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

hierarc/Diagnostics/goodness_of_fit.py

@@ -65,7 +65,9 @@ def plot_ddt_fit(
                     ddt_model_sigma,
                     dd_model_mean,
                     dd_model_sigma,
-                ) = likelihood.ddt_dd_model_prediction(cosmo, kwargs_lens=kwargs_lens, kwargs_los=kwargs_los)
+                ) = likelihood.ddt_dd_model_prediction(
+                    cosmo, kwargs_lens=kwargs_lens, kwargs_los=kwargs_los
+                )
 
                 ddt_name_list.append(name)
                 ddt_model_mean_list.append(ddt_model_mean)
@@ -162,7 +164,10 @@ def kin_fit(self, cosmo, kwargs_lens, kwargs_kin, kwargs_los):
                 sigma_v_predict_mean,
                 cov_error_predict,
             ) = likelihood.sigma_v_measured_vs_predict(
-                cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
+                cosmo,
+                kwargs_lens=kwargs_lens,
+                kwargs_kin=kwargs_kin,
+                kwargs_los=kwargs_los,
             )
 
             if sigma_v_measurement is not None:
@@ -205,7 +210,9 @@ def plot_kin_fit(
         :param color_prediction: color of model prediction
         :return: fig, axes of matplotlib instance
         """
-        logL = self._sample_likelihood.log_likelihood(cosmo, kwargs_lens, kwargs_kin, kwargs_los=kwargs_los)
+        logL = self._sample_likelihood.log_likelihood(
+            cosmo, kwargs_lens, kwargs_kin, kwargs_los=kwargs_los
+        )
         print(logL, "log likelihood")
         (
             sigma_v_name_list,

hierarc/Likelihood/LensLikelihood/double_source_plane.py

@@ -30,7 +30,12 @@ def __init__(
         self._normalized = normalized
 
     def lens_log_likelihood(
-        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None, kwargs_los=None
+        self,
+        cosmo,
+        kwargs_lens=None,
+        kwargs_kin=None,
+        kwargs_source=None,
+        kwargs_los=None,
     ):
         """Log likelihood of the data given a model.
 

hierarc/Likelihood/cosmo_likelihood.py

@@ -108,8 +108,9 @@ def __init__(
         if sigma_v_systematics is True:
             normalized = True
         self._likelihoodLensSample = LensSampleLikelihood(
-            kwargs_likelihood_list, normalized=normalized,
-            los_distributions=los_distributions
+            kwargs_likelihood_list,
+            normalized=normalized,
+            los_distributions=los_distributions,
         )
         self.param = ParamManager(
             cosmology,
@@ -191,8 +192,8 @@ def likelihood(self, args, kwargs_cosmo_interp=None):
             if args[i] < self._lower_limit[i] or args[i] > self._upper_limit[i]:
                 return -np.inf
 
-        kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source, kwargs_los = self.param.args2kwargs(
-            args
+        kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source, kwargs_los = (
+            self.param.args2kwargs(args)
         )
         if self._cosmology == "oLCDM":
             # assert we are not in a crazy cosmological situation that prevents computing the angular distance integral

hierarc/Likelihood/hierarchy_likelihood.py

@@ -127,9 +127,12 @@ def __init__(
         )
         self._num_distribution_draws = int(num_distribution_draws)
 
-        self._los = LOSDistribution(kappa_pdf=kappa_pdf, kappa_bin_edges=kappa_bin_edges,
-                                    global_los_distribution=global_los_distribution,
-                                    los_distributions=los_distributions)
+        self._los = LOSDistribution(
+            kappa_pdf=kappa_pdf,
+            kappa_bin_edges=kappa_bin_edges,
+            global_los_distribution=global_los_distribution,
+            los_distributions=los_distributions,
+        )
         self._mst_ifu = mst_ifu
         self._lambda_scaling_property = lambda_scaling_property
         self._lambda_scaling_property_beta = lambda_scaling_property_beta
@@ -140,7 +143,12 @@ def __init__(
         self._gamma_in_prior_std = gamma_in_prior_std
 
     def lens_log_likelihood(
-        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None, kwargs_los=None,
+        self,
+        cosmo,
+        kwargs_lens=None,
+        kwargs_kin=None,
+        kwargs_source=None,
+        kwargs_los=None,
     ):
         """Log likelihood of the data of a lens given a model (defined with hyper-
         parameters) and cosmology.
@@ -149,7 +157,8 @@ def lens_log_likelihood(
         :param kwargs_lens: keywords of the hyper parameters of the lens model
         :param kwargs_kin: keyword arguments of the kinematic model hyper parameters
         :param kwargs_source: keyword argument of the source model (such as SNe)
-        :param kwargs_los: list of keyword arguments of global line of sight distributions
+        :param kwargs_los: list of keyword arguments of global line of sight
+            distributions
         :return: log likelihood of the data given the model
         """
 
@@ -194,7 +203,8 @@ def hyper_param_likelihood(
         :param kwargs_lens: keywords of the hyper parameters of the lens model
         :param kwargs_kin: keyword arguments of the kinematic model hyper parameters
         :param kwargs_source: keyword argument of the source model (such as SNe)
-        :param kwargs_los: list of keyword arguments of global line of sight distributions
+        :param kwargs_los: list of keyword arguments of global line of sight
+            distributions
         :param cosmo: astropy.cosmology instance
         :return: log likelihood given the single lens analysis for the given hyper
             parameter
@@ -529,7 +539,9 @@ def draw_source(mu_sne=1, sigma_sne=0, lum_dist=0, **kwargs):
         # return linear amplitude with base log 10
         return mag_source
 
-    def sigma_v_measured_vs_predict(self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_los=None):
+    def sigma_v_measured_vs_predict(
+        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_los=None
+    ):
         """Mean and error covariance of velocity dispersion measurement mean and error
         covariance of velocity dispersion predictions.
 

hierarc/Likelihood/lens_sample_likelihood.py

@@ -28,11 +28,19 @@ def __init__(self, kwargs_lens_list, normalized=False, los_distributions=None):
                 )
             else:
                 self._lens_list.append(
-                    LensLikelihood(normalized=normalized, los_distributions=los_distributions, **kwargs_lens)
+                    LensLikelihood(
+                        normalized=normalized,
+                        los_distributions=los_distributions,
+                        **kwargs_lens
+                    )
                 )
 
     def log_likelihood(
-        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None,
+        self,
+        cosmo,
+        kwargs_lens=None,
+        kwargs_kin=None,
+        kwargs_source=None,
         kwargs_los=None,
     ):
         """

hierarc/Likelihood/los_distributions.py

@@ -3,11 +3,15 @@
 
 
 class LOSDistribution(object):
-    """
-    line of sight distribution drawing
-    """
-    def __init__(self, kappa_pdf=None, kappa_bin_edges=None, global_los_distribution=False,
-                 los_distributions=None):
+    """Line of sight distribution drawing."""
+
+    def __init__(
+        self,
+        kappa_pdf=None,
+        kappa_bin_edges=None,
+        global_los_distribution=False,
+        los_distributions=None,
+    ):
         """
 
         :param global_los_distribution: if integer, will draw from the global kappa distribution specified in that
@@ -21,12 +25,19 @@ def __init__(self, kappa_pdf=None, kappa_bin_edges=None, global_los_distribution
         """
 
         self._global_los_distribution = global_los_distribution
-        if isinstance(self._global_los_distribution, int) and self._global_los_distribution is not False:
+        if (
+            isinstance(self._global_los_distribution, int)
+            and self._global_los_distribution is not False
+        ):
             self._draw_kappa_global = True
             self._los_distribution = los_distributions[global_los_distribution]
         else:
             self._draw_kappa_global = False
-        if kappa_pdf is not None and kappa_bin_edges is not None and not self._draw_kappa_global:
+        if (
+            kappa_pdf is not None
+            and kappa_bin_edges is not None
+            and not self._draw_kappa_global
+        ):
             print("test kappa pdf sampling")
             self._kappa_dist = PDFSampling(
                 bin_edges=kappa_bin_edges, pdf_array=kappa_pdf
@@ -36,8 +47,7 @@ def __init__(self, kappa_pdf=None, kappa_bin_edges=None, global_los_distribution
             self._draw_kappa_individual = False
 
     def draw_los(self, kwargs_los, size=1):
-        """
-        Draw from the distribution of line of sight convergence
+        """Draw from the distribution of line of sight convergence.
 
         :param kwargs_los: line of sight parameters
         :type kwargs_los: list of dict
@@ -58,16 +68,18 @@ def draw_los(self, kwargs_los, size=1):
                 sigma = kwargs_los_i["sigma"]
                 xi = kwargs_los_i["xi"]
                 from scipy.stats import genextreme
+
                 kappa_ext_draw = genextreme.rvs(c=xi, loc=mean, scale=sigma, size=size)
             else:
-                raise ValueError("line of sight distribution %s not valid." % self._los_distribution)
+                raise ValueError(
+                    "line of sight distribution %s not valid." % self._los_distribution
+                )
         else:
             kappa_ext_draw = 0
         return kappa_ext_draw
 
     def draw_bool(self, kwargs_los):
-        """
-        whether single-valued or extended distribution (need to draw from)
+        """Whether single-valued or extended distribution (need to draw from)
 
         :param kwargs_los: list of keyword arguments for line of sight distributions
         :return: boolean, True with samples need to be drawn, else False

hierarc/Sampling/ParamManager/los_param.py

@@ -10,7 +10,6 @@ def __init__(
         los_sampling=False,
         los_distributions=None,
         kwargs_fixed=None,
-
     ):
         """
 
