def plot_3d(self, ax_3d: Axes3D, n_angles: int = 30, **kwargs) -> None:
"""
Plot the sphere in 3D.
Parameters
----------
ax_3d : Axes3D
Instance of :class:`~mpl_toolkits.mplot3d.axes3d.Axes3D`.
kwargs : dict, optional
Additional keywords passed to :meth:`~mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface`.
Examples
--------
.. plot::
:include-source:
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.mplot3d import Axes3D
>>> from skspatial.objects import Sphere
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')
>>> sphere = Sphere([1, 2, 3], 2)
>>> sphere.plot_3d(ax, alpha=0.2)
>>> sphere.point.plot_3d(ax, s=100)
"""
X, Y, Z = self.to_mesh(n_angles)
ax_3d.plot_surface(X, Y, Z, **kwargs)
def plot_error_deform(nx, ny, delta1, dt1, dt_an):
filename = 'output/'+str(nx) + '_' + str(ny) + '_deform_heat_t5_delta' + delta1 + '_dt' + dt1 + '.txt'
data = np.fromfile(filename, dtype=np.float32) # np.genfromtxt(filename)#
shape = [ny, nx]
data = np.reshape(data, (int(shape[0]), int(shape[1])))
filename_a = 'output/'+str(nx) + '_' + str(ny) + '_deform_heat_t5_delta' + delta1 + '_dt' + dt_an + '.txt'
data_a = np.fromfile(filename_a, dtype=np.float32) # np.genfromtxt(filename)#
shape_a = shape
data_a = np.reshape(data_a, (int(shape[0]), int(shape[1])))
error = np.divide(data , data_a)
fig = plt.figure()
xc = np.linspace(0, 2, int(shape[1]))
yc = np.linspace(0, 1, int(shape[0]))
fig = plt.figure()
plt.pcolormesh(xc, yc, data, cmap='coolwarm')
plt.colorbar()
fig = plt.figure()
plt.pcolormesh(xc, yc, data_a, cmap='coolwarm')
plt.colorbar()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X,Y = np.meshgrid(xc,yc)
Z = error
ax.plot_surface(X,Y,Z)
fig = plt.figure()
plt.contourf(error)
plt.show()
def add_bands_surface_plot(axes3d: Axes3D,
kpoints: numpy.ndarray,
eigenvalues: numpy.ndarray,
axis: int,
layer: int,
resolution: int,
band_indices: Union[List[int], range,
numpy.ndarray],
offset: Union[List[float], numpy.ndarray] = None):
x, y, zs = get_x_y_zs(kpoints, eigenvalues, axis, layer)
if offset is not None:
x += offset[0]
y += offset[1]
zs += offset[2]
triang = tri.Triangulation(x, y)
x_linspace = numpy.linspace(min(x), max(x), resolution)
y_linspace = numpy.linspace(min(y), max(y), resolution)
for b in band_indices:
interpolator = tri.LinearTriInterpolator(triang, zs[:, b])
x_meshgrid, y_meshgrid = numpy.meshgrid(x_linspace, y_linspace)
z_mesggrid = interpolator(x_meshgrid, y_meshgrid)
axes3d.plot_surface(x_meshgrid,
y_meshgrid,
z_mesggrid,
cmap=pyplot.get_cmap("terrain"),
vmin=numpy.min(z_mesggrid),
vmax=numpy.max(z_mesggrid))
def _draw_plane(ax: Axes3D,
plane: Plane,
point: np.array,
length: float,
elem_num: int = 100,
color: str = "blue",
alpha: float = 0.5,
draw_diagonals: bool = False,
draw_borders: bool = False):
rays = _get_orthogonal_rays(point, plane.n)
corners = [rays[i].point(length) for i in range(4)]
if draw_diagonals:
for i in range(4):
_draw_ray(ax, rays[i], length, color=color, line_style="--")
if draw_borders:
_draw_line(ax, corners[0], corners[1], color=color, line_style="-")
_draw_line(ax, corners[1], corners[2], color=color, line_style="-")
_draw_line(ax, corners[2], corners[3], color=color, line_style="-")
_draw_line(ax, corners[3], corners[0], color=color, line_style="-")
minimum = np.minimum.reduce(corners)
maximum = np.maximum.reduce(corners)
x = np.linspace(minimum[0], maximum[0], elem_num)
y = np.linspace(minimum[1], maximum[1], elem_num)
xx, yy = np.meshgrid(x, y)
d = -plane.position.dot(plane.n)
zz = (-plane.n[0] * xx - plane.n[1] * yy - d) / plane.n[2]
ax.plot_surface(xx, yy, zz, color=color, alpha=alpha)
def Spect(
self
): #, dimension): # Dimension is unit type as V(olt) W(att), etc
Axes3D.plot_surface(self.signalx, self.signaly, self.signalz)
if self.signalx.shape == self.signaly.shape == self.signalz.shape:
pass
elif self.signalx.shape == (len(
self.signalx), ) or self.signaly.shape == (len(
self.signaly), ):
# Check for 1D array both
if self.signalx.shape == (len(
self.signalx), ) and self.signaly.shape == (len(
self.signaly), ):
self.signalx, self.signaly = np.meshgrid(
self.signalx, self.signaly)
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == (len(self.signalx), ):
pass
elif self.signaly.shape == (len(self.signaly), ):
pass
if self.signalx.shape == self.signaly.shape != self.signalz.shape:
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == self.signalz.shape != self.signaly.shape:
nx, mx = np.shape(self.signalx)
ny, my = np.shape(self.signalz)
if nx == ny and mx == my:
pass
elif nx == my and mx == ny:
self.signaly = np.rot90(self.signaly, 1)
# find out if it has to be 1 or 3
elif self.signaly.shape == self.signalz.shape != self.signalx.shape:
ny, my = np.shape(self.signaly)
nx, mx = np.shape(self.signalx)
if ny == nx and my == mx:
pass
elif ny == mx and my == nx:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
else:
raise MeasError.DataError(
"No Valid data found in at least one of the axis")
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def water(self, *argv): # Dimension is unit type as V(olt) W(att), etc
# http://matplotlib.org/examples/mplot3d/polys3d_demo.html
Axes3D.plot_surface(self.signalx, self.signaly, self.signalz) # till better funcion
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def draw(self, axes: Axes3D):
R = (self.orientation * (self.orientation_origin.get_inverse())).to_rot_matrix()
xx = R[0, 0] * self.x + R[0, 1] * self.y + R[0, 2] * self.z
yy = R[1, 0] * self.x + R[1, 1] * self.y + R[1, 2] * self.z
zz = R[2, 0] * self.x + R[2, 1] * self.y + R[2, 2] * self.z
axes.plot_surface(xx, yy, zz, rstride=4, cstride=4, color='b')
return axes,
def water(self, *argv): # Dimension is unit type as V(olt) W(att), etc
# http://matplotlib.org/examples/mplot3d/polys3d_demo.html
Axes3D.plot_surface(self.signalx, self.signaly,
self.signalz) # till better funcion
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def draw(self, axes: Axes3D):
R = (self.orientation *
(self.orientation_origin.get_inverse())).to_rot_matrix()
xx = R[0, 0] * self.x + R[0, 1] * self.y + R[0, 2] * self.z
yy = R[1, 0] * self.x + R[1, 1] * self.y + R[1, 2] * self.z
zz = R[2, 0] * self.x + R[2, 1] * self.y + R[2, 2] * self.z
axes.plot_surface(xx, yy, zz, rstride=4, cstride=4, color='b')
return axes,
def visualize_plane(plane_normal, ax: Axes3D, **kwargs):
"""
Visualize a plane with 3d plot.
