def main(file_path, trade_flow, year, eci_file_path, pci_file_path,
output_path, prev_path, prev5_path):
start = time.time()
step = 0
depths = {"bra": [1, 3, 5, 7, 8, 9], "hs": [2, 6], "wld": [2, 5]}
step += 1
print '''\nSTEP {0}: \nImport file to pandas dataframe'''.format(step)
secex_df = to_df(file_path, False)
secex_df = secex_df.head(1000)
sys.exit()
step += 1
print '''\nSTEP {0}: \nAggregate'''.format(step)
ybpw = aggregate(secex_df)
step += 1
print '''\nSTEP {0}: \nShard'''.format(step)
[yb, ybp, ybw, yp, ypw, yw] = shard(ybpw, depths)
if trade_flow == "export":
step += 1
print '''\nSTEP {0}: \nCalculate PCI & ECI'''.format(step)
[yp, yw] = pci_wld_eci(eci_file_path, pci_file_path, yp, yw)
step += 1
print '''\nSTEP {0}: \nCalculate domestic ECI'''.format(step)
yb = domestic_eci(yp, yb, ybp, depths)
step += 1
print '''\nSTEP {0}: \nCalculate diversity'''.format(step)
yb = calc_diversity(ybp, yb, "bra_id", "hs_id", depths)
yb = calc_diversity(ybw, yb, "bra_id", "wld_id", depths)
yp = calc_diversity(ybp, yp, "hs_id", "bra_id", depths)
yp = calc_diversity(ypw, yp, "hs_id", "wld_id", depths)
yw = calc_diversity(ybw, yw, "wld_id", "bra_id", depths)
yw = calc_diversity(ypw, yw, "wld_id", "hs_id", depths)
if trade_flow == "export":
step += 1
print '''\nSTEP {0}: \nCalculate Brazilian RCA'''.format(step)
yp = brazil_rca(yp, year)
if trade_flow == "export":
step += 1
print '''\nSTEP {0}: \nCalculate RCA, diversity and opp_gain aka RDO'''.format(
step)
ybp = rdo(ybp, yp, year, depths)
if trade_flow == "import":
step += 1
print '''\nSTEP {0}: \nCalculate RCD calculation'''.format(step)
ybp = rcd(ybp, yp, year, depths)
# print ybp.head(20)
# sys.exit()
tables = {
"yb": yb,
"yp": yp,
"yw": yw,
"ybp": ybp,
"ybpw": ybpw,
"ybw": ybw,
"ypw": ypw
}
if prev_path:
step += 1
print '''\nSTEP {0}: \nCalculate 1 year growth'''.format(step)
if prev5_path:
step += 1
print '''\nSTEP {0}: \nCalculate 5 year growth'''.format(step)
for t_name, t in tables.items():
prev_file = os.path.join(prev_path, "{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] +
list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev)
if prev5_path:
prev_file = os.path.join(prev5_path,
"{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] +
list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev, 5)
print "computing column lengths"
for table_name, table_data in tables.items():
tables[table_name] = add_column_length(table_name, table_data)
print '''\nFINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
if not os.path.exists(output_path):
os.makedirs(output_path)
new_file_path = os.path.abspath(
os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True)
total_run_time = (time.time() - start) / 60
print
print
print "Total runtime: {0} minutes".format(int(total_run_time))
print
print
def main(export_file_path, import_file_path, year, eci_file_path,
pci_file_path, ypw_file_path, output_path, prev_path, prev5_path):
output_path = os.path.join(output_path, str(year))
start = time.time()
step = 0
depths = {"bra": [1, 3, 5, 7, 9], "hs": [2, 6], "wld": [2, 5]}
if not os.path.exists(output_path):
os.makedirs(output_path)
d = pd.HDFStore(os.path.join(output_path, 'secex.h5'))
# if "ymb" in d:
if "ymbp" in d:
tables = {}
tables["ymb"] = d["ymb"]
tables["ymp"] = d["ymp"]
tables["ymw"] = d["ymw"]
tables["ymbp"] = d["ymbp"]
tables["ymbw"] = d["ymbw"]
tables["ympw"] = d["ympw"]
tables["ymbpw"] = d["ymbpw"]
else:
step += 1
print '''\nSTEP {0}: \nImport file to pandas dataframe'''.format(step)
secex_exports = to_df(export_file_path, False)
secex_imports = to_df(import_file_path, False)
step += 1
print '''\nSTEP {0}: \nMerge imports and exports'''.format(step)
secex_df = merge(secex_exports, secex_imports)
step += 1
print '''\nSTEP {0}: \nAggregate'''.format(step)
ymbpw = aggregate(secex_df)
step += 1
print '''\nSTEP {0}: \nShard'''.format(step)
[ymb, ymbp, ymbw, ymp, ympw, ymw] = shard(ymbpw)
step += 1
print '''\nSTEP {0}: \nCalculate PCI & ECI'''.format(step)
[ymp, ymw] = pci_wld_eci(eci_file_path, pci_file_path, ymp, ymw, year)
step += 1
print '''\nSTEP {0}: \nCalculate diversity'''.format(step)
ymb = calc_diversity(ymbp, ymb, "bra_id", "hs_id")
ymb = calc_diversity(ymbw, ymb, "bra_id", "wld_id")
ymp = calc_diversity(ymbp, ymp, "hs_id", "bra_id")
ymp = calc_diversity(ympw, ymp, "hs_id", "wld_id")
ymw = calc_diversity(ymbw, ymw, "wld_id", "bra_id")
ymw = calc_diversity(ympw, ymw, "wld_id", "hs_id")
step += 1
print '''\nSTEP {0}: \nCalculate domestic ECI'''.format(step)
ymb = domestic_eci(ymp, ymb, ymbp, depths["bra"])
step += 1
print '''\nSTEP {0}: \nCalculate domestic ECI'''.format(step)
ymb = domestic_eci(ymp, ymb, ymbp, depths["bra"])
step += 1
print '''\nSTEP {0}: \nCalculate Brazilian RCA'''.format(step)
ymp = brazil_rca(ymp, ypw_file_path, year)
step += 1
print '''\nSTEP {0}: \nCalculate RCA, diversity and opp_gain aka RDO'''.format(
step)
ymbp = rdo(ymbp, ymp, year, depths["bra"], ypw_file_path)
tables = {
"ymb": ymb,
"ymp": ymp,
"ymw": ymw,
"ymbp": ymbp,
"ymbpw": ymbpw,
"ymbw": ymbw,
"ympw": ympw
}
for tbln, tbl in tables.items():
d[tbln] = tbl
if prev_path:
step += 1
print '''\nSTEP {0}: \nCalculate 1 year growth'''.format(step)
if prev5_path:
step += 1
print '''\nSTEP {0}: \nCalculate 5 year growth'''.format(step)
for t_name, t in tables.items():
print t_name
prev_file = os.path.join(prev_path, "{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] +
list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev)
if prev5_path:
prev_file = os.path.join(prev5_path,
"{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] +
list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev, 5)
print "computing column lengths"
for table_name, table_data in tables.items():
tables[table_name] = add_column_length(table_name, table_data)
print '''\nFINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
if not os.path.exists(output_path):
os.makedirs(output_path)
new_file_path = os.path.abspath(
os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True)
total_run_time = (time.time() - start) / 60
print
print
print "Total runtime: {0} minutes".format(int(total_run_time))
print
print
def main(file_path, year, output_path, prev_path, prev5_path, requireds_only):
print; print "~~~**** YEAR: {0} ****~~~".format(year); print;
start = time.time()
step = 0
# regions state, meso, micro, planning region, munic
depths = {
"bra": [1, 3, 5, 7, 8, 9],
"cnae": [1, 3, 6],
"cbo": [1, 4],
"demo": [1, 4]
}
if file_path:
if not os.path.exists(output_path): os.makedirs(output_path)
d = pd.HDFStore(os.path.join(output_path, 'rais_df_raw.h5'))
if "rais_df" in d:
rais_df = d['rais_df']
else:
step+=1; print; print '''STEP {0}: \nImport file to pandas dataframe'''.format(step)
rais_df = to_df(file_path, False)
try:
d['rais_df'] = rais_df
# d.close()
except OverflowError:
print "WARNING: Unable to save dataframe, Overflow Error."
