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

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

appletree/components/er.py

@@ -31,6 +31,7 @@ def __init__(self, *args, **kwargs):
         self.register_all(apt.plugins.reconstruction)
         self.register_all(apt.plugins.efficiency)
 
+
 class ERPeak(ComponentSim):
     norm_type = "on_pdf"
     add_eps_to_hist = False

appletree/plugins/er_sr_microphys.py

@@ -2,7 +2,7 @@
 from jax import scipy as jsp
 from jax import jit
 from functools import partial
-import scipy 
+import scipy
 
 from appletree import randgen
 from appletree.plugin import Plugin
@@ -11,14 +11,21 @@
 
 export, __all__ = exporter(export_self=False)
 
+
 def atanh_clip(x):
-    return jnp.arctanh((x + 1)%2 - 1)
+    return jnp.arctanh((x + 1) % 2 - 1)
+
 
 def n_photon_mean_model0(num_quanta, p_mu_0):
-    return jnp.sin(jsp.special.erf(atanh_clip(jnp.sqrt(jnp.arcsinh(num_quanta))))) * num_quanta / p_mu_0
+    return (
+        jnp.sin(jsp.special.erf(atanh_clip(jnp.sqrt(jnp.arcsinh(num_quanta)))))
+        * num_quanta
+        / p_mu_0
+    )
+
 
 def n_photon_std_model0(num_quanta, p_sigma_0):
-    return (num_quanta * p_sigma_0) + jnp.log2((jnp.log2(jnp.log2(jnp.arcsinh(num_quanta))))**6)
+    return (num_quanta * p_sigma_0) + jnp.log2((jnp.log2(jnp.log2(jnp.arcsinh(num_quanta)))) ** 6)
 
 
 @export
@@ -40,21 +47,25 @@ def simulate(self, key, parameters, energy):
 
 @export
 class Emission(Plugin):
+    """A symbolic-regression-based empirical simulation for microphysics aiming at parameter number
+    reduction.
+
+    Wrapped up IonizationER + mTI + RecombFluct + TrueRecombER + RecombinationER in traditional ER
+    microphysics.
+
     """
-    A symbolic-regression-based empirical simulation for microphysics aiming at parameter number reduction.
-    Wrapped up IonizationER + mTI + RecombFluct + TrueRecombER + RecombinationER in traditional ER microphysics.
-    """
+
     depends_on = ["num_quanta"]
     provides = ["num_photon", "num_electron"]
     parameters = ("p_mu_0", "p_sigma_0")
 
     @partial(jit, static_argnums=(0,))
     def simulate(self, key, parameters, num_quanta):
-        n_photon_mean = n_photon_mean_model0(num_quanta, parameters['p_mu_0'])
-        n_photon_std = n_photon_std_model0(num_quanta, parameters['p_sigma_0'])
-        key, _num_photon = randgen.truncate_normal(key, n_photon_mean, n_photon_std, 
-                                                  vmin=0, vmax=num_quanta)
+        n_photon_mean = n_photon_mean_model0(num_quanta, parameters["p_mu_0"])
+        n_photon_std = n_photon_std_model0(num_quanta, parameters["p_sigma_0"])
+        key, _num_photon = randgen.truncate_normal(
+            key, n_photon_mean, n_photon_std, vmin=0, vmax=num_quanta
+        )
         num_photon = _num_photon.round().astype(int)
         num_electron = num_quanta - num_photon
         return key, num_photon, num_electron
-