Remove from __future__ imports as we move away from Python 2
Aurélien Geron
parent
936e2cf50f
commit
f6dfa0ff76
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@ -20,7 +20,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import print_function, division, unicode_literals\n",
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"import numpy as np\n",
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"\n",
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"%matplotlib nbagg\n",
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@ -11,22 +11,6 @@
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"*Machine Learning relies heavily on Linear Algebra, so it is essential to understand what vectors and matrices are, what operations you can perform with them, and how they can be useful.*"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before we start, let's ensure that this notebook works well in both Python 2 and 3:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import division, print_function, unicode_literals"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -26,24 +26,6 @@
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"# Plotting your first graph"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First let's make sure that this notebook works well in both python 2 and 3:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from __future__ import division, print_function, unicode_literals"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -53,10 +35,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib"
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@ -71,10 +51,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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@ -91,9 +69,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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@ -115,7 +91,6 @@
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -134,9 +109,7 @@
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
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@ -154,9 +127,7 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@ -177,9 +148,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(x, y)\n",
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@ -207,9 +176,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0])\n",
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@ -228,9 +195,7 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0], \"g--\")\n",
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@ -250,9 +215,7 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"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",
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@ -270,9 +233,7 @@
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\")\n",
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@ -292,9 +253,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-1.4, 1.4, 30)\n",
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@ -313,7 +272,6 @@
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -338,7 +296,6 @@
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -360,7 +317,6 @@
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -394,9 +350,7 @@
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.subplot(2, 2, 1) # 2 rows, 2 columns, 1st subplot = top left\n",
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@ -418,9 +372,7 @@
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.subplot2grid((3,3), (0, 0), rowspan=2, colspan=2)\n",
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@ -453,7 +405,6 @@
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -494,9 +445,7 @@
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import this"
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@ -513,7 +462,6 @@
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -556,9 +504,7 @@
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-1.5, 1.5, 30)\n",
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@ -588,9 +534,7 @@
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(x, x**2, px, py, \"ro\")\n",
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@ -611,7 +555,6 @@
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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@ -637,9 +580,7 @@
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with plt.xkcd():\n",
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@ -665,9 +606,7 @@
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-1.4, 1.4, 50)\n",
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@ -690,7 +629,6 @@
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -739,9 +677,7 @@
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-2, 2, 100)\n",
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@ -784,9 +720,7 @@
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"radius = 1\n",
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@ -811,7 +745,6 @@
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -840,9 +773,7 @@
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.contourf(X, Y, Z, cmap=matplotlib.cm.coolwarm)\n",
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@ -867,9 +798,7 @@
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from numpy.random import rand\n",
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@ -888,9 +817,7 @@
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x, y, scale = rand(3, 100)\n",
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@ -910,7 +837,6 @@
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -938,9 +864,7 @@
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from numpy.random import randn\n",
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@ -971,9 +895,7 @@
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"data = [1, 1.1, 1.8, 2, 2.1, 3.2, 3, 3, 3, 3]\n",
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@ -992,7 +914,6 @@
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -1028,9 +949,7 @@
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.image as mpimg\n",
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@ -1049,9 +968,7 @@
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(img)\n",
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@ -1068,9 +985,7 @@
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(img)\n",
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@ -1088,9 +1003,7 @@
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"img = np.arange(100*100).reshape(100, 100)\n",
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@ -1110,7 +1023,6 @@
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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@ -1130,7 +1042,6 @@
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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@ -1155,7 +1066,6 @@
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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@ -1198,9 +1108,7 @@
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-1, 1, 100)\n",
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@ -1234,9 +1142,7 @@
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"Writer = animation.writers['ffmpeg']\n",
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],
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"metadata": {
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"kernelspec": {
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"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
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -16,24 +16,14 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"First, let's make sure this notebook works well in both python 2 and 3:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from __future__ import division, print_function, unicode_literals"
|
||||
"# Setup"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now let's import `pandas`. People usually import it as `pd`:"
|
||||
"First, let's import `pandas`. People usually import it as `pd`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -2817,7 +2807,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.3"
|
||||
"version": "3.7.4"
|
||||
},
|
||||
"toc": {
|
||||
"toc_cell": false,
|
||||
|
|
|
|||
Loading…
Reference in New Issue