diff --git a/tutorials/Models-Quick-Fit/Models-Quick-Fit.ipynb b/tutorials/Models-Quick-Fit/Models-Quick-Fit.ipynb
index 619fdf7f..13349fa1 100755
--- a/tutorials/Models-Quick-Fit/Models-Quick-Fit.ipynb
+++ b/tutorials/Models-Quick-Fit/Models-Quick-Fit.ipynb
@@ -53,7 +53,7 @@
     "from astroquery.vizier import Vizier\n",
     "import scipy.optimize\n",
     "# Make plots display in notebooks\n",
-    "%matplotlib inline "
+    "%matplotlib inline"
    ]
   },
   {
@@ -85,7 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This catalog has a lot of information, but for this tutorial we are going to work only with periods and magnitudes. Let's grab them using the keywords `'Period'` and `__Ksmag__`.  Note that `'e__Ksmag_'` refers to the error bars in the magnitude measurements."
+    "This catalog has a lot of information, but for this tutorial we are going to work only with periods and magnitudes. Let's grab them using the keywords `'Period'` and `<Ksmag>`.  Note that `'e_<Ksmag>'` refers to the error bars in the magnitude measurements."
    ]
   },
   {
@@ -94,10 +94,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "period = np.array(catalog[0]['Period']) \n",
+    "period = np.array(catalog[0]['Period'])\n",
     "log_period = np.log10(period)\n",
-    "k_mag = np.array(catalog[0]['__Ksmag_'])\n",
-    "k_mag_err = np.array(catalog[0]['e__Ksmag_'])"
+    "k_mag = np.array(catalog[0]['<Ksmag>'])\n",
+    "k_mag_err = np.array(catalog[0]['e_<Ksmag>'])"
    ]
   },
   {
@@ -214,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fitter = fitting.LinearLSQFitter() "
+    "fitter = fitting.LinearLSQFitter()"
    ]
   },
   {
@@ -257,7 +257,7 @@
    "outputs": [],
    "source": [
     "plt.errorbar(log_period,k_mag,k_mag_err,fmt='k.')\n",
-    "plt.plot(log_period, best_fit(log_period), color='g', linewidth=3)  \n",
+    "plt.plot(log_period, best_fit(log_period), color='g', linewidth=3)\n",
     "plt.xlabel(r'$\\log_{10}$(Period [days])')\n",
     "plt.ylabel('Ks')"
    ]
@@ -318,11 +318,11 @@
    "source": [
     "N = 100\n",
     "x1 = np.linspace(0, 4, N)  # Makes an array from 0 to 4 of N elements\n",
-    "y1 = x1**3 - 6*x1**2 + 12*x1 - 9 \n",
+    "y1 = x1**3 - 6*x1**2 + 12*x1 - 9\n",
     "# Now we add some noise to the data\n",
     "y1 += np.random.normal(0, 2, size=len(y1)) #One way to add random gaussian noise\n",
     "sigma = 1.5\n",
-    "y1_err = np.ones(N)*sigma "
+    "y1_err = np.ones(N)*sigma"
    ]
   },
   {
@@ -339,7 +339,7 @@
    "outputs": [],
    "source": [
     "plt.errorbar(x1, y1, yerr=y1_err,fmt='k.')\n",
-    "plt.xlabel('$x_1$')  \n",
+    "plt.xlabel('$x_1$')\n",
     "plt.ylabel('$y_1$')"
    ]
   },
@@ -357,7 +357,7 @@
    "outputs": [],
    "source": [
     "model_poly = models.Polynomial1D(degree=3)\n",
-    "fitter_poly = fitting.LinearLSQFitter() \n",
+    "fitter_poly = fitting.LinearLSQFitter()\n",
     "best_fit_poly = fitter_poly(model_poly, x1, y1, weights = 1.0/y1_err)"
    ]
   },
@@ -477,7 +477,7 @@
    "outputs": [],
    "source": [
     "plt.errorbar(x1, y1, yerr=y1_err,fmt='k.')\n",
-    "plt.plot(x1, best_fit_poly(x1), color='r', linewidth=3, label='LinearLSQFitter()')  \n",
+    "plt.plot(x1, best_fit_poly(x1), color='r', linewidth=3, label='LinearLSQFitter()')\n",
     "plt.plot(x1, best_fit_poly_2(x1), color='g', linewidth=3, label='SimplexLSQFitter()')\n",
     "plt.xlabel(r'$\\log_{10}$(Period [days])')\n",
     "plt.ylabel('Ks')\n",
@@ -753,7 +753,7 @@
     "y3 = 5.0 * np.sin(2 * np.pi * x3)\n",
     "y3 = np.array([y_point + np.random.normal(0, 1) for y_point in y3])\n",
     "sigma = 1.5\n",
-    "y3_err = np.ones(N)*sigma "
+    "y3_err = np.ones(N)*sigma"
    ]
   },
   {