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"_model_module": "@jupyter-widgets/output" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "xN2EzjQOV5mK", "colab_type": "text" }, "source": [ "# Beautifulsoup in Pandas " ] }, { "cell_type": "markdown", "metadata": { "id": "xDI9V50XV5mN", "colab_type": "text" }, "source": [ "
\n", "Dejan Križaj, 2019\n", "\n", "**Namen:** Zvezek (Notebook) je namenjen seznanjanju študentov z uporabo Jupytra za vnos podatkov iz spleta in njihovo predstavitvijo. To vključuje predvsem osnovni prikaz uporabe modulov Beautifulsoup in Pandas. \n", "\n", "**Zvezek se nanaša na:**\n", "* Impedančna spektroskopija \n" ] }, { "cell_type": "markdown", "metadata": { "id": "57ZTxQf7V5mO", "colab_type": "text" }, "source": [ "
\n", "Namig: Obstajata dve verziji tega dokumenta. Ena je v obliki html datoteke (končnica html), ki je ni mogoče izvajati, druga pa ima končnico ipny (Jupyter Notebook), ki jo lahko izvajamo z Jupyter aplikacijo. To aplikacijo imate lahko naloženo na vašem računalniku in se izvaja v brskalniku, lahko jo ogledujete s spletno aplikacijo nbViewer, s spletnimi aplikacijami Binder ali Google Colab pa jo lahko tudi zaganjate in spreminjate. Več o tem si preberite v \n", "tem članku.\n", "
\n", "Za izvajanje tega zvezka ne potrebujete posebnega znanja programiranja v Pythonu, lahko pa poljubno spreminjate kodo in se sproti učite tudi uporabe programskega jezika. Več podobnih primerov je na Githubu na https://github.com/osnove/Dodatno/\n", "
\n", "\n", "\"Filtri\"" ] }, { "cell_type": "markdown", "metadata": { "id": "eHYvCt8uV5mP", "colab_type": "text" }, "source": [ "Ta zvezek je v bistvu dodatek k zvezku Impedančna spektroskopija, v katerem smo prikazali uporabo impedančne spektroskopije za analizo kemijskih procesov in bioloških sistemov. \n", "\n", "Beautifulsoup in Pandas sta dve knjižnici (modula), ki omogočata dodatno funkcionalnost uporabe Pythona. S pomočjo Beautifulsoup lahko razčlenimo (ang. parsing) določeno datoteko in iz nje izluščimo kar nas zanima. Recimo, pri dielektrični spektroskopiji smo uporabili podatke iz tabele na strani http://niremf.ifac.cnr.it/docs/DIELECTRIC/AppendixC.html. Podatke za en primer (za model dielektričnih lastnosti krvi) smo \"na roko\" prepisali v spremenljivke v Jupytru. Z nekaj ukaznimi vrsticamo to lahko naredimo programsko, pa ne le za eno samo snov pač pa povlečemo celotno tabelo in iz nje izvlečemo podatke. Beautifulsoup deluje kot razčlenjevalec, za oblikovanje v uporabno tabelo in delo s tabelo pa uporabimo še modul Pandas. \n", "\n", "V podpoglavju 1.1 najprej prikažemo \"klasičen\" način obravnave, kjer parametre za model pridobimo iz tabele na spletni strani, kasneje pa to naredimo programsko." ] }, { "cell_type": "markdown", "metadata": { "id": "qgZLpXOxV5mQ", "colab_type": "text" }, "source": [ "## Dielektrične lastnosti bioloških tkiv\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Ifb4I41GV5mR", "colab_type": "text" }, "source": [ "Cole-Cole model ni primeren le za modeliranje dielektričnih lastnosti \"preprostih\" dielektrikov, pač pa v razširjeni obliki zelo dobro modelira tudi dielektrične lastnosti bioloških tkiv. Trenutno, v bistvu že kar nekaj časa, je v veljavi več-parametrski model, ki sta ga predlagala Gabriel & Gabriel () in temelji na uporabi več osnovnih Cole-Cole enačb. S tem omogoči, da se modelira relaksacijske mehanizme v bioloških sistemih, ki nastopajo pri več različnih frekvencah. Model, ki sta ga uporabila \n", "\n", "$$\\underline{\\epsilon} = \\epsilon _{\\infty} + \\sum_{n=1}^{4} \\frac{\\Delta \\epsilon_n}{1+(j\\omega\\tau _n)^{1-\\alpha _n}} + \\frac{\\gamma }{j \\omega \\epsilon _0}$$. \n", "\n", "Osnovni izris grafa prikazuje spodnja celica." ] }, { "cell_type": "code", "metadata": { "code_folding": [ 0 ], "id": "G6lkKI2AV5mT", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 307 }, "outputId": "c1bfe0d1-5b83-44ad-834f-bab41da2fcb6" }, "source": [ "## več parametrski Cole-Cole model\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "ef=4.000\n", "del1=56\n", "tau1=8.377e-12\n", "alf1=.1\n", "del2=5200\n", "tau2=132.629e-9\n", "alf2=.1\n", "sig=.7\n", "del3=0.7\n", "tau3=159.155\n", "alf3=0.2\n", "del4=0\n", "tau4=15e-3\n", "alf4=0\n", "e0=8.854e-12\n", "\n", "eksponent=np.linspace(1,12,100) # izdelamo 10 točk od 0 do 2000, linearno\n", "freq=10**(eksponent)\n", "omega=2*np.pi*freq\n", "\n", "eps=ef+del1/(1+(1j*omega*tau1)**(1-alf1))+del2/(1+(1j*omega*tau2)**(1-alf2))\n", "eps=eps+del3/(1+(1j*omega*tau3)**(1-alf4))+del4/(1+(1j*omega*tau4)**(1-alf4))+sig/(1j*omega*e0)\n", "sigma=1j*eps*omega*e0\n", "\n", "\n", "plt.xlabel('Frekvenca (Hz)')\n", "plt.ylabel('Eps in Gama / ') \n", "plt.loglog(freq,(eps).real, color='b',linewidth=3,label='eps.real')\n", "plt.loglog(freq,sigma.real, color='r',linewidth=3,label='gama.real')\n", "plt.loglog(freq,(sigma).imag, color='r',linestyle=':',label='gama.imag')\n", "\n", "plt.grid(True,which=\"both\")\n", "plt.ylim(.1,1e4)\n", "plt.legend() # klikni puščico za razširitev" ], "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 2 }, { "output_type": "display_data", "data": { "image/png": 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qfF9FtLDcu+3b3dOQ778PH34Ie2sZONqokVsDe9Ag98seMMCti52cXONh9YlN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\nm2/2/Zi77vK283TurFpYeNB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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "vTG5jBztV5mY", "colab_type": "text" }, "source": [ "## Beautiful Soup\n", "\n", "V naslednjih vrsticah bomo povlekli podatke za dielektrični model iz html tabele na strani \n", "http://niremf.ifac.cnr.it/docs/DIELECTRIC/AppendixC.html. V ta namen bomo uporabili modul *requests*, s pomočjo katerega bomo prenesli vsebino datoteke iz spleta v spremenljivko res.\n", "Nato bomo uporabili modul BeautifulSoup, s pomočjo katerega bomo analizirali vsebino datoteke (z razčlenjevalnikom (parserjem) xlml. \n", "\n", "Če modul Beautifulsoup ni nameščen na računalniku, ga je potrebno predhodno namestiti. \n", "Lahko tudi v ukazni vrstici Jupytra z ukazom !pip install bs4. \n", "Enako je potrebno narediti tudi za pandas in tudi za lxml.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "TXGMx0WRV5mZ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "9437fc48-08d4-4db8-b4d0-5469699dfd77" }, "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "\n", "res = requests.get(\"http://niremf.ifac.cnr.it/docs/DIELECTRIC/AppendixC.html\") # potegne file iz spleta\n", "soup = BeautifulSoup(res.content,'lxml') # sparsa vsebino z lxml\n", "\n", "table2 = soup.find_all('table') # poiščemo tabelo\n", "table2[0] # izpišemo tabelo, da vidimo, če je prava" ], "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", 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Tissue Type \\ Parameter ef del1tau1 (ps) alf1 del2tau2 (ns)alf2 sigdel3tau3 (us) alf3del4tau4 (ms) alf4
Aorta4.000 40.00 8.842 0.100 50 3.183 0.100 0.250 1.00E+5 159.155 0.200 1.00E+7 1.592 0.000
Bladder2.500 16.00 8.842 0.100 400 159.155 0.100 0.200 1.00E+5 159.155 0.200 1.00E+7 15.915 0.000
Blood4.000 56.00 8.377 0.100 5200 132.629 0.100 0.700 0.00E+0 159.155 0.200 0.00E+0 15.915 0.000
Bone (Cancellous) 2.500 18.00 13.263 0.220 300 79.577 0.250 0.070 2.00E+4 159.155 0.200 2.00E+7 15.915 0.000
Bone (Cortical) 2.500 10.00 13.263 0.200 180 79.577 0.200 0.020 5.00E+3 159.155 0.200 1.00E+5 15.915 0.000
Bone Marrow (Infiltrated) 2.500 9.00 14.469 0.200 80 15.915 0.100 0.100 1.00E+4 1591.549 0.100 2.00E+6 15.915 0.100
Bone Marrow (Not Infiltrated)\n", "2.500 3.00 7.958 0.200 25 15.915 0.100 0.001 5.00E+3 1591.549 0.100 2.00E+6 15.915 0.100
Brain (Grey Matter) 4.000 45.00 7.958 0.100 400 15.915 0.150 0.020 2.00E+5 106.103 0.220 4.50E+7 5.305 0.000
Brain (White Matter) 4.000 32.00 7.958 0.100 100 7.958 0.100 0.020 4.00E+4 53.052 0.300 3.50E+7 7.958 0.020
Breast fat2.500 3.00 17.680 0.100 15 63.660 0.100 0.010 5.00E+4 454.700 0.100 2.00E+7 13.260 0.000
Cartilage4.000 38.00 13.263 0.150 2500 144.686 0.150 0.150 1.00E+5 318.310 0.100 4.00E+7 15.915 0.000
Cerebellum4.000 40.00 7.958 0.100 700 15.915 0.150 0.040 2.00E+5 106.103 0.220 4.50E+7 5.305 0.000
Cerebro Spinal Fluid 4.000 65.00 7.958 0.100 40 1.592 0.000 2.000 0.00E+0 159.155 0.000 0.00E+0 15.915 0.000
