64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sys import exit
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columns = ["Addr", "Hash"]
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core_number = 8 # FIXME
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for i in range(0, core_number):
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for stat in ["Min", "Med", "Max"]:
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for op in ["Hit", "Miss"]:
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columns.append(op + str(i) + stat)
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columns.append("Hmm")
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df = pd.read_csv("./results_lite.csv")
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print(df.head())
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g = sns.FacetGrid(df, col="core", row="hash", legend_out=True)
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def custom_hist(x,y, **kwargs):
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sns.distplot(x, range(100,150), hist_kws={"weights": y, "histtype":"step"}, kde=False, **kwargs)
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g.map(custom_hist, "time", "clflush_hit")
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# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)
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plt.show()
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# test = pd.DataFrame({"value" : [0, 5], "weight": [5, 1]})
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# plt.figure()
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# sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)
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exit(0)
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selected_columns = columns[:-1]
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df = df[selected_columns]
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print(df.head())
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median_columns = list(filter(lambda s: s.endswith("Med"), columns))
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median_hits_col = list(filter(lambda s: s.startswith("Hit"), median_columns))
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median_miss_col = list(filter(lambda s: s.startswith("Miss"), median_columns))
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print(list(median_columns))
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print(list(median_hits_col), list(median_miss_col))
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hashes = df["Hash"].drop_duplicates()
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print(hashes)
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# def distrib(x, y, **kwargs):
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# sns.distplot()
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separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_hits_col)
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g = sns.FacetGrid(separate_core_df, row="variable")
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g.map(sns.distplot, "value")
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plt.figure()
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separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_miss_col)
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g = sns.FacetGrid(separate_core_df, row="variable")
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g.map(sns.distplot, "value", hist_kws={"range": (75, 115)})
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plt.show()
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# sns.distplot(df["values"], hist_kws={"weights": df["count"]})
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