def plot_dp(storm, datafile1, datafile2=None):
#Single file netCDF reading
ncf=datafile1
nco=netCDF4.Dataset(ncf)
#Get fields to plot
lon=nco.variables['longitude'][:]
lat=nco.variables['latitude'][:]
timeindays=nco.variables['time'][:]
dp=nco.variables['dp'][:]
triangles=nco.variables['tri'][:,:]
reflon=np.linspace(lon.min(),lon.max(),1000)
reflat=np.linspace(lat.min(),lat.max(),1000)
#reflon=np.linspace(-80.40, -74.75, 1000)
#reflat=np.linspace(32.50, 36.60, 1000)
#reflon=np.linspace(-75.70, -71.05, 1000)
#reflat=np.linspace(38.50, 41.40, 1000)
reflon,reflat=np.meshgrid(reflon,reflat)
plt.figure(figsize = [6.4, 3.8])
flatness=0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles=triangles-1 # Correct indices for Python's zero-base
tri=Triangulation(lon,lat,triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
dt = datetime.datetime.combine(datetime.date(1990, 1, 1), datetime.time(0, 0)) + datetime.timedelta(days=timeindays[ind])
dstr = datetime.date.strftime(dt,'%Y%m%d%H:%M:%S')
dstr = dstr[0:8]+' '+dstr[8:17]
print('Plotting '+dstr)
par=np.double(dp[ind,:])
tli=LinearTriInterpolator(tri,par)
par_interp=tli(reflon,reflat)
plt.pcolormesh(reflon,reflat,par_interp,vmin=0.0,vmax=360.0,shading='flat',cmap=plt.cm.jet, transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
coast = cfeature.GSHHSFeature(scale='high',edgecolor='black',facecolor='none',linewidth=0.25)
ax.add_feature(coast)
plt.xticks(fontsize=9)
plt.yticks(fontsize=9)
figtitle = storm.capitalize()+': Peak Dir (deg): '+dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt,'%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8]+'_'+dtlabel[8:14]
filenm = 'nsem_'+storm+'_dp_'+dtlabel+'.png'
plt.savefig(filenm,dpi=150,bbox_inches='tight',pad_inches=0.1)
del(par)
del(par_interp)
def plot_trisurf(data, out=None, ax=None, angles=None):
if ax is None:
fig = plt.figure()
ax = fig.gca(projection='3d')
Xs, Ys = np.meshgrid(data.coords[data.dims[0]].values,
data.coords[data.dims[1]].values)
triang = Triangulation(Xs.ravel(), Ys.ravel())
mask = np.any(np.isnan(data.transpose().values.ravel()[triang.triangles]),
axis=1)
triang.set_mask(mask)
values = data.copy(deep=True).transpose().values
values[np.isnan(values)] = 0
ax.plot_trisurf(triang,
values.ravel(),
antialiased=True,
cmap=cm.coolwarm,
linewidth=0)
if angles is not None:
ax.view_init(*angles)
#plt.show()
return ax
def mesh_tri(grid_file):
ncmesh = Dataset(grid_file,'r')
lon_mesh = np.rad2deg(ncmesh.variables['lonCell'][:])-360.0
#lon_mesh = np.rad2deg(np.mod(ncmesh.variables['lonCell'][:] + np.pi, 2.0*np.pi) - np.pi)
lat_mesh = np.rad2deg(ncmesh.variables['latCell'][:])
nEdgesOnCell = ncmesh.variables['nEdgesOnCell'][:]
cellsOnCell = ncmesh.variables['cellsOnCell'][:,:]
# Triangulate cells
triangles = Triangulation(lon_mesh,lat_mesh)
# Compute triangulation mask (needs to be vectorized)
mask = np.array(np.zeros((triangles.triangles.shape[0],)),dtype=bool)
ntri = triangles.neighbors.shape[0]
for i in range(ntri):
k = 0
for j in range(3):
n = triangles.triangles[i,j]
if nEdgesOnCell[n] != np.where(cellsOnCell[n,:] != 0)[0].size: # Mask triangles
k = k +1 # containing
if k == 3: # 3 boundary
mask[i] = True # cells
triangles.set_mask(mask)
return triangles
def plot(self, variable, show=False, index=None):
if index is None:
self.animation(
variable,
# show=True,
save='/home/jreniel/pyschism/examples/example_1/test.gif',
vmin=0,
vmax=3,
# start_frame=200,
# end_frame=300,
)
else:
var = self.nc[variable]
ugrid = UGrid.from_nc_dataset(self.nc)
x = ugrid.nodes[:, 0]
y = ugrid.nodes[:, 1]
triangulation = Triangulation(x, y, ugrid.faces[:, :3])
triangulation.set_mask(self.nc['wetdry_elem'][index])
plt.tricontourf(triangulation,
var[index, :],
levels=256,
cmap='jet')
plt.gca().axis('scaled')
if show:
plt.show()
def buildTriangulation( self, x, y ):
trig=None
if qgis_qhull_fails:
trig=self._buildtrig_workaround(x,y)
else:
trig=Triangulation(x,y)
analyzer=TriAnalyzer(trig)
mask=analyzer.get_flat_tri_mask()
trig.set_mask(mask)
return trig
def buildTriangulation( self, x, y ):
trig=None
if qgis_qhull_fails:
trig=self._buildtrig_workaround(x,y)
else:
trig=Triangulation(x,y)
analyzer=TriAnalyzer(trig)
mask=analyzer.get_flat_tri_mask()
trig.set_mask(mask)
return trig
def makeTri(projx, projy, max_l):
tri = Triangulation(projx, projy)
triangles = tri.triangles
# Mask off unwanted triangles.
xtri = projx[triangles] - np.roll(projx[triangles], 1, axis=1)
ytri = projy[triangles] - np.roll(projy[triangles], 1, axis=1)
ds = np.sqrt(xtri**2 + ytri**2)
maxi = np.max(ds, axis=1)
tdist = np.mean(ds)
tri.set_mask(maxi > max_l)
return tri, tdist
def generate_triangles(
topology, geometry, mask: Optional[Sequence[bool]] = None
) -> Triangulation:
"""Convert topology and geometry to triangles - optionally set mask as well.
:param topology: Topology of the triangles (connectivity)
:param geometry: Geometry of the triangles (location of the nodes)
:param mask: Valid matplotlib mask
:return: Triangulation object for plotting
"""
tri = Triangulation(geometry[:, 0], geometry[:, 1], topology)
if mask:
tri.set_mask(mask)
return tri
def plot_magnetic_field_contour(self, ax=None):
if ax is None:
fig, ax = plt.subplots()
else:
fig = plt.gcf()
ax.set_aspect('equal')
from matplotlib.tri import Triangulation, TriAnalyzer, UniformTriRefiner
import matplotlib.cm as cm
element_to_magnetic_field = self.magnetic_field_per_element()
x = []
y = []
Z = []
for group in self.mesh.elements_groups:
for element in group.elements:
x_center, y_center = element.center
x.append(x_center)
y.append(y_center)
Z.append(element_to_magnetic_field[element].Norm())
tri = Triangulation(x, y)
#-----------------------------------------------------------------------------
# Improving the triangulation before high-res plots: removing flat triangles
#-----------------------------------------------------------------------------
# masking badly shaped triangles at the border of the triangular mesh.
min_circle_ratio = -1
mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio)
tri.set_mask(mask)
# refining the data
refiner = UniformTriRefiner(tri)
subdiv = 3
tri_refi, z_test_refi = refiner.refine_field(Z, subdiv=subdiv)
levels = npy.arange(0., 1., 0.05)
cmap = cm.get_cmap(name='Blues', lut=None)
ax.tricontour(tri_refi, z_test_refi, levels=levels, #cmap=cmap,
linewidths=[2.0, 0.5, 1.0, 0.5])
# ax.triplot(tri_refi, color='0.97')
# ax.triplot(tri, color='0.7')
# ax.tricontour(x, y, Z)
return ax
def plot_samples(num):
samples_hundred = BM_sampling_method(num)
tri = Triangulation(samples_hundred[0], samples_hundred[1])
random_gen = np.random.mtrand.RandomState(seed=127260)
init_mask_frac = 0.0
min_circle_ratio = .01
subdiv = 3
ntri = tri.triangles.shape[0]
print 'hi'
mask_init = np.zeros(ntri, dtype=np.bool)
masked_tri = random_gen.randint(0, ntri, int(ntri * init_mask_frac))
mask_init[masked_tri] = True
tri.set_mask(mask_init)
print 'hey'
z_exp = experiment_res(tri.x, tri.y)
print z_exp
mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio)
tri.set_mask(mask)
# refining the data
refiner = UniformTriRefiner(tri)
tri_refi, z_test_refi = refiner.refine_field(z_exp, subdiv=subdiv)
# analytical 'results' for comparison
z_expected = experiment_res(tri_refi.x, tri_refi.y)
plt.tricontour(tri_refi,
z_expected,
levels=levels,
cmap=cmap,
linestyles='--')
plt.show()
x, y = np.mgrid[-1:1:0.001, -1:1:0.001]
pos = np.empty(x.shape + (2, ))
pos[:, :, 0] = x
pos[:, :, 1] = y
rv = multivariate_normal([0, 0], [[1.0, 0.0], [0.0, 1.0]])
plt.contour(x, y, rv.pdf(pos))
c2 = np.empty([num,3])
for i in range (num):
c1[i, 0] = b1[i, 0] - b1[i, 1] # X
c1[i, 1] = b1[i, 1] - b1[i, 2]
c1[i, 2] = b1[i, 0] - b1[i, 2]
c2[i, 0] = b2[i, 0] - b2[i, 1] # Y
c2[i, 1] = b2[i, 1] - b2[i, 2]
c2[i, 2] = b2[i, 0] - b2[i, 2]
c1 = c1 / 1.67e-04
c2 = c2 / 1.67e-04
n =np.hypot(c1,c2).sum(axis=1)
# tri.set_mask(n>0.004)
tri.set_mask(n > MA)
fig1, ax1 = plt.subplots()
ax1.set_aspect('equal')
ax1.triplot(tri, 'bo-', lw=1)
tcf = ax1.tricontourf(tri, z_test)
fig1.colorbar(tcf)
ax1.tricontour(tri, z_test, colors='k')
ax1.set_title('triplot of Delaunay triangulation')
plt.show()
newdata = gpd.GeoDataFrame()
newdata['geometry'] = None
def plot_wnd(storm, datafile1, datafile2=None):
#Single file netCDF reading
ncf = datafile1
nco = netCDF4.Dataset(ncf)
#Get fields to plot
lon = nco.variables['x'][:]
lat = nco.variables['y'][:]
timeinsec = nco.variables['time'][:]
uwnd = nco.variables['windx'][:]
vwnd = nco.variables['windy'][:]
triangles = nco.variables['element'][:, :]
reflon = np.linspace(lon.min(), lon.max(), 1000)
reflat = np.linspace(lat.min(), lat.max(), 1000)
#reflon=np.linspace(-80.40, -74.75, 1000)
#reflat=np.linspace(32.50, 36.60, 1000)
#reflon=np.linspace(-75.70, -71.05, 1000)
#reflat=np.linspace(38.50, 41.40, 1000)
#reflon=np.linspace(-80.40, -73.35, 1000)
#reflat=np.linspace(32.50, 39.50, 1000)
#reflon=np.linspace(-85.30, -78.50, 1000)
#reflat=np.linspace(23.00, 29.70, 1000)
reflon, reflat = np.meshgrid(reflon, reflat)
plt.figure(figsize=[6.4, 3.8])
flatness = 0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles = triangles - 1 # Correct indices for Python's zero-base
tri = Triangulation(lon, lat, triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeinsec)):
besttrack = pd.read_csv(PARMnsem + '/storms/' + STORM +
'/best_track.txt',
header=None,
skiprows=4,
delim_whitespace=True)
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
ax.set_extent([-100.00, -50.00, 4.00, 48.00], crs=ccrs.PlateCarree())
#ax.set_extent([-80.40, -74.75, 32.50, 36.60], crs=ccrs.PlateCarree())
#ax.set_extent([-80.40, -73.35, 32.50, 39.50], crs=ccrs.PlateCarree())
#ax.set_extent([-85.30, -78.50, 23.00, 29.70], crs=ccrs.PlateCarree())
dt = base_info.tide_spin_start_date + datetime.timedelta(
seconds=timeinsec[ind])
dstr = datetime.date.strftime(dt, '%Y%m%d%H:%M:%S')
dstr = dstr[0:8] + ' ' + dstr[8:17]
print('Plotting ' + dstr)
par = np.sqrt(
np.square(np.double(uwnd[ind, :])) +
np.square(np.double(vwnd[ind, :])))
tli = LinearTriInterpolator(tri, par)
par_interp = tli(reflon, reflat)
plt.pcolormesh(reflon,
reflat,
par_interp,
vmax=60.0,
shading='flat',
cmap=plt.cm.jet,
transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
coast = cfeature.GSHHSFeature(scale='high',
edgecolor='black',
facecolor='none',
linewidth=0.25)
ax.add_feature(coast)
plt.plot(besttrack.iloc[:, 3].values,
besttrack.iloc[:, 2].values,
'k--',
linewidth=1.0,
transform=ccrs.PlateCarree())
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlines = False
gl.ylines = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 6, 'color': 'black'}
gl.ylabel_style = {'size': 6, 'color': 'black'}
figtitle = storm.capitalize() + ': U10 (m/s): ' + dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt, '%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8] + '_' + dtlabel[8:14]
filenm = 'nsem_' + storm + '_wnd_' + dtlabel + '.png'
plt.savefig(filenm, dpi=150, bbox_inches='tight', pad_inches=0.1)
del (par)
del (par_interp)
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
triangulation = Triangulation(x, y)
xmid = x[triangulation.triangles].mean(axis=1)
ymid = y[triangulation.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triangulation.set_mask(mask)
