{
"metadata": {
"name": "",
"signature": "sha256:f5748c68e1879e4433d354f5b17c64fc482002cbd89c3ab74199f42d231729ec"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Kako nari\u0161emo graf meritve"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zakaj rezultate meritev prika\u017eemo na grafu?\n",
"\n",
"- \u010clove\u0161ki mo\u017egani tako la\u017eje procesirajo kompleksne ideje in povezave.\n",
"
- Graf meritve pogosto razkrije zna\u010dilnosti, ki jih, \u010de podatke predstavimo v tabeli, ne opazimo. \n",
"
- Ve\u010dina bralcev znanstvenih \u010dlankov najprej pogleda slike in se na osnovi njih odlo\u010di, ali bodo \u010dlanek prebrali. \n",
"
"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Orodja za risanje grafov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obstaja vrsta programov, namenjenih risanju grafov:\n",
"\n",
"- matplotlib\n",
"
- gnuplot \n",
"
- ROOT\n",
"
- Asymptote\n",
"
- Grace\n",
"
- Wolfram Mathematica (pla\u010dljiv, instaliran na ra\u010dunalnik FMF) \n",
"
- MATLAB (pla\u010dljiv, instaliran na ra\u010dunalnik FMF)\n",
"
- Origin (pla\u010dljiv)\n",
"
- IGOR Pro (pla\u010dljiv)\n",
"
- ...\n",
"
- Excel (ni priporo\u010dljiv)\n",
"
\n",
"Zgledi na predavanjih bodo prikazani v programu matplotlib."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Napotki"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Znanstvene revije obi\u010dajno podajo napotke, kako naj grafi v objavljenih \u010dlankih izgledajo. Nekaj primerov iz pomembnej\u0161ih fizikalnih revij:\n",
"\n",
"Pomembno je, da so grafi konsistentni (imajo vsi osi enake debeline, enako velikost pisave -- le ta naj bo enako velika kot v tekstu, ...) To najla\u017eje dose\u017eemo tako, da si pripravimo standarden nabor nastavitev, ki jih potem uporabimo pri vseh grafih. Primer:"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"from pylab import *\n",
"%matplotlib inline\n",
"\n",
"# nastavitve za izris grafov (http://matplotlib.org/1.3.1/users/customizing.html)\n",
"rc('text', usetex = True)\n",
"rc('font', size = 12, family = 'serif', serif = ['Computer Modern'])\n",
"rc('xtick', labelsize = 'small')\n",
"rc('ytick', labelsize = 'small')\n",
"rc('legend', frameon = False, fontsize = 'medium')\n",
"rc('figure', figsize = (5, 3))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Primer enostavnega grafa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pri nekem poskusu so merili odgovor mi\u0161i\u010dnega vlakna na adrenalin. Mi\u0161i\u010dno vlakno iz \u017eabjega srca so napeli na mikrosilomer. Vlakno so oblivali z raztopino adrenalina razli\u010dnih koncentracij in merili silo skr\u010denja. Rezultati teh meritev so shranjeni v datoteki adrenalin.dat. V prvem stolpcu datoteke je koncentracija adrenalina v $\\mu$g/l, v drugem pa sila skr\u010denja, ki so jo normirali na 100% pri najve\u010dji koncentraciji.\n",
"\n",
"Podatke najprej preberemo iz datoteke v dvodimenzionalno polje. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"podatki = loadtxt('adrenalin.dat')\n",
"print podatki"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 1. 0. ]\n",
" [ 2. 0. ]\n",
" [ 7. 15.3]\n",
" [ 10. 34.6]\n",
" [ 20. 49.3]\n",
" [ 70. 82.6]\n",
" [ 200. 96. ]\n",
