"""
Diversity calculations (:mod:`skbio.diversity`)
===============================================

.. currentmodule:: skbio.diversity

This package provides functionality for analyzing biological diversity. It
implements metrics of alpha and beta diversity, and provides two "driver
functions" that are intended to be the primary interface for computing alpha
and beta diversity with scikit-bio. Functions are additionally provided that
support discovery of the available diversity metrics. This document provides a
high-level discussion of how to work with the ``skbio.diversity`` module, and
should be the first document you read before working with the module.

Driver functions
----------------

The driver functions, ``skbio.diversity.alpha_diversity`` and
``skbio.diversity.beta_diversity``, are designed to compute alpha diversity for
one or more samples, or beta diversity for one or more pairs of samples. The
diversity driver functions accept a matrix containing vectors of frequencies of
OTUs within each sample.

We use the term "OTU" here very loosely, as these can in practice represent
diverse feature types including bacterial species, genes, and metabolites. The
term "sample" is also loosely defined for these purposes. These are intended to
represent a single unit of sampling, and as such what a single sample
represents can vary widely. For example, in a microbiome survey, these could
represent all 16S rRNA gene sequences from a single oral swab. In a comparative
genomics study on the other hand, a sample could represent an individual
organism's genome.

Each frequency in a given vector represents the number of individuals observed
for a particular OTU. We will refer to the frequencies associated with a single
sample as a *counts vector* or ``counts`` throughout the documentation. Counts
vectors are `array_like`: anything that can be converted into a 1-D numpy array
is acceptable input. For example, you can provide a numpy array or a native
Python list and the results will be identical. As mentioned above, the driver
functions accept one or more of these vectors (representing one or more
samples) in a matrix which is also `array_like`. Each row in the matrix
represents a single sample's count vector, so that rows represent samples and
columns represent OTUs.

Some diversity metrics incorporate relationships between the OTUs in their
computation through reference to a phylogenetic tree. These metrics
additionally take a ``skbio.TreeNode`` object and a list of OTU identifiers
mapping the values in the counts vector to tips in the tree.

The driver functions are optimized so that computing a diversity metric more
than one time (i.e., for more than one sample for alpha diversity metrics, or
more than one pair of samples for beta diversity metrics) is often much faster
than repeated calls to the metric. For this reason, the driver functions take
matrices of counts vectors rather than a single counts vector for alpha
diversity metrics or two counts vectors for beta diversity metrics. The
``alpha_diversity`` driver function will thus compute alpha diversity for all
counts vectors in the matrix, and the ``beta_diversity`` driver function will
compute beta diversity for all pairs of counts vectors in the matrix.

Input validation
----------------

The driver functions perform validation of input by default. Validation can be
slow so it is possible to disable this step by passing ``validate=False``. This
can be dangerous however. If invalid input is encountered when validation is
disabled it can result in difficult-to-interpret error messages or incorrect
results. We therefore recommend that users are careful to ensure that their
input data is valid before disabling validation.

The conditions that the driver functions validate follow. If disabling
validation, users should be confident that these conditions are met.

* the data in the counts vectors can be safely cast to integers
* there are no negative values in the counts vectors
* each counts vector is one dimensional
* the counts matrix is two dimensional
* all counts vectors are of equal length

Additionally, if a phylogenetic diversity metric is being computed, the
following conditions are also confirmed:

* the provided OTU identifiers are all unique
* the length of each counts vector is equal to the number of OTU identifiers
* the provided tree is rooted
* the tree has more than one node
* all nodes in the provided tree except for the root node have branch lengths
* all tip names in the provided tree are unique
* all provided OTU identifiers correspond to tip names in the provided tree

Count vectors
-------------

There are different ways that count vectors are represented in the ecological
literature and in related software. The diversity measures provided here
*always* assume that the input contains abundance data: each count represents
the number of individuals observed for a particular OTU in the sample. For
example, if you have two OTUs, where three individuals were observed from the
first OTU and only a single individual was observed from the second OTU, you
could represent this data in the following forms (among others).