# Use the triangulation's default TriFinder object.
trifinder = triangulation.get_trifinder()
# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triangulation, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)
plt.show()
def visualization(self, savename='figure'):
# --> Sets figure.
fig = plt.figure()
# Mesh arrays.
x = self.x[:self.nde]
y = self.y[:self.nde]
# Vorticity array.
vorticity = self.vorticity[:self.nde]
# Delaunay triangulation.
tri = Triangulation(x, y)
# Mask unwanted triangles.
radius = 0.5
xmid = tri.x[tri.triangles].mean(axis=1)
ymid = tri.y[tri.triangles].mean(axis=1)
xc1, yc1 = 0, 0.75
xc2, yc2 = 0, -0.75
xc3, yc3 = -1.5 * np.sqrt(0.75), 0
mask = np.where((xmid - xc1)**2 + (ymid - yc1)**2 < radius**2, 1, 0)
mask += np.where((xmid - xc2)**2 + (ymid - yc2)**2 < radius**2, 1, 0)
mask += np.where((xmid - xc3)**2 + (ymid - yc3)**2 < radius**2, 1, 0)
tri.set_mask(mask)
# Plot the vorticity field.
ax = fig.gca()
cmap = plt.cm.RdBu
h = ax.tripcolor(tri, vorticity, shading='gouraud', cmap=cmap)
h.set_clim(-4, 4)
ax.set_aspect('equal')
ax.set_xlim(-5, 20)
ax.set_ylim(-4, 4)
# Set up the colorbar.
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
cb = plt.colorbar(h, cax=cax)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
# Highlight the cylinders.
xc, yc, r = 0., 0.75, 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
xc, yc, r = 0., -0.75, 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
xc, yc, r = -1.5 * np.sqrt(0.75), 0., 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
# Titles, labels, ...
ax.set_xlabel(r'$x$')
ax.locator_params(axis='y', nbins=5)
ax.set_ylabel(r'$y$', rotation=0)
# Save the figure.
fig.savefig(savename + '.png', bbox_inches='tight', dpi=300)
return fig, ax
class BaseTrig:
"""" Base class to be inherited by CTrig (charges) and GTrig (FE calculation). The class handles loading
the triangulation, calculating relevant fields and plotting. Has two plotting functions: heatmap to
plot spatially varying fields and boundary_plot to plot just the boundary.
"""
def __init__(self, lattice, p, method):
self.method = method
self.lattice = lattice
self.p = p # porosity
self.holes = None # list of hole positions
self.boundary = None
self.N_holes = None
self.tri = None # A matplotlib Tri object.
self.real_nodes = None
self.mask = None # mask for Tri object, specifies which nodes to plot.
self.length_factor = None
def calc_tri(self):
self.N_holes = self.holes.shape[1]
self.tri = Triangulation(*self.lagrange_nodes(only_real=False))
self._set_mask()
def _set_mask(self, crop=np.inf):
# mask tris which involve a hole
c1 = self.tri.triangles.min(axis=1) <= self.N_holes
# mask tris with points outside crop
c2 = np.abs(self.tri.x[self.tri.triangles]).max(axis=1) > crop
c3 = np.abs(self.tri.y[self.tri.triangles]).max(axis=1) > crop
self.mask = c1 | c2 | c3
self.tri.set_mask(self.mask)
def lagrange_nodes(self, only_real=True):
if only_real:
return self.real_nodes
else:
return np.c_[self.holes, self.real_nodes]
def euler_nodes(self, only_real=True):
nodes = np.array([self.tri.x, self.tri.y])
if only_real:
nodes = nodes[:, self.N_holes:]
return nodes
def deform(self, strain, kind='lin'):
"""
Deforms the mesh. Kind specifiec by which field to deform the mesh. It can be either 'lin'
for linear response or 'mode' for most unstable mode. strain is the amplitude.
"""
if 'linear'.startswith(kind):
H = self.lagrange_nodes()[1].max() - self.lagrange_nodes()[1].min()
scale = H * strain / 2
sol = scale * self.d_lin
self.stress *= scale
elif 'mode'.startswith(kind):
sol = strain * self.d_mode
else:
raise ValueError(f'didnt understand kind={kind}')
all_nodes = self.lagrange_nodes(only_real=False)
self.tri.x = all_nodes[0] + self.append_shit(sol[0])
self.tri.y = all_nodes[1] + self.append_shit(sol[1])
def heatmap(self,
c,
ax=None,
cbar=True,
cmap=None,
clim=None,
cax=None,
crop=None):
if ax is None:
ax = plt.gca()
single_color = c is None
if single_color:
c = np.full_like(self.lagrange_nodes()[0], 0)
clim = [-1, 1]
color = (0.2980392156862745, 0.4470588235294118,
0.6901960784313725)
cmap = mpl.colors.ListedColormap((color, color))
if clim is None:
levels = np.linspace(c.min(), c.max(), 20)
else:
levels = np.linspace(*clim, 20)
if crop:
self._set_mask(crop)
if cmap is None and not single_color:
cmap = sns.color_palette("RdBu_r", 20)
cmap = mpl.colors.ListedColormap(cmap)
hf = ax.tricontourf(self.tri,
self.append_shit(c),
levels=levels,
cmap=cmap)
if not single_color:
hc = ax.tricontour(self.tri,
self.append_shit(c),
levels=levels,
colors=['0.25', '0.5', '0.5', '0.5', '0.5'],
linewidths=[1.0, 0.5, 0.5, 0.5, 0.5])
ax.set_aspect(1)
ax.axis('off')
if cbar:
if cax is None:
cbar = plt.colorbar(hf, ax=ax)
else:
cbar = plt.colorbar(hf, cax=cax)
return ax, cbar
def boundary_plot(self,
deformation='mode',
fill=True,
scale=1,
ax=None,
facecolor='b',
edgecolor='None',
alpha=1,
skip=1):
if deformation == 'mode':
d = self.d_mode[:, self.boundary]
elif deformation == 'lin':
d = self.d_lin[:, self.boundary]
if ax is None:
ax = plt.gca()
reference = self.lagrange_nodes()[:, self.boundary]
if hasattr(self, 'cyc'):
cyc = self.cyc
else:
print('no cyclifier, calculating')
cyc = Cyclifier(reference)
self.cyc = cyc
print('done')
pos = reference + scale * d
patch = patchify(cyc(pos, as_patches=True, skip=skip))
patch.set_facecolor(facecolor)
patch.set_edgecolor(edgecolor)
patch.set_alpha(alpha)
patch = ax.add_patch(patch)
ax.axis('off')
ax.set_xticks([])
ax.set_yticks([])
ax.axis('equal')
return patch
def append_shit(self, x, fill_value=None):
"""Prepend zeros to x on hole nodes."""
