expanded on descriptions in examples

docs/Makefile

@@ -165,5 +165,5 @@ figures:
 	conkit.plot cmap -o examples/images/toxd_cmap_advanced.png -e _tmp/conkit-examples/toxd/toxd.psicov -ef psicov -p _tmp/conkit-examples/toxd/toxd.pdb _tmp/conkit-examples/toxd/toxd.fasta fasta _tmp/conkit-examples/toxd/toxd.mat ccmpred
 	conkit.plot cmap -o examples/images/toxd_cmap_confidence.png --confidence -e _tmp/conkit-examples/toxd/toxd.psicov -ef psicov -p _tmp/conkit-examples/toxd/toxd.pdb _tmp/conkit-examples/toxd/toxd.fasta fasta _tmp/conkit-examples/toxd/toxd.mat ccmpred
 	conkit.plot scov -o examples/images/toxd_scov_plot.png _tmp/conkit-examples/toxd/toxd.a3m a3m
-	conkit.plot peval -o examples/images/toxd_peval_plot.png -j 0.1 -min 0 -max 5 -d 5 _tmp/conkit-examples/toxd/toxd.pdb pdb _tmp/conkit-examples/toxd/toxd.fasta fasta _tmp/conkit-examples/toxd/toxd.mat ccmpred
+	conkit.plot peval -o examples/images/toxd_peval_plot.png -j 0.1 -min 0 -max 5 _tmp/conkit-examples/toxd/toxd.pdb pdb _tmp/conkit-examples/toxd/toxd.fasta fasta _tmp/conkit-examples/toxd/toxd.mat ccmpred
 	rm -rf _tmp

docs/examples/rst/python_plot_scov.rst

@@ -7,5 +7,11 @@ Sequence Coverage Plotting
    >>> msa = conkit.io.read('toxd/toxd.a3m', 'a3m')
    >>> conkit.plot.SequenceCoverageFigure(msa)
 
+The following plot will be produced. Your alignment coverage is shown with the black line with each point corresponding to a residue in the alignment. The red and green lines give you indicators of how good your alignment is.
+
+If parts or all of your coverage fall below the red line, the suitability for covariance-based contact prediction is very little. If you parts of the alignment are well above the red line, possibly even the green, and bigger chunks are below, then you might want to consider re-defining your sequence boundaries to predict contacts only for the well-covered area.
+
+If most residues in your alignment have a better coverage than the green line, the alignment is well-suited for covariance-based predictions.
+
 .. image:: ../images/toxd_scov_plot.png
    :alt: Toxd Sequence Coverage Plot

docs/examples/rst/script_plot_peval.rst

@@ -4,7 +4,7 @@ Precision Evaluation Plotting
 
 .. code-block:: bash
 
-   $> conkit.plot peval -j 0.1 -min 0 -max 5 -d 5 toxd/toxd.pdb pdb toxd/toxd.fasta fasta toxd/toxd.mat ccmpred
+   $> conkit.plot peval -j 0.1 -min 0 -max 5 toxd/toxd.pdb pdb toxd/toxd.fasta fasta toxd/toxd.mat ccmpred
 
 Three command line flags above are important to note. The ``-min`` flag is the minimum factor for contact selection, i.e. ``L * min`` number of contacts. The ``-max`` flag is the maximum factor for contact selection, i.e. ``L * max`` number of contacts. The ``-j`` flag defines the stepwise increase inbetween ``-min`` and ``-max``.
 

docs/examples/rst/script_plot_scov.rst

@@ -2,9 +2,17 @@
 Sequence Coverage Plotting
 --------------------------
 
+The ``scov`` subcommand of the ``conkit.plot`` script is used to plot the coverage plot of the multiple sequence alignment.
+
 .. code-block:: bash
 
    $> conkit.plot scov toxd/toxd.a3m a3m
 
+The following plot will be produced. Your alignment coverage is shown with the black line with each point corresponding to a residue in the alignment. The red and green lines give you indicators of how good your alignment is.
+
+If parts or all of your coverage fall below the red line, the suitability for covariance-based contact prediction is very little. If you parts of the alignment are well above the red line, possibly even the green, and bigger chunks are below, then you might want to consider re-defining your sequence boundaries to predict contacts only for the well-covered area.
+
+If most residues in your alignment have a better coverage than the green line, the alignment is well-suited for covariance-based predictions.
+
 .. image:: ../images/toxd_scov_plot.png
    :alt: Toxd Sequence Coverage Plot

docs/examples/rst/script_predict_pipeline.rst

@@ -12,14 +12,14 @@ The script to run contact prediction using `HHblits <https://github.com/soedingl
 
 .. code-block:: bash
 
-   $> conkit.predict sequence <path/to/hhblits> <path/to/hhblits_database> <path/to/ccmpred> toxd/toxd.fasta fasta
+   $> conkit.predict seq <path/to/hhblits> <path/to/hhblits_database> <path/to/ccmpred> toxd/toxd.fasta fasta
 
 The call above uses your sequence file ``toxd/toxd.fasta`` in ``fasta`` format to first generate a Multiple Sequence Alignment. It will then analyse your alignment identical to the ``conkit.msatool`` script. It will also sort out all the required conversions before executing CCMpred to run the contact prediction. Finally, it will analyse your contact prediciton and plot a contact map, just like the ``conkit.plot`` script does.
 
 **2. Starting with a Multiple Sequence Alignment**
 
 .. code-block:: bash
 
-   $> conkit.predict alignment <path/to/ccmpred> toxd/toxd.a3m a3m
+   $> conkit.predict aln <path/to/ccmpred> toxd/toxd.a3m a3m
 
 This call performs identical operations to the full call under point 1, except that it skips the generation of the alignment. This might be particularly useful if you have limited disk space and cannot store the rather large sequence database that HHblits requires. You can generate your alignment using online servers, such as the `HHblits Server <https://toolkit.tuebingen.mpg.de/hhblits>`_ or the `Jackhmmer Server <https://www.ebi.ac.uk/Tools/hmmer/search/jackhmmer>`_. Both formats are accepted by ConKit, the keywords can be found in the :ref:`file_formats`.

scripts/conkit.predict

@@ -37,7 +37,7 @@ def add_default_args(parser):
 
 def add_alignment_args(subparsers):
     """Parser with alignment as starting point"""
-    from_alignment_subparser = subparsers.add_parser('alignment')
+    from_alignment_subparser = subparsers.add_parser('aln')
     add_default_args(from_alignment_subparser)
     from_alignment_subparser.add_argument('ccmpred', help='Path to the CCMpred executable')
     from_alignment_subparser.add_argument('alignment_file', help='Path to alignment file')
@@ -47,7 +47,7 @@ def add_alignment_args(subparsers):
 
 def add_sequence_args(subparsers):
     """Parser with sequence as starting point"""
-    from_sequence_subparser = subparsers.add_parser('sequence')
+    from_sequence_subparser = subparsers.add_parser('seq')
     add_default_args(from_sequence_subparser)
     from_sequence_subparser.add_argument('--nodca', default=False, action='store_true',
                                          help=argparse.SUPPRESS)