diff --git a/.gitignore b/.gitignore index 8980eee45..33ec05c02 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,3 @@ *.egg-info build/ simple/version.py - diff --git a/data/reference/Instruments.json b/data/reference/Instruments.json index 52ff22607..e998615e1 100644 --- a/data/reference/Instruments.json +++ b/data/reference/Instruments.json @@ -509,5 +509,12 @@ "telescope": "Lick Shane 3m", "description": null, "reference": null + }, + { + "instrument": "EMIR", + "mode": "Missing", + "telescope": "GTC", + "description": "Espectrografo Multiobjeto Infra-Rojo", + "reference": null } ] \ No newline at end of file diff --git a/data/reference/Telescopes.json b/data/reference/Telescopes.json index 9bc3950b0..ac11882dc 100644 --- a/data/reference/Telescopes.json +++ b/data/reference/Telescopes.json @@ -126,7 +126,7 @@ }, { "telescope": "GTC", - "description": null, + "description": "Gran Telescopio de Canarias", "reference": null }, { diff --git a/data/source/cwisep_j181006.00-101001.1.json b/data/source/cwisep_j181006.00-101001.1.json index 85a230910..81cb7d729 100644 --- a/data/source/cwisep_j181006.00-101001.1.json +++ b/data/source/cwisep_j181006.00-101001.1.json @@ -94,6 +94,34 @@ "reference": "Lodi22" } ], + "Spectra": [ + { + "access_url": "https://bdnyc.s3.us-east-1.amazonaws.com/data_target_WISE1810_comb_Jun2021_YJ_STD_bb.fits", + "original_spectrum": null, + "local_spectrum": null, + "regime": "nir", + "telescope": "GTC", + "instrument": "EMIR", + "mode": "Missing", + "observation_date": "2021-06-01T00:00:00", + "comments": null, + "reference": "Lodi22", + "other_references": null + }, + { + "access_url": "https://bdnyc.s3.us-east-1.amazonaws.com/WISE1810m10_OB0001_R1000R_06Sept2020.fits", + "original_spectrum": null, + "local_spectrum": null, + "regime": "optical", + "telescope": "GTC", + "instrument": "OSIRIS", + "mode": "Missing", + "observation_date": "2020-09-06T00:00:00", + "comments": null, + "reference": "Lodi22", + "other_references": null + } + ], "SpectralTypes": [ { "spectral_type_string": "esdT0", diff --git a/scripts/ingests/WISE_1810/ingest_WISE1810.py b/scripts/ingests/WISE_1810/ingest_WISE1810.py new file mode 100644 index 000000000..a6356aa5a --- /dev/null +++ b/scripts/ingests/WISE_1810/ingest_WISE1810.py @@ -0,0 +1,90 @@ +import logging +from astrodb_utils import load_astrodb, AstroDBError +from astrodb_utils.instruments import ingest_instrument +from simple import REFERENCE_TABLES +from simple.utils.spectra import ingest_spectrum +from datetime import datetime + +# set up logging for ASTRODB +astrodb_utils_logger = logging.getLogger("astrodb_utils") +astrodb_utils_logger.setLevel(logging.INFO) + +# set up logging for this ingest script +logger = logging.getLogger("astrodb_utils.WISE_1810") +logger.setLevel(logging.INFO) + +# Load Database +recreate_db = True +save_db = True + +SCHEMA_PATH = "simple/schema.yaml" +db = load_astrodb( + "SIMPLE.sqlite", + recreatedb=recreate_db, + reference_tables=REFERENCE_TABLES, + felis_schema=SCHEMA_PATH +) + +# Ingest Instruments ---- +def add_instrument(): + """ + Telescope: GTC (existed) + Instrument: EMIR (ingestion needed) + """ + try: + ingest_instrument( + db, + telescope="GTC", + instrument="EMIR", + mode="Missing", + raise_error=True + ) + print ("Instrument added successfully") + + + except AstroDBError as e: + logger.error(f"Error adding instruments: {e}") + +# convert obs date format +format_str = "%Y-%m-%d %H:%M:%S0000" + +# same sources with different instruments and FITS file +spectra_data = [{ + "access_url": "https://bdnyc.s3.us-east-1.amazonaws.com/WISE1810m10_OB0001_R1000R_06Sept2020.fits", + "regime": "optical", + "instrument": 'OSIRIS', + "observation_date": datetime.strptime("2020-09-06 00:00:000000", format_str) + }, + { + "access_url": "https://bdnyc.s3.us-east-1.amazonaws.com/data_target_WISE1810_comb_Jun2021_YJ_STD_bb.fits", + "regime": "nir", + "instrument": 'EMIR', + "observation_date":datetime.strptime("2021-06-01 00:00:000000", format_str) + } +] + +# Ingest Spectra ---- +def add_spectra(): + for data in spectra_data: + try: + ingest_spectrum( + db, + source="CWISEP J181006.00-101001.1", + spectrum=data["access_url"], + regime=data["regime"], + mode="Missing", + telescope="GTC", + instrument=data["instrument"], + obs_date=data["observation_date"], + reference="Lodi22" + ) + logger.info(f"Successfully ingested spectrum for CWISEP J181006.00-101001.1 with {data['instrument']}") + except AstroDBError as e: + logger.error(f"Error ingesting spectrum: {e}") + +# Run ingestion function +add_instrument() +add_spectra() + +if save_db: + db.save_database(directory="data/") diff --git a/scripts/ingests/beiler24/~$BeilerSIMPLE.Photometry.Ingest.1.xlsx b/scripts/ingests/beiler24/~$BeilerSIMPLE.Photometry.Ingest.1.xlsx deleted file mode 100644 index 529221cbc..000000000 Binary files a/scripts/ingests/beiler24/~$BeilerSIMPLE.Photometry.Ingest.1.xlsx and /dev/null differ diff --git a/scripts/spectra_convert/WISE1810_txt.py b/scripts/spectra_convert/WISE1810_txt.py new file mode 100644 index 000000000..18b01afb3 --- /dev/null +++ b/scripts/spectra_convert/WISE1810_txt.py @@ -0,0 +1,93 @@ +import astropy.units as u +from astropy.io.fits import getheader, Header +from astropy.io import fits +from astrodb_utils.spectra import check_spectrum_plottable +from astrodb_utils.fits import add_wavelength_keywords +from specutils import Spectrum1D +import numpy as np +from astroquery.simbad import Simbad +import os + + +file= ["/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/data_target_WISE1810_comb_Jun2021_YJ_STD_bb.txt", + "/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/WISE1810m10_OB0001_R1000R_06Sept2020.txt"] + +for filename in file: + + # Read the data, there are 2 columns: wavelength and flux + data = np.loadtxt(filename, comments="#") + if "data_target_" in filename: + print("Reading", filename) + + # select the range limit from tested file handle_WISE1810_txt.ipynb + wave = data[224:1380, 0] * u.AA + flux = data[224:1380, 1] * (u.erg / u.cm**2 / u.s / u.AA) + + # create spectrum object + spectrum = Spectrum1D(spectral_axis=wave, flux=flux) + + # convert spectrum + header = Header() + header.set('DATE-OBS', "2021-06-01T00:00:00") + header.set('INSTRUME', "EMIR") + + + else: + print(f"Reading {filename}\n") + + wave = data[:1870, 0] * u.AA + flux = data[:1870, 1] * (u.erg / u.cm**2 / u.s / u.AA) + + # create spectrum object + spectrum = Spectrum1D(spectral_axis=wave, flux=flux) + + # convert spectrum + header = Header() + header.set('DATE-OBS', "2020-09-06T00:00:00") + header.set('INSTRUME', "OSIRIS") + + + # --- modify the following header to both spectrum --- ## + header.set('OBJECT', "CWISEP J181006.00-101001.1") + header.set('BUNIT', "erg / (cm2 s Angstrom)") + header.set('TELESCOP', "GTC") + header.set('VOREF', "2022A&A...663A..84L") + header.set('TITLE', "Physical properties and trigonometric distance of the peculiar dwarf WISE J181005.5 101002.3") + header.set("AUTHOR", "N. Lodieu, et al.") + header.set("CONTRIB1", "Guan Ying Goh, converted to SIMPLE format") + + + # get