{ "cells": [ { "cell_type": "code", "execution_count": 96, "id": "c7f80873-306a-4f53-8944-42df92f2bad3", "metadata": {}, "outputs": [], "source": [ "import random \n", "import matplotlib.pyplot as plt\n", "import numpy as np \n", "\n", "#inizialmente pensavo di inserire i parametri da un file o da tastiera, tuttavia ho pensato che considerando che i parametri sono soltanto quattro e che il Python è precompilato sarebbe stato più veloce cambiarli direttamente nel codice qui sotto.\n", "t = 10000 #int(input()) #tempo totale della simulazione\n", "n = 100 #int(input()) #numero di intervalli per ogni step\n", "x0 = 0 #float(input()) #valore iniziale\n", "k = 0.1 #float(input()) #valore di k\n", "\n", "dt = 1/n #lasso di tempo per ogni intervallo\n", "N = t*n #numero totali di punti" ] }, { "cell_type": "code", "execution_count": 97, "id": "e848056a-3a27-4847-9ae4-e4001d2fd6d3", "metadata": {}, "outputs": [], "source": [ "Z = np.zeros(N) #creo un array vuoto pieno di zeri\n", "\n", "for i in range(0,N):\n", " Z[i] = random.gauss(0,2*dt) #popolo Z di numeri generati gaussianamente, la media è 0 e la varianza 2*dt" ] }, { "cell_type": "code", "execution_count": 98, "id": "30b0a214-a4b2-46c8-b5a1-411abf88f2b2", "metadata": {}, "outputs": [ { "data": { "image/png": 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PSNotaY+kX0ra06yemZlZlQv/nwHeExEP5W6MmZmNLVWuyexwgLHxyMNEZoNXpSfTLelmimVl9tYSI+KbuRplZmZjQ5WezKEUi1f+GfCe9Hp3zkaZ2cjiXp0NVJW1y85rVsbMzKyRKrPLjpV0l6QH0uc3Svqf+ZtmZmajXZVrMlcD/w34Z4CI2CDpRuDvczbMbLh4aMhs6FS5JvOSiLi3Lm1fjsaYmdnYUiXI/ELSa4AAkHQm0O8jks3MzKBakLmAYqjs9ZIeAz4G/HWVjUuaK2mTpB5JSxvkS9IVKX+DpDmlvBWSdtauBZXSD5d0Z1qF4E5Jh5XyLkrb2iTp9CptNDOzfKqsXbYlIk4DOoDXR8RbImJrs3qSJgBXAvOAWcDZkmbVFZsHzEyvRcBVpbxrgbkNNr0UuCsiZgJ3pc+kbS8Ejkv1vpzaYGZmw6TphX9Jf1v3GYCI+GSTqicCPRGxJdVbCSwAHiyVWQBcFxEBrJE0SdKUiNgeEfdImt5guwsonm8D8FXgX4ELU/rKiNgLPCqpJ7XhB82O0cyaq02I2HrZu4a5JTaaVBku+3XptZ+i9zG9Qr2pwLbS596U1mqZekdExHaA9POVg9iWmZllVOVmzMvLnyX9E6VnzPSj0dMzYwBlqqq0LUmLKIbmOOqoowa4KxurPH3ZbGhV6cnUewlwTIVyvcCRpc/TgMcHUKbeDklTANLPna1sKyKWR0RnRHR2dHQ0PQgzMxu4Knf8/zjN/NogaSOwCfhihW2vBWZKmiFpIsVF+foeUBdwTppldjKwuzYU1o8u4Nz0/lzg26X0hZIOljSDYjJB/f09ZmbWRlXu+C8vhrmPYun/pjdjRsQ+SUuA1cAEYEVEbJS0OOUvA1YB84EeikU4n18nTdJNFBf4J0vqBS6JiGuAy4BbJJ0P/Ax4f9reRkm3UEws2AdcEBH7KxyfmZllomJiVz8FpMP7y4+IJ4e0RW3U2dkZ3d3dw90MG0F8TaYazzAb3ySti4jOKmWr9GTuo7jW8RTFxfVJFD0IKC6sV7k+Y2Zm41CVC///QvH45ckR8QqK4bNvRsSMiHCAMTOzPlXpybwpIhbXPkTEdyR9KmObzNrOw2RmeVQJMr9Iz4+5nmJ47C+BJ7K2yszMxoQqw2VnU6xbdlt6daQ0MzOzflW54/9J4KOSXhYRv2pDm8xshPM6ZlZVlZsx3yzpQdLClpL+RNKXs7fMzMxGvSrDZZ8HTiddh4mIHwGn5myUmZmNDZXWLouIbXVJvpPexgzPLDPLp0qQ2SbpzUBImijpE8BDmdtlZqOAA7Q1UyXILKZ4BPNUipWOZ6fPZmZm/ep3dll6fPEXIuKDbWqPWdv4r3Cz/PrtyaRVjDvSUv1mZmYtqXLH/1bg3yV1UTyCGYCI+FyuRpmZ2djQZ09G0tfS27OA21PZQ0ovMzOzfvXXkzlB0tEUy/r/7za1x8xGGd/9b/3pL8gso1jmfwZQfrKX8HNkzMysgj6HyyLiioj4I+ArEXFM6eXnyJiZWSVN75OJiL8e6MYlzZW0SVKPpKUN8iXpipS/QdKcZnUl3SxpfXptlbQ+pU+X9Gwpb9lA221mZkOjyuyyAUn32FwJvJPiJs61kroi4sFSsXnAzPQ6CbgKOKm/uhFxVmkflwO7S9vbHBGzcx2TjR2+R8asPSqtXTZAJwI9EbElIp4DVgIL6sosAK6LwhpgkqQpVepKEvAB4KaMx2BmFTlwWyM5g8xUoLywZm9Kq1KmSt1TgB0R8UgpbYak+yXdLemURo2StEhSt6TuXbt2VT8aMzNrWc4gowZpUbFMlbpnc2AvZjtwVEQcD/wNcKOkQ1+wkYjlEdEZEZ0dHR19Nt7MzAYv2zUZit7HkaXP04DHK5aZ2F9dSQcB7wNOqKVFxF5gb3q/TtJm4FgOnH5tZmZtlDPIrAVmSpoBPAYsBP5TXZkuYImklRQX/ndHxHZJu5rUPQ14OCJ6awmSOoAnI2K/pGMoJhNsyXRsNkr5uoFZe2ULMhGxT9ISYDUwAVgRERslLU75y4BVwHygB3gGOK+/uqXNL+SFF/xPBT4paR/FQ9UWR8STuY7PzF7Id/9bvZw9GSJiFUUgKactK70P+ng2TaO6pbwPNUi7Fbh1EM01M7MhlvPCv5mZjXMOMmZmlo2DjJmZZeMgY+OGZ5aZtZ+DjJkNOQd0q3GQMTOzbBxkzMwsm6z3yZiNBB66MRs+7smYmVk2DjJmZpaNg4yZZTF96R0eqjQHGTMzy8dBxsY0/yVtNrwcZMwsKwf68c1BxszMsvF9MjYm+a9ns5HBPRkzM8sma5CRNFfSJkk9kpY2yJekK1L+BklzmtWVdKmkxyStT6/5pbyLUvlNkk7PeWxmZtZctuEySROAK4F3Ar3AWkldEfFgqdg8YGZ6nQRcBZxUoe7nI+Kf6vY3C1gIHAe8GviepGMjYn+uYzQzs/7l7MmcCPRExJaIeA5YCSyoK7MAuC4Ka4BJkqZUrFtvAbAyIvZGxKNAT9qOmQ0z35g5fuUMMlOBbaXPvSmtSplmdZek4bUVkg5rYX9IWiSpW1L3rl27WjkeMzNrUc4gowZpUbFMf3WvAl4DzAa2A5e3sD8iYnlEdEZEZ0dHR4MqZmY2VHJOYe4Fjix9ngY8XrHMxL7qRsSOWqKkq4HbW9ifjQMeljEbOXL2ZNYCMyXNkDSR4qJ8V12ZLuCcNMvsZGB3RGzvr266ZlPzXuCB0rYWSjpY0gyKyQT35jo4M2udr82MP9l6MhGxT9ISYDUwAVgRERslLU75y4BVwHyKi/TPAOf1Vzdt+jOSZlMMhW0F/irV2SjpFuBBYB9wgWeWmZkNL0W84LLFuNHZ2Rnd3d3D3QwbIv4LefTYetm7hrsJNgiS1kVEZ5WyvuPfzNrOfxCMHw4yZmaWjRfItFHPfxWbjVzuyZjZsPBMs/HBQcbMzLJxkDEzs2wcZMzMLBsHGRvVPKZvNrI5yJjZsPIfCmObg4yZmWXj+2RsVPJfv2NL7ffp5WbGHvdkzMwsGwcZMzPLxkHGRh0PlZmNHg4yZjZi+A+IscdBxszMsnGQMTOzbLIGGUlzJW2S1CNpaYN8Sboi5W+QNKdZXUmflfRwKn+bpEkpfbqkZyWtT69lOY/N2s+r9o4P/j2PLdmCjKQJwJXAPGAWcLakWXXF5gEz02sRcFWFuncCb4iINwI/AS4qbW9zRMxOr8V5jsyGg//TMRudcvZkTgR6ImJLRDwHrAQW1JVZAFwXhTXAJElT+qsbEd+NiH2p/hpgWsZjMLNh4j8sxoacQWYqsK30uTelVSlTpS7Ah4HvlD7PkHS/pLslndKoUZIWSeqW1L1r165qR2JmZgOSM8ioQVpULNO0rqSLgX3ADSlpO3BURBwP/A1wo6RDX7CRiOUR0RkRnR0dHU0OwczMBiPn2mW9wJGlz9OAxyuWmdhfXUnnAu8G3hERARARe4G96f06SZuBY4HuoTgYGx4eMhnfvKbZ6JezJ7MWmClphqSJwEKgq65MF3BOmmV2MrA7Irb3V1fSXOBC4IyIeKa2IUkdacIAko6hmEywJePxmZlZE9mCTLo4vwRYDTwE3BIRGyUtllSb+bWKIhD0AFcDH+mvbqrzJeAQ4M66qcqnAhsk/Qj4BrA4Ip7MdXyWn3sxVuPvwuiVdan/iFhFEUjKactK7wO4oGrdlP7aPsrfCtw6mPaamdnQ8vNkbMTxX63WiK/PjE5eVsbMzLJxkDEzs2wcZGxE8VCZNePvyOjiIGNmZtn4wr+NCP7r1FrhSQCjh3syNuwcYGyg/N0Z+dyTsWHj/yDMxj73ZMxsVPNDzkY2BxkbFv5PwWx88HCZtZWDi+VS/m55QsDI4Z6MmZll4yBjbeNejLWLv2sjh4fLLDv/g7fh4OGzkcE9GcvKAcZsfHNPxoacA4uNNO7VDB8HGRsSDiw2WnhJmvZykLEBcVCx0c69m/bIGmQkzQW+CEwA/k9EXFaXr5Q/H3gG+FBE3NdfXUmHAzcD04GtwAci4qmUdxFwPrAf+K8RsTrn8Y03Diw2VjX6bjvwDI1sQUbSBOBK4J1AL7BWUldEPFgqNg+YmV4nAVcBJzWpuxS4KyIuk7Q0fb5Q0ixgIXAc8Grge5KOjYj9uY5xrHNQsfHMPZ2hkbMncyLQExFbACStBBYA5SCzALguIgJYI2mSpCkUvZS+6i4A3pbqfxX4V+DClL4yIvYCj0rqSW34QcZjHBUcLMwGZ7D/hsZzkMoZZKYC20qfeyl6K83KTG1S94iI2A4QEdslvbK0rTUNtnUASYuARenjryRtqnpAdSYDvxhg3ZxGarvAbRsot611I6pd+vQBH0dU2+pUbdvRVTeYM8ioQVpULFOl7kD2R0QsB5Y32VZTkrojonOw2xlqI7Vd4LYNlNvWupHaLhh/bct5M2YvcGTp8zTg8Ypl+qu7Iw2pkX7ubGF/ZmbWRjmDzFpgpqQZkiZSXJTvqivTBZyjwsnA7jQU1l/dLuDc9P5c4Nul9IWSDpY0g2Iywb25Ds7MzJrLNlwWEfskLQFWU0xDXhERGyUtTvnLgFUU05d7KKYwn9df3bTpy4BbJJ0P/Ax4f6qzUdItFJMD9gEXZJ5ZNught0xGarvAbRsot611I7VdMM7apmJil5mZ2dDzAplmZpaNg4yZmWXjIFMi6XBJd0p6JP08rI9yKyTtlPRA1fqSLpLUI2mTpNMztm1u2kdPWhGhln6zpPXptVXS+pQ+XdKzpbxlw9C2SyU9VmrD/FLecJ+3z0p6WNIGSbdJmpTSB3Te+tpPKV+Srkj5GyTNqdDGSseYq22SjpT0fUkPSdoo6aOlOn3+btvRtpS3VdKP0/67S+mDPm+DOGevK52T9ZL2SPpYymvXOXu9pB9I2ivpE1XqDuicRYRf6QV8Blia3i8FPt1HuVOBOcADVeoDs4AfAQcDM4DNwIShbhvFJInNwDHAxLTPWQ3KXQ78bXo/vf44cpy3/toGXAp8okGdYT9vwJ8BB6X3ny79Tls+b1V+PxQTYb5Dcd/XycAPK7Sx0vc2Y9umAHPS+0OAnzT73barbSlvKzB5IN+NnO2q287PgaPbfM5eCbwJ+Ify/ob6u+aezIEWUCxVQ/r5540KRcQ9wJMt1H9+yZuIeJRiNt2JGdr2/FI+EfEcUFuO53mSBHwAuKnF/WdvWx/bHdbzFhHfjYh9qdwaivuvBqrKOXh+qaWIWAPUllrqr26l722utkXE9kgL20bEL4GHaLDaxiAM5rz1Z7Dnbaja9Q5gc0T8tMX9D6ptEbEzItYCv22hbsvnzEHmQAcsWUMR6Yeifl/L5wx126rs5xRgR0Q8UkqbIel+SXdLOqXFdg1V25ak4YQVpS74SDpvAB+m+Ku0ptXzVmU/rSy1VKs72O/tYNv2PEnTgeOBH5aSG/1u29m2AL4raZ2KZaVqBnvehuScUdwHWP9HXzvO2UDqtnzOxt3zZCR9D3hVg6yLc+62QdoL5o4PQduq7OdsDvxCbweOiognJJ0AfEvScRGxp41tuwr4VPr8KYrhvA83qdOuttX2cTHF/Vc3pKRK563V/fRTZiBLLbViMG0rMqWXAbcCHyudh75+t+1s259GxOMq1jm8U9LDaTRisIbinE0EzgAuKuW365zlqPsC4y7IRMRpfeVJ2lHr/uvAJWuq6qt+pSVvhqBt/e5H0kHA+4ATSvvcC+xN79dJ2gwcC3SXtpO1bRGxo7Stq4HbqxxPO9qWtnEu8G7gHZEGo6uet1b206TMxH7qDvZ7O9i2IelFFAHmhoj4Zq1AP7/btrUtImo/d0q6jWI46B4Gf94G1a5kHnBf+Ty18ZwNpG7L58zDZQfqa8mawdYfiiVvqrSt2VI+pwEPR0RvLUFSh4rn9yDpmNS2Le1sW90Y9XuB2qy9YT9vKh6edyFwRkQ8U6swwPPW7qWWWjHgtqXrfNcAD0XE58oV+vndtqttL5V0SGrLSykmcpS/X4M5b4P5fdbUjyy085wNpG7r56zZzIDx9AJeAdwFPJJ+Hp7SXw2sKpW7iWK45LcUUf/8/uqnvIspZmxsAuZlbNt8itk9m4GL67ZxLbC4Lu0vgI0UM0juA97T7rYBXwN+DGxIX+IpI+W8UUw22AasT69lgzlvjfYDLK79XiiGKq5M+T8GOiu0sc/vXYvnakBtA95CMZyyoXSe5jf73bapbcek39GP0u9rSM/bIH+fLwGeAF5et812nbNXUfz/tQd4Or0/dKi/a15WxszMsvFwmZmZZeMgY2Zm2TjImJlZNg4yZmaWjYOMmZll4yBjNsJI+tUA6vy/HG0xGyxPYTYbYST9KiJeVrHshMj7mHGzQXFPxiwzSZ+W9JHS50slXSLpLkn3qXjWyQtWpE53iX9W0gOpzFkp/W0qnt9yI8VNewPq/Zi1g3syZplJOh74QkS8NX1+EJgLPB0ReyRNpniMwMyIiFpPRtJfUNyhPReYTLHcx0nA64A7gDdE8QiElno/Zu007hbINGu3iLhf0islvRroAJ6iWJbo85JOBX5HsZT6ERQPr6p5C3BTGg7bIeluiodM7QHurQUYs5HMQcasPb4BnEmxXtRK4IMUAeeEiPitpK3Ai+vqNFpyvebXORppNtR8TcasPVZSrGZ7JkXAeTmwMwWYtwNHN6hzD3CWpAmSOige+93qKtRmw8o9GbM2iIiNacn5x6JYgv4G4P9K6qZYtfjhBtVuA/4DxQrCAfz3iPi5pNe3q91mg+UL/2Zmlo2Hy8zMLBsHGTMzy8ZBxszMsnGQMTOzbBxkzMwsGwcZMzPLxkHGzMyy+f9rkTHqDWzESAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#visualizziamo Z per vedere se è effettivamente la gaussiana voluta\n", "wts1 = np.ones_like(Z) / len(Z) #np.ones_like(Z) crea un array lungo quanto Z ma popolato solo da 1s\n", "\n", "plt.title(\"gaussiana\")\n", "plt.xlabel(\"valori\")\n", "plt.ylabel(\"frequenza\")\n", "plt.hist(Z,200,weights=wts1) #weights=wts1 rende l'istogramma normalizzato\n", "plt.show() #in una IDE non serve a niente, da terminale meglio metterlo perchè a volte non mostra i grafici nonostante li computi" ] }, { "cell_type": "code", "execution_count": 99, "id": "2178d82f-e33e-4ab5-8252-64a75593bd00", "metadata": { "tags": [] }, "outputs": [], "source": [ "x = np.zeros(N) #creo