def add_multiline_to_plot(figure: 'Bokeh Figure', legend: tuple, column_data_source: 'CDS for Bokeh figure', line_width: int=2, line_1_color: str='#CC0000', line_2_color: str='orange') -> 'Bokeh Figure':
"""Adds multiple lines on Bokeh figure, initializes legend and return Bokeh figure."""
figure.line(x='x', y='y1', line_width=line_width, line_color=line_1_color, legend=legend[0], source=column_data_source)
figure.line(x='x', y='y2', line_width=line_width, line_color=line_2_color, legend=legend[1], source=column_data_source)
figure = DataVisualizationUtils.initialise_legend_properties(figure)
return figure
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 render_bytes_sent_received(
figure: Figure, df: pd.DataFrame, label: str, read_col: str, write_col: str
):
time = df[DATETIME_KEY]
def accumulate(column):
values = df[column]
values = values - values.min()
return resample(values, time)
read = accumulate(read_col)
write = accumulate(write_col)
data = ColumnDataSource(
dict(rx=read, rx_kb=read / 1024, tx=write, tx_kb=write / 1024, x=read.index)
)
tooltips = [("RX", "@rx_kb KiB"), ("TX", "@tx_kb KiB")]
figure.add_tools(HoverTool(tooltips=tooltips))
figure.yaxis[0].formatter = NumeralTickFormatter(format="0.0b")
y_max = max(max(data.data["rx"]), max(data.data["tx"]))
figure.y_range = Range1d(0, y_max + 0.01)
figure.line(
x="x", y="rx", color="blue", legend_label="{} RX".format(label), source=data
)
figure.line(
x="x", y="tx", color="red", legend_label="{} TX".format(label), source=data
)
def render(**figure_kwargs):
figure = Figure(**figure_kwargs)
figure.add_layout(Legend())
figure.legend.location = "top_left"
figure.legend.click_policy = "hide"
figure.line(legend_label=yfast,
source=onesource,
x='datetime',
y=yfast,
color='red')
figure.line(legend_label=yslow,
source=onesource,
x='datetime',
y=yslow,
color='blue')
figure.segment(legend_label=y1hist,
source=onesource,
x0='datetime',
x1='datetime',
y0=0,
y1=y1hist,
line_width=6,
color='black',
alpha=0.5)
return figure
def step(counts, bins, label, color, figure):
'''
Add a step plot to 'figure', inplace of a bar plot
Input:
- counts : array-like
Y-axis values
- bins : array-like
Limiting values for x-axis bins
- label : string
Label for this distribution
- color : string
Code defining a color; see ~booq.plot.Colors
- figure : ~pandas.plotting.figure
Output:
- Figure : ~bokeh.plotting.figure
'''
_y,_x = histogram2stepfunction(counts,bins)
figure.line(x=_x,
y=_y,
#line_color="#D95B43",line_width=2,
line_color=color,line_width=2,
legend='label')
import numpy as np
_x = np.diff(bins)/2+bins[:-1]
figure.circle(x=_x,
y=_y,
#line_color="#D95B43",line_width=2,
line_color=color,line_width=2,
fill_color="white",fill_alpha=1,
size=9,
legend='label')
return figure
def plot_pleak(figure, section):
'''
Plot list of peaks given by the user
'''
# Get all values from columns plotted by user to find minimum value
all_values = [
plotObject.data.loc[:, col]
for col in plotObject.plots[section]['columns']
]
all_values = [j for i in all_values for j in i if not pd.isna(j)]
min_value = np.min(all_values)
min_value = min_value - abs(0.1 * min_value)
max_value = np.max(all_values) * 1.1
y_axis = (min_value, 0, max_value)
for peak, peak_name in plotObject.peaks:
x_axis = np.ones_like(y_axis) * peak
figure.line(x_axis,
y_axis,
line_color='green',
line_width=2,
name=peak_name)
return figure
def render_global_percent_resource_usage(
figure: Figure,
source: ColumnDataSource,
report: ClusterReport,
hostnames: List[str],
):
node_count = len(hostnames)
figure.y_range = Range1d(0, node_count + 1)
figure.yaxis.axis_label = "Usage (%)"
# Draw resource usage
colors = select_colors(hostnames)
figure.vbar_stack(
hostnames,
x="x_start",
width=1000.0,
color=colors,
line_color="black",
legend_label=[get_node_description(report, hostname) for hostname in hostnames],
source=source,
)
# Draw ceiling
figure.line(
source.data["x"],
[node_count] * len(source.data["x"]),
color="red",
line_width=2,
)
def bscoreCumulative(self, figurename, dataname):
"""-----------------------------------------------------------------------------------------
Bokeh plot of cumulative distribution as a line on right hand axis
:param figurename: string, name of figures (stored in self.figure)
:param dataname: string, name of data frame (stored in self.frame)
:return: True
-----------------------------------------------------------------------------------------"""
data = self.frame[dataname]
figure = self.figure[figurename]
source = ColumnDataSource(data)
figure.extra_y_ranges = {"cumulative": Range1d(start=0.0, end=1.0)}
axis2 = LinearAxis(y_range_name="cumulative")
axis2.ticker.num_minor_ticks = 10
figure.add_layout(axis2, 'right')
figure.line(source=source,
x='score',
y='cumulative',
y_range_name='cumulative',
line_width=2,
color='#1122cc')
# shaded box showing 95% level
box = BoxAnnotation(bottom=0.95,
top=1.0,
y_range_name='cumulative',
fill_color='#FFBBBB',
line_width=3,
line_dash='dashed')
figure.add_layout(box)
return True
def bokeh_draw_court(figure, line_width=1, line_color='gray'):
"""Returns a figure with the basketball court lines drawn onto it"""
# hoop
figure.circle(x=0, y=0, radius=7.5, fill_alpha=0,
line_color=line_color, line_width=line_width)
# backboard
figure.line(x=range(-30,31), y=-7.5, line_color=line_color)
# The paint
# outerbox
figure.rect(x=0, y=47.5, width=160, height=190,fill_alpha=0,
line_color=line_color, line_width=line_width)
# innerbox
# left inner box line
figure.line(x=-60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# right inner box line
figure.line(x=60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# Restricted Zone
figure.arc(x=0, y=0, radius=40, start_angle=pi, end_angle=0,
line_color=line_color, line_width=line_width)
# top free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=pi, end_angle=0,
line_color=line_color)
# bottome free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_dash="dashed")
# Three point line
# corner three point lines
figure.line(x=-220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
figure.line(x=220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
# # three point arc
figure.arc(x=0, y=0, radius=237.5, start_angle=3.528, end_angle=-0.3863,
line_color=line_color, line_width=line_width)
# add center court
# outer center arc
figure.arc(x=0, y=422.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# inner center arct
figure.arc(x=0, y=422.5, radius=20, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# outer lines, consistting of half court lines and out of bounds
# lines
figure.rect(x=0, y=187.5, width=500, height=470, fill_alpha=0,
line_color=line_color, line_width=line_width)
return figure
def bokeh_draw_court(figure, line_width=1, line_color='gray'):
"""Returns a figure with the basketball court lines drawn onto it"""
# hoop
figure.circle(x=0, y=0, radius=7.5, fill_alpha=0,
line_color=line_color, line_width=line_width)
# backboard
figure.line(x=range(-30,31), y=-7.5, line_color=line_color)
# The paint
# outerbox
figure.rect(x=0, y=47.5, width=160, height=190,fill_alpha=0,
line_color=line_color, line_width=line_width)
# innerbox
# left inner box line
figure.line(x=-60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# right inner box line
figure.line(x=60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# Restricted Zone
figure.arc(x=0, y=0, radius=40, start_angle=pi, end_angle=0,
line_color=line_color, line_width=line_width)
# top free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=pi, end_angle=0,
line_color=line_color)
# bottome free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_dash="dashed")
# Three point line
# corner three point lines
figure.line(x=-220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
