def forecast_trend(self, region, trend=None):
"""Forecast for a region via one of several interpolation algorithms.
World:
Linear: SolarPVUtil 'TAM Data'!BX50:BZ96 Degree2: SolarPVUtil 'TAM Data'!CE50:CH96
Degree3: SolarPVUtil 'TAM Data'!CM50:CQ96 Exponential: SolarPVUtil 'TAM Data'!CV50:CX96
OECD90:
Linear: SolarPVUtil 'TAM Data'!BX168:BZ214 Degree2: SolarPVUtil 'TAM Data'!CE168:CH214
Degree3: SolarPVUtil 'TAM Data'!CM168:CQ214 Exponential: SolarPVUtil 'TAM Data'!CV168:CX214
Eastern Europe:
Linear: SolarPVUtil 'TAM Data'!BX232:BZ278 Degree2: SolarPVUtil 'TAM Data'!CE232:CH278
Degree3: SolarPVUtil 'TAM Data'!CM232:CQ278 Exponential: SolarPVUtil 'TAM Data'!CV232:CX278
Asia (Sans Japan):
Linear: SolarPVUtil 'TAM Data'!BX295:BZ341 Degree2: SolarPVUtil 'TAM Data'!CE295:CH341
Degree3: SolarPVUtil 'TAM Data'!CM295:CQ341 Exponential: SolarPVUtil 'TAM Data'!CV295:CX341
Middle East and Africa:
Linear: SolarPVUtil 'TAM Data'!BX358:BZ404 Degree2: SolarPVUtil 'TAM Data'!CE358:CH404
Degree3: SolarPVUtil 'TAM Data'!CM358:CQ404 Exponential: SolarPVUtil 'TAM Data'!CV358:CX404
Latin America:
Linear: SolarPVUtil 'TAM Data'!BX421:BZ467 Degree2: SolarPVUtil 'TAM Data'!CE421:CH467
Degree3: SolarPVUtil 'TAM Data'!CM421:CQ467 Exponential: SolarPVUtil 'TAM Data'!CV421:CX467
China:
Linear: SolarPVUtil 'TAM Data'!BX484:BZ530 Degree2: SolarPVUtil 'TAM Data'!CE484:CH530
Degree3: SolarPVUtil 'TAM Data'!CM484:CQ530 Exponential: SolarPVUtil 'TAM Data'!CV484:CX530
India:
Linear: SolarPVUtil 'TAM Data'!BX548:BZ594 Degree2: SolarPVUtil 'TAM Data'!CE548:CH594
Degree3: SolarPVUtil 'TAM Data'!CM548:CQ594 Exponential: SolarPVUtil 'TAM Data'!CV548:CX594
EU:
Linear: SolarPVUtil 'TAM Data'!BX612:BZ658 Degree2: SolarPVUtil 'TAM Data'!CE612:CH658
Degree3: SolarPVUtil 'TAM Data'!CM612:CQ658 Exponential: SolarPVUtil 'TAM Data'!CV612:CX658
US:
Linear: SolarPVUtil 'TAM Data'!BX677:BZ723 Degree2: SolarPVUtil 'TAM Data'!CE677:CH723
Degree3: SolarPVUtil 'TAM Data'!CM677:CQ723 Exponential: SolarPVUtil 'TAM Data'!CV677:CX723
"""
main_region = dd.REGIONS[0]
if main_region in region and 'PDS' in region:
data_sources = self._get_data_sources(
data_sources=self.tam_pds_data_sources, region=region)
else:
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region=region)
growth = self.tamconfig.loc['growth', region]
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig[region],
data_sources=data_sources)
data = self.forecast_low_med_high(region).loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_' + self._name_to_identifier(
region) + '_' + str(trend).lower()
return result
def _adoption_trend(self, low_med_high, growth, trend):
"""Adoption prediction via one of several interpolation algorithms."""
