basic

alpsplot.utils.normalize(x, _min=None, _max=None, tgt_min=0.0, tgt_max=1.0)

Normalize a numpy.ndarray into desired range.

$$\frac{tgt\_max - tgt\_min}{\_max - \_min}(x - \_min) + tgt\_min$$

Parameters:

• x (numpy.ndarray) – The unnormalized array.

• _min (float) – The lower bound of original x. Defaults to min(x).

• _max (float) – The upper bound of original x. Defaults to max(x).

• tgt_min (float) – The lower bound of original x. Defaults to 0.0.

• tgt_max (float) – The upper bound of original x. Defaults to 1.0.

Returns:

numpy.ndarray – A normalized array.

Example:

>>> import numpy as np
>>> from alpsplot.utils import normalize
>>>
>>> x = np.array([-10, -5, 0, 5, 10])
>>> normalize(x)
array([0.  , 0.25, 0.5 , 0.75, 1.  ])
>>> normalize(x, _min=-100, _max=100)
array([0.45 , 0.475, 0.5  , 0.525, 0.55 ])
>>> normalize(x, tgt_min=-1, tgt_max=1)
array([-1. , -0.5,  0. ,  0.5,  1. ])

alpsplot.utils.avg_smooth(x, window=3, method='mean')

Average smooth a numpy.ndarray using a given window size.

Paddings are added at head and tail.

Parameters:

• x (numpy.ndarray) – The unsmoothed array.

• window (int) – The window size (number of points). Defaults to 3.

• method (str) – Choose from ['mean', 'median', 'min', 'max']. Defaults to 'mean'.

Returns:

numpy.ndarray – A smoothed array.

Example:

import numpy as np
import matplotlib.pyplot as plt
from alpsplot.utils import avg_smooth

x = np.arange(10, step=1)
y = x + 3 * np.sin(x)
y2 = avg_smooth(y)
y3 = avg_smooth(y, window=5)

plt.plot(x, y, color='red', label='original')
plt.plot(x, y2, color='green', label='window = 3')
plt.plot(x, y3, color='blue', label='window = 5')
plt.legend()
plt.show()

alpsplot.utils.monotone(x, increase=True, reverse=False)

Monotonize a numpy.ndarray.

All non-monotonic points would be clipped as previous value.

Parameters:

• x (numpy.ndarray) – The non-monotonic array.

• increase (bool) – Monotonically increase or decrease. Defaults to True.

• reverse (bool) – Enable to clip from right to left. Defaults to False.

Returns:

numpy.ndarray – A monotonic array.

Example:

import numpy as np
import matplotlib.pyplot as plt
from alpsplot.utils import monotone

x = np.arange(10, step=0.1)
y = x + 3 * np.sin(x)
y2 = monotone(y)

plt.plot(x, y, color='blue', label='original')
plt.plot(x, y2, color='red', label='monotone')
plt.legend()
plt.show()