def plotTable(v, vol, prob, lbds, MOQ, name):
"""
DEFINITION:
v: index in volatility array
vol: volatility array. Contains simulated volatilities.
name: name of the plot.
"""
np, _, nr, _ = shape(MOQ)
MOQshape = zeros((np, nr))
for p in xrange(len(prob)):
for r in xrange(len(lbds)):
MOQshape[p, r] = mean(MOQ[p, v, r, :])
fig = plt.figure()
ax = fig.gca(projection='3d')
X = lbds
Y = prob
X, Y = meshgrid(X, Y)
Z = MOQshape
plt.xlabel('Lambdas')
plt.ylabel('Probabilities')
plt.title(name + ' for fixed lambdas - v=' + str(vol[v]))
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.jet,
linewidth=0,
antialiased=False)
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
fig.colorbar(surf, shrink=0.5, aspect=5)
def study_consts_proximity(in_file, acc = 3):
print 'studying consts proximity..'
try:
params, asurf = read_bender_output(in_file)[:2] # + 'params.txt')
except IOError as e:
print 'there, is no file for this input... got ' + e
#print params[("consts")]
phi_period = params[("phiperiod")]
hangle = params[("height")] / 2 / pi
system = params[("system")]
ue = u(hangle, phi_period, asurf, system = system, consts = params[("consts")])
energies = deform_energies(ue)
delx = abs(params[("consts")][0]) / 10.
delA = abs(params[("consts")][1]) / 2.
x, A = params[("consts")][:2]
if delA == 0.:
Amin = 0
Amax = params[("height")]/70.
else:
Amin, Amax = A - 2*delA, A + delA
x_set = np.linspace(x - delx, x + delx, acc)
A_set = np.linspace(Amin, Amax, acc)
curve_start = params[("consts")][2]
mid = params[("consts")][3]
n_wave = params[("consts")][4]
mesh_x, mesh_A \
= meshgrid(x_set, A_set)
E_b_mat = np.zeros(mesh_x.shape)
E_s_mat = np.zeros(mesh_x.shape)
for index, x in np.ndenumerate(mesh_x):
A = mesh_A[index]
consts = [x, A, curve_start, mid, n_wave]
E_b_mat[index], E_s_mat[index] \
= energies.calc_energies(consts)[:2]
#print E_s_mat
#print E_b_mat
folder = parse_path(params, params[("moldy_opm")]) + 'opm_consts_proxm/'
if not exists(folder):
makedirs(folder)
np.save(folder + 'mesh_x' , mesh_x)
np.save(folder + 'mesh_A' , mesh_A)
np.save(folder + 'E_b_mat_consts_proxm', E_b_mat)
np.save(folder + 'E_s_mat_consts_proxm', E_s_mat)
plot_consts_proxm(mesh_x, mesh_A, E_b_mat, E_s_mat, folder, from_file = False, acc = acc)
def plot3D(a):
b = np.array(range(-100,100))
c = np.array(range(-100,100))
B,C = meshgrid(b,c)
d=eval(a)
d=np.expand_dims(d,axis=0)
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(B,C,d, rstride=1, cstride=1,
cmap='viridis', edgecolor='none')
plt.xlabel('b')
plt.ylabel('c')
plt.savefig("An\\plot3D.png")
plt.close()
def plot_hipothesys_fit(feats, X, theta_opt):
def _minmax(v1, v2):
return min(v1), max(v1), min(v2), max(v2)
f1 = figure('fig1')
ax = f1.gca(projection='3d')
minx, maxx, miny, maxy = _minmax(X[:, 1], X[:, 2])
zi = np.matrix([
[1, minx, miny],
[1, minx, maxy],
[1, maxx, miny],
[1, maxx, maxy],
]).dot(theta_opt)
zi = [z[0, 0] for z in zi]
minf0, maxf0, minf1, maxf1 = _minmax(feats[:, 0], feats[:, 1])
f0i = [minf0, minf0, maxf0, maxf0]
f1i = [minf1, maxf1, minf1, maxf1]
f0im, f1im = meshgrid(f0i, f1i)
ax.plot_surface(f0im, f1im, zi)
def main():
path = "/home/huziy/skynet3_exec1/from_guillimin/cell_area.rpn"
r = RPN(path)
tile = r.get_first_record_for_name("TILE")
lkid = r.get_first_record_for_name("LKID")
larea = r.get_first_record_for_name("AREA")
r.close()
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")
facc = rObj.get_first_record_for_name("FACC")
lkfr = rObj.get_first_record_for_name("LKFR")
lkou = rObj.get_first_record_for_name("LKOU")
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1,1,0,-1,-1,-1,0,1])
dj_list = np.array([0,-1,-1,-1,0,1,1,1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / np.sqrt(di ** 2 + dj ** 2)
dv = dj / np.sqrt(di ** 2 + dj ** 2)
for i in range(100):
print(du[i], dv[i], np.log2(dirs[dirs > 0][i]))
du2d = np.ma.masked_all(dirs.shape)
dv2d = np.ma.masked_all(dirs.shape)
du2d[dirs>0] = du
dv2d[dirs>0] = dv
print(du2d[161,145],dv2d[161,145],dirs[161,145])
iv = range(dirs.shape[0])
jv = range(dirs.shape[1])
jv, iv = meshgrid(jv, iv)
iv = iv.astype(float)
jv = jv.astype(float)
iv -= 0.5
jv -= 0.5
print(iv.min(), iv.max())
plt.figure()
#field = np.ma.masked_where((field > 1e10) | (field < 1e7) , field)
#tile1 = np.ma.masked_where((tile != 25) & (tile != 26), tile)
plt.pcolormesh(iv,jv,tile,cmap = cm.get_cmap("jet", 36))
plt.colorbar()
plt.xlim(75, 100)
plt.ylim(130,142)
plt.figure()
lkid = lkid.astype(int)
print(lkid[161,146], lkid[161,145],lkid[160,145:148])
for i in range(1,121):
x = np.ma.masked_where(lkid != i, lkid)
if x.count() == 1 or x.count() == 0:
print(i, x.count())
#lkid = np.ma.masked_where(~((lkid == 27)|(lkid == 28)) , lkid)
lkid = np.ma.masked_where(lkid <= 0, lkid)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(
file_path=path,
lon1=-68, lat1=52, lon2=16.65, lat2=0
)
x,y = b(lons2d, lats2d)
img = b.pcolormesh(x,y,lkid,cmap = cm.get_cmap("jet", 100))
plt.colorbar(img, ticks = MultipleLocator(base = 10))
b.contour(x, y,tile, levels = range(48), colors = "k", linewidth = 0.5)
b.drawcoastlines(linewidth=0.5)
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 30, width = 0.005, color="k", pivot="middle")
#plt.xlim(30, 70)
#plt.ylim(10,35)
plt.figure()
plt.pcolormesh(iv,jv,lkfr,cmap = cm.get_cmap("jet", 11))
plt.colorbar()
plt.xlim(155, 165)
plt.ylim(140,150)
plt.figure()
d = np.ma.masked_all(dirs.shape)
d[dirs > 0] = np.log2(dirs[dirs > 0])
plt.pcolormesh(iv, jv, d,cmap = cm.get_cmap("jet", 8))
plt.xlim(155, 165)
plt.ylim(140,150)
plt.colorbar(ticks = MultipleLocator(base = 1))
plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.figure()
plt.title("Lake area")
plt.pcolormesh(iv, jv, np.ma.masked_where( larea < 1.0e8, np.log(larea) ),cmap = cm.get_cmap("jet", 8))
#plt.xlim(155, 165)
#plt.ylim(140,150)
print(larea.min(), larea.max())
plt.colorbar(ticks = LinearLocator(numticks = 10))
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.show()
pass
def read_directions():
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
i_start = 20
j_start = 20
i_end = i_start + 200
j_end = j_start + 200
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")[i_start:i_end, j_start:j_end]
facc = rObj.get_first_record_for_name("FACC")[i_start:i_end, j_start:j_end]
lkfr = rObj.get_first_record_for_name("LKFR")[i_start:i_end, j_start:j_end]
lkou = rObj.get_first_record_for_name("LKOU")[i_start:i_end, j_start:j_end]
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1,1,0,-1,-1,-1,0,1])
dj_list = np.array([0,-1,-1,-1,0,1,1,1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / (di ** 2 + dj ** 2)
dv = dj / (di ** 2 + dj ** 2)
du_2d = np.ma.masked_all(dirs.shape)
dv_2d = np.ma.masked_all(dirs.shape)
du_2d[dirs > 0] = du
dv_2d[dirs > 0] = dv
assert isinstance(delta_indices, np.ndarray)
print(delta_indices.shape)
di_field = np.ma.masked_all(dirs.shape)
dj_field = np.ma.masked_all(dirs.shape)
di_field[dirs > 0] = di_list[delta_indices]
dj_field[dirs > 0] = dj_list[delta_indices]
di_field = di_field.astype(int)
dj_field = dj_field.astype(int)
nx, ny = dirs.shape
i_p = range(nx)
j_p = range(ny)
j_p, i_p = meshgrid(j_p, i_p)
i_n, j_n = np.zeros(i_p.shape), np.zeros(j_p.shape)
i_n[dirs > 0] = i_p[dirs > 0] + di_field[dirs > 0]
j_n[dirs > 0] = j_p[dirs > 0] + dj_field[dirs > 0]
cells = []
for i in range(nx):
cells.append([])
for j in range(ny):
cells[i].append(Cell(i, j))
for i in range(nx):
for j in range(ny):
cells[i][j].lakefr = lkfr[i,j]
if dirs[i, j] <= 0:
cells[i][j].set_next(None)
continue
di = di_field[i, j]
dj = dj_field[i, j]
cells[i][j].set_next(cells[i+di][j+dj])
lkou = lkou.astype(int)
iou, jou = np.where(lkou == 1)
for i,j in zip(iou, jou):
print((i,j, cells[i][j].get_lake_size_in_indexes(), cells[i][j].lakefr))
print(dirs[54, 150], dirs[53, 151])
ii = 33
jj = 96
outlet = cells[ii][jj]
i_lake, j_lake = outlet.get_prev_lake_indexes()
save = di_field[i_lake, j_lake]
di_field = np.ma.masked_all(di_field.shape)
di_field[i_lake, j_lake] = save
save = dj_field[i_lake, j_lake]
dj_field = np.ma.masked_all(dj_field.shape)
dj_field[i_lake, j_lake] = save
plt.figure()
#plt.contourf(dirs, levels = map( lambda x, p: x ** p, [2]*8, range(8)))
#plt.pcolormesh(facc.transpose())
plt.quiver(i_p,j_p, du_2d, dv_2d , scale = 0.5, width = 0.1 ,
units = "xy", pivot = "middle", zorder = 5)
plt.pcolormesh(i_p, j_p, dirs)
plt.colorbar()
print(list(map( lambda x, p: x ** p, [2]*8, list(range(8)))))
#plt.colorbar()
#plot
plt.show()
def main():
path = "/home/huziy/skynet3_exec1/from_guillimin/cell_area.rpn"
r = RPN(path)
tile = r.get_first_record_for_name("TILE")
lkid = r.get_first_record_for_name("LKID")
larea = r.get_first_record_for_name("AREA")
r.close()
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")
facc = rObj.get_first_record_for_name("FACC")
lkfr = rObj.get_first_record_for_name("LKFR")
lkou = rObj.get_first_record_for_name("LKOU")
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1, 1, 0, -1, -1, -1, 0, 1])
dj_list = np.array([0, -1, -1, -1, 0, 1, 1, 1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / np.sqrt(di**2 + dj**2)
dv = dj / np.sqrt(di**2 + dj**2)
for i in range(100):
print(du[i], dv[i], np.log2(dirs[dirs > 0][i]))
du2d = np.ma.masked_all(dirs.shape)
dv2d = np.ma.masked_all(dirs.shape)
du2d[dirs > 0] = du
dv2d[dirs > 0] = dv
print(du2d[161, 145], dv2d[161, 145], dirs[161, 145])
iv = range(dirs.shape[0])
jv = range(dirs.shape[1])
jv, iv = meshgrid(jv, iv)
iv = iv.astype(float)
jv = jv.astype(float)
iv -= 0.5
jv -= 0.5
print(iv.min(), iv.max())
plt.figure()
#field = np.ma.masked_where((field > 1e10) | (field < 1e7) , field)
#tile1 = np.ma.masked_where((tile != 25) & (tile != 26), tile)
plt.pcolormesh(iv, jv, tile, cmap=cm.get_cmap("jet", 36))
plt.colorbar()
plt.xlim(75, 100)
plt.ylim(130, 142)
plt.figure()
lkid = lkid.astype(int)
print(lkid[161, 146], lkid[161, 145], lkid[160, 145:148])
for i in range(1, 121):
x = np.ma.masked_where(lkid != i, lkid)
if x.count() == 1 or x.count() == 0:
print(i, x.count())
#lkid = np.ma.masked_where(~((lkid == 27)|(lkid == 28)) , lkid)
lkid = np.ma.masked_where(lkid <= 0, lkid)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords(file_path=path,
lon1=-68,
lat1=52,
lon2=16.65,
lat2=0)
x, y = b(lons2d, lats2d)
img = b.pcolormesh(x, y, lkid, cmap=cm.get_cmap("jet", 100))
plt.colorbar(img, ticks=MultipleLocator(base=10))
b.contour(x, y, tile, levels=range(48), colors="k", linewidth=0.5)
b.drawcoastlines(linewidth=0.5)
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 30, width = 0.005, color="k", pivot="middle")
#plt.xlim(30, 70)
#plt.ylim(10,35)
plt.figure()
plt.pcolormesh(iv, jv, lkfr, cmap=cm.get_cmap("jet", 11))
plt.colorbar()
plt.xlim(155, 165)
plt.ylim(140, 150)
plt.figure()
d = np.ma.masked_all(dirs.shape)
d[dirs > 0] = np.log2(dirs[dirs > 0])
plt.pcolormesh(iv, jv, d, cmap=cm.get_cmap("jet", 8))
plt.xlim(155, 165)
plt.ylim(140, 150)
plt.colorbar(ticks=MultipleLocator(base=1))
plt.quiver(iv + 0.5,
jv + 0.5,
du2d,
dv2d,
scale=4.5,
width=0.035,
color="k",
pivot="middle",
units="inches")
plt.figure()
plt.title("Lake area")
plt.pcolormesh(iv,
jv,
np.ma.masked_where(larea < 1.0e8, np.log(larea)),
cmap=cm.get_cmap("jet", 8))
#plt.xlim(155, 165)
#plt.ylim(140,150)
print(larea.min(), larea.max())
plt.colorbar(ticks=LinearLocator(numticks=10))
#plt.quiver(iv+0.5, jv+0.5, du2d, dv2d, scale = 4.5, width = 0.035, color="k", pivot="middle", units="inches")
plt.show()
pass
def read_directions():
#path = "/home/huziy/skynet3_exec1/from_guillimin/infocell.rpn"
path = "infocell_260x260.rpn"
i_start = 20
j_start = 20
i_end = i_start + 200
j_end = j_start + 200
rObj = RPN(path)
dirs = rObj.get_first_record_for_name("FLDR")[i_start:i_end, j_start:j_end]
facc = rObj.get_first_record_for_name("FACC")[i_start:i_end, j_start:j_end]
lkfr = rObj.get_first_record_for_name("LKFR")[i_start:i_end, j_start:j_end]
lkou = rObj.get_first_record_for_name("LKOU")[i_start:i_end, j_start:j_end]
lons2d, lats2d = rObj.get_longitudes_and_latitudes()
rObj.close()
di_list = np.array([1, 1, 0, -1, -1, -1, 0, 1])
dj_list = np.array([0, -1, -1, -1, 0, 1, 1, 1])
delta_indices = np.log2(dirs[dirs > 0])
delta_indices = delta_indices.astype(int)
di = di_list[delta_indices].astype(float)
dj = dj_list[delta_indices].astype(float)
du = di / (di**2 + dj**2)
dv = dj / (di**2 + dj**2)
du_2d = np.ma.masked_all(dirs.shape)
dv_2d = np.ma.masked_all(dirs.shape)
du_2d[dirs > 0] = du
dv_2d[dirs > 0] = dv
assert isinstance(delta_indices, np.ndarray)
print(delta_indices.shape)
di_field = np.ma.masked_all(dirs.shape)
dj_field = np.ma.masked_all(dirs.shape)
di_field[dirs > 0] = di_list[delta_indices]
dj_field[dirs > 0] = dj_list[delta_indices]
di_field = di_field.astype(int)
dj_field = dj_field.astype(int)
nx, ny = dirs.shape
i_p = range(nx)
j_p = range(ny)
j_p, i_p = meshgrid(j_p, i_p)
i_n, j_n = np.zeros(i_p.shape), np.zeros(j_p.shape)
i_n[dirs > 0] = i_p[dirs > 0] + di_field[dirs > 0]
j_n[dirs > 0] = j_p[dirs > 0] + dj_field[dirs > 0]
cells = []
for i in range(nx):
cells.append([])
for j in range(ny):
cells[i].append(Cell(i, j))
for i in range(nx):
for j in range(ny):
cells[i][j].lakefr = lkfr[i, j]
if dirs[i, j] <= 0:
cells[i][j].set_next(None)
continue
di = di_field[i, j]
dj = dj_field[i, j]
cells[i][j].set_next(cells[i + di][j + dj])
lkou = lkou.astype(int)
iou, jou = np.where(lkou == 1)
for i, j in zip(iou, jou):
print(
(i, j, cells[i][j].get_lake_size_in_indexes(), cells[i][j].lakefr))
print(dirs[54, 150], dirs[53, 151])
ii = 33
jj = 96
outlet = cells[ii][jj]
i_lake, j_lake = outlet.get_prev_lake_indexes()
save = di_field[i_lake, j_lake]
di_field = np.ma.masked_all(di_field.shape)
di_field[i_lake, j_lake] = save
save = dj_field[i_lake, j_lake]
dj_field = np.ma.masked_all(dj_field.shape)
dj_field[i_lake, j_lake] = save
plt.figure()
#plt.contourf(dirs, levels = map( lambda x, p: x ** p, [2]*8, range(8)))
#plt.pcolormesh(facc.transpose())
plt.quiver(i_p,
j_p,
du_2d,
dv_2d,
scale=0.5,
width=0.1,
units="xy",
pivot="middle",
zorder=5)
plt.pcolormesh(i_p, j_p, dirs)
plt.colorbar()
print(list(map(lambda x, p: x**p, [2] * 8, list(range(8)))))
#plt.colorbar()
#plot
plt.show()
