Result plot from file

README.md

@@ -3,7 +3,7 @@ OTPQA
 
 Keep track of changes in OTP performance as development progresses, and catch breaking changes to input data sets by observing changes in routing results.
 
-You will need Python and some Python libraries. For the libraries, on a Debian based system like Ubuntu you can run:
+You will need Python (2.x) and some Python libraries. For the libraries, on a Debian based system like Ubuntu you can run:
 
 `$ sudo apt-get install python-simplejson python-scipy`
 
@@ -26,15 +26,16 @@ It takes no arguments, assumes the presence of endpoints_random.csv and endpoint
 Then run the profiler with

 
     $ python otpprofiler.py hostname
-    
+
 That will generate run_summary.TIMESTAMP.json and full_itins.TIMESTAMP.json
 That one can do with what one pleases.
 
 To generate a report run
 
     $ python report.py filename1.json filename2.json
 
+Where a file name corresponds to a run_summary file created earlier
+
 To generate an HTML report, run
 
     $ python hreport.py f1 [fn2 [fn3 ...]] > report.html
-

otpprofiler.py

@@ -10,7 +10,7 @@
 # python-requests wraps urllib2 providing a much nicer API.
 import grequests
 
-DATE = '2014-12-30'
+DATE = '2015-12-30'
 # split out base and specific endpoint
 SHOW_PARAMS = False
 SHOW_URL = False
@@ -119,7 +119,7 @@ def summarize_plan (itinerary) :
             waits.append(wait)
     ret = {
         'start_time' : time.asctime(time.gmtime(itinerary['startTime'] / 1000)) + ' GMT',
-        'duration' : '%d msec' % int(itinerary['duration']),
+        'duration' : '%d sec' % int(itinerary['duration']),
         'n_legs' : n_legs,
         'n_vehicles' : n_vehicles,
         'walk_distance' : itinerary['walkDistance'],

result_plot_from_file.py

@@ -0,0 +1,25 @@
+import json, violin
+
+def plot_results(dataset, label):
+
+    data = []
+    total_times = []
+
+    if "responses" in dataset:
+        for resp in dataset['responses']:
+            if "total_time" in resp:
+                total_times.append( float(resp["total_time"][:-5]) / 1000.00) #to seconds
+
+        data.append(total_times)
+        violin.violin_plot(data, bp=True, scale=True, labels=[label])
+
+
+if __name__=='__main__':
+    import sys
+
+    if len(sys.argv)<3:
+        print "usage: cmd json_summary_file_name plot_label"
+        exit()
+
+    dataset = json.load(open(sys.argv[1]))
+    plot_results(dataset, sys.argv[2])