def empiricalDistributionPlot(self, sample, bounds=None):
if bounds:
lower = bounds[0]
upper = bounds[1]
else:
lower = min(sample)
upper = max(sample)
plotDomain = linspace(lower, upper, len(sample))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(sample))])
empiricalCDF.observations = sample
obs = []
for j in range(len(sample)):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample = array(obs)
plt.plot(plotDomain, ecdf_sample)
plt.show()
def empiricalDistributionPlot(self, sample, bounds=None):
if bounds:
lower = bounds[0]
upper = bounds[1]
else:
lower = min(sample)
upper = max(sample)
plotDomain = linspace(lower, upper, len(sample))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(sample))])
empiricalCDF.observations = sample
obs = []
for j in range(len(sample)):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample = array(obs)
plt.plot(plotDomain, ecdf_sample)
plt.show()
for t in range(sampleSize):
defosequence.append(defo_ad_pf(initial_land, costshocks[t]) - initial_land)
defosequence_ad = array(defosequence) * landtoemissions / 1e6
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_pf(initial_land, costshocks[t]) - initial_land)
defosequence_defo = array(defosequence) * landtoemissions / 1e6
lower = min(min(defosequence_defo), min(defosequence_ad))
upper = max(max(defosequence_defo), max(defosequence_ad))
plotDomain = linspace(lower, upper, len(costshocks))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(costshocks))])
empiricalCDF.observations = defosequence_ad
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_ad = array(obs)
plt.plot(plotDomain, ecdf_sample_ad, label='AD')
empiricalCDF.observations = defosequence_defo
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_defo = array(obs)
plt.plot(plotDomain, ecdf_sample_defo, label='Defo')
plt.legend()
plt.show()
plt.plot(defo_ad_pf(gridLand, 1.0) - gridLand, label='AD')
for t in range(sampleSize):
defosequence.append(defo_ad_pf(initial_land, costshocks[t]) - initial_land)
defosequence_ad = array(defosequence) * landtoemissions / 1e6
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_pf(initial_land, costshocks[t]) - initial_land)
defosequence_defo = array(defosequence) * landtoemissions / 1e6
lower = min(min(defosequence_defo), min(defosequence_ad))
upper = max(max(defosequence_defo), max(defosequence_ad))
plotDomain = linspace(lower, upper, len(costshocks))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(costshocks))])
empiricalCDF.observations = defosequence_ad
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_ad = array(obs)
plt.plot(plotDomain, ecdf_sample_ad, label='AD')
empiricalCDF.observations = defosequence_defo
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_defo = array(obs)
plt.plot(plotDomain, ecdf_sample_defo, label='Defo')
plt.legend()
plt.show()
plt.plot(defo_ad_pf(gridLand, 1.0) - gridLand, label='AD')
