def get_data(ds): player = LogPlayer( ds ) analyzer = XYAnalyzer( mapper=lambda e: (datetime.fromtimestamp(mktime(e.timestamp)).isocalendar()[1] + datetime.fromtimestamp(mktime(e.timestamp)).isocalendar()[2]/7.0, 1) ) player.add_analyzer(analyzer) player.forward() X,Y = analyzer.get_xy() return aggregate(X,Y)
def get_data(ds): player = LogPlayer(ds) analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_hour + (e.timestamp.tm_min/30*30)/60.0, e.proc_ms )) player.add_analyzer(analyzer) player.forward() X,Y = analyzer.get_xy() return aggregate(X,Y)
def get_data(ds): player = LogPlayer( ds ) analyzer = XYAnalyzer( mapper=lambda e: (e.timestamp.tm_wday + (e.timestamp.tm_hour / 24.0), 1) ) player.add_analyzer(analyzer) player.forward() X,Y = analyzer.get_xy() return aggregate(X,Y)
def get_data(path, mpr, aggregator):
ds = CsvFileSource(path, event_type="WMS")
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=mpr)
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
return aggregator(X, Y)
def get_data(path, mapper, reducer): ds = CsvFileSource(path, event_type='WMS') player = LogPlayer(ds) analyzer = XYAnalyzer(mapper=mapper) player.add_analyzer(analyzer) player.forward() X,Y = analyzer.get_xy() #print X,Y return reducer(X,Y)
def make_plot(ds, title, color):
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_hour + (e.timestamp.tm_min/15*15)/60.0, e.answer_size / 1024.0), filter=lambda e: e.answer_size > 0)
player.add_analyzer(analyzer)
player.forward()
X,Y = analyzer.get_xy()
X, Ymin, Yavg, Ymax = aggregate(X,Y)
#plt.axis([0, len(X), 0, max(Yavg)])
plt.title(title)
plt.xlabel('Hour of day')
plt.ylabel('Average answer size in kilobytes (KB)')
plt.plot(X, Yavg, color)
plt.show()
def get_data(path, mpr, aggregator):
ds = CsvFileSource(path, event_type='WMS')
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=mpr)
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
return aggregator(X, Y)
def get_data(ds): player = LogPlayer(ds) #analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_hour + (e.timestamp.tm_min/15*15)/60.0, e.proc_ms )) analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_hour + (e.timestamp.tm_min/15*15)/60.0, 256.0**2 * e.proc_ms / float(e.query.width*e.query.height) )) player.add_analyzer(analyzer) player.forward() X,Y = analyzer.get_xy() return aggregate(X,Y)
def get_data(ds):
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_hour + (
e.timestamp.tm_min / 30 * 30) / 60.0, 1 / 30.0))
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
return aggregate(X, Y)
def get_data(ds):
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=lambda e: (e.timestamp.tm_wday +
(e.timestamp.tm_hour / 24.0), 1))
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
return aggregate(X, Y)
def get_data(path, mapper, reducer):
ds = CsvFileSource(path, event_type='WMS')
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=mapper)
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
#print X,Y
return reducer(X, Y)
def get_data(ds):
player = LogPlayer(ds)
analyzer = XYAnalyzer(mapper=lambda e: (datetime.fromtimestamp(
mktime(e.timestamp)).isocalendar()[1] + datetime.fromtimestamp(
mktime(e.timestamp)).isocalendar()[2] / 7.0, 1))
player.add_analyzer(analyzer)
player.forward()
X, Y = analyzer.get_xy()
return aggregate(X, Y)