def compute_probable_emergence(export_dataset, prox_cutoff): #-Export System-# dynples = export_dataset.to_dynamic_productlevelexportsystem() dynples = dynples.dynamic_global_panel() dynples.rca_matrices(complete_data=True) dynples.mcp_matrices() dynples.proximity_matrices() #-Compute Product Changes-# Mcp_BothYears, Mcp_NewProducts, Mcp_DieProducts = dynples.compute_product_changes() df_BothYears = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_BothYears)) Mc_BothYears = df_BothYears.groupby(level=['country', 'to_year']).sum() df_NewProducts = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_NewProducts)) Mc_NewProducts = df_NewProducts.groupby(level=['country', 'to_year']).sum() df_DieProducts = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_DieProducts)) Mc_DieProducts = df_DieProducts.groupby(level=['country', 'to_year']).sum() #-Compute Probable Emergence-# Mc_ProbableProducts, Mc_ImProbableProducts = dynples.compute_probable_improbable_emergence(prox_cutoff=prox_cutoff, style=style) Mc_ProbableProducts = from_dict_of_series_to(Mc_ProbableProducts, series_name='ProbableProducts') #-Compute Probable + Persistence-# Mcp_ProbableProducts, Mcp_ImProbableProducts = dynples.compute_probable_improbable_emergence(prox_cutoff=prox_cutoff, style=style, output='reduced') Mc_ProbablePersistent = from_dict_of_series_to(compute_persistence(dynples.mcp, Mcp_ProbableProducts, output="summary"), series_name="ProbablePersistent") return Mc_ProbableProducts, Mc_ProbablePersistent
def compute_probable_emergence(export_dataset, prox_cutoff):
#-Export System-#
dynples = export_dataset.to_dynamic_productlevelexportsystem()
dynples = dynples.dynamic_global_panel()
dynples.rca_matrices(complete_data=True)
dynples.mcp_matrices()
dynples.proximity_matrices()
#-Compute Product Changes-#
Mcp_BothYears, Mcp_NewProducts, Mcp_DieProducts = dynples.compute_product_changes(
)
df_BothYears = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_BothYears))
Mc_BothYears = df_BothYears.groupby(level=['country', 'to_year']).sum()
df_NewProducts = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_NewProducts))
Mc_NewProducts = df_NewProducts.groupby(level=['country', 'to_year']).sum()
df_DieProducts = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_DieProducts))
Mc_DieProducts = df_DieProducts.groupby(level=['country', 'to_year']).sum()
#-Compute Probable Emergence-#
Mc_ProbableProducts, Mc_ImProbableProducts = dynples.compute_probable_improbable_emergence(
prox_cutoff=prox_cutoff, style=style)
Mc_ProbableProducts = from_dict_of_series_to(
Mc_ProbableProducts, series_name='ProbableProducts')
#-Compute Probable + Persistence-#
Mcp_ProbableProducts, Mcp_ImProbableProducts = dynples.compute_probable_improbable_emergence(
prox_cutoff=prox_cutoff, style=style, output='reduced')
Mc_ProbablePersistent = from_dict_of_series_to(
compute_persistence(dynples.mcp,
Mcp_ProbableProducts,
output="summary"),
series_name="ProbablePersistent")
return Mc_ProbableProducts, Mc_ProbablePersistent
def compute_regression_dataset(export_dataset):
"""
Compute a Regression Dataset Helper Function
"""
#-Export System-#
dynples = export_dataset.to_dynamic_productlevelexportsystem()
dynples = dynples.dynamic_global_panel()
dynples.rca_matrices(complete_data=True)
dynples.mcp_matrices()
dynples.proximity_matrices()
#-Compute Product Changes-#
Mcp_BothYears, Mcp_NewProducts, Mcp_DieProducts = dynples.compute_product_changes()
df_BothYears = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_BothYears))
Mc_BothYears = df_BothYears.groupby(level=['country', 'to_year']).sum()
df_NewProducts = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_NewProducts))
Mc_NewProducts = df_NewProducts.groupby(level=['country', 'to_year']).sum()
df_DieProducts = reindex_dynamic_dataframe(from_dict_to_dataframe(Mcp_DieProducts))
Mc_DieProducts = df_DieProducts.groupby(level=['country', 'to_year']).sum()
#-Compute Probable and Improbable Emergence-#
Mc_ProbableProducts, Mc_ImProbableProducts = dynples.compute_probable_improbable_emergence(prox_cutoff='median', style='average')
