233 lines
10 KiB
Python
Executable File
233 lines
10 KiB
Python
Executable File
#!/usr/bin/env 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 exists as path_exists
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from os.path import join as path_join
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from warnings import filterwarnings
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import astropy.units as u
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import numpy as np
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from astropy.table import Column, unique
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from astropy.time import Time, TimeDelta
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from astroquery.exceptions import NoResultsWarning
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from astroquery.mast import MastMissions, Observations
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filterwarnings("error", category=NoResultsWarning)
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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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same_filt = np.unique(np.array(np.sum([obs["Filters"] == filt for filt in obs["Filters"]], axis=2) >= len(obs["Filters"][0]), 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] if fi[:-1] != "CLEAR"])]
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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(
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[[np.abs(TimeDelta(obs["Start"][i].unix - date.unix, format="sec")) < 7.0 * u.d for i in range(len(obs))] for date in obs["Start"]], axis=0
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)
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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)] = "_".join(
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[obs["Proposal ID"][date][0], str(obs["Start"][date][0])[:10]]
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)
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return products
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def get_product_list(target=None, proposal_id=None, instrument="foc"):
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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_pep_id",
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"sci_pi_last_name",
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"sci_targname",
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"sci_aper_1234",
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"sci_spec_1234",
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"sci_central_wavelength",
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"sci_actual_duration",
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"sci_instrume",
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"sci_operating_mode",
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"sci_data_set_name",
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"sci_start_time",
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"sci_stop_time",
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"sci_refnum",
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]
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cols = [
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"Proposal ID",
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"PI last name",
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"Target name",
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"Aperture",
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"Filters",
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"Central wavelength",
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"Exptime",
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"Instrument",
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"Operating Mode",
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"Dataset",
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"Start",
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"Stop",
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"References",
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]
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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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if instrument == "foc":
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results = mission.query_object(
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target, radius=radius, select_cols=select_cols, sci_spec_1234="POL*", sci_obs_type="image", sci_aec="S", sci_instrume="foc"
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)
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dataproduct_type = "image"
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description = "DADS C0F file - Calibrated exposure WFPC/WFPC2/FOC/FOS/GHRS/HSP"
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elif instrument == "fos":
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results = mission.query_object(
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target, radius=radius, select_cols=select_cols, sci_operating_mode="SPECTROPOLARIMETRY", sci_obs_type="spectrum", sci_aec="S", sci_instrume="fos"
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)
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dataproduct_type = "spectrum"
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description = ["DADS C0F file - Calibrated exposure WFPC/WFPC2/FOC/FOS/GHRS/HSP", "DADS C3F file - Calibrated exposure GHRS/FOS/HSP"]
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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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if instrument == "foc":
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results["POLFilters"] = Column(np.array([filt.split(";")[0] for filt in results["Filters"]], dtype=str))
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results["Filters"] = Column(np.array([filt.split(";")[1:] for filt in results["Filters"]], dtype=str))
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else:
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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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if instrument == "foc":
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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["POLFilters"]]] += 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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# Remove observations for which a spectropolarization has not been reduced
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if instrument == "fos":
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for pid in np.unique(obs["Proposal ID"]):
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observations = Observations.query_criteria(proposal_id=pid.split("_")[0])
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c3prod = Observations.filter_products(
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Observations.get_product_list(observations),
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productType=["SCIENCE"],
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dataproduct_type=dataproduct_type,
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calib_level=[2],
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description=description[1],
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)
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if len(c3prod) < 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", "Aperture", "Target name", "Proposal ID", "PI last name"]], "Obs")
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except IndexError:
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raise ValueError("There is no observation with polarimetry for {0:s} in HST/{1:s} Legacy Archive".format(target, instrument.upper()))
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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 = [
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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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]
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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(
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Observations.get_product_list(observations), productType=["SCIENCE"], dataproduct_type=dataproduct_type, calib_level=[2], description=description
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)
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products["proposal_id"] = Column(products["proposal_id"], dtype="U35")
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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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tab = unique(products, "proposal_id")
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products["Obs"] = [np.argmax(tab["proposal_id"] == data["proposal_id"]) + 1 for data in products]
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return target, products
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def retrieve_products(target=None, proposal_id=None, instrument="foc", 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, instrument=instrument)
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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(
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file, Observations.download_file(products["dataURI"][products["productFilename"] == file][0], local_path=fpath)[0]
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)
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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, 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, help="the proposal id of the data products", type=int, default=None)
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parser.add_argument("-i", "--instrum", metavar="instrum", required=False, help="the instrument used for observation", type=str, default="foc")
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parser.add_argument(
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"-o", "--output_dir", metavar="directory_path", required=False, help="output directory path for the data products", type=str, default="./data"
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)
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args = parser.parse_args()
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print(args.target)
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prodpaths = retrieve_products(target=args.target, proposal_id=args.proposal_id, instrument=args.instrum.lower(), output_dir=args.output_dir)
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print(prodpaths)
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