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

# In[1]:


get_ipython().run_line_magic('pylab', 'inline')
import random
random.seed(1337)


# # Documentation for `pyannote.core`

# ## Install

# `pyannote.core` package can be installed the easy way:
# ```bash
# $ pip install pyannote.core
# ```
# 
# Adding support for Jupyter/IPython Notebook graphical representations is as easy as:
# ```bash
# $ pip install pyannote.core[notebook]
# ```

# ## Available data structures

# In[2]:


from pyannote.core import *


# ### Segment ([pyannote.core.segment](pyannote.core.segment.ipynb))

# In[3]:


segment = Segment(start=0, end=10)
segment


# ### Timeline ([pyannote.core.timeline](pyannote.core.timeline.ipynb))

# In[4]:


timeline = Timeline([Segment(1, 5), Segment(6, 8), Segment(12, 18), Segment(7, 20)])
timeline


# ### Annotation ([pyannote.core.annotation](pyannote.core.annotation.ipynb))

# In[5]:


annotation = Annotation()
annotation[Segment(1, 5)] = 'John'
annotation[Segment(6, 8)] = 'Jack'
annotation[Segment(12, 18)] = 'John'
annotation[Segment(7, 20)] = 'Alice'
annotation


# ### Feature ([pyannote.core.feature](pyannote.core.feature.ipynb))

# In[6]:


# one 4-dimensional feature vector extracted every 100ms from a 200ms window
frame = SlidingWindow(start=0.0, step=0.100, duration=0.200)
data = np.random.randn(100, 4)
features = SlidingWindowFeature(data, frame)


# In[9]:


features


# In[7]:


features.crop(Segment(2, 3))


# ### Scores ([pyannote.core.scores](pyannote.core.scores.ipynb))

# In[8]:


scores = Scores()
scores[Segment(3, 5), '_', 'Penny'] = 0.8
scores[Segment(3, 5), '_', 'Leonard'] = 0.15
scores[Segment(3, 5), '_', 'Sheldon'] = 0.05
scores[Segment(5.5, 7), '_', 'Penny'] = 0.4
scores[Segment(5.5, 7), '_', 'Leonard'] = 0.5
scores[Segment(5.5, 7), '_', 'Sheldon'] = 0.1
scores[Segment(8, 10), '_', 'Penny'] = 0.4
scores[Segment(8, 10), '_', 'Leonard'] = 0.25
scores[Segment(8, 10), '_', 'Sheldon'] = 0.35
scores


# ## Contribute

# `pyannote.core` is on [Github](https://github.com/pyannote/pyannote-core).  
#  
#  
#  Fork and pull requests are very welcome!