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Remove from __future__ imports as we move away from Python 2

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This commit is contained in:
Aurélien Geron
2019-10-12 16:40:05 +09:30
parent 936e2cf50f
commit f6dfa0ff76
5 changed files with 211 additions and 684 deletions
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172
tools_matplotlib.ipynb
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@@ -26,24 +26,6 @@
"# Plotting your first graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's make sure that this notebook works well in both python 2 and 3:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division, print_function, unicode_literals"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -53,10 +35,8 @@
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib"
@@ -71,10 +51,8 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -91,9 +69,7 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -115,7 +91,6 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -134,9 +109,7 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
@@ -154,9 +127,7 @@
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -177,9 +148,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot(x, y)\n",
@@ -207,9 +176,7 @@
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0])\n",
@@ -228,9 +195,7 @@
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0], \"g--\")\n",
@@ -250,9 +215,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\", [0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
@@ -270,9 +233,7 @@
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\")\n",
@@ -292,9 +253,7 @@
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-1.4, 1.4, 30)\n",
@@ -313,7 +272,6 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -338,7 +296,6 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -360,7 +317,6 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -394,9 +350,7 @@
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.subplot(2, 2, 1) # 2 rows, 2 columns, 1st subplot = top left\n",
@@ -418,9 +372,7 @@
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.subplot2grid((3,3), (0, 0), rowspan=2, colspan=2)\n",
@@ -453,7 +405,6 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -494,9 +445,7 @@
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import this"
@@ -513,7 +462,6 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -556,9 +504,7 @@
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-1.5, 1.5, 30)\n",
@@ -588,9 +534,7 @@
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot(x, x**2, px, py, \"ro\")\n",
@@ -611,7 +555,6 @@
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
@@ -637,9 +580,7 @@
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"with plt.xkcd():\n",
@@ -665,9 +606,7 @@
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-1.4, 1.4, 50)\n",
@@ -690,7 +629,6 @@
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -739,9 +677,7 @@
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-2, 2, 100)\n",
@@ -784,9 +720,7 @@
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"radius = 1\n",
@@ -811,7 +745,6 @@
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -840,9 +773,7 @@
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.contourf(X, Y, Z, cmap=matplotlib.cm.coolwarm)\n",
@@ -867,9 +798,7 @@
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from numpy.random import rand\n",
@@ -888,9 +817,7 @@
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x, y, scale = rand(3, 100)\n",
@@ -910,7 +837,6 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -938,9 +864,7 @@
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from numpy.random import randn\n",
@@ -971,9 +895,7 @@
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"data = [1, 1.1, 1.8, 2, 2.1, 3.2, 3, 3, 3, 3]\n",
@@ -992,7 +914,6 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -1028,9 +949,7 @@
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.image as mpimg\n",
@@ -1049,9 +968,7 @@
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img)\n",
@@ -1068,9 +985,7 @@
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img)\n",
@@ -1088,9 +1003,7 @@
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"img = np.arange(100*100).reshape(100, 100)\n",
@@ -1110,7 +1023,6 @@
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
@@ -1130,7 +1042,6 @@
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -1155,7 +1066,6 @@
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
@@ -1198,9 +1108,7 @@
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-1, 1, 100)\n",
@@ -1234,9 +1142,7 @@
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"Writer = animation.writers['ffmpeg']\n",
@@ -1255,21 +1161,21 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"display_name": "Python 3",
"language": "python",
"name": "python2"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
"pygments_lexer": "ipython3",
"version": "3.7.4"
},
"toc": {
"toc_cell": true,
@@ -1280,5 +1186,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}