本文整理汇总了Python中matplotlib.pyplot.streamplot方法的典型用法代码示例。如果您正苦于以下问题:Python pyplot.streamplot方法的具体用法?Python pyplot.streamplot怎么用?Python pyplot.streamplot使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.pyplot
的用法示例。
在下文中一共展示了pyplot.streamplot方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_colormap
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_colormap():
X, Y, U, V = velocity_field()
plt.streamplot(X, Y, U, V, color=U, density=0.6, linewidth=2,
cmap=plt.cm.autumn)
plt.colorbar()
示例2: test_linewidth
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_linewidth():
X, Y, U, V = velocity_field()
speed = np.sqrt(U*U + V*V)
lw = 5*speed/speed.max()
df = 25. / 30. # Compatibility factor for old test image
plt.streamplot(X, Y, U, V, density=[0.5 * df, 1. * df], color='k',
linewidth=lw)
示例3: test_masks_and_nans
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_masks_and_nans():
X, Y, U, V = velocity_field()
mask = np.zeros(U.shape, dtype=bool)
mask[40:60, 40:60] = 1
U = np.ma.array(U, mask=mask)
U[:20, :20] = np.nan
with np.errstate(invalid='ignore'):
plt.streamplot(X, Y, U, V, color=U, cmap=plt.cm.Blues)
示例4: test_startpoints
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_startpoints():
X, Y, U, V = velocity_field()
start_x = np.linspace(X.min(), X.max(), 10)
start_y = np.linspace(Y.min(), Y.max(), 10)
start_points = np.column_stack([start_x, start_y])
plt.streamplot(X, Y, U, V, start_points=start_points)
plt.plot(start_x, start_y, 'ok')
示例5: test_masks_and_nans
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_masks_and_nans():
X, Y, U, V = velocity_field()
mask = np.zeros(U.shape, dtype=bool)
mask[40:60, 40:60] = 1
U[:20, :20] = np.nan
U = np.ma.array(U, mask=mask)
with np.errstate(invalid='ignore'):
plt.streamplot(X, Y, U, V, color=U, cmap=plt.cm.Blues)
示例6: test_maxlength
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def test_maxlength():
x, y, U, V = swirl_velocity_field()
plt.streamplot(x, y, U, V, maxlength=10., start_points=[[0., 1.5]],
linewidth=2, density=2)
示例7: plt_stream
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def plt_stream(transform, ax, npts=200, title="Density streamflow", device="cpu"):
side = np.linspace(LOW, HIGH, npts)
xx, yy = np.meshgrid(side, side)
x = np.hstack([xx.reshape(-1, 1), yy.reshape(-1, 1)])
with torch.no_grad():
logqx, z = transform(torch.tensor(x).float().to(device))
d_z_x = -(x - z.cpu().numpy())[:, 0].reshape(xx.shape)
d_z_y = -(x - z.cpu().numpy())[:, 1].reshape(xx.shape)
plt.streamplot(xx, yy, d_z_x, d_z_y, color=(d_z_y**2 + d_z_x**2)/2, cmap='autumn')
示例8: plot_vec_field
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def plot_vec_field(fld, field="field", fig=None, ax=None): # pragma: no cover
"""
Plot a spatial random vector field.
Parameters
----------
fld : :class:`Field`
The given field class instance.
field : :class:`str`, optional
Field that should be plotted. Default: "field"
fig : :class:`Figure` or :any:`None`, optional
Figure to plot the axes on. If `None`, a new one will be created.
Default: `None`
ax : :class:`Axes` or :any:`None`, optional
Axes to plot on. If `None`, a new one will be added to the figure.
Default: `None`
"""
if fld.mesh_type != "structured":
raise RuntimeError(
"Only structured vector fields are supported"
+ " for plotting. Please create one on a structured grid."
)
plot_field = getattr(fld, field)
assert not (fld.pos is None or plot_field is None)
norm = np.sqrt(plot_field[0, :].T ** 2 + plot_field[1, :].T ** 2)
fig, ax = _get_fig_ax(fig, ax)
title = "Field 2D " + fld.mesh_type + ": " + str(plot_field.shape)
x, y, __ = pos2xyz(fld.pos, max_dim=2)
sp = plt.streamplot(
x,
y,
plot_field[0, :].T,
plot_field[1, :].T,
color=norm,
linewidth=norm / 2,
)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_title(title)
fig.colorbar(sp.lines)
fig.show()
return ax
示例9: integrate_streamline
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import streamplot [as 别名]
def integrate_streamline(
X, Y, U, V, integration_direction, init_states, interpolation_num=250, average=True
):
"""use streamline's integrator to alleviate stacking of the solve_ivp. Need to update with the correct time."""
import matplotlib.pyplot as plt
n_cell = init_states.shape[0]
res = np.zeros((n_cell * interpolation_num, 2))
j = -1 # this index will become 0 when the first trajectory found
for i in tqdm(range(n_cell), "integration with streamline"):
strm = plt.streamplot(
X,
Y,
U,
V,
start_points=init_states[i, None],
integration_direction=integration_direction,
density=100,
)
strm_res = np.array(strm.lines.get_segments()).reshape((-1, 2))
if len(strm_res) == 0:
continue
else:
j += 1
t = np.arange(strm_res.shape[0])
t_linspace = np.linspace(t[0], t[-1], interpolation_num)
f = interpolate.interp1d(t, strm_res.T)
cur_rng = np.arange(j * interpolation_num, (j + 1) * interpolation_num)
res[cur_rng, :] = f(t_linspace).T
res = res[: cur_rng[-1], :] # remove all empty trajectories
n_cell = int(res.shape[0] / interpolation_num)
if n_cell > 1 and average:
t_len = len(t_linspace)
avg = np.zeros((t_len, 2))
for i in range(t_len):
cur_rng = np.arange(n_cell) * t_len + i
avg[i, :] = np.mean(res[cur_rng, :], 0)
res = avg
plt.close()
return t_linspace, res
# ---------------------------------------------------------------------------------------------------
# fate related