Cervix4.000 45.00 7.958 0.100 200 15.915 0.100 0.300 1.50E+5 106.103 0.180 4.00E+7 1.592 0.000
Colon4.000 50.00 7.958 0.100 3000 159.155 0.200 0.010 1.00E+5 159.155 0.200 4.00E+7 1.592 0.000
Cornea4.000 48.00 7.958 0.100 4000 159.155 0.050 0.400 1.00E+5 15.915 0.200 4.00E+7 15.915 0.000
Dura4.000 40.00 7.958 0.150 200 7.958 0.100 0.500 1.00E+4 159.155 0.200 1.00E+6 15.915 0.000
Eye Tissues (Sclera) 4.000 50.00 7.958 0.100 4000 159.155 0.100 0.500 1.00E+5 159.155 0.200 5.00E+6 15.915 0.000
Fat (Average Infiltrated) 2.500 9.00 7.958 0.200 35 15.915 0.100 0.035 3.30E+4 159.155 0.050 1.00E+7 15.915 0.010
Fat (Not Infiltrated) 2.500 3.00 7.958 0.200 15 15.915 0.100 0.010 3.30E+4 159.155 0.050 1.00E+7 7.958 0.010
Gall Bladder4.000 55.00 7.579 0.050 40 1.592 0.000 0.900 1.00E+3 159.155 0.200 1.00E+4 15.915 0.000
Gall Bladder Bile 4.000 66.00 7.579 0.050 50 1.592 0.000 1.400 0.00E+0 159.155 0.200 0.00E+0 15.915 0.200
Heart4.000 50.00 7.958 0.100 1200 159.155 0.050 0.050 4.50E+5 72.343 0.220 2.50E+7 4.547 0.000
Kidney4.000 47.00 7.958 0.100 3500 198.944 0.220 0.050 2.50E+5 79.577 0.220 3.00E+7 4.547 0.000
Lens Cortex4.000 42.00 7.958 0.100 1500 79.577 0.100 0.300 2.00E+5 159.155 0.100 4.00E+7 15.915 0.000
Lens Nucleus3.000 32.00 8.842 0.100 100 10.610 0.200 0.200 1.00E+3 15.915 0.200 5.00E+3 15.915 0.000
Liver4.000 39.00 8.842 0.100 6000 530.516 0.200 0.020 5.00E+4 22.736 0.200 3.00E+7 15.915 0.050
Lung (Deflated) 4.000 45.00 7.958 0.100 1000 159.155 0.100 0.200 5.00E+5 159.155 0.200 1.00E+7 15.915 0.000
Lung (Inflated) 2.500 18.00 7.958 0.100 500 63.662 0.100 0.030 2.50E+5 159.155 0.200 4.00E+7 7.958 0.000
Muscle4.000 50.00 7.234 0.100 7000 353.678 0.100 0.200 1.20E+6 318.310 0.100 2.50E+7 2.274 0.000
Nerve4.000 26.00 7.958 0.100 500 106.103 0.150 0.006 7.00E+4 15.915 0.200 4.00E+7 15.915 0.000
Ovary4.000 40.00 8.842 0.150 400 15.915 0.250 0.300 1.00E+5 159.155 0.270 4.00E+7 15.915 0.000
Skin (Dry)4.000 32.00 7.234 0.000 1100 32.481 0.200 0.000 0.00E+0 159.155 0.200 0.00E+0 15.915 0.200
Skin (Wet)4.000 39.00 7.958 0.100 280 79.577 0.000 0.000 3.00E+4 1.592 0.160 3.00E+4 1.592 0.200
Small Intestine 4.000 50.00 7.958 0.100 10000 159.155 0.100 0.500 5.00E+5 159.155 0.200 4.00E+7 15.915 0.000
Spleen4.000 48.00 7.958 0.100 2500 63.662 0.150 0.030 2.00E+5 265.258 0.250 5.00E+7 6.366 0.000
Stomach4.000 60.00 7.958 0.100 2000 79.577 0.100 0.500 1.00E+5 159.155 0.200 4.00E+7 15.915 0.000
Tendon4.000 42.00 12.243 0.100 60 6.366 0.100 0.250 6.00E+4 318.310 0.220 2.00E+7 1.326 0.000
Testis4.000 55.00 7.958 0.100 5000 159.155 0.100 0.400 1.00E+5 159.155 0.200 4.00E+7 15.915 0.000
Thyroid4.000 55.00 7.958 0.100 2500 159.155 0.100 0.500 1.00E+5 159.155 0.200 4.00E+7 15.915 0.000
Tongue4.000 50.00 7.958 0.100 4000 159.155 0.100 0.250 1.00E+5 159.155 0.200 4.00E+7 15.915 0.000
Trachea2.500 38.00 7.958 0.100 400 63.662 0.100 0.300 5.00E+4 15.915 0.200 1.00E+6 15.915 0.000
Uterus4.000 55.00 7.958 0.100 800 31.831 0.100 0.200 3.00E+5 159.155 0.200 3.50E+7 1.061 0.000
Vitreous Humor4.000 65.00 7.234 0.000 30 159.155 0.100 1.500 0.00E+0 159.155 0.000 0.00E+0 15.915 0.000
" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "markdown", "metadata": { "id": "pGAfMWNPV5md", "colab_type": "text" }, "source": [ "Če se je izpisala tabela v obliki html, smo na dobri poti. Sedaj je potrebno razdelati to tabelo, da se iz nje izvleče podatke. V ta namen uporabimo metodo findAll, kjer bomo poiskali indekse, kjer nastopa oznaka za začetek vrste \"tr\" in začetek celice \"td\". Nato shranjujemo po 16 vrednosti iz ene vrste v spremenljivko record in nato vse vrste v records.\n", "\n", "Nato s pomočjo metode DataFrame znotraj modula Pandas kreiramo Dataframe, ki je neke vrste dvodimenzionalni niz z dodatnimi možnostmi indeksiranja itd. " ] }, { "cell_type": "code", "metadata": { "id": "ZVdyfwXJV5me", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "ec3ccb8e-5ef2-4a1a-f142-da9d8928b42f" }, "source": [ "# Poiščem vrednosti v tabeli in jih shranim v niz records\n", "import pandas as pd\n", "records = []\n", "for tr in table2[0].findAll(\"tr\"):\n", " trs = tr.findAll(\"td\")\n", " record = []\n", " for i in range(15): # 16 stolpcev v vrsti shranim v record\n", " record.append(trs[i].text)\n", " records.append(record) # zložim vrste v records\n", "\n", "df = pd.DataFrame(data=records) # naredim DataFrame s Pandas\n", "df # izrišem DataFrame" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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01234567891011121314
0Tissue Type \\ Parameterefdel1tau1 (ps)alf1del2tau2 (ns)alf2sigdel3tau3 (us)alf3del4tau4 (ms)alf4
1
2Aorta4.00040.008.8420.100503.1830.1000.2501.00E+5159.1550.2001.00E+71.5920.000
3Bladder2.50016.008.8420.100400159.1550.1000.2001.00E+5159.1550.2001.00E+715.9150.000
4Blood4.00056.008.3770.1005200132.6290.1000.7000.00E+0159.1550.2000.00E+015.9150.000
5Bone (Cancellous)2.50018.0013.2630.22030079.5770.2500.0702.00E+4159.1550.2002.00E+715.9150.000
6Bone (Cortical)2.50010.0013.2630.20018079.5770.2000.0205.00E+3159.1550.2001.00E+515.9150.000
7
8Bone Marrow (Infiltrated)2.5009.0014.4690.2008015.9150.1000.1001.00E+41591.5490.1002.00E+615.9150.100
9Bone Marrow (Not Infiltrated)\\n2.5003.007.9580.2002515.9150.1000.0015.00E+31591.5490.1002.00E+615.9150.100
10Brain (Grey Matter)4.00045.007.9580.10040015.9150.1500.0202.00E+5106.1030.2204.50E+75.3050.000
11Brain (White Matter)4.00032.007.9580.1001007.9580.1000.0204.00E+453.0520.3003.50E+77.9580.020
12Breast fat2.5003.0017.6800.1001563.6600.1000.0105.00E+4454.7000.1002.00E+713.2600.000
13
14Cartilage4.00038.0013.2630.1502500144.6860.1500.1501.00E+5318.3100.1004.00E+715.9150.000
15Cerebellum4.00040.007.9580.10070015.9150.1500.0402.00E+5106.1030.2204.50E+75.3050.000
16Cerebro Spinal Fluid4.00065.007.9580.100401.5920.0002.0000.00E+0159.1550.0000.00E+015.9150.000
17Cervix4.00045.007.9580.10020015.9150.1000.3001.50E+5106.1030.1804.00E+71.5920.000
18Colon4.00050.007.9580.1003000159.1550.2000.0101.00E+5159.1550.2004.00E+71.5920.000
19
20Cornea4.00048.007.9580.1004000159.1550.0500.4001.00E+515.9150.2004.00E+715.9150.000
21Dura4.00040.007.9580.1502007.9580.1000.5001.00E+4159.1550.2001.00E+615.9150.000
22Eye Tissues (Sclera)4.00050.007.9580.1004000159.1550.1000.5001.00E+5159.1550.2005.00E+615.9150.000
23Fat (Average Infiltrated)2.5009.007.9580.2003515.9150.1000.0353.30E+4159.1550.0501.00E+715.9150.010
24Fat (Not Infiltrated)2.5003.007.9580.2001515.9150.1000.0103.30E+4159.1550.0501.00E+77.9580.010
25
26Gall Bladder4.00055.007.5790.050401.5920.0000.9001.00E+3159.1550.2001.00E+415.9150.000
27Gall Bladder Bile4.00066.007.5790.050501.5920.0001.4000.00E+0159.1550.2000.00E+015.9150.200
28Heart4.00050.007.9580.1001200159.1550.0500.0504.50E+572.3430.2202.50E+74.5470.000
29Kidney4.00047.007.9580.1003500198.9440.2200.0502.50E+579.5770.2203.00E+74.5470.000
30Lens Cortex4.00042.007.9580.100150079.5770.1000.3002.00E+5159.1550.1004.00E+715.9150.000
31
32Lens Nucleus3.00032.008.8420.10010010.6100.2000.2001.00E+315.9150.2005.00E+315.9150.000
33Liver4.00039.008.8420.1006000530.5160.2000.0205.00E+422.7360.2003.00E+715.9150.050
34Lung (Deflated)4.00045.007.9580.1001000159.1550.1000.2005.00E+5159.1550.2001.00E+715.9150.000
35Lung (Inflated)2.50018.007.9580.10050063.6620.1000.0302.50E+5159.1550.2004.00E+77.9580.000
36Muscle4.00050.007.2340.1007000353.6780.1000.2001.20E+6318.3100.1002.50E+72.2740.000
37
38Nerve4.00026.007.9580.100500106.1030.1500.0067.00E+415.9150.2004.00E+715.9150.000
39Ovary4.00040.008.8420.15040015.9150.2500.3001.00E+5159.1550.2704.00E+715.9150.000
40Skin (Dry)4.00032.007.2340.000110032.4810.2000.0000.00E+0159.1550.2000.00E+015.9150.200
41Skin (Wet)4.00039.007.9580.10028079.5770.0000.0003.00E+41.5920.1603.00E+41.5920.200
42Small Intestine4.00050.007.9580.10010000159.1550.1000.5005.00E+5159.1550.2004.00E+715.9150.000
43
44Spleen4.00048.007.9580.100250063.6620.1500.0302.00E+5265.2580.2505.00E+76.3660.000
45Stomach4.00060.007.9580.100200079.5770.1000.5001.00E+5159.1550.2004.00E+715.9150.000
46Tendon4.00042.0012.2430.100606.3660.1000.2506.00E+4318.3100.2202.00E+71.3260.000
47Testis4.00055.007.9580.1005000159.1550.1000.4001.00E+5159.1550.2004.00E+715.9150.000
48Thyroid4.00055.007.9580.1002500159.1550.1000.5001.00E+5159.1550.2004.00E+715.9150.000
