def load_soi_farm_prop(data_tree=naics.generate_tree(),
blue_tree=None,
blueprint=None,
from_out=False,
out_path=_FARM_PROP_OUT_PATH):
""" This function loads the soi nonfarm proprietorship data:
:param data_tree: The NAICS tree to read the data into.
:param cols_dict: A dictionary mapping dataframe columns to the name of
the column names in the input file
:param blueprint: The key corresponding to a dataframe in a tree to be
used as a "blueprint" for populating the df_list dataframes forward.
:param blue_tree: A NAICS tree with the "blueprint" dataframe. The default
is the original NAICS tree.
:param from_out: Whether to read in the data from output.
:param output_path: The path of the output file.
"""
# If from_out, load the data tree from output:
if from_out:
data_tree = naics.load_tree_dfs(input_path=out_path, tree=data_tree)
return data_tree
# Load Farm Proprietorship data:
farm_data = pd.read_csv(_FARM_IN_PATH)
new_farm_cols = ["Land", "FA"]
#
data_tree.append_all(df_nm=_FARM_DF_NM, df_cols=new_farm_cols)
#
land_mult = ((farm_data["R_sp"][0] + farm_data["Q_sp"][0]) *
(float(farm_data["A_sp"][0]) / farm_data["A_p"][0]))
total = farm_data["R_p"][0] + farm_data["Q_p"][0]
total_pa = 0
cur_codes = [111, 112]
proportions = np.zeros(len(cur_codes))
proportions = naics.get_proportions(cur_codes, data_tree, _AST_PRT_DF_NM,
[_LAND_COL_NM, _DEPR_COL_NM])
#
for ind_code in cur_codes:
cur_ind = naics.find_naics(data_tree, ind_code)
cur_df = cur_ind.data.dfs[_AST_PRT_DF_NM]
total_pa += (cur_df[_LAND_COL_NM][0] + cur_df[_DEPR_COL_NM][0])
#
for i in xrange(0, len(cur_codes)):
cur_ind = naics.find_naics(data_tree, cur_codes[i])
cur_ind.data.dfs[_FARM_DF_NM]["Land"][0] = (
land_mult * cur_ind.data.dfs[_AST_PRT_DF_NM][_LAND_COL_NM][0] /
total_pa)
cur_ind.data.dfs[_FARM_DF_NM]["FA"][0] = (
(proportions.iloc[1, i] * total) -
cur_ind.data.dfs[_FARM_DF_NM]["Land"][0])
# Default:
if blueprint == None and _TOT_CORP_DF_NM in data_tree.enum_inds[
0].data.dfs.keys():
blueprint = _TOT_CORP_DF_NM
naics.pop_back(tree=data_tree, df_list=[_FARM_DF_NM])
naics.pop_forward(tree=data_tree,
df_list=[_FARM_DF_NM],
blueprint=blueprint,
blue_tree=blue_tree)
#
return data_tree
def load_soi_farm_prop(data_tree = naics.generate_tree(),
blue_tree = None, blueprint = None,
from_out=False, out_path=_FARM_PROP_OUT_PATH):
""" This function loads the soi nonfarm proprietorship data:
:param data_tree: The NAICS tree to read the data into.
:param cols_dict: A dictionary mapping dataframe columns to the name of
the column names in the input file
:param blueprint: The key corresponding to a dataframe in a tree to be
used as a "blueprint" for populating the df_list dataframes forward.
:param blue_tree: A NAICS tree with the "blueprint" dataframe. The default
is the original NAICS tree.
:param from_out: Whether to read in the data from output.
:param output_path: The path of the output file.
