diff --git a/docs/tutorials/companion_data.ipynb b/docs/tutorials/companion_data.ipynb
index 8fdb9f14..dd817994 100644
--- a/docs/tutorials/companion_data.ipynb
+++ b/docs/tutorials/companion_data.ipynb
@@ -68,19 +68,22 @@
       "\n",
       "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
       "Creating species_config.ini... [DONE]\n",
+      "\n",
       "Configuration settings:\n",
       "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
       "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
-      "   - Interpolation method: linear\n",
       "   - Magnitude of Vega: 0.03\n",
       "Creating species_database.hdf5... [DONE]\n",
-      "Creating data folder... [DONE]\n"
+      "Creating data folder... [DONE]\n",
+      "\n",
+      "Multiprocessing: mpi4py installed\n",
+      "Process number 1 out of 1...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<species.core.species_init.SpeciesInit at 0x145f4ef10>"
+       "<species.core.species_init.SpeciesInit at 0x10f0218d0>"
       ]
      },
      "execution_count": 2,
@@ -134,94 +137,38 @@
    "id": "39f50cac",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
-      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 181MB/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding Vega spectrum... [DONE]\n",
-      "Reference: Bohlin et al. 2014, PASP, 126\n",
-      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n",
-      "Adding object: beta Pic b [DONE]\n"
+      "Adding companion: ['beta Pic b', 'beta Pic c', 'HIP 65426 b', '51 Eri b', 'HR 8799 b', 'HR 8799 c', 'HR 8799 d', 'HR 8799 e', 'HD 95086 b', 'PDS 70 b', 'PDS 70 c', '2M 1207 B', 'AB Pic B', 'HD 206893 B', 'RZ Psc B', 'GQ Lup B', 'PZ Tel B', 'kappa And b', 'HD 1160 B', 'ROXs 12 B', 'ROXs 42 Bb', 'GJ 504 b', 'GJ 758 B', 'GU Psc b', '2M0103 ABb', '1RXS 1609 B', 'GSC 06214 B', 'HD 72946 B', 'HIP 64892 B', 'HD 13724 B', 'YSES 1 b', 'YSES 1 c', 'YSES 2 b', 'HD 142527 B', 'CS Cha B', 'CT Cha B', 'SR 12 C', 'DH Tau B', 'HD 4747 B', 'HR 3549 B', 'CHXR 73 B', 'HD 19467 B', 'b Cen (AB)b', 'VHS 1256 B']\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/tomasstolker/applications/species/species/data/database.py:1296: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n",
-      "  warnings.warn(\n"
+      "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
+      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 149MB/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding object: beta Pic c [DONE]\n",
-      "Adding object: HIP 65426 b [DONE]\n",
-      "Adding object: 51 Eri b [DONE]\n"
+      "Adding spectrum: VegaReference: Bohlin et al. 2014, PASP, 126\n",
+      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/tomasstolker/applications/species/species/data/database.py:1356: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n",
+      "  warnings.warn(\n",
       "/Users/tomasstolker/applications/species/species/data/filter_data/filter_data.py:227: UserWarning: The minimum transmission value of Subaru/CIAO.z is smaller than zero (-1.80e-03). Wavelengths with negative transmission values will be removed.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Adding object: HR 8799 b [DONE]\n",
-      "Adding object: HR 8799 c [DONE]\n",
-      "Adding object: HR 8799 d [DONE]\n",
-      "Adding object: HR 8799 e [DONE]\n",
-      "Adding object: HD 95086 b [DONE]\n",
-      "Adding object: PDS 70 b [DONE]\n",
-      "Adding object: PDS 70 c [DONE]\n",
-      "Adding object: 2M 1207 B [DONE]\n",
-      "Adding object: AB Pic B [DONE]\n",
-      "Adding object: HD 206893 B [DONE]\n",
-      "Adding object: RZ Psc B [DONE]\n",
-      "Adding object: GQ Lup B [DONE]\n",
-      "Adding object: PZ Tel B [DONE]\n",
-      "Adding object: kappa And b [DONE]\n",
-      "Adding object: HD 1160 B [DONE]\n",
-      "Adding object: ROXs 12 B [DONE]\n",
-      "Adding object: ROXs 42 Bb [DONE]\n",
-      "Adding object: GJ 504 b [DONE]\n",
-      "Adding object: GJ 758 B [DONE]\n",
-      "Adding object: GU Psc b [DONE]\n",
-      "Adding object: 2M0103 ABb [DONE]\n",
-      "Adding object: 1RXS 1609 B [DONE]\n",
-      "Adding object: GSC 06214 B [DONE]\n",
-      "Adding object: HD 72946 B [DONE]\n",
-      "Adding object: HIP 64892 B [DONE]\n",
-      "Adding object: HD 13724 B [DONE]\n",
-      "Adding object: YSES 1 b [DONE]\n",
-      "Adding object: YSES 1 c [DONE]\n",
-      "Adding object: YSES 2 b [DONE]\n",
-      "Adding object: HD 142527 B [DONE]\n",
-      "Adding object: CS Cha B [DONE]\n",
-      "Adding object: CT Cha B [DONE]\n",
-      "Adding object: SR 12 C [DONE]\n",
-      "Adding object: DH Tau B [DONE]\n",
-      "Adding object: HD 4747 B [DONE]\n",
-      "Adding object: HR 3549 B [DONE]\n",
-      "Adding object: CHXR 73 B [DONE]\n",
-      "Adding object: HD 19467 B [DONE]\n",
-      "Adding object: b Cen (AB)b [DONE]\n",
-      "Adding object: VHS 1256 B [DONE]\n"
-     ]
     }
    ],
    "source": [
@@ -434,7 +381,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Getting object: beta Pic b... [DONE]\n"
+      "\n",
+      "----------\n",
+      "Get object\n",
+      "----------\n",
+      "\n",
+      "Object name: beta Pic b\n",
+      "Include photometry: True\n",
+      "Include spectra: False\n"
      ]
     }
    ],
diff --git a/docs/tutorials/data_model.ipynb b/docs/tutorials/data_model.ipynb
index 8bce9eb3..9aaf5d57 100644
--- a/docs/tutorials/data_model.ipynb
+++ b/docs/tutorials/data_model.ipynb
@@ -63,19 +63,22 @@
       "\n",
       "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
       "Creating species_config.ini... [DONE]\n",
+      "\n",
       "Configuration settings:\n",
       "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
       "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
-      "   - Interpolation method: linear\n",
       "   - Magnitude of Vega: 0.03\n",
       "Creating species_database.hdf5... [DONE]\n",
-      "Creating data folder... [DONE]\n"
+      "Creating data folder... [DONE]\n",
+      "\n",
+      "Multiprocessing: mpi4py installed\n",
+      "Process number 1 out of 1...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<species.core.species_init.SpeciesInit at 0x10f11cc10>"
+       "<species.core.species_init.SpeciesInit at 0x1540ff790>"
       ]
      },
      "execution_count": 2,
@@ -126,8 +129,26 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/atmo.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/atmo.tgz'.\n",
-      "100%|████████████████████████████████████████| 445M/445M [00:00<00:00, 387GB/s]\n",
+      "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/atmo.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/atmo.tgz'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-------------------------\n",
+      "Add grid of model spectra\n",
+      "-------------------------\n",
+      "\n",
+      "Model name: atmo\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████| 445M/445M [00:00<00:00, 208GB/s]\n",
       "SHA256 hash of downloaded file: f8bde62bf0809c6c5d636cd52d1f2b82457205981095e14ef269d69e46268764\n",
       "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
      ]
@@ -138,18 +159,21 @@
      "text": [
       "Unpacking 42/231 model spectra from ATMO (425 MB)... [DONE]\n",
       "Please cite Phillips et al. (2020) when using ATMO in a publication\n",
-      "Reference URL: https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract\n",
+      "Reference URL: https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P\n",
+      "\n",
       "Wavelength range (um) = 0.4 - 6000\n",
       "Spectral resolution = 10000\n",
       "Teff range (K) = 2500.0 - 3000.0\n",
-      "Adding ATMO model spectra... [DONE]                          \n",
+      "\n",
+      "                                                             \n",
       "Grid points stored in the database:\n",
       "   - Teff = [2500. 2600. 2700. 2800. 2900. 3000.]\n",
       "   - log(g) = [2.5 3.  3.5 4.  4.5 5.  5.5]\n",
+      "\n",
       "Number of grid points per parameter:\n",
       "   - teff: 6\n",
       "   - logg: 7\n",
-      "Fix missing grid points with a linear interpolation:\n",
+      "\n",
       "Number of stored grid points: 42\n",
       "Number of interpolated grid points: 0\n",
       "Number of missing grid points: 0\n"
@@ -179,22 +203,26 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Add companion: ['PZ Tel B']\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
-      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 146MB/s]"
+      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 195MB/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding Vega spectrum... [DONE]\n",
-      "Reference: Bohlin et al. 2014, PASP, 126\n",
-      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n",
-      "Adding object: PZ Tel B"
+      "Adding spectrum: Vega"
      ]
     },
     {
@@ -208,7 +236,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " [DONE]\n"
+      "Reference: Bohlin et al. 2014, PASP, 126\n",
+      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n"
      ]
     }
    ],
@@ -232,7 +261,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Database content:\n",
+      "\n",
+      "---------------------\n",
+      "List database content\n",
+      "---------------------\n",
+      "\n",
       "- filters: <HDF5 group \"/filters\" (2 members)>\n",
       "\t- Gemini: <HDF5 group \"/filters/Gemini\" (2 members)>\n",
       "\t\t- NICI.ED286: <HDF5 dataset \"NICI.ED286\": shape (387, 2), type \"<f8\">\n",
@@ -452,7 +485,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Getting object: PZ Tel B... [DONE]\n"
+      "\n",
+      "----------\n",
+      "Get object\n",
+      "----------\n",
+      "\n",
+      "Object name: PZ Tel B\n",
+      "Include photometry: True\n",
+      "Include spectra: True\n"
      ]
     }
    ],
@@ -483,7 +523,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating synthetic photometry... [DONE]\n"
+      "\n",
+      "--------------------------\n",
+      "Calculate multi-photometry\n",
+      "--------------------------\n",
+      "\n",
+      "Data type: model\n",
+      "Spectrum name: atmo\n",
+      "\n",
+      "Parameters:\n",
+      "   - teff = 2900.00\n",
+      "   - logg = 4.50\n",
+      "   - radius = 2.20\n",
+      "   - distance = 47.13\n",
+      "   - luminosity = 3.26e-03\n",
+      "   - mass = 61.75\n",
+      "\n",
+      "Magnitudes:\n",
+      "   - Gemini/NICI.ED286 = 11.82\n",
+      "   - Gemini/NIRI.H2S1v2-1-G0220 = 11.32\n",
+      "   - Paranal/NACO.H = 11.79\n",
+      "   - Paranal/NACO.J = 12.37\n",
+      "   - Paranal/NACO.Ks = 11.52\n",
+      "   - Paranal/NACO.Lp = 10.97\n",
+      "   - Paranal/NACO.Mp = 11.04\n",
+      "   - Paranal/NACO.NB405 = 10.89\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_2 = 11.82\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_3 = 11.58\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_1 = 11.59\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_2 = 11.33\n",
+      "   - Paranal/SPHERE.ZIMPOL_I_PRIM = 14.44\n",
+      "   - Paranal/SPHERE.ZIMPOL_R_PRIM = 16.97\n",
+      "\n",
+      "Fluxes (W m-2 um-1):\n",
+      "   - Gemini/NICI.ED286 = 2.40e-14\n",
+      "   - Gemini/NIRI.H2S1v2-1-G0220 = 1.11e-14\n",
+      "   - Paranal/NACO.H = 2.20e-14\n",
+      "   - Paranal/NACO.J = 3.37e-14\n",
+      "   - Paranal/NACO.Ks = 1.11e-14\n",
+      "   - Paranal/NACO.Lp = 2.10e-15\n",
+      "   - Paranal/NACO.Mp = 8.16e-16\n",
+      "   - Paranal/NACO.NB405 = 1.71e-15\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_2 = 2.41e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_3 = 2.54e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_1 = 1.11e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_2 = 1.09e-14\n",
+      "   - Paranal/SPHERE.ZIMPOL_I_PRIM = 2.11e-14\n",
+      "   - Paranal/SPHERE.ZIMPOL_R_PRIM = 4.10e-15\n"
      ]
     }
    ],
@@ -517,23 +603,88 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating synthetic photometry... [DONE]\n",
-      "Calculating residuals... [DONE]\n",
+      "\n",
+      "-------------------\n",
+      "Calculate residuals\n",
+      "-------------------\n",
+      "\n",
+      "Data type: model\n",
+      "Spectrum name: atmo\n",
+      "\n",
+      "Include photometry: True\n",
+      "Include spectra: False\n",
+      "\n",
+      "Parameters:\n",
+      "   - teff = 2900.00\n",
+      "   - logg = 4.50\n",
+      "   - radius = 2.20\n",
+      "   - distance = 47.13\n",
+      "   - luminosity = 3.26e-03\n",
+      "   - mass = 61.75\n",
+      "\n",
+      "--------------------------\n",
+      "Calculate multi-photometry\n",
+      "--------------------------\n",
+      "\n",
+      "Data type: model\n",
+      "Spectrum name: atmo\n",
+      "\n",
+      "Parameters:\n",
+      "   - teff = 2900.00\n",
+      "   - logg = 4.50\n",
+      "   - radius = 2.20\n",
+      "   - distance = 47.13\n",
+      "   - luminosity = 3.26e-03\n",
+      "   - mass = 61.75\n",
+      "\n",
+      "Magnitudes:\n",
+      "   - Gemini/NICI.ED286 = 11.82\n",
+      "   - Gemini/NIRI.H2S1v2-1-G0220 = 11.32\n",
+      "   - Paranal/NACO.H = 11.79\n",
+      "   - Paranal/NACO.J = 12.37\n",
+      "   - Paranal/NACO.Ks = 11.52\n",
+      "   - Paranal/NACO.Lp = 10.97\n",
+      "   - Paranal/NACO.Mp = 11.04\n",
+      "   - Paranal/NACO.NB405 = 10.89\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_2 = 11.82\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_3 = 11.58\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_1 = 11.59\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_2 = 11.33\n",
+      "   - Paranal/SPHERE.ZIMPOL_I_PRIM = 14.44\n",
+      "   - Paranal/SPHERE.ZIMPOL_R_PRIM = 16.97\n",
+      "\n",
+      "Fluxes (W m-2 um-1):\n",
+      "   - Gemini/NICI.ED286 = 2.40e-14\n",
+      "   - Gemini/NIRI.H2S1v2-1-G0220 = 1.11e-14\n",
+      "   - Paranal/NACO.H = 2.20e-14\n",
+      "   - Paranal/NACO.J = 3.37e-14\n",
+      "   - Paranal/NACO.Ks = 1.11e-14\n",
+      "   - Paranal/NACO.Lp = 2.10e-15\n",
+      "   - Paranal/NACO.Mp = 8.16e-16\n",
+      "   - Paranal/NACO.NB405 = 1.71e-15\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_2 = 2.41e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_3 = 2.54e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_1 = 1.11e-14\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_2 = 1.09e-14\n",
+      "   - Paranal/SPHERE.ZIMPOL_I_PRIM = 2.11e-14\n",
+      "   - Paranal/SPHERE.ZIMPOL_R_PRIM = 4.10e-15\n",
+      "\n",
       "Residuals (sigma):\n",
-      "   - Gemini/NICI.ED286: 0.95\n",
-      "   - Gemini/NIRI.H2S1v2-1-G0220: -0.53\n",
-      "   - Paranal/NACO.H: -1.06\n",
-      "   - Paranal/NACO.J: -0.52\n",
-      "   - Paranal/NACO.Ks: -0.09\n",
-      "   - Paranal/NACO.Lp: -0.31\n",
-      "   - Paranal/NACO.Mp: 3.38\n",
-      "   - Paranal/NACO.NB405: -0.72\n",
-      "   - Paranal/SPHERE.IRDIS_D_H23_2: 0.22\n",
-      "   - Paranal/SPHERE.IRDIS_D_H23_3: -0.36\n",
-      "   - Paranal/SPHERE.IRDIS_D_K12_1: 0.29\n",
-      "   - Paranal/SPHERE.IRDIS_D_K12_2: 0.36\n",
-      "   - Paranal/SPHERE.ZIMPOL_I_PRIM: -8.54\n",
-      "   - Paranal/SPHERE.ZIMPOL_R_PRIM: -4.28\n",
+      "   - Gemini/NICI.ED286 = 0.95\n",
+      "   - Gemini/NIRI.H2S1v2-1-G0220 = -0.53\n",
+      "   - Paranal/NACO.H = -1.06\n",
+      "   - Paranal/NACO.J = -0.52\n",
+      "   - Paranal/NACO.Ks = -0.09\n",
+      "   - Paranal/NACO.Lp = -0.31\n",
+      "   - Paranal/NACO.Mp = 3.38\n",
+      "   - Paranal/NACO.NB405 = -0.72\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_2 = 0.22\n",
+      "   - Paranal/SPHERE.IRDIS_D_H23_3 = -0.36\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_1 = 0.29\n",
+      "   - Paranal/SPHERE.IRDIS_D_K12_2 = 0.36\n",
+      "   - Paranal/SPHERE.ZIMPOL_I_PRIM = -8.54\n",
+      "   - Paranal/SPHERE.ZIMPOL_R_PRIM = -4.28\n",
+      "\n",
       "Reduced chi2 = 9.66\n",
       "Number of degrees of freedom = 11\n"
      ]
diff --git a/docs/tutorials/flux_magnitude.ipynb b/docs/tutorials/flux_magnitude.ipynb
index a59211e0..eada96f9 100644
--- a/docs/tutorials/flux_magnitude.ipynb
+++ b/docs/tutorials/flux_magnitude.ipynb
@@ -53,19 +53,22 @@
       "\n",
       "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
       "Creating species_config.ini... [DONE]\n",
+      "\n",
       "Configuration settings:\n",
       "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
       "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
