def fit_spindle_refractory():
data = [[88, 317], [118, 99], [125, 93], [131, 97], [137, 115], [144, 143],
[151, 194], [158, 223], [175, 245], [197, 265], [239, 287],
[285, 297], [355, 304], [432, 307], [454, 313]]
xscale = [0, 30]
yscale = [0, 0.08]
data_df = get_target_curve(data, xscale, yscale, scale=False)
sample_data = np.random.choice(a=data_df['x'], p=data_df['y'], size=1000)
with pm.Model() as model:
a = pm.HalfNormal('a', 100 * 10)
b = pm.HalfNormal('b', 100 * 10)
pm.Wald('spindle_duration', mu=a, lam=b, observed=sample_data)
trace = pm.sample(2000, njobs=1)
summary_df = pm.summary(trace)
a_est = summary_df.loc['a', 'mean']
b_est = summary_df.loc['b', 'mean']
n_samples = 10000
with pm.Model() as model:
pm.Wald('spindle_density_mean_params', mu=a_est, lam=b_est)
outcome = pm.sample(n_samples, njobs=1, nchains=1)
# pm.traceplot(trace)
# plt.show()
samples = outcome['spindle_density_mean_params']
sns.distplot(samples, kde=True, bins=100)
x = data_df['x']
y = data_df['y'] * len(samples) * (x[1] - x[0])
sns.lineplot(x, y)
plt.show()
print(summary_df)
return samples * 30 + 0.5
def _sample_pymc3(cls, dist, size):
"""Sample from PyMC3."""
import pymc3
pymc3_rv_map = {
'BetaDistribution': lambda dist:
pymc3.Beta('X', alpha=float(dist.alpha), beta=float(dist.beta)),
'CauchyDistribution': lambda dist:
pymc3.Cauchy('X', alpha=float(dist.x0), beta=float(dist.gamma)),
'ChiSquaredDistribution': lambda dist:
pymc3.ChiSquared('X', nu=float(dist.k)),
'ExponentialDistribution': lambda dist:
pymc3.Exponential('X', lam=float(dist.rate)),
'GammaDistribution': lambda dist:
pymc3.Gamma('X', alpha=float(dist.k), beta=1/float(dist.theta)),
'LogNormalDistribution': lambda dist:
pymc3.Lognormal('X', mu=float(dist.mean), sigma=float(dist.std)),
'NormalDistribution': lambda dist:
pymc3.Normal('X', float(dist.mean), float(dist.std)),
'GaussianInverseDistribution': lambda dist:
pymc3.Wald('X', mu=float(dist.mean), lam=float(dist.shape)),
'ParetoDistribution': lambda dist:
pymc3.Pareto('X', alpha=float(dist.alpha), m=float(dist.xm)),
'UniformDistribution': lambda dist:
pymc3.Uniform('X', lower=float(dist.left), upper=float(dist.right))
}
dist_list = pymc3_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
with pymc3.Model():
pymc3_rv_map[dist.__class__.__name__](dist)
return pymc3.sample(size, chains=1, progressbar=False)[:]['X']
def fit_refractory_minus_duration():
sample_data = pd.read_pickle(
'../data/raw/refractory_prior_samples.pkl')['samples'].values
with pm.Model() as model:
a = pm.HalfNormal('a', 100 * 10)
b = pm.HalfNormal('b', 100 * 10)
pm.Wald('prior', mu=a, lam=b, observed=sample_data)
trace = pm.sample(2000, njobs=1)
summary_df = pm.summary(trace)
a_est = summary_df.loc['a', 'mean']
b_est = summary_df.loc['b', 'mean']
n_samples = 10000
with pm.Model() as model:
pm.Wald('prior_check', mu=a_est, lam=b_est)
outcome = pm.sample(n_samples, njobs=1, nchains=1)
samples = outcome['prior_check']
sns.distplot(samples, kde=True)
sns.distplot(sample_data, kde=True)
plt.show()
print(summary_df)