"""
# If from_out, load the data tree from output:
if from_out:
data_tree = naics.load_tree_dfs(input_path=out_path, tree=data_tree)
return data_tree
# Load Farm Proprietorship data:
farm_data = pd.read_csv(_FARM_IN_PATH)
new_farm_cols = ["Land", "FA"]
#
for i in data_tree.enum_inds:
i.append_dfs((_FARM_DF_NM,
pd.DataFrame(np.zeros((1,len(new_farm_cols))),
columns=new_farm_cols)))
#
land_mult = ((farm_data["R_sp"][0] + farm_data["Q_sp"][0]) *
(float(farm_data["A_sp"][0])/farm_data["A_p"][0]))
total = farm_data.iloc[0,0] + farm_data.iloc[0,2]
total_pa = 0
cur_codes = [111,112]
proportions = np.zeros(len(cur_codes))
proportions = naics.get_proportions(cur_codes, data_tree, "PA_assets",
["Land (Net)","Depreciable assets (Net)"])
#
for i in xrange(0, len(cur_codes)):
cur_ind = naics.find_naics(data_tree, cur_codes[i])
cur_df = cur_ind.data.dfs["PA_assets"]
total_pa += (cur_df["Land (Net)"][0] +
cur_df["Depreciable assets (Net)"][0])
#
for i in xrange(0,len(cur_codes)):
cur_ind = naics.find_naics(data_tree, cur_codes[i])
cur_ind.data.dfs[_FARM_DF_NM]["Land"][0] = (land_mult *
cur_ind.data.dfs["PA_assets"]["Land (Net)"][0]/
total_pa)
cur_ind.data.dfs[_FARM_DF_NM]["FA"][0] = ((proportions.iloc[1,i]*total)
- cur_ind.data.dfs[_FARM_DF_NM]["Land"][0])
# Default:
if blueprint == None and _TOT_CORP_DF_NM in data_tree.enum_inds[0].data.dfs.keys():
blueprint = _TOT_CORP_DF_NM
naics.pop_back(tree=data_tree, df_list=[_FARM_DF_NM])
naics.pop_forward(tree=data_tree, df_list=[_FARM_DF_NM],
blueprint=blueprint, blue_tree=blue_tree)
#
return data_tree
def load_soi_farm_prop(data_tree = None, blue_tree = None, blueprint = None):
#
if data_tree == None:
data_tree = naics.generate_tree()
#Load Farm Proprietorship data:
farm_data = pd.read_csv(os.path.abspath(prop_dir + "\\Farm_Data.csv"))
new_farm_cols = ["Land", "FA"]
#
for i in data_tree.enum_inds:
i.append_dfs(("farm_prop",
pd.DataFrame(np.zeros((1,len(new_farm_cols))),
columns=new_farm_cols)))
#
land_mult = ((farm_data["R_sp"][0] + farm_data["Q_sp"][0]) *
(float(farm_data["A_sp"][0])/farm_data["A_p"][0]))
total = farm_data.iloc[0,0] + farm_data.iloc[0,2]
total_pa = 0
cur_codes = [111,112]
proportions = np.zeros(len(cur_codes))
proportions = naics.get_proportions(cur_codes, data_tree, "PA_assets",
["Land (Net)","Depreciable assets (Net)"])
#
for i in xrange(0, len(cur_codes)):
cur_ind = naics.find_naics(data_tree, cur_codes[i])
cur_df = cur_ind.data.dfs["PA_assets"]
total_pa += (cur_df["Land (Net)"][0] +
cur_df["Depreciable assets (Net)"][0])
#
for i in xrange(0,len(cur_codes)):
cur_ind = naics.find_naics(data_tree, cur_codes[i])
cur_ind.data.dfs["farm_prop"]["Land"][0] = (land_mult *
cur_ind.data.dfs["PA_assets"]["Land (Net)"][0]/
total_pa)
cur_ind.data.dfs["farm_prop"]["FA"][0] = ((proportions.iloc[1,i]*total)
- cur_ind.data.dfs["farm_prop"]["Land"][0])
# Default:
if blueprint == None and "tot_corps" in data_tree.enum_inds[0].data.dfs.keys():
blueprint = "tot_corps"
naics.pop_back(tree=data_tree, df_list=["farm_prop"])
naics.pop_forward(tree=data_tree, df_list=["farm_prop"],
blueprint=blueprint, blue_tree=blue_tree)
#
return data_tree
