From 862282ce99760832d3e9e5b4b1171b861105e004 Mon Sep 17 00:00:00 2001 From: Ebus-at-dockstar Date: Mon, 25 Mar 2013 10:24:28 +0100 Subject: move old stuff away --- ebus/webhdf/__init__.py | 133 ------------------------------------------------ 1 file changed, 133 deletions(-) delete mode 100644 ebus/webhdf/__init__.py (limited to 'ebus/webhdf/__init__.py') diff --git a/ebus/webhdf/__init__.py b/ebus/webhdf/__init__.py deleted file mode 100644 index f661800..0000000 --- a/ebus/webhdf/__init__.py +++ /dev/null @@ -1,133 +0,0 @@ -# -*- coding: utf-8 -*- -import os -import json -import datetime -import time -import itertools -import logging -logging.basicConfig(level=logging.DEBUG) - -import numpy - -import bottle - -import ebus.datastore - -datastore = ebus.datastore.Datastore("hdf-data") -app = bottle.Bottle("ebus") - -lastflush = -1 - -@app.route('/') -def index_file(): - return static_files("index.html") - -@app.route('/static/:filename#.+#') -def static_files(filename): - return bottle.static_file(filename, root=os.path.join(os.path.dirname(__file__),"static")) - - -@app.get('/sensor/:name') -def sensor_data_get(name): - try: - table = datastore.getTable(name) - with datastore: - data = table.readSorted(sortby="timestamp", checkCSI=True, start=0, stop=1,step=-1).tolist()[0] - return {'sensor':name,'error':None,'data':data} - except Exception,e: - return {'sensor':name,'data':None, 'error':str(e)} - -@app.put('/sensor/:name') -@app.put('/sensor/:name/:timestamp') -def sensor_data_put(name,timestamp=None): - FLUSH_INTERVAL=30 #sec - global lastflush - - if not timestamp: timestamp = int(time.time()) - try: - value = bottle.request.POST.value - type = bottle.request.POST.type - - if type == "int": - klass = ebus.datastore.ValueInt - elif type == "float": - 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)} - - msg = "Storing {0} of type {1} with timestamp {2} to {3}".format(value,type,timestamp,name) - logging.info(msg) - - datastore.addValue(name, timestamp, value, klass, flush=False) - - if time.time() - FLUSH_INTERVAL > lastflush: - logging.info("call datastore.flush()") - datastore.flush() - lastflush = time.time() - - return {'error':None,'msg':msg} - except Exception,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 - NUMBER_OF_VALUES=250 - 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)", - 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 - - if len(timestamps) > 10: - 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 / float(NUMBER_OF_VALUES)) - - 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: - # 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))) - return {'sensor':name,'data':None, 'error':str(e)} - -@app.route('/avg/:name/:startdate') -@app.route('/avg/:name/:startdate/:period') -def sensor_avg_start(name, startdate, period=60*15): #15min - AVG_STEPSIZE=50 - try: - startdate, enddate = int(startdate), int(time.time()) - logging.info("/avg/ start={0} end={1}".format(startdate, enddate)) - table=datastore.getTable(name) - with datastore: - sel_rows = table.where("(timestamp >= startdate) & (timestamp <=enddate)", step=AVG_STEPSIZE) - 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, value), data) - return {'sensor':name, 'error':None,'data':data} - except Exception,e: - return {'sensor':name, 'error':str(e), 'data':None} - -- cgit v1.2.1