def render_generic(summary): template_variables = {} # render_common(summary) info = Overview( anchor_id=summary["varid"], warnings=summary["warnings"], var_type="Unsupported", var_name=summary["varname"], ) table = Table([ { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt", "class": "alert" if "n_missing" in summary["warn_fields"] else "", }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", "class": "alert" if "p_missing" in summary["warn_fields"] else "", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) return { "top": Sequence([info, table, HTML("")], sequence_type="grid"), "bottom": None, "ignore": "ignore", }
def render_url(summary): n_freq_table_max = config["n_freq_table_max"].get(int) n_obs_cat = config["vars"]["cat"]["n_obs"].get(int) # TODO: merge with boolean/categorical mini_freq_table_rows = freq_table(freqtable=summary["value_counts"], n=summary["n"], max_number_to_print=n_obs_cat) template_variables = render_common(summary) keys = ["scheme", "netloc", "path", "query", "fragment"] for url_part in keys: template_variables["freqtable_{}".format(url_part)] = freq_table( freqtable=summary["{}_counts".format(url_part)], n=summary["n"], max_number_to_print=n_freq_table_max, ) full_frequency_table = FrequencyTable( template_variables["freq_table_rows"], name="Full", anchor_id="{varid}full_frequency".format(varid=summary["varid"]), ) scheme_frequency_table = FrequencyTable( template_variables["freqtable_scheme"], name="Scheme", anchor_id="{varid}scheme_frequency".format(varid=summary["varid"]), ) netloc_frequency_table = FrequencyTable( template_variables["freqtable_netloc"], name="Netloc", anchor_id="{varid}netloc_frequency".format(varid=summary["varid"]), ) path_frequency_table = FrequencyTable( template_variables["freqtable_path"], name="Path", anchor_id="{varid}path_frequency".format(varid=summary["varid"]), ) query_frequency_table = FrequencyTable( template_variables["freqtable_query"], name="Query", anchor_id="{varid}query_frequency".format(varid=summary["varid"]), ) fragment_frequency_table = FrequencyTable( template_variables["freqtable_fragment"], name="Fragment", anchor_id="{varid}fragment_frequency".format(varid=summary["varid"]), ) items = [ full_frequency_table, scheme_frequency_table, netloc_frequency_table, path_frequency_table, query_frequency_table, fragment_frequency_table, ] template_variables["bottom"] = Sequence(items, sequence_type="tabs") # Element composition info = Overview(summary["varid"], summary["varname"], "URL", summary["warnings"]) table = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt" }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent" }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt" }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) fqm = FrequencyTableSmall(mini_freq_table_rows) # TODO: settings 3,3,6 template_variables["top"] = Sequence([info, table, fqm], sequence_type="grid") return template_variables
def render_categorical(summary): n_obs_cat = config["vars"]["cat"]["n_obs"].get(int) template_variables = render_common(summary) # TODO: merge with boolean mini_freq_table_rows = freq_table( freqtable=summary["value_counts"], n=summary["count"], max_number_to_print=n_obs_cat, ) # Top # Element composition info = Overview( summary["varid"], summary["varname"], "Categorical", summary["warnings"] ) table = Table( [ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt", "class": "alert" if "n_unique" in summary["warn_fields"] else "", }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent", "class": "alert" if "p_unique" in summary["warn_fields"] else "", }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt", "class": "alert" if "n_missing" in summary["warn_fields"] else "", }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", "class": "alert" if "p_missing" in summary["warn_fields"] else "", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ] ) fqm = FrequencyTableSmall(mini_freq_table_rows) # TODO: settings 3,3,6 template_variables["top"] = Sequence([info, table, fqm], sequence_type="grid") # Bottom items = [] frequency_table = FrequencyTable( # 'frequency_table', template_variables["freq_table_rows"], name="Common Values", anchor_id="{varid}common_values".format(varid=summary["varid"]), ) items.append(frequency_table) check_compositions = config["vars"]["cat"]["check_composition"].get(bool) if check_compositions: composition = Table( [ { "name": "Contains chars", "value": summary["composition"]["chars"], "fmt": "fmt", }, { "name": "Contains