@@ -47,21 +46,21 @@ def param_list(self, latex_style=False):
                 if los_distribution in ["GEV", "GAUSSIAN"]:
                     if "mean" not in self._kwargs_fixed[i]:
                         if latex_style is True:
-                            name_list.append(r"$\mu_{\rm los %s}$" %i)
+                            name_list.append(r"$\mu_{\rm los %s}$" % i)
                         else:
-                            name_list.append(str("mean_los_"+str(i)))
+                            name_list.append(str("mean_los_" + str(i)))
                     if "sigma" not in self._kwargs_fixed[i]:
                         if latex_style is True:
-                            name_list.append(r"$\sigma_{\rm los %s}$" %i)
+                            name_list.append(r"$\sigma_{\rm los %s}$" % i)
                         else:
-                            name_list.append(str("sigma_los_"+str(i)))
+                            name_list.append(str("sigma_los_" + str(i)))
                 if los_distribution in ["GEV"]:
                     if "xi" not in self._kwargs_fixed[i]:
                         if latex_style is True:
-                            name_list.append(r"$\xi_{\rm los} %s$" %i)
+                            name_list.append(r"$\xi_{\rm los} %s$" % i)
                         else:
                             name_list.append(str("xi_los_" + str(i)))
-        #str(name + "_" + type + str(k))
+        # str(name + "_" + type + str(k))
         return name_list
 
     def args2kwargs(self, args, i=0):

hierarc/Sampling/ParamManager/param_manager.py

@@ -124,9 +124,11 @@ def __init__(
             z_apparent_m_anchor=z_apparent_m_anchor,
             kwargs_fixed=kwargs_fixed_source,
         )
-        self._los_param = LOSParam(los_sampling=los_sampling,
-                                   los_distributions=los_distributions,
-                                   kwargs_fixed=kwargs_fixed_los,)
+        self._los_param = LOSParam(
+            los_sampling=los_sampling,
+            los_distributions=los_distributions,
+            kwargs_fixed=kwargs_fixed_los,
+        )
         self._kwargs_upper_cosmo, self._kwargs_lower_cosmo = (
             kwargs_upper_cosmo,
             kwargs_lower_cosmo,
@@ -185,8 +187,12 @@ def args2kwargs(self, args):
         return kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source, kwargs_los
 
     def kwargs2args(
-        self, kwargs_cosmo=None, kwargs_lens=None, kwargs_kin=None, kwargs_source=None,
-        kwargs_los=None
+        self,
+        kwargs_cosmo=None,
+        kwargs_lens=None,
+        kwargs_kin=None,
+        kwargs_source=None,
+        kwargs_los=None,
     ):
         """
 

test/test_Diagnostics/test_goodness_of_fit.py

@@ -115,7 +115,9 @@ def test_plot_ddt_fit(self):
     def test_plot_kin_fit(self):
         kwargs_lens = {"lambda_mst": 1}
         kwargs_kin = {}
-        f, ax = self.goodnessofFit.plot_kin_fit(self.cosmo, kwargs_lens, kwargs_kin, kwargs_los=None)
+        f, ax = self.goodnessofFit.plot_kin_fit(
+            self.cosmo, kwargs_lens, kwargs_kin, kwargs_los=None
+        )
         plt.close()
 
     def test_plot_ifu_fit(self):

test/test_Likelihood/test_hierarchy_likelihood.py

@@ -169,17 +169,26 @@ def test_lens_log_likelihood(self):
 
         kwargs_kin = {"a_ani": 1, "a_ani_sigma": 0.1}
         ln_likelihood = self.likelihood.lens_log_likelihood(
-            self.cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
+            self.cosmo,
+            kwargs_lens=kwargs_lens,
+            kwargs_kin=kwargs_kin,
+            kwargs_los=kwargs_los,
         )
         npt.assert_almost_equal(ln_likelihood, -0.5, decimal=1)
 
         ln_likelihood_zero = self.likelihood_zero_dist.lens_log_likelihood(
-            self.cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
+            self.cosmo,
+            kwargs_lens=kwargs_lens,
+            kwargs_kin=kwargs_kin,
+            kwargs_los=kwargs_los,
         )
         assert ln_likelihood_zero == -np.inf
 
         ln_likelihood_kappa_ext = self.likelihood_kappa_ext.lens_log_likelihood(
-            self.cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
+            self.cosmo,
+            kwargs_lens=kwargs_lens,
+            kwargs_kin=kwargs_kin,
+            kwargs_los=kwargs_los,
         )
         npt.assert_almost_equal(ln_likelihood, ln_likelihood_kappa_ext, decimal=1)
 
@@ -192,7 +201,10 @@ def test_lens_log_likelihood(self):
         kwargs_los = [{"mean": 0, "sigma": 0}]
         kwargs_kin = {"a_ani": 1, "a_ani_sigma": 0}
         ln_inf = self.likelihood_single.lens_log_likelihood(
-            self.cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
+            self.cosmo,
+            kwargs_lens=kwargs_lens,
+            kwargs_kin=kwargs_kin,
+            kwargs_los=kwargs_los,
         )
         assert ln_inf < -10000000