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https://github.com/TREX-CoE/Sherman-Morrison.git
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1d1da00931
The vfc_ci tool has been directly added to the repository, since it's not integrated into Verificarlo yet. The vfc_test_h5.cpp file defines a test inspired by test_h5.cpp that reads a list of cycles and dump the vfc_probes for these cycles.
589 lines
20 KiB
Python
589 lines
20 KiB
Python
# Manage the view comparing the variables of a run
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from math import pi
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from functools import partial
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import pandas as pd
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import numpy as np
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from bokeh.plotting import figure, curdoc
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from bokeh.embed import components
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from bokeh.models import Select, ColumnDataSource, Panel, Tabs, HoverTool,\
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RadioButtonGroup, CheckboxGroup, CustomJS
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import helper
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import plot
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################################################################################
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class InspectRuns:
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# Helper functions related to InspectRun
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# Returns a dictionary mapping user-readable strings to all run timestamps
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def gen_runs_selection(self):
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runs_dict = {}
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# Iterate over timestamp rows (runs) and fill dict
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for row in self.metadata.iloc:
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# The syntax used by pandas makes this part a bit tricky :
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# row.name is the index of metadata (so it refers to the
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# timestamp), whereas row["name"] is the column called "name"
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# (which is the display string used for the run)
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# runs_dict[run's name] = run's timestamp
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runs_dict[row["name"]] = row.name
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return runs_dict
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def gen_boxplot_tooltips(self, prefix):
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return [
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("Name", "@%s_x" % prefix),
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("Min", "@" + prefix + "_min{%0.18e}"),
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("Max", "@" + prefix + "_max{%0.18e}"),
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("1st quartile", "@" + prefix + "_quantile25{%0.18e}"),
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("Median", "@" + prefix + "_quantile50{%0.18e}"),
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("3rd quartile", "@" + prefix + "_quantile75{%0.18e}"),
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("μ", "@" + prefix + "_mu{%0.18e}"),
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("Number of samples (tests)", "@nsamples")
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]
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def gen_boxplot_tooltips_formatters(self, prefix):
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return {
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"@%s_min" % prefix : "printf",
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"@%s_max" % prefix : "printf",
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"@%s_quantile25" % prefix : "printf",
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"@%s_quantile50" % prefix : "printf",
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"@%s_quantile75" % prefix : "printf",
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"@%s_mu" % prefix : "printf"
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}
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# Data processing helper
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# (computes new distributions for sigma, s2, s10)
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def data_processing(self, dataframe):
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# Compute aggragated mu
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dataframe["mu"] = np.vectorize(np.average)(dataframe["mu"], weights=dataframe["nsamples"])
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# nsamples is the number of aggregated elements (as well as the number
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# of samples for our new sigma and s distributions)
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dataframe["nsamples"] = dataframe["nsamples"].apply(lambda x: len(x))
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dataframe["mu_x"] = dataframe.index
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# Make sure that strings don't excede a certain length
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dataframe["mu_x"] = dataframe["mu_x"].apply(
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lambda x: x[:17] + "[...]" + x[-17:] if len(x) > 39 else x
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)
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# Get quantiles and mu for sigma, s10, s2
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for prefix in ["sigma", "s10", "s2"]:
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dataframe["%s_x" % prefix] = dataframe["mu_x"]
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dataframe[prefix] = dataframe[prefix].apply(np.sort)
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dataframe["%s_min" % prefix] = dataframe[prefix].apply(np.min)
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dataframe["%s_quantile25" % prefix] = dataframe[prefix].apply(np.quantile, args=(0.25,))
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dataframe["%s_quantile50" % prefix] = dataframe[prefix].apply(np.quantile, args=(0.50,))
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dataframe["%s_quantile75" % prefix] = dataframe[prefix].apply(np.quantile, args=(0.75,))
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dataframe["%s_max" % prefix] = dataframe[prefix].apply(np.max)
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dataframe["%s_mu" % prefix] = dataframe[prefix].apply(np.average)
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del dataframe[prefix]
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return dataframe
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# Plots update function
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def update_plots(self):
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groupby_display = self.widgets["groupby_radio"].labels[
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self.widgets["groupby_radio"].active
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]
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groupby = self.factors_dict[groupby_display]
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filterby_display = self.widgets["filterby_radio"].labels[
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self.widgets["filterby_radio"].active
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]
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filterby = self.factors_dict[filterby_display]
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# Groupby and aggregate lines belonging to the same group in lists
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groups = self.run_data[
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self.run_data.index.isin(
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[self.widgets["select_filter"].value],
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level=filterby
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)
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].groupby(groupby)
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groups = groups.agg({
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"sigma": lambda x: x.tolist(),
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"s10": lambda x: x.tolist(),
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"s2": lambda x: x.tolist(),
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"mu": lambda x: x.tolist(),
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# Used for mu weighted average first, then will be replaced
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"nsamples": lambda x: x.tolist()
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})
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# Compute the new distributions, ...
