diff --git a/demos/getting_started_density.py b/demos/getting_started_density.py
index 55be5200a2050c92977f70026bcffa5003851d5f..f4f92306a56f1654ed34e6fff48ede75e99b1090 100644
--- a/demos/getting_started_density.py
+++ b/demos/getting_started_density.py
@@ -31,9 +31,7 @@ import numpy as np
 import nifty7 as ift
 
 
-def density_estimator(
-    domain, pad=1., cf_fluctuations=None, cf_azm_uniform=None
-):
+def density_estimator(domain, pad=1.0, cf_fluctuations=None, cf_azm_uniform=None):
     cf_azm_uniform_sane_default = (1e-4, 1.0)
     cf_fluctuations_sane_default = {
         "scale": (0.5, 0.3),
@@ -109,40 +107,34 @@ if __name__ == "__main__":
 
     # Generate mock signal and data
     rng = ift.random.current_rng()
-    mock_position = ift.from_random(signal.domain, 'normal')
-    data = ift.Field.from_raw(
-        data_space, rng.poisson(signal(mock_position).val)
-    )
+    mock_position = ift.from_random(signal.domain, "normal")
+    data = ift.Field.from_raw(data_space, rng.poisson(signal(mock_position).val))
 
     # Rejoin domains for plotting
     plotting_domain = ift.DomainTuple.make(ift.RGSpace((npix1, npix2)))
-    plotting_domain_expanded = ift.DomainTuple.make(
-        ift.RGSpace((2 * npix1, 2 * npix2))
-    )
+    plotting_domain_expanded = ift.DomainTuple.make(ift.RGSpace((2 * npix1, 2 * npix2)))
 
     plot = ift.Plot()
     plot.add(
         ift.Field.from_raw(
-            plotting_domain_expanded,
-            ift.exp(correlated_field(mock_position)).val
+            plotting_domain_expanded, ift.exp(correlated_field(mock_position)).val
         ),
-        title='Pre-Slicing Truth'
+        title="Pre-Slicing Truth",
     )
     plot.add(
-        ift.Field.from_raw(plotting_domain,
-                           signal(mock_position).val),
-        title='Ground Truth'
+        ift.Field.from_raw(plotting_domain, signal(mock_position).val),
+        title="Ground Truth",
     )
-    plot.add(ift.Field.from_raw(plotting_domain, data.val), title='Data')
+    plot.add(ift.Field.from_raw(plotting_domain, data.val), title="Data")
     plot.output(ny=1, nx=3, xsize=10, ysize=10, name=filename.format("setup"))
     print("Setup saved as", filename.format("setup"))
 
     # Minimization parameters
     ic_sampling = ift.AbsDeltaEnergyController(
-        name='Sampling', deltaE=0.01, iteration_limit=100
+        name="Sampling", deltaE=0.01, iteration_limit=100
     )
     ic_newton = ift.AbsDeltaEnergyController(
-        name='Newton', deltaE=0.01, iteration_limit=35
+        name="Newton", deltaE=0.01, iteration_limit=35
     )
     ic_sampling.enable_logging()
     ic_newton.enable_logging()
@@ -169,37 +161,27 @@ if __name__ == "__main__":
         plot = ift.Plot()
         plot.add(
             ift.Field.from_raw(
-                plotting_domain_expanded,
-                ift.exp(correlated_field(mock_position)).val
+                plotting_domain_expanded, ift.exp(correlated_field(mock_position)).val
             ),
-            title="Ground truth"
+            title="Ground truth",
         )
         plot.add(
-            ift.Field.from_raw(plotting_domain,
-                               signal(mock_position).val),
-            title="Ground truth"
+            ift.Field.from_raw(plotting_domain, signal(mock_position).val),
+            title="Ground truth",
         )
         plot.add(
-            ift.Field.from_raw(plotting_domain,
-                               signal(kl.position).val),
-            title="Reconstruction"
+            ift.Field.from_raw(plotting_domain, signal(kl.position).val),
+            title="Reconstruction",
         )
         plot.add(
-            (
-                ic_newton.history, ic_sampling.history,
-                minimizer.inversion_history
-            ),
-            label=['kl', 'Sampling', 'Newton inversion'],
-            title='Cumulative energies',
+            (ic_newton.history, ic_sampling.history, minimizer.inversion_history),
+            label=["kl", "Sampling", "Newton inversion"],
+            title="Cumulative energies",
             s=[None, None, 1],
-            alpha=[None, 0.2, None]
+            alpha=[None, 0.2, None],
         )
         plot.output(
-            nx=3,
-            ny=2,
-            ysize=10,
-            xsize=15,
-            name=filename.format(f"loop_{i:02d}")
+            nx=3, ny=2, ysize=10, xsize=15, name=filename.format(f"loop_{i:02d}")
         )
 
     # Done, draw posterior samples
@@ -211,25 +193,18 @@ if __name__ == "__main__":
 
     # Plotting
     plot = ift.Plot()
+    plot.add(ift.Field.from_raw(plotting_domain, sc.mean.val), title="Posterior Mean")
     plot.add(
-        ift.Field.from_raw(plotting_domain, sc.mean.val),
-        title="Posterior Mean"
-    )
-    plot.add(
-        ift.Field.from_raw(plotting_domain,
-                           ift.sqrt(sc.var).val),
-        title="Posterior Standard Deviation"
+        ift.Field.from_raw(plotting_domain, ift.sqrt(sc.var).val),
+        title="Posterior Standard Deviation",
     )
     plot.add(
         ift.Field.from_raw(plotting_domain_expanded, sc_unsliced.mean.val),
-        title="Posterior Unsliced Mean"
+        title="Posterior Unsliced Mean",
     )
     plot.add(
-        ift.Field.from_raw(
-            plotting_domain_expanded,
-            ift.sqrt(sc_unsliced.var).val
-        ),
-        title="Posterior Unsliced Standard Deviation"
+        ift.Field.from_raw(plotting_domain_expanded, ift.sqrt(sc_unsliced.var).val),
+        title="Posterior Unsliced Standard Deviation",
     )
     filename_res = filename.format("results")
     plot.output(ny=2, nx=2, xsize=15, ysize=15, name=filename_res)