def plotpuppy():
import matplotlib
matplotlib.use('Agg')
from matplotlib.colors import LogNorm, Normalize
from matplotlib.ticker import FormatStrFormatter
from mpl_toolkits.axes_grid1 import ImageGrid
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as font_manager
from itertools import product
font_path = '/usr/share/fonts/truetype/msttcorefonts/Arial.ttf'
font_prop = font_manager.FontProperties(fname=font_path)
mpl.rcParams['svg.fonttype'] = u'none'
mpl.rcParams['pdf.fonttype'] = 42
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--cmap",
type=str,
required=False,
default='coolwarm',
help="""Colourmap to use
(see https://matplotlib.org/users/colormaps.html)""")
parser.add_argument(
"--symmetric",
type=bool,
required=False,
default=True,
help="""Whether to make colormap symmetric around 1, if log
scale""")
parser.add_argument("--vmin",
type=float,
required=False,
help="""Value for the lowest colour""")
parser.add_argument("--vmax",
type=float,
required=False,
help="""Value for the highest colour""")
parser.add_argument(
"--scale",
type=str,
default='log',
required=False,
choices={"linear", "log"},
help="""Whether to use linear or log scaling for mapping
colours""")
parser.add_argument("--cbar_mode",
type=str,
default='single',
required=False,
choices={"single", "edge", "each"},
help="""Whether to show a single colorbar, one per row
or one for each subplot""")
parser.add_argument("--n_cols",
type=int,
default=0,
required=False,
help="""How many columns to use for plotting the data.
If 0, automatically make the figure as square as
possible""")
parser.add_argument('--col_names',
type=str,
required=False,
help="""A comma separated list of column names""")
parser.add_argument('--row_names',
type=str,
required=False,
help="""A comma separated list of row names""")
parser.add_argument(
"--norm_corners",
type=int,
required=False,
default=0,
help="""Whether to normalize pileups by their top left and
bottom right corners. 0 for no normalization, positive
number to define the size of the corner squares whose
values are averaged""")
parser.add_argument("--enrichment",
type=int,
required=False,
default=1,
help="""Whether to show the level of enrichment in the
central pixels. 0 to not show, odd positive number to
define the size of the central square whose values are
averaged""")
# parser.add_argument("--n_rows", type=int, default=0,
# required=False,
# help="""How many rows to use for plotting the data""")
parser.add_argument("--output",
type=str,
required=False,
default='pup.pdf',
help="""Where to save the plot""")
parser.add_argument("pileup_files",
type=str,
nargs='*',
help="""All files to plot""")
args = parser.parse_args()
pups = [np.loadtxt(f) for f in args.pileup_files]
if args.norm_corners > 0:
pups = [norm_cis(pup) for pup in pups]
n = len(pups)
if args.n_cols == 0:
n_rows, n_cols = auto_rows_cols(n)
elif args.n_cols < n:
n_rows = int(round(n / args.n_cols))
n_cols = args.n_cols
else:
n_cols = args.n_cols
n_rows = 1
if args.col_names is not None:
args.col_names = args.col_names.strip(', ').split(',')
if args.row_names is not None:
args.row_names = args.row_names.strip(', ').split(',')
if args.col_names != None and n_cols != len(args.col_names):
raise ValueError("""Number of column names is not equal to number of
columns! You specified %s columns and %s column
names""" % (n_cols, len(args.col_names)))
if args.row_names is not None and n_rows != len(args.row_names):
raise ValueError("""Number of row names is not equal to number of
rows!""")
