import argparse

import os

import numpy as np

import pandas as pd

import pkg_resources

from keras import backend as K

from keras.layers import Conv2D

from keras.layers import GlobalAveragePooling2D

from keras.layers import Maximum

from janggu import Janggu

from janggu import Scorer

from janggu import inputlayer

from janggu import outputdense

from janggu.data import Array

from janggu.data import Bioseq

from janggu.layers import Complement

from janggu.layers import DnaConv2D

from janggu.layers import Reverse

from janggu.utils import ExportClustermap

from janggu.utils import ExportTsv

import matplotlib

matplotlib.use('Agg')

np.random.seed(1234)

# Fetch parser arguments

PARSER = argparse.ArgumentParser(description='Command description.')

PARSER.add_argument('model', choices=['single', 'double', 'dnaconv'],

                    help="Single or double stranded model.")

PARSER.add_argument('-path', dest='path',

                    default='tf_results',

                    help="Output directory for the examples.")

PARSER.add_argument('-order', dest='order', type=int,

                    default=1,

                    help="One-hot order.")

args = PARSER.parse_args()

os.environ['JANGGU_OUTPUT'] = args.path

# helper function

def nseqs(filename):

    """Extract the number of rows in the file.

    Note however, that this is a simplification

    that might not always work. In general, one would

    need to parse for '>' occurrences.

    """

    return sum((1 for line in open(filename) if line[0] == '>'))

# load the dataset

DATA_PATH = pkg_resources.resource_filename('janggu', 'resources/')

SAMPLE_1 = os.path.join(DATA_PATH, 'sample.fa')

SAMPLE_2 = os.path.join(DATA_PATH, 'sample2.fa')

# DNA sequences in one-hot encoding will be used as input

DNA = Bioseq.create_from_seq('dna', fastafile=[SAMPLE_1, SAMPLE_2],

                             order=args.order, cache=True)

# An array of 1/0 will be used as labels for training

Y = np.asarray([[1] for line in range(nseqs(SAMPLE_1))] +

               [[0] for line in range(nseqs(SAMPLE_2))])

LABELS = Array('y', Y, conditions=['TF-binding'])

annot = pd.DataFrame(Y[:], columns=LABELS.conditions).applymap(

    lambda x: 'Oct4' if x == 1 else 'Mafk').to_dict(orient='list')

# Define the model templates

@inputlayer

@outputdense('sigmoid')

def single_stranded_model(inputs, inp, oup, params):

    """ keras model that scans a DNA sequence using

    a number of motifs.

    This model only scans one strand for sequence patterns.

    """

    with inputs.use('dna') as layer:

        # the name in inputs.use() should be the same as the dataset name.

        layer = Conv2D(params[0], (params[1], 1), activation=params[2])(layer)

    output = GlobalAveragePooling2D(name='motif')(layer)

    return inputs, output

@inputlayer

@outputdense('sigmoid')

def double_stranded_model(inputs, inp, oup, params):

    """ keras model for scanning both DNA strands.

    Sequence patterns may be present on either strand.

    By scanning both DNA strands with the same motifs (kernels)

    the performance of the model will generally improve.

    In the model below, this is achieved by reverse complementing

    the input tensor and keeping the convolution filters fixed.

    """

    with inputs.use('dna') as layer:

        # the name in inputs.use() should be the same as the dataset name.

        forward = layer

    convlayer = Conv2D(params[0], (params[1], 1), activation=params[2])

    revcomp = Reverse()(forward)

    revcomp = Complement()(revcomp)

    forward = convlayer(forward)

    revcomp = convlayer(revcomp)

    revcomp = Reverse()(revcomp)

    layer = Maximum()([forward, revcomp])

    output = GlobalAveragePooling2D(name='motif')(layer)

    return inputs, output

@inputlayer

@outputdense('sigmoid')

def double_stranded_model_dnaconv(inputs, inp, oup, params):

    """ keras model for scanning both DNA strands.

    A more elegant way of scanning both strands for motif occurrences

    is achieved by the DnaConv2D layer wrapper, which internally

    performs the convolution operation with the normal kernel weights

    and the reverse complemented weights.

    """

    with inputs.use('dna') as layer:

        # the name in inputs.use() should be the same as the dataset name.

        conv = DnaConv2D(Conv2D(params[0],

                                (params[1], 1),

                                activation=params[2]), name='conv1')(layer)

    output = GlobalAveragePooling2D(name='motif')(conv)

    return inputs, output

if args.model == 'single':

    modeltemplate = single_stranded_model

elif args.model == 'double':

    modeltemplate = double_stranded_model

else:

    modeltemplate = double_stranded_model_dnaconv

K.clear_session()

# create a new model object

model = Janggu.create(template=modeltemplate,

                      modelparams=(30, 21, 'relu'),

                      inputs=DNA,

                      outputs=LABELS,

                      name='fasta_seqs_m{}_o{}'.format(args.model, args.order))

model.compile(optimizer='adadelta', loss='binary_crossentropy',

              metrics=['acc'])

model.summary()

# fit the model

hist = model.fit(DNA, LABELS, epochs=100)

print('#' * 40)

print('loss: {}, acc: {}'.format(hist.history['loss'][-1],

                                 hist.history['acc'][-1]))

print('#' * 40)

# load test data

SAMPLE_1 = os.path.join(DATA_PATH, 'sample_test.fa')

SAMPLE_2 = os.path.join(DATA_PATH, 'sample2_test.fa')

DNA_TEST = Bioseq.create_from_seq('dna', fastafile=[SAMPLE_1, SAMPLE_2],

                                  order=args.order, cache=True)

Y = np.asarray([[1] for _ in range(nseqs(SAMPLE_1))] +

               [[0] for _ in range(nseqs(SAMPLE_2))])

LABELS_TEST = Array('y', Y, conditions=['TF-binding'])

annot_test = pd.DataFrame(Y[:], columns=LABELS_TEST.conditions).applymap(

    lambda x: 'Oct4' if x == 1 else 'Mafk').to_dict(orient='list')

# clustering plots based on hidden features

heatmap_eval = Scorer('heatmap', exporter=ExportClustermap(annot=annot_test,

                                                           z_score=1.))

# output the predictions as tables or json files

pred_tsv = Scorer('pred', exporter=ExportTsv(annot=annot_test,

                                             row_names=DNA_TEST.gindexer.chrs))

# do the evaluation on the independent test data

# after the evaluation and prediction has been performed,

# the callbacks further process the results allowing

# to automatically generate summary statistics or figures

# into the JANGGU_OUTPUT directory.

model.evaluate(DNA_TEST, LABELS_TEST, datatags=['test'],

               callbacks=['auc', 'auprc', 'roc', 'auroc'])

pred = model.predict(DNA_TEST, datatags=['test'],

              callbacks=[pred_tsv, heatmap_eval],

              layername='motif')

pred = model.predict(DNA_TEST)

print('Oct4 predictions scores should be greater than Mafk scores:')

print('Prediction score examples for Oct4')

for i in range(4):

    print('{}.: {}'.format(i, pred[i]))

print('Prediction score examples for Mafk')

for i in range(1, 5):

    print('{}.: {}'.format(i, pred[-i]))