def animate(t):
global pf
n, s = divmod(t, numrec)
if s == 0:
pf = ParticleFile(particle_files[n])
X, Y = pf.position(s)
particle_dist.set_data(X, Y)
timestamp.set_text(pf.time(s))
return particle_dist, timestamp
ax = plt.axes(xlim=(0, grid.imax), ylim=(0, grid.jmax), aspect="equal")
# Landmask
Xb = np.arange(-0.5, grid.imax)
Yb = np.arange(-0.5, grid.jmax)
constmap = plt.matplotlib.colors.ListedColormap([0.2, 0.6, 0.4])
M = np.ma.masked_where(grid.M > 0, grid.M)
plt.pcolormesh(Xb, Yb, M, cmap=constmap)
# Draw the cirular boundary
T = np.linspace(0, np.pi)
plt.plot(grid.X0 + grid.R * np.cos(T), grid.R * np.sin(T), color="black")
# Plot initial particle distribution
X, Y = pf.position(0)
particle_dist, = ax.plot(X, Y, ".", color="red", markeredgewidth=0.5, lw=0.5)
time0 = pf.time(0) # Save start-time
timestr = "00:00"
timestamp = ax.text(0.02, 0.93, timestr, fontsize=16, transform=ax.transAxes)
# Update function
def animate(t):
X, Y = pf.position(t)
particle_dist.set_data(X, Y)
# Time since start in minutes
# dtime = int((pf.time(t) - time0).total_seconds() / 60)
dtime = (pf.time(t) - time0) / 60
dtimestr = str(dtime)
timestamp.set_text(dtimestr)
return particle_dist, timestamp
f.close()
jmax, imax = H.shape
# ---------------------
# Read particle file
# ---------------------
pf = ParticleFile(particle_file)
# Find record numbers
n0 = -99
# n1 = -99
for n in range(pf.num_times):
if pf.time(n) < date0:
continue
if n0 < 0: # First time
n0 = n
if pf.time(n) < date1:
n1 = n
print("start: ", n0, pf.time(n0))
print("stop : ", n1, pf.time(n1))
first = True
for n in range(n0, n1 + 1):
print(n)
X0, Y0 = pf.position(n)
S0 = pf["super", n]
A = pf["age", n]
constmap = plt.matplotlib.colors.ListedColormap([0.2, 0.6, 0.4])
M = np.ma.masked_where(M > 0, M)
plt.pcolormesh(Xb, Yb, M, cmap=constmap)
# Plot border of submodel
# plt.plot([135, 172, 172, 135, 135],
# [42, 42, 81, 81, 42],
# color='black')
# Plot initial particle distribution
X, Y = pf.position(0)
particle_dist, = ax.plot(X, Y, ".", color="red", markeredgewidth=0, lw=0.5)
# title = ax.set_title(pf.time(0))
timestamp = ax.text(0.01,
0.97,
pf.time(0),
fontsize=15,
transform=ax.transAxes)
# Update function
def animate(t):
X, Y = pf.position(t)
particle_dist.set_data(X, Y)
timestamp.set_text(pf.time(t))
return particle_dist, timestamp
anim_running = True
ax.contourf(Xcell, Ycell, H, cmap=cmap, alpha=0.3)
# Lon/lat lines
ax.contour(Xcell, Ycell, lat, levels=range(56, 83, 2), colors="black", linestyles=":")
ax.contour(Xcell, Ycell, lon, levels=range(-4, 40, 5), colors="black", linestyles=":")
# Landmask
constmap = plt.matplotlib.colors.ListedColormap([0.2, 0.6, 0.4])
M = np.ma.masked_where(M > 0, M)
plt.pcolormesh(Xb, Yb, M, cmap=constmap)
# Plot initial particle distribution
X, Y = pf.position(0)
particle_dist, = ax.plot(X, Y, ".", color="red", markeredgewidth=0, lw=0.5)
# title = ax.set_title(pf.time(0))
timestamp = ax.text(0.01, 0.92, pf.time(0), fontsize=15, transform=ax.transAxes)
# Update function
def animate(t):
X, Y = pf.position(t)
particle_dist.set_data(X, Y)
timestamp.set_text(pf.time(t))
return particle_dist, timestamp
# Do the animation
anim = FuncAnimation(
fig,
animate,
frames=num_times,
# ROMS grid, plot domain
# Slight overkill to use roppy, could be more stand alone
f0 = Dataset(grid_file)
g = roppy.SGrid(f0, subgrid=(i0, i1, j0, j1))
# particle_file
pf = ParticleFile(particle_file)
fig = plt.figure(figsize=(12, 10))
ax = fig.add_subplot(1, 1, 1)
# Make background map
cmap = plt.get_cmap("Blues")
h = ax.contourf(g.X, g.Y, g.h, cmap=cmap, alpha=0.3)
roppy.mpl_util.landmask(g, color=(0.6, 0.8, 0.0))
ax.contour(g.X, g.Y, g.lat_rho, levels=range(57, 64), colors="black", linestyles=":")
ax.contour(g.X, g.Y, g.lon_rho, levels=range(-4, 10, 2), colors="black", linestyles=":")
# Plot particle distribution
X, Y = pf.position(time=t)
timestring = pf.time(t)
ax.plot(X, Y, ".", color="red", markeredgewidth=0, lw=0.5)
ax.set_title(timestring)
# Show the results
plt.axis("image")
plt.axis((i0 + 1, i1 - 1, j0 + 1, j1 - 1))
plt.show()
cmap = plt.get_cmap("Blues")
h = ax.contourf(Xcell, Ycell, H, cmap=cmap, alpha=0.3)
# Landmask
constmap = plt.matplotlib.colors.ListedColormap([0.2, 0.6, 0.4])
M = np.ma.masked_where(M > 0, M)
plt.pcolormesh(Xb, Yb, M, cmap=constmap)
# Graticule
ax.contour(Xcell,
Ycell,
lat,
levels=range(55, 64),
colors="black",
linestyles=":")
ax.contour(Xcell,
Ycell,
lon,
levels=range(-4, 10, 2),
colors="black",
linestyles=":")
# Plot particle distribution
ax.plot(X, Y, ".", color="red", markeredgewidth=0, lw=0.5)
ax.set_title(pf.time(t))
# Show the results
plt.axis("image")
plt.axis((i0 + 1, i1 - 1, j0 + 1, j1 - 1))
plt.show()
lat = f0.variables["lat_rho"][j0:j1, i0:i1]
# Cell centers and boundaries
Xcell = np.arange(i0, i1)
Ycell = np.arange(j0, j1)
Xb = np.arange(i0 - 0.5, i1)
Yb = np.arange(j0 - 0.5, j1)
# particle_files
particle_files = glob.glob(f"{particle_file[:-3]}*.nc")
particle_files.sort()
# Inital particle distribution
pf = ParticleFile(particle_files[0])
X, Y = pf.position(0)
timestr = pf.time(0)
pf.close()
# Suppose all files (except possible last) has same number of records
numrec = pf.num_times
num_times = (len(particle_files) - 1) * numrec
# Add the records from the last file
pf = ParticleFile(particle_files[-1])
num_times += pf.num_times
# Set up the plot area
fig = plt.figure(figsize=(12, 10))
ax = plt.axes(xlim=(i0 + 1, i1 - 1), ylim=(j0 + 1, j1 - 1), aspect="equal")
# Background bathymetry
cmap = plt.get_cmap("Blues")
ax.contourf(Xcell, Ycell, H, cmap=cmap, alpha=0.3)