waves_testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# waves - testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import math\n",
    "from scipy import io\n",
    "import scipy.signal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.fftpack\n",
    "import scipy.integrate\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "import time\n",
    "import matplotlib.colors as colors\n",
    "import random\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**main variables**\n",
    "<br> f - main frequency (Hz)\n",
    "<br> full_f - the entire time-frequency function (Hz) (\"x\" as x axis)\n",
    "<br> frangemax - x axis length of Fourier-transform, note, that this does not affect Fourier calculation\n",
    "<br> samples - sample points of the first graph\n",
    "<br> s_rate - sample rate (Hz)\n",
    "<br> amp - amplitude of signal\n",
    "<br> randamp - amplitude of noise (random noise)\n",
    "<br> <br> First, we create our signal and noise. In deafult: a sine wave, and a white noise.\n",
    "<br> Or import one in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000\n"
     ]
    }
   ],
   "source": [
    "#%%script false\n",
    "f=100\n",
    "t=4\n",
    "samples=20000 #random.randint(400,50000)\n",
    "s_rate=4000\n",
    "t=samples/s_rate\n",
    "randamp=0#1\n",
    "nyquist=samples/2/t\n",
    "frangemax=200#nyquist\n",
    "\n",
    "xf=np.linspace(0.0, frangemax, int(frangemax*t+1))\n",
    "x = np.linspace(0.0, t, samples)\n",
    "full_f=f+x/100000#*16#(f-60/((x/1.5)+1.8))*7-30  #np.linspace(f, f, samples) #      f+x*0.01     15+10*np.sin(np.pi*x)#   \n",
    "full_f_phase = np.zeros(samples)\n",
    "for i in range (samples):\n",
    "    full_f_phase[i]=np.sum(full_f[:i])*2/s_rate\n",
    "    \n",
    "amp=x/10+10#np.sin(np.pi*x/t)#*(0.2*abs(np.sin(2*np.pi*x)+1)+0.1)#0.4+x/t*0.6\n",
    "shot = \"testing\"\n",
    "\n",
    "s_det = amp*np.sin(full_f_phase*np.pi) #Changing freqency. old way: *(1+(x*10**2)**0.5)\n",
    "s_noise = (np.random.rand(samples)-0.5)*randamp\n",
    "s=(s_det+s_noise)#+200000#/(0.5*randamp+amp.max())*amp.max()\n",
    "print (samples)\n",
    "#f1=f; f2=f/np.sqrt(2); s=np.sin(f1*2*np.pi*x)+np.sin(f2*2*np.pi*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "path ='C:\\\\Users\\\\Marci\\\\Documents\\\\transientwaves\\\\imputdata\\\\in\\\\'#pc\n",
    "#path ='C:\\\\Users\\\\win10\\\\Downloads\\\\transient-waves\\\\inputdata\\\\in\\\\'#laptop\n",
    "\n",
    "for filename in glob.glob(os.path.join(path, '*.sav')):\n",
    "    sav1 = io.readsav(filename, python_dict = True)\n",
    "    print('Loaded: '+filename)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "\n",
    "shot_i=0\n",
    "#print(sav1)\n",
    "s=np.array(sav1['data'])#[shot_i]\n",
    "x=np.array(sav1['timeax'])\n",
    "#print(\"\\n\", sav1['channels'])\n",
    "print(\"\\n\", s)\n",
    "#print(\"\\n\", sav1['channels'][shot_i])\n",
    "sxr_name=str(sav1['channels'][shot_i])[2:4]+'R'+str(sav1['channels'][shot_i])[5:-1]\n",
    "shot=os.path.basename(filename)[:4]+' '+os.path.basename(filename)[5:10]+', '+sxr_name#str(sav1['channels'][shot_i])[2:-1]\n",
    "print(\"\\n\",shot)\n",
    "\n",
    "\n",
    "#C:\\\\Users\\\\win10\\\\Downloads\\\\transient-waves\\\\inputdata\\\\AUGD_34924_2208_2238_Loaded-with-MTR.sav - nullközepű \n",
    "#AUGD_34924_J051_240_245_Loaded-with-MTR.sav - nem nullközepű SXR\n",
    "#AUGD_28881_Loaded-with-MTR.sav - toroidális mágneses tekercs AUGD_28881_Loaded-with-MTR.sav\n",
