diff --git a/docs/tutorials/Arbitrary-density-SCF.py b/docs/tutorials/Arbitrary-density-SCF.py
index 6f305da5..79169d12 100644
--- a/docs/tutorials/Arbitrary-density-SCF.py
+++ b/docs/tutorials/Arbitrary-density-SCF.py
@@ -58,7 +58,6 @@
 import gala.potential as gp
 from gala.potential.scf import compute_coeffs
 
-
 # -
 
 # ## SCF representation of an analytic density distribution
@@ -75,9 +74,10 @@
 # coordinates (x, y, z) and returns the (scalar) value of the density at that
 # location:
 
+
 def density_func(x, y, z):
     r = np.sqrt(x**2 + y**2 + z**2)
-    return 1 / (r**1.8 * (1 + r)**2.7)
+    return 1 / (r**1.8 * (1 + r) ** 2.7)
 
 
 # Let's visualize this density function. For comparison, let's also over-plot
@@ -90,20 +90,20 @@ def density_func(x, y, z):
 
 # +
 x = np.logspace(-1, 1, 128)
-plt.plot(x, density_func(x, 0, 0), marker='', label='custom density')
+plt.plot(x, density_func(x, 0, 0), marker="", label="custom density")
 
 # need a 3D grid for the potentials in Gala
 xyz = np.zeros((3, len(x)))
 xyz[0] = x
-plt.plot(x, hern.density(xyz), marker='', label='Hernquist')
+plt.plot(x, hern.density(xyz), marker="", label="Hernquist")
 
-plt.xscale('log')
-plt.yscale('log')
+plt.xscale("log")
+plt.yscale("log")
 
-plt.xlabel('$r$')
-plt.ylabel(r'$\rho(r)$')
+plt.xlabel("$r$")
+plt.ylabel(r"$\rho(r)$")
 
-plt.legend(loc='best');
+plt.legend(loc="best")
 # -
 
 # These functions are not *too* different, implying that we probably don't need
@@ -114,9 +114,9 @@ def density_func(x, y, z):
 # m$ terms, so we set `lmax=0`. We can also neglect the sin() terms of the
 # expansion ($T_{nlm}$):
 
-(S, Serr), _ = compute_coeffs(density_func,
-                              nmax=10, lmax=0,
-                              M=1., r_s=1., S_only=True)
+(S, Serr), _ = compute_coeffs(
+    density_func, nmax=10, lmax=0, M=1.0, r_s=1.0, S_only=True
+)
 
 # The above variable `S` will contain the expansion coefficients, and the
 # variable `Serr` will contain an estimate of the error in this coefficient
@@ -125,27 +125,26 @@ def density_func(x, y, z):
 
 S
 
-pot = gp.SCFPotential(m=1., r_s=1,
-                      Snlm=S, Tnlm=np.zeros_like(S))
+pot = gp.SCFPotential(m=1.0, r_s=1, Snlm=S, Tnlm=np.zeros_like(S))
 
 # Now let's visualize the SCF estimated density with the true density:
 
 # +
 x = np.logspace(-1, 1, 128)
-plt.plot(x, density_func(x, 0, 0), marker='', label='custom density')
+plt.plot(x, density_func(x, 0, 0), marker="", label="custom density")
 
 # need a 3D grid for the potentials in Gala
 xyz = np.zeros((3, len(x)))
 xyz[0] = x
-plt.plot(x, pot.density(xyz), marker='', label='SCF density')
+plt.plot(x, pot.density(xyz), marker="", label="SCF density")
 
-plt.xscale('log')
-plt.yscale('log')
+plt.xscale("log")
+plt.yscale("log")
 
-plt.xlabel('$r$')
-plt.ylabel(r'$\rho(r)$')
+plt.xlabel("$r$")
+plt.ylabel(r"$\rho(r)$")
 
-plt.legend(loc='best');
+plt.legend(loc="best")
 
 
 # -
@@ -172,9 +171,10 @@ def density_func(x, y, z):
 # Cartesian coordinates (x, y, z) and returns the (scalar) value of the density
 # at that location:
 
+
 def density_func_flat(x, y, z, q):
-    r = np.sqrt(x**2 + y**2 + (z / q)**2)
-    return 1 / (r * (1 + r)**3) / (2*np.pi)
+    r = np.sqrt(x**2 + y**2 + (z / q) ** 2)
+    return 1 / (r * (1 + r) ** 3) / (2 * np.pi)
 
