def test_histogram1d1(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
histogram1d = Histogram1D("_2", scheduler=s) # columns are called 1..30
histogram1d.input[0] = csv.output.result
histogram1d.input.min = min_.output.result
histogram1d.input.max = max_.output.result
pr = Every(proc=self.terse, scheduler=s)
pr.input[0] = histogram1d.output.result
aio.run(s.start())
_ = histogram1d.trace_stats()
last = notNone(histogram1d.table.last()).to_dict()
h1 = last["array"]
bounds = (last["min"], last["max"])
df = pd.read_csv(
get_dataset("bigfile"), header=None, usecols=[2] # type: ignore
)
v = df.to_numpy().reshape(-1)
h2, _ = np.histogram( # type: ignore
v, bins=histogram1d.params.bins, density=False, range=bounds
)
self.assertListEqual(h1.tolist(), h2.tolist())
def t_histogram1d_impl(self, **kw: Any) -> None:
s = self.scheduler()
csv = CSVLoader(
get_dataset("bigfile"), index_col=False, header=None, scheduler=s
)
stirrer = Stirrer(update_column="_2", fixed_step_size=1000, scheduler=s, **kw)
stirrer.input[0] = csv.output.result
min_ = Min(scheduler=s)
min_.input[0] = stirrer.output.result
max_ = Max(scheduler=s)
max_.input[0] = stirrer.output.result
histogram1d = Histogram1D("_2", scheduler=s) # columns are called 1..30
histogram1d.input[0] = stirrer.output.result
histogram1d.input.min = min_.output.result
histogram1d.input.max = max_.output.result
# pr = Print(scheduler=s)
pr = Every(proc=self.terse, scheduler=s)
pr.input[0] = histogram1d.output.result
aio.run(s.start())
_ = histogram1d.trace_stats()
last = notNone(histogram1d.table.last()).to_dict()
h1 = last["array"]
bounds = (last["min"], last["max"])
tab = stirrer.table.loc[:, ["_2"]]
assert tab is not None
v = tab.to_array().reshape(-1)
h2, _ = np.histogram( # type: ignore
v, bins=histogram1d.params.bins, density=False, range=bounds
)
self.assertEqual(np.sum(h1), np.sum(h2))
self.assertListEqual(h1.tolist(), h2.tolist())
def test_hub_if_else(self):
s = Scheduler()
random = RandomTable(2, rows=100000, scheduler=s)
stirrer = Stirrer(
update_column="_1",
delete_rows=5,
update_rows=5,
fixed_step_size=100,
scheduler=s,
)
stirrer.input[0] = random.output.result
switch = Switch(condition=lambda x: False, scheduler=s)
switch.input[0] = stirrer.output.result
max_ = Max(name="max_" + str(hash(random)), scheduler=s)
max_.input[0] = switch.output.result
min_ = Min(name="min_" + str(hash(random)), scheduler=s)
min_.input[0] = switch.output.result_else
hub = Hub(scheduler=s)
hub.input.table = min_.output.result
hub.input.table = max_.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = hub.output.result
aio.run(s.start())
res1 = stirrer.result.min()
res2 = hub.result
self.compare(res1, res2)
def test_idxmax2(self) -> None:
s = self.scheduler()
random = RandomTable(10, rows=10000, throttle=1000, scheduler=s)
stirrer = Stirrer(update_column="_1",
delete_rows=5,
fixed_step_size=100,
scheduler=s)
stirrer.input[0] = random.output.result
idxmax = IdxMax(scheduler=s)
idxmax.input[0] = stirrer.output.result
max_ = Max(scheduler=s)
max_.input[0] = stirrer.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = idxmax.output.result
pr2 = Print(proc=self.terse, scheduler=s)
pr2.input[0] = max_.output.result
aio.run(s.start())
# import pdb;pdb.set_trace()
max1 = max_.psdict
# print('max1', max1)
max = idxmax.max()
assert max is not None
max2 = notNone(max.last()).to_dict()
# print('max2', max2)
self.compare(max1, max2)
def test_hist_index_min_max(self):
"Test min_out and max_out on HistogramIndex"
s = self.scheduler()
random = RandomTable(2, rows=100000, scheduler=s)
t_min = Table(name=None, dshape='{_1: float64}', data={'_1': [0.3]})
min_value = Constant(table=t_min, scheduler=s)
t_max = Table(name=None, dshape='{_1: float64}', data={'_1': [0.8]})
max_value = Constant(table=t_max, scheduler=s)
range_qry = RangeQuery(column='_1', scheduler=s)
range_qry.create_dependent_modules(random,
'table',
min_value=min_value,
max_value=max_value)
prt = Print(proc=self.terse, scheduler=s)
prt.input.df = range_qry.output.table
hist_index = range_qry.hist_index
min_ = Min(name='min_' + str(hash(hist_index)), scheduler=s)
min_.input.table = hist_index.output.min_out
prt2 = Print(proc=self.terse, scheduler=s)
prt2.input.df = min_.output.table
max_ = Max(name='max_' + str(hash(hist_index)), scheduler=s)
max_.input.table = hist_index.output.max_out
pr3 = Print(proc=self.terse, scheduler=s)
pr3.input.df = max_.output.table
s.start()
s.join()
res1 = random.table().min()['_1']
res2 = min_.table().last().to_dict()['_1']
self.assertAlmostEqual(res1, res2)
res1 = random.table().max()['_1']
res2 = max_.table().last().to_dict()['_1']
self.assertAlmostEqual(res1, res2)
def test_hist_index_min_max(self) -> None:
"Test min_out and max_out on HistogramIndex"
s = self.scheduler()
with s:
random = RandomTable(2, rows=100000, scheduler=s)
t_min = PsDict({"_1": 0.3})
min_value = Constant(table=t_min, scheduler=s)
t_max = PsDict({"_1": 0.8})
max_value = Constant(table=t_max, scheduler=s)
range_qry = RangeQuery(column="_1", scheduler=s)
range_qry.create_dependent_modules(
random, "result", min_value=min_value, max_value=max_value
)
prt = Print(proc=self.terse, scheduler=s)
prt.input[0] = range_qry.output.result
hist_index = range_qry.hist_index
assert hist_index is not None
min_ = Min(name="min_" + str(hash(hist_index)), scheduler=s)
min_.input[0] = hist_index.output.min_out
prt2 = Print(proc=self.terse, scheduler=s)
prt2.input[0] = min_.output.result
max_ = Max(name="max_" + str(hash(hist_index)), scheduler=s)
max_.input[0] = hist_index.output.max_out
pr3 = Print(proc=self.terse, scheduler=s)
pr3.input[0] = max_.output.result
aio.run(s.start())
res1 = cast(float, random.table.min()["_1"])
res2 = cast(float, min_.psdict["_1"])
self.assertAlmostEqual(res1, res2)
res1 = cast(float, random.table.max()["_1"])
res2 = cast(float, max_.psdict["_1"])
self.assertAlmostEqual(res1, res2)
def t_histogram2d_impl(self, **kw: Any) -> None:
s = self.scheduler()
random = RandomTable(3, rows=100000, scheduler=s)
stirrer = Stirrer(update_column="_2", fixed_step_size=1000, scheduler=s, **kw)
stirrer.input[0] = random.output.result
min_ = Min(scheduler=s)
min_.input[0] = stirrer.output.result
max_ = Max(scheduler=s)
max_.input[0] = stirrer.output.result
histogram2d = Histogram2D(
0, 1, xbins=100, ybins=100, scheduler=s
) # columns are called 1..30
histogram2d.input[0] = stirrer.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(s.start())
last = notNone(histogram2d.table.last()).to_dict()
h1 = last["array"]
bounds = [[last["ymin"], last["ymax"]], [last["xmin"], last["xmax"]]]
t = stirrer.table.loc[:, ["_1", "_2"]]
assert t is not None
v = t.to_array()
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.assertEqual(np.sum(h1), np.sum(h2))
self.assertListEqual(h1.reshape(-1).tolist(), h2.reshape(-1).tolist())
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_max(self):
s=Scheduler()
random = RandomTable(10, rows=10000, scheduler=s)
max=Max(scheduler=s)
max.input.df = random.output.df
pr=Print(scheduler=s)
pr.input.df = max.output.df
s.start()
res1 = random.df()[random.columns.difference([random.UPDATE_COLUMN])].max()
res2 = last_row(max.df(), remove_update=True)
self.assertTrue(np.allclose(res1, res2))
def test_max(self) -> None:
s = self.scheduler()
random = RandomTable(10, rows=10000, scheduler=s)
max_ = Max(name="max_" + str(hash(random)), scheduler=s)
max_.input[0] = random.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = max_.output.result
aio.run(s.start())
# s.join()
res1 = random.table.max()
res2 = max_.psdict
self.compare(res1, res2)
def test_max(self):
s = self.scheduler()
random = RandomTable(10, rows=10000, scheduler=s)
max_ = Max(name='max_' + str(hash(random)), scheduler=s)
max_.input.table = random.output.table
pr = Print(proc=self.terse, scheduler=s)
pr.input.df = max_.output.table
s.start()
s.join()
res1 = random.table().max()
res2 = max_.table().last()
self.compare(res1, res2)
def test_max(self):
s = self.scheduler()
random = SimpleCSVLoader(
get_dataset("bigfile_multiscale"), nrows=10_000, scheduler=s
)
max_ = Max(name="max_" + str(hash(random)), scheduler=s)
max_.input[0] = random.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = max_.output.result
aio.run(s.start())
# s.join()
res1 = random.result.max()
res2 = max_.result
self.compare(res1, res2)
def test_idxmax(self):
s=Scheduler()
random = RandomTable(10, rows=10000,throttle=1000, scheduler=s)
idxmax=IdxMax(scheduler=s)
idxmax.input.df = random.output.df
max=Max(scheduler=s)
max.input.df = random.output.df
pr=Print(scheduler=s)
pr.input.df = idxmax.output.max
s.start()
max1=last_row(max.df(),remove_update=True)
#print max1
max2=last_row(idxmax.max(),remove_update=True)
#print max2
self.assertTrue((max1==max2).all())
async def _add_max(scheduler: Scheduler, run_number: int) -> None:
with scheduler:
print("adding new modules")
m = Max(name="max", scheduler=scheduler)
prt = Print(name="print_max", proc=proc, scheduler=scheduler)
m.input.table = table.output.result
prt.input.df = m.output.result
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_idxmax(self):
s = self.scheduler()
random = RandomTable(10, rows=10000, throttle=1000, scheduler=s)
idxmax = IdxMax(scheduler=s)
idxmax.input.table = random.output.table
max_ = Max(scheduler=s)
max_.input.table = random.output.table
pr = Print(proc=self.terse, scheduler=s)
pr.input.df = idxmax.output.max
s.start()
s.join()
max1 = max_.table().last().to_dict()
#print('max1', max1)
max2 = idxmax.max().last().to_dict()
#print('max2', max2)
self.assertAlmostEqual(max1, max2)
def test_histogram1d(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
histogram1d = Histogram1D("_2", scheduler=s) # columns are called 1..30
histogram1d.input[0] = csv.output.result
histogram1d.input.min = min_.output.result
histogram1d.input.max = max_.output.result
pr = Every(proc=self.terse, scheduler=s)
pr.input[0] = histogram1d.output.result
aio.run(s.start())
_ = histogram1d.trace_stats()
def p10s_random_min_max(n):
StorageEngine.default = "hdf5"
s = Scheduler()
random = RandomTable(10, rows=n * L, scheduler=s)
min_ = Min(name='min_' + str(hash(random)), scheduler=s)
min_.input.table = random.output.table
max_ = Max(name='max_' + str(hash(random)), scheduler=s)
max_.input.table = random.output.table
s.start()
def p10s_random_min_max(self):
n = self.current_step
StorageEngine.default = "hdf5"
s = Scheduler()
random = RandomTable(10, rows=n * L, scheduler=s)
min_ = Min(mid='min_' + str(hash(random)), scheduler=s)
min_.input.table = random.output.table
max_ = Max(id='max_' + str(hash(random)), scheduler=s)
max_.input.table = random.output.table
s.start()
def test_stirrer(self) -> None:
s = Scheduler()
random = RandomTable(2, rows=100000, scheduler=s)
stirrer = Stirrer(
update_column="_1",
delete_rows=5,
update_rows=5,
fixed_step_size=100,
scheduler=s,
)
stirrer.input[0] = random.output.result
max_ = Max(name="max_" + str(hash(random)), scheduler=s)
max_.input[0] = stirrer.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = max_.output.result
aio.run(s.start())
res1 = stirrer.table.max()
res2 = max_.result
self.compare(res1, res2)
def test_idxmax(self) -> None:
s = self.scheduler()
random = RandomTable(10, rows=10000, throttle=1000, scheduler=s)
idxmax = IdxMax(scheduler=s)
idxmax.input[0] = random.output.result
max_ = Max(scheduler=s)
max_.input[0] = random.output.result
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = idxmax.output.result
pr2 = Print(proc=self.terse, scheduler=s)
pr2.input[0] = max_.output.result
aio.run(s.start())
max1 = max_.psdict
# print('max1', max1)
max = idxmax.max()
assert max is not None
max2 = notNone(max.last()).to_dict()
# print('max2', max2)
self.compare(max1, max2)
async def _add_max_remove_min(scheduler: Scheduler,
run_number: int) -> None:
with scheduler as dataflow:
print("adding new modules")
m = Max(name="max", scheduler=scheduler)
prt = Print(name="print_max", proc=proc, scheduler=scheduler)
m.input.table = table.output.result
prt.input.df = m.output.result
print("removing min module")
dataflow.delete_modules("min", "print_min")
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_switch_if_then(self):
s = Scheduler()
random = RandomTable(2, rows=100000, scheduler=s)
stirrer = Stirrer(
update_column="_1",
delete_rows=5,
update_rows=5,
fixed_step_size=100,
scheduler=s,
)
stirrer.input[0] = random.output.result
switch = Switch(condition=lambda x: True, scheduler=s)
switch.input[0] = stirrer.output.result
max_ = Max(name="max_" + str(hash(random)), scheduler=s)
max_.input[0] = switch.output.result
pr_else = Print(proc=self.terse, scheduler=s)
pr_else.input[0] = switch.output.result_else
pr = Print(proc=self.terse, scheduler=s)
pr.input[0] = max_.output.result
aio.run(s.start())
res1 = stirrer.result.max()
res2 = max_.result
self.compare(res1, res2)
def test_dummy(self):
s = Scheduler()
random = RandomTable(2, rows=100000, scheduler=s)
dummy_ = DummyMod(update_column='_1',
delete_rows=5,
update_rows=5,
fixed_step_size=100,
scheduler=s)
dummy_.input.table = random.output.table
max_ = Max(name='max_' + str(hash(random)), scheduler=s)
max_.input.table = dummy_.output.table
pr = Print(proc=self.terse, scheduler=s)
pr.input.df = max_.output.table
s.start()
s.join()
def main():
"Main function"
csvmod = RandomTable(columns=['a', 'b', 'c'],
rows=1000000,
random=np.random.randn,
throttle=1000,
scheduler=s)
minmod = Min(scheduler=s)
minmod.input.table = csvmod.output.table
maxmod = Max(scheduler=s)
maxmod.input.table = csvmod.output.table
histograms = Histograms(scheduler=s)
histograms.input.table = csvmod.output.table
histograms.input.min = minmod.output.table
histograms.input.max = maxmod.output.table
prlen = Every(scheduler=s)
prlen.input.df = histograms.output.table
return csvmod
def test_histogram1d(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
histogram1d = Histogram1D('_2',
scheduler=s) # columns are called 1..30
histogram1d.input.table = csv.output.table
histogram1d.input.min = min_.output.table
histogram1d.input.max = max_.output.table
#pr = Print(scheduler=s)
pr = Every(proc=self.terse, scheduler=s)
pr.input.df = csv.output.table
s.start(tick_proc=lambda s, r: csv.is_terminated() and s.stop())
s.join()
s = histogram1d.trace_stats()
from progressivis.io import CSVLoader
from progressivis.stats import Histogram2D, Min, Max
from progressivis.datasets import get_dataset
from progressivis.vis import Heatmap
print("Loading test_histogram2d")
print("Type of default_scheduler is %s" % type(Scheduler.default))
csv = CSVLoader(get_dataset('bigfile'),
index_col=False,
header=None,
engine='c')
pr = Every()
pr.input.df = csv.output.table
min_ = Min()
min_.input.table = csv.output.table
max_ = Max()
max_.input.table = csv.output.table
histogram2d = Histogram2D('_1', '_2', xbins=128, ybins=128)
histogram2d.input.table = csv.output.table
histogram2d.input.min = min_.output.table
histogram2d.input.max = max_.output.table
# heatmap
heatmap = Heatmap(filename='histo_%03d.png')
heatmap.input.array = histogram2d.output.table
pr = Print(name='print')
pr.input.df = csv.output.table
if __name__ == '__main__':
csv.start()
#SUFFIX= ''
PREFIX= '../nyc-taxi/'
SUFFIX= '.bz2'
URLS = [
PREFIX+'yellow_tripdata_2015-01.csv'+SUFFIX,
PREFIX+'yellow_tripdata_2015-02.csv'+SUFFIX,
PREFIX+'yellow_tripdata_2015-03.csv'+SUFFIX,
PREFIX+'yellow_tripdata_2015-04.csv'+SUFFIX,
PREFIX+'yellow_tripdata_2015-05.csv'+SUFFIX,
PREFIX+'yellow_tripdata_2015-06.csv'+SUFFIX,
]
filenames = pd.DataFrame({'filename': URLS})
cst = Constant(Table('filenames', data=filenames), scheduler=s)
csv = CSVLoader(index_col=False,skipinitialspace=True,usecols=['dropoff_longitude', 'dropoff_latitude'], filter_=filter_, scheduler=s)
csv.input.filenames = cst.output.table
min = Min(scheduler=s)
min.input.table = csv.output.table
max = Max(scheduler=s)
max.input.table = csv.output.table
histogram2d = Histogram2D('dropoff_longitude', 'dropoff_latitude', xbins=RESOLUTION, ybins=RESOLUTION, scheduler=s)
histogram2d.input.table = csv.output.table
histogram2d.input.min = min.output.table
histogram2d.input.max = max.output.table
heatmap = Heatmap(filename='nyc_dropoff_yellow%d.png', history=5, scheduler=s)
heatmap.input.array = histogram2d.output.table
if __name__=='__main__':
s.start()
