diff --git a/json_benchmarks/benchmarker/result_analysis.py b/json_benchmarks/benchmarker/result_analysis.py index ade9f36..da9ce05 100644 --- a/json_benchmarks/benchmarker/result_analysis.py +++ b/json_benchmarks/benchmarker/result_analysis.py @@ -821,18 +821,18 @@ class ResultAnalysis(ub.NiceRepr): >>> kwargs = {'xscale': 'log', 'yscale': 'log'} >>> self.plot(xlabel, metric_key, group_labels, **kwargs) """ - print('Init seaborn and pyplot') + print("Init seaborn and pyplot") import seaborn as sns sns.set() from matplotlib import pyplot as plt # NOQA - print('Starting plot') + print("Starting plot") data = self.table data = data.sort_values(metric_key) - print('Compute group labels') + print("Compute group labels") for gname, labels in group_labels.items(): if len(labels): new_col = [] @@ -886,7 +886,7 @@ class ResultAnalysis(ub.NiceRepr): plots = [] base_fnum = 1 - print('Start plots') + print("Start plots") for fnum, (fig_key, group) in enumerate(groups, start=base_fnum): # TODO: seaborn doesn't give us any option to reuse an existing # figure or even specify what it's handle should be. A patch should @@ -898,7 +898,7 @@ class ResultAnalysis(ub.NiceRepr): facet = sns.relplot( data=group, - kind='line', + kind="line", # kind="scatter", facet_kws=facet_kws, **plot_kws, @@ -917,7 +917,7 @@ class ResultAnalysis(ub.NiceRepr): } plots.append(plot) - print('Adjust plots') + print("Adjust plots") for plot in plots: xscale = kwargs.get("xscale", None) yscale = kwargs.get("yscale", None) @@ -932,7 +932,7 @@ class ResultAnalysis(ub.NiceRepr): ax.set_yscale(yscale) except ValueError: pass - print('Finish') + print("Finish") return plots diff --git a/json_benchmarks/core.py b/json_benchmarks/core.py index 509e0c2..69b38c0 100644 --- a/json_benchmarks/core.py +++ b/json_benchmarks/core.py @@ -180,7 +180,7 @@ def analyze_results(result_fpaths): import json results = [] - for fpath in ub.ProgIter(result_fpaths, desc='load results'): + for fpath in ub.ProgIter(result_fpaths, desc="load results"): data = json.loads(fpath.read_text()) for row in data["rows"]: result = benchmarker.BenchmarkerResult.load(fpath)