As a vector of counts. This is the expected type of input for the diversity
measures in this module. There are 3 individuals from the OTU at index 0, and 1
individual from the OTU at index 1:

>>> counts = [3, 1]

As a vector of indices. The OTU at index 0 is observed 3 times, while the
OTU at index 1 is observed 1 time:

>>> indices = [0, 0, 0, 1]

As a vector of frequencies. We have 1 OTU that is a singleton and 1 OTU that
is a tripleton. We do not have any 0-tons or doubletons:

>>> frequencies = [0, 1, 0, 1]

Always use the first representation (a counts vector) with this module.

Specifying a diversity metric
-----------------------------

The driver functions take a parameter, ``metric``, that specifies which
diversity metric should be applied. The value that you provide for ``metric``
can be either a string (e.g., ``"faith_pd"``) or a function (e.g.,
``skbio.diversity.alpha.faith_pd``). The metric should generally be passed as a
string, as this often uses an optimized version of the metric. For example,
passing  ``metric="faith_pd"`` (a string) to ``alpha_diversity`` will be tens
of times faster than passing ``metric=skbio.diversity.alpha.faith_pd`` (a
function) when computing Faith's PD on about 100 samples.  Similarly, passing
``metric="unweighted_unifrac"`` (a string) will be hundreds of times
faster than passing ``metric=skbio.diversity.beta.unweighted_unifrac`` (a
function) when computing unweighted UniFrac on about 100 samples. The latter
may be faster if computing only one alpha or beta diversity value, but in
these cases the run times will likely be so small that the difference will be
negligible. **We therefore recommend that you always pass the metric as a
string when possible.**

Passing a metric as a string will not be possible if the metric you'd like to
run is not one that scikit-bio knows about. This might be the case, for
example, if you're applying a custom metric that you've developed. To discover
the metric names that scikit-bio knows about as strings that can be passed as
``metric`` to ``alpha_diversity`` or ``beta_diversity``, you can call
``get_alpha_diversity_metrices`` or ``get_beta_diversity_metrics``,
respectively. These functions return lists of alpha and beta diversity metrics
that are implemented in scikit-bio. There may be additional metrics that can be
passed as strings which won't be listed here, such as those implemented in
``scipy.spatial.distance.pdist``.

Subpackages
-----------

.. autosummary::
   :toctree: generated/

   alpha
   beta

Functions
---------

.. autosummary::
   :toctree: generated/

    alpha_diversity
    beta_diversity
    get_alpha_diversity_metrics
    get_beta_diversity_metrics

Examples
--------

Create a matrix containing 6 samples (rows) and 7 OTUs (columns):

.. plot::
   :context:

   >>> data = [[23, 64, 14, 0, 0, 3, 1],
   ...         [0, 3, 35, 42, 0, 12, 1],
   ...         [0, 5, 5, 0, 40, 40, 0],
   ...         [44, 35, 9, 0, 1, 0, 0],
   ...         [0, 2, 8, 0, 35, 45, 1],
   ...         [0, 0, 25, 35, 0, 19, 0]]
   >>> ids = list('ABCDEF')

   First, we'll compute observed OTUs, an alpha diversity metric, for each
   sample using the ``alpha_diversity`` driver function:

   >>> from skbio.diversity import alpha_diversity
   >>> adiv_obs_otus = alpha_diversity('observed_otus', data, ids)
   >>> adiv_obs_otus
   A    5
   B    5
   C    4
   D    4
   E    5
   F    3
   dtype: int64

   Next we'll compute Faith's PD on the same samples. Since this is a
   phylogenetic diversity metric, we'll first create a tree and an ordered
   list of OTU identifiers.

