def _plotly_vector_plot2(x, y, Field, NQ, scale_x, scale_y, vector_color):
"""Draws quiver plot of field vectors
Args:
x (float/array): x coordinates
y (float/array): y coordinates
Field (Vector2): Field structure
NQ (int): Plot every NQth vector
scale_x (float): unit scaling
scale_y (float): unit scaling
vector_color (string): quiver color
"""
plot_object = []
NPx, NPy = x.shape
if NQ != 0:
NSx, NSy = NPx // NQ, NPy // NQ
with _np.errstate(divide="ignore", invalid="ignore"):
plot_object = _ff.create_quiver(
x[::NSx, ::NSy] / scale_x,
y[::NSx, ::NSy] / scale_y,
Field.x[::NSx, ::NSy] / Field.n[::NSx, ::NSy],
Field.y[::NSx, ::NSy] / Field.n[::NSx, ::NSy],
scale=1.5,
arrow_scale=0.5, # name='quiver',
line_width=1,
line=dict(color="white"),
)
return plot_object
def trace_vector(vect, option='origin', scalvect=1, titre="sans titre"):
if isinstance(vect, Vector):
vect = [vect]
uxL = [v[0] for v in vect]
uyL = [v[1] for v in vect]
xL = [0] * len(vect)
yL = [0] * len(vect)
if option == 'a_la_suite':
for i, v in enumerate(vect):
if i != 0:
xL[i] = scalvect * uxL[i - 1] + xL[i - 1]
yL[i] = scalvect * uyL[i - 1] + yL[i - 1]
quiver_fig = ff.create_quiver(
xL,
yL,
uxL,
uyL,
scale=scalvect,
arrow_scale=.05, # Sets arrow scale
name='vecteur',
angle=np.pi / 12,
line=dict(width=3))
for x, y, v in zip(xL, yL, vect):
data = go.Scatter(x=np.array(x),
y=np.array(y),
mode='markers',
marker=dict(size=8, color='grey'),
name=f'{v.name} norme={v.norm():.2f} N')
quiver_fig.add_trace(data)
layout = go.Layout(title=titre,
xaxis=dict(constrain='domain', title='x'),
yaxis=dict(scaleanchor='x', title='y'))
quiver_fig['layout'].update(layout)
return iplot(quiver_fig, filename='vecteur')
def draw_vector_field(self, vector_field, library):
x, y = numpy.meshgrid(numpy.arange(0, vector_field.shape[1], 1),
numpy.arange(0, vector_field.shape[0], 1))
if library == 'dash':
if self.draw_arrows_backward:
return ff.create_quiver(x - vector_field[..., 0],
y - vector_field[..., 1],
vector_field[..., 0],
vector_field[..., 1],
scale=1.0)
else:
return ff.create_quiver(x,
y,
vector_field[..., 0],
vector_field[..., 1],
scale=1.0)
else:
raise NotImplementedError()
def ShowVectors(gV):
plotly.tools.set_credentials_file(username='******',
api_key='AHpO2zYe4pzOOddMe4nz')
init_notebook_mode(connected=True)
# Getting data points
'''
sX = []
sY = []
sC = []
rX = []
rY = []
rC = []
for i in range( len(hV) ):
if hCol[i] == 'red' :
sX.append( hV[i][0] )
sY.append( hV[i][1] )
sC.append( 'red' )
else :
rX.append( hV[i][0] )
rY.append( hV[i][1] )
rC.append( 'black' )
'''
# Adding vectors
X = 0
Y = 0
j = 0
vX = []
vY = []
U = []
V = []
for i in gV:
dX = i[0] - X
dY = i[1] - Y
vX.append(X)
vY.append(Y)
U.append(dX)
V.append(dY)
#print(" i= " + str(i) + "( " + str( i[0]) + ", " + str(i[1]) + ")" )
#plt.quiver( X, Y, dX, dY, angles='xy', scale_units='xy',scale=1, color= colorV[j] )
X += dX
Y += dY
j += 1
fig = ff.create_quiver(vX, vY, U, V)
# adding retract points
#aplt.scatter( sX, sY,s=20, c=sC, marker= '^')
#plt.scatter( rX, rY,s=20, c=rC, marker= 'o')
plt.iplot(fig)
def quiver(self, factor=1):
xg, yg = self.xpixels.pixel_grids
xg = 0.5 * (xg[1:] + xg[:-1])
yg = 0.5 * (yg[1:] + yg[:-1])
x, y = np.meshgrid(xg, yg)
fig = ff.create_quiver(
x,
y,
self.xpixels.pixels.T,
self.ypixels.pixels.T,
scale=factor,
)
return fig
def vector_plot(result, titles, verbose=False, **kwargs):
variable = titles['variable']
val0 = list(result.values())[0]
if (result[variable].shape == val0.shape) & (val0.shape[1] == 2):
if verbose:
print('\t 2-d output', result[variable].shape)
u = result[variable][:, 0]
v = result[variable][:, 1]
x = val0[:, 0]
y = val0[:, 1]
trace = ff.create_quiver(
x,
y,
u,
v,
# scale=.25,
# arrow_scale=.4,
name='quiver',
line=dict(width=1))['data'][0] # extracts quiver trace
layout = go.Layout(title=titles['title'],
xaxis=dict(title='x'),
yaxis=dict(title='y'))
chart_type = '2d-vector'
elif (result[variable].shape == val0.shape) & (val0.shape[1] == 3):
if verbose:
print('\t 3-d output', result[variable].shape)
u = result[variable][:, 0].tolist()
v = result[variable][:, 1].tolist()
w = result[variable][:, 2].tolist()
x = val0[:, 0].tolist()
y = val0[:, 1].tolist()
z = val0[:, 2].tolist()
trace = go.Cone(x=x, y=y, z=z, u=u, v=v, w=w)
layout = go.Layout(title=titles['title'],
scene=dict(
xaxis=dict(title='x'),
yaxis=dict(title='y'),
zaxis=dict(title='z'),
))
chart_type = '3d-vector'
else:
raise NotImplementedError('plot type not supported yet')
return [trace], chart_type, layout
def iplot(
self,
scale: float = 1.,
height: int = 1000,
width: int = 1000,
) -> plotly.graph_objs._figure.Figure:
pointing = self.azimuthal_equidistant_projection_with_orientation
temp = pointing[:, :2] * (180. / np.pi)
x = temp[:, 0]
y = temp[:, 1]
u = np.cos(pointing[:, 2])
v = np.sin(pointing[:, 2])
fig = ff.create_quiver(x, y, u, v, scale=scale, scaleratio=1.)
fig.update_layout(height=height, width=width)
return fig
def plot_2D(func,
plane='xy',
limits=[(-20e-3, 20e-3), (-20e-3, 20e-3)],
numpoints=40,
quiver=True,
quiver_scale=5e-4,
component='all'):
''' Generates a 2D plot of the passed vector function in the given plane. '''
i = ord(plane[0]) - 120 # ordinate index
j = ord(plane[1]) - 120 # abscissa index
X, agrid, a = sample_2d(func,
plane,
limits,
numpoints,
component=component)
xi = np.unique(X[:, i])
xj = np.unique(X[:, j])
## create heatmap for norm of function
fig = go.Figure()
surf = go.Heatmap(x=xi,
y=xj,
z=agrid,
colorscale="Rainbow",
zsmooth='best',
colorbar={
'title': 'Acceleration',
'titleside': 'right'
})
if quiver:
n = numpoints
X2, agrid2, a2 = X, agrid, a
## create quiver plot
xg = X2[:, i].reshape(n, n)
yg = X2[:, j].reshape(n, n)
ax = (a2[:, i] / np.linalg.norm(a2, axis=1)).reshape(n, n)
ay = (a2[:, j] / np.linalg.norm(a2, axis=1)).reshape(n, n)
fig = ff.create_quiver(xg, yg, ax, ay, scale=quiver_scale)
fig['data'][0]['line']['color'] = 'rgb(255,255,255)'
fig.add_trace(surf)
fig.update_xaxes(range=[xi.min(), xi.max()])
fig.update_yaxes(range=[xj.min(), xj.max()])
return fig
def plot_2d_quiver(omega, theta):
# skew_matrix = skew2d(omega)
skew_matrix = np.array([[0, -omega], [omega, 0]])
x,y = np.meshgrid(np.arange(-2, 2, .5),
np.arange(-2, 2, .5))
u = skew_matrix[0][0] * x + skew_matrix[0][1] * y
v = skew_matrix[1][0] * x + skew_matrix[1][1] * y
# Create quiver figure
fig = ff.create_quiver(x, y, u, v,
scale=.25,
arrow_scale=.4,
name='quiver',
line_width=1)
# Add points to figure
fig.add_trace(go.Scatter(x=[0.], y=[0.],
mode='markers',
marker_size=12,
name='points'))
x0 = 1.0
y0 = 0.6
x_traj = [x0]
y_traj = [y0]
dt = 0.05
t = 0
while t < theta:
t += dt
dx = skew_matrix[0][0] * x_traj[-1] + skew_matrix[0][1] * y_traj[-1]
dy = skew_matrix[1][0] * x_traj[-1] + skew_matrix[1][1] * y_traj[-1]
x_traj.append(x_traj[-1] + dx * dt)
y_traj.append(y_traj[-1] + dy * dt)
if theta > 0:
fig.add_trace(go.Scatter(x=x_traj, y=y_traj,
mode="lines",
line=dict(color='firebrick', width=4)))
# Add calculation
return fig
def showsolution(node,elem,u,**kwargs):
'''
show 2D solution either of a scalar function or a vector field
'''
markersize = 3000/len(node)
if u.ndim == 1:
uplot = ff.create_trisurf(x=node[:,0], y=node[:,1], z=u,
simplices=elem,
colormap="Viridis", # similar to matlab's default colormap
showbackground=False,
aspectratio=dict(x=1, y=1, z=1),
)
fig = go.Figure(data=uplot)
elif u.ndim == 2 and u.shape[-1] == 2:
assert u.shape[0] == elem.shape[0]
u /= (np.abs(u)).max()
center = node[elem].mean(axis=1)
uplot = ff.create_quiver(x=center[:,0], y=center[:,1],
u=u[:,0], v=u[:,1],
scale=.05,
arrow_scale=.5,
name='gradient of u',
line_width=1,
)
# uplot.add_trace(go.Scatter(x=center[:,0], y=center[:,1],
# mode='markers',
# marker=dict(
# color='LightSkyBlue',
# size=markersize,
# # line=dict(
# # color='MediumPurple',
# # width=12
# # )
# ),
# name='nodes in mesh'))
fig = go.Figure(data=uplot)
fig.update_layout(template='plotly_dark',
margin=dict(l=5, r=5, t=5, b=5),
**kwargs)
fig.show()
def velocities(tracking_data):
velocity_quivers = []
for i, side in enumerate(['Home', 'Away']):
team_data = tracking_data[i]
player_nums = list(set(item for subitem in team_data.keys() for item in subitem.split('_')
if item.isdigit()))
xlocs = [team_data['{}_{}_{}'.format(
side, num, 'x')] for num in player_nums]
ylocs = [team_data['{}_{}_{}'.format(
side, num, 'y')] for num in player_nums]
xvels = [team_data['{}_{}_{}'.format(
side, num, 'vx')] for num in player_nums]
yvels = [team_data['{}_{}_{}'.format(
side, num, 'vy')] for num in player_nums]
trace = ff.create_quiver(x=xlocs, y=ylocs, u=xvels, v=yvels,
scale=.5, line_color=player_marker_args[side]['marker_color'], name=side+'_vel')
velocity_quivers.append(trace.data[0])
return velocity_quivers
def plot_gradient(self, func, bounds=[-5, 5], seperate_window=False):
'''Quiver plot to the visualize the gradient.
'''
f_x = func.diff(self.x)
f_y = func.diff(self.y)
fx = lambdify([self.x, self.y], f_x, 'numpy')
fy = lambdify([self.x, self.y], f_y, 'numpy')
# Define numerical space
x_range = np.arange(bounds[0], bounds[1], 1)
y_range = np.arange(bounds[0], bounds[1], 1)
x_, y_ = np.meshgrid(x_range, y_range)
u = fx(x_, y_)
v = fy(x_, y_)
fig = ff.create_quiver(x_, y_, u, v)
# Show
if seperate_window:
plot(fig, show_link=False)
else:
iplot(fig, show_link=False)
def velocity_traces(self, frame_data):
player_ids = (self.home_players, self.away_players)
velocity_quivers = []
for i, side in enumerate(["Home", "Away"]):
players = player_ids[i]
xlocs = [frame_data[f"{player_id}_x"] for player_id in players]
ylocs = [frame_data[f"{player_id}_y"] for player_id in players]
xvels = [frame_data[f"{player_id}_vx"] for player_id in players]
yvels = [frame_data[f"{player_id}_vy"] for player_id in players]
trace = ff.create_quiver(
x=xlocs,
y=ylocs,
u=xvels,
v=yvels,
scale=0.5,
line_color=prm.player_marker_args[side]["marker_color"],
name=side + "_vel",
)
velocity_quivers.append(trace.data[0])
return velocity_quivers
def plot_quiver():
x, y = np.meshgrid(np.arange(-2, 2, .2), np.arange(-2, 2, .25))
z = x * np.exp(-x**2 - y**2)
v, u = np.gradient(z, .2, .2)
# Create quiver figure
fig = ff.create_quiver(x,
y,
u,
v,
scale=.25,
arrow_scale=.4,
name='quiver',
line_width=1)
# Add points to figure
fig.add_trace(
go.Scatter(x=[-.7, .75],
y=[0, 0],
mode='markers',
marker_size=12,
name='points'))
return fig
def velocity_traces(self, frame_data):
velocity_quivers = []
for i, side in enumerate(["Home", "Away"]):
team_data = frame_data[i]
player_nums = list(
set(item for subitem in team_data.keys()
for item in subitem.split("_") if item.isdigit()))
xlocs = [
team_data["{}_{}_{}".format(side, num, "X")]
for num in player_nums
]
ylocs = [
team_data["{}_{}_{}".format(side, num, "Y")]
for num in player_nums
]
xvels = [
team_data["{}_{}_{}".format(side, num, "vx")]
for num in player_nums
]
yvels = [
team_data["{}_{}_{}".format(side, num, "vy")]
for num in player_nums
]
trace = ff.create_quiver(
x=xlocs,
y=ylocs,
u=xvels,
v=yvels,
scale=0.5,
line_color=prm.player_marker_args[side]["marker_color"],
name=side + "_vel",
)
velocity_quivers.append(trace.data[0])
return velocity_quivers
def draw_wires(fig, toDraw, TimeStamp=0, PathInd=[]):
wires_toDraw = []
sectionCenterX = np.array([])
sectionCenterY = np.array([])
sectionCurrentX = np.array([])
sectionCurrentY = np.array([])
for this_wire in toDraw.Nanowires:
if not this_wire in wires_toDraw:
wires_toDraw.append(this_wire)
sectionCenterX = np.append(sectionCenterX,
this_wire.sectionCenterX)
sectionCenterY = np.append(sectionCenterY,
this_wire.sectionCenterY)
sectionCurrentX = np.append(
sectionCurrentX, this_wire.sectionCurrentX[TimeStamp, :])
sectionCurrentY = np.append(
sectionCurrentY, this_wire.sectionCurrentY[TimeStamp, :])
for this_wire in wires_toDraw:
xa, ya, xb, yb = this_wire.wireEnds
xc, yc = this_wire.centerPosition
this_opacity = 0.2 + 0.8 * (this_wire.onPathChecker[TimeStamp] != 0)
if this_wire.index in PathInd:
this_color = 'yellow'
else:
this_color = 'white'
line = go.Scatter(x=[xa, xb, xc],
y=[ya, yb, yc],
name='Nanowire #' + str(this_wire.index),
mode="lines",
hoverinfo='name',
opacity=this_opacity,
line=dict(color=this_color, width=1.5))
fig.add_trace(line)
if this_wire.isElectrode != 0:
if this_wire.isElectrode == 1:
electrodeColor = 'green'
type = 'source'
else:
electrodeColor = 'red'
type = 'drain'
temp = go.Scatter(x=[this_wire.contactEnd[0]],
y=[this_wire.contactEnd[1]],
mode='markers',
name=type,
opacity=0.9,
marker=dict(symbol='hexagon',
color=electrodeColor,
size=30),
hoverinfo='name')
fig.add_trace(temp)
quivers = ff.create_quiver(sectionCenterX,
sectionCenterY,
1e8 * sectionCurrentX,
1e8 * sectionCurrentY,
scale=0.1,
arrow_scale=0.3,
name='Current',
opacity=0.9,
hoverinfo='none',
line=dict(color='red', width=3))
fig.add_trace(quivers.data[0])
return fig
def create_quiver(*args, **kwargs):
FigureFactory._deprecated('create_quiver')
from plotly.figure_factory import create_quiver
return create_quiver(*args, **kwargs)
def create_quiver(*args, **kwargs):
FigureFactory._deprecated('create_quiver')
from plotly.figure_factory import create_quiver
return create_quiver(*args, **kwargs)
def update_2d_sis(beta, gamma, delta, sigma, epsilon, p, S_0, I_0, aux):
'''Updates the 3d Plot'''
# Test values
T = 200
# Handle None Case
if S_0 is None: S_0 = 80
if I_0 is None: I_0 = 20
# Data for trajectory
t, y = SIS(S_0, I_0, beta, gamma, delta, sigma, epsilon, p, T)
# Draw vector field
S_max = y[0].max()
I_max = y[1].max()
grid_max = np.array([S_max, I_max])
xx, uu = eval_on_grid_2d(func=f_sis,
x_min=np.zeros_like(grid_max),
x_max=grid_max,
t=0,
n_points=20,
beta=beta,
gamma=gamma,
delta=delta,
sigma=sigma,
epsilon=epsilon,
p=p)
fig = ff.create_quiver(
x=xx[:, 0],
y=xx[:, 1],
u=uu[:, 0],
v=uu[:, 1],
name='Vector Field',
scale=.4,
)
# Make figure
fig.add_traces([
go.Scatter(
name='Intial Conditions',
x=[S_0],
y=[I_0],
mode='markers',
marker=dict(size=10, opacity=.5),
),
go.Scatter(
name='Sample Trajectory',
x=y[0],
y=y[1],
mode='lines+markers',
marker=dict(
color=t,
colorscale='Viridis',
size=2,
),
),
])
fig.update_layout(
title="Trajectories of the SIS Model",
xaxis_title='Susceptible',
yaxis_title='Infected',
margin=dict(l=0, r=0, b=0, t=0),
legend=dict(
x=0,
y=1,
bgcolor='rgba(255,255,255,.5)',
),
)
return fig
def plot_quiver(self, p_meshgrid_spacing=0.02):
