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

# <img style="float: right;" src="images/hyperstream.svg">
# 
# # HyperStream Tutorial 3: Stream composition
# 
# We will be ussing the tool created in the previous tutorial and we will compose the output of the stream with a new one.

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')

import sys
from datetime import datetime

sys.path.append("../") # Add parent dir in the Path

from hyperstream import HyperStream
from hyperstream import TimeInterval
from hyperstream.utils import UTC

from utils import plot_high_chart

get_ipython().run_line_magic('watermark', '-v -m -p hyperstream -g')


# In[2]:


hs = HyperStream(loglevel=20)
print hs

reader_tool = hs.plugins.example.tools.csv_reader('data/sea_ice.csv')
sea_ice_stream = hs.channel_manager.memory.get_or_create_stream("sea_ice")


ti = TimeInterval(datetime(1990, 1, 1).replace(tzinfo=UTC), datetime(2012, 1, 1).replace(tzinfo=UTC))

reader_tool.execute(sources=[], sink=sea_ice_stream, interval=ti)
for key, value in sea_ice_stream.window().items()[:10]:
    print '[%s]: %s' % (key, value)


# ## Stream composition
# 
# We can compose a chain of streams using different tools to get a new stream. As an example, we can use the tool **read_csv** to generate a stream from a csv file. Then, we can apply the tool **list_mean**, that computes the mean of all the values of each instance of a stream, and outputs a new stream. Finally, we can define the new stream to store the output in **memory** or in a  **MongoDB** database. In this case, we will store the final Stream in the MongoDB database.
# 
# |~stream||tool||stream||tool||stream|
# |:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
# | csv_file | $\rightarrow$ | reader_tool | $\rightarrow$ | sea_ice_stream | $\rightarrow$ | list_mean_tool | $\rightarrow$ | sea_ice_mean_stream |
# |filesystem||memory||memory||memory||MongoDB|

# In[3]:


list_mean_tool = hs.tools.list_mean()

sea_ice_means_stream = hs.channel_manager.mongo.get_or_create_stream('sea_ice_means')
list_mean_tool.execute(sources=[sea_ice_stream], sink=sea_ice_means_stream, interval=ti)

for key, value in sea_ice_means_stream.window().items()[:10]:
    print '[%s]: %s' % (key, value)


# ## Visualization
# 
# We can now plot all the values of the last computed window. In this case there is only one window with all the data computed by the tool.

# In[4]:


my_time, my_data = zip(*[(key.__str__(), value) for key, value in sea_ice_means_stream.window().items()])

plot_high_chart(my_time, my_data, type="high_stock", 
                title='Mean of sea levels in the Artic and the Antartica', yax='meters')


# In[ ]: