{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AST415 Astronomide Sayısal Çözümleme - I #\n",
"## Ders - 06 Grafik Çizimi ##"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Doç. Dr. Özgür Baştürk \n",
"Ankara Üniversitesi, Astronomi ve Uzay Bilimleri Bölümü \n",
"obasturk at ankara.edu.tr \n",
"http://ozgur.astrotux.org"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bu derste neler öğreneceksiniz?#\n",
"## Grafik Çizimi ##\n",
"\n",
"* [Matplotlib Kütüphanesi](#Matplotlib-Kütüphanesi)\n",
" * [PyPlot Modülü](#PyPlot-Modülü)\n",
" * [Birkaç Grafiği Aynı Anda Çizdirmek](#Birkaç-Grafiği-Aynı-Anda-Çizdirmek)\n",
" * [PyPlot ve Metin Yönetimi](#Pyplot-ve-Metin-Yönetimi)\n",
"* [Eğri Uyumlama](#Eğri-Uyumlama)\n",
" * [Basit Bir Doğru Uyumlama Örneği](#Basit-Bir-Doğru-Uyumlama-Örneği)\n",
" * [Yüksek Dereceden Polinom Uyumlama](#Yüksek-Dereceden-Polinom-Uyumlama)\n",
"* [PyPlot Dekorasyon Parametreleri](#PyPlot-Dekorasyon-Parametreleri)\n",
"* [Alıştırma Soruları](#Alıştırmalar)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Matplotlib Kütüphanesi #\n",
"## PyPlot Modülü ##\n",
"\n",
"Python’da grafik çizdirmek için $matplotlib$ kütüphanesinde yer alan ve $matplotlib$ ’in $MATLAB$ stilinde grafik üretmesini sağlayan komutların bir koleksiyonu olarak tanımlanabilecek $matplolib.pyplot$ modülü sıklıkla kullanılmaktadır. Matplotlib kütüphanesinin bazı çok önemli avantajlarının bulunması sebebiyle kullanımı oldukça yaygındır. Bu önemli avantajlar:\n",
"\n",
"* Kolay kullanımı,\n",
"* $\\LaTeX$ sembollerinin kullanılabilmesi,\n",
"* Programlanabilir olması nedeniyle grafikler ve stilleri üzerinde geniş kontrol şansı vermesi, \n",
"* Pe çok türde (PNG, PDF, SVG, EPS, ...), yüksek yayın kalitesinde çıktı üretebilmesi, \n",
"* Grafikleri detaylı inceleyebilmek için basit bir arayüz (GUI) sağlaması\n",
"\n",
"olarak sıralanbilir. Matplotlib oldukça hacimli bir modül olup, hakkında geniş bilgiye http://matplotlib.org/ adresinden ulaşılabilir.\n",
"\n",
"Aşağıdaki kod parçasında basit bir örnek görülmektedir."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"# matplotlib grafiklerinin juypyter'da goruntulenebilmesi icin\n",
"# %matplotlib inline sihirli kelimesi (ing. magic keyword) verilmelidir\n",
"%matplotlib inline\n",
"plt.plot([1,2,3,4])\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gördüğünüz grafik yukarıdaki Python kodunun çıktısıdır. İstediğiniz formatta kaydedip saklayabilir, ya da üzerinde istediğiniz bölümüne yaklaştırma (ing.zoom-in) ya da uzaklaştırma (ing. zoom-out) yapabilirsiniz. $PyPlot$'un $plot$, $xlabel$ ve $ylabel$ fonksiyonları sırasıyla bir grafik (tek bir dizi (liste ya da demet) verildiğinde, $y$-ekseni değeri olarak alınır, $x$-ekseninin varsayılan listesi 0'dan başlar ve $y$-eksenini oluşturan dizi kadar eleman içerir), $x$ ve $y$ eksenleri için birer başlık nesnesini varsayılan parametrelerle oluşturur. $show()$ metodu ise onu ekrana getirir!\n",
"\n",
