def test_scheduler(self):
s = MTScheduler()
csv = CSVLoader(get_dataset('bigfile'),index_col=False,header=None,scheduler=s)
smp = Sample(n=10,scheduler=s)
smp.input.df = csv.output.df
csv.scheduler().start()
sleep(1)
self.assertTrue(csv.scheduler().is_running())
smp2 = Sample(n=15, scheduler=s)
smp2.input.df = csv.output.df
def add_min():
m = Min(scheduler=s)
# Of course, sleeping here is a bad idea. this is to illustrate
# that add_min will be executed atomically by the scheduler.
# using a sleep outside of add_oneshot_tick_proc would lead to an inconsistent
# state.
#sleep(1)
m.input.df = smp2.output.df
prt = Print(scheduler=s)
prt.input.df = m.output.df
s.add_oneshot_tick_proc(add_min)
sleep(1)
self.assertTrue(s._runorder.index(smp.id) > s._runorder.index(csv.id))
self.assertTrue(s._runorder.index(smp2.id) > s._runorder.index(csv.id))
#self.assertTrue(s._runorder.index(m.id) > s._runorder.index(smp2.id))
s.stop()
s.join()
def test_histogram2d(self):
s = self.scheduler()
csv = CSVLoader(get_dataset('bigfile'),
index_col=False,
header=None,
scheduler=s)
min_ = Min(scheduler=s)
min_.input.table = csv.output.table
max_ = Max(scheduler=s)
max_.input.table = csv.output.table
histogram2d = Histogram2D(1, 2, xbins=100, ybins=100,
scheduler=s) # columns are called 1..30
histogram2d.input.table = csv.output.table
histogram2d.input.min = min_.output.table
histogram2d.input.max = max_.output.table
heatmap = Heatmap(filename='histo_%03d.png', scheduler=s)
heatmap.input.array = histogram2d.output.table
#pr = Print(scheduler=s)
pr = Every(proc=self.terse, scheduler=s)
#pr.input.df = heatmap.output.heatmap
#pr.input.df = histogram2d.output.df
pr.input.df = csv.output.table
csv.scheduler().start()
s.join()
#self.scheduler.thread.join()
s = histogram2d.trace_stats()
def test_sample(self):
s = Scheduler()
csv = CSVLoader(get_dataset('bigfile'),index_col=False,header=None,scheduler=s)
smp = Sample(n=10,scheduler=s)
smp.input.df = csv.output.df
prt = Print(scheduler=s)
prt.input.df = smp.output.df
csv.scheduler().start()
def test_scatterplot(self):
s=Scheduler()
csv = CSVLoader(get_dataset('bigfile'),index_col=False,header=None,force_valid_ids=True,scheduler=s)
sp = ScatterPlot(x_column='_1', y_column='_2', scheduler=s)
sp.create_dependent_modules(csv,'df')
cnt = Every(proc=print_len,constant_time=True,scheduler=s)
cnt.input.df = csv.output.df
prt = Print(scheduler=s)
prt.input.df = sp.histogram2d.output.df
csv.scheduler().start(None,idle_proc)
self.assertEquals(len(csv.df()), 1000000)
def test_sample(self):
s = self.scheduler()
csv = CSVLoader(get_dataset('bigfile'),
index_col=False,
header=None,
scheduler=s)
smp = Sample(samples=10, scheduler=s)
smp.input.table = csv.output.table
prt = Print(proc=self.terse, scheduler=s)
prt.input.df = smp.output.table
csv.scheduler().start()
s.join()
#print(repr(smp.table()))
self.assertEqual(len(smp.table()), 10)
def test_histogram2d1(self) -> None:
s = self.scheduler()
csv = CSVLoader(
get_dataset("bigfile"), index_col=False, header=None, scheduler=s
)
min_ = Min(scheduler=s)
min_.input[0] = csv.output.result
max_ = Max(scheduler=s)
max_.input[0] = csv.output.result
histogram2d = Histogram2D(
1, 2, xbins=100, ybins=100, scheduler=s
) # columns are called 1..30
histogram2d.input[0] = csv.output.result
histogram2d.input.min = min_.output.result
histogram2d.input.max = max_.output.result
heatmap = Heatmap(filename="histo_%03d.png", scheduler=s)
heatmap.input.array = histogram2d.output.result
pr = Every(proc=self.terse, scheduler=s)
pr.input[0] = heatmap.output.result
aio.run(csv.scheduler().start())
last = notNone(histogram2d.table.last()).to_dict()
h1 = last["array"]
bounds = [[last["ymin"], last["ymax"]], [last["xmin"], last["xmax"]]]
df = pd.read_csv(
get_dataset("bigfile"), header=None, usecols=[1, 2] # type: ignore
)
v = df.to_numpy() # .reshape(-1, 2)
bins = [histogram2d.params.ybins, histogram2d.params.xbins]
h2 = fh.histogram2d(v[:, 1], v[:, 0], bins=bins, range=bounds)
h2 = np.flip(h2, axis=0) # type: ignore
self.assertTrue(np.allclose(h1, h2))
def test_scatterplot(self):
s = self.scheduler()
csv = CSVLoader(get_dataset('smallfile'),
index_col=False,
header=None,
force_valid_ids=True,
scheduler=s)
sp = MCScatterPlot(scheduler=s,
classes=[('Scatterplot', '_1', '_2')],
approximate=True)
sp.create_dependent_modules(csv, 'table')
cnt = Every(proc=self.terse, constant_time=True, scheduler=s)
cnt.input.df = csv.output.table
prt = Print(proc=self.terse, scheduler=s)
prt.input.df = sp.output.table
csv.scheduler().start(idle_proc=idle_proc)
s.join()
self.assertEqual(len(csv.table()), 30000)
def test_dataflow(self):
s = Scheduler()
with Dataflow(s):
csv = CSVLoader(get_dataset('bigfile'),
name="csv",
index_col=False,
header=None)
m = Min()
m.input.table = csv.output.table
prt = Print(proc=self.terse)
prt.input.df = m.output.table
self.assertIs(s["csv"], csv)
csv.scheduler().start()
sleep(1)
self.assertTrue(csv.scheduler().is_running())
s.stop()
s.join()
def test_sample(self) -> None:
s = self.scheduler()
csv = CSVLoader(get_dataset("bigfile"),
index_col=False,
header=None,
scheduler=s)
smp = Sample(samples=10, scheduler=s)
smp.input[0] = csv.output.result
prt = Print(proc=self.terse, scheduler=s)
prt.input[0] = smp.output.result
aio.run(csv.scheduler().start())
# print(repr(smp.result))
self.assertEqual(len(smp.table), 10)
def test_scheduler(self):
s = Scheduler()
csv = CSVLoader(get_dataset('bigfile'),
name="csv",
index_col=False,
header=None,
scheduler=s)
#smp = Sample(n=10,scheduler=s)
#smp.input.df = csv.output.table
self.assertIs(s["csv"], csv)
csv.scheduler().start()
sleep(1)
self.assertTrue(csv.scheduler().is_running())
#smp2 = Sample(n=15, scheduler=s)
#smp2.input.df = csv.output.df
def add_min():
m = Min(scheduler=s)
# Of course, sleeping here is a bad idea. this is to illustrate
# that add_min will be executed atomically by the scheduler.
# using a sleep outside of add_oneshot_tick_proc would lead to an inconsistent
# state.
#sleep(1)
m.input.table = csv.output.table
prt = Print(proc=self.terse, scheduler=s)
prt.input.df = m.output.table
s.on_tick_once(add_min)
sleep(1)
#self.assertTrue(s._runorder.index(smp.id) > s._runorder.index(csv.id))
#self.assertTrue(s._runorder.index(smp2.id) > s._runorder.index(csv.id))
#self.assertTrue(s._runorder.index(m.id) > s._runorder.index(smp2.id))
s.stop()
s.join()
def test_histogram2d(self) -> None:
s = self.scheduler()
csv = CSVLoader(
get_dataset("bigfile"), index_col=False, header=None, scheduler=s
)
min_ = Min(scheduler=s)
min_.input[0] = csv.output.result
max_ = Max(scheduler=s)
max_.input[0] = csv.output.result
histogram2d = Histogram2D(
1, 2, xbins=100, ybins=100, scheduler=s
) # columns are called 1..30
histogram2d.input[0] = csv.output.result
histogram2d.input.min = min_.output.result
histogram2d.input.max = max_.output.result
heatmap = Heatmap(filename="histo_%03d.png", scheduler=s)
heatmap.input.array = histogram2d.output.result
pr = Every(proc=self.terse, scheduler=s)
pr.input[0] = heatmap.output.result
aio.run(csv.scheduler().start())
_ = histogram2d.trace_stats()
def test_scatterplot(self) -> None:
s = self.scheduler(clean=True)
with s:
csv = CSVLoader(
get_dataset("smallfile"),
index_col=False,
header=None,
force_valid_ids=True,
scheduler=s,
)
sp = MCScatterPlot(scheduler=s,
classes=[("Scatterplot", "_1", "_2")],
approximate=True)
sp.create_dependent_modules(csv, "result")
cnt = Every(proc=self.terse, constant_time=True, scheduler=s)
cnt.input[0] = csv.output.result
prt = Print(proc=self.terse, scheduler=s)
prt.input[0] = sp.output.result
# sts = sleep_then_stop(s, 5)
s.on_loop(self._stop, 5)
aio.run(csv.scheduler().start())
self.assertEqual(len(csv.table), 30000)