"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Module 5: Design\n",
"\n",
"The aims of this lab are:\n",
"\n",
"1. Learn about `matplotlib`'s colormaps, including the awesome `vidiris`. \n",
"1. Learn how to adjust the design element of a basic plot in `matplotlib`. \n",
"1. Understand the differences between bitmap and vector graphics. \n",
"1. Learn what is SVG and how to create simple shapes in SVG. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, import `numpy` and `matplotlib` libraries (don't forget the `matplotlib inline` magic command if you are using Jupyter Notebook). "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Colors\n",
"\n",
"We discussed colors for *categorical* and *quantitative* data. We can further specify the quantitative cases into *sequential* and *diverging*. \"Sequential\" means that the underlying value has a sequential ordering and the color also just needs to change sequentially and monotonically. \n",
"\n",
"In the \"diverging\" case, there should be a meaningful anchor point. For instance, the correlation values may be positive or negative. Both large positive correlation and large negative correlation are important and the sign of the correlation has an important meaning. Therefore, we would like to stitch two sequential colormap together, one from zero to +1, the other from zero to -1. \n",
"\n",
"### Categorical (qualitative) colormaps\n",
"\n",
"To experiment with colormpas, let's create some data first. We will use the `numpy`'s `random` module to create some random data.\n",
"\n",
"#### `numpy` \n",
"\n",
"`numpy` is one of the most important packages in Python. As the name suggests (`num` + `py`), it handles all kinds of numerical manipulations and is the basis of pretty much all scientific packages. Actually, a `pandas` \"series\" is essentially a `numpy` array and a dataframe is essentially a bunch of `numpy` arrays grouped together. If you use it wisely, it can easily give you 10x, 100x or even 1000x speed-up! \n",
"\n",
"If you use `pandas` or other packages, they may do all these numpy optimization under the hood for you. However, it is still good to know some basic `numpy` operations. If you want to study `numpy` more, check out the official tutorial and \"From Python to Numpy\" book:\n",
"\n",
"- [Numpy Quickstart tutorial](https://docs.scipy.org/doc/numpy/user/quickstart.html)\n",
"- [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)\n",
"\n",
"#### Plotting some trigonometric functions\n",
"\n",
"Let's plot a sine and cosine function. By the way, a common trick to plot a function is creating a list of x coordinate values (evenly spaced numbers over an interval) first. `numpy` has a function called [`linspace`](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html) for that (\"LINear SPACE\"). By default, it creates 50 numbers that fill the interval that you pass. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.06122449, 0.12244898, 0.18367347, 0.24489796,\n",
" 0.30612245, 0.36734694, 0.42857143, 0.48979592, 0.55102041,\n",
" 0.6122449 , 0.67346939, 0.73469388, 0.79591837, 0.85714286,\n",
" 0.91836735, 0.97959184, 1.04081633, 1.10204082, 1.16326531,\n",
" 1.2244898 , 1.28571429, 1.34693878, 1.40816327, 1.46938776,\n",
" 1.53061224, 1.59183673, 1.65306122, 1.71428571, 1.7755102 ,\n",
" 1.83673469, 1.89795918, 1.95918367, 2.02040816, 2.08163265,\n",
" 2.14285714, 2.20408163, 2.26530612, 2.32653061, 2.3877551 ,\n",
" 2.44897959, 2.51020408, 2.57142857, 2.63265306, 2.69387755,\n",
" 2.75510204, 2.81632653, 2.87755102, 2.93877551, 3. ])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(0, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's just work with 10 numbers to make it easier to see. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.33333333, 0.66666667, 1. , 1.33333333,\n",
" 1.66666667, 2. , 2.33333333, 2.66666667, 3. ])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.linspace(0, 3, 10) # 10 numbers instead of 50 \n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A nice thing about `numpy` is that you can apply many mathematical operations as if you are dealing with a single number. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1.33333333 1.66666667 2. 2.33333333 2.66666667\n",
" 3. 3.33333333 3.66666667 4. ]\n",
"[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
"[0. 0.11111111 0.44444444 1. 1.77777778 2.77777778\n",
" 4. 5.44444444 7.11111111 9. ]\n",
"[0. 0.57735027 0.81649658 1. 1.15470054 1.29099445\n",
" 1.41421356 1.52752523 1.63299316 1.73205081]\n"
]
}
],
"source": [
"# add 1 to each element of the array\n",
"a_plus_1 = a + 1\n",
"print(a_plus_1)\n",
"\n",
"# multiply each element of the array by 3\n",
"a_times_3 = a * 3\n",
"print(a_times_3)\n",
"\n",
"# raise each element of the array to the power of 2\n",
"a_squared = a ** 2\n",
"print(a_squared)\n",
"\n",
"# take the square root of each element of the array\n",
"a_sqrt = np.sqrt(a)\n",
"print(a_sqrt)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are called \"**vectorized**\" operations. Whenever you can, you should use vectorized operations instead of looping over the elements because they are way way faster and efficient. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q: Let's plot some `sin` and `cos` functions.**\n",
"\n",
"use `numpy`'s `sin` and `cos` functions with `matplotlib`'s `plot` function to plot. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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