Python 使用numpy.logspace（）时出错：如何生成在对数刻度上均匀分布的数字

我正在尝试使用

numpy.logspace（）

从

1e-10

到

1e-14

生成50个值

我得到的输出不正确：

[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]

我的其他选择是什么

对于，边界以基数的指数形式给出，基数默认为

10.0

：

>>> import numpy as np
>>> np.logspace(-10, -14, 50)
array([  1.00000000e-10,   8.28642773e-11,   6.86648845e-11,
         5.68986603e-11,   4.71486636e-11,   3.90693994e-11,
         3.23745754e-11,   2.68269580e-11,   2.22299648e-11,
         1.84206997e-11,   1.52641797e-11,   1.26485522e-11,
         1.04811313e-11,   8.68511374e-12,   7.19685673e-12,
         5.96362332e-12,   4.94171336e-12,   4.09491506e-12,
         3.39322177e-12,   2.81176870e-12,   2.32995181e-12,
         1.93069773e-12,   1.59985872e-12,   1.32571137e-12,
         1.09854114e-12,   9.10298178e-13,   7.54312006e-13,
         6.25055193e-13,   5.17947468e-13,   4.29193426e-13,
         3.55648031e-13,   2.94705170e-13,   2.44205309e-13,
         2.02358965e-13,   1.67683294e-13,   1.38949549e-13,
         1.15139540e-13,   9.54095476e-14,   7.90604321e-14,
         6.55128557e-14,   5.42867544e-14,   4.49843267e-14,
         3.72759372e-14,   3.08884360e-14,   2.55954792e-14,
         2.12095089e-14,   1.75751062e-14,   1.45634848e-14,
         1.20679264e-14,   1.00000000e-14])

>>> np.logspace(-10, -14, num=50, base=10)
array([1.00000000e-10, 8.28642773e-11, 6.86648845e-11, 5.68986603e-11,
       4.71486636e-11, 3.90693994e-11, 3.23745754e-11, 2.68269580e-11,
       2.22299648e-11, 1.84206997e-11, 1.52641797e-11, 1.26485522e-11,
       1.04811313e-11, 8.68511374e-12, 7.19685673e-12, 5.96362332e-12,
       4.94171336e-12, 4.09491506e-12, 3.39322177e-12, 2.81176870e-12,
       2.32995181e-12, 1.93069773e-12, 1.59985872e-12, 1.32571137e-12,
       1.09854114e-12, 9.10298178e-13, 7.54312006e-13, 6.25055193e-13,
       5.17947468e-13, 4.29193426e-13, 3.55648031e-13, 2.94705170e-13,
       2.44205309e-13, 2.02358965e-13, 1.67683294e-13, 1.38949549e-13,
       1.15139540e-13, 9.54095476e-14, 7.90604321e-14, 6.55128557e-14,
       5.42867544e-14, 4.49843267e-14, 3.72759372e-14, 3.08884360e-14,
       2.55954792e-14, 2.12095089e-14, 1.75751062e-14, 1.45634848e-14,
       1.20679264e-14, 1.00000000e-14])

要绝对指定边界，可以使用：

>>> np.logspace(-10, -14, num=50, base=10)
array([1.00000000e-10, 8.28642773e-11, 6.86648845e-11, 5.68986603e-11,
       4.71486636e-11, 3.90693994e-11, 3.23745754e-11, 2.68269580e-11,
       2.22299648e-11, 1.84206997e-11, 1.52641797e-11, 1.26485522e-11,
       1.04811313e-11, 8.68511374e-12, 7.19685673e-12, 5.96362332e-12,
       4.94171336e-12, 4.09491506e-12, 3.39322177e-12, 2.81176870e-12,
       2.32995181e-12, 1.93069773e-12, 1.59985872e-12, 1.32571137e-12,
       1.09854114e-12, 9.10298178e-13, 7.54312006e-13, 6.25055193e-13,
       5.17947468e-13, 4.29193426e-13, 3.55648031e-13, 2.94705170e-13,
       2.44205309e-13, 2.02358965e-13, 1.67683294e-13, 1.38949549e-13,
       1.15139540e-13, 9.54095476e-14, 7.90604321e-14, 6.55128557e-14,
       5.42867544e-14, 4.49843267e-14, 3.72759372e-14, 3.08884360e-14,
       2.55954792e-14, 2.12095089e-14, 1.75751062e-14, 1.45634848e-14,
       1.20679264e-14, 1.00000000e-14])

>>> np.geomspace(1e-10, 1e-14, num=50)
array([1.00000000e-10, 8.28642773e-11, 6.86648845e-11, 5.68986603e-11,
       4.71486636e-11, 3.90693994e-11, 3.23745754e-11, 2.68269580e-11,
       2.22299648e-11, 1.84206997e-11, 1.52641797e-11, 1.26485522e-11,
       1.04811313e-11, 8.68511374e-12, 7.19685673e-12, 5.96362332e-12,
       4.94171336e-12, 4.09491506e-12, 3.39322177e-12, 2.81176870e-12,
       2.32995181e-12, 1.93069773e-12, 1.59985872e-12, 1.32571137e-12,
       1.09854114e-12, 9.10298178e-13, 7.54312006e-13, 6.25055193e-13,
       5.17947468e-13, 4.29193426e-13, 3.55648031e-13, 2.94705170e-13,
       2.44205309e-13, 2.02358965e-13, 1.67683294e-13, 1.38949549e-13,
       1.15139540e-13, 9.54095476e-14, 7.90604321e-14, 6.55128557e-14,
       5.42867544e-14, 4.49843267e-14, 3.72759372e-14, 3.08884360e-14,
       2.55954792e-14, 2.12095089e-14, 1.75751062e-14, 1.45634848e-14,
       1.20679264e-14, 1.00000000e-14])