# Arrange data.
x, y = np.meshgrid(
np.arange(self.msh.x0, self.msh.xf, p_meshgrid_spacing),
np.arange(self.msh.y0, self.msh.yf, p_meshgrid_spacing))
the_problem = self.usr.get('problem', 'Smith-Hutton')
if the_problem == 'Diagonal flow':
u = np.ones(x.shape) * self.fields.vx
v = np.ones(x.shape) * self.fields.vy
the_scale = 0.0025
the_arrow_scale = 0.5
else:
# Prepare the mesh and velocity field for the quiver plot function.
n_nodes = self.msh.M * self.msh.N
# Rearrange the nodes x,y coords in a single array, and flatten the vx,vy velocities.
the_points = np.zeros((n_nodes, 2))
u = np.zeros((n_nodes, ))
v = np.zeros((n_nodes, ))
i = 0
for row in range(self.msh.M):
for col in range(self.msh.N):
the_points[i, 0] = self.msh.cells_x_coords[row, col]
the_points[i, 1] = self.msh.cells_y_coords[row, col]
u[i] = self.fields.vx[row, col]
v[i] = self.fields.vy[row, col]
i = i + 1
x_ = np.linspace(self.msh.x0, self.msh.xf, 50)
y_ = np.linspace(self.msh.y0, self.msh.yf, 50)
x, y = np.meshgrid(x_, y_)
u = griddata(the_points, u, (x, y), method='cubic')
v = griddata(the_points, v, (x, y), method='cubic')
the_scale = 0.02
the_arrow_scale = 0.3
# Build the figure.
fig = ff.create_quiver(x,
y,
u,
v,
name='Velocity field',
scale=the_scale,
arrow_scale=the_arrow_scale)
# Plot origin.-
fig.add_trace(
go.Scatter(
x=[0],
y=[0],
name='Origin',
mode='markers' #,
# marker_size=15
))
# Figure config.
separator = '-'
space_m = '\:'
problem = self.usr.get('problem', '')
if problem == 'Smith-Hutton':
problem = 'Smith \: Hutton'
title = 'Quiver' + separator + problem
title = '$' + title + '$'
fig.update_layout(title={
'text': title,
'x': 0.05,
'xanchor': 'left',
'yanchor': 'top'
},
xaxis_title="x (m)",
yaxis_title="y (m)",
font={'size': 15})
fig.show()
def vector_field(field2d, settings):
assert isinstance(field2d, (CenteredGrid, StaggeredGrid))
if isinstance(field2d, StaggeredGrid):
field2d = field2d.at_centers()
assert isinstance(field2d, CenteredGrid)
assert field2d.rank == 2
batch = settings.get('batch', 0)
batch = min(batch, field2d.data.shape[0])
arrow_origin = settings.get('arrow_origin', 'tip')
assert arrow_origin in ('base', 'center', 'tip')
max_resolution = settings.get('max_arrow_resolution', 40)
max_arrows = settings.get('max_arrows', 2000)
min_arrow_length = settings.get('min_arrow_length', 0.005) * np.max(
field2d.box.size)
draw_full_arrows = settings.get('draw_full_arrows', False)
y, x = field2d.points.data[0, ..., (physics_config.y, physics_config.x)]
data_y, data_x = field2d.data[batch, ...,
(physics_config.y, physics_config.x)]
while np.prod(x.shape) > max_resolution**2:
y = y[::2, ::2]
x = x[::2, ::2]
data_y = data_y[::2, ::2]
data_x = data_x[::2, ::2]
y = y.flatten()
x = x.flatten()
data_y = data_y.flatten()
data_x = data_x.flatten()
if max_arrows is not None or min_arrow_length > 0:
length = np.sqrt(data_y**2 + data_x**2)
keep_indices = np.argsort(length)
keep_indices = keep_indices[length[keep_indices] > min_arrow_length]
if len(keep_indices) > max_arrows:
keep_indices = keep_indices[-max_arrows:]
y = y[keep_indices]
x = x[keep_indices]
data_y = data_y[keep_indices]
data_x = data_x[keep_indices]
if len(x) == 0:
return EMPTY_FIGURE
if arrow_origin == 'tip':
x -= data_x
y -= data_y
elif arrow_origin == 'center':
x -= 0.5 * data_x
y -= 0.5 * data_y
x_range, y_range = [field2d.box.get_lower(1),
field2d.box.get_upper(1)], [
field2d.box.get_lower(0),
field2d.box.get_upper(0)
]
if physics_config.is_x_first:
x_range, y_range = y_range, x_range
if draw_full_arrows:
result = plotly_figures.create_quiver(x, y, data_x, data_y,
scale=1.0) # 7 points per arrow
result.update_xaxes(range=x_range)
result.update_yaxes(range=y_range)
return result
else:
lines_y = np.stack([y, y + data_y, [None] * len(x)],
-1).flatten() # 3 points per arrow
lines_x = np.stack([x, x + data_x, [None] * len(x)], -1).flatten()
return {
'data': [{
'mode': 'lines',
'x': lines_x,
'y': lines_y,
'type': 'scatter',
}],
'layout': {
'xaxis': {
'range': x_range
},
'yaxis': {
'range': y_range
},
}
}
def vector_field(field2d, settings):
assert isinstance(field2d, (CenteredGrid, StaggeredGrid))
if isinstance(field2d, StaggeredGrid):
field2d = field2d.at_centers()
assert isinstance(field2d, CenteredGrid)
assert field2d.rank == 2
batch = settings.get('batch', 0)
batch = min(batch, field2d.data.shape[0])
arrow_origin = settings.get('arrow_origin', 'tip')
assert arrow_origin in ('base', 'center', 'tip')
max_resolution = settings.get('max_arrow_resolution', 40)
max_arrows = settings.get('max_arrows', 300)
draw_full_arrows = settings.get('draw_full_arrows', False)
y, x = math.unstack(field2d.points.data[0, ..., -2:], axis=-1)
data_y, data_x = math.unstack(field2d.data[batch, ...], -1)[-2:]
while np.prod(x.shape) > max_resolution**2:
y = y[::2, ::2]
x = x[::2, ::2]
data_y = data_y[::2, ::2]
data_x = data_x[::2, ::2]
y = y.flatten()
x = x.flatten()
data_y = data_y.flatten()
data_x = data_x.flatten()
if max_arrows is not None and len(x) > max_arrows:
length = np.sqrt(data_y**2 + data_x**2)
keep_indices = np.argsort(length)[-max_arrows:]
# size = np.max(field2d.box.size)
# threshold = size * negligible_threshold
# keep_condition = (np.abs(data_x) > threshold) | (np.abs(data_y) > threshold)
# keep_indices = np.where(keep_condition)
y = y[keep_indices]
x = x[keep_indices]
data_y = data_y[keep_indices]
data_x = data_x[keep_indices]
if arrow_origin == 'tip':
x -= data_x
y -= data_y
elif arrow_origin == 'center':
x -= 0.5 * data_x
y -= 0.5 * data_y
if draw_full_arrows:
result = plotly_figures.create_quiver(x, y, data_x, data_y,
scale=1.0) # 7 points per arrow
result.update_xaxes(
range=[field2d.box.get_lower(1),
field2d.box.get_upper(1)])
result.update_yaxes(
range=[field2d.box.get_lower(0),
field2d.box.get_upper(0)])
return result
else:
lines_y = np.stack([y, y + data_y, [None] * len(x)],
-1).flatten() # 3 points per arrow
lines_x = np.stack([x, x + data_x, [None] * len(x)], -1).flatten()
return {
'data': [{
'mode': 'lines',
'x': lines_x,
'y': lines_y,
'type': 'scatter',
}],
'layout': {
'xaxis': {
'range':
[field2d.box.get_lower(1),
field2d.box.get_upper(1)]
},
'yaxis': {
'range':
[field2d.box.get_lower(0),
field2d.box.get_upper(0)]
},
}
}
def plot_2D_quiver(pixels: Union[torch.Tensor, dict],
grad_pixels: Union[torch.Tensor, dict],
img_mask_gt: torch.Tensor,
img_mask_grad=None,
img=None,
n_debug_points=400,
save_html=None) -> go.Figure:
"""
Plot 2D quiver plots, for each point, point the negative gradient projected on 2D image space.