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groups = self.data_processing(groups).to_dict("list")
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# Update source
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# Assign each ColumnDataSource, starting with the boxplots
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for prefix in ["sigma", "s10", "s2"]:
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dict = {
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"%s_x" % prefix: groups["%s_x" % prefix],
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"%s_min" % prefix: groups["%s_min" % prefix],
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"%s_quantile25" % prefix: groups["%s_quantile25" % prefix],
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"%s_quantile50" % prefix: groups["%s_quantile50" % prefix],
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"%s_quantile75" % prefix: groups["%s_quantile75" % prefix],
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"%s_max" % prefix: groups["%s_max" % prefix],
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"%s_mu" % prefix: groups["%s_mu" % prefix],
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"nsamples": groups["nsamples"]
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}
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# Filter outliers if the box is checked
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if len(self.widgets["outliers_filtering_inspect"].active) > 0:
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# Boxplots will be filtered by max then min
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top_outliers = helper.detect_outliers(dict["%s_max" % prefix])
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helper.remove_boxplot_outliers(dict, top_outliers, prefix)
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bottom_outliers = helper.detect_outliers(dict["%s_min" % prefix])
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helper.remove_boxplot_outliers(dict, bottom_outliers, prefix)
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self.sources["%s_source" % prefix].data = dict
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# Finish with the mu plot
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dict = {
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"mu_x": groups["mu_x"],
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"mu": groups["mu"],
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"nsamples": groups["nsamples"]
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}
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self.sources["mu_source"].data = dict
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# Filter outliers if the box is checked
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if len(self.widgets["outliers_filtering_inspect"].active) > 0:
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mu_outliers = helper.detect_outliers(groups["mu"])
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groups["mu"] = helper.remove_outliers(groups["mu"], mu_outliers)
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groups["mu_x"] = helper.remove_outliers(groups["mu_x"], mu_outliers)
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# Update plots axis/titles
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# Get display string of the last (unselected) factor
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factors_dict = self.factors_dict.copy()
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del factors_dict[groupby_display]
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del factors_dict[filterby_display]
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over_all = list(factors_dict.keys())[0]
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# Update all display strings for plot title (remove caps, plural)
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groupby_display = groupby_display.lower()
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filterby_display = filterby_display.lower()[:-1]
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over_all = over_all.lower()
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self.plots["mu_inspect"].title.text = \
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"Empirical average μ of %s (groupped by %s, for all %s)" \
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% (filterby_display, groupby_display, over_all)
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self.plots["sigma_inspect"].title.text = \
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"Standard deviation σ of %s (groupped by %s, for all %s)" \
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% (filterby_display, groupby_display, over_all)
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self.plots["s10_inspect"].title.text = \
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"Significant digits s of %s (groupped by %s, for all %s)" \
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% (filterby_display, groupby_display, over_all)
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self.plots["s2_inspect"].title.text = \
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"Significant digits s of %s (groupped by %s, for all %s)" \
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% (filterby_display, groupby_display, over_all)
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# Update x_ranges
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helper.reset_x_range(self.plots["mu_inspect"], groups["mu_x"])
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helper.reset_x_range(self.plots["sigma_inspect"], groups["sigma_x"])
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helper.reset_x_range(self.plots["s10_inspect"], groups["s10_x"])
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helper.reset_x_range(self.plots["s2_inspect"], groups["s2_x"])
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# Widets' callback functions
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# Run selector callback
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def update_run(self, attrname, old, new):
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filterby = self.widgets["filterby_radio"].labels[
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self.widgets["filterby_radio"].active
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]
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filterby = self.factors_dict[filterby]
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# Update run selection (by using dict mapping)
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self.current_run = self.runs_dict[new]
