Revision 9f4f3f35 timeAnalysis.py

View differences:

timeAnalysis.py
73 73

  
74 74
os.chdir("plots"+nick)
75 75
# Plotting
76
code.interact(local=dict(globals(), **locals()))
76

  
77 77

  
78 78
# ACF boxplots
79 79
bcACF.T.boxplot(column=[1]+range(5,lags,5))
......
172 172
toplotdf = pd.DataFrame([[1, a, b],
173 173
                         [a, 1, c],
174 174
                         [b, c, 1]])
175

  
176
#code.interact(local=dict(globals(), **locals()))
175 177
import seaborn as sns
176 178
sns.set()
177 179

  
178
#TODO
179
#sns.heatmap(degdf.corr(), cmap="RdBu_r", center=0.0, vmin=-1.0, vmax=1.0)
180 180

  
181
'''f250=bcdf.iloc[750:1000,:]
182
f250.columns=map(str, range(0, len(f250.columns)))
183
sns.heatmap(f250.corr(), cmap="RdBu_r", center=0.0, vmin=-1.0, vmax=1.0,
184
    cbar_kws={"label": "Cross-nodes BC Pearson Correlation"})
185
plt.xlabel("Nodes")
186
plt.ylabel("Nodes")
187
plt.savefig(nick+"bcNodesCorrHM.pdf", format='pdf')
188
plt.clf()
189

  
190
cg=sns.clustermap(f250.corr(), cmap="RdBu_r", robust=True)
191
cg.ax_row_dendrogram.set_visible(False)
192
cg.ax_col_dendrogram.set_visible(False)
193
cg.savefig(nick+"bcNodesClusteredCorr.pdf", format='pdf')
194
plt.clf()'''
181 195

  
182 196
sns.heatmap(toplotdf, cmap="RdBu_r", center=0.0, vmin=-1.0, vmax=1.0)
183 197
plt.savefig(nick+"meanMetricsCorrelation.pdf", format='pdf')

Also available in: Unified diff