diff --git a/docs/tutorials/color_magnitude_broadband.ipynb b/docs/tutorials/color_magnitude_broadband.ipynb index bb2b02c8..b11ac5b6 100644 --- a/docs/tutorials/color_magnitude_broadband.ipynb +++ b/docs/tutorials/color_magnitude_broadband.ipynb @@ -54,12 +54,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initiating species v0.5.5... [DONE]\n", - "Creating species_config.ini... [DONE]\n", - "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", - "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "==============\n", + "species v0.7.1\n", + "==============\n", "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", - "Grid interpolation method: linear\n", + "Creating species_config.ini... [DONE]\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" ] @@ -101,10 +105,52 @@ "Adding filter: Paranal/NACO.NB374... [DONE]\n", "Adding filter: Paranal/NACO.Lp... [DONE]\n", "Adding filter: Paranal/NACO.NB405... [DONE]\n", - "Adding filter: Paranal/NACO.Mp... [DONE]\n", - "Downloading Vega spectrum (270 kB)... [DONE]\n", + "Adding filter: Paranal/NACO.Mp..." + ] + }, + { + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [DONE]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 123MB/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Adding Vega spectrum... [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 object: beta Pic b" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [DONE]\n", "Adding filter: MKO/NSFCam.K..." ] }, @@ -112,7 +158,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/data/database.py:1273: 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:1341: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n", " warnings.warn(\n" ] }, @@ -134,14 +180,15 @@ "Adding filter: Keck/NIRC2.Ms... [DONE]\n", "Adding object: 51 Eri b [DONE]\n", "Adding filter: Subaru/CIAO.z... [DONE]\n", - "Adding filter: Paranal/SPHERE.IRDIS_B_J..." + "Adding filter: Paranal/SPHERE.IRDIS_B_J... [DONE]\n", + "Adding filter: Keck/NIRC2.H..." ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/data/filters.py:209: 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", + "/Users/tomasstolker/applications/species/species/data/filters.py:222: 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" ] }, @@ -150,7 +197,6 @@ "output_type": "stream", "text": [ " [DONE]\n", - "Adding filter: Keck/NIRC2.H... [DONE]\n", "Adding filter: Keck/NIRC2.Ks... [DONE]\n", "Adding object: HR 8799 b [DONE]\n", "Adding object: HR 8799 c [DONE]\n", @@ -237,7 +283,11 @@ "Adding filter: HST/ACS_WFC.F850LP... [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: b Cen (AB)b [DONE]\n", + "Adding filter: 2MASS/2MASS.J... [DONE]\n", + "Adding filter: 2MASS/2MASS.H... [DONE]\n", + "Adding filter: 2MASS/2MASS.Ks... [DONE]\n", + "Adding object: VHS 1256 B [DONE]\n" ] } ], @@ -332,11 +382,20 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/ames-cond.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/ames-cond.tgz'.\n", + "100%|████████████████████████████████████████| 156M/156M [00:00<00:00, 146GB/s]\n", + "SHA256 hash of downloaded file: 325f3ea3ed13c51e08daacbd08811fde04762c29994bfbcc17f42223bf3432ea\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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Downloading AMES-Cond model spectra (150 MB)... [DONE]\n", "Unpacking AMES-Cond model spectra (150 MB)... [DONE]\n", "Wavelength range (um) = 0.5 - 40\n", "Spectral resolution = 4000\n", @@ -385,12 +444,21 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/ames-dusty.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/ames-dusty.tgz'.\n", + "100%|█████████████████████████████████████| 60.4M/60.4M [00:00<00:00, 33.3GB/s]\n", + "SHA256 hash of downloaded file: c389dd52b35bcba2d9850921c97eee043c2ff0654cc24efc47baa2345c05254e\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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Downloading AMES-Dusty model spectra (59 MB)... [DONE]\n", - "Unpacking AMES-Dusty model spectra (59 MB)... [DONE]\n", + "Unpacking AMES-Dusty model spectra (58 MB)... [DONE]\n", "Wavelength range (um) = 0.5 - 40\n", "Spectral resolution = 4000\n", "Teff range (K) = 100.0 - 4000.0\n", @@ -404,8 +472,11 @@ " - teff: 36\n", " - logg: 6\n", "Fix missing grid points with a linear interpolation:\n", + " - teff = 900.0, logg = 6.0\n", + " - teff = 1200.0, logg = 5.5\n", " - teff = 2100.0, logg = 3.5\n", " - teff = 2100.0, logg = 4.5\n", + " - teff = 2200.0, logg = 3.5\n", " - teff = 2400.0, logg = 5.0\n", " - teff = 3100.0, logg = 3.5\n", " - teff = 3200.0, logg = 3.5\n", @@ -422,17 +493,8 @@ " - teff = 4000.0, logg = 4.5\n", " - teff = 4000.0, logg = 5.5\n", " - teff = 4000.0, logg = 6.0\n", - "Could not interpolate 24 grid points so storing zeros instead. [WARNING]\n", + "Could not interpolate 15 grid points so storing zeros instead. [WARNING]\n", "The grid points that are missing:\n", - " - teff = 900.0, logg = 6.0\n", - " - teff = 1100.0, logg = 5.5\n", - " - teff = 1100.0, logg = 6.0\n", - " - teff = 1200.0, logg = 5.5\n", - " - teff = 1200.0, logg = 6.0\n", - " - teff = 2100.0, logg = 3.5\n", - " - teff = 2100.0, logg = 4.0\n", - " - teff = 2200.0, logg = 3.5\n", - " - teff = 2200.0, logg = 4.0\n", " - teff = 3100.0, logg = 3.5\n", " - teff = 3200.0, logg = 3.5\n", " - teff = 3300.0, logg = 3.5\n", @@ -449,8 +511,8 @@ " - teff = 4000.0, logg = 5.5\n", " - teff = 4000.0, logg = 6.0\n", "Number of stored grid points: 216\n", - "Number of interpolated grid points: 3\n", - "Number of missing grid points: 24\n" + "Number of interpolated grid points: 6\n", + "Number of missing grid points: 15\n" ] } ], @@ -530,77 +592,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4019.3207105807364, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4019.3207105807364, 'logg': 4.351948444496957, 'radius': 10.170788710818773, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4019.3207105807364, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4019.3207105807364, 'logg': 4.351948444496957, 'radius': 10.170788710818773, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4164.404431717177, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4164.404431717177, 'logg': 4.329081153206135, 'radius': 10.739016824226589, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4164.404431717177, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4164.404431717177, 'logg': 4.329081153206135, 'radius': 10.739016824226589, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.9180307893338338, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.3730414241427, 'logg': 2.9180307893338338, 'radius': 1.4638670830301985, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9180307893338338, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.3730414241427, 'logg': 2.9180307893338338, 'radius': 1.4638670830301985, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.9730768802029495, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.5270292688421, 'logg': 2.9730768802029495, 'radius': 1.441670727659954, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9730768802029495, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.5270292688421, 'logg': 2.9730768802029495, 'radius': 1.441670727659954, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.0355631548565696, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.9233097703563, 'logg': 3.0355631548565696, 'radius': 1.4150903616033272, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.0355631548565696, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.9233097703563, 'logg': 3.0355631548565696, 'radius': 1.4150903616033272, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.1025651389931803, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 507.4927628068724, 'logg': 3.1025651389931803, 'radius': 1.3865891063317703, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.1025651389931803, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 507.4927628068724, 'logg': 3.1025651389931803, 'radius': 1.3865891063317703, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.207337823869265, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 