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我有一个适合格式的伽马射线图（具有表面亮度的图像）以及 .hpx 作为 Aladin 转换器的输出。

我希望计算角功率谱。如何创建可读的文件 healpy.anafast？我似乎弄错了数据格式（TypeErrors）。

我试过的伽马射线图像之一是费米银河漫反射。该文件是一个公共 LAT 银河漫反射贴图，名称为gll_iem_v02_P6_V11_DIFFUSE.fit：

http://fermi.gsfc.nasa.gov/ssc/data/access/lat/BackgroundModels.html

我在使用时粘贴了下面的代码，但它本质上是plot_wmap_power_spectra在astroml上调用的脚本：

    """
    WMAP power spectrum analysis with HealPy
    ----------------------------------------

    This demonstrates how to plot and take a power spectrum of the WMAP data
    using healpy, the python wrapper for healpix.  Healpy is available for
    download at the `github site <https://github.com/healpy/healpy>`_
    """
    # Author: Jake VanderPlas <vanderplas@astro.washington.edu>
    # License: BSD
    #   The figure is an example from astroML: see http://astroML.github.com
    import numpy as np
    from matplotlib import pyplot as plt

    # warning: due to a bug in healpy, importing it before pylab can cause
    #  a segmentation fault in some circumstances.
    import pylab
    import healpy as hp
    ###
    from astroML.datasets import fetch_wmap_temperatures
    ###

    #------------------------------------------------------------
    # Fetch the data
    ###
    wmap_unmasked = fetch_wmap_temperatures(masked=False)

    #PredictedSurfaceFluxFromModelMap =           np.arange(hp.read_map('PredictedSurfaceFluxFromModelMap.hpx[1]'))
    PredictedSurfaceFluxFromModelMap =   hp.read_map('gll_iem_v02_p6_V11_DIFFUSE.fit',dtype=np.float,verbose=True)
    #PredictedSurfaceFluxFromModelMap = hp.read_map('all.fits',dtype=np.float,verbose=True)
    #cl_out =  hp.read_cl('PredictedSurfaceFluxFromModelMap.hpx',dtype=np.float)#,verbose=True)
    wmap_masked = fetch_wmap_temperatures(masked=True)
    ###
    white_noise = np.ma.asarray(np.random.normal(0, 0.062, wmap_masked.shape))
    len(cl_out)

    #------------------------------------------------------------
    # plot the unmasked map
    fig = plt.figure(1)
    #hp.mollview(wmap_unmasked, min=-1, max=1, title='Unmasked map',
    #            fig=1, unit=r'$\Delta$T (mK)')
    ########----------------
    ##hp.mollview(PredictedSurfaceFluxFromModelMap, min=-1, max=1, title='Unmasked map',
    ##            fig=1, unit=r'$\Delta$T (mK)')
    ########----------------
    #------------------------------------------------------------
    # plot the masked map
    #  filled() fills the masked regions with a null value.
    ########----------------
    #fig = plt.figure(2)
    #hp.mollview(wmap_masked.filled(), title='Masked map',
    #            fig=2, unit=r'$\Delta$T (mK)')

    ########----------------
    #------------------------------------------------------------
    # compute and plot the power spectrum
    ########----------------
    #cl = hp.anafast(wmap_masked.filled(), lmax=1024)
    cl = hp.anafast(PredictedSurfaceFluxFromModelMap, lmax=1024)
    #cl = cl_out
    ########----------------
    ell = np.arange(len(cl))

    cl_white = hp.anafast(white_noise, lmax=1024)

    fig = plt.figure(3)
    ax = fig.add_subplot(111)
    ax.scatter(ell, ell * (ell + 1) * cl,
       s=4, c='black', lw=0,
       label='data')
     ax.scatter(ell, ell * (ell + 1) * cl_white,
       s=4, c='gray', lw=0,
       label='white noise')

     ax.set_xlabel(r'$\ell$')
     ax.set_ylabel(r'$\ell(\ell+1)C_\ell$')
     ax.set_title('Angular Power (not mask corrected)')
     ax.legend(loc='upper right')
     ax.grid()
     ax.set_xlim(0, 1100)

     plt.show()
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1 回答 1

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我已经将你的地图也上传到了 Figshare，将来可能会在那里提供。

一旦你有了 HEALPIx 格式的地图，就很容易阅读它healpy：

import healpy as hp
m = hp.ma(hp.read_map("gll_iem_v02_p6_V11_DIFFUSE.hpx"))

掩码 NaN 像素：

m.mask = np.isnan(m)

绘制它：

hp.mollview(m, min=-1e-5, max=1e-5, xsize=2000)
title("gll_iem_v02_p6_V11_DIFFUSE")

莫尔维尤

计算并绘制角功率谱：

plt.loglog(hp.anafast(m))

功率谱

另请参阅 IPython 笔记本： http: //nbviewer.ipython.org/7553252

于 2013-11-19T21:56:01.233 回答