"Çizdirilmek istenen grafik iki eksen (x ve y eksenleri) için ayrı ayrı tanımlanan iki veri grubu üzerinden çizilecekse her iki eksene ilişkin veri sağlanmalıdır."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# sembol turunu mavi bir cizgi yerine \n",
"# kirmizi (r) bir ici dolu daire (o) olarak tanimlayalim\n",
"plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
"# x ve y eksenlerini daha iyi bir gorunum icin sinirlayalim\n",
"plt.axis([0, 6, 0, 20]) \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Görüldüğü üzere grafiğin eksen limitlerini belirlemek üzere $axis$ metodunu kullandık. Alternatif olarak $plt.xlim((0,6))$ ve $plt.ylim((0,20))$ şeklinde demet (ya da istenirse liste) nesneleriyle limitlerin tanımlandığı iki metod da kullanılabilir. $plot$ ifadesinde \"$ro$\" şeklinde bir metinle verilen ise noktaların şeklini \"o\" rengini kırmızı (red) belirleyen bir parametredir. \"$b+$\" aynı şekilde noktaları mavi birer \"$+$\" işareti ile gösterecektir (deneyiniz!). \n",
"\n",
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Birkaç Grafiği Aynı Anda Çizdirmek #\n",
"\n",
"$PyPlot$ modülünde birkaç eğriyi aynı anda tek bir grafik üzerine çizdirebilmek için grafikleri sırayla çizdirip, en sonunda göstermek ($show()$) yapılması gereken bir şey yoktur."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"# 0 ile 5 arasinda 0.2 esit araliklarla ayrilmis noktalarimiz olsun\n",
"t = np.arange(0., 5., 0.2)\n",
"# Asagidaki her bir grafigi farkli bir renk ve sembolle gosterelim\n",
"plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$PyPlot$ modülü ile birkaç grafiği aynı şeklin (pencerenin) farklı yerlerine de çizdirmek mümkündür."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def f(t):\n",
" return np.exp(-t) * np.cos(2*np.pi*t)\n",
"\n",
"t1 = np.arange(0.0, 5.0, 0.1)\n",
"t2 = np.arange(0.0, 5.0, 0.02)\n",
"\n",
"plt.figure(1)\n",
"plt.subplot(211)\n",
"plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
"\n",
"plt.subplot(212)\n",
"plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Burada $figure(1)$ bir şekil nesnesi oluşturmak için kullanılan bir metottur. $subplot$ ise her bir grafik için şekil üzerinde bir grafik nesnesi oluşturur. ($211$) bu şekil nesnesi üzerinde $2.$ satır ve $1.$ sütunda grafik oluşturacağımızı, bu grafiğin $1$ numaralı grafik olduğunu, $(212$) ise bu grafiğin $2.$ satır $1.$ sütundaki $2$ numaralı grafik olduğunu göstermektedir. Eğer bu grafikleri bir satır ve iki sütunda oluşturmak isteseydik birncisini ($121$), ikincisini ($122$) ile oluşturmamız gerekecekti. Gördüğünüz gibi \"$k$\" siyah renk için kullanılmakta, sembol türü için herhangi bir tercih yapılmadığında kesiksiz bir eğri, \"$--$\" kullanıldığında ise kesikli bir eğri çizdirilmektedir.\n",
"\n",
"$PyPlot$ modülünde birkaç eğriyi aynı anda farklı iki şeklin (çerçeve) farklı yerlerine de çizdirebilirsiniz."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# birinci sekil\n",
"plt.figure(1) \n",
"# ilk grafik\n",
"grafik1 = plt.subplot(121)\n",
"grafik1.plot([1,2,3])\n",
"# ikinci grafik\n",
"grafik2 = plt.subplot(122) \n",
"grafik2.plot([4,5,6])\n",
"# ikinci sekil, ilk grafik varsayilan olarak (111) \n",
"plt.figure(2) \n",