assert x.ndim == 1
if hasattr(x, 'values'):
x = x.values
if fill_value is None:
fill = x.mean().astype(x.dtype)
else:
fill = fill_value
return np.r_[fill + np.zeros(self.N_holes, dtype=x.dtype), x]
@property
def sxy(self):
return self.stress[2]
@property
def syy(self):
return self.stress[1]
@property
def sxx(self):
return self.stress[0]
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
triangulation = Triangulation(x, y)
xmid = x[triangulation.triangles].mean(axis=1)
ymid = y[triangulation.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triangulation.set_mask(mask)
# Use the triangulation's default TriFinder object.
trifinder = triangulation.get_trifinder()
# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triangulation, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)
plt.show()
def extract_wnd(storm,
datafile1,
stations,
df,
lonmin=None,
lonmax=None,
latmin=None,
latmax=None):
#Single file netCDF reading
#ncf='ww3.field.2018_wnd.nc'
ncf = datafile1
nco = netCDF4.Dataset(ncf)
#ncf2='ww3.field.2018_dp.nc'
#ncf2=datafile2
#nco2=netCDF4.Dataset(ncf2)
#Get fields to plot
lon = nco.variables['longitude'][:]
lat = nco.variables['latitude'][:]
pnt_lon = stations.iloc[0, :].values
pnt_lat = stations.iloc[1, :].values
timeindays = nco.variables['time'][:]
uwnd = nco.variables['uwnd'][:]
vwnd = nco.variables['vwnd'][:]
#timeindays2=nco2.variables['time'][:]
#dir=nco2.variables['dp'][:]
if lonmin != None:
reflon = np.linspace(lonmin, lonmax, 1000)
reflat = np.linspace(latmin, latmax, 1000)
else:
reflon = np.linspace(lon.min(), lon.max(), 1000)
reflat = np.linspace(lat.min(), lat.max(), 1000)
reflon, reflat = np.meshgrid(reflon, reflat)
flatness = 0.10 # flatness is from 0-.5 .5 is equilateral triangle
tri = Triangulation(lon, lat)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
#Read obs point locations
#data=np.loadtxt('erie_ndbc.loc', comments = '$')
#buoylon=data[:,0]
#buoylat=data[:,1]
plt.figure(figsize=[6.4, 3.8])
df_dates = pd.DataFrame(columns=['Date'])
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
dt = datetime.datetime.combine(datetime.date(
1990, 1, 1), datetime.time(
0, 0)) + datetime.timedelta(days=timeindays[ind])
dstr = datetime.date.strftime(dt, '%Y%m%d%H:%M:%S')
dstr = dstr[0:8] + ' ' + dstr[8:17]
print('Extracting ' + dstr)
par = np.sqrt(
np.square(np.double(uwnd[ind, :])) +
np.square(np.double(vwnd[ind, :])))
#par2=np.double(dir[ind,:])
tli = LinearTriInterpolator(tri, par)
par_interp = tli(pnt_lon, pnt_lat)
#print(par_interp)
df.loc[len(df)] = par_interp
df_dates.loc[len(df_dates)] = pd.to_datetime(
dt, format="%Y-%m-%d %H:%M:%S")
#line = pd.to_datetime(dt, format="%Y-%m-%d %H:%M:%S")
#new_row = pd.DataFrame(par_interp, columns=stations.columns, index=line)
#df = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
del (par)
del (par_interp)
df['Date'] = df_dates
df.set_index('Date', inplace=True)
return df
# Random points random_gen = np.random.mtrand.RandomState(seed=127260) x_test = random_gen.uniform(-1., 1., size=n_test) y_test = random_gen.uniform(-1., 1., size=n_test) z_test = experiment_res(x_test, y_test) # meshing with Delaunay triangulation tri = Triangulation(x_test, y_test) ntri = tri.triangles.shape[0] # Some invalid data are masked out mask_init = np.zeros(ntri, dtype=np.bool) masked_tri = random_gen.randint(0, ntri, int(ntri*init_mask_frac)) mask_init[masked_tri] = True tri.set_mask(mask_init) #----------------------------------------------------------------------------- # Improving the triangulation before high-res plots: removing flat triangles #----------------------------------------------------------------------------- # masking badly shaped triangles at the border of the triangular mesh. mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio) tri.set_mask(mask) # refining the data refiner = UniformTriRefiner(tri) tri_refi, z_test_refi = refiner.refine_field(z_test, subdiv=subdiv) # analytical 'results' for comparison z_expected = experiment_res(tri_refi.x, tri_refi.y)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
V = dipole_potential(x, y)
# Create the Triangulation; no triangles specified so Delaunay triangulation
# created.
triang = Triangulation(x, y)
# Mask off unwanted triangles.
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)
#-----------------------------------------------------------------------------
# Refine data - interpolates the electrical potential V
#-----------------------------------------------------------------------------
refiner = UniformTriRefiner(triang)
tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)
#-----------------------------------------------------------------------------
# Computes the electrical field (Ex, Ey) as gradient of electrical potential
#-----------------------------------------------------------------------------
tci = CubicTriInterpolator(triang, -V)
# Gradient requested here at the mesh nodes but could be anywhere else:
(Ex, Ey) = tci.gradient(triang.x, triang.y)
E_norm = np.sqrt(Ex**2 + Ey**2)
def plot_cur(storm, datafile1, datafile2=None):
#Single file netCDF reading
ncf=datafile1
nco=netCDF4.Dataset(ncf)
#ncf2=datafile2
#nco2=netCDF4.Dataset(ncf2)
#Get fields to plot
lon=nco.variables['longitude'][:]
lat=nco.variables['latitude'][:]
timeindays=nco.variables['time'][:]
ucur=nco.variables['ucur'][:]
vcur=nco.variables['vcur'][:]
triangles=nco.variables['tri'][:,:]
#timeindays2=nco2.variables['time'][:]
#dir=nco2.variables['dp'][:]
reflon=np.linspace(lon.min(),lon.max(),1000)
reflat=np.linspace(lat.min(),lat.max(),1000)
#reflon=np.linspace(-80.40, -74.75, 1000)
#reflat=np.linspace(32.50, 36.60, 1000)
#reflon=np.linspace(-75.70, -71.05, 1000)
#reflat=np.linspace(38.50, 41.40, 1000)
reflon,reflat=np.meshgrid(reflon,reflat)
plt.figure(figsize = [6.4, 3.8])
flatness=0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles=triangles-1 # Correct indices for Python's zero-base
tri=Triangulation(lon,lat,triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
dt = datetime.datetime.combine(datetime.date(1990, 1, 1), datetime.time(0, 0)) + datetime.timedelta(days=timeindays[ind])