d.close()
os.remove(os.path.join(output_path, 'rais_df_raw.h5'))
# rais_df = to_df(file_path, False)
if "yb" in d and not requireds_only:
tables = {"yb":d["yb"], "yo":d["yo"], "yi":d["yi"], "ybi":d["ybi"], "ybo":d["ybo"], "yio":d["yio"], "ybio":d["ybio"]}
else:
step+=1; print; print '''STEP {0}: \nAggregate'''.format(step)
tables = aggregate(rais_df, depths)
step+=1; print; print 'STEP {0}: \nImportance'.format(step)
tables["yio"] = importance(tables["ybio"], tables["ybi"], tables["yio"], tables["yo"], year, depths)
try:
d["yb"] = tables["yb"]; d["yo"] = tables["yo"]; d["yi"] = tables["yi"]; d["ybi"] = tables["ybi"]; d["ybo"] = tables["ybo"]; d["yio"] = tables["yio"]; d["ybio"] = tables["ybio"]
d.close()
except OverflowError:
print "WARNING: Unable to save dataframe, Overflow Error."
d.close()
os.remove(os.path.join(output_path, 'rais_df_raw.h5'))
step+=1; print; print 'STEP {0}: \nRequired'.format(step)
[tables["ybi"], tables["ybio"]] = required(tables["ybio"], tables["ybi"], tables["yi"], year, depths, output_path)
# print tables["ybi"].head()
# sys.exit()
step+=1; print; print 'STEP {0}: \nDiversity'.format(step)
tables["yb"] = calc_diversity(tables["ybi"], tables["yb"], "bra_id", "cnae_id", year, depths)
tables["yb"] = calc_diversity(tables["ybo"], tables["yb"], "bra_id", "cbo_id", year, depths)
tables["yi"] = calc_diversity(tables["ybi"], tables["yi"], "cnae_id", "bra_id", year, depths)
tables["yi"] = calc_diversity(tables["yio"], tables["yi"], "cnae_id", "cbo_id", year, depths)
tables["yo"] = calc_diversity(tables["ybo"], tables["yo"], "cbo_id", "bra_id", year, depths)
tables["yo"] = calc_diversity(tables["yio"], tables["yo"], "cbo_id", "cnae_id", year, depths)
step+=1; print; print 'STEP {0}: \nCalculate RCA, diversity and opportunity gain aka RDO'.format(step)
tables["ybi"] = rdo(tables["ybi"], tables["yi"], year, depths)
for table_name, table_data in tables.items():
table_data = add_column_length(table_name, table_data)
print; print '''FINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
new_file_path = os.path.abspath(os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True, float_format="%.3f")
if prev_path:
print; print '''Calculating growth:'''
for current_year_file_path in findFiles(output_path, '*.tsv.bz2'):
if "growth" in current_year_file_path: continue
current_year_file_name = os.path.basename(current_year_file_path)
prev_year_file_path = os.path.join(prev_path, current_year_file_name)
prev5_year_file_path = None
if prev5_path:
prev5_year_file_path = os.path.join(prev5_path, current_year_file_name)
if not os.path.exists(prev_year_file_path):
print "Unable to find", current_year_file_name, "for previous year."