 
 # Let's compute the density along a diagonal line for a few different
@@ -186,19 +186,23 @@ def density_func_flat(x, y, z, q):
 xyz[0] = x
 xyz[2] = x
 
-for q in np.arange(0.6, 1+1e-3, 0.2):
-    plt.plot(x, density_func_flat(xyz[0], 0., xyz[2], q), marker='',
-             label=f'custom density: q={q}')
+for q in np.arange(0.6, 1 + 1e-3, 0.2):
+    plt.plot(
+        x,
+        density_func_flat(xyz[0], 0.0, xyz[2], q),
+        marker="",
+        label=f"custom density: q={q}",
+    )
 
-plt.plot(x, hern.density(xyz), marker='', ls='--', label='Hernquist')
+plt.plot(x, hern.density(xyz), marker="", ls="--", label="Hernquist")
 
-plt.xscale('log')
-plt.yscale('log')
+plt.xscale("log")
+plt.yscale("log")
 
-plt.xlabel('$r$')
-plt.ylabel(r'$\rho(r)$')
+plt.xlabel("$r$")
+plt.ylabel(r"$\rho(r)$")
 
-plt.legend(loc='best');
+plt.legend(loc="best")
 # -
 
 # Because this is an axisymmetric density distribution, we need to also compute
@@ -208,13 +212,19 @@ def density_func_flat(x, y, z, q):
 # pass `progress=True`, it will also display a progress bar:
 
 q = 0.6
-(S_flat, Serr_flat), _ = compute_coeffs(density_func_flat,
-                                        nmax=4, lmax=6, args=(q, ),
-                                        M=1., r_s=1., S_only=True,
-                                        skip_m=True, progress=True)
-
-pot_flat = gp.SCFPotential(m=1., r_s=1,
-                           Snlm=S_flat, Tnlm=np.zeros_like(S_flat))
+(S_flat, Serr_flat), _ = compute_coeffs(
+    density_func_flat,
+    nmax=4,
+    lmax=6,
+    args=(q,),
+    M=1.0,
+    r_s=1.0,
+    S_only=True,
+    skip_m=True,
+    progress=True,
+)
+
+pot_flat = gp.SCFPotential(m=1.0, r_s=1, Snlm=S_flat, Tnlm=np.zeros_like(S_flat))
 
 # +
 x = np.logspace(-1, 1, 128)
@@ -222,18 +232,22 @@ def density_func_flat(x, y, z, q):
 xyz[0] = x
 xyz[2] = x
 
-plt.plot(x, density_func_flat(xyz[0], xyz[1], xyz[2], q), marker='',
-         label=f'true density q={q}')
+plt.plot(
+    x,
+    density_func_flat(xyz[0], xyz[1], xyz[2], q),
+    marker="",
+    label=f"true density q={q}",
+)
 
-plt.plot(x, pot_flat.density(xyz), marker='', ls='--', label='SCF density')
+plt.plot(x, pot_flat.density(xyz), marker="", ls="--", label="SCF density")
 
-plt.xscale('log')
-plt.yscale('log')
+plt.xscale("log")
+plt.yscale("log")
 
-plt.xlabel('$r$')
-plt.ylabel(r'$\rho(r)$')
+plt.xlabel("$r$")
+plt.ylabel(r"$\rho(r)$")
 
-plt.legend(loc='best');
+plt.legend(loc="best")
 # -
 
 # The SCF potential object acts like any other `gala.potential` object, meaning
@@ -242,25 +256,23 @@ def density_func_flat(x, y, z, q):
 # +
 grid = np.linspace(-8, 8, 128)
 
-fig, axes = plt.subplots(1, 2, figsize=(10, 5),
-                         sharex=True, sharey=True)
+fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharex=True, sharey=True)
 _ = pot_flat.plot_contours((grid, grid, 0), ax=axes[0])
-axes[0].set_xlabel('$x$')
-axes[0].set_ylabel('$y$')
+axes[0].set_xlabel("$x$")
+axes[0].set_ylabel("$y$")
 
 _ = pot_flat.plot_contours((grid, 0, grid), ax=axes[1])
-axes[1].set_xlabel('$x$')
-axes[1].set_ylabel('$z$')
+axes[1].set_xlabel("$x$")
+axes[1].set_ylabel("$z$")
 
 for ax in axes:
-    ax.set_aspect('equal')
+    ax.set_aspect("equal")
 # -
 
 # And numerically integrate orbits by passing in initial conditions and
 # integration parameters:
 
-w0 = gd.PhaseSpacePosition(pos=[3.5, 0, 1],
-                           vel=[0, 0.4, 0.05])
+w0 = gd.PhaseSpacePosition(pos=[3.5, 0, 1], vel=[0, 0.4, 0.05])
 
