Python io.readsav函数代码示例

def main():
	postx=readsav('samples_x1.sav', python_dict=True)
	postt=readsav('samples_t2.sav', python_dict=True)
	#alpha is the intercept and beta is the slope

	alpha=postx['postx1']['alpha']
	beta=postx['postx1']['beta']

def find_photo_z(type, file=None, file_z=None, z=None, mu=None,
                 sigma=None):
    """Type: file or gauss
       Note: file_z implementation NOT tested.
    """

    if type == 'file':
        try:
            pz_file = io.readsav(file)
            pz = pz_file['p_z']
        except:
            pz_file = np.genfromtxt(file, dtype=None, names=True)
            pz = pz_file['pdf']
        if file_z is not None:
            try:
                my_z = io.readsav(file_z)
                my_z = my_z['z']
            except:
                my_z = np.genfromtxt(file, dtype=None, names=True)
                my_z = my_z['z']
        else:
            my_z = np.arange(0, 5, .01)
        if np.shape(pz) != np.shape(my_z):
            raise ValueError("pz array and z array are different sizes!")
        func_my_photo_z = interpolate.interp1d(my_z, pz)
        my_photo_z = func_my_photo_z(z)
        my_photo_z = np.asarray(my_photo_z/my_photo_z.max())

    elif type == 'gauss':
        my_photo_z = stats.norm.pdf(z, mu, sigma)
        my_photo_z = np.asarray(my_photo_z/my_photo_z.max())
    return my_photo_z

def get_dict_from_file(date, prefix="eis3"):
    """
    Reads an IDL .sav file containing EIS housekeeping data and returns its
    contents as a python dictionary. For speed, if the file has already been
    read, it may return the contents from a hidden memo. If the file is not
    found in the location specified it will attempt to download it once and
    save the file in the location originally specified.

    Parameters
    ----------
    date: date or datetime object
        Date of the observation required. If the file is present in the sunpy
        data directory, it will be read from there, or downloaded to that
        location if it isn't.
    prefix: str
        file prefix (eis3 for thermal correction, fpp1 for doppler shift)
    """
    key = '{0}_{1:%Y%m}.sav'.format(prefix, date)
    if key in __housekeeping_memo__:
        file_dict = __housekeeping_memo__[key]
    else:
        download_dir = os.path.join(sunpy.util.config._get_home(),
                                    'EISpy', 'eispy', 'data', key)
        try:
            file_dict = readsav(download_dir, python_dict=True)
        except IOError:
            url = "http://sdc.uio.no/eis_wave_corr_hk_data/" + key
            urllib.urlretrieve(url, filename=download_dir)
            file_dict = readsav(download_dir, python_dict=True)
            warnings.warn("File was not found, so it was downloaded and " +
                          "placed at the given location", UserWarning)
        __housekeeping_memo__.update({key: file_dict})
    return file_dict

def run_master(file_int, file_vv, db_images, db_vel, zero):

    print(" * MASTER : reading {0}...".format(file_int))
    im = io.readsav('/net/duna/scratch1/aasensio/deepLearning/opticalFlow/database/{0}.save'.format(file_int))['int']
    print(" * MASTER : reading {0}...".format(file_vv))
    vel = io.readsav('/net/duna/scratch1/aasensio/deepLearning/opticalFlow/database/{0}.save'.format(file_vv))

    n_timesteps, nx_orig, ny_orig = im.shape

    tasks = [i for i in range(n_timesteps)]

