{
"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",
"* [Şekil Büyüklük, Boyut ve Çözünürlükleri](#Şekil-Büyüklük,-Boyut-ve-Çözünürlükleri)\n",
"* [Şekillerin Dosyaya Kaydı](#Şekillerin-Dosyaya-Kaydı)\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": 1,
"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. Ancak bu tür işlemler için ilgili jupyter \"sihirli\" kelimesini inline komutu ile değil notebook komutuyla kullanmanız gerekir. \n",
"\n",
"Yukarıdaki kod parçasında $PyPlot$'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": 2,
"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",
"# sadece tek bir eksende limit de belirleyebilirsiniz\n",
"plt.ylim((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 ($show()$) üzere yapılması gereken bir şey yoktur."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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., 2., 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.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eğrilerin düzgün görünmesi için nokta sıklığına dikkat edilmesi gerekir. Zira matplotlib noktaları doğru ile birleştirerek eğri çizilmesine olanak sağlar."
]
},
{
"cell_type": "code",
"execution_count": 4,
"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 0.5 esit araliklarla ayrilmis noktalarimiz olsun\n",
"t = np.arange(0., 2., 0.5)\n",
"plt.plot(t, t, 'r-')\n",
"plt.plot(t, t**2, 'b-') \n",
"plt.plot(t, t**3, 'g-')\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ancak $PyPlot$ modülü ile birkaç grafiği aynı şekil (pencerenin) farklı yerlerine çizdirilmek istendiğinde subplot() fonksiyonundan yararlanmak gerekir. Fonksiyona çzidirilmek istenen grafiğin hangi satır ve sütünde oluşturulacağı ve oluşturulan kaçıncı grafik olduğu bilgisi argüman olarak verilir. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 6,
"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",
"grafik1.set_xlabel(\"x\")\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": 7,
"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",
"iq = mu + sigma * np.random.randn(10000)\n",
"# verimizden bir histogram olusturalim\n",
"n, bins, patches = plt.hist(iq, 50, density=1, \n",
" facecolor='g', alpha=0.75)\n",
"# x eksenine bir baslik verelim\n",
"plt.xlabel('IQ')\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": 8,
"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",
"cost = np.cos(2*np.pi*t)\n",
"# grafigimizi cizdirelim\n",
"plt.plot(t, cost, 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": [
"# Etiketler #\n",
"\n",
"Çizilen bir grafik üzerinde istenilen her veri grubu ayrı bir etiketle (ing. legend) gösterilerek grafiğe bakan kişinin daha kolay anlamasına yardımcı olanabilir. Bu işlem $legend$ fonksiyonuyla gerçekleştirilir."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ldzZLEhP/GTDZu1eWYasMJSdLQcoTJ+T9zpfPdETKdt59V6ZAfvaZ7B0dQLQLy8d89RWwfDnw1lsuJA8ACAsDPvhAStDqbkqZCg6WeQdnz8qWK0rd4OxZYNAgGV9s0sR0NLahLRAbSkwEqlSRBW+xsbL1h8saNwZWr5bL6hLpbvio0nj1VRkf3b1b/g+UAiBT42fPBvbsAf71L9PReJ22QHzI+PEy/vHee9lMHqkvdv060Lu3W2Lzd8OGAbfdBkRF6YC6smzbBsyYId1XAZg8HNEEYjOnTsnAeZMmUicx2+66S1Yhzp8P/O9/bnhB/1aokCSRtWuBJUtMR6OMS0kBunaV/c21b/MWmkBsJjpaBnTHjXPzi955p/wh6Ar1TL3yisw/6NlTV6gHvNmzpQUyZow0TdUNjCYQIqpPRAeJKI6IotN5vB0RJRDRbuvWMc1jbYnosHVr693IPWPLFqmQ0KuXm6sj5MolGSk2VpriyqEcOWTCzbFjwDvvmI5GGXPxokyHf+ABoFUr09HYkrFBdCIKBnAIQD0ApyB7pLdIuzUtEbUDEMnMr9/03IIAYgBEAmAAOwBUZ+YLjs5p50F0ZplGevy4lNbIk8cDJ3jkEXnxw4f1asoJzz8PfP21vF0uzYRTvm3gQKluvXmzJJEAZsdB9JoA4pg5npmvAVgAoJGTz/0vgFXMfN5KGqsAZGWpne3Mnw9s3SoL2tyePABZoT5hgkxHHD3aAyfwP2PGyPyDAQNMR6K87sQJabW3aBHwycMRkwmkBICTab4/Zd13syZE9CMRLSaiO7P4XBBRJyKKIaKYhIQEd8Ttdpcvy7YC1at7eFuBGjWkKT5+vPTPKIfuuksqVsycKdN6VQDp21f+1Ysth+w+iL4cQGlm/jeklTErqy/AzFOYOZKZI4sUKeL2AN1hwgSZfTVhQppdBj1l1Cg5SfQtQ04qHf37S4X8nj11Wm/A2LpVqmv27AmULGk6GlszmUBOA7gzzfcR1n1/Y+ZzzJy6R+s0ANWdfa6vOHtWukoaN5YhCo+LiJA1IQsXyqi9cih/ftk3ZO1aYMUK09Eoj2OWxHHHHXqR5QSTCWQ7gPJEVIaIQgE0B7As7QFElLYWeUMAB6yvvwXwBBEVIKICAJ6w7vM5Q4ZIF5ZXW8q9ewNFi8q/elmdqVdeASpUkNlxSUmmo1EetXQp8P33UizRI4OR/sVYAmHm6wBeh3zwHwCwiJn3EdFQImpoHdaNiPYRUSyAbgDaWc89D2AYJAltBzDUus+nHDwolaE7d5YPKK/Jk0cy16ZNssm6cigkREq+Hzoke6krP5WUJIORFSsC7dubjsYnaC0sgxo3lq6RI0cArw/PXL/+T7GnvXtl8YPKEDNQu7ZM6Y2L04tTvzRpkhRDW7YMeOYZ09HYih2n8Qa0TZuktRwdbSB5AJIwxoyRZpBeVmeKSFohZ87IJDblZ/76Swa7atcGGjQwHY3P0ARiALO0lIsXl/psxjRsCDz8sJSpvnjRYCC+4cEHgeeeA8aOlckPyo+MGyf/qWPHytWCcoomEAOWLwd++EE+t53aptZT0l5WT5xoMBDfMXKk1McaNsx0JMptzpyRmjVNmwI1a5qOxqdoAvGy5GSgXz/g7ruBl14yHQ3ksrpRI7ny+u0309HYXoUKwMsvAx9/LGNXyg+MGCGb8IwYYToSn6MJxMvmzAH27ZPfVduMW48YIV1YuurWKW+9JTOzBg0yHYnKtqNH5WqgQwe5qlNZognEixIT5UOnRg2b7YpZubLUUPngA+DkycyPD3DFikmJk3nzZIM65cMGDZL9jN96y3QkPkkTiBdNniw12kaNsuE43eDBMro/ZIjpSHxCnz5S0Lh/f9ORKJft2QPMnStXA7rds0s0gXjJxYvSU/Sf/8jNdkqVkjnwn3wC/PST6Whsr0AB2egxdUKE8kH9+8tVQJ8+piPxWZpAvOTdd4GEBJuP0/XrB4SHa+e+k6KipCJMv35aEcbnbNki2b93b6mWqVyiCcQLLlyQSU4NGwL33286GgeKFJGFKYsWaf1yJ+TOLXsObdgArF5tOhqVJQMHyu97VJTpSHyaJhAvGDsW+PNPH1k70KuXlKDVQUWndOwoFb8HDNBWiM9Yv14yft++WpMmmzSBeNiZM9J91bw58O9/m47GCfnzSxJZvlzLvTshZ07p8du2Td4yZXPMMvZRvLhUMVXZognEw0aNAq5elUlOPiMqSpr3uperU9q0AcqVk16RlBTT0SiHVq6UWQ8DB8p4n8oWTSAedPq0rFFq08bH1ijlySPN+zVrgHXrTEdjezlyyOznH38EFi82HY3KELMkjjJlbFIGwvdpAvGgkSOldMnAgaYjcUHnztLMHzRIO/ed0KyZrMd86y2plK9saOlSYMcO+U8KDTUdjV8wmkCIqD4RHSSiOCK6Zf9IIupBRPuJ6EciWkNEpdI8lkxEu62b7XZFOn4cmDpVKiSUKWM6GheEh8v81P/9T1oiyqHgYGmFHDwIzJ9vOhp1i5QUuRgqXx5o1cp0NH7D2IZSRBQM4BCAegBOQXYWbMHM+9Mc8ziArcx8mYi6AHiMmZtZj11k5ixNofDmhlIvvwzMni2bD915Z+bH29LVq/IHFxEh23zabvm8vaSkAPfdB1y6BBw4YKNaZ0r6Fp9/Xlaev/ii6Wh8jh03lKoJII6Z45n5GoAFABqlPYCZ1zHzZevbLQAivByjS44ckQXdr7ziw8kDkClGAwYAmzfL4KNyKChIWiFxcfI5pWwiOVlaH//6l0yHVG5jMoGUAJC2ct8p676MdADwTZrvw4gohoi2EFHjjJ5ERJ2s42ISEhKyF7GThg+Xaq19+3rldJ7Vrh1QurT0G+tYSKYaNpRWyLBhssW2soFFi4D9+2UqZHCw6Wj8ik8MohNRKwCRAMamubuU1aRqCWAiEZVN77nMPIWZI5k5sogX9o6Ni5OS7V26SNVWnxcaKrMAYmKAFStMR2N7RNIKiY+XLkxlWHKy/IdUqSJdWMqtTCaQ0wDSdvBEWPfdgIjqAugPoCEzX029n5lPW//GA1gP4F5PBuusYcPkM/fNN01H4kZt2gBly/5TsVc59PTTUrJ/+HDg2jXT0QS4BQtkZsOgQdLHqNzK5Du6HUB5IipDRKEAmgO4YTYVEd0LYDIkeZxNc38BIsppfV0YQC0A+2HY4cPS992lC3DHHaajcaMcOWQsZOdObYU4IbUVcuwYMGuW6WgCWHIyMHQoULWqbGav3M5YAmHm6wBeB/AtgAMAFjHzPiIaSkQNrcPGAsgD4LObputWBBBDRLEA1gEYnXb2linDhsm4s1+1PlK1aqWtkCyoX1+21x4xQlshxixYABw6pK0PDzI2jdcET07jPXQIqFgR6N4dGDfOI6cwb+ZMoH17WZDVsGGmhwe6b74BnnoKmDJFpnUrL7p+XVZ2hoUBu3ZpAskmO07j9SvDh/tx6yOVtkKyRFshBmnrwyv0nXWDQ4eATz+VDf1uv910NB6UOhayaxewzHaL/22HSHLt8eM6I8urrl+X/uR//xtonOEMf+UGmkDcYMQIaX307m06Ei9IbYUMHaqtECdoK8SAhQu19eEl+u5mU1yctD46d5btTf1ejhyyn8LOncBXX5mOxvZSWyHHjmkrxCuSk6X1UbWqtj68QBNINo0YIavO/Xrs42atWkmFyCFDtBXihPr1ZV3IyJG6Ot3jFi2SdR9vvaWtDy/QdzgbjhyRVeevvOJn6z4yExIilXpjYmSqkXKISD7Pjh7VGlkeldr6qFxZ1314iSaQbBg1Snp0Aqr1kapNG6BUKR0LcdLTT0uNrBEjdL8Qj/n8cymDPHCgtj68RN9lF6WuMu7USfZdCjihodIK2boV+O4709