un array vuoto piena di zeri\n", "x[0] = x0 #ci aggiungo l'elemento x0\n", "\n", "#adesso applichiamo eulero, il coefficiente di drift k è positivo o negativo in base al segno di x[i]\n", "for i in range(0, N-1):\n", " if x[i] > 0 :\n", " x[i+1] = x[i] + (-k) * dt + Z[i] #h=k, g=1\n", " else:\n", " x[i+1] = x[i] + (+k) * dt + Z[i] #h=-k, g=1" ] }, { "cell_type": "code", "execution_count": 100, "id": "7e6a12a8-3ea7-4b96-8ad2-3b3b55b950c0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#visualizziamo l'andamento dei punti\n", "plt.title(\"andamento temporale dei punti x\")\n", "plt.xlabel(\"tempo\")\n", "plt.ylabel(\"x\")\n", "plt.plot(x) \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 101, "id": "fc950556-202a-4792-86c1-48b92dc32046", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#visualizziamo la distribuzione dei punti, normalizzata\n", "wts2 = np.ones_like(x) / len(x) #np.ones_like(x) crea un array lungo quanto x ma popolato solo da 1s\n", "\n", "plt.title(\"distribuzione normalizzata dei punti\")\n", "plt.xlabel(\"punti\")\n", "plt.ylabel(\"frequenza\")\n", "plt.hist(x,200, weights=wts2) #weights è il fattore moltiplicativo dato ad ogni valore\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 102, "id": "9a166f23-f82f-491d-8714-f9c91331c9e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "la media è: -0.0023016017678946393\n", "la varianza è: 0.07325140119578938\n" ] } ], "source": [ "print(\"la media è:\", np.mean(x))\n", "print(\"la varianza è:\", np.var(x))" ] }, { "cell_type": "code", "execution_count": 103, "id": "8495d3c8-670b-4ba2-a95e-42f0b449933b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#qui facciamo l'autocorrelazione e vediamo che viene\n", "plt.acorr(x,maxlags=10000) #il maxlags è 100*100 perché in realtà fra x0 e x1 ci sono 100 punti di distanza nel nostro array, questo sarebbe il lag a 100\n", "plt.yscale('log') #questo setta l'asse y logaritmico\n", "plt.xlim(left=0,right=1500) #questo limita l'asse x\n", "plt.ylim(top=1) #questo limite superiormente l'asse y\n", "plt.title(\"autocorrelazione\")\n", "plt.xlabel(\"lag\")\n", "plt.ylabel(\"log autocorrelation\")\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }