def generate_distribution_violin(data_path: str, num_ranks: int,
timesteps: int, bw_value: float):
num_ranks = 2
vpic_reader = VPICReader(data_path, num_ranks=num_ranks)
fig = go.Figure()
for tsidx in range(0, timesteps):
data = vpic_reader.read_global(tsidx)
print(len(data))
# plotted_data = np.random.choice(data, 50000)
plotted_data = np.random.choice(data, 500)
head_cutoff = 0.5
head_cutoff_2 = 4
tail_cutoff = 10
head_data = len([i for i in plotted_data if i < head_cutoff])
head_data_2 = len([i for i in plotted_data if i < head_cutoff_2])
tail_data = len([i for i in plotted_data if i > tail_cutoff])
percent_head = head_data * 100.0 / len(plotted_data)
percent_head_2 = head_data_2 * 100.0 / len(plotted_data)
percent_tail = tail_data * 100.0 / len(plotted_data)
print('TS {0}, < {1}: {2:.2f}'.format(tsidx, head_cutoff,
percent_head))
print('TS {0}, < {1}: {2:.2f}'.format(tsidx, head_cutoff_2,
percent_head_2))
print('TS {0}, > {1}: {2:.2f}'.format(tsidx, tail_cutoff,
percent_tail))
plotted_data = list(map(lambda x: log_tailed(x, 10), plotted_data))
ts_name = 'Timestep {0}'.format(vpic_reader.get_ts(tsidx), )
violin_data = go.Violin(y=plotted_data,
box_visible=False,
meanline_visible=False,
name=ts_name,
side='positive',
points=False,
bandwidth=bw_value,
scalemode='width',
line=dict(width=1))
fig.add_trace(violin_data)
fig.update_traces(width=1.8)
fig.update_layout(
title_text='Energy distribution from 4 timesteps of a VPIC simulation'
' (tail is logarithmic)',
yaxis=dict(tickmode='array',
tickvals=list(range(0, 18, 2)),
ticktext=[
'{0:.0f}'.format(log_tailed_reverse(x, 10))
for x in range(0, 18, 2)
]),
)
fig.show()
def generate_distribution_box(data_path: str, num_ranks: int, timesteps: int):
# num_ranks = 4
vpic_reader = VPICReader(data_path, num_ranks=num_ranks)
fig = go.Figure()
for tsidx in range(0, timesteps):
data = vpic_reader.read_global(tsidx)
print(len(data))
plotted_data = np.random.choice(data, 50000)
head_cutoff = 2
tail_cutoff = 10
head_data = len([i for i in plotted_data if i < head_cutoff])
tail_data = len([i for i in plotted_data if i > tail_cutoff])
percent_head = head_data * 100.0 / len(plotted_data)
percent_tail = tail_data * 100.0 / len(plotted_data)
print('TS {0}, < {1}: {2:.2f}'.format(tsidx, head_cutoff,
percent_head))
print('TS {0}, > {1}: {2:.2f}'.format(tsidx, tail_cutoff,
percent_tail))
plotted_data = list(map(lambda x: log_tailed(x, 10), plotted_data))
ts_name = 'Timestep {0}'.format(vpic_reader.get_ts(tsidx), )
box_data = go.Box(y=plotted_data,
name=ts_name,
line_width=1,
marker=dict(size=4, line=dict(width=0)))
fig.add_trace(box_data)
fig.update_layout(
title_text='Energy distribution from 4 timesteps of a VPIC simulation'
' (tail is logarithmic)',
yaxis=dict(tickmode='array',
tickvals=list(range(0, 18, 2)),
ticktext=[
'{0:.0f}'.format(log_tailed_reverse(x, 10))
for x in range(0, 18, 2)
]),
)
# fig.show()
fig.write_image('../vis/poster/vpic32.distrib.box.pdf')