continue
tbl_name, tbl_w_growth = calc_growth(year, current_year_file_path, prev_year_file_path, prev5_year_file_path)
print tbl_name
new_file_path = os.path.abspath(os.path.join(output_path, "{0}_growth.tsv.bz2".format(tbl_name)))
tbl_w_growth.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True, float_format="%.3f")
# os.remove(current_year_file_path)
print("--- %s minutes ---" % str((time.time() - start)/60))
def main(file_path, trade_flow, year, eci_file_path, pci_file_path, output_path, prev_path, prev5_path):
start = time.time()
step = 0
depths = {
"bra": [1, 3, 5, 7, 8, 9],
"hs": [2, 6],
"wld": [2, 5]
}
step += 1; print '''\nSTEP {0}: \nImport file to pandas dataframe'''.format(step)
secex_df = to_df(file_path, False)
secex_df = secex_df.head(1000)
sys.exit()
step += 1; print '''\nSTEP {0}: \nAggregate'''.format(step)
ybpw = aggregate(secex_df)
step += 1; print '''\nSTEP {0}: \nShard'''.format(step)
[yb, ybp, ybw, yp, ypw, yw] = shard(ybpw, depths)
if trade_flow == "export":
step += 1; print '''\nSTEP {0}: \nCalculate PCI & ECI'''.format(step)
[yp, yw] = pci_wld_eci(eci_file_path, pci_file_path, yp, yw)
step += 1; print '''\nSTEP {0}: \nCalculate domestic ECI'''.format(step)
yb = domestic_eci(yp, yb, ybp, depths)
step += 1; print '''\nSTEP {0}: \nCalculate diversity'''.format(step)
yb = calc_diversity(ybp, yb, "bra_id", "hs_id", depths)
yb = calc_diversity(ybw, yb, "bra_id", "wld_id", depths)
yp = calc_diversity(ybp, yp, "hs_id", "bra_id", depths)
yp = calc_diversity(ypw, yp, "hs_id", "wld_id", depths)
yw = calc_diversity(ybw, yw, "wld_id", "bra_id", depths)
yw = calc_diversity(ypw, yw, "wld_id", "hs_id", depths)
if trade_flow == "export":
step += 1; print '''\nSTEP {0}: \nCalculate Brazilian RCA'''.format(step)
yp = brazil_rca(yp, year)
if trade_flow == "export":
step += 1; print '''\nSTEP {0}: \nCalculate RCA, diversity and opp_gain aka RDO'''.format(step)
ybp = rdo(ybp, yp, year, depths)
if trade_flow == "import":
step += 1; print '''\nSTEP {0}: \nCalculate RCD calculation'''.format(step)
ybp = rcd(ybp, yp, year, depths)
# print ybp.head(20)
# sys.exit()
tables = {"yb": yb, "yp": yp, "yw": yw, "ybp": ybp, "ybpw": ybpw, "ybw": ybw, "ypw": ypw}
if prev_path:
step += 1; print '''\nSTEP {0}: \nCalculate 1 year growth'''.format(step)
if prev5_path:
step += 1; print '''\nSTEP {0}: \nCalculate 5 year growth'''.format(step)
for t_name, t in tables.items():
prev_file = os.path.join(prev_path, "{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] + list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev)
if prev5_path:
prev_file = os.path.join(prev5_path, "{0}.tsv.bz2".format(t_name))
t_prev = to_df(prev_file, t_name)
t_prev = t_prev.reset_index(level="year")
t_prev["year"] = int(year)
t_prev = t_prev.set_index("year", append=True)
t_prev = t_prev.reorder_levels(["year"] + list(t_prev.index.names)[:-1])
t = calc_growth(t, t_prev, 5)
print "computing column lengths"
for table_name, table_data in tables.items():
tables[table_name] = add_column_length(table_name, table_data)
print '''\nFINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
if not os.path.exists(output_path):
os.makedirs(output_path)
new_file_path = os.path.abspath(os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True)
total_run_time = (time.time() - start) / 60
print; print;
print "Total runtime: {0} minutes".format(int(total_run_time))
print; print;
def main(file_path, year, output_path, prev_path, prev5_path, requireds_only):
print
print "~~~**** YEAR: {0} ****~~~".format(year)
print
start = time.time()
step = 0
# regions state, meso, micro, planning region, munic
depths = {
"bra": [1, 3, 5, 7, 8, 9],
"cnae": [1, 3, 6],
"cbo": [1, 4],
"demo": [1, 4]
}
if file_path:
if not os.path.exists(output_path): os.makedirs(output_path)
d = pd.HDFStore(os.path.join(output_path, 'rais_df_raw.h5'))
if "rais_df" in d:
rais_df = d['rais_df']
else:
step += 1
print
print '''STEP {0}: \nImport file to pandas dataframe'''.format(
step)
rais_df = to_df(file_path, False)
try:
d['rais_df'] = rais_df
# d.close()
except OverflowError:
print "WARNING: Unable to save dataframe, Overflow Error."