   >>> from skbio import TreeNode
   >>> from io import StringIO
   >>> tree = TreeNode.read(StringIO(
   ...                      '(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,'
   ...                      '(OTU4:0.75,(OTU5:0.5,(OTU6:0.5,OTU7:0.5):0.5):'
   ...                      '0.5):1.25):0.0)root;'))
   >>> otu_ids = ['OTU1', 'OTU2', 'OTU3', 'OTU4', 'OTU5', 'OTU6', 'OTU7']
   >>> adiv_faith_pd = alpha_diversity('faith_pd', data, ids=ids,
   ...                                 otu_ids=otu_ids, tree=tree)
   >>> adiv_faith_pd
   A    6.75
   B    7.00
   C    6.25
   D    5.75
   E    6.75
   F    5.50
   dtype: float64

   Now we'll compute Bray-Curtis distances, a beta diversity metric, between
   all pairs of samples. Notice that the ``data`` and ``ids`` parameters
   provided to ``beta_diversity`` are the same as those provided to
   ``alpha_diversity``.

   >>> from skbio.diversity import beta_diversity
   >>> bc_dm = beta_diversity("braycurtis", data, ids)
   >>> print(bc_dm)
   6x6 distance matrix
   IDs:
   'A', 'B', 'C', 'D', 'E', 'F'
   Data:
   [[ 0.          0.78787879  0.86666667  0.30927835  0.85714286  0.81521739]
    [ 0.78787879  0.          0.78142077  0.86813187  0.75        0.1627907 ]
    [ 0.86666667  0.78142077  0.          0.87709497  0.09392265  0.71597633]
    [ 0.30927835  0.86813187  0.87709497  0.          0.87777778  0.89285714]
    [ 0.85714286  0.75        0.09392265  0.87777778  0.          0.68235294]
    [ 0.81521739  0.1627907   0.71597633  0.89285714  0.68235294  0.        ]]

   Next, we'll compute weighted UniFrac distances between all pairs of samples.
   Because weighted UniFrac is a phylogenetic beta diversity metric, we'll need
   to pass the ``skbio.TreeNode`` and list of OTU ids that we created above.
   Again, these are the same values that were provided to ``alpha_diversity``.

   >>> wu_dm = beta_diversity("weighted_unifrac", data, ids, tree=tree,
   ...                        otu_ids=otu_ids)
   >>> print(wu_dm)
   6x6 distance matrix
   IDs:
   'A', 'B', 'C', 'D', 'E', 'F'
   Data:
   [[ 0.          2.77549923  3.82857143  0.42512039  3.8547619   3.10937312]
    [ 2.77549923  0.          2.26433692  2.98435423  2.24270353  0.46774194]
    [ 3.82857143  2.26433692  0.          3.95224719  0.16025641  1.86111111]
    [ 0.42512039  2.98435423  3.95224719  0.          3.98796148  3.30870431]
    [ 3.8547619   2.24270353  0.16025641  3.98796148  0.          1.82967033]
    [ 3.10937312  0.46774194  1.86111111  3.30870431  1.82967033  0.        ]]

   Next we'll do some work with these beta diversity distance matrices. First,
   we'll determine if the UniFrac and Bray-Curtis distance matrices are
   significantly correlated by computing the Mantel correlation between them.
   Then we'll determine if the p-value is significant based on an alpha of
   0.05.

   >>> from skbio.stats.distance import mantel
   >>> r, p_value, n = mantel(wu_dm, bc_dm)
   >>> print(r)
   0.922404392093
   >>> alpha = 0.05
   >>> print(p_value < alpha)
   True

   Next, we'll perform principal coordinates analysis (PCoA) on our weighted
   UniFrac distance matrix.

   >>> from skbio.stats.ordination import pcoa
   >>> wu_pc = pcoa(wu_dm)

   PCoA plots are only really interesting in the context of sample metadata, so
   let's define some before we visualize these results.