-      "   - Interpolation method: linear\n",
       "   - Magnitude of Vega: 0.03\n",
       "Creating species_database.hdf5... [DONE]\n",
-      "Creating data folder... [DONE]\n"
+      "Creating data folder... [DONE]\n",
+      "\n",
+      "Multiprocessing: mpi4py installed\n",
+      "Process number 1 out of 1...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<species.core.species_init.SpeciesInit at 0x111259410>"
+       "<species.core.species_init.SpeciesInit at 0x12bd0b110>"
       ]
      },
      "execution_count": 2,
@@ -101,7 +104,7 @@
      "output_type": "stream",
      "text": [
       "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
-      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 122MB/s]\n"
+      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 113MB/s]\n"
      ]
     },
     {
diff --git a/docs/tutorials/running_species.ipynb b/docs/tutorials/running_species.ipynb
index 8160778c..ce27f63a 100644
--- a/docs/tutorials/running_species.ipynb
+++ b/docs/tutorials/running_species.ipynb
@@ -62,19 +62,22 @@
       "\n",
       "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
       "Creating species_config.ini... [DONE]\n",
+      "\n",
       "Configuration settings:\n",
       "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
       "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
-      "   - Interpolation method: linear\n",
       "   - Magnitude of Vega: 0.03\n",
       "Creating species_database.hdf5... [DONE]\n",
-      "Creating data folder... [DONE]\n"
+      "Creating data folder... [DONE]\n",
+      "\n",
+      "Multiprocessing: mpi4py installed\n",
+      "Process number 1 out of 1...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<species.core.species_init.SpeciesInit at 0x154c69c50>"
+       "<species.core.species_init.SpeciesInit at 0x152ada3d0>"
       ]
      },
      "execution_count": 2,
@@ -125,29 +128,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading data from 'http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes_files/vlm-plx-all.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/vlm-plx-all.fits'.\n",
-      "100%|████████████████████████████████████████| 314k/314k [00:00<00:00, 173MB/s]"
+      "Downloading data from 'http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes_files/vlm-plx-all.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/vlm-plx-all.fits'.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding Database of Ultracool Parallaxes..."
+      "\n",
+      "-----------------------\n",
+      "Add photometric library\n",
+      "-----------------------\n",
+      "\n",
+      "Database tag: vlm-plx\n",
+      "Library: Database of Ultracool Parallaxes\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " [DONE]\n"
+      "100%|████████████████████████████████████████| 314k/314k [00:00<00:00, 136MB/s]\n"
      ]
     }
    ],
@@ -174,22 +175,26 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding companion: 51 Eri b\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
-      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 157MB/s]"
+      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 183MB/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding Vega spectrum... [DONE]\n",
-      "Reference: Bohlin et al. 2014, PASP, 126\n",
-      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n",
-      "Adding object: 51 Eri b"
+      "Adding spectrum: Vega"
      ]
     },
     {
@@ -203,15 +208,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " [DONE]\n",
-      "Adding object: beta Pic b [DONE]\n"
+      "Reference: Bohlin et al. 2014, PASP, 126\n",
+      "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n",
+      "Adding companion: beta Pic b\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/tomasstolker/applications/species/species/data/database.py:1296: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n",
+      "/Users/tomasstolker/applications/species/species/data/database.py:1356: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -219,7 +225,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding object: PZ Tel B [DONE]\n"
+      "Adding companion: PZ Tel B\n"
      ]
     }
    ],
@@ -247,7 +253,23 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------\n",
+      "Read color-magnitude\n",
+      "--------------------\n",
+      "\n",
+      "Database tag: vlm-plx\n",
+      "Library type: phot_lib\n",
+      "Filters color: ('MKO/NSFCam.J', 'MKO/NSFCam.H')\n",
+      "Filter magnitude: MKO/NSFCam.J\n"
+     ]
+    }
+   ],
    "source": [
     "colormag = ReadColorMagnitude(library='vlm-plx',\n",
     "                              filters_color=('MKO/NSFCam.J', 'MKO/NSFCam.H'),\n",
@@ -265,7 +287,21 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-------------------\n",
+      "Get color-magnitude\n",
+      "-------------------\n",
+      "\n",
+      "Object type: field\n",
+      "Returning ColorMagBox with 241 objects\n"
+     ]
+    }
+   ],
    "source": [
     "colorbox = colormag.get_color_magnitude(object_type='field')"
    ]
@@ -304,48 +340,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "----------------------------\n",
+      "Plot color-magnitude diagram\n",
+      "----------------------------\n",
+      "\n",
+      "Boxes:\n",
+      "   - ColorMagBox\n",
+      "\n",
+      "Objects:\n",
+      "   - 51 Eri b: ('MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.J')\n",