Mc_ProbableProducts = from_dict_of_series_to(Mc_ProbableProducts, series_name='ProbableProducts')
Mc_ImProbableProducts = from_dict_of_series_to(Mc_ImProbableProducts, series_name = 'ImprobableProducts')
#-Compute Probable\Improbable + Persistence-#
Mcp_ProbableProducts, Mcp_ImProbableProducts = dynples.compute_probable_improbable_emergence(prox_cutoff='median', style='average', output='reduced')
Mc_ProbablePersistent = from_dict_of_series_to(compute_persistence(dynples.mcp, Mcp_ProbableProducts, output="summary"), series_name="ProbablePersistent")
Mc_ImProbablePersistent = from_dict_of_series_to(compute_persistence(dynples.mcp, Mcp_ImProbableProducts, output="summary"), series_name="ImProbablePersistent")
#-Compute Persistence Total Products as a Check-#
Mc_PersistentProducts = from_dict_of_series_to(compute_persistence(dynples.mcp, reindex_dynamic_dict(Mcp_NewProducts, base='finish'), output="summary"), series_name="NewPersistent")
#Compute Centrality
AvgCentrality = dynples.compute_average_centrality(sum_not_mean=True)
AvgCentrality = from_dict_of_series_to(AvgCentrality, series_name='AvgCentrality')
#-Compute Diffusion Properties-#
Mc_ProxAvgDiff, Mc_ProxVarDiff, Mc_ProxWidthDiff = compute_diffusion_properties_nx(dynples.mcp, dynples.proximity)
Mc_ProxAvgDiff = from_dict_of_series_to(Mc_ProxAvgDiff, series_name='AvgProx')
Mc_ProxVarDiff = from_dict_of_series_to(Mc_ProxVarDiff, series_name='VarProx')
Mc_ProxWidthDiff = from_dict_of_series_to(Mc_ProxWidthDiff, series_name='WidthProx')
#GDP and GDPPC Series
wdi = WDI(WDI_SOURCE_DIR)
GDP = wdi.series_long('NY.GDP.MKTP.CD')
GDPPC = wdi.series_long('NY.GDP.PCAP.CD')
GDPPCConst = wdi.series_long('NY.GDP.PCAP.KD')
GDPPCPPP = wdi.series_long('NY.GDP.PCAP.PP.CD') #Only Available 1990 (USE PENN)
GDPPCPPPConst = wdi.series_long('NY.GDP.PCAP.PP.KD') #Only Available 1990 (Use PENN)
GDPGrowth = wdi.series_long('NY.GDP.MKTP.KD.ZG')
GDPPCGrowth = wdi.series_long('NY.GDP.PCAP.KD.ZG')
GNIAtlas = wdi.series_long('NY.GNP.ATLS.CD')
GNIPPP = wdi.series_long('NY.GNP.MKTP.PP.CD')
GNIPCAtlas = wdi.series_long('NY.GNP.PCAP.CD')
NetBarterToT = wdi.series_long('TT.PRI.MRCH.XD.WD')
#-Infrastructure-#
AirDepartures = wdi.series_long('IS.AIR.DPRT')
RailLinesKm = wdi.series_long('IS.RRS.TOTL.KM')
ElectricityUsePC = wdi.series_long('EG.USE.ELEC.KH.PC')
#-Others-#
Population = wdi.series_long('SP.POP.TOTL')
LandArea = wdi.series_long('AG.LND.TOTL.K2')
#Trade Liberalisation Data
trade_lib = pd.read_csv(WAZIARG_DIR+'trade_lib_wacziarg.csv')
trade_lib = tradelib_stats(trade_lib) #-Add in TradeLib Stats-#
trade_lib = trade_lib.set_index(keys=['iso3c', 'year'])
#Merge and Export File (Country-Year Data)
df = AvgCentrality
for item in [Mc_ProbableProducts, Mc_ImProbableProducts, Mc_ProbablePersistent, Mc_ImProbablePersistent, Mc_ProxAvgDiff, Mc_ProxVarDiff, Mc_ProxWidthDiff]:
df = df.join(item, how='outer')
#-Single Index for Merging-#
df.index = pd.Index(df.index)
for item in [GDP, GDPPC, GDPPCConst, GDPPCPPP, GDPPCPPPConst, GDPGrowth, GDPPCGrowth, GNIAtlas, GNIPPP, GNIPCAtlas, NetBarterToT, AirDepartures, RailLinesKm, ElectricityUsePC, Population, LandArea]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
for item in [Mc_BothYears, Mc_NewProducts, Mc_DieProducts, Mc_PersistentProducts]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
for item in [trade_lib]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
#-Restore MultiIndex-#
df.index = pd.MultiIndex.from_tuples(df.index, names=['country', 'year'])
return df
def compute_regression_dataset(export_dataset):
"""
Compute a Regression Dataset Helper Function
"""
#-Export System-#
dynples = export_dataset.to_dynamic_productlevelexportsystem()
dynples = dynples.dynamic_global_panel()
dynples.rca_matrices(complete_data=True)
dynples.mcp_matrices()
dynples.proximity_matrices()
#-Compute Product Changes-#
Mcp_BothYears, Mcp_NewProducts, Mcp_DieProducts = dynples.compute_product_changes(
)
df_BothYears = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_BothYears))
Mc_BothYears = df_BothYears.groupby(level=['country', 'to_year']).sum()