520.0556848108937, 'logg': 3.207337823869265, 'radius': 1.328194511799377, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.207337823869265, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 520.0556848108937, 'logg': 3.207337823869265, 'radius': 1.328194511799377, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.3065271787010397, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 534.0174915985033, 'logg': 3.3065271787010397, 'radius': 1.274758290992437, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.3065271787010397, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 534.0174915985033, 'logg': 3.3065271787010397, 'radius': 1.274758290992437, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.3387964574578715, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 543.9916323051601, 'logg': 3.3387964574578715, 'radius': 1.2725698348137873, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.3387964574578715, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 543.9916323051601, 'logg': 3.3387964574578715, 'radius': 1.2725698348137873, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.372282732356024, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 556.2475974396231, 'logg': 3.372282732356024, 'radius': 1.2706911157956755, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.372282732356024, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 556.2475974396231, 'logg': 3.372282732356024, 'radius': 1.2706911157956755, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.407135965646942, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 570.8634694648467, 'logg': 3.407135965646942, 'radius': 1.269118495295348, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.407135965646942, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 570.8634694648467, 'logg': 3.407135965646942, 'radius': 1.269118495295348, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.444507945285873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 586.5355899585918, 'logg': 3.444507945285873, 'radius': 1.2674322258801856, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.444507945285873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 586.5355899585918, 'logg': 3.444507945285873, 'radius': 1.2674322258801856, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.484580694074335, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 603.3402910634309, 'logg': 3.484580694074335, 'radius': 1.2656240944588282, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.484580694074335, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 603.3402910634309, 'logg': 3.484580694074335, 'radius': 1.2656240944588282, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4081.4355631859694, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4081.4355631859694, 'logg': 4.667985184351259, 'radius': 5.914180524946677, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4081.4355631859694, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4081.4355631859694, 'logg': 4.667985184351259, 'radius': 5.914180524946677, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4185.2608377397555, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4185.2608377397555, 'logg': 4.656638159809863, 'radius': 6.199078262481065, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4185.2608377397555, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4185.2608377397555, 'logg': 4.656638159809863, 'radius': 6.199078262481065, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4325.615910494432, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4325.615910494432, 'logg': 4.641153787722841, 'radius': 6.55382759835061, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4325.615910494432, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4325.615910494432, 'logg': 4.641153787722841, 'radius': 6.55382759835061, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4512.024881795706, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4512.024881795706, 'logg': 4.610559348746733, 'radius': 7.038423776652953, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4512.024881795706, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4512.024881795706, 'logg': 4.610559348746733, 'radius': 7.038423776652953, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4704.664806542345, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4704.664806542345, 'logg': 4.571845570260296, 'radius': 7.623463332054268, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4704.664806542345, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4704.664806542345, 'logg': 4.571845570260296, 'radius': 7.623463332054268, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 4915.122843504473, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4915.122843504473, 'logg': 4.527793777987827, 'radius': 8.265784389535268, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 4915.122843504473, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 4915.122843504473, 'logg': 4.527793777987827, 'radius': 8.265784389535268, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:935: UserWarning: The value of teff is 5129.6908172285885, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 5129.6908172285885, 'logg': 4.487243459618406, 'radius': 8.971175402210688, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:980: UserWarning: The value of teff is 5129.6908172285885, which is above the upper bound of the model grid (4000.0). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 5129.6908172285885, 'logg': 4.487243459618406, 'radius': 8.971175402210688, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.7961969975244876, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.58438710058044, 'logg': 2.7961969975244876, 'radius': 1.5064919873572373, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.7961969975244876, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.58438710058044, 'logg': 2.7961969975244876, 'radius': 1.5064919873572373, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.8072667505973876, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6880174178661, 'logg': 2.8072667505973876, 'radius': 1.5031419477567405, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.8072667505973876, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6880174178661, 'logg': 2.8072667505973876, 'radius': 1.5031419477567405, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.820805889271204, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6648632173275, 'logg': 2.820805889271204, 'radius': 1.4989194659748202, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.820805889271204, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6648632173275, 'logg': 2.820805889271204, 'radius': 1.4989194659748202, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.8353234638853895, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.640035727038, 'logg': 2.8353234638853895, 'radius': 1.494391837164531, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.8353234638853895, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.640035727038, 'logg': 2.8353234638853895, 'radius': 1.494391837164531, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.855670027860674, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.65383479103104, 'logg': 2.855670027860674, 'radius': 1.4883035183626285, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.855670027860674, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.65383479103104, 'logg': 2.855670027860674, 'radius': 1.4883035183626285, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.879927968286292, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6892732862044, 'logg': 2.879927968286292, 'radius': 1.4811452905054687, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.879927968286292, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.6892732862044, 'logg': 2.879927968286292, 'radius': 1.4811452905054687, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.905938962678162, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.7272728229761, 'logg': 