49
50Tongue4.00050.007.9580.1004000159.1550.1000.2501.00E+5159.1550.2004.00E+715.9150.000
51Trachea2.50038.007.9580.10040063.6620.1000.3005.00E+415.9150.2001.00E+615.9150.000
52Uterus4.00055.007.9580.10080031.8310.1000.2003.00E+5159.1550.2003.50E+71.0610.000
53Vitreous Humor4.00065.007.2340.00030159.1550.1001.5000.00E+0159.1550.0000.00E+015.9150.000
\n", "
" ], "text/plain": [ " 0 1 ... 13 14\n", "0 Tissue Type \\ Parameter ef ... tau4 (ms) alf4\n", "1 ... \n", "2 Aorta 4.000 ... 1.592 0.000 \n", "3 Bladder 2.500 ... 15.915 0.000 \n", "4 Blood 4.000 ... 15.915 0.000 \n", "5 Bone (Cancellous) 2.500 ... 15.915 0.000 \n", "6 Bone (Cortical) 2.500 ... 15.915 0.000 \n", "7 ... \n", "8 Bone Marrow (Infiltrated) 2.500 ... 15.915 0.100 \n", "9 Bone Marrow (Not Infiltrated)\\n 2.500 ... 15.915 0.100 \n", "10 Brain (Grey Matter) 4.000 ... 5.305 0.000 \n", "11 Brain (White Matter) 4.000 ... 7.958 0.020 \n", "12 Breast fat 2.500 ... 13.260 0.000 \n", "13 ... \n", "14 Cartilage 4.000 ... 15.915 0.000 \n", "15 Cerebellum 4.000 ... 5.305 0.000 \n", "16 Cerebro Spinal Fluid 4.000 ... 15.915 0.000 \n", "17 Cervix 4.000 ... 1.592 0.000 \n", "18 Colon 4.000 ... 1.592 0.000 \n", "19 ... \n", "20 Cornea 4.000 ... 15.915 0.000 \n", "21 Dura 4.000 ... 15.915 0.000 \n", "22 Eye Tissues (Sclera) 4.000 ... 15.915 0.000 \n", "23 Fat (Average Infiltrated) 2.500 ... 15.915 0.010 \n", "24 Fat (Not Infiltrated) 2.500 ... 7.958 0.010 \n", "25 ... \n", "26 Gall Bladder 4.000 ... 15.915 0.000 \n", "27 Gall Bladder Bile 4.000 ... 15.915 0.200 \n", "28 Heart 4.000 ... 4.547 0.000 \n", "29 Kidney 4.000 ... 4.547 0.000 \n", "30 Lens Cortex 4.000 ... 15.915 0.000 \n", "31 ... \n", "32 Lens Nucleus 3.000 ... 15.915 0.000 \n", "33 Liver 4.000 ... 15.915 0.050 \n", "34 Lung (Deflated) 4.000 ... 15.915 0.000 \n", "35 Lung (Inflated) 2.500 ... 7.958 0.000 \n", "36 Muscle 4.000 ... 2.274 0.000 \n", "37 ... \n", "38 Nerve 4.000 ... 15.915 0.000 \n", "39 Ovary 4.000 ... 15.915 0.000 \n", "40 Skin (Dry) 4.000 ... 15.915 0.200 \n", "41 Skin (Wet) 4.000 ... 1.592 0.200 \n", "42 Small Intestine 4.000 ... 15.915 0.000 \n", "43 ... \n", "44 Spleen 4.000 ... 6.366 0.000 \n", "45 Stomach 4.000 ... 15.915 0.000 \n", "46 Tendon 4.000 ... 1.326 0.000 \n", "47 Testis 4.000 ... 15.915 0.000 \n", "48 Thyroid 4.000 ... 15.915 0.000 \n", "49 ... \n", "50 Tongue 4.000 ... 15.915 0.000 \n", "51 Trachea 2.500 ... 15.915 0.000 \n", "52 Uterus 4.000 ... 1.061 0.000 \n", "53 Vitreous Humor 4.000 ... 15.915 0.000 \n", "\n", "[54 rows x 15 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "markdown", "metadata": { "id": "aluqiPjFV5mh", "colab_type": "text" }, "source": [ "Če je vse potekalo OK, smo dobili izpis DataFrame-a z 53 vrsticami in 15 stolpci. Poglejmo si nekaj osnovnih ukazov za delo z Dataframe-om." ] }, { "cell_type": "code", "metadata": { "id": "xJFuSS2gV5mj", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "outputId": "797164a0-d488-4b09-f9cd-4cee29fded45" }, "source": [ "# Za osnovno delo z Dataframe, uporabi print za izpis, sicer izpiše le rezultat zadnje vrstice\n", "print(type(df)) # ugotovimo tip spremenljivke\n", "print(df.values[0]) # izpis prve vrstice\n", "print(df.values[2][1]) # tretja vrstica, drugi stolpec\n", "#df.values # celoten niz" ], "execution_count": 45, "outputs": [ { "output_type": "stream", "text": [ "\n", "['Tissue Type \\\\ Parameter ' 'ef ' 'del1' 'tau1 (ps) ' 'alf1 ' 'del2'\n", " 'tau2 (ns)' 'alf2 ' 'sig' 'del3' 'tau3 (us) ' 'alf3' 'del4' 'tau4 (ms) '\n", " 'alf4']\n", "4.000 \n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "nr0o_MkBV5mm", "colab_type": "text" }, "source": [ "Za boljše delo je potrebno določiti indeks, po katerem bomo lahko naslavljali vrstice. Trenutno je indeks kar na začetnem stolpcu, določenim s črkami od 0 do 53. Mi želimo naslavljati vrstice z imeni snovi, npr. Aorta, Bladder itd." ] }, { "cell_type": "code", "metadata": { "id": "WDGWmdEmV5mo", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "06e14097-7808-48db-da55-b96040c4eff5" }, "source": [ "df.set_index(0, inplace=True) # nastavimo indeks na stolpec z imeni snovi\n", "df # na začetku ni več številk, indeksi so izpisani bold\n", "# če to vrstico zaženeš še enkrat boš naredil napako, ker črke 0 ni več v prvi vrsti. \n", "# V tem primeru se moraš vrniti na začetek in še enkrat prebrati tabelo" ], "execution_count": 46, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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0
Tissue Type \\ Parameterefdel1tau1 (ps)alf1del2tau2 (ns)alf2sigdel3tau3 (us)alf3del4tau4 (ms)alf4
Aorta4.00040.008.8420.100503.1830.1000.2501.00E+5159.1550.2001.00E+71.5920.000
Bladder2.50016.008.8420.100400159.1550.1000.2001.00E+5159.1550.2001.00E+715.9150.000
Blood4.00056.008.3770.1005200132.6290.1000.7000.00E+0159.1550.2000.00E+015.9150.000
Bone (Cancellous)2.50018.0013.2630.22030079.5770.2500.0702.00E+4159.1550.2002.00E+715.9150.000
Bone (Cortical)2.50010.0013.2630.20018079.5770.2000.0205.00E+3159.1550.2001.00E+515.9150.000
Bone Marrow (Infiltrated)2.5009.0014.4690.2008015.9150.1000.1001.00E+41591.5490.1002.00E+615.9150.100
Bone Marrow (Not Infiltrated)\\n2.5003.007.9580.2002515.9150.1000.0015.00E+31591.5490.1002.00E+615.9150.100
Brain (Grey Matter)4.00045.007.9580.10040015.9150.1500.0202.00E+5106.1030.2204.50E+75.3050.000
Brain (White Matter)4.00032.007.9580.1001007.9580.1000.0204.00E+453.0520.3003.50E+77.9580.020
Breast fat2.5003.0017.6800.1001563.6600.1000.0105.00E+4454.7000.1002.00E+713.2600.000
Cartilage4.00038.0013.2630.1502500144.6860.1500.1501.00E+5318.3100.1004.00E+715.9150.000
Cerebellum4.00040.007.9580.10070015.9150.1500.0402.00E+5106.1030.2204.50E+75.3050.000
Cerebro Spinal Fluid4.00065.007.9580.100401.5920.0002.0000.00E+0159.1550.0000.00E+015.9150.000
Cervix4.00045.007.9580.10020015.9150.1000.3001.50E+5106.1030.1804.00E+71.5920.000
Colon4.00050.007.9580.1003000159.1550.2000.0101.00E+5159.1550.2004.00E+71.5920.000
Cornea4.00048.007.9580.1004000159.1550.0500.4001.00E+515.9150.2004.00E+715.9150.000
Dura4.00040.007.9580.1502007.9580.1000.5001.00E+4159.1550.2001.00E+615.9150.000
Eye Tissues (Sclera)4.00050.007.9580.1004000159.1550.1000.5001.00E+5159.1550.2005.00E+615.9150.000
Fat (Average Infiltrated)2.5009.007.9580.2003515.9150.1000.0353.30E+4159.1550.0501.00E+715.9150.010
Fat (Not Infiltrated)2.5003.007.9580.2001515.9150.1000.0103.30E+4159.1550.0501.00E+77.9580.010
Gall Bladder4.00055.007.5790.050401.5920.0000.9001.00E+3159.1550.2001.00E+415.9150.000
Gall Bladder Bile4.00066.007.5790.050501.5920.0001.4000.00E+0159.1550.2000.00E+015.9150.200
Heart4.00050.007.9580.1001200159.1550.0500.0504.50E+572.3430.2202.50E+74.5470.000
Kidney4.00047.007.9580.1003500198.9440.2200.0502.50E+579.5770.2203.00E+74.5470.000
Lens Cortex4.00042.007.9580.100150079.5770.1000.3002.00E+5159.1550.1004.00E+715.9150.000
Lens Nucleus3.00032.008.8420.10010010.6100.2000.2001.00E+315.9150.2005.00E+315.9150.000
Liver4.00039.008.8420.1006000530.5160.2000.0205.00E+422.7360.2003.00E+715.9150.050
Lung (Deflated)4.00045.007.9580.1001000159.1550.1000.2005.00E+5159.1550.2001.00E+715.9150.000
Lung (Inflated)2.50018.007.9580.10050063.6620.1000.0302.50E+5159.1550.2004.00E+77.9580.000
Muscle4.00050.007.2340.1007000353.6780.1000.2001.20E+6318.3100.1002.50E+72.2740.000
Nerve4.00026.007.9580.100500106.1030.1500.0067.00E+415.9150.2004.00E+715.9150.000
Ovary4.00040.008.8420.15040015.9150.2500.3001.00E+5159.1550.2704.00E+715.9150.000
Skin (Dry)4.00032.007.2340.000110032.4810.2000.0000.00E+0159.1550.2000.00E+015.9150.200
Skin (Wet)4.00039.007.9580.10028079.5770.0000.0003.00E+41.5920.1603.00E+41.5920.200
Small Intestine4.00050.007.9580.10010000159.1550.1000.5005.00E+5159.1550.2004.00E+715.9150.000
Spleen4.00048.007.9580.100250063.6620.1500.0302.00E+5265.2580.2505.00E+76.3660.000
Stomach4.00060.007.9580.100200079.5770.1000.5001.00E+5159.1550.2004.00E+715.9150.000
Tendon4.00042.0012.2430.100606.3660.1000.2506.00E+4318.3100.2202.00E+71.3260.000
Testis4.00055.007.9580.1005000159.1550.1000.4001.00E+5159.1550.2004.00E+715.9150.000
Thyroid4.00055.007.9580.1002500159.1550.1000.5001.00E+5159.1550.2004.00E+715.9150.000
Tongue4.00050.007.9580.1004000159.1550.1000.2501.00E+5159.1550.2004.00E+715.9150.000
Trachea2.50038.007.9580.10040063.6620.1000.3005.00E+415.9150.2001.00E+615.9150.000