-orbit_flat = pot_flat.integrate_orbit(w0, dt=1., n_steps=5000)
+orbit_flat = pot_flat.integrate_orbit(w0, dt=1.0, n_steps=5000)
 _ = orbit_flat.plot()
diff --git a/docs/tutorials/Milky-Way-model.py b/docs/tutorials/Milky-Way-model.py
index aa4479dc..9653f3d8 100644
--- a/docs/tutorials/Milky-Way-model.py
+++ b/docs/tutorials/Milky-Way-model.py
@@ -39,20 +39,21 @@
 
 # +
 # Third-party
-import astropy.units as u
 import astropy.coordinates as coord
+import astropy.units as u
 import matplotlib.pyplot as plt
 import numpy as np
 
 # Gala
 import gala.dynamics as gd
 import gala.potential as gp
+
 # -
 
 # We will also set the default Astropy Galactocentric frame parameters to the
 # values adopted in Astropy v4.0:
 
-coord.galactocentric_frame_defaults.set('v4.0');
+coord.galactocentric_frame_defaults.set("v4.0")
 
 # For the Milky Way model, we'll use the built-in potential class in `gala` (see
 # above for definition):
@@ -67,20 +68,22 @@
 
 # +
 icrs = coord.SkyCoord(
-    ra=coord.Angle('17h 20m 12.4s'),
-    dec=coord.Angle('+57° 54′ 55″'),
-    distance=76*u.kpc,
-    pm_ra_cosdec=0.0569*u.mas/u.yr,
-    pm_dec=-0.1673*u.mas/u.yr,
-    radial_velocity=-291*u.km/u.s)
+    ra=coord.Angle("17h 20m 12.4s"),
+    dec=coord.Angle("+57° 54′ 55″"),
+    distance=76 * u.kpc,
+    pm_ra_cosdec=0.0569 * u.mas / u.yr,
+    pm_dec=-0.1673 * u.mas / u.yr,
+    radial_velocity=-291 * u.km / u.s,
+)
 
 icrs_err = coord.SkyCoord(
-    ra=0*u.deg,
-    dec=0*u.deg,
-    distance=6*u.kpc,
-    pm_ra_cosdec=0.009*u.mas/u.yr,
-    pm_dec=0.009*u.mas/u.yr,
-    radial_velocity=0.1*u.km/u.s)
+    ra=0 * u.deg,
+    dec=0 * u.deg,
+    distance=6 * u.kpc,
+    pm_ra_cosdec=0.009 * u.mas / u.yr,
+    pm_dec=0.009 * u.mas / u.yr,
+    radial_velocity=0.1 * u.km / u.s,
+)
 # -
 
 # Let's start by transforming the measured values to a Galactocentric reference
@@ -106,7 +109,7 @@
 # steps (5 Gyr):
 
 w0 = gd.PhaseSpacePosition(galcen.data)
-orbit = potential.integrate_orbit(w0, dt=-0.5*u.Myr, n_steps=10000)
+orbit = potential.integrate_orbit(w0, dt=-0.5 * u.Myr, n_steps=10000)
 
 # Let's visualize the orbit:
 
@@ -122,19 +125,19 @@
 # time series of the Galactocentric radius of the orbit:
 
 # +
-plt.plot(orbit.t, orbit.spherical.distance, marker='None')
+plt.plot(orbit.t, orbit.spherical.distance, marker="None")
 
 per, per_times = orbit.pericenter(return_times=True, func=None)
 apo, apo_times = orbit.apocenter(return_times=True, func=None)
 
 for t in per_times:
-    plt.axvline(t.value, color='#67a9cf')
+    plt.axvline(t.value, color="#67a9cf")
 
 for t in apo_times:
-    plt.axvline(t.value, color='#ef8a62')
+    plt.axvline(t.value, color="#ef8a62")
 
-plt.xlabel('$t$ [{0}]'.format(orbit.t.unit.to_string('latex')))
-plt.ylabel('$r$ [{0}]'.format(orbit.x.unit.to_string('latex')))
+plt.xlabel("$t$ [{0}]".format(orbit.t.unit.to_string("latex")))
+plt.ylabel("$r$ [{0}]".format(orbit.x.unit.to_string("latex")))
 # -
 
