def run():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(
open(data_file, 'rb'),
delimiter = "\t",
skiprows = 1
)
for axis_y in config.interesting_y_axes:
x = data[:, tsv_get_column_index(data_file, 'config_Z')]
y = data[:, tsv_get_column_index(data_file, axis_y['column'])]
plt.figure()
plt.plot(x, y)
plt.title(axis_y['name'])
plt.xlabel(r'Hub Location $Z$')
plt.ylabel(axis_y['name'])
plt.grid(True)
#plt.show()
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, axis_y['column'])
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path)
def run():
data_file = "%s/latest.tsv" % config.sink_dir
data = np.loadtxt(open(data_file, "rb"), delimiter="\t", skiprows=1)
for axis_y in config.interesting_y_axes:
x = data[:, tsv_get_column_index(data_file, "config_count_hubs")]
y = data[:, tsv_get_column_index(data_file, axis_y["column"])]
plt.figure()
plt.plot(x, y)
plt.xlim(min(x), max(x))
plt.xticks(x)
plt.ylim(0.0, 0.05)
plt.title(axis_y["name"])
plt.xlabel(r"Hub Count $H$")
plt.ylabel(axis_y["name"])
plt.grid(True)
# plt.show()
fig_path = "%s/plot_%s.pdf" % (config.sink_dir, axis_y["column"])
fig_path = fig_path.replace("/runs/", "/plots/")
fig_path = fig_path.replace("/sink/", "/")
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path)
def run_graph():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(open(data_file, 'rb'), delimiter="\t", usecols=(4, ))
x = np.arange(0.01, 1, .01)
y = []
for criterium in x:
count = 0
for value in data:
if value >= criterium:
count += 1
y.append(count)
plt.title(r'Formation Suitability')
plt.ylabel(r'Number of aircraft')
plt.xlabel(r'Suitability Criterium $C_{min}$')
plt.plot(x, y)
#plt.show()
fig_path = '%s/plot.pdf' % (config.sink_dir)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def plot_flow_rate(data):
timestamps = []
values = []
for key in sorted(data.iterkeys()):
timestamps.append(key)
values.append(data[key])
# For testing/debugging
#timestamps = np.arange(0, 1440, 60)
#values = np.linspace(0, 35, len(timestamps))
time_labels = []
for timestamp in timestamps:
# Normalize time to be reset after midnight (if > 1440, then subtract 1440)
timestamp = timestamp % 1440
hours = math.floor(timestamp / 60)
minutes = math.floor(timestamp - hours * 60)
time_labels.append('%02d:%02d' % (hours, minutes))
plt.rc(('xtick.major', 'ytick.major'), pad=10)
plt.bar(timestamps,
values,
width=interval_length - 5,
linewidth=0,
color='#999999')
t = plt.title(r'Traffic Density at hub ($C_{min}=%s$)' % config.min_P)
#t.set_y(1.03)
plt.subplots_adjust(top=0.85, bottom=0.2)
plt.grid()
plt.xlabel(r'Time of day (UTC)', labelpad=10)
plt.ylabel(r'Number of flights', labelpad=5)
plt.xlim(0, 1440)
plt.ylim(0, 40)
xt = plt.xticks([0, 240, 480, 720, 960, 1200, 1440], [
'00:00', '04:00', '08:00', '12:00', '16:00', '18:00', '22:00', '00:00'
])
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, str(config.min_P).replace(
'.', '_'))
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
#plt.show()
plt.savefig(fig_path, bbox_inches='tight')
def run_graph():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(
open(data_file, 'rb'),
delimiter = "\t",
usecols = (4,)
)
x = np.arange(0.01, 1, .01)
y = []
for criterium in x:
count = 0
for value in data:
if value >= criterium:
count += 1
y.append(count)
plt.title(r'Formation Suitability')
plt.ylabel(r'Number of aircraft')
plt.xlabel(r'Suitability Criterium $C_{min}$')
plt.plot(x, y)
#plt.show()
fig_path = '%s/plot.pdf' % (config.sink_dir)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(
fig_path,
