt')
>>> try:
...     file = open('output/test/aggregation.txt', 'r')
...     for line in file:
...         print line.rstrip('\n')
... finally:
...     file.close() 
Results for expression (5 keys): sum[-1:-1](stratum:HgM[stratum:SP eq 2 and stratum:DgM gt 19])
 stratum:SP; stratum:DgM; 08/02/09-08/02/09
        1.0;        20.0;               0.0
        2.0;         0.0;               0.0
        2.0;        18.0;               0.0
        2.0;        30.0;              62.0
        3.0;        27.0;               0.0

Proportions for expression (5 keys): sum[0:99](stratum:SP*stratum:HgM)
 stratum:HgM; stratum:DgM; 01/01/09-08/02/09
         0.0;         0.0;               0.0
        18.5;        18.0;               0.2
        19.0;        20.0;               0.1
        26.0;        27.0;               0.4
        31.0;        30.0;               0.3

Results for expression (1 keys): sum[0:99](operation:Volume)
 01/01/09-08/02/09
             123.2

Results for expression (1 keys): sum[0:99](operation:cash_flow)
 01/01/09-08/02/09
            3000.0

Results for expression (2 keys): sum[0:99](operation:cash_flow)
 comp_unit:AREA; 01/01/09-08/02/09
            1.0;            3000.0
            2.0;               0.0

Results for expression (2 keys): sum[0:99](operation:cash_flow)
 operation:op_name; comp_unit:SC; comp_unit:DEV_CLASS; 01/01/09-08/02/09
          clearcut;          3.0;                 4.0;            3456...
          planting;          3.0;                 4.0;            -456...