Question

我加载了一个拟合文件并将fitsrec数据转换为numpy ndarray：

import pyfits
import os, numpy as np
dataPath ='irac1_dataset.fits'

hduTab=pyfits.open(dataPath)
data_rec = np.array(hduTab[1].data)
data=data_rec.view(np.float64).reshape(data_rec.shape + (-1,))

我发现数据中有一些nan在rec中不存在：

data_rec[3664]
(2.52953742092, 3.636058484, -3.0, 1.16584000133, 0.13033115092, 0.0545114121049, 0.0977915267677, 0.0861630982921, 0.0935291710016)
data[3664]
array([  8.01676073e+230,  -1.68253090e-183,   1.10670705e-320,
        -5.38247269e-235,               nan,   3.19504591e+186,
        -6.19704421e+125,  -1.40287783e+079,   1.94744862e+094])

并且，正如您所看到的，这些值会以显着的方式发生变化，如何实现？

关于hduTab [1] .data：

data_rec = hduTab[1].data
>>> data_rec.dtype
dtype((numpy.record, [('entr_35_1', '>f8'), ('kurt_5_1', '>f8'), ('skew_23_1', '>f8'), ('skew_35_1', '>f8'), ('mean_23_2', '>f8'), ('mean_35_2', '>f8'), ('stdDev_23_1', '>f8'), ('stdDev_35_1', '>f8'), ('pixVal', '>f8')]))

是一个numpy记录

Answer 1

它是＆gt; f8＆＃39;搞砸了你。

In [380]: dt= [('entr_35_1', '>f8'), ('kurt_5_1', '>f8'), ('skew_23_1', '>f8'), 
     ...: ('skew_35_1', '>f8'), ('mean_23_2', '>f8'), ('mean_35_2', '>f8'), ('st
     ...: dDev_23_1', '>f8'), ('stdDev_35_1', '>f8'), ('pixVal', '>f8')]

In [382]: np.dtype(dt)
Out[382]: dtype([('entr_35_1', '>f8'),....('pixVal', '>f8')])

In [383]: np.array([(2.52953742092, 3.636058484, -3.0, 1.16584000133, 0.13033115
     ...: 092, 0.0545114121049, 0.0977915267677, 0.0861630982921, 0.093529171001
     ...: 6)],dtype=dt)
Out[383]: 
array([ ( 2.52953742,  3.63605848, -3.,  1.16584,  0.13033115,  0.05451141,  0.09779153,  0.0861631,  0.09352917)], 
      dtype=[('entr_35_1', '>f8'), ('kurt_5_1', '>f8'), ('skew_23_1', '>f8'), ('skew_35_1', '>f8'), ('mean_23_2', '>f8'), ('mean_35_2', '>f8'), ('stdDev_23_1', '>f8'), ('stdDev_35_1', '>f8'), ('pixVal', '>f8')])
In [384]: x=_

float视图包含nan和无法识别的值：

In [385]: x.view(float)
Out[385]: 
array([  8.01676073e+230,  -1.68253090e-183,   1.10670705e-320,
        -5.38247269e-235,               nan,   3.19504591e+186,
        -6.19704421e+125,  -1.40287783e+079,   1.94744862e+094])

但>f8的视图与输入匹配：

In [386]: x.view('>f8')
Out[386]: 
array([ 2.52953742,  3.63605848, -3.        ,  1.16584   ,  0.13033115,
        0.05451141,  0.09779153,  0.0861631 ,  0.09352917])

然后，我可以使用astype转换为float，（显然是<f8）：

In [387]: _.astype(float)
Out[387]: 
array([ 2.52953742,  3.63605848, -3.        ,  1.16584   ,  0.13033115,
        0.05451141,  0.09779153,  0.0861631 ,  0.09352917])

In [389]: np.dtype('<f8')
Out[389]: dtype('float64')
In [390]: np.dtype('>f8')
Out[390]: dtype('>f8')

使用astype可能很棘手，但似乎如果我保持字段布局相同，我可以直接使用它。所以我可以用它来改变'>f8' to

In [407]: dt1= [('entr_35_1', '<f8'), ('kurt_5_1', '<f8'), ('skew_23_1', '<f8'),
     ...:  ('skew_35_1', '<f8'), ('mean_23_2', '<f8'), ('mean_35_2', '<f8'), ('s
     ...: tdDev_23_1', '<f8'), ('stdDev_35_1', '<f8'), ('pixVal', '<f8')]
In [408]: x.astype(dt1)
Out[408]: 
array([ ( 2.52953742,  3.63605848, -3.,  1.16584,  0.13033115,  0.05451141,  0.09779153,  0.0861631,  0.09352917)], 
      dtype=[('entr_35_1', '<f8'), ('kurt_5_1', '<f8'), ('skew_23_1', '<f8'), ('skew_35_1', '<f8'), ('mean_23_2', '<f8'), ('mean_35_2', '<f8'), ('stdDev_23_1', '<f8'), ('stdDev_35_1', '<f8'), ('pixVal', '<f8')])

我仍然需要使用view来更改字段数量：

In [409]: x.astype(dt1).view(float)
Out[409]: 
array([ 2.52953742,  3.63605848, -3.        ,  1.16584   ,  0.13033115,
        0.05451141,  0.09779153,  0.0861631 ,  0.09352917])

从适合rec转换为ndaray时丢失的信息

1 个答案: