Mayank mittal 2018A7ps0375

2018A7ps0375G mayank mittal.py

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+print(2+0+1+8+7+0+3+7+5)

DATASET_CREATION.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DATASET_CREATION.ipynb",
+      "provenance": [],
+      "authorship_tag": "ABX9TyNjpeX20P2ml8p5yu4let+2",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Mkmittal/CTEPractice1/blob/master/DATASET_CREATION.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lFiYDUrViFs1"
+      },
+      "source": [
+        "import pandas as pd\r\n",
+        "import numpy as np\r\n",
+        "import math as m"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yrZ1Sm5miR9g"
+      },
+      "source": [
+        "sample_size = 8192*2\r\n",
+        "class Gausian_glitch:\r\n",
+        "  def __init__(self,h0,tao,t0):\r\n",
+        "    self.h=[]\r\n",
+        "    self.t_start = t0 - 1 \r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      self.h.append(h0*(m.exp(-1*((t-t0)/2*(tao**2)))))"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6BpgHWPxljbl"
+      },
+      "source": [
+        "class sine_Gaussian_glitch:\r\n",
+        "  def __init__(self,h0,f0,Q,t0):\r\n",
+        "    self.h=[]\r\n",
+        "    self.tao=Q/((2**0.5)*f0*m.pi)  \r\n",
+        "    self.t_start = t0 - 1\r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      self.h.append(h0*(m.exp(-1*(((t-t0)**2)/2*((tao)**2))))*m.sin(2*m.pi*f0*(t-t0)))"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5oY-KvWHlj-5"
+      },
+      "source": [
+        "class ring_Down_glitch:\r\n",
+        "  def __init__(self,h0,f0,Q,t0):\r\n",
+        "    self.h=[]\r\n",
+        "    self.tao=Q/((2**0.5)*f0*m.pi)  \r\n",
+        "    self.t_start = t0 - 1 \r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      if(t>t0):\r\n",
+        "        self.h.append(h0*(m.exp((-1*((t-t0)**2))/(2*tao))*m.sin(2*m.pi*f0*(t-t0))))\r\n",
+        "      else:\r\n",
+        "        self.h.append(0)"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6_Tat7w_l_-3"
+      },
+      "source": [
+        "class Chirp_like_glitch:\r\n",
+        "  def __init__(self,h0,m1,m2,t0):\r\n",
+        "    self.h = []\r\n",
+        "    self.Mc = ((m1*m2)**(3/5))/((m1+m2)**(1/5)) \r\n",
+        "    self.t_start = t0 - 1 \r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      taoc = t-t0\r\n",
+        "      self.hplus.append(h0*(taoc**(-1/4))*m.cos(self.phi(t,taoc)))\r\n",
+        "      self.hcross.append(h0*(taoc**(-1/4))*m.sin(self.phi(t,taoc)))\r\n",
+        "      self.h.append(hcross+hplus)\r\n",
+        "  \r\n",
+        "  def phi(self,t,taoc):\r\n",
+        "    G = 6.673*(10**-11)\r\n",
+        "    return (-2*((5*G*self.Mc)/(c**3))**(-1*(5/8)))*(taoc**(5/8))"
+      ],
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "K_5i5MN8rSd6"
+      },
+      "source": [
+        "class Scattered_light_like_glitch:\r\n",
+        "  def __init__(self,h0,f0,tao,t0):\r\n",
+        "    self.h = []\r\n",
+        "    self.t_start = t0 - 1 \r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      self.h.append(h0*m.sin(sel.phiSL(t,t0,f0))*m.exp(-1*(((t-t0)**2)/2*tao)))\r\n",
+        "  \r\n",
+        "  def phiSL(self,t,t0,f0):\r\n",
+        "    k = 0.5\r\n",
+        "    return 2*m.pi*f0*(t-t0)*(1-k*((t - t0)**2))"
+      ],
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "y0z-AgHtu0oG"
+      },
+      "source": [
+        "class Whistle_like_glitch:\r\n",
+        "  def __init__(self,h0,f0,tao,t0):\r\n",
+        "    self.h = []\r\n",
+        "    self.t_start = t0 - 1 \r\n",
+        "    for i in range(0,sample_size):\r\n",
+        "      t = self.t_start + i*(2/sample_size)\r\n",
+        "      self.h.append(h0*m.sin(sel.phiSL(t,t0,f0,tao))*m.exp(-1*(((t-t0)**2)/2*tao)))\r\n",
+        "  \r\n",
+        "  def phiSL(self,t,t0,f0,tao):\r\n",
+        "    k = 3*tao\r\n",
+        "    return 2*m.pi*f0*(t-t0)*(1-k*((t - t0)**2))"
+      ],
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VvPTlSu7vKbe"
+      },
+      "source": [
+        "#glitch families have been defined above\r\n",
+        "#gaussian noise data of sensitivity curve of H1 detector is required to be added into h "
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ku7WE1IawKfo"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}