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dataset_construct_nber.py
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dataset_construct_nber.py
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"""
Compute NBER Datasets
=====================
Author: Matthew McKay (mamckay@gmail.com)
Filename rules: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}-{{ raw/cleaned }}.h5
This archive includes all varients of this type of trade data
Required Keys
-------------
'A' = pd.DataFrame('Dataset A')
'A-OPTIONS' = Dict{Record of Options}
'A-DESC' = str
Supporting Python Files
-----------------------
1. dataset-info.py Contains Information about the relevant datasets
2. dataset-compile-raw.py Compiles RAW data files to a single dataset file (HDF Format)
Sources
-------
1. nber
md5: 36a376e5a01385782112519bddfac85e
notes: NBER World Trade Flow RAW Dataset
Supporting Files
----------------
{SITCR2L4}
1. Need to compute country code intertemporal consistency for Level 4 Data and update pyeconlab
"""
#-External Packages-#
import numpy as np
import pandas as pd
import sys
import gc
import warnings
#-Package Imports-#
from pyeconlab.trade.dataset.NBERWTF.meta import countryname_to_iso3c, iso3c_recodes_for_1962_2000, incomplete_iso3c_for_1962_2000
from pyeconlab.util import concord_data
from pyeconlab.trade.classification import SITC
from pyeconlab.trade.dataset.NBERWTF import construct_sitcr2
#-Local Imports-#
from dataset_info import TARGET_RAW_DIR, TARGET_DATASET_DIR, YEARS #-Dataset Information
#-Update to Pull from PyEconLab?-#
from dataset_construct_nber_options import DATA_DESCRIPTION, RAW_DATA_DESCRIPTION, DATA_OPTIONS, RAW_DATA_OPTIONS
#---------#
#-Control-#
#---------#
#-1962 to 2000-#
Y6200 = True
#-L4-#
SITCR2L4 = True
SITCR2L4_TRADE = True
SITCR2L4_EXPORT = True
SITCR2L4_IMPORT = True
#-L3-#
SITCR2L3 = True
SITCR2L3_TRADE = True
SITCR2L3_EXPORT = True
SITCR2L3_IMPORT = True
#-L2-#
SITCR2L2 = True
SITCR2L2_TRADE = True
SITCR2L2_EXPORT = True
SITCR2L2_IMPORT = True
#-L1-#
SITCR2L1 = True
SITCR2L1_TRADE = True
SITCR2L1_EXPORT = True
SITCR2L1_IMPORT = True
#-RAW DATA-#
RAW_SITCR2L4 = True
RAW_WORLD_YEARLY = True #Simple Total World Trade
RAW_COUNTRY_YEARLY = True #Simple Total Country Trade & Various Aggregations
RAW_PRODUCT_YEARLY = True #Levels 1,2,3,4 & Various Product Code Aggregations
#-1974 to 2000-#
Y7400 = True
Y7400Trade = True
Y7400Export = True
Y7400Import = True
#-1984 to 2000-#
Y8400 = True
Y8400Trade = True
Y8400Export = True
Y8400Import = True
#-Setup General Directories-#
source_dir = TARGET_RAW_DIR['nber']
target_dir = TARGET_DATASET_DIR['nber']
start_year, end_year = YEARS['nber']
#------------------#
#-Helper Functions-#
#------------------#
def load_raw_dataset(fn, start_year, end_year, verbose=False):
"""
Load Raw NBER Dataset
"""
data = pd.DataFrame()
for year in range(start_year, end_year+1, 1):
print "Loading Year: %s" % year
data = data.append(pd.read_hdf(fn, "Y%s"%year))
data = data.reset_index()
del data["index"]
if verbose: print data.year.unique()
return data
def fix_raw_data(data):
"""
Apply Fixes to NBER Data for ZWE, MWI
ZWE - Drop Data in 1963,1964
MWI - Drop Data in 1963,1964
"""
print "[INFO] Adjustments to RAW DATA based on NBER FAQ ..."