figure.line(x=220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
# # three point arc
figure.arc(x=0, y=0, radius=237.5, start_angle=3.528, end_angle=-0.3863,
line_color=line_color, line_width=line_width)
# add center court
# outer center arc
figure.arc(x=0, y=422.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# inner center arct
figure.arc(x=0, y=422.5, radius=20, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# outer lines, consistting of half court lines and out of bounds
# lines
figure.rect(x=0, y=187.5, width=500, height=470, fill_alpha=0,
line_color=line_color, line_width=line_width)
return figure
def render_node_network_connections(figure: Figure, df: pd.DataFrame):
connections_series = df[NETWORK_CONNECTIONS_KEY]
connections = resample(connections_series, df[DATETIME_KEY])
figure.y_range = Range1d(0, connections_series.max() + 10)
figure.add_tools(HoverTool(tooltips=[("Connection count", "@count")]))
data = ColumnDataSource(dict(count=connections, x=connections.index))
figure.line(x="x", y="count", legend_label="Network connections", source=data)
def render_node_mem_pct_utilization(figure: Figure, df: pd.DataFrame):
mem = resample(df[MEM_KEY], df[DATETIME_KEY])
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
figure.add_tools(HoverTool(tooltips=[("Memory", "@mem")]))
data = ColumnDataSource(dict(mem=mem / 100.0, x=mem.index))
figure.line(x="x", y="mem", color="red", legend_label="Memory", source=data)
def addPlotsToFigure(figure, section):
"""
Add lines or scatter to the figure
"""
try:
figure.xaxis.ticker.desired_num_ticks = 30
except AttributeError as err:
logging.info(f"AttributeError: {err}")
print("bokeh package needs to be updated (pip install bokeh -U)")
sys.exit()
for i, column in enumerate(plotObject.plots[section]["columns"]):
# assert that it is present in dataframe
if column not in plotObject.data.columns:
logging.info(f"Column not found: {column}")
continue
# get boolean with rows without na. Only those will be plotted
bool_not_na = (~pd.isna(plotObject.data.loc[:, column])).to_list()
if re.search(r"^line$", plotObject.plots[section]['plotType'][i],
re.IGNORECASE):
# If line...
figure.line(
plotObject.data.loc[bool_not_na,
plotObject.plots[section]['x_axis']],
plotObject.data.loc[bool_not_na, column],
line_width=2,
color=d3['Category20'][20][plotObject.color],
legend_label=column,
name=column)
elif re.search(r"^scatter$", plotObject.plots[section]['plotType'][i],
re.IGNORECASE):
# If scatter...
figure.circle(
plotObject.data.loc[bool_not_na,
plotObject.plots[section]['x_axis']],
plotObject.data.loc[bool_not_na, column],
size=1,
color=d3['Category20'][20][plotObject.color],
legend_label=column,
name=column)
# Change color for the next plot
plotObject.color += 2 if plotObject.color < 18 else 17
# plot threshold
figure = plot_threshold(figure, section)
# plot peaks of interest
figure = plot_pleak(figure, section)
return figure
def second_limit(H_fit1_file, H_fit2_df, figure=None):
Xs2 = np.asarray(v_serie)
Xs2 = 1 / Xs2
Ys2 = H_fit1_file.to_numpy()
Ys2 = np.array(Ys2, dtype='float')
# Main loop for 2nd extrapolation and plotting the figure
for T2 in np.arange(0, len(T_serie), 1):
poly_X = np.append(
Xs2, 0) # Adding a 0 to the X set to have the value at this point
if len(v_serie) > 1: # Fit only if there are enough points to do so.
coeff_poly = np.polyfit(
Xs2, Ys2[:, T2],
fit_order_2) # Fitting a 4th order polynom on the data
poly_Y = np.polyval(
coeff_poly, poly_X
) # Calculating the values for each point and get the value at 0
# The point at X=0 is at the end of the array, putting it at the right place in the dataframe.
H_fit2_df.iloc[[0], [T2]] = poly_Y[-1]
else: # If only one point, just take the value of the point.