if growth is None or trend is None:
result = pd.DataFrame(np.nan, index=low_med_high.index.copy(), columns=['adoption'])
else:
data = low_med_high[growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
return result
def forecast_trend_usa(self, trend=None):
"""Forecast for the 'USA' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX677:BZ723 Degree2: SolarPVUtil 'TAM Data'!CE677:CH723
Degree3: SolarPVUtil 'TAM Data'!CM677:CQ723 Exponential: SolarPVUtil 'TAM Data'!CV677:CX723
"""
growth = self.tamconfig.loc['growth', 'USA']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='USA')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['USA'],
data_sources=data_sources)
data = self.forecast_low_med_high_usa().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_usa_' + str(trend).lower()
return result
def forecast_trend_india(self, trend=None):
"""Forecast for the 'India' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX548:BZ594 Degree2: SolarPVUtil 'TAM Data'!CE548:CH594
Degree3: SolarPVUtil 'TAM Data'!CM548:CQ594 Exponential: SolarPVUtil 'TAM Data'!CV548:CX594
"""
growth = self.tamconfig.loc['growth', 'India']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='India')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['India'],
data_sources=data_sources)
data = self.forecast_low_med_high_india().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_india_' + str(trend).lower()
return result
def forecast_trend_eu(self, trend=None):
"""Forecast for the 'EU' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX612:BZ658 Degree2: SolarPVUtil 'TAM Data'!CE612:CH658
Degree3: SolarPVUtil 'TAM Data'!CM612:CQ658 Exponential: SolarPVUtil 'TAM Data'!CV612:CX658
"""
growth = self.tamconfig.loc['growth', 'EU']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='EU')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['EU'],
data_sources=data_sources)
data = self.forecast_low_med_high_eu().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_eu_' + str(trend).lower()
return result
def forecast_trend_latin_america(self, trend=None):
"""Forecast for the 'Latin America' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX421:BZ467 Degree2: SolarPVUtil 'TAM Data'!CE421:CH467
Degree3: SolarPVUtil 'TAM Data'!CM421:CQ467 Exponential: SolarPVUtil 'TAM Data'!CV421:CX467
"""
growth = self.tamconfig.loc['growth', 'Latin America']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='Latin America')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['Latin America'],
data_sources=data_sources)
data = self.forecast_low_med_high_latin_america().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_latin_america_' + str(trend).lower()
return result
def forecast_trend_asia_sans_japan(self, trend=None):
"""Forecast for the 'Asia (Sans Japan)' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX295:BZ341 Degree2: SolarPVUtil 'TAM Data'!CE295:CH341
Degree3: SolarPVUtil 'TAM Data'!CM295:CQ341 Exponential: SolarPVUtil 'TAM Data'!CV295:CX341
"""
growth = self.tamconfig.loc['growth', 'Asia (Sans Japan)']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='Asia (Sans Japan)')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['Asia (Sans Japan)'],
data_sources=data_sources)
data = self.forecast_low_med_high_asia_sans_japan().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_asia_sans_japan_' + str(trend).lower()
return result
def forecast_trend_oecd90(self, trend=None):
"""Forecast for the 'OECD90' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX168:BZ214 Degree2: SolarPVUtil 'TAM Data'!CE168:CH214
Degree3: SolarPVUtil 'TAM Data'!CM168:CQ214 Exponential: SolarPVUtil 'TAM Data'!CV168:CX214
"""
growth = self.tamconfig.loc['growth', 'OECD90']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='OECD90')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['OECD90'],
data_sources=data_sources)
data = self.forecast_low_med_high_oecd90().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_oecd90_' + str(trend).lower()
return result
def forecast_trend_global(self, trend=None):
"""Forecast for the 'World' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX50:BZ96 Degree2: SolarPVUtil 'TAM Data'!CE50:CH96
Degree3: SolarPVUtil 'TAM Data'!CM50:CQ96 Exponential: SolarPVUtil 'TAM Data'!CV50:CX96
"""
growth = self.tamconfig.loc['growth', 'World']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources, region='World')
trend = self._get_trend(trend=trend,
tamconfig=self.tamconfig['World'],
data_sources=data_sources)
data = self.forecast_low_med_high_global().loc[:, growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_global_' + str(trend).lower()
return result
def forecast_trend_middle_east_and_africa(self, trend=None):
"""Forecast for the 'Middle East and Africa' region via one of several interpolation algorithms.
Linear: SolarPVUtil 'TAM Data'!BX358:BZ404 Degree2: SolarPVUtil 'TAM Data'!CE358:CH404
Degree3: SolarPVUtil 'TAM Data'!CM358:CQ404 Exponential: SolarPVUtil 'TAM Data'!CV358:CX404
"""
growth = self.tamconfig.loc['growth', 'Middle East and Africa']
data_sources = self._get_data_sources(
data_sources=self.tam_ref_data_sources,
region='Middle East and Africa')
trend = self._get_trend(
trend=trend,
tamconfig=self.tamconfig['Middle East and Africa'],
data_sources=data_sources)
data = self.forecast_low_med_high_middle_east_and_africa().loc[:,
growth]
result = interpolation.trend_algorithm(data=data, trend=trend)
result.name = 'forecast_trend_middle_east_and_africa_' + str(
trend).lower()
return result