/
mp_vis.py
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/
mp_vis.py
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import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
root = os.path.dirname(os.path.realpath(__file__))
bgimg = plt.imread(root + "/extra/bg.png")
def plot_ac(mp, ax, zlevel):
mask = (mp.AC_Z > zlevel - mp.GRID_BOND_Z) & (mp.AC_Z < zlevel + mp.GRID_BOND_Z)
if len(mp.AC_X[mask]) < 2:
return
for i, (x, y, vx, vy) in enumerate(
zip(mp.AC_X[mask], mp.AC_Y[mask], mp.AC_WX[mask], mp.AC_WY[mask])
):
ax.scatter(x, y, c="k")
ax.arrow(
x, y, vx / 3, vy / 3, lw=2, head_width=10, head_length=10, ec="k", fc="k"
)
# cir = plt.Circle((x, y), radius=np.sqrt(AREA*0.8),
# color='k', fc='none', ls='--', lw=2)
# ax.add_patch(cir)
ax.set_xlim(mp.AREA_XY)
ax.set_ylim(mp.AREA_XY)
ax.set_aspect("equal")
def plot_particle_samples(mp, ax, zlevel, sample=10, draw_hdg=None):
mask = (mp.PTC_Z > zlevel - mp.GRID_BOND_Z) & (mp.PTC_Z < zlevel + mp.GRID_BOND_Z)
if len(mp.PTC_X[mask]) < 2:
return
sortidx = np.argsort(mp.PTC_AGE[mask][::sample])[::-1]
xs = mp.PTC_X[mask][::sample][sortidx]
ys = mp.PTC_Y[mask][::sample][sortidx]
vxs = mp.PTC_WX[mask][::sample][sortidx]
vys = mp.PTC_WY[mask][::sample][sortidx]
ages = mp.PTC_AGE[mask][::sample][sortidx]
if max(ages) == min(ages):
Color = "gray"
else:
Color = cm.Blues(1 + 0.2 - (ages - min(ages)) / (max(ages) - min(ages)))
ax.scatter(xs, ys, s=3, color=Color)
if draw_hdg:
for i, (x, y, vx, vy) in enumerate(zip(xs, ys, vxs, vys)):
ax.plot([x, x + vx / 2], [y, y + vy / 2], color="k", alpha=0.5, lw=1)
ax.set_xlim(mp.AREA_XY)
ax.set_ylim(mp.AREA_XY)
ax.set_aspect("equal")
def plot_wind_grid_at_z(mp, ax, zlevel, data=None, barbs=False, scale=1, showbg=True):
xs, ys, zs, vxs, vys, temps, confws, confts = (
mp.construct() if data is None else data
)
mask1 = (zs == zlevel) & np.isfinite(vxs)
mask2 = (zs == zlevel) & np.isnan(vxs)
if showbg:
ax.imshow(bgimg, extent=[-300, 300, -300, 300])
ax.scatter(xs[mask1], ys[mask1], s=4, color="k")
ax.scatter(xs[mask2], ys[mask2], s=4, color="grey", facecolors="none")
if barbs:
ax.barbs(
xs[mask1], ys[mask1], vxs[mask1], vys[mask1], length=5 * scale, lw=1 * scale
)
else:
ax.quiver(
xs[mask1],
ys[mask1],
vxs[mask1] * 0.7 * scale,
vys[mask1] * 0.7 * scale,
color="k",
)
vmean = np.mean(np.sqrt(vxs[mask1] ** 2 + vys[mask1] ** 2))
vmean = 0 if np.isnan(vmean) else vmean
ax.set_aspect("equal")
ax.set_title("H: %d km | $\\bar v_w$: %d m/s" % (zlevel, vmean), fontsize=10)
def plot_wind_confidence(mp, ax, zlevel, data=None, nxy=None, colorbar=True):
xs, ys, zs, vxs, vys, temps, confws, confts = (
mp.construct() if data is None else data
)
mask = zs == zlevel
x = xs[mask]
y = ys[mask]
confw = confws[mask]
if nxy is None:
nx = ny = int(np.sqrt(len(x)))
else:
nx, ny = nxy
CSw = ax.contourf(
x.reshape(nx, ny),
y.reshape(nx, ny),
confw.reshape(nx, ny),
levels=np.linspace(0, 1, 10),
# vmin=0, vmax=1,
cmap=cm.get_cmap(cm.BuGn),
alpha=0.8,
)
if colorbar:
CB = plt.colorbar(CSw, fraction=0.046, pad=0.01)
CB.set_ticks(np.linspace(0, 1, 10), update_ticks=True)
ax.set_aspect("equal")
def plot_temperature_at_z(
