{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example case of simulating observations of a cluster with multiple pointings #\n", "\n", "This thread shows how to use _M2_ProposalTools_ to simulate observations of a target." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "### Useful libraries\n", "import numpy as np\n", "import astropy.units as u\n", "from astropy.io import fits \n", "#### Modules within our library\n", "import M2_ProposalTools.WorkHorse as WH\n", "import M2_ProposalTools.MakeRMSmap as MRM\n", "import M2_ProposalTools.ModelFitting as MF\n", "from astropy.wcs import WCS\n", "import os\n", "from scipy.ndimage import filters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Supply an output directory." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "outdir=\"/home/data/MUSTANG2/SimulatedObservations/LightWeight/\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define some options for the input cluster, mapping parameters, and scanning strategy\n", "\n", "If an entry in offsets is greater than zero, then the scan strategy is taken to be a set of four scans which are offset by the respective distance, in arcminutes, from the central pointing towards: (1) the west, (2) north, (3) east, and (4) south." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "WHpath = os.path.abspath(WH.__file__)\n", "HDUpath = WHpath.replace(\"WorkHorse.py\",\"\")\n", "FileName = \"a1.0005_cl00003_proj_z_SZonly.fits.gz\"\n", "SkyFile = os.path.join(HDUpath,FileName)\n", "SkyHDU = fits.open(SkyFile)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.01\n" ] } ], "source": [ "SZext = 1 # The Compton y map is in the second extension (index = 1)\n", "Redshift = SkyHDU[SZext].header[\"REDSHIFT\"] # The simulation recorded this image at this redshift\n", "print(Redshift) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5.45236651 5.45236651]\n", "['kpc', 'kpc']\n", "[659772.86778532 623087.80682914]\n", "Reference, in pixel coordinates: [1024.5 1024.5]\n", "Shape of image: (2048, 2048)\n", "['linear', 'linear']\n" ] } ], "source": [ "myWCS = WCS(SkyHDU[SZext].header) # Get the WCS information\n", "print(myWCS.wcs.cdelt) # Pixel size information\n", "print(myWCS.wcs.cunit) # Note that the units are not degrees\n", "print(myWCS.wcs.crval) # Check units of CRVAL\n", "print(\"Reference, in pixel coordinates: \",myWCS.wcs.crpix) # Check units of CRPIX\n", "print(\"Shape of image: \",SkyHDU[SZext].shape) # Check shape of the image\n", "print(myWCS.wcs.ctype) # Check the type of projection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lightweight observing suite assumes that the units are in degrees. Even though there are a second set of coordinate deltas (CRDELT1A and CRDELTA2A) in the header which are in degrees, the observing suite is not built to find potential secondary coordinates. Even if it were, we might want to change the redshift so that the cluster is not quite so large on the sky. \n", "\n", "Note, the Compton y signal itself is redshift independent, so we don't face an intrinsic problem in changing the redshift of a simulated observation. Of course, it's possible that you then see a cluster at a stage in evolution which is not fully correct for that redshift, but for the point of demonstrating what this tool requires as in input HDU file, we can proceed." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "z = 0.3 # Let's push the cluster to this redshift\n", "d_ang = WH.get_d_ang(z) # angular distance\n", "d_a_kpc = d_ang.to(\"kpc\").value\n", "r2d = u.rad.to(\"deg\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Now we can redo the WCS information\n", "cdelt = [cdin*r2d/d_a_kpc for cdin in myWCS.wcs.cdelt]\n", "myWCS.wcs.cdelt = cdelt # Set the updated values\n", "myWCS.wcs.cunit = [\"deg\",\"deg\"] # Set the updated units\n", "myWCS.wcs.crval = [180.,45.] # Coordinates in degrees: (RA,Dec). Largely an arbitrary choice for this suite.\n", "myWCS.wcs.ctype = ['RA---TAN','DEC--TAN']\n", "myWCS.wcs.lonpole = 180 # Might as well put the pole close to our object\n", "myWCS.wcs.latpole = 45 # Might as well put the pole close to our object\n", "#myWCS.wcs.crpix = [] # Not needed; CRPIX was near the center of the image." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.22409959 1.22409959]\n" ] } ], "source": [ "pixsize = np.array(cdelt)*3600 # for those of us who don't think in degrees\n", "print(pixsize) # it may be easier to parse arcseconds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This astrometry seems fine. It's definitely a higher resolution than necessary; one could `reproject` it to have larger pixels if a smaller array is desired.\n", "\n", "We'll proceed with this and smooth it by the MUSTANG-2 beam" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "M2sky = WH.smooth_by_M2_beam(SkyHDU[SZext].data,pixsize=pixsize[0]) # Smooth by the MUSTANG-2 beam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation map was in Compton $y$; We'll perform a rough conversion to (micro)Kelvin (Rayleigh-Jeans), the default units for MUSTANG-2 sensitivities used in this package." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "y2k = -3.4 # Conversion from Compton y to Kelvin (RJ) for kT_e ~ 7 keV; good for most clusters\n", "M2_uK = M2sky*y2k*1e6 # Now you have a sky map with respect to MUSTANG-2, in units of microKelvin (RJ) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a new HDUList to use in making our sensitivity (RMS) map." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "NewHdr = myWCS.to_header() # New header\n", "NewSkyM2 = fits.PrimaryHDU(M2_uK,header=NewHdr)\n", "NewSkyOrig = fits.ImageHDU(SkyHDU[SZext].data,header=NewHdr)\n", "NewSky = fits.HDUList([NewSkyM2,NewSkyOrig]) # Make it a list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your choice on what to put in the filename. Saving is not obligatory, but it's probably useful." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "pixstr = \"{:.1f}\".format(pixsize[0]).replace(\".\",\"p\")\n", "zstr = \"{:.1f}\".format(z).replace(\".\",\"z\")\n", "Mstr = \"CL00003\"\n", "SkyName = \"_\".join([Mstr,pixstr,zstr])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "Skyname = \"FromNumericalSimulation_\"+SkyName+\".fits\"\n", "NewSky.writeto(outdir+Skyname,overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, we can define our scanning strategy." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "times = [5,5] # A list of time per scan strategy, in hours\n", "ptgs = [myWCS.wcs.crval,myWCS.wcs.crval ] # A list of centroid pairs (RA,Dec) in degrees\n", "sizes = [3.5,3.5] # Scan sizes, in arcminutes\n", "offsets = [1.5,0] # Pointing offsets, in arcminutes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above image is what MUSTANG-2 would see if data processing imparted no filtering effects. Unfortunately, data processing does act as a filter, and so we combined the filtering effects with the sensitivity mapmaking in the following routine. It returns two HDULists. The first HDUList has a filtered map and a corresponding weight map, where the weight map is the inverse variance. The second HDUList is the same, but the filtered map has an additional smoothing. This smoothing is the standard smoothing applied to final MIDAS maps; it is with this smoothing that the inverse variance maps, as calculated, are appropriate comparisons." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "FilterHDU,SmoothHDU = WH.lightweight_simobs_hdu(NewSky,ptgs=ptgs,sizes=sizes,times=times,offsets=offsets,center=None,WIKID=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Depending on what you want to track, this adds some information to the filenames" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "sss = [\"{:.1f}\".format(mysz).replace(\".\",\"s\") for mysz in sizes]\n", "sts = [\"{:.1f}\".format(mytime).replace(\".\",\"h\") for mytime in times]\n", "ssstr = \"_\".join(sss)\n", "ststr = \"_\".join(sts)\n", "InputStr = \"_\".join([zstr,Mstr,ssstr,ststr,pixstr])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Saving to fits files may be desirable." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "filename = \"SimulatedObs_Unsmoothed_\"+InputStr+\".fits\"\n", "FilterHDU.writeto(outdir+filename,overwrite=True)\n", "filename2 = \"SimulatedObs_Smoothed_\"+InputStr+\".fits\"\n", "SmoothHDU.writeto(outdir+filename2,overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, part of the reason to pass in a simulated HDU is not just to use its astrometry, but to see where features may align in the RMS map.\n", "From a previous notebook, one might recall that the RMS plotting routine will assume the second extension in an HDUList is a weight map and calculate an RMS map from that. To decouple us from that assumption, we can explicitly calculate the RMS map:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "RMSmap = np.zeros(FilterHDU[1].data.shape)\n", "nzwts = (FilterHDU[1].data > 0)\n", "RMSmap[nzwts] = 1.0/np.sqrt(FilterHDU[1].data[nzwts])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we want to make an image which will itself highlight the features we want, or when passed through a GGM filter can highlight features.\n", "\n", "Below, I've just played around by hand to get a file that will highlight features when a GGM filter is applied.\n", "\n", "I define one mask which I already apply to the image, and a second one which will be applied *after* the GGM filter is applied. The latter is smaller than the first so that we're not picking up on the edge of the former mask." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "HeavilySmoothed = filters.gaussian_filter(NewSky[1].data, 25)\n", "HSxymap = MRM.make_xymap(NewSky[1].data,NewSky[1].header,*myWCS.wcs.crval)\n", "HSrmap = MRM.make_rmap(HSxymap) # In arcminutes\n", "MyMask = np.ones(HSrmap.shape)\n", "zind = (HSrmap > 7) # Mask anything more than 7 arcminutes away from the center.\n", "MyMask[zind] = 0\n", "NewSky[0].data = MyMask*NewSky[1].data / HeavilySmoothed" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "ggmMask = np.ones(HSrmap.shape)\n", "zind = (HSrmap > 6.5) # Mask anything more than 6.5 arcminutes away from the center.\n", "ggmMask[zind] = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example, the very center of the map gets down to 20.2 $\\mu$K. We'll set ggm=True so that the routine takes the GGM of the imgext extension of NewSky. We only need one contour, so ncnts=1." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pngname = \"SimulatedObservations_MUSTANG-2_\"+InputStr+\"_RMSimage.png\"\n", "vmin = 20.0 # uK\n", "vmax = 520.0 # uK\n", "MRM.plot_rms_general(NewSky,outdir+pngname,ncnts=1,ggm=True,rmsmap=RMSmap,vmin=vmin,vmax=vmax,prntinfo=True,zoom=3,imgext=0,ggmIsImg=False,mask=ggmMask)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 }