def data_color(rollout_idx: int, point_idx: int, task_size: int):
rollout_alpha = ((original_rollout_size - rollout_idx) /
original_rollout_size) * 0.5
index_color = colors.RGB(255, 0, 0)
point_color = colors.RGB(0, 0, 255).lighten(
rollout_alpha
) if rollout_idx < original_rollout_size else colors.RGB(
255, 200, 255)
return index_color if point_idx < task_size else point_color
def test_from_rgb(self):
c = colors.RGB(10, 20, 30)
c2 = c.from_rgb(c)
self.assertTrue(c is not c2)
self.assertEqual(c.a, c2.a)
self.assertEqual(c.r, c2.r)
self.assertEqual(c.g, c2.g)
self.assertEqual(c.b, c2.b)
c = colors.RGB(10, 20, 30, 0.1)
c2 = c.from_rgb(c)
self.assertTrue(c is not c2)
self.assertEqual(c.a, c2.a)
self.assertEqual(c.r, c2.r)
self.assertEqual(c.g, c2.g)
self.assertEqual(c.b, c2.b)
def blot(gdata,
args=(),
figure=None,
squeeze=False,
streamline=False,
quiver=False,
contour=False,
diverging=False,
group=None,
xscale=1.0,
yscale=1.0,
style=None,
legend=True,
labelPrefix='',
xlabel=None,
ylabel=None,
title=None,
logx=False,
logy=False,
logz=False,
color=None,
fixaspect=False,
vmin=None,
vmax=None,
edgecolors=None,
**kwargs):
"""Plots Gkeyll data
Unifies the plotting across a wide range of Gkyl applications. Can
be used for both 1D an 2D data. Uses a proper colormap by default.
Args:
"""
#-----------------------------------------------------------------
#-- Data Loading -------------------------------------------------
numDims = gdata.getNumDims(squeeze=True)
if numDims > 2:
raise Exception('Only 1D and 2D plots are currently supported')
#end
# Get the handles on the grid and values
grid = gdata.getGrid()
values = gdata.getValues()
lower, upper = gdata.getBounds()
cells = gdata.getNumCells()
# Squeeze the data (get rid of "collapsed" dimensions)
axLabel = ['z_0', 'z_1', 'z_2', 'z_3', 'z_4', 'z_5']
if len(grid) > numDims:
idx = []
for d in range(len(grid)):
if cells[d] <= 1:
idx.append(d)
#end
#end
if idx:
grid = np.delete(grid, idx)
lower = np.delete(lower, idx)
upper = np.delete(upper, idx)
cells = np.delete(cells, idx)
axLabel = np.delete(axLabel, idx)
values = np.squeeze(values, tuple(idx))
#end
numComps = values.shape[-1]
if streamline or quiver:
step = 2
else:
step = 1
#end
idxComps = range(int(np.floor(numComps / step)))
numComps = len(idxComps)
if xscale != 1.0:
axLabel[0] = axLabel[0] + r' $\times$ {:.3e}'.format(xscale)
#end
if numDims == 2 and yscale != 1.0:
axLabel[1] = axLabel[1] + r' $\times$ {:.3e}'.format(yscale)
#end
sr = np.sqrt(numComps) #determine the number of rows and columns
if sr == np.ceil(sr):
numRows = int(sr)
numCols = int(sr)
elif np.ceil(sr) * np.floor(sr) >= numComps:
numRows = int(np.floor(sr))
numCols = int(np.ceil(sr))
else:
numRows = int(np.ceil(sr))
numCols = int(np.ceil(sr))
# Prepare the figure
if figure is None:
fig = []
if numDims == 1:
tooltips = [(axLabel[0], "$x"), ("value", "$y")
] #getting tooltips ready for different dimensions
else:
if streamline or quiver:
tooltips = None
else:
tooltips = [(axLabel[0], "$x"), (axLabel[1], "$y"),
("value", "@image")]
#end
#end
for comp in idxComps:
if logx and logy:
fig.append(
blt.figure(tooltips=tooltips,
x_axis_type="log",
y_axis_type="log",
frame_height=int(600.0 / numRows),
frame_width=int(600.0 / numRows),
outline_line_color='black',
min_border_left=70,
min_border_right=50,
min_border_bottom=10))
elif logx:
fig.append(
blt.figure(
tooltips=tooltips,
x_axis_type="log",
frame_height=int(
600.0 / numRows
), #adjust figures with the size based on the screen size
frame_width=int(600.0 / numRows),
outline_line_color='black',
min_border_left=70,
min_border_right=50,
min_border_bottom=10)
) #adjust spacings betweewn subplots to be aligned
elif logy:
fig.append(
blt.figure(tooltips=tooltips,
y_axis_type="log",
frame_height=int(600.0 / numRows),
frame_width=int(600.0 / numRows),
outline_line_color='black',
min_border_left=70,
min_border_right=50,
min_border_bottom=10))
else:
fig.append(
blt.figure(tooltips=tooltips,
frame_height=int(600.0 / numRows),
frame_width=int(600.0 / numRows),
outline_line_color='black',
min_border_left=70,
min_border_right=60,
min_border_bottom=10))
#-- Preparing the Axes -------------------------------------------
for comp in idxComps:
fig[comp].xaxis.minor_tick_line_color = None #deleting minor ticks
fig[comp].yaxis.minor_tick_line_color = None
fig[comp].xaxis.major_label_text_font_size = '12pt' #tick font size adjustment
fig[comp].yaxis.major_label_text_font_size = '12pt'
fig[comp].xaxis.axis_label_text_font_size = '12pt' #label font size adjustment
fig[comp].yaxis.axis_label_text_font_size = '12pt'
fig[comp].xaxis.formatter = bm.BasicTickFormatter(
precision=1) #adjust floating numbers of ticks
fig[comp].yaxis.formatter = bm.BasicTickFormatter(precision=1)
if numDims != 1:
if comp % numCols != 0: #hiding labels for unnecessary locations
fig[comp].yaxis.major_label_text_font_size = '0pt'
#end
if comp < (numRows - 1) * numCols:
fig[comp].xaxis.major_label_text_font_size = '0pt'
#end
#end
if comp >= (numRows - 1) * numCols:
if xlabel is None:
fig[comp].xaxis.axis_label = axLabel[0]
else: #if there is xlabel to be specified
fig[comp].xaxis.axis_label = xlabel
#end
#end
#end
if comp % numCols == 0:
if ylabel is None:
if numDims == 2:
fig[comp].yaxis.axis_label = axLabel[1]
#end
else:
fig[comp].yaxis.axis_label = ylabel
#end
#end
#end
#-- Main Plotting Loop -------------------------------------------
for comp in idxComps:
if len(idxComps) > 1:
if labelPrefix == "":
label = str(comp)
else:
label = '{:s}_c{:d}'.format(labelPrefix, comp)
#end
else:
label = labelPrefix
#end
#end
# Special plots
if numDims == 1:
x = 0.5 * (grid[0][1:] + grid[0][:-1])
fig[comp].line(x, values[..., comp], line_width=2, legend=label)
elif numDims == 2:
if contour:
pass
elif streamline:
magnitude = np.sqrt(values[..., 2 * comp]**2 +
values[..., 2 * comp + 1]**2)
gridCC = _gridNodalToCellCentered(grid, cells)
plt.subplots(numRows, numCols, sharex=True, sharey=True)
strm = plt.streamplot(
gridCC[0] * xscale,
gridCC[1] *
yscale, # make streamline plot by matplotlib first
values[..., 2 * comp].transpose(),
values[..., 2 * comp + 1].transpose(),
color=magnitude.transpose(),
linewidth=2)
lines = strm.lines # get the line and color data of matplotlib streamline
pathes = lines.get_paths()
arr = lines.get_array().data
points = np.stack([p.vertices.T for p in pathes], axis=0)
X = points[:, 0, :].tolist()
Y = points[:, 1, :].tolist()
mapper = bt.linear_cmap(field_name="color",
palette=bp.Inferno256,
low=arr.min(),
high=arr.max())
# use the data to create a multiline, use linear_map and palette to set the color of the lines:
source = bm.ColumnDataSource(dict(x=X, y=Y, color=arr))
fig[comp].multi_line("x",
"y",
line_color=mapper,
source=source,
line_width=3)
colormapper = bm.LinearColorMapper(
palette='Inferno256',
low=np.amin(magnitude.transpose()),
high=np.amax(magnitude.transpose())) #adding a color bar
color_bar = bm.ColorBar(
color_mapper=colormapper,
width=7,
location=(0, 0),
formatter=bm.BasicTickFormatter(
precision=1), #deleting unnecessary floating numbers
ticker=bm.BasicTicker(desired_num_ticks=4),
label_standoff=13,
major_label_text_font_size='12pt',
border_line_color=None,
padding=2,
bar_line_color='black')
fig[comp].add_layout(color_bar, 'right')
elif quiver:
gridCC = _gridNodalToCellCentered(grid, cells)
x_range = gridCC[0] * xscale #setting x coordinates
y_range = gridCC[1] * yscale #setting y coordinates
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
freq = 7
v_x = values[..., 2 * comp].transpose()
v_y = values[..., 2 * comp + 1].transpose()
X, Y = np.meshgrid(x_range, y_range)
speed = np.sqrt(v_x**2 + v_y**2)
theta = np.arctan2(v_y * dy, v_x * dx) #arctan(y/x)
maxSpeed = speed.max()
x0 = X[::freq, ::freq].flatten()
y0 = Y[::freq, ::freq].flatten()
length = speed[::freq, ::freq].flatten() / maxSpeed
angle = theta[::freq, ::freq].flatten()
x1 = x0 + 0.9 * freq * dx * v_x[::freq, ::freq].flatten(
) / speed[::freq, ::freq].max()
y1 = y0 + 0.9 * freq * dy * v_y[::freq, ::freq].flatten(
) / speed[::freq, ::freq].max()
fig[comp].segment(x0, y0, x1, y1, color='black') #vector line
fig[comp].triangle(x1,
y1,
size=4.0,
angle=angle - np.pi / 2,
color='black') #vector arrow
elif diverging:
gridCC = _gridNodalToCellCentered(grid, cells)
vmax = np.abs(values[..., comp]).max()
x_range = grid[0] * xscale #setting x coordinates
y_range = grid[1] * yscale #setting y coordinates
CmToRgb = (255 * cm.RdBu_r(range(256))).astype(
'int') #extract colors from maplotlib colormap
RgbToHexa = [
bc.RGB(*tuple(rgb)).to_hex() for rgb in CmToRgb
] # convert RGB numbers into colormap hexacode string
mapper = bm.LinearColorMapper(palette=RgbToHexa,
low=-vmax,
high=vmax) #adding a color bar
fig[comp].image(image=[values[..., comp].transpose()],
x=x_range[0],
y=y_range[0],
dw=(x_range[-1] - x_range[0]),
dh=(y_range[-1] - y_range[0]),
color_mapper=mapper)
color_bar = bm.ColorBar(
color_mapper=mapper,
width=7,
location=(0, 0),
formatter=bm.BasicTickFormatter(
precision=1), #deleting unnecessary floating numbers
ticker=bm.BasicTicker(desired_num_ticks=4),
label_standoff=14,
major_label_text_font_size='12pt',
border_line_color=None,
padding=2,
bar_line_color='black')
fig[comp].add_layout(color_bar, 'right')
# Basic plots
else:
gridCC = _gridNodalToCellCentered(grid, cells)
x_range = grid[0] * xscale #setting x coordinates
y_range = grid[1] * yscale #setting y coordinates
if logz:
tmp = np.array(values[..., comp])
if vmin is not None or vmax is not None:
for i in range(tmp.shape[0]):
for j in range(tmp.shape[1]):
if vmin and tmp[i, j] < vmin:
tmp[i, j] = vmin
#end
if vmax and tmp[i, j] > vmax:
tmp[i, j] = vmax
#end
#end
if vmin is not None:
vminTemp = vmin
else:
vminTemp = np.amin(values[..., comp])
#end
if vmax is not None:
vmaxTemp = vmax
else:
vmaxTemp = np.amax(values[..., comp])
#end
mapper = bm.LogColorMapper(palette='Inferno256',
low=vminTemp,
high=vmaxTemp)
fig[comp].image(image=[tmp.transpose()],
x=x_range[0],
y=y_range[0],
dw=(x_range[-1] - x_range[0]),
dh=(y_range[-1] - y_range[0]),
color_mapper=mapper)
color_bar = bm.ColorBar(
color_mapper=mapper, #adding a colorbar
width=7,
location=(0, 0),
formatter=bm.BasicTickFormatter(
precision=1
), #deleting unnecessary floating numbers
ticker=bm.BasicTicker(),
label_standoff=13,
major_label_text_font_size='12pt',
border_line_color=None,
padding=2,
bar_line_color='black')
fig[comp].add_layout(color_bar, 'right')
else:
mapper = bm.LinearColorMapper(palette='Inferno256',
low=np.amin(values[...,
comp]),
high=np.amax(values[...,
comp]))
fig[comp].image(image=[values[..., comp].transpose()],
x=x_range[0],
y=y_range[0],
dw=(x_range[-1] - x_range[0]),
dh=(y_range[-1] - y_range[0]),
color_mapper=mapper)
color_bar = bm.ColorBar(
color_mapper=mapper, #adding a colorbar
width=7,
location=(0, 0),
formatter=bm.BasicTickFormatter(
precision=1
), #deleting unnecessary floating numbers
ticker=bm.BasicTicker(desired_num_ticks=4),
label_standoff=13,
major_label_text_font_size='12pt',
border_line_color=None,
padding=2,
bar_line_color='black')
fig[comp].add_layout(color_bar, 'right')
#end
#end
else:
raise ValueError("{:d}D data not yet supported".format(numDims))
#end
#-- Additional Formatting ------------------------------------
if not quiver:
fig[comp].x_range.range_padding = 0
if numDims == 2:
fig[comp].y_range.range_padding = 0
#end
#end
if legend:
if numDims == 2:
x_range = grid[0] * xscale
y_range = grid[1] * yscale
#The legends are not embedded into the plot but the text numbers on the top of plots. Refer to 1D line plot.
legend_number = bm.Label(
x=x_range[0] + (x_range[-1] - x_range[0]) * 0.005,
y=y_range[-1] - (y_range[-1] - y_range[0]) * 0.115,
text=label,
render_mode='css',
background_fill_color='white',
background_fill_alpha=0.9,
border_line_cap='round')
fig[comp].add_layout(legend_number)
#end
#end
if title:
if comp < numCols:
fig[comp].title.text = title
#end
#end
if not legend:
if numDims == 1:
fig[comp].legend.visible = False
#end
#end
gp = bl.gridplot(children=fig,
toolbar_location='right',
ncols=numCols,
merge_tools=True)
return gp
def test_to_hex(self):
c = colors.RGB(10, 20, 30)
self.assertEqual(c.to_hex(), "#%02X%02X%02X" % (c.r, c.g, c.b))
def test_to_css(self):
c = colors.RGB(10, 20, 30)
self.assertEqual(c.to_css(), "rgb(10, 20, 30)")
c = colors.RGB(10, 20, 30, 0.3)
self.assertEqual(c.to_css(), "rgba(10, 20, 30, 0.3)")
def test_repr(self):
c = colors.RGB(10, 20, 30)
self.assertEqual(repr(c), c.to_css())
c = colors.RGB(10, 20, 30, 0.3)
self.assertEqual(repr(c), c.to_css())
def test_basic(self):
c = colors.RGB(10, 20, 30)
assert c
c = colors.RGB(10, 20, 30, 0.3)
assert c
def test_basic(self):
c = colors.RGB(10, 20, 30)
c = colors.RGB(10, 20, 30, 0.3)
st_viz.create_canvas(
title=f'Prototype Plot',
sizing_mode='scale_width',
plot_height=540,
tools="pan,box_zoom,lasso_select,wheel_zoom,previewsave,reset")
st_viz.add_map_tile('CARTODBPOSITRON')
st_viz.add_numerical_colormap('Viridis256',
'count',
colorbar=True,
cb_orientation='vertical',
cb_location='right',
label_standoff=12,
border_line_color=None,
location=(0, 0),
nan_color=bokeh_colors.RGB(1, 1, 1, 0))
st_viz.add_polygon(fill_color=st_viz.cmap,
line_color=st_viz.cmap,
fill_alpha=0.6,
muted_alpha=0,
legend_label=f'GPS Locations (Choropleth)')
data_points = st_visualizer(limit=len(gdf))
data_points.set_data(gdf)
data_points.set_figure(st_viz.figure)
categorical_name = 'label'
class Callback(callbacks.BokehFilters):
def __init__(self, vsn_instance, widget):