"grafik3 = plt.plot([4,5,6]) \n",
"# birinci sekil ikinci grafigin basligini belirleyelim\n",
"plt.figure(1) \n",
"plt.title('Python Ogreniyorum')\n",
"# grafiklerimizi gosterelim\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PyPlot ve Metin Yönetimi#\n",
"\n",
"$Pyplot$ grafikleri üzerinde metin yönetimi oldukça kolaydır. Başlıklar, $title$, $xlabel$ ve $ylabel$ fonksiyonları ile yönetilirken, grafiğin herhangi bir yerine metin $text$ fonksiyonu ile $(x,y)$ koordinatları grafik biriminde verilerek yerleştirilir. $text$ fonksiyonu $\\LaTeX$ sembolleri kullanabilmek de dahil olmak üzere pek çok özellik sağlar. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Bir Histogram cizmek uzere verimizi olusturalim\n",
"# Ortalama (mu) ve standart sapma (sigma)\n",
"# degerlerimizi verelim\n",
"mu, sigma = 100, 15\n",
"# np.random fonksiyonu randn ile 10000 tane\n",
"# rastgele sayidan olusan bir dizi yaratalim\n",
"x = mu + sigma * np.random.randn(10000)\n",
"# verimizden bir histogram olusturalim\n",
"n, bins, patches = plt.hist(x, 50, density=1, \\\n",
"facecolor='g', alpha=0.75)\n",
"# x eksenine bir baslik verelim\n",
"plt.xlabel('Zeka')\n",
"# y eksenine bir baslik verelim\n",
"plt.ylabel('Olasilik')\n",
"# grafigimize bir baslik verelim\n",
"plt.title('IQ Histogrami')\n",
"# ortalama ve standart sapmayi gosterelim\n",
"plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# eksen sinirlarimizi belirleyelim\n",
"plt.xlim((40,160))\n",
"plt.ylim((0,0.03))\n",
"# grid (izgara) gosterelim\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$PyPlot$ grafiklerinde grafikteki noktalarınızı $annotate$ fonksiyonunu kullanarak etiketleyebilirsiniz."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# t eksenini numpy arange fonksiyonu ile olusturalim\n",
"t = np.arange(0.0, 5.0, 0.01)\n",
"# s = cos(2 * PI * t) ifadesiyle fonksiyonumuzu olusturalim\n",
"s = np.cos(2*np.pi*t)\n",
"# grafigimizi cizdirelim\n",
"plt.plot(t, s, lw=2)\n",
"# noktamizi etiketleyelim\n",
"plt.annotate('yerel maksimum', xy=(2, 1), xytext=(3, 1.5),\n",
" arrowprops=dict(facecolor='black', shrink=0.05),)\n",
"# y ekseninin limitlerini belirleyelim\n",
"plt.ylim(-2,2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Eğri Uyumlama #\n",
"\n",
"## Basit Bir Doğru Uyumlama Örneği ##\n",
"\n",
"$NumPy$ ve $PyPlot$ fonksiyonlarını kullanarak gözlemsel veriye bir eğri uyumlayabilir ve bunu grafik üzerinde gösterebiliriz. Basit bir doğru uyumlama örneği ile başlayalım."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Polinomun katsayilari: [ 0.10160693 -0.02865838]\n",
"Polinom: \n",
"0.1016 x - 0.02866\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# x ve y eksenleri birer liste olarak olusturulmus olsun\n",
"x = [-7.30000, -4.10000, -1.70000, -0.02564,\n",
" 1.50000, 4.50000, 9.10000]\n",
"y = [-0.80000, -0.50000, -0.20000, 0.00000,\n",
" 0.20000, 0.50000, 0.80000]\n",
"# NumPy polyfit fonksiyonuna x ve y degerlerini\n",
"# ve dogru uyumlayacagimizi (1) soyleyelim\n",
"katsayilar = np.polyfit(x, y, 1)\n",
