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Add the effects in the report

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Paul Fd 2020-03-11 18:31:19 +01:00
parent 60c0e7763a
commit 71c1b468ce
1 changed files with 10 additions and 8 deletions
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18
scripts/performance_report.py
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@ -6,7 +6,7 @@ import os
from bokeh.io import output_file, show from bokeh.io import output_file, show
from bokeh.plotting import figure from bokeh.plotting import figure
from bokeh.layouts import column, row from bokeh.layouts import column, row
from bokeh.palettes import Dark2_5 as palette from bokeh.palettes import Dark2_6 as palette
from bokeh.models.widgets import Div from bokeh.models.widgets import Div
from bokeh.models import ColumnDataSource from bokeh.models import ColumnDataSource
import itertools import itertools
@ -18,7 +18,7 @@ file_log_suffix = "_file_log.csv"
file_prefix_length = 14 # length of the pointer prefix file_prefix_length = 14 # length of the pointer prefix
# sfizz 0.3.0 logs # sfizz 0.3.0 logs
callback_log_columns = ['Dispatch', 'RenderMethod', 'Data', 'Amplitude', 'Filters', 'Panning', 'NumVoices', 'NumSamples'] callback_log_columns = ['Dispatch', 'RenderMethod', 'Data', 'Amplitude', 'Filters', 'Panning', 'Effects', 'NumVoices', 'NumSamples']
file_log_columns = ['WaitDuration', 'LoadDuration', 'FileSize', 'FileName'] file_log_columns = ['WaitDuration', 'LoadDuration', 'FileSize', 'FileName']
# Helper functions # Helper functions
@ -149,12 +149,13 @@ for file_name in callback_log_list:
csv_data = pd.read_csv(file_name) csv_data = pd.read_csv(file_name)
# Scale the data and add some columns # Scale the data and add some columns
scale_columns(csv_data, ['Dispatch', 'RenderMethod', 'Data', 'Amplitude', 'Panning', 'Filters'], 1e6) scale_columns(csv_data, ['Dispatch', 'RenderMethod', 'Data', 'Amplitude', 'Panning', 'Filters', 'Effects'], 1e6)
csv_data['DataPerVoice'] = csv_data['Data'] / csv_data['NumVoices'] csv_data['DataPerVoice'] = csv_data['Data'] / csv_data['NumVoices']
csv_data['AmplitudePerVoice'] = csv_data['Amplitude'] / csv_data['NumVoices'] csv_data['AmplitudePerVoice'] = csv_data['Amplitude'] / csv_data['NumVoices']
csv_data['FiltersPerVoice'] = csv_data['Filters'] / csv_data['NumVoices'] csv_data['FiltersPerVoice'] = csv_data['Filters'] / csv_data['NumVoices']
csv_data['PanningPerVoice'] = csv_data['Panning'] / csv_data['NumVoices'] csv_data['PanningPerVoice'] = csv_data['Panning'] / csv_data['NumVoices']
csv_data['Residual'] = (csv_data['RenderMethod'] - csv_data['Panning'] - csv_data['Filters'] - csv_data['Amplitude'] - csv_data['Data']) / csv_data['NumVoices'] csv_data['EffectsPerVoice'] = csv_data['Effects'] / csv_data['NumVoices']
csv_data['Residual'] = (csv_data['RenderMethod'] - csv_data['Panning'] - csv_data['Filters'] - csv_data['Amplitude'] - csv_data['Data'] - csv_data['Effects']) / csv_data['NumVoices']
# Prep the summary # Prep the summary
summary_title = f"Callback statistics summary for {sfz_file_name} ({file_prefix[-4:]})" summary_title = f"Callback statistics summary for {sfz_file_name} ({file_prefix[-4:]})"
@ -166,20 +167,21 @@ for file_name in callback_log_list:
f"Source data reading/generation (avg/max): {csv_data['Data'].mean():.2f}/{csv_data['Data'].max():.2f} µs", f"Source data reading/generation (avg/max): {csv_data['Data'].mean():.2f}/{csv_data['Data'].max():.2f} µs",
f"Amplitude processing (avg/max): {csv_data['Amplitude'].mean():.2f}/{csv_data['Amplitude'].max():.2f} µs", f"Amplitude processing (avg/max): {csv_data['Amplitude'].mean():.2f}/{csv_data['Amplitude'].max():.2f} µs",
f"Panning processing (avg/max): {csv_data['Panning'].mean():.2f}/{csv_data['Panning'].max():.2f} µs", f"Panning processing (avg/max): {csv_data['Panning'].mean():.2f}/{csv_data['Panning'].max():.2f} µs",
f"Filter processing (avg/max): {csv_data['Filters'].mean():.2f}/{csv_data['Filters'].max():.2f} µs" f"Filter processing (avg/max): {csv_data['Filters'].mean():.2f}/{csv_data['Filters'].max():.2f} µs",
f"Effect processing (avg/max): {csv_data['Effects'].mean():.2f}/{csv_data['Effects'].max():.2f} µs"
] ]
callback_figures.append(Div(text=f"<h3>{summary_title}</h3>" + html_list(summary_lines), width=600)) callback_figures.append(Div(text=f"<h3>{summary_title}</h3>" + html_list(summary_lines), width=600))
if args.verbose: if args.verbose:
print_summary_to_console(summary_title, summary_lines) print_summary_to_console(summary_title, summary_lines)
# Callback breakdown figure # Callback breakdown figure
stacked_column_names = ['DataPerVoice', 'AmplitudePerVoice', 'FiltersPerVoice', 'PanningPerVoice', 'Residual'] stacked_column_names = ['DataPerVoice', 'AmplitudePerVoice', 'FiltersPerVoice', 'PanningPerVoice', 'EffectsPerVoice', 'Residual']
stacked_column_legends = ['Data', 'Amplitude', 'Filters', 'Panning', 'Residual'] stacked_column_legends = ['Data', 'Amplitude', 'Filters', 'Panning', 'Effects', 'Residual']
source = ColumnDataSource(csv_data) source = ColumnDataSource(csv_data)
source.add(csv_data.index, 'index') source.add(csv_data.index, 'index')
fig_breakdown = figure(plot_width=600, plot_height=400, title=f"{sfz_file_name} - Callback breakdown") fig_breakdown = figure(plot_width=600, plot_height=400, title=f"{sfz_file_name} - Callback breakdown")
fig_breakdown.varea_stack(stacked_column_names, x='index', source=source, legend_label=stacked_column_legends, color=palette[:5]) fig_breakdown.varea_stack(stacked_column_names, x='index', source=source, legend_label=stacked_column_legends, color=palette[:6])
set_axis_and_legend(fig_breakdown, 'Callback index', 'Aggregate duration (per voice, average, µs)') set_axis_and_legend(fig_breakdown, 'Callback index', 'Aggregate duration (per voice, average, µs)')
# Breakdown histogram figure # Breakdown histogram figure