    task_index = 0
    num_workers = size - 1
    closed_workers = 0
    print("*** Master starting with {0} workers".format(num_workers))
    while closed_workers < num_workers:
        dataReceived = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)                
        source = status.Get_source()
        tag = status.Get_tag()
        if tag == tags.READY:
            # Worker is ready, so send it a task
            if task_index < len(tasks):
                dataToSend = {'index': task_index+zero, 'image': im[task_index,0:1008,0:1008], 'vx1': vel['vx1'][task_index,0:1008,0:1008], 'vz1': vel['vz1'][task_index,0:1008,0:1008],
                    'vx01': vel['vx01'][task_index,0:1008,0:1008], 'vz01': vel['vz01'][task_index,0:1008,0:1008], 'vx001': vel['vx001'][task_index,0:1008,0:1008], 'vz001': vel['vz001'][task_index,0:1008,0:1008]}
                comm.send(dataToSend, dest=source, tag=tags.START)
                print(" * MASTER : sending task {0} to worker {1}".format(task_index, source), flush=True)
                task_index += 1
            else:
                print("Sending termination")
                comm.send(None, dest=source, tag=tags.EXIT)
        elif tag == tags.DONE:
            index = dataReceived['index']
            im_r = dataReceived['image']
            vx1_r = dataReceived['vx1']
            vz1_r = dataReceived['vz1']
            vx01_r = dataReceived['vx01']
            vz01_r = dataReceived['vz01']
            vx001_r = dataReceived['vx001']
            vz001_r = dataReceived['vz001']
                        
            db_images[index,:,:] = im_r
            db_vel[0,index,:,:] = vx1_r
            db_vel[1,index,:,:] = vz1_r
            db_vel[2,index,:,:] = vx01_r
            db_vel[3,index,:,:] = vz01_r
            db_vel[4,index,:,:] = vx001_r
            db_vel[5,index,:,:] = vz001_r
    
            print(" * MASTER : got block {0} from worker {1}".format(index, source), flush=True)
            
        elif tag == tags.EXIT:
            print(" * MASTER : worker {0} exited.".format(source))
            closed_workers += 1

    print("Master block finished")
    return len(tasks)

def loadfhdsav(savfile):
    # get the other poles
    pol = savfile.split("_")[-1][:2]
    if pol == "xx":
        uvfile_altpol = savfile.replace("xx", "yy")
        pols = [0, 1]
    elif pol == "yy":
        uvfile_altpol = savfile.replace("yy", "xx")
        pols = [1, 0]
    else:
        print "polarization not found in filename. skipping"
        raise (StandardError)
    if not os.path.exists(uvfile_altpol):
        print "pol file", uvfile_altpol, "not found. please find"
        raise (StandardError)
    # paramfile = savfile.split('_')[0]+'_params.sav'
    paramfile = savfile.replace("vis_%s" % (["xx", "yy"][pols[0]]), "params")
    if not os.path.exists(paramfile):
        print "error: paramfile=", paramfile, "not found. please find"
        raise (StandardError)
    # weightfile = savfile.split('_')[0]+'_flags.sav'
    weightfile = savfile.replace("vis_%s" % (["xx", "yy"][pols[0]]), "flags")
    if not os.path.exists(weightfile):
        print "error: weightfile", weightfile, "not found, please find"
        raise (StandardError)
    print "loading:", savfile
    uvfile = readsav(savfile)
    ant1 = uvfile["obs"]["baseline_info"][0]["tile_a"][0] - 1
    print "min(ant1)=", ant1.min(), "should be 0"
    ant2 = uvfile["obs"]["baseline_info"][0]["tile_b"][0] - 1
    print "max(ant2)=", ant2.max(), "should be 127"
    data = uvfile["vis_ptr"]
    # times = uvfile['obs']['baseline_info'][0]['jdate'][0]
    baselines = (ant2) * 256 + ant1
    freqs = uvfile["obs"]["baseline_info"][0]["freq"][0]

    print "loading alternate polarization", uvfile_altpol
    uv_altpol = readsav(uvfile_altpol)
    data_altpol = uv_altpol["vis_ptr"]

