def plot_alignment4mummer( seqs, ax, heatmap, size, fn ):
with open( fn , 'r' ) as file:
for line in file:
buf = line.rstrip( '\n' ).split( )
i = common.detect_index( buf[11], int( buf[0] ), int( buf[1] ), seqs )
j = common.detect_index( buf[12], int( buf[3] ), int( buf[4] ), seqs )
color = heatmap.convert_identity2color( float( buf[9] ))
if( i == -1 or j == -1 ):
continue
x, y = convert_position2coord( seqs[i][buf[11]], seqs[j][buf[12]], int( buf[0] ), int( buf[1] ), int( buf[3] ), int( buf[4] ), size.margin_bw_scaffold_alignment, size.histograms[i], size.histograms[j] )
ax.fill( x, y, color=color, alpha=heatmap.alpha, lw=0 )
def plot_histogram(seqs, ax, size, fn):
min_y = 0
max_y = 4
color = 'tomato'
msize = 3
with open(fn, 'r') as file:
x_list = []
y_list = []
for line in file:
buf = line.rstrip('\n').split('\t')
i = common.detect_index(buf[0], int(buf[2]), int(buf[2]), seqs)
if (i == -1):
continue
if (float(buf[4]) < min_y or max_y < float(buf[4])):
continue
x = seqs[i][buf[0]].convert_position2xcoord(int(buf[2]))
y0 = seqs[i][buf[0]].origin_y + seqs[i][
buf[0]].height + size.margin_bw_scaffold_alignment
y1 = seqs[i][buf[0]].origin_y + size.histograms[
i] - size.margin_bw_scaffold_alignment
y = ((y1 - y0) * (float(buf[4]) - min_y)) / (max_y - min_y) + y0
x_list.append(x)
y_list.append(y)
if (len(x_list) != 0):
ax.scatter(x_list,
y_list,
color=color,
marker='.',
s=msize,
zorder=2,
lw=0)
def plot_alignment4lastz( seqs, ax, heatmap, size, fn ):
with open( fn , 'r' ) as file:
for line in file:
if( line[0:1] == "#" ):
continue
buf = line.rstrip( '\n' ).split( '\t' )
i = common.detect_index( buf[1], int( buf[4] ), int( buf[5] ), seqs )
if( buf[7] == '+' ):
s_pos = int( buf[9] )
e_pos = int( buf[10] )
j = common.detect_index( buf[6], s_pos, e_pos, seqs )
else:
s_pos = int( buf[8] ) - int( buf[9] )
e_pos = int( buf[8] ) - int( buf[10] )
j = common.detect_index( buf[6], e_pos, s_pos, seqs )
color = heatmap.convert_identity2color( float( buf[12][:-1] ))
if( i == -1 or j == -1 ):
continue
x, y = convert_position2coord( seqs[i][buf[1]], seqs[j][buf[6]], int( buf[4] ), int( buf[5] ), s_pos, e_pos, size.margin_bw_scaffold_alignment, size.histograms[i], size.histograms[j] )
ax.fill( x, y, color=color, alpha=heatmap.alpha, lw=0 )
def plot_mark_v(seqs, ax, size, fn):
height = size.ylim_max / 50
width = size.xlim_max / 150
#color = 'tomato'
color = '#043c78'
with open(fn, 'r') as file:
for line in file:
buf = line.rstrip('\n').split('\t')
i = common.detect_index(buf[0], int(buf[3]), int(buf[4]), seqs)
if (i == -1):
continue
aninstance = Vmark(seqs[i][buf[0]], buf, width, height, color)
aninstance.plot(ax)
def plot_genes(seqs, ax, size, fn):
color = 'darkorange'
RATIO = 3
#color = 'tomato'
with open(fn, 'r') as file:
for line in file:
buf = line.rstrip('\n').split('\t')
if (len(buf) < 4):
continue
if (buf[2] != 'gene'):
continue
i = common.detect_index(buf[0], int(buf[3]), int(buf[4]), seqs)
if (i == -1):
continue
height = seqs[i][buf[0]].height * RATIO
tail_length = (height / 2) * (size.xlim_max / size.ylim_max) * (
size.figsize_inch[1] / size.figsize_inch[0])
aninstance = Gene(seqs[i][buf[0]], buf, height, tail_length, color)
aninstance.plot(ax)