 # Now we'll sample from the error distribution over the distance, proper
@@ -144,35 +147,47 @@
 # +
 n_samples = 128
 
-dist = np.random.normal(icrs.distance.value, icrs_err.distance.value,
-                        n_samples) * icrs.distance.unit
+dist = (
+    np.random.normal(icrs.distance.value, icrs_err.distance.value, n_samples)
+    * icrs.distance.unit
+)
 
-pm_ra_cosdec = np.random.normal(icrs.pm_ra_cosdec.value,
-                                icrs_err.pm_ra_cosdec.value,
-                                n_samples) * icrs.pm_ra_cosdec.unit
+pm_ra_cosdec = (
+    np.random.normal(icrs.pm_ra_cosdec.value, icrs_err.pm_ra_cosdec.value, n_samples)
+    * icrs.pm_ra_cosdec.unit
+)
 
-pm_dec = np.random.normal(icrs.pm_dec.value,
-                          icrs_err.pm_dec.value,
-                          n_samples) * icrs.pm_dec.unit
+pm_dec = (
+    np.random.normal(icrs.pm_dec.value, icrs_err.pm_dec.value, n_samples)
+    * icrs.pm_dec.unit
+)
 
-rv = np.random.normal(icrs.radial_velocity.value,
-                      icrs_err.radial_velocity.value,
-                      n_samples) * icrs.radial_velocity.unit
+rv = (
+    np.random.normal(
+        icrs.radial_velocity.value, icrs_err.radial_velocity.value, n_samples
+    )
+    * icrs.radial_velocity.unit
+)
 
 ra = np.full(n_samples, icrs.ra.degree) * u.degree
 dec = np.full(n_samples, icrs.dec.degree) * u.degree
 # -
 
-icrs_samples = coord.SkyCoord(ra=ra, dec=dec, distance=dist,
-                              pm_ra_cosdec=pm_ra_cosdec,
-                              pm_dec=pm_dec, radial_velocity=rv)
+icrs_samples = coord.SkyCoord(
+    ra=ra,
+    dec=dec,
+    distance=dist,
+    pm_ra_cosdec=pm_ra_cosdec,
+    pm_dec=pm_dec,
+    radial_velocity=rv,
+)
 
 icrs_samples.shape
 
 galcen_samples = icrs_samples.transform_to(galcen_frame)
 
 w0_samples = gd.PhaseSpacePosition(galcen_samples.data)
-orbit_samples = potential.integrate_orbit(w0_samples, dt=-1*u.Myr, n_steps=4000)
+orbit_samples = potential.integrate_orbit(w0_samples, dt=-1 * u.Myr, n_steps=4000)
 
 orbit_samples.shape
 
@@ -187,10 +202,10 @@
 fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)
 
 axes[0].hist(peris.to_value(u.kpc), bins=np.linspace(20, 80, 32))
-axes[0].set_xlabel('pericenter [kpc]')
+axes[0].set_xlabel("pericenter [kpc]")
 
 axes[1].hist(apos.to_value(u.kpc), bins=np.linspace(60, 140, 32))
-axes[1].set_xlabel('apocenter [kpc]')
+axes[1].set_xlabel("apocenter [kpc]")
 
 axes[2].hist(eccs.value, bins=np.linspace(0.3, 0.5, 41))
-axes[2].set_xlabel('eccentricity');
+axes[2].set_xlabel("eccentricity")
diff --git a/docs/tutorials/define-milky-way-model.py b/docs/tutorials/define-milky-way-model.py
index 58218103..ca491114 100644
--- a/docs/tutorials/define-milky-way-model.py
+++ b/docs/tutorials/define-milky-way-model.py
@@ -23,15 +23,16 @@
 
 # +
 # Third-party dependencies
-from astropy.io import ascii
 import astropy.units as u
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+from astropy.io import ascii
 from scipy.optimize import leastsq
 
 # Gala
 import gala.potential as gp
 from gala.units import galactic
+
 # -
 
 # ## Introduction
@@ -43,7 +44,7 @@
 # measurements are provided with the documentation of `gala` and are shown
 # below. The radius units are kpc, and mass units are solar masses:
 