Uterus4.00055.007.9580.10080031.8310.1000.2003.00E+5159.1550.2003.50E+71.0610.000
Vitreous Humor4.00065.007.2340.00030159.1550.1001.5000.00E+0159.1550.0000.00E+015.9150.000
\n", "
" ], "text/plain": [ " 1 2 ... 13 14\n", "0 ... \n", "Tissue Type \\ Parameter ef del1 ... tau4 (ms) alf4\n", " ... \n", "Aorta 4.000 40.00 ... 1.592 0.000 \n", "Bladder 2.500 16.00 ... 15.915 0.000 \n", "Blood 4.000 56.00 ... 15.915 0.000 \n", "Bone (Cancellous) 2.500 18.00 ... 15.915 0.000 \n", "Bone (Cortical) 2.500 10.00 ... 15.915 0.000 \n", " ... \n", "Bone Marrow (Infiltrated) 2.500 9.00 ... 15.915 0.100 \n", "Bone Marrow (Not Infiltrated)\\n 2.500 3.00 ... 15.915 0.100 \n", "Brain (Grey Matter) 4.000 45.00 ... 5.305 0.000 \n", "Brain (White Matter) 4.000 32.00 ... 7.958 0.020 \n", "Breast fat 2.500 3.00 ... 13.260 0.000 \n", " ... \n", "Cartilage 4.000 38.00 ... 15.915 0.000 \n", "Cerebellum 4.000 40.00 ... 5.305 0.000 \n", "Cerebro Spinal Fluid 4.000 65.00 ... 15.915 0.000 \n", "Cervix 4.000 45.00 ... 1.592 0.000 \n", "Colon 4.000 50.00 ... 1.592 0.000 \n", " ... \n", "Cornea 4.000 48.00 ... 15.915 0.000 \n", "Dura 4.000 40.00 ... 15.915 0.000 \n", "Eye Tissues (Sclera) 4.000 50.00 ... 15.915 0.000 \n", "Fat (Average Infiltrated) 2.500 9.00 ... 15.915 0.010 \n", "Fat (Not Infiltrated) 2.500 3.00 ... 7.958 0.010 \n", " ... \n", "Gall Bladder 4.000 55.00 ... 15.915 0.000 \n", "Gall Bladder Bile 4.000 66.00 ... 15.915 0.200 \n", "Heart 4.000 50.00 ... 4.547 0.000 \n", "Kidney 4.000 47.00 ... 4.547 0.000 \n", "Lens Cortex 4.000 42.00 ... 15.915 0.000 \n", " ... \n", "Lens Nucleus 3.000 32.00 ... 15.915 0.000 \n", "Liver 4.000 39.00 ... 15.915 0.050 \n", "Lung (Deflated) 4.000 45.00 ... 15.915 0.000 \n", "Lung (Inflated) 2.500 18.00 ... 7.958 0.000 \n", "Muscle 4.000 50.00 ... 2.274 0.000 \n", " ... \n", "Nerve 4.000 26.00 ... 15.915 0.000 \n", "Ovary 4.000 40.00 ... 15.915 0.000 \n", "Skin (Dry) 4.000 32.00 ... 15.915 0.200 \n", "Skin (Wet) 4.000 39.00 ... 1.592 0.200 \n", "Small Intestine 4.000 50.00 ... 15.915 0.000 \n", " ... \n", "Spleen 4.000 48.00 ... 6.366 0.000 \n", "Stomach 4.000 60.00 ... 15.915 0.000 \n", "Tendon 4.000 42.00 ... 1.326 0.000 \n", "Testis 4.000 55.00 ... 15.915 0.000 \n", "Thyroid 4.000 55.00 ... 15.915 0.000 \n", " ... \n", "Tongue 4.000 50.00 ... 15.915 0.000 \n", "Trachea 2.500 38.00 ... 15.915 0.000 \n", "Uterus 4.000 55.00 ... 1.061 0.000 \n", "Vitreous Humor 4.000 65.00 ... 15.915 0.000 \n", "\n", "[54 rows x 14 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 46 } ] }, { "cell_type": "markdown", "metadata": { "id": "zAxPL4j-V5mr", "colab_type": "text" }, "source": [ "Sedaj lahko naslavljaš posamezno vrstico in iz nje izvečeš podatke. (Za več o delu z Dataframe si poglej na spletu, za začetek npr. https://www.geeksforgeeks.org/python-pandas-dataframe/)" ] }, { "cell_type": "code", "metadata": { "id": "5Zgk__KJV5ms", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "68560cce-6a9f-4e96-9915-e8c350b62541" }, "source": [ "print(df.index.tolist()[2:10]) # spremeniš indeks v niz, bomo rabili v kratkem" ], "execution_count": 47, "outputs": [ { "output_type": "stream", "text": [ "['Aorta', 'Bladder', 'Blood', 'Bone (Cancellous) ', 'Bone (Cortical) ', '', 'Bone Marrow (Infiltrated) ', 'Bone Marrow (Not Infiltrated)\\n']\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "zsyvWXaqV5mv", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 295 }, "outputId": "08644e04-95c5-441d-810b-bf42b10ffa90" }, "source": [ "vrstica = df.loc['Blood'] # vrednosti vrstice z indeksom Blood\n", "print(vrstica) # izpis\n", "print(vrstica[1]) # prvi člen" ], "execution_count": 61, "outputs": [ { "output_type": "stream", "text": [ "1 4.000 \n", "2 56.00 \n", "3 8.377 \n", "4 0.100 \n", "5 5200 \n", "6 132.629 \n", "7 0.100 \n", "8 0.700 \n", "9 0.00E+0 \n", "10 159.155 \n", "11 0.200 \n", "12 0.00E+0 \n", "13 15.915 \n", "14 0.000 \n", "Name: Blood, dtype: object\n", "4.000 \n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "q2rFmO5PZN8t", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mhctRBsejvV9", "colab_type": "text" }, "source": [ "Če želimo razvrstiti (sortirati?!) tabelo tako, da poiščemo vrstice, ki izpolnjujejo določene pogoje, npr., da je specifična prevodnost većja od 1 moramo storiti sledeče:\n", "1. Ker vrednosti v tabeli niso numerične, jih je najprej potrebno spremeniti v numerične. V ta namen uporabmo funkcijo oz. metodo *apply* in znotraj nje *Dataframe.to_numeric*. Dobimo dataframe df2.\n", "2. Uporabimo df[stolpec] >< pogoj, da izvlečemo vrstice, ki izpolnjuje pogoj." ] }, { "cell_type": "code", "metadata": { "id": "p7R1SXoXgJ4Z", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 233 }, "outputId": "283408c0-3e54-431c-8abc-9b348edf3734" }, "source": [ "df2 = df.apply(pd.to_numeric, errors='coerce') # spremeni str vrednosti v numerične, kjer ne gre izpiše NaN\n", "df3=df2[df2[8] > 0.5]\n", "df3" ], "execution_count": 65, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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1234567891011121314
0
Blood4.056.08.3770.105200.0132.6290.10.70.0159.1550.20.015.9150.0
Cerebro Spinal Fluid4.065.07.9580.1040.01.5920.02.00.0159.1550.00.015.9150.0
Gall Bladder4.055.07.5790.0540.01.5920.00.91000.0159.1550.210000.015.9150.0
Gall Bladder Bile4.066.07.5790.0550.01.5920.01.40.0159.1550.20.015.9150.2
Vitreous Humor4.065.07.2340.0030.0159.1550.11.50.0159.1550.00.015.9150.0
\n", "
" ], "text/plain": [ " 1 2 3 4 ... 11 12 13 14\n", "0 ... \n", "Blood 4.0 56.0 8.377 0.10 ... 0.2 0.0 15.915 0.0\n", "Cerebro Spinal Fluid 4.0 65.0 7.958 0.10 ... 0.0 0.0 15.915 0.0\n", "Gall Bladder 4.0 55.0 7.579 0.05 ... 0.2 10000.0 15.915 0.0\n", "Gall Bladder Bile 4.0 66.0 7.579 0.05 ... 0.2 0.0 15.915 0.2\n", "Vitreous Humor 4.0 65.0 7.234 0.00 ... 0.0 0.0 15.915 0.0\n", "\n", "[5 rows x 14 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 65 } ] }, { "cell_type": "markdown", "metadata": { "id": "CU98C69X-LT6", "colab_type": "text" }, "source": [ "Lahko pa tudi razvrstimo vrstice, npr. po velikosti stolpca 8, v katerem so vrednosti prevodnosti snovi. Od največje do najmanjše vrednosti. Ugotovimo, da so najbolj prevodne snovi v naših možganih (cerebro-spinalna tekočina CSF) in očesna steklovina (VH)." ] }, { "cell_type": "code", "metadata": { "id": "6UXCF-as66QQ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "714b536f-d375-47e2-fe6b-b6df03244318" }, "source": [ "# Razvrščanje glede na vrednosti v stolpcu\n", "df2.sort_values(by=[8],ascending=False)" ], "execution_count": 92, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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1234567891011121314
0
Cerebro Spinal Fluid4.065.07.9580.1040.01.5920.002.0000.0159.1550.000.015.9150.00
Vitreous Humor4.065.07.2340.0030.0159.1550.101.5000.0159.1550.000.015.9150.00
Gall Bladder Bile4.066.07.5790.0550.01.5920.001.4000.0159.1550.200.015.9150.20
Gall Bladder4.055.07.5790.0540.01.5920.000.9001000.0159.1550.2010000.015.9150.00
Blood4.056.08.3770.105200.0132.6290.100.7000.0159.1550.200.015.9150.00
Stomach4.060.07.9580.102000.079.5770.100.500100000.0159.1550.2040000000.015.9150.00
Dura4.040.07.9580.15200.07.9580.100.50010000.0159.1550.201000000.015.9150.00
Small Intestine4.050.07.9580.1010000.0159.1550.100.500500000.0159.1550.2040000000.015.9150.00
Thyroid4.055.07.9580.102500.0159.1550.100.500100000.0159.1550.2040000000.015.9150.00
Eye Tissues (Sclera)4.050.07.9580.104000.0159.1550.100.500100000.0159.1550.205000000.015.9150.00
Testis4.055.07.9580.105000.0159.1550.100.400100000.0159.1550.2040000000.015.9150.00
Cornea4.048.07.9580.104000.0159.1550.050.400100000.015.9150.2040000000.015.9150.00
Ovary4.040.08.8420.15400.015.9150.250.300100000.0159.1550.2740000000.015.9150.00
Lens Cortex4.042.07.9580.101500.079.5770.100.300200000.0159.1550.1040000000.015.9150.00
Cervix4.045.07.9580.10200.015.9150.100.300150000.0106.1030.1840000000.01.5920.00
Trachea2.538.07.9580.10400.063.6620.100.30050000.015.9150.201000000.015.9150.00
Tendon4.042.012.2430.1060.06.3660.100.25060000.0318.3100.2220000000.01.3260.00
Tongue4.050.07.9580.104000.0159.1550.100.250100000.0159.1550.2040000000.015.9150.00
Aorta4.040.08.8420.1050.03.1830.100.250100000.0159.1550.2010000000.01.5920.00