"""
# Create plane for visualization
xx, yy = np.meshgrid(np.linspace(-1, 1), np.linspace(-1, 1))
d = -np.array([0, 0, 0]).dot(plane_normal)
# Only for visualization purposes
if plane_normal[2] == 0:
plane_normal[2] = 0.00000000001
zz = (-plane_normal[0] * xx - plane_normal[1] * yy - d) * 1.0 / plane_normal[2]
ax.plot_surface(xx, yy, zz, alpha=0.2, **kwargs)
def Spect(self): #, dimension): # Dimension is unit type as V(olt) W(att), etc
Axes3D.plot_surface(self.signalx, self.signaly, self.signalz)
if self.signalx.shape == self.signaly.shape == self.signalz.shape:
pass
elif self.signalx.shape == (len(self.signalx), ) or self.signaly.shape == (len(self.signaly), ):
# Check for 1D array both
if self.signalx.shape == (len(self.signalx), ) and self.signaly.shape == (len(self.signaly), ):
self.signalx, self.signaly = np.meshgrid(self.signalx, self.signaly)
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == (len(self.signalx), ):
pass
elif self.signaly.shape == (len(self.signaly), ):
pass
if self.signalx.shape == self.signaly.shape != self.signalz.shape:
nx, mx = np.shape(self.signalx)
nz, mz = np.shape(self.signalz)
if nx == nz and mx == mz:
pass
elif nx == mz and mx == nz:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
elif self.signalx.shape == self.signalz.shape != self.signaly.shape:
nx, mx = np.shape(self.signalx)
ny, my = np.shape(self.signalz)
if nx == ny and mx == my:
pass
elif nx == my and mx == ny:
self.signaly = np.rot90(self.signaly, 1)
# find out if it has to be 1 or 3
elif self.signaly.shape == self.signalz.shape != self.signalx.shape:
ny, my = np.shape(self.signaly)
nx, mx = np.shape(self.signalx)
if ny == nx and my == mx:
pass
elif ny == mx and my == nx:
self.signalz = np.rot90(self.signalz, 1)
# find out if it has to be 1 or 3
else:
raise MeasError.DataError("No Valid data found in at least one of the axis")
plt.xlabel('time [s]') # Or Sample number
plt.ylabel('Frequency [Hz]') # Or Freq Bins number
plt.zlabel('voltage [mV]') # auto add unit here
plt.title(' ') # set title
plt.grid(True)
plt.show()
def surface_plot():
from mpl_toolkits.mplot3d import Axes3D
C = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]
p = [2, 3, 4, 5, 6]
Z = [[50.5, 50.5, 50.5, 90.4, 95.7, 96.7, 97.13, 97.13],
[50.5, 50.5, 50.5, 92.3, 96.2, 96.7, 97.23, 97.23],
[50.5, 50.5, 50.5, 93.12, 96.89, 97.33, 97.16, 97.16],
[50.5, 50.5, 56.4, 93.7, 97.6, 97.4, 97.4, 97.4],
[50.5, 50.5, 67.9, 94.3, 97.7, 97.3, 97.3, 97.3]]
fig = plt.figure()
ax = Axes3D(fig)
Axes3D.plot_surface(ax, C, p, Z)
plt.show()
def plot_velocity(self):
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
Axes3D.plot_surface(ax, self._tt, self._yy, self._vv)
ax.set_xlabel("Time [ns]")
ax.set_ylabel("x [mm]")
ax.set_zlabel("Velocity [km s-1]")
fig = plt.figure()
im = plt.pcolormesh(self._tt, self._yy, self._vv)
cb = fig.colorbar(im)
plt.xlabel("Time [ns]")
plt.ylabel("x [mm]")
cb.set_label("Velocity [km s-1]")
def plot(self, axes: Axes3D, highlight_max=True):
# Plot all unpicked relay positions
unpicked = list(
filter(lambda x: x not in self.relays, self.input.relays))
rx = list(map(lambda r: r.x, unpicked))
ry = list(map(lambda r: r.y, unpicked))
rz = list(map(lambda r: r.height, unpicked))
axes.scatter(rx, ry, rz, c='#a5b6d3', label='Potential positions.')
# Highlight ground
gx = np.arange(0, self.input.width + 10)
gy = np.arange(0, self.input.height + 10)
gx, gy = np.meshgrid(gx, gy)
gz = gx * 0
axes.plot_surface(gx, gy, gz, alpha=0.2, color='brown')
# Plot assignments
for rn, sns in self.relay_to_sensors.items():
for sn in sns:
tx, ty = self.interm_point(sn, rn)
x = [rn.x, tx, sn.x]
y = [rn.y, ty, sn.y]
z = [rn.height, 0, -sn.depth]
axes.plot(x, y, z, linewidth=0.5, c='#000000')
# Highlight max pair
if highlight_max:
ms, mr = self.max_loss_pair
tx, ty = self.interm_point(ms, mr)
mx = [ms.x, tx, mr.x]
my = [ms.y, ty, mr.y]
mz = [-ms.depth, 0, mr.height]
axes.plot(mx,
my,
mz,
c='tab:orange',
label='Max assignment (%.2f)' % self.max_loss)
# Plot all sensors
sx = list(map(lambda s: s.x, self.sensors))
sy = list(map(lambda s: s.y, self.sensors))
sz = list(map(lambda s: -s.depth, self.sensors))
axes.scatter(sx, sy, sz, c='r', label='Sensors')
# Plot all relays
rx = list(map(lambda r: r.x, self.relays))
ry = list(map(lambda r: r.y, self.relays))
rz = list(map(lambda r: r.height, self.relays))
axes.scatter(rx, ry, rz, c='b', label='Relays')
def updatePlot(self, X, Y, Z, population=None):
self.activePlot = (X,Y,Z)
x, y = np.meshgrid(X,Y)
if self.surface is not None:
self.surface.remove()
if self.scatter is not None:
self.scatter.remove()
# surface
self.surface = Axes3D.plot_surface(
self.axes,
x, y, Z,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False,
shade=False,
alpha=0.5
)
# population
if population is not None:
self.activePopulation = population
x, y, z = self.preparePopulationData(population)
self.scatter = Axes3D.scatter(self.axes, x, y, z, c="r", marker="o")
self.scatter.set_alpha(1.0)
# Draw all
self.canvas.draw()
self.canvas.flush_events()
def plot_noise(self, Z):
fig = plt.figure()
X, Y = np.meshgrid(self.bits_expanded, self.bits_expanded)
ax = fig.add_subplot(111, projection='3d')
ax.set_zlim(0, 1.1)
ax.set_xlim(0, 20)
ax.set_ylim(0, 20)
ax.yaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.view_init(35, 230)
ax.set_xlabel('i')
ax.set_ylabel('j')
ax.set_zlabel('R(i,j)')
ax.zaxis.set_rotate_label(False)
Axes3D.plot_surface(ax, X, Y, Z, cmap=cm.coolwarm)
fig.savefig('noise.png')
def Plot_surf(cost_list): #画出曲面图
ax = plt.figure().add_subplot(111, projection='3d')
x = np.arange(min_s, max_s, 1)
y = np.arange(min_S, max_S, 1)
'''
for i in range(s_range):
for j in range(S_range):
x.append(i)
y.append(j)
'''
s, S = np.meshgrid(x, y)
z = np.array(cost_list)
Total_Cost = z.reshape(s.shape)
surf = ax.plot_surface(s,
S,
Total_Cost,
rstride=1,
cstride=1,
cmap='coolwarm')
plt.colorbar(surf, shrink=0.5, aspect=5)
#ax.plot_trisurf(x,y,z)
'''
plt.pcolor(total_cost)
plt.colorbar()
plt.axis("tight")
plt.title("Average total cost")
plt.xlabel("bigs")
plt.ylabel("smalls")
'''
plt.show()