2.905938962678162, 'radius': 1.473469757406555, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.905938962678162, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.7272728229761, 'logg': 2.905938962678162, 'radius': 1.473469757406555, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.9339822921321925, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.84752531806237, 'logg': 2.9339822921321925, 'radius': 1.465193882836573, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9339822921321925, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.84752531806237, 'logg': 2.9339822921321925, 'radius': 1.465193882836573, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 2.9644919021316207, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.20555197078545, 'logg': 2.9644919021316207, 'radius': 1.4561883961908293, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9644919021316207, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.20555197078545, 'logg': 2.9644919021316207, 'radius': 1.4561883961908293, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.0026726111414517, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.0100115950047, 'logg': 3.0026726111414517, 'radius': 1.4449586973534068, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.0026726111414517, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.0100115950047, 'logg': 3.0026726111414517, 'radius': 1.4449586973534068, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.0444294083637633, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.93545547538565, 'logg': 3.0444294083637633, 'radius': 1.4326823296312547, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.0444294083637633, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.93545547538565, 'logg': 3.0444294083637633, 'radius': 1.4326823296312547, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.0892038533213797, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.92777861412816, 'logg': 3.0892038533213797, 'radius': 1.4195187829174454, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.0892038533213797, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.92777861412816, 'logg': 3.0892038533213797, 'radius': 1.4195187829174454, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.1490344602161873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 506.1240352668346, 'logg': 3.1490344602161873, 'radius': 1.4012157774301066, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.1490344602161873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 506.1240352668346, 'logg': 3.1490344602161873, 'radius': 1.4012157774301066, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.2286358747595356, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 506.35983582050795, 'logg': 3.2286358747595356, 'radius': 1.3753758265566598, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.2286358747595356, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 506.35983582050795, 'logg': 3.2286358747595356, 'radius': 1.3753758265566598, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.331188032984285, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 505.06156677568043, 'logg': 3.331188032984285, 'radius': 1.3405155245172165, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.331188032984285, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 505.06156677568043, 'logg': 3.331188032984285, 'radius': 1.3405155245172165, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:910: UserWarning: The value of logg is 3.4411513507951, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.6694754335613, 'logg': 3.4411513507951, 'radius': 1.3031359652909418, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.4411513507951, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 503.6694754335613, 'logg': 3.4411513507951, 'radius': 1.3031359652909418, 'distance': 10.0}.\n", " warnings.warn(\n" ] } @@ -813,14 +875,12 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -893,7 +953,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/tutorials/color_magnitude_narrowband.ipynb b/docs/tutorials/color_magnitude_narrowband.ipynb index 73e0fc03..03324a44 100644 --- a/docs/tutorials/color_magnitude_narrowband.ipynb +++ b/docs/tutorials/color_magnitude_narrowband.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ "output_type": "stream", "text": [ "==============\n", - "species v0.6.0\n", + "species v0.7.1\n", "==============\n", "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_config.ini... [DONE]\n", @@ -71,10 +71,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -168,7 +168,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 204MB/s]" + "100%|████████████████████████████████████████| 288k/288k [00:00<00:00, 198MB/s]" ] }, { @@ -185,7 +185,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n" + "\n", + "/Users/tomasstolker/applications/species/species/data/database.py:1341: UserWarning: Found 33 fluxes with NaN in the data of GPI_YJHK. Removing the spectral fluxes that contain a NaN.\n", + " warnings.warn(\n" ] }, { @@ -208,7 +210,22 @@ "Adding object: 51 Eri b [DONE]\n", "Adding filter: Subaru/CIAO.z... [DONE]\n", "Adding filter: Paranal/SPHERE.IRDIS_B_J... [DONE]\n", - "Adding filter: Keck/NIRC2.H... [DONE]\n", + "Adding filter: Keck/NIRC2.H..." + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tomasstolker/applications/species/species/data/filters.py:222: 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": [ + " [DONE]\n", "Adding filter: Keck/NIRC2.Ks... [DONE]\n", "Adding object: HR 8799 b [DONE]\n", "Adding object: HR 8799 c [DONE]\n", @@ -289,1832 +306,48 @@ "Adding object: SR 12 C [DONE]\n", "Adding object: DH Tau B [DONE]\n", "Adding object: HD 4747 B [DONE]\n", - "Adding filter: Paranal/SPHERE.IRDIS_B_Y... [DONE]\n", - "Adding object: HR 3549 B [DONE]\n", - "Adding filter: HST/ACS_WFC.F775W... [DONE]\n", - "Adding filter: HST/ACS_WFC.F850LP... [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 filter: 2MASS/2MASS.J... [DONE]\n", - "Adding filter: 2MASS/2MASS.H... [DONE]\n", - "Adding filter: 2MASS/2MASS.Ks... [DONE]\n", - "Adding object: VHS 1256 B [DONE]\n" - ] - } - ], - "source": [ - "database.add_companion(name=None, verbose=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The spectra from the SpeX Prism Spectral Library are also downloaded and added to the database. For each spectrum, the [SIMBAD Astronomical Database](http://simbad.u-strasbg.fr/simbad/) is queried for the SIMBAD identifier. The identifier is then used to extract the [distance](https://home.strw.leidenuniv.nl/~stolker/species/distance.dat) of the object (calculated from the parallax). A NaN value is stored for the distance if the object could not be identified in the SIMBAD database so these objects are not used in the color-magnitude diagram." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading SpeX Prism Spectral Library... [DONE] \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J21225635+3656002\n", - " warnings.warn(\"Warning: The script line number %i raised \"\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J12304562+2827583\r", - " \r", - "Adding spectra... 2MASSI J0825196+211552\r", - " \r", - "Adding spectra... 2MASSI J0415195-093506\r", - " \r", - "Adding spectra... 2MASS J12474944-1117551\r", - " \r", - "Adding spectra... SSSPM 0829-1309\r", - " \r", - "Adding spectra... 2MASS J17320014+2656228\r", - " \r", - "Adding spectra... 2MASS J18355309-3217129\r", - " \r", - "Adding spectra... SSSPM 1013-1356\r", - " \r", - "Adding spectra... 2MASSW J0310599+164816\r", - " \r", - "Adding spectra... 2MASS J04414825+2534304\r", - " \r", - "Adding spectra... LSR 2122+3656\r", - " \r", - "Adding spectra... 2MASS J18411320-4000124\r", - " \r", - "Adding spectra... 2MASS J01423153+0523285\r", - " \r", - "Adding spectra... CFHT 3\r", - " \r", - "Adding spectra... 2MASSW J1645221-131951\r", - " \r", - "Adding spectra... 2MASS J17033593+2119071" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J11220826-3512363\r", - " \r", - "Adding spectra... 2MASS J12154938-0036387\r", - " \r", - "Adding spectra... SDSS J085834.42+325627.7\r", - " \r", - "Adding spectra... 