Lung (Deflated)4.045.07.9580.101000.0159.1550.100.200500000.0159.1550.2010000000.015.9150.00
Uterus4.055.07.9580.10800.031.8310.100.200300000.0159.1550.2035000000.01.0610.00
Bladder2.516.08.8420.10400.0159.1550.100.200100000.0159.1550.2010000000.015.9150.00
Lens Nucleus3.032.08.8420.10100.010.6100.200.2001000.015.9150.205000.015.9150.00
Muscle4.050.07.2340.107000.0353.6780.100.2001200000.0318.3100.1025000000.02.2740.00
Cartilage4.038.013.2630.152500.0144.6860.150.150100000.0318.3100.1040000000.015.9150.00
Bone Marrow (Infiltrated)2.59.014.4690.2080.015.9150.100.10010000.01591.5490.102000000.015.9150.10
Bone (Cancellous)2.518.013.2630.22300.079.5770.250.07020000.0159.1550.2020000000.015.9150.00
Heart4.050.07.9580.101200.0159.1550.050.050450000.072.3430.2225000000.04.5470.00
Kidney4.047.07.9580.103500.0198.9440.220.050250000.079.5770.2230000000.04.5470.00
Cerebellum4.040.07.9580.10700.015.9150.150.040200000.0106.1030.2245000000.05.3050.00
Fat (Average Infiltrated)2.59.07.9580.2035.015.9150.100.03533000.0159.1550.0510000000.015.9150.01
Lung (Inflated)2.518.07.9580.10500.063.6620.100.030250000.0159.1550.2040000000.07.9580.00
Spleen4.048.07.9580.102500.063.6620.150.030200000.0265.2580.2550000000.06.3660.00
Liver4.039.08.8420.106000.0530.5160.200.02050000.022.7360.2030000000.015.9150.05
Brain (White Matter)4.032.07.9580.10100.07.9580.100.02040000.053.0520.3035000000.07.9580.02
Brain (Grey Matter)4.045.07.9580.10400.015.9150.150.020200000.0106.1030.2245000000.05.3050.00
Bone (Cortical)2.510.013.2630.20180.079.5770.200.0205000.0159.1550.20100000.015.9150.00
Breast fat2.53.017.6800.1015.063.6600.100.01050000.0454.7000.1020000000.013.2600.00
Colon4.050.07.9580.103000.0159.1550.200.010100000.0159.1550.2040000000.01.5920.00
Fat (Not Infiltrated)2.53.07.9580.2015.015.9150.100.01033000.0159.1550.0510000000.07.9580.01
Nerve4.026.07.9580.10500.0106.1030.150.00670000.015.9150.2040000000.015.9150.00
Bone Marrow (Not Infiltrated)\\n2.53.07.9580.2025.015.9150.100.0015000.01591.5490.102000000.015.9150.10
Skin (Dry)4.032.07.2340.001100.032.4810.200.0000.0159.1550.200.015.9150.20
Skin (Wet)4.039.07.9580.10280.079.5770.000.00030000.01.5920.1630000.01.5920.20
Tissue Type \\ ParameterNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " 1 2 3 ... 12 13 14\n", "0 ... \n", "Cerebro Spinal Fluid 4.0 65.0 7.958 ... 0.0 15.915 0.00\n", "Vitreous Humor 4.0 65.0 7.234 ... 0.0 15.915 0.00\n", "Gall Bladder Bile 4.0 66.0 7.579 ... 0.0 15.915 0.20\n", "Gall Bladder 4.0 55.0 7.579 ... 10000.0 15.915 0.00\n", "Blood 4.0 56.0 8.377 ... 0.0 15.915 0.00\n", "Stomach 4.0 60.0 7.958 ... 40000000.0 15.915 0.00\n", "Dura 4.0 40.0 7.958 ... 1000000.0 15.915 0.00\n", "Small Intestine 4.0 50.0 7.958 ... 40000000.0 15.915 0.00\n", "Thyroid 4.0 55.0 7.958 ... 40000000.0 15.915 0.00\n", "Eye Tissues (Sclera) 4.0 50.0 7.958 ... 5000000.0 15.915 0.00\n", "Testis 4.0 55.0 7.958 ... 40000000.0 15.915 0.00\n", "Cornea 4.0 48.0 7.958 ... 40000000.0 15.915 0.00\n", "Ovary 4.0 40.0 8.842 ... 40000000.0 15.915 0.00\n", "Lens Cortex 4.0 42.0 7.958 ... 40000000.0 15.915 0.00\n", "Cervix 4.0 45.0 7.958 ... 40000000.0 1.592 0.00\n", "Trachea 2.5 38.0 7.958 ... 1000000.0 15.915 0.00\n", "Tendon 4.0 42.0 12.243 ... 20000000.0 1.326 0.00\n", "Tongue 4.0 50.0 7.958 ... 40000000.0 15.915 0.00\n", "Aorta 4.0 40.0 8.842 ... 10000000.0 1.592 0.00\n", "Lung (Deflated) 4.0 45.0 7.958 ... 10000000.0 15.915 0.00\n", "Uterus 4.0 55.0 7.958 ... 35000000.0 1.061 0.00\n", "Bladder 2.5 16.0 8.842 ... 10000000.0 15.915 0.00\n", "Lens Nucleus 3.0 32.0 8.842 ... 5000.0 15.915 0.00\n", "Muscle 4.0 50.0 7.234 ... 25000000.0 2.274 0.00\n", "Cartilage 4.0 38.0 13.263 ... 40000000.0 15.915 0.00\n", "Bone Marrow (Infiltrated) 2.5 9.0 14.469 ... 2000000.0 15.915 0.10\n", "Bone (Cancellous) 2.5 18.0 13.263 ... 20000000.0 15.915 0.00\n", "Heart 4.0 50.0 7.958 ... 25000000.0 4.547 0.00\n", "Kidney 4.0 47.0 7.958 ... 30000000.0 4.547 0.00\n", "Cerebellum 4.0 40.0 7.958 ... 45000000.0 5.305 0.00\n", "Fat (Average Infiltrated) 2.5 9.0 7.958 ... 10000000.0 15.915 0.01\n", "Lung (Inflated) 2.5 18.0 7.958 ... 40000000.0 7.958 0.00\n", "Spleen 4.0 48.0 7.958 ... 50000000.0 6.366 0.00\n", "Liver 4.0 39.0 8.842 ... 30000000.0 15.915 0.05\n", "Brain (White Matter) 4.0 32.0 7.958 ... 35000000.0 7.958 0.02\n", "Brain (Grey Matter) 4.0 45.0 7.958 ... 45000000.0 5.305 0.00\n", "Bone (Cortical) 2.5 10.0 13.263 ... 100000.0 15.915 0.00\n", "Breast fat 2.5 3.0 17.680 ... 20000000.0 13.260 0.00\n", "Colon 4.0 50.0 7.958 ... 40000000.0 1.592 0.00\n", "Fat (Not Infiltrated) 2.5 3.0 7.958 ... 10000000.0 7.958 0.01\n", "Nerve 4.0 26.0 7.958 ... 40000000.0 15.915 0.00\n", "Bone Marrow (Not Infiltrated)\\n 2.5 3.0 7.958 ... 2000000.0 15.915 0.10\n", "Skin (Dry) 4.0 32.0 7.234 ... 0.0 15.915 0.20\n", "Skin (Wet) 4.0 39.0 7.958 ... 30000.0 1.592 0.20\n", "Tissue Type \\ Parameter NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", " NaN NaN NaN ... NaN NaN NaN\n", "\n", "[54 rows x 14 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 92 } ] }, { "cell_type": "markdown", "metadata": { "id": "VrxU_gM1V5m1", "colab_type": "text" }, "source": [ "Sedaj smo pripravljeni, da uporabimo podatke za izrise. Npr., tvorimo niz snovi, v katere vpišemo snov, katerega model želimo izpisati.\n", "\n", "Preden uporabimo podatke, jih moramo spremeniti iz tipa string v float. To se da narediti na več načinov, mi bomo uporabili najbolj enostavnega, z uporabo funkcije float.\n", "\n", "Izris bo narejen za snov kri, lahko pa enostavno zamenjamo snov, npr. za \"Aorta\" in na grafu bo potek permitivnosti in prevodnosti za aorto." ] }, { "cell_type": "code", "metadata": { "id": "SzKwSQ1LV5m2", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 324 }, "outputId": "117c37fc-12b7-4b16-fd10-3e9ed8a196f0" }, "source": [ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "\n", "snov=['Blood']\n", "v = df.loc[snov] # najde vrstico in shrani vrednosti v niz\n", "#print(v)\n", "\n", "ef=float(v[1])\n", "del1=float(v[2])\n", "tau1=float(v[3])*1e-12\n", "alf1=float(v[4])\n", "del2=float(v[5])\n", "tau2=float(v[6])*1e-9\n", "alf2=float(v[7])\n", "sig=float(v[8])\n", "del3=float(v[9])\n", "tau3=float(v[10])*1e-6\n", "alf3=float(v[11])\n", "del4=float(v[12])\n", "tau4=float(v[13])*1e-3\n", "alf4=float(v[14])\n", "print(ef,del1, tau1, alf1,del2,tau2,alf2,del3,tau3,alf3,del4,tau4,alf4)\n", "e0=8.854e-12\n", "\n", "eksponent=np.linspace(1,12,100) # izdelamo 10 točk od 0 do 2000, linearno\n", "freq=10**(eksponent)\n", "omega=2*np.pi*freq\n", "\n", "eps=ef+del1/(1+(1j*omega*tau1)**(1-alf1))+del2/(1+(1j*omega*tau2)**(1-alf2))\n", "eps=eps+del3/(1+(1j*omega*tau3)**(1-alf4))+del4/(1+(1j*omega*tau4)**(1-alf4))+sig/(1j*omega*e0)\n", "sigma=1j*eps*omega*e0\n", "\n", "\n", "plt.xlabel('Frekvenca (Hz)')\n", "plt.ylabel('Eps in Gama / ') \n", "plt.loglog(freq,(eps).real, color='b',linewidth=2,label='eps.real')\n", "plt.loglog(freq,sigma.real, color='r',linewidth=2,label='gama.real')\n", "plt.loglog(freq,(sigma).imag, color='r',linestyle=':',label='gama.imag')\n", "\n", "\n", "plt.grid(True,which=\"both\")\n", "plt.ylim(.1,1e4)\n", "plt.legend()" ], "execution_count": 93, "outputs": [ { "output_type": "stream", "text": [ "4.0 56.0 8.377000000000001e-12 0.1 5200.0 1.32629e-07 0.1 0.0 0.000159155 0.2 0.0 0.015915 0.0\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 93 }, { "output_type": "display_data", "data": { "image/png": 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uXQ6tW+fRunUurVrl06JFPo0aFQRlPu/aCumzU6XhqlW0mDuXFp9/Tr2DXmTJ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\nsgO3X33VlRxKTJoEKSml208/Hfg0GFM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"text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "d0MjW7tPV5m5", "colab_type": "text" }, "source": [ "## Izbira s spustnim seznamom - dropdown list\n", "\n", "Sedaj lahko dodamo še spustni seznam, v katerem bodo imena snovi. Ob izbiri pa izrišemo ustrezen graf.