dstr = datetime.date.strftime(dt,'%Y%m%d%H:%M:%S')
dstr = dstr[0:8]+' '+dstr[8:17]
print('Plotting '+dstr)
par=np.sqrt( np.square(np.double(ucur[ind,:])) + np.square(np.double(vcur[ind,:])) )
#par2=np.double(dir[ind,:])
tli=LinearTriInterpolator(tri,par)
par_interp=tli(reflon,reflat)
#u=np.cos(np.pi/180*(270-par_interp))
#v=np.sin(np.pi/180*(270-par_interp))
plt.pcolormesh(reflon,reflat,par_interp,vmin=0.0,vmax=2.0,shading='flat',cmap=plt.cm.jet, transform=ccrs.PlateCarree())
#plt.contourf(reflon, reflat, par_interp, vmax=60.0, cmap=plt.cm.jet, transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
#rowskip=np.floor(par_interp.shape[0]/25)
#colskip=np.floor(par_interp.shape[1]/25)
rowskip = 50
colskip = 50
#plt.quiver(reflon[0::rowskip,0::colskip],reflat[0::rowskip,0::colskip],\
# u[0::rowskip,0::colskip],v[0::rowskip,0::colskip], \
# scale = 50, color='black',pivot='middle',units='xy',alpha=0.7)
coast = cfeature.GSHHSFeature(scale='high',edgecolor='black',facecolor='none',linewidth=0.25)
ax.add_feature(coast)
plt.xticks(fontsize=9)
plt.yticks(fontsize=9)
figtitle = storm.capitalize()+': Cur (m/s): '+dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt,'%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8]+'_'+dtlabel[8:14]
filenm = 'nsem_'+storm+'_cur_'+dtlabel+'.png'
plt.savefig(filenm,dpi=150,bbox_inches='tight',pad_inches=0.1)
del(par)
del(par_interp)
def plot_wlv(storm, datafile1, datafile2=None, lonmin=None, lonmax=None, latmin=None, latmax=None, domain=None):
#Single file netCDF reading
ncf=datafile1
nco=netCDF4.Dataset(ncf)
ncf2=datafile2
nco2=netCDF4.Dataset(ncf2)
#Get fields to plot
lon=nco.variables['longitude'][:]
lat=nco.variables['latitude'][:]
timeindays=nco.variables['time'][:]
wlv=nco.variables['wlv'][:]
triangles=nco.variables['tri'][:,:]
dpt=nco2.variables['dpt'][:]
if lonmin != None:
reflon=np.linspace(lonmin, lonmax, 1000)
reflat=np.linspace(latmin, latmax, 1000)
else:
reflon=np.linspace(lon.min(),lon.max(),1000)
reflat=np.linspace(lat.min(),lat.max(),1000)
reflon,reflat=np.meshgrid(reflon,reflat)
plt.figure(figsize = [6.4, 3.8])
flatness=0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles=triangles-1 # Correct indices for Python's zero-base
tri=Triangulation(lon,lat,triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
besttrack = pd.read_csv(PARMnsem+'/storms/'+STORM+'/best_track.txt', header=None, skiprows=4, delim_whitespace=True)
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
if lonmin != None:
ax.set_extent([lonmin, lonmax, latmin, latmax], crs=ccrs.PlateCarree())
dt = datetime.datetime.combine(datetime.date(1990, 1, 1), datetime.time(0, 0)) + datetime.timedelta(days=timeindays[ind])
dstr = datetime.date.strftime(dt,'%Y%m%d%H:%M:%S')
dstr = dstr[0:8]+' '+dstr[8:17]
print('Plotting '+dstr)
par=np.double(wlv[ind,:])
tli=LinearTriInterpolator(tri,par)
par_interp=tli(reflon,reflat)
plt.pcolormesh(reflon,reflat,par_interp,vmin=-2.0,vmax=2.0, shading='flat',cmap=plt.cm.bwr, transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
coast = cfeature.GSHHSFeature(scale='high',edgecolor='black',facecolor='none',linewidth=0.25)
ax.add_feature(coast)
plt.plot(besttrack.iloc[:,3].values, besttrack.iloc[:,2].values, 'k--', linewidth=1.0, transform=ccrs.PlateCarree())
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlines = False
gl.ylines = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 6, 'color': 'black'}
gl.ylabel_style = {'size': 6, 'color': 'black'}
figtitle = storm.capitalize()+': WL (m MSL): '+dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt,'%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8]+'_'+dtlabel[8:14]
filenm = 'nsem_'+storm+'_wlv_'+dtlabel+'_'+domain+'.png'
plt.savefig(filenm,dpi=150,bbox_inches='tight',pad_inches=0.1)
del(par)
del(par_interp)
def extract_wlv(storm,
datafile1,
stations,
df,
lonmin=None,
lonmax=None,
latmin=None,
latmax=None):
print('Reading', datafile1)
#Single file netCDF reading
#ncf='ww3.field.2018_wnd.nc'
ncf = datafile1
nco = netCDF4.Dataset(ncf)
#ncf2='ww3.field.2018_dp.nc'
#ncf2=datafile2
#nco2=netCDF4.Dataset(ncf2)
#Get fields to plot
lon = nco.variables['x'][:]
lat = nco.variables['y'][:]
pnt_lon = stations.iloc[0, :].values
pnt_lat = stations.iloc[1, :].values
timeinsec = nco.variables['time'][:]
#base_date=nco.variables['time:base_date']
#print(base_date)
wlv = nco.variables['zeta'][:]
triangles = nco.variables['element'][:, :]
#timeindays2=nco2.variables['time'][:]
#dir=nco2.variables['dp'][:]
if lonmin != None:
reflon = np.linspace(lonmin, lonmax, 1000)
reflat = np.linspace(latmin, latmax, 1000)
else:
reflon = np.linspace(lon.min(), lon.max(), 1000)
reflat = np.linspace(lat.min(), lat.max(), 1000)
reflon, reflat = np.meshgrid(reflon, reflat)
flatness = 0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles = triangles - 1 # Correct indices for Python's zero-base
tri = Triangulation(lon, lat, triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
#Read obs point locations
#data=np.loadtxt('erie_ndbc.loc', comments = '$')
#buoylon=data[:,0]
#buoylat=data[:,1]
df_dates = pd.DataFrame(columns=['Date'])
# Loop through each time step and plot results
for ind in range(0, len(timeinsec)):
#plt.clf()
#ax = plt.axes(projection=ccrs.Mercator())
dt = base_info.tide_spin_start_date + datetime.timedelta(
seconds=timeinsec[ind])
dstr = datetime.date.strftime(dt, '%Y%m%d%H:%M:%S')
dstr = dstr[0:8] + ' ' + dstr[8:17]
print('Plotting ' + dstr)
par = np.double(wlv[ind, :])
tli = LinearTriInterpolator(tri, par)
par_interp = tli(pnt_lon, pnt_lat)
#print(par_interp)
df.loc[len(df)] = par_interp
df_dates.loc[len(df_dates)] = pd.to_datetime(
dt, format="%Y-%m-%d %H:%M:%S")
#line = pd.to_datetime(dt, format="%Y-%m-%d %H:%M:%S")
#new_row = pd.DataFrame(par_interp, columns=stations.columns, index=line)
#df = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
del (par)
del (par_interp)
df['Date'] = df_dates
df.set_index('Date', inplace=True)
return df
def mesh_2D(fname,
var=None,
flabels=None,
fformat='csv',
xlabel='X axis',
ylabel='Y axis',
vmins=None,
output_path=None):
"""Visualization of specific variable on a user provided 2D mesh.
