Improved folder structure and organization

esc.ipynb

@@ -266,10 +266,10 @@
     "        n_f814w_fluxes.append(n_f814w_flux)\n",
     "\n",
     "        # Get the Starburst99 fit's wavelength bins and flux densities\n",
-    "        w_sb99, f_sb99 = np.loadtxt(f'{data}/spectra/mage/cfit/planckarc-{slit_id}-sb99-fit.txt', comments='#', usecols=(0,3), unpack=True)\n",
+    "        w_sb99, f_sb99 = np.loadtxt(f'{data}/mage/stellar_continuum_fits/planckarc-{slit_id}-sb99-fit.txt', comments='#', usecols=(0,3), unpack=True)\n",
     "\n",
     "        # Get the wavelength bins and flux densities and associated uncertainties of the MagE spectrum\n",
-    "        w, f, n = np.loadtxt(f'{data}/spectra/mage/{file}', delimiter='\\t', comments=('#','w'), usecols=(0,1,2), unpack=True)\n",
+    "        w, f, n = np.loadtxt(f'{data}/mage/{file}', delimiter='\\t', comments=('#','w'), usecols=(0,1,2), unpack=True)\n",
     "    \n",
     "        # Create a mask excluding extreme flux density outliers\n",
     "        f_mask = f < 1e-20    \n",

lya.ipynb

@@ -183,8 +183,8 @@
     "    # magnification of the slit, a tuple specifying a range of Lya peculiar velocities used to set \n",
     "    # initial parameter estimates of the central Lya peak, spectral resolution, and uncertainty in the spectral resolution\n",
     "    slits = {\n",
-    "        'NL' : ['rest_sba-nonleaker-no_m3_MWdr.txt', 0, 1, [60,140], 5400, 200],\n",
-    "        'L' : ['rest_sba-leaker-no_m0_MWdr.txt', 0, 1, [20,130], 5300, 200],\n",
+    "        'NL' : ['sunburst_arc_nonleaker_stack_mage.txt', 0, 1, [60,140], 5400, 200],\n",
+    "        'L' : ['sunburst_arc_leaker_stack_mage.txt', 0, 1, [20,130], 5300, 200],\n",
     "        'M5' : ['sunburst_M-5-comb1_MWdr.txt', 2.37086, 51, [0,100], 5500, 400],\n",
     "        'M4' : ['sunburst_M-4-comb1_MWdr.txt', 2.37073, 14.6, [0,85], 5400, 300],\n",
     "        'M6' : ['sunburst_M-6-comb1_MWdr.txt', 2.37021, 147, [10,130], 5300, 300],\n",
@@ -224,7 +224,7 @@
     "        factor = 1e16 if 'M' in slit_id else 1\n",
     "\n",
     "        # Extract the data from the .txt file\n",
-    "        w, f, n = extract_data(f'{data}/spectra/mage/{file_name}')\n",
+    "        w, f, n = extract_data(f'{data}/mage/{file_name}')\n",
     "\n",
     "        # Cut the data to just the relevant portion of the spectrum\n",
     "        f = f[(w >= 1195 * (1 + z)) & (w <= 1235 * (1 + z))]\n",
@@ -784,7 +784,7 @@
     "        z, mag, c, model = slits[slit_id][1], slits[slit_id][2], slits[slit_id][3], slits[slit_id][4]\n",
     "\n",
     "        # Get the data\n",
-    "        w, f, n = extract_data(f'{data}/spectra/mage/{slits[slit_id][0]}')\n",
+    "        w, f, n = extract_data(f'{data}/mage/{slits[slit_id][0]}')\n",
     "\n",
     "        # Convert the data to the rest frame\n",
     "        w, f, n = w / (1 + z), f * (1 + z) / mag, n * (1 + z) / mag\n",
@@ -861,7 +861,7 @@
     "        z, c, model = slits[slit_id][1], slits[slit_id][3], slits[slit_id][4]\n",
     "\n",
     "        # Get the data\n",
-    "        w, f, n = extract_data(f'{data}/spectra/mage/{slits[slit_id][0]}')\n",
+    "        w, f, n = extract_data(f'{data}/mage/{slits[slit_id][0]}')\n",
     "\n",
     "        # Convert the data to the rest frame\n",
     "        w, f, n = w / (1 + z), f * (1 + z), n * (1 + z)\n",