H_fit2_df.iloc[[0], [T2]] = Ys2[[0], [T2]]
poly_Y = np.concatenate((Ys2[[0], [T2]], Ys2[[0], [T2]]))
if figure != None:
figure.square(
Xs2,
Ys2[:, T2], # Data points
legend_label="T Values",
line_width=1,
color='black',
size=10,
fill_color='white')
figure.line(
poly_X,
poly_Y, # Lines to check the quality of the fit.
legend_label="Fit",
line_width=2,
color=colors[T2])
figure.diamond(0,
poly_Y[-1],
line_width=1,
color='black',
size=15,
fill_color='white')
return H_fit2_df
def render_node_per_cpu_pct_utilization(figure: Figure, df: pd.DataFrame):
time = df[DATETIME_KEY]
cpu_series = df[CPU_KEY]
cpu_count = len(cpu_series.iloc[0])
cpus = [
resample(cpu_series.apply(lambda res: res[i]), time) for i in range(cpu_count)
]
cpu_mean = resample(cpu_series.apply(lambda res: average(res)), time)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
data = {}
tooltips = []
for (i, cpu) in enumerate(cpus):
key = f"cpu_{i}"
data[key] = cpu / 100.0
data[f"{key}_x"] = cpu.index
tooltips.append((f"CPU #{i}", f"@{key}"))
data["cpu_mean"] = cpu_mean / 100.0
data["cpu_mean_x"] = cpu_mean.index
tooltips.append(("CPU avg.", "@cpu_mean"))
figure.add_tools(HoverTool(tooltips=tooltips))
data = ColumnDataSource(data)
colors = select_colors(cpus)
for (i, color) in enumerate(colors):
figure.line(
x=f"cpu_{i}_x",
y=f"cpu_{i}",
color=color,
legend_label=f"CPU #{i}",
source=data,
)
figure.line(
x="cpu_mean_x",
y="cpu_mean",
color="red",
legend_label="Average CPU",
line_dash="dashed",
line_width=5,
source=data,
)
def plot_threshold(figure, section):
'''
Plot thresholds
'''
min_mz = np.min(plotObject.data.loc[:, plotObject.plots[section]['x_axis']])
max_mz = np.max(plotObject.data.loc[:, plotObject.plots[section]['x_axis']])
x_axis = (min_mz, max_mz)
for threshold_i in plotObject.plots[section]['threshold']:
y_axis = np.ones_like(x_axis)*threshold_i
figure.line(x_axis, y_axis, line_color='black', line_dash="4 4", name="Threshold")
return figure
def make_new_graph(self):
"""
Creates a new set of graphs.
"""
self.active_lines = {}
legend_strings = []
figure = default_figure(self.get_figure_opts())
for y, i in zip(self.key_group, range(len(self.key_group))):
print("make_new_graph(): plotting {}".format(y))
if y == self.indep_var:
print("make_new_graph(): y={} is the indep var".format(y))
continue
print("make_new_graph(): y={} is a dep var".format(y))
# storing the line renderer objects will allow us to update the
# lines without triggering a page redraw
line = figure.line(self.indep_var, y, source=self.source,
color=self.colors[i], line_width=1.5)
self.active_lines[y] = line
legend_strings.append((y, [line]))
l = Legend(legends=legend_strings, location=(0, 0))
figure.add_layout(l, 'right')
return figure
def third_limit(H_fit2_file, figure=None):
Xs3 = np.asarray(
T_serie) * 1 # sampling value to get the T in s (sampling is 0.001)
Xs3 = 1 / Xs3
Ys3 = H_fit2_file.to_numpy()
Ys3 = Ys3.reshape(
len(Xs3)) * Xs3 # Ys3 is a 2 dimensional array with only one row.
if figure != None:
figure.diamond(Xs3,
Ys3,
legend_label="R",
line_width=1,
color='black',
size=15,
fill_color='white')
Xs3 = Xs3[:n_points]
Ys3 = Ys3[:n_points]
coeff_poly = np.polyfit(
Xs3, Ys3, fit_order_3) # Linear fit on the data in the paper.
poly_X = np.append(
Xs3, 0) # Adding a 0 to the X set to have the value at this point.
