def plot_contours(obj, top_bottom=True):
'''A function that plots the BRF as an azimuthal projection
with contours over the TOC and soil.
Input: rt_layers object, top_bottom - True if only TOC plot, False
if both TOC and soil.
Output: contour plot of brf.
'''
sun = ((np.pi - obj.sun0[0]) * np.cos(obj.sun0[1] + np.pi), \
(np.pi - obj.sun0[0]) * np.sin(obj.sun0[1] + np.pi))
theta = obj.views[:, 0]
x = np.cos(obj.views[:, 1]) * theta
y = np.sin(obj.views[:, 1]) * theta
z = obj.I_top_bottom #* -obj.mu_s
if top_bottom == True:
if np.max > 1.:
maxz = np.max(z)
else:
maxz = 1.
else:
maxz = np.max(z[:obj.n / 2])
minz = 0. #np.min(z)
space = np.linspace(minz, maxz, 11)
x = x[:obj.n / 2]
y = y[:obj.n / 2]
zt = z[:obj.n / 2]
zb = z[obj.n / 2:]
fig = plt.figure()
if top_bottom == True:
plt.subplot(121)
plt.plot(sun[0], sun[1], 'ro')
triang = tri.Triangulation(x, y)
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zt, space, vmax=maxz, vmin=minz)
plt.title('TOC BRF')
plt.ylabel('Y')
plt.xlabel('X')
if top_bottom == True:
plt.subplot(122)
plt.plot(sun[0], sun[1], 'ro')
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zb, space, vmax=maxz, vmin=minz)
plt.title('Soil Absorption')
plt.ylabel('Y')
plt.xlabel('X')
s = obj.__repr__()
if top_bottom == True:
cbaxes = fig.add_axes([0.11, 0.1, 0.85, 0.05])
plt.suptitle(s, x=0.5, y=0.93)
plt.colorbar(orientation='horizontal', ticks=space,\
cax = cbaxes, format='%.3f')
else:
plt.suptitle(s, x=0.5, y=0.13)
plt.colorbar(orientation='horizontal', ticks=space,\
format='%.3f')
#plt.tight_layout()
plt.show()
def plot_contours(obj, top_bottom=True):
'''A function that plots the BRF as an azimuthal projection
with contours over the TOC and soil.
Input: rt_layers object, top_bottom - True if only TOC plot, False
if both TOC and soil.
Output: contour plot of brf.
'''
sun = ((np.pi - obj.sun0[0]) * np.cos(obj.sun0[1] + np.pi), \
(np.pi - obj.sun0[0]) * np.sin(obj.sun0[1] + np.pi))
theta = obj.views[:,0]
x = np.cos(obj.views[:,1]) * theta
y = np.sin(obj.views[:,1]) * theta
z = obj.I_top_bottom # * -obj.mu_s
if top_bottom == True:
if np.max > 1.:
maxz = np.max(z)
else:
maxz = 1.
else:
maxz = np.max(z[:obj.n/2])
minz = 0. #np.min(z)
space = np.linspace(minz, maxz, 11)
x = x[:obj.n/2]
y = y[:obj.n/2]
zt = z[:obj.n/2]
zb = z[obj.n/2:]
fig = plt.figure()
if top_bottom == True:
plt.subplot(121)
plt.plot(sun[0], sun[1], 'ro')
triang = tri.Triangulation(x, y)
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zt, space, vmax=maxz, vmin=minz)
plt.title('TOC BRF')
plt.ylabel('Y')
plt.xlabel('X')
if top_bottom == True:
plt.subplot(122)
plt.plot(sun[0], sun[1], 'ro')
plt.gca().set_aspect('equal')
plt.tricontourf(triang, zb, space, vmax=maxz, vmin=minz)
plt.title('Soil Absorption')
plt.ylabel('Y')
plt.xlabel('X')
s = obj.__repr__()
