def generate(region, year, temps, **kw):
temps2 = self.weather_change(temps)
result = np.nansum(curve(temps2)) / len(temps2)
if diagnostic.is_recording():
diagnostic.record(region, year, 'avgv', float(np.nansum(temps2)) / len(temps2))
if not np.isnan(result):
yield (year, result)
if isinstance(curve, AdaptableCurve):
curve.update(year, temps) # XXX: pass the original temps (labor decision, generalize?)
def generate(region, year, temps, **kw):
coeffs = self.getter(region, year, temps, curve)
if len(temps) == len(coeffs):
result = np.sum(self.weather_change(region, temps).dot(coeffs))
else:
raise RuntimeError("Unknown format for temps: " + str(temps.shape) + " <> len " + str(coeffs))
if diagnostic.is_recording():
for ii in range(temps.shape[0]):
diagnostic.record(region, year, 'var-' + str(ii), temps[ii])
if not np.isnan(result):
yield (year, result)
if isinstance(curve, AdaptableCurve):
curve.update(year, temps) # Passing in original (not weather-changed data)
def generate(region, year, temps, **kw):
temps = self.weather_change(temps)
assert temps.shape[1] == len(curve.curr_curve.ccs), "%d <> %d" % (temps.shape[1], len(curve.curr_curve.ccs))
#result = np.nansum(np.dot(temps, curve.curr_curve.ccs)) / len(temps)
result = np.dot(np.sum(temps, axis=0), curve.curr_curve.ccs) / len(temps)
if diagnostic.is_recording():
sumtemps = np.sum(temps, axis=0) / len(temps)
for ii in range(temps.shape[1]):
diagnostic.record(region, year, 'avgtk_' + str(ii+1), sumtemps[ii])
if not np.isnan(result):
yield (year, result)
if isinstance(curve, AdaptableCurve):
curve.update(year, temps)