Accept dictionaries of {str: tensor(N,3)} or tensors (N,3). In case dictionaries are proviced,
the keys are used for hovertext, otherwise, the indices of points are used.
Args:
pts_ndc (tensor): (P, 3) or dictionary {name: tensor(P,3)}
pts_grad_ndc (tensor): the vector projected to ndc space, i.e. proj(pts+grad)
img_mask_gt (tensor): (H,W,1) value from ground truth mask
img_mask_grad (tensor): (H,W,1) value from occupancy map gradient
img (tensor): (H,W,3) value of the rendered rgb, will be ignored if img_mask_grad is provided
n_debug_points (int): plot limited number of quiver to better readability,
if < 0, plot all.
save_html (str): If given, then save to file
"""
H, W = img_mask_gt.shape[:2]
img_mask_gt = img_mask_gt.cpu()
resolution = torch.tensor((W, H),
device=img_mask_gt.device,
dtype=torch.float)
if img_mask_gt.ndim == 3:
img_mask_gt.squeeze_(-1)
assert (img_mask_gt.ndim == 2)
n_pts_per_cat = None
text_to_id = None
if isinstance(pixels, torch.Tensor):
pixels = pixels.cpu()
grad_pixels = grad_pixels.cpu()
text = np.array([str(i) for i in range(pixels.shape[0])])
n_pts_per_cat = OrderedDict(all=pixels.shape[0])
elif isinstance(pixels, dict):
n_pts_per_cat = OrderedDict(
(k, pts.shape[0]) for k, pts in pixels.items())
text_to_id = {k: i for i, k in enumerate(pixels)}
pixels = torch.cat([pixels[k] for k in n_pts_per_cat.keys()], dim=0)
grad_pixels = torch.cat([grad_pixels[k] for k in n_pts_per_cat.keys()],
dim=0)
pixels = pixels.cpu()
grad_pixels = grad_pixels.cpu()
text = np.array(
[k for k, pts in n_pts_per_cat.items() for i in range(pts)])
assert (
grad_pixels.shape == pixels.shape), 'Found unequal pts and pts_grad.'
# convert ndc to pixel
# pixels = ndc_to_pix(pixels, resolution).cpu()
# grad_pixels = ndc_to_pix(pts_grad_ndc, resolution).cpu()
uv = grad_pixels - pixels
valid_mask = ((pixels < resolution) & (pixels >= 0)).all(dim=-1)
pixels = pixels[valid_mask]
grad_pixels = grad_pixels[valid_mask]
uv = uv[valid_mask]
# scale uv to less than 1/5 image size
# based on the gradient magnitude, select top points to visualize
if n_debug_points > 0:
_, indices = torch.sort(torch.norm(uv, dim=-1), dim=0, descending=True)
indices = indices[:n_debug_points].cpu()
# plot quiver for gradient
pixels_selected = pixels[indices].numpy()
grad_pixels_selected = grad_pixels[indices].numpy()
text_selected = text[indices.numpy()]
uv_selected = uv[indices].numpy()
else:
# plot quiver for gradient
pixels_selected = pixels.numpy()
grad_pixels_selected = grad_pixels.numpy()
text_selected = text
uv_selected = uv.numpy()
uv_selected = uv_selected / \
np.linalg.norm(uv_selected, axis=-1).max().item() * max(H, W) * 0.1
fig = ff.create_quiver(
x=pixels_selected[:, 0],
y=pixels_selected[:, 1],
u=-uv_selected[:, 0],
v=-uv_selected[:, 1],
name=('proj_grad'),
hoverinfo='none',
marker_color='orange',
scale=.25,
arrow_scale=.4,
)
# draw all points with different colors
_start_pts = 0
for k, n_pts in n_pts_per_cat.items():
_pixels = pixels[_start_pts:(n_pts + _start_pts)].cpu().numpy()
t0 = time.time()
fig.add_traces(
go.Scatter(x=_pixels[:, 0],
y=_pixels[:, 1],
name=k,
mode='markers',
marker_size=5,
hoverinfo='text',
text=text[_start_pts:(n_pts + _start_pts)]))
t1 = time.time()
# print('2D Scatter {} {:.3f}'.format(k, t1-t0))
_start_pts += n_pts
fig.update_layout(title_text=('2D quiver'),
xaxis=dict(range=(0, W),
constrain='domain',
visible=False),
yaxis=dict(range=(H, 0),
visible=False,
scaleanchor="x",
scaleratio=1))
# background is the ground truth mask overlayed with the rendered image
if img_mask_grad is not None:
img_mask_grad = img_mask_grad.cpu()
# convert to 0-1
img_mask_grad /= eps_denom(2 * img_mask_grad.abs().max())
assert (img_mask_grad.min().item() >= -0.5
and img_mask_grad.max().item() <= 0.5)
img_mask_grad = 0.5 + img_mask_grad
img = Image.fromarray(
np.array(img_mask_grad.squeeze() * 255).astype(dtype=np.uint8))
elif img is not None:
img = img.cpu()
# overlay two images with a *soft mask
img_mask_gt = img_mask_gt.float()
mask = (img_mask_gt == 0)
img_mask_gt[mask] = 0.35
img_mask_gt[mask == 0] = 0.65
img_mask_gt = img_mask_gt.unsqueeze(-1)
if img.ndim == 2:
img = img.unsqueeze(-1)
overlayed_image = torch.where(img < 0.5, 2 * img * img_mask_gt,
1 - 2 * (1 - img) * (1 - img_mask_gt))
img = Image.fromarray(
np.array(overlayed_image.squeeze() * 255).astype(dtype=np.uint8))
else:
img_mask_gt = img_mask_gt.float()
img = Image.fromarray(
np.array(img_mask_gt * 255).astype(dtype=np.uint8))
fig.add_layout_image(
dict(source=img,
xref="x",
yref="y",
sizex=img.size[0],
sizey=img.size[1],
opacity=1.0,
x=0,
y=0,
layer='below'))
fig.update_layout(height=800, width=W / H * 800, template="plotly_white")
if save_html is not None:
os.makedirs(os.path.dirname(save_html), exist_ok=True)
# figures_to_html([fig], save_html)
fig.write_html(save_html)
return fig
#marker={'color': iris["species"], 'symbol': 'circle'},
),
layout_title_text="..."