\n", "\n", "Najprej si oglejmo, kako se naredi spustni seznam z uporabo ukaza widgets iz modula ipywitgets." ] }, { "cell_type": "code", "metadata": { "id": "crnFMJ44V5m6", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 84, "referenced_widgets": [ "cdb8af3fa0f84d1ca867ccc8aa787c9f", "cff35e2864554869ba0891ad9f02c63f", "75fdb500c5504f8c9cd469a6820b66f2" ] }, "outputId": "08329614-df16-4b5f-dd9b-dc9b44370c84" }, "source": [ "import ipywidgets as widgets\n", "\n", "lista=df.index.tolist()[2:53] # iz indeksa naredim niz za izpustni seznam\n", "#print(lista)\n", "\n", "#Tvorim spustni seznam lista in izbranim default prikazom Aorta\n", "w = widgets.Dropdown( \n", " options=lista,\n", " value='Aorta',\n", " description='Task:',\n", ")\n", "\n", "# Ob spremembi izbire izpišem novo izbiro\n", "def on_change(change):\n", " if change['type'] == 'change' and change['name'] == 'value':\n", " print(\"changed to %s\" % change['new'])\n", " print(w.value)\n", "\n", "w.observe(on_change)\n", "\n", "display(w)" ], "execution_count": 94, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cdb8af3fa0f84d1ca867ccc8aa787c9f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Dropdown(description='Task:', options=('Aorta', 'Bladder', 'Blood', 'Bone (Cancellous) ', 'Bone (Cortical) ', …" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "changed to Colon\n", "Colon\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ifIKjZfqV5m-", "colab_type": "text" }, "source": [ "Sedaj, ko imamo izbirni seznam, ga le še povežemo z novim izrisom grafa za izbrano snov." ] }, { "cell_type": "code", "metadata": { "id": "vWQtSYBmV5m-", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 999, "referenced_widgets": [ "e80972215ca34d0d91932b07673a1022", "8a2a0d2c5fec42b4ae3228351b8f5dfb", "9c90b2c343bb46b1939406fed16bfe83" ] }, "outputId": "204f3266-b82d-44d3-e82d-a28dcd9f1f04" }, "source": [ "import ipywidgets as widgets\n", "\n", "lista=df.index.tolist()[2:53]\n", "#print(lista)\n", "\n", "w = widgets.Dropdown(\n", " options=lista,\n", " value='Aorta',\n", " description='Task:',\n", ")\n", "\n", "def on_change(change):\n", " if change['type'] == 'change' and change['name'] == 'value':\n", " print(\"changed to %s\" % change['new'])\n", " print(w.value)\n", " plot() # ob spremembi se izvrši funkcija plot(), ki izriše graf\n", "\n", "def plot():\n", " snov=w.value #dobim vrednost od izbire \n", " v = df.loc[snov] # najde vrstico in shrani vrednosti v niz\n", " #print(v)\n", "\n", " ef=float(v[1])\n", " del1=float(v[2])\n", " tau1=float(v[3])*1e-12\n", " alf1=float(v[4])\n", " del2=float(v[5])\n", " tau2=float(v[6])*1e-9\n", " alf2=float(v[7])\n", " sig=float(v[8])\n", " del3=float(v[9])\n", " tau3=float(v[10])*1e-6\n", " alf3=float(v[11])\n", " del4=float(v[12])\n", " tau4=float(v[13])*1e-3\n", " alf4=float(v[14])\n", " e0=8.854e-12\n", "\n", " eksponent=np.linspace(1,12,100) # izdelamo 10 točk od 0 do 2000, linearno\n", " freq=10**(eksponent)\n", " omega=2*np.pi*freq\n", "\n", " eps=ef+del1/(1+(1j*omega*tau1)**(1-alf1))+del2/(1+(1j*omega*tau2)**(1-alf2))\n", " eps=eps+del3/(1+(1j*omega*tau3)**(1-alf4))+del4/(1+(1j*omega*tau4)**(1-alf4))+sig/(1j*omega*e0)\n", " sigma=1j*eps*omega*e0\n", " \n", " plt.title(snov)\n", " plt.xlabel('Frekvenca (Hz)')\n", " plt.ylabel('Eps in Gama / ') \n", " plt.loglog(freq,(eps).real, color='b',linewidth=3,label='eps.real')\n", " plt.loglog(freq,sigma.real, color='r',linewidth=3,label='gama.real')\n", " plt.loglog(freq,(sigma).imag, color='r',linestyle=':',label='gama.imag')\n", "\n", " plt.grid(True,which=\"both\")\n", " plt.ylim(.1,1e4)\n", " plt.legend()\n", " \n", "w.observe(on_change)\n", "display(w)" ], "execution_count": 95, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e80972215ca34d0d91932b07673a1022", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Dropdown(description='Task:', options=('Aorta', 'Bladder', 'Blood', 'Bone (Cancellous) ', 'Bone (Cortical) ', …" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "changed to Colon\n", "Colon\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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klYisEZGHAFT1c1W9FbgNuDIc5TUmmtSuDd98A8OGebdt2wb9+7thtenp4Sub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\nqrpOVf8cjnIaYyKYKtx8M4w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"text/plain": [ "
" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "changed to Cerebellum\n", "Cerebellum\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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mT4eDB10FEcIO7IK8anqaAgwquEFEYoEXgYuA9sA1ItK+wC4P+743xpRDcjJ8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\nvdC3/gCAqj7hW3+7tKYnERk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/ZiiMiOSUU2DmTKhTx5X374f+/eHll72VyzDKI2YojIilWzf47jto2tSVs7Nh\n+HC4/XbIzPRUNMMoV5ihMCKaNm3gxx8h2c/F9vTTcNZZsHOnd3IZRnnCDIUR8fzlLzBrFgwcmFv3\n5ZfOeCxY4JlYhlFusOWxHmNL9QInOxsmT27KpElND9dVqJDNNdf8xsUXbzqcQS8cxPJzg9jWz3Qr\nGFseG8HYUr2S89FHqjVrap4ESH37qm7eHJLuCiSWn5tqbOtnuhUMpVkeKyKDROSoUpkmwwghAwbA\n/Pl5/RYzZ7rc3K+8Yrm4DSPYFDVYbwK8KyLfisgoEekhIhIuwQyjKI47Dn74Ae6+22XOA9i7F667\nDnr3huXLvZXPMGKJQg2Fqj6hqqcBZwOLgKHAzyLypohcKSJJ4RLSMAqiYkV49FH4+mto0SK3/rvv\n4Pjj4frrYft27+QzjFihWPefqqao6geqep2qdgYeAeoBk0MunWEEQO/esHgx3HMPVKjg6rKy4MUX\noXlzeOgh+PNPb2U0jGimxOtEVHW5qj6tqmeGQiDDKA1VqsDo0fDzzy4SbQ6pqfDgg9CkiZumshGG\nYZQc20dhxBQdOrg9Fp98Am3b5tanpMDjj0PjxvC3vzn/RgytDDfKOyEOVWCGwog5RODss2HRIpg8\n2e3uzuHQIXjjDejZ0/kxnnjCEiQZUU5mJvTqBU8+GbIuzFAYMUuFCjB4MCxdCtOmQffuec8vXQp3\n3QXHHAMnn+z+n61caSMNI8o4eNANn3OCooWAiDcUInKeiLwqIlNF5K9ey2NEH3FxcOGF8NNPbv/F\n1Vc7n4Y/338Pd97pRh8tWrhltlOnmk/DiAKqVYPx4+GSS0LWhSeGQkQmiMh2EVmar76fiKwSkbUi\ncheAqn6oqtcCw4FLvZDXiB26doXXXoOtW2HSJDjzTI4I/bFundu4d9llkJQELVvCkCGu7pdfICPD\nE9ENIy9ZWfCPf8Bvv4W8qwoh76FgJgJj8VtiKyLxwDjgDGAzME9EPlbVnK1T9/nOG0aZqVkTrrzS\nHTt2OOf39OkuYdL+/XmvXbPGHZMmuXJCAhx7bGdOPRW6dHHGp21bt6/DMMLG8uXw6qvQqRM0axbS\nroo1FCJyAvBvoA1QCYgH9qtqzdJ2qqqzRaRpvuruwFpVXefr921goIisAB4HPlPVn0vbp2EURr16\nbsQwZIib7v3pJxcS5KuvYO5c5wD3Jz0dli9PzLP7OyHB/X9NToYTT4STTnK+D4tlYISMDh3cL5j6\n9UPeVbHRY0VkPnAZ8C6QDFwJtFTVu8vUsTMUM1S1va98EdBPVa/xlQcDPYDVwFXAPGChqr5USHvD\ngGEASUlJXd9+++2yiBc2LDraXe0AACAASURBVJJlZHPoUByrV1dn6dJEVqyoyapVNdi2LSGge486\n6iCdOv1JcvJukpP3ULfuoeJvihBi4dkVRtTrlp1N4pIl7O3Y8YhTnkWPxRdREFjsV/dLcfcF0G5T\nYKlf+SLgNb/yYGBsadq26LGRQazqtm2b6uOPL9JHHlE9/3zVxo3zRrIt7EhOVn3qKdUNG7zWoHhi\n9dmpxoBukye7f1AF6BGq6LGBjChmA6cDrwFbgS3AEFU90pyVgAJGFCcCo9S341tE7vYZssdK0Kbl\no4ggypNue/ZUZPXqGqxYUZNly2qyYkVN9u8vfGa3c+c9XHzxZnr02BXWPBqBUp6eXbQhGRnUnzmT\nbWeeecTcppcjimOAKkBN4EHgGaBFcfcF0G5T8o4oKgDrgGY4X8gioF1p2rYRRWRQnnXLzFRdsED1\n8cddroyKFQseZbRsqfraa+76SKI8P7uIJStLNT29yEs8G1GEAhF5C+gD1AW2AQ+q6ngRORt4Ducw\nn6Cqo0vYro0oIgjTzf/6Cnz3XV2++qo+CxbUJjs77y/B5s1TufHGNXTqtDfYopYKe3aRR8MPPuDo\njz9m4bPPklGrVoHXeDmiOBf4BdgN7ANSgH3F3eflYSOKyMB0K5j161X/8Y8js/SB6iWXqO7cGTw5\nS4s9uwjk889Vhw5Vzc4u9BIvfRRrgQuAJVrcxR5jI4rIwnQrmrS0eN59txFvvdWEgwfjD9fXrXuQ\ne+9d7unowp5ddOLliOJrIK646yLpsBFFZGC6BcbGjaqXX553ZCGi+sADblraC+zZRRAvvqg6blyR\nI4kcwp4z249/Ap+KyN0iclvOUSqTZRjGETRu7CLafvwxHOXLUq/qEi5dfrnbBGiUU1RduIDPPvNU\njECmnv4HpAJLgMNp61X1X6EVreTY1FNkYbqVnB07KvHoo21YuLD24bqOHf/k4YeXUqNGaHMO+GPP\nLoJQJT49naz8kSwLwMupp6XFXRNph009RQamW+nIzFS98ca8U1Ht2qn+8UfIujwCe3YRwIwZqrt2\nlegWL6eePrXw3oYRPuLj4YUX8uahWbYM+va1sOflht274dJLXcKUCCAQQzEC+FxEDojIPhFJEZF9\noRbMMMozInDHHc53Ee9bELViBZx+Ouza5a1sRhioUwdmz3aJ4CMATzbchQrzUUQWpltw+Prrejzy\nSNvDm/SOOy6FZ55ZRPXqofNZ2LPzjko7d3Kobt1S3euZj8JnSGrjwoCfknMEcp9Xh/koIgPTLXhM\nmeKWzOb4LHr3Vj1wIHT92bPziDlzVCtVUv3gg1Ld7pmPQkSuAWYD/wX+5fs7qlQmyzCMUnHFFS4z\nXw7ffOOSLmVnF36PEYW0bg0jR8Jpp3ktSR4C8VHcDHQDNqjqqUBn4M+QSmUYxhEMHQpPPJFbfvdd\nuO02N8YwYgBVSEyEMWNcCsYIIpB9FPNUtZuILAR6qOpBEVmmqu3CI2LgmI8isjDdgo8qjB3bgvff\nb3S4bsSItVxyyeag9mPPLrwc9f33NH73XZaNGlVowL9A8HIfxQdALdx002zgI+DT4u7z8jAfRWRg\nuoWGzEzViy7SPOE+pk0Lbh/27MLMW2+pnnKK6sGDZWomVD6KYnNmq+r5vo+jRORrIBH4vFQmyzCM\nMhMfD//5D2zdCt9958zF3/4GRx/t8nUbUchll7l9ExGaZD3g3Foi0hD4DViIXygPwzDCT0ICfPgh\ntGzpyunpMGAArF3rrVxGCZkwwQX5gog1ElCEofAFAXzAr+pHYAbwP+COUAtmGEbRHHUUfPop5Cy5\n37kTzjwTtm3zVi4jQLKz3VK2V16J+BUJhTqzReRnoJeq7veVf1HVziISD3yjqieHUc6AMGd2ZGG6\nhYfly2ty660dOXTIbeE+7rgUnntuIVWrZpW6zUjSL9hEkm5y6BDxBw+SWaNGUNoLuzMb+DlfeYjf\n5wWF3RcJhzmzIwPTLXx89JFqXFyug/v008vmF400/YJJROj27rsh2THpxYa76iJS0c+gTAQQkcpA\nZC3yNYxyzoAB8PLLueUvv3S5LDIyvJPJKIQlS+Dii2HsWK8lCZiiDMU04GURqZpTISLVgJd85wzD\niCCuucYlO8rhvffcju7M8KWxMAKhQweYORNuuslrSQKmKENxP7Ad2CgiC0RkAbAe2OY7ZxhGhHHf\nfXDrrbnld991S2fNWEQAhw7BmjXu82mnQaVK3spTAgo1FKqapap3AY2BIb6jiarepar2z84wIhAR\nePrpvD9Wp06FSy6BAwe8k8vADfc6d4bff/dakhITyIa7A7g0qIZhRAEi8NxzbvVlzjT4Bx+4H7Ef\nfwz16nkrX7llxAi3