diff --git a/docs/dust_extinction/fit_extinction.rst b/docs/dust_extinction/fit_extinction.rst
index 35afb25..5bc6751 100644
--- a/docs/dust_extinction/fit_extinction.rst
+++ b/docs/dust_extinction/fit_extinction.rst
@@ -20,55 +20,63 @@ extinction curve for the LMC outside of the LMC2 supershell region
 .. plot::
    :include-source:
 
-   import warnings
-   import matplotlib.pyplot as plt
-   import numpy as np
+    import warnings
+    import matplotlib.pyplot as plt
+    import numpy as np
 
-   from astropy.modeling.fitting import LevMarLSQFitter
-   import astropy.units as u
+    from astropy.modeling.fitting import LevMarLSQFitter
+    import astropy.units as u
 
-   from dust_extinction.averages import G03_LMCAvg
-   from dust_extinction.shapes import FM90
+    from dust_extinction.averages import G03_LMCAvg
+    from dust_extinction.shapes import FM90
 
-   # get an observed extinction curve to fit
-   g03_model = G03_LMCAvg()
+    # get an observed extinction curve to fit
+    g03_model = G03_LMCAvg()
 
-   x = g03_model.obsdata_x / u.micron
-   # convert to E(x-V)/E(B0V)
-   y = (g03_model.obsdata_axav - 1.0) * g03_model.Rv
-   # only fit the UV portion (FM90 only valid in UV)
-   (gindxs,) = np.where(x > 3.125 / u.micron)
+    x = g03_model.obsdata_x / u.micron
+    # convert to E(x-V)/E(B0V)
+    y = (g03_model.obsdata_axav - 1.0) * g03_model.Rv
+    # only fit the UV portion (FM90 only valid in UV)
+    (gindxs,) = np.where(x > 3.125 / u.micron)
 
-   # initialize the model
-   fm90_init = FM90()
+    # initialize the model
+    fm90_init = FM90()
 
-   # pick the fitter
-   fit = LevMarLSQFitter()
+    # pick the fitter
+    fit = LevMarLSQFitter()
 
-   # fit the data to the FM90 model using the fitter
-   #   use the initialized model as the starting point
+    # fit the data to the FM90 model using the fitter
+    #   use the initialized model as the starting point
 
-   # ignore some warnings
-   #   UserWarning is to avoid the units of x warning
-   with warnings.catch_warnings():
-       warnings.simplefilter("ignore", category=UserWarning)
-       g03_fit = fit(fm90_init, x[gindxs].value, y[gindxs])
+    # ignore some warnings
+    #   UserWarning is to avoid the units of x warning
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=UserWarning)
+        g03_fit = fit(fm90_init, x[gindxs].value, y[gindxs])
 
-   # plot the observed data, initial guess, and final fit
-   fig, ax = plt.subplots()
+    # plot the observed data, initial guess, and final fit
+    fig, ax = plt.subplots()
 
-   ax.plot(x, y, "ko", label="Observed Curve")
-   ax.plot(x[gindxs], fm90_init(x[gindxs]), label="Initial guess")
-   ax.plot(x[gindxs], g03_fit(x[gindxs]), label="Fitted model")
+    ax.plot(x, y, "ko", label="Observed Curve")
+    ax.plot(x[gindxs], fm90_init(x[gindxs]), label="Initial guess")
+    ax.plot(x[gindxs], g03_fit(x[gindxs]), label="Fitted model")
 
-   ax.set_xlabel("$x$ [$\mu m^{-1}$]")
-   ax.set_ylabel("$E(x-V)/E(B-V)$")
+    ax.set_xlabel("$x$ [$\mu m^{-1}$]")
+    ax.set_ylabel("$E(x-V)/E(B-V)$")
 
-   ax.set_title("Example FM90 Fit to G03_LMCAvg curve")
+    # for 2nd x-axis with lambda values
+    axis_xs = np.array([0.12, 0.15, 0.2, 0.3, 0.5, 1.0, 2.0])
+    new_ticks = 1 / axis_xs
+    new_ticks_labels = ["%.2f" % z for z in axis_xs]
+    tax = ax.twiny()
+    tax.set_xlim(ax.get_xlim())
+    tax.set_xticks(new_ticks)
+    tax.set_xticklabels(new_ticks_labels)
+    tax.set_xlabel(r"$\lambda$ [$\mu$m]")
 