+      "   - beta Pic b: ('Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J')\n",
+      "   - PZ Tel B: ('Paranal/NACO.J', 'Paranal/NACO.H', 'Paranal/NACO.J')\n",
+      "\n",
+      "Spectral range: ('late M', 'late T')\n",
+      "Companion labels: True\n",
+      "Accretion markers: False\n",
+      "\n",
+      "Mass labels: None\n",
+      "Teff labels: None\n",
+      "\n",
+      "Reddening:\n",
+      "   - (('MKO/NSFCam.J', 'MKO/NSFCam.H'), ('MKO/NSFCam.J', 1.0), 'MgSiO3', 0.1, (-0.8, 10.0))\n",
+      "\n",
+      "Figure size: (4.0, 4.8)\n",
+      "Limits x axis: (-1.2, 1.5)\n",
+      "Limits y axis: (21.0, 8.0)\n",
+      "\n",
       "Downloading optical constants (87 kB)... [DONE]\n",
+      "\n",
       "Unpacking optical constants... [DONE]\n",
+      "\n",
       "Adding optical constants of MgSiO3... [DONE]\n",
       "Adding optical constants of Fe... [DONE]\n",
+      "\n",
       "Downloading log-normal dust cross sections (231 kB)... [DONE]\n",
+      "\n",
       "Adding log-normal dust cross sections:\n",
       "   - Data shape (n_wavelength, n_radius, n_sigma): (67, 20, 20)\n",
       "   - Wavelength range: 0.4 - 10.0 um\n",
       "   - Mean geometric radius range: 0.001 - 10.0 um\n",
       "   - Geometric standard deviation range: 1.0 - 10.0\n",
+      "\n",
       "Downloading power-law dust cross sections (231 kB)... [DONE]\n",
-      "Adding power-law dust cross sections\n",
+      "\n",
+      "Adding power-law dust cross sections:\n",
       "   - Data shape (n_wavelength, n_radius, n_exponent): (132, 50, 50)\n",
       "   - Wavelength range: 0.4 - 10.0 um\n",
       "   - Maximum grain radius range: 0.01 - 100.0 um\n",
-      "   - Power-law exponent range: -10.0 - 10.0\n",
-      "Plotting color-magnitude diagram..."
+      "   - Power-law exponent range: -10.0 - 10.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x480 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " [DONE]\n"
-     ]
     }
    ],
    "source": [
@@ -372,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -381,7 +442,7 @@
        "[<Axes: xlabel='$J - H$', ylabel='M$_J$'>, <Axes: >]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/tutorials/synthetic_photometry.ipynb b/docs/tutorials/synthetic_photometry.ipynb
index 7da99e74..5c074ddf 100644
--- a/docs/tutorials/synthetic_photometry.ipynb
+++ b/docs/tutorials/synthetic_photometry.ipynb
@@ -64,19 +64,22 @@
       "\n",
       "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
       "Creating species_config.ini... [DONE]\n",
+      "\n",
       "Configuration settings:\n",
       "   - Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
       "   - Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
-      "   - Interpolation method: linear\n",
       "   - Magnitude of Vega: 0.03\n",
       "Creating species_database.hdf5... [DONE]\n",
-      "Creating data folder... [DONE]\n"
+      "Creating data folder... [DONE]\n",
+      "\n",
+      "Multiprocessing: mpi4py installed\n",
+      "Process number 1 out of 1...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<species.core.species_init.SpeciesInit at 0x153898450>"
+       "<species.core.species_init.SpeciesInit at 0x1507b0610>"
       ]
      },
      "execution_count": 2,
@@ -110,7 +113,7 @@
     {
      "data": {
       "text/plain": [
-       "('data/plnt_Jupiter.txt', <http.client.HTTPMessage at 0x159753e90>)"
+       "('data/plnt_Jupiter.txt', <http.client.HTTPMessage at 0x150809850>)"
       ]
      },
      "execution_count": 3,
@@ -177,26 +180,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding filter: MKO/NSFCam.J... [DONE]\n",
-      "Plotting spectrum..."
+      "\n",
+      "-------------\n",
+      "Plot spectrum\n",
+      "-------------\n",
+      "\n",
+      "Boxes:\n",
+      "   - SpectrumBox\n",
+      "\n",
+      "Object type: planet\n",
+      "Quantity: flux density\n",
+      "Filter profiles: ['MKO/NSFCam.J']\n",
+      "\n",
+      "Figure size: (7.0, 3.0)\n",
+      "Legend parameters: None\n",
+      "Include model name: False\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " [DONE]\n"
-     ]
     }
    ],
    "source": [
@@ -261,24 +270,16 @@
      "output_type": "stream",
      "text": [
       "Downloading data from 'https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/alpha_lyr_stis_011.fits' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/alpha_lyr_stis_011.fits'.\n",
-      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 128MB/s]"
+      "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 158MB/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding Vega spectrum... [DONE]\n",
-      "Reference: Bohlin et al. 2014, PASP, 126\n",
+      "Adding spectrum: VegaReference: Bohlin et al. 2014, PASP, 126\n",
       "URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -301,7 +302,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Flux (W m-2 um-1) = 1.80e-09 +/- 7.68e-14\n"
+      "Flux (W m-2 um-1) = 1.80e-09 +/- 8.87e-14\n"
      ]
     }
    ],
@@ -326,7 +327,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Apparent magnitude = 0.58 +/- 5.78e-05\n"
+      "Apparent magnitude = 0.58 +/- 5.16e-05\n"
      ]
     }
    ],
diff --git a/species/data/companion_data/companion_spectra.py b/species/data/companion_data/companion_spectra.py
index 9aa5b8a9..c6708338 100644
--- a/species/data/companion_data/companion_spectra.py
+++ b/species/data/companion_data/companion_spectra.py
@@ -50,7 +50,8 @@ def companion_spectra(
         comp_spec = json.load(json_file)
 