bbox_inches='tight'
)
def init(directory):
path = '%s/%s.tsv' % (directory,
datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
print "Sinking to %s" % path
global writer, print_header
print_header = True
make_sure_path_exists(os.path.dirname(path))
writer = csv.writer(open(path, 'w'), delimiter='\t')
# Update the symlink to point to the latest data file
force_symlink(path, '%s/latest.tsv' % os.path.dirname(path))
def execute():
planes = run.single_run()
segments = create_segments(planes)
plt, ax = run.render(segments)
name = str(config.min_P).replace('.','_')
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, name)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def execute():
planes = run.single_run()
segments = create_segments(planes)
plt, ax = run.render(segments)
name = 'count_hubs_%d' % config.count_hubs
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, name)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def do_plot(data, title, ylabel, filename, formatter = None):
means = []
errors = []
for hub_savings_list in data:
means.append(np.mean(hub_savings_list))
errors.append(np.std(hub_savings_list))
means = np.array(means)
errors = np.array(errors)
plt.figure()
plt.grid(True)
(mean_line, caps, _) = plt.errorbar(
np.arange(1, len(means)+1),
means, yerr = errors, elinewidth = 2
)
mean_line.set_color('#000000')
for cap in caps:
cap.set_markeredgewidth(2)
plt.xlim(0.5, len(means)+.5)
plt.xticks(config.simhubs)
plt.ylim(0, max(means)*1.1)
plt.title(title)
plt.xlabel(r'Number of hubs $H$')
plt.ylabel(ylabel)
if formatter is not None:
locs, labels = plt.yticks()
plt.yticks(locs, formatter(locs, labels))
fig_path = '%s/%s.pdf' % (config.sink_dir, filename)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(
fig_path,
bbox_inches='tight'
)
def init(directory):
path = '%s/%s.tsv' % (
directory,
datetime.now().strftime('%Y-%m-%d %H:%M:%S')
)
print "Sinking to %s" % path
global writer, print_header
print_header = True
make_sure_path_exists(os.path.dirname(path))
writer = csv.writer(open(path, 'w'), delimiter = '\t')
# Update the symlink to point to the latest data file
force_symlink(
path,
'%s/latest.tsv' % os.path.dirname(path)
)
def do_plot(data, title, ylabel, filename, formatter=None):
means = []
errors = []
for hub_savings_list in data:
means.append(np.mean(hub_savings_list))
errors.append(np.std(hub_savings_list))
means = np.array(means)
errors = np.array(errors)
plt.figure()
plt.grid(True)
(mean_line, caps, _) = plt.errorbar(np.arange(1,
len(means) + 1),
means,
yerr=errors,
elinewidth=2)
mean_line.set_color('#000000')
for cap in caps:
cap.set_markeredgewidth(2)
plt.xlim(0.5, len(means) + .5)
plt.xticks(config.simhubs)
plt.ylim(0, max(means) * 1.1)
plt.title(title)
plt.xlabel(r'Number of hubs $H$')
plt.ylabel(ylabel)
if formatter is not None:
locs, labels = plt.yticks()
plt.yticks(locs, formatter(locs, labels))
fig_path = '%s/%s.pdf' % (config.sink_dir, filename)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def execute():
planes = run.single_run()
segments = create_segments(planes)
plt, ax = run.render(segments)
name = 'count_hubs_%d' % config.count_hubs
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, name)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.title(r'$H=%d$, $Z=%.2f$, $S_f=%.2f$' %
(config.count_hubs, config.Z,
statistics.vars['formation_success_rate']))
plt.savefig(fig_path, bbox_inches='tight')
def execute():
planes = run.single_run()
segments = create_segments(planes)
plt, ax = run.render(segments)
name = 'count_hubs_%d' % config.count_hubs