" [ 1000. 100. ]]\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prebrane podatke prika\u017eemo na grafu, da dobimo ob\u010dutek o obna\u0161anju izmerjene koli\u010dine. Pri poskusih obi\u010dajno kontroliramo eno koli\u010dino in opazujemo, kako le-ta vpliva na neko drugo koli\u010dino. Prvo (v na\u0161em primeru koncentracijo adrenalina) prika\u017eemo na vodoravni osi grafa, drugo (normirano silo skr\u010denja) pa na navpi\u010dni osi."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(podatki[:, 0], podatki[:, 1]);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Graf priredimo:\n",
"\n",
"- Rezultate poskusa prika\u017eemo s to\u010dkami, saj so daljice, ki povezujejo izmerjene vrednosti na zgornjem grafu, zavajajo\u010de.\n",
"
- Odstranimo barve, \u010de le-te niso nujno potrebne.\n",
"
- Ob oseh zapi\u0161emo fizikalne koli\u010dine. \u010ce je prostora dovolj, jih opi\u0161emo z besedami, sicer uporabimo ustrezno oznako (oznaka naj bo v le\u017ee\u010di pisavi). \n",
"
- Ob vseh fizikalnih koli\u010dinah zapi\u0161emo, v katerih enotah so prikazane (enota naj bo v pokon\u010dni pisavi).\n",
"
- Dodamo mre\u017eo, da la\u017eje od\u010ditavamo vrednosti. \n",
"
"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = figure()\n",
"plot(podatki[:, 0], podatki[:, 1], 'ko', clip_on = False)\n",
"xlabel(r'Koncentracija adrenalina ($\\mu$g/l)')\n",
"ylabel(r'$F/F_\\textrm{max}$ (\\%)')\n",
"yticks(linspace(0, 100, 5))\n",
"grid(True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Graf shranimo v datoteko adrenalin.pdf. \u010ce je le mogo\u010de, uporabimo vektorski format, ki omogo\u010da poljubno pove\u010davo brez izgube kvalitete slike."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig.tight_layout(pad = 0.4)\n",
"fig.savefig('adrenalin.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Datoteko z grafom vklju\u010dimo v \u010dlanek (vektorski format, bitni format). Nad ali pod grafom dodamo opisno vrstico, ki naj vsebuje \u0161tevilko grafa in opis na grafu prikazanih koli\u010din. Graf, skupaj z opisno vrstico, naj bo bralcu razumljiv sam po sebi, brez branja \u010dlanka!\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Naloge"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"- Nari\u0161i graf s podatki iz datoteke praznenjeKondenzatorja.txt. To je meritev napetosti na kondenzatorju v voltih (drugi stolpec) v odvisnosti od \u010dasa v sekundah (prvi stolpec).\n",
"\n",
"
- V datoteki Maribor.zip so zbrani vremenski podatki z merilne postaje Maribor-letali\u0161\u010de za obdobje od 1. 1. 1977 do 31. 1. 2015. Datoteka TG_STAID003331.txt vsebuje podatke o povpre\u010dni dnevni temperaturi (zapis podatkov je razlo\u017een v glavi datoteke). Na grafu prika\u017ei, kako se je povpre\u010dna dnevna temperatura spreminjala v letu 2010.\n",
"\n",
"
- Nari\u0161i graf s podatki iz datoteke CSL123.MuD. To je meritev absorpcije rentgenskih \u017earkov (logaritem razmerja vpadnega in prepu\u0161\u010denega toka, drugi stolpec), z energijo fotonov (prvi stolpec) v podro\u010dju robov L (L3 = 5017.8 eV, L2 = 5365.6 eV, L1 = 5720.4 eV), v cezijevi pari, izvedena na sinhrotronu v Hamburgu. Nari\u0161i posebej \u0161e o\u017eje obmo\u010dje robu L3 (-50 eV, 150 eV), da bo profil robu razlo\u010dnej\u0161i. Ali meni\u0161, da je ostri vrh pri\u0161krnjen zaradi pregrobega koraka v energiji?\n",
"
"
]
}
],
"metadata": {}
}
]
}