demos/detection-test-Kravchuk-zeros.ipynb

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      "Populating the interactive namespace from numpy and matplotlib\n",
      "\n",
      "  There is a binary version available but the source version is later:\n",
      "              binary source needs_compilation\n",
      "spatstat.geom  3.2-1  3.2-2              TRUE\n",
      "\n",
      "Do you want to install from sources the package which needs compilation? (Yes/no/cancel) yes\n"
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     "text": [
      "R[write to console]: installing the source package ‘spatstat.geom’\n",
      "\n",
      "\n",
      "R[write to console]: trying URL 'https://cloud.r-project.org/src/contrib/spatstat.geom_3.2-2.tar.gz'\n",
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      "\n",
      "R[write to console]: downloaded 749 KB\n",
      "\n",
      "\n",
      "* installing *source* package ‘spatstat.geom’ ...\n",
      "** package ‘spatstat.geom’ successfully unpacked and MD5 sums checked\n",
      "** using staged installation\n",
      "** libs\n"
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      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c areadiff.c -o areadiff.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c auctionbf.c -o auctionbf.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c bdrymask.c -o bdrymask.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c close3pair.c -o close3pair.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c closepair.c -o closepair.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c connect.c -o connect.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c dinfty.c -o dinfty.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c discarea.c -o discarea.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c discs.c -o discs.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c dist2dpath.c -o dist2dpath.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c distan3.c -o distan3.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c distances.c -o distances.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c distmapbin.c -o distmapbin.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c dwpure.c -o dwpure.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c exactPdist.c -o exactPdist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c exactdist.c -o exactdist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c fardist.c -o fardist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c hasclose.c -o hasclose.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c hotrod.c -o hotrod.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c init.c -o init.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c knndistance.c -o knndistance.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c knngrid.c -o knngrid.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c loccum.c -o loccum.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c metricPdist.c -o metricPdist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c minnnd.c -o minnnd.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c nearestpix.c -o nearestpix.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c nn3Ddist.c -o nn3Ddist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c nnMDdist.c -o nnMDdist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c nndistance.c -o nndistance.o\n",
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      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c quasirandom.c -o quasirandom.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c raster.c -o raster.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c rasterfilter.c -o rasterfilter.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c scan.c -o scan.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c seg2pix.c -o seg2pix.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c tabnum.c -o tabnum.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c trigraf.c -o trigraf.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c uniquemap.c -o uniquemap.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c veegraf.c -o veegraf.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c whist.c -o whist.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c xyseg.c -o xyseg.o\n",
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     "text": [
      "installing to /Library/Frameworks/R.framework/Versions/4.1/Resources/library/00LOCK-spatstat.geom/00new/spatstat.geom/libs\n",
      "** R\n",
      "** inst\n",
      "** byte-compile and prepare package for lazy loading\n"
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      "Warning message:\n",
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      "** help\n",
      "*** installing help indices\n",
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      "** checking absolute paths in shared objects and dynamic libraries\n",
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      "* DONE (spatstat.geom)\n",
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      "R[write to console]: The downloaded source packages are in\n",
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     "text": [
      "Warning: package ‘spatstat.data’ was built under R version 4.1.2\n",
      "\n",
      "  There is a binary version available but the source version is later:\n",
      "                 binary source needs_compilation\n",
      "spatstat.explore  3.1-0  3.2-1              TRUE\n",
      "\n",
      "Do you want to install from sources the package which needs compilation? (Yes/no/cancel) yes\n"
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     "text": [
      "R[write to console]: installing the source package ‘spatstat.explore’\n",
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      "R[write to console]: downloaded 740 KB\n",
      "\n",
      "\n",
      "* installing *source* package ‘spatstat.explore’ ...\n",
      "** package ‘spatstat.explore’ successfully unpacked and MD5 sums checked\n",
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      "** libs\n"
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      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c init.c -o init.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c k3.c -o k3.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c localpcf.c -o localpcf.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c loccum.c -o loccum.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c pcf3.c -o pcf3.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c raster.c -o raster.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c scan.c -o scan.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c segdens.c -o segdens.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c sphefrac.c -o sphefrac.o\n",
      "clang -mmacosx-version-min=10.13 -I\"/Library/Frameworks/R.framework/Resources/include\" -DNDEBUG   -I/usr/local/include   -fPIC  -Wall -g -O2  -c sphevol.c -o sphevol.o\n",
      "clang -mmacosx-version-min=10.13 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o spatstat.explore.so Kborder.o Knone.o Krect.o call3d.o corrections.o denspt.o densptcross.o digber.o f3.o g3.o idw.o init.o k3.o localpcf.o loccum.o pcf3.o raster.o scan.o segdens.o sphefrac.o sphevol.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "installing to /Library/Frameworks/R.framework/Versions/4.1/Resources/library/00LOCK-spatstat.explore/00new/spatstat.explore/libs\n",
      "** R\n",
      "** inst\n",
      "** byte-compile and prepare package for lazy loading\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "package ‘spatstat.data’ was built under R version 4.1.2 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "** help\n",
      "*** installing help indices\n",
      "** building package indices\n",
      "** testing if installed package can be loaded from temporary location\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: package ‘spatstat.data’ was built under R version 4.1.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "** checking absolute paths in shared objects and dynamic libraries\n",
      "** testing if installed package can be loaded from final location\n",
      "** testing if installed package keeps a record of temporary installation path\n",
      "* DONE (spatstat.explore)\n",
      "R[write to console]: \n",
      "\n",
      "R[write to console]: \n",
      "R[write to console]: The downloaded source packages are in\n",
      "\t‘/private/var/folders/r3/zcg_8chn0f94nyfhyj18j1240000gn/T/RtmpnKAMXj/downloaded_packages’\n",
      "R[write to console]: \n",
      "R[write to console]: \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: package ‘spatstat.data’ was built under R version 4.1.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%pylab inline\n",
    "\n",
    "%run ../include/preamble.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection test based on the zeros of the Kravchuk spectrogram\n",
    "\n",
    "Hypothesis testing followes a Monte Carlo envelope methodology in order to discriminate between\n",
    "- the null hypothesis $\\textbf{H}_0$, ``the observations consist in pure noise\"\n",
    "$$\n",
    "\\boldsymbol{y} = \\boldsymbol{\\xi}\n",
    "$$\n",
    "- the alternative $\\textbf{H}_1$, ``the data contain a deterministic signal of interest\".\n",
    "$$\n",
    "\\boldsymbol{y} =\\mathsf{snr} \\times \\boldsymbol{x} + \\boldsymbol{\\xi}\n",
    "$$\n",
    "with $\\mathsf{snr}>0$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate a synthetic signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of points\n",
    "N        = 64\n",
    "\n",
    "# bound of the observation window\n",
    "observe  = 20\n",
    "\n",
    "# duration of the chirp\n",
    "duration = 15\n",
    "\n",
    "# signal-to-noise ratio\n",
    "snr      = 50\n",
    "\n",
    "# generate a synthetic signal corrupted by complex white Gaussian noise\n",
    "nsignal, time_t = the_noisy_chirp(N,snr,observe,duration)\n",
    "\n",
    "# display the real part of the signal\n",
    "_ = display_signal(nsignal,time_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform the test\n",
    "\n",
    "1. Generate $m$ samples of complex white Gaussian noise and compute\n",
    "- their Kravchuk spectrogram and its zeros,\n",
    "- their standard spectrogram and its zeros,\n",
    "- the $K$ and $F$ functional statistics of the two point processes.\n",
    "\n",
    "2. Compute the Kravchuk and standard spectrograms of the analyzed signal and their zeros.\n",
    "\n",
    "3. Compute the $K$ and $F$ functional statistics of the zeros of the signal spectrograms.\n",
    "4. Deduce the summary statistics.\n",
    "5. Perform the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The null hypothesis is rejected. A signal is detected.\n",
      " \n",
      "--------------------------------------------------------------\n",
      " \n",
      "Threshold of rejection: 01\n",
      "Value of the summary statistics: 07\n"
     ]
    }
   ],
   "source": [
    "# desired minimal significance of the test (if not specified set to 199 by the function 'noise_samples')\n",
    "alpha = 0.05\n",
    "\n",
    "# number of samples (if not specified set to 199 by the function 'noise_samples')\n",
    "m = 99\n",
    "\n",
    "# generate the complex white noise samples and store them in '../folder'\n",
    "alpha, m, folder = noise_samples(N,m=m, time_t=time_t,folder = 'samples')\n",
    "\n",
    "\n",
    "# perform the detection test\n",
    "the_test(nsignal,alpha,m,folder)"
   ]
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}