    "#C:\\\\Users\\\\Marci\\\\Documents\\\\transientwaves\\\\imputdata\\\\AUGD_34924_2208_2238_Loaded-with-MTR.sav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "200\n",
      "20000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\Anaconda3\\Lib\\site-packages\\ipykernel_launcher.py:12: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "#%%script false\n",
    "min_t=0#1.9\n",
    "max_t=t#2.2\n",
    "\n",
    "s = s.astype(float)\n",
    "x = x.astype(float)\n",
    "samples=s.size\n",
    "#full_f = np.zeros(samples)\n",
    "t=max(x)-min(x)\n",
    "nyquist=samples/2/t\n",
    "#frangemax=nyquist#250000#\n",
    "xf=np.linspace(0.0, frangemax, frangemax*t+1)\n",
    "s_rate=x.size/t\n",
    "\n",
    "t=max_t-min_t\n",
    "min_x=int(s_rate*min_t)\n",
    "max_x=int(s_rate*max_t)\n",
    "samples=max_x-min_x\n",
    "\n",
    "x=x[min_x:max_x]\n",
    "s=s[min_x:max_x]\n",
    "\n",
    "print(max_t)\n",
    "print(frangemax)\n",
    "print(x.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The widget below controls the wisible range of the chart under it. Note that this is only for visualisation, the program calculates with the whole 0 - t range. The zooming is exponential. Value 0 displays the whole range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c765b6cbbd4248948340a636a4702847",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%script false\n",
    "usewidgets=1\n",
    "import ipywidgets as widgets\n",
    "view=widgets.FloatSlider()\n",
    "if usewidgets==1:\n",
    "    display(view)\n",
    "else:\n",
    "    view.value=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.00005\n"
     ]
    }
   ],
   "source": [
    "#%%script false\n",
    "#plt.figure(figsize=(14,5))\n",
    "#plt.title(shot+' szonda jele')\n",
    "#plt.plot(x,s)\n",
    "#yaxismax=1/2**(view.value/12)\n",
    "#plt.xlim(min_t,max_t-(max_t-min_t)*view.value/100.6)\n",
    "#plt.ylabel('radiális mágneses tér változás (T/s)')\n",
    "#plt.xlabel('idő (s)')\n",
    "#plt.savefig(shot+' in.pdf')\n",
    "#plt.show()\n",
    "\n",
    "plt.figure(figsize=(9,3.5))\n",
    "plt.title('Bemenő teszt csörp jel')\n",
    "plt.plot(x,s)\n",
    "#yaxismax=1/2**(view.value/12)\n",
    "#plt.axis([0,t,min(s),max(s)])\n",
    "plt.ylabel('Amplitúdó')\n",
    "plt.xlabel('idő (s)')\n",
    "plt.grid(True)\n",
    "#plt.savefig(shot+' s_in_z.pdf')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(9,3.5))\n",
    "plt.title(\"A bemenő jel frekvenciája\")\n",
    "plt.plot(x,full_f)\n",
    "yaxismax=1/2**(view.value/12)\n",
    "plt.xlim(-0.01,t/(1.05**view.value))\n",
    "plt.ylim(0,f+1)\n",
    "plt.ylabel('Frekvencia (Hz)')\n",
    "plt.xlabel('idő (s)')\n",
    "plt.grid(True)\n",
    "#plt.savefig('freq_in.pdf')\n",
    "plt.show()\n",
    "\n",
    "print(full_f[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%script false\n",
    "yf = scipy.fftpack.fft(s)\n",
    "yfabs=np.abs(yf[:xf.size])/samples*2\n",
    "#yfabs=(np.abs(yf[:frangemax])*np.abs(yfimag)*np.abs(yfreal))\n",
    "\n",
    "plt.figure(figsize=(9,3.5))\n",
    "plt.plot(xf,yfabs,\".-\")\n",
    "#plt.axis([0,frangemax,0,1.5/rs])\n",
    "plt.xlim((0,frangemax))\n",
    "plt.xlabel('Frekvencia (Hz)')\n",
    "plt.ylabel('Fourier amplitúdó')\n",
    "#plt.xscale('log')\n",
    "plt.title('Fourier-transzformáció abszolút értéke (Teszt jel)')\n",
    "plt.grid(True)\n",
    "plt.savefig('t_fft2.pdf')\n",
    "plt.show()\n",
    "\n",
