from smap.archiver.client import SmapClient

from smap.contrib import dtutil

from matplotlib import pyplot

from matplotlib import dates

import os

# make a client

c = SmapClient("http://new.openbms.org/backend")

# start and end values are Unix timestamps

t_start = "6-12-2013 8:00"

t_end = "6-19-2013 8:00"

start = 1000*dtutil.dt2ts(dtutil.strptime_tz(t_start, "%m-%d-%Y %H:%M"))

end = 1000*dtutil.dt2ts(dtutil.strptime_tz(t_end, "%m-%d-%Y %H:%M"))

stnc = "select distinct Metadata/Location/RoomNumber where Metadata/SourceName='KETI Motes'"

roomlist = c.query(stnc) #the result is a list

#roomlist = roomlist[16:]

#roomlist = ['621A','621B','621C','621D','621E']

for room in roomlist:

print "==========Fetching streams in Room %s=========="%room

stnc = "select Path where Metadata/Location/RoomNumber='%s' and not Path ~ '.*pir.*'" %room

streams = c.query(stnc)

if len(streams)>0:

# print "----%d streams in Room %s----"%(len(streams), room)

for s in streams:

# fetch the metadata of path wanted

tags = c.tags("Path='%s'"%s['Path'])

# mkdir for each room

path = "/Users/hdz_1989/Documents/Dropbox/SDB/KETI_tmp"

folder = room

if not os.path.exists(path+'/'+folder):

os.makedirs(path+'/'+folder)

# ft = open(path+'/'+folder+'/' + 'date.txt', 'w')

# ft.write(t_start + ' ~ ' + t_end)

# ft.close()

for timeseries in tags:

uuid = timeseries['uuid']

filename = timeseries['Path'].split('/')[-1]

clause = "select data in (%.0f, %.0f) limit -1 where uuid = '%s'" \

%(start, end, uuid)

result = c.query(clause) #the result is a list

d = result[0]['Readings']

f = open(path+'/'+folder+'/'+filename + '.csv', 'w')

f.writelines(["%.0f, %.2f\n"%(float(i[0])/1000, float(i[1])) for i in d])

# f.writelines(["%.2f\n"%(float(i[1])) for i in d])

f.close()

# # plot all the data

# for timeseries in tags:

# d = data_map[timeseries['uuid']]

# # since we have the tags, we can add some metadata

# label = "%s (%s)" % (timeseries['Path'],

# timeseries['Properties/UnitofMeasure'])

# # we can plot all of the series in their appropriate time zones

# pyplot.plot_date(dates.epoch2num(d[:, 0] / 1000), d[:, 1], '-',

# label=label,

# tz=timeseries['Properties/Timezone'])

# pyplot.legend(loc="upper center")

# pyplot.show()