#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('pylab', 'inline')
from pyannote.core import notebook


# # SlidingWindowFeature (`pyannote.core.feature.SlidingWindowFeature`)

# In[2]:


from pyannote.core import SlidingWindowFeature, SlidingWindow


# **SlidingWindowFeature** are used to manage feature vectors extracted on a sliding window (e.g. MFCC in audio processing).

# In[3]:


# one 4-dimensional feature vector extracted every 100ms from a 200ms window
frame = SlidingWindow(start=0.0, step=0.100, duration=0.200)

# random for illustration purposes
data = np.random.randn(100, 4)

features = SlidingWindowFeature(data, frame)


# ### Cropping

# You may use `crop` to extract a temporal subset:

# In[4]:


help(features.crop)


# In[5]:


from pyannote.core import Segment
features.crop(Segment(2, 3))


# ### Need help?

# You can always try the following...  
# Who knows? It might give you the information you are looking for!

# In[6]:


help(SlidingWindowFeature)