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# Update run data
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self.run_data = self.data[self.data["timestamp"] == self.current_run]
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# Save old selected option
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old_value = self.widgets["select_filter"].value
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# Update filter options
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options = self.run_data.index\
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.get_level_values(filterby).drop_duplicates().tolist()
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self.widgets["select_filter"].options = options
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if old_value not in self.widgets["select_filter"].options:
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self.widgets["select_filter"].value = options[0]
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# The update_var callback will be triggered by the assignment
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else:
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# Trigger the callback manually (since the plots need to be updated
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# anyway)
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self.update_filter("", "", old_value)
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# "Group by" radio
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def update_groupby(self, attrname, old, new):
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# Update "Filter by" radio list
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filterby_list = list(self.factors_dict.keys())
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del filterby_list[self.widgets["groupby_radio"].active]
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self.widgets["filterby_radio"].labels = filterby_list
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filterby = self.widgets["filterby_radio"].labels[
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self.widgets["filterby_radio"].active
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]
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filterby = self.factors_dict[filterby]
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# Save old selected option
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old_value = self.widgets["select_filter"].value
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# Update filter options
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options = self.run_data.index\
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.get_level_values(filterby).drop_duplicates().tolist()
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self.widgets["select_filter"].options = options
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if old_value not in self.widgets["select_filter"].options:
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self.widgets["select_filter"].value = options[0]
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# The update_var callback will be triggered by the assignment
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else:
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# Trigger the callback manually (since the plots need to be updated
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# anyway)
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self.update_filter("", "", old_value)
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# "Filter by" radio
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def update_filterby(self, attrname, old, new):
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filterby = self.widgets["filterby_radio"].labels[
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self.widgets["filterby_radio"].active
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]
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filterby = self.factors_dict[filterby]
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# Save old selected option
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old_value = self.widgets["select_filter"].value
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# Update filter selector options
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options = self.run_data.index\
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.get_level_values(filterby).drop_duplicates().tolist()
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self.widgets["select_filter"].options = options
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if old_value not in self.widgets["select_filter"].options:
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self.widgets["select_filter"].value = options[0]
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# The update_var callback will be triggered by the assignment
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else:
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# Trigger the callback manually (since the plots need to be updated
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# anyway)
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self.update_filter("", "", old_value)
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# Filter selector callback
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def update_filter(self, attrname, old, new):
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self.update_plots()
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# Filter outliers checkbox callback
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def update_outliers_filtering(self, attrname, old, new):
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# The status (checked/unchecked) of the checkbox is also verified inside
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# self.update_plots(), so calling this function is enough
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self.update_plots()
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# Bokeh setup functions
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# (for both variable and backend selection at once)
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def setup_plots(self):
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tools = "pan, wheel_zoom, xwheel_zoom, ywheel_zoom, reset, save"
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# Tooltips and formatters
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dotplot_tooltips = [
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("Name", "@mu_x"),
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("μ", "@mu{%0.18e}"),
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("Number of samples (tests)", "@nsamples")
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]
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dotplot_formatters = {
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"@mu" : "printf"
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}
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sigma_boxplot_tooltips = self.gen_boxplot_tooltips("sigma")
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sigma_boxplot_tooltips_formatters = self.gen_boxplot_tooltips_formatters("sigma")
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s10_boxplot_tooltips = self.gen_boxplot_tooltips("s10")
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s10_boxplot_tooltips_formatters = self.gen_boxplot_tooltips_formatters("s10")
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s2_boxplot_tooltips = self.gen_boxplot_tooltips("s2")
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s2_boxplot_tooltips_formatters = self.gen_boxplot_tooltips_formatters("s2")
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# Plots
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# Mu plot
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self.plots["mu_inspect"] = figure(
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name="mu_inspect",
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title="",
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plot_width=900, plot_height=400, x_range=[""],
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tools=tools, sizing_mode="scale_width"
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)
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plot.fill_dotplot(
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self.plots["mu_inspect"], self.sources["mu_source"], "mu",
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tooltips = dotplot_tooltips,
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tooltips_formatters = dotplot_formatters
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)
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self.doc.add_root(self.plots["mu_inspect"])
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# Sigma plot
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self.plots["sigma_inspect"] = figure(
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name="sigma_inspect",
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title="",
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plot_width=900, plot_height=400, x_range=[""],
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tools=tools, sizing_mode="scale_width"
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)
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plot.fill_boxplot(
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self.plots["sigma_inspect"], self.sources["sigma_source"], prefix="sigma",
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tooltips = sigma_boxplot_tooltips,
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tooltips_formatters = sigma_boxplot_tooltips_formatters
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)
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self.doc.add_root(self.plots["sigma_inspect"])
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# s plots
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self.plots["s10_inspect"] = figure(
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name="s10_inspect",
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title="",
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plot_width=900, plot_height=400, x_range=[""],
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tools=tools, sizing_mode='scale_width'
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)
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plot.fill_boxplot(
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self.plots["s10_inspect"], self.sources["s10_source"], prefix="s10",
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tooltips = s10_boxplot_tooltips,
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tooltips_formatters = s10_boxplot_tooltips_formatters
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)
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s10_tab_inspect = Panel(child=self.plots["s10_inspect"], title="Base 10")
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self.plots["s2_inspect"] = figure(
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name="s2_inspect",
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title="",
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plot_width=900, plot_height=400, x_range=[""],
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tools=tools, sizing_mode='scale_width'
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)
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plot.fill_boxplot(
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self.plots["s2_inspect"], self.sources["s2_source"], prefix="s2",
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tooltips = s2_boxplot_tooltips,
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tooltips_formatters = s2_boxplot_tooltips_formatters
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)
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s2_tab_inspect = Panel(child=self.plots["s2_inspect"], title="Base 2")
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s_tabs_inspect = Tabs(
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name = "s_tabs_inspect",
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tabs=[s10_tab_inspect, s2_tab_inspect], tabs_location = "below"
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)
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self.doc.add_root(s_tabs_inspect)
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def setup_widgets(self):
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# Generation of selectable items
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# Dict contains all inspectable runs (maps display strings to timestamps)
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# The dict structure allows to get the timestamp from the display string
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# in O(1)
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self.runs_dict = self.gen_runs_selection()
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# Dict maps display strings to column names for the different factors
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# (var, backend, test)
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self.factors_dict = {
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"Variables": "variable",
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"Backends": "vfc_backend",
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"Tests": "test"