   >>> import pandas as pd
   >>> sample_md = [
   ...    ('A', ['gut', 's1']),
   ...    ('B', ['skin', 's1']),
   ...    ('C', ['tongue', 's1']),
   ...    ('D', ['gut', 's2']),
   ...    ('E', ['tongue', 's2']),
   ...    ('F', ['skin', 's2'])]
   >>> sample_md = pd.DataFrame.from_items(
   ...     sample_md, columns=['body_site', 'subject'], orient='index')
   >>> sample_md
     body_site subject
   A       gut      s1
   B      skin      s1
   C    tongue      s1
   D       gut      s2
   E    tongue      s2
   F      skin      s2

   Now let's plot our PCoA results, coloring each sample by the subject it
   was taken from:

   >>> fig = wu_pc.plot(sample_md, 'subject',
   ...                  axis_labels=('PC 1', 'PC 2', 'PC 3'),
   ...                  title='Samples colored by subject', cmap='jet', s=50)

.. plot::
   :context:

   We don't see any clustering/grouping of samples. If we were to instead color
   the samples by the body site they were taken from, we see that the samples
   from the same body site (those that are colored the same) appear to be
   closer to one another in the 3-D space then they are to samples from
   other body sites.

   >>> import matplotlib.pyplot as plt
   >>> plt.close('all') # not necessary for normal use
   >>> fig = wu_pc.plot(sample_md, 'body_site',
   ...                  axis_labels=('PC 1', 'PC 2', 'PC 3'),
   ...                  title='Samples colored by body site', cmap='jet', s=50)

.. plot::
   :context:

   Ordination techniques, such as PCoA, are useful for exploratory analysis.
   The next step is to quantify the strength of the grouping/clustering that we
   see in ordination plots. There are many statistical methods available to
   accomplish this; many operate on distance matrices. Let's use ANOSIM to
   quantify the strength of the clustering we see in the ordination plots
   above, using our weighted UniFrac distance matrix and sample metadata.

   First test the grouping of samples by subject:

   >>> from skbio.stats.distance import anosim
   >>> results = anosim(wu_dm, sample_md, column='subject', permutations=999)
   >>> results['test statistic']
   -0.33333333333333331
   >>> results['p-value'] < 0.1
   False

   The negative value of ANOSIM's R statistic indicates anti-clustering and the
   p-value is insignificant at an alpha of 0.1.

   Now let's test the grouping of samples by body site:

   >>> results = anosim(wu_dm, sample_md, column='body_site', permutations=999)
   >>> results['test statistic']
   1.0
   >>> results['p-value'] < 0.1
   True

   The R statistic indicates strong separation of samples based on body site.
   The p-value is significant at an alpha of 0.1.

   We can also explore the alpha diversity in the context of sample metadata.
   To do this, let's add the Observed OTU and Faith PD data to our sample
   metadata. This is straight-forward beause ``alpha_diversity`` returns a
   Pandas ``Series`` object, and we're representing our sample metadata in a
   Pandas ``DataFrame`` object.

   >>> sample_md['Observed OTUs'] = adiv_obs_otus
   >>> sample_md['Faith PD'] = adiv_faith_pd
   >>> sample_md
     body_site subject  Observed OTUs  Faith PD
   A       gut      s1              5      6.75
   B      skin      s1              5      7.00
   C    tongue      s1              4      6.25
   D       gut      s2              4      5.75
   E    tongue      s2              5      6.75
   F      skin      s2              3      5.50

   We can investigate these alpha diversity data in the context of our metadata
   categories. For example, we can generate boxplots showing Faith PD by body
   site.

   >>> import matplotlib.pyplot as plt
   >>> plt.close('all') # not necessary for normal use
   >>> fig = sample_md.boxplot(column='Faith PD', by='body_site')

We can also compute Spearman correlations between all pairs of columns in this
``DataFrame``. Since our alpha diversity metrics are the only two numeric
columns (and thus the only columns for which Spearman correlation is relevant),
this will give us a symmetric 2x2 correlation matrix.

>>> sample_md.corr(method="spearman")
               Observed OTUs  Faith PD
Observed OTUs       1.000000  0.939336
Faith PD            0.939336  1.000000

"""

# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------

from __future__ import absolute_import, division, print_function

from skbio.util import TestRunner

from ._driver import (alpha_diversity, beta_diversity,
                      get_alpha_diversity_metrics, get_beta_diversity_metrics)

__all__ = ["alpha_diversity", "beta_diversity", "get_alpha_diversity_metrics",
           "get_beta_diversity_metrics"]

test = TestRunner(__file__).test
