def make_plot(source, title_text, league):
hover = HoverTool(tooltips=[(
"Team",
"@team"), ("Expected Runs",
"@expected_runs"), ("Actual Runs",
"@actual_runs"), ("League", "@league")])
p = figure(plot_width=400,
plot_height=400,
tools=[hover],
title=title_text)
p.x_range = Range1d(3, 7)
p.y_range = Range1d(3, 7)
p.xaxis.axis_label = "Expected Runs Per Game"
p.yaxis.axis_label = "Actual Runs Per Game"
p.circle(x="expected_runs",
y="actual_runs",
size=10,
line_color="black",
fill_color="firebrick",
fill_alpha=0.5,
source=source)
p.line(x=[3, 9], y=[3, 9], line_width=4, line_color="navy", alpha=0.7)
p.line(x=[3, 9], y=[3, 9], line_width=175, line_color="navy", alpha=0.05)
print(league)
if 1 == 7:
print("hello world")
p.add_layout(
Label(x=6.638,
y=5.42,
x_offset=-17,
y_offset=10,
text="BOS",
text_color="black"))
p.add_layout(
Label(x=5.121,
y=3.77,
x_offset=-15,
y_offset=-30,
text="PHI",
text_color="black"))
p.add_layout(
Label(x=4.705,
y=4.030,
x_offset=-17,
y_offset=-30,
text="OAK",
text_color="black"))
p.add_layout(
Label(x=5.917,
y=4.14,
x_offset=-12,
y_offset=-30,
text="MIL",
text_color="black"))
return p # show the results
def error_graph(self):
"""plotting error graph in case of misspelling to avoid errors breaking
the program"""
p = figure(title='Uh oh!',
x_range=(0, 1),
y_range=(0, 2),
tools="wheel_zoom,reset",
match_aspect=True,
name='plot')
text1 = 'Uh oh! It looks like you misspelled a word. If you are searching for'
text2 = 'a player make sure that their full name is spelled correctly. If you'
text3 = 'are looking for a team make sure that their 3 letter, NBA specific'
text4 = 'abbreviation is correct.'
mytext1 = Label(x=0.1, y=1.4, text=text1)
mytext2 = Label(x=0.1, y=1.2, text=text2)
mytext3 = Label(x=0.1, y=1, text=text3)
mytext4 = Label(x=0.35, y=.8, text=text4)
p.add_layout(mytext1)
p.add_layout(mytext2)
p.add_layout(mytext3)
p.add_layout(mytext4)
p.grid.visible = False
p.axis.visible = False
p.toolbar.logo = None
p.toolbar_location = None
return p
def f11(city, title):
df = city_bus_level_re[[
'城市对', '发车城市', '到达城市', '最快到达时间', '总得分', '经度_x', '纬度_x', '经度_y', '纬度_y'
]]
x = df[df['城市对'].str.contains(city) & (df['最快到达时间'] <= 120)]
x['总得分'] = x['总得分'] * 10
x['color'] = '待分类'
x['color'][x['最快到达时间'] <= 60] = '#00A896'
x['color'][(x['最快到达时间'] > 60) & (x['最快到达时间'] <= 120)] = '#457B9D'
colors = x[x['发车城市'] != city]['color'].tolist() + x[
x['到达城市'] != city]['color'].tolist()
citys = x[x['发车城市'] != city]['发车城市'].tolist() + x[
x['到达城市'] != city]['到达城市'].tolist()
lng = x[x['发车城市'] != city]['经度_x'].tolist() + x[
x['到达城市'] != city]['经度_y'].tolist()
lat = x[x['发车城市'] != city]['纬度_x'].tolist() + x[
x['到达城市'] != city]['纬度_y'].tolist()
#time = x[x['发车城市'] != city]['最快到达时间'].tolist() + x[x['到达城市'] != city]['最快到达时间'].tolist()
size = x[x['发车城市'] != city]['总得分'].tolist() + x[
x['到达城市'] != city]['总得分'].tolist()
p = figure(
title=title,
plot_width=800,
plot_height=800,
tools=['box_select,reset,xwheel_zoom,ywheel_zoom,pan,crosshair,save'])
p.circle(x=lng,
y=lat,
size=size,
color=colors,
fill_alpha=0.8,
line_width=0.8,
line_color='#1C191D',
legend='绿色(一小时圈)蓝色(两小时圈)')
p.circle_cross(x=x[x['发车城市'] == city]['经度_x'].tolist(),
y=x[x['发车城市'] == city]['纬度_x'].tolist(),
size=26,
fill_alpha=0.8,
line_width=0.8,
line_color='#1C191D',
color='#F0374D',
legend='散点大小(%s与该市公交化得分)' % city)
for i, j, k, m in zip(lng, lat, citys, size):
label = Label(x=i - 0.09, y=j + 0.165, text=k, text_font_size='9pt')