RA and DEC from Simbad + try: + result = Simbad.query_object("CWISEP J181006.00-101001.1") + header["RA_TARG"] = result[0]["ra"] + header["DEC_TARG"] = result[0]["dec"] + except Exception as e: + print(f"Error getting ra/deg: {e}") + + header.set('RA_TARG', 272.52575) # got from simbad + header.set('DEC_TARG', -10.16675) + + spectrum.meta["header"] = header + + # add Spectrum to FITS file + output_file = os.path.splitext(filename)[0] + ".fits" + spectrum.write(output_file, format="tabular-fits", overwrite=True) + print(f"Wrote to fits format") + +output_dir = os.path.dirname(output_file) +new_files = os.listdir(output_dir) +new_files = [f for f in new_files if f.endswith('.fits')] + +# check header and spectrum +for file in new_files: + new_file_path = os.path.join(output_dir, file) + spectrum = Spectrum1D.read(new_file_path, format="tabular-fits") + + header = spectrum.meta["header"] + add_wavelength_keywords(header, spectrum.spectral_axis) + spectrum.meta["header"] = header + spectrum.write(new_file_path, format="tabular-fits", overwrite=True) + + if check_spectrum_plottable(spectrum, show_plot=True): + print(f"{file} is plottable.") diff --git a/scripts/spectra_convert/convert_WISE1810.ipynb b/scripts/spectra_convert/convert_WISE1810.ipynb new file mode 100644 index 000000000..104e5c0d8 --- /dev/null +++ b/scripts/spectra_convert/convert_WISE1810.ipynb @@ -0,0 +1,117 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "from specutils import Spectrum1D\n", + "from astrodb_utils.spectra import check_spectrum_plottable\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/data_target_WISE1810_comb_Jun2021_YJ_STD_bb.txt is plottable\n", + "\n" + ] + } + ], + "source": [ + "file1 = \"/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/data_target_WISE1810_comb_Jun2021_YJ_STD_bb.txt\"\n", + "\n", + "# Read the data, there are 2 columns: wavelength and flux\n", + "data = np.loadtxt(file1, comments=\"#\")\n", + "\n", + "wave = data[224:1380, 0] * u.AA\n", + "flux = data[224:1380, 1] * (u.erg / u.cm**2 / u.s / u.AA)\n", + "\n", + "# create spectrum object\n", + "spectrum = Spectrum1D(spectral_axis=wave, flux=flux)\n", + "\n", + "if (check_spectrum_plottable(spectrum, show_plot=True)):\n", + " print(f\"{file1} is plottable\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/WISE1810m10_OB0001_R1000R_06Sept2020.txt is plottable\n" + ] + } + ], + "source": [ + "file2 = \"/Users/guanying/SIMPLE_Archive/SIMPLE-db/scripts/spectra_convert/WISE1810m10_OB0001_R1000R_06Sept2020.txt\"\n", + "\n", + "data = np.loadtxt(file2, comments=\"#\")\n", + "\n", + "# try with different range\n", + "wave = data[:1870, 0] * u.AA\n", + "flux = data[:1870, 1] * (u.erg / u.cm**2 / u.s / u.AA)\n", + "\n", + "# create spectrum object\n", + "spectrum = Spectrum1D(spectral_axis=wave, flux=flux)\n", + "\n", + "if (check_spectrum_plottable(spectrum, show_plot=True)):\n", + " print(f\"{file2} is plottable\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "simple-db", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/test_data_spectra.py b/tests/test_data_spectra.py index f8ccbf597..25b912390 100644 --- a/tests/test_data_spectra.py +++ b/tests/test_data_spectra.py @@ -4,14 +4,14 @@ def test_spectra_count(db): n_spectra = db.query(db.Spectra).count() - assert n_spectra == 1606, f"found {n_spectra} sources" + assert n_spectra == 1608, f"found {n_spectra} sources" @pytest.mark.parametrize( ("regime", "n_spectra"), [ - ("optical", 743), - ("nir", 636), + ("optical", 744), + ("nir", 637), ("mir", 227), ("unknown", 0), ],