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, name)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.title(r'$H=%d$, $Z=%.2f$, $S_f=%.2f$' % (
config.count_hubs,
config.Z,
statistics.vars['formation_success_rate']
))
#plt.show()
plt.savefig(fig_path, bbox_inches='tight')
def run():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(open(data_file, 'rb'), delimiter="\t", skiprows=1)
axis_x = config.axis_x
axis_y = config.axis_y
i = 0
for axis_z in config.interesting_z_axes:
i += 1
print 'Plotting %s (%d of %d)' % (axis_z['column'], i,
len(config.interesting_z_axes))
plt.figure()
x = data[:, tsv_get_column_index(data_file, axis_x['column'])]
y = data[:, tsv_get_column_index(data_file, axis_y['column'])]
z = data[:, tsv_get_column_index(data_file, axis_z['column'])]
# Note that we must convert the lock time into the lock distance L
if axis_x['column'] == 'config_lock_time':
x = 300 * x / 60
if axis_y['column'] == 'config_lock_time':
y = 300 * y / 60
try:
nx = config.output_nx
ny = config.output_ny
except AttributeError:
N = len(z)
nx = math.sqrt(N)
ny = nx
#
#print 'variable: %s, nx = %d, ny = %d, count z = %d. z = %s' % (
# axis_z['column'],
# nx, ny, len(z), z
#)
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
z = z.reshape(nx, ny)
plt.xlabel(axis_x['name'])
plt.ylabel(axis_y['name'])
plt.grid(True)
try:
cs = plt.contour(x, y, z, axis_z['levels'])
except KeyError:
cs = plt.contour(x, y, z, 10)
plt.clabel(cs)
plt.colorbar()
#plt.title(r'%s ($n=%d$)' % (axis_z['name'], config.count_hubs))
t = plt.title(r'%s' % axis_z['name'])
#t.set_y(1.03)
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, axis_z['column'])
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
print fig_path
#plt.show()
#return
plt.savefig(fig_path)
def plot_flow_rate(data):
timestamps = []
values = []
for key in sorted(data.iterkeys()):
timestamps.append(key)
values.append(data[key])
# For testing/debugging
#timestamps = np.arange(0, 1440, 60)
#values = np.linspace(0, 35, len(timestamps))
time_labels = []
for timestamp in timestamps:
# Normalize time to be reset after midnight (if > 1440, then subtract 1440)
timestamp = timestamp % 1440
hours = math.floor(timestamp / 60)
minutes = math.floor(timestamp - hours * 60)
time_labels.append('%02d:%02d' % (hours, minutes))
plt.rc(('xtick.major', 'ytick.major'), pad = 10)
plt.bar(
timestamps,
values,
width = interval_length - 5,
linewidth = 0,
color = '#999999'
)
t = plt.title(r'Traffic Density at hub ($C_{min}=%s$)' % config.min_P)
#t.set_y(1.03)
plt.subplots_adjust(top = 0.85, bottom = 0.2)
plt.grid()
plt.xlabel(r'Time of day (UTC)', labelpad = 10)
plt.ylabel(r'Number of flights', labelpad = 5)
plt.xlim(0, 1440)
plt.ylim(0, 40)
xt = plt.xticks(
[0,
240,
480,
720,
960,
1200,
1440],
['00:00',
'04:00',
'08:00',
'12:00',
'16:00',
'18:00',
'22:00',
'00:00']
)
fig_path = '%s/plot_%s.pdf' % (
config.sink_dir,
str(config.min_P).replace('.','_')
)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
#plt.show()
plt.savefig(
fig_path,
bbox_inches='tight'
)
def run():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(
open(data_file, 'rb'),
delimiter = "\t",
skiprows = 1
)
axis_x = config.axis_x
axis_y = config.axis_y
i = 0
for axis_z in config.interesting_z_axes:
i += 1
print 'Plotting %s (%d of %d)' % (
axis_z['column'], i, len(config.interesting_z_axes)
)
plt.figure()
x = data[:, tsv_get_column_index(data_file, axis_x['column'])]
y = data[:, tsv_get_column_index(data_file, axis_y['column'])]
z = data[:, tsv_get_column_index(data_file, axis_z['column'])]