HYXmor5MgRYN4809H4oZQUaX1UrAg0bWo6moChCcRFo0fLRU5Atj5StWsHlCypYyFOatgQuOceWTOUnGw6Gj+zZAmwd69MM9eKu16jCcQFJ08CM2bIboMRPrFDiYeEhgLR0bJfyNq1pqOxvdRWyOHDss5NuUlKinSl3n237C2svEYTiAvGjJF/o2/ZhDcAvfQSUKKE/AGrTDVuLJXFR4zQVojbLF8O/Pijtj4M0ASSRadPy17n7dtL703Ay5kT6NMH2LgR2LDBdDS2FxQkY7wHDsiYr8omZrl4KVsWaNHCdDQBRxNIFo0dKy1mv9ht0F06dpR5zMOGmY7EJzRpImO9w4fL75LKhm++kUWt/frpJvQGaALJgl9/BSZPBlq3ll1elSU8XGYTrFkDfP+96WhsLzhYelv27JHCxspFqa2PUqXkj1J5nSaQLHjnHaln1K+f6Uhs6JVXgCJFdCzESc2aAeXLS6NNJ7C5aNUqmUbet68sblVepwnESQkJwEcfAS1bAuXKmY7GhnLlAnr1kjUh27aZjsb2goOlpNiuXbrJo0tSWx8RETKdXBlhNIEQUX0iOkhEcUR0y5wmIspJRAutx7cSUek0j/W17j9IRP/1dKzjxwNXrsgfvcpAly5AwYI6FuKkF18E7rpLWyEuWb9eukujo2UihzLCWAIhomAAHwJ4EkAlAC2IqNJNh3UAcIGZywGYAGCM9dxKkD3UKwOoD+Aj6/U84vx54IMPgBdeAP71L0+dxQ/kzQv06CGX1Lt2mY7G9nLkkN6X7duBb781HY2PGTYMKFZMFmMpYzJNIETUlYgKeODcNQHEMXM8M18DsABAo5uOaQRglvX1YgD/ISKy7l/AzFeZ+SiAOOv1PGLiRODiRRn4VJl4/XUgf35thTipTRuZDq4lxbJg0yZg3TqZuBEWZjoa2/vpJ6BBAyA+3v2v7UwLpCiA7US0yOpyIjeduwSAk2m+P2Xdl+4xzHwdwB8ACjn5XAAAEXUiohgiiklISHAp0LNngeeflwVgKhP58gFRUVJaYs8e09HYni7md8GwYbL1Z6dOpiPxCSNGSL7Nm9f9r51pAmHmAQDKA5gOoB2Aw0Q0kojKuj8c92PmKcwcycyRRYoUcek1Pv4YmD/fzYH5s6go+W0dPtx0JD4hdTG/NtqckFq8s1cvmbihHIqLk+KdXbrIJEl3c2oMhJkZwK/W7TqAAgAWE9Hb2Tj3aQB3pvk+wrov3WOIKAeAfADOOflct9IKCVlQoADQtSvw2Wey5Fo5lDOn9MZs2CAL+pUDw4YBhQrJJ6LK1MiR0srt1cszr+/MGEgUEe0A8DaA7wFUZeYuAKoDaJKNc28HUJ6IyhBRKGRQfNlNxywD0Nb6uimAtVYyWwaguTVLqwykhaRzR+2ke3e5QhwxwnQkPuHll3Uxf6Z27JBtlHv2BPLkMR2N7R09KhvederkuQ3vnGmBFATwHDP/l5k/Y+YkAGDmFAANXD2xNabxOoBvARwAsIiZ9xHRUCJqaB02HUAhIooD0ANAtPXcfQAWAdgPYCWA15hZS9PZSeHCwKuvSt/f4cOmo7G98HCgd29g9Wrghx9MR2NTw4ZJ6/a110xH4hO8seUEcQBN/YiMjOSYmBjTYQSOM2dk7/RmzYBPPjEdje1duiQlciIjdafgW8TGAtWqyd4zb71lOhrbO3lS6kt27CgLoLOLiHYwc+TN9+tKdOU5RYtKiZM5czwzh9DP5M4tfdUrV+pi/lsMHw7cdhvQrZvpSHzCmDEyLbxPH8+eRxOI8qzevWXF3KhRpiPxCa++Kov5dQJbGvv2Se37bt1kjZFyKO2WE6VKefZcmkCUZxUvLiPEM2cCx4+bjsb28uaV+QfLl+ti/r8NHy7NszfeMB2JTxg7VjYr88aWE5pAlOf16SOjeaNHm47EJ3TtKhfaWtgYsox64UKpcFCokOlobC91y4k2bWT40dM0gSjPi4iQ1XLTp8vonnIodTH/l1/KTq0BbcQImaLWo4fpSHzCuHHe3XJCE4jyjtT2dOqG8sqhqCgZMw7odSGHDsky6tde88wyaj9z9iwwaZJUefbWlhOaQJR3lCwp+zZMnSqjfMqhAgVkzHjxYmDvXtPRGDJypCzT79nTdCQ+4Z13vL/lhCYQ5T19+8ro3tvZqYATOLp3lwXXATkj68gRYO5coHNnmQ6uHPrtN+DDD4HmzYEKFbx3Xk0gynvKlJHRvSlTgF9+MR2N7QAPAy4AACAASURBVBUsKAPqixYB+/ebjsbLRo6UbWp79zYdiU945x3g8mVg4EDvnlcTiPKu/v2BpCRthTipRw8pKRZQrZD4eGDWLFmEWqyY6Whs79y5fza8q1jRu+fWBKK8q2xZoHVrqZH/66+mo7G9woVlBuuCBTKjNSCMHCmLTz1ZxMmPjB8vZXC83foANIEoE7QVkiU9e0orJCBmZB09Kq2PTp1kEapy6Px54P33gaZNgcqVvX9+TSDK+8qVA1q10laIk4oUkVbI/PkB0AoZOVI23/F0ESc/MWEC8NdfZlofgCYQZcqAAbLiaexY05H4hJ49ZT2dX7dCjh2TkjcvvyxbNCqHzp8H3n1XWh9Vq5qJQROIMiO1FTJpkrZCnBAQrZARI6TkjbY+nDJ+vLQ+Bg0yF4MmEGVOaitEx0Kc0quXH7dCjh6V1kenTlL6Rjl07py0Pp5/HqhSxVwcRhIIERUkolVEdNj6t0A6x1Qjos1EtI+IfiSiZmkem0lER4lot3Wr5t2fQLlFuXIyI2vSJF0X4oS0rRC/22p++HAZ+/BGCVk/8M47MvPKZOsDMNcCiQawhpnLA1hjfX+zywDaMHNlAPUBTCSitJsB9GbmatZtt+dDVh4xYIDMyNIaWU7p3Vsqmw8ZYjoSNzpy5J91HzrzKlO//SYzr154wczMq7RMJZBGAGZZX88C0PjmA5j5EDMftr7+GcBZAFpRzd+ULQu0bSszsn7+2XQ0tle48D+r0/2mRtbw4bLqPDq960h1s9TWhx129jWVQIoyc2qfxa8AHBa7IaKaAEIBHElz9wira2sCEeV08NxORBRDRDEJCQnZDlx5wIABUiNL9wtxSs+eUiPLL1ohcXGy5XGXLrrq3AlnzwLvvQc0awZUqmQ6Gg8mECJaTUR707k1SnscMzMAdvA6xQDMAdCemVOsu/sC+BeAGgAKAshw2gYzT2HmSGaOLKIloe2pTBmp1Dt5su4X4oRChWRzvsWL/WC/kKFDgdBQXXXupLffBhITgcGDTUciPJZAmLkuM1dJ57YUwBkrMaQmiLPpvQYR3QbgKwD9mXlLmtf+hcVVAJ8AqOmpn0N5yYABALNM5VSZ6t5dNp6yyweJSw4ckIq7r78O3HGH6Whs75dfpOJuq1berbjriKkurGUA2lpftwWw9OYDiCgUwBIAs5l58U2PpSYfgoyf+EtvcOAqVUoWkE2fLlM6lUMFCkgSWbIE2LHDdDQuGjxYZgRo68Mpo0bJfBM7jH2kMpVARgOoR0SHAdS1vgcRRRLRNOuYFwDUBtAunem6nxLRHgB7ABQGEEi1Sv1X//5SRE83A3dK9+5S8t1UGYtsiY2VmQBvvCEzA5RDJ09KD2+7djLvxC5IhiACQ2RkJMfExJgOQznSo4eskDpwALj7btPR2N7bb8vC7U2bgFq1TEeTBY0bA+vXS2uzwC3LwNRNOncGZswADh+Wxrq3EdEOZo68+X5dia7sJToaCAvz8c5973ntNdmwz6daIdu3A0uXynQyTR6Zio+Xnt2OHc0kD0c0gSh7uf122Qx8wQJgzx7T0dhe7txAv37AunXA2rWmo3HSwIEylSwqynQkPmHwYOnZHTDAdCS30gSi7Kd3b+C22+z5F2NDqeWj+veXiWy2tmED8O23UrLktttMR2N7+/bJRLWuXe25SF8TiLKfggUliSxbBmzZkvnxAS4sTGbmbNkCLF9uOhoHmKW5VLw48OqrpqPxCW+9JYtG7VqgWBOIsqeoKOnO6t/fdCQ+oV07oHx5ebuSk01Hk4GvvwZ++EE+FcPDTUdjezExwBdfyFBRoUKmo0mfJhBlT3nyyKfh2rXA6tWmo7G9kBAp8753r1TrtZ2UFPn/LFsWeOkl09H4hP79JXF07246koxpAlH29corQMmS0u1h+859855/HqhWTS7wr10zHc1NFi2StR9Dh0q2Uw6tXQt89539h4o0gSj7yplTpqBs3y5LrpVDQUGypfjRo8DUqaajSSMpSSZEVK0KNG9uOhrbY5bZ7HfeKdO07UwTiLK31q2l7GjfvsD166ajsb369YFHHpHurIsXTUdjmTpV9vwYPVqynHLo88/lmmnIEJkgYWf6v6nsLUcOKQJ06JAsxVUOEcneXGfOABMmmI4GksWGDAEefRR48knT0dje9esy9lGpEtCmjeloMqcJRNnfM89InY7Bg2UnHeXQgw8Czz4rZU7Oplvn2ovGj5cgxoyR7KYcmjFDrpVGjpQdfu1OE4iyv9TL6l9+kTpZKlOjRgFXrkhXljFnzwJjxwJNmgD3328wEN9w6ZJcIz34INCwoelonKMJRPmGWrWARo0kkfz2m+lobK9CBamd9PHHsumfEcOGSRbTPV6cMn68XCONHes7jTVNIMp3jBwpfepa7t0pgwbJZn9G1mIeOiTZq2NH++x+ZGNnzkiX47PP+lZVZU0gyndUqiSbTk2aJB9QyqFixWQV86JFBirCpFZV9ouN2z1v8GDZqnb0aNORZI2RBEJEBYloFREdtv5Nt6YzESWn2UxqWZr7yxDRViKKI6KF1u6FKhCkzm20a3Egm+ndW8q99+jhxbWYGzfKup3oaDm5cujAAZnp3Lmz722BY6oFEg1gDTOXB7DG+j49V5i5mnVLO6w0BsAEZi4H4AKADp4NV9lG0aLywfTll/JBpRzKmxcYPhzYvBlYvDjz47MtJQXo1QsoUcLeNThspE8fKctvp61qnWUqgTQCMMv6ehZkX3OnWPug1wGQ+ueQpecrP9C9u3xA9eolH1jKofbtZRF4nz7A1asePtnChbIKbsQIIFcuD5/M961ZIxWU+/YFihQxHU3WmUogRZn5F+vrXwFk1M4NI6IYItpCRKlJohCA35k5dVnyKQAlPBirsptcueQDavt2YN4809HYXnCwzPA5ehR4/30PnujKFWkdVqsGtGrlwRP5h+RkuRYqXVq2hvdFHksgRLSaiPamc2uU9jiWTdkz6p0tZe3D2xLARCLK8nbyRNTJSkIxCQkJWf9BlD21bg1Ury6X1bap2WFfdesCTz0l3Vke+zMYNw44cUKWwPvCKjjDpk+XTTfHjrV/yZKMeCyBMHNdZq6Szm0pgDNEVAwArH/TXS/LzKetf+MBrAdwL4BzAPITUQ7rsAgApx3EMYWZI5k5sogvthFV+oKCgPfeA37+WdaGqEyNGyeL1Tyy0ePJk7J6sWlT4LHHPHAC//LHH/L/8Mgjss7SV5nqwloGoK31dVsAS28+gIgKEFFO6+vCAGoB2G+1WNYBaOro+SoAPPQQ0LKlXMIdO2Y6GturWBF4/XWZ8bNrl5tfPDpaxqPGjnXzC/un4cNlPezEib6zaDA9phLIaAD1iOgwgLrW9yCiSCKaZh1TEUAMEcVCEsZoZt5vPdYHQA8iioOMiUz3avTKPlIrvL75pulIfMKgQUDhwkC3bm6c1vvDDzIW1auXdOgrhw4dkoo87doB991nOprsIQ6gjXoiIyM5JibGdBjK3YYOlU/Gdeu0+8QJ06bJesz5892wPUdyMvDAA9KVePCg7CSpMsQsRYk3b5ZE4ivLZIhohzUefQNdia58X+/ecuX7+uuyeZFyqH17ufLt3dsNxY2nT5fNu99+W5OHE5YuBb79Vq55fCV5OKIJRPm+8HDpE9i3z8PzVP1DcLC8TadOSV+8y377TRYwPPqojEUph65ckem6VarYf6dBZ2kCUf7hmWeAp5+WrqzTGU7KU5aHHpKWyLhxwP79mR+frr59ZTrRhx/69kiwl4weDRw/DnzwgeyT5g80gSj/QCStkKQkGcxVmRozRkqdvPaaCwPqW7bIYMobbwCVK3skPn8SFyfvd4sW0mDzF5pAlP8oW1amky5YIDUilENFishV8fr1WVzQn5wsWad4cWnxKYeYgVdfldL648aZjsa9NIEo/9KnjySSzp2l01k51LEjULOmlH3//Xcnn/T++8DOnVIfJW9ej8bnD+bPB1atknWWxYubjsa9NIEo/xIe/s82fLoTXqaCguTtSkiQxlumTpyQJdRPPQW88ILH4/N1Fy5IvauaNeWaxt9oAlH+p25dqZU1Zgywd6/paGzv3ntlv5DJkzOpkM/8z4CJDpw7JToaOHdO3lt/LA+mCUT5p3feAfLlAzp10pLvThgyBChTRhYYJiZmcNDnnwMrVshe57riPFMbNwJTpsg8g2rVTEfjGZpAlH8qUkT66Ddvli1wlUO5cslV8qFDGawNuXAB6NpVViB26+b1+HzN5ctAhw6SlP15V19NIMp/tW4N1KsnA+tHj5qOxvbq1QPatpWev9jYmx6MipKBkmnT/GcRgwe99ZYMw02fLrsN+itNIMp/EckHXlAQ8NJL2pXlhHfeAQoVkkRy7Zp15/LlwJw5QL9+MmCiHNqyRbZE6dwZePxx09F4liYQ5d9KlpRPxfXrZbqRcqhQIenKio21urLOn5dxpH//20MbifiXxES5VilRIjC2qdEEovxfx47AE09Iyff4eNPR2F6jRkCbNsDIkcC5VlFS82rmTFkJpxwaMAA4cEAGz2+7zXQ0nqcJRPk/ItlFKShI+maSk01HZHvvvgt0yLcYhb6Zi+tvateVM9atk3kbXboA9eubjsY7NIGowFCypKxd2LRJ6ncoh/JfPIUPrnXCdkSiz1/adZWZ33+Xa5Ny5QJrU0Yj0ymIqCCAhQBKAzgG4AVmvnDTMY8DmJDmrn8BaM7MXxLRTACPAvjDeqwdM+92JZakpCScOnUKiRlOfldhYWGIiIhASEiI6VCyp1Ur4OuvpX5TvXqyPFjdKiUFaNsWISlX8U2reRj/fgjqPikbIan0vfaa7Kn1ww/+PevqFszs9RuAtwFEW19HAxiTyfEFAZwHkMv6fiaAplk9b/Xq1flm8fHxnJCQwCkpKbc8pphTUlI4ISGB4+PjTYfiHhcuMJcsyVyuHPNff5mOxp7GjmUGmKdN4ytXmKtWZb79duZffzUdmD3NnStv15AhpiPxHAAxnM5nqqkurEYAZllfzwLQOJPjmwL4hpkvuzuQxMREFCpUCKRlGdJFRChUqJD/tNDy55cpqUeOyA6G6kYxMTJd97nngJdeQliYFDf+80/potGZ0Dc6eBB45RXg4YflbQs0phJIUWb+xfr6VwCZbe7YHMD8m+4bQUQ/EtEEIsqZ0ROJqBMRxRBRTEJCQkbHOBt3QPK796d2bWDgQGDWLGDGDNPR2MeFC8DzzwPFisk0Iuv/vVIlWdfw7bf+V448O65ckbcrPFwq7gbi+kqPJRAiWk1Ee9O5NUp7nNU8ynA7GyIqBqAqgG/T3N0XMiZSA9K91Sej5zPzFGaOZObIIkWKZOdHUv7krbeA//xHOq9vWXYdgFJSZO7u6dPAokWyICSNV14BmjaVTQjXrzcTot1ERQF79kiDNiLCdDRmeCyBMHNdZq6Szm0pgDNWYkhNEGcdvNQLAJYwc1Ka1/7F6pq7CuATAH41GtqxY0fsd3mfUeWU4GDZRalgQflk/OOPzJ/jz8aOlUKJ77wD3H//LQ8TSWOtfHmgWTPdNXjOHJkZHh0dOFN202OqC2sZgLbW120BLHVwbAvc1H2VJvkQZPzEr2p2T5s2DZUqVTIdhv+7/XZg4UKpk9WuXeB28K9dC/TvL/0xDsaF8uYFvvgCuHRJtgL5u9RJgImJkarFjz4qhYkDmaleu9EAFhFRBwDHIa0MEFEkgM7M3NH6vjSAOwFsuOn5nxJREQAEYDcAt2zV8sYbwG6XJgNnrFo1YOLEjB+/dOkSXnjhBZw6dQrJyckYOHAgJk2ahHHjxiEyMhJ58uRBVFQUVqxYgfDwcCxduhRFixZFu3bt0KBBAzRt2hQAkCdPHly8eBG//PILmjVrhj///BPXr1/HpEmT8Mgjj7j3h/InDz8sHfvduwODBwNDh5qOyLvi4qQFVqGC1A3LZLyrUiVpiTRrJn8vH33kpTht4swZ4NlngTvuAD77LDDHPdIy0gJh5nPM/B9mLm91dZ237o9JTR7W98eYuQQzp9z0/DrMXNXqEmvFzBe9/TO4y8qVK1G8eHHExsZi7969qH9Te/jSpUt44IEHEBsbi9q1a2Pq1KkOX2/evHn473//i927dyM2NhbV/HUjAneKipICRsOGSYskUPzxB9CwoSSNZcucrr3xwgtA795SJf/99z0co41cuwY0aSIbRH35pewYEOgCPH/eyFFLwVOqVq2Knj17ok+fPmjQoMEtrYXQ0FA0aNAAAFC9enWsWrXK4evVqFEDL730EpKSktC4cWNNIM4gkkvpQ4ekK6tsWSAy0nRUnpWcDLRoARw+DHz3nfzMWTBqlLxdb7whq6/9fZEhs0wk+P57mdasf1ZCS5kYdvfdd2Pnzp2oWrUqBgwYgKE3daGEhIT8PY02ODgY169fBwDkyJEDKVaffUpKCq5ZHdK1a9fGxo0bUaJECbRr1w6zZ8/24k/jw3LmlB33ihYFnnnGv4suMsvmUN98I00IF2qOBwcDc+dKkd5mzfx/5+BBg6Se5ODB8vMqoQnEsJ9//hm5cuVCq1at0Lt3b+zcudOp55UuXRo7duwAACxbtgxJSTJJ7fjx4yhatChefvlldOzY0enXU5BB9a+/Bq5eBf77X+Cso8mBPmzYMOl/6t1bNq1wUZ48slVInjwyE+nYMfeFaCdTpshb1qGDzP5W/9AEYtiePXtQs2ZNVKtWDUOGDMEAJ/dcePnll7Fhwwbcc8892Lx5M3JbBXjWr1+Pe+65B/feey8WLlyIqKgoT4bvfypVAr76SuapPvUU8NdfpiNyr8mT5XI6devBbIqIAFaulJlZ9erJILM/WbZMqus++aTkXH9bU5tt6dU38ddberWw9u/ff8t96lYB9z4tX84cHMxcpw7zpUumo3GPhQuZg4KYn3qK+do1t770Dz8w58rFfM89Um7MH3zzDXNoKHONGlo2DTarhaWUvTVoAHzyiWzy8MwzwGW3l2HzrgULgJYtgYcekpXmbq6s/OCDwJIlwP790p31++9ufXmvW7UKaNwYqFxZSrjkyWM6InvSBKJURlq3lnpZ69ZJQrl0yXRErpk3D3jxRaBWLRk491C98SeekLURO3cCderIRoa+aM0amd1coYIkkgIFTEdkX5pAlHKkdWtg9mxgwwYZE/G1S+tPPpGfoXZtmSDg4UvpRo2ApUtlW9fHHwd+/dWjp3O7xYvlv7lsWWD16ltKgqmbaAJRKjOtWgGffgps3iwr10+cMB1R5phlzulLL0nRyBUrvLbT0ZNPyjyE+Hh5u376ySunzbaPPpJFkpGRwMaNulDQGZpAlHJG8+Yy3ejkSeCBB9xf88adkpIkcQwZIgsjv/rK69vk1akjXUF//injI2vWePX0WZKcLFWGX3sNePpp6bYqWNB0VL5BE4hSzqpTR5YiBwfLpfW8eaYjutXPP0uLY+ZMma47Y4bbB8yd9cADwLZtQIkSMrD+8cfSMLKTc+ckaYweDXTqJBMBcuUyHZXv0ARiA8eOHUOVKlWy9RoPPfSQm6JRDlWpAmzdCtx7rwxMd+4M2GW3xtWrpcbGjh2yTHzwYOMLF0qXlpxbt66sp2jRwj6V83ftku6qdetkseDkyVocMas0gfiJH374wXQIgaN4cSmB/uab8qljuksrMVH2U33iCem4375dkptN5MsnQzAjR8ogdbVqMpxkSlKSrCy//375euNGKc+usk7zbVom6rnfJD4+Hk2aNMF7772HGTNmICYmBjly5MD48ePx+OOPY9++fWjfvj2uXbuGlJQUfP755yhfvvzf5dzXr1+PwYMHo3Dhwti7dy+qV6+OuXPngohQunRpxMTEoHDhwoiJiUGvXr2wfv16bNiw4e8V60SEjRs3Im/evO59H/xNSIis5H74YaBjR7mU7dlTuo282Qeybp1U+Tt8WMY93nvP6+MdzggOlnGGxx6TVkitWtIiGT7cu9NkY2OB9u2l9dGihZQC05lWrtMWiI0cPHgQTZo0wcyZM7Ft2zYQEfbs2YP58+ejbdu2SExMxMcff4yoqCjs3r0bMTExiEhnL81du3Zh4sSJ2L9/P+Lj4/H99987PO+4cePw4YcfYvfu3fjf//6H8PBwT/2I/ueZZ2TOatu2wNtvA1WrythIcrJnzxsXJ7PD6tSRc61aBUyfbsvkkdaDD8qHeNeuMiZSoYIM01g1Qj3m1CnJr/fdJ1VqvvhC/ps0eWRTesvT/fVm11ImR48e5dtvv50rVKjA+/btY2bmxo0b85o1a/4+5uGHH+bY2Fj+9NNPuVKlSjx69Gg+dOjQ34/nzp2bmZnXrVvHdevW/fv+zp0785w5c5iZuVSpUpyQkMDMzNu3b+dHH32UmZlHjRrFNWvW5HfffZdPnjyZbox2eJ9sb+1a5qpVmQHmSpWYP/uMOSnJvec4coS5QwcpsxIezty3r8+WWtm1i/nBB+Xtuusu5smTmRMT3XuOo0eZe/ZkDguTsiQ9ejCfO+fecwQC2KmUCRE9T0T7iCjF2oUwo+PqE9FBIoojoug095choq3W/QuJKNQ7kXtOvnz5ULJkSWzatMnhcS1btsSyZcsQHh6Op556CmvXrr3lmJw5c/79dUYl4BPTDPxGR0dj2rRpuHLlCmrVqoWffGXivt08/rh0gS5cKNvjPv88UKqUbBd75Ijrr5uYCMyfL9UKy5WTDblfe00WWowc6bPThqpVAzZtks2ZChWSnrjSpYFevWQ1u6szthITZeZyw4bAXXcBEybIposHD8qW7zpF131MdWHtBfAcgI0ZHUBEwQA+BPAkgEoAWhBR6kbhYwBMYOZyAC4A6ODZcD0vNDQUS5YswezZszFv3jw88sgj+PTTTwEAhw4dwokTJ1ChQgXEx8fjrrvuQrdu3dCoUSP8+OOPTp8jbQn4zz///O/7jxw5gqpVq6JPnz6oUaOGJpDsCAqS1Wh790o/yb33yhzRcuWAihXlg3/xYikalV59rZQU6WPZuFGe98QT8onXsqV0Ww0ZIonj3XdlX1UfFxQkq9e3bpV9rWrWlGGc6tWle6tTJ5mRfOBA+m8Xs1TdX79exjOeeUbergYNgC1bZNzl6FHJuaVLe/mHCwBGBtGZ+QCAvzdKykBNAHHMHG8duwBAIyI6AKAOgJbWcbMADAYwyVPxekvu3LmxYsUK1KtXDwMHDsSePXtQtWpV5MiRAzNnzkTOnDmxaNEizJkzByEhIbjjjjvQr18/p19/0KBB6NChAwYOHIjHHnvs7/snTpyIdevWISgoCJUrV8aT/r69nDcEB8vm2c8+Kwlh4UJZTTd79o0bid9++z8tiJQUqYd+9eo/j1epIlOEGjaUFk6Qfw5bEkkDq1494Px5ybFffim1tdLu4nzbbTLRLCVFWhoXL95Ycb9MGdm348knZTlMmsa48gBigyt7iGg9gF7MHJPOY00B1Gdrj3Qiag3gfkiy2GK1PkBEdwL4hpnTXUhBRJ0AdAKAkiVLVj9+/PgNjx84cAAVK1Z014/kt/R9cpOkJJkCFBcnl8bHj9+YMIoWlU/BMmWk9VK0qLlYbSAlRUqh7Nghefjnn4GEBJkEFxYGhIdLN1WlStLAK1HC+NIXv0REO5j5luEGj7VAiGg1gPTa2P2ZeamnznszZp4CYAoAREZG2mwdrAo4ISHST1OzpulIfEJQkCSHSpUyP1Z5n8cSCDPXzeZLnAZwZ5rvI6z7zgHIT0Q5mPl6mvuVUkp5kZ07VLcDKG/NuAoF0BzAMmtK2ToATa3j2gLIVovGZDeeL9D3RymVHlPTeJ8lolMAHgTwFRF9a91fnIi+BgCrdfE6gG8BHACwiJn3WS/RB0APIooDUAjAdFdjCQsLw7lz5/RDMgPMjHPnziEsLMx0KEopmzE6iO5tkZGRHBNz43h9UlISTp06dcO6CHWjsLAwREREIMRQVVellFleH0T3FSEhIShTpozpMJRSyufYeQxEKaWUjWkCUUop5RJNIEoppVwSUIPoRJQA4HimB6avMIDf3BiOt/l6/IDv/wy+Hj/g+z+Dr8cPmPkZSjFzkZvvDKgEkh1EFJPeLARf4evxA77/M/h6/IDv/wy+Hj9gr59Bu7CUUkq5RBOIUkopl2gCcd4U0wFkk6/HD/j+z+Dr8QO+/zP4evyAjX4GHQNRSinlEm2BKKWUcokmEKWUUi7RBOIEIqpPRAeJKI6Iok3HkxVENIOIzhLRXtOxuIKI7iSidUS0n4j2EVGU6ZiyiojCiGgbEcVaP8MQ0zG5goiCiWgXEa0wHYsriOgYEe0hot1EdMsuqHZHRPmJaDER/UREB4joQeMx6RiIY0QUDOAQgHoATkH2KWnBzPuNBuYkIqoN4CKA2Rlt+2tnRFQMQDFm3klEeQHsANDYV95/ACAiApCbmS8SUQiATQCimHmL4dCyhIh6AIgEcBszNzAdT1YR0TEAkczskwsJiWgWgP8x8zRrj6RczPy7yZi0BZK5mgDimDmema8BWACgkeGYnMbMGwGcNx2Hq5j5F2beaX39F2RvmBJmo8oaFhetb0Osm09duRFRBICnAUwzHUsgIqJ8AGrD2vuIma+ZTh6AJhBnlABwMs33p+BjH2D+gohKA7gXwFazkWSd1f2zG8BZAKuY2dd+hokA3gSQYjqQbGAA3xHRDiLqZDqYLCoDIAHAJ1Y34jQiym06KE0gyicQUR4AnwN4g5n/NB1PVjFzMjNXAxABoCYR+Ux3IhE1AHCWmXeYjiWbHmbm+wA8CeA1q3vXV+QAcB+AScx8L4BLAIyPx2oCydxpAHem+T7Cuk95iTVu8DmAT5n5C9PxZIfV7bAOQH3TsWRBLQANrTGEBQDqENFcsyFlHTOftv49C2AJpHvaV5wCcCpNy3UxJKEYpQkkc9sBlCeiMtbAVXMAywzHFDCsAejpAA4w83jT8biCiIoQUX7r63DIhIyfzEbl3PYiCgAAAYtJREFUPGbuy8wRzFwa8vu/lplbGQ4rS4gotzUJA1bXzxMAfGZmIjP/CuAkEVWw7voPAOMTSQJ+S9vMMPN1InodwLcAggHMYOZ9hsNyGhHNB/AYgMJEdArAIGaebjaqLKkFoDWAPdYYAgD0Y+avDcaUVcUAzLJm9AUBWMTMPjkV1ocVBbBErkeQA8A8Zl5pNqQs6wrgU+tCNh5Ae8Px6DRepZRSrtEuLKWUUi7RBKKUUsolmkCUUkq5RBOIUkopl2gCUUop5RJNIEoppVyiCUQppZRLNIEoZRAR1SCiH609Q3Jb+4X4TJ0sFdh0IaFShhHRcABhAMIh9Y5GGQ5JKadoAlHKMKs0xXYAiQAeYuZkwyEp5RTtwlLKvEIA8gDIC2mJKOUTtAWilGFEtAxSJr0MZPve1w2HpJRTtBqvUgYRURsAScw8z6rW+wMR1WHmtaZjUyoz2gJRSinlEh0DUUop5RJNIEoppVyiCUQppZRLNIEopZRyiSYQpZRSLtEEopRSyiWaQJRSSrnk/3NK8C5JP9atAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"x = np.linspace(0,2*np.pi,100)\n",
