Пример #1
0
def grab_calfiles():
    workingdir = os.getcwd()
    handler = Eso()

    ### Set your username here!
    USERNAME = ""

    handler.ROW_LIMIT = 10000
    handler.login(USERNAME, store_password=True)

    def mkdir_safe(dirname):
        '''
        Check if directory exists - if it doesn't, make it, if it does, clear it out
        '''
        if os.path.isdir(dirname) == True:
            flist = glob.glob(dirname + "/*")
            for f in flist:
                os.remove(f)
        else:
            os.mkdir(dirname)

    mkdir_safe("flats")
    mkdir_safe("flatdarks")
    mkdir_safe("darks")

    # Read the first FITS in the folder
    filelist = glob.glob("obj/*.fits")
    print(os.getcwd())
    temphdu = fits.open(filelist[0])
    header = temphdu[0].header
    print("FITS header loaded")

    # Extract relevant query params from science frame
    prop_ID = header["HIERARCH ESO OBS PROG ID"]
    date = Time(header["DATE-OBS"])
    sci_exp = header["EXPTIME"]
    # Set start and end time of search - may need to tweak this manually to find the right calfiles.
    # Best to be generous with this window since need to find flats, darks, and flat-darks for the pipeline to run.
    stime = date
    etime = date + 18 * u.hour
    win_size = header["HIERARCH ESO DET WINDOW NY"]
    sci_wav = header["HIERARCH ESO INS WLEN CWLEN"]
    #print(filelist[0], sci_wav, date)

    # Query flat frames - check they match
    print("Querying ESO Archive")
    flat_table = handler.query_instrument("crires",
                                          column_filters={
                                              'stime': stime.value,
                                              'etime': etime.value,
                                              'dp_type': 'FLAT',
                                              'ins_wlen_cwlen': sci_wav
                                          })
    flat_header = handler.get_headers(flat_table["DP.ID"])
    mask = flat_header["HIERARCH ESO DET WINDOW NY"] != win_size
    flat_table = flat_table[~mask]

    #### if flat_exp_time not all the same value, choose the highest one
    #### Download flat fields
    flat_exp_time = np.max(flat_table["EXPTIME"])
    flat_files = handler.retrieve_data(flat_table["DP.ID"])
    #print(flat_files)

    for f in flat_files:
        shutil.copy(f, "flats")

    #### Grab the dark frames matching the science exposure time
    dark_table = handler.query_instrument("crires",
                                          column_filters={
                                              'stime': stime.value,
                                              'etime': etime.value,
                                              'dp_type': 'DARK',
                                              'exptime': sci_exp
                                          })
    dark_header = handler.get_headers(dark_table['DP.ID'])
    mask = dark_header["HIERARCH ESO DET WINDOW NY"] != win_size
    dark_table = dark_table[~mask]
    dark_files = handler.retrieve_data(dark_table["DP.ID"])

    for d in dark_files:
        shutil.copy(d, "darks")

    #### Grab darks matched to flat fields
    flatdark_table = handler.query_instrument("crires",
                                              column_filters={
                                                  'stime': stime.value,
                                                  'etime': etime.value,
                                                  'dp_type': 'DARK',
                                                  'exptime': flat_exp_time
                                              })
    flatdark_header = handler.get_headers(flatdark_table["DP.ID"])
    mask = flatdark_header["HIERARCH ESO DET WINDOW NY"] != win_size
    flatdark_table = flatdark_table[~mask]
    flatdark_files = handler.retrieve_data(flatdark_table["DP.ID"])

    for d in flatdark_files:
        shutil.copy(d, "flatdarks")

    print("Unpacking and moving!")

    ### Unpack all the files -- several possible commands for thisself.

    ### maximum compatibility use "gzip -d *.Z"
    ### pigz is a parallel gzip, but also it can't decompress in parallel apparently.
    ### if you want to use it despite this, "pigz -d *.Z"

    ### For maximum SPEED you could try "ls *.Z | parallel pigz -d" if you have GNU parallel installed.

    os.chdir("flats")
    os.system("pigz -d *.Z")

    os.chdir("../flatdarks")
    os.system("pigz -d *.Z")

    os.chdir("../darks")
    os.system("pigz -d *.Z")

    os.chdir("../")
    print("Calibration selection complete!")
varmask = [not x for x in varstat]

### Needs to be both not variable and not a GKM/whatever else star
totalmask = np.logical_and(typemask, varmask)

new_subset = cat_subset[totalmask]
print("Filter by spectral_type: %s of %s targets remain" %
      (np.sum(typemask), len(initmask)))

science_frames = new_subset["DP.ID"]

eso = Eso()
eso.login("tkillestein")

### Mask all that have non-512 window
heads = eso.get_headers(science_frames)

goodmsk = heads['HIERARCH ESO DET WINDOW NY'] == 512
print("Filter by windowing: %s of %s targets remain" %
      (np.sum(goodmsk), len(initmask)))

check_calib_list = heads[goodmsk]

final_frames = []
frames_rejected = 0

for head in tqdm(check_calib_list,
                 ascii=True,
                 desc="Checking availability of calfiles"):
    prop_ID = head["HIERARCH ESO OBS PROG ID"]
    date = Time(head["DATE-OBS"])