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
# Set up the plot area
fig = plt.figure(figsize=(12, 8))
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)
# 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]
I = (ddmin <= A) & (A < ddmax)
if first:
X = X0[I]
Y = Y0[I]
S = S0[I]
first = False
else:
X = np.concatenate((X, X0[I]))
Y = np.concatenate((Y, Y0[I]))
S = np.concatenate((S, S0[I]))
# pf.close()
tstep = 40 # Time step to show
# Output file (and type)
output_file = "line_hv.png"
#output_file = "line_hv.html"
scale = 5 # Figure size factor
# --- Data files ---
ladim_dir = Path("../../")
grid_file = ladim_dir / "examples/data/ocean_avg_0014.nc"
particle_file = ladim_dir / "examples/line/line.nc"
# --- Read particle data ---
pf = ParticleFile(particle_file)
X, Y = pf.position(tstep)
# --- Background bathymetry data ---
# Read bathymetry and land mask
with xr.open_dataset(grid_file) as A:
H = A.h
M = A.mask_rho
jmax, imax = M.shape
H = H.where(M > 0) # Mask out land
M = M.where(M < 1) # Mask out sea
# --- Holoviews elements ---
# Land image
land = hv.Image(data=M, kdims=["xi_rho", "eta_rho"], group="Land")
ax.contourf(Xcell, Ycell, H, cmap=cmap, alpha=0.3)
# Lon/lat lines
ax.contour(Xcell,
Ycell,
lat,
levels=range(57, 64),
colors="black",
linestyles=":")
ax.contour(Xcell,
Ycell,
lon,
levels=range(-4, 10, 2),
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)
# Particle distribution after 48 hours
X, Y = pf.position(48)
particle_dist, = ax.plot(X, Y, ".", color="red", markeredgewidth=0, lw=0.5)
plt.xticks([])
plt.yticks([])
plt.savefig("logo.png", dpi=200, bbox_inches="tight")
plt.show()
# 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()
ax = fig.add_subplot(1, 1, 1)
# Make background map
cmap = plt.get_cmap("Blues")
ax.contourf(g.X, g.Y, g.h, cmap=cmap, alpha=0.3)
roppy.mpl_util.landmask(g, (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 initial particle distribution
X, Y = pf.position(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()
# ----------------
# particle_file
pf0 = ParticleFile(unsplit_file)
pf1 = ParticleFile(split_file)
pf2 = ParticleFile(restarted_file)
fig = plt.figure(figsize=(12, 10))
ax = fig.add_subplot(1, 1, 1)
# Make bathymetry background
with Dataset(grid_file) as f0:
H = f0.variables["h"][j0:j1, i0:i1]
cmap = plt.get_cmap("Blues")
ax.contourf(range(i0, i1), range(j0, j1), H, cmap=cmap, alpha=0.3)
# Plot initial particle distribution
X0, Y0 = pf0.position(t0)
X1, Y1 = pf1.position(t1)
X2, Y2 = pf2.position(t2)
ax.plot(X0, Y0, "o", color="green", label="unsplit")
ax.plot(X1, Y1, "o", color="red", label="split")
ax.plot(X2, Y2, "o", color="blue", label="restart")
# Show the results
plt.legend()
plt.axis("image")
plt.axis((i0 + 1, i1 - 1, j0 + 1, j1 - 1))
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from postladim import ParticleFile, cellcount
pf = ParticleFile("line.nc")
X, Y = pf.position(80)
C = cellcount(X, Y)
i0 = int(round(X.min()))
j0 = int(round(Y.min()))
jmax, imax = C.shape
x_edges = np.arange(i0 - 0.5, i0 + imax)
y_edges = np.arange(j0 - 0.5, j0 + jmax)
plt.set_cmap("magma_r")
plt.pcolormesh(x_edges, y_edges, C)
plt.colorbar()
plt.plot(X, Y, ".k")
plt.show()