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Update on r codes

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mhjensen
2017-12-08 11:32:08 +01:00
parent 3eaf9e4578
commit dd90513eb3
30 changed files with 1431 additions and 206 deletions
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doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
+66 -59
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Dec 6, 2017**\n",
"Date: **Dec 8, 2017**\n",
"\n",
"Copyright 1999-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -125,9 +125,24 @@
"To add more entropy, **cython** can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook \n",
"setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility, \n",
"including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook \n",
"can easily be converted into a nicely rendered **PDF** file or a Latex file for further processing.\n",
"\n",
"This never ends.\n",
"can easily be converted into a nicely rendered **PDF** file or a Latex file for further processing. For example, convert to latex as"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jupyter nbconvert filename.ipynb --to latex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you use the light mark-up language **doconce** you can convert a standard ascii text file into various HTML \n",
"formats, ipython notebooks, latex files, pdf files etc. \n",
"\n",
@@ -138,7 +153,7 @@
},
{
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"metadata": {
"collapsed": false
},
@@ -173,7 +188,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {
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@@ -283,7 +298,7 @@
},
{
"cell_type": "code",
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"execution_count": 4,
"metadata": {
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@@ -540,7 +555,7 @@
},
{
"cell_type": "code",
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"execution_count": 5,
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@@ -614,7 +629,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
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@@ -648,32 +663,28 @@
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"cell_type": "markdown",
"metadata": {},
"source": [
"HudsonBay = read.csv(\"src/Hudson_Bay.csv\",header=T)\n",
"fix(HudsonBay)\n",
"dim(HudsonBay)\n",
"names(HudsonBay)\n",
"plot(HudsonBay$Year, HudsonBay$Hares..x1000.)\n",
"attach(HudsonBay)\n",
"plot(Year, Hares..x1000.)\n",
"plot(Year, Hares..x1000., col=\"red\", varwidth=T, xlab=\"Years\", ylab=\"Haresx 1000\")\n",
"summary(HudsonBay)\n",
"summary(Hares..x1000.)\n",
"library(MASS)\n",
"library(ISLR)\n",
"scatter.smooth(x=Year, y = Hares..x1000.)\n",
"linearMod = lm(Hares..x1000. ~ Year)\n",
"print(linearMod)\n",
"summary(linearMod)\n",
"plot(linearMod)\n",
"confint(linearMod)\n",
"predict(linearMod,data.frame(Year=c(1910,1914,1920)),interval=\"confidence\")"
" HudsonBay = read.csv(\"src/Hudson_Bay.csv\",header=T)\n",
" fix(HudsonBay)\n",
" dim(HudsonBay)\n",
" names(HudsonBay)\n",
" plot(HudsonBay$Year, HudsonBay$Hares..x1000.)\n",
" attach(HudsonBay)\n",
" plot(Year, Hares..x1000.)\n",
" plot(Year, Hares..x1000., col=\"red\", varwidth=T, xlab=\"Years\", ylab=\"Haresx 1000\")\n",
" summary(HudsonBay)\n",
" summary(Hares..x1000.)\n",
" library(MASS)\n",
" library(ISLR)\n",
" scatter.smooth(x=Year, y = Hares..x1000.)\n",
" linearMod = lm(Hares..x1000. ~ Year)\n",
" print(linearMod)\n",
" summary(linearMod)\n",
" plot(linearMod)\n",
" confint(linearMod)\n",
" predict(linearMod,data.frame(Year=c(1910,1914,1920)),interval=\"confidence\")\n"
]
},
{
@@ -684,32 +695,28 @@
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"cell_type": "markdown",
"metadata": {},
"source": [
"set.seed(1485)\n",
"len = 24\n",
"x = runif(len)\n",
"y = x^3+rnorm(len, 0,0.06)\n",
"ds = data.frame(x = x, y = y)\n",
"str(ds)\n",
"plot( y ~ x, main =\"Known cubic with noise\")\n",
"s = seq(0,1,length =100)\n",
"lines(s, s^3, lty =2, col =\"green\")\n",
"m = nls(y ~ I(x^power), data = ds, start = list(power=1), trace = T)\n",
"class(m)\n",
"summary(m)\n",
"power = round(summary(m)$coefficients[1], 3)\n",
"power.se = round(summary(m)$coefficients[2], 3)\n",
"plot(y ~ x, main = \"Fitted power model\", sub = \"Blue: fit; green: known\")\n",
"s = seq(0, 1, length = 100)\n",
"lines(s, s^3, lty = 2, col = \"green\")\n",
"lines(s, predict(m, list(x = s)), lty = 1, col = \"blue\")\n",
"text(0, 0.5, paste(\"y =x^ (\", power, \" +/- \", power.se, \")\", sep = \"\"), pos = 4)"
" set.seed(1485)\n",
" len = 24\n",
" x = runif(len)\n",
" y = x^3+rnorm(len, 0,0.06)\n",
" ds = data.frame(x = x, y = y)\n",
" str(ds)\n",
" plot( y ~ x, main =\"Known cubic with noise\")\n",
" s = seq(0,1,length =100)\n",
" lines(s, s^3, lty =2, col =\"green\")\n",
" m = nls(y ~ I(x^power), data = ds, start = list(power=1), trace = T)\n",
" class(m)\n",
" summary(m)\n",
" power = round(summary(m)$coefficients[1], 3)\n",
" power.se = round(summary(m)$coefficients[2], 3)\n",
" plot(y ~ x, main = \"Fitted power model\", sub = \"Blue: fit; green: known\")\n",
" s = seq(0, 1, length = 100)\n",
" lines(s, s^3, lty = 2, col = \"green\")\n",
" lines(s, predict(m, list(x = s)), lty = 1, col = \"blue\")\n",
" text(0, 0.5, paste(\"y =x^ (\", power, \" +/- \", power.se, \")\", sep = \"\"), pos = 4)\n"
]
}
],