"# polyfit polinom katsayilarini hesaplar bu katsayilari poly1d'ye \n",
"# verilirse bulunan katsayilara gore bir polinom fonksiyonu \n",
"# yaratilir. x degerlerine denk gelen dogrusal y degerleri \n",
"# hesaplanir\n",
"polinom = np.poly1d(katsayilar)\n",
"y_polinom = polinom(x)\n",
"print(\"Polinomun katsayilari: \", katsayilar)\n",
"print(\"Polinom: \", polinom)\n",
"plt.plot(x, y, 'o')\n",
"plt.plot(x, y_polinom)\n",
"plt.ylabel('y')\n",
"plt.xlabel('x')\n",
"plt.xlim(-10,10)\n",
"plt.ylim(-1,1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Yüksek Dereceden Polinom Uyumlama ##\n",
"\n",
"Şimdi de gözlemsel veriye 6. dereceden bir eğri uyduralım."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
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],
"source": [
"# x'e karsilik y seklinde gozlemsel veri:\n",
"x = [2.53240, 1.91110, 1.18430, 0.95784, 0.33158,\n",
" -0.19506, -0.82144, -1.64770, -1.87450, -2.2010]\n",
"y = [-2.50400, -1.62600, -1.17600, -0.87400, -0.64900,\n",
" -0.477000, -0.33400, -0.20600, -0.10100, -0.00600]\n",
"# 6. dereceden polinom fitinin katsayilari\n",
"katsayilar = np.polyfit(x, y, 6)\n",
"polinom_6 = np.poly1d(katsayilar)\n",
"# 0.1 araiklarla yeni bir x ekseni\n",
"x_polinom6 = np.arange(-2.2, 2.6, 0.1)\n",
"y_polinom6= polinom_6(x_polinom6)\n",
"# Grafik cizimi\n",
"plt.plot(x, y, 'o')\n",
"plt.plot(x_polinom6, y_polinom6)\n",
"plt.ylabel('y')\n",
"plt.xlabel('x')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyPlot Dekorasyon Parametreleri #\n",
"\n",
"Aşağıda verilen tüm dekorasyon parametrelerinin kombinasyonlarını da kullanabilirsiniz. Örneğin \"$kx$\" siyah renkli çarpı işaretleri, \"$m--$ mor\" (magenta) renkli kesikli eğri anlamına gelir!\n",
"\n",
"### Renkler ###\n",
"\n",
"Grafiklerinizi dekore etmek üzere aşağıdaki renklere ingilizce isimlerinin baş harfleriyle ulaşabilirsiniz:\n",
"\n",
"Sarı: “y” , \n",
"Mor: “m” , \n",
"Açık mavi: “c”, \n",
"Kırmızı: “r” , \n",
"Mavi: “b” , \n",
"Beyaz: “w” , \n",
"Mavi: “k” \n",
"\n",
"### Eğri Türleri ###\n",
"\n",
"Grafiklerinizde kullanacağınız eğrilere aşağıdaki stilleri uygulayabilirsiniz:\n",
"\n",
"Kesiksiz Eğri: \"-\" (varsayılan), \n",
"Kesikli Eğri: \"--\", \n",
"Noktalı Eğri: \":\", \n",
"Kesikli Noktalı Eğri: \"-.\" \n",
"\n",
"### Sembol Stilleri ###\n",
"\n",
"Grafiklerinizde kullanacağınız noktalara aşağıdaki stilleri uygulayabilirsiniz:\n",
"\n",
"Artı işareti \"+\", \n",
"İçi dolu yuvarlak: \"o\", \n",
"Yıldız \"*\", \n",
"Nokta: \".\", \n",
"Çarpı işareti: \"x\" , \n",
"İçi dolu kare: \"s\" , \n",
"İçi dolu baklava dilimi: \"d\" , \n",
"Bir köşesi yukarıya bakan üçgen: \"^\", \n",
"Bir köşesi aşağıya bakan üçgen: \"v\", \n",
"Bir köşesi sağa bakan üçgen: \">\", \n",
"Bir köşesi sola bakan üçgen: \"<\", \n",
"Beş köşeli yldız (pentagram): \"p\", \n",
"Altı köşeli yıldız (hexagram): \"h\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Alıştırmalar #\n",
"\n",
"1. Dikey atış problemini $y_{0} = 0$, $V_{0} = 10 m/s$, $g = 9.81 m/s^2$ için aşağıdaki ifadeden faydalanarak $t \\in [0, 2V_0 / g]$ aralığında eşit aralıklı a) 10, b)100 zaman ($t$) değeri için çözünüz ve zamana ($t$) karşılık yükseklik ($y$) grafiğini mavi kesiksiz eğri ile iki ayrı grafikte çiziniz.\n",