    print "loading baselines from params file:", paramfile
    params = readsav(paramfile)
    U = params["params"]["uu"][0] * 1e9
    V = params["params"]["vv"][0] * 1e9
    W = params["params"]["ww"][0] * 1e9
    uvw = n.array(zip(U, V, W))
    times = params["params"]["time"][0]
    print "loading weights from :", weightfile
    flags = readsav(weightfile)
    mask = n.dstack([flags["flag_arr"][0], flags["flag_arr"][1]]) == 0  # the zeros are the flags
    # create the new fits file
    outdata = n.zeros((data.shape[0], data.shape[1], 2)).astype(n.complex64)
    outdata[:, :, pols[0]] = data
    outdata[:, :, pols[1]] = data_altpol
    ant1, ant2 = i2a(ant1), i2a(ant2)
    return uvw, ant1, ant2, baselines, times, freqs, outdata, mask

    def __init__(self, root):

        self.root = root
        self.noise = noise
        self.batch_size = 256

        self.dataFile = "/net/duna/scratch1/aasensio/deepLearning/milne/database/database_6301_hinode_1component.h5"

        f = h5py.File(self.dataFile, 'r')
        self.pars = f.get("parameters")                
        self.lower = np.min(self.pars, axis=0)
        self.upper = np.max(self.pars, axis=0)
        f.close()

        
        self.root_hinode = "/net/nas4/fis/aasensio/scratch/HINODE/SUNSPOT/"

        self.label_files = ["sunspot_stokesI_512x512.sav", "sunspot_stokesQ_512x512.sav", "sunspot_stokesU_512x512.sav", "sunspot_stokesV_512x512.sav"]

        self.std_values = np.load('{0}_normalization.npy'.format(self.root))

        labels_data = ['data_ii', 'data_qq', 'data_uu', 'data_vv']

        self.stokes = np.zeros((512,512,50,4))

        for i in range(4):
            print("Reading file {0}".format(self.label_files[i]))
            stokes = io.readsav("/net/nas4/fis/aasensio/scratch/HINODE/SUNSPOT/{0}".format(self.label_files[i]))[labels_data[i]]
            if (i == 0):
                mean_stokesi = np.mean(stokes[400:500,0:100,0])

            stokes = stokes[:,:,0:50] / mean_stokesi
            self.stokes[:,:,:,i] = stokes / self.std_values[None,None,:,i]

        self.stokes = self.stokes.reshape((512*512,50,4))

def z_from_photo_z(photo_z_file, n, my_z_array=None):
    my_p_z = io.readsav(photo_z_file)
    pz = my_p_z['p_z']
    if my_z_array is None:
        z = np.arange(0, 5, .01)
    else:
        z = my_z_array
    if np.shape(pz) != np.shape(z):
        raise ValueError("p_z array and z array are different sizes")
    dz = z[1] - z[0]
    pz /= (dz * pz).sum()
    ecdf = np.cumsum(pz * dz)
    cdf = interpolate.interp1d(z, ecdf)

    def func(x, *args):
        my_cdf = args[0]
        cdf = args[1]
        return abs(my_cdf - cdf(x))
    out_z = []
    for i in range(n):
        my_cdf = np.random.uniform(0, 1)
        my_z = optimize.fmin(func, (1.5), args=(my_cdf, cdf), disp=0)
        out_z.append(my_z[0])
    out_z = np.asarray(out_z)
    return out_z

def makeWeightsArray(X):
    savfile = readsav(X)
    wuv = savfile['weights_uv']
    wxy = n.fft.fft2(wuv)
    wuv = n.abs(wuv)
    wxy = n.abs(wxy)
    return wuv,wxy

def read_padsav(file_name, disable_UserWarnings=True):
    """
        Reads data from an idl_dict object into XPadDataItem objects

        Parameters
        ----------
        file_name : str
            Path to XPad*.padsav file