lya_and_lyc_maps.ipynb

@@ -188,8 +188,8 @@
     "    # Establish file paths to the necessary files\n",
     "    hst_f275w_file = f'{data}/hst/V5.0_PSZ1G311.65-18.48_F275W_0.03g0.6_crsc1.2_0.7crsn3.5_3.0_drc_sci.fits'\n",
     "    hst_f606w_file = f'{data}/hst/V5.0_PSZ1G311.65-18.48_F606W_0.03g0.6_crsc1.2_0.7crsn3.5_3.0_drc_sci.fits'\n",
-    "    lya_nb_f390w_file = f'{results}/Lya_cont_sub_F390W.fits'\n",
-    "    lya_nb_f555w_file = f'{results}/Lya_cont_sub_F555W.fits'\n",
+    "    lya_nb_f390w_file = f'{results}/lya_maps/Lya_cont_sub_F390W.fits'\n",
+    "    lya_nb_f555w_file = f'{results}/lya_maps/Lya_cont_sub_F555W.fits'\n",
     "\n",
     "    # Put the files we will display data from into an array. We will loop over this array\n",
     "    # to make the figure\n",
@@ -406,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

masks.ipynb

@@ -111,8 +111,8 @@
     "        # Save the mask with the new header\n",
     "        fits.writeto(f'{results}/masks/{slit_id}_mask_v5.fits', mask, header=slit_mask_header, overwrite=True)\n",
     "\n",
-    "    # The file path to the mask of the two largest arcs in the v4 HST reduction WCS\n",
-    "    arc_mask_file = f'{data}/hst/masks/arcMask.fits'\n",
+    "    # The file path to the mask of the two largest arcs in a VLT/MUSE IFU footprint\n",
+    "    arc_mask_file = f'{data}/psz1550_1909_v2_mask.fits'\n",
     "\n",
     "    # Reproject and interpolate the mask to the v5 HST reduction WCS\n",
     "    arc_mask, _ = reproject_interp(arc_mask_file, header, hdu_in=0)\n",
@@ -131,9 +131,8 @@
     "    arc_mask_header['DATE'] = (date.today().strftime('%d %B %Y'), 'The date this file was created')\n",
     "    arc_mask_header['FILENAME'] = (f'arc_mask_v5.fits', 'Name of file')\n",
     "    arc_mask_header['DESC'] = ('This file is a binary mask of the Sunburst Arc, aligned to the WCS of the \\\n",
-    "        v5 reduction of the HST observations. The original mask was made for the VLT/MUSE footprint, and \\\n",
-    "        then reprojected to the v4 HST reduction WCS. This file was created from the mask reprojected to \\\n",
-    "        the v4 HST reduction WCS.', 'Description of file')\n",
+    "        v5 reduction of the HST observations. The original mask was made for the VLT/MUSE footprint.', \n",
+    "        'Description of file')\n",
     "\n",
     "    # Save the new arc mask\n",
     "    hdu = fits.PrimaryHDU(data=arc_mask, header=header)\n",
@@ -168,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

nb.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "id": "a8da6247",
    "metadata": {},
    "outputs": [],
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "f6755dd9",
    "metadata": {
     "scrolled": true
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "id": "0d3c414b",
    "metadata": {},
    "outputs": [],
@@ -64,7 +64,7 @@
     "    results = f'{home}/results'\n",
     "\n",
     "    # The filepaths of the SEDs fitted to complete images of the entire Sunburst Arc galaxy\n",
-    "    SEDs = np.array([i for i in sorted(glob.glob(f'{data}/bestfit_model_image*_spec_filter6_cont.txt'))], dtype=str)\n",
+    "    SEDs = np.array([i for i in sorted(glob.glob(f'{data}/sed_fits/bestfit_model_image*_spec_filter6_cont.txt'))], dtype=str)\n",
     "\n",
     "    # Create a blank dictionary\n",
     "    df = {}\n",
@@ -152,7 +152,7 @@
     "    for i, label in enumerate(['nonleaker', 'leaker']):\n",
     "\n",
     "        # Get the file path\n",
-    "        file = glob.glob(f'{data}/spectra/mage/sunburst_arc_{label}_rest_no_m*.txt')[0]\n",
+    "        file = glob.glob(f'{data}/mage/sunburst_arc_{label}_stack_mage.txt')[0]\n",
     "\n",
     "        # Get the wavelength bins and flux densities of the stacked spectrum\n",
     "        w, f = np.loadtxt(file, delimiter='\\t', comments='#', usecols=(0,1), unpack=True)\n",
@@ -271,7 +271,7 @@
     "\n",
     "    # Make a mask of the region and save it\n",
     "    mask = pixel_region.to_mask().to_image((v5_header['NAXIS2'], v5_header['NAXIS1']))\n",
-    "    fits.writeto(f'{home}/results/box_for_median_imcoords_mask_v5.fits', mask.data, header=v5_header, overwrite=True)\n",
+    "    fits.writeto(f'{home}/results/masks/box_for_median_imcoords_mask_v5.fits', mask.data, header=v5_header, overwrite=True)\n",
     "\n",
     "    # Subtract the continuum using both the F390W and F555W-based continuum estimates, and for all ...\n",
     "    #for i, linelabel in enumerate(linelabels):\n",
@@ -312,8 +312,17 @@
     "        header_f = wcs.WCS(header_on).to_header()\n",
     "        header_n = wcs.WCS(header_on).to_header()\n",
     "\n",
+    "        cd_matrix = wcs.WCS(header_on).wcs.cd\n",
+    "\n",
+    "        #print(repr(header_f))\n",
+    "\n",
     "        # For the headers of the data and uncertainty extensions\n",
-    "        for i, header in enumerate([header_f, header_n]):\n",
+    "        for j, header in enumerate([header_f, header_n]):\n",
+    "\n",
+    "            header['CD1_1'] = cd_matrix[0,0]\n",
+    "            header['CD1_2'] = cd_matrix[0,1]\n",
+    "            header['CD2_1'] = cd_matrix[1,0]\n",
+    "            header['CD2_2'] = cd_matrix[1,1]\n",
     "\n",
     "            header.insert(1, ('CREATOR', 'M. Riley Owens', 'Creator of this file'))\n",
     "            header.insert(2, ('CREATOR_EMAIL', 'm.riley.owens@gmail.com', 'Email of the creator of this file'))\n",
@@ -326,7 +335,7 @@
     "            header['SCALEU'] = (cont_scale_std[i], 'The standard deviation of the continuum scaling factor, as inferred from the distribution of scaling factors derived from the 4 SED fits and 2 stacked MagE spectra')\n",
     "            \n",
     "            # For each keyword in an array of useful keywords that the observations share\n",
-    "            for j, keyword in enumerate(np.array(['FILETYPE', 'TELESCOP', 'INSTRUME', 'EQUINOX', 'IMAGETYP',\n",
+    "            for k, keyword in enumerate(np.array(['FILETYPE', 'TELESCOP', 'INSTRUME', 'EQUINOX', 'IMAGETYP',\n",
     "                'PRIMESI', 'OBSTYPE', 'BUNIT', ], dtype=str)):\n",
     "\n",
     "                header[keyword] = (header_on[keyword], header_on.comments[keyword])\n",
@@ -344,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "id": "b7314114",
    "metadata": {},
    "outputs": [],
@@ -369,7 +378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

seeing.ipynb

@@ -184,7 +184,7 @@
     "\n",
     "    # File path to the mask of the two largest arcs of the Sunburst Arc\n",
     "    # in the v5 HST data reduction WCS\n",
-    "    arc_mask_file = f'{results}/arc_mask_v5.fits'\n",
+    "    arc_mask_file = f'{results}/masks/arc_mask_v5.fits'\n",
     "\n",
     "    # Get a cutout of the arc mask. This is necessary because we will\n",
     "    # convolve the Lya maps later, which is computationally expensive for\n",
@@ -212,7 +212,7 @@
     "        #table = table + f'{slit_id} & '\n",
     "    \n",
     "        # File path to the slit mask\n",
-    "        slit_mask_file = f'{results}/{slit_id}_mask_v5.fits'\n",
+    "        slit_mask_file = f'{results}/masks/{slit_id}_mask_v5.fits'\n",
     "\n",
     "        # Get a cutout of the mask\n",
     "        slit_mask = fits.getdata(slit_mask_file)[4000:5600, 4400:5700]\n",
@@ -481,7 +481,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   },
   "orig_nbformat": 4
  },

