Python pyfits.open方法代碼示例

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def load_spectrum(filename, grid):
    """
    Load a single spectrum
    """
    file_in = pyfits.open(filename)
    wl = np.array(file_in[0].data[2])
    flux = np.array(file_in[0].data[0])
    ivar = np.array((file_in[0].data[1]))
    # correct for radial velocity of star
    redshift = file_in[0].header['Z']
    wl_shifted = wl - redshift * wl
    # resample
    flux_rs = (interpolate.interp1d(wl_shifted, flux))(grid)
    ivar_rs = (interpolate.interp1d(wl_shifted, ivar))(grid)
    ivar_rs[ivar_rs < 0] = 0. # in interpolating you can end up with neg
    return flux_rs, ivar_rs 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def load_data():
    direc = "/Users/annaho/Data/LAMOST/Mass_And_Age"
    hdulist = pyfits.open("%s/catalog_paper.fits" %direc)
    tbdata = hdulist[1].data
    hdulist.close()
    snr = tbdata.field("snr")
    chisq = tbdata.field("chisq")
    teff = tbdata.field("cannon_teff")
    in_martig_range = tbdata.field("in_martig_range")
    choose = np.logical_and(in_martig_range, snr > 80)
    print(sum(choose))
    mh = tbdata.field("cannon_mh")[choose]
    afe = tbdata.field("cannon_am")[choose]
    age = tbdata.field("cannon_age")[choose]
    age_err = tbdata.field("cannon_age_err")[choose]
    return mh, afe, age, age_err 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def load_comparison():
    direc = "/Users/annaho/Data/LAMOST/Mass_And_Age"
    hdulist = pyfits.open("%s/age_vs_age_catalog.fits" %direc)
    tbdata = hdulist[1].data
    hdulist.close()
    snr = tbdata.field("snr")
    mass_raw = tbdata.field("cannon_mass")
    choose = np.logical_and(snr > 30, mass_raw > 0)
    mass = np.log10(tbdata.field("cannon_mass")[choose])
    age = tbdata.field("cannon_age")[choose]
    age_err = tbdata.field("cannon_age_err")[choose]
    mass_err = tbdata.field("cannon_mass_err")[choose]
    ness_age = np.log10(np.exp(tbdata.field("lnAge")[choose]))
    ness_age_err = tbdata.field("e_logAge")[choose]
    ness_mass = np.log10(np.exp(tbdata.field("lnM")[choose]))
    ness_mass_err = tbdata.field("e_logM")[choose]
    return mass, age, ness_mass, ness_age 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def find_colors(ref_id, ref_flux, ref_ivar):
    # Find colors
    print("Finding colors")
    a = pyfits.open(DATA_DIR + "/lamost_catalog_colors.fits")
    data = a[1].data
    a.close()
    all_ids = data['LAMOST_ID_1']
    all_ids = np.array([val.strip() for val in all_ids])
    ref_id_col = np.intersect1d(all_ids, ref_id)
    inds = np.array([np.where(all_ids==val)[0][0] for val in ref_id_col])
    all_col, all_col_ivar = get_colors(
            DATA_DIR + "/lamost_catalog_colors.fits")
    col = all_col[:,inds]
    col_ivar = all_col_ivar[:,inds]
    bad_ivar = np.logical_or(np.isnan(col_ivar), col_ivar==np.inf)
    col_ivar[bad_ivar] = 0.0
    bad_flux = np.logical_or(np.isnan(col), col==np.inf)
    col[bad_flux] = 1.0
    col_ivar[bad_flux] = 0.0
    # add them to the wl, flux and ivar arrays
    inds = np.array([np.where(ref_id==val)[0][0] for val in ref_id_col])
    ref_flux_col = np.hstack((ref_flux[inds], col.T))
    ref_ivar_col = np.hstack((ref_ivar[inds], col_ivar.T))
    return ref_id_col, ref_flux_col, ref_ivar_col 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def load_all_ref_label():
    DATA_DIR = "/Users/annaho/Data/LAMOST/Abundances"
    a = pyfits.open(
            DATA_DIR + "/casey_lamost_paper_one_cross_match_with_colors.fits")
    tbl = a[1].data
    a.close()
    ref_id = tbl['lamost_id']
    ref_id = np.array(ref_id)
    ref_id = np.array([val.strip() for val in ref_id])
    snrg = tbl['snrg']
    labels = ['TEFF', 'LOGG', 'AK_WISE',
            'AL_H', 'CA_H', 'C_H', 'FE_H', 'MG_H', 'MN_H',
            'NI_H', 'N_H', 'O_H', 'SI_H', 'TI_H']
    np.savez("label_names.npz", labels)
    nlabel = len(labels)
