def plot(self):
g = Plot(
dict(
width=640,
height=480,
graph_title="Plot",
show_graph_title=True,
no_css=True,
key=True,
scale_x_integers=True,
scale_y_integers=True,
min_x_value=0,
min_y_value=0,
show_data_labels=True,
show_x_guidelines=True,
show_x_title=True,
x_title="Time",
show_y_title=True,
y_title="Ice Cream Cones",
y_title_text_direction='bt',
))
# add a few random datasets
for n in range(1, 4):
g.add_data(
dict(data=flatten(get_data_set()), title='series %d' % n))
res = XML(g.burn())
return render(chart=res)
def min_value(self):
if self.min_scale_value:
return self.min_scale_value
data = map(itemgetter('data'), self.data)
if self.stacked:
data = self.get_cumulative_data()
return min(flatten(data))
def min_value(self):
if self.min_scale_value:
return self.min_scale_value
data = map(itemgetter('data'), self.data)
if self.stacked:
data = self.get_cumulative_data()
return min(flatten(data))
def plot(self): g = Plot(dict( width = 640, height = 480, graph_title = "Plot", show_graph_title = True, no_css = True, key = True, scale_x_integers = True, scale_y_integers = True, min_x_value = 0, min_y_value = 0, show_data_labels = True, show_x_guidelines = True, show_x_title = True, x_title = "Time", show_y_title = True, y_title = "Ice Cream Cones", y_title_text_direction = 'bt', )) # add a few random datasets for n in range(1, 4): g.add_data(dict( data = flatten(get_data_set()), title = 'series %d' % n, )) res = XML(g.burn()) return render(chart=res)
def test_lines_completes(gzip_stream): """ When reading lines from a gzip stream, the operation should complete when the stream is exhausted. """ chunks = gzip.read_chunks(gzip_stream) streams = gzip.load_streams(chunks) lines = flatten(map(gzip.lines_from_stream, streams)) consume(lines)
def test_iterable_data_flat(self): g = Plot() spec = dict( data=flatten(self.get_data()), title='labels', ) g.add_data(spec) svg = g.burn() assert 'text="(1.00, 0.00)"' in svg
def test_iterable_data_flat(self): g = Plot() spec = dict( data=flatten(self.get_data()), title='labels', ) g.add_data(spec) svg = g.burn() assert b'text="(1.00, 0.00)"' in svg
def test_lines_from_stream(gzip_stream):
chunks = gzip.read_chunks(gzip_stream)
streams = gzip.load_streams(chunks)
lines = flatten(map(gzip.lines_from_stream, streams))
first_line = next(lines)
assert first_line == '['
second_line = next(lines)
result = json.loads(second_line.rstrip('\n,'))
assert isinstance(result, dict)
assert 'id' in result
def nearest_weekday(self, calendar):
"""
Return the nearest weekday to self.
"""
weekend_days = calendar.get_weekend_days()
deltas = (timedelta(n) for n in itertools.count())
candidates = recipes.flatten(
(self - delta, self + delta)
for delta in deltas
)
matches = (
day for day in candidates
if day.weekday() not in weekend_days
)
return next(matches)
def nearest_weekday(self, calendar):
"""
Return the nearest weekday to self.
"""
weekend_days = calendar.get_weekend_days()
deltas = (timedelta(n) for n in itertools.count())
candidates = recipes.flatten(
(self - delta, self + delta)
for delta in deltas
)
matches = (
day for day in candidates
if day.weekday() not in weekend_days
)
return next(matches)
def __main():
models = (
"4300u,4450u,4500u,4600u,4650u,"
"4600h,4600hs,4700u,4750u,4800u,4800h,4800hs,4900h,4900HS".split(",")
)
models = """4450u,4650u,4900HS""".split(",")
query_data = __prepare_query_data(models)
pprint(query_data)
data = flatten(concurrent_map(__get_geekbench_results, query_data))
DataFrame(data).to_excel(
os.path.join(os.path.dirname(__file__), "result.xlsx"), index=False
)
def __init__(
self,
df: pd.DataFrame,
exposure_path: Path,
raw_dir: Path,
name_list: Path,
transform=transforms.ToTensor(),
metric="mse",
):
self.exposure_path = exposure_path
self.transform = transform
names = flatten(pd.read_csv(name_list).to_numpy())
by_ev = df.pivot_table(index="name",
columns=["metric", "ev"],
values="score")
by_ev = by_ev.loc[by_ev.index.intersection(names)]
exp_min = -3
exp_max = 6
exp_step = 0.25
evs: np.ndarray = np.linspace(exp_min, exp_max,
int((exp_max - exp_min) / exp_step + 1))
self.evs = np.array([*evs[evs < 0], *evs[evs > 0]])
self.ev_indices = {ev: i for (i, ev) in enumerate(self.evs)}
self.opt_choices = by_ev[metric].idxmin(axis=1)
self.metric = metric
self.data = by_ev
self.names = pd.Series(self.data.index)
self.generator = DataGenerator(
raw_path=raw_dir,
out_path=exposure_path.parent,
store_path=None,
compute_scores=False,
)
def max_value(self):
data = map(itemgetter('data'), self.data)
if self.stacked:
data = self.get_cumulative_data()
return max(flatten(data))
def max_value(self):
data = map(itemgetter('data'), self.data)
if self.stacked:
data = self.get_cumulative_data()
return max(flatten(data))