[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ ci:
 
 repos:
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.8.0
     hooks:
       - id: black
         # It is recommended to specify the latest version of Python
@@ -19,7 +19,7 @@ repos:
         # https://pre-commit.com/#top_level-default_language_version
         language_version: python3
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.8.0
     hooks:
       - id: black-jupyter
         language_version: python3

hierarc/Likelihood/hierarchy_likelihood.py

@@ -129,7 +129,7 @@ def __init__(
             global_los_distribution=global_los_distribution,
             los_distributions=los_distributions,
             individual_distribution=los_distribution_individual,
-            kwargs_individual=kwargs_los_individual
+            kwargs_individual=kwargs_los_individual,
         )
         kwargs_min, kwargs_max = self.param_bounds_interpol()
         self._lens_distribution = LensDistribution(

hierarc/Sampling/Distributions/los_distributions.py

@@ -41,14 +41,15 @@
             self._los_distribution = los_distributions[global_los_distribution]
         else:
             self._draw_kappa_global = False
-        if (not self._draw_kappa_global and individual_distribution is not None
-        ):
+        if not self._draw_kappa_global and individual_distribution is not None:
             if individual_distribution == "PDF":
                 self._kappa_dist = PDFSampling(**kwargs_individual)
             elif individual_distribution == "GEV":
                 self._kappa_dist = GEV(**kwargs_individual)
             else:
-                raise ValueError("individual_distribution %s not supported. Chose among 'GEV' and 'PDF'")
+                raise ValueError(
+                    "individual_distribution %s not supported. Chose among 'GEV' and 'PDF'"
+                )
             self._draw_kappa_individual = True
         else:
             self._draw_kappa_individual = False
@@ -99,9 +100,8 @@
 
 
 class GEV(object):
-    """
-    draw from General Extreme Value distribution
-    """
+    """Draw from General Extreme Value distribution."""
+
     def __init__(self, xi, mean, sigma):
         """
 
@@ -114,12 +114,13 @@
         self._sigma = sigma
 
     def draw(self, n=1):
-        """
-        draws from the PDF of the GEV distribution
+        """Draws from the PDF of the GEV distribution.
 
         :param n: number of draws from distribution
         :type n: int
         :return: draws according to the PDF of the distribution
         """
-        kappa_ext_draw = genextreme.rvs(c=self._xi, loc=self._mean, scale=self._sigma, size=n)
+        kappa_ext_draw = genextreme.rvs(
+            c=self._xi, loc=self._mean, scale=self._sigma, size=n
+        )
         return kappa_ext_draw

test/test_Diagnostics/test_goodness_of_fit.py

@@ -72,7 +72,10 @@ def setup_method(self):
             {
                 "ddt_samples": ddt_samples,
                 "los_distribution_individual": "PDF",
-                "kwargs_los_individual": {"bin_edges": kappa_bin_edges, "pdf_array": kappa_pdf},
+                "kwargs_los_individual": {
+                    "bin_edges": kappa_bin_edges,
+                    "pdf_array": kappa_pdf,
+                },
             },
             {"ddt_samples": ddt_samples},
             {

test/test_Likelihood/test_hierarchy_likelihood.py

@@ -101,7 +101,10 @@ def setup_method(self):
             # los_distributions=["GAUSSIAN"],
             global_los_distribution=False,
             los_distribution_individual="PDF",
-            kwargs_los_individual={"bin_edges": kappa_bin_edges, "pdf_array": kappa_pdf},
+            kwargs_los_individual={
+                "bin_edges": kappa_bin_edges,
+                "pdf_array": kappa_pdf,
+            },
             mst_ifu=False,
             **kwargs_likelihood,
             **kwargs_model

test/test_Likelihood/test_los_distribution.py

@@ -38,7 +38,7 @@ def test_gev(self):
             global_los_distribution=1,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
 
         kappa_dist_drawn = dist_gev.draw_los(kwargs_los, size=10000)
@@ -50,7 +50,7 @@ def test_gev(self):
             global_los_distribution=False,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
 
         kappa_dist_drawn = dist_gev.draw_los(kwargs_los, size=10000)
@@ -62,7 +62,7 @@ def test_gev(self):
             global_los_distribution=0,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
 
         kappa_dist_drawn = dist_gev.draw_los(kwargs_los, size=10000)
@@ -95,7 +95,7 @@ def test_draw_bool(self):
             global_los_distribution=1,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
         bool_draw = dist.draw_bool(kwargs_los)
         assert bool_draw is True
@@ -104,7 +104,7 @@ def test_draw_bool(self):
             global_los_distribution=0,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
         bool_draw = dist.draw_bool(kwargs_los)
         assert bool_draw is False
@@ -113,7 +113,7 @@ def test_draw_bool(self):
             global_los_distribution=False,
             los_distributions=los_distribution,
             individual_distribution="PDF",
-            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges}
+            kwargs_individual={"pdf_array": kappa_pdf, "bin_edges": kappa_bin_edges},
         )
         bool_draw = dist.draw_bool(kwargs_los)
         assert bool_draw is True