)
fig5
#%% Figure Factories
import numpy as np
import plotly.figure_factory as ff
x1,y1 = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
u1 = np.cos(x1)*y1
v1 = np.sin(x1)*y1
fig6 = ff.create_quiver(x1, y1, u1, v1)
fig6.show()
#%% Make Subplots
from plotly.subplots import make_subplots
fig7 = make_subplots(rows=1, cols=2)
fig7.add_trace(go.Scatter(y=[4,2,1], mode="lines"), row=1, col=1)
fig7.add_trace(go.Bar(y=[2,1,3]), row=1, col=2)
fig7
#%%
#%% Updating Figures
def render_output(function_digraph):
if function_digraph == {}:
print("Empty input")
return False
graph_network = nx.DiGraph()
key = 0
# lookup is used to map function to graph_network's first int
lookup = {}
for i in function_digraph:
# Add functions as Nodes and with X,Y positioning
graph_network.add_node(key, name=i, x=0, y=0)
twopi = math.pi * 2
x0, y0 = math.cos(twopi * key / len(function_digraph)), math.sin(
twopi * key / len(function_digraph))
graph_network.add_node(key, name=i, x=x0, y=y0)
# Fills lookup
lookup[i] = key
key += 1
for i in function_digraph:
for j in function_digraph[i]:
graph_network.add_edge(lookup[i], lookup[j])
# Builds a plotly scatter diagram from the edge connectors
edge_trace = Scatter(x=[],
y=[],
line=Line(width=5, color='#000'),
hoverinfo='all',
mode='Lines')
for edge in graph_network.edges():
# print(edge, G.node[edge[0]])
x0, y0 = graph_network.node[edge[0]]['x'], graph_network.node[
edge[0]]['y']
x1, y1 = graph_network.node[edge[1]]['x'], graph_network.node[
edge[1]]['y']
edge_trace['x'] += [x0, x1, None]
edge_trace['y'] += [y0, y1, None]
node_trace = Scatter(
x=[],
y=[],
text=[],
mode='marker+text',
hovertext='None',
hoverinfo='text',
textfont=dict(size=25, family="Courier New"),
marker=Marker(
showscale=True,
# colorscale options
# 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' |
# Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'
colorscale='YIGnBu',
reversescale=True,
color=[],
size=2,
colorbar=dict(thickness=15,
title='Node Connections',
xanchor='left',
titleside='right'),
line=dict(width=2)))
for node in graph_network.nodes():
x, y = graph_network.node[node]['x'], graph_network.node[node]['y']
node_trace['x'].append(x)
node_trace['y'].append(y)
node_trace['text'].append(graph_network.node[node]['name'])
list = [[], [], [], []]
for i in (range(int(len((edge_trace['x'])) / 3))):
list[0].append(edge_trace['x'][i * 3] +
edge_trace['x'][i * 3 + 1] * 0.1)
list[1].append(edge_trace['y'][i * 3] +
edge_trace['y'][i * 3 + 1] * 0.1)
list[2].append(
(edge_trace['x'][i * 3 + 1] - edge_trace['x'][i * 3]) * 9)
list[3].append(
(edge_trace['y'][i * 3 + 1] - edge_trace['y'][i * 3]) * 9)
print(list)
fig = ff.create_quiver(list[0], list[1], list[2], list[3], arrow_scale=0.1)
fig['data'].append(node_trace)
offline.plot(fig, filename='networkx.html')
def vector_plot(result, titles, verbose = False, **kwargs):
variable = titles['variable']
val0 = list(result.values())[0]
# check if this is a 2d vector plot: input and output should have shape 2d
if (result[variable].shape == val0.shape) & (val0.shape[1] == 2):
if verbose:
print('\t 2-d output', result[variable].shape)
u = result[variable][:, 0]
v = result[variable][:, 1]
x = val0[:, 0]
y = val0[:, 1]
# plotly's quiver plot has its own defaults
quiver_defaults = get_defaults(ff.create_quiver)
for k, v_ in kwargs.items():
if k in quiver_defaults:
quiver_defaults[k] = v_
if verbose:
print(quiver_defaults)
try:
trace = ff.create_quiver(
x, y, u, v,
name='quiver',
line=dict(width=1),
**quiver_defaults)['data'][0] # extracts quiver trace
except:
print('problem with quiver, u,v,x,y shapes:')
for v_ in [u, v, x, y]:
print(v_.shape)
raise
layout = go.Layout(title=titles['title'],
xaxis=dict(title='x'),
yaxis=dict(title='y'))
chart_type = '2d-vector'
# check if this is a 3d vector plot: input and output should have shape 3d
elif (result[variable].shape == val0.shape) & (val0.shape[1] == 3):
if verbose:
print('\t 3-d output', result[variable].shape, val0.shape)
print('\t 3d vector plot')
if type(result[variable]) == pd.DataFrame:
u = result[variable].values[:, 0].tolist()
v = result[variable].values[:, 1].tolist()
w = result[variable].values[:, 2].tolist()
else:
u = result[variable][:, 0].tolist()
v = result[variable][:, 1].tolist()
w = result[variable][:, 2].tolist()
if type(val0) == pd.DataFrame:
x = val0.values[:, 0].tolist()
y = val0.values[:, 1].tolist()
z = val0.values[:, 2].tolist()
else:
x = val0[:, 0].tolist()
y = val0[:, 1].tolist()
z = val0[:, 2].tolist()
# normalize each vector to get the length
norms = np.linalg.norm(result[variable], axis=1)
trace = go.Cone(
x=x, y=y, z=z, u=u, v=v, w=w,
hoverinfo='x+y+z+u+v+w+norm',
colorscale='Reds',
cmin=0, cmax=norms.max(),
)
layout = go.Layout(
title=titles['title'],
scene=dict(
xaxis=dict(title='x'),
yaxis=dict(title='y'),
zaxis=dict(title='z'),
))
chart_type = '3d-vector'
else:
raise NotImplementedError('plot type not supported yet')
return [trace], chart_type, layout
def _plot(data: SampledField,
fig: graph_objects.Figure,
size: tuple,
colormap: str or None,
show_color_bar: bool,
row: int = None, col: int = None,
):
subplot = fig.get_subplot(row, col)
vector = data.points.shape['vector']
if data.spatial_rank == 1 and isinstance(data, Grid):
x = data.points.vector[0].numpy().flatten()
channels = data.values.shape.channel
if channels.rank == 1 and channels.get_item_names(0) is not None:
for i, name in enumerate(channels.get_item_names(0)):
y = math.reshaped_native(real_values(data[{channels.name: i}]), [data.shape.spatial], to_numpy=True)
fig.add_trace(graph_objects.Scatter(x=x, y=y, mode='lines+markers', name=name), row=row, col=col)
fig.update_layout(showlegend=True)
else:
for ch_idx in channels.meshgrid():
y = math.reshaped_native(real_values(data[ch_idx]), [data.shape.spatial], to_numpy=True)
fig.add_trace(graph_objects.Scatter(x=x, y=y, mode='lines+markers', name='Multi-channel'), row=row, col=col)
fig.update_layout(showlegend=False)
elif data.spatial_rank == 2 and isinstance(data, Grid) and 'vector' not in data.shape: # heatmap
dims = spatial(data)
values = real_values(data).numpy(dims.reversed)
x = data.points.vector[dims[0].name].dimension(dims[1].name)[0].numpy()
y = data.points.vector[dims[1].name].dimension(dims[0].name)[0].numpy()
min_val, max_val = numpy.nanmin(values), numpy.nanmax(values)
min_val, max_val = min_val if numpy.isfinite(min_val) else 0, max_val if numpy.isfinite(max_val) else 0
color_scale = get_div_map(min_val, max_val, equal_scale=True, colormap=colormap)
# color_bar = graph_objects.heatmap.ColorBar(x=1.15) , colorbar=color_bar
fig.add_heatmap(row=row, col=col, x=x, y=y, z=values, zauto=False, zmin=min_val, zmax=max_val, colorscale=color_scale, showscale=show_color_bar)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain', title=dims.names[0])
subplot.yaxis.update(constrain='domain', title=dims.names[1])
elif data.spatial_rank == 2 and isinstance(data, Grid): # vector field
if isinstance(data, StaggeredGrid):
data = data.at_centers()
x, y = math.reshaped_native(data.points.vector[spatial(data)], [vector, data.shape.without(vector)], to_numpy=True, force_expand=True)
extra_channels = data.shape.channel.without('vector')
data_x, data_y = math.reshaped_native(data.values, [vector, extra_channels, spatial(data)], to_numpy=True, force_expand=True)
lower_x, lower_y = [float(l) for l in data.bounds.lower.vector.unstack_spatial('x,y')]
upper_x, upper_y = [float(u) for u in data.bounds.upper.vector.unstack_spatial('x,y')]
x_range = [lower_x, upper_x]
y_range = [lower_y, upper_y]
for ch in range(data_x.shape[0]):
# quiver = figure_factory.create_quiver(x, y, data_x[ch], data_y[ch], scale=1.0) # 7 points per arrow
# fig.add_trace(quiver, row=row, col=col)
data_y_flat = data_y[ch].flatten()
data_x_flat = data_x[ch].flatten()
# lines_y = numpy.stack([y, y + data_y_flat, [None] * len(x)], -1).flatten() # 3 points per arrow
# lines_x = numpy.stack([x, x + data_x_flat, [None] * len(x)], -1).flatten()
lines_y = numpy.stack([y - data_y_flat / 2, y + data_y_flat / 2, [None] * len(x)], -1).flatten() # 3 points per arrow
lines_x = numpy.stack([x - data_x_flat / 2, x + data_x_flat / 2, [None] * len(x)], -1).flatten()