mp, ax, zlevel, data=None, nxy=None, colorbar=True, showbg=True
):
xs, ys, zs, vxs, vys, temps, confws, confts = (
mp.construct() if data is None else data
)
temps = temps - 273.15
tmin = np.nanmean(temps)
tmax = np.nanmax(temps)
mask = zs == zlevel
x = xs[mask]
y = ys[mask]
temp = temps[mask]
conft = confts[mask]
if nxy is None:
nx = ny = int(np.sqrt(len(x)))
else:
nx, ny = nxy
norm = matplotlib.colors.Normalize(vmin=tmin, vmax=tmax)
color_map = cm.get_cmap("jet")
color = color_map(norm(temp))
color[np.isnan(temp)] = (1, 1, 1, 1)
tmean = np.nanmean(temp)
tmean = "n/a" if np.isnan(tmean) else "%d C$^\circ$" % tmean
if showbg:
ax.imshow(bgimg, extent=[-300, 300, -300, 300])
ax.scatter(x, y, color=color)
# ax.contourf(
# x.reshape(nx, ny),
# y.reshape(nx, ny),
# temp.reshape(nx, ny),
# cmap=cm.get_cmap(cm.Reds),
# alpha=0.6
# )
ax.set_aspect("equal")
ax.set_title("H: %d km | $\\bar T$: %s" % (zlevel, tmean), fontsize=10)
def plot_all_level_wind(
mp,
data=None,
nxy=None,
return_plot=False,
landscape_view=False,
barbs=False,
showbg=True,
):
if data is None:
data = mp.construct()
zlevels = np.unique(data[2])
n = int(np.sqrt(len(zlevels)))
for i, z in enumerate(zlevels):
if landscape_view:
ax = plt.subplot(n, n + 1, i + 1)
else:
ax = plt.subplot(n + 1, n, i + 1)
plot_wind_confidence(mp, ax, z, data=data, nxy=nxy, colorbar=False)
plot_wind_grid_at_z(mp, ax, z, data=data, barbs=barbs, showbg=showbg)
ax.set_xticks([])
ax.set_yticks([])
if not return_plot:
plt.tight_layout()
plt.show()
else:
return plt
def plot_all_level_temp(
mp, data=None, nxy=None, return_plot=False, landscape_view=False
):
if data is None:
data = mp.construct()
zlevels = np.unique(data[2])
n = int(np.sqrt(len(zlevels)))
for i, z in enumerate(zlevels):
if landscape_view:
ax = plt.subplot(n, n + 1, i + 1)
else:
ax = plt.subplot(n + 1, n, i + 1)
plot_temperature_at_z(mp, ax, z, data=data, nxy=nxy, colorbar=False)
ax.set_xticks([])
ax.set_yticks([])
if not return_plot:
plt.tight_layout()
plt.show()
else:
return plt
def draw_map():
from mpl_toolkits.basemap import Basemap
lat0 = 51.989884
lon0 = 4.375374
m = Basemap(
width=600000,
height=600000,
resolution="i",
projection="stere",
lat_0=lat0,
lon_0=lon0,
)
m.drawcoastlines(color="grey", linewidth=1)
m.drawcountries(color="grey", linewidth=1)
m.fillcontinents(color="#eeeeee")
plt.show()
def gpr(res):
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import (
RBF,
WhiteKernel,
ConstantKernel,
Matern,
)
xs, ys, zs, data = res
nlows = [5, 5, 1]
for d in data:
if d.shape[0] < 20:
continue
X = d[:, 0:1]
X_ = np.arange(min(X), 300, 0.1).reshape(-1, 1)
Y = d[:, 1:]
for i in range(3):
y = Y[:, i]
k = (
ConstantKernel()
+ 10 ** 2 * RBF(length_scale=10.0, length_scale_bounds=(10.0, 100.0))
+ WhiteKernel(noise_level_bounds=(nlows[i], 20.0))
)
gpr = GaussianProcessRegressor(kernel=k)
gpr.fit(X, y)
print(gpr.kernel_)
y_pred, y_std = gpr.predict(X_, return_std=True)
plt.subplot(3, 1, i + 1)
plt.scatter(X, y, c="k", s=5)
plt.plot(X_, y_pred)
plt.fill_between(
X_[:, 0], y_pred - y_std, y_pred + y_std, alpha=0.5, color="k"
)
plt.xlim(X_.min(), X_.max())
plt.tight_layout()
plt.draw()
plt.waitforbuttonpress(-1)
plt.clf()