    nobj = len(ref_id)
    ref_label = np.zeros((nobj, nlabel))
    for ii,label in enumerate(labels):
        ref_label[:,ii] = tbl[label]
    np.savez("ref_id.npz", ref_id)
    np.savez("ref_label.npz", ref_label)
    return ref_id 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def find_colors(ref_id, ref_flux, ref_ivar):
    # Find colors
    DATA_DIR = "/Users/annaho/Data/LAMOST/Mass_And_Age"
    print("Finding colors")
    a = pyfits.open(DATA_DIR + "/lamost_catalog_colors.fits")
    data = a[1].data
    a.close()
    all_ids = data['LAMOST_ID_1']
    all_ids = np.array([val.strip() for val in all_ids])
    ref_id_col = np.intersect1d(all_ids, ref_id)
    inds = np.array([np.where(all_ids==val)[0][0] for val in ref_id_col])
    all_id, all_col, all_col_ivar = get_colors(
            DATA_DIR + "/lamost_catalog_colors.fits")
    col = all_col[:,inds]
    col_ivar = all_col_ivar[:,inds]
    bad_ivar = np.logical_or(np.isnan(col_ivar), col_ivar==np.inf)
    col_ivar[bad_ivar] = 0.0
    bad_flux = np.logical_or(np.isnan(col), col==np.inf)
    col[bad_flux] = 1.0
    col_ivar[bad_flux] = 0.0
    # add them to the wl, flux and ivar arrays
    inds = np.array([np.where(ref_id==val)[0][0] for val in ref_id_col])
    ref_flux_col = np.hstack((ref_flux[inds], col.T))
    ref_ivar_col = np.hstack((ref_ivar[inds], col_ivar.T))
    return ref_id_col, ref_flux_col, ref_ivar_col 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def get_labels(ids_find):
    """ Labels to make Cannon model spectra """
    a = pyfits.open("%s/lamost_catalog_full.fits" %LAB_DIR)
    data = a[1].data
    a.close()
    id_all = data['lamost_id']
    id_all = np.array(id_all)
    id_all = np.array([val.strip() for val in id_all])
    snr_all = data['cannon_snrg']
    chisq_all = data['cannon_chisq']
    teff = data['cannon_teff']
    logg = data['cannon_logg']
    feh = data['cannon_m_h']
    afe = data['cannon_alpha_m']
    ak = data['cannon_a_k']
    labels = np.vstack((teff,logg,feh,afe,ak))
    choose = np.in1d(id_all, ids_find)
    id_choose = id_all[choose]
    label_choose = labels[:,choose]
    snr_choose = snr_all[choose]
    chisq_choose = chisq_all[choose]
    inds = np.array([np.where(id_choose==val)[0][0] for val in ids_find])
    print(id_choose[inds][100])
    print(ids_find[100])
    return label_choose[:,inds], snr_choose[inds], chisq_choose[inds] 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def readphoenix(fitsphoe, wmin=3500, wmax=8000):
   #fitsphoe = '/home/astro56/husser/PhoenixModels/HiResFITS/PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte03900-4.00-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'
   #fitsphoe = '/home/astro115/carmenes/serval/phoenix_tmp/lte03400-5.00-0.0_carm.fits'
   #fitsphoe = '/home/astro115/carmenes/serval/phoenix_tmp/lte03400-5.00-0.0_carm.fits'
   print 'readphoenix', fitsphoe
   hdulist = pyfits.open(fitsphoe)
   flux = hdulist[0].data
   #hdulist = pyfits.open('/home/astro56/husser/PhoenixModels/HiResFITS/WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')
   hdulist = pyfits.open(os.path.dirname(fitsphoe)+os.sep+'/WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')
   wave = hdulist[0].data
   hdulist.close()
   #if flux.shape!=wave.shape:
      #flux = flux.T
   imap = (wmin<wave) * (wave<wmax)
   w = wave[imap]
   f = flux[imap]
   return w, f 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def StatsToCSV(campaign, model='nPLD'):
    '''
    Generate the CSV file used in the search database for the documentation.
    '''
    statsfile = os.path.join(EVEREST_SRC, 'missions', 'k2',
                             'tables', 'c%02d_%s.cdpp' % (campaign, model))
    csvfile = os.path.join(os.path.dirname(EVEREST_SRC), 'docs',
                           'c%02d.csv' % campaign)
    epic, kp, cdpp6r, cdpp6, _, _, _, _, saturated = \
        np.loadtxt(statsfile, unpack=True, skiprows=2)