    "#print(yfang[f*int(t)+1])\n",
    "#print(yfang[f*int(t)])\n",
    "#print(yfang[f*int(t)-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short-time Fourier transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherent gain of window: 0.18795013305919642\n",
      "a\n",
      "9.628514286924541e-21\n",
      "15498799.05937216\n",
      "300\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t0=time.time()\n",
    "win_r = 2000 #1000\n",
    "d_win=100#random.randint(200,300)\n",
    "std=300 #150\n",
    "win_base = scipy.signal.gaussian(2*win_r+1, std=std)#np.wining(2*win_r+1)\n",
    "coherent_gain=np.sum(win_base)/(2*win_r+1)\n",
    "print(\"Coherent gain of window:\", coherent_gain)\n",
    "\n",
    "plt.plot(win_base,\".-\")\n",
    "plt.title('Gaussian window')\n",
    "plt.xlabel('sample no.')\n",
    "plt.ylabel('Gaussian value')\n",
    "#plt.show()\n",
    "\n",
    "z_padded_s = np.zeros (samples+2*win_r)\n",
    "z_0=z_padded_s\n",
    "z_padded_s[win_r:-win_r] = s\n",
    "z_padded_s[0:win_r]=np.average(z_padded_s[win_r+1:2*win_r])\n",
    "z_padded_s[-win_r:]=np.average(z_padded_s[-2*win_r:-win_r-1])\n",
    "x_win=np.linspace(0.0, t+(2*win_r/s_rate), samples+2*win_r)\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.plot(z_padded_s,\"-\",linewidth = 2)#x_win,idő (s),max(x_win)\n",
    "plt.xlim((0,samples+2*win_r))\n",
    "plt.title('A kiegészített jel')\n",
    "plt.xlabel('Adatpont')\n",
    "plt.ylabel('Amplitúdó')\n",
    "plt.savefig('sxr2_p.pdf')\n",
    "#plt.show()\n",
    "\n",
    "win = np.zeros((int(samples/d_win+1), 2*win_r+1))\n",
    "for i in range(int(samples/d_win+1)):\n",
    "    win [i]=win_base\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.imshow(win,aspect='auto')\n",
    "plt.title('Sliding Hann window')\n",
    "plt.xlabel('sample no')\n",
    "plt.ylabel('sliding value')\n",
    "#plt.subplots_adjust(right=0.95)\n",
    "cax = plt.axes([0.8, 0.15, 0.03, 0.5])\n",
    "plt.colorbar(cax=cax).set_label('Signal value')\n",
    "#plt.show()\n",
    "\n",
    "z_win = np.zeros((int(samples/d_win+0.9), win_r*2+1))\n",
    "for i in range(int(samples/d_win+0.9)):\n",
    "    if i<1000001:\n",
    "        z_win[i] = win[i] * (z_padded_s [d_win*i:d_win*i+win_r+win_r+1]-np.average(z_padded_s [d_win*i:d_win*i+win_r+win_r+1]))\n",
    "    #z_win[i] = win[i] * (z_padded_s [d_win*i:d_win*i+win_r+win_r+1])\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "plt.imshow(z_win,aspect='auto',cmap='bwr')#, extent=(min_t*s_rate/d_win,max_t*s_rate/d_win,max_t*s_rate,min_t*s_rate)\n",
    "plt.title('Gauss ablak + eredeti jel')\n",
    "plt.xlabel('Adatpont')\n",
    "plt.ylabel('Ablak elcsúsztatása')\n",
    "plt.subplots_adjust(right=0.88)\n",
    "cax = plt.axes([0.90, 0.23, 0.03, 0.54])\n",
    "plt.colorbar(cax=cax).set_label('Amplitúdó')\n",
    "plt.savefig('z_win.pdf')\n",
    "#plt.show()\n",
    "\n",
    "z_win_fft = np.zeros((int(samples/d_win+0.9), win_r*2+1))\n",
    "for i in range(int(samples/d_win+0.9)):\n",
    "    if i % 1000 == 0:\n",
    "        print('a')\n",
    "    if i<1000001:\n",
    "        z_win_fft[i] = np.abs(scipy.fftpack.fft(z_win[i]))  \n",
    "t1=time.time()\n",
    "\n",
    "print(z_win_fft.min()**2)\n",
    "print(z_win_fft.max()**2)\n",
    "print(std)\n",
    "\n",
    "z_win_fft_cutted = (z_win_fft.T)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "z_win_fft_cutted = np.zeros((int(frangemax*(2*win_r+1)/s_rate), int(samples/d_win)))\n",