digits", "value": summary["composition"]["digits"], "fmt": "fmt", }, { "name": "Contains whitespace", "value": summary["composition"]["spaces"], "fmt": "fmt", }, { "name": "Contains non-words", "value": summary["composition"]["non-words"], "fmt": "fmt", }, ], name="Composition", anchor_id="{varid}composition".format(varid=summary["varid"]), ) length = Table( [ { "name": "Max length", "value": summary["max_length"], "fmt": "fmt_numeric", }, { "name": "Mean length", "value": summary["mean_length"], "fmt": "fmt_numeric", }, { "name": "Min length", "value": summary["min_length"], "fmt": "fmt_numeric", }, ], name="Length", anchor_id="{varid}lengthstats".format(varid=summary["varid"]), ) tbl = Sequence( [composition, length], anchor_id="{varid}tbl".format(varid=summary["varid"]), name="Composition", sequence_type="grid", ) items.append(tbl) histogram_bins = 10 length = Image( histogram(summary["length"], summary, histogram_bins), alt="Scatter", name="Length", anchor_id="{varid}length".format(varid=summary["varid"]), ) items.append(length) template_variables["bottom"] = Sequence( items, sequence_type="tabs", anchor_id="{varid}bottom".format(varid=summary["varid"]), ) return template_variables
def render_categorical(summary): n_obs_cat = config["vars"]["cat"]["n_obs"].get(int) image_format = config["plot"]["image_format"].get(str) template_variables = render_common(summary) # TODO: merge with boolean mini_freq_table_rows = freq_table( freqtable=summary["value_counts"], n=summary["count"], max_number_to_print=n_obs_cat, ) # Top # Element composition info = Overview(summary["varid"], summary["varname"], "Categorical", summary["warnings"]) table = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt", "class": "alert" if "n_unique" in summary["warn_fields"] else "", }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent", "class": "alert" if "p_unique" in summary["warn_fields"] else "", }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt", "class": "alert" if "n_missing" in summary["warn_fields"] else "", }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", "class": "alert" if "p_missing" in summary["warn_fields"] else "", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) fqm = FrequencyTableSmall(mini_freq_table_rows) # TODO: settings 3,3,6 template_variables["top"] = Sequence([info, table, fqm], sequence_type="grid") # Bottom items = [] frequency_table = FrequencyTable( template_variables["freq_table_rows"], name="Common Values", anchor_id="{varid}common_values".format(varid=summary["varid"]), ) items.append(frequency_table) check_compositions = config["vars"]["cat"]["check_composition"].get(bool) if check_compositions: length_table = Table( [ { "name": "Max length", "value": summary["max_length"], "fmt": "fmt_numeric", }, { "name": "Mean length", "value": summary["mean_length"], "fmt": "fmt_numeric", }, { "name": "Min length", "value": summary["min_length"], "fmt": "fmt_numeric", }, ], name="Length", anchor_id="{varid}lengthstats".format(varid=summary["varid"]), ) histogram_bins = 10 length = Image( histogram(summary["length"], summary, histogram_bins), image_format=image_format, alt="Scatter", name="Length", anchor_id="{varid}length".format(varid=summary["varid"]), ) tbl = Sequence( [length, length_table], anchor_id="{varid}tbl".format(varid=summary["varid"]), name="Length", sequence_type="grid", ) items.append(tbl) n_freq_table_max = config["n_freq_table_max"].get(int) citems = [] vc = pd.Series(summary["category_alias_values"]).value_counts() citems.append( FrequencyTable( freq_table(freqtable=vc, n=vc.sum(), max_number_to_print=n_freq_table_max), name="Categories", anchor_id="{varid}category_long_values".format( varid=summary["varid"]), )) vc = pd.Series(summary["script_values"]).value_counts() citems.append( FrequencyTable( freq_table(freqtable=vc, n=vc.sum(), max_number_to_print=n_freq_table_max), name="Scripts", anchor_id="{varid}script_values".format( varid=summary["varid"]), )) vc = pd.Series(summary["block_alias_values"]).value_counts() citems.append( FrequencyTable( freq_table(freqtable=vc, n=vc.sum(), max_number_to_print=n_freq_table_max), name="Blocks", anchor_id="{varid}block_alias_values".format( varid=summary["varid"]), )) characters = Sequence( citems, name="Characters", sequence_type="tabs", anchor_id="{varid}characters".format(varid=summary["varid"]), ) items.append(characters) template_variables["bottom"] = Sequence( items, sequence_type="tabs", anchor_id="{varid}bottom".format(varid=summary["varid"]), ) return template_variables
def render_complex(summary): template_variables = {} image_format = config["plot"]["image_format"].get(str) # Top info = Overview( summary["varid"], summary["varname"], "Complex number (ℂ)", summary["warnings"], ) table1 = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt" }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent" }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt" }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) table2 = Table([ { "name": "Mean", "value": summary["mean"], "fmt": "fmt" }, { "name": "Minimum", "value": summary["min"], "fmt": "fmt" }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt" }, { "name": "Zeros", "value": summary["n_zeros"], "fmt": "fmt" }, { "name": "Zeros (%)", "value": summary["p_zeros"], "fmt": "fmt_percent" }, ]) placeholder = HTML("") template_variables["top"] = Sequence([info, table1, table2, placeholder], sequence_type="grid") # Bottom items = [ Image( scatter_complex(summary["scatter_data"]), image_format=image_format, alt="Scatterplot", caption="Scatterplot in the complex plane", name="Scatter", anchor_id="{varid}scatter".format(varid=summary["varid"]), ) ] bottom = Sequence(items, sequence_type="tabs", anchor_id=summary["varid"]) template_variables["bottom"] = bottom return template_variables
def render_date(summary): # TODO: render common? template_variables = {} # Top info = Overview(summary["varid"], summary["varname"], "Date", []) table1 = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt" }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent" }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt" }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) table2 = Table([ { "name": "Minimum", "value": summary["min"], "fmt": "fmt" }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt" }, # {'name': '', 'value': '', 'fmt': 'fmt'}, # {'name': '', 'value': '', 'fmt': 'fmt'}, # {'name': '', 'value': '', 'fmt': 'fmt'}, # {'name': '', 'value': '', 'fmt': 'fmt'}, ]) mini_histo = Image( mini_histogram(summary["histogram_data"], summary, summary["histogram_bins"]), "Mini histogram", ) template_variables["top"] = Sequence([info, table1, table2, mini_histo], sequence_type="grid") # Bottom bottom = Sequence( [ Image( histogram(summary["histogram_data"], summary, summary["histogram_bins"]), alt="Histogram", caption="Histogram", name="Histogram", anchor_id="{varid}histogram".format(varid=summary["varid"]), ) ], sequence_type="tabs", anchor_id=summary["varid"], ) template_variables["bottom"] = bottom return template_variables
def render_real(summary): template_variables = render_common(summary) image_format = config["plot"]["image_format"].get(str) if summary["min"] >= 0: name = "Real number (ℝ<sub>≥0</sub>)" else: name = "Real number (ℝ)" # Top info = Overview(summary["varid"], summary["varname"], name, summary["warnings"]) table1 = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt", "class": "alert" if "n_unique" in summary["warn_fields"] else "", }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent", "class": "alert" if "p_unique" in summary["warn_fields"] else "", }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt", "class": "alert" if "n_missing" in summary["warn_fields"] else "", }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", "class": "alert" if "p_missing" in summary["warn_fields"] else "", }, { "name": "Infinite", "value": summary["n_infinite"], "fmt": "fmt", "class": "alert" if "n_infinite" in summary["warn_fields"] else "", }, { "name": "Infinite (%)", "value": summary["p_infinite"], "fmt": "fmt_percent", "class": "alert" if "p_infinite" in summary["warn_fields"] else "", }, ]) table2 = Table([ { "name": "Mean", "value": summary["mean"], "fmt": "fmt" }, { "name": "Minimum", "value": summary["min"], "fmt": "fmt" }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt" }, { "name": "Zeros", "value": summary["n_zeros"], "fmt": "fmt", "class": "alert" if "n_zeros" in summary["warn_fields"] else "", }, { "name": "Zeros (%)", "value": summary["p_zeros"], "fmt": "fmt_percent", "class": "alert" if "p_zeros" in summary["warn_fields"] else "", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) histogram_bins = 10 # TODO: replace with SmallImage... mini_histo = Image( mini_histogram(summary["histogram_data"], summary, histogram_bins), image_format=image_format, alt="Mini histogram", ) template_variables["top"] = Sequence([info, table1, table2, mini_histo], sequence_type="grid") quantile_statistics = Table( [ { "name": "Minimum", "value": summary["min"], "fmt": "fmt_numeric" }, { "name": "5-th percentile", "value": summary["5%"], "fmt": "fmt_numeric" }, { "name": "Q1", "value": summary["25%"], "fmt": "fmt_numeric" }, { "name": "median", "value": summary["50%"], "fmt": "fmt_numeric" }, { "name": "Q3", "value": summary["75%"], "fmt": "fmt_numeric" }, { "name": "95-th percentile", "value": summary["95%"], "fmt": "fmt_numeric" }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt_numeric" }, { "name": "Range", "value": summary["range"], "fmt": "fmt_numeric" }, { "name": "Interquartile range (IQR)", "value": summary["iqr"], "fmt": "fmt_numeric", }, ], name="Quantile statistics", ) descriptive_statistics = Table( [ { "name": "Standard deviation", "value": summary["std"], "fmt": "fmt_numeric", }, { "name": "Coefficient of variation (CV)", "value": summary["cv"], "fmt": "fmt_numeric", }, { "name": "Kurtosis", "value": summary["kurtosis"], "fmt": "fmt_numeric" }, { "name": "Mean", "value": summary["mean"], "fmt": "fmt_numeric" }, { "name": "Median Absolute Deviation (MAD)", "value": summary["mad"], "fmt": "fmt_numeric", }, { "name": "Skewness", "value": summary["skewness"], "fmt": "fmt_numeric", "class": "alert" if "skewness" in summary["warn_fields"] else "", }, { "name": "Sum", "value": summary["sum"], "fmt": "fmt_numeric" }, { "name": "Variance", "value": summary["variance"], "fmt": "fmt_numeric" }, ], name="Descriptive statistics", ) statistics = Sequence( [quantile_statistics, descriptive_statistics], anchor_id="{varid}statistics".format(varid=summary["varid"]), name="Statistics", sequence_type="grid", ) seqs = [ Image( histogram(summary["histogram_data"], summary, histogram_bins), image_format=image_format, alt="Histogram", caption="<strong>Histogram with fixed size bins</strong> (bins={})" .format(histogram_bins), name="Histogram", anchor_id="{varid}histogram".format(varid=summary["varid"]), ) ] fq = FrequencyTable( template_variables["freq_table_rows"], name="Common values", anchor_id="{varid}common_values".format(varid=summary["varid"]), ) evs = Sequence( [ FrequencyTable( template_variables["firstn_expanded"], name="Minimum 5 values", anchor_id="{varid}firstn".format(varid=summary["varid"]), ), FrequencyTable( template_variables["lastn_expanded"], name="Maximum 5 values", anchor_id="{varid}lastn".format(varid=summary["varid"]), ), ], sequence_type="tabs", name="Extreme values", anchor_id="{varid}extreme_values".format(varid=summary["varid"]), ) if "histogram_bins_bayesian_blocks" in summary: histo_dyn = Image( histogram( summary["histogram_data"], summary, summary["histogram_bins_bayesian_blocks"], ), image_format=image_format, alt="Histogram", caption= '<strong>Histogram with variable size bins</strong> (bins={}, <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S/abstract" target="_blank">"bayesian blocks"</a> binning strategy used)' .format( fmt_array(summary["histogram_bins_bayesian_blocks"], threshold=5)), name="Dynamic Histogram", anchor_id="{varid}dynamic_histogram".format( varid=summary["varid"]), ) seqs.append(histo_dyn) template_variables["bottom"] = Sequence( [ statistics, Sequence( seqs, sequence_type="tabs", name="Histogram(s)", anchor_id="{varid}histograms".format(varid=summary["varid"]), ), fq, evs, ], sequence_type="tabs", anchor_id="{varid}bottom".format(varid=summary["varid"]), ) return template_variables
def render_boolean(summary): n_obs_bool = config["vars"]["bool"]["n_obs"].get(int) # Prepare variables template_variables = render_common(summary) mini_freq_table_rows = freq_table( freqtable=summary["value_counts"], n=summary["n"], max_number_to_print=n_obs_bool, ) # Element composition info = Overview( anchor_id=summary["varid"], warnings=summary["warnings"], var_type="Boolean", var_name=summary["varname"], ) table = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt", "class": "alert" if "n_unique" in summary["warn_fields"] else "", }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent", "class": "alert" if "p_unique" in summary["warn_fields"] else "", }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt", "class": "alert" if "n_missing" in summary["warn_fields"] else "", }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", "class": "alert" if "p_missing" in summary["warn_fields"] else "", }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) fqm = FrequencyTableSmall(mini_freq_table_rows) template_variables["top"] = Sequence([info, table, fqm], sequence_type="grid") freqtable = FrequencyTable( template_variables["freq_table_rows"], name="Frequency Table", anchor_id="{varid}frequency_table".format(varid=summary["varid"]), ) template_variables["bottom"] = Sequence( [freqtable], sequence_type="tabs", anchor_id="{varid}bottom".format(varid=summary["varid"]), ) return template_variables