def globe_map(hist: Union[Histogram2D, DirectionalHistogram],
ax: Axes3D,
*,
show_zero: bool = True,
**kwargs):
"""Heat map plotted on the surface of a sphere."""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
lw = kwargs.pop("lw", 1)
r = 1
xs = r * np.outer(np.sin(hist.numpy_bins[0]), np.cos(hist.numpy_bins[1]))
ys = r * np.outer(np.sin(hist.numpy_bins[0]), np.sin(hist.numpy_bins[1]))
zs = r * np.outer(np.cos(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
col.set_edgecolor("black")
col.set_linewidth(lw)
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b")
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-1.1, 1.1)
ax.set_zlim(-1.1, 1.1)
return ax
def cylinder_map(hist: Union[Histogram2D, CylinderSurfaceHistogram],
ax: Axes3D,
*,
show_zero: bool = True,
**kwargs):
"""Heat map plotted on the surface of a cylinder."""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
if hasattr(hist, "radius"):
r = kwargs.pop("radius", hist.radius)
else:
r = kwargs.pop("radius", 1)
xs = r * np.outer(np.cos(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
ys = r * np.outer(np.sin(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
zs = np.outer(np.ones(hist.shape[0] + 1), hist.numpy_bins[1])
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b")
ax.set_xlim(-r * 1.1, r * 1.1)
ax.set_ylim(-r * 1.1, r * 1.1)
ax.set_zlim(zs.min(), zs.max())
def plot_my_fun():
def func(z, r):
return 0.5*(z-r)/(r+z)+0.5
data = np.zeros(shape=(3, 54))*np.nan
i = 0
X = np.arange(7)
Y = np.arange(1, 10)
X, Y = np.meshgrid(X, Y)
Z = func(X, Y)
# for z in range(7):
# for r in range(1, 10):
# data[:,i] = np.array([z,r,func(z,r)])
fig = plt.figure()
ax = fig.gca(projection='3d')
Axes3D.plot_surface(ax, X,Y,Z)
plt.show()
def visualise(x,y):
thetaval0 = np.linspace(-10,10,num=100)
thetaval1 = np.linspace(-1,4,100)
jval=np.zeros((100,100),dtype=int)
for i in range(len(thetaval0)):
for j in range(len(thetaval1)):
th=np.array([thetaval0[i],thetaval1[j]])
th.shape=(2,1)
b=compute_costFunction(x,y,th)
jval[i][j]=b
np.transpose(jval)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Axes3D.plot_surface(ax,thetaval0, thetaval1, jval)
plt.show()
def show_plot(approx_values, x_max, t_max, error, title=""):
n = approx_values.shape[1] - 1
m = approx_values.shape[0] - 1
exact_values = count_exact_values(n, m, x_max, t_max)
fig = plt.figure()
ax = fig.gca(projection='3d')
Axes3D.plot_surface(ax,
*create_grid(x_max, t_max, n, m, exact_values),
color='royalblue')
Axes3D.plot_surface(ax,
*create_grid(x_max, t_max, n, m, approx_values),
color='orangered')
ax.set_xlabel('x')
ax.set_ylabel('t')
ax.set_title((f"{title}\n" if title != "" else "") +
f"n = {n}, m = {m}, Error is {error}")
plt.show()
def plot_3d(self,
ax_3d: Axes3D,
lims_x: array_like = (-1, 1),
lims_y: array_like = (-1, 1),
**kwargs) -> None:
"""
Plot a 3D plane.
Parameters
----------
ax_3d : Axes3D
Instance of :class:`~mpl_toolkits.mplot3d.axes3d.Axes3D`.
lims_x, lims_y : (2,) tuple
x and y limits of the plane.
Tuple of form (min, max). The default is (-1, 1).
The point on the plane is used as the origin.
kwargs : dict, optional
Additional keywords passed to :meth:`~mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface`.
Examples
--------
.. plot::
:include-source:
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.mplot3d import Axes3D
>>> from skspatial.objects import Plane
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')
>>> plane = Plane([5, 3, 1], [1, 0, 1])
>>> plane.plot_3d(ax, alpha=0.2)
>>> plane.point.plot_3d(ax, s=100)
"""
X, Y, Z = self.to_mesh(lims_x, lims_y)
ax_3d.plot_surface(X, Y, Z, **kwargs)
def plot(self, Z, which, format_axis=True):
fig = plt.figure()
X, Y = np.meshgrid(self.bits_expanded, self.bits_expanded)
ax = fig.add_subplot(111, projection='3d')
if format_axis == True:
ax.set_zlim(0, 2500000)
ax.set_xlim(0, 20)
ax.set_ylim(0, 20)
ax.zaxis.set_major_formatter(
FuncFormatter(lambda x, pos: '%dk' % int(x / 1000)))
ax.yaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.view_init(35, 230)
ax.set_xlabel('i')
ax.set_ylabel('j')
ax.set_zlabel('Fitness')
ax.zaxis.labelpad = 15
ax.zaxis.set_rotate_label(False)
Axes3D.plot_surface(ax, X, Y, Z, cmap=cm.coolwarm)
fig.savefig(which)
def plot_error_ftle(nx, ny, delta1, dt1, dt_an):
filename = 'output/'+str(nx) + '_' + str(ny) + '_ftle_heat_t5_delta' + delta1 + '_dt' + dt1 + '.txt'
data = np.fromfile(filename, dtype=np.float32) # np.genfromtxt(filename)#
shape = [ny, nx]
data = np.reshape(data, (int(shape[0]), int(shape[1])))
filename_a ='output/'+ str(nx) + '_' + str(ny) + '_ftle_heat_t5_delta' + delta1 + '_dt' + dt_an + '.txt'
data_a = np.fromfile(filename_a, dtype=np.float32) # np.genfromtxt(filename)#
shape_a = shape
data_a = np.reshape(data_a, (int(shape[0]), int(shape[1])))
difference = abs(data - data_a)
error = difference / (abs(data_a) + 1 * 1e-15)
print(np.min(abs(data_a[np.nonzero(data_a)])))
xc = np.linspace(0, 2, int(shape[1]))
yc = np.linspace(0, 1, int(shape[0]))
sum_diff = np.sum(difference)
sum_data_a = np.sum(abs(data_a))
print(sum_diff / sum_data_a * 100)
fig = plt.figure()
plt.pcolormesh(xc, yc, data, cmap='GnBu', label=dt1)
plt.title(dt1)
plt.colorbar()
fig = plt.figure()
plt.pcolormesh(xc, yc, data_a, cmap='GnBu', label=dt_an)
plt.title(dt_an)
plt.colorbar()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y = np.meshgrid(xc, yc)
Z = error
ax.plot_surface(X, Y, Z)
#ax.set_zlim(bottom=-0, top=0.5, emit=True, auto=False)
fig = plt.figure()
square = 1;
plt.show()
plt.pcolormesh(xc[square:len(xc) - square], yc[square:len(yc) - square], error[square:len(yc) - square, square:len(xc) - square])
plt.colorbar()
plt.show()
def plot_3d(self, ax_3d: Axes3D, n_along_axis: int = 100, n_angles: int = 30, **kwargs) -> None:
"""
Plot a 3D cylinder.
Parameters
----------
ax_3d : Axes3D
Instance of :class:`~mpl_toolkits.mplot3d.axes3d.Axes3D`.
n_along_axis : int
Number of intervals along the axis of the cylinder.
n_angles : int
Number of angles distributed around the circle.
kwargs : dict, optional
Additional keywords passed to :meth:`~mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface`.
