def get_more_thermal_params(N=100,F_2x=3.84):
from copulas.multivariate import GaussianMultivariate
d1_d2_q1_copula = GaussianMultivariate.load(Path(__file__).parent / "./Parameter_Sets/d1_d2_q1_CMIP6_copula.pkl")
d1_d2_q1_df = d1_d2_q1_copula.sample(10*N)
while (d1_d2_q1_df<0).any(axis=1).sum() != 0:
d1_d2_q1_df.loc[(d1_d2_q1_df<0).any(axis=1)] = d1_d2_q1_copula.sample((d1_d2_q1_df<0).any(axis=1).sum()).values
d2_samples = d1_d2_q1_df['d2'].values
d3_samples = d1_d2_q1_df['d1'].values
q3_samples = d1_d2_q1_df['q1'].values
d1_samples = sp.stats.truncnorm(-2,2,loc=283,scale=116).rvs(10*N)
TCR_samples = np.random.lognormal(np.log(2.5)/2,np.log(2.5)/(2*1.645),10*N)
RWF_samples = sp.stats.truncnorm(-2.75,2.75,loc=0.582,scale=0.06).rvs(10*N)
ECS_samples = TCR_samples/RWF_samples
d = np.array([d1_samples,d2_samples,d3_samples])
k = 1-(d/70)*(1-np.exp(-70/d))
q = ((TCR_samples/F_2x - k[2]*q3_samples)[np.newaxis,:] - np.roll(k[:2],axis=0,shift=1)*(ECS_samples/F_2x - q3_samples)[np.newaxis,:])/(k[:2] - np.roll(k[:2],axis=0,shift=1))
sample_df = pd.DataFrame(index=['d','q'],columns = [1,2,3]).apply(pd.to_numeric)
df_list = []
i=0
j=0
while j<N:
curr_df = sample_df.copy()
curr_df.loc['d'] = d[:,i]
curr_df.loc['q',3] = q3_samples[i]
curr_df.loc['q',[1,2]] = q[:,i]
if curr_df.loc['q',2]<=0:
i+=1
continue
df_list += [curr_df]
j+=1
i+=1
thermal_params = pd.concat(df_list,axis=1,keys=['therm'+str(x) for x in np.arange(N)])
return thermal_params
def test_save_load(self):
data = sample_trivariate_xyz()
model = GaussianMultivariate()
model.fit(data)
sampled_data = model.sample(10)
path_to_model = os.path.join(self.test_dir.name, "model.pkl")
model.save(path_to_model)
model2 = GaussianMultivariate.load(path_to_model)
pdf = model.probability_density(sampled_data)
pdf2 = model2.probability_density(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
cdf = model.cumulative_distribution(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def _gaussian(self, dataset):
"""
For the given dataset, this runs "everything but the kitchen sink" (i.e.
every feature of GaussianMultivariate that is officially supported) and
makes sure it doesn't crash.
"""
model = GaussianMultivariate({
dataset.columns[0]: GaussianKDE() # Use a KDE for the first column
})
model.fit(dataset)
for N in [10, 100, 50]:
assert len(model.sample(N)) == N
sampled_data = model.sample(10)
pdf = model.probability_density(sampled_data)
cdf = model.cumulative_distribution(sampled_data)
# Test Save/Load from Dictionary
config = model.to_dict()
model2 = GaussianMultivariate.from_dict(config)
for N in [10, 100, 50]:
assert len(model2.sample(N)) == N
pdf2 = model2.probability_density(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
assert np.all(np.isclose(cdf, cdf2, atol=0.01))
path_to_model = os.path.join(self.test_dir.name, "model.pkl")
model.save(path_to_model)
model2 = GaussianMultivariate.load(path_to_model)
for N in [10, 100, 50]:
assert len(model2.sample(N)) == N
pdf2 = model2.probability_density(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
assert np.all(np.isclose(cdf, cdf2, atol=0.01))