"y1 = np.sin(x)\n",
"y2 = np.cos(x)\n",
"plt.plot(x,y1,\"b-\", label=\"sinus\")\n",
"plt.plot(x,y2,\"r-\", label=\"kosinus\")\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.legend(loc=\"best\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Görüldüğü üzere her bir grafiğe $label$ argümanı üzerinden verilen etiketler, $legends$ fonksiyonunun etiketlerin grafiğin neresine yerleştirileceğini belirleyen $location$ (kısaca $loc$) parametresi üzerinden belirlenen yere yerleştirilmektedir. $loc$ aşağıdaki seçeneklerden biri olabilir:\n",
"\n",
"* plt.legend(loc=0), plt.legend(loc=\"best\") # matplotlib en uygun yeri belirler \n",
"* plt.legend(loc=1), plt.legend(loc=\"upper right\") # sağ üst köşe\n",
"* plt.legend(loc=2), plt.legend(loc=\"upper left\") # sol üst köşe\n",
"* plt.legend(loc=3), plt.legend(loc=\"lower left\") # sol alt köşe\n",
"* plt.legend(loc=4), plt.legend(loc=\"lower righ\") # sağ alt köşe\n",
"* plt.legend(loc=5), plt.legend(loc=\"right\") # sağ taraf \n",
"* plt.legend(loc=6), plt.legend(loc=\"center left\") # merkezin solu\n",
"* plt.legend(loc=7), plt.legend(loc=\"center left\") # merkezin sağı\n",
"* plt.legend(loc=8), plt.legend(loc=\"lower center\") # ortanın aşağısı \n",
"* plt.legend(loc=9), plt.legend(loc=\"upper center\") # ortanın yukarısı\n",
"* plt.legend(loc=10), plt.legend(loc=\"center\") # tam orta\n",
"\n",
"Ayrıca koordinatlar üzerinden istenen koordinatlara da koymak mümkün olduğu gibi pek çok başka seçenek ve ayar da bulunmaktadır. "
]
},
{
"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": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"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",
"plt.plot(x,y,'ro')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gözlemsel hataları çizdirebilmek için $pyplot.errorbar$ fonkisyonu kullanılır."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"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",
"xhata = np.ones(len(x))*0.5\n",
"yhata = np.ones(len(y))*0.1\n",
"plt.errorbar(x,y,xerr=xhata,yerr=yhata,fmt=\".\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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",
"# Isterseniz katsayilari kullanarak da dogru uyumlamanizi\n",
"# verinizin uzerine cizdirebilirsiniz.\n",
"#print(katsayilar[0])\n",
"#yfit = katsayilar[0]*np.array(x) + katsayilar[1]\n",
"#plt.plot(x,yfit,'b-')\n",
"#plt.show()\n",
"#print(\"{:.2f}x + {:.2f}\".format(katsayilar[0], katsayilar[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": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 6 5 4 3 2\n",
"-0.004129 x - 0.001608 x + 0.0363 x - 0.0202 x - 0.1747 x - 0.3066 x - 0.5139\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"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.linspace(x[0], x[-1], 1000)\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",
"print(polinom_6)\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",
"Siyah: “k” \n",
"\n",
