Major upgrade.

More documentation. Additional functionality and so on.

Author: Kevin Dungs

__init__.py

@@ -0,0 +1,5 @@
+# A small library that helps doing timing measurements.
+#
+# Author:  Kevin Dungs <kevin.dungs@cern.ch>
+# Version: 2.0
+# Date:    2014-03-10

analysor.py

@@ -1,16 +1,19 @@
+# Useful functions for analysing studies.
+# Helps by calculating means as ufloats.
 #
-#
-#
+# Author:  Kevin Dungs <kevin.dungs@cern.ch>
+# Version: 2.0
+# Date:    2014-03-10
 
 
 def mean_and_error(xs):
-    from scipy.stats import sem
-    from uncertainties import ufloat
     """
         Calculate mean and SEM of a numpy array xs.
 
         Returns a ufloat.
     """
+    from scipy.stats import sem
+    from uncertainties import ufloat
     return ufloat(xs.mean(), sem(xs))
 
 
@@ -19,4 +22,8 @@ def means_and_errors(timings):
     return {
         key: mean_and_error(np.array([t[key] for t in timings]))
         for key in timings[0].keys()
-    }
+    }
+
+
+def total(timing):
+    return sum(timing.values())

extractor.py

@@ -1,7 +1,8 @@
-# A small library that helps doing timing measurements.
+# Helpers for extracting information from logfiles generated from Gaudi output.
 #
-# Author: Kevin Dungs <kevin.dungs@cern.ch>
-# Date:   2014-02-17
+# Author:  Kevin Dungs <kevin.dungs@cern.ch>
+# Version: 2.0
+# Date:    2014-03-10
 
 
 def timings_from_file(filename):
@@ -18,23 +19,24 @@ def timings_from_file(filename):
         ))
 
 
-def extract_relevant(timings):
+def extract_relevant(timings, relevant=None):
     """
         Extract relevant information from a timing dictionary that has been
         obtained from timings_from_file().
     """
-    relevant = {  # Relevant information, list elements are summed over.
-        'VELO tracking': ['FstVPDecoding', 'FstPixel'],
-        'PV finding': ['HltPVsPV3D'],
-        'VELO-UT tracking': ['CreateUTLiteClusters', 'PrVeloUT'],
-        'Forward tracking': ['FTRawBankDecoder', 'FstForward'],
-    }
+    if not relevant:
+        relevant = {  # Relevant information, list elements are summed over.
+            'VELO tracking': ['FstVPDecoding', 'FstPixel'],
+            'PV finding': ['HltPVsPV3D'],
+            'VELO-UT tracking': ['CreateUTLiteClusters', 'PrVeloUT'],
+            'Forward tracking': ['FTRawBankDecoder', 'FstForward'],
+        }
     return {k: sum(float(timings[t]) for t in v) for k, v in relevant.items()}
 
 
-def extract_multiple(filenames):
+def extract_multiple(filenames, relevant=None):
     """
         For a list of filenames, extracts the relevant information from them and
         returns a list of dictionaries containing the timings.
-    """    
-    return [extract_relevant(timings_from_file(f)) for f in filenames]
+    """
+    return [extract_relevant(timings_from_file(f), relevant) for f in filenames]

printor.py

@@ -1,12 +1,15 @@
+# Prints a readymade LaTeX table of a timing study for use in publications.
 #
-#
-#
+# Author:  Kevin Dungs <kevin.dungs@cern.ch>
+# Version: 2.0
+# Date:    2014-03-10
+
 
 def latex(study, significant_digits=None):
+    from TimingTools import analysor
+    formatstring = '{:L}'
+    if significant_digits:
+        formatstring = '{:.' + str(significant_digits) + 'L}'
     for k, v in study.items():
-        if significant_digits:
-            print(('{} & {:' + str(significant_digits) + 'L}').format(
-                k.ljust(30), v
-            ))
-        else:
-            print('{} & {:L}'.format(k.ljust(30), v))
+        print(('{}  &  ' + formatstring).format(k.ljust(30), v))
+    print(('{}  &  ' + formatstring).format('Total'.ljust(30), analysor.total(study)))

study.py

@@ -1,14 +1,32 @@
+# Quickly load whole studies.
 #
-#
-#
+# Author:  Kevin Dungs <kevin.dungs@cern.ch>
+# Version: 2.0
+# Date:    2014-03-10
 
 
-def load_study(path, runs=10):
-    """
+def load_study(path, runs=10, prefix='', relevant=None):
+    """ Load a study from {path} with {runs} runs.
+        Additional dictionary of {relevant} algorithms can be used and is
+        passed to the extractor.
     """
     from os.path import join
-    import analysor
-    import extractor
-    return analysor.means_and_errors(extractor.extract_multiple(
-        [join(path, '{}.log'.format(i)) for i in range(1, runs+1)]
-    ))
+    from TimingTools import (
+        analysor,
+        extractor
+    )
+    return extractor.extract_multiple(
+        [join(path, '{}{}.log'.format(prefix, i)) for i in range(1, runs+1)],
+        relevant
+    )
+
+
+def load_means(path, runs=10, prefix='', relevant=None):
+    """ Load the means for a study from {path} with {runs} runs.
+        Additional dictionary of {relevant} algorithms can be used and is
+        passed to the extractor.
+    """
+    from TimingTools import (
+        analysor
+    )
+    return analysor.means_and_errors(load_study(path, runs, prefix, relevant))