poly_Y = np.polyval(
coeff_poly,
poly_X) # Calculating the values for each point and get the value at 0
# The point at X=0 is at the end of the array.
R = poly_Y[-1]
if figure != None:
figure.line(poly_X,
poly_Y,
legend_label="R= " + "{:6.4f}".format(R),
line_width=2,
color='red')
figure.legend.location = "bottom_right"
return R
def draw_bytes(title, read_col, write_col):
if resources.iloc[0].get(read_col) is None or resources.iloc[
0].get(write_col) is None:
return
def accumulate(column):
values = resources.apply(lambda res: res[column])
values = values - values.min()
return resample(values, time)
read = accumulate(read_col)
write = accumulate(write_col)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0.0b")
figure.line(read.index,
read,
color="blue",
legend_label="{} RX".format(title))
figure.line(write.index,
write,
color="red",
legend_label="{} TX".format(title))
def bokeh_draw_court(figure, line_color='gray', line_width=1):
"""Returns a figure with the basketball court lines drawn onto it
This function draws a court based on the x and y-axis values that the NBA
stats API provides for the shot chart data. For example the center of the
hoop is located at the (0,0) coordinate. Twenty-two feet from the left of
the center of the hoop in is represented by the (-220,0) coordinates.
So one foot equals +/-10 units on the x and y-axis.
Parameters
----------
figure : Bokeh figure object
The Axes object to plot the court onto.
line_color : str, optional
The color of the court lines. Can be a a Hex value.
line_width : float, optional
The linewidth the of the court lines in pixels.
Returns
-------
figure : Figure
The Figure object with the court on it.
"""
# hoop
figure.circle(x=0,
y=0,
radius=7.5,
fill_alpha=0,
line_color=line_color,
line_width=line_width)
# backboard
figure.line(x=range(-30, 31), y=-12.5, line_color=line_color)
# The paint
# outerbox
figure.rect(x=0,
y=47.5,
width=160,
height=190,
fill_alpha=0,
line_color=line_color,
line_width=line_width)
# innerbox
# left inner box line
figure.line(x=-60,
y=np.arange(-47.5, 143.5),
line_color=line_color,
line_width=line_width)
# right inner box line
figure.line(x=60,
y=np.arange(-47.5, 143.5),
line_color=line_color,
line_width=line_width)
# Restricted Zone
figure.arc(x=0,
y=0,
radius=40,
start_angle=pi,
end_angle=0,
line_color=line_color,
line_width=line_width)
# top free throw arc
figure.arc(x=0,
y=142.5,
radius=60,
start_angle=pi,
end_angle=0,
line_color=line_color)
# bottome free throw arc
figure.arc(x=0,
y=142.5,
radius=60,
start_angle=0,
end_angle=pi,
line_color=line_color,
line_dash="dashed")
# Three point line
# corner three point lines
figure.line(x=-220,
y=np.arange(-47.5, 92.5),
line_color=line_color,
line_width=line_width)
figure.line(x=220,
y=np.arange(-47.5, 92.5),
line_color=line_color,
line_width=line_width)
# # three point arc
figure.arc(x=0,
y=0,
radius=237.5,
start_angle=3.528,
end_angle=-0.3863,
line_color=line_color,
line_width=line_width)
# add center court
# outer center arc
figure.arc(x=0,
y=422.5,
radius=60,
start_angle=0,
end_angle=pi,
line_color=line_color,
line_width=line_width)
# inner center arct
figure.arc(x=0,
y=422.5,
radius=20,
start_angle=0,
end_angle=pi,
line_color=line_color,
line_width=line_width)
# outer lines, consistting of half court lines and out of bounds lines
figure.rect(x=0,
y=187.5,
width=500,
height=470,
fill_alpha=0,
line_color=line_color,
line_width=line_width)
return figure
def first_limit(H_file, H_fit1_df, figure=None):
for value_v in iterate_on_v:
# Selecting each subset of the dataframe corresponding to the desired v value.
H_subset = H_file[H_file['v_value'] == v_serie[value_v]]
# Shifting to numpy for the fit.
H_np_subset = H_subset.to_numpy()
# Building all the Xs values for the fits. Repetition of the same array, as the X values are all the same.
Xs = H_np_subset[:, 1] # Size values are the 2nd column, index = 1
Xs = 1 / Xs # Inverting the values as the graph shows 1/size
poly_X = np.insert(
Xs, 0,
0) # Adding a 0 to the X set to have the value at this point
# Hard coded indexes to take all the T_values, should be ok as long as I do not change the dataframe structure
H_Ys = H_np_subset[:, 2:np.shape(H_np_subset)[1]]
# Creating a figure for the desired v value.
if (v_serie[value_v] == watch_v and figure != None):
ListY = H_Ys.T.tolist()
for plots in np.arange(0, len(ListY), 1):
figure.circle(Xs,
ListY[plots],
legend_label="All T For v=" + str(watch_v),
line_width=1,
color='black',
size=10,
fill_color='white')
#This is the main loop, populating the array with the fitted values
for T in np.arange(0, len(T_serie), 1):
if len(s_serie
) > 1: # Fit only if there are enough points to do so.
