income and entity information. Using the different asset and income information (ratios of assets to income),
data can be allocated to all the industries in the different partner entities.
Last updated: 7/26/2016.
"""
# Packages:
from __future__ import unicode_literals
import os.path
import re
import numpy as np
import pandas as pd
import xlrd
from btax.util import get_paths, to_str
globals().update(get_paths())
# Constants
_AST_FILE_FCTR = 10**3
_SHAPE = (131,4)
_CODE_RANGE = ['32', '33', '45', '49']
_PARENTS = {'32':'31','33':'31','45':'44','49':'48'}
def load_partner_data(entity_dfs):
"""Reads in the partner data and creates new dataframes for each partner type and stores them in the soi dictionary
:param entity_dfs: Contains all the soi data by entity type
:type entity_dfs: dictionary
:returns: The soi dictionary updated with the partner dataframe
:rtype: dictionary
import sys
from btax.soi_processing import pull_soi_data
from btax import calc_final_outputs
from btax import check_output
from btax.util import (get_paths, read_from_egg, output_by_asset_to_json_table,
output_by_industry_to_json_table, diff_two_tables,
filter_user_params_for_econ)
from btax import read_bea
import btax.soi_processing as soi
import btax.parameters as params
from btax import format_output
from btax import visuals
from btax import visuals_plotly
globals().update(get_paths())
TABLE_ORDER = [
'base_output_by_asset',
'reform_output_by_asset',
'changed_output_by_asset',
'base_output_by_industry',
'reform_output_by_industry',
'changed_output_by_industry',
]
ModelDiffs = namedtuple('ModelDiffs', TABLE_ORDER + ['row_grouping'])
ASSET_PRE_CACHE_FILE = 'asset_data.pkl'
def run_btax(test_run,
baseline=False,