        Returns
        -------
        items : list
            A list of XPadDataItems
    """

    warning_action = "default"
    if (disable_UserWarnings):
        warning_action = "ignore"

    with catch_warnings():
        simplefilter(warning_action, UserWarning)
        idl_dict = readsav(file_name)

    return parse_padsav(idl_dict)

    def __init__(self,infile):
        mypath=os.getcwd()
        if mypath.find('/Users/rfinn') > -1:
            print "Running on Rose's mac pro or laptop"
            homedir='/Users/rfinn/'
        elif mypath.find('Users/kellywhalen') > -1:
            print "Running on Kelly's Laptop"
            homedir='/Users/kellywhalen/Github/Virgo/'

        cefile=readsav(infile)
        self.nulnu_iras25=cefile['nulnu_iras25']
        self.nulnu_iras100=cefile['nulnu_iras100']
        self.nulnu_iras12=cefile['nulnu_iras12']
        self.nulnu_iras60=cefile['nulnu_iras60']
        self.nulnuinlsun=cefile['nulnuinlsun']
        self.lir_sanders=cefile['lir_sanders']
        self.lir=cefile['lir']
        self.nulnu_lw3=cefile['nulnu_lw3']
        self.nulnu_lw2=cefile['nulnu_lw2']
        self.lamb=cefile['lambda']
        #
        # convert all to double-precision arrays
        #
        self.lamb=array(self.lamb,'d')
        self.nulnu_iras25=array(self.nulnu_iras25,'d')
        self.nulnu_iras100=array(self.nulnu_iras100,'d')
        self.nulnu_iras12=array(self.nulnu_iras12,'d')
        self.nulnu_iras60=array(self.nulnu_iras60,'d')
        self.nulnuinlsun=array(self.nulnuinlsun,'d')
        self.lir_sanders=array(self.lir_sanders,'d')
        self.lir=array(self.lir,'d')
        self.nulnu_lw3=array(self.nulnu_lw3,'d')
        self.nulnu_lw2=array(self.nulnu_lw3,'d')

def import_cosmology(filename, structure_name="fid"):
    r""" Loads an icosmo cosmology from a fiducial structure stored in an
    idl save file into a cosmicpy cosmology.

    Parameters
    ----------
    filename : str
        Name of the idl save from which to load the cosmology.
    structure_name : str, optional
        Name of the icosmo fiducial structure stored in the save file.

    Returns
    -------
    cosmo : cosmology
        cosmicpy cosmology corresponding to the icosmo input.
    """
    icosmo_file = readsav(filename)
    icosmo = icosmo_file.get(structure_name)

    h = icosmo['cosmo'][0]['h'][0]
    Omega_m = icosmo['cosmo'][0]['omega_m'][0]
    Omega_de = icosmo['cosmo'][0]['omega_l'][0]
    Omega_b = icosmo['cosmo'][0]['omega_b'][0]
    w0 = icosmo['cosmo'][0]['w0'][0]
    wa = icosmo['cosmo'][0]['wa'][0]
    tau = icosmo['cosmo'][0]['tau'][0]
    n = icosmo['cosmo'][0]['n'][0]
    sigma8 = icosmo['cosmo'][0]['sigma8'][0]

    cosmo = cosmicpy.cosmology(h=h, Omega_m=Omega_m, Omega_de=Omega_de,
                              Omega_b=Omega_b, w0=w0, wa=wa, tau=tau,
                              n=n, sigma8=sigma8)
    return cosmo

 def calc_specifics(self, Temp):
     """A separate method to calculate the specific line list properties based on an input T."""
     if self.specs_calced == 0:
         #make sure we don't inadvertently try and do this twice
         if self.ll_name == 'HITRAN04':
             self.Temp = Temp
             self.specs_calced = 1
             #lets make sure the relevant temperature is now carried around with the linelist.                
             
             props = HT04_globals(self.spec, self.iso)
             
             if Temp == 296.0 and self.ll_name == 'HITRAN04':
                 Q=props.Q296
             else:
                 Q=getQ(self.spec, self.iso, self.ll_name, Temp)    
             
  
             E_temp = -1.0 * self.epp * c2 / Temp
             #print E_temp
             w_temp = -1.0 * self.wave * c2 / Temp
             #print w_temp
             self.strength = self.strength * (props.abund/ Q) * (np.exp(E_temp) * (1.0-np.exp(w_temp))) * apc.c.cgs.value
             #I have no idea why Jan multiplies by C here, but he does, so lets copy it.
             