"\n",
"$$ y = y_0 + V_0 t - \\frac{1}{2} g t^2 $$\n",
"\n",
"Grafiğin eksenlerini $zaman (s)$ ve $yukseklik (m)$, başlığını $Dikey Atis Problemi$ olarak adlandırınız.\n",
"\n",
"2. Fahrenheit dereceden Santigrad dereceye hızlı bir dönüşüm C = (F - 30) / 2 formülüyle yapılabilir. $[20, 120]$ kapalı aralığında birbirinden eşit uzaklıkta 250 Fahrenheit dereceyi bu formülle ve standart dönüşüm formülüyle (C = 9 / 5 * (F - 32) + 180) Santigrad dereceye dönüştürünüz. Oluşan Santigrad derece değerlerini Fahrenheit dereceye karşılık çizdirerek (standart dönüşüm mavi kesiksiz doğru, yaklaşık dönüşümü kırmızı noktalı-kesikli doğru) yaklaşık formülün başarısını karşılaştırma yoluyla test ediniz.\n",
"\n",
"3. Bir önceki soruda oluşturulan grafikte görüldüğü gibi iki ifade belirli bir fahrenheit değerinde eşit olmakta (doğrular kesişmekte) ve bu doğrudan uzaklaştıkça yaklaşık ifade tam ifadeden giderek uzaklaşmaktadır. Bu değeri bularak, grafik üzerinde yeşil, içi dolu bir kare ile gösteriniz.\n",
"\n",
"4. $V_0$ hızı ile yatayla $\\theta$ açı yapacak şekilde $g$ yerçekim ivmeli ortamda fırlatılan bir cismin izleyeceği yol aşağıdaki formülle verilir.\n",
"\n",
"$$ y = x tan(\\theta) - \\frac{1}{2 V_0^2} \\frac{g x^2}{cos^2 \\theta} + y_0 $$\n",
"\n",
"Verilen bu ifadeyi $g = 9.81$ ve $y_0 = 0$ parametrelerini bu varsayılan değerlerle anahtar kelime argümanları, diğer tüm parametreleri ($x$, $\\theta$, $V_0$) ise konum argümanı olarak alan, $yatay\\_atis$ adında bir fonksiyon olarak kodlayınız.\n",
"\n",
"Burada x cismin yatay eksendeki koordinatı, y ise yüksekliğidir. $x \\in [0, \\frac{V_{0}^2 sin 2 \\theta}{g}]$ aralığında oluşturacağınız a) 10 $x$ değeri için ($x_1$) cismin aldığı yolu (x'e karşılık y) kırmızı kesiksiz doğru, b) 100 $x$ değeri için ($x_2$) ise yeşil noktalı doğru ile çizdiriniz. $V_0 = 10$ m/s, $\\theta = \\pi / 6$ radyan alınız.\n",
"\n",
"Cismin maksimum yüksekliğe çıktığı andaki x koordinatını ($xmax$) $\\frac{V_0^2 sin 2\\theta}{2 g}$ formülünden hesaplattıktan sonra $yatay\\_atis$ fonksiyonunuzda yerine koyarak y koordinatını ($ymax$) bulunuz ve grafiğinizde bu ($xmax$, $ymax$) koordinatını mavi bir baklava dilimi (ing. diamond) işareti ile işaretleyiniz.\n",
"\n",
"5. $L$ uzunluğunda kütlesiz bir sarkaç ipine bağlı $m$ kütlesi $T$ salınma dönemiyle salınıyor olsun. Aşağıda farklı uzunluklardaki sarkaçlarla yapılan salınma dönemi ölçümleri birer $numpy$ dizisi ile verilmiştir. L dizisine karşılık T dizisini kırmızı içi dolu dairelerle çizdiriniz. L ile T arasındaki ilişkinin sırasıyla 1, 2, ve 3. dereceden polinomlarla ifade edilebileceğini varsayarak ve $np.polyfit$ ile $np.poly1d$ fonksiyonlarını kullanarak elde edeceğiniz polinomları gözlemsel noktalarınızın üzerine çizdiriniz. Sizce hangi dereceden polinom veriyi daha iyi temsil etmektedir?\n",
"\n",
"[Başa Dön](#Grafik-Çizimi)"
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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