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# filter_design.normalize

All Samples(9)  |  Call(6)  |  Derive(0)  |  Import(3)

```#

from .filter_design import tf2zpk, zpk2tf, normalize, freqs
import numpy
from numpy import product, zeros, array, dot, transpose, ones, \
```
```    #   A, B, C, and D follow quite naturally.
#
num, den = normalize(num, den)   # Strips zeros, checks arrays
nn = len(num.shape)
if nn == 1:
```
```        N = len(args)
if N == 2:  # Numerator denominator transfer function input
self._num, self._den = normalize(*args)
self._update(N)
self.inputs = 1
```

```#

from filter_design import tf2zpk, zpk2tf, normalize
import numpy
from numpy import product, zeros, array, dot, transpose, ones, \
```
```    #   A, B, C, and D follow quite naturally.
#
num, den = normalize(num, den)   # Strips zeros, checks arrays
nn = len(num.shape)
if nn == 1:
```
```        N = len(args)
if N == 2:  # Numerator denominator transfer function input
self.__dict__['num'], self.__dict__['den'] = normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = tf2zpk(*args)
```

```#

from filter_design import tf2zpk, zpk2tf, normalize
import numpy
from numpy import product, zeros, array, dot, transpose, ones, \
```
```    #   A, B, C, and D follow quite naturally.
#
num, den = normalize(num, den)   # Strips zeros, checks arrays
nn = len(num.shape)
if nn == 1:
```
```        N = len(args)
if N == 2:  # Numerator denominator transfer function input
self.__dict__['num'], self.__dict__['den'] = normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = tf2zpk(*args)
```