if top_bottom == True:
cbaxes = fig.add_axes([0.11,0.1,0.85,0.05])
plt.suptitle(s,x=0.5,y=0.93)
plt.colorbar(orientation='horizontal', ticks=space,\
cax = cbaxes, format='%.3f')
else:
plt.suptitle(s,x=0.5,y=0.13)
plt.colorbar(orientation='horizontal', ticks=space,\
format='%.3f')
#plt.tight_layout()
plt.show()
def triplot(x,y,z,r=0.001,title = 'band'):
# z = c-c.min()
# z /= z.max()
triang = tri.Triangulation(x,y)
xmid = x[triang.triangles].var(axis=1)
ymid = y[triang.triangles].var(axis=1)
mask = sp.where(xmid*xmid + ymid*ymid > r*r, 1, 0)
triang.set_mask(mask)
pl.figure()
pl.gca().set_aspect('equal')
pl.tricontourf(triang, z)
pl.colorbar()
V = sp.arange(-10,10,dtype=sp.double)/10*z.max()
pl.tricontour(triang, z,V)#, colors='k')
pl.title(title)
def triplot(x, y, z, r=0.001, title='band'):
# z = c-c.min()
# z /= z.max()
triang = tri.Triangulation(x, y)
xmid = x[triang.triangles].var(axis=1)
ymid = y[triang.triangles].var(axis=1)
mask = sp.where(xmid * xmid + ymid * ymid > r * r, 1, 0)
triang.set_mask(mask)
pl.figure()
pl.gca().set_aspect('equal')
pl.tricontourf(triang, z)
pl.colorbar()
V = sp.arange(-10, 10, dtype=sp.double) / 10 * z.max()
pl.tricontour(triang, z, V) #, colors='k')
pl.title(title)
vvv = np.zeros(len(p))
ppp = np.zeros(len(p))
# speed
sound = np.sqrt(gamma * pp / dd)
veloc = np.sqrt(uu**2 + vv**2) / sound # normalized to local Mach speed
for i in range(len(p)):
ind0 = np.where(tri[:, 0] == i)
ind1 = np.where(tri[:, 1] == i)
ind2 = np.where(tri[:, 2] == i)
vvv[i] = np.mean(np.concatenate((veloc[ind0], veloc[ind1], veloc[ind2])))
ppp[i] = np.mean(np.concatenate((pp[ind0], pp[ind1], pp[ind2])))
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, vvv, 30)
plt.colorbar()
plt.title('SPEED [IN MACH UNITS]')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# subsonic region
#v_reg = np.zeros(len(vvv))
v_reg = vvv
inda = np.where(vvv <= 1)
indb = np.where(vvv > 1)
#v_reg[inda] = -1
v_reg[indb] = 2
plt.figure()
import numpy as np
import matplotlib.pylab as pl
x = np.loadtxt('./data/x.txt')
y = np.loadtxt('./data/y.txt')
p = np.loadtxt('./data/p.txt')
print(sum(p))
pl.figure(figsize=(10., 10.), dpi=1000)
pl.xlim((300, 500))
pl.ylim((250, 450))
pl.tricontourf(x, y, p, 300, cmap='hot')
#pl.tricontour(x,y,p, 300,cmap='hot')
#pl.scatter(x,y,c=p, cmap = 'hot')
pl.colorbar()
pl.savefig('TW.png')
def __call__(self):
x1, x2, y1, y2, t = self.x1, self.x2, self.y1, self.y2, self.t
p, tri = self.p, self.tri
pp, dd, uu, vv = self.pp, self.dd, self.uu, self.vv
Ibrief = self.Ibrief
atm_units = 0 # False
if np.max(pp) > 1.0e5:
# change from Pascal units to 1 atm units
atm_units = 1 # True
p_1atm = 1.01325e5
pp = pp / p_1atm
# cell location (middle of the triangle)
cell = (1.0 / 3.0) * (p[tri[:, 0]] + p[tri[:, 1]] + p[tri[:, 2]])
# tricontour uses the pressure and density at the nodes, instead
# of the calculated value of pp and dd in the cells, so we have
# to assign a value to pressure and density to the nodes.
# Sometimes, a few nodes do not appear in the triangulation
# The pressure and density on these nodes will be defined as
# the mean value of the pressure and density arrays. For normal
# size meshes, this will not affect the visual plot
ppp = np.zeros(len(p))
ddd = np.zeros(len(p))
pp_mean = np.mean(pp)
dd_mean = np.mean(dd)
for i in range(len(p)):
ind0 = np.where(tri[:, 0] == i)
ind1 = np.where(tri[:, 1] == i)
ind2 = np.where(tri[:, 2] == i)
aa = list(np.concatenate((ind0[0], ind1[0], ind2[0])))
if len(aa): # not empty
ppp[i] = np.mean(pp[aa])
ddd[i] = np.mean(dd[aa])
else: # node not in tri
ppp[i] = pp_mean
ddd[i] = dd_mean
if Ibrief == 1:
plt.figure()
plt.subplot(2, 2, 1)
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ppp, 256)
plt.colorbar()
if atm_units == 0:
plt.title('PRESSURE')
else:
plt.title('PRESSURE [ATM]')
plt.ylabel('y')
plt.subplot(2, 2, 2)
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ddd, 256)
plt.colorbar()
plt.title('DENSITY')
plt.xlabel('x')
plt.subplot(2, 2, 3)
# --------------------------------
# u, v
# --------------------------------
xx = cell[:, 0]
yy = cell[:, 1]
uu_tri = uu[0:len(tri)]
vv_tri = vv[0:len(tri)]
# generate 100 points for (u,v) plot
if len(xx) * len(yy) <= 100 or len(xx) < 10 or len(yy) < 10:
plt.quiver(xx, yy, uu_tri, vv_tri)
plt.axis('equal')
plt.xlabel('velocity field')
plt.show()
else:
mxx = np.zeros(100)
myy = np.zeros(100)
muu = np.zeros(100)
mvv = np.zeros(100)
ddx = 0.1 * (x2 - x1)
ddy = 0.1 * (y2 - y1)
n = 0
for i in range(10):
for j in range(10):
ind = (xx > x1 + ddx * i) & (
xx <= x1 + ddx *
(i + 1)) & (yy > y1 + ddy * j) & (yy <= y1 + ddy *
(j + 1))
if np.any(ind == True):
mxx[n] = np.mean(xx[np.where(ind == True)])
myy[n] = np.mean(yy[np.where(ind == True)])
muu[n] = np.mean(uu_tri[np.where(ind == True)])
mvv[n] = np.mean(vv_tri[np.where(ind == True)])
else:
mxx[n] = x1 + 0.5 * ddx * (2 * i + 1)
myy[n] = y1 + 0.5 * ddy * (2 * j + 1)
muu[n] = 0.0
mvv[n] = 0.0
n += 1
plt.quiver(mxx, myy, muu, mvv)
plt.axis('equal')
plt.xlabel('velocity field')
ind = np.argmin(pp)
pmin = pp[ind]
# cell location (middle of the triangle)
pmin_cell = (1.0 / 3.0) * (p[tri[ind, 0]] + p[tri[ind, 1]] +
p[tri[ind, 2]])
ind = np.argmax(pp)
pmax = pp[ind]
# cell location (middle of the triangle)
pmax_cell = (1.0 / 3.0) * (p[tri[ind, 0]] + p[tri[ind, 1]] +
p[tri[ind, 2]])
ind = np.argmin(dd)
dmin = dd[ind]
# cell location (middle of the triangle)
dmin_cell = (1.0 / 3.0) * (p[tri[ind, 0]] + p[tri[ind, 1]] +
p[tri[ind, 2]])
ind = np.argmax(dd)
dmax = dd[ind]
# cell location (middle of the triangle)
dmax_cell = (1.0 / 3.0) * (p[tri[ind, 0]] + p[tri[ind, 1]] +
p[tri[ind, 2]])
plt.text(
1.2 * x2, 0.8 * y2, 'max p = %g @ (%+5.2f,%+5.2f)' %
(pmax, pmax_cell[0], pmax_cell[1]))
plt.text(
1.2 * x2, 0.6 * y2, 'min p = %g @ (%+5.2f,%+5.2f)' %
(pmin, pmin_cell[0], pmin_cell[1]))
plt.text(
1.2 * x2, 0.4 * y2, 'max d = %g @ (%+5.2f,%+5.2f)' %
(dmax, dmax_cell[0], dmax_cell[1]))
plt.text(
1.2 * x2, 0.2 * y2, 'min d = %g @ (%+5.2f,%+5.2f)' %
