IMP: merge fastq_stats_* visualizers into a single action

[angrybee]

Closes #70

[angrybee]

Hi there,
I need help with the format to import in test_stats.py.

The plugin itself transforms SingleLanePerSamplePairedEndFastqDirFmt and SingleLanePerSampleSingleEndFastqDirFmt to CasavaOneEightSingleLanePerSampleDirFmt. In the unittests it doesn't work like this so I get a TypeError: 'BoundFile' object is not subscriptable because of the different format of the manifest in Type CasavaOneEightSingleLanePerSampleDirFmt.

I would like to import these formats to use the given MANIFEST for both tests. I also tried to transform the input manual to CasavaOneEightSingleLanePerSampleDirFmt with no success.

Any suggestions?

[thermokarst]

Hey @angrybee! The easiest solution is to just invoke the plugin action via the framework (rather than importing the function manually). You can see an example of that here:

q2-vsearch/q2_vsearch/tests/test_cluster_features.py

Lines 567 to 612 in f718620

	class ClusterFeaturesOpenReference(TestPluginBase):
	package = 'q2_vsearch.tests'
	def setUp(self):
	super().setUp()
	self.open_reference = \
	self.plugin.pipelines['cluster_features_open_reference']
	input_sequences_fp = self.get_data_path('dna-sequences-2.fasta')
	self.input_sequences = Artifact.import_data('FeatureData[Sequence]',
	input_sequences_fp)
	ref_sequences_fp = self.get_data_path('ref-sequences-1.fasta')
	self.ref_sequences = Artifact.import_data('FeatureData[Sequence]',
	ref_sequences_fp)
	input_table = biom.Table(np.array([[100, 101, 103],
	[1, 1, 2],
	[4, 5, 6],
	[7, 8, 9],
	[99, 98, 97]]),
	['feature1', 'feature2', 'feature3',
	'feature4', 'feature5'],
	['sample1', 'sample2', 'sample3'])
	self.input_table = Artifact.import_data('FeatureTable[Frequency]',
	input_table)
	self.input_sequences_list = _read_seqs(self.input_sequences)
	def test_100_percent_clustering(self):
	# feature1 clusters into r1 and feature4 clusters into r4 during
	# closed-ref clustering; feature2, feature3, and feature5 cluster into
	# their own clusters during de-novo clustering.
	exp_table = biom.Table(np.array([[100, 101, 103],
	[1, 1, 2],
	[4, 5, 6],
	[7, 8, 9],
	[99, 98, 97]]),
	['r1', 'feature2', 'feature3', 'r2',
	'feature5'],
	['sample1', 'sample2', 'sample3'])
	with redirected_stdio(stderr=os.devnull):
	obs_table, rep_seqs, new_ref_seqs = self.open_reference(
	sequences=self.input_sequences, table=self.input_table,
	reference_sequences=self.ref_sequences, perc_identity=1.0)

[angrybee]

Thx @thermokarst for the hint which opened a lot of more questions and now I know way to much about qiime2-code-details :)

For me it was not possible to reuse demux-1 because qiime complained that the data I wanted to import as se-Reads are having forward and reverse reads. So you also did a good job there. Because of this I copied the forward reads to a new folder and changed the MANIFEST there. If anybody has a better solution feel free to tell me.

Otherwise I would say, job done, happy to be reviewed.

[thermokarst]

LGTM, thanks @angrybee!