     if comp_name in comp_spec:
-        print_section("Get companion spectra")
+        if verbose:
+            print_section("Get companion spectra")
 
         data_folder = os.path.join(input_path, "companion_data/")
 
diff --git a/species/data/database.py b/species/data/database.py
index 3cc136e3..e647eba0 100644
--- a/species/data/database.py
+++ b/species/data/database.py
@@ -327,6 +327,9 @@ def add_companion(
         if name is None:
             name = list(comp_data.keys())
 
+        if not verbose:
+            print(f"Add companion: {name}")
+
         for item in name:
             spec_dict = companion_spectra(self.data_folder, item, verbose=verbose)
 
@@ -902,11 +905,9 @@ def add_object(
             None
         """
 
-        print_section("Add object")
-
-        print(f"Object name: {object_name}")
-
         if verbose:
+            print_section("Add object")
+            print(f"Object name: {object_name}")
             print(f"Units: {units}")
             print(f"Deredden: {deredden}")
 
@@ -1535,9 +1536,6 @@ def add_object(
                         print(f"      - {spec_item}: {spec_value[2]:.1f}")
                     dset.attrs["specres"] = spec_value[2]
 
-        if not verbose:
-            print(" [DONE]")
-
         hdf5_file.close()
 
     @typechecked
@@ -1554,16 +1552,18 @@ def add_photometry(self, phot_library: str) -> None:
             None
         """
 
-        with h5py.File(self.database, "a") as hdf5_file:
-            if "photometry" not in hdf5_file:
-                hdf5_file.create_group("photometry")
+        print_section("Add photometric library")
 
+        print(f"Database tag: {phot_library}")
+
+        with h5py.File(self.database, "a") as hdf5_file:
             if f"photometry/{phot_library}" in hdf5_file:
                 del hdf5_file[f"photometry/{phot_library}"]
 
             if phot_library[0:7] == "vlm-plx":
                 from species.data.phot_data.phot_vlm_plx import add_vlm_plx
 
+                print("Library: Database of Ultracool Parallaxes")
                 add_vlm_plx(self.data_folder, hdf5_file)
 
             elif phot_library[0:7] == "leggett":
diff --git a/species/data/misc_data/dust_data.py b/species/data/misc_data/dust_data.py
index daf144eb..68efab3c 100644
--- a/species/data/misc_data/dust_data.py
+++ b/species/data/misc_data/dust_data.py
@@ -40,18 +40,18 @@ def add_optical_constants(input_path: str, database: h5py._hl.files.File) -> Non
     data_file = os.path.join(input_path, "optical_constants.zip")
 
     if not os.path.isfile(data_file):
-        print("Downloading optical constants (87 kB)...", end="", flush=True)
+        print("\nDownloading optical constants (87 kB)...", end="", flush=True)
         urllib.request.urlretrieve(url, data_file)
         print(" [DONE]")
 
-    print("Unpacking optical constants...", end="", flush=True)
+    print("\nUnpacking optical constants...", end="", flush=True)
 
     with zipfile.ZipFile(data_file, "r") as zip_ref:
         zip_ref.extractall(input_path)
 
     print(" [DONE]")
 
-    print("Adding optical constants of MgSiO3...", end="")
+    print("\nAdding optical constants of MgSiO3...", end="")
 
     nk_file = os.path.join(
         input_path,
@@ -135,11 +135,11 @@ def add_cross_sections(input_path: str, database: h5py._hl.files.File) -> None:
 
     data_file = os.path.join(input_path, "lognorm_mgsio3_c_ext.fits")
 
-    print("Downloading log-normal dust cross sections (231 kB)...", end="", flush=True)
+    print("\nDownloading log-normal dust cross sections (231 kB)...", end="", flush=True)
     urllib.request.urlretrieve(url, data_file)
     print(" [DONE]")
 
-    print("Adding log-normal dust cross sections:")
+    print("\nAdding log-normal dust cross sections:")
 
     with fits.open(os.path.join(input_path, "lognorm_mgsio3_c_ext.fits")) as hdu_list:
         database.create_dataset(
@@ -175,11 +175,11 @@ def add_cross_sections(input_path: str, database: h5py._hl.files.File) -> None:
 
     data_file = os.path.join(input_path, "powerlaw_mgsio3_c_ext.fits")
 
-    print("Downloading power-law dust cross sections (231 kB)...", end="", flush=True)
+    print("\nDownloading power-law dust cross sections (231 kB)...", end="", flush=True)
     urllib.request.urlretrieve(url, data_file)
     print(" [DONE]")
 
-    print("Adding power-law dust cross sections")
+    print("\nAdding power-law dust cross sections:")
 
     with fits.open(os.path.join(input_path, "powerlaw_mgsio3_c_ext.fits")) as hdu_list:
         database.create_dataset(
diff --git a/species/data/phot_data/phot_vlm_plx.py b/species/data/phot_data/phot_vlm_plx.py
index 03f340f0..a74db3d5 100644
--- a/species/data/phot_data/phot_vlm_plx.py
+++ b/species/data/phot_data/phot_vlm_plx.py
@@ -49,8 +49,6 @@ def add_vlm_plx(input_path, database):
             progressbar=True,
         )
 
-    print("Adding Database of Ultracool Parallaxes...", end="", flush=True)
-
     database.create_group("photometry/vlm-plx")
 
     with fits.open(data_file) as hdu_list:
@@ -110,6 +108,4 @@ def add_vlm_plx(input_path, database):
         "photometry/vlm-plx/2MASS/2MASS.Ks", data=phot_data["K2MAG"]
     )
 
-    print(" [DONE]")
-
     database.close()
diff --git a/species/data/spec_data/spec_vega.py b/species/data/spec_data/spec_vega.py
index 1eede0c4..4b14e5bc 100644
--- a/species/data/spec_data/spec_vega.py
+++ b/species/data/spec_data/spec_vega.py
@@ -78,13 +78,11 @@ def add_vega(input_path, database):
     error_stat *= 1e-3 * 1e4  # (erg s-1 cm-2 A-1) -> (W m-2 um-1)
     error_sys *= 1e-3 * 1e4  # (erg s-1 cm-2 A-1) -> (W m-2 um-1)
 
-    print("Adding Vega spectrum...", end="", flush=True)
+    print("Adding spectrum: Vega", end="", flush=True)
 
     database.create_dataset(
         "spectra/calibration/vega", data=np.vstack((wavelength, flux, error_stat))
     )
 
-    print(" [DONE]")
-
     print("Reference: Bohlin et al. 2014, PASP, 126")
     print("URL: https://ui.adsabs.harvard.edu/abs/2014PASP..126..711B/abstract")
diff --git a/species/plot/plot_color.py b/species/plot/plot_color.py
index 51319937..8c5e190d 100644
--- a/species/plot/plot_color.py
+++ b/species/plot/plot_color.py
@@ -22,6 +22,7 @@
 from species.core.box import IsochroneBox, ColorMagBox, ColorColorBox
 from species.read.read_filter import ReadFilter
 from species.read.read_object import ReadObject
+from species.util.core_util import print_section
 from species.util.dust_util import calc_reddening, ism_extinction
 from species.util.plot_util import (
     convert_model_name,
@@ -107,8 +108,8 @@ def plot_color_magnitude(
         Plot accreting, directly imaged objects with a different symbol
         than the regular, directly imaged objects. The object names
         from ``objects`` will be compared with the data from
-        `data/companion_data.json` to check if a companion is
-        accreting or not.
+        `data/companion_data/companion_data.json` to check if a
+        companion is accreting or not.
     reddening : list(tuple(tuple(str, str), tuple(str, float),
             str, float, tuple(float, float))), None
         Include reddening arrows by providing a list with tuples. Each
@@ -166,6 +167,41 @@ def plot_color_magnitude(
         customization of the plot.
     """
 
+    print_section("Plot color-magnitude diagram")
+
+    print("Boxes:")
+    for item in boxes:
+        if isinstance(item, list):
+            item_type = item[0].__class__.__name__
+            print(f"   - List with {len(item)} x {item_type}")
+        else:
+            print(f"   - {item.__class__.__name__}")
+
+    if objects is None:
+        print(f"\nObjects: {object_type}")
+    else:
+        print(f"\nObjects:")
+        for item in objects:
+            print(f"   - {item[0]}: {item[1:]}")
+
+    print(f"\nSpectral range: {field_range}")
+    print(f"Companion labels: {companion_labels}")
+    print(f"Accretion markers: {accretion}")
+
+    print(f"\nMass labels: {mass_labels}")
+    print(f"Teff labels: {teff_labels}")
+
+    if reddening is None:
+        print(f"\nReddening: {object_type}")
+    else:
+        print(f"\nReddening:")
+        for item in reddening:
+            print(f"   - {item}")
+
+    print(f"\nFigure size: {figsize}")
+    print(f"Limits x axis: {xlim}")
+    print(f"Limits y axis: {ylim}")
+
     plt.rcParams["font.family"] = "serif"
     plt.rcParams["mathtext.fontset"] = "dejavuserif"
 
@@ -782,7 +818,9 @@ def plot_color_magnitude(
             x_color = objcolor1[0] - objcolor2[0]
 
             species_folder = str(pathlib.Path(__file__).parent.resolve())[:-4]
-            data_file = os.path.join(species_folder, "data/companion_data.json")
+            data_file = os.path.join(
+                species_folder, "data/companion_data/companion_data.json"
+            )
 
             with open(data_file, "r", encoding="utf-8") as json_file:
                 comp_data = json.load(json_file)
@@ -834,11 +872,6 @@ def plot_color_magnitude(
                     **kwargs,
                 )
 
-    if output is None:
-        print("Plotting color-magnitude diagram...", end="", flush=True)
-    else:
-        print(f"Plotting color-magnitude diagram: {output}...", end="", flush=True)
-
     if legend is not None:
         handles, labels = ax1.get_legend_handles_labels()
 
@@ -862,10 +895,9 @@ def plot_color_magnitude(
     if output is None:
         plt.show()
     else:
+        print(f"\nOutput: {output}")
         plt.savefig(output, bbox_inches="tight")
 
-    print(" [DONE]")
-
     return fig
 
 
@@ -994,6 +1026,40 @@ def plot_color_color(
         customization of the plot.
     """
 
+    print_section("Plot color-color diagram")
+
+    print("Boxes:")
+    for item in boxes:
+        if isinstance(item, list):
+            item_type = item[0].__class__.__name__
+            print(f"   - List with {len(item)} x {item_type}")
+        else:
+            print(f"   - {item.__class__.__name__}")
+
+    if objects is None:
+        print(f"\nObjects: {object_type}")
+    else:
+        print(f"\nObjects:")
+        for item in objects:
+            print(f"   - {item[0]}: {item[1:]}")
+
+    print(f"\nSpectral range: {field_range}")
+    print(f"Companion labels: {companion_labels}")
+
+    print(f"\nMass labels: {mass_labels}")
+    print(f"Teff labels: {teff_labels}")
+
+    if reddening is None:
+        print(f"\nReddening: {object_type}")
+    else:
+        print(f"\nReddening:")
+        for item in reddening:
+            print(f"   - {item}")
+
+    print(f"\nFigure size: {figsize}")
+    print(f"Limits x axis: {xlim}")
+    print(f"Limits y axis: {ylim}")
+
     plt.rcParams["font.family"] = "serif"
     plt.rcParams["mathtext.fontset"] = "dejavuserif"
 