(dmin, dmin_cell[0], dmin_cell[1]))
plt.text(1.2 * x2, 0.0, 'simulation time t = %6.4f' % (t))
plt.show()
else: # normal detailed separate plots
# ------------------------------
# pressure
# ------------------------------
# method 1: almost continuous contour plot
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ppp, 256)
plt.colorbar()
if atm_units == 0:
plt.title('PRESSURE')
else:
plt.title('PRESSURE [ATM]')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# method 2: 30 contours
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ppp, 30)
plt.colorbar()
if atm_units == 0:
plt.title('PRESSURE')
else:
plt.title('PRESSURE [ATM]')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# method 3: just the contour lines
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontour(p[:, 0], p[:, 1], tri, ppp, 30)
plt.colorbar()
if atm_units == 0:
plt.title('PRESSURE')
else:
plt.title('PRESSURE [ATM]')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# --------------------
# density
# --------------------
# method 1: almost continuous contour plot
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ddd, 256)
plt.colorbar()
plt.title('DENSITY')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# method 2: 30 contours
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontourf(p[:, 0], p[:, 1], tri, ddd, 30)
plt.colorbar()
plt.title('DENSITY')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
# method 3: just the contour lines
plt.figure()
plt.gca().set_aspect('equal')
plt.tricontour(p[:, 0], p[:, 1], tri, ddd, 30)
plt.colorbar()
plt.title('DENSITY')
plt.xlabel('x')
plt.ylabel('y')
# --------------------------------
# u, v
# --------------------------------
xx = cell[:, 0]
yy = cell[:, 1]
uu_tri = uu[0:len(tri)]
vv_tri = vv[0:len(tri)]
# generate 100 points for (u,v) plot
if len(xx) * len(yy) <= 100 or len(xx) < 10 or len(yy) < 10:
plt.figure()
plt.quiver(xx, yy, uu_tri, vv_tri)
plt.title('VELOCITY FIELD')
plt.axis('equal')
plt.xlabel('pixel # in x')
plt.ylabel('pixel # in y')
plt.show()
else:
mxx = np.zeros(100)
myy = np.zeros(100)
muu = np.zeros(100)
mvv = np.zeros(100)
ddx = 0.1 * (x2 - x1)
ddy = 0.1 * (y2 - y1)
n = 0
for i in range(10):
for j in range(10):
ind = (xx > x1 + ddx * i) & (
xx <= x1 + ddx *
(i + 1)) & (yy > y1 + ddy * j) & (yy <= y1 + ddy *
(j + 1))
if np.any(ind == True):
mxx[n] = np.mean(xx[np.where(ind == True)])
myy[n] = np.mean(yy[np.where(ind == True)])
muu[n] = np.mean(uu_tri[np.where(ind == True)])
mvv[n] = np.mean(vv_tri[np.where(ind == True)])
else:
mxx[n] = x1 + 0.5 * ddx * (2 * i + 1)
myy[n] = y1 + 0.5 * ddy * (2 * j + 1)
muu[n] = 0.0
mvv[n] = 0.0
n += 1
plt.figure()
plt.quiver(mxx, myy, muu, mvv)
plt.title('VELOCITY FIELD')
plt.axis('equal')
plt.xlabel('pixel # in x')
plt.ylabel('pixel # in y')
plt.show()
return 0
w = np.sqrt(eigenvalues[posmin])
period = 2.*np.pi/w
wavelength = np.sqrt(C2)*period
eta = eigenvectors[:, posmin]
print 'wavelength = ', wavelength
for i in np.arange(ID.shape[0]):
if ID[i] != 0:
Modo1[i, 0] = eta[ID[i]-1,0]
else:
Modo1[i, 0] = eta_g
x = xyz[:, 0]
y = xyz[:, 1]
triangles = IEN-1
plt.tricontourf(x, y, triangles, Modo1[:, 0])
Tri3.Plot2DQuadGeometry(xyz,IEN,nodeId=True,elemId=False)
plt.colorbar()
plt.show()
######## Narrow Bay #######
CBdirichlet = np.arange(ndx+1, (ndx+1)*(ndy+2), ndy+1)
ID = np.zeros(xyz.shape[0], dtype=int)
aux1 = 1
for i in np.arange(ID.shape[0]):
aux2 = 1
for j in np.arange(CBdirichlet.shape[0]):
if i+1 == CBdirichlet[j]:
aux2 = aux1*0
else:
def createContour(self, data, value=None, col=None):
""" calculation of a contour deom value : value[0] : min
[1] : max, [2] nb contours, [3] decimals, [4] : 'lin', log' or 'fix',
if [4]:fix, then [5] is the series of contours"""
X, Y, Z = data
#print 'visu controu',value,col
self.cnv.collections = self.cnv.collections[:3]
self.cnv.artists = []
V = 11
Zmin = amin(amin(Z))
Zmax = amax(amax(Z * (Z < 1e5)))
if Zmax == Zmin: # test min=max -> pas de contour
onMessage(self.gui, ' values all equal to ' + str(Zmin))
return
if value == None or len(value) < 4:
value = [Zmin, Zmax, (Zmax - Zmin) / 10., 2, 'auto', []]
# adapt the number and values of the contours
val2 = [float(a) for a in value[:3]]
if value[4] == 'log': # cas echelle log
n = int((log10(val2[1]) - log10(max(val2[0], 1e-4))) / val2[2]) + 1
V = logspace(log10(max(val2[0], 1e-4)), log10(val2[1]), n)
elif (value[4]
== 'fix') and (value[5] != None): # fixes par l'utilisateur
V = value[5] * 1
V.append(V[-1] * 2.)
n = len(V)
elif value[4] == 'lin': # cas echelle lineaire
n = int((val2[1] - val2[0]) / val2[2]) + 1
V = linspace(val2[0], val2[1], n)
else: # cas automatique
n = 11
V = linspace(Zmin, Zmax, n)
# ONE DIMENSIONAL
if self.mesh == False:
r, c = shape(X)
if r == 1:
X = concatenate([X, X])
Y = concatenate([Y - Y * .45, Y + Y * .45])
Z = concatenate([Z, Z])
Z2 = ma.masked_where(Z.copy() > 1e5, Z.copy())
#print value,n,V
# definir les couleurs des contours
if col == None or type(col) < type(
5): # or (col==[(0,0,0),(0,0,0),(0,0,0),10]):
cmap = mpl.cm.jet
col = [(0, 0, 255), (0, 255, 0), (255, 0, 0), 10]
else:
r, g, b = [], [], []
lim=((0.,1.,0.,0.),(.1,1.,0.,0.),(.25,.8,0.,0.),(.35,0.,.8,0.),(.45,0.,1.,0.),\
(.55,0.,1.,0.),(.65,0.,.8,0.),(.75,0.,0.,.8),(.9,0.,0.,1.),(1.,0.,0.,1.))
for i in range(len(lim)):
c1 = lim[i][1] * col[0][0] / 255. + lim[i][2] * col[1][
0] / 255. + lim[i][3] * col[2][0] / 255.
r.append((lim[i][0], c1, c1))
c2 = lim[i][1] * col[0][1] / 255. + lim[i][2] * col[1][
1] / 255. + lim[i][3] * col[2][1] / 255.
g.append((lim[i][0], c2, c2))
c3 = lim[i][1] * col[0][2] / 255. + lim[i][2] * col[1][
2] / 255. + lim[i][3] * col[2][2] / 255.
b.append((lim[i][0], c3, c3))
cdict = {'red': r, 'green': g, 'blue': b}
cmap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict,
256)
if self.mesh == False:
cf = pl.contourf(pl.array(X), pl.array(Y), Z2, V, cmap=cmap)
c = pl.contour(pl.array(X), pl.array(Y), Z2, V, cmap=cmap)
else:
cf = pl.tricontourf(self.Triangles, Z2, V, cmap=cmap)
c = pl.tricontour(self.Triangles, Z2, V, cmap=cmap)
#print col[3]
for c0 in cf.collections:
c0.set_alpha(int(col[3]) / 100.)
#print cl
if value == None: fmt = '%1.3f'
else: fmt = '%1.' + str(value[3]) + 'f'
cl = pl.clabel(c, color='black', fontsize=9, fmt=fmt)
self.Contour = c
self.ContourF = cf
self.ContourLabel = cl
self.Contour.data = data
self.redraw()