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138 lines
1.6 KiB
Markdown
138 lines
1.6 KiB
Markdown
# 记录数组
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记录数组（`record array`）与结构数组类似：
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In [1]:
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```py
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import numpy as np
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```
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质点类型：
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In [2]:
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```py
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partical_dtype = np.dtype([('mass', 'float'),
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('velocity', 'float')])
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```
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生成记录数组要使用 `numpy.rec` 里的 `fromrecords` 方法：
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In [3]:
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```py
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from numpy import rec
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particals_rec = rec.fromrecords([(1,1), (1,2), (2,1), (1,3)],
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dtype = partical_dtype)
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```
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In [4]:
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```py
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particals_rec
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```
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Out[4]:
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```py
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rec.array([(1.0, 1.0), (1.0, 2.0), (2.0, 1.0), (1.0, 3.0)],
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dtype=[('mass', '<f8'), ('velocity', '<f8')])
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```
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在记录数组中，域可以通过属性来获得：
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In [5]:
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```py
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particals_rec.mass
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```
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Out[5]:
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```py
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array([ 1., 1., 2., 1.])
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```
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也可以通过域来查询：
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In [6]:
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```py
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particals_rec['mass']
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```
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Out[6]:
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```py
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array([ 1., 1., 2., 1.])
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```
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不过，记录数组的运行效率要比结构化数组要慢一些。
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也可以通过将一个结构化数组看成记录数组：
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In [7]:
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```py
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particals = np.array([(1,1), (1,2), (2,1), (1,3)],
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dtype = partical_dtype)
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```
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使用 `view` 方法看成 `recarray` ：
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In [8]:
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```py
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particals_rec = particals.view(np.recarray)
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```
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In [9]:
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```py
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particals_rec.mass
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```
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Out[9]:
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```py
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array([ 1., 1., 2., 1.])
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```
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In [10]:
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```py
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particals_rec.velocity
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```
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Out[10]:
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```py
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array([ 1., 2., 1., 3.])
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```
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对于自定义的类型，可以通过它的 `names` 属性查看它有哪些域：
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In [11]:
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```py
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particals.dtype.names
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```
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Out[11]:
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```py
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('mass', 'velocity')
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``` |