2MASS J05314149+6856293\r", - " \r", - "Adding spectra... Gliese 417BC\r", - " \r", - "Adding spectra... SDSS J173101.41+531047.9\r", - " \r", - "Adding spectra... 2MASSI J0439010-235308\r", - " \r", - "Adding spectra... 2MASS J11263991-5003550\r", - " \r", - "Adding spectra... 2MASS J22425317+2542573\r", - " \r", - "Adding spectra... 2MASSW J0820299+450031\r", - " \r", - "Adding spectra... SDSSp J083008.12+482847.4\r", - " \r", - "Adding spectra... 2MASS J02055138-0759253\r", - " \r", - "Adding spectra... 2MASS J13313310+3407583\r", - " \r", - "Adding spectra... SDSS J000013.54+255418.6\r", - " \r", - "Adding spectra... 2MASS J18112466+3748513\r", - " \r", - "Adding spectra... SDSSp J092615.38+584720.9\r", - " \r", - "Adding spectra... 2MASS J21513979+3402444" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSS J120602.51+281328.7\r", - " \r", - "Adding spectra... LSPM J0734+5810\r", - " \r", - "Adding spectra... HD 89744B\r", - " \r", - "Adding spectra... SDSS J154009.36+374230.3\r", - " \r", - "Adding spectra... GJ 1001B, LHS 102B\r", - " \r", - "Adding spectra... 2MASSI J1217110-031113\r", - " \r", - "Adding spectra... 2MASSW J2130446-084520\r", - " \r", - "Adding spectra... SDSS J075840.33+324723.4\r", - " \r", - "Adding spectra... 2MASS J14113696+2112471\r", - " \r", - "Adding spectra... SDSS J082030.12+103737.0\r", - " \r", - "Adding spectra... 2MASSI J1534498-295227\r", - " \r", - "Adding spectra... 2MASS J15561873+1300527\r", - " \r", - "Adding spectra... SDSS J232804.58-103845.7\r", - " \r", - "Adding spectra... 2MASS J19163888-3700026\r", - " \r", - "Adding spectra... 2MASS J23392527+3507165\r", - " \r", - "Adding spectra... 2MASS J23294790-1607550\r", - " \r", - "Adding spectra... 2MASS J11323833-1446374\r", - " \r", - "Adding spectra... 2MASS J11304030+1206306" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J04024153-4037476\r", - " \r", - "Adding spectra... 2MASS J05363713-0328492\r", - " \r", - "Adding spectra... SDSS J000250.98+245413.8\r", - " \r", - "Adding spectra... NLTT 57259\r", - " \r", - "Adding spectra... 2MASS J12373441+3028596\r", - " \r", - "Adding spectra... 2MASS J14403186-1303263\r", - " \r", - "Adding spectra... 2MASSI J2107316-030733\r", - " \r", - "Adding spectra... SDSS J024749.90-163112.6\r", - " \r", - "Adding spectra... 2MASS J01472702+4731142\r", - " \r", - "Adding spectra... MHO 4" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J14044941-3159329\n", - " warnings.warn(\"Warning: The script line number %i raised \"\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J15201746-1755307\r", - " \r", - "Adding spectra... 2MASS J00412179+3547133\r", - " \r", - "Adding spectra... 2MASS J16262034+3925190\r", - " \r", - "Adding spectra... 2MASS J21580457-1550098\r", - " \r", - "Adding spectra... SDSS J035104.37+481046.8\r", - " \r", - "Adding spectra... SDSS J020735.60+135556.3\r", - " \r", - "Adding spectra... 2MASSI J1526140+204341\r", - " \r", - "Adding spectra... KPNO 12\r", - " \r", - "Adding spectra... 2MASS J09153413+0422045\r", - " \r", - "Adding spectra... 2MASS J15590462-0356280\r", - " \r", - "Adding spectra... 2MASSW J1239272+551537\r", - " \r", - "Adding spectra... 2MASS J14044941-3159329\r", - " \r", - "Adding spectra... 2MASS J04035944+1520502\r", - " \r", - "Adding spectra... 2MASS J17330480+0041270\r", - " \r", - "Adding spectra... SDSS J111320.16+343057.9\r", - " \r", - "Adding spectra... CFHT 4\r", - " \r", - "Adding spectra... SDSS J213240.36+102949.4\r", - " \r", - "Adding spectra... 2MASS J05103520-4208140" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J05103520-4208140\n", - " warnings.warn(\"Warning: The script line number %i raised \"\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J00115060-1523450\r", - " \r", - "Adding spectra... 2MASS J13023811+5650212\r", - " \r", - "Adding spectra... 2MASS J01170586+1752568\r", - " \r", - "Adding spectra... 2MASSI J0727182+171001\r", - " \r", - "Adding spectra... 2MASS J12255432-2739466\r", - " \r", - "Adding spectra... 2MASS J12471472-0525130\r", - " \r", - "Adding spectra... 2MASS J16304206-0232224\r", - " \r", - "Adding spectra... 2MASS J15200224-4422419A\r", - " \r", - "Adding spectra... 2MASS J00345157+0523050\r", - " \r", - "Adding spectra... SDSS J205235.31-160929.8\r", - " \r", - "Adding spectra... Gl 584C\r", - " \r", - "Adding spectra... 2MASSW J1728114+394859\r", - " \r", - "Adding spectra... SDSS J213352.72+101841.0\r", - " \r", - "Adding spectra... 2MASS J21210987+6557255\r", - " \r", - "Adding spectra... NLTT 37409\r", - " \r", - "Adding spectra... 2MASS J15243203+0934386\r", - " \r", - "Adding spectra... CXOGC J174516.1-290315" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... CFHT 6\r", - " \r", - "Adding spectra... SDSS J105213.51+442255.7\r", - " \r", - "Adding spectra... 2MASS J11582077+0435014\r", - " \r", - "Adding spectra... 2MASS J01045111-3327380\r", - " \r", - "Adding spectra... LHS 3566\r", - " \r", - "Adding spectra... 2MASS J10430758+2225236\r", - " \r", - "Adding spectra... 2MASS J01311838+3801554\r", - " \r", - "Adding spectra... 2MASS J00552554+4130184\r", - " \r", - "Adding spectra... 2MASS J19475198-3438572\r", - " \r", - "Adding spectra... 2MASS J04071296+1710474\r", - " \r", - "Adding spectra... SDSS J073922.26+661503.5\r", - " \r", - "Adding spectra... 2MASS J20025073-0521524\r", - " \r", - "Adding spectra... SDSS J065405.63+652805.4\r", - " \r", - "Adding spectra... 2MASS J05185995-2828372\r", - " \r", - "Adding spectra... SV P 2312\r", - " \r", - "Adding spectra... DENIS-P J1058.7-1548\r", - " \r", - "Adding spectra... 2MASSW J1439284+192915\r", - " \r", - "Adding spectra... NLTT 6012a" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J11463232+0203414\r", - " \r", - "Adding spectra... 2MASSW J2101154+175658\r", - " \r", - "Adding spectra... 2MASS J14343616+2202463\r", - " \r", - "Adding spectra... 2MASS J17364839+0220426\r", - " \r", - "Adding spectra... 2MASS J05341594-0631397\r", - " \r", - "Adding spectra... 2MASS J09395909-3817217\r", - " \r", - "Adding spectra... 2MASS J19415458+6826021\r", - " \r", - "Adding spectra... 2MASS J20342948+6727398\r", - " \r", - "Adding spectra... 2MASS J18244344+2937133\r", - " \r", - "Adding spectra... 2MASS J00531899-3631102\r", - " \r", - "Adding spectra... SDSS J151643.01+305344.4\r", - " \r", - "Adding spectra... 2MASS J05363776+1000232\r", - " \r", - "Adding spectra... NLTT 184\r", - " \r", - "Adding spectra... 2MASS J21512543-2441000\r", - " \r", - "Adding spectra... 2MASS J20343769+0827009\r", - " \r", - "Adding spectra... 2MASS J15293306+6730215\r", - " \r", - "Adding spectra... 2MASSI J0937347+293142\r", - " \r", - "Adding spectra... 2MASS J11150577+2520467" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASSW J0920122+351742\r", - " \r", - "Adding spectra... 2MASSW J2244316+204343\r", - " \r", - "Adding spectra... 2MASSW J1448256+103159\r", - " \r", - "Adding spectra... SDSS J121951.45+312849.4\r", - " \r", - "Adding spectra... LSR J01253-4545\r", - " \r", - "Adding spectra... 2MASSI J0847287-153237\r", - " \r", - "Adding spectra... 2MASS J16423481-2355027\r", - " \r", - "Adding spectra... V Z Cep\r", - " \r", - "Adding spectra... 2MASS J00335534-0908247\r", - " \r", - "Adding spectra... SDSS J163359.23-064056.5\r", - " \r", - "Adding spectra... 2MASS J01470204+2120242\r", - " \r", - "Adding spectra... 2MASS J16403561+2922225\r", - " \r", - "Adding spectra... 2MASS J23512200+3010540\r", - " \r", - "Adding spectra... NLTT 53908\r", - " \r", - "Adding spectra... DENIS-P J0255-4700\r", - " \r", - "Adding spectra... 2MASSI J0856479+223518\r", - " \r", - "Adding spectra... SDSS J141530.05+572428.7\r", - " \r", - "Adding spectra... SDSS J134203.11+134022.2" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASSI J2356547-155310\r", - " \r", - "Adding spectra... SDSSp J175032.96+175903.9\r", - " \r", - "Adding spectra... 2MASSI J1546291-332511\r", - " \r", - "Adding spectra... 2MASS J15461461+4932114\r", - " \r", - "Adding spectra... 2MASS J19010601+4718136\r", - " \r", - "Adding spectra... 