Examples
--------
.. plot::
:include-source:
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.mplot3d import Axes3D
>>> from skspatial.objects import Cylinder
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')
>>> cylinder = Cylinder([5, 3, 1], [1, 0, 1], 2)
>>> cylinder.plot_3d(ax, alpha=0.2)
>>> cylinder.point.plot_3d(ax, s=100)
"""
X, Y, Z = self.to_mesh(n_along_axis, n_angles)
ax_3d.plot_surface(X, Y, Z, **kwargs)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
a = np.arange(round(P[4, 0] - 500), round(P[4, 0] + 500))
b = np.arange(round(P[4, 1] - 500), round(P[4, 1] + 500))
teste = np.zeros([len(a), len(b)])
a, b = np.meshgrid(a, b)
v = np.zeros(4)
for k in range(1000):
for j in range(1000):
for i in range(4):
v[i] = ((P[i, 0] - a[k, j])**2 + (P[i, 1] - b[k, j])**2)**0.5
teste[k, j] = np.dot(v - dist[4, 0:4], v - dist[4, 0:4])
fig = plt.figure()
ax = fig.gca(projection='3d')
Axes3D.plot_surface(ax, a, b, teste)
def surface_map(hist,
ax: Axes3D,
*,
show_zero: bool = True,
x=(lambda x, y: x),
y=(lambda x, y: y),
z=(lambda x, y: 0),
**kwargs):
"""Coloured-rectangle plot of 2D histogram, placed on an arbitrary surface.
Each bin is mapped to a rectangle in 3D space using the x,y,z functions.
Parameters
----------
hist : Histogram2D
show_zero : Optional[bool]
Whether to show coloured box for bins with 0 frequency (otherwise background).
x : function
Function with 2 parameters used to map bins to spatial x coordinate
y : function
Function with 2 parameters used to map bins to spatial y coordinate
z : function
Function with 2 parameters used to map bins to spatial z coordinate
Returns
-------
matplotlib.axes._subplots.Axes3DSubplot
See Also
--------
map, cylinder_map, globe_map
"""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
xs = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
ys = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
zs = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
edges_x = hist.numpy_bins[0]
edges_y = hist.numpy_bins[1]
for i in range(hist.shape[0] + 1):
for j in range(hist.shape[1] + 1):
xs[i, j] = x(edges_x[i], edges_y[j])
ys[i, j] = y(edges_x[i], edges_y[j])
zs[i, j] = z(edges_x[i], edges_y[j])
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b") # Dummy plot
ax.set_xlim(xs.min(), xs.max())
ax.set_ylim(ys.min(), ys.max())
ax.set_zlim(zs.min(), zs.max())
# ax.plot_surface(x, y, z, rstride=hist.shape[0], color="b")
return ax
theta0_vals = np.linspace(-10, 10, 100)
theta1_vals = np.linspace(-1, 4, 100)
# initialize J_vals to a matrix of 0's
J_vals = np.zeros((len(theta0_vals),len(theta1_vals)))
# Fill out J_Vals
# Note: There is probably a more efficient way to do this that uses
# broadcasting instead of the nested for loops
for i in range(len(theta0_vals)):
for j in range(len(theta1_vals)):
t = np.array([theta0_vals[i],theta1_vals[j]])
J_vals[i][j] = computeCost(X,y,t)
# Surface plot using J_Vals
fig = plt.figure()
ax = plt.subplot(111,projection='3d')
Axes3D.plot_surface(ax,theta0_vals,theta1_vals,J_vals,cmap=cm.coolwarm)
plt.show()
# Contour plot
# TO DO: Currently does not work as expected. Need to find a way to mimic
# the logspace option in matlab
fig = plt.figure()
ax = plt.subplot(111)
plt.contour(theta0_vals,theta1_vals,J_vals)
# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4
x = a
step = (abs(a) + abs(b)) / n
for i in range(n + 1):
pointsX.append(x)
pointsY.append(f(x))
x += step
return pointsX, pointsY
def generate3D(x, y, a, b, s):
pointsX = []
pointsY = []
pointsZ = []
s = int(s)
step = (abs(a) + abs(b)) / s
for i in range(s + 1):
pointsX.append(f3d(x, y))
pointsY.append(f3d(x, y))
pointsZ.append(f3d(x, y))
x += step
return pointsX, pointsY, pointsZ
x, y, z = generate3D(1.1, math.sqrt(2), 0, 100, 100)
print(x, y)
print(z)
Axes3D.plot_surface(x, y, z)
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 22 20:56:55 2015
@author: jderoo
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#D is a vector from 0 to 3, 100 spaces in between
D = np.linspace(0,3,100)
#U is a vector from 0 to 3, 100 spaces in between
U = np.linspace(0,3,100)
#A is a blank matrix/list
A = []
#for loop that ranges i from 0 to 100 steps of 1
for i in range(0,100,1):
#for loop that ranges j from 0 to 100 steps of 1
for j in range(0,100,1):
#The base equation we were given in the homework
r = ((3*D[i]*(U[j])^.7)/(1 + D[i]^.95 + U[j])^.3)
#in cell location i,j place the calced value of r
A[i,j] = r
#3D plot D, U, and the now filled matrix A
Axes3D.plot_surface(D,U,A)
import math
import numpy as np
from numpy import exp
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
mask = [[1, 2, 1], [2, 4, 2], [1, 2, 1]]
transformed = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
K = M = 3
L = N = 3
for k in range(K):
for l in range(L):
accum = 0
for m in range(M):
for n in range(N):
accum += mask[m][n] * exp(-2j * math.pi * k * m / K) * exp(
-2j * math.pi * l * n / L)
complexAccum = accum / 16
transformed[k][l] = accum / 16
mask_dft = np.fft.fft2(mask)
print(mask_dft)
Axes3D.plot_surface(transformed)
plt.show()
def saveoutput(Ec, Ne, Ie, Ne_sub, E_sub, Te_sub, converge, Vd_temp):
transport_model = transportmodel.value
N_sd = Nsd1.value
N_body = Nbody1.value
fermi_flag = fermiflag1.value
Vd = Vdc.value
# Rohit- N_dos, Trans and E
###########################################################
#SPECIFY XI, YI, Vg_bias, and Vd_bias for plotting use only
###########################################################
Nx = round((2 * Lsd + Lg_top) / dx) + 1
XI = np.linspace(-(Lsd + Lg_top / 2) * 1e9, (Lsd + Lg_top / 2) * 1e9,
Nx) # in nanometers
Ny = round((t_top + t_bot + t_si) / dy) + 1
YI = np.linspace(-t_top * 1e9, (t_si + t_bot) * 1e9, Ny)
Vg_bias = np.linspace(Vg1, Vg1 + Vg_step * Ng_step, Ng_step + 1)
Vd_bias = np.linspace(Vd_temp, Vd_temp + Vd_step * Nd_step, Nd_step + 1)
###########################################################
# POST PROCESSING
###########################################################
MEc = np.reshape(Ec[Nd_step, :, Ng_step], (Ny, Nx)).transpose()
trMEc = MEc.transpose()
MNe = np.reshape(Ne[Nd_step, :, Ng_step], (Ny, Nx)).transpose()
trMNe = MNe.transpose()
#mkdir(dirname)
#cd(dirname)
#raw_data is always stored!!!!!
#--------------------------------------------------
#save output_rawdata
#Save convergence data in a file
#save DOS.dat N_dos -ascii
Trans = globvars.Trans
#save trans.dat Trans -ascii
E = globvars.E
#save E.dat E -ascii
N_dos = globvars.N_dos
fid = open("convergence.dat", "w")
fid.write(
'****************************************************************\n')
fid.write('*******You ran a Nanomos simulation on the %s *********\n' %
datetime.datetime.now())
fid.write(
'********************* Using Nanomos 2.0 ************************\n')
fid.write(
'********** The input file you used was the following **********\n')
fid.write(
'****************************************************************\n')
#cd ..
