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Peter Norvig authored and 
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+34
-28
@@ -18,20 +18,9 @@
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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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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1028a2be0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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@@ -47,8 +36,26 @@
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"for (dist, climb, time) in csv.reader(open('dist-climb-time.csv')):\n",
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" if miles(dist) > 30:\n",
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" X.append(feet(climb) / miles(dist))\n",
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" Y.append(miles(dist) / hours(time))\n",
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"\n",
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" Y.append(miles(dist) / hours(time))"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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"text/plain": [
|
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"<matplotlib.figure.Figure at 0x105166cc0>"
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]
|
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
||||
"plt.plot(X, Y, 'o');"
|
||||
]
|
||||
},
|
||||
@@ -63,14 +70,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x108dd82e8>"
|
||||
"<matplotlib.figure.Figure at 0x10b1d1390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -90,12 +97,13 @@
|
||||
" plt.style.use('fivethirtyeight')\n",
|
||||
" plt.plot(X, Y, 'o')\n",
|
||||
" X1 = list(range(int(max(X))))\n",
|
||||
" plt.plot(X1, [F(x) for x in X1], 'o')\n",
|
||||
" plt.plot(X1, [F(x) for x in X1], '-')\n",
|
||||
" plt.ylabel('Speed (mph)')\n",
|
||||
" plt.xlabel('Grade (feet/mile)')\n",
|
||||
" plt.minorticks_on()\n",
|
||||
" plt.grid(True, which='major')\n",
|
||||
" plt.grid(True, which='minor', alpha=0.2)\n",
|
||||
" plt.grid(True, which='minor', alpha=0.4)\n",
|
||||
" plt.title('Average speed vs Grade on {} rides'.format(len(X)))\n",
|
||||
" \n",
|
||||
"show(X, Y) "
|
||||
]
|
||||
@@ -104,17 +112,15 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"So, I average about 14 mph when the road is fairly flat, with a lot of variability from 12 to 16 mph, depending more on my level of effort than on the grade of the road. But from 60 ft/mile and up, speed falls off quickly at 1 mph for every 20 ft/mile, and by 140 ft/mile, I'm down around 8 mph. Note that 140 ft/mile is only 2.7% grade, but if you figure a typical route is 1/3 up, 1/3 down, and 1/3 flatish, then that's 8% grade on the up part.\n",
|
||||
"So, I average about 14 mph when the road is fairly flat, with a lot of variability from 12 to 16 mph, depending more on my level of effort than on the grade of the road. But from 60 ft/mile and up, speed falls off quickly at 1 mph for every 20 ft/mile, and by 120 ft/mile, I'm down around 10 mph, and 8.5mph at 140 ft/mile. Note that 140 ft/mile is only 2.7% grade, but if you figure a typical route is 1/3 up, 1/3 down, and 1/3 flat-ish, then that's 8% grade on the up part.\n",
|
||||
"\n",
|
||||
"I can use the polynomial `F` to estimate the duration of a route:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def duration(dist, climb, F=F):\n",
|
||||
@@ -131,16 +137,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"6.7314081227852185"
|
||||
"6.2641828504384378"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
Reference in new issue
Block a user