-   ax.legend(loc="best")
-   plt.tight_layout()
-   plt.show()
+    ax.legend(loc="best")
+    plt.tight_layout()
+    plt.show()
 
 Example: P92 Fit
 ================
@@ -144,7 +152,15 @@ between data points.
    ax.set_xlabel('$x$ [$\mu m^{-1}$]')
    ax.set_ylabel('$A(x)/A(V)$')
 
-   ax.set_title('Example P92 Fit to GCC09_MWAvg average curve')
+   # for 2nd x-axis with lambda values
+   axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+   new_ticks = 1 / axis_xs
+   new_ticks_labels = ["%.2f" % z for z in axis_xs]
+   tax = ax.twiny()
+   tax.set_xlim(ax.get_xlim())
+   tax.set_xticks(new_ticks)
+   tax.set_xticklabels(new_ticks_labels)
+   tax.set_xlabel(r"$\lambda$ [$\mu$m]")
 
    ax.legend(loc='best')
    plt.tight_layout()
diff --git a/docs/index.rst b/docs/index.rst
index 6f332d7..d077803 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -8,7 +8,7 @@ Interstellar Dust Extinction
 extinction curves.
 
 Extinction describes the effects of dust on observations of single star due to
-the dust along the line-of-sight to a star removiong flux by absorbing photons
+the dust along the line-of-sight to a star removing flux by absorbing photons
 and scattering photons out of the line-of-sight. The wavelength dependence of
 dust extinction (also know as extinction curves) provides fundamental
 information about the size, composition, and shape of interstellar dust grain.
diff --git a/dust_extinction/averages.py b/dust_extinction/averages.py
index 197aa12..0135b8c 100644
--- a/dust_extinction/averages.py
+++ b/dust_extinction/averages.py
@@ -260,7 +260,7 @@ class B92_MWAvg(BaseExtModel):
         # generate the curves and plot them
         x = np.arange(1.0/ext_model.x_range[1], 1.0/ext_model.x_range[0], 0.1) * u.micron
 
-        ax.plot(x,ext_model(x),label='B1992')
+        ax.plot(x,ext_model(x),label='B92')
         ax.plot(1.0/ext_model.obsdata_x, ext_model.obsdata_axav, 'ko',
                 label='obsdata')
 
@@ -295,7 +295,7 @@ class B92_MWAvg(BaseExtModel):
 
     def evaluate(self, in_x):
         """
-        B1992 function
+        B92 function
 
         Parameters
         ----------
@@ -373,6 +373,16 @@ class G03_SMCBar(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -494,6 +504,16 @@ class G03_LMCAvg(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -616,6 +636,16 @@ class G03_LMC2(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -1048,6 +1078,16 @@ class GCC09_MWAvg(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -1062,9 +1102,13 @@ def __init__(self, **kwargs):
         ref = importlib_resources.files("dust_extinction") / "data"
         with importlib_resources.as_file(ref) as data_path:
             # GCC09 sigma clipped average of 75 sightlines
-            a = Table.read(data_path / "GCC09_FUSE.dat", format="ascii.commented_header")
+            a = Table.read(
+                data_path / "GCC09_FUSE.dat", format="ascii.commented_header"
+            )
             b = Table.read(data_path / "GCC09_IUE.dat", format="ascii.commented_header")
-            c = Table.read(data_path / "GCC09_PHOT.dat", format="ascii.commented_header")
+            c = Table.read(
+                data_path / "GCC09_PHOT.dat", format="ascii.commented_header"
+            )
 
         # FUSE range
         self.obsdata_x_fuse = a["x"].data
@@ -1559,6 +1603,16 @@ class G24_SMCAvg(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -1573,7 +1627,9 @@ def __init__(self, **kwargs):
         ref = importlib_resources.files("dust_extinction") / "data"
         with importlib_resources.as_file(ref) as data_path:
             # D22 sigma clipped average of 13 diffuse sightlines
-            a = Table.read(data_path / "G24_SMCAvg.dat", format="ascii.commented_header")
+            a = Table.read(
+                data_path / "G24_SMCAvg.dat", format="ascii.commented_header"
+            )
 
         # data
         self.obsdata_x = 1.0 / a["wave"].data
@@ -1682,6 +1738,16 @@ class G24_SMCBumps(BaseExtModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -1696,7 +1762,9 @@ def __init__(self, **kwargs):
         ref = importlib_resources.files("dust_extinction") / "data"
         with importlib_resources.as_file(ref) as data_path:
             # D22 sigma clipped average of 13 diffuse sightlines
-            a = Table.read(data_path / "G24_SMCBumps.dat", format="ascii.commented_header")
+            a = Table.read(
+                data_path / "G24_SMCBumps.dat", format="ascii.commented_header"
+            )
 