The provided mesh should contain two columns (x,y coordinates for each
mesh point) and be one of :func:`batman.input_output.available_formats`.
(x, y) must be respectively the first and second column. Any other column
is treated as an extra variable and will be used to plot a figure.
If :attr:`var` is not `None`, its content will be used as plotting
variables.
:param str fname: name of mesh file.
:param array_like var: data to be plotted shape (n_coords, n_vars).
:param list(str) flabels: names of the variables.
:param str fformat: format of the mesh file.
:param str xlabel: name of the x-axis.
:param str ylabel: name of the y-axis.
:param lst(double) vmins: value of the minimal output for data filtering.
:param str output_path: name of the output path.
:returns: figure.
:rtype: Matplotlib figure instances.
"""
# Read the mesh file
io = formater(fformat)
mesh = io.read(fname)
if var is not None:
var = np.asarray(var)
else:
var = mesh[:, 2:]
if flabels is None:
flabels = ['y' + str(i) for i in range(var.shape[1])]
# Input variables
var_len = var.shape[0]
if var_len != len(mesh):
raise ValueError(
'Variable size not equal: Variable {} - Mesh {}'.format(
var_len, len(mesh)))
if vmins is None:
vmins = [None] * var_len
# Meshing with Delaunay triangulation
tri = Triangulation(mesh[:, 0], mesh[:, 1])
# Masking badly shaped triangles at the border of the triangular mesh
mask = TriAnalyzer(tri).get_flat_tri_mask(0.01)
tri.set_mask(mask)
# Loop over input parameters
figs, axs = [], []
for i, _ in enumerate(var[0]):
fig, ax = plt.subplots()
figs.append(fig)
axs.append(ax)
cmap = cm.viridis
cmap.set_bad(alpha=0.0)
cmap.set_under('w', alpha=0.0)
plt.tricontourf(tri,
var[:, i],
antialiased=True,
cmap=cmap,
vmin=vmins[i])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tick_params(axis='x')
plt.tick_params(axis='y')
cbar = plt.colorbar()
cbar.set_label(flabels[i])
cbar.ax.tick_params()
bat.visualization.save_show(output_path, figs, extend='neither')
return figs, axs
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi / n_angles
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
V = dipole_potential(x, y)
# Create the Triangulation; no triangles specified so Delaunay triangulation
# created.
triang = Triangulation(x, y)
# Mask off unwanted triangles.
triang.set_mask(
np.hypot(x[triang.triangles].mean(axis=1), y[triang.triangles].mean(
axis=1)) < min_radius)
#-----------------------------------------------------------------------------
# Refine data - interpolates the electrical potential V
#-----------------------------------------------------------------------------
refiner = UniformTriRefiner(triang)
tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)
#-----------------------------------------------------------------------------
# Computes the electrical field (Ex, Ey) as gradient of electrical potential
#-----------------------------------------------------------------------------
tci = CubicTriInterpolator(triang, -V)
# Gradient requested here at the mesh nodes but could be anywhere else:
(Ex, Ey) = tci.gradient(triang.x, triang.y)
E_norm = np.sqrt(Ex**2 + Ey**2)
def plot_cur(storm, datafile1, datafile2=None):
#Single file netCDF reading
ncf = datafile1
nco = netCDF4.Dataset(ncf)
#ncf2=datafile2
#nco2=netCDF4.Dataset(ncf2)
#Get fields to plot
lon = nco.variables['longitude'][:]
lat = nco.variables['latitude'][:]
timeindays = nco.variables['time'][:]
ucur = nco.variables['ucur'][:]
vcur = nco.variables['vcur'][:]
triangles = nco.variables['tri'][:, :]
#timeindays2=nco2.variables['time'][:]
#dir=nco2.variables['dp'][:]
reflon = np.linspace(lon.min(), lon.max(), 1000)
reflat = np.linspace(lat.min(), lat.max(), 1000)
#reflon=np.linspace(-80.40, -73.35, 1000)
#reflat=np.linspace(32.50, 39.50, 1000)
reflon, reflat = np.meshgrid(reflon, reflat)
plt.figure(figsize=[6.4, 3.8])
flatness = 0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles = triangles - 1 # Correct indices for Python's zero-base
tri = Triangulation(lon, lat, triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
besttrack = pd.read_csv(PARMnsem + '/storms/' + STORM +
'/best_track.txt',
header=None,
skiprows=4,
delim_whitespace=True)
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
ax.set_extent([-100.00, -50.00, 4.00, 48.00], crs=ccrs.PlateCarree())
#ax.set_extent([-80.40, -73.35, 32.50, 39.50], crs=ccrs.PlateCarree())
dt = base_info.tide_spin_start_date + datetime.timedelta(
days=timeindays[ind])
dstr = datetime.date.strftime(dt, '%Y%m%d%H:%M:%S')
dstr = dstr[0:8] + ' ' + dstr[8:17]
print('Plotting ' + dstr)
par = np.sqrt(
np.square(np.double(ucur[ind, :])) +
np.square(np.double(vcur[ind, :])))
#par2=np.double(dir[ind,:])
tli = LinearTriInterpolator(tri, par)
par_interp = tli(reflon, reflat)
#u=np.cos(np.pi/180*(270-par_interp))
#v=np.sin(np.pi/180*(270-par_interp))
plt.pcolormesh(reflon,
reflat,
par_interp,
vmin=0.0,
vmax=2.0,
shading='flat',
cmap=plt.cm.jet,
transform=ccrs.PlateCarree())
#plt.contourf(reflon, reflat, par_interp, vmax=60.0, cmap=plt.cm.jet, transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
#rowskip=np.floor(par_interp.shape[0]/25)
#colskip=np.floor(par_interp.shape[1]/25)
rowskip = 50
colskip = 50
#plt.quiver(reflon[0::rowskip,0::colskip],reflat[0::rowskip,0::colskip],\
# u[0::rowskip,0::colskip],v[0::rowskip,0::colskip], \
# scale = 50, color='black',pivot='middle',units='xy',alpha=0.7)
coast = cfeature.GSHHSFeature(scale='high',
edgecolor='black',
facecolor='none',
linewidth=0.25)
ax.add_feature(coast)
plt.plot(besttrack.iloc[:, 3].values,
besttrack.iloc[:, 2].values,
'k--',
linewidth=1.0,
transform=ccrs.PlateCarree())
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlines = False
gl.ylines = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 6, 'color': 'black'}
gl.ylabel_style = {'size': 6, 'color': 'black'}
figtitle = storm.capitalize() + ': Cur (m/s): ' + dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt, '%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8] + '_' + dtlabel[8:14]
filenm = 'nsem_' + storm + '_cur_' + dtlabel + '.png'
plt.savefig(filenm, dpi=150, bbox_inches='tight', pad_inches=0.1)
del (par)
del (par_interp)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) angles[:, 1::2] += math.pi/n_angles x = (radii*np.cos(angles)).flatten() y = (radii*np.sin(angles)).flatten() V = dipole_potential(x, y) # Create the Triangulation; no triangles specified so Delaunay triangulation # created. triang = Triangulation(x, y) # Mask off unwanted triangles. xmid = x[triang.triangles].mean(axis=1) ymid = y[triang.triangles].mean(axis=1) mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0) triang.set_mask(mask) #----------------------------------------------------------------------------- # Refine data - interpolates the electrical potential V #----------------------------------------------------------------------------- refiner = UniformTriRefiner(triang) tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3) #----------------------------------------------------------------------------- # Computes the electrical field (Ex, Ey) as gradient of electrical potential #----------------------------------------------------------------------------- tci = CubicTriInterpolator(triang, -V) # Gradient requested here at the mesh nodes but could be anywhere else: (Ex, Ey) = tci.gradient(triang.x, triang.y) E_norm = np.sqrt(Ex**2 + Ey**2)
def density(model, refinement=0):
"""
Create a Voronoi mesh and calculate the local particle density on its vertices.
