def plot_expression(equation):
y = []
expressions = [equation.replace('x', str(i)) for i in range(600)]
for expression in expressions:
calc = Calculator(expression)
y.append(calc.calculate())
plot(y, title="Simple X-Y Graph")
def test_multi_series_plotting():
ys = [
[math.sin(i / (10 + i / 50)) - math.sin(i / 100) for i in range(1000)],
# Make sure we also support plotting series of different length
[math.sin(i / (10 + i / 50)) - math.sin(i / 100) - 1 for i in range(800)],
]
plot(ys, title="Double sine wave", color=True)
def set_uni(self, data, time):
y = data
yo = time
uni.plot([y, yo],
lines=1,
x_gridlines=[10, 20, 30],
y_gridlines=[0, 2, 4, 6, 8, 10])
def test_plotting_one_million_points():
nr_samples = 1_000_000
acceptable_time_in_seconds = 0.3
ys = np.random.normal(0, 1, nr_samples)
start_time = time.time()
plot(ys)
assert time.time() - start_time < acceptable_time_in_seconds
import pandas as pd
import numpy as np
import sys
argv = list(sys.argv)
from IPython.terminal.embed import InteractiveShellEmbed
my_shell = InteractiveShellEmbed()
result = my_shell.getoutput("pip install uniplot")
print(result)
result = my_shell.getoutput("cat output.log | grep %s | awk '{print $NF}'" % (argv[1]))
from uniplot import plot
result = list(map(float, result))
result = pd.DataFrame({0:result}).ewm(alpha=float(argv[2])).mean().fillna(0.0)[0].clip(0.0, 1e9)
plot(result)
def plot_beliefs(
sensors: List[Sensor],
start: datetime,
duration: timedelta,
belief_time_before: Optional[datetime],
source: Optional[DataSource],
):
"""
Show a simple plot of belief data directly in the terminal.
"""
sensors = list(sensors)
min_resolution = min([s.event_resolution for s in sensors])
# query data
beliefs_by_sensor = TimedBelief.search(
sensors=sensors,
event_starts_after=start,
event_ends_before=start + duration,
beliefs_before=belief_time_before,
source=source,
one_deterministic_belief_per_event=True,
resolution=min_resolution,
sum_multiple=False,
)
# only keep non-empty
for s in sensors:
if beliefs_by_sensor[s.name].empty:
click.echo(f"No data found for sensor '{s.name}' (Id: {s.id})")
beliefs_by_sensor.pop(s.name)
sensors.remove(s)
if len(beliefs_by_sensor.keys()) == 0:
click.echo("No data found!")
raise click.Abort()
first_df = beliefs_by_sensor[sensors[0].name]
# Build title
if len(sensors) == 1:
title = f"Beliefs for Sensor '{sensors[0].name}' (Id {sensors[0].id}).\n"
else:
title = f"Beliefs for Sensor(s) [{','.join([s.name for s in sensors])}], (Id(s): [{','.join([str(s.id) for s in sensors])}]).\n"
title += f"Data spans {naturaldelta(duration)} and starts at {start}."
if belief_time_before:
title += f"\nOnly beliefs made before: {belief_time_before}."
if source:
title += f"\nSource: {source.description}"
title += f"\nThe time resolution (x-axis) is {naturaldelta(min_resolution)}."
uniplot.plot(
[
beliefs.event_value
for beliefs in [beliefs_by_sensor[sn] for sn in [s.name for s in sensors]]
],
title=title,
color=True,
lines=True,
y_unit=first_df.sensor.unit
if len(beliefs_by_sensor) == 1
or all(sensor.unit == first_df.sensor.unit for sensor in sensors)
else "",
legend_labels=[s.name for s in sensors],
)
def test_normal_plotting():
x = [math.sin(i / 20) + i / 300 for i in range(600)]
plot(xs=x, ys=x, title="Sine wave")
def test_just_single_point_plotting():
"""
Testing this because this has caused problems since for a single point min == max
"""
x = [2.34]
plot(x)
def test_massively_multi_series_plotting():
x = [math.sin(i / 20) + i / 300 for i in range(600)]
xt = np.array([x]).T
plot(xt, title="Many colored dots", color=True)
def test_normal_plotting_with_x_series():
x = [math.sin(i / 20) + i / 300 for i in range(600)]
plot(xs=x, ys=x, title="Diagonal")
def test_random_line_plotting():
xs = np.random.normal(size=100)
ys = np.random.normal(size=100)
plot(xs=xs, ys=ys, lines=True)