             strengths_jan = readsav('/home/dstock/sf/idl/code/ff.xdr')

def grabdate(d):
  global rundir, srcdir, outdir
  # Limit output to one line per date. 
  status(d)
  # Loop over both probes. 
  for p in ('a', 'b'):
    status(p)
    # Nuke the run directory. Leave stdout and stderr. 
    [ os.remove(x) for x in os.listdir(rundir) if x not in ('stdoe.txt',) ]
    # Create and execute an IDL script to grab position, electric field, and
    # magnetic field data for the day and and dump it into a sav file. 
    out, err = spedas( idlcode(probe=p, date=d) )
    # Make sure there's somewhere to store the pickles. 
    pkldir = outdir + d.replace('-', '') + '/' + p + '/'
    if not os.path.exists(pkldir):
      os.makedirs(pkldir)
    # Read in the IDL output. 
    if not os.path.exists('temp.sav'):
      status('X')
      continue
    else:
      temp = io.readsav('temp.sav')
    # Rewrite the data as pickles. (Pickles are Python data files. They are
    # reasonably efficient in terms of both storage size and load time.)
    for key, arr in temp.items():
      with open(pkldir + key + '.pkl', 'wb') as handle:
        pickle.dump(arr, handle, protocol=-1)
    # Acknowledge successful date access. 
    status('OK')
  # Move to the next line. 
  return status()

def _get_dusty_array(y_window, x_window):
    """
    Returns the sliced array of dusty pixels
    """
    url = darts + 'data/cal/dp/dusty_pixels.sav'
    http_down = urllib.urlretrieve(url)
    dusties = readsav(http_down[0]).dp_data
    return dusties[y_window[0]:y_window[1], x_window[0]: x_window[1]]

    def __init__(self, root, output, name_of_variable):

# Only allocate needed memory
        config = tf.ConfigProto()
        config.gpu_options.allow_growth=True
        session = tf.Session(config=config)
        ktf.set_session(session)

        self.root = root
        self.nx = 576
        self.ny = 576
        self.n_times = 2
        self.n_filters = 64
        self.batch_size = 1
        self.n_conv_layers = 20
        self.stride = 1
        self.skip_frequency = 2
        self.n_frames = 1        
        self.output = output
        self.name_of_variable = name_of_variable

        telescope_radius = 0.5 * 0.965 * u.meter
        pixel_size = 0.02759 * u.arcsec / u.pixel
        fov = 1152 * u.pixel
        lambda0 = 500 * u.nm
        imax = imax_degradation(telescope_radius, pixel_size, fov)
        imax.compute_psf(lambda0)


        res = io.readsav('/net/viga/scratch1/deepLearning/opticalFlow/mancha/c3d_1152_cont4_4bin_012000_continuum.sav')['continuum']

        self.images = np.zeros((2,576,576), dtype='float32')

# 576 pixels are obtained by resampling 1152 pixels of 0.02759 "/px to 0.0545 "/px for IMaX
        self.images[0,:,:] = congrid.resample(imax.apply_psf(res[0,:,:]), (576, 576))
        self.images[1,:,:] = congrid.resample(imax.apply_psf(res[1,:,:]), (576, 576))

        res = io.readsav('/net/viga/scratch1/deepLearning/opticalFlow/mancha/c3d_1152_cont4_4bin_012000.isotau.sav')

        self.vx = np.zeros((3,576,576), dtype='float32')
        self.vy = np.zeros((3,576,576), dtype='float32')

        for i in range(3):
            self.vx[i,:,:] = congrid.resample(imax.apply_psf(res['vx'][i,:,:]), (576, 576))
            self.vy[i,:,:] = congrid.resample(imax.apply_psf(res['vy'][i,:,:]), (576, 576))