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# Copyright 2013-2015, The James Hutton Insitute
# Author: Leighton Pritchard
#
# This code is part of the pyani package, and is governed by its licence.
# Please see the LICENSE file that should have been included as part of
# this package.
"""Configuration settings for the pyani package.
"""
from matplotlib.colors import LinearSegmentedColormap
# Defaults assume that common binaries are on the $PATH
NUCMER_DEFAULT = "nucmer"
FILTER_DEFAULT = "delta-filter"
BLASTN_DEFAULT = "blastn"
MAKEBLASTDB_DEFAULT = "makeblastdb"
BLASTALL_DEFAULT = "blastall"
FORMATDB_DEFAULT = "formatdb"
QSUB_DEFAULT = "qsub"
# Stems for output files
ANIM_FILESTEMS = ("ANIm_alignment_lengths", "ANIm_percentage_identity",
"ANIm_alignment_coverage", "ANIm_similarity_errors",
"ANIm_hadamard")
ANIB_FILESTEMS = ("ANIb_alignment_lengths", "ANIb_percentage_identity",
"ANIb_alignment_coverage", "ANIb_similarity_errors",
"ANIb_hadamard")
TETRA_FILESTEMS = ("TETRA_correlations",)
ANIBLASTALL_FILESTEMS = ("ANIblastall_alignment_lengths",
"ANIblastall_percentage_identity",
"ANIblastall_alignment_coverage",
"ANIblastall_similarity_errors",
"ANIblastall_hadamard")
# Output subdirectory names for each method
ALIGNDIR = {'ANIm': 'nucmer_output',
'ANIb': 'blastn_output',
'ANIblastall': 'blastall_output'}
# Any valid matplotlib colour map can be used here
# See, e.g. http://matplotlib.org/xkcd/examples/color/colormaps_reference.html
MPL_CBAR = 'Spectral'
# Parameters for analyses
FRAGSIZE = 1020 # Default ANIb fragment size
# SGE/OGE scheduler parameters
SGE_WAIT = 0.01 # Base unit of time (s) to wait between polling SGE
# Custom Matplotlib colourmaps
# 1a) Map for species boundaries (95%: 0.95), blue for values at
# 0.9 or below, red for values at 1.0; white at 0.95.
# Also, anything below 0.7 is 70% grey
cdict_spbnd_BuRd = {'red': ((0.0, 0.0, 0.7),
(0.7, 0.7, 0.0),
(0.9, 0.0, 0.0),
(0.95, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.7),
(0.7, 0.7, 0.0),
(0.9, 0.0, 0.0),
(0.95, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.7),
(0.7, 0.7, 1.0),
(0.95, 1.0, 1.0),
(1.0, 0.0, 0.0))}
CMAP_SPBND_BURD = LinearSegmentedColormap("spbnd_BuRd",
cdict_spbnd_BuRd)
# 1b) Map for species boundaries (95%: 0.95), blue for values at
# 0.9 or below, red for values at 1.0; white at 0.9.
# Also, anything below 0.8 is 70% grey
cdict_hadamard_BuRd = {'red': ((0.0, 0.0, 0.7),
(0.8, 0.7, 0.0),
(0.9, 0.0, 0.0),
(0.9, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.7),
(0.8, 0.7, 0.0),
(0.9, 0.0, 0.0),
(0.9, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.7),
(0.8, 0.7, 1.0),
(0.9, 1.0, 1.0),
(1.0, 0.0, 0.0))}
CMAP_HADAMARD_BURD = LinearSegmentedColormap("hadamard_BuRd",
cdict_hadamard_BuRd)
# 2) Blue for values at 0.0, red for values at 1.0; white at 0.5
cdict_BuRd = {'red': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0))}
CMAP_BURD = LinearSegmentedColormap("BuRd", cdict_BuRd)
# Graphics parameters for each output file. Note that this should be
# in sync with the output file stems above
def params_mpl(df):
"""Returns dict of matplotlib parameters, dependent on dataframe."""
return {'ANIb_alignment_lengths': ('afmhot', df.values.min(),
df.values.max()),
'ANIb_percentage_identity': ('spbnd_BuRd', 0, 1),
'ANIb_alignment_coverage': ('BuRd', 0, 1),
'ANIb_hadamard': ('hadamard_BuRd', 0, 1),
'ANIb_similarity_errors': ('afmhot', df.values.min(),
df.values.max()),
'ANIm_alignment_lengths': ('afmhot', df.values.min(),
df.values.max()),
'ANIm_percentage_identity': ('spbnd_BuRd', 0, 1),
'ANIm_alignment_coverage': ('BuRd', 0, 1),
'ANIm_hadamard': ('hadamard_BuRd', 0, 1),
'ANIm_similarity_errors': ('afmhot', df.values.min(),
df.values.max()),
'TETRA_correlations': ('spbnd_BuRd', 0, 1),
'ANIblastall_alignment_lengths': ('afmhot', df.values.min(),
df.values.max()),
'ANIblastall_percentage_identity': ('spbnd_BuRd', 0, 1),
'ANIblastall_alignment_coverage': ('BuRd', 0, 1),
'ANIblastall_hadamard': ('hadamard_BuRd', 0, 1),
'ANIblastall_similarity_errors': ('afmhot', df.values.min(),
df.values.max())}
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