for year in xrange(1963,1964+1,1):
for country in ["Malawi", "Zimbabwe"]:
print
print "[INFO] Removing %s trade values in year %s ..."%(country, year)
drop = data.loc[(data.year == year)&((data.exporter == country)|(data.importer == country))]
print "...... Dropping"
print drop.to_string()
print "[INFO] Number of Observations = %s"%data.shape[0]
print "[INFO] Dropping ... %s observations"%len(drop)
data = data.drop(drop.index)
print "[INFO] Number of Observations after drop = %s"%data.shape[0]
gc.collect()
return data
#-------------------------#
#-NBER DATA: 1962 to 2000-#
#-------------------------#
if Y6200:
print "[INFO] Running Y6200 ..."
#-Source Information-#
#~~~~~~~~~~~~~~~~~~~~#
print "---> Loading RAW Data (1962 to 2000) <---"
print
fn = source_dir + "nber_year.h5"
rawdata = load_raw_dataset(fn, start_year, end_year)
rawdata = fix_raw_data(rawdata) #Adjustments Made for FAQ
print "---> Loading RAW HK-CHINA Adjustments <---"
fn, start_year, end_year = (source_dir + "nber_supp_year.h5", 1988, 2000)
rawdata_hk = load_raw_dataset(fn, start_year, end_year)
#-Precomputed Data Options--#
from pyeconlab.trade.dataset.NBERWTF.meta import IntertemporalProducts #Should make a Local Static Copy
ICP = IntertemporalProducts().IC6200
#-Construct SITC R2 Level 4 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
DATA_TYPES = []
if SITCR2L4_TRADE: DATA_TYPES.append('trade')
if SITCR2L4_EXPORT: DATA_TYPES.append('export')
if SITCR2L4_IMPORT: DATA_TYPES.append('import')
#-!!-Future Work: Collapse these to Level loops-!!-#
if SITCR2L4:
print
print "---> COMPUTING SITC REVISION 2 LEVEL 4 DATASETS <---"
print
# from pyeconlab.trade.dataset.NBERWTF import construct_sitcr2l4
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l4-1962to2000.h5" % data_type #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print "\n[SITCR2L4] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l3 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[4])
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=4, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
#-Construct SITC R2 Level 3 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
DATA_TYPES = []
if SITCR2L3_TRADE: DATA_TYPES.append('trade')
if SITCR2L3_EXPORT: DATA_TYPES.append('export')
if SITCR2L3_IMPORT: DATA_TYPES.append('import')
if SITCR2L3:
print
print "---> COMPUTING SITC REVISION 2 LEVEL 3 DATASETS <---"
print
#-Import this as a Function from pyeconlab-#
# from pyeconlab.trade.dataset.NBERWTF import construct_sitcr2l3
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l3-1962to2000.h5" % data_type #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print "\n[SITCR2L3] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l3 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[3])
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=3, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
#-Construct SITC R2 Level 2 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
DATA_TYPES = []
if SITCR2L2_TRADE: DATA_TYPES.append('trade')
if SITCR2L2_EXPORT: DATA_TYPES.append('export')
if SITCR2L2_IMPORT: DATA_TYPES.append('import')
if SITCR2L2:
print
print "---> COMPUTING SITC REVISION 2 LEVEL 2 DATASETS <---"
print
#-Import this as a Function from pyeconlab-#
# from pyeconlab.trade.dataset.NBERWTF import construct_sitcr2l2
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l2-1962to2000.h5" % data_type #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print "[SITCR2L2] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l2 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[2])
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=2, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
#-Construct SITC R2 Level 1 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
DATA_TYPES = []
if SITCR2L1_TRADE: DATA_TYPES.append('trade')
if SITCR2L1_EXPORT: DATA_TYPES.append('export')
if SITCR2L1_IMPORT: DATA_TYPES.append('import')
if SITCR2L1:
print
print "---> COMPUTING SITC REVISION 2 LEVEL 1 DATASETS <---"
print
#-Import this as a Function from pyeconlab-#
# from pyeconlab.trade.dataset.NBERWTF import construct_sitcr2l3
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l1-1962to2000.h5" % data_type #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print "[SITCR2L3] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l1 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
print "[INFO] This operation cannot occur at this level ... continuing"
continue
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=1, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
#---------------#
#- RAW DATASET -#
#---------------#
def load_all_rawdata(start_year, end_year):
print "---> Loading RAW Data (1962 to 2000) <---"
print
fn = source_dir + "nber_year.h5"
rawdata = load_raw_dataset(fn, start_year, end_year)
rawdata = fix_raw_data(rawdata) #Adjustments Made for FAQ
print "---> Loading RAW HK-CHINA Adjustments <---"
fn, start_year, end_year = (source_dir + "nber_supp_year.h5", 1988, 2000)
rawdata_hk = load_raw_dataset(fn, start_year, end_year)
return rawdata, rawdata_hk
if RAW_SITCR2L4:
print
print "---> COMPUTING SITC REVISION 2 LEVEL 4 RAW DATASETS <---"
print
rawdata, rawdata_hk = load_all_rawdata(start_year, end_year)
#-Setup Store-#
fn = "raw_nber-sitcr2l4-1962to2000.h5" #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(RAW_DATA_OPTIONS.keys()):
print "[SITCR2L4] Computing Dataset %s for %s" % (dataset, 'trade')
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if RAW_DATA_OPTIONS[dataset]['adjust_hk'] == True:
RAW_DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l3 function, then remove so not saved as an attribute
else:
RAW_DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if RAW_DATA_OPTIONS[dataset]['intertemp_productcode']:
RAW_DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[4])
else:
RAW_DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type='trade', level=4, **RAW_DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if RAW_DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
RAW_DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
RAW_DATA_OPTIONS[dataset]['adjust_hk'] = False
if RAW_DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
RAW_DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
RAW_DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = RAW_DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = 'trade'
store.get_storer(dataset).attrs.description = RAW_DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
del data
gc.collect()
#-Notes
# ~~~~~
# This Captures Total Export and Total Import Values in the NBER Dataset.
# Total TRADE = X + M = 2 x Values Below
if RAW_WORLD_YEARLY:
print
print "---> COMPUTING WORLD YEARLY VALUES FROM RAW NBER DATASET <---"
print
rawdata, rawdata_hk = load_all_rawdata(start_year, end_year)
#-Setup Store-#
fn = "raw_nber_world_yearly-1962to2000.h5"
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Values Using World-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "value"]].loc[(data.exporter == "World")&(data.importer == "World")].groupby(["year"]).sum()
store.put("World", data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
# #-Compute Values Dropping World-#
# #-This method should be equivalent - check then delete!-#
# data = rawdata.copy(deep=True)
# data = data[["year", "exporter", "importer", "value"]].loc[(data.exporter != "World")&(data.importer != "World")].groupby(["year"]).sum()
# store.put("World2", data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
print store
store.close()
del rawdata, rawdata_hk
gc.collect()
if RAW_COUNTRY_YEARLY:
print
print "---> COMPUTING COUNTRY YEARLY VALUES FROM RAW NBER DATASET <---"
print
from pyeconlab.trade.dataset.NBERWTF.meta import countryname_to_iso3c
rawdata, rawdata_hk = load_all_rawdata(start_year, end_year)
#-Setup Store-#
fn = "raw_nber_country_year-1962to2000.h5"
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Export Values Using World-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "value"]].loc[(data.exporter != "World")&(data.importer == "World")].groupby(["year", "exporter"]).sum().reset_index()
data['eiso3c'] = data['exporter'].apply(lambda x: countryname_to_iso3c[x])
data = data.set_index(["year", "eiso3c", "exporter"]) #.unstack("year")
# data.columns = data.columns.droplevel()
store.put("CountryExport", data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
#-Country Values not using World-#
#-This method should be equivalent - check then delete!-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "value"]].loc[(data.exporter != "World")&(data.importer != "World")].groupby(["year", "exporter"]).sum().reset_index()
data['eiso3c'] = data['exporter'].apply(lambda x: countryname_to_iso3c[x])
data = data.set_index(["year", "eiso3c", "exporter"]) #.unstack("year")
# data.columns = data.columns.droplevel()
store.put("CNTRY2", data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
#-Compute Import Values Using World-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "value"]].loc[(data.exporter != "World")&(data.importer == "World")].groupby(["year", "importer"]).sum().reset_index()
data['iiso3c'] = data['importer'].apply(lambda x: countryname_to_iso3c[x])
data = data.set_index(["year", "iiso3c", "importer"]) #.unstack("year")
# data.columns = data.columns.droplevel()
store.put("CountryImport", data, format='table')
print store
store.close()
del rawdata, rawdata_hk
gc.collect()
#-Need Aggregation Version(?): No as they can be computed from this RAW DATA without reference to original RAW-#
if RAW_PRODUCT_YEARLY:
print
print "---> COMPUTING PRODUCT YEAR VALUES FROM RAW NBER DATASET"
print
rawdata, rawdata_hk = load_all_rawdata(start_year, end_year)
#-Setup Store-#
fn = "raw_nber_product_year-1962to2000.h5"
store = pd.HDFStore(target_dir+fn, complevel=9, complib='zlib')
#-Compute Values Using World-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "sitc4", "value"]].loc[(data.exporter == "World")&(data.importer == "World")].groupby(["year", "sitc4"]).sum().reset_index()
store.put("L4", data, format='table')
for level in [3,2,1]:
data["sitc%s"%level] = data["sitc%s"%(level+1)].apply(lambda x: x[0:level])
data = data.groupby(["year", "sitc%s"%level]).sum().reset_index()
store.put("L%s"%level, data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
#-Country Values not using World-#
#-This method should be equivalent - check then delete!-#
data = rawdata.copy(deep=True)
data = data[["year", "exporter", "importer", "sitc4", "value"]].loc[(data.exporter != "World")&(data.importer != "World")].groupby(["year", "sitc4"]).sum().reset_index()
store.put("L42", data, format='table')
for level in [3,2,1]:
data["sitc%s"%level] = data["sitc%s"%(level+1)].apply(lambda x: x[0:level])
data = data.groupby(["year", "sitc%s"%level]).sum().reset_index()
store.put("L%s2"%level, data, format='table')
##--!!--CHECK THIS THEN DELETE--!!-##
print store
store.close()
del rawdata, rawdata_hk
gc.collect()
#------------------------------------- OTHER YEARS --------------------------------------------#
#--------------#
#-1974 to 2000-#
#--------------#
local_dir = "Y7400/"
if Y7400:
print
print "[INFO] Running Y7400 ..."
#-Source Information-#
print "---> Loading RAW Data <---"
print
fn = source_dir + "nber_year.h5"
rawdata = load_raw_dataset(fn, 1974, 2000)
rawdata = fix_raw_data(rawdata) #Adjustments Made for FAQ
print "---> Loading RAW HK-CHINA Adjustments <---"
fn, start_year, end_year = (source_dir + "nber_supp_year.h5", 1988, 2000)
rawdata_hk = load_raw_dataset(fn, start_year, end_year)
#-Precomputed Data Options--#
from pyeconlab.trade.dataset.NBERWTF.meta import IntertemporalProducts #Should make a Local Static Copy
ICP = IntertemporalProducts().IC7400
from dataset_construct_nber_options import DATA_DESCRIPTION, DATA_OPTIONS #-Update to Pull from PyEconLab?-#
DATA_TYPES = []
if Y7400Trade: DATA_TYPES.append('trade')
if Y7400Export: DATA_TYPES.append('export')
if Y7400Import: DATA_TYPES.append('import')
#-Construct SITC R2 Level 4,3,2 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
for level in [4,3,2,1]:
print
print "---> [74to00] COMPUTING SITC REVISION 2 LEVEL %s DATASETS <---" % level
print
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l%s-1974to2000.h5" % (data_type,level) #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+local_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print
print "[SITCR2L4] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l3 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[level])
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=level, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
del rawdata
gc.collect()
#--------------#
#-1984 to 2000-#
#--------------#
local_dir = "Y8400/"
if Y8400:
print
print "[INFO] Running Y8400 ..."
#-Precomputed Data Options--#
from pyeconlab.trade.dataset.NBERWTF.meta import IntertemporalProducts #Should make a Local Static Copy
ICP = IntertemporalProducts().IC8400
#-Source Information-#
#~~~~~~~~~~~~~~~~~~~~#
print "---> Loading RAW Data <---"
print
fn = source_dir + "nber_year.h5"
rawdata = load_raw_dataset(fn, 1984, 2000)
rawdata = fix_raw_data(rawdata) #Adjustments Made for FAQ
print "---> Loading RAW HK-CHINA Adjustments <---"
fn, start_year, end_year = (source_dir + "nber_supp_year.h5", 1988, 2000)
rawdata_hk = load_raw_dataset(fn, start_year, end_year)
#-Construct SITC R2 Level 4,3,2 Trade Datasets-#
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
DATA_TYPES = []
if Y8400Trade: DATA_TYPES.append('trade')
if Y8400Export: DATA_TYPES.append('export')
if Y8400Import: DATA_TYPES.append('import')
for level in [4,3,2,1]:
print
print "---> [84to00] COMPUTING SITC REVISION 2 LEVEL %s DATASETS <---" % level
print
for data_type in DATA_TYPES:
#-Setup Store-#
fn = "nber-%s-sitcr2l%s-1984to2000.h5" % (data_type,level) #-Write File: {{ source }}-{{ flow }}-{{ classification }}-{{ years }}.h5-#
store = pd.HDFStore(target_dir+local_dir+fn, complevel=9, complib='zlib')
#-Compute Datasets-#
for dataset in sorted(DATA_OPTIONS.keys()):
print
print "[SITCR2L4] Computing Dataset %s for %s" % (dataset, data_type)
#-IF Adjust Hong Kong Data then Add Data to the Tuple-#
if DATA_OPTIONS[dataset]['adjust_hk'] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = (True, rawdata_hk) #Add Data for construct_sitcr2l3 function, then remove so not saved as an attribute
else:
DATA_OPTIONS[dataset]['adjust_hk'] = (False, None)
#-Intertemporal Product Codes-#
if DATA_OPTIONS[dataset]['intertemp_productcode']:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (True, ICP[level])
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = (False, None)
#-Compute Data-#
data = construct_sitcr2(rawdata.copy(deep=True), data_type=data_type, level=level, values_only=True, **DATA_OPTIONS[dataset])
store.put(dataset, data, format='table')
#-Remove Hong Kong Data-#
if DATA_OPTIONS[dataset]['adjust_hk'][0] == True:
DATA_OPTIONS[dataset]['adjust_hk'] = True
else:
DATA_OPTIONS[dataset]['adjust_hk'] = False
if DATA_OPTIONS[dataset]['intertemp_productcode'][0] == True:
DATA_OPTIONS[dataset]['intertemp_productcode'] = True
else:
DATA_OPTIONS[dataset]['intertemp_productcode'] = False
store.get_storer(dataset).attrs.options = DATA_OPTIONS[dataset]
store.get_storer(dataset).attrs.data_type = data_type
store.get_storer(dataset).attrs.description = DATA_DESCRIPTION[dataset]
print store
#-Close-#
store.close()
del rawdata
gc.collect()