name = extra_channels.get_item_names(0)[ch] if extra_channels.rank == 1 and extra_channels.get_item_names(0) is not None else None
fig.add_scatter(x=lines_x, y=lines_y, mode='lines', row=row, col=col, name=name)
if data_x.shape[0] == 1:
fig.update_layout(showlegend=False)
subplot.xaxis.update(range=x_range)
subplot.yaxis.update(range=y_range)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif data.spatial_rank == 3 and isinstance(data, Grid) and data.shape.channel.volume == 1: # 3D heatmap
values = real_values(data).numpy('z,y,x')
x = data.points.vector['x'].numpy('z,y,x')
y = data.points.vector['y'].numpy('z,y,x')
z = data.points.vector['z'].numpy('z,y,x')
min_val, max_val = numpy.nanmin(values), numpy.nanmax(values)
min_val, max_val = min_val if numpy.isfinite(min_val) else 0, max_val if numpy.isfinite(max_val) else 0
color_scale = get_div_map(min_val, max_val, equal_scale=True, colormap=colormap)
fig.add_volume(x=x.flatten(), y=y.flatten(), z=z.flatten(), value=values.flatten(),
showscale=show_color_bar, colorscale=color_scale, cmin=min_val, cmax=max_val, cauto=False,
isomin=0.1, isomax=0.8,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=17, # needs to be a large number for good volume rendering
row=row, col=col)
fig.update_layout(uirevision=True)
elif data.spatial_rank == 3 and isinstance(data, Grid): # 3D vector field
if isinstance(data, StaggeredGrid):
data = data.at_centers()
u = real_values(data).vector['x'].numpy('z,y,x')
v = real_values(data).vector['y'].numpy('z,y,x')
w = real_values(data).vector['z'].numpy('z,y,x')
x = data.points.vector['x'].numpy('z,y,x')
y = data.points.vector['y'].numpy('z,y,x')
z = data.points.vector['z'].numpy('z,y,x')
fig.add_cone(x=x.flatten(), y=y.flatten(), z=z.flatten(), u=u.flatten(), v=v.flatten(), w=w.flatten(),
colorscale='Blues',
sizemode="absolute", sizeref=1,
row=row, col=col)
elif isinstance(data, PointCloud) and data.spatial_rank == 2 and 'vector' in channel(data):
x, y = math.reshaped_native(data.points, [vector, data.shape.without('vector')], to_numpy=True, force_expand=True)
u, v = math.reshaped_native(data.values, [vector, data.shape.without('vector')], to_numpy=True, force_expand=True)
lower_x, lower_y = [float(d) for d in data.bounds.lower.vector]
upper_x, upper_y = [float(d) for d in data.bounds.upper.vector]
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
quiver = figure_factory.create_quiver(x, y, u, v, scale=1.0).data[0] # 7 points per arrow
if data.color.shape:
# color = data.color.numpy(data.shape.non_channel).reshape(-1)
warnings.warn("Multi-colored vector plots not yet supported")
else:
color = data.color.native()
quiver.line.update(color=color)
fig.add_trace(quiver, row=row, col=col)
if data.points.vector.item_names:
subplot.xaxis.update(title=data.points.vector.item_names[0])
subplot.yaxis.update(title=data.points.vector.item_names[1])
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif isinstance(data, PointCloud) and data.spatial_rank == 2:
lower_x, lower_y = [float(d) for d in data.bounds.lower.vector]
upper_x, upper_y = [float(d) for d in data.bounds.upper.vector]
if data.points.shape.non_channel.rank > 1:
data_list = field.unstack(data, data.points.shape.non_channel[0].name)
for d in data_list:
_plot(d, fig, size, colormap, show_color_bar, row, col)
else:
x, y = [d.numpy() for d in data.points.vector.unstack_spatial('x,y')]
color = data.color.native()
subplot_height = (subplot.yaxis.domain[1] - subplot.yaxis.domain[0]) * size[1]
if isinstance(data.elements, Sphere):
symbol = 'circle'
marker_size = data.elements.bounding_radius().numpy() * 1.9
elif isinstance(data.elements, BaseBox):
symbol = 'square'
marker_size = math.mean(data.elements.bounding_half_extent(), 'vector').numpy() * 1
elif isinstance(data.elements, Point):
symbol = 'x'
marker_size = 12 / (subplot_height / (upper_y - lower_y))
else:
symbol = 'asterisk'
marker_size = data.elements.bounding_radius().numpy()
marker_size *= subplot_height / (upper_y - lower_y)
marker = graph_objects.scatter.Marker(size=marker_size, color=color, sizemode='diameter', symbol=symbol)
fig.add_scatter(mode='markers', x=x, y=y, marker=marker, row=row, col=col)
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
fig.update_layout(showlegend=False)
subplot.xaxis.update(scaleanchor=f'y{subplot.yaxis.plotly_name[5:]}', scaleratio=1, constrain='domain')
subplot.yaxis.update(constrain='domain')
elif isinstance(data, PointCloud) and data.spatial_rank == 3:
lower_x, lower_y, lower_z = [float(d) for d in data.bounds.lower.vector.unstack_spatial('x,y,z')]
upper_x, upper_y, upper_z = [float(d) for d in data.bounds.upper.vector.unstack_spatial('x,y,z')]
if data.points.shape.non_channel.rank > 1:
data_list = field.unstack(data, data.points.shape.non_channel[0].name)
for d in data_list:
_plot(d, fig, size, colormap, show_color_bar, row, col)
else:
x, y, z = [d.numpy() for d in data.points.vector.unstack_spatial('x,y,z')]
color = data.color.native()
# if data.color.shape.instance_rank == 0:
# color = str(data.color)
# else:
# color = [str(d) for d in math.unstack(data.color, instance)]
domain_y = fig.layout[subplot.plotly_name].domain.y
if isinstance(data.elements, Sphere):
symbol = 'circle'
marker_size = data.elements.bounding_radius().numpy() * 2
elif isinstance(data.elements, BaseBox):
symbol = 'square'
marker_size = math.mean(data.elements.bounding_half_extent(), 'vector').numpy() * 1
elif isinstance(data.elements, Point):
symbol = 'x'
marker_size = 4 / (size[1] * (domain_y[1] - domain_y[0]) / (upper_y - lower_y) * 0.5)
else:
symbol = 'asterisk'
marker_size = data.elements.bounding_radius().numpy()
marker_size *= size[1] * (domain_y[1] - domain_y[0]) / (upper_y - lower_y) * 0.5
marker = graph_objects.scatter3d.Marker(size=marker_size, color=color, sizemode='diameter', symbol=symbol)
fig.add_scatter3d(mode='markers', x=x, y=y, z=z, marker=marker, row=row, col=col)
subplot.xaxis.update(range=[lower_x, upper_x])
subplot.yaxis.update(range=[lower_y, upper_y])
subplot.zaxis.update(range=[lower_z, upper_z])
fig.update_layout(showlegend=False)
else:
raise NotImplementedError(f"No figure recipe for {data}")
rowVecY = []
for x in X:
desiredPoint = np.array([x, y, z], dtype=float)
arrLength = perfectDelta.inverseKinetic(x, y, z)
reachedPoint = imperfectDelta.forwardKinetic(*arrLength)
rowVecX.append(reachedPoint[0] - desiredPoint[0])
rowVecY.append(reachedPoint[1] - desiredPoint[1])
vecX.append(rowVecX)
vecY.append(rowVecY)
arrX, arrY = np.meshgrid(X, Y)
fig = ff.create_quiver(x=arrX,
y=arrY,
u=vecX,
v=vecY,
scale=0.1,
arrow_scale=.4,
name='quiver',
line_width=1)
fig.update_layout(title="Error in plan Z = 0",
xaxis_title="X in mm",
yaxis_title="Y in mm",
font=dict(family="Courier New, monospace"))
fig.show()
def _process_series(self, series):
self._init_cyclers()
# if legend=True and both 3d lines and surfaces are shown, then hide the
# surfaces color bars and only shows line labels in the legend.
# TODO: can I show both colorbars and legends by scaling down the color
# bars?
show_3D_colorscales = True
show_2D_vectors = False
for s in series:
if s.is_3Dline:
show_3D_colorscales = False
if s.is_2Dvector:
show_2D_vectors = True
self._fig.data = []
count = 0
for ii, s in enumerate(series):
if s.is_2Dline:
line_kw = self._kwargs.get("line_kw", dict())
if s.is_parametric:
x, y, param = s.get_data()
# hides/show the colormap depending on self._use_cm
mode = "lines+markers" if not s.is_point else "markers"
if (not s.is_point) and (not self._use_cm):
mode = "lines"
lkw = dict(
name=s.label,
line_color=next(self._cl),
mode=mode,
marker=dict(
color=param,
colorscale=(next(
self._cyccm) if self._use_cyclic_cm(
param, s.is_complex) else next(self._cm)),
size=6,
showscale=self.legend and self._use_cm,
colorbar=self._create_colorbar(ii, s.label, True),
),
customdata=param,
hovertemplate=(
"x: %{x}<br />y: %{y}<br />u: %{customdata}"
if not s.is_complex else
"x: %{x}<br />y: %{y}<br />Arg: %{customdata}"),
)
self._fig.add_trace(
go.Scatter(x=x, y=y, **merge({}, lkw, line_kw)))
else:
x, y = s.get_data()
x, y, _ = self._detect_poles(x, y)
lkw = dict(
name=s.label,
mode="lines" if not s.is_point else "markers",
line_color=next(self._cl),
)
self._fig.add_trace(
go.Scatter(x=x, y=y, **merge({}, lkw, line_kw)))
elif s.is_3Dline:
# NOTE: As a design choice, I decided to show the legend entry
# as well as the colorbar (if use_cm=True). Even though the
# legend entry shows the wrong color (black line), it is useful
# in order to hide/show a specific series whenever we are
# plotting multiple series.
x, y, z, param = s.get_data()
if not s.is_point:
lkw = dict(
name=s.label,
mode="lines",
line=dict(
width=4,
colorscale=(next(self._cm) if self._use_cm else
self._solid_colorscale()),
color=param,
showscale=self.legend and self._use_cm,
colorbar=self._create_colorbar(ii, s.label, True),
),
)
else:
lkw = dict(name=s.label,
mode="markers",
line_color=next(self._cl))
line_kw = self._kwargs.get("line_kw", dict())
self._fig.add_trace(
go.Scatter3d(x=x, y=y, z=z, **merge({}, lkw, line_kw)))
elif (s.is_3Dsurface and
(not s.is_complex)) or (s.is_3Dsurface and s.is_complex and
(s.real or s.imag)):
xx, yy, zz = s.get_data()
# create a solid color to be used when self._use_cm=False
col = next(self._cl)
colorscale = [[0, col], [1, col]]
colormap = next(self._cm)
skw = dict(
name=s.label,
showscale=self.legend and show_3D_colorscales,
colorbar=self._create_colorbar(ii, s.label),
colorscale=colormap if self._use_cm else colorscale,
)
surface_kw = self._kwargs.get("surface_kw", dict())
self._fig.add_trace(
go.Surface(x=xx, y=yy, z=zz, **merge({}, skw, surface_kw)))
# TODO: remove this? Making it works with iplot is difficult
if self._kwargs.get("wireframe", False):
line_marker = dict(color=next(self._wfcm), width=2)
for i, j, k in zip(xx, yy, zz):
self._fig.add_trace(
go.Scatter3d(
x=i,
y=j,
z=k,
mode="lines",
line=line_marker,
showlegend=False,
))
for i, j, k in zip(xx.T, yy.T, zz.T):
self._fig.add_trace(
go.Scatter3d(
x=i,
y=j,
z=k,
mode="lines",
line=line_marker,
showlegend=False,
))
count += 1
elif s.is_contour and (not s.is_complex):
xx, yy, zz = s.get_data()
xx = xx[0, :]
yy = yy[:, 0]
# default values
ckw = dict(
contours=dict(
coloring=None,
showlabels=False,
),
colorscale=next(self._cm),
colorbar=self._create_colorbar(ii, s.label,
show_2D_vectors),
)
# user-provided values
contour_kw = self._kwargs.get("contour_kw", dict())
self._fig.add_trace(
go.Contour(x=xx, y=yy, z=zz, **merge({}, ckw, contour_kw)))
count += 1
elif s.is_implicit:
points = s.get_data()
if len(points) == 2:
# interval math plotting
x, y, pixels = self._get_pixels(s, points[0])
ckw = dict(
colorscale=[
[0, "rgba(0,0,0,0)"],
[1, next(self._cl)],
],
showscale=False,
name=s.label,
)
contour_kw = self._kwargs.get("contour_kw", dict())
self._fig.add_trace(
go.Heatmap(x=x,
y=y,
z=pixels,
**merge({}, ckw, contour_kw)))
else:
x, y, z, ones, plot_type = points
zf = z.flatten()
m, M = min(zf), max(zf)
col = next(self._cl)
# default values
ckw = dict(
contours=dict(
coloring="none"
if plot_type == "contour" else None,
showlabels=False,
type="levels"
if plot_type == "contour" else "constraint",
operation="<",
value=[(m + M) / 2],
),
colorscale=[
[0, "rgba(0,0,0,0)"],
[1, next(self._cl)],
],
fillcolor=col,
showscale=True,
name=s.label,
line=dict(color=col),
)
contour_kw = self._kwargs.get("contour_kw", dict())
# TODO: sadly, Plotly does not support yet setting contour
# levels, hence the visualization will look ugly whenever
# plot_type="contour".
# https://github.com/plotly/plotly.js/issues/4503
self._fig.add_trace(
go.Contour(x=x,
y=y,
z=ones,
**merge({}, ckw, contour_kw)))
count += 1
elif s.is_vector:
if s.is_2Dvector:
xx, yy, uu, vv = s.get_data()
streamlines = self._kwargs.get("streamlines", False)
if streamlines:
# NOTE: currently, it is not possible to create streamlines with
# a color scale: https://community.plotly.com/t/how-to-make-python-quiver-with-colorscale/41028
# default values
skw = dict(line_color=next(self._qc),
arrow_scale=0.15,
name=s.label)
# user-provided values
stream_kw = self._kwargs.get("stream_kw", dict())
stream = create_streamline(xx[0, :], yy[:, 0], uu, vv,
**merge({}, skw, stream_kw))
self._fig.add_trace(stream.data[0])
else:
# NOTE: currently, it is not possible to create quivers with
# a color scale: https://community.plotly.com/t/how-to-make-python-quiver-with-colorscale/41028
# default values
qkw = dict(line_color=next(self._qc),
scale=0.075,
name=s.label)
# user-provided values
quiver_kw = self._kwargs.get("quiver_kw", dict())
quiver = create_quiver(
xx, yy, uu, vv,
**merge({}, qkw,
quiver_kw)) # merge two dictionaries
self._fig.add_trace(quiver.data[0])
else:
xx, yy, zz, uu, vv, ww = s.get_data()
streamlines = self._kwargs.get("streamlines", False)
if streamlines:
seeds_points = get_seeds_points(xx, yy, zz, uu, vv, ww)
# default values
skw = dict(
colorscale=next(self._cm),
sizeref=0.3,
colorbar=self._create_colorbar(ii, s.label),
starts=dict(
x=seeds_points[:, 0],
y=seeds_points[:, 1],
z=seeds_points[:, 2],
),
)
# user-provided values
stream_kw = self._kwargs.get("stream_kw", dict())
self._fig.add_trace(
go.Streamtube(x=xx.flatten(),
y=yy.flatten(),
z=zz.flatten(),
u=uu.flatten(),
v=vv.flatten(),
w=ww.flatten(),
**merge({}, skw, stream_kw)))
else:
# default values
qkw = dict(
showscale=(not s.is_slice) or self.legend,
colorscale=next(self._cm),
sizemode="absolute",
sizeref=40,
colorbar=self._create_colorbar(ii, s.label),
)
# user-provided values
quiver_kw = self._kwargs.get("quiver_kw", dict())
self._fig.add_trace(
go.Cone(x=xx.flatten(),
y=yy.flatten(),
z=zz.flatten(),
u=uu.flatten(),
v=vv.flatten(),
w=ww.flatten(),
**merge({}, qkw, quiver_kw)))
count += 1
elif s.is_complex:
if s.is_domain_coloring:
x, y, magn_angle, img, colors = s.get_data()
xmin, xmax = x.min(), x.max()
ymin, ymax = y.min(), y.max()
self._fig.add_trace(
go.Image(
x0=xmin,
y0=ymin,
dx=(xmax - xmin) / s.n1,
dy=(ymax - ymin) / s.n2,
z=img,
name=s.label,
customdata=magn_angle,
hovertemplate=
("x: %{x}<br />y: %{y}<br />RGB: %{z}" +
"<br />Abs: %{customdata[0]}<br />Arg: %{customdata[1]}"
),
))
if colors is not None:
# chroma/phase-colorbar
self._fig.add_trace(
go.Scatter(
x=[xmin, xmax],
y=[ymin, ymax],
showlegend=False,
mode="markers",
marker=dict(
opacity=0,
colorscale=[
"rgb(%s, %s, %s)" % tuple(c)
for c in colors
],
color=[-self.pi, self.pi],
colorbar=dict(
tickvals=[
-self.pi,
-self.pi / 2,
0,
self.pi / 2,
self.pi,
],
ticktext=[
"-π",
"-π / 2",
"0",
"π / 2",
"π",
],
x=1 + 0.1 * count,
title="Argument",
titleside="right",
),
showscale=True,
),
))
if s.coloring == "f":
# lightness/magnitude-colorbar
self._fig.add_trace(
go.Scatter(
x=[xmin, xmax],
y=[ymin, ymax],
showlegend=False,
mode="markers",
marker=dict(
opacity=0,
colorscale=[[0, "black"], [1, "white"]],
color=[0, 1e20],
colorbar=dict(
tickvals=[0, 1e20],
ticktext=["0", "∞"],
x=1 + 0.1 * (count + 1),
title="Magnitude",
titleside="right",
),
showscale=True,
),
))
count += 2
else:
xx, yy, mag_angle, colors, colorscale = s.get_data()
mag, angle = mag_angle[:, :, 0], mag_angle[:, :, 1]
if s.coloring != "a":
warnings.warn(
"Plotly doesn't support custom coloring " +
"over surfaces. The surface color will show the " +
"argument of the complex function.")
# create a solid color to be used when self._use_cm=False
col = next(self._cl)
colorscale = [[0, col], [1, col]]
colormap = next(self._cm) if not s.is_complex else next(
self._cyccm)
skw = dict(
name=s.label,
showscale=self.legend and show_3D_colorscales,
colorbar=dict(
x=1 + 0.1 * count,
title=s.label,
titleside="right",
),
colorscale=colormap if self._use_cm else colorscale,
surfacecolor=angle,
customdata=angle,
hovertemplate=
"x: %{x}<br />y: %{y}<br />Abs: %{z}<br />Arg: %{customdata}",
)
m, M = min(angle.flatten()), max(angle.flatten())
# show pi symbols on the colorbar if the range is close
# enough to [-pi, pi]
if (abs(m + self.pi) < 1e-02) and (abs(M - self.pi) <
1e-02):
skw["colorbar"]["tickvals"] = [
m,
-self.pi / 2,
0,
self.pi / 2,
M,
]
skw["colorbar"]["ticktext"] = [
"-π",
"-π / 2",
"0",
"π / 2",
"π",
]
surface_kw = self._kwargs.get("surface_kw", dict())
self._fig.add_trace(
go.Surface(x=xx,
y=yy,
z=mag,
**merge({}, skw, surface_kw)))
count += 1
elif s.is_geometry:
x, y = s.get_data()
lkw = dict(name=s.label,
mode="lines",
fill="toself",
line_color=next(self._cl))
line_kw = self._kwargs.get("line_kw", dict())
self._fig.add_trace(
go.Scatter(x=x, y=y, **merge({}, lkw, line_kw)))
else:
raise NotImplementedError("{} is not supported by {}".format(
type(s),
type(self).__name__))
def vector_field(field2d, settings):
assert isinstance(field2d, (CenteredGrid, StaggeredGrid))
if isinstance(field2d, StaggeredGrid):
field2d = field2d.at_centers()
assert isinstance(field2d, CenteredGrid)
assert field2d.spatial_rank == 2
batch = settings.get('batch', 0)
batch = min(batch, field2d.values.shape.batch.volume)
arrow_origin = settings.get('arrow_origin', 'tip')
assert arrow_origin in ('base', 'center', 'tip')
max_resolution = settings.get('max_arrow_resolution', 40)
max_arrows = settings.get('max_arrows', 2000)
min_arrow_length = settings.get('min_arrow_length', 0.005) * math.max(field2d.box.size)
draw_full_arrows = settings.get('draw_full_arrows', False)
x, y = field2d.points.vector.unstack_spatial('x,y', to_numpy=True)
data = math.join_dimensions(field2d.values, field2d.shape.batch, 'batch').batch[batch]
data_x, data_y = data.vector.unstack_spatial('x,y', to_numpy=True)
while np.prod(x.shape) > max_resolution ** 2:
y = y[::2, ::2]
x = x[::2, ::2]
data_y = data_y[::2, ::2]
data_x = data_x[::2, ::2]
y = y.flatten()
x = x.flatten()
data_y = data_y.flatten()
data_x = data_x.flatten()
if max_arrows is not None or min_arrow_length > 0:
length = np.sqrt(data_y**2 + data_x**2)
keep_indices = np.argsort(length)
keep_indices = keep_indices[length[keep_indices] > min_arrow_length]
if len(keep_indices) > max_arrows:
keep_indices = keep_indices[-max_arrows:]
y = y[keep_indices]
x = x[keep_indices]
data_y = data_y[keep_indices]
data_x = data_x[keep_indices]
if len(x) == 0:
return EMPTY_FIGURE
if arrow_origin == 'tip':
x -= data_x
y -= data_y
elif arrow_origin == 'center':
x -= 0.5 * data_x
y -= 0.5 * data_y
lower = field2d.bounds.lower.vector.unstack_spatial('x,y', to_python=True)
upper = field2d.bounds.upper.vector.unstack_spatial('x,y', to_python=True)
x_range = [lower[0], upper[0]]
y_range = [lower[1], upper[1]]
if draw_full_arrows:
result = plotly_figures.create_quiver(x, y, data_x, data_y, scale=1.0) # 7 points per arrow
result.update_xaxes(range=x_range)
result.update_yaxes(range=y_range)
return result
else:
lines_y = np.stack([y, y + data_y, [None] * len(x)], -1).flatten() # 3 points per arrow
lines_x = np.stack([x, x + data_x, [None] * len(x)], -1).flatten()
return {
'data': [
{
'mode': 'lines',
'x': lines_x,
'y': lines_y,
'type': 'scatter',
}
],
'layout': {
'xaxis': {'range': x_range},
'yaxis': {'range': y_range},
}
}
def _update_interactive(self, params):
for i, s in enumerate(self.series):
if s.is_interactive:
self.series[i].update_data(params)
if s.is_2Dline and s.is_parametric:
x, y, param = self.series[i].get_data()
self.fig.data[i]["x"] = x
self.fig.data[i]["y"] = y
self.fig.data[i]["marker"]["color"] = param
self.fig.data[i]["customdata"] = param
elif s.is_2Dline:
x, y = self.series[i].get_data()
x, y, _ = self._detect_poles(x, y)
if s.is_geometry:
self.fig.data[i]["x"] = x
self.fig.data[i]["y"] = y
elif s.is_3Dline:
x, y, z, param = s.get_data()
self.fig.data[i]["x"] = x
self.fig.data[i]["y"] = y
self.fig.data[i]["z"] = z
self.fig.data[i]["line"]["color"] = param
elif s.is_3Dsurface and s.is_parametric:
x, y, z = self.series[i].get_data()
self.fig.data[i]["x"] = x
self.fig.data[i]["y"] = y
self.fig.data[i]["z"] = z
elif (s.is_3Dsurface and
(not s.is_complex)) or (s.is_3Dsurface and s.is_complex
and (s.real or s.imag)):
x, y, z = self.series[i].get_data()
self.fig.data[i]["z"] = z
elif s.is_contour and (not s.is_complex):
_, _, zz = s.get_data()
self.fig.data[i]["z"] = zz
elif s.is_implicit:
points = s.get_data()
if len(points) == 2:
raise NotImplementedError
else:
_, _, zz, ones, _ = points
self.fig.data[i]["z"] = ones
elif s.is_vector and s.is_3D:
streamlines = self._kwargs.get("streamlines", False)
if streamlines:
raise NotImplementedError
_, _, _, u, v, w = self.series[i].get_data()
self.fig.data[i]["u"] = u.flatten()
self.fig.data[i]["v"] = v.flatten()
self.fig.data[i]["w"] = w.flatten()
elif s.is_vector:
x, y, u, v = self.series[i].get_data()
streamlines = self._kwargs.get("streamlines", False)
if streamlines:
streams = create_streamline(x[0, :], y[:, 0], u, v)
data = streams.data[0]
# TODO: iplot doesn't work with 2D streamlines. Why?
# Is it possible that the sequential update of x and y
# is the cause of the error? Since at every update,
# len(x) = len(y) but those are different from before.
raise NotImplementedError
else:
# default values
qkw = dict(line_color=self.quivers_colors[i],
scale=0.075,
name=s.label)
# user-provided values
quiver_kw = self._kwargs.get("quiver_kw", dict())
quivers = create_quiver(x, y, u, v,
**merge({}, qkw, quiver_kw))
data = quivers.data[0]
self.fig.data[i]["x"] = data["x"]
self.fig.data[i]["y"] = data["y"]
elif s.is_complex:
if s.is_domain_coloring:
raise NotImplementedError
# TODO: for some unkown reason, domain_coloring and
# interactive plot don't like each other...
# x, y, z, magn_angle, img, discr, colors = self._get_image(s)
# self.fig.data[i]["z"] = img
# self.fig.data[i]["x0"] = -4
# self.fig.data[i]["y0"] = -5
# # self.fig.data[i]["customdata"] = magn_angle
else:
xx, yy, mag_angle, colors, colorscale = s.get_data()
mag, angle = mag_angle[:, :, 0], mag_angle[:, :, 1]
self.fig.data[i]["z"] = mag
self.fig.data[i]["surfacecolor"] = angle
self.fig.data[i]["customdata"] = angle
m, M = min(angle.flatten()), max(angle.flatten())
# show pi symbols on the colorbar if the range is close
# enough to [-pi, pi]
if (abs(m + self.pi) < 1e-02) and (abs(M - self.pi) <
1e-02):
self.fig.data[i]["colorbar"]["tickvals"] = [
m,
-self.pi / 2,
0,
self.pi / 2,
M,
]
self.fig.data[i]["colorbar"]["ticktext"] = [
"-π",
"-π / 2",
"0",
"π / 2",
"π",
]
elif s.is_geometry and not (s.is_2Dline):
x, y = self.series[i].get_data()
self.fig.data[i]["x"] = x
self.fig.data[i]["y"] = y
