{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5227f550-ed14-42d5-87b1-d6ce2047f307",
   "metadata": {},
   "source": [
    "# Qick view and study signal from UHF/VHF [50-3000] MHz SRSP \"Ra\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb5a69f-b1c4-4d00-892e-b2c5dc888e0a",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "373c6651-ea40-4eb1-9647-6d4ddbd98d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff4a1151-13bf-4823-a119-3f85c179da4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import requests\n",
    "import fnmatch\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.ticker import StrMethodFormatter, NullFormatter\n",
    "\n",
    "from datetime import date\n",
    "from datetime import datetime, timezone, timedelta\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "371406e4-3409-48ac-94b3-103aae594134",
   "metadata": {},
   "outputs": [],
   "source": [
    "gc.enable()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01805dda-0bd7-4ab4-b07d-9a73d4279fd7",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d22dd6e9-14d7-4922-8746-55d2230a6e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def listFD(url, ext=''):\n",
    "    page = requests.get(url).text\n",
    "    soup = BeautifulSoup(page, 'html.parser')\n",
    "    return [url + '/' + node.get('href') for node in soup.find_all('a') if node.get('href').endswith(ext)]\n",
    "\n",
    "def download(url: str, dest_folder: str):\n",
    "    if not os.path.exists(dest_folder):\n",
    "        os.makedirs(dest_folder)  # create folder if it does not exist\n",
    "\n",
    "    filename = url.split('/')[-1].replace(\" \", \"_\")  # be careful with file names\n",
    "    file_path = os.path.join(dest_folder, filename)\n",
    "\n",
    "    r = requests.get(url, stream=True)\n",
    "    if r.ok:\n",
    "        print(\"saving to\", os.path.abspath(file_path))\n",
    "        with open(file_path, 'wb') as f:\n",
    "            for chunk in r.iter_content(chunk_size=1024 * 8):\n",
    "                if chunk:\n",
    "                    f.write(chunk)\n",
    "                    f.flush()\n",
    "                    os.fsync(f.fileno())\n",
    "    else:  # HTTP status code 4XX/5XX\n",
    "        print(\"Download failed: status code {}\\n{}\".format(r.status_code, r.text))\n",
    "\n",
    "def hhmm_format(t, pos=0):\n",
    "    hh = (int)(t / 3600.)\n",
    "    t -= hh*3600.\n",
    "    mm = (int)(t / 60.)\n",
    "    ss = (int)(t % 60)\n",
    "    return '%02d:%02d:%02d' % (hh,mm,ss)\n",
    "\n",
    "def to_ticks(hhmmstr, hhmmend):\n",
    "    try:\n",
    "        hhs, mms = hhmmstr.split(':')\n",
    "        hhe, mme = hhmmend.split(':')\n",
    "        startobs = int(hhs) * 3600 + int(mms) * 60 + 3600 * 3\n",
    "        endobs = int(hhe) * 3600 + int(mme) * 60 + 3600 * 3\n",
    "    except:\n",
    "        try:\n",
    "            hhs, mms, sss = hhmmstr.split(':')\n",
    "            hhe, mme, sse = hhmmend.split(':')\n",
    "            startobs = int(hhs) * 3600 + int(mms) * 60 + int(sse) * 60 + 3600 * 3\n",
    "            endobs = int(hhe) * 3600 + int(mme) * 60 + int(sse) * 60 + 3600 * 3\n",
    "        except:\n",
    "            print(\"Time's format undefined\")\n",
    "        \n",
    "    \n",
    "    return (startobs, endobs)\n",
    "\n",
    "def tics_coff_dt(t_1, t_2):\n",
    "    if (t_2 - t_1) // 4 == 0:\n",
    "        coff_dt = (t_2 - t_1) / 4\n",
    "    else:\n",
    "        coff_dt = (t_2 - t_1) // 4\n",
    "    return coff_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7653675-1bde-4719-9725-059eb51057f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotSpectra(radiodata, freqrange=[0,-1], pol=0, yscalelog=False, \n",
    "                 vmin=-21, vmax=-11,\n",
    "                 dateview = datetime.now(),\n",
    "                 t1 = \"03:00\", t2 = \"16:00\",\n",
    "                 cmap=\"rainbow\", improve=False, saveplot=False):\n",
    "    \n",
    "    frequencies = []\n",
    "    common_times = None\n",
    "    intensities = []\n",
    "    \n",
    "    for row in radiodata[freqrange[0]:freqrange[1]]:\n",
    "        freq = row[0]\n",
    "        time_lcp = np.array(row[1])\n",
    "        time_rcp = np.array(row[3])\n",
    "        \n",
    "        if improve == True:\n",
    "            flux_lcp = np.array(row[2]) / 10 ** int(np.log10(np.min(np.array(row[2][4000:-4000]))))\n",
    "            flux_rcp = np.array(row[4]) / 10 ** int(np.log10(np.min(np.array(row[4][4000:-4000]))))\n",
    "\n",
    "        else:\n",
    "            flux_lcp = np.array(row[2])\n",
    "            flux_rcp = np.array(row[4])\n",
    "    \n",
    "        fluxI = flux_lcp + flux_rcp\n",
    "\n",
    "        fluxV = flux_lcp - flux_rcp\n",
    "        flux = [fluxI, fluxV]\n",
    "        stokes = [\"I\", \"V\"]\n",
    "    \n",
    "        if common_times is None:\n",
    "            common_times = time_lcp\n",
    "    \n",
    "        frequencies.append(freq)\n",
    "        intensities.append(flux[pol])\n",