2MASSI J1047538+212423\r", - " \r", - "Adding spectra... 2MASS J09384022-2748184\r", - " \r", - "Adding spectra... 2MASS J03001631+2130205\r", - " \r", - "Adding spectra... 2MASS J10461875+4441149\r", - " \r", - "Adding spectra... 2MASS J22120345+1641093\r", - " \r", - "Adding spectra... DENIS J124514.1-442907\r", - " \r", - "Adding spectra... 2MASS J16130315+6502051\r", - " \r", - "Adding spectra... 2MASSI J2057540-025230\r", - " \r", - "Adding spectra... 2MASSW J1507476-162738\r", - " \r", - "Adding spectra... 2MASS J14185890+6000194\r", - " \r", - "Adding spectra... 2MASS J10315064+3349595\r", - " \r", - "Adding spectra... SDSS J080531.84+481233.0\r", - " \r", - "Adding spectra... 2MASS J12095613-1004008" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASSI J1553022+153236\r", - " \r", - "Adding spectra... DENIS-P J1228.2-1547\r", - " \r", - "Adding spectra... 2MASS J14171672-0407311\r", - " \r", - "Adding spectra... APMPM 0559-2903\r", - " \r", - "Adding spectra... SDSS J202820.32+005226.5\r", - " \r", - "Adding spectra... SDSS J003609.26+241343.3\r", - " \r", - "Adding spectra... 2MASS J11070582+2827226\r", - " \r", - "Adding spectra... SDSS J083506.16+195304.4\r", - " \r", - "Adding spectra... 2MASS J11240487+3808054\r", - " \r", - "Adding spectra... 2MASSI J2104149-103736\r", - " \r", - "Adding spectra... SDSS J175805.46+463311.9\r", - " \r", - "Adding spectra... 2MASS J12070374-3151298\r", - " \r", - "Adding spectra... DENIS-P J220002.05-303832.9A\r", - " \r", - "Adding spectra... 2MASS J02304442-3027275\r", - " \r", - "Adding spectra... 2MASSI J2339101+135230\r", - " \r", - "Adding spectra... Gl 337CD\r", - " \r", - "Adding spectra... 2MASS J02271036-1624479\r", - " \r", - "Adding spectra... SDSS J102552.43+321234.0" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... LRL 10176\r", - " \r", - "Adding spectra... SDSSp J111010.01+011613.1\r", - " \r", - "Adding spectra... ITG 2\r", - " \r", - "Adding spectra... 2MASS J20491972-1944324\r", - " \r", - "Adding spectra... SDSS J154849.02+172235.4\r", - " \r", - "Adding spectra... 2MASS J04360273+1547536\r", - " \r", - "Adding spectra... 2MASS J09393548-2448279\r", - " \r", - "Adding spectra... SDSS J090900.73+652527.2\r", - " \r", - "Adding spectra... SDSSp J224953.45+004404.2\r", - " \r", - "Adding spectra... SDSS J032553.17+042540.1\r", - " \r", - "Adding spectra... LHS 217\r", - " \r", - "Adding spectra... NLTT 57956\r", - " \r", - "Adding spectra... 2MASS J19285196-4356256\r", - " \r", - "Adding spectra... 2MASS J11061191+2754215\r", - " \r", - "Adding spectra... 2MASS J01405263+0453302\r", - " \r", - "Adding spectra... 2MASSW J0208183+254253\r", - " \r", - "Adding spectra... 2MASS J13243553+6358281" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSSp J003259.36+141036.6\r", - " \r", - "Adding spectra... 2MASS J17252029-0024508\r", - " \r", - "Adding spectra... LHS 377\r", - " \r", - "Adding spectra... 2MASS J01303563-4445411A\r", - " \r", - "Adding spectra... 2MASSW J1506544+132106\r", - " \r", - "Adding spectra... 2MASS J06244595-4521548\r", - " \r", - "Adding spectra... KPNO 9\r", - " \r", - "Adding spectra... 2MASS J12531161+2728145\r", - " \r", - "Adding spectra... 2MASS J20033545+1158552\r", - " \r", - "Adding spectra... IPMS J013656.57+093347.3" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J17331764+1529116\r", - " \r", - "Adding spectra... 2MASSW J0205034+125142\r", - " \r", - "Adding spectra... 2MASS J04480182-0557254\r", - " \r", - "Adding spectra... 2MASS J01574939-1146113\r", - " \r", - "Adding spectra... 2MASS J21542494-1023022\r", - " \r", - "Adding spectra... 2MASSW J0036159+182110\r", - " \r", - "Adding spectra... 2MASS J10073369-4555147\r", - " \r", - "Adding spectra... 2MASSW J0320284-044636\r", - " \r", - "Adding spectra... KPNO 4\r", - " \r", - "Adding spectra... DENIS-P J170548.38-051645.7\r", - " \r", - "Adding spectra... 2MASS J13184794+1736117\r", - " \r", - "Adding spectra... 2MASS J23092857+3246175\r", - " \r", - "Adding spectra... SDSS J165329.69+623136.5\r", - " \r", - "Adding spectra... 2MASS J13272391+0946446\r", - " \r", - "Adding spectra... LSR 1826+3014\r", - " \r", - "Adding spectra... Gliese 866AB\r", - " \r", - "Adding spectra... 2MASS J17264629-1158036\r", - " \r", - "Adding spectra... 2MASS J22282889-4310262" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... LP 508-14\r", - " \r", - "Adding spectra... SDSS J153417.05+161546.1AB\r", - " \r", - "Adding spectra... KPNO 5\r", - " \r", - "Adding spectra... 2MASS J22341394+2359559\r", - " \r", - "Adding spectra... SDSS J151114.66+060742.9\r", - " \r", - "Adding spectra... 2MASS J17343053-1151388\r", - " \r", - "Adding spectra... 2MASSW J0030300-145033\r", - " \r", - "Adding spectra... 2MASSI J1807159+501531\r", - " \r", - "Adding spectra... SDSS J085116.20+181730.0\r", - " \r", - "Adding spectra... 2MASSW J0929336+342952\r", - " \r", - "Adding spectra... 2MASSW J1632291+190441\r", - " \r", - "Adding spectra... SDSS J151506.11+443648.3\r", - " \r", - "Adding spectra... 2MASS J15141384+1201451\r", - " \r", - "Adding spectra... 2MASS J04070885+1514565\r", - " \r", - "Adding spectra... 2MASS J04062677-3812102\r", - " \r", - "Adding spectra... SDSS J1256-0224\r", - " \r", - "Adding spectra... 2MASS J15412408+5425598\r", - " \r", - "Adding spectra... 2MASS J14192618+5919047" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... KPNO 7\r", - " \r", - "Adding spectra... SDSSp J085758.45+570851.4\r", - " \r", - "Adding spectra... SDSS J153453.33+121949.2\r", - " \r", - "Adding spectra... LEHPM 2-59\r", - " \r", - "Adding spectra... SDSS J162255.27+115924.1\r", - " \r", - "Adding spectra... 2MASS J18284076+1229207\r", - " \r", - "Adding spectra... 2MASS J03444306+3137338\r", - " \r", - "Adding spectra... 2MASS J04574903+3015195\r", - " \r", - "Adding spectra... 2MASS J16452207+3004071\r", - " \r", - "Adding spectra... KPNO 6\r", - " \r", - "Adding spectra... SDSS J103931.35+325625.5\r", - " \r", - "Adding spectra... 2MASS J05160945-0445499\r", - " \r", - "Adding spectra... 2MASS J23174712-4838501\r", - " \r", - "Adding spectra... 2MASS J03280716+3022432\r", - " \r", - "Adding spectra... 2MASS J14182962-3538060\r", - " \r", - "Adding spectra... LSPM J2142+2252\r", - " \r", - "Adding spectra... SDSSp J162414.37+002915.6\r", - " \r", - "Adding spectra... SDSS J121659.17+300306.3" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSSp J134646.45-003150.4\r", - " \r", - "Adding spectra... 2MASS J09490860-1545485\r", - " \r", - "Adding spectra... 2MASS J11000965+4957470\r", - " \r", - "Adding spectra... SDSS J100711.74+193056.2\r", - " \r", - "Adding spectra... SDSS J140023.12+433822.3\r", - " \r", - "Adding spectra... 2MASS J00013044+1010146\r", - " \r", - "Adding spectra... 2MASS J14075361+1241099\r", - " \r", - "Adding spectra... KPNO 2\r", - " \r", - "Adding spectra... 2MASS J00554279+1301043\r", - " \r", - "Adding spectra... 2MASS J13080147+3553169\r", - " \r", - "Adding spectra... 2MASS J22021302-0228558\r", - " \r", - "Adding spectra... 2MASSI J2254188+312349\r", - " \r", - "Adding spectra... 2MASS J18212815+1414010\r", - " \r", - "Adding spectra... 2MASS J12425052+2357231\r", - " \r", - "Adding spectra... LP 589-7\r", - " \r", - "Adding spectra... SDSS J104829.21+091937.8\r", - " \r", - "Adding spectra... 2MASS J22120703+3430351\r", - " \r", - "Adding spectra... 2MASS J03422594+3148496" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSSp J010752.33+004156.1\r", - " \r", - "Adding spectra... 2MASS J17072343-0558249A\r", - " \r", - "Adding spectra... 2MASS J17561080+2815238\r", - " \r", - "Adding spectra... 2MASS J01414839-1601196\r", - " \r", - "Adding spectra... 2MASS J12314753+0847331\r", - " \r", - "Adding spectra... SDSS J104409.43+042937.6\r", - " \r", - "Adding spectra... 2MASS J01415823-4633574\r", - " \r", - "Adding spectra... SDSSp J042348.57-041403.5\r", - " \r", - "Adding spectra... 2MASS J23312378-4718274\r", - " \r", - "Adding spectra... 2MASSI J0117474-340325\r", - " \r", - "Adding spectra... 2MASS J08234818+2428577\r", - " \r", - "Adding spectra... 