if args.enrichment % 2 == 0 and args.enrichment > 0:
raise ValueError("""Side of the square to calculate enrichment has
to be an odd number""")
f = plt.figure(dpi=300, figsize=(max(3.5, n_cols + 0.5), max(3, n_rows)))
grid = ImageGrid(
f,
111,
share_all=True, # similar to subplot(111)
nrows_ncols=(n_rows, n_cols),
# direction='column',
axes_pad=0.05,
add_all=True,
label_mode="L",
cbar_location="right",
cbar_mode=args.cbar_mode,
cbar_size="5%",
cbar_pad="3%",
)
axarr = np.array(grid).reshape((n_rows, n_cols))
# f, axarr = plt.subplots(n_rows, n_cols, sharex=True, sharey=True,# similar to subplot(111)
# figsize=(max(3.5, n_cols+0.5), max(3, n_rows)),
# dpi=300, squeeze=False,
# constrained_layout=True
# )
sym = False
if args.scale == 'log':
norm = LogNorm
if args.symmetric:
sym = True
else:
norm = Normalize
if args.cbar_mode == 'single':
vmin, vmax = get_min_max(pups, args.vmin, args.vmax, sym=sym)
elif args.cbar_mode == 'edge':
colorscales = [
get_min_max(row, args.vmin, args.vmax, sym=sym) for row in pups
]
elif args.cbar_mode == 'each':
grid.cbar_axes = np.asarray(grid.cbar_axes).reshape((n_rows, n_cols))
n_grid = n_rows * n_cols
extra = [None for i in range(n_grid - len(pups))]
pupsarray = np.empty(n_rows * n_cols, dtype=object)
for i, pup in enumerate(pups + extra):
pupsarray[i] = pup
pups = pupsarray.reshape((n_rows, n_cols))
cbs = []
for i in range(n_rows):
if args.cbar_mode == 'edge':
vmin, vmax = colorscales[i]
for j in range(n_cols):
# n = i*n_cols+(j%n_cols)
if pups[i, j] is not None:
if args.cbar_mode == 'each':
vmin = np.nanmin(pups[i, j])
vmax = np.nanmax(pups[i, j])
ax = axarr[i, j]
m = ax.imshow(pups[i, j],
interpolation='nearest',
norm=norm(vmax=vmax, vmin=vmin),
cmap=args.cmap,
extent=(0, 1, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
if args.enrichment > 0:
enr = round(get_enrichment(pups[i, j], args.enrichment), 2)
ax.text(s=enr,
y=0.95,
x=0.05,
ha='left',
va='top',
size='x-small',
transform=ax.transAxes)
if args.cbar_mode == 'each':
cbs.append(plt.colorbar(m, cax=grid.cbar_axes[i, j]))
else:
axarr[i, j].axis('off')
grid.cbar_axes[i, j].axis('off')
if args.cbar_mode == 'edge':
cbs.append(plt.colorbar(m, cax=grid.cbar_axes[i]))
if args.col_names is not None:
for i, name in enumerate(args.col_names):
axarr[-1, i].set_xlabel(name)
if args.row_names is not None:
for i, name in enumerate(args.row_names):
axarr[i, 0].set_ylabel(name)
if args.cbar_mode == 'single':
cbs.append(plt.colorbar(
m, cax=grid.cbar_axes[0])) #, format=FormatStrFormatter('%.2f'))
# plt.setp(cbs, ticks=mpl.ticker.LogLocator())
# if sym:
# cb.ax.yaxis.set_ticks([vmin, 1, vmax])
plt.savefig(args.output, bbox_inches='tight')
def main():
mpl.rcParams["svg.fonttype"] = u"none"
mpl.rcParams["pdf.fonttype"] = 42
parser = parse_args_plotpuppy()
args = parser.parse_args()
pups = [load_array_with_header(f)['data'] for f in args.pileup_files]
if args.norm_corners > 0:
pups = [normCis(pup) for pup in pups]
n = len(pups)
if args.n_cols == 0:
n_rows, n_cols = auto_rows_cols(n)
elif args.n_cols < n:
n_rows = int(round(n / args.n_cols))
n_cols = args.n_cols
else:
n_cols = args.n_cols
n_rows = 1
if args.col_names is not None:
args.col_names = args.col_names.strip(", ").split(",")
if args.row_names is not None:
args.row_names = args.row_names.strip(", ").split(",")
if args.col_names != None and n_cols != len(args.col_names):
raise ValueError(
f"""Number of column names is not equal to number of
columns! You specified {n_cols} columns and {len(args.col_names)}
column names"""
% (n_cols, len(args.col_names))
)
if args.row_names is not None and n_rows != len(args.row_names):
raise ValueError(
"""Number of row names is not equal to number of
rows!"""
