python datastore + webhdf

1 files changed, 16 insertions, 10 deletions

diff --git a/ebus/webhdf/__init__.py b/ebus/webhdf/__init__.py
index af13f87..c1f34dd 100644
--- a/ebus/webhdf/__init__.py
+++ b/ebus/webhdf/__init__.py
@@ -13,7 +13,7 @@ import bottle
 
 import ebus.datastore
 
-datastore = ebus.datastore.Datastore("testhdffiles")
+datastore = ebus.datastore.Datastore("hdf-data")
 
 app = bottle.Bottle("ebus")
 
@@ -50,6 +50,8 @@ def sensor_data_put(name,timestamp=None):
             klass = ebus.datastore.ValueFloat
         elif type == "string":
             klass = ebus.datastore.ValueString
+	elif type == "":
+	    klass = None
         else:
             return {'error':'INVALID_TYPE', msg:'Type {0} is invalid'.format(type)}
 
@@ -58,38 +60,42 @@ def sensor_data_put(name,timestamp=None):
 	logging.info(msg)
         return {'error':None,'msg':msg}
     except Exception,e:
-        return {'error':e,'msg':e}
+	logging.error("Error: " + "{0} name={1} value={2} type={3}".format(e, name, value, type))
+        return {'error':str(e),'msg':str(e)}
 
 
 
 @app.route('/sensor/:name/:startdate/:enddate')
 def sensor_name_start_end(name,startdate,enddate):
+    SAMPLING_STEPSIZE=500
     try:
         startdate, enddate = int(startdate), int(enddate)
 	logging.info("/sensor/ start={0} end={1}".format(startdate, enddate))
         table=datastore.getTable(name)
         with datastore:
-	    i = table.where("(timestamp >= startdate) & (timestamp <= enddate)",step=100)
+	    i = table.where("(timestamp >= startdate) & (timestamp <= enddate)",
+			    condvars={'startdate':startdate,'enddate':enddate,'timestamp':table.cols.timestamp},
+			    step=SAMPLING_STEPSIZE)
 	    timestamps = []
 	    try:
 	        for x in range(20):
 		    i.next()
 		    timestamps.append(i['timestamp'])
-	    except:
-		pass
+	    except: pass
+
 	    if len(timestamps) > 10:
-		    diff = map(lambda (x1,x2): (x2-x1)/100, zip(timestamps[:-1], timestamps[1:]))
+		    diff = map(lambda (x1,x2): (x2-x1)/SAMPLING_STEPSIZE, zip(timestamps[:-1], timestamps[1:]))
 		    diff_avg = numpy.average(diff)
 		    time_period = enddate - startdate
 		    samples = time_period / diff_avg
 		    step = numpy.ceil(samples / 400.0)
 
-		    data = [(x['timestamp']*1000, x['value']) 
+		    data = [(x['timestamp'], x['value']) 
 			    for x in table.where("(timestamp >= startdate) & (timestamp <= enddate)", step=step)]
 		    logging.info("diff={0} samples={1} step={2} len={3} ({4})".format(diff_avg, samples, step, len(data),name))
 	    else:
-	            logging.info("No data found ({0})".format(name))
-		    data = []
+		    # Not enough data, sampling approach useless, deliver all data
+		    data = [(x['timestamp'], x['value']) for x in table.where("(timestamp >= startdate) & (timestamp <= enddate)")]
             return {'sensor':name, 'error':None,'data':data}
     except Exception,e:
 	logging.error("Error: " + str(e) + str(type(e)))
@@ -107,7 +113,7 @@ def sensor_avg_start(name, startdate, period=60*15): #15min
             f_group = range(startdate, enddate, period)
             data = map(lambda (group_id, grouped_rows): (group_id, numpy.average([row['value'] for row in grouped_rows])),
                     itertools.groupby(sel_rows, lambda t: (t['timestamp']/period)*period))
-            data = map(lambda (timestamp,value): (timestamp*1000, value), data)
+            data = map(lambda (timestamp,value): (timestamp, value), data)
             return {'sensor':name, 'error':None,'data':data}
     except Exception,e:
             return {'sensor':name, 'error':str(e), 'data':None}