K7JhQ68lKTEBb7gzDCN6EHFZ8m67Lbduzhy3c3vVKu/kKpfs2eMWozVsCNdf\n77U0pcISF3lMJK3pDjamW2Tw/vsNGTu2Bapu529CQhYjR67hrLO2FroZOJr0Kynh1C0uPZ2uI0aw\np0sX1o4cGfL+vE5c1BA4CUtcFHQiYk13iDDdIocPP1StUiV3nwWoXnyx6u7dBV8fbfqVhLDqlpWl\n+tBDql98EZbuvExc9ATwPXAfLnTHHcDtpTJZhmF4wsCB8MMPLi9ODu++62JFvfQSZJV+E7dRGIcO\nQVwc3H+/S3YexQTiozgPaKWqZ6tqf98xINSCGYYRXDp1gvnzYdiw3LqdO52PtXNnmDHDMuYFjc8/\nh7ZtYyZKYyCGYh1QsdirDMOIeKpVczu4P/wQjjkmt37JEujf373bXn4ZDhywdS5l4qijoFUraNDA\na0mCQrHLY4E0YKGIzAQO5lSqavRsKzQMIw8DB8Jf/wrPPAOPPQb797v6Vatg+HBISOjJeee5FAln\nnglVqngrb9SQleUShnTrBp984rU0QSOQnw0fAw8DPwAL/A7DMKKYKlXg3nth9Wq4/fa8aZrT0+N5\n+204/3yoXRv69oVHH4Vvv4XUVO9kjmgOHnTW94UXvOl/2TKavv46fPNN0JsOZMPdpKD3ahhGxHD0\n0fDUU/DAAzB+vJt6Wrky9/zBg/DVV+4At0ejdWvo2BHatHGfW7aEZs0gMdEbHSKC7GxnVevUCV+f\ny5fDO++4Y8UKmoJb1Na7d1C7KdRQiMg7qnqJiCwBjthsoarHB1USwzA8pUYNuOUWuPlmmDBhHuvW\ndeP99/MaDXDvoRUr3JGf2rWd76NRI3c0bOim6Y8+2v1t0MAlWoqPD49OYSMryw3R3n039Jnq1qxx\ncVmmToWlS488/9FHLuVhQkLQuixqRHGz7++5QevNMIyIRwSaN9/P1VfD6NEuNNFXX8HXX7tVU8uW\nFb46as8edyxcWHj7cXGQlJRrPBo2dEalcWNo0gSOPdaVo8aYvPIKvPGGi48SqiHV77/D22/DW2/B\ngkJm/qtWZXv37tS/8Ub3JQeRQg2Fqm7x/d0Q1B5LiIgcC9wLJKrqRV7KYhjlkYYNYfBgdwCkpcGi\nRW7WI2dksW4drF/vfsgWR3Y2bNnijsKoWNGNTNq0cSux2rZ1S3jbtIEKgSzBCSeJiW66qWrV4q8t\nCXv3uljxU6bArFkFp0utUgXOOcetOjj7bJbPnUv9Pn2CKweBrXoKOiIyATdS2a6q7f3q+wHPA/HA\na6r6uKquA64WEcuBYRgRQNWqLmbUiSfmrVeFrVth0ybYvNn93bIF/vgj1zBs2QK7dxffR0aG24Kw\ndi1Mn563786doVcvF+SwZ8/gv58DJi3NdX7ppS48bzCmnDIz4X//g0mT3BTSwYNHXlOpEpx1luu3\nf38IQzgSr2zzRGAsMDmnwpeLexxwBrAZmCciH6vqck8kNAyjRIjk+iG6dy/8uoMHnUHJMSKbN+ca\nlg0b3Ohk27aC701Lg++/d8fjj7t3Zp8+cOGFbslvUlJIVDuSH3+E885zYXlPOqnsRmLVKpgwASZP\ndl9OfkScZbz8crjgAqhVq2z9lZASBQUUkdpAY1VdXOaORZoCM3JGFCJyIjBKVc/0le8GUNXHfOVp\nRU09icgwYBhAUlJS17fffrusIoYFC74WncSybuC9fgcOxPH771XYuLEa69dX5ddfq7N6dQ127qxc\n6D1xcUrnzns499wt9Oy5k4oVC363BUO3Srt3c9yzz7L6ttvIqF27VG3EHTxIvW++4ejp00ksyCkN\npLRowbYzzmD7aadxqG7dYtv0LCggMAuXI7sO8BvwE/BMcfcF0G5TYKlf+SLcdFNOeTBu1HEULv3q\nr8DdgbRtQQEjA9MteolU/f74Q/X991VHjlRt1y5vkEP/o1491XvuUd227cg2yqTb6tWq2dmlv19V\ndc0a1dtuU61Tp2Dh//IX1dtvV128uMRNhyooYLEjChH5RVU7i8g1uNHEgyKyWMu4PLaAEcVFQD9V\nvcZXHgz0UNUbS9CmhRmPIEy36CVa9Nu+vTLffluX2bPrsWRJ4uFQ6jlUrpzFOeds4bLLNlGvnpvv\nL61uCX/8Qberr2bD4MFsvPzykt2sSu0FC2j03nvU+eknJN97Nzs+nl0nncSWs85iT/fuaCmXfHk5\nolgCNAD+B3Tz1S0u7r4A2m1K3hHFicB//cp3E+AIIv9hI4rIwHSLXqJRvw0bVB98ULVhwyN/pFeu\nrHrnnap//lkG3bKzVR9/XHXLlsDvOXRI9T//Ue3QoeDRQ7Nmqo89prp1a+lkyoeXI4qLgfuB71V1\nhG+56lOqemGpzFZuu03JO6KoAKwG+gK/A/OAy1V1WQnatBFFBGG6RS/RrF9WlvDdd3V5440mrFlT\nI8+5WrUOMWjQKi68cFfA+zTqzp7NvjZtOFSCHLKSkUGDzz6jyZtvklCAZ37XCSfw+/nnszs5Oah7\nHjxNXBTsA3gL2AJk4FY4Xe2rPxtnLH4F7i1t+zaiiAxMt+glFvTLzlb99FPVbt2O/CHfvbvqL78E\n0MiuXaqJiarXXBNYp4cOqb7yimqTJkd2Wq2ac66sXl0mvYrCy8RFx4rIdBHZISLbReQj36ii1Kjq\nIFVtoKoVVbWRqo731X+qqi1Vtbmqji5LH4ZhlG9E3HaDOXPcxukmTXLPzZ0Lyclwxx1uyW2h1Knj\nNrsVF+hP1e3MbtfOJfzYuDH3XN268NBDru6FF+C448qilicEMvU0B7e/4S1f1WXASFXtEWLZSoxN\nPUUWplv0Eov6HTwYx9tvN2bKlCZkZubOOzVunMadd66kXbt9AEhmJi2feYY9XbqwPYDMdNV+/ZUW\n48ZR+5df8tQfqlWLjYMG8ceAAWQHMe5SUXjpzD7CcQ0sKu4+Lw+beooMTLfoJZb1mzRpjvbpk3dW\nKC5O9Y47VA8cUNX0dNU+fVQffrjohvbvdx7y+Pi8jSUmOqd3ampY9PHHs6kn4DMRuUtEmorIMSLy\nT+BTEakjImGMp2sYhlF2mjQ5wFdfwauvuoi54OJPTXpqG726prFwRWUXRuO++wpvZOZM6NABnngi\nN+F4fDzceKOLO3LnnS6dYIwQyNTTb0WcVlUtk78imNjUU2RhukUvsayfv27btlXmqadasWxBFZbS\nnh85kb9XmMxVV61n0KBNxMfnfT9KRgbNxo+nydSpeer/7NiR1bfeSpp/flkPiKlVT6E+bOopMjDd\nopdY1i+/btnZquPGqY6o+Kp2Zd7hGaQePVRXrvS7cPVq1a5d804z1a6tOn582XdrB4mwTz35pphy\nPl+c79yjpTJZhmEYkcT06cjPC7j+erhl6TVU6JH7g/qnn6BTJ3juOcie/gl07Zo3F0S/fi7W+tCh\noU9W5DGFTj2JyM+q2iX/54LKkYJNPUUWplv0Esv65egmGRl0HzKEtCZNWPLYY4DbrPfmm02YPPkY\nMjPjAOU2nuEp7iDOl+gzu2JF1g0bxuYLLgh6gqCyEvapJ+CXgj4XVI60w6aeIgPTLXqJZf3y6Pbr\nr6opKUdcs3ChaucOGfoqV+eZatqT2ETTf1gQPmFLiBernrSQzwWVDcMwIh9Vjpk0CUaNcuVjjy0w\n8U/HNoeY3/xSrmH84brv6EnLvfNofXkXpk0rOOFcrFKUoegoIvtEJAU43vc5p9whTPIZhmEElYSt\nW90u6cLe9AcOwHnnEffh+4erPq13FX2ZyQ7qs349XHwx9O7t/BjlgRIlLop0zEcRWZhu0UvM6adK\nXHo62VWqkLp3L9Vr1CjQvxCXnk6He+7Js8t60yWXsGbYcGZ80pDx45uxb1/FPPd0776Lq67aQNu2\n+0KuRnHY8ljzUUQdplv0EnP63XuvaufOqvv2Fa5bRoZq//55fBL6wAN5lr7u3q16yy2qFSrkvQxU\nTzlF9d13XTNe4eXObMMwjOjm5JPdXFFhv7ZV4brrYPr03LpHH4V//SvP0tfateHZZ2HpUpe+2n9V\n7OzZbkqqWTO4/363cjZWMENhGEbs8vvv7m+/fu4NX9h+h/vugwkTcst33gl3311os61auYi0y5fD\nFVeQJ7fF5s3wyCMukGzHjs7WzJvnwoREK2YoDMOITd57D1q0gB9/LPq61193o4ccrroKfPsqiqN1\na5gyBdavd6OI+vXznl+82C2w6t4d/vIXuPRSeP55mD8fMjJKpI2nVPBaAMMwjJBw6qkuSF+XIvYG\nz58PI0bkls85x0ULLOFO60aNXMqJe++FTz+Ft95ys1jp6bnX7NgB77zjDoCKFaFtWzfqaNcOWrZ0\nqSqOPRaqVClR9yHHVj15TMytLvHDdIteolm/ylu3crB+/UJ3TefoVnHPHroOH07C9u2uvlkzfhk3\njqwgvaX374/np5+O4qef6jBvXh327KkU8L21ah2ifv2D1K17kDp1DlG79iFq186gZk131KiRSbVq\n7qhePYuKFbMRsVVPtuopCjHdopeo1W/nTtWkJNWbby70kq+//totTTr11NwlS7Vqqa5ZEzKxsrJc\n6tWxY1UHDVJt2vTIVVNlOeLjXRqMunXT9dxzSycjRax6sqknwzBihzp14J57oLjMdA89BF9/7T6L\nOM90ixYhEysuzgUY7NQJbrjB1f35p/NhLF4Mq1bB6tWwZg1s2gSZmSVrPysL9u4FqMyOHcGW3nwU\nhmHEApmZsGsXJCXBTTcVeWnNJUtg9OjcilGj4OyzQytfAdSqBaec4g5/srJg61bYsAG2bIFt21x5\nxw6n4q5dsGePMwx798K+fXkd46GYMTRDYRhG9PPgg25566JFRy498mffPto89ljuWtU+fZwHOoKI\nj4eGDd0RKIcOwf798MUXP3LSSScGXSYzFIZhRD+DBrmlQkUZCYCbbqLKli3uc2IiTJ6cdxNElFKp\nkjvq1z9Io0bBb98MhWEY0Utqqptrad/eHUXx3nswaVJu+aWXoHHj0MoXI9jyWI+J5mWIxWG6RS/R\noF+FffvoOmIEv593HpsvvrjYa7sPGUKlPXsA2HrGGay8555wiBlWbHmsLY+NOky36CUq9EtLU73u\nOtU5c4q/dujQw2tJ0+vWVd2zJ/TyeUCoggLa1JNhGNGHqvNJvPRS8dfOnJknjtPqm2+mQ61aIRQu\n9rBYT4ZhRBezZ7tosH/8Ufy1aWkwbFhu+aKL2HXyyaGTLUYxQ2EYRnSRs3GgRo3ir33oIVi3zn2u\nVQv+/e/QyhajmKEwDCO6OPdcl4O0OEOxciU8/XRuecwYF8LVKDFmKAzDiA7GjYOpU93n4qK7qsLI\nkbmxME4+GYYODa18MYwZCsMwIp/s7NwY3YEs6Z82Db780n2Oj3dGpoShw41cbNWTYRiRT1yce/Gn\npxf/wk9NhVtvzS3fcAMcf3xo5YtxbERhGEZkM3GiC2RUsWJgDuzRo3NToCYluVykRpmIeEMhItVE\nZJKIvCoiV3gtj2EYYWTxYudbePXVwK5fuxaeeSa3/OSTbrWTUSY8MRQiMkFEtovI0nz1/URklYis\nFZG7fNUXANNU9VpgQNiFNQzDO44/3uW8vvHGwK6/7TYXShXgxBNh8ODQyVaO8GpEMRHo518hIvHA\nOOAsoC0wSETaAo2ATb7LssIoo2EYXpGV5TL5APToARUCcKf+978uUTU4P8YLL5gDO0h4YihUdTaw\nO191d2Ctqq5T1UPA28BAYDPOWEAUTJUZhhEEnn/ejSZWrQrs+oyMvA7sv/8dkguOb2eUHM+ix4pI\nU2CGqrb3lS8C+qnqNb7yYKAHcCcwFkgHvlPVNwppbxgwDCApKanr22+/HWoVgkI0ROksLaZb9OK1\nfhX37CHpiy9cVNgARgWNpk2jxbhxAGRWrcrc//yHQ3XqFHit17qFkpiLHgs0BZb6lS8CXvMrDwbG\nlqZtix4bGZhu0Ytn+mVkqGZnl+ye7dtVExMPR4fVp54q8vJYfnblIXrs74B/FpFGvrqA8ctHwaxZ\ns4IoWuhITU2NGllLiukWvXilX/Nx40jYupXlo0ahAWaeazlmDEfv3QtAWqNGzOvYES1C9lh+diHT\nrTALEuqDI0cUFYB1QDOgErAIaFeatm1EERmYbtGLZ/o984zqbbcFfv2CBaoiuaOJGTOKvSWWn12o\nRhSe+ChE5C2gD1AX2AY8qKrjReRs4DkgHpigqqNL2K5luIsgTLfoJSr0U6XzTTeRuNStst/VowdL\nHn+82NuiQrdSEnM+ilAeNqKIDEy36CXs+t1zj+qsWSW75803c0cSFSqorlwZ0G2x/OxiakQRKmxE\nEVmYbtFLOPWLT02l6/DhbD/tNNYHGOE1Pi2N7lddReWdOwHYdMkl/DpiRED3xvKzsxGFjSiiDtMt\negm7fqmpqgcPBn79P/6RO5pISlL988+Ab43lZxeqEYVtYDMMwxtUXTjwzEyoVg0qVQrsviVL4Lnn\ncstPPw2JiaGR0QA83HAXCmzqKbIw3aKXcOiXuHAhnW+9lZX//CdbzzorsJtU6XTzzdRasgSAPZ06\nseiZZ0oUqiOWn51NPdnUU9RhukUvYdEvO1v1009VMzMDv2fixLwO7GXLStxtLD87m3oyDCM2UIUd\nO9wo4KyzXAa6QNi+HW6/Pbd8223Qtm1oZDTyYIbCMIzw8tpr0KpVbnTYQLnhBvCtcqJxY7j//uDL\nZhSI+Sg8xuZLo5NY1g1Cq1+VjRs5evp0t5w1LrDfqvVmzaKdX6a6RU8+yZ5u3UrVfyw/O/NRmI8i\n6jDdopeI0m/7dtW6dXN9E9dcU6bmIkq3IGM+CsMwopt//9tNH+VkoAsEVbj++twpp0aNYMyY0Mhn\nFIoZCsMwwsOWLbBxI1SsGPg9L77o9lrk8OqrtmfCA8xH4TE2XxqdxLJuEEL9srICXuVUY9UqOo8c\nSVxGBgB/nHsuq//xjzKLEMvPznwU5qOIOky36CWo+v3nP6pLl5bsnt27VZs2zfVLdO6seuBAUMSJ\n5WdnPgrDMKKPAwfgrrvgkUcCvyczEwYPhvXrXTkxEd59FxISQiKiUTyRlOHOMIxYo0oVWLAg8E11\nqjB8OHzySW7d669D8+ahkc8ICBtRGIYRGhYvdi/+pCSoWzewe+6+G8aPzy3feSecf35o5DMCxpzZ\nHmOOtegklnWDsutX7bffSL7mGtZefz2/X3hhQPc0fvttmr/88uHy1jPPZOWdd5Yo4F8gxPKzM2e2\nObOjDtMteimzfpmZqmPHqu7ZU/y1WVmqd9yR67gG1f79VTMyyiZDIcTyswuVM9t8FIZhBJeMDLdX\n4oYbir/2wAG48sq8eyV69YKpU6GCvZ4iBfNRGIYRPL75Blq3hhUrir925Uro3TuvkejfHz77zDnB\njYjBDIVhGMEjIQFatoQmTQq/JjMTnngCOnWCefNy62+6CT74wGW7MyIKG9sZhhE8evRwI4KCUIXp\n0+HBB2Hhwtz6ChXgmWdg5MjwyGiUGBtRGIZRdj7+GB591IXoyE96uvM5dOkCAwfmNRJdu7p9FmYk\nIhozFIZhlJ3//tf5GnIMxcGD8PXXMGwYNGgAl12W10AkJDjDMmcOHH+8NzIbAWP7KDzG1nRHJ7Gs\nG5Rcv/jUVGouWUL1jRup9csv1Fq0iPj09COuy0pI4I8BA9h06aUcqlMnmCIHTCw/u1Dto4gpQ5FD\ncnKyzp8/32sxAmLWrFn06dPHazFCgukWvRSoX3Y2bN0K69bBr7+6VUuzZrlc1r/95nwQhdGsmVsG\ne8MNUK9eKEUvllh+dmXRTUQKNRTmzDYMw73kd++G33+HzZtpMHMmfPUVbNrkjo0b3d8CRgmF0rw5\n9OsHl18OJ54Y9B3WRvgwQ2EY5YHsbPjjDzcS+O03F5l1w4ZcA7B5s9v85qNVSduPj4d27SA5GU44\nAU4/3Y0ijJjADIVhxBJZWbB6tQvIt3w5LFsGq1Y5A+FnCEpNnTpw7LFQvz507gxt2rijdWuoWrXs\n7RsRiRkKw4hWVJ2/YM4cd8yf7wxEWlrp2qteHRo3hoYN2VKhAg26dYOGDd3muSZN3LmaNeH77+GU\nU5