Fixing the language

notebooks/intro.ipynb

@@ -111,14 +111,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PyMKS can handle both numpy and Dask arrays. For simplicity, we will only use Numpy arrays in this notebook. Note that PyMKS requires certain data shapes for proper 2-point spatial correlation calculations. For a two-dimensional microstructure, PyMKS requires a 4-dimensional array. The first dimension is the sample axis, the second dimension is the x-axis, the third dimension is the y-axis, and the fourth dimension is the phase axis. In the phase axis, the specific phase gets '1's and the rest of the image gets '0's. This is equivalent to one-hot encoding in image analysis. For this example, we have a 2-phase segmented microstructure (986 x 1376 pixels) so we need to perform some array manipulations to obtain a 1 x 986 x 1376 x 2 array. "
+    "PyMKS can handle both Numpy and Dask arrays. For simplicity, we will only use Numpy arrays in this notebook. Note that PyMKS requires certain data shapes for proper 2-point spatial correlation calculations. For a two-dimensional microstructure, PyMKS requires a 4-dimensional array. The first dimension is the sample axis, the second dimension is the x-axis, the third dimension is the y-axis, and the fourth dimension is the phase axis. In the phase axis, the specific phase gets '1's and the rest of the image gets '0's. This is equivalent to one-hot encoding in image analysis. For this example, we have a 2-phase segmented microstructure (986 x 1376 pixels) so we need to perform some array manipulations to obtain a 1 x 986 x 1376 x 2 array. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Convert the image to a Numpy array and add the sample axis by using `np.expand_dims`function. If you have multiple images you can simply stack them by using Numpy data manipulation functions."
+    "Convert the image to a Numpy array and add the sample axis by using the `np.expand_dims` function. Multiple images can be stacked along the sample axis."
    ]
   },
   {
@@ -310,7 +310,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous section, we demonstrated a 2-point spatial correlation calculation for a single experimental image. Now we will demonstrate calculating 2-point correlations for multiple microstructure images without using any for loops. In the following, the data array has axes representing samples, x-direction, y-direction and phase. \n",
+    "In the previous section, we demonstrated a 2-point spatial correlation calculation for a single experimental image. Now we will demonstrate calculating 2-point correlations for multiple microstructure images without using any for loops. In the following, the data array has axes representing the sample axis, the x axis, the y axis and the phase axis. \n",
     "\n",
     "Here we generate 40 different 2-phased synthetic microstructures each with a `100x100` shape using the function `generate_multiphase`. [Effective Siffness of a Composite Material example](./stress.ipynb) notebook has detailed explanation for the `generate_multiphase` function. \n",
     "\n",
@@ -1549,7 +1549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,