def render_count(summary): template_variables = render_common(summary) # Top info = Overview( summary["varid"], summary["varname"], "Real number (ℝ / ℝ<sub>≥0</sub>)", summary["warnings"], ) table1 = Table([ { "name": "Distinct count", "value": summary["n_unique"], "fmt": "fmt" }, { "name": "Unique (%)", "value": summary["p_unique"], "fmt": "fmt_percent" }, { "name": "Missing", "value": summary["n_missing"], "fmt": "fmt" }, { "name": "Missing (%)", "value": summary["p_missing"], "fmt": "fmt_percent", }, # {'name': 'Infinite', 'value': summary['n_infinite'], 'fmt': 'fmt'}, # {'name': 'Infinite (%)', 'value': summary['p_infinite'], 'fmt': 'fmt_percent'}, ]) table2 = Table([ { "name": "Mean", "value": summary["mean"], "fmt": "fmt" }, { "name": "Minimum", "value": summary["min"], "fmt": "fmt" }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt" }, { "name": "Zeros", "value": summary["n_zeros"], "fmt": "fmt" }, { "name": "Zeros (%)", "value": summary["p_zeros"], "fmt": "fmt_percent" }, { "name": "Memory size", "value": summary["memory_size"], "fmt": "fmt_bytesize", }, ]) # TODO: replace with SmallImage... mini_histo = Image( mini_histogram(summary["histogram_data"], summary, summary["histogram_bins"]), "Mini histogram", ) template_variables["top"] = Sequence([info, table1, table2, mini_histo], sequence_type="grid") quantile_statistics = { "name": "Quantile statistics", "items": [ { "name": "Minimum", "value": summary["min"], "fmt": "fmt_numeric" }, { "name": "5-th percentile", "value": summary["quantile_5"], "fmt": "fmt_numeric", }, { "name": "Q1", "value": summary["quantile_25"], "fmt": "fmt_numeric" }, { "name": "median", "value": summary["quantile_50"], "fmt": "fmt_numeric" }, { "name": "Q3", "value": summary["quantile_75"], "fmt": "fmt_numeric" }, { "name": "95-th percentile", "value": summary["quantile_95"], "fmt": "fmt_numeric", }, { "name": "Maximum", "value": summary["max"], "fmt": "fmt_numeric" }, { "name": "Range", "value": summary["range"], "fmt": "fmt_numeric" }, { "name": "Interquartile range", "value": summary["iqr"], "fmt": "fmt_numeric", }, ], } descriptive_statistics = { "name": "Descriptive statistics", "items": [ { "name": "Standard deviation", "value": summary["std"], "fmt": "fmt_numeric", }, { "name": "Coefficient of variation", "value": summary["cv"], "fmt": "fmt_numeric", }, { "name": "Kurtosis", "value": summary["kurt"], "fmt": "fmt_numeric" }, { "name": "Mean", "value": summary["mean"], "fmt": "fmt_numeric" }, { "name": "MAD", "value": summary["mad"], "fmt": "fmt_numeric" }, { "name": "Skewness", "value": summary["skew"], "fmt": "fmt_numeric" }, { "name": "Sum", "value": summary["sum"], "fmt": "fmt_numeric" }, { "name": "Variance", "value": summary["var"], "fmt": "fmt_numeric" }, ], } # TODO: Make sections data structure # statistics = ItemRenderer( # 'statistics', # 'Statistics', # 'table', # [ # quantile_statistics, # descriptive_statistics # ] # ) seqs = [ Image( histogram(summary["histogram_data"], summary, summary["histogram_bins"]), alt="Histogram", caption="<strong>Histogram with fixed size bins</strong> (bins={})" .format(summary["histogram_bins"]), name="Histogram", anchor_id="histogram", ) ] fq = FrequencyTable( template_variables["freq_table_rows"], name="Common values", anchor_id="common_values", ) evs = Sequence( [ FrequencyTable( template_variables["firstn_expanded"], name="Minimum 5 values", anchor_id="firstn", ), FrequencyTable( template_variables["lastn_expanded"], name="Maximum 5 values", anchor_id="lastn", ), ], sequence_type="tabs", name="Extreme values", anchor_id="extreme_values", ) if "histogram_bins_bayesian_blocks" in summary: histo_dyn = Image( histogram( summary["histogram_data"], summary, summary["histogram_bins_bayesian_blocks"], ), alt="Histogram", caption= '<strong>Histogram with variable size bins</strong> (bins={}, <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S/abstract" target="_blank">"bayesian blocks"</a> binning strategy used)' .format( fmt_array(summary["histogram_bins_bayesian_blocks"], threshold=5)), name="Dynamic Histogram", anchor_id="dynamic_histogram", ) seqs.append(histo_dyn) template_variables["bottom"] = Sequence( [ # statistics, Sequence(seqs, sequence_type="tabs", name="Histogram(s)", anchor_id="histograms"), fq, evs, ], sequence_type="tabs", anchor_id=summary["varid"], ) return template_variables