The local density is calculated as follows:
for each vertex, compute the density of each neighbour region as
one over the area and assign the average of
the neighbouring density to the vertex.
Parameters
----------
model : simulation.builder.Model
the Model object containing
refinement : int (defaults : 0)
number of subdivision for refining the mesh (0 == None)
Returns
-------
tri : matplotlib.tri.Triangulation
the triangulation mesh (refined if set as)
vert_density : numpy.array
the array containing the local denstity associated with the tri mesh
Example
-------
To plot the result using matplotlib use :
.. code-block:: python
import matplotlib.pyplot as plt
tri, density = data_proc.density(model)
plt.tricontour(tri, density) # to draw contours
plt.tricontourf(tri, density) # ot draw filled contours
plt.show()
Note
----
As of now, the numerical results may not be quantitatively accurate
but should qualitatively represent the density.
"""
vor = Voronoi(model.pos)
vert_density = np.zeros(max(vor.vertices.shape)) # density vector
reg_num = np.zeros(max(
vor.vertices.shape)) # nbr of regions per vertex --> averaging
for point_index, reg in enumerate(vor.point_region):
vertices = vor.regions[reg]
if vertices:
if -1 not in vertices:
area = ConvexHull(vor.vertices[vertices]).area # gets the area
vert_density[
vertices] += 1 / area # makes it a density (sort-of)
reg_num[vertices] += 1
vert_density /= reg_num # averaging
# getting rid of really ugly border points
new_vert, vert_density = (
vor.vertices[vor.vertices[:, 0] >= np.min(model.pos[:, 0])],
vert_density[vor.vertices[:, 0] >= np.min(model.pos[:, 0])])
new_vert, vert_density = (
new_vert[new_vert[:, 0] <= np.max(model.pos[:, 0])],
vert_density[new_vert[:, 0] <= np.max(model.pos[:, 0])])
new_vert, vert_density = (
new_vert[new_vert[:, 1] >= np.min(model.pos[:, 1])],
vert_density[new_vert[:, 1] >= np.min(model.pos[:, 1])])
new_vert, vert_density = (
new_vert[new_vert[:, 1] <= np.max(model.pos[:, 1])],
vert_density[new_vert[:, 1] <= np.max(model.pos[:, 1])])
# for triangulation refinement
tri2 = Triangulation(*new_vert.T)
if refinement:
tri2.set_mask(TriAnalyzer(tri2).get_flat_tri_mask(0.1))
refiner = UniformTriRefiner(tri2)
print(len(tri2.neighbors), vert_density.shape)
tri, vert_density = refiner.refine_field(vert_density,
subdiv=refinement)
else:
tri, vert_density = tri2, vert_density
return tri, vert_density
z,
breaks,
linewidths=[0.5, 0.25],
colors='saddlebrown')
plt.clabel(CS, inline=5, fontsize=8)
ax.set_ylabel('X[m]')
ax.set_xlabel('Y[m]')
plt.grid()
plt.savefig('plot.png', dpi=300)
plt.show()
# Tworzenie mapy hipsometrycznej
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
tri = Triangulation(y, x)
mask = TriAnalyzer(tri).get_flat_tri_mask(0.02)
tri.set_mask(mask)
fig, ax = plt.subplots()
fig.set_size_inches(10, 15)
ax.tick_params(labelsize=15)
ax.yaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.set_aspect('equal')
ax.set_title("Mapa warstwicowa")
CS = ax.tricontourf(tri, z, cmap='RdBu')
ax.tricontour(tri, z, breaks, linewidths=[0.5, 0.25], colors='saddlebrown')
plt.clabel(CS, inline=1, fontsize=10)
ax.set_ylabel('X[m]')
ax.set_xlabel('Y[m]')
plt.grid()
plt.savefig('plotHipso.png', dpi=300)
plt.show()
# Read data from mesh file
ncmesh = Dataset(cfg['mesh_file'], 'r')
lon_mesh = np.rad2deg(
np.mod(ncmesh.variables['lonCell'][:] + np.pi, 2.0 * np.pi) - np.pi)
lat_mesh = np.rad2deg(ncmesh.variables['latCell'][:])
nEdgesOnCell = ncmesh.variables['nEdgesOnCell'][:]
cellsOnCell = ncmesh.variables['cellsOnCell'][:, :]
# Triangulate cells
triangles = Triangulation(lon_mesh, lat_mesh)
# Compute triangulation mask (needs to be vectorized)
mask = np.array(np.zeros((triangles.triangles.shape[0], )), dtype=bool)
ntri = triangles.neighbors.shape[0]
for i in range(ntri):
k = 0
for j in range(3):
n = triangles.triangles[i, j]
if nEdgesOnCell[n] != np.where(
cellsOnCell[n, :] != 0)[0].size: # Mask triangles
k = k + 1 # containing
if k == 3: # 3 boundary
mask[i] = True # cells
triangles.set_mask(mask)
# Write out geotiff image
output_name = cfg['output_variable'] + '.tif'
write_to_geotiff(var, triangles, cfg['nx'], cfg['ny'], cfg['bbox'],
output_name)
angles[:, 1::2] += math.pi/n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
z = function_z(x, y)