         # data
         self.obsdata_x = 1.0 / a["wave"].data
diff --git a/dust_extinction/parameter_averages.py b/dust_extinction/parameter_averages.py
index 7434ab8..6b1023e 100644
--- a/dust_extinction/parameter_averages.py
+++ b/dust_extinction/parameter_averages.py
@@ -70,6 +70,16 @@ class CCM89(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -196,6 +206,16 @@ class O94(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -325,6 +345,16 @@ class F99(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -464,6 +494,16 @@ class F04(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -599,6 +639,16 @@ class VCG04(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.12, 0.15, 0.2, 0.3])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -709,6 +759,16 @@ class GCC09(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.09, 0.1, 0.12, 0.15, 0.2, 0.3])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
@@ -834,6 +894,16 @@ class M14(BaseExtRvModel):
            ext_model = M14(Rv=cur_Rv)
            ax.plot(x,ext_model(x),label='R(V) = ' + str(cur_Rv))
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.3, 0.5, 1.0, 2.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
@@ -1029,6 +1099,16 @@ class G16(BaseExtRvAfAModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best', title=r'$f_A = 1.0$')
         plt.show()
 
@@ -1058,6 +1138,16 @@ class G16(BaseExtRvAfAModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best', title=r'$R_A(V) = 3.1$')
         plt.show()
     """
@@ -1165,6 +1255,16 @@ class F19(BaseExtRvModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$A(x)/A(V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
diff --git a/dust_extinction/shapes.py b/dust_extinction/shapes.py
index 33f3cee..6a7a74c 100644
--- a/dust_extinction/shapes.py
+++ b/dust_extinction/shapes.py
@@ -227,14 +227,27 @@ class FM90(Fittable1DModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$E(\lambda - V)/E(B - V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.12, 0.15, 0.2, 0.3])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
+
     n_inputs = 1
     n_outputs = 1
 
     # bounds based on Gordon et al. (2024) results
-    C1 = Parameter(description="linear term: y-intercept", default=0.10, bounds=(-10.0, 5.0))
+    C1 = Parameter(
+        description="linear term: y-intercept", default=0.10, bounds=(-10.0, 5.0)
+    )
     C2 = Parameter(description="linear term: slope", default=0.70, bounds=(-0.1, 5.0))
     C3 = Parameter(description="bump: amplitude", default=3.23, bounds=(-1.0, 6.0))
     C4 = Parameter(description="FUV rise: amplitude", default=0.41, bounds=(-0.5, 1.5))
@@ -395,14 +408,27 @@ class FM90_B3(Fittable1DModel):
         ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
         ax.set_ylabel(r'$E(\lambda - V)/E(B - V)$')
 
+        # for 2nd x-axis with lambda values
+        axis_xs = np.array([0.12, 0.15, 0.2, 0.3])
+        new_ticks = 1 / axis_xs
+        new_ticks_labels = ["%.2f" % z for z in axis_xs]
+        tax = ax.twiny()
+        tax.set_xlim(ax.get_xlim())
+        tax.set_xticks(new_ticks)
+        tax.set_xticklabels(new_ticks_labels)
+        tax.set_xlabel(r"$\lambda$ [$\mu$m]")
+
         ax.legend(loc='best')
         plt.show()
     """
+
     n_inputs = 1
     n_outputs = 1
 
     # bounds based on Gordon et al. (2024) results
-    C1 = Parameter(description="linear term: y-intercept", default=0.10, bounds=(-10.0, 5.0))
+    C1 = Parameter(
+        description="linear term: y-intercept", default=0.10, bounds=(-10.0, 5.0)
+    )
     C2 = Parameter(description="linear term: slope", default=0.70, bounds=(-0.1, 5.0))
     B3 = Parameter(description="bump: amplitude", default=3.23, bounds=(-1.0, 6.0))
     C4 = Parameter(description="FUV rise: amplitude", default=0.41, bounds=(-0.5, 1.5))
@@ -592,6 +618,7 @@ class P92(Fittable1DModel):
         ax.legend(loc='best')
         plt.show()
     """
+
     n_inputs = 1
     n_outputs = 1