# -*- coding: utf-8 -*-
# (c) The James Hutton Institute 2017-2019
# (c) The University of Strathclude 2019-2020
# Author: Leighton Pritchard
#
# Contact:
# leighton.pritchard@strath.ac.uk
#
# Leighton Pritchard,
# Strathclyde Institute of Pharmaceutical and Biomedical Sciences
# The University of Strathclyde
# 161 Cathedral Street
# Glasgow
# G4 0RE
# Scotland,
# UK
#
# The MIT License
#
# Copyright (c) 2013-2019 The James Hutton Institute
# (c) The University of Strathclude 2019-2020
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""Configuration settings for the pyani package."""
from pathlib import Path
from typing import Any, Dict, Tuple
import pandas as pd # type: ignore
from matplotlib.colors import LinearSegmentedColormap # type: ignore
# Defaults assume that common binaries are on the $PATH
NUCMER_DEFAULT = Path("nucmer")
FILTER_DEFAULT = Path("delta-filter")
BLASTN_DEFAULT = Path("blastn")
MAKEBLASTDB_DEFAULT = Path("makeblastdb")
BLASTALL_DEFAULT = Path("blastall")
FORMATDB_DEFAULT = Path("formatdb")
QSUB_DEFAULT = Path("qsub")
FASTANI_DEFAULT = Path("fastANI")
# 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",
"fastANI": "fastani_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
# 0.64 (0.8 * 0.8) or below, red for values at 1.0; white at 0.9.
# Also, anything below 0.25 (0.5 * 0.5) is 70% grey
cdict_hadamard_BuRd = {
"red": (
(0.0, 0.0, 0.7),
(0.25, 0.7, 0.0),
(0.64, 0.0, 0.0),
(0.64, 1.0, 1.0),
(1.0, 1.0, 1.0),
),
"green": (
(0.0, 0.0, 0.7),
(0.25, 0.7, 0.0),
(0.64, 0.0, 0.0),
(0.64, 1.0, 1.0),
(1.0, 0.0, 0.0),
),
"blue": ((0.0, 0.0, 0.7), (0.25, 0.7, 1.0), (0.64, 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
[docs]def params_mpl(dfm: pd.DataFrame) -> Dict[str, Tuple[str, Any, Any]]:
"""Return dict of matplotlib parameters, dependent on dataframe.
:param dfm:
DEPRECATED FROM v0.3 onwards
"""
return {
"ANIb_alignment_lengths": ("afmhot", dfm.values.min(), dfm.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", dfm.values.min(), dfm.values.max()),
"ANIm_alignment_lengths": ("afmhot", dfm.values.min(), dfm.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", dfm.values.min(), dfm.values.max()),
"TETRA_correlations": ("spbnd_BuRd", 0, 1),
"ANIblastall_alignment_lengths": ("afmhot", dfm.values.min(), dfm.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", dfm.values.min(), dfm.values.max()),
}
[docs]def get_colormap(dataframe: pd.DataFrame, matname: str) -> Tuple[str, Any, Any]:
"""Return colormap parameters for a dataframe.
:param dataframe:
:param matname:
The colormap is dependent on the type of analysis that was done.
"""
paramdict = {
"identity": ("spbnd_BuRd", 0, 1),
"coverage": ("BuRd", 0, 1),
"aln_lengths": ("afmhot", dataframe.values.min(), dataframe.values.max()),
"sim_errors": ("afmhot", dataframe.values.min(), dataframe.values.max()),
"hadamard": ("hadamard_BuRd", 0, 1),
}
return paramdict[matname]