reformat code

gammapy/spectrum/models.py

@@ -227,14 +227,14 @@ def from_dict(cls, val):
         return model
 
     def plot(
-        self,
-        energy_range,
-        ax=None,
-        energy_unit="TeV",
-        flux_unit="cm-2 s-1 TeV-1",
-        energy_power=0,
-        n_points=100,
-        **kwargs
+            self,
+            energy_range,
+            ax=None,
+            energy_unit="TeV",
+            flux_unit="cm-2 s-1 TeV-1",
+            energy_power=0,
+            n_points=100,
+            **kwargs
     ):
         """Plot spectral model curve.
 
@@ -288,14 +288,14 @@ def plot(
         return ax
 
     def plot_error(
-        self,
-        energy_range,
-        ax=None,
-        energy_unit="TeV",
-        flux_unit="cm-2 s-1 TeV-1",
-        energy_power=0,
-        n_points=100,
-        **kwargs
+            self,
+            energy_range,
+            ax=None,
+            energy_unit="TeV",
+            flux_unit="cm-2 s-1 TeV-1",
+            energy_power=0,
+            n_points=100,
+            **kwargs
     ):
         """Plot spectral model error band.
 
@@ -420,7 +420,6 @@ def inverse(self, value, emin=0.1 * u.TeV, emax=100 * u.TeV):
 
         energies = []
         for val in np.atleast_1d(value):
-
             def f(x):
                 # scale by 1e12 to achieve better precision
                 energy = u.Quantity(x, eunit, copy=False)
@@ -468,7 +467,7 @@ def __init__(self, model1, model2, operator):
         self.model2 = model2
         self.operator = operator
         parameters = (
-            self.model1.parameters.parameters + self.model2.parameters.parameters
+                self.model1.parameters.parameters + self.model2.parameters.parameters
         )
         super().__init__(parameters)
 
@@ -624,7 +623,7 @@ def energy_flux(self, emin, emax):
             return temp * np.log(emax / emin)
         else:
             prefactor = (
-                pars["amplitude"].quantity * pars["reference"].quantity ** 2 / val
+                    pars["amplitude"].quantity * pars["reference"].quantity ** 2 / val
             )
             upper = (emax / pars["reference"].quantity) ** val
             lower = (emin / pars["reference"].quantity) ** val
@@ -712,7 +711,7 @@ class PowerLaw2(SpectralModel):
     __slots__ = ["index", "amplitude", "emin", "emax"]
 
     def __init__(
-        self, amplitude="1e-12 cm-2 s-1", index=2, emin="0.1 TeV", emax="100 TeV"
+            self, amplitude="1e-12 cm-2 s-1", index=2, emin="0.1 TeV", emax="100 TeV"
     ):
         self.amplitude = Parameter("amplitude", amplitude)
         self.index = Parameter("index", index)
@@ -840,11 +839,11 @@ class ExponentialCutoffPowerLaw(SpectralModel):
     __slots__ = ["index", "amplitude", "reference", "lambda_"]
 
     def __init__(
-        self,
-        index=1.5,
-        amplitude="1e-12 cm-2 s-1 TeV-1",
-        reference="1 TeV",
-        lambda_="0.1 TeV-1",
+            self,
+            index=1.5,
+            amplitude="1e-12 cm-2 s-1 TeV-1",
+            reference="1 TeV",
+            lambda_="0.1 TeV-1",
     ):
         self.index = Parameter("index", index)
         self.amplitude = Parameter("amplitude", amplitude)
@@ -919,11 +918,11 @@ class ExponentialCutoffPowerLaw3FGL(SpectralModel):
     __slots__ = ["index", "amplitude", "reference", "ecut"]
 
     def __init__(
-        self,
-        index=1.5,
-        amplitude="1e-12 cm-2 s-1 TeV-1",
-        reference="1 TeV",
-        ecut="10 TeV",
+            self,
+            index=1.5,
+            amplitude="1e-12 cm-2 s-1 TeV-1",
+            reference="1 TeV",
+            ecut="10 TeV",
     ):
         self.index = Parameter("index", index)
         self.amplitude = Parameter("amplitude", amplitude)
@@ -982,12 +981,12 @@ class PLSuperExpCutoff3FGL(SpectralModel):
     __slots__ = ["index_1", "index_2", "amplitude", "reference", "ecut"]
 