@@ -1695,11 +1761,6 @@ def plot_color_color(
                     **kwargs,
                 )
 
-    if output is None:
-        print("Plotting color-color diagram...", end="", flush=True)
-    else:
-        print(f"Plotting color-color diagram: {output}...", end="", flush=True)
-
     handles, labels = ax1.get_legend_handles_labels()
 
     if legend is not None:
@@ -1725,8 +1786,7 @@ def plot_color_color(
     if output is None:
         plt.show()
     else:
+        print(f"\nOutput: {output}")
         plt.savefig(output, bbox_inches="tight")
 
-    print(" [DONE]")
-
     return fig
diff --git a/species/plot/plot_spectrum.py b/species/plot/plot_spectrum.py
index 916a04de..4d33f88b 100644
--- a/species/plot/plot_spectrum.py
+++ b/species/plot/plot_spectrum.py
@@ -1188,9 +1188,6 @@ def plot_spectrum(
     if filters is not None:
         ax2.set_ylim(0.0, 1.1)
 
-    if output is not None:
-        print(f"\nOutput: {output}")
-
     if title is not None:
         if filters:
             ax2.set_title(title, y=1.02, fontsize=13)
@@ -1325,6 +1322,7 @@ def plot_spectrum(
     if output is None:
         plt.show()
     else:
+        print(f"\nOutput: {output}")
         plt.savefig(output, bbox_inches="tight")
 
     return fig
diff --git a/species/read/read_color.py b/species/read/read_color.py
index 908e9825..c8ee95b8 100644
--- a/species/read/read_color.py
+++ b/species/read/read_color.py
@@ -16,6 +16,7 @@
 from species.core.box import ColorColorBox, ColorMagBox, create_box
 from species.read.read_spectrum import ReadSpectrum
 from species.util.convert_util import apparent_to_absolute, parallax_to_distance
+from species.util.core_util import print_section
 
 
 class ReadColorMagnitude:
@@ -49,6 +50,8 @@ def __init__(
             None
         """
 
+        print_section("Read color-magnitude")
+
         self.library = library
         self.filters_color = filters_color
         self.filter_mag = filter_mag
@@ -72,6 +75,11 @@ def __init__(
                     f"The '{self.library}' library is not present in the database."
                 )
 
+        print(f"Database tag: {self.library}")
+        print(f"Library type: {self.lib_type}")
+        print(f"Filters color: {self.filters_color}")
+        print(f"Filter magnitude: {self.filter_mag}")
+
     @typechecked
     def get_color_magnitude(self, object_type: Optional[str] = None) -> ColorMagBox:
         """
@@ -92,6 +100,10 @@ def get_color_magnitude(self, object_type: Optional[str] = None) -> ColorMagBox:
             Box with the colors and magnitudes.
         """
 
+        print_section("Get color-magnitude")
+
+        print(f"Object type: {object_type}")
+
         if self.lib_type == "phot_lib":
             with h5py.File(self.database, "r") as h5_file:
                 sptype = np.asarray(h5_file[f"photometry/{self.library}/sptype"])
@@ -221,6 +233,8 @@ def get_color_magnitude(self, object_type: Optional[str] = None) -> ColorMagBox:
                 names=obj_names[indices],
             )
 
+            n_objects = sptype[indices].size 
+
         elif self.lib_type == "spec_lib":
             read_spec_0 = ReadSpectrum(
                 spec_library=self.library, filter_name=self.filters_color[0]
@@ -250,6 +264,9 @@ def get_color_magnitude(self, object_type: Optional[str] = None) -> ColorMagBox:
                 names=None,
             )
 
+        n_objects = colormag_box.sptype.size
+        print(f"Returning ColorMagBox with {n_objects} objects")
+
         return colormag_box
 
 
@@ -281,6 +298,8 @@ def __init__(
             None
         """
 
+        print_section("Read color-magnitude")
+
         self.library = library
         self.filters_colors = filters_colors
 
@@ -303,6 +322,10 @@ def __init__(
                     f"The '{self.library}' library is not present in the database."
                 )
 
+        print(f"Database tag: {self.library}")
+        print(f"Library type: {self.lib_type}")
+        print(f"Filters colors: {self.filters_colors}")
+
     @typechecked
     def get_color_color(self, object_type: Optional[str] = None) -> ColorColorBox:
         """
@@ -323,6 +346,10 @@ def get_color_color(self, object_type: Optional[str] = None) -> ColorColorBox:
             Box with the colors.
         """
 
+        print_section("Get color-magnitude")
+
+        print(f"Object type: {object_type}")
+
         if self.lib_type == "phot_lib":
             h5_file = h5py.File(self.database, "r")
 
@@ -420,4 +447,7 @@ def get_color_color(self, object_type: Optional[str] = None) -> ColorColorBox:
                 names=None,
             )
 
+        n_objects = colormag_box.sptype.size
+        print(f"Returning ColorColorBox with {n_objects} objects")
+
         return colorbox
diff --git a/species/util/dust_util.py b/species/util/dust_util.py
index de2b178c..6c00f83d 100644
--- a/species/util/dust_util.py
+++ b/species/util/dust_util.py
@@ -19,15 +19,15 @@
 
 
 @typechecked
-def check_dust_database() -> None:
+def check_dust_database() -> str:
     """
     Function to check if the dust data is present in the
     database and add the data if needed.
 
     Returns
     -------
-    NoneType
-        None
+    str
+        Path of the HDF5 database.
     """
 
     config_file = os.path.join(os.getcwd(), "species_config.ini")
@@ -43,6 +43,7 @@ def check_dust_database() -> None:
             add_optical_constants(data_folder, hdf5_file)
             add_cross_sections(data_folder, hdf5_file)
 
+    return database_path
 
 @typechecked
 def log_normal_distribution(