{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n# Compute the H/V spectral ratio from the imaginary part of the CCFs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import & getting the data from the computed autocorrelations (ZZ, EE, NN)\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "import os\nif \"SPHINX_DOC_BUILD\" in os.environ:\n    if \"MSNOISE_DOC\" in os.environ:\n        os.chdir(os.environ[\"MSNOISE_DOC\"])\n\nimport matplotlib\nmatplotlib.use(\"agg\")\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\nimport datetime\nregister_matplotlib_converters()\n\nplt.style.use(\"ggplot\")\n\nfrom msnoise.api import connect, get_results, build_movstack_datelist, get_params, get_t_axis, xr_get_ccf\n\n\n# connect to the database\ndb = connect()\n\n# Obtain a list of dates between ``start_date`` and ``enddate``\nstart, end, datelist = build_movstack_datelist(db)\n\n# Get the list of parameters from the DB:\nparams = get_params(db)\n\n# Get the time axis for plotting the CCF:\ntaxis = get_t_axis(db)\n\n# Get the first mov_stack configured:\nmov_stack = params.mov_stack[0]\n\n# Get the results for two station, filter id=1, ZZ component, mov_stack=1 and the results as a 2D array:\nZZ = xr_get_ccf(\"PF.FJS.00\", \"PF.FJS.00\", \"ZZ\", 2, mov_stack, taxis, format=\"dataframe\")\nEE = xr_get_ccf(\"PF.FJS.00\", \"PF.FJS.00\", \"EE\", 2, mov_stack, taxis, format=\"dataframe\")\nNN = xr_get_ccf(\"PF.FJS.00\", \"PF.FJS.00\", \"NN\", 2, mov_stack, taxis, format=\"dataframe\")"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the data has the same time base (index) and joining\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "r = \"1D\"\nrZZ = ZZ.resample(r).mean()\nrEE = EE.resample(r).mean()\nrNN = NN.resample(r).mean()\n\nmerged = pd.concat({'ZZ': rZZ, 'EE': rEE, 'NN': rNN}, axis=0)\nmerged.index.names = ['channel', 'date']\n\n# Swap the levels of the MultiIndex\nresult = merged.swaplevel('channel', 'date').sort_index(level='date') #.iloc[:500]"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper functions\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "def get_imag(d, fs):\n    NFFT = 1024*32\n    iX = np.imag(np.fft.fft(d, n=NFFT)[:NFFT//2])\n    freqs = np.fft.fftfreq(NFFT, d=1/fs)[:NFFT//2]\n\n    return freqs, iX"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the job !\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "HVSRs = {}\nfor date, group in result.groupby(level='date'):\n    print(f\"Date: {date}\")\n    group= group.droplevel(0)\n    try:\n        f, iZZ = get_imag(group.loc[\"ZZ\"].values, 20)\n        f, iEE = get_imag(group.loc[\"EE\"].values, 20)\n        f, iNN = get_imag(group.loc[\"NN\"].values, 20)\n    except:\n        continue\n    hvsr = np.sqrt((iEE+iNN)/iZZ)\n    HVSRs[date] = hvsr\n\nHVSR = pd.DataFrame(HVSRs, index=f)\nX = HVSR.copy().fillna(0.0)\nX = X.T.resample(r).mean().T"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "plt.subplots(figsize=(8,10))\nplt.pcolormesh(X.index, X.columns, X.T, rasterized=True, vmax=5)\nplt.xlabel(\"Frequency (Hz)\")\nplt.ylabel(\"Date\")\nplt.grid()\nplt.xlim(1,10)\nplt.show()"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting & zooming around 1-4 Hz\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "plt.subplots(figsize=(8,10))\nplt.pcolormesh(X.index, X.columns, X.T, rasterized=True, vmax=3)\nplt.xlabel(\"Frequency (Hz)\")\nplt.ylabel(\"Date\")\nplt.grid()\nplt.xlim(1,4)\nplt.show()"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the HVSR curve\n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "source": [
    "plt.subplots(figsize=(8,10))\nhvsr = X.mean(axis=1)\nplt.plot(hvsr.index, hvsr)\nplt.show()\n\n\n#EOF"
   ],
   "outputs": []
  }
 ],
 "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}