H_coeff_poly = np.polyfit(
Xs, H_Ys[:,
T], fit_order_1) # Fitting a polynom on the data
H_poly_Y = np.polyval(
H_coeff_poly, poly_X
) # Calculating the values for each point and get the value at 0
# The point at X=0 is at the beginning of the array, putting it at the right place in the dataframe.
H_fit1_df.iloc[[value_v], [T]] = H_poly_Y[0]
else: # If only one point, just take the value of the point.
H_fit1_df.iloc[[value_v], [T]] = H_Ys[:, T]
H_poly_Y = H_Ys[:, T]
# Showing the fit for the desired v_value
if (v_serie[value_v] == watch_v and figure != None):
figure.line(
poly_X,
H_poly_Y, # Adding points to check the quality of the fit.
legend_label="Fits",
line_width=2,
color=colors[T])
figure.square(0,
H_poly_Y[0],
line_width=1,
color='black',
size=10,
fill_color='white')
return H_fit1_df
def _draw_court(self, figure, line_width=1):
import numpy as np
pi = 3.14
# hoop
figure.circle(x=0,
y=0,
radius=7.5,
fill_alpha=0,
line_color=self.f_color,
line_width=line_width)
# backboard
figure.line(x=range(-30, 31), y=-12.5, line_color=self.f_color)
# The paint
# outerbox
figure.rect(x=0,
y=47.5,
width=160,
height=190,
fill_alpha=0,
line_color=self.f_color,
line_width=line_width)
# innerbox
# left inner box line
figure.line(x=-60,
y=np.arange(-47.5, 143.5),
line_color=self.f_color,
line_width=line_width)
# right inner box line
figure.line(x=60,
y=np.arange(-47.5, 143.5),
line_color=self.f_color,
line_width=line_width)
# Restricted Zone
figure.arc(x=0,
y=0,
radius=40,
start_angle=pi,
end_angle=0,
line_color=self.f_color,
line_width=line_width)
# top free throw arc
figure.arc(x=0,
y=142.5,
radius=60,
start_angle=pi,
end_angle=0,
line_color=self.f_color)
# bottome free throw arc
figure.arc(x=0,
y=142.5,
radius=60,
start_angle=0,
end_angle=pi,
line_color=self.f_color,
line_dash="dashed")
# Three point line
# corner three point lines
figure.line(x=-220,
y=np.arange(-47.5, 92.5),
line_color=self.f_color,
line_width=line_width)
figure.line(x=220,
y=np.arange(-47.5, 92.5),
line_color=self.f_color,
line_width=line_width)
# # three point arc
figure.arc(x=0,
y=0,
radius=237.5,
start_angle=3.528,
end_angle=-0.3863,
line_color=self.f_color,
line_width=line_width)
return figure
x_values.append(walk[0])
y_values.append(walk[1])
path.append(walk)
min_distance = min(distances)
max_distance = max(distances)
distances_length = len(distances)
distances_sum = 0
for i in distances:
distances_sum += i
avg_distance = int(distances_sum / distances_length)
print(path)
print(f'Minimun distance: {min_distance}')
print(f'Maximun distance: {max_distance}')
print(f'average distance: {avg_distance}')
return x_values, y_values
if __name__ == "__main__":
number_steps = int(input('How many steps do you want to walk: '))
number_reps = int(input('How many reps do you want to do: '))
main(number_steps, number_reps)
output_file('simple__graphic.html')
figure = figure()
figure.line(x_values, y_values, line_width=2)
show(figure)
def bokeh_draw_court(figure, line_color='gray', line_width=1):
"""Returns a figure with the basketball court lines drawn onto it
This function draws a court based on the x and y-axis values that the NBA
stats API provides for the shot chart data. For example the center of the
hoop is located at the (0,0) coordinate. Twenty-two feet from the left of
the center of the hoop in is represented by the (-220,0) coordinates.