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}
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# Run selection
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# Contains all options strings
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runs_display = list(self.runs_dict.keys())
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# Will be used when updating plots (contains actual number)
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self.current_run = self.runs_dict[runs_display[-1]]
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# Contains the selected option string, used to update current_n_runs
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current_run_display = runs_display[-1]
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# This contains only entries matching the run
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self.run_data = self.data[self.data["timestamp"] == self.current_run]
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change_run_callback_js="updateRunMetadata(cb_obj.value);"
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self.widgets["select_run"] = Select(
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name="select_run", title="Run :",
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value=current_run_display, options=runs_display
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)
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self.doc.add_root(self.widgets["select_run"])
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self.widgets["select_run"].on_change("value", self.update_run)
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self.widgets["select_run"].js_on_change("value", CustomJS(
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code = change_run_callback_js,
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args=(dict(
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metadata=helper.metadata_to_dict(
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helper.get_metadata(self.metadata, self.current_run)
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)
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))
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))
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# Factors selection
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# "Group by" radio
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self.widgets["groupby_radio"] = RadioButtonGroup(
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name="groupby_radio",
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labels=list(self.factors_dict.keys()), active=0
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)
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self.doc.add_root(self.widgets["groupby_radio"])
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# The functions are defined inside the template to avoid writing too
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# much JS server side
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self.widgets["groupby_radio"].on_change(
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"active",
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self.update_groupby
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)
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# "Filter by" radio
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# Get all possible factors, and remove the one selected in "Group by"
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filterby_list = list(self.factors_dict.keys())
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del filterby_list[self.widgets["groupby_radio"].active]
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self.widgets["filterby_radio"] = RadioButtonGroup(
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name="filterby_radio",
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labels=filterby_list, active=0
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)
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self.doc.add_root(self.widgets["filterby_radio"])
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||
# The functions are defined inside the template to avoid writing too
|
||
# much JS server side
|
||
self.widgets["filterby_radio"].on_change(
|
||
"active",
|
||
self.update_filterby
|
||
)
|
||
|
||
|
||
# Filter selector
|
||
|
||
filterby = self.widgets["filterby_radio"].labels[
|
||
self.widgets["filterby_radio"].active
|
||
]
|
||
filterby = self.factors_dict[filterby]
|
||
|
||
options = self.run_data.index\
|
||
.get_level_values(filterby).drop_duplicates().tolist()
|
||
|
||
self.widgets["select_filter"] = Select(
|
||
# We need a different name to avoid collision in the template with
|
||
# the runs comparison's widget
|
||
name="select_filter", title="Select a filter :",
|
||
value=options[0], options=options
|
||
)
|
||
self.doc.add_root(self.widgets["select_filter"])
|
||
self.widgets["select_filter"]\
|
||
.on_change("value", self.update_filter)
|
||
|
||
|
||
# Toggle for outliers filtering
|
||
|
||
self.widgets["outliers_filtering_inspect"] = CheckboxGroup(
|
||
name="outliers_filtering_inspect",
|
||
labels=["Filter outliers"], active = []
|
||
)
|
||
self.doc.add_root(self.widgets["outliers_filtering_inspect"])
|
||
self.widgets["outliers_filtering_inspect"]\
|
||
.on_change("active", self.update_outliers_filtering)
|
||
|
||
|
||
|
||
# Communication methods
|
||
# (to send/receive messages to/from master)
|
||
|
||
# When received, switch to the run_name in parameter
|
||
def switch_view(self, run_name):
|
||
self.widgets["select_run"].value = run_name
|
||
|
||
|
||
|
||
# Constructor
|
||
|
||
def __init__(self, master, doc, data, metadata):
|
||
|
||
self.master = master
|
||
|
||
self.doc = doc
|
||
self.data = data
|
||
self.metadata = metadata
|
||
|
||
|
||
self.sources = {
|
||
"mu_source": ColumnDataSource(data={}),
|
||
"sigma_source": ColumnDataSource(data={}),
|
||
"s10_source" :ColumnDataSource(data={}),
|
||
"s2_source": ColumnDataSource(data={})
|
||
}
|
||
|
||
self.plots = {}
|
||
self.widgets = {}
|
||
|
||
# Setup Bokeh objects
|
||
self.setup_plots()
|
||
self.setup_widgets()
|
||
|
||
# Pass the initial metadata to the template (will be updated in CustomJS
|
||
# callbacks). This is required because metadata is not displayed in a
|
||
# Bokeh widget, so we can't update this with a server callback.
|
||
initial_run = helper.get_metadata(self.metadata, self.current_run)
|
||
self.doc.template_variables["initial_timestamp"] = self.current_run
|
||
|
||
# At this point, everything should have been initialized, so we can
|
||
# show the plots for the first time
|
||
self.update_plots()
|