"Tüm renkler için [bkz.](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)\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": [
"# Şekil Büyüklük, Boyut ve Çözünürlükleri #\n",
"\n",
"Matplotlib , $figsize$ ve $dpi$ argümanları kullanılarak bir şekil nesnesi oluşturulduğunda en boy oranının (ing. aspect ratio), inç başına nokta sayısı (Dot per Inch, DPI) biriminde çözünürlük ve şekil boyutunun belirlenmesine izin verir. $figsize$, şeklin inç cinsinden genişlik ve yüksekliğini belirleyen bir demet değişkene (tuple) atanabilir. Örnepin 800x600 piksel, 100 nokta / inç rakam oluşturmak için aşağıdaki kod kullanılabilir."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"fig = plt.figure(figsize=(8,6), dpi=100)\n",
"x = np.linspace(0,2*np.pi,100)\n",
"y = np.sin(x)\n",
"plt.plot(x,y,\"b-\")\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"sin(x)\")\n",
"plt.suptitle(\"Sinus Fonksiyonu\")\n",
"plt.title(\"14 Aralik 2020, Pazartesi\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Başa Dön](#Grafik-Çizimi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Şekillerin Dosyaya Kaydı #\n",
"\n",
"Oluşturduğunuz bir şekli istediğiniz bir formatta otomatik olarak bir dosyaya kaydetmek isterseniz bunu matplotlib.pyplot modülünün $savefig$ fonksiyonuyla kolaylıkla yapabilirsiniz. Örneğin bir önceki kod parçasıyla oluşturulan sinüs fonksiyonunun görüntüsünü oluşturulan büyüklükte ve “Portable Graphics Format\" (PNG) formatında kaydetmek için aşağıdaki kodu yazmak yeterlidir. \n",
"\n",
"Şekliniz bu durumda kodu çalıştırdğınız klasöre kaydedilir. Eğer başka bir klasöre kaydetmek isterseniz, bu durumda söz konusu klasörün nerede olduğunu göreli (relative path) ya da mutlak (absolute path) olarak tanımlamanız gerekir."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"fig.savefig(\"sinus_fonksiyonu.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib, PNG, JPG, EPS, SVG, PGF ve PDF gibi çeşitli formatlarda yüksek kaliteli çıktılar üretebilir. Bilimsel makaleler için, mümkün olduğunda EPS ya da PDF kullanmanızı öneririm. (Pdflatex ile derlenen LaTeX belgeleri $includegraphics$ komutu kullanılarak PDF'ler içerebilir). Dosya türünü sadece uzantıyla kontrol edebildiğiniz gibi dosya türlerinin ilgili özellikleri de bu fonksiyonla kontrol edilebilir; ancak bu dersin kapsamı dışındadır."
]
},
{
"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 zaman ($t$) değeri, b) 100 zaman ($t$) değeri için çözünüz. Her iki çözümün zamana ($t$) karşılık yükseklik ($y$) grafiğini, mavi kesiksiz eğri ile 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 adet Fahrenheit derece değerini, 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 hesaplanan iki ifade belirli bir Fahrenheit değerinde eşit olmakta (doğrular kesişmekte) ve yaklaşık ifade ile hesaplanan değerler, tam ifade ile hesaplananlardan giderek uzaklaşmaktadır. Her iki doğrunun kesiştiği 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 $yatay\\_atis$ adında bir fonksiyon olarak kodlayınız. $g = 9.81$ ve $y_0 = 0$ parametrelerini bu varsayılan değerlerle anahtar kelime argümanları olarak, diğer tüm parametreleri ($x$, $\\theta$, $V_0$) ise konum argümanı olarak alınız.\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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
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}