2MASS J04442713+2512164\r", - " \r", - "Adding spectra... TWA 30B\r", - " \r", - "Adding spectra... V 1451 Aql\r", - " \r", - "Adding spectra... 2MASSW J0051107-154417\r", - " \r", - "Adding spectra... 2MASSI J0908380+503208\r", - " \r", - "Adding spectra... 2MASS J05161597-3332046\r", - " \r", - "Adding spectra... 2MASS J09171104-1650010" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... KPNO 1\r", - " \r", - "Adding spectra... 2MASS J16403197+1231068\r", - " \r", - "Adding spectra... SDSS J152103.24+013142.7\r", - " \r", - "Adding spectra... 2MASS J07290002-3954043\r", - " \r", - "Adding spectra... 2MASS J05460407-0003228\r", - " \r", - "Adding spectra... 2MASS J16382073+1327354\r", - " \r", - "Adding spectra... VB 8\r", - " \r", - "Adding spectra... 2MASS J18131803+5101246\r", - " \r", - "Adding spectra... 2MASS J06020638+4043588\r", - " \r", - "Adding spectra... 2MASP J0345432+254023" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSS J120747.17+024424.8\r", - " \r", - "Adding spectra... SDSS J213154.43-011939.3\r", - " \r", - "Adding spectra... 2MASS J00501994-3322402\r", - " \r", - "Adding spectra... 2MASS J16002647-2456424\r", - " \r", - "Adding spectra... 2MASS J16150413+1340079\r", - " \r", - "Adding spectra... 2MASS J02572581-3105523\r", - " \r", - "Adding spectra... 2MASS J10462067+2354307\r", - " \r", - "Adding spectra... 2MASSW J0208236+273740\r", - " \r", - "Adding spectra... 2MASS J21420580-3101162\r", - " \r", - "Adding spectra... 2MASS J20261584-2943124\r", - " \r", - "Adding spectra... 2MASS J01151621+3130061\r", - " \r", - "Adding spectra... SDSS J161731.65+401859.7\r", - " \r", - "Adding spectra... 2MASS J17502484-0016151\r", - " \r", - "Adding spectra... DENIS-P J225210.73-173013.4\r", - " \r", - "Adding spectra... SDSS J121440.95+631643.4\r", - " \r", - "Adding spectra... 2MASS J09054654+5623117\r", - " \r", - "Adding spectra... 2MASS J14283132+5923354" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... SDSS J112118.57+433246.5\r", - " \r", - "Adding spectra... 2MASS J02481204+2445141\r", - " \r", - "Adding spectra... NLTT 2914\r", - " \r", - "Adding spectra... SDSS J163030.53+434404.0\r", - " \r", - "Adding spectra... SDSS J175024.01+422237.8\r", - " \r", - "Adding spectra... SDSS J085234.90+472035.0\r", - " \r", - "Adding spectra... 2MASSI J0103320+193536\r", - " \r", - "Adding spectra... 2MASS J12341814+0008359\r", - " \r", - "Adding spectra... 2MASS J00583814-1747311\r", - " \r", - "Adding spectra... 2MASS J03185403-3421292\r", - " \r", - "Adding spectra... 2MASS J17392515+2454421\r", - " \r", - "Adding spectra... SDSS J074149.15+235127.5\r", - " \r", - "Adding spectra... 2MASS J22114470+6856262\r", - " \r", - "Adding spectra... 2MASS J15500845+1455180\r", - " \r", - "Adding spectra... 2MASS J19495702+6222440\r", - " \r", - "Adding spectra... 2MASS J1754544+164920\r", - " \r", - "Adding spectra... 2MASS J04382218+2553503" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" + "Adding filter: Paranal/SPHERE.IRDIS_B_Y... [DONE]\n", + "Adding object: HR 3549 B [DONE]\n", + "Adding filter: HST/ACS_WFC.F775W... [DONE]\n", + "Adding filter: HST/ACS_WFC.F850LP... [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 filter: 2MASS/2MASS.J... [DONE]\n", + "Adding filter: 2MASS/2MASS.H... [DONE]\n", + "Adding filter: 2MASS/2MASS.Ks... [DONE]\n", + "Adding object: VHS 1256 B [DONE]\n" ] - }, + } + ], + "source": [ + "database.add_companion(name=None, verbose=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The spectra from the SpeX Prism Spectral Library are also downloaded and added to the database. For each spectrum, the [SIMBAD Astronomical Database](http://simbad.u-strasbg.fr/simbad/) is queried for the SIMBAD identifier. The identifier is then used to extract the [distance](https://home.strw.leidenuniv.nl/~stolker/species/distance.dat) of the object (calculated from the parallax). A NaN value is stored for the distance if the object could not be identified in the SIMBAD database so these objects are not used in the color-magnitude diagram." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J11181292-0856106\r", - " \r", - "Adding spectra... SO 0253+1625\r", - " \r", - "Adding spectra... 2MASS J14044948-3159330\r", - " \r", - "Adding spectra... 2MASS J00150206+2959323\r", - " \r", - "Adding spectra... 2MASS J11061197+2754225\r", - " \r", - "Adding spectra... 2MASS J01532750+3631482\r", - " \r", - "Adding spectra... 2MASS J00163761+3448368\r", - " \r", - "Adding spectra... 2MASS J01330461-1355271\r", - " \r", - "Adding spectra... 2MASS J03023398-1028223\r", - " \r", - "Adding spectra... SDSS J133148.92-011651.4\r", - " \r", - "Adding spectra... 2MASS J11145133-2618235\r", - " \r", - "Adding spectra... 2MASS J12490872+4157286\r", - " \r", - "Adding spectra... 2MASS J00193927-3724392\r", - " \r", - "Adding spectra... SDSS J115553.86+055957.5\r", - " \r", - "Adding spectra... 2MASS J11395113-3159214\r", - " \r", - "Adding spectra... DENIS-P J0205.4-1159\r", - " \r", - "Adding spectra... SDSS J142227.25+221557.1\r", - " \r", - "Adding spectra... SDSS J152039.82+354619.8" + "Downloading SpeX Prism Spectral Library... [DONE] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", + "/Users/tomasstolker/applications/species/species/analysis/photometry.py:87: UserWarning: Please note that a manually provided zero-point flux is by default equalized to a magnitude of 0.03 for all filters. The magnitude of Vega can be adjusted in the configuration file (see https://species.readthedocs.io/en/latest/configuration.html) by setting the 'vega_mag' parameter. Currently the parameter is set to 0.03.\n", " warnings.warn(\n" ] }, @@ -2123,323 +356,66 @@ "output_type": "stream", "text": [ "\r", - " \r", - "Adding spectra... 2MASS J00165953-4056541\r", - " \r", - "Adding spectra... 2MASS J14370929-3512430\r", + " \r", + "Adding spectra... 2MASS J12304562+2827583\r", " \r", - "Adding spectra... 2MASSW J2224438-015852\r", + "Adding spectra... 2MASSI J0825196+211552\r", " \r", - "Adding spectra... SDSS J141659.78+500626.4\r", - " \r", - "Adding spectra... 2MASSI J0652307+471034\r", + "Adding spectra... 2MASSI J0415195-093506\r", " \r", - "Adding spectra... 2MASS J22225588-4446197\r", + "Adding spectra... 2MASS J12474944-1117551\r", " \r", - "Adding spectra... 2MASS J05381018-0554261\r", + "Adding spectra... SSSPM 0829-1309\r", + " \r", + "Adding spectra... 2MASS J17320014+2656228\r", " \r", - "Adding spectra... 2MASS J23531922+3656457\r", + "Adding spectra... 2MASS J18355309-3217129\r", " \r", - "Adding spectra... 2MASSW J1515008+484742\r", - " \r", - "Adding spectra... VB 10\r", - " \r", - "Adding spectra... 2MASSW J0228110+253738\r", + "Adding spectra... SSSPM 1013-1356\r", + " \r", + "Adding spectra... 2MASSW J0310599+164816\r", " \r", - "Adding spectra... SDSS J104842.84+011158.5\r", - " \r", - "Adding spectra... 2MASS J23343177-1509294\r", - " \r", - "Adding spectra... SDSS J024256.98+212319.6\r", - " \r", - "Adding spectra... 2MASS J10334372-3337056\r", - " \r", - "Adding spectra... 2MASS J04470652-1946392\r", - " \r", - "Adding spectra... SDSSp J125453.90-012247.4\r", - " \r", - "Adding spectra... 2MASS J21481628+4003593" + "Adding spectra... 2MASS J04414825+2534304" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" + "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J21225635+3656002\n", + " warnings.warn(\"Warning: The script line number %i raised \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - " \r", - "Adding spectra... HN Peg B\r", - " \r", - "Adding spectra... SDSS J011912.22+240331.6\r", - " \r", - "Adding spectra... LHS 2924\r", - " \r", - "Adding spectra... 2MASS J01340281+0508125\r", - " \r", - "Adding spectra... 2MASS J17220990-1158127AB\r", - " \r", - "Adding spectra... 2MASS J01550354+0950003\r", - " \r", - "Adding spectra... SDSS J102109.69-030420.1\r", - " \r", - "Adding spectra... 2MASS J13571237+1428398\r", - " \r", - "Adding spectra... 2MASS J21555848+2345307\r", - " \r", - "Adding spectra... 2MASSW J1036530-344138\r", - " \r", - "Adding spectra... GJ 1048B\r", - " \r", - "Adding spectra... SDSS J151603.03+025928.9\r", - " \r", - "Adding spectra... 