[pre-commit.ci] pre-commit autoupdate (#281)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 22.12.0 → 23.1.0](psf/black@22.12.0...23.1.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
     exclude: docs/tutorials
 
 - repo: https://github.com/psf/black
-  rev: "22.12.0"
+  rev: "23.1.0"
   hooks:
   - id: black
 

docs/tutorials/data-and-models.md

@@ -240,7 +240,6 @@ rv_obs = 5.0 * np.sin(2 * np.pi * t / 10.0) + np.sqrt(
 ) * random.normal(size=len(t))
 
 with pm.Model():
-
     # Period, semi-amplitude, and eccentricity
     log_period = pm.Normal("log_period", mu=np.log(10.0), sigma=1.0)
     period = pm.Deterministic("period", tt.exp(log_period))
@@ -333,7 +332,6 @@ rho_err = random.uniform(0.05, 0.1, len(t))
 theta_err = random.uniform(0.05, 0.1, len(t))
 
 with pm.Model():
-
     # Period, semi-major axis, eccentricity, and t0
     log_period = pm.Normal("log_period", mu=np.log(25.0 * 365.25), sigma=1.0)
     period = pm.Deterministic("period", tt.exp(log_period))
@@ -452,7 +450,6 @@ t = np.arange(0, 35, 0.02)
 yerr = 5e-4
 
 with pm.Model():
-
     # The baseline flux
     mean = pm.Normal("mean", mu=0.0, sigma=1.0)
 

docs/tutorials/intro-to-pymc3.md

@@ -106,7 +106,6 @@ The documentation for the distributions available in PyMC3's modeling language c
 import pymc3 as pm
 
 with pm.Model() as model:
-
     # Define the priors on each parameter:
     m = pm.Uniform("m", lower=-5, upper=5)
     b = pm.Uniform("b", lower=-5, upper=5)
@@ -249,7 +248,6 @@ import aesara_theano_fallback.tensor as tt
 import exoplanet as xo
 
 with pm.Model() as model:
-
     # Parameters
     logK = pm.Uniform(
         "logK",

src/exoplanet/distributions/eccentricity.py

@@ -141,13 +141,11 @@ def vaneylen19(
         )
 
         with pm.Model(name=name):
-
             if fixed:
                 sigma_gauss = sigma_gauss_mu
                 sigma_rayleigh = sigma_rayleigh_mu
                 frac = frac_mu
             else:
-
                 bounded_normal = pm.Bound(pm.Normal, lower=0)
                 sigma_gauss = bounded_normal(
                     "sigma_gauss",