)
if args.enrichment % 2 == 0 and args.enrichment > 0:
raise ValueError(
"""Side of the square to calculate enrichment has
to be an odd number"""
)
f = plt.figure(dpi=args.dpi, figsize=(max(3.5, n_cols + 0.5), max(3, n_rows)))
grid = ImageGrid(
f,
111,
share_all=True, # similar to subplot(111)
nrows_ncols=(n_rows, n_cols),
# direction='column',
axes_pad=0.05,
add_all=True,
label_mode="L",
cbar_location="right",
cbar_mode=args.cbar_mode,
cbar_size="5%",
cbar_pad="3%",
)
axarr = np.array(grid).reshape((n_rows, n_cols))
# f, axarr = plt.subplots(n_rows, n_cols, sharex=True, sharey=True,# similar to subplot(111)
# figsize=(max(3.5, n_cols+0.5), max(3, n_rows)),
# dpi=300, squeeze=False,
# constrained_layout=True
# )
sym = False
if args.scale == "log":
norm = LogNorm
if args.symmetric:
sym = True
else:
norm = Normalize
n_grid = n_rows * n_cols
extra = [None for i in range(n_grid - len(pups))]
pupsarray = np.empty(n_rows * n_cols, dtype=object)
for i, pup in enumerate(pups + extra):
pupsarray[i] = pup
pups = pupsarray.reshape((n_rows, n_cols))
if args.cbar_mode == "single":
vmin, vmax = get_min_max(pups, args.vmin, args.vmax, sym=sym)
elif args.cbar_mode == "edge":
colorscales = [get_min_max(row, args.vmin, args.vmax, sym=sym) for row in pups]
elif args.cbar_mode == "each":
grid.cbar_axes = np.asarray(grid.cbar_axes).reshape((n_rows, n_cols))
cbs = []
for i in range(n_rows):
if args.cbar_mode == "edge":
vmin, vmax = colorscales[i]
for j in range(n_cols):
# n = i*n_cols+(j%n_cols)
if pups[i, j] is not None:
if args.cbar_mode == "each":
vmin = np.nanmin(pups[i, j])
vmax = np.nanmax(pups[i, j])
ax = axarr[i, j]
m = ax.imshow(
pups[i, j],
interpolation="none",
norm=norm(vmax=vmax, vmin=vmin),
cmap=args.cmap,
extent=(0, 1, 0, 1),
)
ax.set_xticks([])
ax.set_yticks([])
if args.enrichment > 0:
enr = round(get_enrichment(pups[i, j], args.enrichment), 2)
ax.text(
s=enr,
y=0.95,
x=0.05,
ha="left",
va="top",
size="x-small",
transform=ax.transAxes,
)
if args.cbar_mode == "each":
cbs.append(plt.colorbar(m, cax=grid.cbar_axes[i, j]))
else:
axarr[i, j].axis("off")
grid.cbar_axes[i, j].axis("off")
if args.cbar_mode == "edge":
cbs.append(plt.colorbar(m, cax=grid.cbar_axes[i]))
if args.col_names is not None:
for i, name in enumerate(args.col_names):
axarr[-1, i].set_xlabel(name)
if args.row_names is not None:
for i, name in enumerate(args.row_names):
axarr[i, 0].set_ylabel(name)
if args.cbar_mode == "single":
cbs.append(
plt.colorbar(m, cax=grid.cbar_axes[0])
) # , format=FormatStrFormatter('%.2f'))
# plt.setp(cbs, ticks=mpl.ticker.LogLocator())
# if sym:
# cb.ax.yaxis.set_ticks([vmin, 1, vmax])
plt.savefig(args.output, bbox_inches="tight")
