diff --git a/library_scipy.ipynb b/library_scipy.ipynb
index ea8a056..6746bbf 100644
--- a/library_scipy.ipynb
+++ b/library_scipy.ipynb
@@ -1687,8 +1687,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "xdata = [ -10.0, -9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n",
-    "ydata = [1.2, 4.2, 6.7, 8.3, 10.6, 11.7, 13.5, 14.5, 15.7, 16.1, 16.6, 16.0, 15.4, 14.4, 14.2, 12.7, 10.3, 8.6, 6.1, 3.9, 2.1]"
+    "ydata = [1.2, 4.2, 6.7, 8.3, 10.6, 11.7, 13.5, 14.5, 15.7, 16.1, 16.6, 16.0, 15.4, 14.4, 14.2, 12.7, 10.3, 8.6, 6.1, 3.9, 2.1]\n",
+    "xdata = np.array(xdata)\n",
+    "ydata = np.array(ydata)"
    ]
   },
   {
@@ -1726,8 +1730,18 @@
     "def solution_gaussian(): # do not change the function signature\n",
     "    # 2. TODO: define function to fit: the Gaussian.\n",
     "\n",
-    "    # 3. TODO: call the curve_fit function here and return the parameters and covariance matrix AS A TUPLE\n",
-    "    pass"
+    "    def gaussian_func(x, a, b):\n",
+    "        \"\"\"\n",
+    "        Use this function signature, and these parameters A and B.\n",
+    "        a: coefficient of the Gaussian\n",
+    "        b: coefficient of the x^2 term\n",
+    "\n",
+    "        Will probably return an error if you change this.\n",
+    "\n",
+    "        Returns: the Gaussian function using the parameters a and b\n",
+    "        \"\"\"\n",
+    "    \n",
+    "    # 3. TODO: call the curve_fit function here and return the parameters and covariance matrix AS A TUPLE"
    ]
   },
   {
diff --git a/tutorial/tests/test_library_scipy.py b/tutorial/tests/test_library_scipy.py
index 17cf2ce..cf16784 100644
--- a/tutorial/tests/test_library_scipy.py
+++ b/tutorial/tests/test_library_scipy.py
@@ -118,10 +118,12 @@ def reference_gaussian():
         3.9,
         2.1,
     ]
+    xdata = np.array(xdata)
+    ydata = np.array(ydata)
 
     # 2. TODO: define the gaussian function here:
-    def ref_gaussian_math(x, sigma, mu):
-        return 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
+    def ref_gaussian_math(x, a, b):
+        return a * np.exp(-b * x**2)
 
     # 3. TODO: call the curve_fit function here:
     parameters, covariance = curve_fit(ref_gaussian_math, xdata, ydata)