load_rawtf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key backend.qt4 in file /home/lbondonneau/.config/matplotlib/matplotlibrc, line 6 ('backend.qt4        : PyQt4')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.7.5/lib/matplotlib/mpl-data/matplotlibrc\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from NenuRaw_module import Rawtf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_raw = Path('/databf2/nenufar-tf/ES00/2022/02/20220228_154600_20220228_155400_B0809+74_TRACKING/B0809+74_TRACKING_20220228_154637_2.raw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int8 67043328\n",
      "  blocksize_header  is: 67043328 -> 0.447 sec\n",
      "  blocksize_file    is: 67043328\n",
      "  overlap    is: 0\n",
      "  nbit       is: 8\n",
      "  head_size  is: 2316\n"
     ]
    }
   ],
   "source": [
    "file_format = Rawtf(fn_raw, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87296, 192, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_num = 0\n",
    "block = file_format.data['data'][block_num]\n",
    "block.shape = (int(file_format.nschan_file / file_format.nof_polcpx), file_format.nchan_file, file_format.nof_polcpx)\n",
    "nbin, nchan, npol = np.shape(block)\n",
    "np.shape(block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# block = block.transpose((1, 0, 2))\n",
    "# np.shape(block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using a single block can go down to 2.2374 Hz with fftlen of 87296\n"
     ]
    }
   ],
   "source": [
    "block_length = nbin * file_format.tbin\n",
    "min_freq_res = file_format.chan_bw / nbin\n",
    "print(f\"Using a single block can go down to {min_freq_res * 1e6:.4f} Hz with fftlen of {nbin}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16760832, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.fft.fft(block, axis=0)\n",
    "test = test.transpose((1, 0, 2))\n",
    "test = test.reshape((nbin * nchan, npol))\n",
    "np.shape(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
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      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n",
      "findfont: Font family 'Bitstream Vera Sans' not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(np.abs(test[:,0]) + np.abs(test[:,1]))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}