    "        # intensities.append(flux_V)\n",
    "    \n",
    "    frequencies = np.array(frequencies)\n",
    "    intensities = np.array(intensities)  # shape: (N_freq, N_time)\n",
    "\n",
    "    time_dt = np.array([\n",
    "        datetime.utcfromtimestamp(ts / 1000) + timedelta(hours=-3)\n",
    "        for ts in common_times\n",
    "    ])\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(13.5,7.25/2))\n",
    "    fig.subplots_adjust(wspace=0, hspace=0,left = 0.08, right = 0.98, top = 0.9, bottom = 0.08)\n",
    "\n",
    "    im = ax.pcolormesh(\n",
    "        time_dt, \n",
    "        frequencies,\n",
    "        intensities,\n",
    "        # np.log(intensities),\n",
    "        cmap=cmap,\n",
    "        shading=None,\n",
    "        vmin = vmin,\n",
    "        vmax = vmax,\n",
    "    )\n",
    "    \n",
    "    yearfits = dateview.year\n",
    "    monthfits = dateview.month\n",
    "    dayfits = dateview.day\n",
    "\n",
    "    t1 = datetime(yearfits, monthfits, dayfits, int(t1[0:2]), int(t1[3:5]), 0, tzinfo=timezone(timedelta(hours=0)))\n",
    "    t2 = datetime(yearfits, monthfits, dayfits, int(t2[0:2]), int(t2[3:5]), 0, tzinfo=timezone(timedelta(hours=0)))\n",
    "    \n",
    "    ax.set_xlim(mdates.date2num(t1), mdates.date2num(t2))\n",
    "    ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "    ax.xaxis.set_minor_locator(mdates.MinuteLocator(interval=10))\n",
    "    \n",
    "    plt.xlabel(\"UTC Time, hours\",fontsize=12)\n",
    "    plt.ylabel(\"Frequency, GHz\",fontsize=12)\n",
    "    \n",
    "    if yscalelog == True:\n",
    "        plt.yscale('log')\n",
    "    \n",
    "        ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "        ax.yaxis.set_minor_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "\n",
    "    ax.invert_yaxis()\n",
    "    ax = plt.gca()\n",
    "\n",
    "    plt.title(str(dateview)[:10] + \"           Stokes \" + stokes[pol] + \"           Spectropolarimeter SRSP \" + str(frequencies[0]) + \"-\" + str(frequencies[-1]) + \" GHz\", fontsize=13, y = 1.02)\n",
    "    cbar = fig.colorbar(im)\n",
    "    if saveplot == True:\n",
    "        plt.savefig(f'img/Stokes_{stokes[pol]}_SHF_daily_{int(frequencies[0])}_{int(frequencies[-1])}_GHz.png', format='png', dpi=1200)\n",
    "    plt.show()\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de49b28-a3f7-40f2-a717-99810cce99e5",
   "metadata": {},
   "source": [
    "## Observation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96396303-b58e-4db3-9d97-d69804d6d011",
   "metadata": {},
   "source": [
    "input data observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56711e5c-bed0-412c-8abe-971101149b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2025/05/19/', '20250519')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dateview = datetime.now().strftime(\"%Y/%m/%d/\") # Today\n",
    "dateview = date(2025, 5, 19).strftime(\"%Y/%m/%d/\") # Choos day for observe\n",
    "filenamedate = dateview.replace(\"/\", \"\")\n",
    "\n",
    "dateview, filenamedate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41e6dca7-07de-4596-aef0-4bcd83152181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://srsp.cosmos.ru/fits/3_24_GHz/2025/05/19/\n"
     ]
    }
   ],
   "source": [
    "url = \"https://srsp.cosmos.ru/fits/3_24_GHz/\" + dateview\n",
    "ext = 'fits'\n",
    "\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "222ce16d-4df9-4eb7-9b43-9a3bfcf8d4ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving to /home/greg/SDRepo/Data/fits/3_24_GHz/2025/05/19/3_24_GHz_20250519.fits\n"
     ]
    }
   ],
   "source": [
    "for file in listFD(url, ext):\n",
    "    download(file, './Data/fits/3_24_GHz/' + dateview)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbe79ceb-d336-4158-8c45-e1859889629a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fits_filename = \"./Data/fits/3_24_GHz/\" + dateview + \"3_24_GHz_\" + filenamedate + \".fits\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d48bde3e-02b9-4879-b546-092fa48c597f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdulist = fits.open(fits_filename)\n",
    "radiodata = hdulist[1].data\n",
    "dateview = hdulist[0].header['DATE-OBS']\n",
    "dateview[:-4]\n",
    "date_format = '%Y-%m-%dT%H:%M:%S'\n",
    "dateviewfits = datetime.strptime(dateview[:-4], date_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91e72e68-d6f5-4f9f-97c2-9ea636035884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1350x362.5 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotSpectra(radiodata, freqrange=[0, 39], pol=0, yscalelog=True,\n",
    "             vmin=5.3e-8, vmax=8.3e-7, # 3-24 GHz\n",
    "             dateview=dateviewfits,\n",
    "             t1 = \"02:00\", t2 = \"16:30\",\n",
    "             # cmap='hot',\n",
    "             cmap='rainbow',\n",
    "             # improve= True, \n",
    "             # saveplot=True\n",
    "            )"
   ]
  }
 ],
 "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": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}