2MASS J20360316+1051295\r", - " \r", - "Adding spectra... 2MASS J13032137+2351110\r", - " \r", - "Adding spectra... 2MASS J21513839-4853542\r", - " \r", - "Adding spectra... 2MASSW J1146345+223053\r", - " \r", - "Adding spectra... SDSS J074201.41+205520.5\r", - " \r", - "Adding spectra... 2MASSI J1711457+223204" + "Adding spectra... CFHT 4 " ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J14162394+1348363\n", + "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J14044941-3159329\n", " warnings.warn(\"Warning: The script line number %i raised \"\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " \r", - "Adding spectra... 2MASS J13342806+5258199\r", - " \r", - "Adding spectra... 2MASSW J0717163+570543\r", - " \r", - "Adding spectra... SDSS J134525.57+521634.0\r", - " \r", - "Adding spectra... 2MASS J21403907+3655563\r", - " \r", - "Adding spectra... 2MASSI J0755480+221218\r", - " \r", - "Adding spectra... 2MASS J18283572-4849046\r", - " \r", - "Adding spectra... 2MASSW J0801405+462850\r", - " \r", - "Adding spectra... 2MASS J03382862+0001296\r", - " \r", - "Adding spectra... ULAS J141623.94+134836.3\r", - " \r", - "Adding spectra... 2MASS J21321145+1341584\r", - " \r", - "Adding spectra... 2MASS J12212770+0257198\r", - " \r", - "Adding spectra... SDSS J020608.97+223559.2\r", - " \r", - "Adding spectra... 2MASS J03305571+3146272\r", - " \r", - "Adding spectra... SDSS J104335.08+121314.1\r", - " \r", - "Adding spectra... SDSSp J132629.82-003831.5\r", - " \r", - "Adding spectra... 2MASS J12373919+6526148" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" + "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J05103520-4208140\n", + " warnings.warn(\"Warning: The script line number %i raised \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r", - " \r", - "Adding spectra... SDSS J083048.80+012831.1\r", - " \r", - "Adding spectra... Gliese 570D\r", - " \r", - "Adding spectra... 2MASS J15031961+2525196\r", - " \r", - "Adding spectra... 2MASS J15575011-2952431\r", - " \r", - "Adding spectra... SDSS J103321.92+400549.5\r", - " \r", - "Adding spectra... 2MASSI J0241536-124106\r", - " \r", - "Adding spectra... 2MASSI J0243137-245329\r", - " \r", - "Adding spectra... 2MASS J17461199+5034036\r", - " \r", - "Adding spectra... 2MASS J18282794+1453337\r", - " \r", - "Adding spectra... 2MASS J10454932+1254541\r", - " \r", - "Adding spectra... 2MASS J04070752+1546457\r", - " \r", - "Adding spectra... 2MASS J23254530+4251488\r", - " \r", - "Adding spectra... 2MASS J23515044-2537367\r", - " \r", - "Adding spectra... SDSS J212413.89+010000.3\r", - " \r", - "Adding spectra... 2MASSI J0835425-081923\r", - " \r", - "Adding spectra... 2MASS J21543318+5942187" + "Adding spectra... 2MASS J12373919+6526148 " ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" + "/Users/tomasstolker/.pyenv/versions/3.11.5/envs/species3.11/lib/python3.11/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J14162394+1348363\n", + " warnings.warn(\"Warning: The script line number %i raised \"\n" ] }, { @@ -2448,40 +424,6 @@ "text": [ "Adding spectra... [DONE] \n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/analysis/photometry.py:531: DeprecationWarning: The 'zp_flux' parameter is deprecated and will be removed in a future release. Please use the 'zero_point' parameter of the SyntheticPhotometry constructor instead.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -2497,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2505,7 +447,7 @@ "output_type": "stream", "text": [ "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/ames-cond.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/ames-cond.tgz'.\n", - "100%|████████████████████████████████████████| 156M/156M [00:00<00:00, 106GB/s]\n", + "100%|███████████████████████████████████████| 156M/156M [00:00<00:00, 78.0GB/s]\n", "SHA256 hash of downloaded file: 325f3ea3ed13c51e08daacbd08811fde04762c29994bfbcc17f42223bf3432ea\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" ] @@ -2529,21 +471,7 @@ " - log(g) = [2.5 3. 3.5 4. 4.5 5. 5.5]\n", "Number of grid points per parameter:\n", " - teff: 65\n", - " - logg: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tomasstolker/applications/species/species/util/data_util.py:379: RuntimeWarning: divide by zero encountered in log10\n", - " flux = np.log10(flux)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " - logg: 7\n", "Fix missing grid points with a linear interpolation:\n", " - teff = 200.0, logg = 5.5\n", " - teff = 900.0, logg = 2.5\n", @@ -2565,6 +493,14 @@ "Number of interpolated grid points: 16\n", "Number of missing grid points: 0\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tomasstolker/applications/species/species/util/data_util.py:380: RuntimeWarning: divide by zero encountered in log10\n", + " flux = np.log10(flux)\n" + ] } ], "source": [ @@ -2580,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -2588,7 +524,7 @@ "output_type": "stream", "text": [ "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/ames-dusty.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/ames-dusty.tgz'.\n", - "100%|█████████████████████████████████████| 60.4M/60.4M [00:00<00:00, 28.1GB/s]\n", + "100%|█████████████████████████████████████| 60.4M/60.4M [00:00<00:00, 34.8GB/s]\n", "SHA256 hash of downloaded file: c389dd52b35bcba2d9850921c97eee043c2ff0654cc24efc47baa2345c05254e\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" ] @@ -2668,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -2704,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -2722,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -2749,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -2766,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -2778,34 +714,34 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 2.9180307893338338, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.3730414241427, 'logg': 2.9180307893338338, 'radius': 1.4638670830301985, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9180307893338338, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 501.3730414241427, 'logg': 2.9180307893338338, 'radius': 1.4638670830301985, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 2.9730768802029495, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.5270292688421, 'logg': 2.9730768802029495, 'radius': 1.441670727659954, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 2.9730768802029495, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 502.5270292688421, 'logg': 2.9730768802029495, 'radius': 1.441670727659954, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.0355631548565696, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.9233097703563, 'logg': 3.0355631548565696, 'radius': 1.4150903616033272, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.0355631548565696, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 504.9233097703563, 'logg': 3.0355631548565696, 'radius': 1.4150903616033272, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.1025651389931803, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 507.4927628068724, 'logg': 3.1025651389931803, 'radius': 1.3865891063317703, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.1025651389931803, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 507.4927628068724, 'logg': 3.1025651389931803, 'radius': 1.3865891063317703, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.207337823869265, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 520.0556848108937, 'logg': 3.207337823869265, 'radius': 1.328194511799377, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.207337823869265, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 520.0556848108937, 'logg': 3.207337823869265, 'radius': 1.328194511799377, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.3065271787010397, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 534.0174915985033, 'logg': 3.3065271787010397, 'radius': 1.274758290992437, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.3065271787010397, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 534.0174915985033, 'logg': 