source_plane.ipynb

@@ -48,7 +48,7 @@
     "    slits = np.array(['M5', 'M4', 'M6', 'M3'], dtype=str)\n",
     "\n",
     "    # Make an array of the filepaths to the source images\n",
-    "    files = np.array([f'{home}/figs/artist_impression_sunburst_source_labels_and_slilts_Riley_{slit}.png' for slit in slits], dtype=str)\n",
+    "    files = np.array([f'{home}/figs/source_plane_reconstructions/artist_impression_sunburst_source_labels_and_slilts_Riley_{slit}.png' for slit in slits], dtype=str)\n",
     "\n",
     "    # Instantiate the figure and axes\n",
     "    fig, ax = plt.subplots(2,2, figsize=(5,5), constrained_layout=True)\n",
@@ -100,7 +100,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

stack.ipynb

@@ -80,10 +80,10 @@
     "\n",
     "    # Establish basic directories\n",
     "    home = os.getcwd()\n",
-    "    data = home + '/data/spectra/mage'\n",
+    "    data = f'{home}/data/mage'\n",
     "\n",
     "    # Get the individual MagE spectra\n",
-    "    files = glob.glob(data + '/*.txt')\n",
+    "    files = glob.glob(f'{data}/*.txt')\n",
     "\n",
     "    # Dictionary of the spectroscopic redshifts of each spectrum,\n",
     "    # as measured by Mainali et al. (2022) (bibliographic information \n",
@@ -202,11 +202,11 @@
     "    n_nonleaker = n_nonleaker / 3\n",
     "\n",
     "    # Save the stacked spectra to .txt files\n",
-    "    np.savetxt(data + '/sunburst_arc_leaker_stack_mage.txt', np.array([w_leaker, f_leaker, n_leaker]).T, delimiter='\\t', encoding='utf-8',\n",
+    "    np.savetxt(f'{data}/mage/sunburst_arc_leaker_stack_mage.txt', np.array([w_leaker, f_leaker, n_leaker]).T, delimiter='\\t', encoding='utf-8',\n",
     "        header=f'Stacked spectrum of the LyC-leaking region of the Sunburst Arc from the MagE slits M2, M7, M8, M9, created by stack.ipynb.' + '\\n' \n",
     "        + 'The stack excludes slit M0 because of poor observing conditions that caused a poor fluxing.' + '\\n' + '\\n'\n",
     "        + 'The columns below are, from left to right: rest wavelength (angstroms), flux density (arbitrary units in wavelength space), and the 1σ Gaussian uncertainty of the flux density.')\n",
-    "    np.savetxt(data + '/sunburst_arc_nonleaker_stack_mage.txt', np.array([w_nonleaker, f_nonleaker, n_nonleaker]).T, delimiter='\\t', encoding='utf-8',\n",
+    "    np.savetxt(f'{data}/mage/sunburst_arc_nonleaker_stack_mage.txt', np.array([w_nonleaker, f_nonleaker, n_nonleaker]).T, delimiter='\\t', encoding='utf-8',\n",
     "        header=f'Stacked spectrum of the non-LyC-leaking regions of the Sunburst Arc from the MagE slits M5, M4, M6, created by stack.ipynb.' + '\\n' \n",
     "        + 'The stack excludes slit M3 because it captures a highly compact region of the galaxy which may not be representative of it\\'s surrounding environment.' + '\\n'\n",
     "        + 'Consult Vanzella et al. (2020) (MNRAS:Letters, 499, 1, L67), Diego et al. (2022) (A&A, 665, A134), or Sharon et al. (2022) (ApJ, 941, 2, 203) for more information about the compact region.' + '\\n' + '\\n'\n",