-tbl = ascii.read('data/MW_mass_enclosed.csv')
+tbl = ascii.read("data/MW_mass_enclosed.csv")
 
 tbl
 
@@ -52,19 +53,28 @@
 # +
 fig, ax = plt.subplots(1, 1, figsize=(4, 4))
 
-ax.errorbar(tbl['r'], tbl['Menc'], yerr=(tbl['Menc_err_neg'],
-                                         tbl['Menc_err_pos']),
-            marker='o', markersize=2, color='k', alpha=1., ecolor='#aaaaaa',
-            capthick=0, linestyle='none', elinewidth=1.)
-
-ax.set_xlim(1E-3, 10**2.6)
-ax.set_ylim(7E6, 10**12.25)
-
-ax.set_xlabel('$r$ [kpc]')
-ax.set_ylabel('$M(<r)$ [M$_\odot$]')
-
-ax.set_xscale('log')
-ax.set_yscale('log')
+ax.errorbar(
+    tbl["r"],
+    tbl["Menc"],
+    yerr=(tbl["Menc_err_neg"], tbl["Menc_err_pos"]),
+    marker="o",
+    markersize=2,
+    color="k",
+    alpha=1.0,
+    ecolor="#aaaaaa",
+    capthick=0,
+    linestyle="none",
+    elinewidth=1.0,
+)
+
+ax.set_xlim(1e-3, 10**2.6)
+ax.set_ylim(7e6, 10**12.25)
+
+ax.set_xlabel("$r$ [kpc]")
+ax.set_ylabel("$M(<r)$ [M$_\odot$]")
+
+ax.set_xscale("log")
+ax.set_yscale("log")
 
 fig.tight_layout()
 
@@ -86,14 +96,19 @@
 # these parameters in log-space, so we'll first define a function that returns a
 # `gala.potential.CCompositePotential` object given these four parameters:
 
+
 def get_potential(log_M_h, log_r_s, log_M_n, log_a):
     mw_potential = gp.CCompositePotential()
-    mw_potential['bulge'] = gp.HernquistPotential(m=5E9, c=1., units=galactic)
-    mw_potential['disk'] = gp.MiyamotoNagaiPotential(m=6.8E10*u.Msun, a=3*u.kpc, b=280*u.pc,
-                                                     units=galactic)
-    mw_potential['nucl'] = gp.HernquistPotential(m=np.exp(log_M_n), c=np.exp(log_a)*u.pc,
-                                                 units=galactic)
-    mw_potential['halo'] = gp.NFWPotential(m=np.exp(log_M_h), r_s=np.exp(log_r_s), units=galactic)
+    mw_potential["bulge"] = gp.HernquistPotential(m=5e9, c=1.0, units=galactic)
+    mw_potential["disk"] = gp.MiyamotoNagaiPotential(
+        m=6.8e10 * u.Msun, a=3 * u.kpc, b=280 * u.pc, units=galactic
+    )
+    mw_potential["nucl"] = gp.HernquistPotential(
+        m=np.exp(log_M_n), c=np.exp(log_a) * u.pc, units=galactic
+    )
+    mw_potential["halo"] = gp.NFWPotential(
+        m=np.exp(log_M_h), r_s=np.exp(log_r_s), units=galactic
+    )
 
     return mw_potential
 
@@ -103,7 +118,7 @@ def get_potential(log_M_h, log_r_s, log_M_n, log_a):
 
 # Initial guess for the parameters- units are:
 #     [Msun, kpc, Msun, pc]
-x0 = [np.log(6E11), np.log(20.), np.log(2E9), np.log(100.)]
+x0 = [np.log(6e11), np.log(20.0), np.log(2e9), np.log(100.0)]
 init_potential = get_potential(*x0)
 
 # +
@@ -112,22 +127,31 @@ def get_potential(log_M_h, log_r_s, log_M_n, log_a):
 
 fig, ax = plt.subplots(1, 1, figsize=(4, 4))
 
-ax.errorbar(tbl['r'], tbl['Menc'], yerr=(tbl['Menc_err_neg'], tbl['Menc_err_pos']),
-            marker='o', markersize=2, color='k', alpha=1., ecolor='#aaaaaa',
-            capthick=0, linestyle='none', elinewidth=1.)
+ax.errorbar(
+    tbl["r"],
+    tbl["Menc"],
+    yerr=(tbl["Menc_err_neg"], tbl["Menc_err_pos"]),
+    marker="o",
+    markersize=2,
+    color="k",
+    alpha=1.0,
+    ecolor="#aaaaaa",
+    capthick=0,
+    linestyle="none",
+    elinewidth=1.0,
+)
 
-fit_menc = init_potential.mass_enclosed(xyz*u.kpc)
-ax.loglog(xyz[0], fit_menc.value, marker='', color="#3182bd",
-          linewidth=2, alpha=0.7)
+fit_menc = init_potential.mass_enclosed(xyz * u.kpc)
+ax.loglog(xyz[0], fit_menc.value, marker="", color="#3182bd", linewidth=2, alpha=0.7)
 
-ax.set_xlim(1E-3, 10**2.6)
-ax.set_ylim(7E6, 10**12.25)
+ax.set_xlim(1e-3, 10**2.6)
+ax.set_ylim(7e6, 10**12.25)
 
-ax.set_xlabel('$r$ [kpc]')
-ax.set_ylabel('$M(<r)$ [M$_\odot$]')
+ax.set_xlabel("$r$ [kpc]")
+ax.set_ylabel("$M(<r)$ [M$_\odot$]")
 
-ax.set_xscale('log')
-ax.set_yscale('log')
+ax.set_xscale("log")
+ax.set_yscale("log")
 
 fig.tight_layout()
 
@@ -138,6 +162,7 @@ def get_potential(log_M_h, log_r_s, log_M_n, log_a):
 # optimize our nucleus and halo parameters. We first need to define an error
 # function:
 
+
 def err_func(p, r, Menc, Menc_err):
     pot = get_potential(*p)
     xyz = np.zeros((3, len(r)))
@@ -151,9 +176,9 @@ def err_func(p, r, Menc, Menc_err):
 # that the uncertainties in the mass measurements are Gaussian (a bad but simple
 # assumption):
 
-err = np.max([tbl['Menc_err_pos'], tbl['Menc_err_neg']], axis=0)
-p_opt, ier = leastsq(err_func, x0=x0, args=(tbl['r'], tbl['Menc'], err))
-assert ier in range(1, 4+1), "least-squares fit failed!"
+err = np.max([tbl["Menc_err_pos"], tbl["Menc_err_neg"]], axis=0)
+p_opt, ier = leastsq(err_func, x0=x0, args=(tbl["r"], tbl["Menc"], err))
+assert ier in range(1, 4 + 1), "least-squares fit failed!"
 fit_potential = get_potential(*p_opt)
 
 # Now we have a best-fit potential! Let's plot the enclosed mass of the fit potential over the data:
@@ -164,22 +189,31 @@ def err_func(p, r, Menc, Menc_err):
 
 fig, ax = plt.subplots(1, 1, figsize=(4, 4))
 
-ax.errorbar(tbl['r'], tbl['Menc'], yerr=(tbl['Menc_err_neg'], tbl['Menc_err_pos']),
-            marker='o', markersize=2, color='k', alpha=1., ecolor='#aaaaaa',
-            capthick=0, linestyle='none', elinewidth=1.)
-
-fit_menc = fit_potential.mass_enclosed(xyz*u.kpc)
-ax.loglog(xyz[0], fit_menc.value, marker='', color="#3182bd",
-          linewidth=2, alpha=0.7)
-
-ax.set_xlim(1E-3, 10**2.6)
-ax.set_ylim(7E6, 10**12.25)
-
-ax.set_xlabel('$r$ [kpc]')
-ax.set_ylabel('$M(<r)$ [M$_\odot$]')
-
-ax.set_xscale('log')
-ax.set_yscale('log')
+ax.errorbar(
+    tbl["r"],
+    tbl["Menc"],
+    yerr=(tbl["Menc_err_neg"], tbl["Menc_err_pos"]),
+    marker="o",
+    markersize=2,
+    color="k",
+    alpha=1.0,
+    ecolor="#aaaaaa",
+    capthick=0,
+    linestyle="none",
+    elinewidth=1.0,
+)
+
+fit_menc = fit_potential.mass_enclosed(xyz * u.kpc)
+ax.loglog(xyz[0], fit_menc.value, marker="", color="#3182bd", linewidth=2, alpha=0.7)
+
+ax.set_xlim(1e-3, 10**2.6)
+ax.set_ylim(7e6, 10**12.25)
+
+ax.set_xlabel("$r$ [kpc]")
+ax.set_ylabel("$M(<r)$ [M$_\odot$]")
+
+ax.set_xscale("log")
+ax.set_yscale("log")
 
 fig.tight_layout()
 # -