    with open(csvfile, 'w') as f:
        print('c%02d' % campaign, file=f)
        for i in range(len(epic)):
            print('%09d,%.3f,%.3f,%.3f,%d' % (epic[i], kp[i],
                                              cdpp6r[i], cdpp6[i],
                                              int(saturated[i])),
                                              file=f) 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def load_all_broadband_images(filename,camera=0,openlist=None):
  if (not os.path.exists(filename)):
    print "file not found:", filename
    sys.exit()

  camera_string = 'CAMERA'+str(camera)+'-BROADBAND-NONSCATTER'

  if openlist is None:
      openlist = fits.open(filename,memmap=False)
      data = openlist[camera_string].data
      #openlist.close()
      print "### Sunpy: opening broadband list: ", openlist.filename()
  else:
      openfn = openlist.filename()
      assert openfn==filename
      data = openlist[camera_string].data

  data[ data < 1e-20 ] = 1e-20

  return data,openlist 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def apply_gain(self):
        """
        Applies the gains set for each DM to the DM actuator commands. 
        Also applies different control law if DM is in "closed" or "open" loop mode
        """
        # Loop through DMs and apply gain
        n_act1 = 0
        for dm_i, dm in self.dms.items():

            n_act2 = n_act1 + dm.n_valid_actuators
            # If loop is closed, only add residual measurements onto old
            # actuator values
            if dm.dmConfig.closed:
                self.actuator_values[n_act1: n_act2] += (dm.dmConfig.gain * self.new_actuator_values[n_act1: n_act2])

            else:
                self.actuator_values[n_act1: n_act2] = ((dm.dmConfig.gain * self.new_actuator_values[n_act1: n_act2])
                                + ( (1. - dm.dmConfig.gain) * self.actuator_values[n_act1: n_act2]) )

            n_act1 += dm.n_valid_actuators 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def runSciCams(self, dmShape=None):
        """
        Runs a single frame of the science Cameras

        Calculates the image recorded by all science cameras in the system for the current phase over the telescope one frame. If a dmShape is present (which it usually will be in AO!) this correction is applied to the science phase before the image is calculated.

        Args:
            correction (list or ndarray, optional): An array of the combined system DM shape to correct the science path. If not given science cameras are in open loop.
        """
        t = time.time()

        self.sciImgNo +=1
        for sci in xrange(self.config.sim.nSci):
            self.sciImgs[sci] += self.sciCams[sci].frame(self.scrns, dmShape)

            # Normalise long exposure psf
            #self.sciImgs[sci] /= self.sciImgs[sci].sum()
            self.sciCams[sci].longExpStrehl = (
                    self.sciImgs[sci].max()/
                    self.sciImgs[sci].sum()/
                    self.sciCams[sci].psfMax)

        self.Tsci +=time.time()-t 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def readin(self):
    """
    readin()
    """
    
    inf = open(self.archive, 'r')
    (self.data, self.time_data, self.cadence, self.labels) = pickle.load(inf)
    inf.close()
    
    # Use hours from 0 as x axis scale
    self.xvals = self.time_data - self.time_data[0]
    
    #print self.data
      
#_______________________________________________________________________________
#_____________________________________plot_item_________________________________
# 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def make_kepler_label_file():
    """ using the values made by The Cannon """
    lamost_key = np.loadtxt('lamost_sorted_by_ra.txt',dtype=str)
    apogee_key = np.loadtxt('apogee_sorted_by_ra.txt', dtype=str)
    apogee_key_short = np.array(
            [(item.split('v603-')[-1]).split('.fits')[0]
            for item in apogee_key])
    nstars = len(lamost_key)

    direc = "/home/annaho/TheCannon/examples/example_apokasc/test_is_lamost_apogee_overlap"
    kepler_ids = np.load("%s/test_ids.npz" %direc)['arr_0']
    kepler_ids = np.array([a.split('/')[-1] for a in kepler_ids])
    kepler_labels = np.load("%s/test_labels.npz" %direc)['arr_0']
    inds = np.array(
            [np.where(apogee_key==a)[0][0] for a in kepler_ids])
    teff = kepler_labels[:,0][inds]
    logg = kepler_labels[:,1][inds]
    feh = kepler_labels[:,2][inds]
    alpha = kepler_labels[:,3][inds]

    outputf = open("apogee_cannon_labels.csv", "w")
    header = "#lamost_id,apogee_id,teff,logg,feh,alpha,snr,vscatter,starflag\n"
    outputf.write(header)
    for i in range(nstars):
        line = lamost_key[i]+','+apogee_key[i]+','+\
               str(teff[i])+','+str(logg[i])+','+str(feh[i])+','+\
               str(alpha[i])+',0'+',0'+',0'+'\n'
        outputf.write(line) 

# 需要導入模塊: import pyfits [as 別名]
# 或者: from pyfits import open [as 別名]
def make_tr_file_list(frac_cut=0.94, snr_cut=100):
    """ make a list of training objects, given cuts

    Parameters
    ----------
    frac_cut: float
        the fraction of pix in the spectrum that must be good

    snr_cut: float
        the snr that the spec

    Returns
    -------
    tr_files: np array
        list of file names of training objects
    """
    allfiles = np.loadtxt(
            "apogee_dr12_labels.csv", delimiter=',', usecols=(0,), 
            dtype=str)
    allfiles_apogee= np.loadtxt(
            "apogee_dr12_labels.csv", delimiter=',', usecols=(1,), 
            dtype=str)
    starflags = np.loadtxt(
            "apogee_dr12_labels.csv", delimiter=',', usecols=(8,), dtype=str)
    good = starflags == "False"
    tr_files = allfiles[good]
    tr_files_apogee = allfiles_apogee[good]
    outputf = open("PAPER_training_step/tr_files.txt", "w")
    for tr_file in tr_files:
        outputf.write(tr_file + '\n')
    outputf.close()
    outputf = open("PAPER_training_step/tr_files_apogee.txt", "w")
    for tr_file in tr_files_apogee:
        outputf.write(tr_file + '\n')
    outputf.close()
    return tr_files