def pull_soi_data(soi_tree=naics.generate_tree(), from_out=False,
get_all=False, get_corp=False,
get_tot=False, get_s=False,
get_c=False, get_prt=False,
get_prop=False, get_farm_prop=False,
output_data=False, out_path=None):
# If get_all, set all booleans to true:
if get_all:
get_corp = True
get_tot = True
get_s = True
get_c = True
get_prt = True
get_prop = True
get_farm = True
# Import the soi_processing custom module:
soi_dir = os.path.join(_DATA_DIR, "soi")
sys.path.append(soi_dir)
import soi_processing as soi
# Loading the soi corporate data into the NAICS tree:
soi_tree = soi.load_corporate(soi_tree=soi_tree, from_out=from_out,
get_all=get_corp, get_tot=get_tot,
get_s=get_s, get_c=get_c,
output_data=output_data, out_path=out_path)
# Loading the soi partnership data into the NAICS tree:
if get_prt:
soi_tree = soi.load_partner(soi_tree=soi_tree, from_out=from_out,
output_data=output_data, out_path=out_path)
# Loading the soi proprietorship data into the NAICS tree:
soi_tree = soi.load_proprietorship(
soi_tree=soi_tree, from_out=from_out,
get_nonfarm=get_prop, get_farm=get_farm_prop,
output_data=output_data, out_path=out_path
)
return soi_tree
def pull_soi_data(soi_tree=naics.generate_tree(),
from_out=False,
get_all=False,
get_corp=False,
get_tot=False,
get_s=False,
get_c=False,
get_prt=False,
get_prop=False,
get_farm_prop=False,
output_data=False,
out_path=None):
# If get_all, set all booleans to true:
if get_all:
get_corp = True
get_tot = True
get_s = True
get_c = True
get_prt = True
get_prop = True
get_farm_prop = True
# Import the soi_processing custom module:
soi_dir = os.path.join(_DATA_DIR, "soi")
sys.path.append(soi_dir)
import soi_processing as soi
# Loading the soi corporate data into the NAICS tree:
if get_corp or get_tot or get_s or get_c:
soi_tree = soi.load_corporate(soi_tree=soi_tree,
from_out=from_out,
get_all=get_corp,
get_tot=get_tot,
get_s=get_s,
get_c=get_c,
output_data=output_data,
out_path=out_path)
# Loading the soi partnership data into the NAICS tree:
if get_prt:
soi_tree = soi.load_partner(soi_tree=soi_tree,
from_out=from_out,
output_data=output_data,
out_path=out_path)
# Loading the soi proprietorship data into the NAICS tree:
if get_prop or get_farm_prop:
soi_tree = soi.load_proprietorship(soi_tree=soi_tree,
from_out=from_out,
get_nonfarm=get_prop,
get_farm=get_farm_prop,
output_data=output_data,
out_path=out_path)
return soi_tree
def init_depr_rates(data_tree=naics.generate_tree(), get_econ=False,
get_tax_est=False, get_tax_150=False,
get_tax_200=False, get_tax_sl=False,
get_tax_ads=False, soi_from_out=False,
output_data=False):
# Reading in the SOI Tax Stats-Corporation data:
soi_tree = naics.generate_tree()
soi_tree = read_soi.load_corporate(soi_tree=soi_tree,
from_out=soi_from_out,
output_data=(not soi_from_out))
# Reading in the SOI Tax Stats-Partnership data:
soi_tree = read_soi.load_partner(soi_tree=soi_tree,
from_out=soi_from_out,
output_data=(not soi_from_out))
# Reading in the SOI Tax Stats-Proprietorship data:
soi_tree = read_soi.load_soi_proprietorship(soi_tree=soi_tree,
from_out=soi_from_out,
output_data=(not soi_from_out))
'''
Many industries are not listed in the SOI datasets. The data for these missing
industries are interpolated.
'''
# Get a list of the names of all the pd dfs besides the list of codes:
#cur_names = soi_tree.enum_inds[0].data.dfs.keys()
#cur_names.remove(_CODE_DF_NM)
# Populate missing industry data backwards throught the tree:
#naics.pop_back(data_tree, cur_names)
# Populate the missing total corporate data forwards through the tree:
#naics.pop_forward(data_tree, ["tot_corps"])
# Populate other missing data using tot_corps as a "blueprint":
#cur_names = ["c_corps", "s_corps", "PA_inc_loss", "PA_assets", "soi_prop"]
#naics.pop_forward(data_tree, cur_names, "tot_corps")
# Calculate c_corps data:
#read_soi.calc_c_corp(data_tree)
#naics.pop_back(data_tree,["c_corps"])
#naics.pop_forward(data_tree, ["c_corps"], "tot_corps")
# Populate pa05 using pa01:
#naics.pop_forward(data_tree, ["PA_types"], "PA_inc_loss")
#
#naics.pop_back(data_tree, ["farm_prop"])
#naics.pop_forward(data_tree, ["farm_prop"], "tot_corps")
#Create an output tree containing only the final data on FA, INV, and LAND.
output_tree = calc_assets.summary_tree(data_tree, _DATA_DIR)
# Create a tree with all the FA's broken down by type of asset:
asset_tree = read_bea.read_bea(output_tree, _DATA_DIR)
naics.pop_back(asset_tree, ["All", "Corp", "Non-Corp"])
#
corp_types = ["C Corporations",
"Corporate general partners",
"Corporate limited partners"]
non_corp_types = ["S Corporations",
"Individual general partners",
"Individual limited partners",
"Partnership general partners",
"Partnership limited partners",
"Tax-exempt organization general partners",
"Tax-exempt organization limited partners",
"Nominee and other general partners",
"Nominee and other limited partners",
"Sole Proprietors"]
naics.pop_forward(asset_tree, ["All"], "FA", output_tree)
naics.pop_forward(asset_tree, ["Corp"], "FA", output_tree, corp_types)
naics.pop_forward(asset_tree, ["Non-Corp"], "FA", output_tree, non_corp_types)
#
inv_tree = read_inv.read_inventories(output_tree, _DATA_DIR)
naics.pop_back(inv_tree, ["Inventories"])
naics.pop_forward(inv_tree, ["Inventories"])
#
land_tree = read_land.read_land(output_tree, _DATA_DIR)
naics.pop_back(land_tree, ["Land"])
naics.pop_forward(land_tree, ["Land"], "LAND", output_tree)
#
econ_depr_tree = calc_rates.calc_depr_rates(asset_tree, inv_tree, land_tree, _DATA_DIR)
tax_depr_tree = calc_rates.calc_tax_depr_rates(asset_tree, inv_tree, land_tree, _DATA_DIR)
naics.pop_rates(tax_depr_tree)
return {"Econ": econ_depr_tree, "Tax": tax_depr_tree}