d.close()
os.remove(os.path.join(output_path, 'rais_df_raw.h5'))
# rais_df = to_df(file_path, False)
if "yb" in d and not requireds_only:
tables = {
"yb": d["yb"],
"yo": d["yo"],
"yi": d["yi"],
"ybi": d["ybi"],
"ybo": d["ybo"],
"yio": d["yio"],
"ybio": d["ybio"]
}
else:
step += 1
print
print '''STEP {0}: \nAggregate'''.format(step)
tables = aggregate(rais_df, depths)
step += 1
print
print 'STEP {0}: \nImportance'.format(step)
tables["yio"] = importance(tables["ybio"], tables["ybi"],
tables["yio"], tables["yo"], year,
depths)
try:
d["yb"] = tables["yb"]
d["yo"] = tables["yo"]
d["yi"] = tables["yi"]
d["ybi"] = tables["ybi"]
d["ybo"] = tables["ybo"]
d["yio"] = tables["yio"]
d["ybio"] = tables["ybio"]
d.close()
except OverflowError:
print "WARNING: Unable to save dataframe, Overflow Error."
d.close()
os.remove(os.path.join(output_path, 'rais_df_raw.h5'))
step += 1
print
print 'STEP {0}: \nRequired'.format(step)
[tables["ybi"],
tables["ybio"]] = required(tables["ybio"], tables["ybi"],
tables["yi"], year, depths, output_path)
# print tables["ybi"].head()
# sys.exit()
step += 1
print
print 'STEP {0}: \nDiversity'.format(step)
tables["yb"] = calc_diversity(tables["ybi"], tables["yb"], "bra_id",
"cnae_id", year, depths)
tables["yb"] = calc_diversity(tables["ybo"], tables["yb"], "bra_id",
"cbo_id", year, depths)
tables["yi"] = calc_diversity(tables["ybi"], tables["yi"], "cnae_id",
"bra_id", year, depths)
tables["yi"] = calc_diversity(tables["yio"], tables["yi"], "cnae_id",
"cbo_id", year, depths)
tables["yo"] = calc_diversity(tables["ybo"], tables["yo"], "cbo_id",
"bra_id", year, depths)
tables["yo"] = calc_diversity(tables["yio"], tables["yo"], "cbo_id",
"cnae_id", year, depths)
step += 1
print
print 'STEP {0}: \nCalculate RCA, diversity and opportunity gain aka RDO'.format(
step)
tables["ybi"] = rdo(tables["ybi"], tables["yi"], year, depths)
for table_name, table_data in tables.items():
table_data = add_column_length(table_name, table_data)
print
print '''FINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
new_file_path = os.path.abspath(
os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'),
sep="\t",
index=True,
float_format="%.3f")
if prev_path:
print
print '''Calculating growth:'''
for current_year_file_path in findFiles(output_path, '*.tsv.bz2'):
if "growth" in current_year_file_path: continue
current_year_file_name = os.path.basename(current_year_file_path)
prev_year_file_path = os.path.join(prev_path,
current_year_file_name)
prev5_year_file_path = None
if prev5_path:
prev5_year_file_path = os.path.join(prev5_path,
current_year_file_name)
if not os.path.exists(prev_year_file_path):
print "Unable to find", current_year_file_name, "for previous year."
continue
tbl_name, tbl_w_growth = calc_growth(year, current_year_file_path,
prev_year_file_path,
prev5_year_file_path)
print tbl_name
new_file_path = os.path.abspath(
os.path.join(output_path,
"{0}_growth.tsv.bz2".format(tbl_name)))
tbl_w_growth.to_csv(bz2.BZ2File(new_file_path, 'wb'),
sep="\t",
index=True,
float_format="%.3f")
# os.remove(current_year_file_path)
print("--- %s minutes ---" % str((time.time() - start) / 60))
def main(export_file_path, import_file_path, year, eci_file_path, pci_file_path, ypw_file_path, output_path):
start = time.time()
step = 0
depths = {
"bra": [1, 3, 5, 7, 9],
"hs": [2, 6],
"wld": [2, 5]
}
if not os.path.exists(output_path): os.makedirs(output_path)
d = pd.HDFStore(os.path.join(output_path, 'secex.h5'))
# if "ymb" in d:
if "ymbp" in d:
tables = {}
tables["ymb"] = d["ymb"]; tables["ymp"] = d["ymp"]; tables["ymw"] = d["ymw"]; tables["ymbp"] = d["ymbp"]; tables["ymbw"] = d["ymbw"]; tables["ympw"] = d["ympw"]; tables["ymbpw"] = d["ymbpw"]
else:
step += 1; print '''\nSTEP {0}: \nImport file to pandas dataframe'''.format(step)
secex_exports = to_df(export_file_path, False)
secex_imports = to_df(import_file_path, False)
# secex_exports = secex_exports.head(1000)
# secex_imports = secex_imports.head(1000)
step += 1; print '''\nSTEP {0}: \nMerge imports and exports'''.format(step)
secex_df = merge(secex_exports, secex_imports)
step += 1; print '''\nSTEP {0}: \nAggregate'''.format(step)
ymbpw = aggregate(secex_df)
step += 1; print '''\nSTEP {0}: \nShard'''.format(step)
[ymb, ymbp, ymbw, ymp, ympw, ymw] = shard(ymbpw)
step += 1; print '''\nSTEP {0}: \nCalculate PCI & ECI'''.format(step)
[ymp, ymw] = pci_wld_eci(eci_file_path, pci_file_path, ymp, ymw, year)
step += 1; print '''\nSTEP {0}: \nCalculate diversity'''.format(step)
ymb = calc_diversity(ymbp, ymb, "bra_id", "hs_id")
ymb = calc_diversity(ymbw, ymb, "bra_id", "wld_id")
ymp = calc_diversity(ymbp, ymp, "hs_id", "bra_id")
ymp = calc_diversity(ympw, ymp, "hs_id", "wld_id")
ymw = calc_diversity(ymbw, ymw, "wld_id", "bra_id")
ymw = calc_diversity(ympw, ymw, "wld_id", "hs_id")
step += 1; print '''\nSTEP {0}: \nCalculate domestic ECI'''.format(step)
ymb = domestic_eci(ymp, ymb, ymbp, depths["bra"])
step += 1; print '''\nSTEP {0}: \nCalculate Brazilian RCA'''.format(step)
ymp = brazil_rca(ymp, ypw_file_path, year)
step += 1; print '''\nSTEP {0}: \nCalculate RCA, diversity and opp_gain aka RDO'''.format(step)
ymbp = rdo(ymbp, ymp, year, depths["bra"], ypw_file_path)
tables = {"ymb": ymb, "ymp": ymp, "ymw": ymw, "ymbp": ymbp, "ymbpw": ymbpw, "ymbw": ymbw, "ympw": ympw}
for tbln, tbl in tables.items():
d[tbln] = tbl
print "computing column lengths"
for table_name, table_data in tables.items():
tables[table_name] = add_column_length(table_name, table_data)
print '''\nFINAL STEP: \nSave files to output path'''
for t_name, t in tables.items():
if not os.path.exists(output_path):
os.makedirs(output_path)
new_file_path = os.path.abspath(os.path.join(output_path, "{0}.tsv.bz2".format(t_name)))
t.to_csv(bz2.BZ2File(new_file_path, 'wb'), sep="\t", index=True)
total_run_time = (time.time() - start) / 60
print; print;
print "Total runtime: {0} minutes".format(int(total_run_time))
print; print;