So one foot equals +/-10 units on the x and y-axis.
Parameters
----------
figure : Bokeh figure object
The Axes object to plot the court onto.
line_color : str, optional
The color of the court lines. Can be a a Hex value.
line_width : float, optional
The linewidth the of the court lines in pixels.
Returns
-------
figure : Figure
The Figure object with the court on it.
"""
# hoop
figure.circle(x=0, y=0, radius=7.5, fill_alpha=0,
line_color=line_color, line_width=line_width)
# backboard
figure.line(x=range(-30, 31), y=-12.5, line_color=line_color)
# The paint
# outerbox
figure.rect(x=0, y=47.5, width=160, height=190, fill_alpha=0,
line_color=line_color, line_width=line_width)
# innerbox
# left inner box line
figure.line(x=-60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# right inner box line
figure.line(x=60, y=np.arange(-47.5, 143.5), line_color=line_color,
line_width=line_width)
# Restricted Zone
figure.arc(x=0, y=0, radius=40, start_angle=pi, end_angle=0,
line_color=line_color, line_width=line_width)
# top free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=pi, end_angle=0,
line_color=line_color)
# bottome free throw arc
figure.arc(x=0, y=142.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_dash="dashed")
# Three point line
# corner three point lines
figure.line(x=-220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
figure.line(x=220, y=np.arange(-47.5, 92.5), line_color=line_color,
line_width=line_width)
# # three point arc
figure.arc(x=0, y=0, radius=237.5, start_angle=3.528, end_angle=-0.3863,
line_color=line_color, line_width=line_width)
# add center court
# outer center arc
figure.arc(x=0, y=422.5, radius=60, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# inner center arct
figure.arc(x=0, y=422.5, radius=20, start_angle=0, end_angle=pi,
line_color=line_color, line_width=line_width)
# outer lines, consistting of half court lines and out of bounds lines
figure.rect(x=0, y=187.5, width=500, height=470, fill_alpha=0,
line_color=line_color, line_width=line_width)
return figure
def draw(args, figure):
node, frame, method = args
time = frame["datetime"]
resources = frame["resources"]
if method == "cpu":
figure.plot_width = 950
figure.plot_height = 400
else:
figure.plot_width = 950
figure.plot_height = 400
figure.min_border_right = 20
if method in ("cpu", "mem"):
figure.y_range = Range1d(0, 1)
if len(resources) == 0:
return
def draw_bytes(title, read_col, write_col):
if resources.iloc[0].get(read_col) is None or resources.iloc[
0].get(write_col) is None:
return
def accumulate(column):
values = resources.apply(lambda res: res[column])
values = values - values.min()
return resample(values, time)
read = accumulate(read_col)
write = accumulate(write_col)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0.0b")
figure.line(read.index,
read,
color="blue",
legend_label="{} RX".format(title))
figure.line(write.index,
write,
color="red",
legend_label="{} TX".format(title))
if method == "cpu":
cpu_count = len(resources.iloc[0]["cpu"])
cpus = [
resample(resources.apply(lambda res: res["cpu"][i]), time)
for i in range(cpu_count)
]
cpu_mean = resample(
resources.apply(lambda res: average(res["cpu"])), time)
processes = (process_name(p) for p in report.cluster.nodes[node]
if process_name(p))
figure.title = Title(
text="{}: {}".format(node, ",".join(processes)))
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
palette = d3["Category20"][20]
for (i, cpu) in enumerate(cpus):
color = palette[i % 20]
figure.line(cpu.index,
cpu / 100.0,
color=color,
legend_label=f"CPU #{i}")
figure.line(cpu_mean.index,
cpu_mean / 100.0,
color="red",
legend_label=f"Average CPU",
line_dash="dashed",
line_width=5)
elif method == "mem":
mem = resample(resources.apply(lambda res: res["mem"]), time)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.line(mem.index,
mem / 100.0,
color="red",
legend_label="Memory")
elif method == "network":
draw_bytes("Net", "net-read", "net-write")
elif method == "net-connections":
connections = resample(
resources.apply(lambda res: res["connections"]), time)
figure.line(connections.index,
connections,
legend_label="Network connections")
elif method == "io":
draw_bytes("Disk", "disk-read", "disk-write")
