Add multi-correlation plots. Add CDF plots.

xlap/analyse/cdf.py

+import math
+import numpy as np
+from .util import cdf, extract_durations
+import matplotlib.pyplot as plt
+
+def multi_cdf(dfs, config, export=False, file_name="CDF.pdf"):
+    durations = [x + "_D" for x in extract_durations(config)]
+    durations.remove("EndToEnd_D")
+
+    cols = 3
+    rows = int(math.ceil(len(durations) / cols))
+    items = len(durations)
+    fig, axes = plt.subplots(nrows=rows, ncols=cols)
+    fig.set_size_inches(4 * cols, 4 * rows, forward=True)
+
+    names = []
+    for df in dfs:
+        names.append(df.name)
+
+    for idx, duration in enumerate(durations):
+        if items > cols:
+            ax = axes[idx // cols, idx % cols]
+        else:
+            ax = axes[idx]
+        for df in dfs:
+            cdf(df[duration], grid=True, ax=ax)
+
+        ax.legend(names)
+        ax.set_xlabel("{} [us]".format(duration))
+        ax.set_ylabel("CDF")
+    plt.subplots_adjust(wspace=0.3,hspace=0.3)
+
+    if export and file_name is not None:
+        fig.savefig(file_name)
+    plt.tight_layout()
+    plt.show()

xlap/analyse/correlation.py

@@ -3,25 +3,54 @@ import matplotlib.pyplot as plt
 from xlap.analyse.util import extract_durations
 
 
-def correlation(data_frame, config, export=False, file_name="Correlation.pdf"):
+def corr(df, duration, grid=False, ax=None, color="black", marker="+"):
+    df.plot.scatter(ax=ax,
+                    y="EndToEnd_D",
+                    x=duration,
+                    grid=grid,
+                    loglog=True,
+                    marker=marker,
+                    color=color)
+
+def corr_multi(dfs, duration, **kwargs):
+    names = []
+    for df in dfs:
+        names.append(df.name)
+
+    colors = ["green","blue","orange"]
+    markers = ["v", "^", ">", "<"]
+    for idf, df in enumerate(dfs):
+        corr(df, duration, color=colors[idf % len(colors)],
+             marker=markers[idf % len(markers)], **kwargs)
+    if len(names) > 1:
+        kwargs["ax"].legend(names)
+    kwargs["ax"].set_xlabel("{} [us]".format(duration))
+
+
+def multi_correlation(dfs, config):
     durations = [x + "_D" for x in extract_durations(config)]
     durations.remove("EndToEnd_D")
 
-    cols = 4
+    cols = 2
     rows = int(math.ceil(len(durations) / cols))
+    items = len(durations)
     fig, axes = plt.subplots(nrows=rows, ncols=cols)
-    fig.set_size_inches(4 * cols, 3.5 * rows, forward=True)
+    fig.set_size_inches(5.5 * cols, 5.5 * rows, forward=True)
+
+
+    for idx, duration in enumerate(durations):
+        if items > cols:
+            ax = axes[idx // cols, idx % cols]
+        else:
+            ax = axes[idx]
+        corr_multi(dfs, duration, grid=True, ax=ax)
 
-    i = 0
-    for duration in durations:
-        ax = data_frame.plot.scatter(ax=axes[i // cols, i % cols], y="EndToEnd_D", x=duration, grid=True, marker="+",
-                                     color="black")
-        ax.set_ylabel("EndToEnd [us]")
-        ax.margins(0.1, 0.1)
-        ax.set_xlabel("{} [us]".format(duration.replace("_D", "")))
-        i += 1
+    plt.subplots_adjust(wspace=0.3,hspace=0.3)
 
+def correlation(df, config, export=False, file_name="Correlation.pdf"):
+    res = multi_correlation([df], config)
     if export and file_name is not None:
         fig.savefig(file_name)
     plt.tight_layout()
-    plt.show()
\ No newline at end of file
+    plt.show()
+    return res

xlap/analyse/jitter.py

@@ -43,4 +43,6 @@ def trace_jitter(data_frame, threshold=None, export=False, file_name=None):
 
 
 def prep(df, config):
-    return df[[x + "_D" for x in extract_durations(config)]]
+    res = df[[x + "_D" for x in extract_durations(config)]]
+    res.name = df.name
+    return res

xlap/analyse/util.py

 import matplotlib.pyplot as plt
-from matplotlib import pyplot as plt
+import numpy as np
+import math
 
 
+def cdf(values, grid=False, ax=None):
+    # empirical CDF
+    def F(x,data):
+        return float(len(data[data <= x]))/len(data)
+
+    vF = np.vectorize(F, excluded=['data'])
+
+    if ax is None:
+        ax = plt
+
+    ax.semilogx(np.sort(values),vF(x=np.sort(values), data=values))
+    if grid:
+        ax.grid()
+
 def hist(df):
     return df.hist(cumulative=True, normed=1, bins=200)
 
@@ -48,4 +63,4 @@ def describe_table(df):
     stats.columns = list(map(lambda x: x.replace("_D", ""), stats.columns))
     table = stats.to_latex(float_format=lambda x: "%.3f" % x)
     print(table)
-    return stats
\ No newline at end of file
+    return stats