zP4dJLg6uTEZGYoTCMaCE7G5Ysgdmzc4/t2wO//6ijoEULNyXUtKk7cgxA48Z5clGvmjWLBoU5\nRbt2hVGj4Nxzy6CMEU2YoTCMSCUzExYtcgbhm2/c3z17ir+vQQMXHqN9e+c3aNMGjjsOatcumzyq\nLuBfQgLcf3/Z2jKiCjMUhhEp7NgBc+fCTz+544cfIDW16Htq1XJhM044Abp3d7/2k5JCI9/zz8OU\nKfDFF2U3OkZUYYbCMMLJwYNuCWrOXoQ1a9x00pIlsGVL8fcnJbmIq717u3Dc7dpBXJgCLBx7LHTo\n4IyTUa6IeEMhIscC9wKJqnqR1/IYHpE3rY2br89fV9C5/NfllLOzj/zsf2Rl5f7NynLTQDl/MzKo\nvWABpKTAoUPu5X/ggNtjkJbmRgEpKe7YvRt27XLHli3ub0lo1MgZhFNOgT59oFUr7/YjDBjgDqPc\nEVJDISITgHOB7ara3q++H/A8EA+8pqqPF9aGqq4DrhaRaYVdExTefBO+/bbgl0ZhLxP/c4G+qPId\nnf78060kyX/OKX/kS3rYbAAAB7hJREFU54KuKe58Wa4J5L5Cjp4ZGW59fYDXF3pEIB1D0WhCAnTs\n6KaSevRwm9SaNvV2o1p2ttswN2CA+2uUS0I9opgIjAUm51SISDwwDjgD2AzME5GPcUbjsXz3D1XV\nEizrKAOzZ4Nfvt5wEcuD+IpeCxCJxMfDX/7iDECLFm73cps2bkqnRQt3PpJISXEb9QJxohsxS8hj\nPYlIU2BGzohCRE4ERqnqmb7y3QCqmt9I5G9nWlFTTyIyDBjmK7YCVpVZ+PBQF9jptRAhwnSLXmJZ\nP9OtYI5R1QIDcXnho2gIbPIrbwZ6FHaxiBwFjAY6i8jdhRkUVX0FeCWYgoYDEZlfWCCuaMd0i15i\nWT/TreREvDNbVXcBw72WwzAMo7ziReKi34HGfuVGvjrDMAwjAvHCUMwDjhORZiJSCbgM+NgDOSKF\nqJsuKwGmW/QSy/qZbiUkpM5sEXkL6INzsGwDHlTV8SJyNvAcbqXTBFUdHTIhDMMwjDIRkxnuDMMw\njODhxdSTYRiGEUWYoTAMwzCKxAxFBCEi54nIqyIyVUT+6rU8wUZEqonIfBGJuUQGIhInIqNF5N8i\ncpXX8gQTEWkiIh+KyAQRuctreYKBiBwrIuP9QwP5/n1O8v0fvMJL+cpKIfqV+v1ihiLE+P5zbReR\npfnq+4nIKhFZm/OfT1U/VNVrcftGIj51WEl083En8E54pSw9JdRvIG6pdwZuE2lEU0LdOgDTVHUo\n0DnswgZICf+vrVPVq/M1cQFOz2uBiIt+WFb9yvJ+MUMReiYC/fwr/OJdnQW0BQaJSFu/S+7znY90\nJhKgbiJyBrAcCE/sruAwkcCfXSvgB1W9DRgRZjlLw0QC120OLjDnV8DnYZazJEyk5P/X/GlEbtSI\nrBDJWBYmUjb9cijx+8UMRYhR1dnA7nzV3YG1Pqt/CHgbGCiOJ4DPVPXncMtaUkqiG26Z9AnA5cC1\nIhLx//ZKqN9mICdyXiS+ZPJQQt3+jlvafhpwTnglDZwS6lQQm3HGAiLw3VhW/cryfom4L6OcUFC8\nq4bASOB04CIRidawJQXqpqr3quotwJvAq6qa7Yl0ZaewZ/c+cKaI/BuY7YVgQaAw3T4HbhKRl4D1\nHshVFgrUSUSO8unTOScwKe4ZXigiLwLTwyxnaSmJfqV+v0R8rKfyhKq+ALzgtRyhRFUnei1DKFDV\nNCD/nHdMoKpLgZhKGlZQDDlV3Y8bPUU9hehX6veLjSi8IZbjXcWybhDb+sWibrGokz9h0c8MhTfE\ncryrWNYNYlu/WNQtFnXyJyz6maEIMb54Vz8CrURks4hcraqZwI3Af4EVwDuqusxLOUtDLOsGsa1f\nLOoWizr546V+FuvJMAzDKBIbURiGYRhFYobCMAzDKBIzFIZhGEaRmKEwDMMwisQMhWEYhlEkZigM\nwzCMIjFDYRiGYRSJGQojZhGRLBFZ6Hc0LcG9TfPH/Y8kRKSBiMzwfe6T89nv/EQRKTQ+k4iMEZHT\nQi2nERtYUEAjljmgqp0KOykiFXw7W6OR24BXy3D/v333fxUccYxYxkYURrlCRIaIyMe+JDwzfXV3\niMg8EVksIv8q4J5jReQXEekmInNEpJ3fuVkikiwujeYEEZnru3agX3/vi8jnIrJGRJ70u7efiPws\nIotEJEeW7iLyo6+NH0SkVSGqXEgASYR8suWMqJaIiAKo6gbgKBH5S+DfnlFesRGFEctUEZGFvs+/\nqer5vs9dgONVdbe43MHH4RLACPCxiJwCbATwvajfBoao6iIRmQpcAjwoIg2ABqo6X0QeBb5S1aEi\nUguYKyJf+vrrhEshehBY5ctZkY77RX+Kqv4mInV8164EeqlqpoicDjyKMwqHEZFmwB5VPehX3ctP\nV4AmwAxVne/rHxF5irzG5WegJ/BewN+oUS4xQ2HEMoVNPX2hqjmZwv7qO37xlavjDMdGoB7wEXCB\nqi73nX8H+B/wIM5gTPNrZ4CI3O4rJ+Be1gAzVXUvgIgsB44BagOzVfU3AD95EoFJInIcoEDFAuRv\nAOzIV/etqp6bUxCRif4nReRSnIH8q1/1duDoAto3jDyYoTDKI/v9Pgv8f3t3rBJXEIVx/P9BhBQG\nC8EmTYolVRDUwkZ8gpCw2KUQSRHSWKSxiiBYCIE8gPgAYghJnTRJp0UguGKhkNJKLCyCIMixmBEH\n0XFht3B3v1+3e++cO7dYDufMwmEtItbLG/LB9ykpYcyQ5n0TEUeSTiSNkwbUvy/izEXEwY0406RK\n4soF9d/dKvArIpp5D79vueeMlIjaIukFsEKqXso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"text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "3gJNoCjkV5nB", "colab_type": "text" }, "source": [ "## Zdaj pa ti\n", "\n", "Graf je preveč natlačen, če bi želeli pogledati dielektrične lastnosti več snovi hkrati. Lahko pa si zamislimo drugače oblikovan graf, na katerem bi recimo prikazovali le permitivnost (realni del kompleksne dielektričnosti) ali pa le prevodnost. Poskusi izdelati kodo, kjer boš lahko izbral nekaj snovi in za te snovi izrisal omenjene grafe. (Ena od možnih rešitev je na koncu strani)" ] }, { "cell_type": "code", "metadata": { "id": "90fjGn2hV5nC", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bJSSr_d4V5nG", "colab_type": "text" }, "source": [ "## Zaključek\n", "\n", "Ta zvezek se nanaša na poglavje o impedančni spektroskopiji, ki ga dopolnjuje z načinom prikaza dielektričnih lastnosti bioloških tkiv na bolj \"eleganten\" način. In sicer tako, da uporabi modula BeautifulSoup in Pandas za obdelavo spletne strani, iz katere izvlečemo tabelo s parametri in jo pretvorimo v Dataframe tablo in nato iz nje izvlečemo parametre za izbrano snov. Nato uporabimo še interaktivni gradnik v obliki izvlečnega seznama, s pomočjo katerega izberemo želen izris grafa.\n", "\n", "V zvezku ne obravnavamo celovito modula BeautifulSoup in še manj Pandas, ki omogočata še marsikaj drugega. Na primer, tabela je bila v našem primeru v originalni html datoteki zapisana s html sintakso, lahko pa bi bila tudi v obliki json ali xml in podobno. Za vsak konkreten primer je potrebno pogledati html obliko dokumenta (običajno z desnim klikom miške in izbiro Page source) in v zmedi html zapisa poiskati, kako so zapisani originalni podatki.Ko pač nekaj konkretnega potrebujete, je potrebno malo pobrskati po spletu in najti usttrezno rešitev.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZkvqcaU9V5nI", "colab_type": "text" }, "source": [ "## Rešitve" ] }, { "cell_type": "code", "metadata": { "code_folding": [], "id": "LT7oapgiV5nJ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "22905fdfe2894673b41d269f746a555f", "e96f87ea32a14933b96e679891582b37", "714c82382a0d40928761897e86c5bea7", "d5e53a73c5054403bd502d4ae717629d" ] }, "outputId": "7e020a6d-f983-4a7c-a562-b28bb1af4f60" }, "source": [ "import ipywidgets as widgets\n", "from IPython.display import clear_output\n", "\n", "lista=df.index.tolist()[2:53]\n", "#print(lista)\n", "\n", "selector = widgets.SelectMultiple(\n", "options=lista,\n", "value=[lista[1]],\n", "rows=10,\n", "description='Variables',\n", "disabled=False)\n", "\n", "output = widgets.Output()\n", "\n", "display(selector)\n", "display(output)\n", "\n", "def plot(snovi):\n", " i=-1\n", " lstyle=['-','-.','--',':']\n", " for snov in snovi:\n", " i=i+1\n", " v = df.loc[snov] # najde vrstico in shrani vrednosti v niz\n", " ef=float(v[1])\n", " del1=float(v[2])\n", " tau1=float(v[3])*1e-12\n", " alf1=float(v[4])\n", " del2=float(v[5])\n", " tau2=float(v[6])*1e-9\n", " alf2=float(v[7])\n", " sig=float(v[8])\n", " del3=float(v[9])\n", " tau3=float(v[10])*1e-6\n", " alf3=float(v[11])\n", " del4=float(v[12])\n", " tau4=float(v[13])*1e-3\n", " alf4=float(v[14])\n", " #print(ef,del1, tau1, alf1,del2,tau2,alf2,del3,tau3,alf3,del4,tau4,alf4)\n", " e0=8.854e-12\n", "\n", " eksponent=np.linspace(1,12,100) # izdelamo 10 točk od 0 do 2000, linearno\n", " freq=10**(eksponent)\n", " omega=2*np.pi*freq\n", "\n", " eps=ef+del1/(1+(1j*omega*tau1)**(1-alf1))+del2/(1+(1j*omega*tau2)**(1-alf2))\n", " eps=eps+del3/(1+(1j*omega*tau3)**(1-alf4))+del4/(1+(1j*omega*tau4)**(1-alf4))+sig/(1j*omega*e0)\n", " sigma=1j*eps*omega*e0\n", "\n", " plt.loglog(freq,(eps).real, color='b',linestyle=lstyle[i],linewidth=2,label='eps '+snov)\n", " plt.loglog(freq,sigma.real, color='r',linestyle=lstyle[i],linewidth=2,label='gama' + snov)\n", "\n", " plt.xlabel('Frekvenca (Hz)')\n", " plt.ylabel('Eps in Gama / ') \n", " plt.grid(True,which=\"both\")\n", " plt.ylim(.1,1e4)\n", " plt.legend()\n", " plt.title(snovi)\n", " \n", "def multiplot(widg):\n", " choices = widg['new']\n", " output.clear_output(wait=False)\n", " \n", " if len(choices)>4: \n", " print('Ne izberi več kot štiri snovi')\n", " else:\n", " plot(choices)\n", "\n", "\n", "selector.observe(multiplot, names='value')\n" ], "execution_count": 96, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "22905fdfe2894673b41d269f746a555f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "SelectMultiple(description='Variables', index=(1,), options=('Aorta', 'Bladder', 'Blood', 'Bone (Cancellous) '…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d5e53a73c5054403bd502d4ae717629d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Output()" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": 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AFaGc2+Z6KrnPPnPr1NSurbprV+nO5bfcwimWc1ON7fwst+AoZK4n3496UtVU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\nlpNDtZ9/dpXHMKsQwjErgQR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nn77mnJ9//pkRI0YAcN9999nqz2//5s2bGT58uG2/ofSpXFkbisxMnQfjo49K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\nXHvtl9xxx07S0qKbfXkintXMBj6uYd+GE73Oy62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2ezdt2sSwYcNo2rQpZrOZkJAQFi1aREhICMuXL2fHjh28/PLLFZYxLCyM+Pji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