    "for i in range(int(samples/d_win)):\n",
    "    for j in range(int(frangemax*(2*win_r+1)/s_rate)): #int(frangemax*max(x_hann))\n",
    "        z_win_fft_cutted [j][i] = z_win_fft [i][j]**2\n",
    "\n",
    "        \n",
    "print(z_win_fft.shape)\n",
    "print(z_win_fft_cutted.shape)\n",
    "#print(z_spy_stft.shape)\n",
    "#print(z_spy_stft_cutted.shape)\n",
    "#print(z_win_fft.max()/abs(z_spy_stft.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000.0 200\n"
     ]
    }
   ],
   "source": [
    "#print(z_win_fft_cutted)\n",
    "##### %%script false\n",
    "fft_max = z_win_fft_cutted.max()\n",
    "colornorm=colors.LogNorm(vmin=fft_max*10**(-6), vmax=fft_max)#40000000#10\n",
    "logax = np.linspace(int(np.log10(fft_max)-6),int(np.log10(fft_max)),7)\n",
    "logax = 10**logax\n",
    "fig, ax = plt.subplots(figsize=(14,5))\n",
    "#extent=[0,t-(2*win_r/s_rate),0,frangemax/t],\n",
    "#plt.figure(figsize=(15,10))\n",
    "#ax.plot(x,np.abs(full_f),color='g',linewidth=3,linestyle='dashed',alpha=0.6)#img = ax.\n",
    "img = plt.imshow(z_win_fft_cutted,cmap='plasma',  aspect='auto', extent=[min_t,max_t,2*nyquist,0], norm=colornorm)#interpolation='gaussian',*30000000/4\n",
    "#plt.title('39424-es kisülés, J051-es szonda STFT-je')\n",
    "plt.title(shot+' szonda saját felüláteresztős szűrt STFT-je')\n",
    "#plt.title('AUGD 34924, B31-40-es szonda STFT-je')\n",
    "plt.ylabel('Frekvencia (Hz)')\n",
    "plt.xlabel('idő (s)')#('time (s)')\n",
    "plt.ylim(0,frangemax)\n",
    "#plt.xlim(1.9,2.4)\n",
    "plt.subplots_adjust(right=0.90)\n",
    "#plt.gca().invert_yaxis()\n",
    "cbar=plt.colorbar(img,cax=plt.axes([0.91, 0.2, 0.02, 0.6]),ticks=None)\n",
    "cbar.set_label('Fourier-amplitúdó (J)')\n",
    "#plt.axis([2.13,2.15,min(s),20000])\n",
    "cbar.set_ticks(logax)\n",
    "plt.savefig(shot+' '+str(min_t)+'-'+str(max_t)+' stft.pdf')#34924_40\n",
    "plt.show()\n",
    "\n",
    "print(nyquist,frangemax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200.0\n",
      "3999.1\n",
      "19950.12468827929\n",
      "65.61419024065383\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,)\n"
     ]
    }
   ],
   "source": [
    "#%%script false\n",
    "#ridge_no = 1\n",
    "print(samples/d_win)\n",
    "\n",
    "print(s_rate-0.9)\n",
    "print(nyquist/((2*win_r+1)/s_rate-0.9))\n",
    "ridge_d=50#00#50# #windth of search if rige is lost\n",
    "ridge_d2=15#00   #width of search if rige is followed\n",
    "ridge_start=f#40000#2#\n",
    "ridge = np.zeros(int(samples/d_win+0.9))  #position (frequency) of the ridge\n",
    "ridge_h = np.zeros(int(samples/d_win+0.9)) #height of the ridge\n",
    "maxi=z_win_fft.max()/60 # minimum treshold\n",
    "print(maxi)\n",
    "\n",
    "for i in range(int(samples/d_win+0.9)):\n",
    "    ridgeval=0  #max(z_spy_stft[i][:])\n",
    "    if (i==0) or (ridge[i-1]==0):\n",
    "        for j in range(int(ridge_d*(2*win_r+1)/(s_rate-0.9))):\n",
    "            j2=j+int((ridge_start)*(2*win_r+1)/(s_rate-0.9))\n",
    "            if ridgeval < z_win_fft[i][j2]:\n",
    "                ridgeval=z_win_fft[i][j2]\n",
    "                ridge[i]= j2\n",
    "    else:\n",
    "        for j in range(int(ridge_d2*(2*win_r+1)/(s_rate-0.9))):\n",
    "            j2=int(j+ridge[i-1]-(ridge_d2/2)*(2*win_r+1)/(s_rate-0.9))\n",
    "            if ridgeval < z_win_fft[i][j2]:\n",
    "                ridgeval=z_win_fft[i][j2]\n",
    "                ridge[i]= j2\n",
    "                #print(j2, z_spy_stft[j2][i])\n",
