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def save_html(self, output_html):
"""Save figure as HTML.
Parameters
----------
output_html : str
Name of output file.
"""
html_script = Tooltip.script_global
html_body = Tooltip.html_body
AbstractPlot.save_html(self, output_html, html_script, html_body)
# along with this program. If not, see . #
# #
###############################################################################
from refinem.plots.gc_plots import GcPlots
from refinem.plots.td_plots import TdPlots
from refinem.plots.cov_perc_plots import CovPercPlots
from refinem.plots.mpld3_plugins import LinkedBrush, Tooltip
import matplotlib
import mpld3
from biolib.plots.abstract_plot import AbstractPlot
class DistributionPlots(AbstractPlot):
def __init__(self, options):
"""Initialize plot."""
AbstractPlot.__init__(self, options)
def plot(self, genome_scaffold_stats,
highlight_scaffold_ids, link_scaffold_ids,
genome_stats,
gc_dist, td_dist,
gc_perc, td_perc, cov_perc):
"""Create figure with the GC, tetranucleotide signature, and coverage distributions.
Parameters
----------
genome_scaffold_stats : d[scaffold_id] -> namedtuple of scaffold stats
Statistics for scaffolds in genome.
highlight_scaffold_ids : d[scaffold_id] -> color
zeros as np_zeros)
import scipy.cluster.hierarchy as cluster
import scipy.spatial.distance as dist
import matplotlib.pyplot as pylab
from matplotlib.colors import ListedColormap
from biolib.plots.abstract_plot import AbstractPlot
from biolib.common import alphanumeric_sort
# import mpld3
# from comparem.plots.mpld3_plugins import Tooltip
class Heatmap(AbstractPlot):
def __init__(self, infile, outfile):
AbstractPlot.__init__(self, None)
self.outfile = outfile
self.genomes = None
self._parse_data(infile)
self.colormap = pylab.cm.bwr
self.discreteColourMap = ListedColormap([(141/255.0, 211/255.0, 199/255.0),(255/255.0, 255/255.0, 179/255.0),
(190/255.0, 186/255.0, 218/255.0),(251/255.0, 128/255.0, 114/255.0),
(128/255.0, 177/255.0, 211/255.0),(253/255.0, 180/255.0, 98/255.0),
(179/255.0, 222/255.0, 105/255.0),(252/255.0, 205/255.0, 229/255.0),
(217/255.0, 217/255.0, 217/255.0), (188/255.0, 128/255.0, 189/255.0),
(204/255.0, 235/255.0, 197/255.0),(255/255.0, 237/255.0, 111/255.0)])
# #
###############################################################################
import matplotlib
import mpld3
from matplotlib.collections import LineCollection
from biolib.plots.abstract_plot import AbstractPlot
from refinem.plots.mpld3_plugins import Tooltip
from numpy import (mean as np_mean)
class BasePlot(AbstractPlot):
"""Base plotting function used by many plots."""
def __init__(self, options):
"""Initialize."""
AbstractPlot.__init__(self, options)
def point_properties(self, scaffold_stats, highlight_scaffold_ids, link_scaffold_ids):
"""Get visual properties for each point to be plotted.
This includes organizing points such that those to be
highlighted or linked are rendered last. This helps ensure
this points will be visible.
Parameters
----------
scaffold_stats : d[scaffold_id] = [x, y]
import mpld3
from biolib.plots.abstract_plot import AbstractPlot
from refinem.plots.scatter import Scatter
from refinem.plots.gc_plots import GcPlots
from refinem.plots.td_plots import TdPlots
from refinem.plots.cov_perc_plots import CovPercPlots
from refinem.plots.gc_cov_plot import GcCovPlot
from refinem.plots.tetra_pca_plot import TetraPcaPlot
from refinem.plots.mpld3_plugins import LinkedBrush, Tooltip
from numpy import (mean as np_mean, abs as np_abs)
class CombinedPlots(AbstractPlot):
def __init__(self, options):
"""Initialize."""
AbstractPlot.__init__(self, options)
def plot(self, genome_scaffold_stats,
highlight_scaffold_ids, link_scaffold_ids,
genome_stats,
gc_dist, td_dist,
gc_perc, td_perc, cov_perc):
"""Create figure containing distribution plots, tetranucleotide PCA plots, and GC vs. coverage plot.
Parameters
----------
genome_scaffold_stats : d[scaffold_id] -> namedtuple of scaffold stats
Statistics for scaffolds in genome.
highlight_scaffold_ids : d[scaffold_id] -> color