Skip to content
This repository has been archived by the owner on Aug 24, 2024. It is now read-only.

Commit

Permalink
Remove prints
Browse files Browse the repository at this point in the history
  • Loading branch information
@jiep
jiep committed Feb 20, 2022
1 parent 53dc339 commit d69a43a
Show file tree
Hide file tree
Showing 2 changed files with 27 additions and 28 deletions.
29 changes: 14 additions & 15 deletions graphics/generate_graphics.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -57,9 +57,9 @@ def conditions(s):
df = data[data['algorithm'].isin(level)]

df2 = df[['algorithm', 'mean_cpu_cycles', 'N', 'operation']]
print(data)
# print(data)
df2 = df2.groupby(['algorithm','N'])['mean_cpu_cycles'].sum().reset_index()
print(df2)
# print(df2)
p = sns.lineplot(ax=axes[j], x="N", y="mean_cpu_cycles", hue="algorithm", data=df2, palette=COLORS, linewidth=2, style="algorithm", markers=True, dashes=False)
axes[j].set_title("Level {}".format(LEVELS_LABELS[j]), fontsize="x-large")
axes[j].set_xlabel('Number of parties', fontsize="x-large")
Expand All @@ -80,9 +80,9 @@ def plot_scalability(data, config):
fig.subplots_adjust(hspace=0.75, wspace=0.4)

df2 = data[['algorithm', 'mean_cpu_cycles', 'N', 'operation']]
print(data)
# print(data)
df2 = df2.groupby(['algorithm','N'])['mean_cpu_cycles'].sum().reset_index()
print(df2)
# print(df2)
p = sns.lineplot(ax=axes, x="N", y="mean_cpu_cycles", hue="algorithm", data=df2, palette=COLORS, linewidth=2, style="algorithm", markers=True, dashes=False)
axes.set_xlabel('Number of parties', fontsize="x-large")
axes.set_ylabel('CPU Cycles', fontsize="x-large")
Expand Down Expand Up @@ -162,7 +162,7 @@ def plot_heatmap(data, config):
df = df[['algorithm', 'operation', 'mean_cpu_cycles']]
df['mean_cpu_cycles'] = np.log(df['mean_cpu_cycles'])
df = df.pivot(index='operation', columns='algorithm', values='mean_cpu_cycles')
print(df)
# print(df)

grid_kws = {"height_ratios": (.9, .01), "hspace": .001}
fig, (axes, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws, figsize=(18,18))
Expand Down Expand Up @@ -206,16 +206,16 @@ def conditions(s):
return ""

data["level"] = data.apply(conditions, axis=1)

operations = ['init', 'commit', 'check']
operations_names = ['Init', 'Commit', 'Check']
for (i, var) in enumerate(operations):
for (j, level) in enumerate(LEVELS):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_cpu_cycles']]
print(df2)
print(level)
print(df2["algorithm"])
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -269,10 +269,9 @@ def conditions(s):
for (i, var) in enumerate(operations):
for (j, level) in enumerate(LEVELS):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_cpu_cycles']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -328,8 +327,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_cpu_cycles']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -384,8 +383,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_cpu_cycles']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down
26 changes: 13 additions & 13 deletions graphics/generate_graphics_time.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -57,9 +57,9 @@ def conditions(s):
df = data[data['algorithm'].isin(level)]

df2 = df[['algorithm', 'mean_time_us', 'N', 'operation']]
print(data)
# print(data)
df2 = df2.groupby(['algorithm','N'])['mean_time_us'].sum().reset_index()
print(df2)
# print(df2)
p = sns.lineplot(ax=axes[j], x="N", y="mean_time_us", hue="algorithm", data=df2, palette=COLORS, linewidth=2, style="algorithm", markers=True, dashes=False)
axes[j].set_title("Level {}".format(LEVELS_LABELS[j]), fontsize="x-large")
axes[j].set_xlabel('Number of parties', fontsize="x-large")
Expand All @@ -80,9 +80,9 @@ def plot_scalability(data, config):
fig.subplots_adjust(hspace=0.75, wspace=0.4)

df2 = data[['algorithm', 'mean_time_us', 'N', 'operation']]
print(data)
# print(data)
df2 = df2.groupby(['algorithm','N'])['mean_time_us'].sum().reset_index()
print(df2)
# print(df2)
p = sns.lineplot(ax=axes, x="N", y="mean_time_us", hue="algorithm", data=df2, palette=COLORS, linewidth=2, style="algorithm", markers=True, dashes=False)
axes.set_xlabel('Number of parties', fontsize="x-large")
axes.set_ylabel('Time (us)', fontsize="x-large")
Expand Down Expand Up @@ -162,7 +162,7 @@ def plot_heatmap(data, config):
df = df[['algorithm', 'operation', 'mean_time_us']]
df['mean_time_us'] = np.log(df['mean_time_us'])
df = df.pivot(index='operation', columns='algorithm', values='mean_time_us')
print(df)
# print(df)

grid_kws = {"height_ratios": (.9, .01), "hspace": .001}
fig, (axes, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws, figsize=(18,18))
Expand Down Expand Up @@ -214,8 +214,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_time_us']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -271,8 +271,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_time_us']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -328,8 +328,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_time_us']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down Expand Up @@ -385,8 +385,8 @@ def conditions(s):
df = data[(data['operation'] == var) & data['algorithm'].isin(level)]

df2 = df[['algorithm','operation', 'mean_time_us']]
print(df2)
print(level)
# print(df2)
# print(level)

# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
# print(df2)
Expand Down

0 comments on commit d69a43a

Please sign in to comment.