label2 = Label(x=i - 0.09,
y=j - 0.044,
text=str(m / 10)[:4],
text_font_size='9pt')
p.add_layout(label)
p.add_layout(label2)
label3 = Label(x=x[x['发车城市'] == city]['经度_x'].tolist()[0] - 0.09,
y=x[x['发车城市'] == city]['纬度_x'].tolist()[0] + 0.165,
text=city,
text_font_size='9pt')
p.add_layout(label3)
p.legend.location = "bottom_right"
p.legend.background_fill_alpha = 0.05
p.legend.border_line_color = None
p.background_fill_alpha = 0.05
df_y = pd.DataFrame({'LNG': lng, 'lat': lat, 'city': citys})
return show(p), df_y, x
def infographic(items, **kwargs):
"""Create and infographic 'plot'.
:param items: 3-tuples of (label, value, unit, icon); the label should be
a one or two word description, the value the headline number, and the
icon the name of a fontawesome icon. If `value` is numeric, it
will be normalised by use of an SI suffix for display after which
`unit` will be appended. If `value` is a string it will be used as is.
:param kwargs: kwargs for bokeh gridplot.
..note:: If `bootstrap_fontawesome` has not already been called, the
function will load the required fonts, however they will not display
the first time an Jupyter labs cell is run. If using the
`InfoGraphItems` helper class, this wrinkle will be taken care of
provided the helper is initiated in a previous cell.
"""
plots = list()
seen = set()
for label, value, icon, unit in items:
if label in seen:
continue
if not isinstance(value, str):
value = si_format(value) + unit
seen.add(label)
width, height = 175, 100
aspect = height / width
p = figure(
output_backend='webgl',
plot_width=width, plot_height=height,
title=None, toolbar_location=None)
p.axis.visible = False
p.grid.visible = False
p.outline_line_color = None
p.rect([0.5], [0.5], [1.0], [1.0], fill_color="#2171b5")
p.x_range = Range1d(start=0.1, end=0.9, bounds=(0.1, 0.9))
p.y_range = Range1d(start=0.1, end=0.9, bounds=(0.1, 0.9))
p.add_layout(
Label(
x=0.15, y=0.45, text=value, text_color="#DEEBF7",
text_font_size="24px"))
p.add_layout(
Label(
x=0.15, y=0.2, text=label, text_color="#C6DBEF",
text_font_size="16px"))
image = fa_icons.rgba(icon, 75, color='#6BAED6')
p.image_rgba(image=[image], x=0.6, y=0.4, dw=0.25, dh=0.25/aspect)
plots.append(p)
defaults = {'toolbar_location': None, "ncols": len(items)}
defaults.update(kwargs)
return gridplot(plots, **defaults)
def test_Label():
label = Label()
assert label.plot is None
assert label.level == 'annotation'
assert label.x is None
assert label.y is None
assert label.x_units == 'data'
assert label.y_units == 'data'
assert label.text == 'text'
assert label.angle == 0
assert label.x_offset == 0
assert label.y_offset == 0
assert label.render_mode == 'canvas'
assert label.x_range_name == 'default'
assert label.y_range_name == 'default'
assert isinstance(label.source, ColumnDataSource)
assert label.source.data == {}
yield check_text, label
yield check_background, label, None, 1.0
yield check_border, label, None, 1.0, 1.0
yield (check_props, label, [
"plot", "level", "x", "y", "x_units", "y_units", "text", "angle",
"x_offset", "y_offset", "render_mode", "x_range_name", "y_range_name",
"source"
], TEXT, ANGLE, BORDER, BACKGROUND)
def __init__(self,
sim_data,
image=PendelWagenSystem(),
speed_factor=1,
fig=None,
state=1,
x_range=None,
wait=0.01):
self.wait = wait
self.set_sim_data(sim_data)
self.set_image(image)
self.figure = fig if fig else self.create_fig(state)
self.k = int(self.x.shape[0] / (np.max(self.t) / self.wait))
self.x = self.x[0:-1:self.k]
self.t = self.t[0:-1:self.k]
self.speed_factor = speed_factor
self.time = Label(x=25,
y=self.figure.plot_height - 50,
x_units='screen',
y_units='screen')
self.figure.add_layout(self.time)
self.figure.renderers += self.image.glyphs
def test_Label():
label = Label()
assert label.plot is None
assert label.level == 'annotation'
assert label.x is None
assert label.y is None
assert label.x_units == 'data'
assert label.y_units == 'data'
assert label.text == 'text'
assert label.angle == 0
assert label.x_offset == 0
assert label.y_offset == 0
assert label.render_mode == 'canvas'
assert label.x_range_name == 'default'
assert label.y_range_name == 'default'
assert isinstance(label.source, ColumnDataSource)
assert label.source.data == {}
yield check_text, label
yield check_fill, label, "background_", None, 1.0
yield check_line, label, "border_", None, 1.0, 1.0
yield (check_props, label, [
"plot", "level", "x", "y", "x_units", "y_units", "text", "angle",
"x_offset", "y_offset", "render_mode", "x_range_name", "y_range_name",
"source"
], TEXT, ANGLE, prefix('border_', LINE), prefix('background_', FILL))
def add_ascending_node_direction(plot):
plot.add_layout(
Arrow(
end=VeeHead(
size=10,
fill_color=colors["ascending-node"],
line_color=colors["ascending-node"],
),
line_width=3,
x_start=0,
y_start=0,
x_end=constants.PLOTS_RANGE,
y_end=0,
line_color=colors["ascending-node"],
)
)
plot.add_layout(
Label(
text="Ascending Node",
x=0.5 * constants.PLOTS_RANGE,
y=-0.1 * constants.PLOTS_RANGE,
text_font_size="8pt",
text_color=colors["ascending-node"],
)
)
def calibrate(current, reference, figure):
r = scipy.stats.linregress(x=current, y=reference, alternative='two-sided')
a, b = current[0], current[-1]
ca, cb = (
a * r.slope + r.intercept,
b * r.slope + r.intercept,
)
figure.line(
x=[a, b],
y=[ca, cb],
color="red",
)
rps = Label(x=min(current),
y=(max(reference) - min(reference)) * .8 + min(reference),
x_units="data",
y_units="data",
text=f"slope={r.slope}\nintercept={r.intercept}\n"
f"rvalue={r.rvalue}\npvalue={r.pvalue}\n"
f"stderr={r.stderr}\nintercept_stderr={r.intercept_stderr}",
render_mode='css',
border_line_color='black',
border_line_alpha=1.0,
background_fill_color='white',
background_fill_alpha=1.0)
figure.add_layout(rps)
return dict(slope=r.slope, intercept=r.intercept)
def __init__(self,
dataf,
labels,
x,
y,
size=5,
alpha=0.5,
width=400,
height=400,
color=None):
self.uuid = str(uuid.uuid4())[:10]
self.x = x
self.y = y
self.plot = figure(width=width, height=height, title=f"{x} vs. {y}")
self.color_column = labels if isinstance(labels, str) else color
self._colors = ["red", "blue", "green", "purple", "cyan"]
if isinstance(labels, str):
self.labels = list(dataf[labels].unique())
d = {k: col for k, col in zip(self.labels, self._colors)}
dataf = dataf.assign(color=[d[lab] for lab in dataf[labels]])
self.source = ColumnDataSource(data=dataf)
else:
if not self.color_column:
dataf = dataf.assign(
color=["gray" for _ in range(dataf.shape[0])])
else:
color_labels = list(dataf[self.color_column].unique())
d = {k: col for k, col in zip(color_labels, self._colors)}
dataf = dataf.assign(
color=[d[lab] for lab in dataf[self.color_column]])
self.source = ColumnDataSource(data=dataf)
self.labels = labels
if len(self.labels) > 5:
raise ValueError("We currently only allow for 5 classes max.")
self.plot.circle(x=x,
y=y,
color="color",
source=self.source,
size=size,
alpha=alpha)
# Create all the tools for drawing
self.poly_patches = {}
self.poly_draw = {}
for k, col in zip(self.labels, self._colors):
self.poly_patches[k] = self.plot.patches([], [],
fill_color=col,
fill_alpha=0.4,
line_alpha=0.0)
icon_path = resource_filename("hulearn", f"images/{col}.png")
self.poly_draw[k] = PolyDrawTool(renderers=[self.poly_patches[k]],
custom_icon=icon_path)
c = self.plot.circle([], [], size=5, color="black")
edit_tool = PolyEditTool(renderers=list(self.poly_patches.values()),
vertex_renderer=c)
self.plot.add_tools(*self.poly_draw.values(), edit_tool)
self.plot.add_layout(Label(x=70, y=70, text="here your text"))
def plot_edge_performance(model):
all_cuts = np.arange(0.001, 1., 0.02)
results = {'eff': [], 'pur': [], 'score cut': all_cuts}
model.to(device)
test_data = model.testset[0].to(device)
with torch.no_grad():
test_results = model.shared_evaluation(test_data, 0, log=False)
auc = roc_auc_score(test_results["truth"].cpu(),
test_results["score"].cpu())
for cut in all_cuts:
preds = test_results["score"] > cut
edge_positive = preds.sum().float()
edge_true = test_results["truth"].sum().float()
edge_true_positive = ((test_results["truth"].bool()
& preds).sum().float())
results["eff"].append(
(edge_true_positive / max(1, edge_true)).cpu().numpy())
results["pur"].append(
(edge_true_positive / max(1, edge_positive)).cpu().numpy())
results = pd.DataFrame(results)
source = ColumnDataSource(results)
cmap = viridis(3)
titles = ['Efficiency', 'Purity']
figures = []
x = 'score cut'
for idx, y in enumerate(['eff', 'pur']):
figures.append(
figure(title=titles[idx], x_axis_label=x, y_axis_label=y))
figures[-1].circle(y=y,
x=x,
source=source,
color=cmap[0],
legend_label=y)
figures[-1].line(x=x,
y=y,
source=source,
color=cmap[0],
legend_label=y)
y_val = results[y][(results[x] - 0.5).abs().idxmin()].item()
label = Label(x=0.1,
y=y_val,
x_offset=10,
y_offset=-10,
text="@ score cut = 0.5, \n" + y + " = " +
str(round(y_val, 3)) + "\n AUC: " + str(auc),
border_line_color='black',
border_line_alpha=1.0,
background_fill_color='white',
background_fill_alpha=0.8)
figures[-1].add_layout(label)
show(row(figures))
def add_equatorial_plane(plot):
plot.add_layout(
Label(
text="Equatorial Plane",
x=-0.7 * constants.PLOTS_RANGE,
y=0.7 * constants.PLOTS_RANGE,
text_align="center",
text_color=colors["equator"],
text_font_size="9pt",
angle=pi / 4,
)
)
def plot_neighbor_performance(model):
all_radius = np.arange(0.001, 0.15, 0.005)
results = {'eff': [], 'pur': [], 'loss': [], 'radius': all_radius}
model.to(device)
test_data = model.testset[0].to(device)
with torch.no_grad():
for r in all_radius:
test_results = model.shared_evaluation(test_data,
0,
r,
1000,
log=False)
for key in results:
if key not in test_results: continue
results[key].append(test_results[key].cpu().numpy())
results = pd.DataFrame(results)
source = ColumnDataSource(results)
cmap = viridis(3)
titles = ['Efficiency', 'Purity', 'Loss']
figures = []
x = 'radius'
for idx, y in enumerate(['eff', 'pur', 'loss']):
figures.append(
figure(title=titles[idx], x_axis_label=x, y_axis_label=y))
figures[-1].circle(y=y,
x=x,
source=source,
color=cmap[0],
legend_label=y)
figures[-1].line(x=x,
y=y,
source=source,
color=cmap[0],
legend_label=y)
y_val = results[y][(results[x] -
model.hparams["r_test"]).abs().idxmin()].item()
label = Label(x=model.hparams["r_test"],
y=y_val,
x_offset=10,
y_offset=-10,
text=f"@ radius = {model.hparams['r_test']}, \n" + y +
" = " + str(round(y_val, 3)),
border_line_color='black',
border_line_alpha=1.0,
background_fill_color='white',
background_fill_alpha=0.8)
figures[-1].add_layout(label)
show(row(figures))
def createMeasureJTool(self):
source = ColumnDataSource(data=dict(x=[], y=[]))
label = Label(x=0,
y=0,
text="",
text_color="#000000",
render_mode="css")
self.plot.add_layout(label)
measureJTool = MeasureJTool(label=label,
frequency=getFrequency(self.udic),
id="measureJTool")
self.plot.add_tools(measureJTool)
def __init__(self, sim_data, image=PendelWagenSystem(), speed_factor=1, fig=None):
self.sim_data = sim_data
self.x, self.t = sim_data
self.figure = fig if fig else self.create_fig()
self.image = image
k = int(self.x.shape[0] / (np.max(self.t)/0.01))
self.x = self.x[0:-1:k]
self.t = self.t[0:-1:k]
self.speed_factor = speed_factor
self.time = Label(x=25, y=self.figure.plot_height - 50, x_units='screen', y_units='screen')
self.figure.add_layout(self.time)
self.figure.renderers += self.image.glyphs
def add_equator_line(plot):
plot.add_layout(
Span(
location=0,
dimension="width",
line_color=colors["equator"],
line_dash="dashed",
line_width=1,
)
)
plot.add_layout(
Label(
text="Equator",
x=-0.9 * constants.PLOTS_RANGE,
y=0.03 * constants.PLOTS_RANGE,
text_font_size="8pt",
text_color=colors["equator"],
)
)
def plot_feature_cumsum(X, y, feature, cutoff, x_range=(-1, 4)):
feature_data = get_feature_cumsum(X, y, feature)
p = figure(x_range=x_range, )
p.line(
feature_data[feature],
feature_data["0"],
legend_label="0",
line_color="navy",
)
p.line(
feature_data[feature],
feature_data["1"],
legend_label="1",
line_color="crimson",
)
span = Span(
location=cutoff,
dimension="height",
line_color="gray",
line_dash="dotted",
)
label = Label(
text="limiar da árvore\nde decisão",
text_align='right',
x=cutoff - 0.1,
y=0.9,
x_units="data",
)
p.add_layout(span)
p.add_layout(label)
p.xaxis.axis_label = feature
p.yaxis.axis_label = "soma cumulativa"
p.legend.location = "top_left"
show(p)
def f9(df, col, hovers, titles, ax, label): #
df.columns = ['city', col]
citys = df['city'].tolist()[::-1]
source2 = ColumnDataSource(df)
hover = HoverTool(tooltips=[('城市', '@city'), (hovers, '@%s' % col)])
p2 = figure(
title=titles,
plot_width=400,
plot_height=550,
y_range=citys,
tools=[hover, 'box_select,reset,xwheel_zoom,pan,crosshair,save'])
p2.hbar(y='city',
source=source2,
color='#02C39A',
height=0.2,
left=1,
right=col,
alpha=0.6)
p2.xgrid.grid_line_color = None
p2.ygrid.grid_line_color = None
p2.xaxis.minor_tick_line_color = None
p2.yaxis.axis_line_color = None
p2.outline_line_color = None
p2.legend.location = "bottom_right"
p2.legend.orientation = "horizontal"
p2.xaxis.axis_label = label
n = 19.2
for k in df[col].tolist():
if n > 0:
label = Label(x=ax,
y=n - 0.05,
text=str(k)[:4],
text_font_size='9pt',
text_color='#898B8E')
n = n - 1
p2.add_layout(label)
output_file('%s.html' % titles)
return show(p2)
def test_Label():
label = Label()
assert label.plot is None
assert label.level == 'annotation'
assert label.x is None
assert label.y is None
assert label.x_units == 'data'
assert label.y_units == 'data'
assert label.text is None
assert label.angle == 0
assert label.angle_units == 'rad'
assert label.x_offset == 0
assert label.y_offset == 0
assert label.render_mode == 'canvas'
assert label.x_range_name == 'default'
assert label.y_range_name == 'default'
check_text_properties(label)
check_fill_properties(label, "background_", None, 1.0)
check_line_properties(label, "border_", None, 1.0, 1.0)
check_properties_existence(label, [
"plot",
"level",
"visible",
"x",
"y",
"x_units",
"y_units",
"text",
"angle",
"angle_units",
"x_offset",
"y_offset",
"render_mode",
"x_range_name",
"y_range_name"],
TEXT,
prefix('border_', LINE),
prefix('background_', FILL))
def add_north_direction(plot):
plot.add_layout(
Arrow(
end=VeeHead(
size=10, fill_color=colors["north"], line_color=colors["north"]
),
line_width=3,
x_start=0,
y_start=0,
x_end=0,
y_end=constants.PLOTS_RANGE,
line_color=colors["north"],
)
)
plot.add_layout(
Label(
text="North",
x=0.05 * constants.PLOTS_RANGE,
y=0.8 * constants.PLOTS_RANGE,
text_font_size="8pt",
text_color=colors["north"],
)
)
def add_vernal_direction(plot):
plot.add_layout(
Arrow(
end=VeeHead(
size=10, fill_color=colors["vernal"], line_color=colors["vernal"]
),
line_width=3,
x_start=0,
y_start=0,
x_end=constants.PLOTS_RANGE,
y_end=0,
line_color=colors["vernal"],
)
)
plot.add_layout(
Label(
text="Vernal Equinox",
x=0.5 * constants.PLOTS_RANGE,
y=-0.1 * constants.PLOTS_RANGE,
text_font_size="8pt",
text_color=colors["vernal"],
)
)
def _label_plot(label, h, w, angle=angle, **kwargs):
# a figure that simply holds a label
p = figure(output_backend='webgl',
plot_height=h,
plot_width=w,
title=None,
toolbar_location=None)
p.axis.visible = False
p.grid.visible = False
p.outline_line_color = None
p.toolbar.active_drag = None
p.x_range = Range1d(start=0.0, end=1.0)
p.y_range = Range1d(start=0.0, end=1.0)
if label is None:
label = ""
p.add_layout(
Label(x=0.5,
y=0.5,
text=label,
text_align='center',
text_baseline='middle',
angle=angle,
**kwargs))
return p
x = Span(location=x_mean,
dimension='height',
line_color='green',
line_alpha=0.7)
y = Span(location=y_mean,
dimension='width',
line_color='green',
line_alpha=0.7)
p.add_layout(x)
p.add_layout(y)
bg1 = BoxAnnotation(bottom=y_mean,
right=x_mean,
fill_alpha=0.1,
fill_color='olive')
label1 = Label(x=0.1, y=0.8, text='少量少打折', text_font_size='10pt')
p.add_layout(bg1)
p.add_layout(label1)
bg2 = BoxAnnotation(bottom=y_mean,
left=x_mean,
fill_alpha=0.1,
fill_color='firebrick')
label2 = Label(x=0.7, y=0.8, text='大量少打折', text_font_size='10pt')
p.add_layout(bg2)
p.add_layout(label2)
bg3 = BoxAnnotation(top=y_mean,
right=x_mean,
fill_alpha=0.1,
fill_color='firebrick')
line_dash=[6, 4])
y = Span(location=y_mean,
dimension='width',
line_color='green',
line_alpha=0.7,
line_width=1.5,
line_dash=[6, 4])
p4.add_layout(x)
p4.add_layout(y)
# 绘制辅助线
bg1 = BoxAnnotation(bottom=y_mean,
right=x_mean,
fill_alpha=0.1,
fill_color='olive')
label1 = Label(x=0.1, y=0.45, text="少量大打折", text_font_size="10pt")
p4.add_layout(bg1)
p4.add_layout(label1)
# 绘制第一象限
bg2 = BoxAnnotation(bottom=y_mean,
left=x_mean,
fill_alpha=0.1,
fill_color='firebrick')
label2 = Label(x=0.5, y=0.45, text="大量大打折", text_font_size="10pt")
p4.add_layout(bg2)
p4.add_layout(label2)
# 绘制第二象限
bg3 = BoxAnnotation(top=y_mean,
right=x_mean,
def test_Label_accepts_datetime_xy():
obj = Label(x=datetime(2018, 8, 7, 0, 0), y=datetime(2018, 8, 7, 0, 0))
assert obj.x == 1533600000000.0
assert obj.y == 1533600000000.0
def plotOutlierHistogram(dataframe, maxOutliers, func,
statisticalOutlierThreshold, userLatencyThreshold,
averageDuration, maxDuration):
global pixelsForTitle
global pixelsPerHeightUnit
global plotWidth
global timeUnitString
cds = ColumnDataSource(dataframe)
figureTitle = "Occurrences of " + func + " that took longer than " \
+ statisticalOutlierThreshold + "."
hover = HoverTool(
tooltips=[("interval start",
"@lowerbound{0,0}"), ("interval end", "@upperbound{0,0}")])
TOOLS = [hover, "tap, reset"]
p = figure(title = figureTitle, plot_width = plotWidth,
plot_height = min(500, (max(5, (maxOutliers + 1)) \
* pixelsPerHeightUnit + \
pixelsForTitle)),
x_axis_label = "Execution timeline (" + timeUnitString + ")",
y_axis_label = "Number of outliers",
tools = TOOLS, toolbar_location="above")
y_ticker_max = p.plot_height // pixelsPerHeightUnit
y_ticker_step = max(1, (maxOutliers + 1) // y_ticker_max)
y_upper_bound = (maxOutliers // y_ticker_step + 2) * y_ticker_step
p.yaxis.ticker = FixedTicker(
ticks=list(range(0, y_upper_bound, y_ticker_step)))
p.ygrid.ticker = FixedTicker(
ticks=list(range(0, y_upper_bound, y_ticker_step)))
p.xaxis.formatter = NumeralTickFormatter(format="0,")
p.y_range = Range1d(0, y_upper_bound)
p.quad(left='lowerbound',
right='upperbound',
bottom='bottom',
top='height',
color=funcToColor[func],
source=cds,
nonselection_fill_color=funcToColor[func],
nonselection_fill_alpha=1.0,
line_color="lightgrey",
selection_fill_color=funcToColor[func],
selection_line_color="grey")
p.x(x='markerX',
y='markerY',
size='markersize',
color='navy',
line_width=1,
source=cds)
# Add an annotation to the chart
#
y_max = dataframe['height'].max()
text = "Average duration: " + '{0:,.0f}'.format(averageDuration) + " " + \
timeUnitString + \
". Maximum duration: " + '{0:,.0f}'.format(maxDuration) + " " + \
timeUnitString + ". "
if (userLatencyThreshold is not None):
text = text + \
"An \'x\' shows intervals with operations exceeding " + \
"a user-defined threshold of " + \
userLatencyThreshold + "."
mytext = Label(x=0,
y=y_upper_bound - y_ticker_step,
text=text,
text_color="grey",
text_font="helvetica",
text_font_size="10pt",
text_font_style="italic")
p.add_layout(mytext)
url = "@bucketfiles"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return p
from bokeh.io import output_file, show
from bokeh.models.annotations import Label, LabelSet
from bokeh.models import ColumnDataSource
#prepare the output
output_file("students.html")
#crate columndatasource
source=ColumnDataSource(dict(average_grades=["B+","A","D-"],
exam_grades=["A+","C","D"],
student_names=["Stephan","Helder","Riazudidn"]))
#create the figure
f = figure(x_range=["F","D-","D","D+","C-","C","C+","B-","B","B+","A-","A","A+"],
y_range=["F","D-","D","D+","C-","C","C+","B-","B","B+","A-","A","A+"])
#add description label
description=Label(x=7,y=1,text="This graph shows average grades and exam grades for 3rd grade students",render_mode="css")
f.add_layout(description)
#add labels for glyphs
labels=LabelSet(x="average_grades",y="exam_grades",text="student_names",x_offset=5, y_offset=5, source=source)
f.add_layout(labels)
#create glyphs
f.circle(x="average_grades", y="exam_grades", source=source, size=8)
show(f)
line_width=2)
span_solid_average_boil = Span(location=solid_average_boil,
dimension='width',
line_color='red',
line_width=2)
# add the spans
# add the instance
f.add_layout(span_gas_average_boil)
f.add_layout(span_liquid_average_boil)
f.add_layout(span_solid_average_boil)
#Add labels to spans
label_span_gas_average_boil = Label(x=80,
y=gas_average_boil,
text="Gas average boiling point",
render_mode="css",
text_font_size="10px")
label_span_liquid_average_boil = Label(x=80,
y=liquid_average_boil,
text="Liquid average boiling point",
render_mode="css",
text_font_size="10px")
label_span_solid_average_boil = Label(x=80,
y=solid_average_boil,
text="Solid average boiling point",
render_mode="css",
text_font_size="10px")
#Add labels to figure
f.add_layout(label_span_gas_average_boil)
top=mean_discount,
left=mean_disper, # 设置矩形四边位置
fill_alpha=0.1,
fill_color='olive' # 设置透明度、颜色
)
p.add_layout(right_down)
## 添加注释
from bokeh.models.annotations import Label
# 设置四个注释的位置
label1 = Label(
x=0.1,
y=0.8, # 标注注释位置
#x_offset=12, # x偏移，同理y_offset
text="少量少打折", # 注释内容
text_font_size="12pt", # 字体大小
)
p.add_layout(label1)
label2 = Label(
x=0.5,
y=0.8, # 标注注释位置
#x_offset=12, # x偏移，同理y_offset
text="少量少打折", # 注释内容
text_font_size="12pt", # 字体大小
)
p.add_layout(label2)
label3 = Label(
def ex_7_create_label_annotations_for_span(self):
# Remove rows with NaN values and then map standard states to colors
elements.dropna(
inplace=True
) # if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements['size'] = elements['van der Waals radius'] / 10
# Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(
elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(
elements[elements['standard state'] == 'solid'])
# Define the output file path
output_file("elements_annotations.html")
# Create the figure object
f = figure()
# adding glyphs
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Liquid',
source=liquid)
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Solid',
source=solid)
# Add axis labels
f.xaxis.axis_label = "Atomic radius"
f.yaxis.axis_label = "Boiling point"
# Calculate the average boiling point for all three groups by dividing the sum by the number of values
gas_average_boil = sum(gas.data['boiling point']) / len(
gas.data['boiling point'])
liquid_average_boil = sum(liquid.data['boiling point']) / len(
liquid.data['boiling point'])
solid_average_boil = sum(solid.data['boiling point']) / len(
solid.data['boiling point'])
# Create three spans
span_gas_average_boil = Span(location=gas_average_boil,
dimension='width',
line_color='yellow',
line_width=2)
span_liquid_average_boil = Span(location=liquid_average_boil,
dimension='width',
line_color='orange',
line_width=2)
span_solid_average_boil = Span(location=solid_average_boil,
dimension='width',
line_color='red',
line_width=2)
# Add spans to the figure
f.add_layout(span_gas_average_boil)
f.add_layout(span_liquid_average_boil)
f.add_layout(span_solid_average_boil)
# Add labels to spans
label_span_gas_average_boil = Label(x=80,
y=gas_average_boil,
text="Gas average boiling point",
render_mode="css",
text_font_size="10px")
label_span_liquid_average_boil = Label(
x=80,
y=liquid_average_boil,
text="Liquid average boiling point",
render_mode="css",
text_font_size="10px")
label_span_solid_average_boil = Label(
x=80,
y=solid_average_boil,
text="Solid average boiling point",
render_mode="css",
text_font_size="10px")
# Add labels to figure
f.add_layout(label_span_gas_average_boil)
f.add_layout(label_span_liquid_average_boil)
f.add_layout(label_span_solid_average_boil)
# Save and show the figure
show(f)