    "    if ridgeval<(maxi):\n",
    "        ridgeval =0\n",
    "        ridge [i]=0\n",
    "    ridge_h[i]=ridgeval#**(1/2)\n",
    "    #print(i, ridge [i])\n",
    "    #print (i,\"  \",ridge[i],\"\\t\", ridgeval,\"\\t\", z_spy_stft[i][int(ridge[i])])\n",
    "\n",
    "ridge=ridge*(s_rate/(2*win_r+1))\n",
    "\n",
    "#proc_x=np.linspace(min_t,max_t,np.size(ridge))\n",
    "ridge[ ridge==0 ] = np.nan\n",
    "plt.figure(figsize=(14,3.5))\n",
    "plt.title(shot+' szonda maximum frekvenciaértékei')\n",
    "plt.plot(np.linspace(min_t,max_t,np.size(ridge)),ridge,'.-',x,full_f)\n",
    "plt.grid(True)\n",
    "plt.legend(('Kiszámolt', 'Eredeti'))\n",
    "#yaxismax=1/2**(view.value/12)\n",
    "plt.xlim(min_t,max_t)\n",
    "#plt.ylim(ridge_start/1100,)\n",
    "#plt.ylim(0,200)\n",
    "#plt.xlim(0.89,0.91)\n",
    "plt.ylabel('Frekvencia (kHz)')\n",
    "plt.xlabel('idő (s)')\n",
    "plt.savefig(shot+' f_max.pdf')\n",
    "plt.show()\n",
    "#print(z_spy_stft.shape)\n",
    "print(ridge.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.02501862780591914"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%script false\n",
    "plt.figure(figsize=(14,3.5))\n",
    "x_ridge=np.linspace(min_t,max_t,np.size(ridge))\n",
    "diff_ridge=full_f[0::d_win]-ridge#15+10*np.sin(np.pi*x_ridge)\n",
    "plt.plot(x_ridge,abs(diff_ridge))#/ridge\n",
    "plt.ylabel('Frekvencia (Hz)')\n",
    "plt.xlabel('idő (s)')\n",
    "plt.grid(True)\n",
    "plt.xlim(min_t,max_t)\n",
    "#plt.ylim(0,4)\n",
    "#plt.savefig('t_diff.pdf')\n",
    "plt.show()\n",
    "#((1/(np.size(ridge)-1))*sum((diff_ridge/ridge)**2))**(1/2)\n",
    "np.average(abs(diff_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,5))\n",
    "#ax.plot(x,np.abs(full_f),color='g',linewidth=3,linestyle='dashed',alpha=0.6)#img = ax.\n",
    "ax.plot(np.linspace(min_t,max_t,np.size(ridge)),ridge,'.',color='g',alpha=0.6)\n",
    "img = plt.imshow(z_win_fft_cutted,cmap='plasma',  aspect='auto', extent=[min_t,max_t,2*nyquist,0], norm=colornorm)#interpolation='gaussian',*30000000/4\n",
    "#plt.title('39424-es kisülés, J051-es szonda STFT-je')\n",
    "plt.title(shot+' szonda saját felüláteresztős szűrt STFT-je')\n",
    "#plt.title('AUGD 34924, B31-40-es szonda STFT-je')\n",
    "plt.ylabel('Frekvencia (Hz)')\n",
    "plt.xlabel('idő (s)')#('time (s)')\n",
    "plt.ylim(0,frangemax)\n",
    "#plt.xlim(1.9,2.4)\n",
    "plt.subplots_adjust(right=0.90)\n",
    "#plt.gca().invert_yaxis()\n",
    "cbar=plt.colorbar(img,cax=plt.axes([0.91, 0.2, 0.02, 0.6]),ticks=None)\n",
    "cbar.set_label('Fourier-amplitúdó (J)')\n",
    "#plt.axis([2.13,2.15,min(s),20000])\n",
    "cbar.set_ticks(logax)\n",
    "plt.savefig(shot+' '+str(min_t)+'-'+str(max_t)+' stft.pdf')#34924_40\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9997500624843789\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,)\n"
     ]
    }
   ],
   "source": [
    "print((s_rate/(2*win_r+1)))\n",
    "\n",
    "ridge_h[ ridge_h==0 ] = np.nan\n",
    "#print(maxi**(1/2))\n",
    "plt.figure(figsize=(10,3.5))\n",
    "plt.title('Az amplitúdó a maximum frekvenciaértékeknél, '+shot)#, AUGD 34924 J051-es szonda\n",
    "plt.plot(np.linspace(min_t,max_t,np.size(ridge_h)),ridge_h*2/(2*win_r+1)/coherent_gain,x,amp,'-',markersize=1)#,x,3*abs(np.sin(2*np.pi*x))+2,\n",
    "plt.grid(True)\n",
    "plt.xlim(min_t,max_t)\n",
    "#plt.xlim(0.89,0.91)\n",
    "#plt.ylim(maxi**(1/2)/2,max(ridge_h)*1.05)\n",
