def update_plot(abs_earliest, abs_latest, abs_num_tasks, tasks):
num_buckets = 100
left, right, top = compute_utilizations(abs_earliest,
abs_latest,
abs_num_tasks,
tasks,
num_buckets)
time_series_source.data = {"left": left,
"right": right,
"top": top}
x_range = (max(0, min(left))
if len(left) else 0,
max(right) if len(right) else 1)
y_range = (0, max(top) + 1 if len(top) else 1)
# Define the axis ranges
x_range = helpers._get_range(x_range)
time_series_fig.x_range.start = x_range.start
time_series_fig.x_range.end = x_range.end
y_range = helpers._get_range(y_range)
time_series_fig.y_range.start = y_range.start
time_series_fig.y_range.end = num_cpus
# Push the updated data to the notebook
push_notebook(handle=handle)
def task_completion_time_update(abs_earliest, abs_latest, abs_num_tasks,
tasks):
if len(tasks) == 0:
return
# Create the distribution to plot
distr = []
for task_id, data in tasks.items():
distr.append(data["store_outputs_end"] -
data["get_arguments_start"])
# Create a histogram from the distribution
top, bin_edges = np.histogram(distr, bins="auto")
left = bin_edges[:-1]
right = bin_edges[1:]
source.data = {"top": top, "left": left, "right": right}
# Set the x and y ranges
x_range = (min(left) if len(left) else 0,
max(right) if len(right) else 1)
y_range = (0, max(top) + 1 if len(top) else 1)
x_range = helpers._get_range(x_range)
p.x_range.start = x_range.start
p.x_range.end = x_range.end
y_range = helpers._get_range(y_range)
p.y_range.start = y_range.start
p.y_range.end = y_range.end
# Push updates to the plot
push_notebook(handle=handle)
def update_plot(abs_earliest, abs_latest, abs_num_tasks, tasks):
num_buckets = 100
left, right, top = compute_utilizations(abs_earliest, abs_latest,
abs_num_tasks, tasks,
num_buckets)
time_series_source.data = {
"left": left,
"right": right,
"top": top
}
x_range = (max(0, min(left)) if len(left) else 0,
max(right) if len(right) else 1)
y_range = (0, max(top) + 1 if len(top) else 1)
# Define the axis ranges
x_range = helpers._get_range(x_range)
time_series_fig.x_range.start = x_range.start
time_series_fig.x_range.end = x_range.end
y_range = helpers._get_range(y_range)
time_series_fig.y_range.start = y_range.start
time_series_fig.y_range.end = num_cpus
# Push the updated data to the notebook
push_notebook(handle=handle)
def task_completion_time_update(abs_earliest,
abs_latest,
abs_num_tasks,
tasks):
if len(tasks) == 0:
return
# Create the distribution to plot
distr = []
for task_id, data in tasks.items():
distr.append(data["store_outputs_end"] -
data["get_arguments_start"])
# Create a histogram from the distribution
top, bin_edges = np.histogram(distr, bins="auto")
left = bin_edges[:-1]
right = bin_edges[1:]
source.data = {"top": top, "left": left, "right": right}
# Set the x and y ranges
x_range = (min(left) if len(left) else 0,
max(right) if len(right) else 1)
y_range = (0, max(top) + 1 if len(top) else 1)
x_range = helpers._get_range(x_range)
p.x_range.start = x_range.start
p.x_range.end = x_range.end
y_range = helpers._get_range(y_range)
p.y_range.start = y_range.start
p.y_range.end = y_range.end
# Push updates to the plot
push_notebook(handle=handle)
def test__get_range_with_float_bounds():
r = bph._get_range((1.2, 10))
assert isinstance(r, Range1d)
assert r.start == 1.2
assert r.end == 10
r = bph._get_range([1.2, 10])
assert isinstance(r, Range1d)
assert r.start == 1.2
assert r.end == 10
def test__get_range_with_float_bounds():
r = _get_range((1.2, 10))
assert isinstance(r, Range1d)
assert r.start == 1.2
assert r.end == 10
r = _get_range([1.2, 10])
assert isinstance(r, Range1d)
assert r.start == 1.2
assert r.end == 10
def test_get_range_with_pandas_group(pd):
from bokeh.sampledata.iris import flowers
g = flowers.groupby('species')
r = bph._get_range(g)
assert isinstance(r, FactorRange)
assert r.factors == ['setosa', 'versicolor',
'virginica'] # should always be sorted
def test__get_range_with_string_seq():
f = ["foo" ,"end", "baz"]
for t in [list, tuple]:
r = _get_range(t(f))
assert isinstance(r, FactorRange)
# FactorRange accepts Seq, but _get_range always sets a list copy
assert r.factors == f
def test__get_range_with_string_seq():
f = ["foo", "end", "baz"]
for t in [list, tuple]:
r = bph._get_range(t(f))
assert isinstance(r, FactorRange)
# FactorRange accepts Seq, but _get_range always sets a list copy
assert r.factors == f
def test__get_range_with_series(pd):
r = bph._get_range(pd.Series([20, 30]))
assert isinstance(r, Range1d)
assert r.start == 20
assert r.end == 30
def test__get_range_with_ndarray_factors():
f = np.array(["Crosby", "Stills", "Nash", "Young"])
r = bph._get_range(f)
assert isinstance(r, FactorRange)
assert r.factors == list(f)
def test__get_range_with_too_long_ndarray():
with pytest.raises(ValueError):
bph._get_range(np.array([10, 20, 30]))
def test__get_range_with_ndarray():
r = bph._get_range(np.array([10, 20]))
assert isinstance(r, Range1d)
assert r.start == 10
assert r.end == 20
def test__get_range_with_Range():
for t in [Range1d, DataRange1d, FactorRange]:
rng = t()
r = bph._get_range(rng)
assert r is rng
def test__get_range_with_ndarray():
import numpy as np
r = _get_range(np.array([10, 20]))
assert isinstance(r, Range1d)
assert r.start == 10
assert r.end == 20
def test__get_range_with_ndarray():
import numpy as np
r = _get_range(np.array([10, 20]))
assert isinstance(r, Range1d)
assert r.start == 10
assert r.end == 20
def test__get_range_with_series(pd):
r = _get_range(pd.Series([20, 30]))
assert isinstance(r, Range1d)
assert r.start == 20
assert r.end == 30
def test__get_range_with_too_long_series(pd):
with pytest.raises(ValueError):
_get_range(pd.Series([20, 30, 40]))
def test__get_range_with_ndarray():
r = bph._get_range(np.array([10, 20]))
assert isinstance(r, Range1d)
assert r.start == 10
assert r.end == 20
def test_get_range_with_pandas_group():
from bokeh.sampledata.iris import flowers
g = flowers.groupby('species')
r = _get_range(g)
assert isinstance(r, FactorRange)
assert r.factors == ['setosa', 'versicolor', 'virginica'] # should always be sorted
def test__get_range_with_None():
r = _get_range(None)
assert isinstance(r, DataRange1d)
def test__get_range_with_ndarray_factors():
import numpy as np
f = np.array(["Crosby", "Stills", "Nash", "Young"])
r = _get_range(f)
assert isinstance(r, FactorRange)
assert r.factors == list(f)
def test__get_range_with_too_long_series(pd):
with pytest.raises(ValueError):
bph._get_range(pd.Series([20, 30, 40]))
def test__get_range_with_too_long_ndarray():
with pytest.raises(ValueError):
_get_range(np.array([10, 20, 30]))
def test__get_range_with_None():
r = bph._get_range(None)
assert isinstance(r, DataRange1d)
p1.title.align = 'center'
hover_tool = HoverTool(
renderers=[plot1],
tooltips=[
('Time (y)', '@time'),
('Discounted TRI', '@discounted_tri'),
('Upper CI', '@upper_ci'),
('Lower CI', '@lower_ci')
],
mode='vline',
)
p1.add_tools(hover_tool)
p1.add_tools(HoverTool(renderers=[plot2, plot3], mode='vline', tooltips=[]))
p1.x_range = _get_range([0, reader.number_of_projection_time_steps])
p1.y_range = _get_range([0.8, 1.2])
theme = Theme(json={
'attrs': {
'Figure': {
'background_fill_color': '#f9f4f4',
# 'border_fill_color': '#e6e6e6',
'outline_line_color': '#444444'
},
# 'Axis': {
# 'axis_line_color': "black",
# 'axis_label_text_color': "black",
# 'major_label_text_color': "black",
# 'major_tick_line_color': "black",
# 'minor_tick_line_color': "black",
def test__get_range_with_Range():
for t in [Range1d, DataRange1d, FactorRange]:
rng = t()
r = _get_range(rng)
assert r is rng