df_NewProducts = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_NewProducts))
Mc_NewProducts = df_NewProducts.groupby(level=['country', 'to_year']).sum()
df_DieProducts = reindex_dynamic_dataframe(
from_dict_to_dataframe(Mcp_DieProducts))
Mc_DieProducts = df_DieProducts.groupby(level=['country', 'to_year']).sum()
#-Compute Probable and Improbable Emergence-#
Mc_ProbableProducts, Mc_ImProbableProducts = dynples.compute_probable_improbable_emergence(
prox_cutoff='median', style='average')
Mc_ProbableProducts = from_dict_of_series_to(
Mc_ProbableProducts, series_name='ProbableProducts')
Mc_ImProbableProducts = from_dict_of_series_to(
Mc_ImProbableProducts, series_name='ImprobableProducts')
#-Compute Probable\Improbable + Persistence-#
Mcp_ProbableProducts, Mcp_ImProbableProducts = dynples.compute_probable_improbable_emergence(
prox_cutoff='median', style='average', output='reduced')
Mc_ProbablePersistent = from_dict_of_series_to(
compute_persistence(dynples.mcp,
Mcp_ProbableProducts,
output="summary"),
series_name="ProbablePersistent")
Mc_ImProbablePersistent = from_dict_of_series_to(
compute_persistence(dynples.mcp,
Mcp_ImProbableProducts,
output="summary"),
series_name="ImProbablePersistent")
#-Compute Persistence Total Products as a Check-#
Mc_PersistentProducts = from_dict_of_series_to(compute_persistence(
dynples.mcp,
reindex_dynamic_dict(Mcp_NewProducts, base='finish'),
output="summary"),
series_name="NewPersistent")
#Compute Centrality
AvgCentrality = dynples.compute_average_centrality(sum_not_mean=True)
AvgCentrality = from_dict_of_series_to(AvgCentrality,
series_name='AvgCentrality')
#-Compute Diffusion Properties-#
Mc_ProxAvgDiff, Mc_ProxVarDiff, Mc_ProxWidthDiff = compute_diffusion_properties_nx(
dynples.mcp, dynples.proximity)
Mc_ProxAvgDiff = from_dict_of_series_to(Mc_ProxAvgDiff,
series_name='AvgProx')
Mc_ProxVarDiff = from_dict_of_series_to(Mc_ProxVarDiff,
series_name='VarProx')
Mc_ProxWidthDiff = from_dict_of_series_to(Mc_ProxWidthDiff,
series_name='WidthProx')
#GDP and GDPPC Series
wdi = WDI(WDI_SOURCE_DIR)
GDP = wdi.series_long('NY.GDP.MKTP.CD')
GDPPC = wdi.series_long('NY.GDP.PCAP.CD')
GDPPCConst = wdi.series_long('NY.GDP.PCAP.KD')
GDPPCPPP = wdi.series_long(
'NY.GDP.PCAP.PP.CD') #Only Available 1990 (USE PENN)
GDPPCPPPConst = wdi.series_long(
'NY.GDP.PCAP.PP.KD') #Only Available 1990 (Use PENN)
GDPGrowth = wdi.series_long('NY.GDP.MKTP.KD.ZG')
GDPPCGrowth = wdi.series_long('NY.GDP.PCAP.KD.ZG')
GNIAtlas = wdi.series_long('NY.GNP.ATLS.CD')
GNIPPP = wdi.series_long('NY.GNP.MKTP.PP.CD')
GNIPCAtlas = wdi.series_long('NY.GNP.PCAP.CD')
NetBarterToT = wdi.series_long('TT.PRI.MRCH.XD.WD')
#-Infrastructure-#
AirDepartures = wdi.series_long('IS.AIR.DPRT')
RailLinesKm = wdi.series_long('IS.RRS.TOTL.KM')
ElectricityUsePC = wdi.series_long('EG.USE.ELEC.KH.PC')
#-Others-#
Population = wdi.series_long('SP.POP.TOTL')
LandArea = wdi.series_long('AG.LND.TOTL.K2')
#Trade Liberalisation Data
trade_lib = pd.read_csv(WAZIARG_DIR + 'trade_lib_wacziarg.csv')
trade_lib = tradelib_stats(trade_lib) #-Add in TradeLib Stats-#
trade_lib = trade_lib.set_index(keys=['iso3c', 'year'])
#Merge and Export File (Country-Year Data)
df = AvgCentrality
for item in [
Mc_ProbableProducts, Mc_ImProbableProducts, Mc_ProbablePersistent,
Mc_ImProbablePersistent, Mc_ProxAvgDiff, Mc_ProxVarDiff,
Mc_ProxWidthDiff
]:
df = df.join(item, how='outer')
#-Single Index for Merging-#
df.index = pd.Index(df.index)
for item in [
GDP, GDPPC, GDPPCConst, GDPPCPPP, GDPPCPPPConst, GDPGrowth,
GDPPCGrowth, GNIAtlas, GNIPPP, GNIPCAtlas, NetBarterToT,
AirDepartures, RailLinesKm, ElectricityUsePC, Population, LandArea
]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
for item in [
Mc_BothYears, Mc_NewProducts, Mc_DieProducts, Mc_PersistentProducts
]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
for item in [trade_lib]:
item.index = pd.Index(item.index)
df = df.merge(item, how='left', left_index=True, right_index=True)
#-Restore MultiIndex-#
df.index = pd.MultiIndex.from_tuples(df.index, names=['country', 'year'])
return df