"""
Kraken module example
=======================

In **Sequana**, we provide tools to quickly assess the taxonomic content of
a reads (FastQ). It is based on the Kraken and Krona software. **Sequana**
bridges the gap between those tools, Kraken databases and a simple interface to
get a quick taxonomic overview of the FastQ content.

For more information, please see the :mod:`sequana.kraken` module documentation.
Note that this feature is also part of the :ref:`pipeline_quality_control` pipeline. 

Although we will use the **Sequana** library hereafter, note that there is also
a standalone application named **sequana_taxonomy**.

"""
###########################################################
# Context
# ----------
# running the kraken analysis takes some time so we do provide
# an output directly. The output file can be analysed within 
# Sequana to plot a Pie chart but also Javascript Krona results
# The kraken format is as follows::
#
#    C   HISEQ:426:C5T65ACXX:5:2301:5633:7203    11234   203 0:2 11234:1 0:1 11234:1 0:2 11234:1 0:13 11234:1 0:1 11234:1 0:3 11234:1 0:16 11234:1 0:5 11234:1 0:6 11234:1 0:13 A:31 0:33 11234:1 0:29 11234:1 0:7
#    C   HISEQ:426:C5T65ACXX:5:2301:5815:7120    11234   203 0:4 11234:1 0:12 11234:1 0:22 11234:1 0:1 0 11234:1 0:5 11234:1 0:7 11234:1 0:5 A:31 0:3 11234:1 0:22 11234:1 0:18 11234:1 0:24 11234:1
#
# Each row correspond to a read in the FastQ file(s). The first column
# is either C (classified) or U (unclassified) and the third column contains
# the taxon the most relevant.
#
# The taxon are not readable so we first need to get the scientific names
# Besides, the lineage would be useful. This is done in Sequana using
# the :class:`sequana.kraken.KrakenResults`. See following example. 



##############################################
# Example 
# --------
#
# In the following example, we use the results of a kraken analysis. The
# original toy data files contains 1500 reads mostly related to Measles virus
#
from sequana import KrakenResults, sequana_data
test_file = sequana_data("test_kraken.out", "testing")
k = KrakenResults(test_file)
df = k.plot(kind='pie')
print(df)


####################################################
# Note that only a subset of taxons are shown in the pie chart
# that is those that cover at least 1% of the total reads. Others
# are put together and labelled "others"
#
# A more interactive plot can be obtained using Krona:
from sequana import KrakenResults, sequana_data
test_file = sequana_data("test_kraken.out", "testing")
k = KrakenResults(test_file)
k.to_js(onweb=False)  # The output filame is krona.html by default

#######################################################################
# An example is available in  `Krona example <../_static/krona.html>`_

