198 lines
8.6 KiB
Python
Executable File
198 lines
8.6 KiB
Python
Executable File
#!/usr/bin/python3
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# -*- coding:utf-8 -*-
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"""
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Library function to query and download datatsets from MAST api.
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"""
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from os import system
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from os.path import join as path_join, exists as path_exists
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from astroquery.mast import MastMissions, Observations
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from astropy.table import unique, Column
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from astropy.time import Time, TimeDelta
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import astropy.units as u
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import numpy as np
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def divide_proposal(products):
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"""
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Divide observation in proposals by time or filter
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"""
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for pid in np.unique(products['Proposal ID']):
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obs = products[products['Proposal ID'] == pid].copy()
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close_date = np.unique(np.array([TimeDelta(np.abs(obs['Start'][0].unix-date.unix), format='sec')
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< 7.*u.d for date in obs['Start']], dtype=bool), axis=0)
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if len(close_date) > 1:
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for date in close_date:
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products['Proposal ID'][np.any([products['Dataset'] == dataset for dataset in obs['Dataset'][date]], axis=0)
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] = "_".join([obs['Proposal ID'][date][0], str(obs['Start'][date][0])[:10]])
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for pid in np.unique(products['Proposal ID']):
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obs = products[products['Proposal ID'] == pid].copy()
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same_filt = np.unique(np.array(np.sum([obs['Filters'][:, 1:] == filt[1:] for filt in obs['Filters']], axis=2) < 3, dtype=bool), axis=0)
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if len(same_filt) > 1:
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for filt in same_filt:
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products['Proposal ID'][np.any([products['Dataset'] == dataset for dataset in obs['Dataset'][filt]], axis=0)] = "_".join(
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[obs['Proposal ID'][filt][0], "_".join([fi for fi in obs['Filters'][filt][0][1:] if fi[:-1] != "CLEAR"])])
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return products
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def get_product_list(target=None, proposal_id=None):
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"""
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Retrieve products list for a given target from the MAST archive
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"""
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mission = MastMissions(mission='hst')
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radius = '3'
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select_cols = [
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'sci_data_set_name',
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'sci_spec_1234',
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'sci_actual_duration',
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'sci_start_time',
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'sci_stop_time',
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'sci_central_wavelength',
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'sci_instrume',
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'sci_aper_1234',
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'sci_targname',
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'sci_pep_id',
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'sci_pi_last_name']
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cols = [
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'Dataset',
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'Filters',
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'Exptime',
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'Start',
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'Stop',
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'Central wavelength',
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'Instrument',
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'Size',
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'Target name',
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'Proposal ID',
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'PI last name']
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if target is None:
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target = input("Target name:\n>")
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# Use query_object method to resolve the object name into coordinates
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results = mission.query_object(
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target,
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radius=radius,
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select_cols=select_cols,
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sci_spec_1234='POL*',
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sci_obs_type='image',
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sci_aec='S',
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sci_instrume='foc')
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for c, n_c in zip(select_cols, cols):
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results.rename_column(c, n_c)
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results['Proposal ID'] = Column(results['Proposal ID'], dtype='U35')
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results['Filters'] = Column(np.array([filt.split(";") for filt in results['Filters']], dtype=str))
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results['Start'] = Column(Time(results['Start']))
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results['Stop'] = Column(Time(results['Stop']))
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results = divide_proposal(results)
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obs = results.copy()
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# Remove single observations for which a FIND filter is used
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to_remove = []
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for i in range(len(obs)):
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if "F1ND" in obs[i]['Filters']:
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to_remove.append(i)
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obs.remove_rows(to_remove)
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# Remove observations for which a polarization filter is missing
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polfilt = {"POL0": 0, "POL60": 1, "POL120": 2}
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for pid in np.unique(obs['Proposal ID']):
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used_pol = np.zeros(3)
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for dataset in obs[obs['Proposal ID'] == pid]:
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used_pol[polfilt[dataset['Filters'][0]]] += 1
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if np.any(used_pol < 1):
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obs.remove_rows(np.arange(len(obs))[obs['Proposal ID'] == pid])
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tab = unique(obs, ['Target name', 'Proposal ID'])
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obs["Obs"] = [np.argmax(np.logical_and(tab['Proposal ID'] == data['Proposal ID'], tab['Target name'] == data['Target name']))+1 for data in obs]
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try:
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n_obs = unique(obs[["Obs", "Filters", "Start", "Central wavelength", "Instrument", "Size", "Target name", "Proposal ID", "PI last name"]], 'Obs')
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except IndexError:
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raise ValueError(
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"There is no observation with POL0, POL60 and POL120 for {0:s} in HST/FOC Legacy Archive".format(target))
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b = np.zeros(len(results), dtype=bool)
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if proposal_id is not None and str(proposal_id) in obs['Proposal ID']:
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b[results['Proposal ID'] == str(proposal_id)] = True
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else:
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n_obs.pprint(len(n_obs)+2)
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a = [np.array(i.split(":"), dtype=str)
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for i in input("select observations to be downloaded ('1,3,4,5' or '1,3:5' or 'all','*' default to 1)\n>").split(',')]
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if a[0][0] == '':
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a = [[1]]
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if a[0][0] in ['a', 'all', '*']:
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b = np.ones(len(results), dtype=bool)
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else:
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a = [np.array(i, dtype=int) for i in a]
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for i in a:
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if len(i) > 1:
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for j in range(i[0], i[1]+1):
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b[np.array([dataset in obs['Dataset'][obs["Obs"] == j] for dataset in results['Dataset']])] = True
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else:
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b[np.array([dataset in obs['Dataset'][obs['Obs'] == i[0]] for dataset in results['Dataset']])] = True
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observations = Observations.query_criteria(obs_id=list(results['Dataset'][b]))
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products = Observations.filter_products(Observations.get_product_list(observations),
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productType=['SCIENCE'],
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dataproduct_type=['image'],
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calib_level=[2],
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description="DADS C0F file - Calibrated exposure WFPC/WFPC2/FOC/FOS/GHRS/HSP")
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products['proposal_id'] = Column(products['proposal_id'], dtype='U35')
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products['target_name'] = Column(observations['target_name'])
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for prod in products:
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prod['proposal_id'] = results['Proposal ID'][results['Dataset'] == prod['productFilename'][:len(results['Dataset'][0])].upper()][0]
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for prod in products:
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prod['target_name'] = observations['target_name'][observations['obsid'] == prod['obsID']][0]
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tab = unique(products, ['target_name', 'proposal_id'])
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if len(tab) > 1 and np.all(tab['target_name'] == tab['target_name'][0]):
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target = tab['target_name'][0]
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products["Obs"] = [np.argmax(np.logical_and(tab['proposal_id'] == data['proposal_id'], tab['target_name'] == data['target_name']))+1 for data in products]
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return target, products
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def retrieve_products(target=None, proposal_id=None, output_dir='./data'):
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"""
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Given a target name and a proposal_id, create the local directories and retrieve the fits files from the MAST Archive
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"""
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target, products = get_product_list(target=target, proposal_id=proposal_id)
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prodpaths = []
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# data_dir = path_join(output_dir, target)
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out = ""
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for obs in unique(products, 'Obs'):
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filepaths = []
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# obs_dir = path_join(data_dir, obs['prodposal_id'])
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# if obs['target_name']!=target:
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obs_dir = path_join(path_join(output_dir, target), obs['proposal_id'])
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if not path_exists(obs_dir):
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system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
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for file in products['productFilename'][products['Obs'] == obs['Obs']]:
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fpath = path_join(obs_dir, file)
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if not path_exists(fpath):
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out += "{0:s} : {1:s}\n".format(file, Observations.download_file(
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products['dataURI'][products['productFilename'] == file][0], local_path=fpath)[0])
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else:
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out += "{0:s} : Exists\n".format(file)
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filepaths.append([obs_dir, file])
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prodpaths.append(np.array(filepaths, dtype=str))
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return target, prodpaths
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Query MAST for target products')
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parser.add_argument('-t', '--target', metavar='targetname', required=False,
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help='the name of the target', type=str, default=None)
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parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False,
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help='the proposal id of the data products', type=int, default=None)
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parser.add_argument('-o', '--output_dir', metavar='directory_path', required=False,
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help='output directory path for the data products', type=str, default="./data")
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args = parser.parse_args()
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prodpaths = retrieve_products(target=args.target, proposal_id=args.proposal_id)
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print(prodpaths)
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