3.3065271787010397, 'radius': 1.274758290992437, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.3387964574578715, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 543.9916323051601, 'logg': 3.3387964574578715, 'radius': 1.2725698348137873, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.3387964574578715, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 543.9916323051601, 'logg': 3.3387964574578715, 'radius': 1.2725698348137873, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.372282732356024, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 556.2475974396231, 'logg': 3.372282732356024, 'radius': 1.2706911157956755, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.372282732356024, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 556.2475974396231, 'logg': 3.372282732356024, 'radius': 1.2706911157956755, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.407135965646942, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 570.8634694648467, 'logg': 3.407135965646942, 'radius': 1.269118495295348, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.407135965646942, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 570.8634694648467, 'logg': 3.407135965646942, 'radius': 1.269118495295348, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.444507945285873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 586.5355899585918, 'logg': 3.444507945285873, 'radius': 1.2674322258801856, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.444507945285873, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 586.5355899585918, 'logg': 3.444507945285873, 'radius': 1.2674322258801856, 'distance': 10.0}.\n", " warnings.warn(\n", - "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:948: UserWarning: The value of logg is 3.484580694074335, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 603.3402910634309, 'logg': 3.484580694074335, 'radius': 1.2656240944588282, 'distance': 10.0}.\n", + "/Users/tomasstolker/applications/species/species/read/read_isochrone.py:955: UserWarning: The value of logg is 3.484580694074335, which is below the lower bound of the model grid (3.5). Setting the magnitudes to NaN for the following isochrone sample: {'teff': 603.3402910634309, 'logg': 3.484580694074335, 'radius': 1.2656240944588282, 'distance': 10.0}.\n", " warnings.warn(\n" ] } @@ -2842,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -2868,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -2880,7 +816,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2921,7 +857,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2930,7 +866,7 @@ "[, ]" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/tutorials/model_spectra.ipynb b/docs/tutorials/model_spectra.ipynb index b5803ba1..472485d2 100644 --- a/docs/tutorials/model_spectra.ipynb +++ b/docs/tutorials/model_spectra.ipynb @@ -53,12 +53,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initiating species v0.5.5... [DONE]\n", - "Creating species_config.ini... [DONE]\n", - "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", - "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "==============\n", + "species v0.7.1\n", + "==============\n", "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", - "Grid interpolation method: linear\n", + "Creating species_config.ini... [DONE]\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" ] @@ -66,7 +70,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -132,7 +136,7 @@ " - Teff range (K): [500, 4000]\n", " - Wavelength range (um): [0.5, 40]\n", " - Resolution lambda/Dlambda: 4000\n", - " - File size: 59 MB\n", + " - File size: 58 MB\n", "\n", " - ATMO:\n", " - Label = atmo\n", @@ -182,7 +186,7 @@ " - Resolution lambda/Dlambda: 3000\n", " - File size: 2.1 GB\n", " - Reference: Petrus et al. (2023)\n", - " - URL: https://arxiv.org/abs/2207.06622\n", + " - URL: https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract\n", "\n", " - blackbody:\n", " - Label = blackbody\n", @@ -253,10 +257,20 @@ " - Exo-REM:\n", " - Label = exo-rem\n", " - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio']\n", - " - Teff range (K): [1000, 2000]\n", - " - Wavelength range (um): [0.9, 5]\n", - " - Resolution lambda/Dlambda: 10000\n", - " - File size: 706 MB\n", + " - Teff range (K): [400, 2000]\n", + " - Wavelength range (um): [0.35, 250.0]\n", + " - Resolution lambda/Dlambda: 500\n", + " - File size: 530 MB\n", + " - Reference: Charney et al. (2018)\n", + " - URL: https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract\n", + "\n", + " - Exo-REM:\n", + " - Label = exo-rem-highres\n", + " - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio']\n", + " - Teff range (K): [400, 2000]\n", + " - Wavelength range (um): [0.67, 250.0]\n", + " - Resolution lambda/Dlambda: 20000\n", + " - File size: 16 GB\n", " - Reference: Charney et al. (2018)\n", " - URL: https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract\n", "\n", @@ -367,7 +381,17 @@ " - Extra details: [Fe/H] = 0.0, log(g) = 5.0\n", " - File size: 112 MB\n", " - Reference: Marley et al. (2021)\n", - " - URL: https://zenodo.org/record/5063476\n" + " - URL: https://zenodo.org/record/5063476\n", + "\n", + " - SPHINX:\n", + " - Label = sphinx\n", + " - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio']\n", + " - Teff range (K): [2000, 4000]\n", + " - Wavelength range (um): [0.1, 20]\n", + " - Resolution lambda/Dlambda: 250\n", + " - File size: 143 Mb\n", + " - Reference: Aishwarya et al. (2022)\n", + " - URL: https://zenodo.org/record/7416042\n" ] } ], @@ -387,11 +411,20 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading data from 'https://home.strw.leidenuniv.nl/~stolker/species/drift-phoenix.tgz' to file '/Users/tomasstolker/applications/species/docs/tutorials/data/drift-phoenix.tgz'.\n", + "100%|████████████████████████████████████████| 240M/240M [00:00<00:00, 115GB/s]\n", + "SHA256 hash of downloaded file: ba71a5e4d3d399a6f8ae249590c2e174e90ec2b55e712d350dad8ca1ae83a907\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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Downloading DRIFT-PHOENIX model spectra (229 MB)... [DONE]\n", "Unpacking DRIFT-PHOENIX model spectra (229 MB)... [DONE]\n", "Please cite Helling et al. (2008) when using DRIFT-PHOENIX in a publication\n", "Reference URL: https://ui.adsabs.harvard.edu/abs/2008ApJ...675L.105H/abstract\n", @@ -609,14 +642,12 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -827,12 +856,30 @@ "execution_count": 20, "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, 135MB/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", "Flux (W m-2 um-1) = 1.39e-17\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ @@ -864,10 +911,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading Vega spectrum (270 kB)... [DONE]\n", - "Adding Vega spectrum... [DONE]\n", - "Apparent magnitude = 15.48\n", - "Absolute magnitude = 10.48\n" + "Apparent magnitude = 15.46\n", + "Absolute magnitude = 10.46\n" ] } ], @@ -901,7 +946,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/species/data/spex.py b/species/data/spex.py index 15bb97d1..bb5c5728 100644 --- a/species/data/spex.py +++ b/species/data/spex.py @@ -121,10 +121,10 @@ def add_spex(input_path: str, database: h5py._hl.files.File) -> None: print_message = "Downloading SpeX Prism Spectral Library... [DONE]" print(f"\r{print_message}") - h_twomass = photometry.SyntheticPhotometry("2MASS/2MASS.H") - # 2MASS H band zero point for 0 mag (Cogen et al. 2003) - h_zp = 1.133e-9 # (W m-2 um-1) + zp_hband = 1.133e-9 # (W m-2 um-1) + + h_twomass = photometry.SyntheticPhotometry("2MASS/2MASS.H", zero_point=zp_hband) for votable in os.listdir(data_path): if votable.startswith("spex_") and votable.endswith(".xml"): @@ -205,7 +205,7 @@ def add_spex(input_path: str, database: h5py._hl.files.File) -> None: except KeyError: sptype_nir = None - h_flux, _ = h_twomass.magnitude_to_flux(h_mag, error=None, zp_flux=h_zp) + h_flux, _ = h_twomass.magnitude_to_flux(h_mag, error=None) phot = h_twomass.spectrum_to_flux(wavelength, flux) # Normalized units flux *= h_flux / phot[0] # (W m-2 um-1)