     def __init__(
-        self,
-        index_1=1.5,
-        index_2=2,
-        amplitude="1e-12 cm-2 s-1 TeV-1",
-        reference="1 TeV",
-        ecut="10 TeV",
+            self,
+            index_1=1.5,
+            index_2=2,
+            amplitude="1e-12 cm-2 s-1 TeV-1",
+            reference="1 TeV",
+            ecut="10 TeV",
     ):
         self.index_1 = Parameter("index_1", index_1)
         self.index_2 = Parameter("index_2", index_2)
@@ -1056,7 +1055,7 @@ class LogParabola(SpectralModel):
     __slots__ = ["amplitude", "reference", "alpha", "beta"]
 
     def __init__(
-        self, amplitude="1e-12 cm-2 s-1 TeV-1", reference="10 TeV", alpha=2, beta=1
+            self, amplitude="1e-12 cm-2 s-1 TeV-1", reference="10 TeV", alpha=2, beta=1
     ):
         self.amplitude = Parameter("amplitude", amplitude)
         self.reference = Parameter("reference", reference, frozen=True)
@@ -1134,7 +1133,7 @@ class TableModel(SpectralModel):
     __slots__ = ["energy", "values", "norm", "meta", "_evaluate"]
 
     def __init__(
-        self, energy, values, norm=1, values_scale="log", interp_kwargs=None, meta=None
+            self, energy, values, norm=1, values_scale="log", interp_kwargs=None, meta=None
     ):
         self.norm = Parameter("norm", norm, unit="")
         self.energy = energy
@@ -1299,7 +1298,7 @@ class Absorption:
     """
 
     def __init__(
-        self, energy_lo, energy_hi, param_lo, param_hi, data, interp_kwargs=None
+            self, energy_lo, energy_hi, param_lo, param_hi, data, interp_kwargs=None
     ):
         self.data = data
 
@@ -1424,7 +1423,7 @@ class AbsorbedSpectralModel(SpectralModel):
     __slots__ = ["spectral_model", "absorption", "parameter", "parameter_name"]
 
     def __init__(
-        self, spectral_model, absorption, parameter, parameter_name="redshift"
+            self, spectral_model, absorption, parameter, parameter_name="redshift"
     ):
         self.spectral_model = spectral_model
         self.absorption = absorption
@@ -1623,7 +1622,7 @@ class Gaussian(SpectralModel):
     """
 
     def __init__(
-        self, norm=1e-12 * u.Unit("cm-2 s-1"), mean=1 * u.TeV, sigma=2 * u.TeV
+            self, norm=1e-12 * u.Unit("cm-2 s-1"), mean=1 * u.TeV, sigma=2 * u.TeV
     ):
         self.norm = Parameter("norm", norm)
         self.mean = Parameter("mean", mean)
@@ -1634,9 +1633,9 @@ def __init__(
     @staticmethod
     def evaluate(energy, norm, mean, sigma):
         return (
-            norm
-            / (sigma * np.sqrt(2 * np.pi))
-            * np.exp((energy - mean) ** 2 / (2 * sigma ** 2))
+                norm
+                / (sigma * np.sqrt(2 * np.pi))
+                * np.exp((energy - mean) ** 2 / (2 * sigma ** 2))
         )
 
     def integral(self, emin, emax, **kwargs):
@@ -1683,5 +1682,4 @@ def energy_flux(self, emin, emax):
         a = pars["norm"].quantity * pars["sigma"].quantity / np.sqrt(2 * np.pi)
         b = pars["norm"].quantity * pars["mean"].quantity / 2
         return a * (np.exp(-u_min ** 2) - np.exp(-u_max ** 2)) + b * (
-            erf(u_max) - erf(u_min) )
-
+                erf(u_max) - erf(u_min))

gammapy/spectrum/tests/test_models.py

@@ -261,9 +261,9 @@ def test_models(spectrum):
     # inverse for ConstantModel is irrelevant.
     # inverse for Gaussian has a degeneracy
     if not (
-        isinstance(model, ConstantModel)
-        or spectrum["name"] == "compound6"
-        or spectrum["name"] == "Gaussian"
+            isinstance(model, ConstantModel)
+            or spectrum["name"] == "compound6"
+            or spectrum["name"] == "Gaussian"
     ):
         assert_quantity_allclose(model.inverse(value), 2 * u.TeV, rtol=0.01)