applied changes after committee review

Author: giumas

COMP_000_Intro_to_NumPy.ipynb

@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The efficient storage and manipulation of numerical arrays is often critical when processing ocean data. This notebook will present a specialized Python package for handling such numerical arrays: [NumPy](https://www.numpy.org/) ."
+    "The efficient storage and manipulation of numerical arrays is often critical when processing ocean data. This notebook will present a specialized Python package for handling such numerical arrays: [NumPy](https://www.numpy.org/)."
    ]
   },
   {
@@ -42,7 +42,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "NumPy (short for Numerical Python) is centered around a powerful N-dimensional array object, but also contains other useful capabilities like linear algebra, Fourier transform, functions to create random numbers, etc."
+    "NumPy (short for Numerical Python) is centered around a powerful N-dimensional array object, but also provides other useful capabilities like linear algebra, Fourier transform, functions to create random numbers, etc."
    ]
   },
   {
@@ -58,7 +58,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before starting to use `numpy`, you have to execute the following cell that, together with code introduced in the past notebooks, imports `numpy` with the commonly-adopted short name of `np`:"
+    "Before starting to use `numpy`, you have to execute the following cell. Together with code introduced in the past notebooks, this imports `numpy` and assign it the commonly-adopted short name of `np`:"
    ]
   },
   {
@@ -106,14 +106,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Creating an Array with Zero Values"
+    "## Creating an Array Filled with Zeros "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first `numpy` function that we introduce is [`zeros()`](https://www.numpy.org/devdocs/reference/generated/numpy.zeros.html?#numpy.zeros). When a single integer value is passed, this function creates an 1D array containing the passed number of zeros:"
+    "The first `numpy` function that we introduce is [`zeros()`](https://www.numpy.org/devdocs/reference/generated/numpy.zeros.html?#numpy.zeros). When a single integer is passed, this function creates an 1D array with a number of zero equal to the passed value:"
    ]
   },
   {
@@ -130,9 +130,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is also possible to create multi-dimensional array by passing a [`tuple`](https://docs.python.org/3.6/library/stdtypes.html?#tuples).\n",
+    "It is also possible to create multi-dimensional array by passing a [`tuple`](https://docs.python.org/3.6/library/stdtypes.html?#tuples). Each value in the `tuple` define the array size for each dimension.\n",
     "\n",
-    "Thus, the following code creates a 2D array with 3 rows and 8 columns passing `(3, 8)`:"
+    "Thus, the following code creates a 2D array with 8 rows and 2 columns passing `(8, 2)`:"
    ]
   },
   {
@@ -141,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "arr = np.zeros((3, 8))\n",
+    "arr = np.zeros((8, 2))\n",
     "print(arr)"
    ]
   },
@@ -216,6 +216,13 @@
     "print(\"Array dimensions: %d\" % arr.ndim)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number of elements along each of the axes is the **length** of the axes."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -354,7 +361,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `zeros()` is just one of the several functions available to create an array in `numpy`. \n",
+    "The `zeros()` function is just one of the several ways to create an array in `numpy`. \n",
     "\n",
     "For instance, to create an array of a given shape, but initialized with a specific value (e.g., `8.0`), you can use the [`full()`](https://www.numpy.org/devdocs/reference/generated/numpy.full.html) function:"
    ]
@@ -373,7 +380,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you want to create an array filled with a linear sequence of values, you may use the [`arange()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html) function or the [`linspace()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) function by selecting the one that fulfills your specific needs:"
+    "If you want to create an array filled with a sequence of values, you may use the [`arange()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html) function or the [`linspace()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) function by selecting the one that fulfills your specific needs:"
    ]
   },
   {