# fid1=fopen(filename,'r')
# while 1
# tline = fgetl(fid1)
# if ~ischar(tline), break, end
# count=fprintf(fid,'%s\n',tline);
# end
# status=fclose(fid1)
# cd(dirname)
fid.write(
'****************************************************************\n')
fid.write(
'********** Convergence data for simulated bias points **********\n')
fid.write(
'****************************************************************\n')
for i in np.arange(0, Ng_step + 1):
for j in np.arange(0, Nd_step + 1):
fid.write('\n')
fid.write('**** Gate voltage = %f ****Drain voltage = %f' %
(Vg_bias[i], Vd_bias[j]))
fid.write('\n')
for item in converge[i, j]:
fid.write('%e\n' % item)
fid.write(
'****************************************************************')
fid.write(
'********** The end of the convergence data file*****************')
fid.write(
'****************************************************************')
fid.close()
############################################# IV plots ##############################################
if plot_IV == 1:
# ID-VD CHARACTERISTICS (A/m), SAVED TO "id_vd.dat"
# -------------------------------------------------
if (Ng_step >= 1 and Nd_step >= 1):
Ng = np.size(Ie, 0)
figure(1)
plot(Vd_bias, Ie[0, :], 'o-')
grid(True)
hold(True)
for i in np.arange(1, Ng):
plot(Vd_bias, Ie[i, :], 'o-')
xlabel('$V_{DS}$ [V]')
ylabel('$I_{DS}$ $[\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{DS}$')
savefig('ID_VD.png')
temmm = Vd_bias
Ie2 = Ie.transpose()
fid = open('ID_VD.dat', 'w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[ind, :]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
# ID-VG CHARACTERISTICS (A/m), SAVED TO "id_vg.dat"
# -------------------------------------------------
if (Ng_step >= 1 and Nd_step == 0):
figure(1)
semilogy(Vg_bias, Ie[:, 0], 'o-')
grid(True)
xlabel('$V_{GS}$ [V]')
ylabel('$I_{DS} [\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{GS}$')
savefig('ID_VG.png')
#temmm=[Vg_bias, Ie[:,0]]
#np.savetxt( 'ID_VG.dat', temmm, fmt='%e', delimiter=';')
temmm = Vg_bias
Ie2 = Ie.transpose()
fid = open('ID_VG.dat', 'w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[:, ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
# ID-VD CHARACTERISTICS (A/m), SAVED TO "id_vd.dat"
# -------------------------------------------------
if (Nd_step >= 1 and Ng_step == 0):
figure(1)
plot(Vd_bias, Ie[0, :], 'o-')
grid(True)
xlabel('$V_{DS}$ [V]')
ylabel('$I_{DS} [\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{DS}$')
savefig('ID_VD.png')
#temmm = [Vd_bias, Ie[0,:]]
#np.savetxt('ID_VD.dat', temmm, fmt='%e', delimiter=';')
temmm = Vd_bias
Ie2 = Ie.transpose()
fid = open('ID_{VD}.dat', 'w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[ind, :]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#if plot_Iv==1 end
#***************************************************************************************
# Ec(X,Y)
# -------------------------------------------
if plot_Ec3d == 1:
figure(6)
[X, Y] = np.meshgrid(XI, YI)
Z = trMEc
ax = gca(projection='3d')
surf = ax.plot_surface(X,
Y,
Z,
rstride=3,
cstride=3,
cmap=cm.coolwarm,
linewidth=0.5,
antialiased=True)
#surf(XI,YI,trMEc)
#shading interp commented out to reduce size of the .ps file
title('3D Conduction band edge potential profile')
ax.set_xlabel('X [nm]')
ax.set_ylabel('Y [nm]')
ax.set_zlabel('Ec [eV]')
ax.view_init(elev=60, azim=50)
ax.dist = 8
savefig('Ec_X_Y.png')
XII = (0, XI)
tem1 = (YI, trMEc)
tem2 = (XII, tem1)
#np.savetxt('Ec_X_Y.dat', tem2, fmt='%e', delimiter=';')
#f1 = open('Ec_X_Y','w')
#writer = csv.writer(f1, delimiter = ',')
#writer.writerows(tem2)
#*******************************************************************************************
if (plot_Ecsub == 1 and max_subband >= 1):
figure(8)
for iii in np.arange(0, max_subband):
plot(XI, E_sub[0, Ng_step, Nd_step, :, iii], 'r-')
hold(True)
grid(True)
if (t_vall == 3):
plot(XI, E_sub[1, Ng_step, Nd_step, :, iii], 'k-')
plot(XI, E_sub[2, Ng_step, Nd_step, :, iii], '-')
title('The Subbands energy profile along the channel')
xlabel('X [nm]')
ylabel('E_{SUB} [eV]')
savefig('Ec_sub_X.png')
############################################################################################
if (plot_Nesub == 1 and max_subband >= 1):
figure(9)
for iii in np.arange(0, max_subband):
semilogy(XI, Ne_sub[0, Ng_step, Nd_step, :, iii], 'r-')
hold(True)
grid(True)
if (t_vall == 3):
semilogy(XI, Ne_sub[1, Ng_step, Nd_step, :, iii], 'k-')
semilogy(XI, Ne_sub[2, Ng_step, Nd_step, :, iii], '-')
title('2D electron density of the subbands along the channel ')
xlabel('X [nm]')
ylabel('N2D [cm^{-2}]')
savefig('Ne_sub_X.png')
if (Ng_step == 0 and Nd_step == 0):
# PLOT the graph of Transmission coefficient versus Energy
#------------------------------------------------------------------
if transport_model == 4:
figure(11)
plot(Trans, E)
xlabel('Transmission Coefficient')
ylabel('Energy (eV)')
savefig('Trans.png')
colorbar()
# PLOT the Density of states versus energy along the device
#------------------------------------------------------------------
if transport_model == 4:
figure(12)
pcolor(XI, E, np.sqrt(N_dos[:, 0:len(E)].transpose()))
#shading interp
# Square root of the DOS to get better visualization
xlabel('Distance along the device (nm)')
ylabel('Energy (eV)')
colorbar()
savefig('DOS.png')
#print -depsc2 DOS.ps
# PLOT the Density of states versus energy along the device
#------------------------------------------------------------------
if transport_model == 5:
figure(12)
pcolor(XI, E, np.sqrt(abs(N_dos[:, 0:len(E)]).transpose()))
#shading interp;
# Square root of the DOS to get better visualization
xlabel('Distance along the device (nm)')
ylabel('Energy (eV)')
colorbar()
savefig('DOS.jpg')
################################################################################################
if plot_Ne_IV == 1:
# SUBBAND CHARGE DENSITY (/cm^2) vs X for Diff. Vg
# --------------------------------------------------------------
if (Ng_step >= 1 or (Ng_step == 0 and Nd_step == 0)):
figure(2)
for iii in np.arange(0, Ng_step + 1):
Ne1_temp = np.squeeze(Ne_sub[:, iii, Nd_step, :, :])
#if len(np.shape(Ne1_temp)) == 1:
Ne1 = Ne1_temp
#else:
# Ne1 = np.sum(Ne1_temp,0)
Ne2 = np.zeros((len(Ne1), Ng_step + 1))
Ne2[:, iii] = (Ne1) * 1e-4
plot(XI, Ne2[:, iii], 'r-')
hold(True)
grid(True)
if Ng_step >= 1:
title('2D electron density along the channel at different Vg')
elif Ng_step == 0:
title('2D electron density along the channel')
xlabel('X [nm]')
ylabel('N2D $[cm^{-2}]$')
savefig('N2D_X1.png')
#figure(11)
#Ne1=sum(squeeze(Ne_sub(:,:,:,1,Nd_step+1)),3);
#Ne2(:,1)=sum(Ne1,2)*1e-4;
#semilogy(XI',Ne2(:,1),'r-');
#hold on
#grid on
#if Ng_step>0
# for iii=2:Ng_step+1
# Ne1=sum(squeeze(Ne_sub(:,:,:,iii,Nd_step+1)),3);
# Ne2(:,iii)=sum(Ne1,2)*1e-4;
# semilogy(XI',Ne2(:,iii),'r-');
# end
#end
#title('2D electron density along the channel @Diff. VG');
#xlabel('X [nm]');
#ylabel('N2D [cm^{-2}]');
#print -depsc ./output/LOG_N2D_X.ps
temmm = [XI, Ne2]
Ne2 = np.squeeze(Ne_sub[0, Ng_step, :, :, 0])
Ne2 = Ne2 * 1e-4
fid = open('N2D_X1.dat', 'w')
ind = 0
print np.shape(XI)
print np.shape(Ne2)
for item1 in XI:
fid.write("%e " % item1)
item2 = Ne2[ind]
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save N2D_X1.dat temmm -ascii;
# SUBBAND CHARGE DENSITY (/cm^2) vs X for Diff. Vd
# --------------------------------------------------------------
if Nd_step >= 1:
figure(3)
for iii in np.arange(0, Nd_step + 1):
Ne1_temp = np.squeeze(Ne_sub[:, Ng_step, iii, :, :])
#if len(np.size(Ne1_temp)) == 1:
Ne1 = Ne1_temp
#else:
#Ne1 = np.sum(Ne1_temp,0)
Ne2 = np.zeros((len(Ne1), Nd_step + 1))
Ne2[:, iii] = (Ne1) * 1e-4
plot(XI, Ne2[:, iii], 'r-')
hold(True)
grid(True)
title('2D electron density along the channel at different Vd')
xlabel('X [nm]')
ylabel('N2D [$cm^{-2}$]')
savefig('N2D_X2.png')
temmm = [XI, Ne2]
Ne2 = np.squeeze(Ne_sub[0, Ng_step, :, :, 0])
Ne2 = Ne2 * 1e-4
fid = open('N2D_X2.dat', 'w')
ind = 0
print np.shape(XI)
print np.shape(Ne2)
for item1 in XI:
fid.write("%e " % item1)
for item2 in Ne2[:, ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save N2D_X2.dat temmm -ascii
#******************************************************************************************
if plot_Ec_IV == 1:
# The First SUBBAND ENERGY PROFILE vs X for Diff. Vg
# ------------------------------------------------------
if (Ng_step >= 1 or (Ng_step == 0 and Nd_step == 0)):
figure(4)
for iii in np.arange(0, Ng_step + 1):
plot(XI, E_sub[0, iii, Nd_step, :, 0], 'r-')
hold(True)
grid(True)
if Ng_step >= 1:
title(
'The First Subband energy profile along the channel at different Vg'
)
elif Ng_step == 0:
title('The First Subband energy profile along the channel')
xlabel('X [nm]')
ylabel('E_{SUB} [eV]')
savefig('Ec_X1.png')
tem = E_sub[0, iii, Nd_step, :, 0]
sq_tem = np.squeeze(tem)
print np.shape(sq_tem)
temmm = [XI, sq_tem]
fid = open('Ec_X1.dat', 'w')
ind = 0
for item1 in XI:
fid.write("%e " % item1)
item2 = sq_tem[ind]
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save Ec_X1.dat temmm -ascii
# The First SUBBAND ENERGY PROFILE vs X for Diff. Vd
# ------------------------------------------------------
if Nd_step >= 1:
figure(5)
for iii in np.arange(0, Nd_step + 1):
plot(XI, E_sub[0, Ng_step, iii, :, 0], 'r-')
hold(True)
grid(True)
title(
'The First Subband energy profile along the channel at different Vd'
)
xlabel('X [nm]')
ylabel('$E_{SUB}$ [eV]')
savefig('Ec_X2.png')
tem = E_sub[0, Ng_step, :, :, 0]
sq_tem = np.squeeze(tem)
fid = open('Ec_X2.dat', 'w')
ind = 0
for item1 in XI:
fid.write("%e " % item1)
for item2 in sq_tem[:, ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#temmm = [XI,sq_tem]
#save Ec_X2.dat temmm -ascii
# 3D CHARGE DENSITY N(X,Y)
# ------------------------------------------------------------
if plot_Ne3d == 1:
figure(7)
#surf(XI,YI,trMNe)
#shading interp commented out to reduce size of the .ps file
[X, Y] = np.meshgrid(XI, YI)
Z = trMNe
ax = gca(projection='3d')
surf = ax.plot_surface(X,
Y,
Z,
rstride=3,
cstride=3,
cmap=cm.coolwarm,
linewidth=0.5,
antialiased=True)
title('3D Electron density profile')
ax.set_xlabel('X [nm]')
ax.set_ylabel('Y [nm]')
ax.set_zlabel('Ne [$m^{-3}$]')
ax.view_init(elev=60, azim=50)
ax.dist = 8
savefig('Ne_X_Y.png')
XII = [0, XI]
tem1 = [YI, trMNe]
tem2 = [XII, tem1]
#save Ne_X_Y.dat tem2 -ascii
return
def saveoutput(Ec,Ne,Ie,Ne_sub,E_sub,Te_sub,converge,Vd_temp):
transport_model = transportmodel.value
N_sd = Nsd1.value
N_body = Nbody1.value
fermi_flag = fermiflag1.value
Vd = Vdc.value
# Rohit- N_dos, Trans and E
###########################################################
#SPECIFY XI, YI, Vg_bias, and Vd_bias for plotting use only
###########################################################
Nx = round((2*Lsd+Lg_top)/dx)+1
XI = np.linspace(-(Lsd+Lg_top/2)*1e9, (Lsd+Lg_top/2)*1e9, Nx) # in nanometers
Ny = round((t_top+t_bot+t_si)/dy)+1
YI = np.linspace(-t_top*1e9, (t_si+t_bot)*1e9, Ny)
Vg_bias = np.linspace(Vg1, Vg1+Vg_step*Ng_step, Ng_step+1)
Vd_bias = np.linspace(Vd_temp, Vd_temp+Vd_step*Nd_step, Nd_step+1)
###########################################################
# POST PROCESSING
###########################################################
MEc = np.reshape(Ec[Nd_step, :, Ng_step], (Ny, Nx)).transpose()
trMEc = MEc.transpose()
MNe = np.reshape(Ne[Nd_step, :, Ng_step], (Ny, Nx)).transpose()
trMNe = MNe.transpose()
#mkdir(dirname)
#cd(dirname)
#raw_data is always stored!!!!!
#--------------------------------------------------
#save output_rawdata
#Save convergence data in a file
#save DOS.dat N_dos -ascii
Trans = globvars.Trans
#save trans.dat Trans -ascii
E = globvars.E
#save E.dat E -ascii
N_dos = globvars.N_dos
fid = open("convergence.dat", "w")
fid.write('****************************************************************\n')
fid.write('*******You ran a Nanomos simulation on the %s *********\n' % datetime.datetime.now())
fid.write('********************* Using Nanomos 2.0 ************************\n')
fid.write('********** The input file you used was the following **********\n')
fid.write('****************************************************************\n')
#cd ..
# fid1=fopen(filename,'r')
# while 1
# tline = fgetl(fid1)
# if ~ischar(tline), break, end
# count=fprintf(fid,'%s\n',tline);
# end
# status=fclose(fid1)
# cd(dirname)
fid.write('****************************************************************\n')
fid.write('********** Convergence data for simulated bias points **********\n')
fid.write('****************************************************************\n')
for i in np.arange(0,Ng_step+1):
for j in np.arange(0,Nd_step+1):
fid.write('\n')
fid.write('**** Gate voltage = %f ****Drain voltage = %f' % (Vg_bias[i], Vd_bias[j]))
fid.write('\n')
for item in converge[i,j]:
fid.write('%e\n' % item)
fid.write('****************************************************************')
fid.write('********** The end of the convergence data file*****************')
fid.write('****************************************************************')
fid.close()
############################################# IV plots ##############################################
if plot_IV == 1:
# ID-VD CHARACTERISTICS (A/m), SAVED TO "id_vd.dat"
# -------------------------------------------------
if (Ng_step>=1 and Nd_step>=1):
Ng = np.size(Ie,0)
figure(1)
plot(Vd_bias,Ie[0,:],'o-')
grid(True)
hold(True)
for i in np.arange(1,Ng):
plot(Vd_bias, Ie[i, :], 'o-')
xlabel('$V_{DS}$ [V]')
ylabel('$I_{DS}$ $[\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{DS}$')
savefig('ID_VD.png')
temmm = Vd_bias
Ie2 = Ie.transpose()
fid = open('ID_VD.dat','w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[ind,:]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
# ID-VG CHARACTERISTICS (A/m), SAVED TO "id_vg.dat"
# -------------------------------------------------
if (Ng_step>=1 and Nd_step==0):
figure(1)
semilogy(Vg_bias, Ie[:,0],'o-')
grid(True)
xlabel('$V_{GS}$ [V]')
ylabel('$I_{DS} [\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{GS}$')
savefig('ID_VG.png')
#temmm=[Vg_bias, Ie[:,0]]
#np.savetxt( 'ID_VG.dat', temmm, fmt='%e', delimiter=';')
temmm = Vg_bias
Ie2 = Ie.transpose()
fid = open('ID_VG.dat','w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[:,ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
# ID-VD CHARACTERISTICS (A/m), SAVED TO "id_vd.dat"
# -------------------------------------------------
if (Nd_step>=1 and Ng_step==0):
figure(1)
plot(Vd_bias, Ie[0,:], 'o-')
grid(True)
xlabel('$V_{DS}$ [V]')
ylabel('$I_{DS} [\mu A/\mu m]$')
title('$I_{DS}$ vs. $V_{DS}$')
savefig('ID_VD.png')
#temmm = [Vd_bias, Ie[0,:]]
#np.savetxt('ID_VD.dat', temmm, fmt='%e', delimiter=';')
temmm = Vd_bias
Ie2 = Ie.transpose()
fid = open('ID_{VD}.dat','w')
ind = 0
for item1 in temmm:
fid.write("%e " % item1)
for item2 in Ie2[ind,:]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#if plot_Iv==1 end
#***************************************************************************************
# Ec(X,Y)
# -------------------------------------------
if plot_Ec3d == 1:
figure(6)
[X, Y] = np.meshgrid(XI, YI)
Z = trMEc
ax = gca(projection = '3d')
surf = ax.plot_surface(X, Y, Z, rstride=3, cstride=3, cmap=cm.coolwarm, linewidth=0.5, antialiased = True)
#surf(XI,YI,trMEc)
#shading interp commented out to reduce size of the .ps file
title('3D Conduction band edge potential profile')
ax.set_xlabel('X [nm]')
ax.set_ylabel('Y [nm]')
ax.set_zlabel('Ec [eV]')
ax.view_init(elev=60, azim=50)
ax.dist=8
savefig('Ec_X_Y.png')
XII = (0, XI)
tem1 = (YI, trMEc)
tem2 = (XII, tem1)
#np.savetxt('Ec_X_Y.dat', tem2, fmt='%e', delimiter=';')
#f1 = open('Ec_X_Y','w')
#writer = csv.writer(f1, delimiter = ',')
#writer.writerows(tem2)
#*******************************************************************************************
if (plot_Ecsub==1 and max_subband>=1):
figure(8)
for iii in np.arange(0,max_subband):
plot(XI, E_sub[0, Ng_step, Nd_step, :, iii],'r-')
hold(True)
grid(True)
if (t_vall==3):
plot(XI, E_sub[1, Ng_step, Nd_step, :,iii],'k-')
plot(XI, E_sub[2, Ng_step, Nd_step, :,iii],'-')
title('The Subbands energy profile along the channel')
xlabel('X [nm]')
ylabel('E_{SUB} [eV]')
savefig('Ec_sub_X.png')
############################################################################################
if (plot_Nesub==1 and max_subband>=1):
figure(9)
for iii in np.arange(0,max_subband):
semilogy(XI, Ne_sub[0, Ng_step, Nd_step, :, iii],'r-')
hold(True)
grid(True)
if (t_vall==3):
semilogy(XI, Ne_sub[1, Ng_step, Nd_step, :, iii],'k-')
semilogy(XI, Ne_sub[2, Ng_step, Nd_step, :, iii],'-')
title('2D electron density of the subbands along the channel ')
xlabel('X [nm]')
ylabel('N2D [cm^{-2}]')
savefig('Ne_sub_X.png')
if (Ng_step==0 and Nd_step==0):
# PLOT the graph of Transmission coefficient versus Energy
#------------------------------------------------------------------
if transport_model==4:
figure(11)
plot(Trans, E)
xlabel('Transmission Coefficient')
ylabel('Energy (eV)')
savefig('Trans.png')
colorbar()
# PLOT the Density of states versus energy along the device
#------------------------------------------------------------------
if transport_model==4:
figure(12)
pcolor(XI, E, np.sqrt(N_dos[:,0:len(E)].transpose()))
#shading interp
# Square root of the DOS to get better visualization
xlabel('Distance along the device (nm)')
ylabel('Energy (eV)')
colorbar()
savefig('DOS.png')
#print -depsc2 DOS.ps
# PLOT the Density of states versus energy along the device
#------------------------------------------------------------------
if transport_model==5:
figure(12)
pcolor(XI,E,np.sqrt(abs(N_dos[:,0:len(E)]).transpose()))
#shading interp;
# Square root of the DOS to get better visualization
xlabel('Distance along the device (nm)')
ylabel('Energy (eV)')
colorbar()
savefig('DOS.jpg')
################################################################################################
if plot_Ne_IV==1:
# SUBBAND CHARGE DENSITY (/cm^2) vs X for Diff. Vg
# --------------------------------------------------------------
if (Ng_step>=1 or (Ng_step==0 and Nd_step==0)):
figure(2)
for iii in np.arange(0,Ng_step+1):
Ne1_temp = np.squeeze(Ne_sub[:, iii, Nd_step, :, :])
#if len(np.shape(Ne1_temp)) == 1:
Ne1 = Ne1_temp
#else:
# Ne1 = np.sum(Ne1_temp,0)
Ne2 = np.zeros((len(Ne1), Ng_step+1))
Ne2[:,iii]=(Ne1)*1e-4
plot(XI, Ne2[:, iii], 'r-')
hold(True)
grid(True)
if Ng_step>=1:
title('2D electron density along the channel at different Vg')
elif Ng_step==0:
title('2D electron density along the channel')
xlabel('X [nm]')
ylabel('N2D $[cm^{-2}]$')
savefig('N2D_X1.png')
#figure(11)
#Ne1=sum(squeeze(Ne_sub(:,:,:,1,Nd_step+1)),3);
#Ne2(:,1)=sum(Ne1,2)*1e-4;
#semilogy(XI',Ne2(:,1),'r-');
#hold on
#grid on
#if Ng_step>0
# for iii=2:Ng_step+1
# Ne1=sum(squeeze(Ne_sub(:,:,:,iii,Nd_step+1)),3);
# Ne2(:,iii)=sum(Ne1,2)*1e-4;
# semilogy(XI',Ne2(:,iii),'r-');
# end
#end
#title('2D electron density along the channel @Diff. VG');
#xlabel('X [nm]');
#ylabel('N2D [cm^{-2}]');
#print -depsc ./output/LOG_N2D_X.ps
temmm=[XI,Ne2]
Ne2 = np.squeeze(Ne_sub[0,Ng_step,:,:,0])
Ne2 = Ne2*1e-4
fid = open('N2D_X1.dat','w')
ind = 0
print np.shape(XI)
print np.shape(Ne2)
for item1 in XI:
fid.write("%e " % item1)
item2 = Ne2[ind]
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save N2D_X1.dat temmm -ascii;
# SUBBAND CHARGE DENSITY (/cm^2) vs X for Diff. Vd
# --------------------------------------------------------------
if Nd_step>=1:
figure(3)
for iii in np.arange(0,Nd_step+1):
Ne1_temp = np.squeeze(Ne_sub[:, Ng_step, iii, :, :])
#if len(np.size(Ne1_temp)) == 1:
Ne1 = Ne1_temp
#else:
#Ne1 = np.sum(Ne1_temp,0)
Ne2 = np.zeros((len(Ne1), Nd_step+1))
Ne2[:, iii] = (Ne1)*1e-4
plot(XI, Ne2[:, iii], 'r-')
hold(True)
grid(True)
title('2D electron density along the channel at different Vd')
xlabel('X [nm]')
ylabel('N2D [$cm^{-2}$]')
savefig('N2D_X2.png')
temmm=[XI,Ne2]
Ne2 = np.squeeze(Ne_sub[0,Ng_step,:,:,0])
Ne2 = Ne2*1e-4
fid = open('N2D_X2.dat','w')
ind = 0
print np.shape(XI)
print np.shape(Ne2)
for item1 in XI:
fid.write("%e " % item1)
for item2 in Ne2[:,ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save N2D_X2.dat temmm -ascii
#******************************************************************************************
if plot_Ec_IV==1:
# The First SUBBAND ENERGY PROFILE vs X for Diff. Vg
# ------------------------------------------------------
if (Ng_step>=1 or (Ng_step==0 and Nd_step==0)):
figure(4)
for iii in np.arange(0,Ng_step+1):
plot(XI, E_sub[0, iii, Nd_step, :, 0],'r-')
hold(True)
grid(True)
if Ng_step>=1:
title('The First Subband energy profile along the channel at different Vg')
elif Ng_step==0:
title('The First Subband energy profile along the channel')
xlabel('X [nm]')
ylabel('E_{SUB} [eV]')
savefig('Ec_X1.png')
tem = E_sub[0,iii, Nd_step, :, 0]
sq_tem = np.squeeze(tem)
print np.shape(sq_tem)
temmm = [XI, sq_tem]
fid = open('Ec_X1.dat','w')
ind = 0
for item1 in XI:
fid.write("%e " % item1)
item2 = sq_tem[ind]
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#save Ec_X1.dat temmm -ascii
# The First SUBBAND ENERGY PROFILE vs X for Diff. Vd
# ------------------------------------------------------
if Nd_step>=1:
figure(5)
for iii in np.arange(0,Nd_step+1):
plot(XI,E_sub[0,Ng_step,iii, :, 0],'r-')
hold(True)
grid(True)
title('The First Subband energy profile along the channel at different Vd')
xlabel('X [nm]')
ylabel('$E_{SUB}$ [eV]')
savefig('Ec_X2.png')
tem = E_sub[0, Ng_step, :, :, 0]
sq_tem = np.squeeze(tem)
fid = open('Ec_X2.dat','w')
ind = 0
for item1 in XI:
fid.write("%e " % item1)
for item2 in sq_tem[:,ind]:
fid.write("%e " % item2)
fid.write('\n')
ind += 1
fid.close()
#temmm = [XI,sq_tem]
#save Ec_X2.dat temmm -ascii
# 3D CHARGE DENSITY N(X,Y)
# ------------------------------------------------------------
if plot_Ne3d==1:
figure(7)
#surf(XI,YI,trMNe)
#shading interp commented out to reduce size of the .ps file
[X, Y] = np.meshgrid(XI, YI)
Z = trMNe
ax = gca(projection = '3d')
surf = ax.plot_surface(X, Y, Z, rstride=3, cstride=3, cmap=cm.coolwarm, linewidth=0.5, antialiased = True)
title('3D Electron density profile')
ax.set_xlabel('X [nm]')
ax.set_ylabel('Y [nm]')
ax.set_zlabel('Ne [$m^{-3}$]')
ax.view_init(elev=60, azim=50)
ax.dist=8
savefig('Ne_X_Y.png')
XII = [0, XI]
tem1 = [YI, trMNe]
tem2 = [XII, tem1]
#save Ne_X_Y.dat tem2 -ascii
return
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 27 20:10:40 2016
@author: Bhuti
"""
import pandas as pd
pd.set_option('display.max_row', 1000)
pd.set_option('display.max_columns', 50)
got = pd.read_csv ('/Users/Bhuti/Desktop/war_of_the_five_kings_dataset-master/5kings_battles_v1.csv')
got = pd.DataFrame(got)
# print(got['attacker_size'])
print(got['defender_king'].value_counts())
got['defender_king'].value_counts().plot(kind='bar')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = got['year']
y = got['battle_number']
z = got['attacker_size']
# Axes3D.plot_surface('defender_1','battle_number','attacker_outcome')
Axes3D.plot_surface(x,y,z)
# Grid over which we will calculate J
theta0_vals = np.linspace(-10, 10, 100)
theta1_vals = np.linspace(-1, 4, 100)
# initialize J_vals to a matrix of 0's
J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))
# Fill out J_Vals
# Note: There is probably a more efficient way to do this that uses
# broadcasting instead of the nested for loops
for i in range(len(theta0_vals)):
for j in range(len(theta1_vals)):
t = np.array([theta0_vals[i], theta1_vals[j]])
J_vals[i][j] = computeCost(X, y, t)
# Surface plot using J_Vals
fig = plt.figure()
ax = plt.subplot(111, projection='3d')
Axes3D.plot_surface(ax, theta0_vals, theta1_vals, J_vals, cmap=cm.coolwarm)
plt.show()
# Contour plot
# TO DO: Currently does not work as expected. Need to find a way to mimic
# the logspace option in matlab
fig = plt.figure()
ax = plt.subplot(111)
plt.contour(theta0_vals, theta1_vals, J_vals)
# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4
predict2 = np.dot([1, 7], theta)
print('For population = 70,000, we predict a profit of ', predict2 * 10000)
input('Program paused. Press enter to continue.\n')
print('Visualizing J(theta_0, theta_1) ...\n')
# Grid over which we will calculate J
theta0_vals = np.linspace(-10, 10, 100)
theta1_vals = np.linspace(-1, 4, 100)
J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))
for i in range(len(theta0_vals)):
for j in range(len(theta1_vals)):
t = np.array([theta0_vals[i], theta1_vals[j]])
J_vals[i][j] = computeCost(X, y, t)
"""
# Surface plot using J_Vals
fig = plt.figure()
ax = plt.subplot(111,projection='3d')
Axes3D.plot_surface(ax,theta0_vals,theta1_vals,J_vals,cmap=cm.coolwarm)
plt.show()
fig = plt.figure()
ax = plt.subplot(111)
plt.contour(theta0_vals,theta1_vals,J_vals)
"""
print('Loading data ...', '\n')
print('Plotting Data ...', '\n')
data = pd.read_csv("ex1data2.txt", names=["size", "bedrooms", "price"])
s = np.array(data.size)
r = np.array(data.bedrooms)
# form a finer level initial guess
n2 = n*2
x0 = np.random.random(4*n2)
x0[0:n*2] = res.x[0:n*2]
x0[n*4:n*5] = res.x[n*2:n*3]
x0[n*6:n*7] = res.x[3*n:4*n]
x0[n*7:n*8] = np.zeros(n)
n = n2
# call a minimizer to find a global min for the finest level
opt = {'ftol':1E-8, 'maxiter':500, 'disp': True}
res = minimize(F, x0, method='SLSQP', options=opt)
#res = minimize(F, x0, method='SLSQP', jac=JacF, options=opt)
sol = res.x
time_end = time()
print( 'CPU time used:', time_end-time_start, 'seconds' )
np.save('data_abd.npy', sol)
# output final energy and plot the solution
npt = 100
tpt1 = np.arange(-1,1,2.0/npt)
tpt2 = np.arange(-1,1,2.0/npt)
a = sol[0:2*n].reshape(n,2)
b = sol[2*n:3*n]
c = sol[3*n:4*n]
ypt = phi(tpt1, tpt2, a, b, d)
Axes3D.plot_surface(tpt1,tp2,ypt,'r:')
plt.title( 'Solution with n = ' + str(n) )
plt.show()