    "plt.ylabel('Amplitúdó')\n",
    "plt.xlabel('idő (s)')\n",
    "#plt.yscale(\"log\")\n",
    "plt.savefig(shot+'_amp.pdf')\n",
    "plt.show()\n",
    "print(ridge.shape)\n",
    "\n",
    "amp_diff=np.zeros(int(samples/d_win+0.9))#\n",
    "for i in range(int(samples/d_win+0.9)):\n",
    "    amp_diff[i]=np.sqrt(ridge_h[i])-3*abs(np.sin(2*np.pi*x[20*i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Small observations\n",
    "Now, we make histograms of the noise and the entire signal. The difference - if the input is a single frequency - only one point. In general: it is the signal. We will fit a half gaussian onto these, and see, how likely it is, that the difference (signal) is produced by the noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "hist_bins = np.linspace(0, 50000, 50)\n",
    "\n",
    "\"\"\"plt.figure(figsize=(13,5))\n",
    "plt.hist(yf_noise,hist_bins, density=True)\n",
    "plt.title('Distribution of Fourier transformation (non-deterministic component)')\n",
    "plt.ylabel('Frequency (%)')\n",
    "plt.xlabel('Fourier amplitude')\n",
    "plt.grid(True)\n",
    "plt.show()\"\"\"\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.hist(yfabs, hist_bins, density=True)\n",
    "plt.title('Distribution of entire Fourier transformation')\n",
    "plt.ylabel('Frequency (%)')\n",
    "plt.xlabel('Fourier amplitude')\n",
    "plt.yscale('log')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.acorr(yfabs, maxlags=1400)\n",
    "plt.axis([-1,1400,0,1])\n",
    "#plt.xlim((0,frangemax))\n",
    "plt.xlabel('$\\Delta$ frequency (Hz)')\n",
    "plt.ylabel('Autocorrelation value')\n",
    "#plt.xscale('log')\n",
    "plt.title('Autocorrelation of fourier value')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "def acf(x):\n",
    "    result = np.correlate(x, x, mode='full')\n",
    "    return result[int(result.size/2):]\n",
    "\n",
    "s_acf=acf(s)\n",
    "s_acf=s_acf/np.max(s_acf)\n",
    "s_acf_fft_abs=abs(scipy.fftpack.fft(s))\n",
    "s_acf_fft_ang=np.angle(scipy.fftpack.fft(s))/np.pi\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.plot(x,s_acf)\n",
    "#plt.axis([0,frangemax,0,1.5/rs])\n",
    "plt.xlim(min_t,max_t)\n",
    "plt.xlabel('$\\Delta$t (s)')\n",
    "plt.ylabel('Autocorrelation value')\n",
    "#plt.xscale('log')\n",
    "plt.title('Autocorrelation of input signal')\n",
    "plt.grid(True)\n",
    "#plt.show()\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.plot(xf,s_acf_fft_abs[:xf.size])\n",
    "#plt.axis([0,frangemax,0,1.5/rs])\n",
    "plt.xlim((0,frangemax))\n",
    "plt.xlabel('frequency (Hz)')\n",
    "plt.ylabel('Fourier value')\n",
    "#plt.xscale('log')\n",
    "plt.title(\"Autocorrelation's Fourier-transform's amplitude\")\n",
    "plt.grid(True)\n",
    "#plt.show()\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.plot(xf,s_acf_fft_ang[:xf.size])\n",
    "#plt.axis([0,frangemax,0,1.5/rs])\n",
    "plt.xlim((0,frangemax))\n",
    "plt.xlabel('frequency (Hz)')\n",
    "plt.ylabel('Fourier value')\n",
    "#plt.xscale('log')\n",
    "plt.title(\"Autocorrelation's Fourier-transform's phase\")\n",
    "plt.grid(True)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks\n",
    "<br> \n",
    "<br> Import and evaluate chirps\n",
    "<br> Reconstruct wave frequencies and amplitudes\n",
    "<br> Get the radial ?function of several wave types"
   ]
  }
 ],
 "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",
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