def test_scatterplot2(self):
s = self.scheduler()
random = RandomTable(2, rows=2000000, scheduler=s)
sp = MCScatterPlot(scheduler=s,
classes=[('Scatterplot', '_1', '_2')],
approximate=True)
sp.create_dependent_modules(random, 'table', with_sampling=False)
cnt = Every(proc=self.terse, constant_time=True, scheduler=s)
cnt.input.df = random.output.table
prt = Print(proc=self.terse, scheduler=s)
prt.input.df = sp.output.table
decorate(s, VariablePatch1("variable_1"))
decorate(s, VariablePatch2("variable_2"))
decorate(s, ScatterPlotPatch("mc_scatter_plot_1"))
sp.scheduler().start(idle_proc=idle_proc)
s.join()
js = sp.to_json()
x, y, _ = zip(*js['sample']['data'])
min_x = min(x)
max_x = max(x)
min_y = min(y)
max_y = max(y)
self.assertGreaterEqual(min_x, LOWER_X)
self.assertGreaterEqual(min_y, LOWER_Y)
self.assertLessEqual(max_x, UPPER_X)
self.assertLessEqual(max_y, UPPER_Y)
def test_scatterplot2(self) -> None:
s = self.scheduler(clean=True)
with s:
random = RandomTable(2, rows=2000000, throttle=1000, scheduler=s)
sp = MCScatterPlot(scheduler=s,
classes=[("Scatterplot", "_1", "_2")],
approximate=True)
sp.create_dependent_modules(random, "result", with_sampling=False)
cnt = Every(proc=self.terse, constant_time=True, scheduler=s)
cnt.input[0] = random.output.result
prt = Print(proc=self.terse, scheduler=s)
prt.input[0] = sp.output.result
async def fake_input_1(scheduler: Scheduler, rn: int) -> None:
module = scheduler["dyn_var_1"]
print("from input dyn_var_1")
await module.from_input({"x": LOWER_X, "y": LOWER_Y})
async def fake_input_2(scheduler: Scheduler, rn: int) -> None:
module = scheduler["dyn_var_2"]
print("from input dyn_var_2")
await module.from_input({"x": UPPER_X, "y": UPPER_Y})
# finp1 = fake_input(s, "dyn_var_1", 6, {"x": LOWER_X, "y": LOWER_Y})
# finp2 = fake_input(s, "dyn_var_2", 6, {"x": UPPER_X, "y": UPPER_Y})
# sts = sleep_then_stop(s, 10)
s.on_loop(self._stop, 10)
# s.on_loop(prt)
s.on_loop(fake_input_1, 3)
s.on_loop(fake_input_2, 3)
# aio.run_gather(sp.scheduler().start(), sts)
aio.run(s.start())
js = sp.to_json()
x, y, _ = zip(*js["sample"]["data"])
min_x = min(x)
max_x = max(x)
min_y = min(y)
max_y = max(y)
self.assertGreaterEqual(min_x, LOWER_X)
self.assertGreaterEqual(min_y, LOWER_Y)
self.assertLessEqual(max_x, UPPER_X)
self.assertLessEqual(max_y, UPPER_Y)
]
FILENAMES = pd.DataFrame({'filename': URLS})
CST = Constant(Table('filenames', data=FILENAMES), scheduler=s)
CSV = CSVLoader(index_col=False,
skipinitialspace=True,
usecols=['pickup_longitude', 'pickup_latitude'],
filter_=_filter,
scheduler=s)
CSV.input.filenames = CST.output.table
PR = Every(scheduler=s)
PR.input.df = CSV.output.table
SCATTERPLOT = MCScatterPlot(scheduler=s,
classes=[('Scatterplot', 'pickup_longitude',
'pickup_latitude')],
approximate=True)
SCATTERPLOT.create_dependent_modules(CSV, 'table')
s.set_interaction_opts(starving_mods=SCATTERPLOT.get_starving_mods(),
max_iter=3,
max_time=1.5)
if __name__ == '__main__':
s.start()
while True:
time.sleep(2)
s.to_json()
SCATTERPLOT.to_json() # simulate a web query
#SCATTERPLOT.get_image()
s.join()
print(len(CSV.table()))
CSV = CSVLoader(index_col=False,
skipinitialspace=True,
usecols=[
'pickup_longitude', 'pickup_latitude', 'dropoff_longitude',
'dropoff_latitude'
],
filter_=_filter,
scheduler=s) # TODO: reimplement filter in read_csv.py
CSV.input.filenames = CST.output.table
PR = Every(scheduler=s)
PR.input.df = CSV.output.table
MULTICLASS = MCScatterPlot(scheduler=s,
classes=[('pickup', 'pickup_longitude',
'pickup_latitude'),
('dropoff', 'dropoff_longitude',
'dropoff_latitude')],
approximate=True)
MULTICLASS.create_dependent_modules(CSV, 'table')
s.set_interaction_opts(starving_mods=MULTICLASS.get_starving_mods(),
max_iter=3,
max_time=1.5)
if __name__ == '__main__':
s.start()
while True:
time.sleep(2)
s.to_json()
MULTICLASS.to_json() # simulate a web query
s.join()
print(len(CSV.table()))