# Now create the Triangulation.
# (Creating a Triangulation without specifying the triangles results in the
# Delaunay triangulation of the points.)
triang = Triangulation(x, y)
# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triang.set_mask(mask)
#-----------------------------------------------------------------------------
# Refine data
#-----------------------------------------------------------------------------
refiner = UniformTriRefiner(triang)
tri_refi, z_test_refi = refiner.refine_field(z, subdiv=3)
#-----------------------------------------------------------------------------
# Plot the triangulation and the high-res iso-contours
#-----------------------------------------------------------------------------
plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(triang, lw=0.5, color='white')
levels = np.arange(0., 1., 0.025)
def __init__(self, **kwargs):
super(ContourMap, self).__init__(**kwargs)
n_test = 200 # Number of test data points, tested from 3 to 5000 for subdiv=3
subdiv = 3 # Number of recursive subdivisions of the initial mesh for smooth
# plots. Values >3 might result in a very high number of triangles
# for the refine mesh: new triangles numbering = (4**subdiv)*ntri
init_mask_frac = 0.0 # Float > 0. adjusting the proportion of
# (invalid) initial triangles which will be masked
# out. Enter 0 for no mask.
min_circle_ratio = .01 # Minimum circle ratio - border triangles with circle
# ratio below this will be masked if they touch a
# border. Suggested value 0.01 ; Use -1 to keep
# all triangles.
# Random points
random_gen = np.random.mtrand.RandomState(seed=127260)
x_test = random_gen.uniform(-1., 1., size=n_test)
y_test = random_gen.uniform(-1., 1., size=n_test)
z_test = experiment_res(x_test, y_test)
# meshing with Delaunay triangulation
tri = Triangulation(x_test, y_test)
ntri = tri.triangles.shape[0]
# Some invalid data are masked out
mask_init = np.zeros(ntri, dtype=np.bool)
masked_tri = random_gen.randint(0, ntri, int(ntri * init_mask_frac))
mask_init[masked_tri] = True
tri.set_mask(mask_init)
#-----------------------------------------------------------------------------
# Improving the triangulation before high-res plots: removing flat triangles
#-----------------------------------------------------------------------------
# masking badly shaped triangles at the border of the triangular mesh.
mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio)
tri.set_mask(mask)
# refining the data
refiner = UniformTriRefiner(tri)
tri_refi, z_test_refi = refiner.refine_field(z_test, subdiv=subdiv)
# analytical 'results' for comparison
z_expected = experiment_res(tri_refi.x, tri_refi.y)
# for the demo: loading the 'flat' triangles for plot
flat_tri = Triangulation(x_test, y_test)
flat_tri.set_mask(~mask)
#-----------------------------------------------------------------------------
# Now the plots
#-----------------------------------------------------------------------------
# User options for plots
plot_tri = True # plot of base triangulation
plot_masked_tri = True # plot of excessively flat excluded triangles
plot_refi_tri = False # plot of refined triangulation
plot_expected = False # plot of analytical function values for comparison
# Graphical options for tricontouring
levels = np.arange(0., 1., 0.025)
cmap = cm.get_cmap(name='Blues', lut=None)
plt.figure()
plt.gca().set_aspect('equal')
plt.title("Filtering a Delaunay mesh\n" +
"(application to high-resolution tricontouring)")
# 1) plot of the refined (computed) data countours:
plt.tricontour(tri_refi,
z_test_refi,
levels=levels,
cmap=cmap,
linewidths=[2.0, 0.5, 1.0, 0.5])
# 2) plot of the expected (analytical) data countours (dashed):
if plot_expected:
plt.tricontour(tri_refi,
z_expected,
levels=levels,
cmap=cmap,
linestyles='--')
# 3) plot of the fine mesh on which interpolation was done:
if plot_refi_tri:
plt.triplot(tri_refi, color='0.97')
# 4) plot of the initial 'coarse' mesh:
if plot_tri:
plt.triplot(tri, color='0.7')
# 4) plot of the unvalidated triangles from naive Delaunay Triangulation:
if plot_masked_tri:
plt.triplot(flat_tri, color='red')
plt.show()
# Random points random_gen = np.random.RandomState(seed=19680801) x_test = random_gen.uniform(-1., 1., size=n_test) y_test = random_gen.uniform(-1., 1., size=n_test) z_test = experiment_res(x_test, y_test) # meshing with Delaunay triangulation tri = Triangulation(x_test, y_test) ntri = tri.triangles.shape[0] # Some invalid data are masked out mask_init = np.zeros(ntri, dtype=bool) masked_tri = random_gen.randint(0, ntri, int(ntri * init_mask_frac)) mask_init[masked_tri] = True tri.set_mask(mask_init) #----------------------------------------------------------------------------- # Improving the triangulation before high-res plots: removing flat triangles #----------------------------------------------------------------------------- # masking badly shaped triangles at the border of the triangular mesh. mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio) tri.set_mask(mask) # refining the data refiner = UniformTriRefiner(tri) tri_refi, z_test_refi = refiner.refine_field(z_test, subdiv=subdiv) # analytical 'results' for comparison z_expected = experiment_res(tri_refi.x, tri_refi.y)
def visualization(mesh,
vorticity,
psi=None,
savename='figure',
var_range=[-4, 4]):
# --> Import various utilities from matplotlib.
from matplotlib.tri import Triangulation
from matplotlib import ticker
from matplotlib.patches import Circle
from mpl_toolkits.axes_grid1 import make_axes_locatable
# --> Get mesh information and build-up the Delaunay triangulation.
x, y = mesh.coordinates()[:, 0], mesh.coordinates()[:, 1]
tri = Triangulation(x, y)
# --> Mask the unwanted triangles.
radius = 0.5
xmid = tri.x[tri.triangles].mean(axis=1)
ymid = tri.y[tri.triangles].mean(axis=1)
xc1, yc1 = 0, 0.75
xc2, yc2 = 0, -0.75
xc3, yc3 = -1.5 * np.sqrt(0.75), 0
mask = np.where((xmid - xc1)**2 + (ymid - yc1)**2 < radius**2, 1, 0)
mask += np.where((xmid - xc2)**2 + (ymid - yc2)**2 < radius**2, 1, 0)
mask += np.where((xmid - xc3)**2 + (ymid - yc3)**2 < radius**2, 1, 0)
tri.set_mask(mask)
# --> Get the vorticity field at the mesh nodes.
vorticity = vorticity.compute_vertex_values(mesh)
#-----------------------------------
#----- PLOT THE FIGURE -----
#-----------------------------------
# --> Create matplotlib figure.
fig = plt.figure()
ax = fig.gca()
# --> Plot the vorticity field.
h = ax.tripcolor(tri, vorticity, shading='gouraud', cmap=plt.cm.RdBu)
h.set_clim(var_range[0], var_range[1])
# --> Set the axis.
ax.set_aspect('equal')
ax.set_xlim(-5, 20)
ax.set_ylim(-4, 4)
# --> Setup the colorbar.
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
cb = plt.colorbar(h, cax=cax)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
# --> Highlight the cylinders.
xc, yc, r = 0., 0.75, 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
xc, yc, r = 0., -0.75, 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
xc, yc, r = -1.5 * np.sqrt(0.75), 0., 0.5
circle = Circle((xc, yc), r, facecolor='None', edgecolor='k')
ax.add_patch(circle)
# --> Titles, labels, ...
ax.set_xlabel(r'$x$')
ax.locator_params(axis='y', nbins=5)
ax.set_ylabel(r'$y$', rotation=0)
# --> Save the figure.
fig.savefig(savename + '.png', bbox_inches='tight', dpi=300)
return