@@ -382,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "arr = np.arange(start=0, stop=40, step=8)  # arange excludes the `stop` value\n",
+    "arr = np.arange(start=0, stop=40, step=8)  # the `stop` value is not included\n",
     "print(arr)"
    ]
   },
@@ -392,7 +399,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "arr = np.linspace(start=0, stop=40, num=5)\n",
+    "arr = np.linspace(start=0, stop=40, num=5)  # the `stop` value is included\n",
     "print(arr)"
    ]
   },
@@ -501,7 +508,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the advantages of using `numpy` arrays in place of `list` containers is the many mathematical and statistical methods available in NumPy."
+    "Numpy arrays have many efficient mathematical and statistical methods. This is one of the motivations to use them in place of `list` containers.\n",
+    "\n",
+    "The cell below shows how to use some of these methods. If you want to learn more about them, visit [this Numpy page](https://www.numpy.org/devdocs/reference/arrays.ndarray.html#calculation)."
    ]
   },
   {
@@ -550,7 +559,7 @@
     "temp_list = [11.2, 11.0, 13.7, 16.0, 16.1, 16.2, 16.1]\n",
     "\n",
     "temp_arr = np.array(temp_list)\n",
-    "avg_temp = temp_arr.mean()\n",
+    "mean_temp = temp_arr.mean()\n",
     "min_temp = temp_arr.min()\n",
     "max_temp = temp_arr.max()\n",
     "print(\"The average value is %f\" % (mean_temp, ))\n",
@@ -559,7 +568,7 @@
     "\n",
     "# plot creation\n",
     "plt.plot(temp_arr, color='orange', marker='o', label='data')\n",
-    "plt.axhline(y=avg_temp, color='green', linestyle='dashed', label='avg')\n",
+    "plt.axhline(y=mean_temp, color='green', linestyle='dashed', label='avg')\n",
     "plt.axhline(y=min_temp, color='blue', linestyle='dotted', label='min')\n",
     "plt.axhline(y=max_temp, color='red', linestyle='dotted', label='max')\n",
     "plt.title(\"Temperature\")  \n",
@@ -910,101 +919,6 @@
     "***"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Array Views and Copies"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img align=\"left\" width=\"6%\" style=\"padding-right:10px;\" src=\"images/key.png\">\n",
-    "\n",
-    "Array slicing returns views rather than copies of the array data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The relevant implication is that, if you modify a sub-array, the original array is modified too!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we extract the temperature row as a sub-array and modify it (by assigning a common value), the original `data_arr` will change too:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a two-dimensional data array\n",
-    "sal_list = [34.4, 34.1, 33.6, 31.7, 31.3, 31.2, 31.0]\n",
-    "temp_list = [11.2, 11.0, 13.7, 16.0, 16.1, 16.2, 16.1]\n",
-    "data_arr = np.array([sal_list, temp_list])\n",
-    "print(\"data array before:\")\n",
-    "print(data_arr)\n",
-    "\n",
-    "# Slice and modify the resulting sub-array\n",
-    "temp_arr = data_arr[1, :]\n",
-    "temp_arr[0] = -9999.9\n",
-    "print(\"temperature sub-array:\")\n",
-    "print(temp_arr)\n",
-    "print(\"data array after:\")\n",
-    "print(data_arr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This default behavior is quite useful when you handle large array since you can access sub-arrays without copying the underlying data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you need a copy of a specific array or sub-array, you can explicitly copy the data using the [`copy()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.copy.html) method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a two-dimensional data array\n",
-    "sal_list = [34.4, 34.1, 33.6, 31.7, 31.3, 31.2, 31.0]\n",
-    "temp_list = [11.2, 11.0, 13.7, 16.0, 16.1, 16.2, 16.1]\n",
-    "data_arr = np.array([sal_list, temp_list])\n",
-    "print(\"before:\")\n",
-    "print(data_arr)\n",
-    "\n",
-    "# Slice, MAKE A COPY, and modify the resulting sub-array\n",
-    "temp_arr = data_arr[1, :].copy()\n",
-    "temp_arr[0] = -9999.9\n",
-    "print(\"temperature sub-array:\")\n",
-    "print(temp_arr)\n",
-    "print(\"after:\")\n",
-    "print(data_arr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

COMP_001_Adopting_NumPy_in_Class_Methods.ipynb

@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The task of this notebook is to use a NumPy array in the previously-coded `WaterLevels` class for storing the data. But first, please execute the following initialization code."
+    "The task of this notebook is to use a NumPy array in the previously-coded `WaterLevels` class to store the data. But first, please execute the following initialization code."
    ]
   },
   {