# Note that we must convert the lock time into the lock distance L
if axis_x['column'] == 'config_lock_time':
x = 300 * x / 60
if axis_y['column'] == 'config_lock_time':
y = 300 * y / 60
try:
nx = config.output_nx
ny = config.output_ny
except AttributeError:
N = len(z)
nx = math.sqrt(N)
ny = nx
#
#print 'variable: %s, nx = %d, ny = %d, count z = %d. z = %s' % (
# axis_z['column'],
# nx, ny, len(z), z
#)
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
z = z.reshape(nx, ny)
plt.xlabel(axis_x['name'])
plt.ylabel(axis_y['name'])
plt.grid(True)
try:
cs = plt.contour(x, y, z, axis_z['levels'])
except KeyError:
cs = plt.contour(x, y, z, 10)
plt.clabel(cs)
plt.colorbar()
#plt.title(r'%s ($n=%d$)' % (axis_z['name'], config.count_hubs))
t = plt.title(r'%s' % axis_z['name'])
#t.set_y(1.03)
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, axis_z['column'])
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
print fig_path
#plt.show()
#return
plt.savefig(fig_path)
def run():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(
open(data_file, 'rb'),
delimiter = "\t",
skiprows = 1
)
j = 0
for axis_y in config.axes_y:
print 'Plotting %s (%d of %d)' % (
axis_y['column'], (j+1), len(config.axes_y)
)
axis_x = config.axis_x
fig = plt.figure(j)
ax = fig.add_subplot(111)
x = data[:, tsv_get_column_index(data_file, axis_x['column'])]
y = data[:, tsv_get_column_index(data_file, axis_y['column'])]
# Note that we must convert the lock time into the lock distance L
if axis_x['column'] == 'config_lock_time':
raise Error('lock time needs conversion')
nx = len(config.x)
ny = len(config.y)
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
plt.xlabel(axis_x['name'])
plt.ylabel(axis_y['name'])
i = 0
for line in config.x:
horizontal = x[0,:]
vertical = y[i,:]
plt.plot(horizontal, vertical, label = config.legend % line)
i += 1
plt.grid(True)
t = plt.title(r'%s' % axis_y['name'])
#t.set_y(1.03)
#plt.subplots_adjust(top = 0.85, bottom = 0.2)
lgd = ax.legend(
loc='upper center',
bbox_to_anchor=(0.5,-0.15),
ncol = 2
)
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, axis_y['column'])
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
locs, labels = plt.yticks()
try:
f = axis_y['yticks_formatter']
plt.yticks(locs, f(locs, labels))
except KeyError:
pass
#plt.show()
#return
plt.savefig(
fig_path,
bbox_extra_artists=(lgd,),
bbox_inches='tight'
)
j += 1
def do_plot(plotconf, data):
plt.figure()
column = plotconf['column']
print 'Drawing plot for %s' % column
data_file = '%s/latest.tsv' % config.sink_dir
x = data[:, tsv_get_column_index(data_file, 'hub_lon')]
y = data[:, tsv_get_column_index(data_file, 'hub_lat')]
z = data[:, tsv_get_column_index(data_file, column)]
minlat = np.min(y)
maxlat = np.max(y)
minlon = np.min(x)
maxlon = np.max(x)
m = Basemap(projection='merc',
resolution='c',
llcrnrlat=minlat,
urcrnrlat=maxlat,
llcrnrlon=minlon,
urcrnrlon=maxlon)
# Reverse Y-axis (high lat = low y)
#y = y[::-1]
#x, y = np.meshgrid(x, y)
N = len(z)
#print N
nx = math.sqrt(N)
ny = nx
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
z = z.reshape(nx, ny)
#print x
#print y
#print z
m.drawcoastlines()
m.drawstates()
m.drawcountries()
# draw lat/lon grid lines every 10 degrees.
parallels = np.arange(-360., 360., 10.)
meridians = np.arange(-360., 360., 20.)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
# Midpoints were determined in runs/singlehub/trunk
#midpoint_origins = Point(50., 8.)
midpoint_origins = Waypoint('LUX')
#midpoint_destinations = Point(35.,-87.)
midpoint_destinations = Waypoint('PIT')
lon1 = midpoint_origins.lon
lat1 = midpoint_origins.lat
lon2 = midpoint_destinations.lon
lat2 = midpoint_destinations.lat
# Draw the trunk route
m.drawgreatcircle(lon1, lat1, lon2, lat2, linewidth=3, color='#000000')
x, y = m(x, y)
cs = m.contourf(x, y, z, plotconf['levels'])
plt.colorbar(cs, orientation='vertical', shrink=0.65)
plt.title(plotconf['title'])
#plt.show()
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, column)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def do_plot(plotconf, data):
plt.figure()
column = plotconf['column']
print 'Drawing plot for %s' % column
data_file = '%s/latest.tsv' % config.sink_dir
x = data[:, tsv_get_column_index(data_file, 'hub_lon')]
y = data[:, tsv_get_column_index(data_file, 'hub_lat')]
z = data[:, tsv_get_column_index(data_file, column)]
minlat = np.min(y)
maxlat = np.max(y)
minlon = np.min(x)
maxlon = np.max(x)
m = Basemap(
projection = 'merc', resolution = 'c',
llcrnrlat = minlat, urcrnrlat = maxlat,
llcrnrlon = minlon, urcrnrlon = maxlon
)
# Reverse Y-axis (high lat = low y)
#y = y[::-1]
#x, y = np.meshgrid(x, y)
N = len(z)
#print N
nx = math.sqrt(N)
ny = nx
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
z = z.reshape(nx, ny)
#print x
#print y
#print z
m.drawcoastlines()
m.drawstates()
m.drawcountries()
# draw lat/lon grid lines every 10 degrees.
parallels = np.arange(-360.,360.,10.)
meridians = np.arange(-360.,360.,20.)
m.drawparallels(parallels,labels=[1,0,0,0])
m.drawmeridians(meridians,labels=[0,0,0,1])
# Midpoints were determined in runs/singlehub/trunk
#midpoint_origins = Point(50., 8.)
midpoint_origins = Waypoint('LUX')
#midpoint_destinations = Point(35.,-87.)
midpoint_destinations = Waypoint('PIT')
lon1 = midpoint_origins.lon
lat1 = midpoint_origins.lat
lon2 = midpoint_destinations.lon
lat2 = midpoint_destinations.lat
# Draw the trunk route
m.drawgreatcircle(lon1, lat1, lon2, lat2, linewidth = 3, color='#000000')
x, y = m(x, y)
cs = m.contourf(x, y, z, plotconf['levels'])
plt.colorbar(cs, orientation = 'vertical', shrink = 0.65)
plt.title(plotconf['title'])
#plt.show()
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, column)
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
plt.savefig(fig_path, bbox_inches='tight')
def run():
data_file = '%s/latest.tsv' % config.sink_dir
data = np.loadtxt(open(data_file, 'rb'), delimiter="\t", skiprows=1)
j = 0
for axis_y in config.axes_y:
print 'Plotting %s (%d of %d)' % (axis_y['column'],
(j + 1), len(config.axes_y))
axis_x = config.axis_x
fig = plt.figure(j)
ax = fig.add_subplot(111)
x = data[:, tsv_get_column_index(data_file, axis_x['column'])]
y = data[:, tsv_get_column_index(data_file, axis_y['column'])]
# Note that we must convert the lock time into the lock distance L
if axis_x['column'] == 'config_lock_time':
raise Error('lock time needs conversion')
nx = len(config.x)
ny = len(config.y)
x = x.reshape(nx, ny)
y = y.reshape(nx, ny)
plt.xlabel(axis_x['name'])
plt.ylabel(axis_y['name'])
i = 0
for line in config.x:
horizontal = x[0, :]
vertical = y[i, :]
plt.plot(horizontal, vertical, label=config.legend % line)
i += 1
plt.grid(True)
t = plt.title(r'%s' % axis_y['name'])
#t.set_y(1.03)
#plt.subplots_adjust(top = 0.85, bottom = 0.2)
lgd = ax.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.15),
ncol=2)
fig_path = '%s/plot_%s.pdf' % (config.sink_dir, axis_y['column'])
fig_path = fig_path.replace('/runs/', '/plots/')
fig_path = fig_path.replace('/sink/', '/')
make_sure_path_exists(os.path.dirname(fig_path))
locs, labels = plt.yticks()
try:
f = axis_y['yticks_formatter']
plt.yticks(locs, f(locs, labels))
except KeyError:
pass
#plt.show()
#return
plt.savefig(fig_path, bbox_extra_artists=(lgd, ), bbox_inches='tight')
j += 1
